Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: figure
Figure

Image /page/13/Figure/2 description: A composite image of three satellite photographs, labeled a, b, and c, showing different glacial landscapes with annotations. Each panel includes coordinates and a date. Panel a, dated June 08, 2015, at 85.88°E, 28.39°N, shows a light-colored glacial lake with a breach leading to a deposition area that has formed a smaller, green 'deposition blocked lake'. Panel b, dated November 01, 2016, at 85.93°E, 28.32°N, displays a large, green glacial lake fed by a glacier, with a breach at its outlet and a 'debrirs fan' (likely debris fan) below it. Panel c, dated October 10, 2017, at 85.78°E, 28.27°N, shows two dark glacial lakes near a glacier, with a 'V-shaped breach' and a prominent 'eroded channel' downstream.


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: figure
Figure

Image /page/13/Figure/3 description: A composite image of three satellite views, labeled d, e, and f, showing different glacial landscapes, all dated November 01, 2016. Each panel includes a north arrow. Panel d, on the left, shows a glacier feeding into a dark glacial lake. Below the lake, labels point to a 'breach', a 'debris fan', and a 'deposition fan'. The location is given as No.81: 85.87°E, 28.19°N. Panel e, in the middle, displays a glacier, a brownish-yellow glacial lake, a 'debris fan', and a 'V-shaped breach'. The location is No.83: 85.87°E, 28.17°N. Panel f, on the right, shows a glacier, a green glacial lake, a 'V-shaped breach', and a 'debris fan'. The location is No.86: 85.92°E, 28.14°N.

data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md

5. Discussion
5.1. Glacial Lake Inventory


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: figure
Figure: Figure 6. Unreported glacial lake outburst flood events were identified based on Google Earth images ( a , c ) and GaoFen-1 (GF-1) images ( b , d - f ) in the Bhote Koshi Basin.

**Figure 6.** Unreported glacial lake outburst flood events were identified based on Google Earth images (**a**,**c**) and GaoFen-1 (GF-1) images (**b**,**d**-**f**) in the Bhote Koshi Basin.


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: text

The six undocumented outburst events found show that high-resolution remote sensing images make it possible to trace minor GLOF events that were unreported because of difficult access or few people living in high mountain regions. It also proves that the GLOF frequency is high in BKB. Other unknown and unpublished GLOF have also been found in Bhutan, Nepal and other parts of the Himalayas, based on long-time-series remote sensing data that show glacial lake changes (disappearance or abrupt shrinkage) and typical topographic features, such as exposed debris fans and sediment tails in downstream river channels [9,17,18]. A database of past GLOF events as complete as possible is essential for robust and reliable GLOF hazard assessment [69]. The gradually improving GLOF inventory helps us better understand the mechanism of GLOF and to do hazard assessment.

Water 2020, 12, 464 15 of 20


Document: 1. Introduction
Section: 5. Discussion > 5.2. Glacial Lake Outburst Hazard
Type: text

A GLOF is a complex process, and the hazard magnitude is determined by the outburst water volume and flood routing [8,22,38]. The outburst water volume is mainly related to the glacial lake volume [54,70]. Since the depth of a glacial lake is hard to acquire by remote sensing data and investigation, the surface area of the lake becomes an important indicator for assessing lake volume and hazard. In previous GLOF hazard assessment studies, glacial lakes smaller than 0.1 km² were assumed not to pose a hazard potential relevant to downstream locations [47]. Khanal et al. [34] identified 10 critical lakes in the BKB, and all of them are larger than 0.2 km². Indeed, small glacial lake outbursts can cause damage to downstream locations. According to the inventory of historical glacial lake outburst floods in the Himalayas, small GLOF, such as Zanaco, Geiqu and Choradari Lakes with areas smaller than 0.1 km², damaged downstream roads and villages [9]. On one hand, a small outburst can create a much larger outburst from another lake located downstream, for examples, the reach of GLOF from lakes Artesoncocha and Chacrucocha [43,67]. On the other hand, a small glacial lake outburst flood can transform into debris flows due to downstream sediment entrainment. For example, a small ice-dammed lake outburst in 2009 (area of 34,000 m²) at Keara in the Andes caused damage 10 km downstream [41].

During the flood routing, landslides on the slopes enter the flood, transforming the flood into debris flows and greatly increasing the discharge, volume and impact. Ignoring the earthquake-induced landslides would underestimate the basin's GLOF hazard. If we do not consider landslides triggered by earthquakes transforming the glacial lake outburst floods into debris flows in the BKB, only nine glacial lakes are identified as having very high hazard, 16 are at high hazard, 12 are at medium hazard and 85 are at low hazard. The hazards rank of two very high hazard small glacial lakes, eight high hazard lakes and 20 medium hazard lakes, accounting for 24.6% of the total lakes, would be decreased. It leads to the GLOF hazard of BKB greatly underestimated.

5. Discussion
5.2. Glacial Lake Outburst Hazard


Document: 1. Introduction
Section: 5. Discussion > 5.2. Glacial Lake Outburst Hazard
Type: text

, volume and impact . Ignoring the earthquake - induced landslides would underestimate the basin ' s GLOF hazard . If we do not consider landslides triggered by earthquakes transforming the glacial lake outburst floods into debris flows in the BKB , only nine glacial lakes are identified as having very high hazard , 16 are at high hazard , 12 are at medium hazard and 85 are at low hazard . The hazards rank of two very high hazard small glacial lakes , eight high hazard lakes and 20 medium hazard lakes , accounting for 24 . 6 % of the total lakes , would be decreased . It leads to the GLOF hazard of BKB greatly underestimated .

In this study, the GLOF caused by GBTSC on 25 July 2016 is a good example. GBTSC is located in the Zhangzangbo Valley on the right bank of the Poiqu River, and the average gradient is 182‰ (Figure 7b). This lake was tiny; the surface area before the outburst was 0.01 km2. After the outburst, the lake was almost empty as shown in Figure 7b. The width of the breach was 27 m, and the depth was 9 m. The peak discharge was 618 m3/s, and increased to 4019 m3/s at the section of the Zhangzangbo Valley mouth (approximately 7.2 km from the breach), according to the investigation and assessment report written by the Institute of Mountain Hazards and Environment (http://www.imde.ac.cn). The discharge increased by almost eight times because the outburst flood changed to a debris flow. The Zhangzangbo was in the seismic intensity zone VII and intensely impacted by the Gorkha earthquake. Many landslides were triggered along the river, and some landslide deposits blocked the river (Figure 7c,d). The loose mass volume increased to  $8.74 \times 10^6$  m3 in Zhangzangbo according to the landslide distribution. These landslide deposits provided rich masses for the debris flow. Once the GLOF occurred, these deposits were easily eroded and entrained, leading the flood to change to a debris flow and amplifying the discharge. Tens of thousands of landslides were triggered by the M 7.8 (Gorkha) and M 7.3(Dolakha) earthquakes [71,72]. It will take some time to transport these landslide deposits, which accumulated on the slope or in the channel. Thus, in the region affected by strong earthquakes, we must strengthen the monitoring of high-hazard glacial lakes and pay special attention to glacial lake outburst debris flows after an earthquake.


Document: 1. Introduction
Section: 5. Discussion > 5.2. Glacial Lake Outburst Hazard
Type: figure
Figure

Image /page/15/Figure/1 description: A composite figure with four panels labeled a, b, c, and d, showing satellite images that compare channel morphology in a mountainous region before and after a geological event.


Document: 1. Introduction
Section: 5. Discussion > 5.2. Glacial Lake Outburst Hazard
Type: text

Panel a is a large overview image dated Oct. 24, 2016. It shows the Zhangzangbo and Poiqu rivers, and two lakes named Gongbatongshaco and Cirenmaco. Three areas along the Zhangzangbo river are highlighted with red dashed boxes and labeled B, C, and D. The image includes a scale bar from 0 to 2,200 meters and a north arrow.

Panel b shows two images of area B. The top image, from Nov. 10, 2015, shows a river channel. The bottom image, from Oct. 24, 2016, shows significant changes, with labels pointing to "Moraine terrace erosion" and a newly formed "debris fan" in the widened river channel.

Panel c displays two images of area C. The left image, from Jun. 01, 2015, shows a river bend. The right image, from Oct. 24, 2016, shows the same bend with significant widening, labeled to indicate "Lateral erosion" and "Bank collapse," which is highlighted with a yellow dashed line.

Panel d presents two images of area D. The left image, from Jun. 01, 2015, shows a "Blockage" and two areas of "Landslide deposits" circled in yellow. The right image, from Oct. 24, 2016, shows that the previous landslide deposits have been eroded, and new features are labeled, including a "Landslide" and a "Boulder" in the debris-filled channel.


Document: 1. Introduction
Section: 5. Discussion > 5.2. Glacial Lake Outburst Hazard
Type: figure
Figure: Figure 7. Comparison of channel changes before and after GBTSC GLOF along the Zhangzangbo Valley. (a) The flow path of GBTSC GLOF; (b) GF-1 images showing that the flood left a large debris fan in the front of the lake, and eroded the moraine terrace; (c) lateral erosion and bank collapse in the moraine terrace and the width of channel increased; (d) landslide deposits distributed along the channel and blocked the channel before bursting, while GLOF eroded landslide deposits and triggered bank slump and landslide afterward, causing an increase in channel width. (Note: the 1 June 2015 images of c and d are from Google Earth, others are GF-1 images).

**Figure 7.** Comparison of channel changes before and after GBTSC GLOF along the Zhangzangbo Valley. (a) The flow path of GBTSC GLOF; (b) GF-1 images showing that the flood left a large debris fan in the front of the lake, and eroded the moraine terrace; (c) lateral erosion and bank collapse in the moraine terrace and the width of channel increased; (d) landslide deposits distributed along the channel and blocked the channel before bursting, while GLOF eroded landslide deposits and triggered bank slump and landslide afterward, causing an increase in channel width. (Note: the 1 June 2015 images of c and d are from Google Earth, others are GF-1 images).


Document: 1. Introduction
Section: 6. Conclusions
Type: text

In this study a new detailed 2016 inventory of glacial lakes in the BKB was established and six unreported GLOF that occurred before 1984 have been detected with geomorphic outburst evidence based on high-resolution remote sensing images. The BKB is one of the most hotspot small river basins for glacial lakes and GLOF in the central Himalayas. High-resolution remote sensing images are useful for detecting unreported GLOF events in high mountainous regions and sparsely populated regions, which is conducive to improving the GLOF inventory and better assessing GLOF hazard. A rough but more comprehensive method was proposed to assess GLOF hazard, which considers the probability for a flood to develop into a debris flow in the downstream, where large numbers of landslides triggered by earthquake are distributed. The GLOF hazard in BKB increases due to landslide deposits volume, which increased approximately  $124.36 \times 10^6$  m3 after the Gorkha earthquake, and 11 glacial lakes are identified as very high hazard, nine are high hazard, 32 are medium hazard and 55 are low hazard. However, about 24.6% of the all lakes' hazards would be underestimated without earthquake-induced landslides, in which most of them are small glacial lakes. Therefore, for regional GLOF hazard assessment, small glacial lakes should not be overlooked for landslide deposit entrainment along a flood route and flood eroding channel bed would increase the peak discharge, especially in earthquake affected areas where large numbers of landslides were triggered. We suggest

Water 2020, 12, 464 17 of 20

that more attention should be paid to the very high and high-hazard glacial lakes and to improving the engineering security standard for defending against flood hazards downstream of BKB.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4441/12/2/464/s1. Table S1: glacial lake inventory of Bhote Koshi.

**Author Contributions:** N.C. and M.L. conceived the original ideas and drafted the original manuscript. Y.Z. and M.D. carried out field investigation and data collection. N.C. and M.L. revised the original manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China (Grant NOs. 41671112 and 41861134008) and the 135 Strategic Program of the IMHE, CAS (Grant NO. SDS-135-1705).

Conflicts of Interest: The authors declare there no conflict of interest.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: Abstract
Type: text

GLOFs, driven by climate change-induced glacier melt, present a serious threat to downstream communities in mountainous regions. The present study addresses this pressing issue by employing a systematic methodology to analyze scientific literature on GLOF risk management and hazard mitigation. The research synthesizes key insights through thematic analysis, focusing on GLOF hazard mitigation, hazard and risk mapping, and the development of a methodological framework for managing GLOF hazards and risks. It evaluates structural measures such as spillways and diversion canals, and non-structural measures, including strategic land-use planning and community-based strategies. The study highlights the importance of Early Warning Systems (EWS), GLOF risk management skills, and knowledge transfer, emphasizing integration with climate change adaptation. It also discusses facilitating factors like policy legislation, institutional support, and addressing knowledge disparities. While the physical aspects of GLOFs have been widely explored, research on their social impacts is relatively limited, leaving a crucial gap in understanding the full extent of these disasters. The findings of the study offer a comprehensive resource for policymakers, planners, and disaster management professionals, providing a holistic approach to mitigating GLOF risks and enhancing resilience in vulnerable regions. This research is poised to significantly contribute to effective GLOF hazard mitigation and climate change adaptation strategies.

**Keywords** GLOFs; Hazard and Risk; Management · Methodological Framework; EWS · Knowledge disparity · Policy legislation

data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
Type: text

Glacial lakes, a prominent feature of the glacial landscape, form as a result of the dynamic interaction between the glacial processes and surrounding topography (Steffen et al. 2022; Peng et al. 2023). These glacial lakes originate from several processes and mechanisms, primarily involving the accumulation of glacial meltwater behind the ice dams, moraines

Department of Geography and Disaster Management, School of Earth and Environmental Sciences, University of Kashmir, Srinagar 190 006, India


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
Type: text

Rayees Ahmed
Rayeesrashid84@gmail.com

or bedrock barriers that obstruct the normal flow of the water (Zhang et al. 2022; Hu et al. 2022). The formation of the glacial lakes is closely linked to the glacier retreat during the deglaciation period (Molg et al. 2021; Rather et al. 2024a, b; Knight and Harrison 2018). These lakes vary in size, volume, depth, and morphology and can be broadly classified into four main types, such as moraine-dammed lake (formed when glacial debris creates a natural dam obstructing the meltwater flow), ice-dammed lake (forms when a glacier temporarily blocks a river impounding meltwater), glacier erosion lake (found in depressions on mountain slopes that have been glacially sculpted out by erosion) and other glacial lakes (Lakes formed within glaciated valleys primarily fed by the direct glacial melt) (Gupta et al. 2023; Ahmed et al. 2021). The glacial lake expansion is considered as one of the important indicators of the climate change (Nie et al. 2013; Zheng et al. 2021). The ongoing lake formation and expansion of different types of glacial lakes is directly linked to the warming climate and rapid melting of the glaciers (Pandey et al. 2021; Ahmed et al. 2022; Mir et al. 2022, 2024).

GLOFs are serious natural hazards that could have devastating impacts on communities that inhabit downstream of glacial lakes. The chance of GLOF incidence is rising as a result of climate change (Harrison et al. 2018). The melting of glaciers leads to the formation of new lakes and increasing water levels, which raises the possibility of severe flooding (Benn et al. 2012; Zheng et al. 2021; Munoz et al. 2024). As such, the drafting of GLOF hazard and disaster management strategies has become a key issue for planners, policymakers and disaster management professionals. A multidisciplinary approach is necessary to design efficient GLOF hazard and disaster management strategies, drawing on knowledge from a variety of domains, such as hydrology, geography, disaster management, meteorology, glaciology, and engineering. These approaches need to take into account the different needs and vulnerabilities of different communities or social groups, as well as the complex interactions between physical, social, and economic factors.

Installing EWS in glacial lake regions requires robust planning, accurate data transfer, and careful assessment of site-specific variables. The Laguna 513 EWS in the Peruvian Andes serves as a successful model (Huggel et al. 2020), highlighting the need for location-specific adaptation. Wireless sensor networks (WSNs) offer cost-effective, real-time monitoring for GLOF risk reduction (Singhal et al. 2022). In Sikkim, India, the GLOF EWS integrates GIS and real-time sensors for effective flood simulation and evacuation planning (Kumar et al. 2022). Effective risk management also demands community engagement and addressing institutional gaps to enhance preparedness and mitigation efforts in vulnerable regions. Recent studies emphasize the importance of risk communication, capacity building, and community involvement in GLOF disaster management. For instance, Pandey et al. (2021) identified key challenges in developing community-centered strategies in Nepal. Effective GLOF management requires cross-disciplinary collaboration and careful execution to protect vulnerable communities. Internationally, GLOFs are prioritized in the Sendai Framework for Disaster Risk Reduction, urging the development of regional and national mitigation plans (UNDRR 2015)


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
Type: text

effective flood simulation and evacuation planning ( Kumar et al . 2022 ) . Effective risk management also demands community engagement and addressing institutional gaps to enhance preparedness and mitigation efforts in vulnerable regions . Recent studies emphasize the importance of risk communication , capacity building , and community involvement in GLOF disaster management . For instance , Pandey et al . ( 2021 ) identified key challenges in developing community - centered strategies in Nepal . Effective GLOF management requires cross - disciplinary collaboration and careful execution to protect vulnerable communities . Internationally , GLOFs are prioritized in the Sendai Framework for Disaster Risk Reduction , urging the development of regional and national mitigation plans ( UNDRR 2015 )

Awareness of GLOFs has grown, leading to efforts like the NDMA's collaboration with Swiss agencies to create management guidelines and a global risk reduction program. These initiatives focus on hazard mapping, early warning systems, and community-based strategies. Effective GLOF management must consider the political and social challenges of remote, marginalized regions, incorporating local knowledge for sustainability. Hazard and disaster management plans need to take into account the distinct social, economic, and environmental conditions of the impacted communities in order to reduce the hazards associated with GLOFs. Comprehensive risk assessments, early warning systems,


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 1 Introduction
Type: text

and emergency preparedness are all essential components of successful plans. Since local communities are frequently the first to respond to GLOF events, the solutions also need to empower and involve them. The efficacy and sustainability of GLOF hazard and disaster management systems can be improved by incorporating traditional knowledge and practices.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 2 Methodology
Type: text

This review-based study employed a methodical approach to thoroughly analyse the scientific literature on risk management, hazard mitigation, and GLOFs, with an emphasis on the consequences of glacial retreat brought on by climate change. The evaluation process was guided by well-crafted research questions that focused on important issues such as risk management techniques, GLOF hazard mitigation strategies, and the relationship between these factors and the effects of climate change in high-mountain areas.

Explicit inclusion and exclusion criteria were developed in order to guarantee a strict selection procedure. Systematic search strings using keywords like "Glacial Lake Outburst Floods," "Hazard Mitigation," "Hazard Assessment," "GLOF modeling," "Risk Management," "Structural and Non-Structural measures," and "Climate Change" were conducted to find pertinent literature on reliable scholarly databases such as Scopus, google scholar, PubMed and Web of Science (Figure S1). Titles and abstracts were used in the first screening process to find research that met the predetermined criteria. This process eventually resulted in the selection of peer-reviewed articles, academic papers, and reports that precisely addressed the themes that were being investigated (Fig. 1).

A comprehensive full-text analysis of the articles selected was carried out, with a focus on rigorous methodology and relevance assessment (Gao et al. 2024). During this procedure, important


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 2 Methodology
Type: figure
Figure

Image /page/2/Figure/7 description: A flowchart illustrating a six-step methodology for a literature review. The process starts with a box labeled "Literature Review" for the "Extraction of relevant research articles for thematic analysis". Step 1 is the "Selection of Keywords," which are categorized into GLOFs, Hazard and Risk Assessment, GLOF mitigation measures, and GLOF management and mitigation. Step 2 is the "Selection of relevant database," with a word cloud of terms like "glacial lake(s)" pointing to four databases: Web of Science, Scopus, Google Scholar, and PubMed. Step 3, "Extraction of research items," shows the number of articles found in each database: WOS (n=612), Scopus (n=590), Google Scholar (n=557), and PubMed (n=334). Step 4 is the "Selection of time frame," which is 1990-2023. Step 5 shows the result after filtering, with "Records after removing duplicate items n=823". Finally, Step 6 shows the final count after further filtering: "Records after removing conference papers, books, reports and book chapters n=685".


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 2 Methodology
Type: text

**Fig. 1** Methodology flow chart for the extraction of research articles from 1990 to 2023. The red boxes show steps for generating the database for the Review analysis


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 2 Methodology
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 2 Methodology
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data was extracted, such as the approaches used in each study, the primary conclusions, and any limitations noted by the authors. After the data were retrieved, a thematic analysis was conducted on the data to classify the studies according to common themes such as risk mapping, early warning systems, structural and non-structural strategies, and climate change adaptation. The synthesis of results was a critical step in developing a thorough understanding of GLOF hazard reduction and risk management measures. Based on the collective insights gleaned from the scholarly literature, the synthesis of knowledge from many investigations allowed for the formulation of clear conclusions and suggestions. The methodology relied heavily on critical reflection, which involved an analysis of the strengths and limitations inherent in the evaluated literature. This critical viewpoint aided in the construction of a well-structured review study by allowing for a sophisticated assessment of the state of knowledge on GLOF hazard reduction. The documentation of the whole review process, including search strategies, inclusion/exclusion decisions, and data synthesis methodologies, guaranteed the study's findings were transparent and reproducible.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 3 GLOF Hazard Mitigation
Type: text

GLOFs are a significant natural hazard in regions where glaciers are retreating due to climate change. Glacial lakes are formed when glaciers melt and are contained by unstable moraine dams composed of loose rock and debris (see Fig. 2c). A GLOF could happen if the dam's foundational stability is compromised by the pressure of the water behind it, causing a sudden and disastrous release of water and debris downstream. GLOFs have the potential to result in significant financial losses, infrastructure destruction, and fatalities. As a result, populations residing downstream of glacial lakes must implement efficient hazard mitigation measures. Effective GLOF hazard mitigation consists of a number of elements, such as hazard mapping, early warning systems, and structural and non-structural mitigation. The schematic representation of the GLOF triggering mechanism and downstream impacts is illustrated in Fig. 3.

A crucial part of GLOF hazard mitigation plans is early warning systems. EWS enables the population downstream to warn in advance of potential GLOF events so they can take the necessary precautions and safety measures to reduce damage and casualties. Monitoring systems, communication networks, and dissemination systems are the parts of a typical early warning system. Physical measurements like lake level, water temperature, and seismic activity can be included in monitoring systems. The state of the moraine dam and the extent and volume of the glacial lake can both be usefully inferred from remote sensing techniques like satellite imagery and aerial photography. To ensure that warnings are efficiently transmitted to populations downstream, communication networks are essential. Radios, TVs, cell phones, and sirens are examples of communication networks. All community members must be able to access and rely on the communication networks, regardless of their socioeconomic level or place of residence. Traditional media, like radio and newspapers, as well as new media, like social media and smartphone apps, can be used as dissemination platforms. Reaching a wider audience through the usage of new media is especially beneficial for younger generations who are more inclined to use social media and mobile apps. A number of countries have successfully put in place EWS for GLOFs. The Department of Hydrology and Meteorology in Nepal, for instance, has reduced the effects of GLOFs in the country by implementing the EWS for GLOFs (Pandey et al. 2021). To offer early warning of possible GLOF incidents, the system combines monitoring systems, communication networks, and dissemination systems.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 3 GLOF Hazard Mitigation
Type: figure
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 3 GLOF Hazard Mitigation
Type: figure
Figure

Image /page/4/Figure/2 description: A figure titled 'Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management...' is presented in three panels labeled a, b, and c. Panel (a) is a schematic diagram illustrating a GLOF monitoring and warning system. It shows a satellite using remote sensing to monitor a glacial lake, with data on 'Hazard Potential' sent to a server. Downstream, gauge and warning stations along a river send 'Alarm' signals to the server. The server then alerts 'End Users'. The system also includes regular monitoring via remote sensing and GIS to assess risk, ultimately leading to a 'GLOF Warning' for communities. Panel (b) is a circular diagram detailing the 'GLOF hazard and Risk Management' cycle. The cycle includes 'Risk Analysis', 'Disaster Risk Reduction' (with measures like lowering lake water level and engineering solutions), 'Response' (including Early Warning Systems and emergency measures), and 'Recovery' (including relief, rehabilitation, and reconstruction). Panel (c) is a visual depiction of a GLOF event, showing a 'Moraine dammed Lake' at a 'Breaching point'. The resulting 'Flood water' and 'Debris flow' travel down through forests and valleys, impacting 'Agriculture fields', a 'Bridge', and a 'Settlement' downstream.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 3 GLOF Hazard Mitigation
Type: text

Fig. 2 a) A Schematic diagram illustrating the location of monitoring stations and the monitoring/warning processes, b) Structural and Non-structural measures for the GLOF Hazard and Risk management, c) Illustration of GLOF event and downstream impacts


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
Type: text

Hazard mapping is another key element for GLOF hazard mitigation. The mapping of various parameters related to GLOF hazard and Risk in the downstream region such as location of the glacial lake and its feeding glacier, moraine dams, ice cores, mass movements, evolution of the lake, downstream area, population, infrastructure, potential flood propagation


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
Type: figure
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
Type: figure
Figure

Image /page/5/Picture/1 description: A diagram illustrating the causes and effects of a Glacial Lake Outburst Flood (GLOF). The diagram is split into two main sections: 'GLOF Triggering Mechanism' at the top and 'Downstream Impacts' at the bottom. The top section shows a mountainous, glacial environment with a large blue glacial lake contained by a moraine. Several triggering mechanisms are shown with red arrows pointing into the lake, including a rock fall, a snow avalanche, ice calving from a glacier, and a cloud burst. The bottom section, labeled 'Downstream Impacts,' shows the floodwater rushing from the breached lake into a valley below. This flood impacts a community, showing houses, agricultural fields, forests, a bridge, and a more developed area with a factory and a road. People are depicted in an orange raft on the floodwaters. A legend on the right side of the diagram explains the various symbols used: Agricultural field, Forest, Bridge, House, Glacial lake, Moraine, Glacier Snout, Snow avalanche, Rock fall, Potential flood from lake in the upstream, Ice calving, Ice cored moraine, and Cloud burst.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
Type: text

Fig. 3 GLOF triggering mechanism and downstream impacts

parameters etc is called as Hazard and Risk mapping. The mapping can be used to create risk evaluations and provide guidance for creating plans to mitigate hazards. The process of GLOF hazard and risk can be carried out using a combination of Remote sensing techniques and hydrodynamic models such as satellite imagery, aerial photographs, field investigations, etc. Besides physical parameters, the mapping process should also take into consideration the social and economic factors that increase the vulnerability of the downstream population. For instance, the National Centre for Hydrology and Meteorology in Bhutan has created an inventory of glacier lakes that contains details about the lakes' locations, sizes, and potential hazards (Bajracharya et al. 2020). The hazard maps created from the inventory have influenced the creation of GLOF hazard mitigation plans in Bhutan. Several studies have been carried out in different parts of the world on the GLOF hazard and Risk assessment. For example, Ives et al., 2010 analysed the evolution of glacial lakes and risk assessment in the Hindu Kush Himalayas, Wang et al. 2022 have done an integrated assessment of GLOF disaster in Qinghai Tibetan Plateau. Similarly, in Nepal GLOF risk assessment was carried out by various researchers (Shrestha et al. 2010; Khanal et al. 2015; Rounce et al. 2016; Hu et al. 2022; Khadka et al. 2019; Sattar et al. 2021; Khanal et al. 2015), Bhutan (Ives et al. 2010; Rinzin et al. 2023; Gurung and Khanal 2017; Hagg et al. 2021; Scapozza et al. 2019; Rinzin et al. 2021; Rawat et al. 2023b; Negi et al. 2021; Ahmed et al. 2023; Wangchuk et al. 2023). A boom of studies on the GLOF risk assessment and Inventorization of glacial lakes have been conducted in several catchments of the Indian Himalayan Region (Sattar et al. 2021; Ahmed et al. 2021, 2022; Mir et al. 2018; Majeed et al. 2021; Rawat et al. 2023; Aggarwal et al. 2016; Mal et al. 2021; Dubey and Goyal 2020; Deswal et al. 2021; Gupta et al. 2022; Rinzin et al. 2023; Thakur et al. 2016). In the Peruvian Andes GLOF hazard and risk have been studied extensively for instance Frey et al. (2018) have proposed a robust GLOF management strategy for a data-scarce region like Santa Teresa in Peru. Emmer and Vilmek (2014) have provided a new method to evaluate the potential danger of glacial lakes in Peru. Similarly, a huge number of studies have been carried out in the region which have focused on the preparation of lake and


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
Type: figure
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 4 Hazard and Risk Mapping
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GLOF inventories, hazard level, risk assessment as well as structural and non-structural measures for the GLOF hazard and risk management (Drenkhan et al. 2019; Ahmed et al. 2022; Frey et al. 2018; Huggel et al. 2020; Emmer and Vilmek 2014; Munoz et al. 2024; Hagen et al. 2023; Batka et al. 2020; Huggel et al. 2020; Carey et al. 2012; Wood et al. 2021). From the above-mentioned studies, it is quite clear that researchers are long been working on the glacial lake hazard and risk mapping, however, only findings from a few studies have been implemented for the planning on the ground. These risk maps along with other useful findings should be taken into consideration while planning and developmental activity in the ecologically fragile areas to lessen the impacts of the potential GLOFs.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 5 Methodological Framework for GLOF hazard and Risk Mapping
Type: text

The development of a comprehensive methodological framework is one of the key elements for evaluating and reducing the possible risks associated with GLOFs. An extensive literature survey was conducted to build this framework, ensuring a strong foundation based on current knowledge and techniques. The initial phase includes the detection and Inventorization of glacial lakes at different scales, employing field surveys and data from remote sensing to create an extensive database. The dynamics of glacial lakes, such as changes in volume, size, and possible triggers for outburst events, are then evaluated using modeling approaches and remote sensing technology. The comprehensive methodological framework for GLOF hazard and risk mapping is illustrated in Fig. 4. Framework for the evaluation of hazard level, physical vulnerability and social vulnerability of GLOFs is illustrated in Supplementary Figure S3.

The following stage is dedicated to hazard assessment and includes identifying susceptible downstream locations, estimating the volume of the flood, and analysing possible flood routes. Simulating possible GLOF scenarios requires the use of sophisticated hydrological and hydraulic modelling techniques in conjunction with GIS. After that, risk mapping combines vulnerability and exposure analyses with hazard data, taking into account downstream areas' infrastructure, population density, and land use. This stage helps to prioritise risk reduction efforts by giving a spatial representation of the potential impact zones.

Stakeholder participation is crucial to this process; local communities, governmental organisations, and scientific specialists should all be included. Their contributions improve data quality, evaluate models, and add useful local knowledge. The developed methodological framework serves as a crucial resource for planners, policymakers and disaster management authorities. Targeted policy interventions, such as land-use planning, early warning systems, and infrastructure development in high-risk zones, are made possible by the spatial visualisation of prospective risk areas. Decision-makers can use the framework's results to help create and carry out policies that lessen communities' susceptibility to GLOF disasters. Additionally, the methodological framework provides a systematic and standardized approach to GLOF hazard and risk assessment, ensuring consistency and comparability across different regions and contexts. Adaptive management strategies can be improved by regular updates that are based on continuous observation and investigation. In conclusion, strengthening policy implementation and protecting vulnerable communities from the potentially disastrous effects of GLOFs depends on the development of a strong GLOF hazard and risk mapping framework that is backed by scientific data and stakeholder cooperation.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 5 Methodological Framework for GLOF hazard and Risk Mapping
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 5 Methodological Framework for GLOF hazard and Risk Mapping
Type: figure
Figure

Image /page/7/Figure/1 description: A flowchart illustrating a framework for the hazard and risk mapping of glacial lakes. The process begins with inputs like Satellite imagery, Maps, Reports, Past inventories, and Toposheets, which are used for 'Mapping glacial lakes at different time points' with the help of 'Geospatial tools (RS and GIS)'. This leads to the creation of a 'Glacial lake data base'. The next step is the 'Identification of PDGLs' (Potentially Dangerous Glacial Lakes) using 'Integrated criteria' and 'Geospatial tools'. This generates a 'List of PDGLs'. These are then ranked for hazard level using various 'Ranking Criteria' such as 'Lake area, volume and Peak discharge', 'Moraine condition', 'Past GLOF history', and others. The output is a 'Hazard Level' categorized as High, Medium, or Low, determined using the 'Natural Jenks Classification method in ArcGIS'. Following this, 'high priority lakes' are selected for further assessment. This involves an 'In-depth analysis through field investigation' covering eight 'Thematic areas of study' including 'Moraine dam condition' and 'Analysis of the downstream agriculture and infrastructure'. This detailed study, including field investigation and modeling, results in 'Inundation maps, risk maps for the planners and policy makers'. After 'Analysis and interpretation of data', the process culminates in a 'Final Report'. The flowchart includes small map images as examples of inundation maps.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 5 Methodological Framework for GLOF hazard and Risk Mapping
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Fig. 4 Framework for the hazard and risk mapping of glacial lakes


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures
Type: text

The structural measures involve the construction and development of physical infrastructure to minimise the risk associated with the potentially dangerous glacial lakes. Structural measures comprise of the construction of spillways, diversion channels, siphoning as well as other engineering approaches to manage the rapid flow of water and debris material in the event of a GLOF. The effective implementation of the structural measures depends on the location and characteristics of the moraine dam. The effectiveness is also determined by the availability of funds and technical expertise. The following steps outline the key components of this framework.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures > 6.1 Site-Specific Vulnerability Assessment
Type: text

Conducting a comprehensive site-specific vulnerability assessment is the first step towards identifying regions that are vulnerable to GLOF events. This entails identifying the most vulnerable areas by carefully examining glacial lakes, possible flood routes, and downstream areas. Essential information is gathered by field surveys and data from remote sensing.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures > 6.2 Infrastructure Development and Engineering Solutions
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The framework presents engineering solutions that are customised to the specific characteristics of every risk zone that has been identified, taking into account the results of the vulnerability assessment. To prevent flooding, structural measures could involve the construction of retaining walls, debris dams, and other barriers. Floodwaters can also be redirected away from inhabited areas by channelizing rivers and creating diversion channels.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures > 6.3 Early Warning Systems and Monitoring Infrastructure
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To give at-risk communities timely warnings, the framework places a strong emphasis on the integration of early warning systems and monitoring infrastructure. Real-time data collection is facilitated by the installation of sensors, weather monitoring equipment, and gauging stations close to glacial lakes. When combined with communication networks, these methods allow authorities to alert people in good time, which facilitates preparation and efficient evacuation (Fig. 2a).


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 6 Structural Measures > 6.4 Community Involvement and Capacity Building
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Local communities must be actively involved for structural measures to be implemented successfully. The framework emphasises how crucial community involvement and capacity-building initiatives are. These programmes inform locals about possible dangers, how to evacuate, and how crucial it is to keep an eye on and maintain the structural safeguards put in place.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 7 Non-Structural Measures
Type: text

Non-structural measures emphasise on community-based strategies for hazard mitigation rather than involving physical infrastructure. Land-use planning, public awareness campaigns, and community readiness and response planning are examples of non-structural measures. Planning for community readiness and response entails collaborating with nearby communities to create strategies for tackling GLOF events. Plans for evacuation routes, emergency shelters, and protocols for community member collaboration and communication can all be included in these plans. Campaigns to raise public awareness are essential to ensuring that communities are aware of the dangers posed by GLOFs and are equipped to respond to emergencies. Media campaigns, instructional materials, and community gatherings are a few examples of public awareness initiatives. By making sure that future development considers the possibility of GLOF risks land-use planning may


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 7 Non-Structural Measures
Type: figure
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 7 Non-Structural Measures
Type: text

contribute to reducing the risk of GLOFs. Zoning regulations, construction restrictions, and other measures to lessen a community's susceptibility to GLOFs are examples of landuse planning (Fig. 2b).

Although GLOF hazard mitigation approaches are important, there are a number of obstacles that prevent them from being implemented effectively. The isolation and political marginalisation of numerous communities at danger from GLOFs is one of the primary obstacles. The lack of finances, technical know-how, and political clout in these communities can make it challenging to put into practice sensible hazard mitigation plans. The lack of funds for GLOF hazard mitigation measures is another issue. Low-income nations with little funding for hazard mitigation and infrastructure development make up a large number of those at risk from GLOFs. Notwithstanding these obstacles, there exist prospects for enhancing GLOF hazard reduction tactics.

GLOF risk reduction involves several strategies to mitigate the catastrophic impacts of GLOFs, which are becoming more frequent due to climate change and glacier retreat (Shijin et al. 2022; Taylor et al. 2023). Key measures include infrastructure development (e.g., spillways, dams), hazard mapping, early warning systems, and land use planning (Ahmed et al. 2024). Effective risk reduction also requires educating and involving local communities, as well as addressing the underlying causes of GLOFs, such as rising temperatures and glacial retreat. International cooperation is essential, particularly in transboundary regions, with organizations like ICIMOD facilitating collaborative efforts to develop and implement regional risk reduction strategies.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 8 Early Warning System (EWS)
Type: text

An EWS is a vital component of GLOF risk reduction. It is intended to identify and monitor changes in glacial lakes and to send alerts to people downstream in the event of a potential GLOF. EWS can give fast and accurate information to decision-makers and local populations, allowing them to take necessary action to mitigate the impact of a GLOF. EWS usually involves a range of monitoring devices, like water level gauges, weather stations, and seismometers, that are installed in and around glacial lakes to track changes in water level, temperature, and seismic activity (Fig. 2a). This monitoring data is sent to a central control center, where it undergoes analysis and processing to determine the likelihood of a GLOF. When a GLOF is detected, the EWS sends an alarm to downstream populations through different means of communication such as sirens, text messaging, and social media. The alert informs individuals about the location and timing of the anticipated flood and urges them to escape to safe areas. In addition to alarms, EWS can give real-time information regarding the extent and magnitude of a GLOF, allowing emergency personnel to better organize their response and deploy resources (Fig. 2a). The efficacy of an EWS, on the other hand, is dependent upon aspects, including the reliability and quality of monitoring data, the timeliness and accuracy of alerts, and the ability of local people to respond to alerts and escape to safe locations. To reduce the impact of GLOFs, EWS should be supplemented with other risk reduction measures such as risk mapping, land use planning, and infrastructure development.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 8 Early Warning System (EWS)
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 9 GLOF Risk Management Skills and Knowledge Transfer
Type: text

Effective GLOF risk reduction hinges on knowledge transfer and skill development. This involves sharing expertise and best practices among stakeholders—scholars, policymakers, emergency responders, and local communities—to enhance local capabilities in recognizing GLOF indicators, responding to alerts, and evacuating safely. Knowledge transfer improves the accuracy of risk assessments through data sharing on glacial dynamics and climate effects, and by incorporating lessons from past GLOF events. It also fosters global collaboration, crucial for managing risks in transboundary regions. By focusing on capacity building, data exchange, and international cooperation, communities can build resilience, refine risk assessments, and implement effective mitigation strategies.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 10 GLOF Hazard Mitigation and Climate Change Adaptation
Type: text

Climate change impacts, such as rising temperatures and changing patterns of precipitation, are increasingly being attributed to GLOFs, which may lead to the development and instability of glacial lakes (Zheng et al. 2021). As a result, measures to lower the danger of future GLOFs should be closely integrated with climate change adaptation initiatives. One important strategy for GLOF hazard prevention and climate change adaptation is to design and build infrastructure, such as dams and spillways. These engineering structures can aid in regulating the flow of water from glacial lakes, lowering the risk of massive flooding. However, such infrastructural solutions must be carefully developed and planned to ensure long-term effectiveness and sustainability.

Another strategy is to encourage ecosystem-based adaptation (EbA) initiatives, which can assist in lowering the risk of GLOFs and increase community resilience to the effects of climate change. Restoration of deteriorated habitats, such as wetlands, marshes, bogs, and forests, may assist with managing the flow of water and lessen the risk of floods as part of EbA efforts. Furthermore, EbA strategies can help to strengthen local populations' capacity to adapt to climate change impacts and resilience to GLOFs. Furthermore, successful GLOF hazard reduction and climate change adaptation necessitate the development of integrated risk management strategies that address the root causes and drivers of GLOFs, such as climate change impacts, glacier retreat, and human activities. To develop and implement appropriate risk reduction strategies, different stakeholders, including researchers, policymakers, emergency responders, and local communities, must work together.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation
Type: text

GLOFs are a severe threat throughout mountainous areas, particularly where glacial lakes exist. Glaciers are melting as a result of climate change, increasing the likelihood of GLOFs and posing a severe threat to people, infrastructure, and the environment. Several supportive factors can promote GLOF hazard mitigation and climate change adaptation activities to decrease the risks associated with GLOFs and adapt to the impacts of climate change. Several


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation
Type: figure
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation
Type: text

actions can help to mitigate GLOF hazards and adapt to climate change. A few examples are given below:

- Strong political will
- Collaboration and partnerships
- Engagement and the empowerment of local people
- Adequate funding
- Integration with other development goals
- Access to technology and data
- Awareness and education


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 12 Policy Legislation and Institutional Support
Type: text

Effective GLOF hazard mitigation and climate change adaptation require strong policy, law, and institutional support. Governments and other stakeholders have to develop and enforce laws and regulations to support risk management plans, provide needed resources, and encourage transparency and accountability. Furthermore, organizations must be built or enhanced to allow policy implementation and risk reduction measures. Climate litigation is being informed by interdisciplinary research on GLOF hazards, particularly concerning anthropogenic climate change. Notably, a Peruvian resident is suing a German energy firm for possible GLOF dangers; the case is presently pending before a German court. Research relates GLOF dangers and glacier changes to greenhouse gas emissions. Even though these incidents have an impact, it's important to take into account larger viewpoints on accountability and justice. An analysis of the Palcacocha case (Figure S2) highlights the different duties at different levels and highlights the impact of governance, socioeconomic conditions, cultural features, and climatic and non-climatic elements in creating exposure and vulnerability to GLOF threats.

- Policy and legislation
- Institutional support
- Financial support
- Capacity building
- Monitoring and assessment

Effective GLOF and climate change management relies on robust policies, institutional support, financial resources, capacity building, and monitoring. Policies should reduce emissions and promote sustainable practices, while institutions coordinate efforts and provide technical support. Financial backing is essential for infrastructure and early warning systems, sourced from various channels. Capacity building enhances stakeholder skills through targeted training. Comprehensive monitoring ensures progress and informs improvements in risk management strategies.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 13 Knowledge Disparity
Type: text

Knowledge disparity, the uneven distribution of information and expertise, significantly impacts GLOF hazard mitigation and climate change adaptation. Communities lacking access to scientific data, technical skills, and early warning systems face challenges


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 13 Knowledge Disparity
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Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 13 Knowledge Disparity
Type: text

in addressing GLOF risks. Financial constraints further exacerbate this issue, limiting the ability to implement protective measures and adapt strategies. Institutional capacity gaps, such as inadequate specialized agencies or poor coordination, also hinder effective responses. Additional factors like language barriers, traditional knowledge, and gender roles can contribute to knowledge imbalances, affecting the success of risk reduction efforts. Addressing these disparities requires a collaborative approach, including information sharing, institutional strengthening, and social inclusivity, to enhance community understanding and resilience against GLOFs and climate change.


Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies
Section: 14 Conclusion
Type: text

This study underscores the critical need for a comprehensive framework to mitigate GLOF hazards, particularly in the face of accelerating glacier retreat driven by climate change. By integrating both structural and non-structural measures, the research highlights the importance of proactive land-use planning, community-based strategies, and early warning systems (EWS) in managing GLOF risks effectively. The study's emphasis on detailed risk mapping and spatial analysis equips decision-makers with the tools necessary for targeted interventions, ensuring resources are allocated efficiently in highrisk areas. The implications of this study extend to policymakers, practitioners, and researchers, providing a roadmap for developing sustainable and adaptable solutions to the complex challenges posed by climate change in high-mountain regions. Legislative support, institutional capacity, and knowledge transfer are identified as crucial elements in enhancing the effectiveness of GLOF risk mitigation efforts. Addressing knowledge disparities and fostering interdisciplinary collaboration are essential for building resilient communities. Looking forward, the study advocates for ongoing research, particularly in understanding the socio-economic impacts of GLOFs, to ensure a holistic approach to disaster risk reduction. By integrating climate change adaptation into GLOF mitigation strategies, the findings offer a pathway for safeguarding vulnerable communities and preserving the stability of high-mountain environments.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1007/s11269-024-03958-x.

**Funding** No funding was received to conduct this study.

Data Availability Not applicable.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Abstract
Type: text

Glacier-associated hazards are becoming a common and serious challenge to the high mountainous regions of the world. Glacial lake outburst floods (GLOFs) are one of the most serious unanticipated glacier hazards, with the potential to release a huge amount of water and debris in a short span of time, resulting in the loss of lives, property, and severe damage to downstream valleys. The present study used multi-temporal Landsat and Google earth imageries to analyze the spatio-temporal dynamism of the selected glacial lake (moraine-dammed) in the Satluj basin of Western Himalayas. Furthermore, GLOF susceptibility of the lake was assessed using a multi-criteria decision-based method. The results show that the lake area has increased from 0.11 to 0.26 km2 over the past 28 years from 1990 to 2018. The susceptibility index value for the lake was calculated as 0.75, which indicates that the lake is highly susceptible to the GLOF. The depth and volume of the lake were estimated to be 16 m and 57×105 m3, respectively, using an empirical formula. HEC-RAS, HECGeo-RAS, and Arc-GIS software were utilized in this study to perform unsteady flow analysis and to determine the GLOF impact on the downstream area. The worst-case GLOF scenario (breach width of 75 m) was revealed during an overtopping failure of the moraine dam, resulted in a peak discharge of  $4060 \text{ m}^3$ /s and releasing a total water volume of  $57 \times 10^5 \text{ m}^3$ . The breach hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river channel. The analysis further reveals that the routed flood waves reach the nearest settlement, i.e., Rajpur town, situated at a distance of 102 km in the downstream valley of the lake at 6 h after the beginning of the lake breach event with a peak discharge/flood of 1757 m3/s and maximum flow velocity of 1.5 m/s. With the ongoing climate warming and glacier retreat, moraine-dammed lakes are becoming more hazardous and thus increasing the total threat. Therefore, it is mandatory to monitor and assess such lakes at regular intervals of time to lessen the disastrous impacts of GLOFs on the livelihood and infrastructure in the downstream valleys. The findings of this study will aid in the creation of risk management plans, preparatory tactics, and risk reduction techniques for GLOF hazards in the region.

 $\textbf{Keywords} \ \ Glacier \ recession \cdot Glacial \ lake \ expansion \cdot GLOF \ susceptibility \cdot Glacial \ lake \ outburst \ flooding \ (GLOFs) \cdot HEC-RAS \cdot Western \ Himalaya-India$

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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Introduction
Type: text

The glacierized basins of the Himalayan region have witnessed a serious effect of the changing climate, and even little changes in climate have a wide range of consequences/implications for the Himalayas and its ecology (Ahmad

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et al. 2022; Gurung et al. 2021; Wani et al. 2022). The ongoing climate change/variability has resulted in the significant mass loss of the glaciers throughout the high terrains of the world and several among them are under threat (Kumar et al. 2020; Ahmed et al. 2021a; Sarkar et al. 2020); consequent to that, a large number of glacial lakes have shown a discernible increase in terms of number and area (Bolch et al. 2012). The meltwater from these retreating glaciers not only increases the water level of the existing lakes, but it has also led to the formation of thousands of new lakes (Mir et al. 2018; Ahmad et al. 2022). For example, over 2070 lakes were formed in the Nepal Himalayas alone during the second


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Introduction
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Introduction
Type: text

half of the twentieth century as a result of changing climatic regimes (Maharjan et al. 2018). Some of these lakes are potentially dangerous/disastrous and pose a serious threat to the life and property in the downstream region.

Snow and glacier melt in the Himalayas is caused by a local weather system driven by mountain-induced dynamic and thermodynamic processes that affect precipitation pattern, cloudiness, and other factors, forming glacial lakes near the terminus (proglacial lakes), on the surface (supraglacial lakes), or within the glaciers (subglacial lakes). Due to the fast accumulation of glacier meltwater in these lakes, the effects of rapid flash flooding in riparian areas downstream have been disastrous (Richardson and Reynolds 2000; Jain et al. 2012). The melting of mountain glaciers and the growth of massive glacial lakes are two of the most visible and dramatic effects of climate change in this environment (Bolch et al. 2012). The inventory of glacial lakes has shown a total of around 2000 glaciers (Prasad et al. 2019) and 769 glacial lakes in the Satluj basin (Randhawa and Sharma 2013). Similar to other parts of the Himalayas, glaciers in the Satluj basin are retreating and glacial lakes are expanding at a greater pace (Gaddam et al. 2016; Mandal and Sharma 2020; Singh et al. 2021) and are expected to lose 53% and 81% of the area at the end of the century under the RCP 8.5 scenarios of CNRMCM5 and GDFDL-CM3 models (Prasad et al. 2019). As a result, the size and number of supraglacial and proglacial lakes that merge to form larger glacier lakes behind the loose moraine may grow faster (IPCC 2012; Worni et al. 2013; Emmer 2017; Harrison et al. 2018). Moraine-dammed glacial lakes have a higher risk of breaching, making them potentially dangerous due to the massive volume of water accumulated. Large outburst flood (GLOF) and risks can be produced in downstream areas during any sort of breaching and rapid discharge of water and debris from such lakes (Richardson and Reynolds 2000; Westoby et al. 2014; Worni et al. 2014). GLOF represents a catastrophic event in the Himalayas and is common in the glacierized areas of the region, thus posing a potential risk to the downstream hydro-power plant projects due to the extreme flow of a river to cause great damage. GLOF phenomenon is related to complex flow characteristics in both spatial and temporal scales (Westoby et al. 2014).

From the last few decades, the Indian Himalayas have witnessed a large number of glaciers associated events, for example, Chog Kumdan GLOF event (1929) in Shyok River basin (Gunn 1930), Chorabari GLOF event (2013) in Mandakini basin (Allen et al. 2016), and recent being Chamoli disaster (2021) in Uttarakhand (Shugar et al. 2021) are some of the events well documented in the scholarly literature. These disastrous GLOF incidents are an example of calamitous GLOF events that have caused havoc on people's lives and livelihoods (Carrivick and Tweed 2016). According to the study carried out by Dubey and Goyal


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Introduction
Type: figure
Figure

Image /page/1/Picture/5 description: A block of black text on a white background. The text reads: "2020, in Indian Himalayas, there are around 23 glacial lakes that are at very high risk and 56 in the high-risk category. Nonetheless, the risk posed by multiple triggering mechanisms should be estimated, particularly in the Himalayan region, where 68 percent of hydropower projects are located on probable GLOF tracks (Schwanghart et al. 2016)." The numbers "2020" and "2016" are in blue text.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Introduction
Type: text

In the year 2000, the Satluj valley witnessed a devastating flood caused by cloud bursts and the resultant GLOF events, causing extensive damage in downstream areas. Thus, the region needs continuous monitoring and assessment of lakes that have the potential to cause a GLOF event (Randhawa et al. 2021). Preparation of an inventory and identification of lakes as potentially dangerous is the initial step for the hazard assessment of the lakes, followed by the GLOF modeling. To prevent and monitor the GLOF hazard and analyze the damage that may occur in the future, the integration of remote sensing and GIS could play an important role in identifying dangerous lakes and monitoring GLOF events in real-time. Geographic information system (GIS) and remote sensing (RS) technology have been successfully used globally for the monitoring and assessment of glaciers, glacial lakes, and associated hazards. The GIS and RS-based tools and techniques allow rapid investigation of huge glaciated areas in a very inexpensive way and are therefore considered very suitable for glacial lake and GLOF studies (Imdad et al. 2022; Ahmed et al. 2021b). Keeping in view the above discussion on how climate change is inducing glacier retreat and thereby glacial lake expansion and increasing hazard potentiality of the GLOFs, the aim of the present study is to achieve the following objectives.

- To analyze the spatio-temporal changes in a selected lake for the GLOFs study using multi-temporal satellite imageries from 1990 to 2018.
- To assess the GLOF susceptibility of the lake using a multi-criteria decision-based method.
- To perform flood routing assessment of glacial lake hazard through hydrodynamic model (HEC-RAS), the study incorporates the following: (i) estimate the volume of a lake using empirical equation, (ii) evaluate multiple dam breach scenarios on the basis of varying breach width and breach formation time, producing breach outflow hydrograph, (iii) one dimensional (1-D) hydrodynamic mathematical modeling to evaluate the hazard potential of the selected lake, and (iv) a detailed study on flow hydraulic of potential GLOF and its downstream impact at different sites along the river.
- The main objective of this research is to create a flood inundation map by employing the HEC-RAS model, RS and GIS, including predictions of water spread, maximum flood depth, travel duration, and peak discharge owing to the GLOF hazard.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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The present study has selected a potentially dangerous glacial lake from the Satluj basin of the Indian Himalayas to carry out a GLOF modeling using HEC-RAS. The lake is located between 31° 39′ 36.47″ N Latitude and 78° 09′ 59.85″ E longitude at an elevation of 4267 m above sea level (Fig. 1). The lake is dammed by an unconsolidated moraine and connected to the snout of the feeding glacier (compound basin). The nearest settlement which is vulnerable to the probable GLOF event is located at a distance of approximately 8.5 km in the downstream region. The HPPCL Kashang hydropower project is also located in the downstream region of the lake at a distance of 39.06 km from the outlet. The snout of the glacier (feeding lake)

has shown a retreat of 153 m at the rate of 7.6 m/year from 2000 to 2020. The current area of the lake and its feeding glacier is estimated to be 0.26 km2 and 5.72 km2, respectively. The glacier meltwater from this lake is supplied to the Satluj River. Satluj River is one of the key rivers in the Indus system. The Satluj River rises in Tibet at Manasarovar and ends in India near Namigia and it runs through the two states of India, i.e., Punjab and Himachal Pradesh. Satluj reaches the Anandpur Dun, a valley area between Sivaliks and Himalayan outer range near Nangal Town. The earlier studies have focused on the mapping, evolution, identification, and hazard assessment of glacial lakes. We have tried to evaluate the impacts of GLOF in the downstream valley and GLOF was routed from the lake outlet to the last selected location (Rajpur town), covering a distance of 102 km (Fig. 1).


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Study area
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Figure

Image /page/2/Figure/5 description: A figure composed of four maps, labeled a, b, c, and d, showing the location of a selected lake for GLOF (Glacial Lake Outburst Flood) hazard assessment.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Study area
Type: text

Map 'a' displays a map of India with the state of Himachal Pradesh highlighted in red. The map includes latitude and longitude lines and a scale bar in kilometers.

Map 'b' is an elevation map of Himachal Pradesh, showing the Sutlej basin outlined in black. The legend indicates elevation ranges from a low of 243 meters to a high of 6619 meters. Major rivers like the Chenab, Sutlej, Yamuna, and Ganges are labeled.

Map 'c' provides a more detailed elevation map of the Sutlej basin, outlined in red. The elevation ranges from a low of 454 meters to a high of 6446 meters. It shows the location of a specific lake, Rajpur town, and HPPCL Karcham. Rivers such as the Sutlej, Chenab, and Indus are also shown.

Map 'd' is a close-up satellite image of the lake. The lake is outlined in red and is fed by a glacier, shown in cyan. Key features are labeled, including the 'Glacier', its 'Snout', the 'Lake' itself, the 'Moraine-dam' that contains the lake, the 'Lake outlet', and the resulting 'Stream'.

Fig. 1 Location map of the selected lake for GLOF hazard assessment, (a) Himachal Pradesh on map of India, (b) Satluj basin on the map of Himachal Pradesh, (c) Lake location on the Satluj basin map and (d) Glacio-geomorphological features around the lake


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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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Satellite data have been widely used for the change detection purpose of various parameters of glaciers and glacial lakes (Paul and Andreassen 2009). Traditional field-based surveys, which are highly recommended for measuring and monitoring changes in glaciers and glacial lakes, are often limited by the ruggedness and harshness of Himalayan terrain (Pratap et al. 2016). To overcome this problem multi-temporal satellite data offers a wide range of opportunities to monitor such glaciers and glacial lakes. In this study, we used satellite data of four decades to analyze the spatio-temporal dynamism of the selected lake. Landsat imagery with its free availability and fine spatial resolution of 30 m hosted by the Global Land Survey (GLS) data system of the United States Geological Survey (USGS) was downloaded for the years, namely 1990 (4–5 Thematic Mapper (TM)), 2000 (4–5 Thematic Mapper (TM)), 2010 (Enhanced Thematic Mapper (ETM)), and 2018 (Operational Land Imager (OLI)). Change detection of the glacial lake on the multi-temporal Landsat images is depicted in Fig. 2. The other data set which is required to

carry out GLOF modeling is digital elevation model (DEM) (Table 1).

In this study, we used Satellite Radar Topography Mission (SRTM) DEM with a spatial resolution of 30 m downloaded from the web portal www.earthexplorer.usgs.gov. The DEM was used to extract the catchment/watershed of the glacial lake and its feeding glacier. It was also used to generate a cross-section at 1 km intervals as well as elevation data. The cross-sections were created downstream from the lake to the final selected location. The cross-sections from


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology
Type: table
Table: Table 1 Satellite data used in the study

| Date of acquisition | Sensor        | Spatial resolution (m) |
|---------------------|---------------|------------------------|
| 3 December 1990     | Landsat 4-5TM | 30                     |
| 19 December 2000    | Landsat 4-5TM | 30                     |
| 22 August 2010      | Landsat 4-5TM | 30                     |
| 6 October 2018      | Landsat 8OLI  | 30                     |
| 9 October 2018      | Google earth  | 1.5–6                  |
| 12 November 2014    | SRTM-DEM      | 30                     |

Date of acquisition
Sensor
Spatial resolution (m)


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology
Type: figure
Figure

Image /page/3/Figure/8 description: A figure displaying four satellite images in a 2x2 grid, illustrating the growth of Satluj lake over time. Each panel is labeled with a letter and includes a title, a north arrow, coordinate grids, and a scale bar.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology
Type: text

- Panel a, titled "Satluj lake - 1990", shows a small, light-blue lake outlined in green.
- Panel b, titled "Satluj lake - 2000", shows the lake has grown in size and is a darker blue, outlined in magenta.
- Panel c, titled "Satluj lake - 2010", depicts a further enlarged, dark blue lake outlined in red.
- Panel d, titled "Satluj lake - 2018", shows the lake at its largest extent in the series, appearing dark teal and outlined in yellow.

All images show the surrounding terrain in reddish-brown hues. The longitude ranges from approximately 78°9'45"E to 78°10'15"E, and the latitude ranges from 31°39'30"N to 31°39'45"N. A scale bar in each image indicates distances up to 0.3 km.

Fig. 2 Time series Landsat images showing growth of glacial lake area from 1990 to 2018, **a** lake area 1990 **b** lake area 2000, **c** lake area 2010, and **d** lake area 2018


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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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the DEM were generated because the field-measured crosssections were not available for this area due to the rugged terrain and harsh weather conditions. Besides that, highresolution Google Earth imagery was used for the crosschecking and validation of the lake extents. Chow, 1959, has suggested a Manning coefficient range between 0.03 to 0.07 for the hilly terrain with a steep slope and no vegetation, gravel, cobbles, boulders, and bushes on the banks. Considering this, we have used a Manning coefficient range of 0.04 to 0.06 as an input parameter for the GLOF routing of the selected lake of Satluj basin in HEC-RAS software. The overall methodology is given in Fig. 3.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
Type: text

In order to assess the GLOF susceptibility, different parameters have been employed in the past studies pertaining to various parts of the Himalayan region (Che et al. 2014; Jain et al. 2012; Aggarwal et al. 2014; Kougkoulos et al. 2018;

Allen et al. 2016). However, as of now, there is no particular set of parameters that can be used to analyze the GLOF susceptibility. In this study, we have used 5 parameters or potential factors that govern the mechanism of potential GLOF to assess the GLOF susceptibility of the selected lake. The parameters were identified and compiled from the literature survey which include lake area, lake expansion, distance between lake and feeding glacier, type of dam, and potential of ice or rock avalanche (Khadka et al. 2018; Gurung et al. 2021). These parameters have been calculated using the satellite imagery (Landsat TM/ETM+/OLI) and digital elevation model. The parameters and their source are given in Table 2.

After the identification and calculation of parameters weights were allocated to each factor using analytical hierarchy process (AHP). AHP model propounded by Satty, 1980, has been widely used for the hazard and risk assessment such as land degradation, landslides, snow avalanche, debris flow (Himan et al. 2014), and GLOF hazard assessment (Khadka

Datasets and methodology
GLOF susceptibility and AHP


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
Type: figure
Figure

Image /page/4/Figure/7 description: A methodology flowchart illustrating the steps for a GLOF (Glacial Lake Outburst Flood) analysis using the HEC-RAS model. The process begins with two primary inputs in yellow boxes: 'SRTM 30m DEM' and 'Landsat data series 1990-2018'. The SRTM data is used to 'Delineate river reach' and then 'Creation of geometry database along the river in Hec-GeoRAS'. This involves creating 'Bank lines', 'Flow path centerlines of river', and 'cross-section generation across the river', which leads to 'Topology creation' and then 'Export RAS data into HECRAS'. This data is then adjusted for 'cross section', 'bank station', and 'Manning coefficient'. The Landsat data is used for 'Mapping of glacial lake', which informs both the geometry creation and the 'Lake area and volume estimation from Huggle's equation'. This leads to generating a 'Lake breach hydrograph' using Froehlich's equation. The core of the model is the '1 D hydrodynamic modeling' which uses 'Boundary condition' inputs of 'Breach Hydrograph' and 'Friction slope'. The adjusted RAS data and the modeling results feed into 'Unsteady flow estimation'. This is followed by 'Flood routing of breach hydrograph along the river channel'. The final outputs of the analysis are 'GLOF inundation mapping' and 'Downstream impact assessment'.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
Type: text

Fig. 3 Methodology flow chart depicting the steps followed in HEC-RAS model for the GLOF analysis


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
Type: table
Table: Table 2 Index values assigned to factors/parameters

| Factors (f) | Factor/parameter                           | Class                                           | Rank                  | Index value (Ci) | Rank | Selected lake | Factor weight (Wi) | Data source                            | Reference                                                                  |
|-------------|--------------------------------------------|-------------------------------------------------|-----------------------|---------------------|------|---------------|--------------------------|----------------------------------------|----------------------------------------------------------------------------|
| f1          | Lake area (km2)                            | > 0.5 0.5–0.1 < 0.1                       | High Medium Low | 1 0.50 0.25   | 5    | 0.262         | 0.04                     | Landsat data (2020)                 | Campbell (2005); Aggarwal et al. (2014); Washakh et al. (2019) |
| f2          | Lake expansion (%)                         | < 50% 50–20% < 20%                        | High Medium Low | 1 0.50 0.25   | 3    | 56.42         | 0.16                     | Multi-temporal satellite data       | Bolch et al. (2012); Che et al. (2014); Jain et al. (2012)     |
| f3          | Distance between lake and glacier    | At snout > 500 < 500                      | High Medium Low | 1 0.50 0.25   | 4    | At the snout  | 0.09                     | Landsat and Google Earth imagery | Che et al. (2014); ICIMOD (2011)                                     |
| f4          | Type of dam                                | Ice dam Moraine dam Other types of dam | High Medium Low | 1 0.50 0.25   | 2    | Moraine dam   | 0.24                     | Google Earth imagery and DEM     | Huggel et al. (2002)                                                    |
| f5          | Potential of ice or rock ava- lanche | Susceptible Not susceptible                  | High Low           | 1 0.25           | 1    | High          | 0.22                     | Google Earth imagery                | Allen et al. (2016); Islam and Patel (2020)                       |

Factors (f)
Factor/parameter
Class
Rank
Index value (Ci)
Rank
Selected lake
Factor weight (Wi)
Data source
Reference


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > GLOF susceptibility and AHP
Type: text

et al. 2018; Gurung et al. 2021). Each parameter/factor were ranked into three classes, viz., high, medium, and low with a corresponding index value of 1, 0.25, and 0.5 (Prakash and Nagrajan 2017; Gurung et al. 2021). The final weights for each of the parameters/factors were determined by multiplying the factor weight (Wi) by the class index values (Ci) based on the measurement of lake factors. At the last stage, final weights of each factor were added to assess the GLOF susceptibility of the lake (Eq. (1)).

GLOF Susceptibility =
$$\sum_{i=0}^{n} (Ci) * (Wi)$$
 (1)

The GLOF susceptibility index value ranges from 0.25 to 1. Using an equal interval class system, the values are divided into five categories: very low (0.25-0.40), low (0.40-0.55), medium (0.55-0.70), high (0.70-0.85), and very high (>8.5).


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Hydrodynamic modeling using HEC-RAS
Type: text

Modeling of GLOF hazard and risk has been carried out in several locations throughout the world, using a wide range of approaches and methodologies. In this study, we have used HEC-RAS hydrodynamic model to perform GLOF modeling and to evaluate impacts of flood caused due to the potential GLOF in the downstream region of the selected lake located in the Satluj basin. HEC-RAS model has been widely used for this purpose across the world (Dortch et al. 2011; Klimeš et al. 2014b; Watson et al. 2015; Hussain

Datasets and methodology
Hydrodynamic modeling using HEC-RAS


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Hydrodynamic modeling using HEC-RAS
Type: figure
Figure

Image /page/5/Picture/9 description: A close-up of a paragraph of text from a document, printed in a black serif font on a white background. The text reads: et al. 2020; Kougkoulos et al. 2018; Wang et al. 2018). It is an integrated system of software that is designed to use in a multi-tasking environment (Hussain et al. 2020) and is most significantly used for glacial hazard studies (Klimeš et al. 2016). It is a user-friendly reliable model that has the dynamic capability of performing complex flow simulations in design, management, and operation of river systems. The model is based on one-dimensional St. Venant equations that are used to simulate flood scenarios generated by a glacial lake outburst. In hydraulic model, two key input data sets (geometry data and flow data) are required to compute Unsteady Flow Analysis (UFA). Cross-section data derived from the field survey is recommended, but due to the rugged terrain and harsh weather conditions, it is not easy to carry out the field survey.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Hydrodynamic modeling using HEC-RAS
Type: text

Geometry data (river cross-sections, center lines, bank lines, flow lines, and elevation data) was derived from the satellite imagery in a plugin of HEC-Geo-RAS in ArcGIS environment. Subsequently, the geometric data was imported into HEC-RAS software for the unsteady flow simulation. The flow data requires upstream and downstream boundary conditions. The dam breach outflow hydrograph is commonly used for upstream and downstream boundary conditions as normal depth, which is calculated using the channel bed slope. The present study considered a glacial lake as a dam failure structure with a specific crest level and crest length. Various parameters such as simulation time series, elevation above the invert level and its breach dimensions were specified to the corresponding lake. An


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Hydrodynamic modeling using HEC-RAS
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Hydrodynamic modeling using HEC-RAS
Type: text

inline structure that characterizes the lateral-frontal moraine is entered at the lower elevation of the lake, and the failure catastrophe is modeled and evaluated by breaching the inline structure before performing a dam break analysis. For an upper boundary condition, glacial lake acts as a storage area by its elevation storage relationship. The total volume and maximum depth acquired from the empirical equations and DEM were used to generate the elevation-storage volume. DEM was also used to get the Triangular Irregular Network (TIN) terrain to set up the 1-D (hydrodynamic) modeling and subsequently minimum elevation of the lake was derived from the TIN network. The storage capacity at the minimum elevation of the lake (last elected location) is zero, whereas it was obtained for the maximum elevation (topmost elevation of lake). On the basis of these two parameters, elevation storage capacity for the lake was generated. Such parameters are required as an input to the empirical equations that estimate the potential peak discharge and the failure time of a GLOF event (moraine dam breach event). For a dam break analysis, the Froehlich model uses fewer input parameters to estimate peak breach outflow hydrograph. At the final stage, unsteady flow analysis was computed to derive important results like maximum flood depth, maximum flood velocity, peak flood hydrographs, and water surface elevation at different crosssections in the downstream region.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Datasets and methodology > Moraine dam breach modeling
Type: text

Glacial lake area, maximum depth, and volume of water in the lake are the important parameters required to perform GLOF modeling. As discussed above, field-based observation was not possible for the current studied lake due to the rugged terrain and harsh weather conditions; thus, we relied on the area-based empirical relationship to obtain the depth and volume of the glacial lake. There are several empirical formulas available in the scholarly literature but the empirical relation developed by Huggel is most extensively employed in past GLOF hazard studies, where bathymetric surveying data are not available. However, this equation was developed for the Swiss Alps region, but due to the unavailability of bathymetry data, we used this equation to derive the volume of the glacial lake. The equation given by Huggel et al. (2002) is given as:

$$V = 0.104A^{1.42} \tag{2}$$

V (m3) and A (m2) are the volume and area of the lake, respectively. By employing Eq. (2), we get a total volume of  $5749 \times 10^3$  m3.

Important parameters such as failure time of dam, extent of overtopping prior to the failure, shape, size, and breach formation time should be considered for the evaluation of the dam failure, because the dam-break flood estimation is dependent upon these parameters. Factors such as initial and final breach width and level, breach shape, breach development formation time, and reservoir level at the time of initial breach are all important breach features that need to be included into existing dam break models (Jain et al. 2012).

We have estimated these essential breach characteristics (breach width and breach formation time) that are required as inputs to carry out dam break analysis have been approximated using Froehlich's empirical relations:

$$B_{w} = 0.1803K_{o}(V_{w})^{0.32}(h_{b})^{0.19}$$
(3)

$$T_{f} = 0.00254 (V_{w})^{0.53} (h_{b})^{0.9}$$
(4)

In the above equations,  $B_w$  and  $V_w$  are the breach width (m) and volume of lake (m³), whereas  $h_b$  and  $T_f$  are the breach height (m) and breach formation time (hr). Equation 1 mentioned in the above section was used to compute the volume of the lake.  $B_w$  is the function of  $V_w$  and  $h_b$ . In the present study, a total of nine dam breach scenarios based on varied breach width and breach formation time are designed for a mechanism of a glacial lake outburst flood. The failure mechanism as an overtopping is considered in breach scenarios to evaluate a potential worst-case GLOF event of a lake. These scenarios produce peak flood hydrographs at the downstream cross-sections generated at various locations.

Datasets and methodology
Moraine dam breach modeling


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Change in glacial lake area
Type: text

Using Landsat imagery in combination with high-resolution google earth imagery, we have identified and mapped the selected glacial lake, its feeding glacier, and other geomorphological features such as moraines, crevasses, snout, supraglacial channel, and streams. The lake extents derived from the multi-temporal satellite images have shown a considerable increase of the lake area from 0.112 km2 in 1990 to 0.26 km2 in 2018. Thus, showing an overall increase of 0.12 km2 (56.4%) over the period of 28 years indicates a rapid expansion at the rate of 0.01 km2/year. The snout of the glacier (feeding lake) has shown a retreat of 153 m at the rate of 7.56 m/year from 2000 to 2020. The lake has been identified as potentially dangerous by employing the various parameters derived from the scholarly literature. Previously, it has been identified as potentially dangerous glacial lake by Prakash et al. (2013). The present glacial lake is situated at the retreating glacier snout dammed by loose and unconsolidated material. Furthermore, it has been identified that there are probable rockfall/avalanche zones in the

Results and discussion
Change in glacial lake area