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Section: ABSTRACT
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This manuscript was handled by Emmanouil Anagnostou, Editor-in-Chief.

Deglaciation due to atmospheric warming has led to the formation and expansion of numerous glacial lakes, especially in the eastern Himalaya. Many of these glacial lakes are susceptible to glacial lake outburst floods (GLOFs), which can cause far-reaching impacts on downstream infrastructure and livelihoods. This study is a comprehensive assessment of GLOF susceptibility, hazard, exposure, vulnerability, and risk for four potentially dangerous glacial lakes (Bechung Tsho, Raphstreng Tsho, Thorthomi Tsho, and Lugge Tsho) located in the Lunana glacier complex of the Phochu basin in Bhutan. Exposure and risk assessments were based on modelled GLOF hydrodynamics, infrastructure data, population and housing census data. Among the four glacial lakes, Thorthormi Tsho and Lugge Tsho are relatively more susceptible to outburst floods than Raphstreng Tsho and Bechung Tsho. Outflow flood volumes from these lakes range between  $6 \times 10^5$  and  $3 \times 10^8$  m3 which can potentially impact over 16,000 people, two hydropower projects, numerous other infrastructures, and agricultural land up to 150 km downstream of the lakes. The GLOF exposed elements are largely in Punakha and Wangdue Phodrang districts, which are located 90 and 100 km downstream of the Lunana glacier complex respectively. Among 17 subdistrict blocks within the basin, one (Lunana) lies in a very high GLOF risk area, while 9 others are in the high GLOF risk zone. The study highlights the importance of multi-source data in improving the knowledge of downstream GLOF risk and serves as a base for improving GLOF risk reduction strategies in high mountain regions.


Document: 1-s2.0-S0022169423002536-main
Section: 1. Introduction
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Glacial lakes can be potential hazard sources as outburst floods originating from these lakes can cause far-reaching damage to infrastructure and people (Allen et al., 2015; Zheng et al., 2021b). Glacial lakes are often dammed by unstable moraine or glacier ice and are surrounded by destabilizing permafrost slopes or hanging glaciers (Otto, 2019; Richardson and Reynolds, 2000). Slope failure originating in the vicinity of a glacial lake can result in a mass movement that can enter a lake, capable of displacing the lake's water creating an impulse wave that overtops the frontal dam. The instability of slopes and glacial lakes

are further aggravated in young and seismically active alpine mountains (Armbruster et al., 1978), such as Himalaya, where hazard chains/cascades are common (Kirschbaum et al., 2019; Sharma et al., 2022). The rapid expansion of glacial lakes resulting from glacier retreat increases the exposure of the lakes to glacial lake outburst flood (GLOF) triggers from the surrounding catchment, where slopes are destabilized due to permafrost degradation making it more susceptible to slope failures and rockfalls (Grämiger et al., 2017). In addition, as lakes grow depending on the basin geometry, the risk of overtopping failures may increase. Thus, lake growth increases the probability of GLOFs being triggered (Rounce et al., 2017, 2016) and increases the potential flood volume

E-mail address: guoqing.zhang@itpcas.ac.cn (G. Zhang).

<sup>\*</sup> Corresponding author.

(Fujita et al., 2013), which are among the main factors contributing to increased GLOF hazard (Westoby et al., 2015). These intrinsic and extrinsic hazard drivers can cause largely unpredictable, rapid, and deadly draining of lake water, which may transform into debris or hyper-concentrated flow as the flood entrains sediment (Richardson and Reynolds, 2000; Sattar et al., 2022).


Document: 1-s2.0-S0022169423002536-main
Section: 1. Introduction
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, 2016 ) and increases the potential flood volume E - mail address : guoqing . zhang @ itpcas . ac . cn ( G . Zhang ) . < sup > \ * < / sup > Corresponding author . ( Fujita et al . , 2013 ) , which are among the main factors contributing to increased GLOF hazard ( Westoby et al . , 2015 ) . These intrinsic and extrinsic hazard drivers can cause largely unpredictable , rapid , and deadly draining of lake water , which may transform into debris or hyper - concentrated flow as the flood entrains sediment ( Richardson and Reynolds , 2000 ; Sattar et al . , 2022 ) .

Since GLOFs are high-magnitude and largely unpredictable catastrophic events, they can cause immense damage and disruption to the lives and infrastructures in the downstream settlements (Allen et al., 2015; Carey et al., 2011; Zheng et al., 2021b). It is estimated that 15 million people globally are exposed to the threat of GLOF, with populations in High Mountain Asia the most exposed, with around 1 million people living within 10 km of a glacial lake (Taylor et al. 2023). Numerous GLOF events have occurred in the past across the world, causing damage to infrastructures and the loss of thousands of lives (Carrivick and Tweed, 2016; Veh et al., 2022). For example, the Jinweng Co GLOF event in 2020 in the southeastern Tibetan Plateau destroyed ten residential houses, washed away eight bridges, and 43.9 km of road, also causing damage to several other structures and agricultural land (Zheng et al., 2021b). In 2013, the debris flow resulting from Chorabari lake combined with cloud-burst-induced landslides and flooding in the Indian state of Uttarakhand caused over 6,000 deaths and heavy damage to infrastructure (Allen et al., 2015). In the Peruvian Cordillera Blanca, GLOFs in 1941 and 1945 killed over 6,000 people, washed away  $\sim$ 35 % of Huaraz city and other essential infrastructures, including hydroelectric power stations (Carey et al., 2011). Bhutan alone has recorded at least 18 GLOF events since the 1950s, the latest being the breaching of subsidiary lake II of Thorthormi Tsho on 20 June 2019 (Gurung et al., 2017; Komori et al., 2012; National Center for Hydrology and Meteorology [NCHM], 2019b; Rinzin et al., 2021). At least seven of these past GLOF events in Bhutan have caused varying degrees of infrastructure damage and loss of lives in the downstream settlements. The most serious was the 1994 GLOF event of Lugge Tsho, amongst best-known contemporary GLOF events in the Himalaya (Veh et al., 2019b), which led to around 21 fatalities, seriously damaged Punakha Dzong, the capital administrative center of Bhutan, and around 90 houses (Komori et al., 2012; Rinzin et al., 2021; Watanbe and Daniel, 1996). All these tragic and disastrous past GLOF experiences underpin the need for systemic flood disaster risk reduction (DRR) activities for which detailed downstream GLOF hazard and risk assessment provides the necessary first step.


Document: 1-s2.0-S0022169423002536-main
Section: 1. Introduction
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loss of lives in the downstream settlements . The most serious was the 1994 GLOF event of Lugge Tsho , amongst best - known contemporary GLOF events in the Himalaya ( Veh et al . , 2019b ) , which led to around 21 fatalities , seriously damaged Punakha Dzong , the capital administrative center of Bhutan , and around 90 houses ( Komori et al . , 2012 ; Rinzin et al . , 2021 ; Watanbe and Daniel , 1996 ) . All these tragic and disastrous past GLOF experiences underpin the need for systemic flood disaster risk reduction ( DRR ) activities for which detailed downstream GLOF hazard and risk assessment provides the necessary first step .

GLOFs often involve a cascade of different processes, variably termed a process chain or hazard cascade (Mergili et al., 2020; Zheng et al., 2021b), which can increase the spatial reach and destructive power of the event (Sattar et al., 2022). In the Himalaya, GLOFs frequently start with rapid mass movement into the lake, often due to slope failure from the mountain flank above the lake, leading to displacement waves, overtopping the dam (Komori et al., 2012; Nie et al., 2018). The overtopping waves cause moraine dam incision and dam failure resulting in a sudden discharge of lake water. As the flood propagates farther downstream, it transforms into debris flow and hyper-concentrated or secondary debris flow, depending on the geologic and topographic characteristics of the river channel and available sediment (GAPHAZ, 2017; Schneider et al., 2014; Westoby et al., 2014; Worni et al., 2014). These complex GLOF process chains present manifold challenges to accurately capturing its physical characteristics by numerical modelling and to a growing extent, the whole hazard cascade. However, emerging models can either better represent phase changes or sediment-laden events and their application to past events has increasingly enabled a better understanding of GLOFs. Some examples of commonly employed mass movement models include RAMMS (Christen et al., 2010), r.avaflow (Mergili et al., 2017), IBER (Bladé et al., 2014), FLOW3D (Flow Science, 2012), D-Claw (Iverson and George, 2014) and, primarily for clear water floods, HEC-RAS (CEIWR-HEC, 2021). Two or more of these models can be combined appropriately to model each process chain separately and sequentially (Frey et al., 2018; Schneider et al., 2014;

Worni et al., 2014), or the entire process chain can be integrated into a single model framework (Mergili et al., 2017). However, due to computation cost and uncertainty of input parameters for practical application of numerically sophisticated models (Westoby et al., 2014), computationally less complex models like HEC-RAS 2D are often used to capture first-order hydrodynamics and impacts of GLOF (Maskey et al., 2020; Sattar et al., 2021a; Sattar et al., 2021b).


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separately and sequentially ( Frey et al . , 2018 ; Schneider et al . , 2014 ; Worni et al . , 2014 ) , or the entire process chain can be integrated into a single model framework ( Mergili et al . , 2017 ) . However , due to computation cost and uncertainty of input parameters for practical application of numerically sophisticated models ( Westoby et al . , 2014 ) , computationally less complex models like HEC - RAS 2D are often used to capture first - order hydrodynamics and impacts of GLOF ( Maskey et al . , 2020 ; Sattar et al . , 2021a ; Sattar et al . , 2021b ) .

Population and infrastructures continue to grow in GLOF-prone areas despite the increasing threat from GLOFs (Allen et al., 2022, Carrivick and Tweed, 2016, Taylor et al. 2023). This is especially true for Bhutan, where most of the settlements (National Statistics Bureau [NSB], 2018) and major hydropower projects (the mainstay of the country's economy) are concentrated along the glacier-fed river valleys; for example, Punatsangchu hydroelectric dams (PHP-I and -II) are fed by meltwater from glaciers in the Lunana region (Ministry of Economic Affairs [MOEA], 2021). Although the positive trend in the frequency of GLOFs has decayed since the 1970s (Veh et al., 2022), some studies have forecasted a 3 to 7-fold increase in GLOF hazard and downstream risk in the Himalaya due to future deglaciation (Allen et al., 2016; Zheng et al., 2021a). The assessment at the global scale has pointed out that Bhutan and Nepal have the greatest national-level economic impact from GLOF (Carrivick and Tweed, 2016), and in absolute normalized GLOF risk scores, Bhutan is ranked 13th in the world (Taylor et al. 2023).


Document: 1-s2.0-S0022169423002536-main
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2021 ) . Although the positive trend in the frequency of GLOFs has decayed since the 1970s ( Veh et al . , 2022 ) , some studies have forecasted a 3 to 7 - fold increase in GLOF hazard and downstream risk in the Himalaya due to future deglaciation ( Allen et al . , 2016 ; Zheng et al . , 2021a ) . The assessment at the global scale has pointed out that Bhutan and Nepal have the greatest national - level economic impact from GLOF ( Carrivick and Tweed , 2016 ) , and in absolute normalized GLOF risk scores , Bhutan is ranked 13th in the world ( Taylor et al . 2023 ) .

The increasing GLOF threat coupled with growing downstream exposure merits an evaluation of systematic downstream risk combining GLOF hydraulics and population and census housing data. While ample existing studies offer comprehensive information about the GLOF process chains and its effects on downstream communities (Frey et al., 2018; Kougkoulos et al., 2018; Sattar et al., 2022; Sattar et al., 2021b; Schneider et al., 2014), these studies have not included the social vulnerability, due to which the GLOF downstream risk has remained far from being understood. Although Allen et al. (2016) have included the social vulnerability to evaluate GLOF downstream risk in the Indian state of Himachal Pradesh, the GLOF risk was evaluated based on the rough estimation of GLOF paths in a GIS environment, falling short of including GLOF hydraulics and intensity. Taylor et al. (2023) assessed GLOF risk, including exposure and vulnerability, but for consistency used open access simplified data and assumptions, for example, populations within a 1 km buffer of river centerlines, and GLOF reach as a fixed 50 km downstream of a lake. Lunana glacier complex, located in the headwater of Punatsangchu has five proglacial lakes, of which four were reported to have high to very high outburst susceptibility in terms of both potential trigger mechanisms and flood volume (Nagai et al., 2017; Rinzin et al., 2021). Therefore, for the first time, this study offers a detailed GLOF risk assessment, including underlying components of hazard, vulnerability, and exposure, using multi-source data including lake bathymetry, OpenStreetMap, and population and housing census data, to improve confidence in the knowledge of GLOF susceptibility and downstream risk from these lakes. This study is guided by the following three main objectives: i) to evaluate the potential GLOF hazard under different moraine-breaching scenarios from the glacial lakes in the Lunana glacier complex; ii) to identify the exposed population, infrastructures, and agricultural land in the valley; and iii) to conduct a detailed downstream risk assessment by considering socio-economic factors along the potentially inundated areas of Punatsangchu and Phochu river basins.


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Section: 2. Study area: significance of the Lunana glacier complex
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Bhutan is a small landlocked country in the Himalaya, where the landscapes are largely dominated by rugged terrains and high mountains. The Himalaya stretch along the entire northern part of the country and host large reserves of glaciers. A total of 1583 ( $\sim$ 1487 km²) glaciers, of which 219 ( $\sim$ 951 km²) debris-covered, and 1364 ( $\sim$ 536 km²) cleanice glaciers have been reported in the Bhutan Himalaya (Nagai et al., 2016). Bhutanese glaciers have undergone significant area and mass loss

since the 1980s. For example, the glacier area shrunk by  $23.3 \pm 0.9$  % during 1980–2010 (Bajracharya et al., 2014). Further, between 2000 and 2020, glacier mass loss occurred at the rate of 0.47 m w.e.yr $^{-1}$ , higher than in other neighboring eastern Himalayan ( $\sim$ 0.33 m w.e.yr $^{-1}$ ) and Nyainqentanglha ( $\sim$ 0.46 m w.e.yr $^{-1}$ ) regions (Hugonnet et al., 2021). Under the future global 1.5 °C warming scenario, the eastern Himalaya, where Bhutan is located, is projected to warm by  $\sim$  1.9 °C, which is predicted to cause a  $\sim$  60 % of glacier mass loss by the end of 21st century (Kraaijenbrink et al., 2017).

Region-wide studies such as the Himalayan scale (Nie et al., 2018), High Mountain Asia (Wang et al., 2020), and Third Pole (Zhang et al., 2015), all have indicated that the eastern Himalaya is one of the regions with the highest concentration of glacial lakes. Using high-resolution ALOS PRISM and AVNIR-2 satellite imagery from 2006 to 2011, Nagai et al. (2017) documented 733 glacial lakes (~83.6 km²) in the Bhutan Himalaya, identified 226 lakes with positive potential flood volume (PFV) (0.04–33.7  $\times$  106 m³), 33 as having larger PFV (>5  $\times$  106 m³), and two with very large-scale PFV (>30  $\times$  106 m³). A more recent inventory (Rinzin et al., 2021) using Sentinel-1, Sentinel-2, and declassified Corona KH-4 data reported 2574 (156.63  $\pm$  7.9 km²) glacial lakes in 2020, from which 1118 (~82.92 km²) were classified as glacier-fed lakes. Their inventory also revealed that Bhutanese glacial lakes have expanded by ~26.5 km² (+17 %) since the 1960s (Rinzin et al., 2021). Further, 65 lakes were found to have high outburst susceptibility based


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Section: 2. Study area: significance of the Lunana glacier complex
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30 $ \ times $ 106 m³ ) . A more recent inventory ( Rinzin et al . , 2021 ) using Sentinel - 1 , Sentinel - 2 , and declassified Corona KH - 4 data reported 2574 ( 156 . 63 $ \ pm $ 7 . 9 km² ) glacial lakes in 2020 , from which 1118 ( ~ 82 . 92 km² ) were classified as glacier - fed lakes . Their inventory also revealed that Bhutanese glacial lakes have expanded by ~ 26 . 5 km² ( + 17 % ) since the 1960s ( Rinzin et al . , 2021 ) . Further , 65 lakes were found to have high outburst susceptibility based

on multiple attributes such as lake area and the likelihood of mass movement into the lake (Rinzin et al., 2021). The two inventories reported different numbers and statistics of glacial lakes in the Bhutan Himalaya due to differences in the approach and criteria employed in mapping glacial lakes and defining potentially dangerous glacial lakes. However, there is a common consensus that the Phochu basin, where the Lunana glacier complex is located contains the largest proportion of glacial lakes and the majority of potentially dangerous glacial lakes (PDGLs). For example, Rinzin et al. (2021) reported the highest proportion (22.6 %) of glacier-fed lakes in the Phochu basin and identified it as the inland basin with the highest number of PDGLs. The two glacial lakes with larger-scale potential flood volume identified by Nagai et al. (2017) are both located in the Phochu basin. Likewise, nearly half (8) of 17 PDGLs in Bhutan, first identified by ICIMOD (Mool et al., 2001) and later verified by NCHM (2019) are located in the Phochu basin. Bhutan is a dense cluster of glacial lakes in the Himalaya, and the Phochu basin is a global hotspot of potential GLOF risk (Taylor et al., 2023).

Four proglacial lakes, namely, Bechung Tsho, Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho in the Lunana glacier complex of Phochu basin in the Bhutan Himalaya were selected for detailed GLOF susceptibility evaluation and downstream hazard and risk assessment (Fig. 1). These four glacial lakes are the primary source of water for the Phochu river, which coalesces with Mochu and forms Punatsangchu, one of the biggest rivers in Bhutan. A first-order hazard assessment had


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Figure

Image /page/2/Figure/6 description: A detailed topographical map of the Lunana glacier complex and downstream settlements in Bhutan. The main map, labeled (a), shows a mountainous region with elevation ranging from 352 to 8590 meters. It displays rivers, lakes, and Gewog boundaries. Key settlements along a river are numbered: 1. Lunana, 2. Punakha, 3. Wangdue Phodrang, 4. PHP-I, and 5. PHP-II. Two inset maps in the top-left corner show the location of the area within High Mountain Asia and Bhutan. On the right, four panels, (b) through (e), show close-ups of specific lakes from the Lunana complex with their respective areas: (b) Area: 0.5 km², (c) Area: 1.3 km², (d) Area: 4.3 km², and (e) Area: 1.6 km². The map includes latitude and longitude lines and a scale bar for 20 km.


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Fig. 1. Lunana glacier complex and downstream settlements. Map (a) shows settlements downstream of the Lunana glacier complex along the Phochu and Punatsangchu basins. Downstream settlements are presented following the subdistrict block administrative boundary. Panels (b–e) show four glacial lakes, which we studied here: Bechung Tsho, Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho, respectively. Background image: ALOS-PALSAR DEM.

already revealed that all these lakes are highly (susceptibility level higher than 0.8) or very highly (susceptibility level higher than 0.6) susceptible to outburst flood (Rinzin et al., 2021). The Lunana glacier complex is also the catchment where at least three past GLOF events have been recorded since 1990, for example, the most devastating 1994 GLOF from Lugge Tsho (Watanbe and Daniel, 1996), and the recent breaching of subsidiary lake II of Thorthormi Tsho in July 2019 (National Center for Hydrology and Meteorology [NCHM], 2019a). Although the artificial draining of Thorthormi Tsho initiated in 2008 reduced the lake water level by 5 m, the lake is still deemed as highly dangerous (NCHM, 2019b). The investigation with persistent scatterer interferometry (PSI) has revealed an unstable left moraine wall for Thorthormi Tsho and significant movement of the side moraine wall of Lugge Tsho (Wangchuk et al., 2022). Although assessment with PSI by Wangchuk et al. (2022) has found no significant movement of the thin side moraine between Thorthormi Tsho and Raphstreng Tsho, a detailed field investigation in 2019 has reported active sliding of this thin sidemoraine (NCHM, 2019a), suggesting a threat of a future cascading flood from Thorthormi Tsho and Raphstreng Tsho. Lunana, the only community-dwelling within the proximity of glaciers in Bhutan, is located about 5-20 km away from these glacial lakes. Punakha Dzong, the former capital, with significant cultural heritage, is situated at the confluence of Phochu and Mochu, about 90 km downstream. Punatsangchu basin also hosts the two largest hydroelectric projects, Punatsangchu hydroelectric project (PHP)-I (1200 MW) and -II (1020 MW), at 110 and 125 km downstream respectively which are expected to be commissioned soon (MOEA, 2021). Taylor et al. (2023) state that the Punatsangchu catchment is the 4th most at risk from GLOF catchments in the world. Located along the Phochu and Punatsangchu riverbanks there are also two district capital towns: Khuruthang town (90 km downstream) for Punakha and Bajo town (100 km downstream) for Wangdue Phodrang districts. This high GLOF potential combined with high downstream exposure means that investigating the hazard and risk status becomes critically important (Fig. 1).


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Section: 3. Data and method > 3.1. Remote sensing and field data
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Declassified satellite data, Corona KH-4 and KH-9 were used to manually digitize glacier and glacial lake boundaries in the 1960s and 1970s, respectively (Table 1). To correct the image distortion of Corona and Hexagon data, the co-registration using ground control points from Sentinel-2 imagery was performed (see Rinzin et al., 2021). A Landsat series (TM, ETM, and OLI) were used to map glacial lakes and glaciers from 1990 to 2020. Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) DEM with 12.5 m spatial resolution was used as a source of terrain information for the GLOF routing. We modified terrain data along the river flow path by adding a line vector and cutting a channel through the artificial flood plain using the Terrain Modification Tool in the RAS Mapper (see supplementary material for detail). The ALOS-PALSAR DEM was also used to map the lake surrounding topographic potential for ice and rock avalanching and steepness of the moraine dam. The bathymetry data for three glacial lakes (Bechung Tsho, Rapshtreng Tsho, and Lugge Tsho) in the Lunana glacier complex were collected during the field expedition from 15 September to 20 October 2021. A Hummingbird 999SI combo (echo sounder) with an inbuilt Global Positioning System was used to collect the bathymetry data. The echo sounder transmits sound waves coupled with coordinate information into the lake bottom and records the returning echoes on a sonar chart with depth information. The lake depth data collected along the transverse transects were interpolated to the entire lake surface. The field-measured lake bathymetry was used to calculate the flood breach volume and determine the bed topography of the lakes.

We used the Hindu Kush Himalaya-scale Landsat-based land cover

3. Data and method
3.1. Remote sensing and field data


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Table: Table 1 Summary of all data used for lake outburst flood susceptibility assessment, downstream hazard, exposure, vulnerability, and risk assessment.

| No. | Period         | Source                        | Resolution (m) | Purpose                                                                                |
|-----|----------------|-------------------------------|----------------|----------------------------------------------------------------------------------------------------------------|
| 1   | 1965–1968      | Corona KH-4                   | ~2.7           | Map glacial lakes and glaciers in the 1960 s                                                                   |
| 2   | 1973–1974      | Hexagon KH-9                  | 6–9            | Map glacial lakes and glaciers in the 1970 s                                                                   |
| 3   | 1990–2020      | Landsat (OLI, ETM, TM)        | 30             | Map glaciers and glacial lakes during 1990–2020                                                                |
| 4   | 2006–2011      | ALOS PALSAR                   | 12.5           | Source of terrain data for multi-scenario flood modelling and lake surrounding topography analysis |
| 5   | 2022/02/ 18 | OpenStreetMap                 | NA             | Quantify structures exposed to the simulated GLOFs                                                       |
| 6   | 2000 & 2018 | Land use and land cover    | 30             | Determine Manning's coefficient and area of agricultural land ( ICIMOD, 2021)                         |
| 7   | 2017           | Population and housing census | NA             | Determine downstream settlement social vulnerability level ( NSB, 2018)                               |
| 8   | 2010–2020      | Glacier mass balance       | 100            | Determine glacier mass balance (Hugonnet et al., 2021)                                                   |
| 9   | 2000–2018      | Glacier velocity data      | 240            | Determine glacier flow velocity (Gardner et al., 2019)                                                   |
| 10  | 2021           | Lake bathymetry               | NA             | Calculate potential flood volume                                                                            |

No.
Period
Source
Resolution (m)
Purpose


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data of 2018 (from ICIMOD) to quantify the area of agricultural land inundated by the modelled GLOF (ICIMOD, 2021). The same data was also used to derive pixel-based Manning's n value for flood routing. We used time-series of Land Ice Velocity and Elevation (ITS\_LIVE) (Gardner et al., 2019) to calculate the flow velocity of glaciers. The glacier mass loss was calculated using the global-scale glacier mass loss dataset available at a  $1^{\circ} \times 1^{\circ}$  grid scale (Hugonnet et al., 2021). The global-scale consensus ice thickness datasets and corresponding surface DEM (Farinotti et al., 2019) were also used to calculate glacier bed overdeepening and determine future glacial lake extent.

OpenStreetMap data was used to count the number of buildings exposed to the multiple GLOF scenarios modelled in the study (data as of 15 June 2022). Bhutan population and housing 2017 census data were used to determine the social vulnerability level of the exposed population (NSB, 2018). The 2020 livestock statistics from the Ministry of Agriculture and Forestry, Bhutan (Renewable Natural Resources Statistics Division [RNR-SD], 2020) were used to quantify the number of livestock exposed to the hazard from GLOFs. The granularity of these data will be further discussed below.


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The GLOF susceptibility assessment for the lakes in the Lunana glacier complex was performed based on a total of 18 conditioning and triggering factors, broadly categorized into atmospheric, cryospheric, geotechnical, and geomorphic factors (after GAPHAZ, 2017) (see Supplementary Table S1). Each factor was either observed or quantified using the presented data, including remote sensing, DEM, and field observation.

The topographic potential of the surrounding slopes for landslide/rockfall and ice/snow avalanche (slope greater than or equal  $30^{\circ}$  and run-out trajectory slope greater than or equal to  $14^{\circ}$ ) were mapped using a GIS-based modelling approach (Allen et al., 2019; Romstad et al., 2008) (Fig. 2). Steep Lakefront Area (SLA) (depression angle  $>10^{\circ}$  from


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Figure

Image /page/4/Figure/2 description: A methodological flowchart is presented in six numbered sections, detailing the process for assessing Glacial Lake Outburst Flood (GLOF) susceptibility, hazard, exposure, vulnerability, and risk.


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1. Glacial lake outburst flood susceptibility: This flowchart shows that data from Landsat series, Hexagon KH-9, Corona KH-4, ALOS-PALSAR DEM, Google Earth Imagery, and Field photos are used to analyze factors like glacier & lake area change, mass balance, glacier velocity (leading to cryospheric factor), topographic potential area, moraine dam stability, and catchment area. These analyses inform intermediate factors such as calving front, avalanche, rockfall/landslide, and geotechnical & geomorphological factors, which all contribute to determining GLOF susceptibility.

2. Dam breach and GLOF routing: This section shows that ALOS-PALSAR DEM and Bathymetry Survey data are used to calculate peak discharge, flood velocity, and flood depth, which together determine the flood intensity.

3. Hazard: A 2x3 matrix shows the hazard level based on Intensity (High, Medium) and Probability (High, Medium, Low). The combinations result in High (H), Medium (M), or Low (L) hazard levels, represented by red, orange, and green colors respectively. For example, High Intensity and High Probability result in a High Hazard level.

4. Exposure: A flowchart indicates that flood intensity affects people, buildings, agricultural land, motorways, and bridges. The overall exposure is rated as High (H), Medium (M), or Low (L), shown in a red, orange, and green bar.

5. Vulnerability: This section shows that population and housing census data are used to create a social vulnerability index, which is then rated as High (H), Medium (M), or Low (L) in a red, orange, and green bar.

6. Risk: A 3x3 matrix determines the risk level based on Exposure (High, Medium, Low) and Hazard (High, Medium, Low). The matrix also aligns Vulnerability levels with Exposure levels. The resulting risk is categorized as High (H), Medium (M), or Low (L), represented by dark red, orange, and green squares. For instance, High Exposure and High Hazard result in a High Risk level.

Fig. 2. Methodological flow chart adopted for GLOF susceptibility, downstream hazard, exposure, vulnerability, and risk assessments for the potentially dangerous glacial lakes in the Lunana glacier complex of the Bhutan Himalaya: downstream GLOF hazard, vulnerability, exposure, and risk are categorized into three classes; high (H), medium (M) and low (L). For the hazard computation, the GLOF intensity used here is translated from the modelled flood depth: high (more than 1 m) and medium (less than 1 m) (after GAPHAZ, 2017).


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Section: 3. Data and method > 3.2. Glacial lake outburst susceptibility assessment
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Exposure and High Hazard result in a High Risk level . Fig . 2 . Methodological flow chart adopted for GLOF susceptibility , downstream hazard , exposure , vulnerability , and risk assessments for the potentially dangerous glacial lakes in the Lunana glacier complex of the Bhutan Himalaya : downstream GLOF hazard , vulnerability , exposure , and risk are categorized into three classes ; high ( H ) , medium ( M ) and low ( L ) . For the hazard computation , the GLOF intensity used here is translated from the modelled flood depth : high ( more than 1 m ) and medium ( less than 1 m ) ( after GAPHAZ , 2017 ) .

the flat lake surface), which provides information on the steepness of the moraine dam, was computed following the approach of Fujita et al. (2013). Hydrological characteristics such as total area of catchment and stream density were calculated from AlOS-PALSAR DEM. Other attributes, such as calving potential, which require finer resolution observation, were validated based on expert interpretation of Google Earth imagery and field photos (Fig. 3). The parent glacier extent was vectorized by modifying RGI v6.0 glacier outlines (RGI Consortium, 2017) and through our expert observation in a false-color composite of Landsat series imagery (OLI, TM, and ETM) for 1990–2020, Corona KH-4 for the 1960s, and Hexagon KH-9 for the 1970s. The same scenes were also used for digitizing the glacial lake boundaries manually. The glacier bed overdeepenings, as an indicator of future lake extent, were calculated by subtracting glacier ice thickness from the surface DEM (Farinotti et al., 2019) following Linsbauer et al. (2016).

Based on the degree to which these factors can condition or trigger a GLOF from a given lake, inferred through either expert judgment or calculated value, the above factors were classified into high, medium, and low susceptibility levels (Allen et al., 2022; GAPHAZ, 2017) with the corresponding score of 3, 2, 1, respectively. The total GLOF susceptibility score of the lake was calculated as the sum of scores across all factors. The susceptibility score was finally used to compare the relative outburst susceptibility level of the four lakes considered here.


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Section: 3. Data and method > 3.3. Dam breach and GLOF routing
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Hydrologic Engineering Center's River Analysis System (HEC-RAS v

6.0), a 2-dimensional (2D) dam breach model was used to simulate a series of potential GLOF events from the lakes in the Lunana glacier complex. Although limited to clear-water flow assumptions, HEC-RAS has been widely used for hydrodynamic modelling of GLOF in the Himalaya, such as in Nepal (Khanal et al., 2015; Sattar et al., 2021b), Tibetan Plateau (Wang et al., 2015), India (Sattar et al., 2021a) and Bhutan (Maurer et al., 2020; Osti et al., 2013). The model solves the 2D Saint Venant equation to calculate the unsteady flow hydraulics of GLOFs (CEIWR-HEC, 2021). The sine wave progression breach model was employed where initial breach forms slowly and with a further gradual incision the outflow velocities and shear stress change which leads to an increase in the outflow discharge (Sattar et al., 2021b). For the majority of moraine-dammed lakes, the outburst events are caused by displacement waves from mass movement followed by accompanying breach development and dam break (GAPHAZ, 2017). Therefore, we assumed that the dam breach was instigated by overtopping due to the mass movement into the lake, with subsequent dam incision and degradation leading to the outburst event. In HEC-RAS this is implemented with the dam failure mode considered as overtopping, where dam breach starts at the top of the dam and progresses toward the base of the moraine as erosion occurs (CEIWR-HEC, 2021). Breach parameters like breach width and formation time for each GLOF scenario were calculated using Froehlich (1995) (Eqs. (1) and (2)). This set of equations has been used previously in calculating breach parameters for GLOF modelling (Anacona et al., 2015; Sattar et al., 2021b; Wang et al.,

3. Data and method
3.3. Dam breach and GLOF routing


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Section: 3. Data and method > 3.3. Dam breach and GLOF routing
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Figure

Image /page/5/Figure/2 description: A figure displaying four photographs of different glacial lakes, each with highlighted geological hazards. A legend at the top defines the colored, dashed boxes used in the photos: black for 'Calving front', blue for 'Avalanche', orange for 'Landslide/Rockfall', and purple for 'Unstable moraine'.


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The top-left photo is of 'Bechung Tsho' on 11/10/2021, showing highlighted areas for avalanches and landslides/rockfalls, with an inset image pointing to the calving front.

The top-right photo is of 'Lugge Tsho' on 8/10/2021, indicating a calving front and a landslide/rockfall area, with an inset showing another view with avalanche and landslide zones.

The bottom-left photo is of 'Raphstreng Tsho' on 11/10/2021, with several areas marked for potential landslides/rockfalls and an avalanche.

The bottom-right photo is of 'Thorthormi Tsho' on 11/10/2021, showing potential avalanche and landslide/rockfall zones, with an inset highlighting an unstable moraine.

Fig. 3. Field photographs show the surrounding conditions that make lakes susceptible to GLOF, including calving front, avalanche, landslide/rockfall, and unstable moraine dam. Photo courtesv: NCHM. Bhutan.

$$B_{w} = 0.1803k_{0}(V_{w})^{0.32}(h_{b})^{0.14}$$
(1)

$$T_f = 0.00254 (V_w)^{0.53} (h_b)^{-0.9}$$
 (2)

where  $B_w$  is breach width,  $T_f$  is breach formation time,  $V_w$  is breach volume, and  $h_b$  is breach height.


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landslide / rockfall , and unstable moraine dam . Photo courtesv : NCHM . Bhutan . $ $ B_ { w } = 0 . 1803k_ { 0 } ( V_ { w } ) ^ { 0 . 32 } ( h_ { b } ) ^ { 0 . 14 } $ $ ( 1 ) $ $ T_f = 0 . 00254 ( V_w ) ^ { 0 . 53 } ( h_b ) ^ { - 0 . 9 } $ $ ( 2 ) where $ B_w $ is breach width , $ T_f $ is breach formation time , $ V_w $ is breach volume , and $ h_b $ is breach height .

For the forward modelling, a series of 12 GLOF scenarios were simulated, considering large, moderate, and small magnitude events originating from each lake: Bechung Tsho, Raphstreng Tsho, Thorthormi Tsho, and Lugge Tsho. For each simulation, a unique set of parameters were considered: breach width  $(B_w)$ , breach formation time  $(T_f)$  and breach volume  $(V_w)$ , and breach height  $(h_b)$  while keeping the downstream valley geomorphology and river hydrological conditions constant. For most past GLOF events in the Himalaya, it is unlikely that the entire moraine fails in case of an outburst event (Maurer et al., 2020; Veh et al., 2019a). Therefore, for the high magnitude GLOF scenario routing, we constructed lake breach models assuming  $h_b$  starting at the crest of the dam down to the mean depth of the lakes (calculated based on the field-measured bathymetry). The  $h_b$  for the moderate magnitude is assumed to be half of the large-magnitude  $h_b$ . Likewise, the smallmagnitude  $h_b$  was calculated as half of the moderate magnitude. Breach volumes were calculated from the field bathymetries as the volume of the water above the lowest point of breaching following the conservative trend in the previous studies, such as of Sattar et al. (2021b). However, owing to the lack of bathymetry data, the  $V_w$  for Thorthormi Tsho was calculated as the product of area and potential lowering height (Hp) following (equation (3)) Fujita et al. (2013). Hp is the anticipated water lowering height after SLA is removed due to moraine dam failure (Fujita et al., 2013). Here, it is worth noting that the GLOF was simulated as a clear water flood and did not account for the complex process chain of a GLOF, such as debris loading from erosion and sediment deposition along the downstream channel. We used pixelbased Manning's n value according to the latest Landsat-derived land use and land cover data (LULC) (ICIMOD, 2021). Depending on different classes of LULC, the pixel-based Manning's *n* ranges from 0.035 to 0.120.

$$PFV = \min[Hp; Dm]A \tag{3}$$

where, *PFV* is potential flood volume, *Hp* is potential height lowering, *Dm* is the mean depth of the lake, and A is a lake area.


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and sediment deposition along the downstream channel . We used pixelbased Manning ' s n value according to the latest Landsat - derived land use and land cover data ( LULC ) ( ICIMOD , 2021 ) . Depending on different classes of LULC , the pixel - based Manning ' s * n * ranges from 0 . 035 to 0 . 120 . $ $ PFV = \ min [ Hp ; Dm ] A \ tag { 3 } $ $ where , * PFV * is potential flood volume , * Hp * is potential height lowering , * Dm * is the mean depth of the lake , and A is a lake area .

We reconstructed the 1994 GLOF event of Lugge Tsho to understand the GLOF hydrodynamics in the Punatsangchu basin before conducting the multi-scenario modelling from the lakes in the Lunana glacier complex. There are limited field and eyewitness information on the 1994 GLOF event, so we drew upon sparse data available in the previous literature to define essential input parameters and initial conditions. For example, the moraine dam breach height was set to 18 m based on the height estimated by Fujita et al. (2008). Likewise, the dam breach width was set to 105 m and the dam crest width to 28 m both of which were constrained by Koike and Takenaka (2012). Combining field measurements and remote sensing observations, Fujita et al. (2008) have estimated a GLOF volume of 17.2  $\pm$  5.3  $\times$  106 m3. Accordingly, we simulated six scenarios of GLOF by changing flood volume between 15  $\times$  106 and 20  $\times$  106 m3 (each time increasing by 1 million m3) and keeping all other initial breach parameters constant. Other essential information available for the 1994 Lugge GLOF is the Peak flow at Wangdue Phodrang hydrological station (1800–2500 m3/s) (Yamada, 2000; Osti et al., 2013), and flood wave arrival time at Punakha through eyewitness (5.1 hr) and estimated using seismometer (4.75 hr) (Maurer et al., 2020; Watanbe and Daniel, 1996). So, we measured peak flow at Wangdue Phodrang hydrological station and flood reach time at Punakha.


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Section: 3. Data and method > 3.4. Downstream risk assessment
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The risk from any disaster event is represented as a function of hazard, exposure, and vulnerability (IPCC, 2014). Here, GLOF modelling

results were first translated into a hazard map. In doing so, the flow height from each scenario modelled GLOF was converted into two classes of intensity: high intensity (more than 1 m) and medium intensity (less than 1 m) (Frey et al., 2018; GAPHAZ, 2017). Later, the hazard map was computed by combining these intensities with the probability of occurrence (low, medium, and high corresponding to potential large, moderate, and small magnitude events, respectively). We combined intensity and probability maps by using the simple matrix approach (GAPHAZ, 2017). Since all the lakes examined here are situated in the same basin and pose a threat to the downstream regions, we evaluate the combined hazard for all four glacial lakes in the Lunana glacier complex. This final hazard map was created by initially producing four GLOF hazard maps (one for each lake) with three hazard levels given as high, medium, and low. Later, a simple matrix technique was used to merge the individual lake's hazard map. The combined hazard map is classified into four hazard levels: very high (when all (four) high hazard level from each lake intersects), high (when at least three high hazard level intersect or two high hazard level and two medium levels intersect), medium (at least two medium hazard level intersect) and low (when all low hazard levels intersect).

Exposure is the presence of people, livelihood, environmental services, and other infrastructure along the flood plain (Allen et al., 2019). Flood exposure calculation involves two components: delineating the flood hazard map and the population, structures, livestock, and agricultural land intersecting with that flood hazard map (Tate et al., 2021). The OpenStreetMap data were used to quantify any buildings, roads, and bridges that intersect with the extent of flood hazard under different scenarios. While there was good coverage of OpenStreetMap in our study area, we manually updated 75 buildings based on high-resolution Google Earth imagery. To estimate the GLOF exposed population we divided the total population by the number of buildings in the 17 identified subdistrict blocks to roughly appraise the number of people per exposed building, following Kougkoulos et al. (2018). The population data were extracted from Bhutan population and housing census 2017 data (NSB, 2018). The same approach was used to calculate the total number of exposed livestock using Bhutan's annual livestock statistics 2020 from the Ministry of Agriculture and Forestry (RNR-SD, 2020). The inundated agricultural land was delineated by overlaying GLOF extent on the 2018 Landsat 8-based LULC (ICIMOD, 2021). The exposure level for each subdistrict block was classified into three classes depending mainly on the number of exposed buildings as high (more than 50), medium (10–50), and low (up to 10 buildings with agricultural land, road, and bridge damage). We identified buildings as highly important since their destruction often directly relates to human casualties during disasters like GLOF (Allen et al., 2016).

3. Data and method
3.4. Downstream risk assessment


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from the Ministry of Agriculture and Forestry ( RNR - SD , 2020 ) . The inundated agricultural land was delineated by overlaying GLOF extent on the 2018 Landsat 8 - based LULC ( ICIMOD , 2021 ) . The exposure level for each subdistrict block was classified into three classes depending mainly on the number of exposed buildings as high ( more than 50 ) , medium ( 10 – 50 ) , and low ( up to 10 buildings with agricultural land , road , and bridge damage ) . We identified buildings as highly important since their destruction often directly relates to human casualties during disasters like GLOF ( Allen et al . , 2016 ) .

Based on Bhutanese population and housing census 2017 data, 15 social vulnerability indicators, and their values were selected, representing a wide range of social and economic conditions of the people (NSB, 2018) (Table 2). Here, we did not differentiate the indicator into positive or negative dependence, since we uniformly chose indicators with positive dependence, where increasing indicator value increases vulnerability. For example, we chose the percentage of households without a car for the indicator, household ownership of the car. Each indicator value of the subdistrict block (Table S2) was normalized to a value ranging from 0 (low vulnerability) to 1 (high vulnerability) with impartial weighting. The individual vulnerability index (VI) of each subdistrict block was calculated as the average across all normalized scores. The vulnerability index for each subdistrict block was also classified into very high (0.75-1), high (0.5-0.75), medium (0.25-0.5), and low (0-0.25). Finally, hazard, vulnerability, and exposure were combined using the simple matrix method to produce the risk level of each subdistrict block (Fig. 2). Here, we treated hazard, exposure, and vulnerability equally and no weighting was used.


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List of indicators and corresponding represented components considered for calculating the social vulnerability index for the downstream settlements. The indicators are extracted from the Bhutan Population and Housing Census 2017 data (NSB, 2018), following the convention used by Allen et al. (2016) and Cutter et al. (2003). The absolute and normalized values for each indicator are also provided in supplementary Table S2.


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Table

| No. | Social vulnerability indicator                                            | Component represented                                    |
|-----|---------------------------------------------------------------------------|----------------------------------------------------------|
| 1   | Old age dependence ratio                                                  | Sensitivity, capacity to prepare, respond and recover |
| 2   | Child dependence ratio                                                    | Sensitivity, capacity to prepare, respond and recover |
| 3   | Disability prevalence rate                                                | Sensitivity, capacity to prepare, respond and recover |
| 4   | Female population                                                         | Sensitivity, capacity to prepare, respond and recover |
| 5   | Household without high school education attainment (adequate literacy) | Capacity to prepare, respond and recover              |
| 6   | Household with insufficiency in food                                      | Sensitivity, capacity to prepare, respond and recover |
| 7   | Rented household                                                          | Capacity to recover                                      |
| 8   | Household without a reliable source of water                           | Capacity to prepare, respond and recover              |
| 9   | Household without an improved sanitation                               | Capacity to recover                                      |
| 10  | Household with more than a 30-minute walk to the road point            | Capacity to prepare, respond and recover              |
| 11  | Household without a smartphone                                            | Capacity to prepare and respond                       |
| 12  | Household living in the house without housing concrete wall            | Capacity to recover                                      |
| 13  | Household without a car                                                   | Capacity to prepare, respond and recover              |
| 14  | Household without a TV                                                    | Capacity to prepare and respond                       |
| 15  | Household without electricity as a main source of energy               | Capacity to prepare and respond                       |

No.
Social vulnerability indicator
Component represented


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The total GLOF susceptibility score (Sc) for the Thorthormi Tsho (Sc = 47) and Lugge Tsho (Sc = 45) was comparatively higher than the Bechung Tsho (Sc = 37) and Raphstreng Tsho (Sc = 34), suggesting that the former two lakes have higher chances of producing GLOF than the latter two. Since all lakes are located within the small catchment (99.3 km2), they had uniform susceptibility scores of 8 resulting from atmospheric factors. Seven of the 18 susceptibility factors considered here were cryospheric factors. With a total score of 17, Thorthormi Tsho had the highest susceptibility score from cryospheric factors, followed by Lugge Tsho with 16. This high score is because, firstly, the likelihood of ice avalanche, an important cryospheric factor, was highest for the Thorthormi Tsho as its total area susceptible to avalanche (4.3 km2) is higher than other lakes, almost by 10-fold. Likewise, for the Lugge Tsho, the meltwater ponds on the moraine indicated a possible degradation of the ice core or permafrost in its terminal moraine dam. Lugge Tsho is also in direct contact with the parent glacier and has a calving front measuring up to 360 m (Fig. 3) indicating the high possibility of impact from the calving. The Thorthormi glacier surface flow velocity (9.37 m a-1), especially in the lower terminus, is almost 2–3-fold higher than other glaciers. The other cryospheric factors, such as frontal glacier retreat, mass loss, and lake expansion, were also higher for Thorthormi and Lugge Tsho (Fig. 4 and Supplementary Table S1). Additionally, the modelling of glacier bed topography suggested that Lugge Tsho will expand up to 1.83 km2 (+11.6 %) Supplementary Fig. S3).

The total susceptibility scores across nine geotechnical and geomorphologic factors ranked Lugge Tsho with the highest GLOF susceptibility level, followed by Thorthormi Tsho with a score of 23 and 20, respectively. Again, this higher score is because most of the calculated


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Image /page/7/Figure/2 description: This is a figure from a scientific paper, labeled "Fig. 4", illustrating glacier retreat and glacial lake expansion between 1966 and 2020 for four different locations: Rapstreng, Thorthormi, Lugge, and Bechung. The figure is organized into four rows, one for each location. Each row contains a series of four satellite images from different years (1966, 1990, 2010, 2020, with specific dates in 1994 for Lugge) and a corresponding line graph. The images visually show the decrease in glacier size (outlined in blue) and the increase in lake size (outlined in yellow) over time. The line graphs plot Lake area (km²) in green on the left y-axis and Glacier length (km) in blue on the right y-axis, against the year on the x-axis. For Rapstreng, the lake area grows from near 0 to 1.25 km², while the glacier length shrinks from about 5.5 to 4.2 km. For Thorthormi, the lake area increases from 0 to about 3.5 km², and the glacier length decreases from about 9.2 to 6.2 km. For Lugge, the graph shows a sharp drop in lake area on 7/10/1994 due to an outburst flood, after which it begins to increase again. For Bechung, the lake area grows from 0 to 0.4 km², while the glacier length decreases from about 6.6 to 5.8 km. A legend at the bottom clarifies that the blue outline represents the glacier and the yellow outline represents the lake.


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Fig. 4. Glacier retreat and glacial lake expansion between 1966 and 2020 in the Lunana glacier complex. Lugge Tsho area recession after the 1994 outburst flood is also shown. The background image: Corona KH-4 (1968) and Landsat series (1990–2020).

values and expert judgment for these factors were in favour of Lugge Tsho and Thorthormi Tsho, as discussed below. All four glacial lakes in the Luanna complex have the most imminent threat from probable mass movements from the surrounding slopes (clearly visible in field photos; Fig. 3). By comparison, the total area susceptible to the mass movement for Thorthormi Tsho is  $10.8 \text{ km}^2$  which is 2–4-fold higher than other lakes making it relatively more susceptible to the impact of the mass movement (Supplementary Table S1). The individual catchment area for the lake ranges from 11.8– $46.2 \text{ km}^2$ . However, owing to the largest catchment area ( $46.2 \text{ km}^2$ ) and high stream order, Lugge Tsho appears to be most exposed, allowing us to assign higher scores for the Lugge Tsho (Supplementary Table S1). Likewise, the field photos showed that Thorthormi Tsho's moraine dam width, especially toward Raphstreng Tsho (right side), is very thin (30–40 m) (Fig. 3). On the other hand,

Raphstreng Tsho's moraine width is thick (500 m) as observed in Google Earth high-resolution imagery. Moraine damming Bechung Tsho seems very wide (700 m) with a good vegetation cover, seemingly the most stable among the lakes in the Lunana glacier complex.


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The glacial lakes in the Lunana glacier complex have potential flood volumes ranging from  $6\times 10^5$  to  $3\times 10^8$  m³, which could produce floods with a peak discharge of 200–76,000 m³/s. Thorthormi Tsho has a comparatively higher GLOF magnitude than the other three lakes (Fig. 5). The resulting peak flow mainly depends on the flood magnitude at the starting point and gradually decreases as the flood routes


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Image /page/8/Figure/2 description: A figure from a scientific paper illustrating breach parameters and outflow hydrographs for four different glacial lakes (Tsho) under three different scenarios: Large, Moderate, and Small. The figure is a 4x3 grid where rows represent the lakes (Thorthormi Tsho, Lugge Tsho, Raphstreng Tsho, Bechung Tsho) and columns represent the scenarios. Each cell contains a schematic of a dam breach showing breach width (Bw) and height (Hb), along with a corresponding hydrograph of discharge versus time. For Thorthormi Tsho, the parameters for Large, Moderate, and Small scenarios are respectively: V=303.5, 151.5, 75.8 (x10⁶ m³); T=1.7, 2.2, 2.9 hr; Bw=293.7, 206.2, 144.8 m; Hb=70.8, 35.4, 17.7 m; with peak discharges around 75,000, 22,000, and 9,000 m³/s. For Lugge Tsho, the parameters are: V=80.2, 40.1, 20.1 (x10⁶ m³); T=1.2, 1.7, 2.0 hr; Bw=178.1, 126.0, 87.9 m; Hb=48, 24, 12 m; with peak discharges around 30,000, 12,000, and 6,000 m³/s. For Raphstreng Tsho, the parameters are: V=61.1, 30.5, 15.3 (x10⁶ m³); T=1.1, 1.4, 1.8 hr; Bw=161.6, 113.5, 79.7 m; Hb=45.4, 22.7, 11.4 m; with peak discharges around 25,000, 10,000, and 5,000 m³/s. For Bechung Tsho, the parameters are: V=4.9, 2.4, 0.6 (x10⁶ m³); T=1.1, 1.4, 1.8 hr; Bw=54.5, 41.0, 26.9 m; Hb=10.4, 5.2, 2.6 m; with peak discharges around 1200, 450, and 200 m³/s. The hydrographs for Large and Moderate scenarios span 200 minutes, while those for the Small scenario span 400 minutes.


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**Fig. 5.** Illustration of breach parameters: breach width  $(B_w)$ , breach height  $(H_b)$ , breach formation time  $(T_f)$ , and outflow hydrograph at the dam for four typical proglacial lakes in the Lunana glacier complex under three different outbursts GLOF magnitude scenarios: large, moderate, and small.

downstream. For example, peak flow from Thorthormi Tsho at Lunana, 10 km from the Lunana glacier complex, is 9000–55,000 m3/s while it decreases to 4200-20,000 m3/s when the flood arrives PHP-II, about 125 km downstream (Fig. 6). The flood from Bechung is the smallest of all, attenuating before reaching Toedwang in Punakha, the first major settlement after Lunana. So, the flood hydraulics hereafter is presented only for the other three lakes (Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho). Unlike peak flow, the disparities in inundation depth and velocity at different downstream sites were also influenced by the channel topography and flood plain (Fig. 7). All 12 simulated GLOFs arrive at Lunana (which is about 10 km downstream) between 0.5 hr (Thorthormi large scenario) to 4.5 hr (Bechung small scenario) (Table 3), and depth ranges between 1 m and 25 m, while flow velocity is 3–14 m s-1 (Table 3). The nine GLOFs (3 each from Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho) take ~2.5 to 4.75 hr to reach Punakha Dzong and flow with a velocity of 4.7–8.0 m s-1 and flow depth of 6.9-27.8 m (Table 3). The flood arrival time increases to 3.0-6.0 hr as the floods arrive at Wangdue Phodrang hydrological station. Here, due to the low gradient (slope =  $\sim 0.02^{\circ}$ ) flood path, the floods flow with the minimum velocity  $(1.7-3.2 \text{ m s}^{-1})$  while their depth becomes the maximum (8.0-29.2 m) (Table 3). The flood proceeds farther downstream and arrives at PHP-I within 4.0 to 8.25 hr with a flow velocity of 1.7– $7.0~m~s^{-1}$  and depth of 9.0–26.2~m~(Table~3). Taking around 4.5-10.5 hr, the flood flows at 3.7 m s-1 at PHP-II while their depth ranges between 7.0 and 29.0 m (Table 3). The detailed flow velocity and arrival time across various stations along the river centreline is also indicated in Fig. 7.


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GLOF downstream risk was computed by combining hazard, exposure, and vulnerability. Fig. 8 is the excerpt of the hazard map from Lunana and Wangdue Phodrang. The simulated flood (up to 150 km downstream) from the lakes in the Lunana glacier complex showed that the flood would inundate about 42.8 km2 downstream area, out of which 15.6 % fall under the very high hazard zone while 42.4 %, 14.5 %, and 27.4 % fall in the high, medium, and low hazard levels, respectively. In terms of total land area, Lunana suffers the maximum (11.3 km2) inundation, followed by Toedwang (5.1 km2). Subdistrict blocks were classified into different GLOF hazard units based on which hazard level map intersected with them. Lunana, Kazhi, and Toedwang lie in very high hazard level zones, so three of them were classified as very high GLOF hazard level subdistrict blocks. The 13 others overlap with the high-hazard level map, so, these 13 were assigned high-hazard levels. Only one (Nahi) does not intersect with the hazard map, given a zerohazard level (Fig. 9a).

The GLOF exposure assessment showed that the simulated clearwater floods from the glacial lakes in the Lunana glacier complex can cause substantial inundation to two hydropower projects (PHP-I and PHP-I) and numerous structures and agricultural land across different downstream sites. The floods expose up to 2,721 buildings which convert to 16,062 people and 6,157 livestock. Besides 11.8 km² of agricultural land, 101.4 km of roads (including highways, farms, and feeder roads) and 71 bridges are also exposed to the different hazard levels of the simulated floods. Of the total, only 4 buildings (all in

Journal of Hydrology 619 (2023) 129311


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Figure

Image /page/9/Figure/2 description: A figure, labeled Fig. 6, displays a grid of flow hydrographs for three different Glacial Lake Outburst Flood (GLOF) scenarios. The grid has four columns, representing four proglacial lakes (Thorthormi Tsho, Lugge Tsho, Raphstreng Tsho, and Bechung Tsho), and five rows, representing downstream sites (Lunana, Punakha dzong, Wangdue hydro. sta, PHP-I, and PHP-II). Each graph plots Flow (m³/s) on the y-axis against Time (min) on the x-axis, from 0 to 1000 minutes. Three scenarios are shown in each plot: Large (red line), Moderate (black line), and Small (blue line). Generally, as the flood wave moves downstream (from top to bottom row), the peak flow decreases and the time to peak increases. For the Thorthormi Tsho lake, the large scenario peak flow decreases from about 55,000 m³/s at Lunana to 18,000 m³/s at PHP-II. For Lugge Tsho, it decreases from 20,000 m³/s to 5,000 m³/s. For Raphstreng Tsho, it decreases from 18,000 m³/s to about 3,500 m³/s. For the Bechung Tsho lake, a flood wave is only shown at the Lunana site, with a peak of about 850 m³/s for the large scenario; for all downstream sites, a note indicates 'Flood wave terminates before reaching here'.


Document: 1-s2.0-S0022169423002536-main
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Fig. 6. Flow hydrograph of three different scenarios of the GLOF magnitudes: large, moderate and small at each site downstream of the four typical proglacial lakes.

Lunana), 0.01 km2 of agricultural land, and 4 bridges (all in Lunana) were located within the very high-hazard flood zone; none of the roads fall within the very high-hazard zone (detail in Table 4). These exposed structures and agricultural land were primarily located in the subdistrict blocks of Wangdue Phodrang and Punakha districts, about 80–100 km

downstream of the Lunana glacier complex. For example, the highest number of buildings are exposed in Guma (753), followed by Thedtsho (639). Guma also suffers from the longest stretch (15.5 km) of road inundation. Likewise, Dzomi is modelled to likely suffer maximum (2.1 km²) damage to agricultural land, while the highest number of bridges


Document: 1-s2.0-S0022169423002536-main
Section: 4. Results > 4.3. Downstream hazard, exposure, and risk
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Figure

Image /page/10/Figure/2 description: A figure with eight graphs, labeled (a) through (h), arranged in two columns and four rows. The figure illustrates flood reach time and velocity for three different Glacial Lake Outburst Flood (GLOF) scenarios (Large, Moderate, and Small) from four different lakes: Bechung tsho, Raphstreng tsho, Lugge tsho, and Thorthormi tsho. A legend indicates that the Large scenario is represented by a red line, Moderate by a blue line, and Small by a green line.


Document: 1-s2.0-S0022169423002536-main
Section: 4. Results > 4.3. Downstream hazard, exposure, and risk
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The left column of graphs (a, b, c, d) shows Flood Reach Time. The x-axis represents 'Station: distance from the lakes (m)' from 0 to 150,000. The y-axis represents 'Time (hr)' from 0 to 20, with the values increasing downwards.
- Graph (a) for Bechung tsho shows the flood traveling less than 100,000 m in over 20 hours.
- Graphs (b) for Raphstreng tsho, (c) for Lugge tsho, and (d) for Thorthormi tsho show the flood traveling the full 150,000 m. In these cases, the 'Large' scenario is the fastest, taking approximately 8-11 hours, while the 'Small' scenario is the slowest, taking 12-18 hours.

The right column of graphs (e, f, g, h) shows Velocity. The x-axis is also 'Station: distance from the lakes (m)' from 0 to 150,000. The right y-axis is 'Velocity (m s⁻¹)' from 0 to 25. The left y-axis, shown in graph (e), is 'Elevation m a.s.l.' from 0 to 4000. Graph (e) also includes a light green line showing the elevation profile, which decreases from over 4000 m to near 0 m over the 150,000 m distance. These graphs have two vertical grey shaded bands.
- Graph (e) for Bechung tsho shows velocities generally below 15 m/s.
- Graphs (f) for Raphstreng tsho, (g) for Lugge tsho, and (h) for Thorthormi tsho show fluctuating velocities along the river path, with peaks exceeding 25 m/s, particularly within the first 25,000 m. Generally, the 'Large' scenario has the highest velocity and the 'Small' scenario has the lowest.

Fig. 7. Flood reach time (a–d) and velocity (e–h) for every 300 m along the river centreline of three different GLOF scenarios (large, moderate, and small) from the lakes in the Lunana glacier complex. Plotting the elevation profile (see e) along the river centreline shows how flow velocity changes depending on the elevation gradient at different places (shaded with grey color in e–h).

(11) exposure would occur in Rubisa (Table 4). Although no road is exposed in Lunana since it is only connected by non-vehicle access, 11.8 km of the footpath is being inundated. There are, however, current plans to extend the road network towards Lunana which will change exposure and risk. For display purposes, we classified exposure into high, medium, and low levels for each subdistrict block. Based on our classification approach (see method), 10 subdistrict blocks were classified as highly exposed, while 3 (Athang, Rubisa, and Patshaling) and 1 (Gase Tshowongm) were labeled as medium and low, respectively. The exposure level was zero for the other three, including Kazhi, Largyab,

and Nahi, since no structure or agricultural land intersects with the flood hazard map (Fig. 9b).


Document: 1-s2.0-S0022169423002536-main
Section: 4. Results > 4.3. Downstream hazard, exposure, and risk
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are , however , current plans to extend the road network towards Lunana which will change exposure and risk . For display purposes , we classified exposure into high , medium , and low levels for each subdistrict block . Based on our classification approach ( see method ) , 10 subdistrict blocks were classified as highly exposed , while 3 ( Athang , Rubisa , and Patshaling ) and 1 ( Gase Tshowongm ) were labeled as medium and low , respectively . The exposure level was zero for the other three , including Kazhi , Largyab , and Nahi , since no structure or agricultural land intersects with the flood hazard map ( Fig . 9b ) .

The calculated normalized score for the social vulnerability index for the 17 subdistrict blocks ranges from 0.3 to 0.7. A total of six, including Lunana, Largyab, Dzomi, Kabisa, Nahi, and Kazhi, had high vulnerability levels. Only Gase Tshowongm had a low vulnerability level, while the other 10 exhibited medium-level vulnerability (Fig. 9c). The risk assessment produced by combining hazard, exposure, and vulnerability revealed that only Lunana gewog is located in a very high-risk zone. Nine of the other subdistrict blocks lie in high-risk categories, and four

Flood arrival time, flow velocity, and depth at five downstream sites under three scenarios: large (L), moderate (M), and small (S) GLOF magnitudes.


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Type: table
Table: Table 3

| Flood hydraulics             | Lake       | Magnitude | Downstream | site    |                     |       |        |
|------------------------------|------------|-----------|------------|---------|---------------------|-------|--------|
|                              |            |           | Lunana     | Punakha | Wangdue Phodrang | PHP-I | PHP-II |
| Flood wave arrival time (hr) | Thorthormi | L         | 0.5        | 2.5     | 3.0                 | 4.0   | 4.5    |
|                              |            | M         | 0.75       | 3.0     | 4.0                 | 5.0   | 6.0    |
|                              |            | S         | 1.0        | 4.25    | 5.25                | 7.0   | 8.0    |
|                              | Lugge      | L         | 1.0        | 3.75    | 4.5                 | 5.75  | 7.0    |
|                              |            | M         | 1.25       | 4.75    | 5.5                 | 7.25  | 8.75   |
|                              |            | S         | 1.25       | 5.5     | 6.5                 | 8.75  | 10.75  |
|                              | Raphstreng | L         | 0.75       | 3.5     | 4.25                | 5.75  | 7.0    |
|                              |            | M         | 0.75       | 4.25    | 5.25                | 7.0   | 8.75   |
|                              |            | S         | 1.0        | 4.75    | 6.0                 | 8.25  | 10.5   |
|                              | Bechung    | L         | 1.4        |         |                     |       |        |
|                              | -          | M         | 2.1        |         |                     |       |        |
|                              |            | S         | 3.25       |         |                     |       |        |
| Flood velocity (m/s)         | Thorthormi | L         | 14.2       | 8.0     | 3.2                 | 7.0   | 8.9    |
| •                            |            | M         | 10.2       | 6.1     | 2.2                 | 5.3   | 7.4    |
|                              |            | S         | 7.4        | 5.0     | 1.7                 | 3.5   | 6.3    |
|                              | Lugge      | L         | 12.3       | 7.1     | 3.2                 | 3.2   | 6.0    |
|                              | 00         | M         | 9.0        | 6.0     | 2.4                 | 2.5   | 5.2    |
|                              |            | S         | 7.4        | 5.0     | 2.0                 | 2.0   | 5.0    |
|                              | Raphstreng | L         | 12.3       | 6.7     | 3.0                 | 3.1   | 5.2    |
|                              | r 0        | M         | 9.7        | 5.6     | 2.2                 | 2.2   | 4.3    |
|                              |            | S         | 8.0        | 4.7     | 1.7                 | 1.7   | 3.7    |
|                              | Bechung    | L         | 6.0        |         |                     |       |        |
|                              |            | M         | 4.0        |         |                     |       |        |
|                              |            | S         | 3.0        |         |                     |       |        |
| Flood depth (m)              | Thorthormi | L         | 25.0       | 28.0    | 29.2                | 26.2  | 29.1   |
| 1                            |            | M         | 14.7       | 18.0    | 20.4                | 18.7  | 20.0   |
|                              |            | S         | 10.0       | 11.4    | 14.0                | 13.3  | 12.8   |
|                              | Lugge      | L         | 13.0       | 13.4    | 15.3                | 16.6  | 15.0   |
|                              | - 00 -     | M         | 9.2        | 10.0    | 11.0                | 12.6  | 10.0   |
|                              |            | S         | 7.2        | 7.3     | 8.6                 | 10.1  | 7.0    |
|                              | Raphstreng | L         | 12.4       | 12.1    | 13.6                | 14.0  | 13.4   |
|                              | 1 0        | M         | 9.2        | 9.0     | 1.0                 | 10.6  | 9.3    |
|                              |            | S         | 6.0        | 7.0     | 8.0                 | 9.0   | 7.0    |
|                              | Bechung    | L         | 2.5        | ,       |                     |       |        |

Flood hydraulics
Lake
Magnitude
Downstream
site


Document: 1-s2.0-S0022169423002536-main
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Table: Table 3

| Flood hydraulics             | Lake       | Magnitude | Downstream | site    |                     |       |        |
|------------------------------|------------|-----------|------------|---------|---------------------|-------|--------|
|                              |            | M         | 1.7        |         |                     |       |        |
|                              |            | S         | 1.0        |         |                     |       |        |


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are in the medium-risk zone. The remaining 3 (Kazhi, Largyab, and Nahi) were classified as zero-risk areas since they have zero exposure and hazard (Fig. 9d).


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The comprehensive analysis of wide-ranging cryospheric, geotechnical, and geomorphological factors showed that Thorthormi Tsho and Lugge Tsho are relatively more susceptible to GLOF than Raphstreng Tsho and Bechung Tsho. This finding resonates well with the earlier GLOF susceptibility assessments undertaken in the Bhutan Himalaya. The first-order GIS-based susceptibility assessment by Rinzin et al. (2021) ranked Thorthormi Tsho as the most dangerous glacial lake in the Bhutan Himalaya. Also, the detailed field investigation by NCHM (2019a) has reported active sliding of a thin moraine between Thorthormi Tsho and Raphstreng Tsho, thus, increasing the susceptibility to GLOF events in the future. The comparatively higher velocity of the Thorthormi glacier, especially in the frontal area, indicates active sliding of ice, the same being identified as the likely cause of Thorthormi subsidiary lake-II draining in 2019 according to NCHM (2019a). Calving of ice into the lake would further increase its hydrostatic gradient and GLOF volume (GAPHAZ, 2017; Rounce et al., 2016) and calving activities can create displacement waves as the calved ice impacts the lake. The high GLOF susceptibility of Lugge Tsho also relates well with the

findings from the earlier studies (NCHM, 2019a; Rinzin et al., 2021; Wangchuk et al., 2022). This is evident as Lugge Tsho has produced the most catastrophic past GLOF events in the Bhutan Himalaya (Komori et al., 2012; Maurer et al., 2020). There is evidence of multiple cases of GLOF events from the same lake in the Himalaya, e.g., Cirenma Co in the Central Himalaya produced three GLOFs in 1964, 1981, and 1983, respectively (Wang et al., 2015). Such experience from the Himalaya combined with the high GLOF susceptibility of Lugge Tsho suggests that GLOF occurrence in the future cannot be neglected and is a subject of further investigation. It is also an actively growing glacial lake in the region, with the potential to grow in the future (Linsbauer et al., 2016), implying that it is likely to become more exposed with time to surrounding slope failure (Rounce et al., 2017; Rounce et al., 2016). Also, Thorthormi Tsho and Lugge Tsho both being in contact with their parents' glaciers means they would exacerbate the melting of the corresponding glacier through increasing mechanical calving activities and subaqueous melting, which in turn would cause enhanced expansion of the lake through glacier-lake interaction. This interaction would make Thorthormi Tsho and Lugge Tsho more susceptible to GLOF while also increasing potential flood volume (King et al., 2018, 2019). Thus, we highly recommend continuous monitoring of Thorthormi Tsho and Lugge Tsho. Nevertheless, we cannot discount the GLOF susceptibility of Raphstreng Tsho and Bechung Tsho entirely since they also have one or more factors indicating high outburst susceptibility (e.g., high potential flood volume of Raphstreng Tsho). Bechung Tsho is also expected to grow in the future by expanding towards its upstream direction and can


Document: 1-s2.0-S0022169423002536-main
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Figure

Image /page/12/Figure/2 description: The image displays two satellite maps, labeled (a) and (b) in the caption, showing hazard levels in different river valleys. The top map, (a), shows a barren, mountainous area around Thanza village. It is marked with longitude from 90°11'30"E to 90°13'0"E and latitude from 28°4'30"N to 28°5'0"N. A scale bar indicates 0.5 km. A color-coded overlay shows hazard levels, with a legend defining green as Low, yellow as Medium, orange as High, and dark red as Very high. The 'Very high' and 'High' zones are concentrated in the main river channel, with 'Medium' and 'Low' zones extending to the banks. The bottom map, (b), shows the area around Wangdue Phodrang, which has more vegetation and a visible settlement. It is marked with longitude from 89°53'0"E to 89°54'0"E and latitude from 27°29'30"N to 27°31'30"N, with a similar 0.5 km scale bar. The hazard overlay here shows 'High' (orange) in the river channel, with 'Medium' (yellow) and 'Low' (green) zones on the banks, partially covering the settlement. The 'Very high' level is not present in this map. The caption identifies map (a) as Thanza village (7 km from the lakes) and map (b) as Wangdue Phodrang (100 km from the lakes).


Document: 1-s2.0-S0022169423002536-main
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Fig. 8. The excerpt of the hazard map from Thanza village of Lunana (7 km from the lakes) (a) and Wangdue Phodrang (100 km from the lakes) (b) is derived from multiple GLOF scenarios originating from the Lunana glacier complex. It should be noted that the GLOF from the Bechung Tsho ultimately terminates before arriving Wangude Phodrang area, and so no very high hazard level is identified in this area based on our classification approach. The background image: ESRI Basemap Imagery.

Journal of Hydrology 619 (2023) 129311


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Figure

Image /page/13/Figure/2 description: A figure from a scientific paper by S. Rinzin et al., published in the Journal of Hydrology, showing four maps labeled (a), (b), (c), and (d). These maps illustrate the hazard, exposure, vulnerability, and risk levels in 2020 for 17 subdistrict blocks in a mountainous region. The maps are gridded with latitude lines from 27°0'N to 28°0'N and longitude lines from 90°0'E to 90°30'E, and include a 50 km scale bar.


Document: 1-s2.0-S0022169423002536-main
Section: 5. Discussion > 5.1. Glacial lakes outburst susceptibility
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Map (a) shows the Hazard level. The legend indicates 'High' (orange) and 'Very high' (dark red). The subdistricts LN, TW, and KZ are colored dark red for 'Very high' hazard, while the rest are orange for 'High' hazard.

Map (b) shows the Exposure level. The legend has four categories: 'No exposure' (white), 'Low' (green), 'Medium' (yellow), and 'High' (orange). Subdistricts KZ and LM have 'No exposure'. GW has 'Low' exposure. AT and PT have 'Medium' exposure. The remaining subdistricts have 'High' exposure.

Map (c) shows the Vulnerability level. The legend has three categories: 'Low' (green), 'Medium' (yellow), and 'High' (orange). Subdistrict GW has 'Low' vulnerability. TW, CH, DZ, AT, and PT have 'Medium' vulnerability. The rest of the subdistricts have 'High' vulnerability.

Map (d) shows the Risk level. The legend has four categories: 'No risk' (white), 'Medium' (yellow), 'High' (orange), and 'Very high' (dark red). Subdistricts KZ and LM have 'No risk'. GW, AT, and PT have 'Medium' risk. Subdistrict LN has 'Very high' risk. The remaining subdistricts have 'High' risk.

Fig. 9. The hazard (a), exposure (b), vulnerability (c), and risk (d) levels in 2020 of 17 subdistrict blocks downstream of the Lunana glacier complex under three different scenarios of GLOF magnitude (large, medium, and small) from each lake. The risk is calculated as a function of hazard, exposure, and vulnerability. The 17 subdistrict blocks are: Lunana (LN), Kazhi (KZ), Toedwang (TD), Chhubu (CH), Guma (GM), Dzomi (DZ), Lingmukha (LM), Kabisa (KB), Thedtsho (TD), Nahi (NH), Gase Tshogongm (GG), Gase Tshowongm (GW), Rubisa (RB), Darkar (DK), Athang (AT), Patshaling (PT), Largyab (LG). Background image: ALOS-PALSAR DEM.

become more exposed to GLOF triggers like an avalanche and landslide/rockfall in the future (Richardson and Reynolds, 2000). Therefore, attention might be needed to monitor Raphstreng Tsho and Bechung Tsho. The NCHM has reported an increasing temperature trend in Bhutan since the 1990s. There has been a decreasing trend in precipitation, but Bhutan experienced high precipitation variability over the

same period (Dorji and Tamang, 2019), leading to numerous extreme weather events in Bhutan (NCHM, 2022). This increasing frequency of extreme precipitation (Dorji and Tamang, 2019) added to increasing temperature would further amplify the GLOF susceptibility level of glacial lakes in the Lunana complex (GAPHAZ, 2017). However, since all lakes are located in the same basin, if the most dangerous glacial lakes

Buildings, people, livestock, road, bridges, and agricultural land within flood hazard level: very high (VH), high (H), medium (M) and low (L).


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Table: Table 4

| No. | Name        | Building (count) |     |     |      | People (count) |      |      |      | Agricultural land (km2) |      |      |      | Livestock (count) |       |       |       | Bridge (count) |    |    |    | Road (km) |     |     |     |
|-----|-------------|------------------|-----|-----|------|----------------|------|------|------|-------------------------|------|------|------|-------------------|-------|-------|-------|----------------|----|----|----|-----------|-----|-----|-----|
|     |             | VH               | H   | M   | L    | VH             | H    | M    | L    | VH                      | H    | M    | L    | VH                | H     | M     | L     | VH             | H  | M  | L  | VH        | H   | M   | L   |
| 1   | Athang      | 0                | 8   | 7   | 11   | 0              | 48   | 42   | 66   | 0                       | 0.4  | 0.1  | 0.2  | 0                 | 18.4  | 16.1  | 25.3  | 0              | 4  | 0  | 1  | 0         | 1.3 | 0.8 | 2.0 |
| 2   | Chhubu      | 0                | 5   | 6   | 97   | 0              | 30   | 36   | 582  | 0                       | 0.4  | 0.2  | 0.5  | 0                 | 11.5  | 13.8  | 223.1 | 0              | 3  | 1  | 2  | 0         | 1.3 | 1.4 | 4.2 |
| 3   | Darkar      | 0                | 49  | 42  | 50   | 0              | 294  | 252  | 300  | 0                       | 0.5  | 0.2  | 0.3  | 0                 | 112.7 | 96.6  | 115   | 0              | 7  | 1  | 2  | 0         | 5.3 | 3.2 | 5.0 |
| 4   | Dzomi       | 0                | 152 | 56  | 75   | 0              | 912  | 336  | 450  | 0                       | 0.8  | 0.3  | 1.0  | 0                 | 349.6 | 128.8 | 172.5 | 0              | 3  | 2  | 1  | 0         | 7.9 | 1.9 | 1.0 |
| 5   | Gase Tgongm | 0                | 36  | 22  | 30   | 0              | 216  | 132  | 180  | 0                       | 0.3  | 0.1  | 0.1  | 0                 | 82.8  | 50.6  | 69    | 0              | 3  | 2  | 1  | 0         | 3.9 | 1.8 | 2.5 |
| 6   | Gase Twongm | 0                | 0   | 2   | 3    | 0              | 0    | 12   | 18   | 0                       | 0.05 | 0.03 | 0.04 | 0                 | 0     | 4.6   | 6.9   | 0              | 1  | 0  | 1  | 0         | 0.3 | 0.4 | 1.5 |
| 7   | Guma        | 0                | 172 | 127 | 454  | 0              | 1032 | 762  | 2724 | 0                       | 0.4  | 0.2  | 1.1  | 0                 | 395.6 | 292.1 | 1044  | 0              | 2  | 0  | 0  | 0         | 3.4 | 3.1 | 9.0 |
| 8   | Kabisa      | 0                | 22  | 11  | 37   | 0              | 132  | 66   | 222  | 0                       | 0.3  | 0.1  | 0.3  | 0                 | 50.6  | 25.3  | 85.1  | 0              | 0  | 0  | 0  | 0         | 2.1 | 0.7 | 1.2 |
| 9   | Kazhi       | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |
| 10  | Largyab     | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |
| 11  | Lingmukha   | 0                | 73  | 39  | 101  | 0              | 438  | 234  | 606  | 0                       | 0    | 0    | 0    | 0                 | 167.9 | 89.7  | 232.3 | 0              | 1  | 0  | 0  | 0         | 0   | 0   | 0   |
| 12  | Lunana      | 1                | 17  | 20  | 21   | 6              | 102  | 120  | 126  | 0                       | 0.2  | 0.05 | 0.1  | 2.3               | 39.1  | 46    | 48.3  | 3              | 0  | 1  | 0  | 0         | 4.5 | 1.0 | 1.2 |
| 13  | Nahi        | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |
| 14  | Patshaling  | 0                | 8   | 3   | 23   | 0              | 48   | 18   | 138  | 0                       | 0.1  | 0.01 | 0.01 | 0                 | 18.4  | 6.9   | 52.9  | 0              | 2  | 1  | 0  | 0         | 0.5 | 0.1 | 0.5 |
| 15  | Rubisa      | 0                | 5   | 9   | 18   | 0              | 30   | 54   | 108  | 0                       | 0.8  | 0.2  | 0.2  | 0                 | 11.5  | 20.7  | 41.4  | 0              | 10 | 3  | 2  | 0         | 4.0 | 1.2 | 2.0 |
| 16  | Thedtsho    | 0                | 199 | 116 | 324  | 0              | 1194 | 696  | 1944 | 0                       | 0.7  | 0.2  | 0.4  | 0                 | 457.7 | 266.8 | 745.2 | 0              | 2  | 1  | 0  | 0         | 5.0 | 2.8 | 5.9 |
| 17  | Toedwang    | 3                | 109 | 59  | 55   | 18             | 654  | 354  | 330  | 0.01                    | 0.4  | 0.1  | 0.3  | 6.9               | 250.7 | 135.7 | 126.5 | 1              | 4  | 2  | 1  | 0.03      | 3.6 | 1.1 | 3   |
|     | Aggregate   | 4                | 855 | 519 | 1299 | 24             | 5130 | 3114 | 7794 | 0.01                    | 5.35 | 1.78 | 4.64 | 9.2               | 1967  | 1194  | 2988  | 4              | 42 | 14 | 11 | 0         | 43  | 19  | 39  |
|     | Total       | 2677             |     |     |      | 16,062         |      |      |      | 11.8                    |      |      |      | 6157              |       |       |       | 71             |    |    |    | 101.4     |     |     |     |

No.
Name
Building (count)
People (count)
Agricultural land (km2)
Livestock (count)
Bridge (count)
Road (km)


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are monitored (e.g., Thorthormi Tsho), and proper disaster management plans are implemented in the downstream settlement, possible downstream damage from the floods from any lakes in the basin could be highly reduced.


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Section: 5. Discussion > 5.2. GLOF risk on downstream settlement
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Our simulation of the 1994 GLOF by setting breach volume at  $20.0 \times 10^6$  yielded a peak flow of 2,129 m³/s at Wangdue Phodrang, within the range of estimated peak flow (1800 to 2500 m³/s) of the 1994 GLOF at the same station (Osti et al., 2013). Likewise, our simulated GLOF takes 4.4 hr to reach Punakha, which also relates well with the arrival time noted by eyewitnesses and the reconstructed arrival time (4.75 hr) using a seismometer (Maurer et al., 2020). Simulations with varying breach times between 0.25 and 1 hr result in a significant difference in peak discharge at the dam, but both arrival time and peak flow remain fairly consistent by the time the flood wave reaches Punakha and Wangdue Phodrang (Supplementary Fig. S2). This model calibration with the GLOF events of 1994 in the same basin reasonably indicates that our model setup is suitable for modelling different scenarios of future GLOF for risk assessment purposes.

The study combines hazard, exposure, and vulnerability to determine the risk posed by the potentially dangerous lakes in the Lunana glacier complex to the downstream region along the Phochu and Punatsangchu banks. The study reaffirms the earlier studies such as Osti et al. (2013) and Koike and Takenaka (2012) that Bhutanese people are inevitably highly exposed to the GLOF. Most of the modelled GLOFs (8/12) from the lakes produce a flood of magnitude higher than the 1994 GLOF of Lugge Tsho (Fujita et al., 2008; Maurer et al., 2020; Osti et al., 2013; Watanbe and Daniel, 1996). Although there is no history of floods of such high magnitudes in Bhutan, with climate change-induced deglaciation and cryospheric system change in the high mountains across the world in recent decades (GAPHAZ, 2017), these potential events in the future cannot be disregarded. Our results offer new insights into the impacts of large-magnitude GLOF events in a typical basin in the Himalaya.

5. Discussion
5.2. GLOF risk on downstream settlement


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Section: 5. Discussion > 5.2. GLOF risk on downstream settlement
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/ 12 ) from the lakes produce a flood of magnitude higher than the 1994 GLOF of Lugge Tsho ( Fujita et al . , 2008 ; Maurer et al . , 2020 ; Osti et al . , 2013 ; Watanbe and Daniel , 1996 ) . Although there is no history of floods of such high magnitudes in Bhutan , with climate change - induced deglaciation and cryospheric system change in the high mountains across the world in recent decades ( GAPHAZ , 2017 ) , these potential events in the future cannot be disregarded . Our results offer new insights into the impacts of large - magnitude GLOF events in a typical basin in the Himalaya .

Our study showed that one among 17 subdistrict blocks (Lunana) lies in the very high GLOF risk zone and 9 others in a high-risk zone. Some of the villages in Lunana, like Thanza village of Lunana, are located very close to the lakes in the Lunana glacier complex (just about 2-7 km). The large and medium scenario GLOFs from Thorthormi Tsho take only 15-30 min to reach some part of Lunana. This also means that the Lunana region is likely impact by all GLOF events with little warning, suggesting high-magnitude damage to the exposed structures (most of which are not built with the concrete wall (NSB, 2018). The capacity to prepare for, respond to, and recover from such a disaster is challenging for Lunana because of its remote location and high social vulnerability level. The nine high-risk subdistrict block clusters (Fig. 9) are located in the Punakha and Wangdue Phodrang districts, about 80-100 km from the Lunana glacier complex. The long travel time for GLOF allows these subdistrict blocks to potentially have more warning time in a GLOF event. Even the large scenario GLOF from the Thorthormi Tsho takes about 2.5 and 3 hr to arrive in Punakha and Wangdue Phodrang, respectively (Table 2). But this is only possible if there is a flood early warning system installed along the riverbank and if authorities and communities are well trained in response procedures, or it becomes entirely reliant on ad-hoc community-to-community communication. Flood early warning instruments have been installed along the banks of Phochu and Punatsangchu (NCHM, 2021), and continuous monitoring and maintenance of these instruments are highly recommended (Huggel et al., 2020). However, due to the high density of settlement, the inundation of infrastructures including buildings and roads is very high in Punakha and Wangdue Phodrang areas; 85 % of exposed buildings and 59 % of the bridge are located within these nine subdistrict blocks (Table 4). Our results suggest that the Lunana subdistrict block needs the highest level of attention for GLOF disaster risk reduction. Also, the

farther downstream settlements in the Punakha and Wangdue Phodrang districts, such as Guma and Thedtsho need to be prioritized (Fig. 9).


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and continuous monitoring and maintenance of these instruments are highly recommended ( Huggel et al . , 2020 ) . However , due to the high density of settlement , the inundation of infrastructures including buildings and roads is very high in Punakha and Wangdue Phodrang areas ; 85 % of exposed buildings and 59 % of the bridge are located within these nine subdistrict blocks ( Table 4 ) . Our results suggest that the Lunana subdistrict block needs the highest level of attention for GLOF disaster risk reduction . Also , the farther downstream settlements in the Punakha and Wangdue Phodrang districts , such as Guma and Thedtsho need to be prioritized ( Fig . 9 ) .

Investigations elsewhere in the world have revealed that eventual damage from disasters not only depend on the magnitude of the hazard (GLOF intensity in this case) but also on the social vulnerability level (crucial for resilience, preparedness, and response capacity of the people) (Cutter and Finch, 2008; Flanagan et al., 2018). Similarly, the importance of data from multiple sources (GLOF hazard data, subdistrict level socio-economic condition, and settlement density) for the downstream GLOF risk assessment is clearly portrayed in this study. Gase Tshogongm falls in a high-hazard zone, but with low vulnerability and exposure level, so it has been classified as a medium GLOF-risk subdistrict block. Likewise, Kazhi, Nahi, and Largyab have high social vulnerability and fall in a high GLOF hazard zone; however, all of them have zero exposure, which allowed us to rule out the GLOF risk in these subdistrict blocks under current conditions. These findings help us improve the GLOF risk knowledge in the Punatsangchu and Phochu basins, which is an essential preliminary assessment for the implementation of an early warning system (Huggel et al., 2020) and risk reduction prioritization. The GLOF early warning system has been put in place by National Centre for Hydrology and Meteorology and is mainly based on experience from the 1994 GLOF event of Lugge Tsho (NCHM, 2021). By contrast, our downstream risk assessment is based on the multiple GLOF scenario modelling results, most of which have a higher magnitude than the 1994 GLOF event. Thus, our result provides new insights for consolidating and improving the existing early warning system. To this end, this study is valuable for the comprehensive and careful design of GLOF risk management strategy, both hard engineering schemes such as improving structural measures (e.g., construction of flood retention walls, identification of safe/evacuation zone, lowering lake levels) and enhancing the response and recovery capacity (e.g., GLOF risk advocacy programs, training populations for GLOF evacuation) in the affected downstream community.