Document: Significance
Section: Results
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The predicted flood volumes and peak discharges span more than 7 orders of magnitude (Fig. 2). Based on a mean posterior rate of 1.26 GLOFs  $y^{-1}$  over the past 3 decades (23), we estimate a contemporary  $V_{100}$  of  $33.5^{+3.7}$ / $_{-3.7} \times 10^6$  m3 s-1 and  $Q_{\rm p100}$  of  $15,600^{+2,000}$ / $_{-1,800}$  m3 s-1 in the entire study area (Fig. 3). Regionally differing GLOF rates cause variation in  $V_{100}$  and  $Q_{\rm p100}$ . For example, GLOF hazard in the Eastern Himalayas is 350% ( $V_{100}$ ) and 338% ( $Q_{p100}$ ) higher than in the next highest region (Fig. 3 and SI Appendix, Fig. S6).

Correcting for regionally varying GLOF rates, we can explore how-for a fixed rate of one GLOF y-1-hazard changes as function of the lake size distribution (Fig. 4). We find that GLOF hazard in the Eastern Himalayas, which currently have the largest lakes and have had the highest GLOF activity in the past 3 decades (23), is on average more than 87% ( $V_{100}$ ) and 57% $(Q_{p100})$  higher than the estimate from the entire Himalayas. In contrast, the Western Himalayas had no known outburst from moraine-dammed lakes in the past 30 y, while the rate-adjusted  $V_{100}$  and  $Q_{\rm p100}$  there are still about 76% and 58% below that of the overall study area. In summary, these figures robustly support a contemporary hotspot of GLOF hazard in the southern Himalayas, mainly due to the larger abundance of glacier lakes and GLOFs in that part of the mountain belt.


Document: Significance
Section: Discussion
Type: text

We offer a consistent and reproducible estimate of present GLOF hazard in the Himalayas. Our Bayesian estimates explore the parameter space of plausible flood volumes and associated peak discharges with roughly a million outburst scenarios for any given lake. Our approach expands previous hazard appraisals by explicitly accounting for regionally varying GLOF rates. The estimated GLOF return periods are consistent with the frequency of reported flood volumes and discharges since 1935 (Fig. 3 and SI Appendix, Fig. S6 and Table S2). The 3 largest reported flood volumes of 71.6, 19.5, and  $17.2 \times 10^6$  m3 from the lakes Sangwang Tsho (1954), Sabai Tsho (1998), and Lugge Tsho (1994), respectively, had return periods of 237, 56, and 49 y, respectively, according to our predictions. The highest reported peak discharge (15,920 m3·s-1) drained from Lake Zhangzangbo in 1981; we estimate this as a 100-y event in the greater Himalayan region. The lakes Sangwang Tsho and Sabai Tsho had similar high peak discharges of ~10,000 m3·s-1, corresponding to a return period of 60 y. Yet reported values of  $V_0$  and  $Q_p$  rely chiefly on point estimates from empirical rating curves, eyewitness accounts, flood marks, and transported clasts in river channels, or measurements several kilometers downstream of the failing dams (28). thus compromising detailed, site-specific validation of our regional predictions. These uncertainties cause some of the scatter in the data that we based our models on (SI Appendix, Figs. S4 and S5). However, our approach explicitly propagates these uncertainties and robustly estimates  $V_0$  and  $Q_p$  from the distribution of meltwater lake areas in the entire Himalayan region. The size of glacier lakes will remain the key determinant of GLOF magnitude, regardless of whether we use alternative discharge rating curves (SI Appendix, Fig. S7), more elaborate numerical dam breach simulations (29), or any other metric of flood potential (20).


Document: Significance
Section: Discussion
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Document: Significance
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Figure

Image /page/2/Figure/2 description: A figure composed of eight scatter plots arranged in a 2x4 grid, labeled A through H. All plots share the same axes. The y-axis is labeled "Frequency density" and ranges logarithmically from 10⁻¹¹ to 10⁻¹. The x-axis is labeled "Peak discharge Qp [m³ s⁻¹] / Flood volume V₀ [m³]" and ranges logarithmically from 10⁻¹ to 10⁹. Each plot displays two data series: one with orange dots and one with dark blue dots. The orange series generally shows a decreasing trend from left to right. The blue series shows a distribution that peaks around 10³ to 10⁵ on the x-axis and then decreases. Each plot is labeled with a region and contains two numbers in gray text. The details for each plot are as follows:


Document: Significance
Section: Discussion
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Plot A: "All regions", numbers 5184 and 426.8.
Plot B: "Hindu Kush", numbers 650 and 30.9.
Plot C: "Karakoram", numbers 351 and 17.2.
Plot D: "Western Himalaya", numbers 688 and 30.8.
Plot E: "Central Himalaya", numbers 958 and 77.7.
Plot F: "Eastern Himalaya", numbers 1600 and 184.8.
Plot G: "Nyainqentanglha", numbers 415 and 41.7.
Plot H: "Hengduan Shan", numbers 527 and 44.

Fig. 2. Frequency densities of simulated GLOF flood volumes  $V_0$  and peak discharge  $Q_p$  for present Himalayan glacier lakes. Bubbles are log-binned densities of all simulations of flood volumes  $V_0$  (blue) and peak discharge  $Q_p$  (orange) in (A) the entire study area and (B–H) 7 subregions. Gray numbers are the number of lakes (top) and their surface areas (square kilometers) (bottom) for a given region.

Our study objectively enriches previous qualitative appraisals, which contrasted in the degree and number of "potentially dangerous" Himalayan lakes (22). The tails of the frequency–size distributions (Fig. 2) contain valuable information for practitioners regarding the physically possible margins of  $V_0$  and  $Q_p$  in the Himalayas. We note that half of the total Himalayan lake volume  $(20.1^{+2.4}/_{-2.1} \text{ km}^3)$  is in the largest 1% of glacier lakes (SI Appendix, Fig. S8). For example, Sangwang Tsho—the largest Himalayan moraine-dammed lake today—accounts for 5% of the total glacier lake volume ( $\sim 1 \text{ km}^3$ ) and could release a flood with a return period of  $5,000^{+1,400}/_{-1,000}$  y, should this volume drain entirely (SI Appendix, Fig. S9). Such flood magnitudes and associated return periods are consistent with reports on Holocene flood volumes up to 3 orders of magnitude larger in the eastern Himalayas (30).

Himalayan glacier lakes are currently limited in their storage capacity to produce floods of these dimensions, particularly in the Hindu Kush, Karakoram, and Western Himalayas, where lakes are smallest today (Figs. 1 and 2 and SI Appendix, Fig. S8). Why glacier lakes occur in clusters and have become largest in the Central and Eastern Himalayas since 1990 (+7.7 to +23% in area) remains debated (19). Glaciers have had negative mass balances in all regions except the Karakoram since the 1970s at least (31), while the highest ice losses in the 21st century were in the Western Himalaya and the Nyaingentanglha (32). The Central and Eastern Himalayas have a high proportion of lakes in direct contact with debris-covered parent glaciers, possibly accelerating glacier mass loss and lake growth (33). The increasing trend of air temperature is consistent over the entire Himalayas, whereas changes in precipitation were mostly insignificant (34), so that the clustering of lakes today may be tied to local, less well-known, controls such as glacier bed topography or valley geometry (35).


Document: Significance
Section: Discussion
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the Karakoram since the 1970s at least ( 31 ) , while the highest ice losses in the 21st century were in the Western Himalaya and the Nyaingentanglha ( 32 ) . The Central and Eastern Himalayas have a high proportion of lakes in direct contact with debris - covered parent glaciers , possibly accelerating glacier mass loss and lake growth ( 33 ) . The increasing trend of air temperature is consistent over the entire Himalayas , whereas changes in precipitation were mostly insignificant ( 34 ) , so that the clustering of lakes today may be tied to local , less well - known , controls such as glacier bed topography or valley geometry ( 35 ) .

Yet the distribution of glacier lakes will likely change in the future, given that a global temperature rise of 1.5 °C could melt half of the Himalayan glacier mass by the end of the 21st century (36). Models of subglacial topography suggest that a complete melting of all glaciers might provide accommodation space for another 16,000 meltwater lakes (>104 m2) with a maximum total volume of 120 km3 (37), hence possibly increasing the current lake volume 6-fold. According to these projections, most of these futures lakes could form in the Karakoram and Western Himalayas, where contemporary glacier cover is highest (13). Yet the errors of the inferred bathymetries can involve tens of meters, and many lakes might fill bedrock depressions that are less prone to catastrophic outbursts. Future flood volumes may increase nevertheless, especially in regions with large ice volumes and few lakes, even if only a fraction of the total meltwater will be trapped by moraines. Regional GLOF hazard could thus change commensurately, should annual GLOF frequencies rise with a growing lake abundance in the future (16). Whether lake outbursts will become more frequent with atmospheric warming remains an open question, however. The average rate of GLOFs in the greater Himalayan region has remained unchanged in the past 3 decades (23), showing that rapid growth of glacier lakes alone is an unsuitable predictor for GLOF activity, let alone GLOF hazard (38, 39).

Both the magnitude and frequency of outburst floods are straightforward to change in our model, motivating a more dynamic assessment with regular updates of outburst frequency and lake size distribution. We also encourage future work to learn more about the triggers and drivers of historic GLOFs. With more of this information becoming available, we can improve our Bayesian framework by assigning more informed (and appropriately weighted) prior probabilities of outburst to each glacier lake. In this spirit, we offer a practical tool that is extensible and


Document: Significance
Section: Discussion
Type: figure
Figure

Image /page/3/Figure/1 description: A figure displaying estimated 100-year peak discharges from Glacial Lake Outburst Floods (GLOFs) in the greater Himalayan region. The figure includes a central map and eight surrounding line graphs.


Document: Significance
Section: Discussion
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The map shows the Tibetan Plateau and surrounding mountain ranges: Hindu Kush, Karakoram, Western Himalaya, Central Himalaya, Eastern Himalaya, Nyainqentanglha, and Hengduan Shan. It is populated with blue circles of varying sizes, representing peak discharge (Qp) in m³ s⁻¹, and white circles representing historical GLOFs.

Each of the eight line graphs plots peak discharge (Qp) on the y-axis against the return period in years on the x-axis. The y-axis ranges from 0 to 40,000 m³ s⁻¹, and the x-axis ranges from 2 to 200 years. Each graph shows an upward-curving blue line with a shaded confidence interval. The data for each region is as follows:

- \*\*Karakoram\*\*: Present Qp100 is 535 (+100, -113) m³ s⁻¹; GLOF rate is 0.06.
- \*\*Western Himalaya\*\*: Present Qp100 is 145 (+117, -166) m³ s⁻¹; GLOF rate is 0.03.
- \*\*Central Himalaya\*\*: Present Qp100 is 2,603 (+500, -433) m³ s⁻¹; GLOF rate is 0.19.
- \*\*Eastern Himalaya\*\*: Present Qp100 is 14,448 (+1,846, -1,891) m³ s⁻¹; GLOF rate is 0.71.
- \*\*Hindu Kush\*\*: Present Qp100 is 574 (+125, -106) m³ s⁻¹; GLOF rate is 0.06.
- \*\*Nyainqentanglha\*\*: Present Qp100 is 4,276 (+766, -720) m³ s⁻¹; GLOF rate is 0.23.
- \*\*Hengduan Shan\*\*: Present Qp100 is 2,529 (+515, -449) m³ s⁻¹; GLOF rate is 0.16.
- \*\*All regions\*\*: Present Qp100 is 15,609 (+2,013, -1,797) m³ s⁻¹; GLOF rate is 1.26.

Fig. 3. Estimated 100-y peak discharges from GLOFs in the greater Himalayan region. Circles on the map are present moraine-dammed lakes with colors coded and radii scaled to the modes of the predicted distributions of Q0 for each lake. Plots show return periods of GLOF peak discharge for the entire study area (Lower Left) and its 7 subregions. Blue curves are estimated GLOF return periods, and numbers are the posterior mean 100-y flood discharge and 95% HDIs from 1,000 simulations for all 5,184 moraine-dammed lakes, calculated from the mean regional GLOF rate per region in the past 3 decades (brown). Thick dark blue lines are the means from 1,000 simulations (shades) drawing on sampled time series of 10,000 y each (Materials and Methods). Red ticks in Lower Left are average return periods of  $Q_p$  estimated for a sample of historic GLOFs since 1935 (SI Appendix, Table S2).


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subregions . Blue curves are estimated GLOF return periods , and numbers are the posterior mean 100 - y flood discharge and 95 % HDIs from 1 , 000 simulations for all 5 , 184 moraine - dammed lakes , calculated from the mean regional GLOF rate per region in the past 3 decades ( brown ) . Thick dark blue lines are the means from 1 , 000 simulations ( shades ) drawing on sampled time series of 10 , 000 y each ( Materials and Methods ) . Red ticks in Lower Left are average return periods of $ Q_p $ estimated for a sample of historic GLOFs since 1935 ( SI Appendix , Table S2 ) .

compatible with flood routing models, design codes, and hazard mitigation. Our appraisal of GLOF hazard complements projections of meteorological flood hazards in a warming climate for the sparsely instrumented Himalayan drainage network. Atmospheric warming is projected to increase mean daily and annual discharges in the Indus, Ganges, and Brahmaputra rivers only by a few percent in this century (40, 41), although the return periods for a given flood stage might drop by 50 to 90% in these rivers in the 21st century. Extreme runoff in headwaters (1), and GLOFs in particular (12), has eluded such projections. We show that

flood hazard due to glacier melt requires urgent attention in light of the rapidly growing population, infrastructure, and hydropower projects in the Himalayas (3, 12). Our appraisal adds to recent calls toward identifying regions that are most prone to GLOF impacts, and complements integrative concepts for climate risk adaptation on local, national, or regional levels (42).


Document: Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard Downstream Impact and Risk Over the Indian
Type: text

**Abstract** Indian Himalayas are home to numerous glacial lakes, which can pose serious threat to downstream communities and lead to catastrophic socioeconomic disasters in case of a glacial lake outburst flood (GLOF). This study first identified 329 glacial lakes of size greater than 0.05 km² in the Indian Himalayas, and then a remote sensing-based hazard and risk assessment was performed on these lakes. Different factors such as avalanche, rockfall, upstream GLOF, lake expansion, identification of the presence of ice cores, and assessment of the stability of moraine were considered for the hazard modeling. Further, a stochastic inundation model was applied to quantify the potential number of buildings, bridges, and hydropower systems that could be inundated by GLOF in each lake. Finally, the hazard parameters and downstream impact were collectively considered to determine the risk linked with each lake. A total of 23 lakes were identified as very high risk lakes and 50 as high-risk lakes. The potential flood volumes associated with various triggering mechanisms were also measured and were used to identify the lakes with the most considerable risk, such as Shakho Cho and Khangchung Tso. This study is anticipated to support stakeholders and decision-makers in identifying critical glacial lakes and make well-informed decisions related to future modeling efforts, field studies, and risk mitigation measures.

data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md


Document: 1. Introduction
Section: 1. Introduction
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The Himalayas have observed extensive shrinkage of glaciers with the most negative mass balance and the most significant decline in glacial length (Cogley, 2016; Gardelle et al., 2011; Kääb et al., 2012; Maurer et al., 2019; Sakai & Fujita, 2017; Yao et al., 2012). This glacial retreat is accompanied by the formation of numerous glacial lakes formed by replacing ice from glacier tongues (King et al., 2017, 2018). Linsbauer et al. (2016) predicted the future emergence of glacial lakes in Himalayas by modeling glacier bed topographies and documented 5,000 overdeepenings that may turn into glacial lakes. Sporadic outbursts in the unstable glacial lake have killed thousands of people with some of the most significant events taking place in the Himalayas (Nie et al., 2018; Veh et al., 2018). Carrivick and Tweed (2016) compiled an inventory of glacier floods and reported that the central Himalayas have observed maximum number of causalities due to glacial hazards, where Nepal and India have observed fewer flood but higher levels of damage. Glacial lake outburst flood (GLOF) is the sudden discharge of a large amount of stored water from glacial lakes (Carrivick & Rushmer, 2006), triggered majorly due to dynamic failure (mass entering the lake in the form of avalanche, rockfall, or GLOF in the upstream portion of the lake) and minorly due to self-destructive failures (settlement of ice-cored moraine or unstable moraine structure), seismic activities, earthquake, and extreme climatic events (Carrivick & Tweed, 2013; Rounce et al., 2016; Worni et al., 2013). The remote location, infrequent occurrence, and interconnection between these triggering mechanisms make it difficult to assess the potential hazard related to these lakes. Nonetheless, the estimated risk from various triggering mechanisms must be quantified, especially for the Himalayas, where 68% of the hydropower plants are located on potential GLOF tracks (Schwanghart et al., 2016).

Hindu Kush Himalayas have been subjected to many glacial lake studies (Emmer, 2018; Fujita et al., 2013; Ives et al., 2010; King et al., 2017, 2018; Maharjan et al., 2018; Schwanghart et al., 2016; Veh et al., 2019), including the ones carried out for specific countries such as Nepal (Mool et al., 2011; Rounce et al., 2017; Somos-Valenzuela et al., 2015), Bhutan (Komori, 2008; Ukita et al., 2011), and Tibet (Chen et al., 2007; Cui et al., 2011; Wang et al., 2011). Studies carried out for the Indian Himalayas include the work of ICIMOD (Ives et al., 2010) that created a comprehensive glacial lake inventory of Hindu Kush Himalayas and included three Indian states. Worni et al. (2013) created a glacial lake inventory for Indian Himalayas

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( Komori , 2008 ; Ukita et al . , 2011 ) , and Tibet ( Chen et al . , 2007 ; Cui et al . , 2011 ; Wang et al . , 2011 ) . Studies carried out for the Indian Himalayas include the work of ICIMOD ( Ives et al . , 2010 ) that created a comprehensive glacial lake inventory of Hindu Kush Himalayas and included three Indian states . Worni et al . ( 2013 ) created a glacial lake inventory for Indian Himalayas © 2020 . American Geophysical Union . All Rights Reserved . DUBEY AND GOYAL 1 of 21 19447973 , 2020 , 4 , Downloaded from https : / / agupubs

(>0.01 km²) and carried out a detailed risk assessment. Indian Himalayas were also subjected to many regional studies focusing particularly on specific region or lake (Abdul Hakeem et al., 2018; Aggarwal et al., 2017; Raj et al., 2013; Sattar et al., 2019). Most of the above-mentioned studies were carried out using glacial lake data derived from medium-resolution Landsat imageries, whereas, with the proliferation of satellite data, higher spatial resolution optical satellite data are available even at no-cost public domain such as Resourcesat-2 (RS-2) Linear Imaging Self Scanning (LISS-3) and Sentinel 2 multispectral instrument (MSI) data set (Drusch et al., 2012).

Documentation of critical glacial lakes and application of remedial measures can prevent events such as the ones occurred at Ayaco Lake in 1969 and 1970 (Liu et al., 2013), Nare Lake in 1977 (Buchroithner et al., 1982), Dig Tsho in 1985 (Vuichard & Zimmermann, 1987), Sabai Tsho in 1998 (Osti & Egashira, 2009), and Chorabari in 2013 (Allen et al., 2016; Kanti et al., 2019); these events are the example of calamitous GLOF events that have caused extensive damage to lives and socioeconomic condition of inhabitants (Carrivick & Tweed, 2016). Assessment of glacial lake hazards has been performed using regional data in China (Chen et al., 2010), Nepal (Bajracharya & Mool, 2009; Mool et al., 2011), Bhutan (Nagai et al., 2017), Tibet (Chen et al., 2010), and India (Worni et al., 2013). The major distinctions among these studies are the choice of parameters and the respective weights assigned to these parameters. The application of various parameters and weights leads to contradictory hazard classification (Emmer et al., 2016; Rounce et al., 2016) which can create confusion among stakeholders. Therefore, the objective of this study is to develop a novel framework to analyze the glacial lakes holistically, that is, accounting for primary triggering mechanisms and framed with an objective approach to enable smooth decision making.

To the best of our knowledge, present investigation is the first to examine the hazard, downstream impact, and risk of glacial hazard in a holistic manner accounting for both self-destructive and dynamic failures accompanied with the determination of downstream impact in terms of public utilities such as buildings, bridges, and hydropower systems over the entire Indian Himalayas. Also, most of the earlier studies in the Indian Himalayas were based on self-destructive failures; however, to represent the complex mechanism of GLOF, more comprehensive framework that accounts for most frequent triggers is required.


Document: 1. Introduction
Section: 2. Method > 2.1. Glacial Lake Inventory
Type: text

This study includes glacial lakes in the Indian Himalayas that are greater than 0.05 km2 (Figure 1). The 0.05 km2 threshold was applied to maximize the number of glacial lakes which is also consistent with past catastrophic GLOF events (Nie et al., 2018). Glacial lakes were delineated for the fall of 2018 using Sentinel-2 Multispectral Instrument (MSI) imageries, and to assess the temporal change in lake dimensions the glacial lakes were delineated for the year 1993 using Landsat 5 Thematic Mapper Plus (TM) imageries. The presence of clouds hinders the visibility of imageries. Therefore, for the cloud masked lakes, the imageries of 2017 were used to assist the lake delineation of 2018 whereas imageries of 1992 were used to assist the lake delineation of 1993. The initial period for the lake delineation was considered to be 1992 as this year represents the initial stage of Landsat 5 data availability during the months of least snow cover for the Indian Himalayas. Glacial lake boundaries were manually delineated, and the presence of glacial lake was identified using the Normalized Difference Water Index (NDWI; McFeeters, 1996). Uncertainty in lake delineation was presumed to be the lake perimeter multiplied by half the pixel size (Rounce et al., 2017; Shukla et al., 2018). Glacial lakes were categorized as moraine-dammed lake, ice-dammed lake, bedrock-dammed lake, and other glacial lakes (Maharjan et al., 2018). Glacier outlines were obtained from Randolph Glacier Inventory Version 5.0 (RGI; Arendt et al., 2012), which is based on satellite imageries acquired between 1999 and 2003. The uncertainty associated with RGI inventory has been estimated to be ~15% (Nuimura et al., 2014).


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
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Hazard assessment implies determination of susceptibility to various triggers such as avalanche, rockfall, upstream GLOF, lake expansion, presence of ice cores, and instability of damming moraine. The present

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Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
Type: figure
Figure

Image /page/2/Figure/3 description: A collection of maps illustrating the locations of lakes in the Indian Himalayas. The main map, labeled (a), shows a large area covering the states of Jammu and Kashmir (dark green), Himachal Pradesh (light green), and Uttarakhand (grey). This map displays glacier boundaries in light blue and lakes as black dots of varying sizes. On the left, there are three smaller maps. The top map shows a portion of India with three major river basins color-coded: Brahmaputra (yellow), Ganga (light blue), and Indus (purple). This map has three boxed sections labeled (a), (b), and (c) that correspond to the other maps. Below this are two zoomed-in maps. Map (b) shows the state of Sikkim (orange) with its lakes and glacier boundaries. Map (c) shows the state of Arunachal Pradesh (red) and a part of Assam, also with lakes and glacier boundaries. Several legends are provided. The 'STATES' legend lists Arunachal Pradesh (red), Uttarakhand (grey), Himachal Pradesh (light green), Ladakh, Jammu and Kashmir (dark green), and Sikkim (orange). The 'LAKES (km.sq)' legend indicates the size of the lakes represented by the black dots: the smallest dot is 0.05 - 0.50, the next size is 0.50 - 1.00, the next is 1.00 - 1.50, and the largest is 1.50 - 1.78.


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
Type: figure
Figure: Figure 1. Glacial lakes in Indian Himalayas, along with major river basins and states. Jammu Kashmir shown in the figure has been recently divided into two union territories (Jammu and Kashmir, and Ladakh).

Figure 1. Glacial lakes in Indian Himalayas, along with major river basins and states. Jammu Kashmir shown in the figure has been recently divided into two union territories (Jammu and Kashmir, and Ladakh).


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
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study aimed at modeling self-destructive failures using hydrostatic pressure, presence of ice core in damming moraine, and significant expansion over time. Whereas, dynamic failure (mass inflowing into the lake) using ice avalanche trajectories, landslide/rockfall, and upstream GLOFs. The workflow of the presented methodology is depicted in Figure 2a.

The combination of average steep lakefront area (SLA; Fujita et al., 2013) angle and the presence of ice-cored moraine was utilized to assess the moraine's stability. Steep lakefront area determines the lowering ( $\rm H_p$ ) of the glacial lake in case of a breach. A threshold of 10° on the SLA angle was applied based on the implementation of SLA concept on five previously outbursted glacial lakes by Fujita et al. (2013). Google Earth and Sentinel imageries were utilized to identify supraglacial ponds or change in the outlet channel to estimate the presence of ice core in the damming structure (Rounce et al., 2016; Watson et al., 2018). Though the bedrock-dammed lakes will not produce a GLOF by dam failure but may produce a GLOF by overtopping, still to apply a standardized analysis, the SLA was determined for all the lakes irrespective of their dam type. Lakes were considered significantly expanded if the areal expansion of lake between 2018 and 1993 exceeded the errors associated with 1993 lake delineation.

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 acquired between 2000 and 2010 with spatial resolution of 30 m and absolute vertical accuracy of ~17 m (Tachikawa et al., 2011), hereon referred as DEM in conjunction with RGI, was used to determine the areas that are prone to an avalanche, that is, any glaciated region with slope ranging between  $45^{\circ}$  and  $60^{\circ}$  (Alean, 1985; Osti et al., 2011). Avalanche-prone areas were then cumulated into maximum avalanche-prone areas using a variable kernel filter with 100% threshold. Here, a  $1 \times 1$  grid was checked, and if all the pixels were prone to an avalanche, it was expanded to a  $2 \times 2$  pixel grid. This expansion of the grid was continued until it fails to meet the threshold of 100%. Furthermore, three scenarios were assumed considering the avalanche thicknesses of 10, 30, and 50 m, respectively; these thicknesses are consistent with their relationship between slope and shear stress and are of the same order as observed in Switzerland, Austria, and Alaska (Alean, 1985). These thicknesses were combined with the maximum

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Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
Type: figure
Figure

Image /page/3/Figure/4 description: A multi-part figure illustrating a methodology for assessing Glacial Lake Outburst Flood (GLOF) risk, divided into three sections labeled (a), (b), and (c).


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
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Part (a) is a flowchart detailing the process. The inputs, shown in blue boxes, are Landsat 5 Thematic Mapper (TM), Aster Global Digital Elevation Model (GDEM v2), and Sentinel 2 Multispectral Instrument (MSI). These feed into a 'Glacial lake inventory (1993, 2018)'. The flowchart then splits into four main analysis streams: 'Avalanche/Rockfall', 'Moraine's stability', 'Upstream GLOF', and 'Downstream Impact'. Each stream uses various inputs like DEM, RGI, Inventory, and Ice cores to produce outputs such as 'Dynamic failure prone lakes', 'Steep lakes with/without ice cores', 'Identification of lakes with upstream GLOF susceptibility', and 'Quantification of hydropower systems, buildings and bridges affected'.

Part (b) is a Venn diagram showing the combination of four factors to determine hazard levels. The four overlapping circles are labeled 'Rockfall/Upstream GLOF', 'Avalanche Prone', 'Steep Moraine', and 'Presence of Ice Core'. The central intersection of all four is colored red, indicating 'Very High Hazard'. The intersection of three factors is light grey for 'High Hazard', two factors is darker grey for 'Moderate Hazard', and the area outside the circles is yellow for 'Low Hazard'. A legend below the diagram clarifies these color codes.

Part (c) is a 'GLOF Risk Chart', a 4x4 matrix that combines 'GLOF Hazard' (on the y-axis) with 'Downstream Impact' (on the x-axis). Both axes have levels: L (Low), M (Medium), H (High), and VH (Very High). The resulting risk levels in the grid are color-coded according to the legend in part (b). The matrix reads as follows:
- VH GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of VH, VH, M, M respectively.
- H GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of VH, H, M, M respectively.
- M GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of H, M, M, L respectively.
- L GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of M, M, L, L respectively.

\*Randolph Glacier Inventory (RGI 5.0);


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
Type: figure
Figure: Figure 2. (a) Workflow of method. (b) Hazard classification flowchart represented using Venn diagrams. (c) Risk chart represented as a function of downstream impact and GLOF hazard.

Figure 2. (a) Workflow of method. (b) Hazard classification flowchart represented using Venn diagrams. (c) Risk chart represented as a function of downstream impact and GLOF hazard.


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avalanche-prone area to determine the avalanche volume. A minimum threshold of  $0.1 \times 10^6$  m3 was applied on lake volume as defined by Richardson and Reynolds (2000), a volume large enough to destroy a village. Data on ice avalanche volume triggering a GLOF are scarce. However, GLOF models attempting to model ice avalanche and wave propagation have found that an avalanche of  $0.5 \times 10^6 \text{ m}^3$  is required to trigger an outburst (Somos-Valenzuela et al., 2016; Worni et al., 2014). Avalanche volumes were then used to determine the average angle between the initial point of the avalanche to the last point based on the equation given by Huggel et al. (2004), that is, equation (1).

$$\tan(\alpha) = 1.11 - 0.118\log(V),\tag{1}$$

where V and  $\alpha$  denote the volume of avalanche (m3) and the average slope trajectory (°) also referred to as "look-up" angle, respectively. The minimum threshold selected for the average look-up angle was 17° as avalanche rarely exceed this limit (Huggel et al., 2004). The avalanche trajectories were then estimated using a flow direction algorithm with the sink filled DEM up to the point where the average look-up angle was attained. Rockfalls were modeled similarly with rock thickness of 4 m and an average look-up angle of

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denote the volume of avalanche ( m < sup > 3 < / sup > ) and the average slope trajectory ( ° ) also referred to as " look - up " angle , respectively . The minimum threshold selected for the average look - up angle was 17 ° as avalanche rarely exceed this limit ( Huggel et al . , 2004 ) . The avalanche trajectories were then estimated using a flow direction algorithm with the sink filled DEM up to the point where the average look - up angle was attained . Rockfalls were modeled similarly with rock thickness of 4 m and an average look - up angle of DUBEY AND GOYAL 4 of 21

20° (Collins & Jibson, 2015). Furthermore, area prone to a rockfall was assumed to be any non-glaciated region with its slope exceeding 30° (Bolch et al., 2011; Rounce et al., 2016). Sensitivity analysis was implemented by changing the average look-up angle by ±3°. Any GLOF event in the upstream of glacial lake having the ability to cascade a series of GLOF events was modeled using Monte Carlo least cost path method (MC-LCP; Watson et al., 2015); MC-LCP uses Monte Carlo loop to model DEM uncertainty and implements an iterative least cost path analysis to produce inundation probabilities for each DEM cell. This method is computationally inexpensive and relies merely on the geometry of the downstream channel acquired from the DEM. It generates the flow path without differentiating between flash floods and debris flows. As the model has no physical basis, the model was implemented considering the conservative flood extent generated when compared to the flood extent of 1985 GLOF of Dig Tsho (Watson et al., 2015) and due to limited data availability on previous GLOFs, against the flood extents of two-dimensional debris flood model FLO-2D for Lmja Tsho (Rounce et al., 2016) and HEC-RAS for South Lhonak Lake (Sattar et al., 2019), Sikkim (supporting information Figure S1). MC-LCP model implemented with both Shuttle Radar Topography Mission (SRTM) DEM v.4 and Aster GDEM v.2 generated reasonable flood extent when compared against the flood extent generated by Sattar et al. (2019) except for few places, where the model does not capture the simulated flood extent. This could be highly problematic for downstream impact assessment if these areas were populated. More detailed analysis on the comparison of the flood extent from both the DEMs revealed that GDEM v2.0 tracked the main channel better. Therefore, MC-LCP along with Aster GDEM v2 was used to model potential GLOF from each lake.


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MC - LCP model implemented with both Shuttle Radar Topography Mission ( SRTM ) DEM v . 4 and Aster GDEM v . 2 generated reasonable flood extent when compared against the flood extent generated by Sattar et al . ( 2019 ) except for few places , where the model does not capture the simulated flood extent . This could be highly problematic for downstream impact assessment if these areas were populated . More detailed analysis on the comparison of the flood extent from both the DEMs revealed that GDEM v2 . 0 tracked the main channel better . Therefore , MC - LCP along with Aster GDEM v2 was used to model potential GLOF from each lake .

Each lake was assessed for its downstream impact using MC-LCP model with a cutoff distance of 50 km to facilitate a standardized comparison between different lakes; the 50 km threshold was consistent with GLOF event at Dig Tsho in 1985 (Watson et al., 2015), Chilleon Valley in 2015 (Wilson et al., 2019), Chorabari in 2013 (Rafiq et al., 2019), and so forth. Although some of the GLOF events have shown runout length up to 200 km (Richardson & Reynolds, 2000), considering such outliers may lead to overestimation of downstream impact. Generated flood extents were used to assess the downstream impact of GLOF for each lake by quantifying the number of bridges, hydropower systems, and buildings that could be affected. The number of hydropower systems that could be affected was primarily assessed using the database available from 43rd report on hydropower by Ministry of India, but it was later acknowledged that the available location of various hydropower systems was representative of the system but not essentially the location of a particular part of system such as dam. To resolve this discrepancy, hydropower systems were manually marked using Google Earth and Google Maps. The locations of the buildings were retrieved from OpenStreetMap (https://www.openstreetmap.org) and were confirmed and updated using Google Earth. For the Indian Himalayas, the buildings were majorly underrepresented; more than 10,000 buildings were updated for the local inventory. Bridges were identified using OpenStreetMap as any road crossing the watercourse.

The potential flood volume (PFV) for each lake was assessed by calculating the maximum PFV of self-destructive and dynamic failures. In case of self-destructive failure, the PFV was estimated using the approach from Fujita et al. (2013), but to determine the mean depth and lake volume, equation from Cook and Quincey (2015) was utilized (equations (2) and (3)).

$$D_m = 0.1217A^{0.4129}, (2)$$

$$V = 0.1217A_l^{1.4129}, (3)$$


Document: 1. Introduction
Section: 2. Method > 2.2. Glacial Hazard and Downstream Impact
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watercourse . The potential flood volume ( PFV ) for each lake was assessed by calculating the maximum PFV of self - destructive and dynamic failures . In case of self - destructive failure , the PFV was estimated using the approach from Fujita et al . ( 2013 ) , but to determine the mean depth and lake volume , equation from Cook and Quincey ( 2015 ) was utilized ( equations ( 2 ) and ( 3 ) ) . $ $ D_m = 0 . 1217A ^ { 0 . 4129 } , ( 2 ) $ $ $ $ V = 0 . 1217A_l ^ { 1 . 4129 } , ( 3 ) $ $

where  $A_l$  is the lake area in 2018 (m2),  $D_m$  is the mean depth (m), and V is the lake volume (m3). These equations were used to limit the PFV to the maximum of lake volume. In case of dynamic failure, the PFV was estimated based on the assumption that the water displaced will be equal to the volume of mass entering the lake. These estimates of PFV have significant uncertainties that need to be taken into account and propagated to the final results; Schwanghart et al. (2016) used a stochastic approach to predict outburst flood volumes and estimated that peak discharge may vary up to 2 order of magnitude for a given lake area. In this study, our aim was to provide a quantitative comparison of glacier flood risks rather than to precisely define the absolute flood of any individual flood event. Therefore, a simplistic sequential perturbation approach was used for PFV error estimation, where we evaluated PFV uncertainty (dPFV) using equation (4), whereas importance of  $d_D$  (error in depth estimation) in estimated uncertainty using equation (5).

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$$Importance = \frac{((D+d_D)A-AD)^2}{dPFV^2}.$$
 (5)

Here  $d_A$  and  $d_D$  represent the errors in area and depth estimation, respectively. For the case where higher PFV is estimated using SLA analysis, the parameters D and A represent the lake mean depth and lake area, respectively. In this case, we computed the error in-depth estimation based on root-mean-square error (RMSE) of bathymetric derived depth for 42 glacial lakes (Cook & Quincey, 2015; supporting information Table S1, Sheet 9) against the mean depth obtained from equation (2) and computed the RMSE value to be 6.7 m. The error in area estimation was determined by multiplying the shoreline value with half the pixel size of Sentinel 2 MSI data (10 m). In the case, where PFV is found to be greater for avalanche (rockfall), D represents the mean depth of avalanche (rockfall) and A represents the maximum prone area for avalanche (rockfall). Due to scarce data availability on avalanche (rockfall) depth. Mean depth of avalanche (rockfall) was assumed to be 30 m (4 m) with error in-depth estimation of 20 m (2 m), whereas the error in area estimation was found to be 15%, based on the uncertainty associated with the glacier inventory (Nuimura et al., 2014).


Document: 1. Introduction
Section: 2. Method > 2.3. Risk
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The focal objective of the study was to assess the risk associated with each lake, which can be represented as a combined characteristic of hazard and downstream impact. The framework was adopted from Rounce et al. (2016) with slight variation to prioritize the lakes with the highest risk in the Indian Himalayas. The classification was carried out based on frequent trigger mechanism of GLOF events and to facilitate even distribution of lakes into very high, high, moderate, and low rankings of risk. Classification of hazards puts the lakes with avalanches hitting the lake with a steep moraine (>10°) in very high hazard category as they represent the two most frequent triggering mechanisms. The high hazard category contains the lakes which either have an avalanche hitting the lake or steep moraine with an ice core. The lakes with an ice core have been given a higher hazard ranking than a lake solely with steep moraine, as it may trigger dam settlement or piping. Moderate hazard includes any lake with either an unstable moraine or a rockfall entering the lake. Rockfall was classified in moderate ranking as there have been only three recorded GLOF events triggered by a rockfall (Byers et al., 2019; Emmer & Cochachin, 2013; Richardson & Reynolds, 2000). Lastly, lakes with low hazard are the ones with gentle (<10°) moraine and no mass entering the lake (Figure 2b).

Classification of downstream impacts puts the lake threatening a large number (100) of buildings and at least one hydropower system into very high downstream impact category, the lakes impacting either a large number of buildings or a hydropower system into high downstream impact category, and the lakes threatening a small number of buildings or bridges in moderate downstream impact category. The lakes threatening no buildings and bridges were put into low downstream impact category. The criteria for the classification of risk were obtained from Rounce et al. (2017) which is an updated approach from Worni et al. (2013) and Rounce et al. (2016); it states that any combination of very high downstream impact and high hazard or vice versa will lead to the categorization of the lake in very high risk category. Subsequently, the lakes with high hazard and high downstream impact were categorized in the high-risk category. Any combination of low hazard and medium downstream impact or vice versa along with low hazard and low downstream impact were categorized in the low-risk category. Lastly, the remaining were categorized in moderate-risk category (Figure 2c).


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Section: 3. Results > 3.1. Inventory
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In the Indian Himalayas, 329 glacial lakes (>0.05 km²) were inventoried for the year 2018. These lakes were scattered in four states (Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh) and two union territories (Ladakh, and Jammu and Kashmir) of India. State-wise distribution of these lakes reveals that the union territories encompass 98 (30%) glacial lakes. Himachal Pradesh comprises 36 (11%) glacial lakes, Uttarakhand comprises 22 (7%) glacial lakes, Sikkim comprises 88 (27%) glacial lakes, and Arunachal Pradesh comprises 85 (26%) glacial lakes (Figure 3a). Major river basin-wise distribution reveals that the

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Figure

Image /page/6/Figure/3 description: The image displays six charts, labeled (a) through (f), analyzing the distribution of lakes across different states and basins. The charts are arranged in two columns: the left column (a, b, e) shows data by state, and the right column (c, d, f) shows data by basin. The states analyzed are Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. The basins are Indus, Ganga, and Brahmaputra.


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Chart (a) is a combination bar and line graph for states. The left y-axis represents Area (km.sq) from 0 to 30, and the right y-axis represents the Number of lakes from 0 to 180. Green bars show the area in 2018, and grey bars show the area in 1993. A dashed line shows the number of lakes. For example, in Jammu and Kashmir, the area in 2018 was about 22 km.sq, and the number of lakes was about 105.

Chart (b) is a horizontal stacked bar chart showing the number of lakes in each state, categorized by formation type: Moraine-dammed (grey), Ice-dammed (green), Bedrock-dammed (orange), and Others (blue). Jammu and Kashmir has the highest number of lakes, over 100.

Chart (e) is a scatter plot showing the elevation of lakes in each state. The y-axis is Elevation (m) from 3000 to 6000. Each circle represents a lake, colored by its formation type, with the size of the circle likely indicating its area. Most lakes are located between 3500m and 5500m.

Chart (c) is a combination bar and line graph for basins, similar to (a). The left y-axis for Area (km.sq) goes up to 40. The Brahmaputra basin has the largest area (over 30 km.sq in 2018) and the highest number of lakes (about 165).

Chart (d) is a horizontal stacked bar chart showing the number of lakes by formation type for each basin. The Brahmaputra basin has the most lakes, exceeding 160.

Chart (f) is a scatter plot showing the elevation of lakes in each basin, similar to (e). The Brahmaputra basin shows a wide distribution of lakes from below 3500m to above 5500m.


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Figure: Figure 3. Areal distribution of lakes in various states (a) and basins (c). The dotted line is plotted in correspondence with the secondary axis to represent the number of lakes. Distribution of various lake types in states (b) and basins (d). Figures (e) and (f) represent glacial lakes in various elevation zones, the color of the circles represent various lake types, whereas the size of the circles represents the size of the lake.

**Figure 3.** Areal distribution of lakes in various states (a) and basins (c). The dotted line is plotted in correspondence with the secondary axis to represent the number of lakes. Distribution of various lake types in states (b) and basins (d). Figures (e) and (f) represent glacial lakes in various elevation zones, the color of the circles represent various lake types, whereas the size of the circles represents the size of the lake.


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Indus basin contains 134 (41%) glacial lakes, Ganga basin contains 22 (7%) glacial lakes, and Brahmaputra basin contains 173 (52%) glacial lakes (Figure 3c). These lakes covered an area of  $65.80 \pm 4.37 \text{ km}^2$  in 2018. Lake size distribution of these lakes revealed that 129 (39%) lakes were smaller than  $0.1 \text{ km}^2$ , 178 (54%) had size ranging between  $0.1 \text{ and } 0.5 \text{ km}^2$ , and only 22 (7%) were found to be greater than  $0.5 \text{ km}^2$ . Distribution of lakes according to their type revealed that moraine-dammed lakes account for 154 (47%) lakes, followed by 57 ice-dammed lakes (17%), 31 bedrock-dammed lakes (9%), and 87 other lakes such as erosional and debris-dammed lakes (26%) (Figures 3b and 3d). Elevation profile of the lakes revealed that the lakes

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Figure

Image /page/7/Figure/3 description: An image displaying six pie charts that analyze various environmental factors, arranged in two rows of three. The top row features charts for 'Avalanche', 'Rockfall', and 'Upstream GLOFs'. The 'Avalanche' chart shows 293 'No hit' and 36 'Avalanche hit', with the 'hit' category further broken down into 28 'Indirect hit' and 8 'Direct hit'. The 'Rockfall' chart shows 277 'No hit' and 52 'Rockfall hit', with the 'hit' category detailed as 37 'Indirect hit' and 15 'Direct hit'. The 'Upstream GLOFs' chart indicates 292 'No threat' and 37 'Potential'. The bottom row includes charts for 'Significant Growth', 'Ice Cored', and 'Moraine Stability'. The 'Significant Growth' chart shows 265 'No threat' and 64 'Potential'. The 'Ice Cored' chart displays 249 'No threat' and 80 'Potential'. The 'Moraine Stability' chart shows 82 'Stable' and 247 'Unstable'.


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Figure: Figure 4. Summary of hazard parameters.

Figure 4. Summary of hazard parameters.


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ranged from 3,000 (meters above sea level) m.a.s.l to 5,661 m.a.s.l with the mean elevation value of 4,484 m.a. s.l. The lakes present above the elevation of 5,000 m.a.s.l were predominantly ice dammed and moraine dammed. Almost all bedrock-dammed lakes were present below 5,000 m.a.s.l, whereas other lakes were uniformly distributed among all the elevation zones (Figure 3e). It was observed in the Indus and Ganga basins, large-sized lakes were present below 4,400 m.a.s.l, whereas for Brahmaputra basin all the large-sized lakes were located above the elevation 4,900 m.a.s.l (Figure 3f). All the lakes of the union territories and Himachal Pradesh were located within the Indus basin, the lakes of Uttarakhand were located within the Ganga basin, whereas all the lakes of Sikkim and Arunachal Pradesh were located within the Brahmaputra basin. In 1993, the area covered by these lakes was  $56.8 \pm 15.69 \text{ km}^2$  which increased to  $65.80 \pm 4.37 \text{ km}^2$  by 2018, that is, a 15.84% increase in lake area over 25 years. Similar increase in lake area has been observed in Tibetan Plateau (Zhang et al., 2014), Nepal (Nie et al., 2013), Bhutan (Komori, 2008), Tien Shan (Narama et al., 2010), Central Andes (Wilson et al., 2018), and Western Greenland (Carrivick & Quincey, 2014).


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The determination of potential hazards was carried out by modeling moraine stability, avalanche, rockfall, GLOF in the upstream portion of the lake, and lake expansion. For avalanche and rockfall, a direct hit on the lake was considered to be any trajectory entering the lake with the starting point located within  $\pm 45^{\circ}$  of the major axis of the lake. A direct hit may have grave implications on wave run-up and moraine stability. Modeling of dynamic failure revealed that out of 329 lakes, 36 (11%) are susceptible to an avalanche entering the lake, 52 (16%) are susceptible to a rockfall, and 37 (11%) are susceptible to an upstream GLOF. It was also observed that only 8 (22%) out of 36 avalanches and 15 (29%) out of 52 rockfalls were entering the lake with a direct hit which implied that most of the mass entering the lake was along its minor axis. Concerning moraine stability, 247 (75%) lakes have an unstable moraine (average SLA angle >10°), and 80 (24) have ice cores present in their damming structure. Assessment of lake expansion revealed that from 1993 to 2018, 64 (19.5%) lakes significantly expanded and 6 (2%) lakes significantly drained (Figure 4).

Modeled hazards were used to classify the lakes into various hazard categories; that is, 28 lakes were categorized as very high hazard, 50 as high hazard, 198 as moderate hazard, and 53 as low hazard. Sikkim has the maximum number (20) of very high hazard lakes followed by the union territories of Ladakh, and Jammu and Kashmir (4). The state of Himachal Pradesh and Arunachal Pradesh contains two very high hazard lakes each, whereas Uttarakhand has no very high hazard lake. River basin-wise, Brahmaputra basin has 20 very high hazard lakes, followed by 6 in Indus basin and 2 in the Ganga basin. Classification of very high hazard lakes based on type revealed that 20 were moraine dammed, 7 were ice dammed, and 1 was the other lake. This is attributed due to elevation dependence and direct connection of ice-dammed and moraine-dammed lakes with their parent glaciers which make them highly susceptible to avalanches. Elevation dependence of lakes revealed that all the very high hazard lakes were lying above the elevation range of 4,000 m (Figure 5a).

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Figure

Image /page/8/Figure/3 description: The image displays three bubble charts, labeled (a), (b), and (c), which analyze hazard, downstream impact (DI), and risk across different elevation zones in five regions of the Himalayas. All three charts share the same axes. The x-axis lists the regions: Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. The y-axis represents Elevation in meters (m), ranging from 3000 to 6000. The data is represented by circles of varying sizes and colors, plotted vertically for each region.


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Chart (a) illustrates Hazard levels. The legend indicates four categories: Low Hazard (blue), Moderate Hazard (light blue), High Hazard (light red), and Very High Hazard (red). Sikkim shows a high concentration of 'Very High Hazard' and 'High Hazard' circles, particularly between 5000 and 5500 meters.

Chart (b) shows Downstream Impact (DI). The legend is similar: Low DI (blue), Moderate DI (light blue), High DI (light red), and Very High DI (red). Jammu and Kashmir and Sikkim exhibit significant clusters of 'High DI' and 'Very High DI' circles.

Chart (c) depicts Risk levels. The legend follows the same color scheme: Low Risk (blue), Moderate Risk (light blue), High Risk (light red), and Very High Risk (red). Sikkim stands out with a large cluster of 'Very High Risk' and 'High Risk' circles between 5000 and 5500 meters. In contrast, Himachal Pradesh and Uttarakhand are dominated by 'Low Risk' and 'Moderate Risk' circles, including a large 'Low Risk' circle around 4200 meters in both regions.


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Figure: Figure 5. Distribution of (a) hazard, (b) downstream impact (DI), and (c) risk in various elevation zones. Color of the circle represents very high, high, moderate, and low rankings of risk, hazard, and downstream impact. The size of the circle represents the size of the lake.

Figure 5. Distribution of (a) hazard, (b) downstream impact (DI), and (c) risk in various elevation zones. Color of the circle represents very high, high, moderate, and low rankings of risk, hazard, and downstream impact. The size of the circle represents the size of the lake.


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The commonly known very high hazard lakes in the Indian Himalayas include Shakho Cho, Khangchung Tso, Bhale Pokhri Lake, and Goecha Lake. Other commonly known high hazard lakes include South Lhonak Lake, Basudhara Tal, Lam Dal Lake, Shaushar Lake, Gadsar Lake, and Lolgul Gali Lake. Few lakes such as Geepang Gath (moderate hazard), Chandra Tal (moderate hazard), Samudri Tapu (moderate hazard), and Gurudongmar (moderate hazard) were not classified as critical because of no avalanche trajectories entering the lake, as well as unavailability of ice cores in damming moraine.

The sensitivity test conducted by varying the "look-up" for avalanche by  $\pm 3^{\circ}$  did not alter the number of lakes prone to avalanche. In case of rockfall, the decrease in look-up angle to 17° increased the number of lakes susceptible to rockfall from 52 to 62 and categorized two low hazard lakes to moderate hazard category, whereas increasing the look-up angle to 23°, reduced the number of lakes prone to rockfall from 52 to 35 and changed the hazard ranking of four lakes from moderate to low. Unlike Rounce et al. (2017), the change in look-up angle for rockfall induced a substantial change in lake categorization.


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Section: 3. Results > 3.3. Downstream Impact
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There are 217 lakes that inundated at least 20 buildings while 138 inundated at least 100 buildings; nine lakes were identified to inundate more than 1,000 buildings. Six lakes inundating more than 1,000 buildings were located in the union territories of Jammu and Kashmir, and Ladakh whereas three lakes were located in Uttarakhand (Figure 6c). The number of buildings inundated varied from 0 to 2010, with a median number of 54. The number of lakes that inundated at least one hydropower system was 67 whereas the number of

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Figure

Image /page/9/Figure/3 description: The image contains three stacked bar charts, labeled (a), (b), and (c), showing the number of lakes that threaten various infrastructures, broken down by region. A single legend applies to all charts, indicating the regions: Arunachal Pradesh (light red), Sikkim (light orange), Uttarakhand (light yellow), Himachal Pradesh (grey-blue), and Jammu and Kashmir (dark blue).


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Chart (a) displays the number of lakes versus the number of 'Bridges threatened'. The y-axis, 'Number of lakes', ranges from 0 to 140. The x-axis categories are '0', '0-5', '5-10', '10-15', and '>15'. The highest bar is in the '0-5' category, representing approximately 132 lakes. The '5-10' category shows about 100 lakes.

Chart (b) shows the number of lakes versus 'Hydropower systems threatened'. The y-axis, 'Number of lakes', ranges from 0 to 300. The x-axis categories are '0', '1', and '2'. The vast majority of lakes, approximately 265, threaten '0' hydropower systems.

Chart (c) illustrates the number of lakes versus 'Buildings threatened'. The y-axis, 'Number of lakes', ranges from 0 to 120. The x-axis categories are '<20', '20-50', '50-100', '100-500', '500-1000', and '>1000'. The categories '<20' and '100-500' have the highest bars, each representing just over 110 lakes.


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Figure: Figure 6. Downstream impact summary representing the potential number of (a) bridges, (b) hydropower systems, and (c) buildings threatened by GLOFs.

Figure 6. Downstream impact summary representing the potential number of (a) bridges, (b) hydropower systems, and (c) buildings threatened by GLOFs.


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lakes that inundated two hydropower systems was 15 (Figure 6b). The number of bridges inundated ranged from 0 to 17 with a median value of 4 (Figure 6a).

The ranking of downstream impact by considering the 100 buildings threshold to differentiate between large and small number of buildings classified 51 lakes as very high downstream impact, 87 as high downstream impact, 148 as moderate downstream impact, and 43 as low downstream impact. The threshold of 100 buildings helped in distributing the lakes uniformly. Fifty-one lakes out of 67 that inundated at least one hydropower system also inundated 100 buildings. This shows that hydropower systems are located near the areas of settlements. Altering the building threshold to 50 buildings increased the number of lakes in very high downstream impact category by 3 and high downstream category by 30. Union territories of Jammu and Kashmir, and Ladakh consist of the highest number of very high downstream impact lakes, followed by Sikkim, Uttarakhand, Himachal Pradesh, and lastly Arunachal Pradesh, which has no lake that shows very high downstream impact. Most of the lakes with very high downstream impact were located below the elevation of 5,000 m except for three lakes in Uttarakhand and one lake in Sikkim (Figure 5b). Some of the commonly known lakes with very high downstream impact are Sheshnag Lake, Tsomgo Lake, and Hangu Lake.

The uncertainty associated with PFV estimation in case where the computation was based on SLA analysis of lake was found to be  $\pm 44\%$ , whereas, in the case of avalanche and rockfall, the uncertainty values were found to be  $\pm 68\%$  and  $\pm 52\%$ , respectively. It was observed that these uncertainty values were majorly caused due to error in-depth estimation (importance = 98%, 95%, and 91% in case of SLA, avalanche, and rockfall, respectively) rather than the error in area estimation.


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The risk categorization of glacial lakes was based on the criteria described in Figure 2c. A total of 23 lakes were classified as very high risk, 50 were classified as high risk, 195 were classified as moderate risk, and 61 were classified as low risk. Out of 51 lakes that were characterized as very high downstream impact, merely 10 were categorized as very high risk due to their moderate hazard. State-wise distribution of very high risk lakes revealed that the union territories of Ladakh, and Jammu and Kashmir have 4 very high risk lakes, Uttarakhand has 2 very high risk lakes, and Sikkim has 17 very high risk lakes, whereas Himachal

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Pradesh and Arunachal Pradesh have no very high risk lakes (Figure 7). There was no apparent trend between risk and elevation of lakes (Figure 5c). Table 1 shows the lake details, associated hazard, downstream impact, and potential risk of the lakes recognized as very high risk lakes and their confrontation against other studies in the Indian Himalayas (Abdul Hakeem et al., 2018; Aggarwal et al., 2017; Ives et al., 2010; Raj & Kumar, 2016; Worni et al., 2013).


Document: 1. Introduction
Section: 4. Discussions > 4.1. Glacial Lake Inventory
Type: text

This study identified 329 glacial lakes in the Indian Himalayas, which strongly agrees with previous studies (Aggarwal et al., 2017; Fujita et al., 2013; Ives et al., 2010; Worni et al., 2013; Zhang et al., 2015) (supporting information Table S1, Sheets 3–7). The threshold of lakes above the elevation of 3,000 m to select the glacial lakes was applied instead of the threshold used by previous studies (only moraine-dammed lakes for Fujita et al., 2013, and any lakes within 10 km of glacier boundaries for Worni et al., 2013), as the uncertainty associated with debris-covered glaciers in these inventories may range up to 30% and it can significantly alter the study area (Yao et al., 2012) and use of only moraine-dammed lakes may lead to underestimation of potential GLOF hazard.

The lakes that did not show any significant expansion between 1993 and 2018 were compared for the year 2018 against the lake area measurement by Fujita et al. (2013). As Fujita et al. (2013) only considered moraine-dammed lakes, the comparison was made only for 128 lakes that were common for both the studies. The mean difference in lake area between these studies was found to be  $0.0183~\rm km^2$  which is comparable with the assumption of error linked with lake delineation which ranged from  $0.0127~\rm to~0.1337~\rm km^2$  with mean value of  $0.0296~\rm km^2$ . Therefore, the assumption used to measure lake error, that is, perimeter multiplied by half the pixel size, is reasonable.

The maximum distance of lake from glaciers experiencing an avalanche and rockfall is 607.5 and 5,079.9 m, respectively; therefore, for the studies focusing on dynamic failures, the distance threshold of 10 km can be reasonably adopted. Lake distance of steep moraine lakes revealed that there are 42 lakes with steep moraine that are located beyond 10 km of glacier boundaries; therefore, for studies incorporating self-destructive failure, the distance threshold of 10 km may lead to underrepresentation of glacial lakes.

4. Discussions
4.1. Glacial Lake Inventory


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: text

This study modeled the most frequent triggers of GLOF such as avalanche or rockfall hit, upstream GLOF, stability of the damming moraine, and presence of ice cores. Other triggers of GLOF include extreme climatic events and seismic activities. These triggers were integrally assessed by the studied hazard parameters as an extreme climatic event, or seismic activity will ultimately alter the hydrostatic pressure on the damming moraine which will more likely trigger the failure of an unstable moraine in comparison to a stable one. The impact of remediation measures was not studied as there has been only one such effort at South Lhonak Lake, Sikkim. The lowering of the lake was by 3 m, that is, well within the associated error of the DEM (RMSE = 17 m); therefore, it was not possible to make any noticeable inferences for its impact on the hazard.

The major struggle accompanied with any hazard assessment is the technique to represent the parameters in terms of its hazard category. This is especially perplexing, as these events are unpredictable and usually occur in a remote location. Therefore, the data from past events and the knowledge of their triggers are limited. Another difficulty associated with hazard assessment is that these events are interconnected; that is, one trigger can evolve into another. Falátková (2016) addresses this issue by demonstrating that the destabilization and breach of damming moraine may take place by the coupled effect of an avalanche, melting of ice cores, increase in hydrostatic pressure, and the effect over time. Additionally, Emmer and Cochachin (2013) described the combined effect of ice cores, hydrostatic pressure, and time in a single hazard parameter called as self-destruction. The SLA analysis illustrated by Fujita et al. (2013) inherently takes into account the combined impact of various triggers as it focuses on the failure of the moraine irrespective of their triggering mechanism.

These aspects of hazard analysis imply that while the underlying triggering mechanisms are understood for GLOF, there is still significant uncertainty concerning how the interconnection of these triggers affects the

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4. Discussions
4.2. Hazard and Downstream Impact


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: figure
Figure

Image /page/11/Figure/3 description: An infographic displaying a map of northern and northeastern India, showing the locations of high-risk lakes and associated risk analysis data for five regions. A legend indicates that red dots are 'Very high risk lake' and yellow circles are 'High risk lake'. Another legend explains the color coding for the pie charts: L (Low) is light green, M (Medium) is yellow, H (High) is light red, and VH (Very High) is dark red. The map shows the regions of Ladakh, Jammu and Kashmir; Himachal Pradesh; Uttarakhand; Sikkim; and Arunachal Pradesh, with a high concentration of lakes in Sikkim. Each region has a corresponding set of pie charts for Hazard, Downstream Impact, and Risk. The data is as follows: Ladakh, Jammu and Kashmir: Hazard (L=29, M=51, H=14, VH=4), Downstream Impact (L=24, M=22, H=30, VH=22), and Risk (L=25, M=55, H=14, VH=4). Himachal Pradesh: The first chart shows L=5, M=23, H=6, VH=2. The second chart shows L=9, M=24, H=2, VH=1. The third chart shows L=13, M=19, H=4. Uttarakhand: The first chart shows L=3, M=13, H=6. The second chart shows M=16, H=1, VH=5. The third chart shows M=16, H=2, VH=2. Sikkim: The first chart shows L=4, M=39, H=28, VH=17. The second chart shows L=31, M=35, H=22. The third chart shows L=5, M=48, H=15, VH=20. Arunachal Pradesh: The first chart shows L=11, M=63, H=9, VH=2. The second chart shows L=10, M=55, H=20. The third chart shows L=17, M=66, H=2, VH=2.


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: figure
Figure: Figure 7. Distribution of hazard, downstream impact, and risk classification (pie charts) for various states.

Figure 7. Distribution of hazard, downstream impact, and risk classification (pie charts) for various states.


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: text

hazard. These inherent uncertainties prevent the quantitative hazard assessment by assignment of weights to various hazard parameters, for example, in the case of Bolch et al. (2011) and Wang et al. (2013). To overcome this, a framework was adopted to reflect the most hazardous situations by frequency of the various triggering mechanisms involved. The expansion of glacial lakes was not used as a parameter of GLOF assessment as Rounce et al. (2017) applied the expansion model to predict the further expansion of glacial lakes in Nepal Himalayas and observed that 15 out of 22 glacial lakes with significant expansion were incapable of further growth and only 1 lake out of 136 exhibited a change in its hazard assessment.

For downstream impact assessment, the GLOF extents were conservatively assessed using the stochastic MC-LCP model by quantifying potential buildings, hydropower systems, and bridges located within the inundation regions. Large-scale applicability, inexpensive computations, and consideration of DEM uncertainty are the benefits of MC-LCP model whereas no accountability for any variations in PFVs is the limitation. It should be noted that GLOFs at Lmja Tso and Dig Tsho had PFV of  $33.5 \times 10^6$  m3 (Somos-Valenzuela et al., 2016) and  $5 \times 10^6$  m3 (Vuichard & Zimmermann, 1987), respectively. Consequently, MC-LCP is producing flood extent linked with large PFV values. A good example describing this is by Khangchung Tso and Lake 228. Both of these lakes have similar flood extent despite having very different PFV of  $19.928 \times 10^6$  m3 and  $0.7612 \times 10^6$  m3. Figure 8 describes the distribution of lakes at different PFV ranges. It shows that the number of lakes dramatically reduces beyond  $5 \times 10^6$  m3. The figure also shows that most of the lakes with PFV higher than  $5 \times 10^6$  m3 are located in Arunachal Pradesh and Sikkim. Past GLOF events of Dig Tsho and Tam Pokhri show that PFV of  $5 \times 10^6$  m3 is significant enough to produce high socioeconomic damage to downstream communities. Conversely, a lake outburst near Chorabari glacier in Uttarakhand with PFV of just  $0.43 \times 10^6$  m3 caused catastrophic damage and killed more than 4,000 people in 2013. Therefore, the risk assessment should be based on downstream impact rather than PFV. We assumed the runout distance for each lake to be 50 km, beyond which the waves were considered to be relatively small in comparison to structures located outside the main channel. This assumption is consistent with most of the past events (Rafiq et al., 2019; Vuichard & Zimmermann, 1987). Whereas, due to exceptions such as Luggye Tsho

DUBEY AND GOYAL 12 of 21


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: table
Table

|         | Downstream Impact, Risk Ranking, and PFV of Lakes Identified as Very High Risk Lakes |  |
|---------|--------------------------------------------------------------------------------------|--|
| Table 1 | Lake Details, Hazard, Downstream Impact, R                                           |  |


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: table
Table

| Lake details               |                  |                  |                 |                  |               |                    |                    | Hazard                   |                   |                      |           | Lake ID (this study) | Hazard   |                             | Downstream impact |         |                    | Ranking        |                           |              | PFV (106 m3)  |
|----------------------------|------------------|------------------|-----------------|------------------|---------------|--------------------|--------------------|--------------------------|-------------------|----------------------|-----------|-------------------------|----------|-----------------------------|-------------------|---------|--------------------|----------------|---------------------------|--------------|---------------|
| Lake ID (this study) | Other studies | Longitude (°) | Latitude (°) | Elevation (m) | Aspect (°) | Area (km²) 1993 | Area (km²) 2018 | Significant expansion | Lakes upstream | Presence of ice core | Avalanche |                         | Rockfall | Steep moraine (slope > 10°) | Buildings         | Bridges | Hydropower systems | Hazard ranking | Downstream impact ranking | Risk ranking |               |
| 6                          |                  | 75.373           | 34.185          | 4,294            | 35            | 0.179              | 0.121              | 0                        | 0                 | 1                    | 1         | 6                       | 0        | 1                           | 1,400             | 10      | 1                  | 3              | 3                         | 3            | 1.86 ± 0.82   |
| 21                         |                  | 75.085           | 34.391          | 4,074            | 300           | 0.051              | 0.061              | 0                        | 0                 | 1                    | 1         | 21                      | 0        | 1                           | 200               | 5       | 0                  | 3              | 2                         | 3            | 0.7 ± 0.31    |
| 27                         |                  | 75.058           | 34.422          | 3,580            | 280           | 0.382              | 0.399              | 0                        | 0                 | 0                    | 1         | 27                      | 0        | 0                           | 650               | 7       | 1                  | 2              | 3                         | 3            | 0.11 ± 0.07   |
| 34                         |                  | 75.377           | 34.139          | 3,722            | 20            | 0.33               | 0.334              | 0                        | 0                 | 1                    | 0         | 34                      | 0        | 1                           | 1,000             | 10      | 1                  | 2              | 3                         | 3            | 0.19 ± 0.08   |
| 139                        |                  | 79.487           | 30.981          | 5,661            | 30            | 0.027              | 0.051              | 1                        | 0                 | 1                    | 0         | 139                     | 1        | 1                           | 1,300             | 13      | 1                  | 2              | 3                         | 3            | 0.55 ± 0.24   |
| 153                        | 4                | 79.46            | 30.976          | 5,574            | 220           | 0.119              | 0.177              | 1                        | 0                 | 1                    | 0         | 153                     | 0        | 1                           | 1,300             | 13      | 1                  | 2              | 3                         | 3            | 3.17 ± 1.4    |
| 159                        | 2, 3             | 88.546           | 27.993          | 5,178            | 75            | 0.562              | 0.595              | 0                        | 0                 | 1                    | 1         | 159                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 8.17 ± 3.6    |
| 160                        | 2, 3, 5          | 88.713           | 28.005          | 5,231            | 340           | 1.155              | 1.247              | 1                        | 0                 | 1                    | 1         | 160                     | 1        | 1                           | 300               | 5       | 0                  | 3              | 2                         | 3            | 49.89 ± 21.95 |
| 161                        | 2, 3             | 88.616           | 27.975          | 5,010            | 30            | 0.574              | 0.576              | 0                        | 0                 | 0                    | 1         | 161                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 16.78 ± 7.38  |
| 164                        |                  | 88.657           | 27.816          | 4,613            | 90            | 0.143              | 0.143              | 0                        | 0                 | 0                    | 1         | 164                     | 0        | 1                           | 300               | 10      | 1                  | 3              | 3                         | 3            | 2.34 ± 1.03   |
| 173                        | 3                | 88.789           | 27.873          | 4,943            | 160           | 0.098              | 0.104              | 0                        | 0                 | 0                    | 1         | 173                     | 0        | 1                           | 275               | 8       | 1                  | 3              | 3                         | 3            | 1.49 ± 0.66   |
| 174                        | 3                | 88.509           | 27.982          | 4,942            | 200           | 0.028              | 0.376              | 1                        | 0                 | 0                    | 1         | 174                     | 1        | 1                           | 152               | 11      | 0                  | 3              | 2                         | 3            | 1.8 ± 0.79    |
| 176                        |                  | 88.638           | 27.873          | 5,147            | 185           | 0.078              | 0.086              | 0                        | 0                 | 0                    | 1         | 176                     | 0        | 1                           | 104               | 6       | 0                  | 3              | 2                         | 3            | 1.14 ± 0.5    |
| 180                        |                  | 88.806           | 27.854          | 5,068            | 210           | 0.09               | 0.097              | 0                        | 0                 | 1                    | 0         | 180                     | 0        | 1                           | 275               | 8       | 1                  | 2              | 3                         | 3            | 1.36 ± 0.6    |
| 202                        | 1, 3, 5          | 88.816           | 27.99           | 5,288            | 300           | 1.647              | 1.775              | 0                        | 0                 | 1                    | 1         | 202                     | 1        | 1                           | 150               | 3       | 0                  | 3              | 2                         | 3            | 19.93 ± 8.77  |
| 207                        |                  | 88.761           | 27.895          | 5,232            | 280           | 0.466              | 0.476              | 0                        | 1                 | 1                    | 1         | 207                     | 0        | 1                           | 172               | 8       | 0                  | 3              | 2                         | 3            | 12.78 ± 5.62  |
| 209                        | 1, 2             | 88.561           | 28.014          | 5,070            | 110           | 0.262              | 0.27               | 0                        | 0                 | 0                    | 1         | 209                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 5.06 ± 2.23   |
| 214                        | 2                | 88.639           | 28.002          | 5,423            | 350           | 0.319              | 0.317              | 0                        | 0                 | 1                    | 1         | 214                     | 1        | 1                           | 395               | 8       | 0                  | 3              | 2                         | 3            | 7.21 ± 3.17   |
| 218                        | 1                | 88.863           | 27.865          | 4,834            | 200           | 0.135              | 0.141              | 0                        | 0                 | 0                    | 1         | 218                     | 1        | 1                           | 195               | 8       | 1                  | 3              | 3                         | 3            | 1.76 ± 0.77   |
| 225                        |                  | 88.672           | 27.92           | 4,857            | 20            | 0.095              | 0.097              | 0                        | 0                 | 0                    | 1         | 225                     | 0        | 1                           | 135               | 10      | 0                  | 3              | 2                         | 3            | 0.3 ± 0.13    |
| 228                        |                  | 88.801           | 27.993          | 5,284            | 300           |                    | 0.086              | 0                        | 0                 | 0                    | 1         | 228                     | 0        | 1                           | 223               | 11      | 0                  | 3              | 2                         | 3            | 0.76 ± 0.33   |
| 231                        | 3                | 88.747           | 27.864          | 5,089            | 280           | 0.117              | 0.185              | 1                        | 0                 | 1                    | 1         | 231                     | 0        | 1                           | 135               | 10      | 0                  | 3              | 2                         | 3            | 0.6 ± 0.26    |
| 329                        |                  | 88.514           | 27.7            | 4,505            | 210           | 0.094              | 0.076              | 0                        | 0                 | 1                    | 0         | 329                     | 0        | 1                           | 130               | 16      | 0                  | 2              | 3                         | 3            | 0.12 ± 0.05   |

Lake details
Hazard
Lake ID (this study)
Hazard
Downstream impact
Ranking
PFV (106 m3)


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: text

Note. In hazard assessment, 1 represents yes, and 0 represents no. Ranking of 0 represents low, 1 represents moderate, 2 represents high, and 3 represents very high. Additional details of all the 329 lakes are provided in the supporting information Table S1 (Sheet 1). 1: Ives et al., 2010 (ICIMOD); 2: Worni et al., 2013; 3: Aggarwal et al., 2017; 4: Raj & Kumar, 2016; 5: Abdul Hakeem et al., 2018.

19447737, 2020, 4, Downloaded from https://gutpubs.onlinebtary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use of the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the artic

DUBEY AND GOYAL 13 of 21

Note. In hazard assessment, 1 represents yes, and 0 represents no. Ranking of 0 represents low, 1 represents moderate, 2 represents high, and 3 represents very high. Additional details of all the 329 lakes are provided in the supporting information Table S1 (Sheet 1). 1: Ives et al., 2010 (ICIMOD); 2: Worni et al., 2013; 3: Aggarwal et al., 2017; 4: Raj & Kumar, 2016; 5: Abdul Hakeem et al., 2018.


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: text

the article and the article and the article and the artic DUBEY AND GOYAL 13 of 21 Note . In hazard assessment , 1 represents yes , and 0 represents no . Ranking of 0 represents low , 1 represents moderate , 2 represents high , and 3 represents very high . Additional details of all the 329 lakes are provided in the supporting information Table S1 ( Sheet 1 ) . 1 : Ives et al . , 2010 ( ICIMOD ) ; 2 : Worni et al . , 2013 ; 3 : Aggarwal et al . , 2017 ; 4 : Raj & Kumar , 2016 ; 5 : Abdul Hakeem et al . , 2018 .

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DUBEY AND GOYAL 14 of 21

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Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: figure
Figure

Image /page/14/Figure/3 description: A stacked bar chart showing the number of lakes categorized by their potential flood volume (PVF) across five different regions in the Indian Himalayas. The y-axis, labeled "Number of lakes," ranges from 0 to 100. The x-axis, labeled "Potential flood volume (PVF; 10⁶ m³)," has categories: 0, 0-1, 1-2, 2-3, 3-4, 4-5, 5-10, 10-15, 15-20, 20-30, 30-40, and 40-50. The legend indicates the regions: Arunachal Pradesh (red), Sikkim (light orange), Uttarakhand (light yellow), Himachal Pradesh (grey-blue), and Jammu and Kashmir (dark blue). The chart shows that the highest number of lakes have a potential flood volume between 0 and 1 million cubic meters, with that bar reaching a total of approximately 93 lakes. The second-highest category is 1-2 million cubic meters, with about 76 lakes. Generally, the number of lakes decreases as the potential flood volume increases. For the 0-1 PVF category, the approximate breakdown is: Jammu and Kashmir ~28, Himachal Pradesh ~15, Uttarakhand ~12, Sikkim ~26, and Arunachal Pradesh ~12.


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: figure
Figure: Figure 8. Distribution of PFV for the lakes in Indian Himalayas.

Figure 8. Distribution of PFV for the lakes in Indian Himalayas.


Document: 1. Introduction
Section: 4. Discussions > 4.2. Hazard and Downstream Impact
Type: text

(Reynolds, 2014; Richardson & Reynolds, 2000) where the runout length of 200 km was observed, determination of runout length should be based on the application of physically based hydrodynamic model using variable discharge values, this can also be incorporated in MC-LCP model by the use of cost function based on runout distance. Additionally, due to the presence of artifacts in the DEM, the river network obtained did not coincide with the actual river path for few lakes (Lake 284, Lake 285, Lake 286, etc.), for such lakes the river network obtained using reconditioned DEM with 20 m vertical buffer was considered as flood extent instead of MC-LCP output. Improved locations of hydropower systems and updated inventory of buildings can significantly improve the downstream impact assessment. Nevertheless, this study can provide a conservative estimate of flood extents for each lake that can provide vital information for planning risk mitigation measures.


Document: 1. Introduction
Section: 4. Discussions > 4.3. Risk
Type: text

This study is meant to assist stakeholders in identifying critical glacial lake that needs an additional field survey. With the available financial resources, field investigation and hydrodynamic modeling of each of these lakes are impractical, but field-based assessment on a few of these lakes to validate remotely sensed based assessment can be useful. The framework for this study has been designed in a manner that data from hazard and downstream impact assessment can even be used by decision-makers who may have an alternate way on the classification of these glacial lakes. This study sought to balance both social and economic effects. Conversely, if economic impacts were to be prioritized, then the lakes affecting more hydropower systems should have been given higher downstream impact, for example, Bhale Pokhri Lake, Goecha La Lake, Hangu Lake, and Tsomgo Lake. Alternatively, if the social impact were to be prioritized, then the lakes affecting more number of buildings should have been given the highest downstream impact, for example, Lakes 12 and 56.

The classification showed here were established by prioritizing the lakes based on their hazard and down-stream impact, while on the contrary, a common way to prioritize the lake impact is based on higher PFV. Hazard, downstream impact, and risk of lakes having PFV higher than  $5 \times 10^6$  m³ (PFV of Dig Tsho) are illustrated in Table 2; there are a total of 32 lakes in Indian Himalayas that have PFV exceeding  $5 \times 10^6$  m³ among which 7 are at very high risk, 3 are at high risk, and 22 lakes are at moderate risk. Gurudongmar Lake has the largest PFV due to its large size (1.120 km²) and steep moraine (42.47°). Shakho Cho Lake being one of the most dangerous lakes in the Indian Himalayas is equipped with water level gauging instrument to monitor high rise in lake. Despite being a well-studied (Worni et al., 2013, 2014) lake in Indian Himalayas, no mitigation measures have been taken so far, and the lake appears to be at very high risk due to the potential of avalanche and rockfall. Other notable lakes that were not included in the list but are at very high risk are Lake 153, Lake 164, and Lake 218. Physically based hydrodynamic modeling of

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