Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Change in glacial lake area
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upstream region of the lake that can be disastrous at some point of time when they hit the lake. The nearest settlement and Kashnag project power house are located just 17 km from the lake outlet. Keeping in view these factors, we have carried out the GLOF modeling of the lake for a distance of 102 km in the downstream region. In this study, we have calculated the breach width and breach formation time by using Eqs. (2) and (3). Breach width is a function of the total volume of water in upstream and the breach height. A breach width of 75 m was obtained by using Eq. (2) to calculate the initial dam-breach hydrograph and subsequently to perform an unsteady flow analysis.

data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md

Results and discussion
Change in glacial lake area


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

In the present study, utilizing satellite data such as Landsat (TM, ETM + and OLI), Google Earth imagery, and DEM, the characteristics of the glacial lake and its feeding glacier were investigated in detail. The major conclusions drawn from the GLOF susceptibility index based on the essential parameters mentioned in Table 2 are as follows. The investigation reveals that the lake has an area of 0.26 km2 which is greater than the threshold area mentioned for the lake to be potentially dangerous (Campbell 2005; Aggarwal et al. 2014; Washakh et al. 2019). The lake area has shown a significant increase in area over a period of 28 years from 1990 to 2018, indicating a growth of 56.42% with a mean increase of 0.003 km2, thus depicting the high index value in terms of GLOF susceptibility. Lakes that are in direct contact with the glacier (at the snout) have a high potential to cause a GLOF event due the sudden calving of the retreating snout (Che et al. 2014; ICIMOD 2011). In our case, the selected lake is located at the snout of the glacier with little glacier calving visible on the Google Earth imagery (Fig. 1). The type of dam plays an important role in determining the hazard level of a particular lake. Lake dammed by ice or moraines are considered potentially most dangerous as compared to the landslide or bedrock dammed lakes. The selected lake has been classified as a moraine dammed lake through the visual interpretation technique (Huggel et al. 2002). It is also crucial to note that certain triggering factors, including as heavy rain, landslides, rockfall, mass movements, avalanches, and cloud bursts, may cause these glacial lakes to suddenly burst. From the Google Earth imagery, potential ice and rock avalanche zones are clearly visible. In case if a lake is hit by such triggering factors, it may cause sudden breaching of the moraine dam and the resultant flood may wash away the agricultural land and infrastructure in the downstream region.

It is quite clear from the Table 2 that all of the selected parameters have shown high index value and their corresponding rank value is also high. The lake is area is greater than 0.2 km2, with an expansion rate of more than 56% and it

is present at the retreating snout of the glacier. Furthermore, lake is dammed by moraine and susceptible to potential rock and ice avalanche. In terms of susceptibility index, the total value of the lake was calculated as 0.75, which indicates that the lake is highly susceptible to GLOF and therefore needs continuous monitoring and assessment with the help of remote sensing GIS and hydrodynamic modeling.

Results and discussion
GLOF susceptibility


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
Type: text

The GLOF simulation has been carried out for the selected lake dammed by an unconsolidated moraine as visible on the Google Earth imagery. The lake is present at the snout of the glacier which is retreating rapidly from the past few decades and adding more and more influx of the glacier melt into the lake. With the increasing temperatures and resultant glacier melting, more expansion is expected from this lake in the coming years. Considering all these factors, the flood routing from a glacial lake up to a distance of 102 km to the basin outlet has been characterized in the model by adding various river cross-sections derived from SRTM DEM. Each cross-section is identified with a different river station and subsequently given as an input in the HEC-RAS model as depicted in Fig. 4.

The downstream effects of the GLOF caused by the dam failure are modelled using the unsteady flow analysis in HEC-RAS model. The one-dimensional moraine breach simulations were used to evaluate the unsteady flow hydrodynamic models understanding and sensitivity to the input dam-breach parameter (breach width and breach formation time). Therefore, to estimate and evaluate its effect on initial breach hydrograph, possible varied breach width  $(B_w)$  and breach failure time  $(T_f)$  of the lake were considered for different worst-case scenarios, in which breach width has been taken as 40, 60, and 75 m and breach formation time has been taken as 40, 30, and 20 min, respectively (Fig. 5).

Results and discussion
Assessment and simulation of GLOF


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
Type: figure
Figure: Figure 5a to f shows the one-dimensional breach hydrographs scenarios, executed with varied breach width (Bw) and breach formation time (Tf). We have considered three scenarios with varied Bw = 75 m, Bw = 60 m, and Bw = 40 mwith a constant breach formation time (Tf). Similarly, keeping breach width (Bw) constant, model sensitivity to breach formation time was also analyzed by considering three scenarios with varied Tf (20, 30, and 40 min). Based on the various breach parameters, overtopping failure mechanism have been used to analyze the various scenarios. The results derived from these scenarios are given in Table 3. It is inferred from this study that, among the nine different breach scenarios, GLOF breach hydrograph for 75 m breach width (Bw) and 30 min breach formation time (Tf), which produced peak discharge of 4507 m3/s, has been considered an input for upstream boundary condition and generates flood routing at different sites along the river flow channel,

Figure 5a to f shows the one-dimensional breach hydrographs scenarios, executed with varied breach width (Bw) and breach formation time (Tf). We have considered three scenarios with varied Bw = 75 m, Bw = 60 m, and Bw = 40 mwith a constant breach formation time (Tf). Similarly, keeping breach width (Bw) constant, model sensitivity to breach formation time was also analyzed by considering three scenarios with varied Tf (20, 30, and 40 min). Based on the various breach parameters, overtopping failure mechanism have been used to analyze the various scenarios. The results derived from these scenarios are given in Table 3. It is inferred from this study that, among the nine different breach scenarios, GLOF breach hydrograph for 75 m breach width (Bw) and 30 min breach formation time (Tf), which produced peak discharge of 4507 m3/s, has been considered an input for upstream boundary condition and generates flood routing at different sites along the river flow channel,


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
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**Fig. 4** Cross-section network generated along the river channel from the lake outlet


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
Type: figure
Figure

Image /page/8/Figure/3 description: A map showing a river system with its source at a glacial lake. The map is gridded with longitude and latitude lines, ranging from 77°45'0"E to 78°15'0"E and 31°25'0"N to 31°40'30"N. The river flows from the 'Glacial lake' in the northeast to an 'Outlet point (Rajpur town)' in the southwest. The river's path is marked with numerous red cross-sections, which are numbered sequentially from 1000 near the outlet to 104000 near the lake. A legend in the bottom right corner defines the symbols used: a light blue area for the glacial lake, a blue line for the river, a purple line for flowpaths, a red line for a cross section, and a green dot for the outlet point. A scale bar at the bottom indicates distances in kilometers, up to 24 km. A north arrow is present in the top right corner.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF
Type: text

which gives different peak flood hydrograph and other significant flood wave parameters like water depth, flow velocity, flood peak arrival time, and the corresponding water surface elevations in the downstream area and so this was used for detailed studies on downstream impact.

The assessment of the different magnitude of flood peak hydrographs at just downstream of the lake and various locations across the river is shown in Fig. 6. In this study, attenuated flood peak hydrographs have been evaluated at five different locations at a distance of 29.3 km, 39.1 km, 44.4 km, 47.7 km, and 102 km (Rajpur town) downstream from the lake. The estimated flood peak 4507 m3/s at GLOF site and the same get mitigated to 464 m3/s at the downstream outlet site, respectively. The flood peak arrival time from lake to the outlet at a distance of 102 km is about 6 h and 5 min. This reflects that the downstream river reach is relatively wide with a gentle slope compared to that narrow valley of upstream glacial area; as a result, flood water spreads across a flood plain resulting in gradually decreasing of peak discharge as it proceeds downstream from the lake as shown in Fig. 6 and Table 4. In addition to this, flood routing along the river reach allowed calculation of other flood hydraulic parameters such as flow depth, flow velocity, and flood peak arrival time which are given in Table 4 for each of the affected locations.

The resultant flood at the nearest site (bridge) located 29.3 km from the lake site will arrive at 01:40 with a maximum discharge of 3229  $\rm m^3~s^{-1}$  and maximum flood depth and velocity of 8.13 m and 8.3 m/s, respectively. At Karcham barrage located 39 km from the lake outlet, a peak flood with

2692 m³ s-1 will arrive at 2:20 with a maximum depth of 3.8 m and a maximum flood velocity of 4.4 m/s. At the settlement 1 situated at a distance of 44 km from the lake site, the maximum discharge was estimated as 2672.12 m³ s-1 with maximum flood depth and velocity of 4.1 m and 4.4, respectively. Similarly, at settlement 2 located at a distance of 48 km, the maximum peak discharge was estimated as 2510 m³ s-1. The maximum flood depth and velocity for the same was estimated by the model as 4.4 m and 5.4 m/s, respectively. The peak flood hydrograph and other flood wave parameters were also calculated for the last selected site, namely, Rajpur town located at a distance of 102 km from the lake site. The results show a maximum peak discharge of 481 m³ s-1 with a maximum flood depth and velocity of 3.3 m and 1.5 m/s (Fig. 6).

After the analysis of Table 4 and Fig. 6, it was observed that the intensity of peak flood hydrographs and other significant flood wave parameters like water depth, flow velocity, and water surface elevation are decreasing from the lake site to the last selected location, i.e., Rajpur town. This is because of the elevation differences that decrease from the lake site to the downstream areas. The water spread area increase with the decrease in elevation and thereby decreasing the peak discharge and other associated parameters such as maximum depth, velocity, and water surface elevation.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: text

In the present study, unsteady flow routing of simulated GLOF hydrograph was computed from the selected lake up

Results and discussion
Assessment and simulation of GLOF
Flood inundation and simulation


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Figure

Image /page/9/Figure/2 description: The image displays six line graphs, labeled a through f, illustrating GLOF (Glacial Lake Outburst Flood) hydrographs under different scenarios. All graphs plot discharge, Q (m³/s), on the y-axis against Time (HH:MM) on the x-axis, which ranges from 00:00 to 14:24. Each graph contains three curves representing different conditions, with their peak discharge values labeled.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: text

Graphs a, b, and c show hydrographs for breach formation time scenarios:
- Graph a (constant breach width = 75m): Shows peak discharges of 5174.62, 4507.01, and 4068.17 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.
- Graph b (constant breach width = 60m): Shows peak discharges of 4817.09, 4151.72, and 3761.88 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.
- Graph c (constant breach width = 40m): Shows peak discharges of 4159.71, 3609.08, and 3362.44 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.

Graphs d, e, and f show hydrographs for breach width scenarios:
- Graph d (Constant Tf = 30 min): Shows peak discharges of 4507.01, 4151.73, and 3609.07 m³/s for breach widths of 75m, 60m, and 40m, respectively.
- Graph e (Constant Tf = 20 min): Shows peak discharges of 5174.62, 4817.09, and 4159.71 m³/s for breach widths of 75m, 60m, and 40m, respectively.
- Graph f (Constant Tf = 40 min): Shows peak discharges of 4068.17, 3761.88, and 3362.44 m³/s for breach widths of 75m, 60m, and 40m, respectively.

Fig. 5 Lake breach outflow hydrographs showing peak discharge obtained for varied (a-c) breach formation time (Tf) keeping breach width constant, similarly (d-e) varied breach width (Bw) keeping the breach formation time constant


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: table
Table: Table 3 Breach scenarios with varied breach width and breach formation time

| Failure mode- overtopping   |            |            |            |            |            |            |            |            |            |
|-----------------------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|
|                             | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | Scenario 7 | Scenario 8 | Scenario 9 |
| Breach width (m)            | 75         | 60         | 40         | 75         | 60         | 40         | 75         | 60         | 40         |
| Breach formation time (min) | 20         | 20         | 20         | 30         | 30         | 30         | 40         | 40         | 40         |
| Peak discharge (m3/s)       | 5174.62    | 4817.09    | 4159.71    | 4507.01    | 4151.72    | 3609.08    | 4068.17    | 3761.88    | 3362.44    |


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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**Fig. 6** Routed flood hydrographs at selected downstream locations along the flow path


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: figure
Figure

Image /page/10/Figure/3 description: A line graph plots Discharge in cubic meters per second (m³/s) on the y-axis versus Time in hours and minutes (HH:MM) on the x-axis. The y-axis ranges from 0 to 5000, and the x-axis ranges from 0:00 to 14:24. The graph shows several colored lines representing discharge at different locations and distances. A legend in the top right corner identifies five data series: GLOF at 0 Km (dark blue), Bridge at 29.3 Km (red), Karcham barrage at 39.06 Km (light blue), Settlement 1 at 44.39 Km (purple), and Settlement 2 at 47.69 Km (red). The GLOF line shows a sharp peak at 4500 m³/s shortly after 0:00. The subsequent lines show peaks that are progressively later in time and lower in magnitude: the Bridge line peaks at about 3250 m³/s around 2:00; the Karcham barrage and Settlement 1 lines peak around 2700 m³/s between 2:24 and 2:45; and the Settlement 2 line peaks at 2500 m³/s around 3:00. There is also a green line on the graph, not listed in the legend, which shows a later, smaller peak of about 1800 m³/s around 6:00.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: table
Table: Table 4 Peak flood and time of peak at different sites along the flow routing

| Sites            | Distance from lake (km) | Peak flood (m3/s) | Maximum depth (m) | Maximum velocity (m/s) | Flood peak arrival time (HH:MM) |
|------------------|-------------------------|-------------------|-------------------|------------------------|---------------------------------------|
| Just d/s of lake | 0                       | 4507              | 8.1               | 8.3                    | 00:20                                 |
| Bridge           | 29.3                    | 3229              | 9.7               | 4.3                    | 01:40                                 |
| Karcham barrage  | 39.1                    | 2692              | 3.8               | 4.4                    | 02:20                                 |
| Settlement 1     | 44.3                    | 2672              | 4.2               | 4.4                    | 02:30                                 |
| Settlement 2     | 47.6                    | 2510              | 3.9               | 5.5                    | 02:40                                 |
| Rajpur town      | 102                     | 481               | 3.3               | 1.5                    | 06:05                                 |

Sites
Distance from lake (km)
Peak flood (m3/s)
Maximum depth (m)
Maximum velocity (m/s)
Flood peak arrival time (HH:MM)


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: text

to the Rajpur town, which is located at a distance of 102 km downstream from lake. The flood wave routed the Rajpur town at 6 h and 5 min after the beginning of the GLOF event. Maximum water depth and maximum flow velocity along the flow channel were estimated as 27.4 m and 13.3 m/s, respectively. Figures 7 and 10 depict the spatial plots of inundation depth and flow velocity along the main flow channel from the Satluj lake to the Rajpur town and also provide a visualization of the three main inundated areas downstream of the lake.

The resultant flooding may cause damage to the infrastructure such as roads and bridges along the river's flow path downstream of the GLOF lake. The flood wave inundates a bridge located at 30 km downstream of the lake shown in Fig. 8a. Within 8 min of the flood wave arriving, flow velocities reach a high of 13.6 m/s (Fig. 9) and a water depth of 10.2 m (Fig. 7) with an arrival timing of 8 min is recorded, potentially causing significant inundation of the bridge and roads. GLOF wave reaches the first bridge at location 27°39′22.79″ N, and 88°36′16.26″ E located at 29.3 km downstream of lake shown in Fig. 8a, where flow depth is estimated up to 9 m and flow velocity of 4.28 m/s.

The GLOF potentially inundates three settlements, one barrage along the river flow path. The flood wave encounters the first settlement at locations 27°36′22.58″ N, and 88°38′31.76″ E along the bank of the channel at 44.4 km downstream, with a maximum flow velocity of 4.5 m/s and maximum flow depth of 4.2 m (Figs. 7 and 10). Figure 8d shows that the maximum flow depth and velocity recorded are 3.932 m and 5.498 m/s in the second settlement.

The analysis shows that the flood routing varies and gradually decreases along with the downstream of lakes. In this flood routing, peak flood decreases gradually from the lake up to 5 km then sharply declined as it proceeds downstream as shown in Fig. 10. We have observed that flood height have shown rise and fall continuously along the downstream of the river reach covering all the various sites located at few kilometers distance. The maximum flood height would be around 16 m at 20 km downstream of the lake and the peak flood at this location was found to be 3126 m³/s. The food peak reached Karcham barrage at 39 km downstream would be around 2692 m³/s.

The estimated flood depth in the downstream area varies depending upon the morphology of the river channel. The


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Figure

Image /page/11/Figure/2 description: A figure from a scientific paper showing a map of a mountainous region with a river valley, illustrating maximum flood depth. The map is bounded by longitudes 77°40'0"E to 78°10'0"E and latitudes 31°27'0"N to 31°37'30"N. A main legend in the bottom right indicates that the maximum flood depth ranges from a low of 0.001 m (blue) to a high of 27.444 m (magenta). There are three inset maps that provide detailed views of specific locations along the river. Each inset includes a line graph plotting flood depth in meters against flood wave arrival time in hours. The top-left inset shows a graph with a peak flood depth of about 8 m. The top-middle inset shows a peak flood depth of about 10 m. The bottom-middle inset shows a peak flood depth of about 4.5 m. Each inset has its own legend for flood depth, showing different ranges. A scale bar at the bottom left of the main map indicates distances from 0 to 32 km.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Fig. 7 Spatial distribution of Maximum Flood inundation


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: figure
Figure

Image /page/11/Figure/4 description: A composite image of four maps, labeled a, b, c, and d, each showing a satellite view of a river with a color overlay indicating the maximum flood depth. Each map includes a legend, a scale bar in kilometers, and geographic coordinates. Map (a) highlights a bridge crossing the river, with a legend for Max Flood Depth (m) showing ranges: 0.02-2.26, 2.26-4.55, 4.55-6.95, 6.95-9.3, and 9.30-13.61. Map (b) shows an area of settlements along a river bend, with a legend showing flood depth ranges: 0.02-2.48, 2.48-4.47, 4.47-6.23, 6.23-8.36, and 8.36-11.64. Map (c) shows a settlement along a wider section of the river, with a legend showing flood depth ranges: 0.04-2.16, 2.16-3.93, 3.93-5.46, 5.46-7.19, and 7.19-10.83. Map (d) shows a dense settlement on a sharp river bend, with a legend showing flood depth ranges: 0.12-2.41, 2.41-4.95, 4.95-7.81, 7.81-11.31, and 11.31-16.32. In all maps, the colors range from light blue for shallower depths to dark purple for the deepest floodwaters.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Fig. 8 Flood inundation maps: a focused view at inundate bridge, b zoomed view of inundated settlement 1, c zoomed view at inundate settlement 1, and d zoomed view of Rajpur town


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Figure

Image /page/12/Figure/2 description: A figure from 'Environmental Science and Pollution Research' showing a map of a mountainous region, illustrating flood data. The main map, with coordinates from 77°40'0"E to 78°10'0"E and 31°27'0"N to 31°37'30"N, displays a yellow outline of a flood path. A legend in the bottom right indicates the 'Max Flow Velocity (m/s)' with a color scale from green (Low: 0.480) to red (High: 13.280). There are three inset maps providing detailed views of specific locations. Each inset includes a color-coded representation of flow velocity and a line graph plotting 'Flow velocity (m/s)' against 'Flood wave arrival time, hr' from 00:00 to 14:24. The top-left inset shows a settlement with flow velocities up to 8.66 m/s and a graph peaking around 1.2 m/s. The top-middle inset shows a bridge with flow velocities up to 9.08 m/s and a graph peaking just below 4 m/s. The bottom-middle inset shows settlements with flow velocities up to 13.28 m/s and a graph also peaking just below 4 m/s. A scale bar at the bottom left of the main map indicates a distance of 32 km.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
Type: text

Fig. 9 Spatial distribution of maximum velocity inundation

**Fig. 10** Peak flood and flood height impact on the downstream of glacial lake


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Figure

Image /page/12/Figure/5 description: A line graph plots two variables, Peak Flood and Flood Height, against the Distance from Lake outlet. The x-axis represents the 'Distance from Lake outlet (km)' and ranges from 0 to 95. There are two y-axes. The left y-axis represents 'Peak Flood (m³/s)' and ranges from 0 to 5000. The right y-axis represents 'Flood Height (m)' and ranges from 0 to 18. The legend indicates that the blue line with circular markers represents 'Peak flood', and the red line with triangular markers represents 'Flood Height'. The 'Peak flood' line shows a general downward trend, starting at approximately 4500 m³/s near the outlet and decreasing to about 1750 m³/s at 95 km. The 'Flood Height' line fluctuates significantly. It starts around 8 m, peaks at nearly 12 m, drops, then spikes to a maximum of about 14.5 m at a distance of 20 km. It then drops to its lowest point of about 5 m at 35 km, followed by several other peaks and troughs, including a double peak of about 11 m between 75 and 80 km, and ends with a sharp rise to about 12.5 m at 95 km.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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flood heights and peak flood discharges were estimated using a one-dimensional hydrodynamic model and to analyze the potential impact of GLOFs on downstream areas. The obtained results of flood simulation were used to generate a flood inundation map. Regular monitoring is required to analyze the behavior of the glacial lake and to prevent and monitor the GLOF hazard and assess the damage that may occur in the future. Over the past few decades, GLOFs are


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation
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becoming common in the glacierized basins of the world, resulting in thousands of deaths and severe damage to the property in the downstream regions. Given the frequency of earthquakes and ongoing warming, the likelihood of GLOFs and related disasters is significant. It is not only the likelihood of a glacial lake outburst that comes under the category of risk but vulnerability and adaptability of various elements such as population, roads, and infrastructure are also part of it.


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

In the current study, we investigated the dynamics of the highly susceptible glacial lake (dammed by moraine) in the Satluj basin of Western Himalayas for a period of 28 years from 1980 to 2018. The results show a clear growth in the glacial lake area from 0.112 km2 in 1990 to 0.262 km2 in 2018 derived from the multi-temporal satellite (Landsat TM, ETM and OLI) with a spatial resolution of 30 m. The glacial lake studies using multi-temporal satellite data, especially Landsat series, have been successfully employed by various researchers in different parts of the Himalayan region. The investigations reveal that glacial lakes are increasing in both number and area; for example, in Jhelum basin of Kashmir Himalayas, the number has increased from 253 in 1990 to 324 in 2018, with a growth rate of 21.4% and the area on the other hand has increased from  $18.84 \pm 0.1 \text{ km}^2$  in 1990 to  $22.13 \pm 0.12 \text{ km}^2$  in 2018 with a growth rate of 14.7% (Ahmed et al. 2021a, 2021b). Similarly in Koshi basin, the glacial lakes have shown an increase of 31.5% between 1975 and 2010 (Shresth et al. 2017), Central Himalayas by 17 and 24% (Nie et al. 2013; Shukla et al. 2018), Nepal Himalayas by 25% during 1977 to 2015 (Khadka et al. 2018), Nepal and Bhutan Himalayas by 20–60% during 1990–2009 (Gardelle et al. 2011), Sikkim Himalayas by 8.3% during 1988–2014 (Debnath et al. 2018), Entire third pole by 23% during 1990-2010 (Zhang et al. 2019), and Eastern Himalayas by (Begam and Sen 2019; Mal et al. 2021). Rapid glacier recession and glacial lake expansion in the high terrains of the Himalayan region are a clear indicator of the changing climate and global warming. Temperature plays a key and dominant role in influencing the sensitivity of the glaciers. Glaciers in the Himalayan region are under the influence of the western disturbances, and Indian and Asian monsoons. Over the last few decades, glaciers in the Himalayan region are retreating and losing mass at a faster rate under the influence of the climate change (increasing temperatures and decreasing precipitation) resulting in the formation and development of various types of glacial lakes especially supraglacial lakes, moraine-dammed lakes, icedammed lakes, etc. Some of these lakes are potentially dangerous, which can burst and may cause huge damage to the life and property in the downstream valleys as in case of the Chorabari lake outburst (Allen et al. 2016) which results in the number of deaths and destruction to the property in the downstream regions. Therefore, monitoring and assessment of the potentially dangerous glacial lakes and evaluating their probable GLOF impacts are a key step to reduce the damage and loss associated with such events.


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Discussions
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temperatures and decreasing precipitation ) resulting in the formation and development of various types of glacial lakes especially supraglacial lakes , moraine - dammed lakes , icedammed lakes , etc . Some of these lakes are potentially dangerous , which can burst and may cause huge damage to the life and property in the downstream valleys as in case of the Chorabari lake outburst ( Allen et al . 2016 ) which results in the number of deaths and destruction to the property in the downstream regions . Therefore , monitoring and assessment of the potentially dangerous glacial lakes and evaluating their probable GLOF impacts are a key step to reduce the damage and loss associated with such events .

Apart from the glacial lake dynamics, we have also evaluated the probable GLOF impacts from the high susceptible glacial lake on various important locations in the downstream region using combined approaches of remote sensing GIS and one-dimensional hydrodynamic model (HEC-RAS). GLOF event with a breach width of 75 m was revealed during the overtopping failure of the moraine dam, which resulted a peak discharge of 4063 m3/s and releasing a total volume of  $57 \times 10^5$  m3 water. The GLOF hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river. The findings of our modeling in Satluj basin revealed that channel roughness is not necessary for the extent of the inundation, but it is critical for the flood's velocity and the flood arrival time. Our results are in line with the findings from other studies pertaining to the HEC-RAS modeling (Sattar et al. 2020; Hagg et al. 2021).

HEC-RAS has been widely used in the past by various researchers for the GLOF hazard assessment and it is considered an important tool to analyze the likelihood of GLOFs resulted due to the breaching of moraine dams. The model output can be used for better planning and preventive measures. In the Himalayan region, a number of hydropower projects are situated on the various rivers and tributaries and a large number are being planned to be constructed on these rivers. There is a considerable potential that these dams will be breached, putting the hydropower plants and projects safety in danger (AHEC 2011). As a result, taking into account GLOF scenarios and estimating hydrologic parameters are now required for effective planning and design of hydroelectric power projects viz a viz their safety. Since field survey was not possible for the current studied lake due to the rough terrain, harsh weather conditions, and lack of instrumentations and funding, therefore, we relied on the satellite data and hydrodynamic modeling (HEC-RAS). But we suggest an in-depth analysis of the selected susceptible glacial lake through field-based investigations.

HEC-RAS has been effectively applied to model GLOF simulations in different parts of the Himalayan region. For example, Sattar et al. (2020) have used the HEC-RAS model for the hazard assessment of the potentially dangerous glacial lake (Safed Lake) in central Himalayas. Thakur et al. (2016) have used the one-dimensional hydrodynamic model (HEC-RS) to evaluate the impacts of GLOFs on the hydropower projects in Dhauliganga River basin. Similarly,


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Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
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Khanal et al. (2015) have applied the HEC-RAS model for the GLOF risk assessment in Nepal transboundary region. Maskey et al. (2020) have also used HEC-RAS for the GLOF modeling of Thulagi and Barun glacial lakes in Nepal Himalayas. Besides that, there are various studies that have successfully this model to evaluate the GLOF impacts on the downstream region in the Himalayas (Bajrachraya et al. 2007; Gilany and Iqbal 2020; Zaidi et al. 2013; Hussain et al. 2020; Amin et al. 2020; Majeed et al. 2021; Sattar et al. 2019) and outside the Himalayan region (Klimeš et al. 2014a; Kougkoulos et al. 2018; Anacona et al. 2015; Somos-Valenzuela and McKinney 2011; Klimes et al. 2015; Ahmed et al. 2022a, b).


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Discussions > Limitations and recommendations
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With the ongoing climate change-induced continuous glacier retreat, new glacial lakes have formed and enlarged the existing ones; therefore, the frequency of glacier lake outburst floods (GLOFs) is anticipated to grow. We have investigated the potentially dangerous glacial lake in the Satluj basin by employing the 1-D hydrodynamic modeling. Glacial lake outburst flow modeling is nothing more than an approximation of a physical phenomenon that can be used to study the physical phenomenon and its impacts on water resource structure design and flood management. In GLOF modeling, assumptions are mostly related to breach parameters, particularly breach breadth and depth, which affect flood peak and flood arrival times. Due to the lack of fieldbased bathymetry data and several other related parameters associated with the dam breach as well as routing of the flood hydrograph are the rough estimates/assumptions that cause errors and uncertainties in the results. The use of DEM with a coarse spatial resolution (30 m) also overestimates the inundation and the magnitude of the flood; therefore, we suggest the use of fine or high-resolution terrain data (DEM) to reduce the uncertainties in the results. Generally, in case if the volume of the storage area (lake) is unknown, empirical equations developed in different parts of the world are used to calculate the volume of the lake. But different equations yield different volumes, thus showing a huge variation in the results. Thus, in this backdrop, we suggest that an average of all these empirical equations should be taken into account while computing the volume of the lake also suggested by Emmer et al. (2021) and Ahmed et al. (2021a). In general, the mechanism of glacial lake bursting and the genesis of glacial lake breaches are not well understood. Irrespective of the limitations and assumptions discussed above, hydrodynamic modeling serves a valuable purpose as it provides a reasonable estimate of glacial lake outburst flood and allows for an effective estimation of the design flood.

Discussions
Limitations and recommendations


Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling
Section: Conclusions
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Glacial lake outburst floods are one of the major concerns in the Himalayan region. With the expansion and formation of new glacial lakes, the risk of GLOF is further increasing in the region. This study used multi-temporal satellite data, SRTM DEM, and HEC-RAS software to analyze the changes in the selected GLOF-susceptible lake and to evaluate the potential impacts of GLOF in the downstream area. Using a multi-criteria decision-based method, the selected lake was found to be highly susceptible to GLOF. The lake area has shown a discernible increase from 0.12 km2 in 1990 to 0.26 km2 in 2018 with an overall increase of 0.11 km2 (56.4%) over the period of 28 years, indicating a rapid expansion at the rate of 0.01 km2/year. Several hydrodynamic simulations were performed for different scenario models with varied breach width, breach formation time and breach depth. The worst case GLOF scenario (breach width=75 m) is revealed during an overtopping failure of the moraine dam, which resulted a peak discharge of 4063 m3/s and releasing a total water volume of  $5749 \times 10^3$  m3. The GLOF hydrograph was routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river. The findings of this study show that the routed flood waves reach the nearest town, i.e., Rajpur located at a distance of 102 km downstream of a lake at 6 h after the initiation of the breach event, with a peak flood of 1756 m3/s and maximum flow velocity of 1.5 m/s. If a GLOF occurs, it can have severe consequences for downstream livelihood and infrastructure. As a result, such lakes must be examined regularly, and mitigating efforts to reduce GLOF vulnerability should be prioritized. With the increasing and apparent GLOF risk across the Himalayan region, the findings of this study highlight the urgent need of forward-looking, collaborative, and long-term initiatives to prevent future impacts of GLOFs as well as to boost the sustainable development in the region.

**Acknowledgements** The authors are thankful to the United States Geological Survey (USGS) for freely providing the satellite data used in this study.

Author contribution Every author has contributed to the successful compilation of this study. MR and RA: conceptualization, methodology, software, writing—original draft, formal analysis. MR, RA, SKJ and AKL: data curation, formal analysis, writing—review and editing. SKJ and AKL: writing—review, editing, supervision. All authors read and approved the final manuscript.

Data availability Not applicable.


Document: Glacial lake outburst flood risk assessment
Section: ARSTRACT
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The ongoing trend of warming climate has made Glacial Lake Outburst Floods (GLOFs) a major cryospheric hazard worldwide, especially in the Himalayas. GLOFs in the Himalayan region are mostly caused by moraine-dammed proglacial lakes and icedammed lakes. These sporadic disasters have resulted in significant loss of life and property. This study offers a comprehensive analysis of the GLOF hazard potential of a potentially dangerous proglacial lake (PDGL) in the Ladakh region. This research explores the GLOF threat from the lake using multi-criteria analysis and advanced 2D hydrodynamic modeling approaches. The mass balance response of the mother glacier, its flow dynamics, and glacier-lake interactions were examined for the past 22 years. The findings show that over this period, the PDGL has had a notable expansion of 78.7%, accompanied by a significant recession of 13.2% in its feeding glacier. The glacier has witnessed an average thickness loss of  $\sim$ 7 m at the rate of 0.32 m  $a^{-1}$  during this period. The average, lowest, and maximum depth of the glacier were found to be 30.95, 14.30, and 50.57 m, respectively and the average velocity of the glacier was estimated as 3.38 m a-1. Because of the lake's rapid expansion and steep surrounding slopes, it was classified as a high-hazard lake. The risk to the downstream community was assessed through 2D hydrodynamic modeling using the HEC-RAS tool. The maximum discharge under the worst-case scenario for the piping and overtopping failures was estimated as 3890.99 m3 s-1 and 5111.39 m3 s-1, respectively. The area potentially under the threat of inundation was calculated to be 4.74 and 5.38 km2 for the moderate and worst-case scenarios respectively. The expected maximum flood velocities range from 18.26 to 23.78 meters, respectively for the moderate and worst-case scenarios. At several locations in the downstream area, routed hydrographs representing the GLOF propagation were generated. The findings show that the flood wave in the worst-case scenario

data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md


Document: Glacial lake outburst flood risk assessment
Section: ARTICLE HISTORY
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Received 5 June 2024 Accepted 3 October 2024


Document: Glacial lake outburst flood risk assessment
Section: KEYWORDS
Type: text

GLOF; glacial lake expansion; risk assessment; HECRAS, Ladakh region; Western Himalayas; Panikhar, Kargil would arrive at the first settlement in  $50\,\text{min}$ , with a peak velocity of  $12.36\,\text{m s}^{-1}$ . The potentially inundated area includes critical infrastructure such as bridges, residential houses, and roads. To mitigate the potential risk associated with this lake, a more detailed and on-site study is highly recommended.


Document: Glacial lake outburst flood risk assessment
Section: 1. Introduction
Type: text

In most glaciated parts of the world, the effects of climate change have resulted in ongoing glacier recession and the formation of glacial lakes behind recently exposed unstable moraines (Zemp et al. 2009; Bolch et al. 2012; Nie et al. 2017). Mountain settlements in the Himalayas, where numerous such glacial lakes are created, are seriously at risk from glacial lake outburst floods (Clague and Mathews 1973; Ding and Liu 1992; Ahmed 2024). Glacial lake outburst floods (GLOFs) are catastrophic occurrences that take place when a glacial lake, impounded by a glacier or moraine, breaks, releasing a significant amount of water and debris downstream causing flash floods in the downstream area, ruining crops, damaging infrastructure like bridges and hydropower plants, and resulting in fatalities. The majority of these flood episodes are linked to processes like dam breach creation and subsequent damming moraine failure (Richardson and Reynolds 2000; Emmer and Vilímek 2013; Worni et al. 2014). The possible triggers of a GLOF event may be glacial calving activity at the lake terminus (Zhang et al. 2020), snow or ice avalanches (Schaub et al. 2016), landslides, extreme weather events (Schmidt et al. 2020) like cloudbursts (Bhambri et al. 2016) or seismic activity (Kargel et al. 2016). These events are potent threats whose effects can reach distances of more than 100 km in the downstream region (Richardson and Reynolds 2000).

GLOFs pose a threat to about 15 million people globally and the most prominent high-vulnerability zone is the Himalayan region followed by the Andes (Taylor et al. 2023). Due to the Himalayan terrain's steep slopes and constrained flow pathways, GLOF episodes have typically been described as being particularly catastrophic (Mool 1995; Reynolds 1998; Richardson and Reynolds 2000; Worni et al. 2013). The recent Kedarnath disaster in the Central Himalayas in June 2013, which was brought on by unrelenting rain and subsequent eruption of the Chorabari glacial lake, serves as an illustration of the degree of fatality these occurrences can bring about (Das et al. 2015; Allen et al. 2016). The increased GLOF hazard in the region requires rigorous research initiatives for the identification and monitoring of hazardous glacial lakes in this region. Given that most of the glacial lakes in the Himalayas are inaccessible because of its tough terrain and harsh climate, remote sensing methods are used to monitor these lakes through time and space. Numerous studies have been conducted in the past to evaluate the distribution and GLOF hazard of glacial lakes throughout the world, especially in the Himalaya, which includes countries like Nepal, Tibet, Bhutan, and India (Bajracharya et al. 2007; Bolch et al. 2008; Fujita et al. 2009; Ives et al. 2010; Shrestha et al. 2010; Raj et al. 2013; Rounce et al. 2017; Emmer et al. 2018; Jiang et al. 2018; Nie et al. 2018; Byers et al. 2018; Emmer et al. 2020; Ashraf et al. 2021; Ahmed et al. 2022a, 2022b; Banerjee and Bhuiyan 2023).


Document: Glacial lake outburst flood risk assessment
Section: 1. Introduction
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and GLOF hazard of glacial lakes throughout the world , especially in the Himalaya , which includes countries like Nepal , Tibet , Bhutan , and India ( Bajracharya et al . 2007 ; Bolch et al . 2008 ; Fujita et al . 2009 ; Ives et al . 2010 ; Shrestha et al . 2010 ; Raj et al . 2013 ; Rounce et al . 2017 ; Emmer et al . 2018 ; Jiang et al . 2018 ; Nie et al . 2018 ; Byers et al . 2018 ; Emmer et al . 2020 ; Ashraf et al . 2021 ; Ahmed et al . 2022a , 2022b ; Banerjee and Bhuiyan 2023 ) .

The Himalayan region, hence, has been the most well-known case for GLOF disaster risk studies. In the central Himalayas, a GLOF hazard vulnerability study by the International Centre for Integrated Mountain Development (ICIMOD), highlighted some priority sites for GLOF risk mitigation. The study mapped 1,947 glacial lakes using data from the Global Land Ice Measurement from Space (GLIMS) program, of which 835 were deemed dangerous because of their locations upstream of populated areas and major roadways. The study showed that Nepal urgently needs to reduce GLOF danger, especially in the regions downstream of the 835 little glacial lakes (Bajracharya et al. 2007). Rounce et al. (2017) evaluated the risk presented by GLOFs in Nepal using satellite data from 2000 to 2015 and the previous GLOF data. The risk connected to each lake was estimated by combining the hazard and projected downstream effects. The results reported 31 high-risk lakes and 11 very high-risk lakes.

For the Upper Indus River basin, Gupta et al. (2021) conducted an assessment of potentially hazardous glacial lakes identifying 20 and 48 high-risk lakes for two different GLOF scenarios respectively. The evolving status and potential consequences of glacier lakes and the GLOF threat in the Hindu Kush Himalaya region were studied by Ashraf et al. (2021). An overall increase of 26% in the number of glacial lakes was found from 2001 to 2013 and 36 lakes were characterized as potentially dangerous. The increased risk posed by GLOFs in the context of a warming climate is highlighted, along with the necessity for proactive monitoring, risk assessment, and adaptation measures. From an Indian perspective, Jammu and Kashmir has the highest cumulative degree of GLOF hazard, followed by Uttarakhand, Arunachal Pradesh, Sikkim, Himachal Pradesh, and Arunachal Pradesh (Mal et al. 2021). Dubey and Goyal (2020) also placed J&K in second place, only after Sikkim, concerning the GLOF threat. Ahmed et al. (2022) analyzed the GLOF hazard scenario in the Jhelum basin using remote sensing and GIS and identified 21 potentially dangerous and 7 highly hazardous lakes.


Document: Glacial lake outburst flood risk assessment
Section: 1. Introduction
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the context of a warming climate is highlighted , along with the necessity for proactive monitoring , risk assessment , and adaptation measures . From an Indian perspective , Jammu and Kashmir has the highest cumulative degree of GLOF hazard , followed by Uttarakhand , Arunachal Pradesh , Sikkim , Himachal Pradesh , and Arunachal Pradesh ( Mal et al . 2021 ) . Dubey and Goyal ( 2020 ) also placed J & K in second place , only after Sikkim , concerning the GLOF threat . Ahmed et al . ( 2022 ) analyzed the GLOF hazard scenario in the Jhelum basin using remote sensing and GIS and identified 21 potentially dangerous and 7 highly hazardous lakes .

Studies focusing on individual glacial lakes have also been conducted in all parts of the Himalayan region. Majeed et al. (2021) reconstructed the 2014 Gya glacial lake outburst flood in Ladakh using the hydrodynamic model HEC-RAS and the results revealed the possible cause of the GLOF to be the piping failure. The study also predicted the possibility of 5 times more severe GLOF in the future from the lake. Mir et al. (2018) evaluated the GLOF risk of Dalung and Padam lakes in the Zanskar Valley using the Mike 11 model. The results declared both the lakes as potentially dangerous and susceptible to GLOFs of significant intensities. Sattar et al. (2021) analyzed the GLOF potential of Lower Barun glacial lake in Sikkim Himalayas and the lake was found to be susceptible to GLOF in case of a strong triggering event. Ahmed et al. (2023) estimated the risk associated with the Gangabal lake in Kashmir Himalayas using the HEC-RAS model. The results indicate that the lake has the potential to generate a peak discharge of 16,601.03 m3/s in the worst-case scenario at the time of a GLOF event. Rawat et al. (2023a) assessed a PDGL in the Satluj River basin in India and reported an areal expansion from 0.11 to 0.26 km2 over the past 28 years from 1990 to 2018 and high GLOF risk. Sattar et al. (2019) conducted the moraine breach modeling of the South Lhonak lake in Sikkim and declared the lake to be highly vulnerable to GLOF, owing to its alarming rate of expansion over the

past few decades and a loosely consolidated moraine dam. This very lake witnessed a severe GLOF disaster on October 4, 2023, killing at least 40 people and destroying public infrastructure in its downstream area. Similar studies have been conducted far and wide in the Himalayas that focus on predictive GLOF modeling of hazardous glacial lakes (Emmer and Vilímek 2013; Haemmig et al. 2014; Klimeš et al. 2014; Round et al. 2017; Wang et al. 2018, Sattar et al. 2019; Hussain et al. 2020; Majeed et al. 2021; Sattar et al. 2021; Ahmed et al. 2022; Wang et al. 2022; Gouli et al. 2023; Das et al. 2024; Schmidt et al., 2020) etc.


Document: Glacial lake outburst flood risk assessment
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least 40 people and destroying public infrastructure in its downstream area . Similar studies have been conducted far and wide in the Himalayas that focus on predictive GLOF modeling of hazardous glacial lakes ( Emmer and Vilímek 2013 ; Haemmig et al . 2014 ; Klimeš et al . 2014 ; Round et al . 2017 ; Wang et al . 2018 , Sattar et al . 2019 ; Hussain et al . 2020 ; Majeed et al . 2021 ; Sattar et al . 2021 ; Ahmed et al . 2022 ; Wang et al . 2022 ; Gouli et al . 2023 ; Das et al . 2024 ; Schmidt et al . , 2020 ) etc .

The present study is one such attempt at understanding the GLOF hazard risk associated with a rapidly expanding moraine-dammed glacial lake in the Ladakh region of the Western Himalayas using multi-criteria decision analysis and 2D GLOF modeling. The identified PDGL is situated above the Panikhar and Pranti villages in the Kargil district of Ladakh. This study focused on the following objectives. (1) Studying the glacier-lake interactions of the Panikhar glacier and glacial lake for the past 22 years (2) Estimating the mass balance, ice thickness, and surface velocity of the Panikhar glacier, (3) Assessing the GLOF hazard level of the lake and associated risk to the downstream community using geospatial analysis and hydrodynamic modeling.


Document: Glacial lake outburst flood risk assessment
Section: 2. Study area
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The glacial lake under investigation in this study is situated above the Panikhar and Pranti villages of the Kargil district in the Suru River catchment of the Western Himalayas. This lake is located between 34°2′2.154″N latitude and 75°46′59.172″E longitude, 4062 meters above sea level while its mother glacier lies, in close proximity, to its West (Figure 1). The Suru catchment is drained by the Suru River which flows through Kargil district and subsequently joins the Indus as its left bank tributary. The total catchment area is 4408 km² and the elevation range is from 2588 masl to 7047 masl. The mother glacier of the lake (GLIMS ID: G075817E34034N) is situated in the southwest of the Suru River catchment at a distance of about 15 kilometers from the well-known Parkachik glacier. The glacial lake or its mother glacier has not been identified with any specific names in previous literature. Because of this, we have given them the names of the village that is closest to them: Panikhar Glacier and Glacial Lake. The Panikhar village is situated about 13 kilometers downstream in the North-East of the glacier-lake complex and is vulnerable to potential GLOF events from the lake.


Document: Glacial lake outburst flood risk assessment
Section: 2. Study area > 2.1. Glacial geomorphology
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Panikhar glacier is oriented in the West-East direction and the lake is proglacially located close to its snout at its eastern side. The ablation zone of the glacier exhibits a bend towards the northeast according to the local geomorphology. The elevation of the glacial valley ranges from 5500 to 4100 meters above sea level (masl) and the glacial lake is situated at a height of 4100 masl. The contour lines clearly show that the steep slope to the northwest of the glacial lake is prone to landslides and snow

2. Study area
2.1. Glacial geomorphology


Document: Glacial lake outburst flood risk assessment
Section: 2. Study area > 2.1. Glacial geomorphology
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Figure

Image /page/5/Figure/3 description: A figure titled "Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village." The figure is composed of several maps and photographs. In the top left, two maps show the location of the study area within India and the region of Jammu & Kashmir. The main map in the top center is a topographic map of the Suru Catchment, with elevations ranging from a low of 2588m to a high of 7047m. This map includes a legend for the Glacial lake, Panikhar Glacier, Suru Catchment, and River Streams. To the right of this map are three photographs: one showing icebergs floating on the lake's surface, a full view of the lake, and a view of the Moraine Dam. The bottom half of the figure is a large Landsat 8 satellite image from October 2022. This image details the Panikhar Glacier, the glacial lake, the downstream flowpath, and Panikhar Village, which are outlined and labeled. A legend for this satellite image indicates that the RGB channels correspond to Band\_5 (Red), Band\_4 (Green), and Band\_3 (Blue).


Document: Glacial lake outburst flood risk assessment
Section: 2. Study area > 2.1. Glacial geomorphology
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Figure: Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.

Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.


Document: Glacial lake outburst flood risk assessment
Section: 2. Study area > 2.1. Glacial geomorphology
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avalanches, which might catalyze a GLOF. The glacial lake is impounded by the glacier's terminal moraine and the Panikhar settlement lies 13 km northeast of the glacial lake (Figure 2).


Document: Glacial lake outburst flood risk assessment
Section: 3. Datasets
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This study used Sentinel-2A (10 m), PlanetScope (3 m), Landsat TM (30 m), Landsat ETM+ (30 m), and Landsat OLI (30 m) for the mapping of glacial lake and its mother glacier and estimating area changes in the glacial lake and its feeding glacier from 2000 to 2022. Moreover, high-resolution Google imagery was also used for verification and validation purposes. Advanced Land Observing Satellite (ALOS) Phased Array Synthetic-Aperture Radar (PALSAR) radiometrically terrain corrected (RTC) Digital Elevation Model (DEM) was downloaded online from https://asf.alaska.edu (accessed on 23rd May 2023), from which topographic information such as elevation, slope, aspect, hill shade, etc. was obtained. Furthermore, ASTER DEM (30 m) was


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Figure

Image /page/6/Figure/1 description: A topographical map showing the glacial geomorphology of the Panikhar glacier. The map includes latitude and longitude lines, with longitude ranging from 75°47'0"E to 75°51'0"E and latitude from 34°1'0"N to 34°3'0"N. A scale bar indicates a range up to 2.25 km. The map displays contour lines with elevations from 4100 to 5800 meters. A legend defines the symbols used on the map: a black outline for Glacial Valley, a thin line for Contour line, light blue shading for Panikhar glacier, darker blue for Glacial lake, a red triangle for U shaped valley, a green asterisk for Cirque, a purple diamond for Roche moutainee, a red dashed line for Lateral moraines, and a thick black-and-white dashed line for Terminal moraine. The map illustrates the glacier, a glacial lake, and the locations of these various geomorphological features. An inset map in the bottom right corner shows a satellite image of the same area.


Document: Glacial lake outburst flood risk assessment
Section: 3. Datasets
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Figure: Figure 2. Glacial geomorphology of the study area.

Figure 2. Glacial geomorphology of the study area.


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Table: Table 1. Datasets used in the study.

| S. No | Data/Tool               | Resolution | Year                   | Source                                                    |
|-------|-------------------------|------------|------------------------|-----------------------------------------------------------|
| 1.    | Planet CubeSat          | 3m         | 2016, 2018, 2020, 2022 | Planet https://www.planet.com/                         |
| 2.    | Landsat 7 ETM+          | 30 m       | 2000, 2010             | USGS Earth Explorer http://www.earthexplorer.usgs.gov  |
| 3.    | Landsat 8 OLI           | 30 m       | 2022                   | USGS Earth Explorer http://www.earthexplorer.usgs.gov  |
| 4.    | Sentinel 2 A            | 10 m       | 2016, 2018, 2020, 2022 | USGS Earth Explorer http://www.earthexplorer.usgs.gov/ |
| 5.    | ALOS-PALSAR DEM      | 12.5 m     | 2015                   | https://asf.alaska.edu/                                   |
| 6.    | SRTM DEM                | 90 m       | 2000                   | USGS Earth Explorer http://www.earthexplorer.usgs.gov/ |
| 7.    | ASTER DEM               | 30 m       | 2022                   | https://www.earthdata.nasa.gov/                           |
| 8.    | Copernicus (GLO 30) DEM | 30 m       | 2023                   | https://spacedata.copernicus.eu/collection                |
| 9.    | Google Earth Imagery    | <1 m       | 2000–2022              | Google Earth Pro Tool                                     |
| 10.   | CRU gridded data        | 0.5°       | 1990–2002              | https://crudata.uea.ac.uk/cru/data/hrg                    |


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also used for glacier mass balance estimation through the geodetic method. PlanetScope imagery (3 m resolution) was acquired from the Planet Labs website www.planet.com. Landsat data was downloaded *via* the USGS Earth Explorer site, https://earthexplorer.usgs.gov, and Sentinel-2A images were obtained from the "Copernicus internet hub of European Space Agency" (ESA). Table 1 gives a summary of the datasets utilized in this study. These datasets have been extensively used in previous studies to analyze changes occurring on the Earth's surface (Ahmad et al. 2022a; Imdad et al. 2023; Malik et al. 2024; Mushtaq et al. 2024; Saleem et al. 2024).


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Type: figure
Figure

Image /page/7/Picture/2 description: A black and white circular icon showing a stylized boat on water. The boat is a simple silhouette with a small mast, floating on a wavy line, all enclosed within a circle.


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.1. Glacial lake mapping and change detection
Type: text

We have employed Planet CubeSat RGB images to delineate the glacial lake outlines for the years 2016, 2018, 2020, and 2022. However, the Planet CubeSat data is available from the year 2016 onwards. Hence glacial lake outline delineation for 2000 and 2010 was carried out from Landsat 7 ETM+ images. Out of several techniques and approaches previously used for glacial lake mapping, we have employed an integrated approach using the normalized difference water index (NDWI) method coupled with manual digitization for the removal of any possible errors. NDWI is a widely employed method for the extraction of water pixels from a satellite image, using the difference in the reflectance values of Blue and Green bands (McFeeters 1996; Ahmed et al. 2022; Zhang et al. 2022; Rather et al. 2024a). However, the NDWI method faces certain issues while classifying pixels containing muddy patches, shadows, cloud cover, or ice (Kaplan and Avdan 2017; Wang et al. 2020; Ahmad et al. 2022b, Mir et al. 2022; Rather et al. 2024b). These issues were tackled through the generation of relief, slope, aspect, and hill shade maps from the ALOS Pulsar DEM accompanied by manual correction using high-resolution Google Earth imagery. Cloud cover acts as a major impediment in the process of mapping different features from satellite imagery, therefore, images were selected from the post-monsoon months. Wherever a cloud-free image was not available, images from the adjacent months were taken to address this problem. The high resolution of the Planet CubeSat images (3 m) proved helpful in the minimization of any errors in the lake boundary delineation.


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge
Type: text

A glacier lake's depth, volume, and peak flow are crucial factors in determining the GLOF threat connected to that specific glacial lake. For calculating the glacial lake depth and volume from satellite datasets, we have to rely upon certain empirical equations which are based on the glacial lake area, developed by researchers across the world. Field surveys of glacial lakes require enormous effort and resources owing to the harsh terrain of the Himalayan cryosphere; therefore, empirical equations are widely employed for this purpose (Evans 1986; Wang et al. 2011; Emmer and Vilímek 2013; Fujita et al. 2013). Several equations for the calculation of glacial lake depth, volume, and potential peak discharge have been developed which include those by Huggel et al. (2002), Wang et al. (2011), Qi et al. (2022), Patel et al. (2017), Fujita et al. (2013), etc. A summary of these equations is given in Table 2. To reduce bias, we computed the lake volume using these empirical equations and took the average of all the equations into account for the final result. For lake volume estimation the following equations: (Equations (1) and (2)), given by Qi et al. (2022), showed results closest to the average values. These equations use the geometry of the lake beside the lake area to calculate the lake volume.

For lakes larger than 0.1 km2

$$V = 40.67^{1.184} - 3.218 \frac{mxw}{mxl} \tag{1}$$


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge
Type: table
Table: Table 2. Empirical equations used for depth and volume calculations.

| Empirical Equation                           | Parameter Calculated | Reference            |
|----------------------------------------------|----------------------|----------------------|
| $D = 0.104 \times A0.42$                     | Lake Depth           | Huggel et al. (2002) |
| $D_{m} = 55 \times A0.25$                    | Lake Depth           | Fujita et al. (2013) |
| $Dm = 4 \times 10^{-5} \times A + 5.0564$    | Lake Depth           | Patel et al. (2017)  |
| $V = 0.104 \times A1.42$                     | Lake Volume          | Huggel et al. (2002) |
| $V = 0.035 \times A1.5$                      | Lake Volume          | Evans (1986)         |
| $V = 0.0578 \times A1.4683$                  | Lake Volume          | (Liu et al., 2020)   |
| $V = 40.67^{1.184} - 3.218 \text{ mxw/mxl}$  | Lake Volume          | Qi et al. (2022)     |
| $V = 557.4^{2.455} + 0.2005 \text{ mxw/mxl}$ | Lake Volume          | Qi et al. (2022)     |
| $V = A \times D_{m}$                         | Lake Volume          | Patel et al. (2017)  |
| $Q_{max} = 0.00077 \times V^{1.017}$         | Peak discharge       | Huggel et al. (2002) |
| $Q_{max} = 0.72 \times V0.53$                | Peak discharge       | Evans (1986)         |

Empirical Equation
Parameter Calculated
Reference


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge
Type: text

For lakes smaller than 0.1 km2

$$V = 557.4^{2.455} + 0.2005 \frac{mxw}{mxl}$$
 (2)


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.2. Mapping the feeding glacier
Type: text

We employed Planet CubeSat images along with Landsat 7 ETM+ and Sentinel 2 A imagery for mapping the feeding glacier of the lake. The Normalized difference Snow Index (NDSI) combined with visual interpretation was used for glacier area change detection analysis. NDSI method has been extensively used in the Himalayan cryosphere by several studies and is considered to be a reliable method for glacier mapping and change detection (Bolch et al. 2012; Kulkarni et al. 2012; (Paul et al. 2015)).

The error encountered in the estimation of glacier and lake areas is directly proportional to the resolution of the satellite images and it was evaluated using Equation (3).

$$A_{er} = 100 (n^{0.5} \times m) / A_{gl}$$
 (3)

Where n is the ratio of the glacial lake's perimeter to the spatial resolution, m is the unit pixel area of the image (m2), and  $A_{ol}$  is the glacial lake area (m2).

4. Methods
4.2. Mapping the feeding glacier


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.2. Mapping the feeding glacier > 4.2.1. Mass balance estimation of the glacier
Type: text

Estimating the mass balance of glaciers can reveal important information about how glaciers behave in response to climate change and how glaciers interact with glacial lakes over a certain period. Various studies have employed different techniques to estimate the mass balance of glaciers globally (Soruco et al. (2009), Zemp et al. (2009), Barrand et al. (2010), Pratap et al. (2016) and Kumar et al. (2019) such as the glaciological method ((Kulkarni, 1992)), geodetic method (Kumar et al. 2017; Majeed et al. 2021), Hydrological method (Bhutiyani 1999; Pratap et al. 2016), etc. However, in this study, we conducted the mass balance estimation of the Panikhar glacier using the geodetic method as this is a convenient method to estimate mass balance keeping in view the harsh terrain of the region ((Rashid et al. 2020)). The basis of the geodetic approach is the calculation of the glacier's surface elevation difference over

time. This method computes the surface elevation difference or thickness change of the glacier using digital elevation models from different time periods. The glacier area and ice density assumptions are then used to translate the elevation change into mass loss. (Braithwaite 2002; Muhammad et al. 2019; Romshoo et al. 2023). SRTM DEM and ASTER DEM (30 m resolution), acquired from https://www.earthdata.nasa.gov/ for the years 2000 and 2022 respectively, were used to calculate the elevation change of the glacier during this period using the DEM differencing technique. The DEMs were pre-processed in ArcMap 10.3 and the elevation difference was computed by comparing the elevation values of corresponding pixels from the two DEMs, using the raster calculator tool. The elevation difference was then translated into volume change using the cell size of the DEM. Assuming the ice density to be constant at 850 kg/m3, the mass loss from the glacier was calculated in kg/m3 which was subsequently converted into meter water equivalent (m.w.e.).


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.2. Mapping the feeding glacier > 4.2.2. Glacier surface velocity and ice thickness estimation
Type: text

Surface velocities for the Panikhar Glacier were determined using the Glacier Image Velocimetry (GIV), a feature-tracking-based technique. GIV is a software package specifically designed for computing glacier velocity fields at high spatial resolution, utilizing unique points or characteristics on the glacier surface visible in consecutive satellite images. This meticulous process involves filtering individual velocity maps based on user-specified criteria, detecting and eliminating anomalous values using outlier detection functions, and computing statistical metrics for each grid cell over time. Sentinel-2 and Landsat true color composites were analyzed using GIV, with 20 multitemporal images with 30-meter spatial resolution from 2017 to 2023. Ice-thickness modeling during the same period utilized Copernicus DEM data at the same spatial resolution. Sentinel-2 images were used as input data for estimating glacier velocity, spanning from 2017 to 2023, with GIV configured to derive surface velocity considering a minimum temporal baseline gap of approximately 11 d and a maximum of three years. The displacement for each pair of images was computed using a multi-pass frequency domain cross-correlation algorithm, with subsequent filtering to remove pixels exceeding maximum velocity thresholds and enhancing result accuracy through spatial smoothing and rectification of systematic georeferencing errors using stable grounds.

The estimation of glacier thickness was conducted in MATLAB. For ice-thickness modeling, we employed the Copernicus DEM at the same spatial resolution. Our study adapted codes originally given by van Wyk de Vries et al. to suit our specific research requirements in our investigation of the Panikhar Glacier. We estimated ice thickness using an ice-velocity-based method. The equation follows the methodology proposed by (Gantayat et al., 2014), with further refinements by van Wyk de Vries et al., particularly focusing on the mean slope and surface velocity computations.

$$H = \frac{4\sqrt{1 \cdot 5U_s}}{Af^3 \ (\rho f sin\alpha)3} \tag{4}$$

In this equation, H depicts the ice thickness, Us denotes the surface velocity, f is the slope factor (often assumed as 0.8, especially suitable for Himalayan glaciers as noted by (Paterson, 1991)), g stands for the acceleration due to gravity (9.8 m/s-2),  $\alpha$  signifies the glacier slope, and  $\rho$  indicates the ice density (typically taken as 900 kg/m3 according to (Farinotti et al., 2009)).

Glacier volume estimation adopted a piece-wise method, involving the summation of the product of the squared ground pixel size and its respective ice thickness. In Equation (5),  $H_{j,k}$  represents the mean value of each pixel in the ice-thickness map, while r depicts the ground resolution of the pixel, assuming square pixels applicable to optical remote sensing data.

$$Vp = \sum_{i=1}^{n_j} \sum_{k=1}^{n_k} H_{j,k} \times r^2$$
 (5)


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.3. GLOF hazard analysis
Type: text

In the Himalayan region, a widely used method for determining GLOF vulnerability is predictive analysis utilizing a multicriteria framework (Bolch et al. 2008; Wang et al. 2011; Worni et al. 2013; Rounce et al. 2017; Wang et al. 2018; Dubey and Goyal 2020; Khadka et al. 2021; Ahmed et al. 2022). Despite the extensive use of this method in GLOF investigations, this method involves some amount of subjective expert judgment. This is because the GLOF susceptibility analysis is a complex process, and local variations in the influencing criteria make it nearly difficult to develop a uniform framework for this purpose globally (Allen et al. 2019; Taylor et al. 2023). In this study, we adopted the framework given by Che et al. (2014) as found to be suitable in our study area. This framework includes 10 relevant criteria pertaining to the glacial lake and its surrounding conditions that affect GLOF susceptibility and classifies the lakes into 5 levels of hazard from level 1 to level 5 using the weighted sum method (Table 3).


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling
Type: text

The two-dimensional (2D) GLOF modeling of the lake was done using HEC-RAS 2D (Version 6.3.1) to simulate two distinct scenarios: piping and overtopping. The aim was to assess the potential flood impacts from the glacial lake on the nearby downstream settlements. This approach has been extensively used to model the GLOF events globally as well as in the Himalayan region (Klimeš et al. 2014; Worni et al.


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling
Type: table
Table: Table 3. Integrated criteria used for assessing GLOF hazard, adopted from Che et al. (2014).

| Index                                        | Criteria                     | Weight | Criteria Presence |
|----------------------------------------------|------------------------------|--------|-------------------|
| Type of glacial lake                         | Terminal moraine dammed lake | 0.15   | Yes               |
| Area of lake                                 | >0.2 km2                     | 0.15   | Yes               |
| Distance between lake and its mother glacier | <500 m                       | 0.15   | Yes               |
| Average slope of the glacier                 | >7°                          | 0.10   | Yes               |
| Slope of the downstream                      | >20°                         | 0.10   | No                |
| Top width of the dam                         | <60 m                        | 0.10   | No                |
| Area of the glacier                          | >2 km2                       | 0.09   | Yes               |
| Slope between lake and its mother glacier    | >8°                          | 0.07   | No                |
| Change of lake area                          | >10% in a decade             | 0.06   | Yes               |
| Elevation of the lake                        | >5000 m                      | 0.03   | No                |


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling
Type: figure
Figure

Image /page/11/Picture/2 description: A close-up, black and white line drawing of a circle. Inside the circle, there is a horizontal line with a small, dark rectangle resting on top of it. Below the horizontal line is a dark, filled, semi-circular shape. There are faint markings that appear to be letters 'd' and 'f' above the rectangle.


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling
Type: text

2014; Sattar et al. 2019; Sattar et al. 2021; Majeed et al. 2021; Ahmed et al. 2022; Rawat et al. 2023b).


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling > 4.4.1. Model input and setup
Type: text

HEC-RAS requires a digital elevation model (DEM), for providing detailed terrain data for the study area. This raster-based DEM serves as the foundation for defining the flow area of interest, spanning from the glacial lake to the vulnerable settlements downstream. Within this defined flow area, a 2D computational mesh is constructed using cells of uniform dimensions. Each cell in the mesh is characterized by: Manning's roughness coefficient (N value), representing the flow resistance and the elevation data extracted from the DEM, specifying the terrain height at each cell location. ALOS PALSAR DEM was employed in this study given its better spatial resolution of 12.5 meters.

4. Methods
4.4. GLOF modeling
4.4.1. Model input and setup


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling > 4.4.2. Simulating two different GLOF failures
Type: text

- Piping Failure: This scenario simulates the failure of the lake dam or natural barrier due to internal erosion (piping). The modeling process involves steps like defining an initial breach hydrograph corresponding to the piping failure, setting the boundary conditions to initiate breach formation, and simulating flood propagation downstream. The 2D hydrodynamic modeling approach captures the breach evolution and flood wave propagation characteristics along the flow path. The characteristics of the potential flood wave were simulated for two different case scenarios presuming 50% and 100% drainage of the lake respectively.
- b. Overtopping Failure: In overtopping failure, flood generation occurs when water levels exceed the capacity of the dam or barrier, causing water to spill over the top. The modeling procedure includes defining an appropriate initial breach hydrograph corresponding to the overtopping event and configuring the boundary conditions to simulate breach initiation and the subsequent flood wave propagation downstream. This is followed by employing the 2D hydraulic model to capture the breach dynamics and the flood wave behavior, considering terrain elevations and flow resistances. This was also done keeping in view the moderate and worst-case scenarios with 50% and 100% volume of the lake.


Document: Glacial lake outburst flood risk assessment
Section: 4. Methods > 4.4. GLOF modeling > 4.4.3. Model outputs and analysis
Type: text

The 2D modeling process generates spatially distributed outputs including water depth distribution, flow velocity profiles indicating flood wave dynamics, and inundation extents depicting areas affected by the flood waters. These outputs enable detailed analysis of flood impacts along the route from the Lake to the settlements for both piping and overtopping scenarios. By integrating terrain data, hydraulic properties, and the breach conditions, the modeling study provides critical insights into potential flood hazards and helps in the development of mitigation frameworks for disaster preparedness and response. This comprehensive approach leverages advanced hydraulic modeling techniques to simulate complex flood scenarios, facilitating informed decision-making and risk assessment in vulnerable regions prone to GLOF events.

4. Methods
4.4. GLOF modeling
4.4.3. Model outputs and analysis


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.1. Depth, volume, and peak discharge of the lake
Type: text

The glacial lake depth and volume estimations were carried out using the various regression equations stated in Table 4. The results of these equations vary considerably and hence an average value of all of these equations was considered as the final estimate of lake depth and lake volume to minimize the bias in the final estimates as suggested by Emmer (2018). Using this approach, the average depth of the lake was estimated as  $31.50\,\mathrm{m}$  while the average volume was estimated as  $14.04\times10^6\,\mathrm{m}^3$ . The equation developed by Fujita showed exaggerated estimates of lake depth in comparison to the other equations and in calculating the lake volume, the maximum value was obtained through Liu's equation while Qi's equation showed results closest to the average value of all equations.

The estimation of the Peak discharge of the lake was done using the equations given by Huggel et al. (2002) and Evans (1986), and the values obtained from these equations are recorded in Table 4. To minimize the bias, the average value of these two equations, which came out to be 7954.19 m3s-1, was taken as the final estimate of the peak discharge of the lake as suggested by Emmer et al. (2018).


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.2. Glacial lake area change
Type: text

The change detection analysis of the lake, using multi-temporal datasets, revealed an overall increase of 78.72% in the lake's area at a rate of  $0.017\,\mathrm{km^2}$  per annum. The lake area has gone up from  $0.10\,\mathrm{km^2}\,\pm\,75.9~\mathrm{m^2}$  in 2000 to  $0.22\,\mathrm{km^2}\,\pm\,0.82~\mathrm{m^2}$  in 2010,  $0.33\,\mathrm{km^2}\,\pm\,0.82~\mathrm{m^2}$  in 2016,  $0.37\,\mathrm{km^2}\,\pm\,0.72\,\mathrm{m^2}$  in 2018,  $0.41\,\mathrm{km^2}\,\pm\,0.67\,\mathrm{m^2}$  in 2020 and  $0.47\,\mathrm{km^2}\,\pm\,0.62~\mathrm{m^2}$  in 2022. The lake area has witnessed an overall increase of  $\sim\!0.37\,\mathrm{km^2}$  in the past 22 years. It has almost doubled in each decade from 2000 to 2020 which depicts a very rapid expansion scenario, much likely to accelerate further in the future decade of 2020–2030 keeping in view the increasing temperature trends in the region. Figure 3 depicts the expansion of the lake through lake outline maps.

5. Results
5.2. Glacial lake area change


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: text

The feeding glacier of Panikhar Lake was also subjected to spatio-temporal change detection analysis. The results revealed a significant decrease of  $\sim 0.61 \, \mathrm{km}^2$  in the glacier area accounting for a 13.2% overall decrease in the glacier area for the past


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: table
Table

|  | Table 4. A summary of | f various equations used | d for depth and vo | lume measurements. |
|--|-----------------------|--------------------------|--------------------|--------------------|
|--|-----------------------|--------------------------|--------------------|--------------------|

Table 4. A summary of
f various equations used
d for depth and vo
lume measurements.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: table
Table

| Emp. Equation | Depth (m) | Volume (106 m3) | Peak Discharge (m3/s) |
|---------------|-----------|-----------------|-----------------------|
| Huggel        | 25.08     | 11.78           | 11,963.14             |
| Fujita        | 45.58     | –               | –                     |
| Evans         | –         | 11.33           | 3945.24               |
| Liu           | –         | 19.17           | –                     |
| Qi            | –         | 14.43           | –                     |
| Patel         | 23.86     | 11.21           | –                     |
| Average Value | 31.50     | 14.04           | 7954.19               |

Emp. Equation
Depth (m)
Volume (106 m3)
Peak Discharge (m3/s)


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: figure
Figure

Image /page/13/Figure/3 description: A figure displaying six satellite images in a 2x3 grid, illustrating the expansion of a lake from the year 2000 to 2022. The images are for the years 2000, 2010, and 2016 in the top row, and 2018, 2020, and 2022 in the bottom row. Each image shows the lake outlined and colored in dark blue against a terrestrial background. Each panel includes a scale bar from 0 to 0.4 kilometers, a north arrow, and coordinate axes for latitude (from 4°2'0"N to 34°2'20"N) and longitude (from 75°50'20"E to 75°51'0"E). The lake appears as two small, separate bodies of water in 2000, which merge and grow significantly in size through 2010, 2016, 2018, 2020, and 2022. The images for 2016, 2018, and 2022 show small white patches on the lake's surface. The caption below the images reads: 'Figure 3. Expansion of Pari lake from 2000 to 2022 depicted on Planet Cube Sat (RGB)'.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: figure
Figure: Figure 3. Expansion of Panikhar glacial lake from 2000 to 2022 depicted on Planet CubeSat (RGB) and Landsat 7 ETM+ images.

Figure 3. Expansion of Panikhar glacial lake from 2000 to 2022 depicted on Planet CubeSat (RGB) and Landsat 7 ETM+ images.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: figure
Figure

Image /page/13/Figure/5 description: A line graph showing the relationship between Lake Area and Glacier Area from the year 2000 to 2022. The x-axis represents the Year, from 1998 to 2024. There are two y-axes: the left y-axis represents Lake Area in square kilometers (km²), ranging from 0 to 0.5, and the right y-axis represents Glacier Area in square kilometers (km²), ranging from 3.8 to 4.8. The graph contains four lines: a solid green line for Lake Area, a solid red line for Glacier Area, a dashed green line for the linear trend of Lake Area, and a dashed red line for the linear trend of Glacier Area. The Lake Area shows a steady increase from 0.1 km² in 2000 to 0.47 km² in 2022. Specific data points for Lake Area are (2000, 0.1), (2010, 0.22), (2016, 0.33), (2018, 0.37), (2020, 0.41), and (2022, 0.47). The Glacier Area shows a general decrease from 4.63 km² in 2000 to 4.02 km² in 2022, with a sharp drop between 2018 and 2020. Specific data points for Glacier Area are (2000, 4.63), (2010, 4.58), (2016, 4.4), (2018, 4.39), (2020, 4.04), and (2022, 4.02). The trend lines confirm the inverse relationship: as the glacier area decreases, the lake area increases.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: figure
Figure: Figure 4. Glacier and glacial lake area trends.

Figure 4. Glacier and glacial lake area trends.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes
Type: text

22 years. The overall area of the glacier has decreased from  $4.63\,\mathrm{km}^2\pm139~\mathrm{m}^2$  in 2000 to  $4.58\,\mathrm{km}^2\pm138.4~\mathrm{m}^2$  in 2010,  $4.50\,\mathrm{km}^2\pm0.01\,\mathrm{m}^2$  in 2016,  $4.39\,\mathrm{km}^2\pm0.01\,\mathrm{m}^2$  in 2018,  $4.04\,\mathrm{km}^2\pm0.02\,\mathrm{m}^2$  in 2020 and  $4.02\,\mathrm{km}^2\pm0.02\,\mathrm{m}^2$  in 2022 with an annual decrease rate of  $\sim\!0.03\,\mathrm{km}^2$  (Figures 4 and 5). The glacier has witnessed a snout retreat of 348 m in this period. The glacier and the glacial lake areas behold a reciprocal relationship with each other which means that the glacial lake is expanding at the expense of its feeding glacier (Figure 4).


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier
Type: text

The glacier mass balance was estimated through the geodetic method and the results reveal that the glacier has, on average, undergone a thickness loss of 7.04 m from 2000


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier
Type: figure
Figure

Image /page/14/Figure/1 description: A map titled "Figure 5. Spatio-temporal evolution of the Penikhar glacier for the last 22 years." The main image is a satellite view of a glacier, shown in light blue, surrounded by mountainous terrain. The map includes latitude and longitude coordinates, a scale bar from 0 to 1.7 Kms, and a north arrow. Overlaid on the glacier are several colored outlines indicating its extent in different years. A legend titled "Glacier Outlines" shows the following: a light purple line for 2022, a light green line for 2020, a blue line for 2018, a black line for 2016, a tan line for 2010, and a red line for 2000. The satellite data is from Sentinel 2A (2022), using Band 11 (red), Band 8 (green), and Band 3 (blue). In the bottom right corner, there is an inset map titled "Glacier Snout," which is a close-up of the glacier's terminus. This inset has its own scale bar from 0 to 0.4 Kms and a legend showing the glacier snout outlines for 2016 (dark green), 2010 (light blue), 2000 (black), 2022 (red), 2020 (pink), and 2018 (light green). The outlines on both maps generally show the glacier receding over time.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier
Type: figure
Figure: Figure 5. Spatio-temporal evolution of the Panikhar glacier for the last 22 years.

Figure 5. Spatio-temporal evolution of the Panikhar glacier for the last 22 years.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier
Type: text

to 2022 at the rate of  $0.32\,\mathrm{m~a}^{-1}$ . The total mass balance of the glacier was estimated to be  $-5.922\,\mathrm{m}$  w.e. which amounts to a total mass loss of  $\sim 5.92$  metric tons per square metre during this period at the rate of 0.27 metric tons per square metre per year (Figure 6). The mass balance values across the glacier fall from  $-0.05\,\mathrm{m}$  w.e. to  $0.06\,\mathrm{m}$  w.e. with a mean negative value. This depicts that despite certain accumulation regions on the glacier, the glacier has an overall negative mean mass balance. These results are in proximity to other studies in the region. For instance, Kumar et al. (2017) reported an annual mass balance of  $-0.53\pm0.16\,\mathrm{m}$  we. per year for the glaciers in the Western Himalayas. Majeed et al. (2021) reported a mass balance of  $-0.24\,\mathrm{m}$  w.e. per year for clean glaciers and  $-0.37\,\mathrm{m}$  w.e. per year for debris-covered ones in the Pangong region.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier
Type: text

According to the velocity estimations, the glacier's mean velocity was estimated to be  $3.38 \,\mathrm{m}$  a-1, while its surface velocity varied from  $0.87 \,\mathrm{m/y}$  to  $9.76 \,\mathrm{m}$  a<sup>-1</sup>. (Figure 7a). The glacier is characterized by a slowly moving snout with slightly increasing velocities in the upper accumulation zone. Previous studies in the region also report the existence of an increasing trend in the surface velocity of the glaciers with increasing elevation until a maximum is reached towards the transient snow lines (Bhushan et al. 2018; Ahmed et al. 2022).

The ice thickness measurement of the glacier, conducted in MATLAB, reveals that the thickness of the glacier varies from 14.39 m to 50.87 m (Figure 7b) while the mean thickness was estimated to be 30.95 m. The thickness of the glacier increases


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier
Type: figure
Figure

Image /page/15/Figure/3 description: A map illustrating the spatial variation of mass balance on a glacier. The map is oriented with a north arrow pointing up. The glacier is outlined with a blue 'Glacier Boundary' line and is overlaid with a color gradient representing the 'Mass Balance (m.w.e)'. The color scale in the legend ranges from red (0.06) to yellow (-0.01) to green (-0.05), indicating areas of mass gain and loss. The western, higher-elevation part of the glacier shows a small red area of positive mass balance, while the majority of the glacier is yellow and green, indicating negative mass balance. Brown 'Contour (100m)' lines show the topography, with elevations ranging from 4100 meters at the eastern tip to over 5100 meters in the west. The map includes a scale bar from 0 to 2 kilometers. The area is located between approximately 75°47'0"E and 75°50'0"E longitude and 34°1'0"N and 34°3'0"N latitude.