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: Figure 6. Spatial variation of geodetic mass balance across Panikhar glacier in meter water equivalent (m.w.e).

Figure 6. Spatial variation of geodetic mass balance across Panikhar glacier in meter water equivalent (m.w.e).


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/5 description: The image displays two maps, labeled A and B, showing characteristics of a glacier. Both maps are georeferenced with latitude and longitude coordinates and include a 1 km scale bar and a north arrow. Map A, titled "Glacier Velocity (m/y)", uses a color scale from blue (Low: 0.87 m/y) to red (High: 9.76 m/y) to show glacier surface velocity. The highest velocities are concentrated in the central, wider part of the glacier, indicated by yellow and red patches. Map B, titled "Glacier Thickness (m)", uses a color scale from blue (Low: 14.39 m) to magenta (High: 50.87 m). This map shows the glacier is thickest in the central area, depicted in magenta, and thins out towards the edges, shown in blue.

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


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: Figure 7. Figure depicting (A) glacier surface velocity and (B) Ice thickness distribution.

Figure 7. Figure depicting (A) glacier surface velocity and (B) Ice thickness distribution.


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/16/Figure/1 description: A line graph displays Mean Temperature and Total annual precipitation from 1990 to 2020. The x-axis represents the Year, from 1990 to 2020. The left y-axis represents Temperature in degrees Celsius, ranging from -1 to 8. The right y-axis represents Precipitation in millimeters (mm), ranging from 50 to 400. There are two data series plotted. The first, in red with square markers, shows the Mean Temperature (T\_mean). It exhibits a clear upward trend over the period, starting near 0 degrees Celsius in 1990 and rising to about 1 degree Celsius by 2020. A red dashed trendline with a shaded confidence interval is shown, with an R-squared value of R²=0.275. The second data series, in green with square markers, shows the Total annual precipitation. This series shows significant year-to-year fluctuation, with values ranging from approximately 150 mm to over 300 mm. A green dashed trendline with a shaded confidence interval is nearly horizontal, indicating no significant trend, which is supported by a very low R-squared value of R²=0.0138.


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: Figure 8. Temperature and precipitation trends in Ladakh.

Figure 8. Temperature and precipitation trends in Ladakh.


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

towards the center and decreases as we move towards the edges. The total glacier volume as estimated from the thickness estimates and the glacier pixel areas was found to be 45,783 m3.


Document: Glacial lake outburst flood risk assessment
Section: 5. Results > 5.4. Temperature and precipitation variability over the region
Type: text

The Ladakh region is overall a data-scarce region with only a few observatories for the generation of observed temperature and precipitation data. Keeping this in mind, we projected the trends in the mean annual temperature (T mean) and Total annual precipitation in the region using a gridded climatic dataset given by the Climate Research Unit (CRU) to peek into the climatic controls of the glacial lake expansion. It was found that the mean annual temperature has witnessed a slight positive trend ( $R^2 = 0.275$ ) during this period while the total annual precipitation in the region has witnessed a slight negative trend with an  $R^2$  value of 0.0138 (Figure 8).

The warming temperature trends in this region and declining precipitation trends are also confirmed by previous studies that use various gridded climate data products to analyze the climatic variability in Ladakh (Shafiq et al. 2016; Ahmad et al. 2019; Mann et al. 2022). This may lead to further glacier recession and subsequent glacial lake expansion in the near future making the local populations vulnerable to cryospheric disasters such as the GLOF.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.1. GLOF susceptibility analysis
Type: text

We carried out the GLOF susceptibility analysis of the glacial lake based on the approach used by Che et al. (2014) which includes criteria pertaining to both the lake and the surrounding conditions that affect GLOF susceptibility (Table 3) and classifies the lakes into 5 levels of hazard from level 1 to level 5. The lake was found to be a High-hazard lake associated with a level 4 hazard. Some of the major reasons that account for the high hazard index of the lake are its large size, unstable moraine dam, direct connection to its mother glacier, marked rate of expansion, and a large-

6. GLOF risk
6.1. GLOF susceptibility analysis


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.1. GLOF susceptibility analysis
Type: figure
Figure

Image /page/17/Figure/3 description: A geographical map illustrating potential zones of mass movement in a mountainous region. The map is set against a satellite image background and includes longitude and latitude markings, a scale bar, and a compass rose. An inset map in the top-left corner provides a closer satellite view of the 'Glacier Snout' and 'Panikhar Lake', with a steep slope outlined in red. Red lines connect this outlined area to the main map, which highlights the 'Potential Zones of Mass Movement'. These zones are color-coded based on slope steepness. A legend in the bottom-right corner defines the symbols and colors used. 'Glacial Lake' is shown in solid light blue, and 'Glacier' is a white area with blue hatching. The 'Slope (degrees)' is categorized by color: green for 0-8, light green for 8-19, yellow for 19-31, orange for 31-45, and red for >45. The map shows that the areas with the highest potential for mass movement (red and orange) are on the steep slopes adjacent to the glacial lake and glacier.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.1. GLOF susceptibility analysis
Type: figure
Figure: Figure 9. The local topographic setting of the Panikhar glacier-lake complex and zones of possible mass movement.

**Figure 9.** The local topographic setting of the Panikhar glacier-lake complex and zones of possible mass movement.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.1. GLOF susceptibility analysis
Type: text

sized mother glacier (4.02 km2) with a slope of 16.5°. Moreover, the lake is prone to rock fall/debris fall from its adjoining steep slopes as evident from the slope profile of the lake surroundings (Figure 9).


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling
Type: text

We analyzed the potential impacts of an outburst event originating from the Panikhar Lake by analyzing the temporal and spatial properties of the flood wave using two-dimensional hydraulic modeling for two different case scenarios. In scenario 1, we assumed that 50% of the total volume of the lake is drained whereas scenario 2 was the worst-case scenario assuming a 100% drainage of the lake.

6. GLOF risk
6.2. Hydrodynamic modelling


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: text

Given the dynamic nature of high-magnitude flood events, we focused on understanding the changing hydraulic properties over time, particularly examining discharge, flow velocity, and depths across the flow channel. Assuming drainage of 50% of the lake's total water volume, we found that the maximum discharge at the point of the breach was 1908.25 m³/s and 3271.50 m³/s for the piping and overtopping failures respectively (Figure 10a). The routed hydrographs depict that the flood wave in this scenario will reach the first settlement in 1 h and 4 min after the dam breach in both the piping and the overtopping failures with a peak discharge of 2701.1 m³/s and 2714.7 m³/s respectively (Figure 11a). The flood depths along the flow path are


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: figure
Figure

Image /page/18/Figure/1 description: A close-up, black and white image showing the number 18 followed by a plus sign enclosed in a circle.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: figure
Figure

Image /page/18/Figure/2 description: The image contains two line graphs, labeled A and B, which are breach hydrographs comparing 'Piping Failure' and 'Overtopping failure' scenarios. Both graphs plot Discharge (m³/s) on the y-axis against Time (hh:mm:ss) on the x-axis.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: text

Graph A has a y-axis ranging from 0 to 3500 m³/s. The blue line represents 'Piping Failure' and shows a peak discharge of 1908.25 m³/s. The orange line represents 'Overtopping failure' and shows a higher peak discharge of 3271.50 m³/s. The x-axis ranges from 1:00:00 to 4:22:55.

Graph B has a y-axis ranging from 0 to 5000 m³/s. The green line represents 'Piping Failure' with a peak discharge of 3890.99 m³/s. The red line represents 'Overtopping Failure' with a higher peak discharge of 5111.39 m³/s. The x-axis ranges from 1:00:00 to 3:25:42.

The caption below the graphs reads: 'Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping'.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: figure
Figure: Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping failures (A) 50% volume of the lake (B) 100% volume of the lake (worst case scenario).

Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping failures (A) 50% volume of the lake (B) 100% volume of the lake (worst case scenario).


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released
Type: text

depicted in Figure 12. The maximum flood velocity was estimated to be 18.26 m/s while the maximum depth of the flood wave was found to be 14.87 m.

The maximum area under the threat of inundation along the valley was calculated as 4.74 km2 for both the overtopping and pipping failure situations. The mean flood depth across the flow channel was estimated to be 3.41 meters and the mean flood velocity was found to be 3.84 m/s. We found that the potential flood wave in this scenario inundates bridges, roads, and a few residential houses along the channel in settlement 2 and near the Suru Valley bridge (Figure 13).


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released
Type: text

The maximum discharge, in the worst-case scenario with 100% water release, at the point of breach was calculated to be 3890.99 m3/s and 5111.39 m3/s for the piping and overtopping scenarios respectively (Figure 10b). A routed flood wave in the simulation reached the closest settlement (settlement 1) of the Panikhar village in 50 min after the lake-breach event with a peak discharge of 6345.06 m3/s and 6453.9 m3/s for the piping and overtopping scenarios respectively (Figure 11b).


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released
Type: figure
Figure

Image /page/19/Figure/3 description: The image displays four line graphs arranged in a 2x2 grid, labeled A and B on the right side for each row. The graphs show discharge over time for different locations under two failure scenarios: "Piping Failure" (left column) and "Overtopping Failure" (right column).


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released
Type: text

Row A contains two graphs with the y-axis representing Discharge (m³/s) from 0 to 3000. The x-axis represents Time in hh:mm:ss format.
- The top-left graph, "Piping Failure," shows five hydrographs for different locations: Settlement 1, Surrur Valley Bridge, Settlement 2, Bridge 2, and Picnic Spot Damasna. The peak discharge for Settlement 1 is approximately 2600 m³/s at around 2:10:36. The other locations have lower peaks that occur later in time.
- The top-right graph, "Overtopping Failure," shows similar hydrographs. The peak discharge for Settlement 1 is also around 2600 m³/s but occurs earlier, at approximately 2:00:04.

Row B contains two graphs with the y-axis representing Discharge (m³/s) from 0 to 8000. The x-axis represents Time in hh:mm format.
- The bottom-left graph, "Piping Failure," shows five hydrographs for: Settlement 1, Suru Valley Bridge, Settlement 2, Bridge 2, and Picnic Spot Damasna. The peak discharge for Settlement 1 and Suru Valley Bridge is approximately 6400 m³/s at around 02:00. The other locations have lower peaks that occur later.
- The bottom-right graph, "Overtopping Failure," shows similar hydrographs. The peak discharge for Settlement 1 and Suru Valley Bridge is also around 6400 m³/s but occurs slightly earlier, at approximately 01:55.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released
Type: figure
Figure: Figure 11. Routed hydrographs at various locations along the downstream flow channel in piping and overtopping failures (A) Assuming release of 50% of the water volume of the lake (B) Assuming release of 100% of the water volumeof the lake.

**Figure 11.** Routed hydrographs at various locations along the downstream flow channel in piping and overtopping failures (A) Assuming release of 50% of the water volume of the lake (B) Assuming release of 100% of the water volumeof the lake.


Document: Glacial lake outburst flood risk assessment
Section: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released
Type: text

The two-dimensional routing of the breach hydrograph is simulated over a distance of 25.6 km along the flow channel, from the lake to the Damasna picnic spot in Panikhar village. The maximum area under the threat of inundation along the valley was calculated as 5.38 km² for both the overtopping and piping failures (Figure 16). Across the entire channel, the mean flow discharge and mean flow depth are recorded at 588 m³/s and 5.31 m for pipping failure 755 m³/s, and 5.33 m for overtopping failure with a mean flow velocity of 5.37 m/s. The flood depth and velocity vary along the flow channel as per the terrain profile of the flow channel (Figure 13). The flood depth and velocity values decline towards the lower reaches of the flow channel, as a result of the increase in the channel width and decrease in the slope.

The potential flood wave inundates infrastructure such as roads, bridges, and settlements along the channel with varying depths and velocities for both the piping and overtopping failures (Figures 14 and 15, Table 5). We evaluated the flood depths and flood velocities along the flow channel at settlements 1 and 2 to determine the maximum flood depths, peak flood velocities, and the time of the peak. Settlements 1 and 2 witness the highest potential flood depths of 7.9 and 7.65 m in the overtopping scenario whereas the lowest flood depths are witnessed at the Damasna picnic spot. Settlement 1 witnesses the highest peak velocities of 12.36 m/s and 11.35 m/s in the Piping and the overtopping scenarios (Figure 15).


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: text

The Himalayan region is a hotspot of GLOF risk (Taylor et al. 2023) and this risk is projected to increase three times by the year 2100 (Zhang et al. 2024). 18% of all the


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/20/Figure/1 description: A close-up, black and white image showing the number 20 next to a circular icon. The icon contains a simple line drawing of an oil lamp with a flame, and a short line extends downwards from the bottom of the circle.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/20/Figure/2 description: This image, labeled Figure 12, displays two sets of maps illustrating flood depth and velocity profiles in a downstream flow channel. The top set of three maps, labeled (A), (B), and (C), shows the 'Flood Depth (m)'. A color scale indicates that the depth ranges from a low of 0.001 m (blue/green) to a high of 14.87 m (red). The maps depict a river channel winding through a mountainous terrain, with varying depths indicated by the color coding. The bottom set of three maps, also labeled (A), (B), and (C) for the same geographical areas, shows the 'Flood Velocity (m)'. The corresponding color scale ranges from a low of 0.001 m (green) to a high of 18.26 m (red). Each map includes latitude and longitude coordinates and a 2 km scale bar.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure: Figure 12. Flood depth and velocity profiles in the downstream flow channel in the first case scenario (50% volume). Parts A, B and C represent the 1st, 2nd and 3rd sections along the flow channel.

Figure 12. Flood depth and velocity profiles in the downstream flow channel in the first case scenario (50% volume). Parts A, B and C represent the 1st, 2nd and 3rd sections along the flow channel.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/21/Figure/3 description: A composite image showing several annotated satellite views of a river valley. The river and its floodplain are highlighted in a translucent green. The main panel on the left shows a wide view of the river with a white arrow indicating the "Flow Direction." Several points are marked with red pins and labels along the river: "Picnic Spot Damasna," "Bridge 2," "Settlement 2," "Settlement 1," and "Suru Valley Bridge." Red boxes highlight the areas of Bridge 2 and Settlement 2. The middle panel is a closer view of the "Suru Valley Bridge" area, with white arrows pointing to structures labeled "Houses," one of which is circled. The right side of the image contains two smaller, zoomed-in panels. The top right panel shows a close-up of "Settlement 2," with a red box around it and an arrow pointing to "Houses." The bottom right panel is a close-up view of "Bridge 2" spanning the river.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure: Figure 13. Buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation in scenario (1) in the downstream flow channel of Panikhar lake.

Figure 13. Buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation in scenario (1) in the downstream flow channel of Panikhar lake.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: text

previous GLOF events have occurred in the High Mountain Asia (HMA) which includes the Himalayan region. Most of the GLOF events in Himalayan region have been reported from the moraine and ice-dammed glacial lakes (Shrestha et al. 2010). The key triggers of GLOF in the region are extreme precipitation events and mass movements such as ice avalanches (Shrestha et al. 2010; Zhang et al. 2024). The rapid expansion of mountainous populations into high-risk zones is one of the major contributors to increasing GLOF risk in this region (Taylor et al. 2023). A large body of literature has been produced by researchers to generate baseline data about the GLOF phenomenon in the data-scarce Himalayan region (Emmer et al. 2022). These studies have focused on the preparation of glacial lake inventories, identification of potentially dangerous glacial lakes, and estimation of GLOF risk associated with hazardous lakes.

Categorizing a lake as potentially dangerous is very difficult given the multitude of factors influencing a GLOF event such as lake size, lake dam, lake surroundings, landslide, avalanche susceptibility, or extreme weather event. Therefore, under particular circumstances, an otherwise stable lake can also produce a GLOF event. Extreme precipitation events have caused notable GLOF events in the past through dam overtopping or collapse. This adds to the complexity of understanding and predicting the GLOF disaster. The same problem arises with the categorization of glacial lakes as potentially dangerous or otherwise based on size. Generally, some area threshold (e.g.  $A > 0.1 \text{ km}^2$ ) is defined to exclude smaller lakes from the list of potentially dangerous lakes. For instance, Worni et al. (2013) did not classify the Gya Lake in Ladakh as potentially hazardous, maybe because it was ice-covered or because its size was below the mapping threshold of the study (0.01 km2), leading to the assumption that it did not pose any significant threat to the settlements downstream. It can be challenging to leave out lakes that may be dangerous, as the same was demonstrated by the 2013 Chorabari lake outburst event (Das et al. 2015; Bhambri et al.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/22/Figure/1 description: This image is Figure 14, which displays flood depth and velocity profiles for a downstream flow channel. The figure is divided into two main horizontal sections. The top section shows flood depth, and the bottom section shows flood velocity. Each section contains three maps labeled (A), (B), and (C), which depict different segments of a river flowing through a mountainous terrain. For the top section, a color scale represents 'Flood Depth (m)', ranging from a low of 0.001 (blue) to a high of 17.60 (red). For the bottom section, a color scale represents 'Flood Velocity (m/s)', ranging from a low of 0.001 (green) to a high of 23.78 (red). The maps include geographical coordinates, scale bars in kilometers, and a north arrow on the 'C' panels.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure: Figure 14. Flood depth and velocity profiles in the downstream flow channel in the worst case scenario (100% volume). Parts A, B and C represent 1st, 2nd and 3rd sections along the flow channel.

Figure 14. Flood depth and velocity profiles in the downstream flow channel in the worst case scenario (100% volume). Parts A, B and C represent 1st, 2nd and 3rd sections along the flow channel.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: text

2016). According to several studies, small glacial lakes have the potential to seriously harm local livelihoods and cause major problems, as demonstrated by the Domkhar food in Ladakh (Ikeda et al. 2016) and other minor foods in Hunza region (Ashraf et al. 2021). The triggering factors of the GLOF are often dynamic and may change from time to time both quantitatively and qualitatively and hence require regular monitoring. e.g. Lakes that are stable as such, may get exposed to unstable


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/23/Figure/3 description: A figure with two side-by-side panels, each showing a map with a flood depth overlay and an inset graph of water velocity over time. The left panel is located at 75°57'0"E and shows a map with a settlement labeled "Settlement 1". The flood depth is represented by a color scale from blue (Low: 0.001 m) to red (High: 17.60 m). The inset graph shows velocity in m/s on the y-axis and time in hh:mm on the x-axis, from 01:26 to 03:50. The velocity peaks at over 10 m/s around 02:00. The right panel is located at 75°58'0"E and 34°8'0"N and shows a map with a settlement labeled "Settlement 2". It uses the same depth color scale. The inset graph shows velocity peaking at over 5 m/s around 02:00 over the same time period. Both maps include a north arrow.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure: Figure 15. Flood depth and flood velocity curves at settlements 1 and 2 in Panikhar village.

Figure 15. Flood depth and flood velocity curves at settlements 1 and 2 in Panikhar village.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: table
Table: Table 5. Flood parameters at various locations of the flow path downstream of Panikhar lake for the worst case scenario i.e.100% water release.

| Location               | Distance from the Lake (km) | Overtopping Failure |                  |                        | Pipping Failure   |                  |                        |
|------------------------|-----------------------------|---------------------|------------------|------------------------|-------------------|------------------|------------------------|
|                        |                             | Mean Depth (m)   | Max Depth (m) | Peak Velocity (m/s) | Mean Depth (m) | Max Depth (m) | Peak Velocity (m/s) |
| Settlement 1           | 15.6                        | 4.35                | 7.97             | 11.35                  | 3.05              | 6.61             | 12.36                  |
| Suru valley Bridge  | 16.11                       | 2.25                | 4.16             | 9.05                   | 1.96              | 4.29             | 8.66                   |
| Settlement 2           | 18.01                       | 4.63                | 7.65             | 5.27                   | 4.71              | 7.67             | 3.70                   |
| Bridge 2               | 19.08                       | 2.73                | 5.33             | 5.82                   | 1.97              | 3.99             | 5.36                   |
| Picnic Spot Damasna | 22.50                       | 1.70                | 6.96             | 6.615                  | 5.13              | 6.70             | 7.187                  |

Location
Distance from the Lake (km)
Overtopping Failure
Pipping Failure


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure

Image /page/23/Picture/7 description: A composite figure displaying satellite maps of a river valley to highlight specific locations and features. The main panel on the left shows a winding river in a mountainous region, with a green shaded area along its banks. The river's flow direction is indicated by a white arrow pointing upwards. Several locations are marked with red pins and labeled, including "Settlement 1", "Suru Valley Bridge", "Settlement 2", "Bridge 2", and "Picnic Spot Damasna". On the right, there are three zoomed-in panels. The top panel shows a close-up of "Settlement 2", with red arrows pointing to "Houses". The middle panel is a close-up of "Bridge 2", with red arrows indicating "Bridge 2 and buildings". The bottom panel provides another view of the area, with a red oval highlighting "Roads and houses in Settlement 2".


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: figure
Figure: Figure 16. Inundation area in scenario 2 (100% water release) with some important areas with buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation.

Figure 16. Inundation area in scenario 2 (100% water release) with some important areas with buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation.


Document: Glacial lake outburst flood risk assessment
Section: 7. Discussion
Type: text

surrounding slopes or ice avalanche zones after expanding into the glacial valleys thus enhancing their susceptibility (Haeberli et al., 2017).

As moraine and ice-dammed lakes are more vulnerable to GLOF, they should receive greater attention when it comes to GLOF prioritization. Also, lakes that are in close proximity to the parent glacier are more likely to produce a GLOF as these lakes are more at threat of Icefalls and glacial calving activity. The likelihood of piping activity leading to a dam collapse is higher in lakes with underground drainage (Narama et al. 2018). Additionally, because thermokarst processes may block the intra-moraine channels, which can result in overflow and subsequent lake outburst through breaching, glacial lakes with subterranean drainage are more prone to GLOFs unlike the ones with stable surface drainage (Narama et al. 2018; Petrakov et al. 2020). Lakes with smaller dam width-to-height ratios and lower freeboard levels should be the focus of the GLOF study as they are important indicators of the dam's stability and can be estimated using high-resolution satellite imagery.

Studies similar to the present study had been previously conducted for the South-Lhonak Lake in Sikkim, which, later on, witnessed a severe GLOF event this year killing at least 40 people. However, effective measures were not taken to prevent this disaster which could have been easily done through certain practical measures taken on time. To avoid such tragedies in the future, a closer examination of the GLOF hazard is necessary, aided by more precise tools and dependable datasets. However, scientific knowledge of the environmental processes at play in the Himalayan region is often hampered by the sparse network of monitoring sites required to generate an ample amount of reliable meteorological and glaciological data. Regularly updated weather data coupled with high-resolution imagery, DEMs and detailed inventories can help in understanding the processes of the Himalayan cryosphere much more closely to prevent any future disasters. In addition to fine satellite datasets, bathymetric surveys are needed to estimate lake volumes more precisely because, even in similar geographic locations, lakes' sizes are not always equally predictable (Cook and Quincey 2015). Although there are numerous empirical equations devised for the estimation of the depth and volume of the lakes, there is always a chance of over or underestimation of these attributes. Hence bathymetric datasets can be extremely helpful in the estimation of the lake volume and the potential maximum discharge from a glacial lake in case of a GLOF event.

Mapping the societal elements of risk associated with the GLOF disaster is a largely ignored domain in the Himalayan region. At the national and state levels, GLOF risk profiles, that map the exposure and vulnerability of human life and property to this specific cryospheric danger must be produced and regularly updated. India lacks an early warning system infrastructure for the GLOF disaster even when the northern and the northeastern states of the country are under serious GLOF danger. Hence effective early warning systems need to be installed at critically hazardous glacial lakes that will help in evacuating the vulnerable population at the time of a future GLOF disaster and evading major risks associated with human life and key infrastructure such as the hydroelectric projects.


Document: Glacial lake outburst flood risk assessment
Section: 8. Conclusion
Type: text

The glacier-lake interactions were studied for the Panikhar glacial lake and its feeding glacier for the past 22 years. We found that the glacial lake has undergone a remarkable expansion during this period. The area of the lake has increased by 0.37 km2, from

~0.10 km2 in 2000 to ~0.47 km2 in 2022, showing a net increase of 78.7%, at the rate of 0.017 km2 per year while as its mother glacier has retreated significantly from ~4.63 km2 in 2000 to ~4.02 km2 in 2022 showing a total decrease of 13.2% in its total area i.e. 0.03 km2 per annum. The glacier and glacial lake area plots show an inverse relationship which shows that the glacial lake is rapidly expanding at the expense of its mother glacier. The mass balance estimate of the Panikhar glacier also reveals a mass loss of 5.92 metric tons per square metre and an average thickness loss of 7.04 m over the past 22 years. The glacial lake is impounded by the terminal moraine of the glacier, rendering it hazardous, due to its swift rate of expansion and various other contextual factors. Notably, the presence of zones susceptible to potential mass movement in the surrounding areas also contributes to the heightened risk associated with this lake. The mean surface velocity and thickness of the glacier were estimated to be 3.38 m s-1 and 30.95 m respectively. The potential outburst flood was modeled for two different GLOF scenarios using the HEC-RAS tool to generate flood depth and velocity profiles along the flow path for a distance of 25 km. The results reveal that the glacial lake is a potential threat to the downstream villages, especially the nearby Panikhar village. A total area of 5.38 km2 which includes key infrastructure such as settlements, bridges, and roads is vulnerable to a potential GLOF event in the worst-case scenario. The routed hydrographs indicate that the flood wave generated at the time of an overtopping failure will arrive at the nearest settlement in 50 min with a peak velocity of 12.36 m/s. This study suggests regular monitoring of the lake and the installation of an early warning system (EWS) to mitigate and manage the risk from the lake in the future, especially at the time of an extreme weather event.


Document: 1 Introduction
Section: 1 Introduction
Type: text

Glaciers are formed by accumulation of snow at higher altitudes where the average temperature lies in the freezing zone. As the accumulation increases, deeply buried snowflakes get tightly packed and crystallized, resulting in glacial ice formation. As the average global temperature has increased due to heightened anthropogenic activities, the glaciers have receded significantly, resulting in formation of a greater number of moraine-impounded glacier lakes [1-4]. The surface area and the depth of these glacial lakes are growing as a result of glacial retreat, raising the possibility of breaching of moraine dams. Breaching of these dams can further be triggered by extreme precipitation, permafrost deterioration, and dead-ice melting [5]. This will result in instant release of large quantities of sediment-laden glacial water that can ignite vast damage to the downstream parts of the dams. These events, known as Glacial Lake Outburst Floods (GLOFs), can get aggravated if erodible materials are present [6]. Due to its high velocity, continued entrainment of sediment in its flow path, and tendency to flow along existing river channels for very long runout distances, these debris floods/flow events pose significant threat to the lives and property located along its flow channel [6].

In India, the Kedarnath disaster in 2013 [7], and the Chamoli disaster in 2021 [6], caused by cloud outbursts and ice/rock avalanches, respectively, claimed thousands of lives and caused substantial socio-economic losses. Two series of occurrences struck Kedarnath in 2013: the first resulted in landslides, breaching, blockades, flooding, and failures of river banks; the second was outburst flooding in Chorabari Tal Lake in conjunction with erosion of the terrain and concomitant landslides. In the Chamoli Disaster in 2021, a significant amount of rubble blocks fell into the glacial pocket,

Department of Civil Engineering, Indian Institute of Technology Jammu, Jagti 181221, India e-mail: 2021pce1008@iitjammu.ac.in

P. Mahajan ( $\boxtimes$ ) · R. Kumar · R. Bhowmik

forcing the lake to burst. This event had endangered lives and inflicted significant financial losses. Nie et al. [8] inventoried and studied over 60 GLOF events in the Himalayan region alone and reported the estimated losses to be over hundreds of billion dollars. Thus, implementation of adequate mitigation measures and land-use and development plans is vital while developing infrastructure in the mountainous regions.

This paper aims to inventory the reported GLOF events, regarding their causes, the methodologies employed for their study, and predictive dam-breach models reported in the literature. The outcome of this study will aid in consolidating the knowledge developed regarding the effect of climate change, especially on the young and fragile Himalayan region.


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs
Type: text

Classification of Glacial lakes into different categories plays a vital role in understanding their origin and evolution. However, there is presently no globally accepted classification system for glacial lakes. Numerous researchers assigned classifications to the glacial lakes in accordance with their knowledge of the region's morphology. In 2018, ICIMOD [9] classified glacial lakes into four groups and seven sub-groups, as shown in Table 1.

An attempt has also been made to summarize the reported cases of moraine-impounded glacial lakes with the objective of understanding the causes of their formation and to study the areas likely to be affected if failure occurs. This summary is given in Table 2. Table 2 further summarizes the volume, depth, and peak discharge of the lakes, probable triggering factors, and methodologies employed to analyze the probability of the GLOF events.


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools
Type: text

In conventional times, data was collected from the topographic maps available by reputed agencies to identify the aerial extent of the glacial lake. With the increase in advancement of technology, various remote sensing (RS) techniques such as multispectral scanner (MSS) [4], linear imaging self-scanning (LISS) [12] satellite data, and thematic mapper (TM) [4] tools are used, provided a minimum cover of snow and cloud is present in the sites. Data elevation models (DEMs) such as SRTM [1] and ASTER [5] are also used to recreate the digital elevation models of the sites and conduct in-depth studies of the concerned areas.

In Zanskar Basin [4], the conventional methodology of using topographic maps is utilized in conjunction with remote sensing methodologies like multi-spectral

2 Glacial Lakes and GLOFs
2.1 Methodologies
2.1.1 Remote Sensing Tools


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools
Type: table
Table: Table 1 ICIMOD classification of Glacial lakes (ICIMOD [9])

| Sl. No | Glacial lak                   | e type                                   | Description                                                                                |
|-----------|-------------------------------|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1         | Moraine dammed lake (M) | End moraine dammed lake                  | The lake's water usually touches the walls of the side moraines, but the water is held back by the end moraine (dam), but not necessarily, in contact with the glacier, and may have glacier ice at the lake bottom |
| 2         |                               | Lateral moraine dammed lake              | Lake formed in the tributary valley, trunk valley, or between the lateral moraine and the valley wall, or at the junction of two moraines. Lake is held back by the outside wall of a lateral moraine      |
| 3         |                               | Other moraine dammed lake                | Lake dammed by other moraines (including kettle lakes and thermokarst lakes)                                                                                |
| 4         | Ice dammed                 | Supra-glacial lake                       | Body of water (ponds or lakes) on the surface of a glacier                                                                                |
| 5         | lake (I)                      | Dammed by tributary valley glacier | Lake dammed by glacier ice with no lateral moraines. Can be at the side of a glacier between the glacier's margin and valley wall                                                                                |
| 6         | Bedrock                       | Cirque lake                              | A small pond occupying a cirque                                                                                |
| 7         | dammed lake (B)            | Other glacier erosion lake               | Body of water occupying depressions formed by glacial erosion. These are usually located on the mid-slope of hills, but not necessarily in a cirque                                                                 |
| 8         | Other dam                     | med lakes                                | Lakes formed in a glaciated valley and fed by glacier melt, but the damming material is not directly part of the glacial process, for example, debris flow, alluvial, or landslide-blocked lakes           |


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools
Type: text

scanner, linear imaging self-scanning, advanced space-borne thermal emission and reflection radiometer to determine the characteristics of the glacial lakes. In Chamlang Lake Tsho [5], images obtained from RS tools and photogrammetric techniques, and field-based methodologies, like sonar-sounding machine, GPS, and fishing line method were utilized to study the area. Figure 1 depicts the various steps involved in processing the data to recreate the profile of the glacial lakes.


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: text

For the study of glacial lakes, empirical formulations and numerical modeling are often used. One of the widely used numerical tools is Hydrologic Engineering Centre's River Analysis System (HEC-RAS) [6]. This tool simulates GLOF phenomenon from the source to the downstream flow path locations. Using the model, cross-sectional measurements of the main river section at regular intervals can be determined, and the same can be utilized for routing floods. Hydrographs can be generated along different sections of the flow channel which can be further used to estimate the peak discharge intensity [1, 6]. However, there are some limitations of

(continued)

2 Glacial Lakes and GLOFs
2.1 Methodologies
2.1.2 Numerical Modeling


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: table
Table

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Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: table
Table

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Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: table
Table

| Table 2 Summary of reported causes, methodology and parameters taken for consideration for the analysis of glacial lake |                                                                  |                                                                               |                                                                                |                                                                                |                                                                                |                                      |                                      |                               |                            |
|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|--------------------------------------|--------------------------------------|-------------------------------|----------------------------|
| Sl. No                                                                                | Events                                                           | Area                                                                          | Triggering factors                                                           | Tools                                                                                | Methodology                                                                         | Volume of lake (million m³) | Lake average depth (meters) | Peak discharge (cumecs) | References                 |
| 1                                                                                | Glacial lake (33° 09′ 32.56″ N and 76° 59′ 05.38″ E) | Zanskar Basin, J&K, India                                                  | Collapse of unstable natural dams                                         | Topographical maps in early days. Replaced by remote sensing using MSS, TM, LISS III, ASTER DEM | Monitoring of extent of glacial lake is observed                              | 4.2                                  | 10                                   | 1.7–196                       | Babu Govinda Raj [4] |
| 2                                                                                | Glacial Lake L2                                               | Dhauliganga River Basin, Pithoragarh Distt, Uttarakhand           | Lake area expansion, dead ice melting, seepage, seismic activity | HEC-RAS (hydrologic engineering centre’s river analysis system)                                       | Analysis of the glacial lake using numerical modeling                      | 2                                    | 30                                   | 4272                          | Jha and Khare [6]       |
| 3                                                                                | Lumi Chimi Lake                                               | Sun-Koshi Basin, Transboundary between Tibet (China) and Nepal | Retreat of glaciers                                                          | Dambreak and hydrodynamic modeling                                                                       | Risk assessment of the event before it occurs                                 | 307.2                                | 80                                   | 5040–8380                     | Shrestha et al. [1]     |
| 4                                                                                | Chamlang South Tsho                                           | Hongu valley, eastern Nepal Himalaya                                    | Lake area expansion, ice avalanches, rock falls, landslides         | Observational data, photographs, ground-based surveys, field and remote sensing data            | GLOF hazards, bathymetry, surrounding material decide the vulnerability | 34.9–35.6                            | 40.2                                 | Not available                 | Lamsal et al. [5]       |


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: text

References Gurung et al. [11] Aggarwal et al. [12] Liu et al. [3] et al. [10] Goodsell Not available discharge (cumecs) 2611.136 Peak 1270 1198 average (meters) depth Lake 6.29 14.1 25 37 Volume of available (million 42.93 0.37 0.97  $m^3$ Not position due to ice 51 houses swept away, 18 bridges channel shifted to 0.976 Nu. Million Five people died, washed away and outburst flooding using MIKE-11 Dam Break agricultural land more than CNY Analysis of the dangerous lake economic loss estimated loss Methodology Four bridges washed away, supra-glacial collapse and affected and 100 million Sub-glacial potential Model from Landsat and Indian Remote Sensing, MIKE-11 Dam Break Model observation and remote Satellite images, field Satellite images, field investigation and past studies Visual representation Satellite data, images data was utilized sensing data Tools composition of dam expansion, ice avalanches blackhole due to collapse of ice Formation of supra-glacial observations Earthquake, draining of temperature and rainfall, Triggering factors Lake area rainfall, Climatic material puod Westland Tai Poutini National Park North-western Bhutan Bomê County, South-eastern Tibet (China) New Zealand, Gasa District, Sikkim Area
 Table 2
 (continued)
 Guangxieco Lake Teesta basin Lemthang Events Joseph Glacier Franz Tsho SI. No S 9 \_  $\infty$

(continued)

References

Khan et al. [13]

Schneider et al. [2]

9000-40,000 Peak discharge (cumecs) 4500 Volume of lakeLakelakeaverage(million depthdepthm³)(meters) Not available 21 0.2 - 0.4 $\infty$ Santa Valley highway affected, people are prone to flood, Karakoram Highway Damaged several bridges swept away and agricultural land affected More than 2000 Methodology Images from Landsat and Sentinel, Global Digital Elevation Model (GDEM), ArcMap 10.5 Remote sensing images, digital elevation data, RAMMS and IBER model—numerical and physically based avalanche and debris Tools flow Blockage of melt water originating from Mochuwar Triggering factors Rock-ice avalanche glacier Gilgit-Baltistan, Pakistan Carhuaz, Peru Area Table 2 (continued) Shishper Glacier lake Lake 513 Sl. Events
No 10 6


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: figure
Figure

Image /page/7/Figure/2 description: A flowchart illustrating a three-step process, with each step represented by a blue, right-pointing chevron arrow. The first step is labeled "Image Processing to Delineate Glacial Lakes." The second step is "Stereo-data Processing for 3D Topographic Maps." The third and final step is "Lake Bathymetry." The text within each chevron is white.


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling
Type: text

Fig. 1 Steps involved for the interpretation for the data

this model, one of which is its inability to consider debris and sediment-laden water in the simulations. In the study on the Dhauliganga Basin [6], HEC-RAS was used to create the glacial lake representation. HEC-RAS was also used to generate the hydrological model for the Mandakini River [7].


Document: 1 Introduction
Section: 2 Glacial Lakes and GLOFs > 2.2 Empirical Equations for Discharge Estimation and Stability of Moraine Dams
Type: text

The simplest and fastest methods for determining the various parameters required for vulnerability assessment of moraine dams are empirical relationships. Empirical relationships, on the other hand, do not incorporate fundamental rules of fluid mechanics and hydraulics as they are derived through statistical study of historical events. Various researchers have proposed empirical equations to determine peak discharge values based on estimated average breach width and time of failure in moraine dams [3].

Peak discharge is an important consideration for the assessment because it helps to visualize the size of the flood downstream. The findings of the multiple regression analysis showed that compared to other factors like embankment width and embankment length  $(E_1)$ , empirical relationships with parameters like volume of water  $(V_w)$  and height of water  $(H_w)$  gave more precise forecasts of maximum flow discharge  $(Q_{\text{max}})$  [14]. The models that predict peak outflow using only  $H_w$  and  $V_w$  do not considerably benefit from the addition of  $E_1$  and  $Q_{\text{max}}$  to the relationship, making  $H_w$  and  $V_w$  more significant features to define peak discharge compared to other parameters. Table 3 shows the summary of the empirical relationships developed by various researchers.


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: text

Using the empirical relationships mentioned in Table 3, the average breach width B, time of failure  $t_f$ , and peak discharge values for 15 different cases of moraine dam are estimated and tabulated in Table 4. The results showed that when the glacial lake's volumetric capacity increases, the peak discharge of the lake also consistently rises.


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: table
Table: Table 3 Summary of empirical relationships

| <b>Table 3</b> Summary of empirical relationships |                             |                                                                                |                                                      |
|---------------------------------------------------|-----------------------------|---------------------------------------------------------------------------------------------------|------------------------------------------------------|
| Sl. No                                            | Parameter                   | Lake volume (m3)**                                                                                | References                                           |
| 1                                                 | Lake volume (m3)         | $V = 0.104A^{1.42}$                                                                               | Huggel et al. [15]                                   |
| 2                                                 |                             | $V = 0.035A^{1.5}$                                                                                |                                                      |
| 3                                                 |                             | $V = 0.104A^{1.42}$                                                                               | Evans [16]                                           |
| 4                                                 |                             | $V = A * MD$                                                                                | Wang et al. [17]                                     |
| 5                                                 | Lake depth (m)              | $D = 0.104A^{0.42}$                                                                               | Huggel et al. [15]                                   |
| 6                                                 |                             | $MD = 4A * 10^{-5} + 5.0564$                                                                      | Wang et al. [17]                                     |
| 7                                                 |                             | $D = 0.087A^{0.434}$                                                                              | Patel et al. [18]                                    |
| 8                                                 | Average breach width (m) | $B = 0.1803V_{W}^{0.32}h_{b}^{0.19}$                                                              | Froehlich [19]                                       |
| 9                                                 | Time of failure (h)      | $t_{f} = 0.00254V_{W}^{0.53}h_{b}^{-0.90}$                                                        |                                                      |
| 10                                                | Peak discharge (m3s-1)   | $Q_{max} = 75V^{0.67}$                                                                            | Clague and Mathews [20]                              |
| 11                                                |                             | $Q_{max} = 0.72V^{0.53}$                                                                          | Evans [16]                                           |
| 12                                                |                             | $Q_{max} = 0.0048V^{0.896}$                                                                       | Popov [21]                                           |
| 13                                                |                             | $Q_{max} = 0.00077V^{1.017}$                                                                      | Huggel et al. [15]                                   |
| 14                                                |                             | $Q_{max} =$ $1.165(\frac{L}{B})^{\frac{1}{10}}(\frac{B}{b})^{\frac{1}{3}}b(H-h)^{\frac{3}{2}}$ | Lue et al. [22]                                      |
| 15                                                |                             | $Q_{max} = 0.00013(PE)^{0.60}$                                                                    | Huggel et al. [15]                                   |
| 16                                                |                             | $Q_{max} = \frac{2V}{T_{w}}$                                                                      | Huggel et al. [15]; Popov [21], and Haeberli [23] |


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: text

<sup>\*\*</sup>Where V is the volume of glacier lake; A is an area of glacier lake in  $m^2$ ; MD is the average depth of glacial lake in m;  $V_w =$  reservoir volume in  $m^3$ ;  $h_b$  is the height of water above breach invert level; L is the lake's length (m), B is the breach's maximum width (m), b is its avg width (m), H is the lake's maximum depth (m), and h is height of remaining dam (m)

However, it has been observed that the average breach width, B, and time of failure,  $t_f$ , change in proportion to the mean bathymetric depth, as shown in Table 5.

For the Safed glacial lake located in Goriganga Basin, Uttrakhand, India [34], Table 5 shows the parametric effect of the breach depth and volume released on the average breach width and time of the failure. When the breach depth is reduced to half, as in Scenario 2, it is shown that the breach breadth decreases by 30%. The average breach width further decreases by 30% if the drop continues to 15 m, as shown in Scenario 2. When the time of failure is taken into account, it can be seen that the moraine dam's failure time increases by 29 and 30% in comparison to scenarios 1 and 2, respectively, based on the equations proposed by Froehlich, 1995 [12]. This shows that the time of failure is directly connected to volume released but indirectly related to breach width, whereas the breach width is directly related to volume released but indirectly related to breach depth. Due to the inverse relationship between average breach width and time of failure, the breach depth has a significantly

(continued)

 Summary of physical information about glacial lakes including average breach width and time of failure


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: table
Table: Table 4

| Table 4 Summary of physical information about glacial lakes including average breach width and time of failure |                  |                  |                    |                |                |                                                                                |                                                                                |
|-----------------------------------------------------------------------------------------------------------------------|------------------|------------------|--------------------|----------------|----------------|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| Sl. No                                                                                | Lake             | Area (m2, × 106) | Volume (m3, × 106) | Mean depth (m) | Type of lake   | Average breach width B = 0.1803*1.4* V w0.32 h b0.19 | Time of failure t f = 0.00254 V w0.53 h b(−0.90) |
| 1                                                                                | Ice Cave Lake    | 0.0035           | 0.01               | 2.9            | Ice-dammed     | 5.9                                                                                | 0.1                                                                            |
| 2                                                                                | Gruben Lake 5    | 0.01             | 0.05               | 5              | Thermokarst    | 10.9                                                                               | 0.2                                                                            |
| 3                                                                                | Crusoe-Baby Lake | 0.017            | 0.08               | 4.7            | Ice-dammed     | 12.6                                                                               | 0.3                                                                            |
| 4                                                                                | Gruben Lake 3    | 0.021            | 0.15               | 7.1            | Ice-dammed     | 16.6                                                                               | 0.2                                                                            |
| 5                                                                                | Gruben Lake 1    | 0.023            | 0.24               | 10.4           | Moraine-dammed | 20.8                                                                               | 0.2                                                                            |
| 6                                                                                | MT' Lake         | 0.0416           | 0.5                | 12             | Ice-dammed     | 27                                                                                | 0.3                                                                            |
| 7                                                                                | Lac d'Arsine     | 0.059            | 0.8                | 13.6           | Moraine-dammed | 32.1                                                                               | 0.3                                                                            |
| 8                                                                                | Nostetuko lake   | 0.2622           | 7.5                | 28.6           | Moraine-dammed | 75.7                                                                               | 0.5                                                                            |
| 9                                                                                | Between Lake     | 0.4              | 7.5                | 18.8           | Ice-dammed     | 69.9                                                                               | 0.8                                                                            |
| 10                                                                                | Abmachimai Co    | 0.565            | 19.4               | 34.3           | Moraine-dammed | 106.2                                                                              | 0.8                                                                            |
| 11                                                                                | Gjanupsvatn      | 0.6              | 20                 | 33.3           | Ice-dammed     | 106.6                                                                              | 0.8                                                                            |
| 12                                                                                | Quongzonk Co     | 0.753            | 21                 | 27.9           | Moraine-dammed | 104.7                                                                              | 1                                                                              |
| 13                                                                                | Laguna Parón     | 1.6              | 75                 | 46.9           | Moraine-dammed | 173.6                                                                              | 1.2                                                                            |
| 14                                                                                | Summit Lake      | 5                | 250                | 50             | Ice-dammed     | 258.4                                                                              | 2.1                                                                            |
| 15                                                                                | Phantom Lake     | 6                | 500                | 83.3           | Ice-dammed     | 355.4                                                                              | 1.9                                                                            |


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: table
Table

| 5 |
|---|
|---|


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: table
Table

| Discharge                  |                          |                                    |                                   |                          |                         | Reference                     |                               |
|----------------------------|--------------------------|------------------------------------|-----------------------------------|--------------------------|-------------------------|-------------------------------|-------------------------------|
| Glacier, tunnel events     |                          | Glacier, non-tunnel events         |                                   | Moraines                 | Earth- and rock-fill |                               |                               |
| Clague and Mathews [20] | Walder and Costa [24] | Haeberli [23]                      | Walder and Costa [24]          | Popov [21]               | Evans [16]              |                               |                               |
| $Qmax = 75(V/106)0.67$     | $Qmax = 46(V/106)0.66$   | $Qmax = 2 V/tw$ where $tw =1000 s$ | $Qmax = 1100(V/106)0.44$          | $Qmax = 0.0048 V0.896$   | $Qmax = 0.72 V0.53$     |                               |                               |
| 3.43                       | 2.2                      | 20                                 | 145.01                            | 18.42                    | 94.91                   | Maag and Janischowsky [25] |                               |
| 10.08                      | 6.37                     | 100                                | 294.4                             | 77.9                     | 222.73                  | Clague and Mathews [20]    |                               |
| 13.81                      | 8.69                     | 160                                | 362.04                            | 118.69                   | 285.74                  | Maag and Janischowsky [25] |                               |
| 21.04                      | 13.15                    | 300                                | 477.39                            | 208.46                   | 398.71                  | Kääb and Haeberli [26]     |                               |
| 28.83                      | 17.93                    | 480                                | 587.06                            | 317.62                   | 511.5                   | Kääb and Haeberli [26]     |                               |
| 47.14                      | 29.11                    | 1000                               | 810.85                            | 613.08                   | 754.72                  | Blown and Church [27]      |                               |
| 64.58                      | 39.7                     | 1600                               | 997.13                            | 934.14                   | 968.21                  | Vallon [28]                   |                               |
| 289.3                      | 173.9                    | 15,000                             | 2669.44                           | 6938.99                  | 3170.41                 | Evans and Clague [29]      |                               |
| 289.3                      | 173.9                    | 15,000                             | 2669.44                           | 6938.99                  | 3170.41                 | Maag and Janischowsky [25] |                               |
| 546.88                     | 325.62                   | 38,800                             | 4055.32                           | 16,259.66                | 5246.47                 | Meon and Schwarz [30]      |                               |
| Table 4 (continued)        |                          |                                    |                                   |                          |                         |                               |                               |
| Sl. No                     | Discharge                |                                    |                                   |                          |                         |                               | Reference                     |
|                            | Glacier, tunnel events   |                                    | Glacier, non-tunnel events        |                          | Moraines                | Earth- and rock-fill          |                               |
|                            | Clague and Mathews [20]  | Walder and Costa [24]              | Haeberli [23]                     | Walder and Costa [24]    | Popov [21]              | Evans [16]                    |                               |
|                            | $Qmax = 75(V/106)0.67$   | $Qmax = 46(V/106)0.66$             | $Qmax = 2 V/tw where tw = 1000 s$ | $Qmax = 1100(V/106)0.44$ | $Qmax = 0.0048 V0.896$  | $Qmax = 0.72 V0.53$           |                               |
| 11                         | 558.15                   | 332.23                             | 40,000                            | 4110.04                  | 16,709.53               | 5331.86                       | Costa and Schuster [31]       |
| 12                         | 576.7                    | 343.1                              | 42,000                            | 4199.23                  | 17,456.2                | 5471.53                       | Meon and Schwarz [30]         |
| 13                         | 1353.17                  | 794.87                             | 150,000                           | 7352.24                  | 54,613.01               | 10,742.74                     | Lliboutry et al. [32]         |
| 14                         | 3031.66                  | 1759.54                            | 500,000                           | 12,487.81                | 160,618.47              | 20,334.84                     | Mathews and Clague [33]       |
| 15                         | 4823.6                   | 2780.21                            | 1,000,000                         | 16,941.02                | 298,894.8               | 29,362.07                     | Maag and Janischowsky [25] |


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: table
Table

| Sl. No | GLOF scenarios | Breach depth (hb) | Volume released (106 × m3) | Average breach width B = 0.1803*1.4*Vw0.32 hb0.19 | Time of failure tf = 0.00254Vw0.53 hb(-0.90) |
|-----------|----------------|----------------------|-------------------------------|---------------------------------------------------------|----------------------------------------------------|
| 1         | Scenario 1     | 60                   | 4.34                          | 73.10                                                   | 0.21                                               |
| 2         | Scenario 2     | 30                   | 2.17                          | 51.30                                                   | 0.27                                               |
| 3         | Scenario 3     | 15                   | 1.08                          | 36.00                                                   | 0.35                                               |

Average breach width B = 0.1803 1.4 Vw0.32 hb0.19


Document: 1 Introduction
Section: 3 Estimation of Peak Discharge of Reported Cases
Type: text

Table 5 Parametric effect of the breach depth and volume released

more noticeable effect even when the effect of volume released is assumed to be the same in all circumstances.


Document: 1 Introduction
Section: 4 Concluding Remarks
Type: text

The high-altitude glaciers have suffered greatly as a result of extensive anthropogenic activities that led to climate change. In areas where glacial lakes are growing in lateral extent and bathymetry, increasing possibility of higher peak discharges and GLOFs are risking the lives of the local inhabitants, local flora, fauna, and other infrastructure.

In this paper, reported studies on glacial lakes are examined in order to ascertain their formational behavior, its after-effects, and potential corrective actions. The approaches that are utilized globally to anticipate the size of these glaciers include remote sensing and numerical simulation utilizing HEC-RAS. These lakes have been categorized by ICIMOD based on the failure behavior that is anticipated to occur in the future. In order to predict the peak discharge, breach width, and time of failure of various structures that result in the abrupt discharge of enormous amounts of water in the downstream region, numerous empirical equations have been developed and reviewed in this paper. The established empirical relationships are then used to estimate the average breach width, B, time of failure,  $t_f$ , and peak discharge for 15 identified cases of glacial lakes.

The peak discharge values aid in identifying the glacial lake's susceptibility in the downstream sections of the impacted area. The results show that as the depth of the breach grows, so does the likelihood of failure. If the breach depth is reduced by 50%, the time of failure increases by 28–29%, and the average breach width drops by about 30%.

Reviewing all the various glacial lakes enables us to spot certain significant problems that, if resolved, can reduce the impact of GLOF to some level: (a) Quantification of the climatic conditions in space—time and further determination of projections for the future, (b) establishment of a database that compiles data on glacier surface areas and their pace of retreat across time, (c) development of an algorithm that aids in projecting the size of lakes based on present-day spatial features, (d) development of hydrological models that account for potential changes in the regional availability of water.


Document: Author(s):
Section: Author(s):
Type: text

By Christian Huggel, Alejo Cochachin, Fabian Drenkhan, Javier Fluixá-Sanmartín, Holger Frey, Javier García Hernández, Christine Jurt, Randy Muñoz, Karen Price, Luis Vicuña

data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md


Document: Author(s):
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Type: figure
Figure

Image /page/0/Picture/16 description: A wide-angle photograph of a majestic mountain landscape. In the background, a large, snow-covered mountain peak with glaciers reaches up into a blue sky with white clouds. Below the mountain, a vibrant turquoise alpine lake sits in a rocky basin, surrounded by steep, rugged cliffs. The foreground is a rocky slope with numerous large boulders and some sparse vegetation, offering a high vantage point looking down towards the lake.


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Figure

Image /page/0/Picture/17 description: An icon of an image file, depicting a green landscape, is shown to the left of the text 'Site location' in a dark sans-serif font on a white background.


Document: Author(s):
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Site location: a) Peru, b) Cordillera Blanca and c) the Hualcán-Carhuaz area (in dashed line).

Glacier shrinkage – accelerated over the last decades due to climate change – is exposing large areas in mountain regions worldwide. But an even dire consequence of the melting ice is the

forming of more glacier lakes, which are increasing in size. Glacial lakes have caused some of the world's most devastating floods, for example, in the Andes, Himalayas and Alps, where thousands of human lives were lost and huge infrastructure damages reported (Carrivick and Tweed, 2013; Bajracharya et al., 2007; Carey 2005). Climate change is rapidly reshaping living conditions in high mountains – altering flood patterns and creating new flood hazards – leaving populations at imminent risk in several regions (Cook et al., 2016; Emmer et al., 2015; Frey et al., 2016; Drenkhan e al., 2019).

Climatic, glaciological and hydrological information and services can play an essential role for early detection of potential hazards and risks, and for effectively reducing risks. However, infrastructure for climate and associated services are poorly developed in many high mountain areas and need to be substantially strengthened. This report on the design, implementation, operation and circumstances around the setting up of an early warning system for glacier lake outburst floods (GLOFs) in the Peruvian Andes highlights the challenges related to accessing and installing equipment in many high mountains regions.


Document: Author(s):
Section: Laguna 513 disaster
Type: figure
Figure

Image /page/1/Picture/5 description: An overview map displaying a mountainous region with a red line tracing the path of an event. The map features a large aerial view and a smaller, inset close-up view. The inset, outlined in a blue box, shows a snow-covered mountain labeled "Mt. Hualcán" and a turquoise lake labeled "Laguna 513". A red oval on the mountain peak is marked as the "Detachment area (11 Apr 2010)". The red line on the main map originates from this detachment area, flows down the mountain, and follows a valley path towards a town labeled "Carhuaz" and the "Santa River". A scale bar at the bottom right indicates a distance of 2 kilometers, and a north arrow is present for orientation.


Document: Author(s):
Section: Laguna 513 disaster
Type: text

Overview map of Lake 513 and Carhuaz, indicating the 2010 ice avalanche source zone producing the GLOF that reached down to Carhuaz.

The Cordillera Blanca in the tropical Andes of Peru is a glaciated mountain range with a long history of disastrous GLOF events (Carey 2005; 2010). GLOF risks result from considerable physical hazard levels and the high levels of vulnerability and exposure of downstream populations (Frey et al., 2018). The Laguna 513 glacier lake (9°12'45"S, 77°33'00"W) is located at 4 428 metres (m) above sea level at the foot of Mount Hualcán (6 104 m) in the Santa River basin (Figs. 1, 2). The lake, which formed in the late 1960s as a result of glacier shrinkage, was declared highly dangerous in 1988 and subjected to exhaustive security works to artificially lower its level by some 20 m until 1994. This did not reduce GLOFs risk to zero, though the probability of occurrence and magnitude of GLOFs was substantially lowered. In 2004, authorities and specialists produced a report indicating that the lake could be considered safe due to the infrastructure in place (INDECI 2004; Muñoz et al. 2016).

However, Laguna 513 was heavily impacted when a 450 000 cubic metres (m3) rock-ice avalanche detached from the southwest slope of Mt. Hualcán (Carey et al., 2012) (Fig. 2) on 11 April 2010, at about 8 a.m. local time. The avalanche caused a tsunami-like push-wave on the lake, resulting in a dam spillover despite the over 20 m freeboard. Traces of the wave indicate an overtopping of the dam by about 5 m – corresponding to a wave height of about 24 to 25 m – with a peak discharge of rate of several tens of thousands m3 per second (Schneider et al., 2014). The resulting GLOF damaged several bridges, water service infrastructure along its trajectory and eventually reached the debris fan of the city of Carhuaz (about 20 000 inhabitants), where the coarse material of the GLOF was deposited. A total of 0.7 km2 of agricultural land was buried and the Santa Valley highway was affected, but no lives were lost.

Local and national authorities, as well as Peruvian and international experts, met in the weeks following the disaster to discuss ways to better protect people and their assets in future incidents. As a result, plans for a GLOF Early Warning System (EWS) were initiated in 2011 and implemented within three years. The GLOF EWS, the first in the Andean region, was established in the framework of the Glacier Project (www.proyectoglaciares.pe) with financial support from the Swiss Agency for Development and Cooperation (SDC). CARE Peru and the University of Zurich jointly implemented the EWS in close collaboration with the municipality of Carhuaz and the National Water Authority of Peru (ANA) and its Office for Glacier and Lake Evaluations (former Glaciology and Water Resources Unit - UGRH) in Huaraz.


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System
Type: text

The design, organization and operation of the GLOF was structured to adhere to internationally recognized EWS components (cf. Fluixá-Sanmartín et al., 2018):

- risk knowledge
- monitoring and warning
- · dissemination and communication
- response capability.


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Risk knowledge
Type: text

Understanding the risks encountered at a particular location is fundamental for the design of an EWS. Risks can be assessed using established methods that analyze physical hazards by means of critical indicators and thresholds (in this case related to the different components involved in the GLOF process), exposure of people and assets (e.g. infrastructure) and the vulnerability (e.g. social, economic) of the elements at risk. Comprehensive risk assessments for GLOFs are rare (Allen et al., 2016) and complex because GLOFs are typically the results of a cascade of triggering and propagating mass flow processes (Schneider et al., 2014; Westoby et al., 2014).

The 2010 GLOF served as a reference to analyze the physical hazards by simulating the process cascade with an iterative approach of coupled, physically-based numerical mass movement and hydrodynamic models (RAMMS and IBER). This model chain was then used to simulate three potential future scenarios of different magnitudes (small, medium, large) and corresponding probabilities of occurrence (high, medium, low, respectively). This hazard assessment procedure followed international standards and was in line with the recently established guidelines of the International Commission on Glacier and Permafrost Hazards in Mountains (www.gaphaz.org), a joint commission of the International Association of Cryopsheric Sciences (IACS) and the International Permafrost Association (IPA). The modelling, together with field surveys, resulted in a GLOF hazard map for local communities and city of Carhuaz (Schneider et al., 2014) (Fig. 3). Exposure and vulnerability were assessed using publicly available data sources (such as census data) and additional surveys in the catchments.

However, risks are perceived in very different ways by different actors. It is a challenge to understand and take these differences in perception into consideration but this is essential for wide user acceptance and the long term success of risk reduction measures. Repeated workshops were conducted in the different communities of the catchment to learn about the risk perceptions and priorities of local leaders and people. At a later stage, ethnographic studies, which included longer (several months) research visits with the local communities, were also conducted in the catchment. These were important to gain more in-depth understanding of how local people perceive their natural environment, and the relationships they maintain with mountains, glaciers and lakes that surround them. This led to a deeper understanding of how they perceived the diverse types of risks and how they understood the connections between those risks and those involved in the GLOF EWS project. Thereby cultural and political aspects were highlighted and people's concerns about water came to the fore, that is water availability, mostly in terms of access to water (e.g. water rights, allocation).


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Monitoring and warning
Type: figure
Figure

Image /page/3/Picture/5 description: A generic image file icon, which depicts a landscape with a green hill and a cloud, is shown next to the text "Glacier Lake" in a dark gray sans-serif font on a white background.


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Monitoring and warning
Type: text

Monitoring and warning are central elements of an EWS. Monitoring instruments and technical measuring must be set up to detect hazards so that timely warnings can be issued. The challenge is to identify the environmental processes and variables that are critical to the early detection of an extreme event (such as a GLOF) – and that are measurable by sensors. The insights and improved understanding of the processes gained from the reconstruction of the 2010 outburst and the modelling of potential future scenarios (risk knowledge element) served to identify where and what to measure and monitor. Knowledge about GLOF travel times – from triggering to reaching population centres – are, for instance, critical for the design of an EWS and for later visualization and planning with local authorities and communities (see "Dissemination and communication").

Reference projects for GLOF EWS were rare at the time of the design for Laguna 513 (2011/2012), and completely non-existent for the Latin American region. The "art" in such a design is in taking account of all possible flood trigger processes while measuring the point that still allows for timely warning. The different types of flood trigger processes – ice avalanches, moraine instabilities, rock slope failures – strongly depend on local conditions. It is critical to adequately understand the physical environment and interplay of processes that can result in different GLOF scenarios.

The harsh, extreme physical environment in which glacier lakes (as origin of GLOFs) form are often the biggest challenge. At high altitudes, such as that of Laguna 513, there are large daily temperature fluctuations, long periods of cloudiness, heavy precipitation and high solar radiation as well as a steep topography in what is a remote environment. All of these factors need to be considered in the design and implementation. The GLOF EWS adventurer/scientists had to make

provisions for reduced energy, for complications with data transmission from the sensors and the limited access for sensor installation and frequent following-up and maintenance.

Another crucial element for monitoring and warning, in particular for an EWS in extreme environments such as in this case, is the redundancy in the system. Even in a well-calibrated and tested EWS, a sensor or data transmission failure is likely to occur at some point, sufficient redundancy is indispensable to avoid that sensor failure results in EWS failure as a whole.

An addition problem is funding the long term maintenance of the EWS. Small municipalities with limited budget have other priorities like investing in health and education services.

With the knowledge and information gathered, the local and international team worked together, each contributing their expertise, to design an EWS that would overcome the challenges of Laguna 513. The design comprised two stations – a main station at the Laguna 513 dam and a station in the Pampa Shonquil, which would include meteorological measuring instruments – a data centre in the municipality of Carhuaz, a warning station in the community of Pariacaca, and a repeater station for transferring the signal from the lake to the data centre (Fig. 3).

The stations were equipped with the following instruments:


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Monitoring and warning
Type: text

other priorities like investing in health and education services . With the knowledge and information gathered , the local and international team worked together , each contributing their expertise , to design an EWS that would overcome the challenges of Laguna 513 . The design comprised two stations – a main station at the Laguna 513 dam and a station in the Pampa Shonquil , which would include meteorological measuring instruments – a data centre in the municipality of Carhuaz , a warning station in the community of Pariacaca , and a repeater station for transferring the signal from the lake to the data centre ( Fig . 3 ) . The stations were equipped with the following instruments :

- Data centre Carhuaz (2 640 m a.s.l.): receiving antenna, screen with real-time data access, server for data storage, infrastructure for launching alarms.
- Repeater station (3 189 m a.s.l.): receiving and sending antenna.
- Station Laguna 513 (4 491 m a.s.l.): 2 cameras taking photos every 5 seconds during daylight times, one looking at the face of Mt. Hualcán, one observing the dam. 4 geophones located close to the station, continuously measuring and sending data in 5 second intervals, in order to detect potential mass movements (e.g. ice avalanche) impacting the lake. Receiving and sending antenna and data logger.
- Station Pampa Shonquil (3 600 m a.s.l.): river discharge station (using pressure sensor), meteorological station with sensors for measuring air temperature and humidity, precipitation, wind speed, and solar radiation. Sending antenna and data logger.
- Information receiving and warning station at Pariacaca (3 138 m a.s.l.): the monitoring system informs locals about events at Laguna 513 and sirens activated from the Data Centre Carhuaz to facilitate evacuation.

All stations were equipped with solar panels and batteries for energy generation and storage, however energy availability remained a limiting factor, in particular at the glacier lake station as the peaks of the Cordillera Blanca experience a high frequency of cloud coverage. Each station had a mast where most of the instruments were fixed, a concrete lockable box for the electronic equipment, and a protective fence. Emergency power aggregates were available in the municipal building to prevent data losses and interrupted access during blackouts.

The geophones (devices recording ground movements and converting them into voltage) were the principle instruments used for registering a potential GLOF trigger. The back-up cameras could be used to get an overview of the current situation and, particularly during the test phase of the system, for relating geophone measurements to the magnitude of (avalanche) events. The pressure sensor in the riverbed at the Pampa Shonquil station added redundancy to the system and, if calibration measurements were taken, could be used to continuously record runoff. Later, it was planned to install wire sensors in the river channel bed below Laguna 513 that would detect unusually high and dangerous river flow discharge, which could be applied in debris flow warning systems.

A permanently manned hut with wardens next to the station at Pampa Shonquil was an important element of the EWS, especially for consideration of redundancy. Its main purpose was to control the freshwater intake for the municipality of Carhuaz but the location gave the wardens a perfect view towards Laguna 513, they could radio warnings to the authorities in case of an event (as was the case in the 2010 event).


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Monitoring and warning
Type: text

to continuously record runoff . Later , it was planned to install wire sensors in the river channel bed below Laguna 513 that would detect unusually high and dangerous river flow discharge , which could be applied in debris flow warning systems . A permanently manned hut with wardens next to the station at Pampa Shonquil was an important element of the EWS , especially for consideration of redundancy . Its main purpose was to control the freshwater intake for the municipality of Carhuaz but the location gave the wardens a perfect view towards Laguna 513 , they could radio warnings to the authorities in case of an event ( as was the case in the 2010 event ) .

For security, all data is first stored in the data logger at each respective station, then transmitted at 5-second intervals to the data centre server, which has a cloud back-up. All data are directly transferred to a website that permits real-time remote access. In the data centre itself – a separate office in the municipality of Carhuaz – a screen continuously displays the data from this webpage.

Warning protocols represent essential elements of an EWS. The protocol documents and defines warning procedures, typically differentiating a number of warning levels and associated actions as well as the responsible institutions, organizations or committees and people. Local, regional and national laws, rules and guidelines had to be taken into consideration in the Laguna 513 warning protocol. The members of the Local Emergency Operation Centre, civil defense, selected government officials and the mayor, whose authority it is to launch the evacuation alarm, had to be involved. Accordingly, the protocol was accompanied by a list of responsible persons and their phone numbers. It defined three warning levels – yellow, orange and red – plus a normal green baseline level and how these warning levels are reached and what type of actions need to be taken. To this purpose, thresholds of physical variables and processes had to be determined based on sensor measurements. Definition of these thresholds is critical and involves an extended period of calibration and testing, typically of many months, especially if no prior measurements are available as in the case of Laguna 513.


Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Dissemination and communication
Type: text

If measurements on a geophone surpasses a defined threshold, a short message to immediately check EWS data and information is automatically sent to the mobile phones of all the responsible personnel identified in the warning protocol. The subsequent steps to be taken are based on the action plan and on the available data. Alarms cannot be automatically launched by the EWS because, under Peruvian law, only the mayor can authorize an evacuation.

Carhuaz's alarm module has two long-range acoustic sirens and the capacity to send predefined text messages to community and district leaders and stakeholders such as school principals, hospital chiefs, the police and firefighters. Communities upstream of Carhuaz receive warnings and associated information through the Local Emergency Operation Centre and the central authorities of Carhuaz. Pariacaca, which is on the flood pathway, has a warning station with sirens. Furthermore, the EWS protocols were adapted to fit with the Peruvian protocols for risk assessment, allowing communication with the National Emergency Operations Centre at Lima to ask for help (Muñoz et al. 2016).