Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: table
Table

| Date          | Area (km2) | Perimeter (m) | Absolute Area Error (km2) | Relative Area Error (%) | Date         | Area (km2) | Perimeter (m) | Absolute Area Error (km2) | Relative Area Error (%) |
|---------------|---------------|---------------|---------------------------------|-------------------------------|--------------|---------------|---------------|---------------------------------|-------------------------------|
| 1 August 2014 | 2.99          | 9.94          | 0.01                            | 0.21                          | 11 June 2022 | 1.25          | 8.52          | 0                               | 0.02                          |
| 12 July 2015  | 2.28          | 9.57          | 0.01                            | 0.27                          | 17 June 2022 | 1.45          | 7.92          | 0.01                            | 0.39                          |
| 25 June 2016  | 1.94          | 8.82          | 0.01                            | 0.31                          | 21 June 2022 | 1.52          | 7.54          | 0.01                            | 0.37                          |
| 6 July 2017   | 1.8           | 8.74          | 0                               | 0.01                          | 8 July 2022  | 2.07          | 8.91          | 0.01                            | 0.29                          |
| 3 August 2018 | 2.5           | 10.15         | 0.01                            | 0.26                          | 12 July 2022 | 2.15          | 10.03         | 0                               | 0.01                          |
| 5 July 2019   | 1.86          | 8.61          | 0                               | 0.01                          | 13 July 2022 | 2.1           | 9.91          | 0                               | 0.01                          |
| 5 August 2020 | 2.05          | 8.25          | 0                               | 0.01                          | 14 July 2022 | 2.09          | 9.76          | 0.01                            | 0.30                          |
| 3 July 2021   | 1.96          | 8.70          | 0.01                            | 0.30                          | 15 July 2022 | 2.08          | 9.85          | 0                               | 0.01                          |
| 18 May 2022   | 0.72          | 5.84          | 0                               | 0.68                          | 16 July 2022 | 2.03          | 9.27          | 0.01                            | 0.30                          |
| 28 May 2022   | 0.95          | 6.71          | 0.01                            | 0.55                          | 17 July 2022 | 2.01          | 9.12          | 0                               | 0.01                          |
| 3 June 2022   | 1.06          | 6.82          | 0.01                            | 0.50                          | 18 July 2022 | 1.28          | 5.26          | 0                               | 0                             |

Date
Area (km2)
Perimeter (m)
Absolute Area Error (km2)
Relative Area Error (%)
Date
Area (km2)
Perimeter (m)
Absolute Area Error (km2)
Relative Area Error (%)


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

Previous research has shown that the area of the lake continues to expand until the outburst occurs, and once the lake surface area starts to decrease, the GLOF event is imminent. Therefore, monitoring changes in the lake area prior to an outburst is crucial. Analysis of satellite images during the research period revealed a decreasing trend in the

Remote Sens. 2023, 15, 1941 8 of 18

maximum lake area (MLA) from 2014 to 2021 (Figure 3b). Over the past nine years, the average MLA was 2.07 km2. The largest MLA was 2.99 km2 on 1 August 2014 (Figure 3a), and the smallest MLA was 1.67 km2 on 12 July 2016 (Figure 3d). From 2014 to 2022, the MLA occurred in July, except in 2014 and 2018 two years, which occurred in August. From the perspective of the average elevation of the lake, which shows a similar change trend as the MLA change from 2014 to 2022 (Figure 3b). The highest and the lowest average elevation of the MLA were observed in 2014 (3318.55 m) and 2019 (3289.97 m), respectively.

data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure

Image /page/7/Figure/2 description: A multi-panel figure showing the evolution of a glacial lake from 2014 to 2022. The figure is composed of ten panels labeled (a) through (j). Panel (a) is a large satellite image from October 1, 2014, showing the lake with colored outlines indicating its extent for each year from 2014 to 2022. A legend details the colors for each year: W2022 (orange), W2021 (dark blue), W2020 (green), W2019 (cyan), W2018 (blue), W2017 (magenta), W2016 (light green), W2015 (yellow), and W2014 (red). Panel (b) is a dual-axis line graph for the years 2014-2022, plotting Elevation (m) on the left y-axis (ranging from 3270 to 3330) and Area (km²) on the right y-axis (ranging from 0 to 3.5). The orange line represents elevation, and the blue line represents area. Panels (c) through (j) are smaller satellite images of the lake on different dates: (c) 2015 12 July, (d) 2016 25 June, (e) 2017 06 July, (f) 2018 04 October, (g) 2019 09 July, (h) 2020 05 October, (i) 2021 05 July, and (j) 2022 15 July, showing variations in the lake's color, ice cover, and clarity over the years.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure: Figure 3. Maximum lake area (MLA) change before outburst since 2014. (a) MLA extent in each year and overlay on the image obtained on 1 August 2014; (b) MLA changes from 2014 to 2022; (c–j) MLA overlay on images obtained from 2015 to 2022.

**Figure 3.** Maximum lake area (MLA) change before outburst since 2014. (a) MLA extent in each year and overlay on the image obtained on 1 August 2014; (b) MLA changes from 2014 to 2022; (c–j) MLA overlay on images obtained from 2015 to 2022.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

Based on the observation of Lake Merzbacher GLOF in 2022, which started at least from 18 May and lasted for about two months (Figure 4). Initially, water coalesces into the southern part of the lake by gravity with an area of  $0.72~\rm km^2$ . With the lake increasingly receiving meltwater from snow and ice, the lake area continues to increase. From 18 May to 28 May, the lake area increased at a rate of  $0.023~\rm km^2/d$ . During the next 11 days (28 May~8 June), the lake area increased at a relatively low speed compared to the first ten days, with a rate of  $0.01~\rm km^2/d$ . After that, the lake expanded rapidly from 8 June to 17 June, with a rate of  $0.04~\rm km^2/d$ . For some time after that (17 June~8 July), the lake area increased rapidly, with a speed of  $0.03~\rm km^2/d$ . The lake area reached its maximum in 12 July with a rate of  $0.03~\rm km^2/d$  from 08 July to 12 July. However, the lake area decreased from 2.19 km² on 12 July to 2.12 km² on 14 July. In the next four days (15 July~18 July), the lake area continued to shrink with an average rate of  $0.21~\rm km^2/d$ , especially during the period from 17 July to 18 July, and the lake area decreased rapidly from 2.01 km² to  $1.28~\rm km^2$ .

Remote Sens. 2023, 15, 1941 9 of 18


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure

Image /page/8/Figure/1 description: This figure, titled "Figure 4. Lake extent change in 2022 before and after the outburst," displays a series of satellite images, a map, and a line graph to illustrate the changing size of a lake. The main part of the figure is a grid of 14 satellite images dated from May 18, 2022, to July 20, 2022. These images show a lake in a mountainous, snowy area, with its extent outlined. The lake's size is observed to increase progressively until mid-July, after which it appears to have drained. In the bottom right, there is a map with a legend showing the superimposed outlines of the lake on various dates, along with a scale bar. Next to the map is a line graph plotting the lake's area in square kilometers against the date. The y-axis, labeled "Area(km²)," ranges from 0 to 2.5. The x-axis, labeled "Date," shows dates from May 18 (0518) to July 18 (0718). The graph shows the area increasing from approximately 0.7 km² on May 18 to a peak of about 2.2 km² on July 14, before sharply declining to around 1.2 km² by July 18, visually representing the outburst event.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure: Figure 4. Lake extent change in 2022 before and after the outburst.

Figure 4. Lake extent change in 2022 before and after the outburst.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

The centroid changes of the lake area from 2014 to 2022 are presented in Figure 5. The centroid of the maximum lake area in the nine-year period was mostly located along the central line in a direction from the northeast to the southwest. When using the southwestern boundary as a reference frame, the farthest centroid of the maximum lake area was observed in 2018, measuring 1.2 km from the centroid point to the southernmost boundary. Second to 2018, the centroid in 2014 was the second farthest centroid of the maximum lake area during the research period. Simultaneously, the two largest lake areas occurred in the above two years. Notably, even in the years with the largest lake area, the centroid of the maximum lake area remained within a certain distance from the southernmost boundary. Based on the dense records in 2022 prior to the outburst, from 18 May to 28 May, the lake area expanded 0.2 km toward the northeast. In the following 14 days (28 May to 11 June), it continued to expand approximately 0.2 km to the northeast, and from 11 June to 21 June, it continued to expand northeastward with a distance of about 0.1 km. Before reaching its maximum, the lake area expanded approximately 0.3 km northeastward. Subsequently, the lake area began to shrink back toward the southwestern boundary. For the first few days (12 July to 15 July), the centroid of the lake area had little change. However, since 15 July, a significant retreat of the centroid of the lake area was observed. The most significant retreat occurred in a 24 h period from 17 July to 18 July.

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Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure

Image /page/9/Figure/1 description: A figure with two side-by-side maps showing changes in a geographical feature, likely a glacier, over time. Each map has an inset scatter plot. Both maps feature a scale bar from 0 to 1 km and a compass rose.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

The left panel displays annual changes from 2014 to 2022. The map shows the glacier with colored outlines and corresponding dots for each year. The legend indicates the colors: 2014 (red), 2015 (yellow), 2016 (green), 2017 (purple), 2018 (blue), 2019 (cyan), 2020 (pink), 2021 (dark blue), and 2022 (brown). The inset scatter plot shows the position of a point over these years, with the X-axis from 79.842 to 79.847 and the Y-axis from 42.2015 to 42.2055.

The right panel displays changes over specific dates, likely within a single year. The map shows the glacier with colored outlines and dots for dates like 0518, 0528, 0608, 0611, 0617, 0621, 0708, 0712, 0714, 0715, 0716, 0717, and 0718, each with a unique color as defined in the legend. The inset scatter plot shows the position of a point on these dates, with the X-axis from 79.84 to 79.845 and the Y-axis from 42.197 to 42.205.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure: Figure 5. Centroid changes of lake area in historical and 2022 before the outburst.

Figure 5. Centroid changes of lake area in historical and 2022 before the outburst.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

3.2. Using Lake Area and Ice Cover to Monitor Hazard of Glacier Lake Outburst Flood of Lake Merzbacher

Previous research has divided the entire outburst process of Lake Merzbacher into four stages: icefall (Stage I), quick water storage (Stage II), early warning (Stage III), and post-drainage (Stage IV). Therefore, the ice cover can be an essential index to predict the outburst. By setting the NDWI threshold value, we classified the lake as water and an ice area, two classes. As depicted in Figure 6, the ice area accounted for 53.3% of the total lake area on average from 2014 to 2022. The ice area proportion is the maximum (69.87%) in 2021, while that of 2016 is the minimum (23.83%). According to the records of the outburst date from 2014 to 2021, Lake Merzbacher outburst on 10 October, 9 July 2015, 25 June 2016~2 July 2016, 7 July 2017, 12 October 2018, 9 July 2019~12 July 2019, 7 October 2020, and 9 July 2021. During the first stage, the ice area is generally higher than the water area. In the second stage, the water area will expand rapidly, and the ice at the bottom of the lake will emerge from the water. Thus, the ice area will expand simultaneously before the outburst. When the dam body is raised, the lake area will shrink, and more floating ice will emerge, which indicates the lake has entered an early warning stage. As shown in Figure 6, water areas in 2014, 2015, 2018, 2019, and 2021 are smaller than ice areas, indicating that these images may have been captured during Stage II or Stage III.

Remote Sens. 2023, 15, 1941 11 of 18


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure

Image /page/10/Figure/1 description: A stacked bar chart, labeled (a), showing the ice area and water area in square kilometers from 2014 to 2022. The x-axis represents the year, and the y-axis represents the area in square kilometers (km²), ranging from 0 to 3.5. The legend indicates that light blue bars represent 'Ice area' and dark blue bars represent 'Water area'. The data for each year is approximately as follows: 2014: Ice area ~1.7 km², Water area ~1.3 km², Total ~3.0 km². 2015: Ice area ~1.45 km², Water area ~0.8 km², Total ~2.25 km². 2016: Ice area ~0.45 km², Water area ~1.5 km², Total ~1.95 km². 2017: Ice area ~0.85 km², Water area ~0.95 km², Total ~1.8 km². 2018: Ice area ~1.55 km², Water area ~0.95 km², Total ~2.5 km². 2019: Ice area ~1.25 km², Water area ~0.6 km², Total ~1.85 km². 2020: Ice area ~0.9 km², Water area ~1.15 km², Total ~2.05 km². 2021: Ice area ~1.35 km², Water area ~0.6 km², Total ~1.95 km². 2022: Ice area ~1.0 km², Water area ~1.25 km², Total ~2.25 km².


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure

Image /page/10/Figure/2 description: A combination chart, labeled (b), showing changes in water and ice area over time. The horizontal axis is labeled 'Date' and shows dates from 0518 to 0717. The chart has two vertical axes, both labeled 'Area(km²)'. The left y-axis ranges from 0 to 2.5 and corresponds to a stacked bar chart. The right y-axis ranges from -0.4 to 0.6 and corresponds to a line chart. The legend indicates that dark blue bars represent 'Water area', light blue bars represent 'Ice area', and a red line represents 'W-I' (Water area minus Ice area). The stacked bars show a general increase in total area over the period, from approximately 0.7 km² on 0518 to a peak of about 2.1 km² on 0714. The red line fluctuates, starting near -0.2, dropping to a low of about -0.35 on 0621, and reaching a peak of approximately 0.55 on 0714.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: figure
Figure: Figure 6. Water area and Ice area of Lake Merzbacher before the outburst from 2014 to 2022 (a); Water area and Ice area daily changes before the outburst (b).

**Figure 6.** Water area and Ice area of Lake Merzbacher before the outburst from 2014 to 2022 (a); Water area and Ice area daily changes before the outburst (b).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022
Type: text

Time series images captured in 2022 before and after the outburst provide more details. At the beginning of the observation period, the lake area was mainly covered by ice on 18 May. As the ice melted, the water area gradually increased and exceeded the ice area on 28 May and 3 June. During the storage process of the glacial lake, the ice originally deposited at the bottom of the lake basin was lifted by the water body and floated on the surface of the lake, resulting in ice dominating the lake area again on 17 June and 21 June. As the meltwater continued to merge, the water area surpassed the ice area again until reaching the maximum lake area on 8 July to 12 July. As the ice dam body rose from 12 July to 17 July, the gap between the water and ice area gradually increased and narrowed. During this period, ice deposited at the bottom of the ice dam was lifted by the water body, and the lake was dominated by ice again before the outburst.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: text

According to the relevant records of the published studies and news reports, Lake Merzbacher has experienced nearly yearly outbursts since 1902 (Figure 7). From the perspective of the outburst date, it can be found that over 80% of outburst events occurred on 25 June, 28 July, and 14 October. From 1981 to 2022, almost every outburst event occurred on 23 July and 16 October, except in 1988 and 1996. Based on ERA5 climate data, the hottest month in every year since 1981 was extracted (Figure 7). As depicted in Figure 7, the hottest month occurred on 24 July or 20 October since 1981. From 1981 to 2022, the hottest month and the outburst date were the same in 25 years. Especially from 2015 to 2022, Lake Merzbacher outburst occurred in the hottest month every year. Lake Merzbacher outburst occurred in the second hottest month in 11 years, of which 5 years is one month ahead of the hottest month, while the rest of the 6 years are one month later than the hottest month. Only two outburst events occurred in the cold season. The main water storage source of Lake Merzbacher is the meltwater of North Inylchek Glacier, so the water storage of the glacial lake strongly depends on air temperature.

Further analysis of the outburst time reveals that since the 1930s, the outbursts of Lake Merzbacher have tended to occur earlier in the year (Figure 8). Over the period from 1902 to 2022, the Lake outburst earlier on average 6 days per decade ( $R^2 = 0.21$ ). Since the water storage of Lake Merzbacher comes from glacial meltwater, one potential reason for this fact can be climate warming, which is melting glaciers earlier. Climate change has raised average temperatures in the surrounding area of Lake Merzbacher (Figure 8). From 1981 to 2021, the average temperature increased from  $-9.59\,^{\circ}\text{C}$  to  $-9.06\,^{\circ}\text{C}$ , increasing at a rate of 0.18  $^{\circ}\text{C}$  per decade. From the perspective of April, May, June, July, and October, the average temperature increased during the same period. Among them, the fastest growth in

Remote Sens. 2023, 15, 1941 12 of 18

average temperature has been seen in April, which increased at a rate of  $0.45\,^\circ\text{C}$  per decade. In other words, Lake Merzbacher has shown an early warming trend.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: figure
Figure

Image /page/11/Figure/2 description: The image displays three charts, labeled (a), (b), and (c), analyzing outburst data, likely related to Lake Merzbacher as mentioned in the text above the charts.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: text

Chart (a) is a histogram showing the frequency of outbursts by month. The x-axis is labeled 'Month' with ranges: [5, 6], (6, 7], (7, 8], (8, 9], (9, 10], (10, 11], and (11, 12]. The y-axis is 'Frequency', from 0 to 30. The frequencies for each month range are as follows: [5, 6] is 3, (6, 7] is 25, (7, 8] is 28, (8, 9] is 14, (9, 10] is 7, (10, 11] is 1, and (11, 12] is 3. The highest frequency of outbursts occurs between months 6 and 8.

Chart (b) is a bar chart showing the 'Outburst date' (month) over time. The x-axis is 'Year', from 1981 to 2020. The y-axis is 'Outburst date', representing the month from 0 to 14. Most outbursts occur in month 7 or 8. There are notable peaks where the outburst occurred in month 12, specifically around 1994 and 2016.

Chart (c) is a grouped bar chart comparing the 'Outburst Date' (blue bars) with the 'Hottest Month' (orange bars) for each year from 1981 to 2021. The x-axis is 'Year', and the y-axis is 'Month', from 0 to 14. In most years, the outburst date and the hottest month are closely aligned, typically around month 7 or 8. However, in some years, such as 1988 and 1996, the outburst date (month 12) is significantly later than the hottest month (month 7).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: figure
Figure: Figure 7. Date of Lake Merzbacher outburst since 1902. (a) Lake Merzbacher outburst Frequency in different months since 1902; (b) Lake Merzbacher outburst date since 1902; (c) The relationship between the outburst date and the hottest date since 1902.

**Figure 7.** Date of Lake Merzbacher outburst since 1902. (a) Lake Merzbacher outburst Frequency in different months since 1902; (b) Lake Merzbacher outburst date since 1902; (c) The relationship between the outburst date and the hottest date since 1902.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: figure
Figure

Image /page/11/Figure/4 description: The image displays two graphs, labeled (a) and (b).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: text

Graph (a) is a scatter plot titled "Outburst date since 1902". The x-axis is labeled "Year" and ranges from 1900 to beyond 2000. The y-axis is labeled "Days" and ranges from 100 to 350. The plot contains numerous blue circular data points. A dotted blue trendline is drawn through the data, showing a negative slope. The equation for the trendline is given as "y = -0.6397x + 1493.3", and the R-squared value is "R² = 0.21".

Graph (b) is a line graph showing temperature data over time. The x-axis is labeled "Year" and ranges from 1981 to 2020. There are two y-axes, both labeled "Temperature (°C)". The left axis ranges from 0 to 5, and the right axis ranges from -15 to 5. The graph contains five colored lines representing different months: July (yellow), April (dark blue), June (gray), October (light blue), and May (orange). A dotted gray line represents the overall "Trendline". The equation for this trendline is "y = 0.0448x - 10.191", and the R-squared value is "R² = 0.1343".


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: figure
Figure: Figure 8. Detailed records of outburst date from Lake Merzbacher from 1902 to 2022 (a). Temperature changes in Lake Merzbacher from 1981 to 2022 (b).

**Figure 8.** Detailed records of outburst date from Lake Merzbacher from 1902 to 2022 (a). Temperature changes in Lake Merzbacher from 1981 to 2022 (b).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980
Type: text

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Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
Type: text

The glacier lake outburst flood (GLOF) significantly threatens downstream communities, infrastructure, and livelihoods [50–52]. Establishing an early warning system is crucial in mitigating disasters and reducing damage [53]. GLOFs release large amounts of water stored in glacier lakes rapidly downstream [54]. Therefore, capturing the signals of an imminent GLOF is essential for early warning. Previous studies have shown Lake Merzbacher outbursts at a high frequency, especially after entering the twenty century. Additionally, there is a certain regularity in Lake Merzbacher's outburst mechanism [19,20]. The lake area expands before reaching its maximum, after which it rapidly shrinks until the outburst. This study analyzed the lake's daily changes in area leading up to the 2022 outburst. To the best of our knowledge, this is the first study to observe these changes daily before a Lake Merzbacher outburst.

When analyzing the maximum lake area (MLA) before the outburst of Lake Merzbacher from 2014 to 2022, it was found that the MLA did not exceed 3 km2 and showed a decreasing trend, which is consistent with the findings of Li et al. [30]. In fact, the MLA has shown a decreasing trend, at least after entering the new century. On the contrary, glacier lakes showed an increasing trend in the Tienshan region reported by Wang et al. [55]. The upper lake area also showed an increasing trend [30]. The lake area expansion or shrinkage are all the consequences of climate change. On the one hand, climate change can lead to glacial ablation and thus provide more melting water for the glacier lakes. Therefore, the quantities and volume of glacier lakes showed an increasing trend. However, Lake Merzbacher was dammed by the southern branch of the Inylchek Glacier. The increase in temperature will also accelerate the piping inside the ice dam [56]. As a result, the outbursts tended to occur earlier (Figure 8). However, this does not mean Lake Merzbacher is not dangerous anymore. Under the effects of climate change, glaciers in the Tien Shan of Central Asia have generally retreated over the past half-century [57]. A general increasing trend has prevailed in the temporal variation of peak and total discharges of the jokulhlaup floods of the lake [49,58]. Furthermore, the southern branch of the Inylchek Glacier proceeds northward and flows into Lake Merzbacher, which may break the stability of the ice dam and thus significantly increase the GLOF risk for Lake Merzbacher. Therefore, the outburst of Lake Merzbacher requires our ongoing attention.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
Type: text

) . However , this does not mean Lake Merzbacher is not dangerous anymore . Under the effects of climate change , glaciers in the Tien Shan of Central Asia have generally retreated over the past half - century [ 57 ] . A general increasing trend has prevailed in the temporal variation of peak and total discharges of the jokulhlaup floods of the lake [ 49 , 58 ] . Furthermore , the southern branch of the Inylchek Glacier proceeds northward and flows into Lake Merzbacher , which may break the stability of the ice dam and thus significantly increase the GLOF risk for Lake Merzbacher . Therefore , the outburst of Lake Merzbacher requires our ongoing attention .

A previous study divided the entire process of the Lake Merzbacher outburst into four periods: icefall, quick water storage, early warning, and post-drainage [18]. According to satellite images in 2022, since at least 4 May, water starts to be injected into the lake (Figure 9). As the temperature keeps increasing, more and more glacier meltwater flows into Lake Merzbacher. The water area would expand at a relatively low speed during this period. This process continues for up to about one month, the same as in previous records. In the next month, the lake area would expand at a relatively high speed until reaching the maximum lake area. As the dam body gradually rises under the water pressure [20], a system of englacial channels hidden in the dam opens, and the lake water discharges through these channels. During this period, the lake area showed a relatively low decreasing speed and can be detected from some corners (S1–S3). This period lasts about 5 days and is significant to early warning. Furthermore, the entire outburst process of Lake Merzbacher will repeat nearly every year [16]. It means that Lake Merzbacher, because of its predictability, would be a promising candidate for an early warning system (EWS). Compared to other engineering measures on glacial lakes, establishing the EWS is a relatively inexpensive option. Generally, the GLOF EWS has been widely established in the HMA [58]. For example, a manual EWS was set up in 1997 by Ropal Tsho, a moraine-dammed located in the Rolwaling Valley of Nepal [49]. The manual EWS by Ropal Tsho evolved into a fully automatic system one year after its establishment, which constituted two components: a sensing system and a warning system [59]. Imja Tsho is another glacial lake located in the Solukhumbu District of Nepal, near the base of Mount Everest [60]. It is a glacial lake that formed as a result of the retreat of the Imja Glacier. The lake has grown rapidly in recent decades due to climate change, and there are

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Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
Type: text

Tsho , a moraine - dammed located in the Rolwaling Valley of Nepal [ 49 ] . The manual EWS by Ropal Tsho evolved into a fully automatic system one year after its establishment , which constituted two components : a sensing system and a warning system [ 59 ] . Imja Tsho is another glacial lake located in the Solukhumbu District of Nepal , near the base of Mount Everest [ 60 ] . It is a glacial lake that formed as a result of the retreat of the Imja Glacier . The lake has grown rapidly in recent decades due to climate change , and there are Remote Sens . 2023 , 15 , 1941 14 of 18

concerns that it could breach its natural dam and cause flooding downstream. The EWS of Imja Tsho combined two monitoring stations (web camera) and two relay stations [56]. EWSs can be complicated or easy. In theory, only the installation of water-level sensors can achieve a simple prediction scheme. However, a comprehensive monitoring system, usually composed of glacial lake monitoring, parent glaciers monitoring, the downstream river environment change detection, formulating a disaster mitigation plan, releasing early-warning information, and training for evacuation, is of great significance to reveal the GLOF generation mechanism [53]. Therefore, though the advantage of dense satellite images has been demonstrated, field-based EWS components are still required.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
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Figure

Image /page/13/Picture/2 description: A low-resolution satellite image of a mountainous, snowy landscape. The image features a mix of white and light blue areas, representing snow or ice, and darker, rocky terrain. A small, dark blue body of water is visible near the center. In the top-left corner, there is a compass rose. In the bottom-left corner, a scale bar indicates a distance from 0 to 1 km. The date "4 May 2022" is written in the bottom-right corner.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
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Figure: Figure 9. Satellite image of Lake Merzbacher captured in 4 May 2022.

Figure 9. Satellite image of Lake Merzbacher captured in 4 May 2022.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 4. Discussion
Type: text

The difficulty in using satellite images to provide warnings of outburst floods from glacier-dammed lakes is to identify the drainage date [61]. The proportion of ice and water could also provide important information during the process. Xie et al. [16] proposed an index by calculating the ratio of floating ice and total lake area change rates. They found that when this index is less than 0.5 and the lake area is larger than 3 km2, the outburst will occur in the next 5–8 days. It is still a rough calculation. This study also calculates the proportion of ice and water before the outburst. As depicted in Figure 6, W-I shows regular fluctuations. Especially in the early warning stage, we found that the W-I showed an obvious decreasing trend, which indicates that more and more ice arose over the lake's surface. The ice and water areas are nearly the same size one day before the outburst. From the view of the satellite image, the lake area was almost dominated by ice. With the channel opening, water storage in the lake begins to flow downstream, and the floating ice over the lake's surface moves to the dam. Therefore, we can also speculate on the status of the lake according to the proportion of floating ice. Historical images from 2014 to 2021 can also help verify the assumption. According to the accurate records of outburst dates in 2020 and 2021, only two or four days are left before the outburst. As is shown in Figure 3, the floating ice almost bestrews the lake's surface in 2020 and 2021. It is speculated that the corresponding date of images in 2018 and 2019 are also near the outburst date. On the other hand, field-based EWS components may improve the timeliness and accuracy of early warning greatly.

The uncertainty and limitations in this research can be summarized below. As mentioned before, this is the first research to fully record the whole outburst process using

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high-resolution images. However, it is a pity that we failed to obtain an effective image on 19 July 2022 because of the cloudy weather. It is on this day the outburst occurred. Synthetic aperture radar (SAR) is not vulnerable to weather conditions and can be applied as an effective reinforcement [62]. Even though we frequently monitor the lake area, hydrographic station data are significant for early warning. As the lake water flows out, the downstream hydrographic station would also witness the process of rapid increase followed by a rapid decrease of water [17]. Therefore, combining hydrographic station records or other field-based EWS components and dense satellite images can achieve accurate surveillance.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 5. Conclusions
Type: text

This study demonstrates the potential of using dense Chinese high-resolution satellite images to monitor glacier lake outburst flood (GLOF) and release early warning. Based on the dense high-resolution satellite images, this study monitored the whole process of the Lake Merzbacher GLOF in 2022 and further analyzed historical GLOF events in Lake Merzbacher. Our results show that the lake area would expand slowly (0.01 km2/d) and then rapidly expand  $(0.04 \text{ km}^2/\text{d})$  until it reaches the maximum lake area. After entering the early warning stage, which would last five days, the lake area would decrease slowly until the outburst. Further analysis shows that the GLOF is not far away when the floating ice area accounts for 50% or larger than 50% of the total lake area. The Lake Merzbacher GLOF is closely related to the temperature, and the outburst mainly occurred in the hottest months (July and October), especially in the last ten years. With the average temperature increase under climate change, Lake Merzbacher outbursted earlier and earlier (6 days earlier per decade since 1902). Our study provides essential references for monitoring Lake Merzbacher and establishing the early warning system (EWS). We also suggest that adding some field observation equipment, including a meteorological station and a hydrologic station, can improve the timeliness and accuracy of early warning.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs15071941/s1, Figure S1: Changes in the lake area were observed at a regional scale; Figure S2: Changes in the connection between the upper and lower parts of the lake.

**Author Contributions:** Conceptualization, C.G. and M.L.; methodology, C.G.; software, C.G.; validation, M.L., K.H. and P.W.; formal analysis, C.G.; investigation, C.G.; resources, S.L.; data curation, C.G.; writing—original draft preparation, C.G.; writing—review and editing, S.L.; visualization, K.H.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2019YFE0127700; the National Key Research and Development Program of China, grant number 2021YFB3901200; and the China High-Resolution Earth Observation System, grant number 03-Y30F03-9001-20/22.

**Data Availability Statement:** Data used in this study will be available upon request from the first author.

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


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Section: Abstract
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Glacial Lake Outburst Floods (GLOFs) have inflicted varying degrees of damage on the ecological environment, infrastructure, and human life in the alpine regions. Consequently, effectively predicting GLOFs has emerged as a critical research focus for disaster prevention and mitigation. This study focuses on the southern Tibetan Plateau and systematically examines the distribution characteristics of glaciers and glacial lakes, which are key contributors to GLOFs, and reviews historical GLOF events and their developmental patterns using remote sensing imagery, geographic information systems (GIS) and machine learning techniques. Based on historical GLOF data, a susceptibility evaluation system was developed by integrating Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms. This system quantitatively assesses the susceptibility of each glacial lake within the study area. The findings indicate that the majority of historical GLOF events occurred between June and September, with triggering factors including icefalls, moraine dam destabilization, and heavy precipitation. Climate warming, particularly during the 1960s and from 1990 to 2020, has significantly influenced the frequency of GLOF events, revealing substantial spatial heterogeneity, glacial dependence, and climate sensitivity of GLOFs. The evaluation results show that approximately 17.4% of the glacial lakes are situated in high and very high susceptibility classes, with the highest susceptibility observed in the central and eastern sub-regions. The MLP model demonstrated slightly higher accuracy than the SVM model, with AUC (Area Under the Receiver Operating Characteristic Curve) values of 0.96 and 0.90, respectively. This study offers a novel methodology and technical support for assessing the risk of glacial lake outbursts in the Tibetan Plateau and similar alpine mountain regions, providing a scientific basis for the development of disaster prevention and mitigation strategies.

**Keywords** Glacial lake outburst flood · Spatial pattern · Susceptibility evaluation · Machine learning method · Tibetan plateau

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Extended author information available on the last page of the article


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Glacial Lake Outburst Floods (GLOFs) are catastrophic natural hazards caused by the sudden release of water from glacial lakes, often resulting in large-scale floods or debris flows. These events pose significant threats to downstream ecosystems, infrastructure, economic development, and human life (Ahmed et al. 2021; Chen et al. 2024). The Tibetan Plateau is one of the world's most densely populated regions with glaciers and glacial lakes, especially in its southern part. The complex geological structures and extreme climatic conditions in this area contribute to a high frequency of GLOFs (Harrison et al. 2018; Liu et al. 2014). In the context of global warming, the Tibetan Plateau and other alpine mountain regions have experienced severe GLOFs that have caused extensive damage to infrastructure and the ecological environment (Tang et al. 2023). Notable historical events include the 1954 Sangwang Glacial Lake outburst in Kangma County, Tibet, which resulted in approximately 400 fatalities and affected over 20,000 people downstream (Liu et al. 2019); the 1988 Guangxie Glacial Lake outburst in Bomi County, destroying 51 houses, causing five deaths and the closure of the Sichuan-Tibet Highway for over 200 days (Liu et al. 2019); the 2016 Gongbashatong Glacial Lake outburst in Nyalamu County, which flooded Zhangmu Port and caused extensive damage to infrastructure in downstream areas of Nepal, including the towns of Kodari and Tatopani; and the 2020 Jinweng Glacial Lake outburst in Nagchu City, causing significant damage to water conservancy and transportation infrastructure downstream (Yang et al. 2022). In recent years, as major engineering projects expand into alpine regions of the Tibetan Plateau, the potential threats posed by GLOFs to infrastructure, as well as their mutual feedback effects, have become increasingly prominent. Therefore, accurately predicting the likelihood of future GLOFs is crucial for developing effective disaster prevention and mitigation strategies.


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Gongbashatong Glacial Lake outburst in Nyalamu County , which flooded Zhangmu Port and caused extensive damage to infrastructure in downstream areas of Nepal , including the towns of Kodari and Tatopani ; and the 2020 Jinweng Glacial Lake outburst in Nagchu City , causing significant damage to water conservancy and transportation infrastructure downstream ( Yang et al . 2022 ) . In recent years , as major engineering projects expand into alpine regions of the Tibetan Plateau , the potential threats posed by GLOFs to infrastructure , as well as their mutual feedback effects , have become increasingly prominent . Therefore , accurately predicting the likelihood of future GLOFs is crucial for developing effective disaster prevention and mitigation strategies .

The formation of GLOFs is influenced by multiple factors, including glacier retreat, the morphological characteristics of glacial lakes, climate change, dynamic processes of glacier-glacial lake interactions, and geotectonic activities (Cook et al. 2018; Mou et al. 2024; Pandey et al. 2022). The interplay of these factors increases the complexity and uncertainty associated with predicting GLOFs (Shi et al. 2024; Wang et al. 2025; Westoby et al. 2014; Worni et al. 2014). Wangchuk et al. (2022) utilized Sentinel-1 SAR radar backscatter intensity data to monitor changes in glacier and lake areas and analyze the susceptibility of GLOFs. Aggarwal et al. (2016) utilized multispectral remote sensing imagery in conjunction with parameters such as lake area, distance to the parent glacier, slope, and distance from the basin outlet to identify 18 lakes with potential outburst risk. Emmer and Vilímek (2014) used decision trees and numerical calculations based on remote sensing imagery and digital terrain models to assess the susceptibility of GLOFs, providing effective monitoring tools for different GLOF scenarios. However, spatial and temporal prediction of GLOFs remains a significant challenge, particularly in the Tibetan Plateau region, characterized by high altitudes, variable climate, and complex topography (Zhang et al. 2021a; Zheng et al. 2021). Current approaches to evaluating GLOF susceptibility can be broadly categorized into statistical models and physical models. Statistical models focus on predicting the likelihood of future GLOFs by quantifying the relationships between historical events and contributing factors through statistical analysis (Chen et al. 2024; Das et al. 2024). Physical models rely on detailed topographic, hydrological, and glacial lake parameter data, employing numerical simulations to estimate the potential extent and magnitude of outburst


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floods with a mechanistic understanding of the processes involved (Zhang et al. 2024). These traditional risk assessment methods are often insufficient for addressing this complex environment and handling numerous evaluation units (Ahmed 2024; Niggli et al. 2024). Analyzing historical outburst events is crucial for studying GLOFs. Through the analysis of historical GLOF events, potential driving factors of glacial lake outbursts can be identified, providing valuable insights for future risk assessment and management (Ahmed 2024; Taylor et al. 2023). Moreover, data-driven, machine learning-based methods have emerged as powerful tools in geohazard prediction and have been widely applied to predict the susceptibility of landslides, avalanches, and debris flow (Chen et al. 2020; Viallon-Galinier et al. 2023; Wen et al. 2022; Zhou et al. 2024). These methods can extract hazard-related features from complex data and construct prediction models that incorporate multi-dimensional and multi-factor considerations. Compared to traditional statistical models, machine learning techniques offer significant advantages in handling large-scale data and capturing nonlinear relationships (Wen et al. 2022). Therefore, machine learning methods hold great potential for enhancing risk prediction of GLOFs.

The aim of this paper is to quantitatively predict the susceptibility of GLOFs in the southern Tibetan Plateau by integrating historical GLOF events with machine learning methods. First, we systematically analyzed the development patterns of glaciers and glacial lakes in the study area and compiled historical records of GLOFs to examine their spatial and temporal patterns. Subsequently, a system of conditioning factors was constructed, and machine learning algorithms were applied to learn from historical events and develop a predictive model for the outburst susceptibility of each glacial lake in the region. The model's prediction accuracy and the relative importance of each conditioning factor were assessed. Finally, the paper discusses the response of GLOFs to climate change, identifies highly susceptible glacial lakes, and explores their dominant control factors. The results provide methodological support for predicting GLOF risks in similar regions and offer a theoretical basis for the prevention and management of GLOF disasters in the southern Tibetan Plateau.


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The study area is situated on the southern Tibetan Plateau, encompassing the Himalayas and portions of the Hengduan Mountains, spanning a total area of 674,000 km². Based on geographical features, the study area is subdivided into western, central, and eastern subzones (Fig. 1a). The topography of the Himalayas varies significantly from north to south: the northern region consists of a high plateau and wide valley basins, with altitudes ranging from 4500 to 5200 m, while the southern part descends abruptly to below 3000 m, showcasing dramatic landscapes of deep valleys and towering mountains. The southern slopes of the Himalayas are lower in elevation, with the lowest point reaching no more than 500 m near the Ganges Plain. The southern Tibetan Plateau is primarily influenced by the Indian monsoon, the southern branch of the westerly circulation, and local climatic factors (Wen et al. 2024). Each year, around late February or early March, the Indian Ocean monsoon begins to impact the study area(Sun et al. 2022). The towering Himalayas block the warm, humid air brought by the monsoon, leading to substantial precipitation and snowfall on the southern side (Xu et al. 2022). In contrast, the southern branches of the westerly wind belt dominate the winter climate, which is relatively dry, with limited rain and snowfall. Most


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Image /page/3/Figure/2 description: A multi-panel figure illustrating a study on glacial lakes in the Tibetan Plateau. The figure is composed of four panels labeled a, b, c, and d. Panel a is a large map of the Tibetan Plateau and surrounding areas, spanning from 75°E to 95°E longitude and approximately 27°N to 35°N latitude. The map shows the locations of glaciers (in light blue), glacial lakes (in pink), and historical Glacial Lake Outburst Floods (GLOFs, marked with purple circles). The study area is divided into Western, Middle, and Eastern subzones. Major cities like New Delhi, Kathmandu, Thimphu, and Lasa are labeled. Panel b is a Landsat 8 satellite image from 2020-05-10 of a glacial lake named Jinwengcuo. The lake is shown in dark blue, surrounded by reddish and cyan terrain. Panel c is another Landsat 8 image of the same lake, Jinwengcuo, from 2020-11-18, showing a change in the lake's appearance and surrounding snow/ice cover. Panel d is a photograph dated 2019-07-13, showing a person in a blue jacket standing on the shore of a lake next to a monitoring station. A small boat or buoy is visible on the water in the background.


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Fig. 1 Glacial Lake inventory map and location of the study area. (a) Glacial Lake inventory map; (b) & (c) Remote sensing images before and after Jinwucuo outburst; (d) Field survey photo of glacial lake

of the winter snow is driven by oscillations in the north-south branch flow or by the intrusion of the strong Siberian cold front (Sun et al. 2022). Locally, the climate is influenced by the foehn wind effect, where the windward slopes of the southern Himalayas receive heavy precipitation, while the leeward side forms hot, dry valleys. This creates a stark contrast in climate between the northern and southern sides of the Himalayas. For example, the northern part of Gyirong County has a semi-arid plateau valley monsoon climate, characterized by an annual mean temperature of 2°C, with the warmest month averaging 10–18 °C and the coldest month averaging -10 °C. Annual precipitation in this region is approximately 300–600 mm. In contrast, the southern part of Jilong County experiences a subtropical mountain monsoon climate, with an average annual temperature of 10–13 °C, the warmest month exceeding 18 °C, and annual precipitation of around 1000 mm.


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Section: 3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events
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The glacial lake data utilized in this study are sourced from the Cataloging Data Set of High Asian Ice Lakes (Wang et al. 2020). This dataset integrates glacier cataloging data and 668 Landsat series images, processed using ArcGIS and ENVI software, with boundaries extracted through manual visual interpretation. The percentage difference between the absolute lake area and GPS-measured boundaries ranges from 5.5 to 25.5%. The dataset covers two time periods: 1990 and 2018. In 1990, the study area contained 9093 glacial lakes covering a total area of 711.2 km², while in 2018, the number of glacial lakes


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increased to 10,173, with a total area of 819.3 km2. The 2018 glacial lakes are illustrated in Fig. 1a. Additionally, the glacier data were derived from the best integrated quality product generated through data fusion in the Assessment of Glacier Inventories for the Third Pole Region (1990–2015) (Xia and Shiqiao 2022). This dataset employs hierarchical analysis to filter eight contributing factors and evaluates the comprehensive quality of eight existing glacier cataloging datasets for the Third Pole Region by classifying them. A new glacier catalog was created by fusing the highest-quality data within each evaluation unit (He and Zhou 2022). The eight glacier datasets used are: Randolph Glacier Inventory (RGI) updated Glacier Area Mapping for Discharge in Asian Mountains (GAMDAM), Second Chinese Glacier Inventory (CGI-2), Hindu Kush-Himalayan Glacier Inventory (HKHGI), Western Himalayas Glacier Inventory (WHGI), Karakoram-Pamir Glacier Inventory (KPGI), Second Pakistan Glacier Inventory (PGI-2), and Southeast Tibetan Plateau Glacier Inventory (SETPGI). Historical GLOFs are primarily reviewed through the literature, focusing on the timing and localization of each event, as shown in Fig. 1a. To ensure the accuracy and reliability of the reported events, all GLOF occurrences since 1986 were cross-validated using historical remote sensing imagery, primarily from Landsat series, to verify their timing, spatial extent, and geomorphic signatures (Fig. 1b&c). Furthermore, ground-truthing was carried out at several key sites. Since 2017, we have performed multiple field surveys focusing on glacial lakes with documented historical GLOF events. During these surveys, UAV (Unmanned Aerial Vehicle) were used to identify potential triggering factors, such as ice and rock avalanches, around these lakes. Additionally, unmanned boats equipped with multibeam sonar systems were employed to map the lakebed topography, providing precise measurements of the glacial lake reservoirs (Fig. 1d). The information on these historical GLOF events is detailed in Table 1. On the basis of these data, statistical analyses were conducted to examine the elevation, flow direction, and slope of glaciers, as well as the elevation, area, type, and expansion trend of glacial lakes. Additionally, temporal and spatial clustering analyses of historical GLOF events were performed using GIS tools.


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Sample selection plays a critical role in evaluating the GLOF susceptibility, as it directly influences the model's training effectiveness and prediction accuracy. In this study, 66 documented GLOF events within the study area were used as positive samples, excluding the Lumu Lake outburst due to unavailable localization data. An equal number of glacial lakes are randomly selected as negative samples using GIS tools to ensure an appropriate balance between positive and negative samples. Evaluation units are defined as the watersheds of individual glacial lakes within the study area. The conditioning factors must be representative, measurable, and based on readily available data. In this study, eight conditioning factors were selected: Glacial Lake Area, Glacial Lake Replenishment Area, Distance to Glacier, Elevation, Elevation Coefficient of Variation (ECV), Topographic Wetness Index (TWI), Peak Ground Acceleration (PGA), and Distance to Potential Icefall (Fig. 2). Among them, the Glacial Lake Area reflects the storage capacity of the glacial lake. The areas of glacial lakes used as a learning sample are reconstructed as comprehensively as possible using remote sensing imagery and geomorphologic evidence. Glacial Lake Replenishment Area determines the lake's ability to receive meltwater and precipitation. Distance to Glacier characterizes the extent of direct glacial activity impact. Those three factors can be


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calculated and obtained using GIS. Additionally, elevation directly influences the local climate around the glacial lake, hydrological processes, and the extent and intensity of flood impacts following an outburst. Elevation data are extracted from the ASTER GDEM with 30 m resolution (https://www.gscloud.cn/). The ECV reflects the degree of terrain relief and the magnitude of elevation change within the region. The TWI is a physical indicator that captures the influence of regional topography on runoff flow direction and water accumulation. Both the ECV and TWI are derived from GIS calculations. Strong earthquakes can trigger landslides, avalanches, or ground deformation in nearby mountainous regions, leading to significant changes in glacial lake levels or even catastrophic outbursts. The seismic factor is quantitatively represented by the PGA, with data sourced from the Global Earthquake Model Global Seismic Hazard Map (Johnson et al. 2023). Icefalls are among the primary natural triggers for GLOFs. In this study, the distance to potential icefall is used to quantitatively assess their impact on GLOFs. The potential icefall data are derived from the research team's previous findings. Relative to the evaluation factors in existing studies, this study places particular emphasis on ECV and TWI to better characterize the impacts of heterogeneous mountainous terrain. These two topographic variables—ECV and TWI—are critical for understanding water routing and flood dynamics, especially within the complex geomorphological settings of the southeastern Tibetan Plateau. Furthermore, we introduce 'Distance to potential icefall' as a novel factor, informed by recent findings on icefall-triggered GLOFs in the Himalaya.

After preparing the conditioning factors, descriptive statistics should be conducted on the values of all positive samples, along with an analysis of their degree of dispersion, to evaluate their effectiveness in differentiating GLOF susceptibility. Specifically, measures such as mean, standard deviation, and coefficient of variation will be used to quantify the variability and discriminatory power of each factor (Wen et al. 2022). A higher coefficient of variation indicates greater dispersion of the factor values, which, to some extent, suggests that the factor has weaker discriminatory power in evaluating GLOF susceptibility. In addition, the combination of conditioning factors must undergo a multicollinearity test. Multicollinearity refers to the reduced objectivity and accuracy of results caused by high correlations between explanatory variables in the model (Thompson et al. 2017). To assess the correlation between factors, the variance inflation factor (VIF) is used. The formula for calculating the VIF index is as follows (Thompson et al. 2017):

$$VIF = \frac{1}{1 - R_i^2} \tag{1}$$

where,  $R_j^2$  is the multiple linear regression coefficient. The VIF indicates the severity of multicollinearity. A VIF value closer to 1 signifies weaker multicollinearity, while a higher VIF value indicates stronger multicollinearity. Typically, a VIF value of 10 is used as the threshold for assessing multicollinearity. When VIF<10, there is no significant multicollinearity, which meets the requirement for model stability.


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is used . The formula for calculating the VIF index is as follows ( Thompson et al . 2017 ) : $ $ VIF = \ frac { 1 } { 1 - R_i ^ 2 } \ tag { 1 } $ $ where , $ R_j ^ 2 $ is the multiple linear regression coefficient . The VIF indicates the severity of multicollinearity . A VIF value closer to 1 signifies weaker multicollinearity , while a higher VIF value indicates stronger multicollinearity . Typically , a VIF value of 10 is used as the threshold for assessing multicollinearity . When VIF < 10 , there is no significant multicollinearity , which meets the requirement for model stability .

To further evaluate the relative importance of each conditioning factor and enhance model interpretability, a feature importance analysis is performed in the Results section. This analysis elucidates which factors dominate susceptibility modeling and how they interact with others to contribute to GLOF occurrence, thereby improving the transparency and physical interpretability of the model outputs. Additionally, the Discussion section provides


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Table

|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |
|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|
|                     |                              |                     |                 |                                     |               |                             |                                          | Data set Acquisi-             |                       |
| _                   | Lumu Lake                    | N/A                 | N/A             | 1931/06/08 (Tibetan calendar) | 72            | Icefall                     | (Yao et al. 2014)                        |                               |                       |
| 7                   | Taraco                       | 86.13°              | 28.29°          | 1935/08/28                          |               | Moraine dam destabilization | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 3                   | Qiongbixiamaco               | 88.92°              | 27.85°          | 1940/07/10                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 4                   | Sangwangco                   | 90.01°              | 28.24°          | 1954/07/16                          | 400           | Icefall                     | (Liu et al. 2019)                        |                               |                       |
| 2                   | Lureco                       | 90.59°              | 28.27°          | $\sim\!1950s$                       |               | Icefall                     | (Yao et al. 2014)                        |                               |                       |
| 9                   | Dacangco                     | 86.35°              | 28.19°          | 1956/06/01                          |               | Heavy rainfall              | (Tong et al. 2019)                       |                               |                       |
| _                   | Cuoalong Glacial lake     | 90.59°              | 28.05°          | ~1956–1966                          |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |
| ∞                   | Cirenmaco                    | .90 . 98 | 28.07°          | 1964/07                             | 200           | Icefall                     | (Veh et al. 2020)                        |                               |                       |
| 6                   |                              | .90 . 98 | 28.07°          | 1981/07/11                          |               | Icefall                     | (Veh et al. 2020; Xu 1988)               |                               |                       |
| 10                  |                              | .90 . 98 | $28.07^{\circ}$ | 1983                                |               | Icefall                     | (Wang et al. 2018)                       |                               |                       |
| 11                  | Longdaco                     | 85.35°              | $28.62^{\circ}$ | 1964/08/25                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 12                  | Jilaico (Gehaipuco)       | 87.81°              | 27.96°          | 1964/09/21                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 13                  | Damenlahaico                 | 93.04°              | 29.87°          | 1964/09/26                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 4                   | Jichudrake North 1           | 89.29°              | 27.87°          | $\sim\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |
| 15                  | Upper Jiejiu Tsho            | 90.72°              | 28.27°          | $\sim\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |
| 16                  | Upper Shegong Tsho        | 90.74°              | 28.30°          | $\sim\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |
| 17                  | 17 Jichudrake North 2 89.31° | 89.31°              | 27.87°          | $\sim\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al.        |                               |                       |
| Table 1 (continued) |                              |                     |                 |                                     |               |                             |                                          |                               |                       |
| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing |                       |
|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |
| 18                  | Ayaco                        | 86.49°              | 28.35°          | 1968/08/15                          |               | Moraine dam destabilization | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |
| 19                  |                              | 86.49°              | 28.35°          | 1969/08/17                          |               | Moraine dam destabilization | (Yao et al. 2014)                        |                               |                       |
| 20                  |                              | 86.49°              | 28.35°          | 1970/08/15                          |               | Moraine dam destabilization | (Yao et al. 2014)                        |                               |                       |
| 21                  | Zhari                        | 90.61°              | 28.30°          | 1981/06/24                          |               | Icefall                     | (Yao et al. 2014)                        |                               |                       |
| 22                  | Yindapuco (Jinco)            | 87.91°              | 27.95°          | 1982/08/27                          |               | Moraine dam destabilization | (Reynolds 2023)                          |                               |                       |
| 23                  | Dig Tsho                     | 86.58               | 27.87           | 1985/08/4                           |               | Icefall                     | (Cenderelli and Wohl 2001)               |                               |                       |
| 24                  |                              |                     |                 | 2015/04/25                          |               | Earthquake                  | (Byers et al. 2017)                      | Landsat-8                     | 2015/05/25            |
| 25                  | Sanga North                  | 93.88°              | 30.12°          | ~1976–1987                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1988/10/09            |
| 26                  | Guangxieco                   | 96.50°              | 29.46°          | 1988/07/15                          | 5             | Icefall                     | (Liu et al. 2019)                        | Landsat-5                     | 1988/10/27            |
| 27                  | Gebumaco Upper               | 96.54°              | 29.75°          | 1991/06/12                          |               | Icefall                     | (Nie et al. 2018)                        | Landsat-5                     | 1993/10/09            |
| 28                  | Gebumaco                     | 96.55°              | 29.75°          | 1991/06/12                          |               | Moraine dam destabilization | (Wang et al. 2011)                       | Landsat-5                     | 1993/10/09            |
| 29                  | Chubung                      | 86.46°              | 27.87°          | 1991/07/12                          |               | Landslide                   | (Reynolds John 1999; Veh et al. 2020)    | Landsat-5                     | 1991/11/15            |
| 30                  | Upper Luggye                 | 90.32°              | 28.08°          | 1991/09/30                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1991/09/30            |
| 31                  |                              |                     |                 | 2010/10/04                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 2010/10/04            |

ID Name
Longitude
Latitude
Date
Death toll
Trigger
Reference
Verified by remote sensing


Document: ORIGINAL PAPER
Section: 3 Data and methodology > 3.2 Learning samples and conditioning factors
Type: table
Table

|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |
|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|
| 32                  | Baitang Weat                 | 92.78°              | 29.55°          | 1991/10/09                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1991/10/09            |
| 33                  | Langbuco Upper               | 86.42°              | 27.92°          | 1992/08                             |               | Icefall                     | (Nie et al. 2018)                        | Landsat-4                     | 1992/09/22            |
| 34                  | Rejieco                      | 88.90°              | 27.97°          | 1992/09/10                          |               | Icefall                     | (Zhang et al. 2021b)                     | Landsat-5                     | 1992/11/10            |
| 35                  | Zangla Tsho                  | 82.11°              | 30.35°          | 1994/08                             |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1994/09/09            |
| 36                  | Luggye Tsho                  | 90.30°              | 28.09°          | 1994/10/07                          |               | Moraine dam destabilization | (Veh et al. 2020; Wang et al. 2012)      | Landsat-5                     | 1994/11/09            |
| 37                  | Xiaga                        | 91.94°              | 28.80°          | 1995/05/26                          |               | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 1995/09/02            |
| 38                  | Zhanabo                      | 85.37°              | 28.66°          | 1995/06/07                          |               | Moraine dam destabilization | (Yao et al. 2014)                        | Landsat-5                     | 1995/11/01            |
| 39                  | Zanglaco West                | 81.99°              | 30.37°          | 1995/09                             |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1995/10/30            |
| 40                  | Kongyangmi La Tsho        | 88.78°              | 27.90°          | 1996/05/13                          |               | Icefall                     | (Nie et al. 2018)                        | Landsat-5                     | 1996/05/13            |
| 41                  | Zhalonggabu                  | 85.48°              | 28.66°          | ~1996                               |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1996/10/18            |
| 42                  | Unnamed Lake                 | 92.38°              | 27.70°          | 1997/10/18                          |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1997/10/18            |
| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing |                       |
|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |
| 43                  | Tam Pokhari                  | 86.84°              | 27.74°          | 1998/09/03                          |               | Icefall                     | (Lamsal et al. 2016; Veh et al. 2020)    | Landsat-5                     | 1998/09/15            |
| 44                  | Upper Gangri Tsho1        | 90.81°              | 27.88°          | 1998/09/10                          |               | Icefall                     | (Komori et al. 2012)                     | Landsat-5                     | 1998/09/10            |
| 45                  | Upper Gangri Tsho2        | 90.81°              | 27.89°          | 1998/09/10                          |               | Upstream inflow             | (Zhang et al. 2021b)                     | Landsat-5                     | 1998/09/10            |
| 46                  | Dareco South                 | 85.92°              | 28.13°          | ~2001–2003                          |               | Icefall                     | (Liu et al. 2019)                        | Landsat-5                     | 2003/12/25            |
| 47                  | Longjiuco                    | 89.74°              | 28.21°          | ~2002–2003                          |               | Heavy rainfall              | (Zhang et al. 2021b)                     | Landsat-5                     | 2003/11/18            |
| 48                  | Jialongco                    | 85.85°              | 28.21°          | 2002/05/23                          |               | Icefall                     | (Chen et al. 2006)                       | Landsat-5                     | 2004/05/17            |
| 49                  | Jialongco                    | 85.85°              | 28.21°          | 2002/06/29                          |               | Icefall                     | (Chen et al. 2006)                       | Landsat-5                     | 2004/05/17            |
| 50                  | Degaco                       | 90.67°              | 28.33°          | 2002/09/18                          |               | Snow avalanche              | (Liu et al. 2013)                        | Landsat-5                     | 2003/11/18            |
| 51                  | Jitang Sourth                | 94.32°              | 30.66°          | 2002–2003                           | 9             | N/A                         | (Liu et al. 2019)                        | Landsat-5                     | 2003/10/19            |
| 52                  | Ouguchongguco Upper       | 93.54°              | 29.63°          | 2004/10/21                          |               | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 2004/10/21            |
| 53                  | Dagonglongba Lake         | 96.46°              | 29.75°          | ~2007–2008                          |               | Moraine dam destabilization | (Zhang et al. 2021b)                     | Landsat-5                     | 2008/06/28            |
| 54                  | Sangwangco East              | 90.22°              | 28.27°          | 2008/10/31                          |               | Moraine dam destabilization | (Liu et al. 2019)                        | Landsat-5                     | 2008/12/01            |
| 55                  | Langco                       | 91.81°              | 27.83°          | 2007/08/10                          |               | Heavy rainfall              | (Yao et al. 2014)                        | Landsat-5                     | 2008/05/16            |
| 56                  | Zhemaico                     | 92.34°              | 28.01°          | 2009/07/03                          |               | Moraine dam destabilization | (Liu et al. 2013)                        | Landsat-5                     | 2009/10/10            |
| 57                  | Cuoga                        | 94.00°              | 30.83°          | 2009/07/29                          | 2             | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 2009/09/17            |
| 58                  | Geiqu Lake                   | 87.99°              | 27.95°          | 2010/07                             |               | Heavy rainfall              | (Yao et al. 2014)                        | Landsat-5                     | 2010/11/12            |
| 59                  | Nalongzangbu Lake         | 94.95°              | 30.53°          | 2014/06/30                          |               | Icefall                     | (Zhang et al. 2021b)                     | Landsat-8                     | 2014/08/23            |
| 60                  | Ranzeranco (Ranzeriaco)   | 93.53°              | 30.47°          | 2013/07/05                          |               | Moraine dam destabilization | (Wang et al. 2021)                       | Landsat-8                     | 2013/09/12            |
| 61                  | Lemthang Tsho                | 89.58°              | 28.06°          | 2015/06/28                          |               | Landslide                   | (Gurung et al. 2017; Veh et al. 2020)    | Landsat-8                     | 2015/10/18            |
| 62                  | Yindapuco Upper              | 87.88°              | 27.93°          | 2015/10/31                          |               | N/A                         | (Reynolds 2023)                          | Landsat-8                     | 2015/11/10            |
| 63                  | Zangbuco East                | 82.11°              | 30.16°          | ~2016                               |               | N/A                         | (Liu et al. 2019)                        | Landsat-8                     | 2016/07/10            |
| Table 1 (continued) |                              |                     |                 |                                     |               |                             |                                          |                               |                       |


Document: ORIGINAL PAPER
Section: 3 Data and methodology > 3.2 Learning samples and conditioning factors
Type: table
Table

|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |
|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|
| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |
|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |
| 64                  | Gongbatongshaco              | 86.05°              | 28.07°          | 2016/07/05                          |               | Icefall                     | (Cook et al. 2018)                       | Landsat-8                     | 2016/07/14            |
| 65                  | Langmale Lake                | 87.14°              | 27.81°          | 2017/04/20                          |               | Rock avalanche              | (Byers et al. 2019)                      | Landsat-8                     | 2017/11/06            |
| 66                  | Jinwengco                    | 93.63°              | 30.35°          | 2020/06/26                          |               | Landslide                   | (Yang et al. 2022)                       | Landsat-8                     | 2020/08/30            |


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Table 1 (continued)


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Image /page/10/Figure/2 description: A figure from a scientific paper titled 'Fig. 2 GLOF conditioning factors', displaying eight different maps (labeled a through h) of a mountainous region. The figure includes a scale bar from 0 to 500 km and a north arrow. The maps are as follows:


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a. 'Glacial lake area': Shows the distribution of glacial lakes, represented by small pink dots within a dashed outline of the region.

b. 'Glacial lake replenishment area (m²)': A map colored mostly dark blue, with a color scale ranging from 0 to 2.88303e+10.

c. 'Glaciers': Displays the location of glaciers in light blue.

d. 'Elevation (m)': A topographic map with a color scale from green (low elevation) to brown (high elevation), ranging from 0 to 8848 meters.

e. 'Elevation variation coefficient': A map with colors from blue to yellow, with a scale from 0 to 1.628.

f. 'Topographic wetness index': A map primarily in shades of green and yellow, with a scale from 1.96 to 26.5.

g. 'Peak ground acceleration (m/s²)': A map with a color gradient from blue to yellow and orange, with a scale from 0.095 to 0.589.

h. 'Potential icefall': Shows the location of potential icefall areas as black dots and a labeled pink shape within the region's outline.

Fig. 2 GLOF conditioning factors


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a comparison of our selected factors with those in previous studies, emphasizing both commonalities and region-specific innovations.


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SVM is a machine learning algorithm primarily employed for classification and regression tasks. The primary objective of SVM is to identify an optimal hyperplane that effectively separates data points of different classes in an N-dimensional feature space, where N denotes the number of features (Yousefi et al. 2020). Simultaneously, SVM aims to maximize the margin, defined as the distance between the hyperplane and the nearest data points from each class. Mathematically, a hyperplane is characterized by its normal vector w and offset b, and can be expressed as:

$$f(x) = w^T x + b (2)$$

where, *x* is the feature vector, serving as the condition factor in the susceptibility evaluation. SVMs can address nonlinear classification problems by employing a kernel function. The kernel function maps the data into a higher-dimensional space, enabling the identification of a linear hyperplane that separates the data. Commonly used kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. In this context, the kernel function computes the similarity between each sample in the high-dimensional space and the support vectors, which can be interpreted as weights. By analyzing the weights assigned to each feature, we can evaluate the relative importance that the model attributes to different condition factors.

SVM was selected in this study due to its effectiveness in handling high-dimensional and small-sample datasets, which are characteristic of the compiled GLOF inventory. SVM often demonstrates higher generalization capacity in sparse datasets and requires fewer hyperparameters to tune (Ragab et al. 2021). Additionally, SVM is less prone to overfitting when appropriately regularized (Yousefi et al. 2020), which is critical given the imbalanced nature of GLOF event distribution.

3 Data and methodology
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3.3.1 Support vector machine (SVM)


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MLP is a feed-forward artificial neural network model primarily employed for supervised learning tasks. In classification tasks, MLP transforms raw input features through multiple fully connected layers, and the predicted probability for each class is obtained using the Softmax function (Ramchoun et al. 2016). MLP can be conceptualized as a directed graph consisting of multiple layers of nodes, where each node, except those in the input layer, represents a neuron with a nonlinear activation function. The architecture of an MLP comprises three types of layers: the input layer, hidden layers, and output layer. The input layer receives the input feature vector, with each feature corresponding to an individual input neuron. The hidden layers, which follow the input layer, may consist of one or more layers. Neurons in the hidden layers process the outputs from the previous layer by applying weights and activation functions. The output layer generates the final prediction. During

3 Data and methodology
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training, the MLP adjusts its weights via back-propagation and gradient descent, thereby determining the relative importance of each condition factor.

The choice of MLP was motivated by its ability to model complex nonlinear interactions among conditioning factors, which is essential in capturing the multifactorial nature of GLOF susceptibility. MLP can be more effectively adapted to address data imbalance through architecture design and loss function adjustments (Castro and Braga 2013). Furthermore, integrating two fundamentally different model architectures (kernel-based SVM and network-based MLP) enables robust cross-validation and allows us to evaluate the consistency of spatial susceptibility patterns across distinct algorithmic paradigms.


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The Receiver Operating Characteristic (ROC) curve is a graphical representation that plots the true positive rate (sensitivity) on the Y-axis and the false positive rate (1-specificity) on the X-axis at various classification thresholds (Mandrekar 2010). This plot serves as a visual tool for evaluating model performance. Sensitivity and specificity reflect the model's ability to correctly identify positive and negative samples, respectively. However, these two metrics alone do not fully capture the overall accuracy of the model. Therefore, the Area Under the Curve (AUC) value is often used to comprehensively evaluate model performance. The AUC represents the area under the ROC curve and is a widely accepted metric for assessing the performance of binary classifiers. The AUC can be calculated using the following formula (Mandrekar 2010):

$$AUC = \frac{\sum_{i=1}^{n_0} r_i - n_0 (n_0 + 1) / 2}{n_0 n_1}$$
 (3)

where,  $n_0$  represents the number of negative samples,  $n_1$  denotes the number of positive samples, and  $r_i$  indicates the positional rank of the i-th negative sample within the entire test dataset. The AUC value ranges from 0 to 1, with values closer to 1 indicating higher prediction accuracy of the model.

Finally, the outburst susceptibility index of all glacial lakes was classified into five categories using the natural breakpoint method: very high susceptibility (VHS), high susceptibility (HS), medium susceptibility (MS), low susceptibility (LS), and very low susceptibility (VLS). Glacial lakes with VHS and HS were analyzed in detail, along with an examination of the significance of each conditioning factor. The complete methodological process of this study is illustrated in the Fig. 3 and can be summarized as follows: (1) Historical GLOF events and randomly selected non-outburst glacial lakes were used as learning samples, combined with multiple conditioning factors to construct a comprehensive dataset; (2) The dataset was randomly partitioned into training (80%) and validation subsets (20%); (3) Two machine learning algorithms—SVM and MLP—were trained using the training subset to develop susceptibility evaluation models for GLOFs; (4) The trained models were assessed through ROC curves, with their performance quantified by AUC values based on the validation subset; (5) The susceptibility index derived from the optimal models was classified using the natural breakpoint method to identify lakes prone to GLOF events. This systematic integration ensures that SVM and MLP effectively capture complementary patterns of

3 Data and methodology
3.3 GLOF modeling and model evaluation
3.3.3 Model evaluation