Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.2. Accuracy of our PDGLs identification
Type: text

Fig. 8. The framework of quantitative and qualitative evaluation of GLOF hazard, including data input, selection of assessment factors, weighting scheme, GLOF susceptibility, and risk assessment.

before. Taking these newly discovered and undated GLOF events into account was initially to increase the number of small-sized drained lakes to fully evaluate their outburst features, but this also brought challenges to our validation work because the expressed outburst features in the assessment factors were not obvious.

In addition, the differences in the final glacial lake hazard levels were compared between the present study and previous studies. Zheng et al. (2021b) assessed GLOF susceptibility using four factors, including lake volume (L3), watershed area (H7), likelihood of ice and/or rock avalanches into lakes (R1), and downstream slope of the lake dam (D3), and applied an equal-weighting scheme. A comparison of our results with those of Zheng et al. (2021b) shows that the consistency in high-and very high-hazard glacial lakes is relatively high, while there are large discrepancies in the medium- and low-level hazard glacial lakes. At our experimental site, the Sunkoshi River basin (B4), the consistency of the very high-hazard levels between the different studies reached 93% (Supplementary materials, Table 5S). This is at least less controversial for determining key potential hazard sources, which is our current focus.

In the present study, the best combination of assessment factors was determined via an optimality analysis, which can effectively guarantee the authenticity and reliability of GLOF susceptibility evaluation results. Additionally, some deficiencies in the selection criteria of assessment factors have been effectively solved. We emphasize the expression effect of assessment factors on the final GLOF susceptibility rather than the status of the factors themselves. We focused on small-sized glacial lakes that were previously neglected to some extent. As a result, 37% of the small-sized glacial lakes were classified as high- and very high-hazard, an observation that was overlooked in previous studies.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.3. Transboundary threats
Type: text

The transboundary threat of GLOFs in the Himalayan region has recently attracted increasing attention (Rounce et al., 2017; Allen et al., 2019; Washakh et al., 2019). GLOFs frequently occur at the border between China and Nepal. Some catastrophic outbursts may intensify GLOF impacts over the 40-km river channel and cause damage to communities and infrastructure, like the Cirenmaco GLOF in 1981 (Wang et al., 2018), the Jialongco GLOF in 2002 (Li et al., 2021), and the Gongbatongshaco GLOF in 2016 (Cook et al., 2018; Nie et al., 2018), all of which displayed transboundary impacts. Although the exposure of settlements and property to GLOFs was not estimated in the present study, the inherent risk is higher than previously reported. We introduced numerous small-sized lakes in the identified inventory that are new sources of outburst potential. Further research should include an exposure and vulnerability risk assessment to enable a detailed quantitative analysis of regional transboundary flows and similar situations. In fact, we need to devote efforts to prevent and mitigate the increasing threat of GLOFs. Since GLOFs have transboundary impacts, it is necessary to establish a regional cooperation mechanism for GLOF hazard adaptation and mitigation. An early warning system covering the basin from upstream to downstream would be practical in this regard.

5. Discussion
5.3. Transboundary threats


Document: GRAPHICAL ABSTRACT
Section: 6. Conclusion
Type: text

Here, we present an optimality analysis of assessment factors before a formal identification of PDGLs and determine the best combination of factors from 57 factors to identify potentially dangerous moraine-dammed lakes whose areas are larger than 0.02 km² and are mainly fed by contemporary glaciers in the Himalayas. It is noteworthy that as assessment factors become increasingly diverse, their effectiveness deserves further consideration. We identified 207 glacial lakes as very high-hazard lakes, 345 as high-hazard lakes, 405 as medium-hazard lakes, 426 as low-hazard lakes, and 267 as very low-hazard lakes, most of which are located in the eastern and central Himalayas. High potential outburst lakes generally have several common features,

including a parent glacier belonging to a hanging glacier or small valley glacier with a relatively small area and a glacier snout and lake surroundings that are steep. This study improves the scientific basis for the selection of assessment factors, which is a reference for future research on PDGL identification. Furthermore, an integrative and detailed inventory of PDGLs in the Himalayas can be used as benchmark data to respond to GLOF threats.


Document: GRAPHICAL ABSTRACT
Section: CRediT authorship contribution statement
Type: text

**Taigang Zhang:** Methodology, Data curation, Writing – original draft. **Weicai Wang:** Methodology, Supervision, Writing – review & editing. **Tanguang Gao:** Writing – review & editing. **Baosheng An:** Supervision, Writing – review & editing. **Tandong Yao:** Project administration, Funding acquisition.


Document: GRAPHICAL ABSTRACT
Section: Appendix A. Supplementary data
Type: text

Supplementary data to this article can be found online at https://www.editorialmanager.com/stoten/download.aspx?id=5076615&guid=f344d3f5-9171-46c4-9da0-37a3f700f7bd&scheme=1.


Document: Increasing risk of glacial lake outburst floods from future Third Pole deglaciation
Type: text

Warming on Earth's Third Pole is leading to rapid loss of ice and the formation and expansion of glacial lakes, posing a severe threat to downstream communities. Here we provide a holistic assessment of past evolution, present state and modelled future change of glacial lakes and related glacial lake outburst flood (GLOF) risk across the Third Pole. We show that the highest GLOF risk is at present centred in the eastern Himalaya, where the current risk level is at least twice that in adjacent regions. In the future, GLOF risk will potentially almost triple as a consequence of further lake development, and additional hotspots will emerge to the west, including within transboundary regions. With apparent increases in GLOF risk already anticipated by the mid-twenty-first century in some regions, the results highlight the urgent need for forward-looking, collaborative, long-term approaches to mitigate future impacts and enhance sustainable development across the Third Pole.

he Hindu Kush-Himalaya, Tibetan Plateau and surrounding areas are widely known as the Third Pole of the Earth as it is home to the largest number of glaciers outside the polar regions1. Widespread retreat of glaciers is taking place over most of its territory and has accelerated in recent decades as one of the consequences of global warming2-4. This glacier wasting is associated with the rapid expansion and new formation of glacial lakes5-8, bringing both large opportunities and risks9,10. Particularly, when water is suddenly released, glacial lake outburst floods (GLOFs)11 can devastate lives and livelihoods up to hundreds of kilometres downstream of their source12,13. This threat is most apparent in the Third Pole14,15 where the warming rates are distinctly higher than the Northern Hemisphere and global mean16, and numerous GLOFs have been recorded, originating from both moraine-dammed and ice-dammed glacial lakes15,17. While outbursts from ice-dammed glacial lakes have been concentrated in the Karakoram and Pamir regions18,19, outbursts from moraine-dammed glacial lakes are observed across the Third Pole, and most frequently along the main Himalayan arc20 where glacial lakes are increasing rapidly in both size and number5,6. The impact of a GLOF can extend across international boundaries21, creating severe challenges for early warning and other risk reduction strategies, particularly in critical transboundary areas22. Despite the severe threat that such large extreme events pose for sustainable mountain development over the Third Pole4, there remains a lack of understanding regarding how and where GLOF risk will evolve in the future. GLOF assessment over the Third Pole has typically been focused on moraine-dammed proglacial lakes owing to their potentially large flood volumes23,24, weak dam composition25 and clear link to climate change17. Outburst

data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md


Document: Increasing risk of glacial lake outburst floods from future Third Pole deglaciation
Type: text

particularly in critical transboundary areas  22  . Despite the severe threat that such large extreme events pose for sustainable mountain development over the Third Pole  4  , there remains a lack of understanding regarding how and where GLOF risk will evolve in the future . GLOF assessment over the Third Pole has typically been focused on moraine - dammed proglacial lakes owing to their potentially large flood volumes  23 , 24  , weak dam composition  25  and clear link to climate change  17  . Outburst

floods from moraine-dammed glacial lakes can be triggered by various mechanisms, including intense precipitation and snowmelt26,27, and most commonly, from the impact of ice and/or rock avalanches into a lake20,28. While robust long-term trends in GLOF frequency are not evident17,29, the GLOF threat is expected to increase in response to future warming as lakes expand towards steep and destabilizing mountain cliffs30,31. With the expansion of communities, tourism, hydropower, transportation and other crucial sectors into exposed areas, substantial risk reduction and adaptation strategies will be required to avoid increased impacts on sustainable development in vulnerable mountain regions. Focusing here on moraine-dammed proglacial lakes, we draw on a comprehensive inventory of glacial lakes and past GLOF events across the Third Pole, to model and evaluate how and where GLOF hazard and risk will change in response to future deglaciation, using a robust and unified evidence-based approach.


Document: Past evolution and present state of glacial lakes
Section: Past evolution and present state of glacial lakes
Type: text

To understand the present state of glacial lakes across the Third Pole and changes over the past decades, all glacial lakes  $\geq$ 0.01 km² were mapped based on archival Landsat satellite images from 1990 to 2015 (Methods). Our inventory reveals the existence of 26,633 glacial lakes in the Third Pole, with a total area of 1,968.8  $\pm$  1.2 km² and approximately one-third of them being dammed by moraines (in total 7,650 with a combined area of 535.1  $\pm$ 0.7 km²; 6,958 of these lakes were formed in proglacial areas or are in a transitional state). The dataset suggests a clear heterogeneity in their spatial distribution (Fig. 1 and Supplementary Table 1) as today's glacial lakes are principally distributed along the Hindu Kush–Himalaya–Hengduan

1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China. 2Climatic Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland. 3University of Chinese Academy of Sciences, Beijing, China. 4Department of Geography, University of Zurich, Zurich, Switzerland. 5China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad, Pakistan. 6Dendrolab.ch, Department of Earth Sciences, University of Geneva, Geneva, Switzerland. 7Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland. 8Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Birmensdorf, Switzerland. 9Department of Geosciences, University of Fribourg, Fribourg, Switzerland. 10Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China. 11CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China. 12Department of F.A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland. 13These authors contributed equally: Guoxiong Zheng, Simon Keith Allen. 188e-mail: baoam@ms.xjb.ac.cn; markus.stoffel@unige.ch


Document: Past evolution and present state of glacial lakes
Section: Past evolution and present state of glacial lakes
Type: figure
Figure

Image /page/1/Figure/1 description: A map of High Mountain Asia, showing the distribution of glacial lakes and projections for aggregated lake area under different climate scenarios. The map spans from approximately 70°E to 110°E longitude and 25°N to 45°N latitude, covering regions like the Tibetan Plateau, Himalayas, Karakoram, and Hindu Kush. The map is divided into several labeled regions, including West Tien Shan, Pamir, West Himalaya, Central Himalaya, East Himalaya, Hengduan Shan, South and East Tibet, Inner Tibet, East Kun Lun, West Kun Lun, Qilian Shan, and Hissar Alay. Bar charts are overlaid on each region, illustrating the aggregated lake area for five conditions: 'Present' (cyan), 'RCP2.6' (light blue), 'RCP4.5' (light purple), 'RCP8.5' (purple), and 'Ice free' (magenta). A scale bar indicates that a specific height corresponds to 250 km². The charts generally show an increase in lake area from present conditions to the 'Ice free' scenario. A dashed line within the bars distinguishes the 'Moraine dammed' lake area. An inset map in the top right corner shows the geographical context of the main map, outlining the area within China and India.


Document: Past evolution and present state of glacial lakes
Section: Past evolution and present state of glacial lakes
Type: text

**Fig. 1** | Region-wide present and projected glacial lakes to 2050, 2100 and on an ice-free Third Pole. Map showing the geographical extent of the Third Pole and the spatial distribution of its present glacial lakes. Bar charts in different colours denote the present and potential future glacial lake areas (present plus projected results under three RCPs, and under the ice-free scenario) that were aggregated into the Global Terrestrial Network for Glaciers (GTN-G) regions46, respectively. The proportional area of present moraine-dammed glacial lakes to all present glacial lakes is shown in grey. Dashed lines show estimated changes in 2050.

Shan mountains and ranges of inner and southeast Tibet as well as the Tien Shan mountains. The Hengduan Shan holds the largest number of glacial lakes  $(6,571;\ 436.2\pm0.5\ km^2)$ , whereas the eastern Himalaya has the most moraine-dammed glacial lakes  $(1,533;\ 183.7\pm0.4\ km^2)$ . At the basin scale, glacial lakes are most abundant in the Brahmaputra River Basin  $(9,665;\ 778.8\pm0.8\ km^2)$ , followed by the Indus, Yangtze, Ganges, Amu Darya and Salween river basins (all with >1,000 lakes, Supplementary Fig. 3 and Supplementary Table 2). The Brahmaputra River Basin is also home to the largest number of moraine-dammed glacial lakes  $(2,331;\ 212.0\pm0.4\ km^2)$ .

Glacial lakes on the Third Pole have undergone a notable increase in total area and number since the 1990s5-7. Our analyses show an overall increase of  $6.8 \pm 0.1\%$  in glacial lake area and 5.9% in lake number across the Third Pole between 1990 and 2015 (Supplementary Fig. 4 and Supplementary Table 1). We propose that this recent increase has principally been driven by an expansion of moraine-dammed glacial lakes that charactize many of the long, flat, debris-covered glacier tongues in the region. To test this hypothesis, we split the glacial lakes into two categories: moraine-dammed and other-dammed glacial lakes (Methods). We find that if only the moraine-dammed glacial lakes are considered, the area gain is substantially larger at  $31.3 \pm 0.3\%$ , with an increase in number of 26.7%. In contrast, the size of other-dammed glacial lakes remains virtually unchanged  $(-0.02 \pm 0.2\%)$ , with a slight decrease in number of -1.4%. Still, changes show clear heterogeneity both at regional and basin scales (Supplementary Figs. 4 and 5).


Document: Past evolution and present state of glacial lakes
Section: Historical GLOF activity
Type: text

As GLOF events across the Third Pole have been heavily reported in previous studies18-20,29, we systematically collected and collated these records to better understand their spatial distributions and the characteristics of different source lakes, which were categorized into moraine, ice, bedrock and complex ones depending on the dam type (Fig. 2a; see Supplementary Table 3 for all records assembled). On the basis of this dataset, we found that at least 296 GLOF events from 109 glacial lakes occurred over the Third Pole since 1560 CE. GLOFs in the eastern Himalaya are primarily from the failures of moraine-dammed glacial lakes (Fig. 2c and Supplementary Table 3) and account for ~45% of the past GLOF sources alone. GLOFs resulting from ice-dammed glacial lakes are all reported in the Pamir-Karakoram regions (Fig. 2a), where the prevalent glacier surges have led to repetitive blockage and eventual release of glacial lakes19. Although most past GLOF sources were related to moraine-dammed glacial lakes (74 out of 109, Fig. 2b), ice- or complex-dammed flood sources (34 out of 109, Fig. 2b) contributed to more than half of the past outbursts (187 out of 296, Fig. 2b) due to the repetitive and high-frequency nature of these events. This is exemplified in the case of Kyagar Tsho (see Fig. 2a for its location and Supplementary Table 3), situated in the headwaters of Yarkant River, which produced at least 33 outburst floods in the past century with an accelerating trend during recent decades32. Practical experience has proven that real-time monitoring and early warning systems are effective in forecasting such active and high-frequency floods resulting from ice dam breaches32,33. The complex-dammed


Document: Past evolution and present state of glacial lakes
Section: Historical GLOF activity
Type: figure
Figure

Image /page/2/Figure/1 description: A multi-panel figure, labeled a through e, presenting data on Glacial Lake Outburst Floods (GLOFs) in the Third Pole region of Asia.


Document: Past evolution and present state of glacial lakes
Section: Historical GLOF activity
Type: text

Panel a is a map showing the spatial distribution of reported GLOFs across Central and South Asia. GLOFs are color-coded by dam type: Moraine (cyan), Ice (orange), Bedrock (magenta), and Complex (green). The map also indicates transboundary GLOF threats, marking present (red dots) and future (hollow red circles) potentially dangerous glacial lakes, along with modeled flow paths and cross-border points.

Panel b is a donut chart illustrating GLOF frequency by dam type. The counts are: Moraine (182 total from segments of 108 and 74), Ice (187 total from segments of 113 and 74), Complex (34 total from segments of 26 and 8), and Bedrock (2 total from segments of 1 and 1).

Panel c is a donut chart showing GLOF frequency by source region. The regions and their respective counts are: Hindu Kush (112, 26, 3, 1), Karakoram (96, 11, 8, 6, 4), East Himalaya (62, 49), Central Himalaya (7), South and East Tibet (5), and Hengduan Shan (3, 1). There is also a segment with a value of 2.

Panel d is a bar chart comparing the number of GLOF sources. For Moraine dams, there are 74 total sources, with 19 having disappeared after a burst. For Bedrock dams, there is 1 source, with 0 disappearing. For Complex dams, there are 8 sources, with 6 disappearing.

Panel e is a detailed map of the Himalayan region, showing towns like Kathmandu and Zhangmu, and a high concentration of potentially dangerous glacial lakes and their modeled flow paths, with five specific points numbered along one such path.

**Fig. 2 | Reported historical GLOFs and potential transboundary threats on the Third Pole. a**, Map showing the spatial distribution of recorded GLOF sources by lake dam type as well as present and projected (ice-free scenario) glacial lakes with possible transboundary GLOF threats across the Third Pole. For ice-dammed GLOF sources, only those with known geographic coordinates are shown (see Supplementary Table 3 for complete records). The black box near the bottom of the panel is the location of **e. b**, Double doughnut chart representing the number of past GLOF sources and flood frequency by lake dam type. **c**, Double doughnut chart showing the number of past GLOF sources and flood frequency per region. **d**, Statistics of GLOF sources by lake dam type. Ice-dammed cases were not counted owing to their repetitive nature. **e**, Amplified map showing a hotspot of potential transboundary GLOF threat between China and Nepal, and a historical GLOF hotspot in the eastern Himalaya. The circled numbers represent five concentrated regions with potential transboundary GLOF threats. The capital cities of Nepal and Bhutan are indicated with yellow squares. The left black box is the location of Supplementary Fig. 13 and the right black box is the location of Supplementary Fig. 14. Base maps: Google, Europa Technologies.


Document: Past evolution and present state of glacial lakes
Section: Historical GLOF activity
Type: text

. * * d * * , Statistics of GLOF sources by lake dam type . Ice - dammed cases were not counted owing to their repetitive nature . * * e * * , Amplified map showing a hotspot of potential transboundary GLOF threat between China and Nepal , and a historical GLOF hotspot in the eastern Himalaya . The circled numbers represent five concentrated regions with potential transboundary GLOF threats . The capital cities of Nepal and Bhutan are indicated with yellow squares . The left black box is the location of Supplementary Fig . 13 and the right black box is the location of Supplementary Fig . 14 . Base maps : Google , Europa Technologies .

glacial lakes are usually positioned on the surface of debris-covered glaciers and are held back by a mix of ice and moraine. These lakes often vanish fully after an outburst (Fig. 2d), but some exceptions exist. For instance, Merzbacher Lake (see Fig. 2a for the specific site and Supplementary Table 3), located upstream of Aksu River, has produced at least 66 recorded outbursts over the last century because of its peculiar geographic situation and water supply modes 14,34. The occurrence of floods from such lakes is sudden and difficult to monitor and mitigate, as drainage can occur within or underneath the glacier 34.


Document: Past evolution and present state of glacial lakes
Section: Present hotspots of GLOF hazard and risk
Type: text

Based on a conceptual model of GLOF hazard and risk optimized for large-scale automated application (Methods), we implemented

the first quantitative assessment of potential GLOF hazard and risk for a total of 6,958 existing moraine-dammed glacial lakes across the Third Pole and provide hierarchical classifications. This study compliments numerous national- to regional-scale GLOF hazard and risk inventories that have previously been undertaken (Supplementary Table 4), allowing a comparison of GLOF hazard and risk across the entire Third Pole. The model considers GLOF hazard on the basis of well-established conditioning and triggering factors30 and identifies infrastructure and communities at risk within downstream simulated flood paths. In contrast to previous work, we evaluate model reliability by validating classification results against those glacial lakes that have generated outburst floods in the past. The model successfully classified GLOF hazard as high or very high for 46 out of the 48 existing glacial lakes (that is,


Document: Past evolution and present state of glacial lakes
Section: Present hotspots of GLOF hazard and risk
Type: figure
Figure

Image /page/3/Figure/1 description: A figure with three panels (a, b, c) illustrating Glacial Lake Outburst Flood (GLOF) hazard and risk. Panels a and b are maps with pie charts, and panel c is a histogram.


Document: Past evolution and present state of glacial lakes
Section: Present hotspots of GLOF hazard and risk
Type: text

Panel a, labeled "GLOF hazard", is a map showing various regions with overlaid pie charts. The size of each pie chart corresponds to the "Aggregated hazard value", with a scale showing concentric circles for values <10, 50, 100, 250, and 500. The slices of the pie charts are colored to represent different hazard levels: dark green for Very low (VL), light green for Low (L), yellow for Medium (M), orange for High (H), and red for Very high (VH).

Panel b, labeled "GLOF risk", is a similar map. The size of its pie charts represents the "Aggregated risk value", with a scale for values <5, 25, 50, 125, and 250. The color coding for risk levels is the same as in panel a.

Panel c is a histogram with the x-axis labeled "Normalized hazard value" (from 0 to 1.0) and the y-axis labeled "Frequency (n = 6,958)" (from 0 to 400). The bars are colored according to the "Hazard class": dark green for "Very low", light green for "Low", yellow for "Medium", orange for "High", and red for "Very high". The distribution is roughly bell-shaped, peaking around a normalized hazard value of 0.4. Above the histogram, brown dots represent "Historical GLOFs (n = 48)", with dashed lines connecting them to the corresponding hazard values. The number of historical GLOFs is indicated for some ranges, showing 1, 1, 9, and 37, with the majority occurring at high and very high hazard values.

**Fig. 3** | Region-wide present GLOF hazard and risk across the Third Pole. a,b, Pie charts showing the proportion of different GLOF hazard (a) and risk (b) levels per region. VL, very low; L, low; M, medium; H, high; VH, very high. Aggregated hazard and risk values are the total sums of normalized values within each region. **c**, Histogram presenting the distribution of GLOF hazard levels across all moraine-dammed glacial lakes assessed. The vertical dashed lines indicate the distribution of assessed GLOF hazard values for glacial lakes where historical GLOFs have been recorded, which were further used to validate the overall model performance. The dots refer to the number of GLOF sources for a given hazard value.


Document: Past evolution and present state of glacial lakes
Section: Present hotspots of GLOF hazard and risk
Type: text

a ) and risk ( b ) levels per region . VL , very low ; L , low ; M , medium ; H , high ; VH , very high . Aggregated hazard and risk values are the total sums of normalized values within each region . * * c * * , Histogram presenting the distribution of GLOF hazard levels across all moraine - dammed glacial lakes assessed . The vertical dashed lines indicate the distribution of assessed GLOF hazard values for glacial lakes where historical GLOFs have been recorded , which were further used to validate the overall model performance . The dots refer to the number of GLOF sources for a given hazard value .

95.8% accuracy, Fig. 3c and Supplementary Table 6), from which the past GLOFs originated. For example, Jialong Co and Cirenma Co, located in eastern Himalaya, have been the origin of several GLOFs since 1960, and are both ranked within the 99th percentile of the most hazardous lakes. Overall, our assessment indicates that one-third (2,323) of all glacial lakes assessed have high to very high hazard levels, and one in six (1,203) glacial lakes would pose a potentially high to very high risk to downstream communities. More than one-third (2,619) of glacial lakes assessed appear not to be dangerous as no downstream infrastructures are evident within the flood paths (see Supplementary Table 5 for all infrastructures considered). On a regional scale, the eastern Himalaya has the highest value in terms of GLOF hazard, which is twice that of the adjacent Hengduan Shan, inner Tibet, central and western Himalaya, and southeast Tibet; three to four times that of the Tien Shan areas; and seven to eight times that of the Pamir, Hindu Kush and Karakoram (Fig. 3a). For GLOF risk, the eastern Himalaya likewise has the highest value, being two to three times that of the central Himalaya, Hengduan Shan and western Himalaya; four times that of southeast Tibet and western Tien Shan; and six to eight times that of inner Tibet, Hindu Kush, eastern Tien Shan and Pamir (Fig. 3b). Notably, some regions such as inner and southeast Tibet present high levels of GLOF hazard (Fig. 3a), but lower levels of GLOF risk (Fig. 3b) owing to much lower exposure values compared with the central and eastern Himalaya (Supplementary Fig. 6). While

mapping of roads and other exposed elements over inner and southeast Tibet is considered less complete than elsewhere35, these results correspond well with other approaches based on gridded data including population density and cropland area that have likewise identified highest risk levels in central and eastern Himalaya36. At the basin scale, the Brahmaputra River Basin has the highest level in terms of GLOF hazard, followed by Ganges, Indus, Salween, Ili, Amu Darya river basins and Tibetan Plateau interior (Supplementary Fig. 7a). However, with respect to GLOF risk, the Ganges River Basin has the highest level, followed by Brahmaputra, Indus, Amu Darya, Ili, Salween and Syr Darya river basins (Supplementary Fig. 7b).


Document: Past evolution and present state of glacial lakes
Section: Anticipation of future changes
Type: text

We expand our assessment to consider how and where GLOF hazard and risk will evolve under continued global warming and glacier shrinking. Future glacial lake formation and associated hazard and risk are considered under three different CO2 emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5)37 and projections of a glacier model38 for the middle (2050) and end (2100) of the twenty-first century (Methods). These results are compared with an ice-free scenario which assumes that the Third Pole's glaciers have totally vanished. Our modelling suggests that >13,000 new glacial lakes with a combined maximum area of ~1,510 km² and a combined maximum volume of ~50 km³ could potentially emerge in an ice-free environment (Supplementary

Table 1). This includes the expansion of existing glacial lakes to reach their topographically constrained extent, and potential new glacial lakes forming in depressions within the former bed of glaciers. In total, ~47% of this additional lake area would have already emerged by 2050, and 86% by 2100 under RCP8.5 (or 44% and 56%, respectively, under RCP2.6) (Supplementary Table 1). This future glacial lake development is mostly clustered along the Pamir-Karakoram-Kunlun Shan-Himalaya ranges as well as the western Tien Shan mountains (Fig. 1 and Supplementary Table 1), where the existing glaciers contribute ~74% of the total Third Pole glacier area (Supplementary Table 1). There is notable regional variation in projected glacial lake development from east to west, with regions such as southeast Tibet and eastern Himalaya already revealing close to their maximum lake area by 2050, under all RCP scenarios (Fig. 1). In contrast, in Karakoram, Pamir and western Himalaya, lake area will continue to increase strikingly into the late twenty-first century and beyond. On a basin scale, the future glacial lakes will be expected chiefly at the headwaters of the Indus and Tarim river basins, followed by Amu Darya, Ganges, Brahmaputra river basins and the interior ranges of the Tibetan Plateau (~89% of all projected glacial lakes, Supplementary Fig. 3 and Supplementary Table 2). The Brahmaputra, Ganges and Yangtze river basins almost approach their maximum lake area in 2050 under the three CO2 emission scenarios, whereas lakes in the Indus and Tarim river basins as well as the Tibetan Plateau interior will continue to expand beyond 2100.


Document: Past evolution and present state of glacial lakes
Section: Anticipation of future changes
Type: text

century and beyond . On a basin scale , the future glacial lakes will be expected chiefly at the headwaters of the Indus and Tarim river basins , followed by Amu Darya , Ganges , Brahmaputra river basins and the interior ranges of the Tibetan Plateau ( ~ 89 % of all projected glacial lakes , Supplementary Fig . 3 and Supplementary Table 2 ) . The Brahmaputra , Ganges and Yangtze river basins almost approach their maximum lake area in 2050 under the three CO  2  emission scenarios , whereas lakes in the Indus and Tarim river basins as well as the Tibetan Plateau interior will continue to expand beyond 2100 .

To assess the possible implications of this future glacial lake development on GLOF hazard and risk, we applied the same model as was used for the present glacial lakes, with some slight modifications to account for future unknowns (Methods). For the whole Third Pole region, results indicate that future GLOF hazard and risk will increase by almost threefold relative to current conditions, with obvious regional variations (Fig. 4 and Supplementary Fig. 8). This is considered a conservative maximum estimate given that glacial lakes will appear gradually, and an unknown quantity of moraine dams could breach, and lakes thereby drain, before other lakes have emerged. Nonetheless, under the high-emission scenario RCP8.5, much of the Third Pole could already be approaching a state of 'peak risk' by the end of the twenty-first century, or even mid-century in some regions, with risk levels comparable to even the ice-free scenario (Fig. 4). In an ice-free environment, average glacial lake volumes will be ~80% larger than today, while topographic potential for ice/rock avalanche triggering will increase by ~230%, as lakes will be positioned closer to steep mountain headwalls (Supplementary Fig. 9). This suggests that the increase in potential GLOF frequency will generally be more important than the future change in magnitude. Meanwhile, in terms of risk, although overall exposure levels will increase due to the larger number of lakes, future lakes will on average have lower exposure levels (-40% compared with today) as they are located higher, and farther away from existing communities. As a consequence, average risk values per lake will be 14% lower in the future, while the overall number of lakes classified to have high or very high risk levels will increase from 1,203 to 2,963 lakes (that is, by a factor of 2.5). This analysis does not consider future changes in the distribution of population and infrastructure, particularly hydropower, that is expanding higher into alpine valleys39. Likewise, tourism expansion into remote mountain valleys could further enhance risks, while outmigration from rural areas could reduce risk in some cases40,41. Collectively, the Karakoram, Pamir and western Himalaya will be the regions with the most substantial increase in GLOF hazard, while in terms of risk, the largest increases will be in central Himalaya, Karakoram and western Himalaya (Fig. 4 and Supplementary Fig. 8). The eastern Himalaya will remain the primary GLOF hotspot under all assessed future scenarios, while the emergence of the Karakoram as an additional major hotspot of GLOF hazard and risk is most notable over longer timescales under RCP4.5 and RCP8.5, and under the ice-free


Document: Past evolution and present state of glacial lakes
Section: Anticipation of future changes
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reduce risk in some cases < sup > 40 , 41 < / sup > . Collectively , the Karakoram , Pamir and western Himalaya will be the regions with the most substantial increase in GLOF hazard , while in terms of risk , the largest increases will be in central Himalaya , Karakoram and western Himalaya ( Fig . 4 and Supplementary Fig . 8 ) . The eastern Himalaya will remain the primary GLOF hotspot under all assessed future scenarios , while the emergence of the Karakoram as an additional major hotspot of GLOF hazard and risk is most notable over longer timescales under RCP4 . 5 and RCP8 . 5 , and under the ice - free

scenario. While lake volumes will on average be larger than today, it is clear that even in the future, it is not necessarily the largest lakes that will be the biggest threat to communities (Supplementary Fig. 10). At the basin scale, the future increase in GLOF risk will be greatest in the Indus, Ganges and Amu Darya river basins, with the Ganges River Basin maintaining the highest risk levels under all future scenarios (Supplementary Figs. 11 and 12). By 2100, the Indus River Basin will exhibit the highest GLOF hazard levels (currently exhibited by the Brahmaputra River Basin), with close to a fourfold increase from today under the RCP4.5 scenario.


Document: Past evolution and present state of glacial lakes
Section: Potential transboundary threats
Type: text

The mountain ranges of the Tibetan Plateau and surrounding areas span 11 nations (Fig. 2a), giving rise to potential transboundary natural disasters, especially GLOFs. In fact, several relevant cases have been reported, for instance, in the transboundary Poiqu River Basin between China and Nepal22 and the Shakhimardan catchment between Kyrgyzstan and Uzbekistan42 (Fig. 2a). A well-known outburst from Cirenma Co (Fig. 2a) in 1981 caused serious devastation, including but not limited to the destruction of the Sino-Nepal Friendship bridge and part of the Nepal Sun Koshi hydropower station, and the loss of 200 lives in Nepal43. Recognizing the broader transboundary threat across the Third Pole, we identified where simulated flood flow paths from existing moraine-dammed glacial lakes intersected with national borders. Consequently, a total of 438 glacial lakes were found to have the potential to produce a transboundary flood, of which 191 were classified to be in the high or very high risk classes (Fig. 2a). Further analysis revealed that ~86% of these potentially dangerous lakes are located between China and Nepal, forming five hotspots of transboundary floods (Fig. 2e). It can be inferred from the illustrated example of one of these hotspots shown in Supplementary Fig. 13 that a possible outburst will have serious effects on downstream transboundary settlements and infrastructures.

The transboundary GLOF threat will amplify in the future. Analysis suggests that the number of future potential transboundary flood sources under the ice-free scenario will roughly double (an additional 464 lakes), with 211 of these lakes classified in the high or very high risk classes (Fig. 2a). The border region between China and Nepal will remain a major hotspot (~42% of all these future dangerous glacial lakes), while the border region between Tajikistan and Afghanistan emerges clearly as an additional potential transboundary hotspot (~36%, up from ~5% at present).


Document: Past evolution and present state of glacial lakes
Section: Summary and implications
Type: text

We conclude that while the most dangerous hotspots of GLOF hazard and risk are now located within the eastern Himalaya, consistent with previous studies21,23,36,44 and recorded GLOF events (Supplementary Fig. 10; see Supplementary Fig. 14 for a visual example in the eastern Himalaya), over the course of the twenty-first century, additional hotspots will emerge in the regions to the west, particularly under high-emission scenarios. For Karakoram and Pamir, where glaciers have recently exhibited a stable or slightly positive mass balance45, repetitive, high-frequency outburst events associated with ice-dammed glacial lakes will probably remain the dominant GLOF threat for several decades to come. However, the eventual depletion of glaciers in these regions projected during the twenty-first century will see conditions becoming less conducive for glacier surging and ice-dam formation19, and the threat of moraine- or bedrock-dammed glacial lakes substantially increasing. It is also important to emphasize that climate and related glacial changes as assessed here are just one driver of future GLOF risk; in some regions, future changes in exposure and societal vulnerability could potentially lead to different risk scenarios, especially where political, economic and social conditions facilitate or impede effective disaster risk reduction. Ultimately, determining exactly which lakes are the highest priority for response actions should be guided by consensus across multiple lines of evidence,


Document: Past evolution and present state of glacial lakes
Section: Summary and implications
Type: figure
Figure

Image /page/5/Figure/1 description: A map of the 'Third Pole' region in Asia, showing projected aggregated risk values for various mountain ranges under different climate scenarios. The map displays longitude from 70°E to 110°E and latitude from approximately 30°N to 40°N. Bar charts are overlaid on specific regions, including West Tien Shan, Hindu Kush, Karakoram, West Himalaya, Central Himalaya, and East Himalaya, among others. Each bar chart consists of five bars representing different conditions, as explained in a legend titled 'Aggregated risk value'. The conditions are: 'Present' (light yellow), 'RCP2.6' (light pink), 'RCP4.5' (pink), 'RCP8.5' (magenta), and 'Ice free' (dark purple). A reference bar indicates a value of 600. In all regions, the aggregated risk value increases progressively from the 'Present' condition to the 'Ice free' condition. A separate legend in the top right corner shows the 'Total risk value' for the entire 'Third Pole' region. The values for the five scenarios are: 1,510, 2,705, 3,197, 3,597, and 3,861, corresponding to the increasing severity of the climate scenarios.


Document: Past evolution and present state of glacial lakes
Section: Summary and implications
Type: text

**Fig. 4 | Region-wide future changes in GLOF risk to 2050, 2100 and on an ice-free Third Pole.** Bar plots indicate projected changes in GLOF risk per region from the present to 2050 and 2100 (under three RCPs) as well as under the ice-free scenario. Dashed lines show estimated changes to 2050. The future GLOF risk was estimated based on currently known infrastructures that are exposed to modelled flood flow paths from potential future lakes and includes the assessed risk from present moraine-dammed glacial lakes (that is, these lakes are assumed to remain in the future). Inset: changes in GLOF risk over the whole Third Pole; note that the scale differs from the regional risk values.

considering not only the wealth of information on existing GLOF hazard and risk (Supplementary Table 4), but also future emerging threats. Particularly where existing and emerging transboundary risks have been identified, risk reduction strategies will require high levels of international planning and cooperation. These transboundary regions represent vital economic corridors of activity, but some suffer from political tensions and challenges that can negatively affect timely data sharing, communication and coordination of early warning and disaster preparedness. We appeal to the relevant nations and international research communities to work together to prevent and mitigate the damages and losses that GLOFs bring to the Third Pole region, and to remain flexible and alert to the new challenges that could emerge under a future warmer climate.


Document: Past evolution and present state of glacial lakes
Section: Online content
Type: text

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41558-021-01028-3.

Received: 2 June 2020; Accepted: 16 March 2021; Published online: 6 May 2021


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

Glacial lake delineation and lake changes. We used Landsat satellite imagery to outline the boundaries of glacial lakes that are defined as water bodies formed primarily by glaciation and fed by glacial/snow melts, and are situated on the surface, adjacent or downstream of a glacier, or on palaeoglaciation landforms47 across the Third Pole. Two time windows,  $1990 \pm 4$  and  $2015 \pm 1$ , were chosen to reveal the present state of glacial lakes and their changes over the last ~25 years. The selection was determined largely by the availability of Landsat datasets in such a large area. To minimize the effects of ice or seasonal snow cover on glacial lake mapping in these high elevations, we picked images mostly from the warm season (June to November), which corresponds roughly to the highest and most stable water areas/levels of the lakes in a hydrological year48. Overall, we used a total of 403 Landsat 4-5 Thematic Mapper (TM) scenes (97.8% from the warm season) for the 1990 epoch and 294 Landsat 8 Operational Land Imager (OLI) scenes (100% in the warm season) for the 2015 period (Supplementary Figs. 1 and 2a,b). We also gave priority to images with no or little cloud coverage, that is, an average cloud cover percentage of 7.7% and 4.2% for 1990 and 2015, respectively (Supplementary Figs. 1 and 2c). Landsat images were obtained from the US Geological Survey (USGS) data portal with a spatial resolution of 30 m. Images were first converted from the digital numbers to the top of atmosphere. Landsat 8 OLI images were further fused using a nearest neighbour diffusion-based pan-sharpening algorithm49 to improve their spatial resolution to 15 m.


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

b ) . We also gave priority to images with no or little cloud coverage , that is , an average cloud cover percentage of 7 . 7 % and 4 . 2 % for 1990 and 2015 , respectively ( Supplementary Figs . 1 and 2c ) . Landsat images were obtained from the US Geological Survey ( USGS ) data portal with a spatial resolution of 30 m . Images were first converted from the digital numbers to the top of atmosphere . Landsat 8 OLI images were further fused using a nearest neighbour diffusion - based pan - sharpening algorithm  49  to improve their spatial resolution to 15 m .

An automated water body classification algorithm50 based on global-local threshold segmentation for water indices greyscale images was applied to extract all water bodies from the Landsat images. A slope threshold of <20° as well as a shaded relief threshold of >0.25 were employed to alleviate the disturbances from mountain shadows50. Subsequently, careful visual inspection and manual re-editing were carried out to remove other water bodies (for example, reservoirs, rivers and small streams) and to correct the wrongly mapped glacial lakes based on corresponding Landsat scenes, online maps and other glacial lake datasets5,4 available for the Third Pole. A 10 km buffer5,52 around the Randolph Glacier Inventory (RGI 6.0)54 was used to preliminarily determine the distribution of what could be considered glacial lakes. However, to ensure the integrity and reliability of our inventory, we also included additional glacial lakes in areas where glaciers are missing from the RGI 6.0 dataset55. Both the glacial lake datasets for 1990 and 2015 were then cross-checked and attributed. The area of all glacial lakes was calculated based on the universal transverse mercator (UTM) projected coordinate system and their uncertainty ( $\delta$ ) was estimated using the formula56:  $\delta = P/G \times G^2/2 \times 0.6872$ , where P is the perimeter of the glacial lake and G is the spatial resolution of the images used. Lastly, we selected all glacial lakes ≥0.01 km2 and superimposed them in a high-resolution Google Earth 3D view mode together with the corresponding Landsat scenes to distinguish the moraine-dammed glacial lakes separately. The remaining lakes were grouped and termed 'other-dammed glacial lakes'. Lakes dammed with ice or lying on the surface of glaciers were not included in the temporal change analyses due to their transient character and large seasonal fluctuations.

The relative change in glacial lake area between 1990 and 2015 and its uncertainty were estimated using the following equations:

$$R = \frac{A_2 - A_1}{A_1} \times 100\%$$

$$\delta R = \sqrt{\left(\frac{\delta A}{A_2 - A_1}\right)^2 + \left(\frac{\delta A_1}{A_1}\right)^2} \times R$$


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

lying on the surface of glaciers were not included in the temporal change analyses due to their transient character and large seasonal fluctuations . The relative change in glacial lake area between 1990 and 2015 and its uncertainty were estimated using the following equations : $ $ R = \ frac { A_2 - A_1 } { A_1 } \ times 100 \ % $ $ $ $ \ delta R = \ sqrt { \ left ( \ frac { \ delta A } { A_2 - A_1 } \ right ) ^ 2 + \ left ( \ frac { \ delta A_1 } { A_1 } \ right ) ^ 2 } \ times R $ $

where R is the relative change in glacial lake area and  $\delta R$  is its uncertainty;  $A_1$ ,  $A_2$  are the lake areas at the beginning and end of the period, respectively; and  $\delta A_1$ ,  $\delta A_2$  are their uncertainties,  $\delta A = \sqrt{\delta A_1^2 + \delta A_2^2}$ .

Modelling of future glacial lakes. Glacier-bed topography, that is, ice-free digital elevation model (DEM) can be estimated using model approaches that determine local ice thickness based on the characteristics of surface topography and considerations of ice-flow dynamics8. The potential sites of future glacial lake formation under scenarios of complete deglaciation can then be projected by detecting overdeepenings on the glacier-bed. Here we used the latest global glacier ice thickness dataset57 consisting of a consensus of five different ice thickness models as well as the 30 m Advanced Land Observing Satellite Global Digital Surface Model (ALOS AW3D30 v2.2) to extract the bed topography of all Third Pole glaciers. Three analytical steps were performed to determine the possible sites of future glacial lake formation: (1) glacier areas with slopes <20° were mapped first as potential locations of glacier-bed overdeepenings as lakes normally form in flat topography; (2) depressions in the glacier bed were detected by filling them using a classic filling algorithm58, while a slope raster was generated based on the filled glacier-bed topographies; and (3) depressions with a slope <1° were identified8 and intersected with the layer of possible overdeepenings obtained in the first step to generate the final layer of the possible future lakes. Ultimately, we selected all modelled overdeepenings ≥0.01 km2 as the potential sites of future lake formation, to ensure comparability to the glacial lake inventories in 1990 and 2015. The area and volume of all modelled future glacial lakes were estimated based on a bathymetry raster that was derived from the difference between the


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

, while a slope raster was generated based on the filled glacier - bed topographies ; and ( 3 ) depressions with a slope < 1 ° were identified8 and intersected with the layer of possible overdeepenings obtained in the first step to generate the final layer of the possible future lakes . Ultimately , we selected all modelled overdeepenings ≥ 0 . 01 km  2  as the potential sites of future lake formation , to ensure comparability to the glacial lake inventories in 1990 and 2015 . The area and volume of all modelled future glacial lakes were estimated based on a bathymetry raster that was derived from the difference between the

overdeepenings-filled DEM and the original glacier-bed topography. The mean and maximum depth of each glacial lake was also estimated using the detected depression extent and its bathymetry raster. In general, ice-thickness models have proven robust in detecting the locations of overdeepenings  $^{\rm sp}$ , although local uncertainties in ice-thickness estimates (and thereby volume of the overdeepenings and associated future lake) could be on the order of  $\pm 100\%$  for individual cases  $^{\rm co}$ . Likewise, dam composition (moraine or bedrock) cannot be distinguished for future lakes, and lake volumes will be ultimately determined by dam geometry and the presence of an outlet channel.

To estimate the time horizon in which potential future glacial lakes could emerge, we simulated glacier recession based on an ensemble of 14 global circulation models of the Climate Model Intercomparison Project Phase 5 (ref. 61) under three different CO2 emission scenarios — RCP2.6, RCP4.5 and RCP8.5, in addition to the hypothetical end case with a completely ice-free Third Pole. Two major time horizons over the course of the twenty-first century, 2050 and 2100, were considered. The Global Glacier Evolution Model (GloGEM)38 was employed to model the evolution of all individual RGI 6.0 glaciers based on projected climate forcing until 2100. GloGEM resolves all components of the glacier surface mass balance and computes changes in glacier geometry (thickness, length) in annual time steps. Results for the ensemble of global circulation models have been averaged for the three considered RCPs. The minimum elevation for each glacier at both 2050 and 2100 under each RCP scenario was calculated and used to determine the exposed overdeepenings at each time step. We assume that the potential glacial lake is forming or has formed if the modelled minimum elevation of its parent glacier is greater than or equal to the elevation of the modelled overdeepening that was retrieved based on the filled glacier-bed topographies.


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

until 2100 . GloGEM resolves all components of the glacier surface mass balance and computes changes in glacier geometry ( thickness , length ) in annual time steps . Results for the ensemble of global circulation models have been averaged for the three considered RCPs . The minimum elevation for each glacier at both 2050 and 2100 under each RCP scenario was calculated and used to determine the exposed overdeepenings at each time step . We assume that the potential glacial lake is forming or has formed if the modelled minimum elevation of its parent glacier is greater than or equal to the elevation of the modelled overdeepening that was retrieved based on the filled glacier - bed topographies .

GLOF hazard assessment and validation. Moraine-dammed glacial lakes dominate past GLOFs recorded in most of the world17, especially in the Himalaya20,29, and their evolution can be directly linked to atmospheric warming and glacial recession17. Hence, for the current GLOF hazard and risk assessment, we focused on such lakes. A conceptual model21 was used to assess hazard and risk for 6,958 existing moraine-dammed proglacial lakes across the Third Pole. The model does not apply to some glacial lakes situated on the surface or flanks of debris-covered glaciers, as predisposing and triggering factors in these cases are different. Furthermore, we recognize the transition and complexity that occurs between different lake types, meaning that some degree of subjectivity in lake classification cannot be avoided. The model defines three indices21: the GLOF risk index as a function of the hazard index (combining likelihood and potential magnitude of a GLOF) and exposure index (potential for people and infrastructure to be affected). Here we improved the model for large-scale implementation so that it can run on a single glacial lake object iteratively. The large-scale assessment was based on a single UTM grid zone. All calculations and analyses described below were based on the 90 m Multi-Error-Removed Improved-Terrain (MERIT) DEM62, which is proven to have the best performance on the Third Pole63


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

three indices21 : the GLOF risk index as a function of the hazard index ( combining likelihood and potential magnitude of a GLOF ) and exposure index ( potential for people and infrastructure to be affected ) . Here we improved the model for large - scale implementation so that it can run on a single glacial lake object iteratively . The large - scale assessment was based on a single UTM grid zone . All calculations and analyses described below were based on the 90 m Multi - Error - Removed Improved - Terrain ( MERIT ) DEM62 , which is proven to have the best performance on the Third Pole  63

The potential hazard of a GLOF was defined based on four key factors21, which encompass well-established processes that drive GLOF triggering and magnitude30. (1) Lake volume was calculated using a lake depth-area-volume empirical relationship64 and used as a proxy for possible maximum flood magnitude. (2) Total watershed area upstream of each glacial lake was used as an indicator of the potential for heavy rain and glacier/snow melt runoff to flow into the lake or cause it to overflow20,26. (3) The likelihood of ice and/or rock avalanches triggering an outburst was calculated based on the concept of topographic potential which includes: the potential for ice and/or rock to detach (parameterized by slope angle); and the potential for the ice and/or rock avalanche to reach a glacial lake (parameterized by the overall trajectory slope or angle of reach). Due to the quality of available glacier datasets at such a large scale, we did not distinguish whether the surrounding slopes were rock or ice-covered. Thus, we assumed that an avalanche was possible from any slope >30°, and the resulting avalanche could feasibly impact the lake where the overall slope trajectory is >14° (tangent angle = 0.25)65. (4) Downstream slope of the lake dam was considered to be an important factor controlling dam stability and the potential for self-destruction. It is also relevant for the erosional capacity of a GLOF event in downstream areas. This factor was calculated as the average horizontal downstream slope of each lake in three different buffer zones depending on different lake areas (A) intervals ( $A \ge 0.1 \text{ km}^2$ , 900 m;  $0.1 > A \ge 0.05 \text{ km}^2$ , 600 m;  $0.05 > A \ge 0.01 \text{ km}^2$ , 300 m). These intervals were derived empirically based on careful analyses of a large number of lakes using high-resolution satellite images.

All factors were then normalized to 0 to 1 using the percent ranking method and assumed to have equal weight to the hazard of a GLOF event. For display and comparative purposes, hazards of all glacial lakes were eventually classified into five levels (very low, low, medium, high, very high) using the natural breaks (Jenks) method and then were aggregated to each region and river basin based on the sum of all lake hazard levels.


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

text { km } ^ 2 $ , 300 m ) . These intervals were derived empirically based on careful analyses of a large number of lakes using high - resolution satellite images . All factors were then normalized to 0 to 1 using the percent ranking method and assumed to have equal weight to the hazard of a GLOF event . For display and comparative purposes , hazards of all glacial lakes were eventually classified into five levels ( very low , low , medium , high , very high ) using the natural breaks ( Jenks ) method and then were aggregated to each region and river basin based on the sum of all lake hazard levels .

The verification of model outcomes is highly dependent on empirical information21. Here, to verify the results from the GLOF hazard assessment and provide the historical context of GLOF activity, we compiled the most complete inventory of past GLOFs over the Third Pole. Thus, we collected and re-evaluated almost all of the historical GLOF events (Supplementary Table 3) that have been

reported on the Third Pole and found a total of 296 GLOF events from 109 glacial lakes. Finally, 48 moraine-dammed glacial lakes that did not disappear after previous GLOFs and remained  $\geq$ 0.01 km² in the 2015 epoch were used to validate the hazard assessment.

GLOF exposure and risk. Exposure represents the presence of people, livestock, infrastructure and other assets that could be threatened by potential GLOF hazard21. Here we used infrastructures (such as buildings, highways, railways and historic places, see Supplementary Table 5 for all features used) to characterize the communities and populations that might be affected within a potential GLOF flow path, which was simulated using the GIS-based modified single-flow hydrological model66. All infrastructure features were downloaded from OpenStreetMap (accessed 1 August 2019) and were converted to a raster grid. The maximum downstream flow distance for each lake path was determined based on an empirically derived worst-case scenario defined by the angle of reach from the source lake, with a tangent angle = 0.05 (3° angle of reach)67. Beyond this flow distance, damages from a GLOF are not expected. The sum of the angle of reach for each OpenStreetMap raster pixel that is exposed to the lake flow path was aggregated as the quantified measure of exposure for each glacial lake. As of 2016, the OpenStreetMap was considered to be 83% globally complete and increasing rapidly, although generally lower (yet relatively homogeneous) levels of completeness are seen over the Third Pole35. The exception is Nepal where intense humanitarian mapping efforts have led to near total completeness.

The exposure values for each glacial lake were also normalized to 0 to 1 using the percent ranking method and then multiplied with the normalized hazard value to give the final risk level associated with each lake. The risk values of all glacial lakes (except those with an exposure value of zero indicating no risk to downstream communities) were divided into five classes using the natural breaks (Jenks) method and aggregated to each region and river basin to obtain their overall risk levels. In the absence of reasonable proxy variables at such a large scale, the vulnerability component of risk was not considered here.


Document: Past evolution and present state of glacial lakes
Section: Methods
Type: text

efforts have led to near total completeness . The exposure values for each glacial lake were also normalized to 0 to 1 using the percent ranking method and then multiplied with the normalized hazard value to give the final risk level associated with each lake . The risk values of all glacial lakes ( except those with an exposure value of zero indicating no risk to downstream communities ) were divided into five classes using the natural breaks ( Jenks ) method and aggregated to each region and river basin to obtain their overall risk levels . In the absence of reasonable proxy variables at such a large scale , the vulnerability component of risk was not considered here .

Future GLOF hazard and risk assessments. The same modelling approach was also applied to assess future GLOF hazards and risks for all modelled glacial lakes formed at 2050 and 2100 under three different RCPs and the ice-free scenario. The difference was that the current topography of the glacier areas was substituted by the modelled glacier-bed topography and then the selected factors were calculated for these future scenarios. However, because we cannot judge whether these potential glacial lakes will be dammed by moraine or bedrock, and resulting dam geometries are highly uncertain, we used the average dam slope of present glacial lakes in each region or river basin to characterize all modelled lakes in the same region or basin. Likewise, no robust basis exists to predict the future development of downstream infrastructure and communities across all of the Third Pole; thus, the future risk assessment considers only the change in exposure of existing infrastructures and communities.


Document: Past evolution and present state of glacial lakes
Section: Code availability
Type: text

The GLOF hazard and risk assessment models are available at Zenodo under the identifier https://doi.org/10.5281/zenodo.4477947. Additional model or code used in this study are available from the corresponding authors on request.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: Abstract
Type: text

Long-term simultaneous tracking of the dynamics of regional glacial lakes can help in identifying lakes prone to climate change and Glacial Lake Outburst Flood (GLOF). This study demonstrates a methodology of tracking the dynamics of glacial lakes using Landsat time series data and SRTM DEM of Sikkim Himalaya over three decades (1987–2020), using a random forest classifier (RFC) and an artificial neural network (ANN). The classifiers were trained with features like slope, hillshade, automated water extraction index, band ratio, modified normalized difference water index, normalized difference water index, and water ratio index. The performance of the classifiers were measured using parameters like Accuracy, Kappa, Sensitivity, Specificity, Precision, F1 score, and Area under the curve. Furthermore, imbalance tests were performed to validate the predictions of the classifiers. On average, RFC marginally outperformed ANN with an accuracy of 98%. The slope was the most important determinant in mapping the glacial lakes, followed by the automated water extraction index. Time series data generated from this method was used in forecasting the fate of numerous glacial lakes of the Sikkim Himalaya. Models like Brown's and Holt's exponential smoothing, and the random walk model were applied for forecasting. The forecasts were validated with varying degrees of accuracy. The proposed methodology helps in overcoming the challenges of mapping glacial lakes over a vast geographic area over a prolonged period and generates time series data of their spatial extent. The methodology demonstrated here will be useful for the long-term mapping and monitoring of glacial lakes all over the world.

**Keywords** Glacial lake, Google earth engine, Machine learning, Random forest, Time series, Forecasting

data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

Glacial Lakes (GLs) are a product of the complex interactions between the glacial terrain, topography, geology, hydrology, and climatic variables of a region [1, 2]. The formation and evolution of these GLs are driven by processes involving the accumulation


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: figure
Figure

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Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

© The Author(s) 2025. **Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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of meltwater from glaciers behind natural dams like ice dams, moraines, or bedrock [3, 4]. The formation and expansion of GLs depend on the retreat of glaciers during deglaciation periods [5]. The expansion of GLs is viewed as a significant indicator of climate change, glacial lake outburst flood (GLOF), and accelerated melting of glaciers [6-12].

The intensity of change and the fate of these lakes are highly uncertain as they are very sensitive to temperature and precipitation [13, 14]. The retreating glaciers often leave behind proglacial lakes. Such lakes are fed by the melting water of the glacier till a critical volume is achieved that breaks the lake's boundary wall, leading to a glacial lake outburst flood (GLOF). The increase in ambient air temperature and land surface temperature in mountainous regions in comparison to other places in the past few decades has made the issue of GLOF a matter of concern [15, 16]. Therefore, continuous monitoring of the long-term dynamics of GLs is necessary for understanding the impacts of global warming on the glacial lakes and for assessing the GLOF risk. However, mapping the glacial lake using coarse-resolution satellite images is a challenging task due to the presence of snow, melting glaciers, debris, and mountain shadows. To address these challenges, water indices, like NDWI, derived from the image bands are used to highlight the GLs from the non-GL objects in the image [17-19]. Other derived indices like NDSI [17], MNDWI [19-21], slope and hillshade [14, 22, 23] have also been used by various researchers for the identification of GLs. These input images are used by experts as well as machine learning algorithms to discriminate pixels belonging to GL from non-GL objects.

Manual digitization of GLs has also been attempted using False Colour Composite (FCC) image in the Pumqu River Basin of Tibet using Landsat 8 and SRTM DEM images [24]. Similarly, manual mapping of GLs of Sikkim Himalaya was done using NDSI and NDWI derived from Resourcesat-2, LISS III sensor images, and SRTM DEM [17]. However, manual digitization of GLs over a large region and several observation periods is prone to subjective error. Often, such methods lack reliability and reproducibility due to the limitation of model validation parameters that machine learning has [17, 25, 26].


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

non - GL objects . Manual digitization of GLs has also been attempted using False Colour Composite ( FCC ) image in the Pumqu River Basin of Tibet using Landsat 8 and SRTM DEM images [ 24 ] . Similarly , manual mapping of GLs of Sikkim Himalaya was done using NDSI and NDWI derived from Resourcesat - 2 , LISS III sensor images , and SRTM DEM [ 17 ] . However , manual digitization of GLs over a large region and several observation periods is prone to subjective error . Often , such methods lack reliability and reproducibility due to the limitation of model validation parameters that machine learning has [ 17 , 25 , 26 ] .

Several strategies have been adopted to demarcate the GLs. For instance, global threshold as well as local threshold of NDWI were used to map GLs, along with static thresholds of slope and hillshade, iteratively to distinguish GLs from other objects using Landsat images and SRTM DEMs [14]. A similar method was used to delineate the GLs of Sikkim Himalaya using Resourcesat-1, LISS III and Cartosat-I images [27]. Threshold-based studies have been used to map high altitude GLs of Asia using NIR, SWIR bands, and NDWI derived from Landsat 8 images along with slope derived from ASTER GDEM [26]. Threshold methods like Double-Window Flexible Pace Search (DFPS) and edge detection were used to determine the NDWI threshold for mapping GLs of Gangotri glacier using Sentinel 2 A images [28]. Beyond the threshold method and manual tracing of GLs, semi-automatic methods [11, 14, 18, 22, 24, 27-29], and automatic methods [20, 26] have also been successfully applied in mapping of GLs. It is worth noting that threshold-based mapping of GLs is only appropriate for GLs that have high spectral contrast for features like NDWI, slope, etc., as compared to their surroundings [14, 27]. It becomes even more difficult to apply the threshold method for small GLs in coarseresolution imageries that have mixed pixels containing debris, snow, and glaciers along with water [30]. Furthermore, mapping of GLs using threshold-based methods gets

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more challenging while considering variation in spatial, spectral, and temporal resolutions, due to the use of different satellites.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

worth noting that threshold - based mapping of GLs is only appropriate for GLs that have high spectral contrast for features like NDWI , slope , etc . , as compared to their surroundings [ 14 , 27 ] . It becomes even more difficult to apply the threshold method for small GLs in coarseresolution imageries that have mixed pixels containing debris , snow , and glaciers along with water [ 30 ] . Furthermore , mapping of GLs using threshold - based methods gets Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 3 of 33 more challenging while considering variation in spatial , spectral , and temporal resolutions , due to the use of different satellites .

Since 2020, GL mapping has slowly migrated away from manual digitization and threshold-based methods to machine learning methods. Threshold-based methods heavily depend on a static threshold or a fixed set of thresholds of one or two features for GL mapping [26, 31]. In contrast, machine learning-based methods can consider multiple features and have the innate adaptability to consider varied thresholds over a wide range of features to map GLs [21, 32]. This adaptability gives an advantage to machine learning in mapping glacial lakes in a coarse-resolution image. Machine learning-based techniques [33] like Random Forest Classifier (RFC) have proven highly effective in GL mapping [30]. Convolutional Neural Network (CNN) like U-Net have also yielded high-accuracy mapping of GLs [21, 34, 35]. In recent times, GL mapping has become much more reliable owing to the availability of satellite imageries, like Landsat 8 and Sentinel 1 A and 2 A, having high spatial, spectral, and temporal resolutions, in public data archives. Additionally, machine learning techniques like deep learning have further reduced the computation time and improved the accuracy of image classification. However, the performance of a neural network heavily depends on a large and balanced dataset as compared to other machine learning methods like RFC [36, 37]. Besides, such studies have only focused on the current state of the GLs and not on the long-term behaviour of the lakes, primarily due to the lack of reliable data sources [21, 34].


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

like Landsat 8 and Sentinel 1 A and 2 A , having high spatial , spectral , and temporal resolutions , in public data archives . Additionally , machine learning techniques like deep learning have further reduced the computation time and improved the accuracy of image classification . However , the performance of a neural network heavily depends on a large and balanced dataset as compared to other machine learning methods like RFC [ 36 , 37 ] . Besides , such studies have only focused on the current state of the GLs and not on the long - term behaviour of the lakes , primarily due to the lack of reliable data sources [ 21 , 34 ] .

The reason for the limited number of long term studies so far on the dynamics of glacial lakes is due to the limited accessibility and heterogeneity of long-term time series data of satellite from different sensors. For instance, Landsat 5 carried 7 spectral bands, including a single thermal band at 120 m. Landsat 7, in contrast, provided 8 bands, with its thermal band operating at 60 m. Both satellites offered 8-bit radiometry, allowing 256 brightness levels [38]. A major improvement came with Landsat 8, which operates with 11 spectral bands, including two thermal bands at 100 m. The multispectral resolution across all Landsat missions remained at 30 m, while thermal bands were resampled to 30 m. The most significant leap with Landsat 8 was its radiometric depth, expanded to 12-bit, providing 4096 brightness levels [38]. Nevertheless, several regions of the world still have historically sparse Landsat coverage. Fortunately, cloud computing platforms such as Google Earth Engine (GEE) now provide access to a large volume of orthorectified surface reflectance products and georeferenced data, including the complete Landsat time series archive. Online geocomputation in GEE enables generation of georeferenced outputs over large areas in very short time frames [39–41].

Change detection of GLs so far has been focused on the changes in the shape, size, number, and type of GLs over time [24, 42–44]. Many of these studies have documented decadal changes in total area and total number of GLs [29], time series analysis of selected GLs [22, 33, 45], observation period-based change detection of total number, total area and total types of GLs [46]. Change detection of regional GLs is a crucial step towards validation of the impacts of climate change on the cryosphere and hydrological regime. Moreover, understanding the dynamics of GLs is of paramount importance in GLOF mitigation. However, change detection can only provide qualitative information about the fate of the GLs under consideration. Only time series analysis and forecasting of individual lakes over a prolonged period can provide reliable data to foresee their change in surface area and volume [46]. Unfortunately, time series analysis of lakes have

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Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 1 Introduction
Type: text

, total area and total types of GLs [ 46 ] . Change detection of regional GLs is a crucial step towards validation of the impacts of climate change on the cryosphere and hydrological regime . Moreover , understanding the dynamics of GLs is of paramount importance in GLOF mitigation . However , change detection can only provide qualitative information about the fate of the GLs under consideration . Only time series analysis and forecasting of individual lakes over a prolonged period can provide reliable data to foresee their change in surface area and volume [ 46 ] . Unfortunately , time series analysis of lakes have Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 4 of 33

been confined to only one or very few lakes. The selection of such lakes remain highly subjective. Such studies do not shed any light on the fate of all the GLs of a region. In contrast, time series analysis of regional GLs has been performed by clubbing the GLs together in terms of the total number and total area. This kind of analysis does not shed any light towards the dynamics of individual lakes in the region. Moreover, time series analysis of selected lakes over a brief period does not provide reliable information on the long-term dynamics of the lakes. Forecasting models of the long-term dynamics of all the GLs over a regional extent have not yet been attempted [47, 48]. Most of these studies have avoided using multiple satellite data sources for time series-based forecasting models [49, 50]. This is most likely due to the lack of geospatial techniques to track individual glacial lakes over a large geographic extent and for long durations, such as several decades. Instead, most authors have manually tracked a few lakes that they considered relevant for their study. Hence, a methodology is required that provides long-term regional GL dynamics.

The objective of this study is to address this research gap by developing a new methodology of time series analysis and forecasting of GLs over a long period. For this, geocomputing using Landsat time series data on the GEE cloud computing platform was performed to prepare triennial median water indices, along with topographic indices. These indices were used to train a random forest classifier and an artificial neural network to identify the GLs. In this study, a glacial lake is defined as a lake, or a water body, formed by the action of a glacier. The dynamics of individual GLs were tracked using a unique lake ID (ULID). The ULIDs were used to prepare a time series dataset of the size of the lakes. Based on the time series dataset, the size of the lakes were projected up to 2026 using forecast models.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.1 Case study for demonstration
Type: text

The area of interest (AOI) in this study is the northern part of Sikkim. Sikkim is a northeastern state of India located in the eastern sector of the Himalayas. It is characterised by mountainous topography along and across its territory. The elevation varies from 280 m in the south at the border with West Bengal to 8,586 m at Mt. Kanchenjunga, bordering Nepal. Sikkim has the highest number of glaciers in comparison to other Indian states or union territories. One of the most prominent glaciers in Sikkim is the Zemu Glacier. It is the largest glacier in the Eastern Himalayas, with a length of approximately 26 km. It is a source of water for numerous rivers, including the River Teesta. Unfortunately, the Zemu Glacier has been receding since the last century at an alarming rate [51], leaving lateral moraine ridges on either side of the glacier. Many glaciers of Sikkim are debris-covered to a varying extent, making them prone to differential melting. The rapid melting of glaciers due to climate warming has resulted in the formation and expansion of many glacial lakes. Several of these lakes are supraglacial and periglacial in location. These lakes are moraine- and ice-dammed. Climate change-induced retreating glaciers [52], acute cloud bursts [53], and melting of ice dams [54] are putting several of these glacial lakes under the category of potentially dangerous glacial lakes (PDGLs). For instance, Sikkim Himalaya currently has 14 PDGLs [55] (Fig. 1).

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2 Materials and methods
2.1 Case study for demonstration


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.1 Case study for demonstration
Type: figure
Figure

Image /page/5/Figure/1 description: A figure from a scientific paper titled "AREA OF INTEREST" showing a map of a study area in the Sikkim Himalaya. An inset map in the upper right corner shows the location of Sikkim, highlighted in red, within India, bordering Nepal, China, and Bangladesh. An arrow points from the inset to the main map. The main map is a satellite image of a mountainous region with a grid overlay. The longitude lines range from 88.0°E to 88.8°E, and the latitude lines range from 27.2°N to 28.0°N. A legend in the top left explains the map's features: red areas are 'Glacial lakes', blue outlines indicate 'Glacier extent', and a semi-transparent overlay represents the 'Area of Interest'. The text explains that the Area of Interest (AOI) is defined by a 10 km buffer from the current glacier extent. The map includes a north arrow and a scale bar showing 0, 7.5, and 15 km. The figure caption at the bottom identifies it as "Fig. 1" and describes the study area as the Northern part of Sikkim.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.1 Case study for demonstration
Type: text

**Fig. 1** The study area or AOI considered in this study is the Northern part of Sikkim, encompassing the glaciers and glacial lakes


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: text

Google Earth Engine (GEE) is an open cloud-based online platform that archives geospatial data and provides a coding environment for geospatial analysis. GEE was used to compute several triennial median water indices from the Landsat 5 to 8 Surface Reflectance (SR) Tier 1 (T1) image archive for the period 1987 to 2020 (Fig. 2). The computation of the indices were confined to AOI. The AOI was prepared by buffering the glacier polygons of the Global Land Ice Measurements from Space (GLIMS) – Current [56]

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Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: figure
Figure

Image /page/6/Figure/1 description: A dual-axis combination chart displaying the number of images per year and the cumulative sum of images from 1987 to 2020. The x-axis represents the year. The primary y-axis on the left, labeled 'Images per year', ranges from 0 to 25. The secondary y-axis on the right, labeled 'Cumulative sum of images', ranges from 0 to 500. Blue vertical bars represent the 'images per year', showing fluctuations with a general upward trend. The number of images per year varies, with notable peaks in 1992 (approx. 16), 2006 (approx. 17), and a significant increase from 2012 onwards, peaking in 2018 at about 23 images. There is a sharp drop in 2002 and 2003. An orange line represents the 'cumulative sum of images', showing a steady and accelerating increase from near 0 in 1987 to approximately 475 by 2020. The chart is annotated with brackets indicating time periods labeled 'L5' (approx. 1988-2011), 'L7' (approx. 2011-2015), and 'L8' (approx. 2015-2019).


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: text

**Fig. 2** Landsat time series data from 1987–2020 of the Sikkim Himalaya. L5 is the Landsat 5 ETM sensor, L7 is the Landsat 7 ETM+sensor, and L8 is the Landsat 8 OLI/TIRS sensor. L5 and L7 have 11 bands, while L8 has 12 bands. The images are atmospherically corrected surface reflectance products using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)/ Land Surface Reflectance Code (LaSRC). The images are masked from the interferences caused by cloud, shadow, water, and snow using the C Function of Mask (CFMASK), as well as a perpixel saturation mask


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: figure
Figure

Image /page/6/Figure/3 description: A flowchart illustrating a multi-step process, likely for geographic or environmental analysis, divided into 11 steps. A large, curved blue arrow indicates the overall workflow direction. Each step is represented by a box containing icons and text labels.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: text

Step 1: Starts with GLIMS data, processed by GEE to create an AOI (Area of Interest).
Step 2: Landsat Time Series Data is processed by GEE to produce Triennial Water Indices Maps.
Step 3: SRTM DEM data is processed by GEE to create Topographic Indices Maps.
Step 4: JRC GSW Unchanged Extent data undergoes 'Clip + Data Pruning + Ground Truthing' to produce an AOI.
Step 5: The 'Unchanged Extent of GLs' is used to generate 'Random Points', resulting in 'Class-1 (True) Points'.
Step 6: Openstreet Map data is used to create an AOI. This is combined with the 'Unchanged Extent of GLs' through a 'Buffer + Clip' process. 'Random Points' are then generated, resulting in 'Class-0 (False) Points'.
Step 7: 'Triennial Water Indices Maps', 'Topographic Indices Maps', 'Class-1 (True) Points', and 'Class-0 (False) Points' are combined to create an 'ML Training-Testing Dataset'.
Step 8: The 'ML Training-Testing Dataset' is used for 'Model Performance Tests' and 'Imbalance Tests' to generate 'Prediction Maps'.
Step 9: 'Prediction Maps' undergo 'Vectorization' to create 'GL Vector Maps'. These are then processed by the 'ULID Method' to produce 'ULID Tagged Maps'.
Step 10: 'ULID Tagged Maps' and 'Buffered JRC GSW Maximum Extent' data are used for 'Selection by Location' to create 'Externally Validated GLs'. These are then processed by 'Selection by Attribute' to produce a 'GL Time Series Dataset'.
Step 11: The 'GL Time Series Dataset' is used to create 'Forecast Models' and conduct 'Model Performance Tests'.

**Fig. 3** Visual summary of the methodology. *AOI* Area of Interest, *GLIMS* Global Land Ice Measurements From Space, *GL* Glacial Lake, *ML* Machine learning algorithm, *SRTM DEM* Shuttle Radar Topography Mission digital elevation model, *GEE* Google Earth Engine cloud computing, *ULID* Unique Lake Identifier, *JRC GSW* Joint Research Centre Global Surface Water Mapping

by 10 km [42] and clipping them with the Sikkim administrative boundary vector map extracted from the OpenStreetMap [57] (Step 1, Fig. 3).

Median values of the triennial period were considered in this study as they are better representatives of the water indices that are influenced by temporal variabilities and missing data [7, 33]. Water indices considered for this study were selected to highlight the water body features based on the spectral signature in the visible and infrared spectra. They included Automated Water Extraction Index with No Shadow (AWEInsh), and Automated Water Extraction Index with Shadow (AWEIsh) [58–60], Band Ratio [46, 61], Modified Normalized Difference Water Index (MNDWI) [20, 62, 63], Normalized

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Difference Water Index using the Blue band (NDWIb) and Normalized Difference Water Index using the Green band (NDWIg) [17, 30], and Water Ratio Index (WRI) [64–66]:


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine
Type: text

, and Automated Water Extraction Index with Shadow ( AWEI < sub > sh < / sub > ) [ 58 – 60 ] , Band Ratio [ 46 , 61 ] , Modified Normalized Difference Water Index ( MNDWI ) [ 20 , 62 , 63 ] , Normalized Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 7 of 33 Difference Water Index using the Blue band ( NDWI < sub > b < / sub > ) and Normalized Difference Water Index using the Green band ( NDWI < sub > g < / sub > ) [ 17 , 30 ] , and Water Ratio Index ( WRI ) [ 64 – 66 ] :

$$AWEI_{nsh} = 4 \times (GREEN - SWIR1) - (0.25 \times NIR + 2.75 \times SWIR2)$$
 (1)

$$AWEI_{sh} = BLUE + 2.5 \times GREEN - 1.5 \times (NIR + SWIR1) - 0.25 \times SWIR2$$
 (2)

$$Band Ratio = \frac{GREEN}{NIR} \tag{3}$$

$$MNDWI = \frac{GREEN - SWIR1}{GREEN + SWIR1} \tag{4}$$

$$NDWI_b = \frac{BLUE - NIR}{BLUE + NIR} \tag{5}$$

$$NDWI_g = \frac{GREEN - NIR}{GREEN + NIR} \tag{6}$$

$$WRI = \frac{GREEN + RED}{NIR + SWIR2} \tag{7}$$

Where, GREEN and BLUE are representative visible bands of the Landsat bands. The NIR, SWIR 1 and 2 are the Near Infrared and Short Wavelength Infrared bands 1 and 2 of the Landsat bands (Step 2, Fig. 3). Beyond the water indices, two topographic features, namely slope [14, 22, 26] and hillshade raster maps [14, 27] were calculated in the GEE framework from the Shuttle Radar Topography Mission (SRTM) Version 3 (V3) digital elevation data of 30-meter resolution [67] (Step 3, Fig. 3). The slope and hillshade raster maps were calculated to further purify the water body feature extraction criteria (Table 1).


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment
Type: text

JRC GSW V 1.3 provides the changes in surface water bodies from 1984 to 2020 with epochs 1984–1999 and 2000–2020, the maximum extent of global surface water, and the average monthly extent of surface water [68]. The unchanged extent of the GLs of the AOI extracted from the JRC GSW V 1.3 was clipped by the AOI, and the product was validated by ground truthing using high-resolution images of Google Earth as well as Sentinel 2 A. The main purpose of this verification was to ensure that the polygons of GLs generated from JRC GSW V 1.3 were confined within the actual images of GLs (Step 4, Fig. 3). After confirmation, these polygons were used to generate 1000 random points. These points represented instances of places with GLs in the Sikkim Himalaya. The point vector map was considered as the GL instances (Class-1) of GLs (Step 5, Fig. 3). Next, a


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment
Type: table
Table: Table 1 Performance of RFC and ANN in the prediction of the GLs

| Dataset                                                                                | Source              | Purpose                                |
|------------------------------------------------------------------------------------------|---------------------|----------------------------------------|
| Global Land Ice Measurements from Space (GLIMS)                                          | Google Earth Engine | Glacier extent map                     |
| Sikkim administrative boundary                                                           | OpenStreetMap       | Study area vector map               |
| Landsat 5 to 8 Surface Reflectance (SR) Tier 1 (T1)                                      | Google Earth Engine | Water indices                          |
| Shuttle Radar Topography Mission (SRTM) Version 3 (V3)                                   | Google Earth Engine | Topographic indices                    |
| Joint Research Centre Global Surface Water Mapping Layers Version 1.3 (JRC GSW V 1.3) | Google Earth Engine | Reference map for visual validation |


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment
Type: text

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buffer of 250 m was created around the unchanged surface water extent polygons. The radius of 250 m of the buffer was determined considering the maximum variation in size of most of these GLs. This buffer was used to erase portions of the AOI vector map to generate the polygon without any GLs. The latter was used to generate 3000 random points as places without GLs in the Sikkim Himalaya. The point vector map was considered as the non-GL instances (Class-0) of GLs (Step 6, Fig. 3). The normalized triennial median water indices and terrain feature raster maps were used to seed the Class-1 point and Class-0-point vector maps [69]. The tables of these point vector maps were used for training the ML algorithms (Step 7, Fig. 3). Redundant data instances, like the ones with an incomplete set of feature values, were removed from the dataset.


Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis
Section: 2 Materials and methods > 2.4 Training and testing of machine learning algorithms using the dataset
Type: text

The dataset was split into 75% for training and the rest for testing the predictions. Next, the algorithms were trained on a specific triennial period (e.g., 1987–1990 or 2017–2020). Two machine learning algorithms were considered for classification, namely ANN and RFC. CNN was not considered in this study as it was not a viable classifier for the study. Unlike the classification of a single image, as has been done in most studies involving U-Net [36, 48, 63], this study required the image classification of varied images from sources like Landsat 5 to 8 and SRTM data, at various times. At different times, the same lake may have a different shape, spectral signature, and snow thickness. Thereby, instead of relying on the image properties as used by CNN for image classification, pixel properties as used by RFC and ANN were incorporated to avoid misclassification of the images.