Document: s41467-023-36033-x
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- 18  . Importantly , these lakes can represent a substantial hazard in the form of glacial lake outburst floods ( GLOFs ) . GLOF triggering is complex , with dam breach initiation caused by mass movement - induced impulse waves  19 , 20  , lake overfilling due to pluvial , nival and glacial runoff  21  , and moraine - or ice dam degradation being variably important dependent on setting  22 , 23  . Consequently , the probability of a lake releasing a GLOF is difficult to accurately quantify without detailed and localised studies .

GLOFs can be highly destructive and can arrive with little prior warning, causing significant damage to property, infrastructure, and agricultural land, and resulting in extensive loss of life. However, the impact varies significantly across the globe; in the last 70 years, several thousand people have been killed by GLOFs in the Cordillera Blanca alone24,25, most from a small number of events26,27, while only 393 deaths in the European Alps can be directly linked to GLOF activity over the last 1000 years28. The continued ice loss and expansion of glacial lakes due to climate change therefore represents a globally important natural hazard that requires urgent attention if future loss of life from GLOF is to be minimised29,30 and the UN's Sustainable Development Goals (particularly Goal 11–Disaster Risk Reduction) are to be met.

Since 1990, the number, area, and volume of glacial lakes globally has grown rapidly, increasing by 53%, 51%, and 48% respectively30. Concurrent with the rapid growth of glacial lakes, many catchments downstream have experienced rapid and large increases in population, infrastructure and hydroelectric power (HEP) schemes, while agriculture has intensified31–35. However, the socio-economic vulnerability

1School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UK. 2School of Earth & Environment, University of Canterbury, Christchurch, New Zealand. 3Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK.

—e-mail: thomas.robinson@canterbury.ac.nz


Document: s41467-023-36033-x
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in population , infrastructure and hydroelectric power ( HEP ) schemes , while agriculture has intensified  31 – 35  . However , the socio - economic vulnerability  1  School of Geography , Politics and Sociology , Newcastle University , Newcastle upon Tyne , UK .  2  School of Earth & Environment , University of Canterbury , Christchurch , New Zealand .  3  Department of Geography and Environmental Sciences , Northumbria University , Newcastle upon Tyne , UK . — e - mail : thomas . robinson @ canterbury . ac . nz

to climate-related hazards is thought to have decreased36, although this decrease is spatially heterogenous and it remains unclear if this heterogeneity is sufficient to offset potential increases in hazard and exposure. Contemporaneous changes in lake conditions and downstream damage potential (i.e., the combination of exposure—the proximity of populations to a potential outburst—and vulnerability—the exposed populations likelihood to be impacted by the GLOF) are all critical components of GLOF danger10,31,37,38. However, how the recent observed changes in each combine to produce contemporary global GLOF danger remains unclear29. While regional scale GLOF risk assessments have been undertaken39,40, to our knowledge, no global scale study has been attempted that considers not just the physical lake conditions, but also societal exposure and vulnerability that directly influence GLOF danger41.

Here we combine the most up-to-date lake condition, exposure, and vulnerability data available to quantify and rank contemporary (2020) damage potential from GLOFs at a global scale, adding to similar recent approaches for hydrometeorological floods42,43. We analyse the spatial distribution of population exposure to determine where populations are in relation to glacial lakes, using necessarily simple estimates of potential GLOF runout paths (50 km runout, with potentially affected populations located within 1 km of a river course), therefore identifying potential GLOF danger hotspots and thus higher priority zones for mitigation and further, local-scale research. While this study captures lake conditions and damage potential as they were in 2020, the methods presented provide a framework to capture changing GLOF danger through time.


Document: Results
Section: Results > Lake conditions
Type: text

As of 2020, regional normalised GLOF lake conditions, represented in terms of the total number and area of glacial lakes, were highest in the Pacific Northwest (PNW; 1.000), and lowest in the European Alps (0.041) (Fig. S1). There was high variability between nations, with individual GLOF lake conditions highest in Greenland and Canada (1.000 and 0.685 respectively) and lowest in Ecuador (0.001). Excluding Uzbekistan (no glacial lakes, normalised hazard score of zero) the largest range in intra-regional GLOF lake condition scores were seen in High Mountain Asia (HMA), ranging from a high score in China (0.319) to a low score in Mongolia (0.006). Generally, normalised national GLOF lake condition scores in HMA are below 0.100, with the exception of China.


Document: Results
Section: Results > Exposure
Type: text

In total, 90 million people across 30 countries live in 1089 basins containing glacial lakes (Fig. 1a). Our analysis indicates that of these, 15 million (16.6%) live within 50 km of a glacial lake and 1 km of potential GLOF runout tracks (Fig. 1a). We find that 62% (-9.3 million) of the globally exposed population are located in the HMA region. Globally, the proportion of exposed population varies significantly between countries; India and Pakistan contain the highest number of exposed people (-3 million and -2 million people respectively, or one-third of the global total combined) while Iceland contains the least (260 people) (Fig. 1b). Just four highly populous countries account for >50% of the globally exposed population: India, Pakistan, Peru, and China (Fig. 2a). As a result, regionally HMA has the highest normalised exposure score (1.000) while the High Arctic and Outlying Countries score the lowest (0.019). India and Pakistan are the highest individually scoring nations (1.000 and 0.701), and Sweden is the lowest (0.001).

Generally, the population exposed to GLOFs increases with distance from a glacial lake, with almost half (48%) of exposed populations globally located between 20 km and 35 km downstream of lakes (Fig. 2). Only 2% (300,000) of the global population exposed to GLOFs live within 5 km of one or more glacial lakes (Fig. 2), with the majority of these (66%; 198,000) found in HMA (Fig. 2). Populations in HMA live,

on average, closer to glacial lakes than anywhere else, with -1 million people living within 10 km downstream of a glacial lake, where any early warning time is likely to be low, and, uncertainty in GLOF magnitude high. In contrast, populations across the PNW and High Arctic and Outlying Countries are generally situated further than 35 km downstream from glacial lakes (Fig. 2). Analysis of exposure at the national scale reveals considerable sub-regional variability, with populations in Pakistan living closest to glacial lakes, (0.8 million within the first 15 km (Fig. S2)), while settled populations in Kyrgyzstan are living at least 35 km downstream (Fig. S2).


Document: Results
Section: Results > Vulnerability
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The three indices which were used to calculate vulnerability (CPI, HDI, and SVI) showed marked variation between and within regions (Fig. S3). Generally, the Andes and HMA have the highest levels of corruption and social vulnerability and lowest levels of human development, while the contrary is true for the European Alps, PNW and High Arctic and Outlying Countries. However, regional summaries mask some substantial national variations; although the Andes region has an average corruption score of 51, country level scores vary from high corruption in Bolivia (33) to lower corruption in Ecuador (88). Similarly, the average human development score in HMA (0.671) masks a range of scores, from a low of 0.511 (Afghanistan) to a high of 0.825 (Kazakhstan). Nonetheless, HMA is identified as the most vulnerable region to GLOF in 2020 (0.768) and the PNW the least (0.336). Overall, Afghanistan and Pakistan are the most vulnerable nations (0.919 and 0.837 respectively) while Switzerland and New Zealand are the least (0.194 and 0.186 respectively).


Document: Results
Section: Results > GLOF Danger
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The combined normalised scores of GLOF lake conditions, exposure and vulnerability reveal HMA to have the highest GLOF danger as of 2020 (0.313), with a total of 9.3 million people exposed to 2211 lakes covering an area 1256.09 km2. Comparatively, the High Arctic and Outlying Countries have the lowest GLOF danger (0.032) with <200,000 people exposed, albeit to a similarly high number and area of glacial lakes (1862 lakes covering an area 1166.09 km2) (Fig. 3). As with the individual components, there is substantial sub-regional variation in GLOF danger itself (Fig. 3). China and Pakistan have the highest danger globally (0.863 and 0.751 respectively). Pakistan has near double the exposed population of China (2.1 million and 1.1 million respectively) and is significantly more vulnerable (0.837 compared to 0.683 in China). However, with more numerous lakes, and of larger area (1109 lakes covering 1094.44 km²) the GLOF lake condition score in China is large enough to more than offset these differences. Both Greenland and Uzbekistan have a danger score of zero because as of 2020 they have, respectively, no exposed population and no glacial lakes.

When all 1089 glacial basins are ranked from highest to lowest risk (Fig. 3), the top three are found in Pakistan (Khyber Pakhtunkhwa basin), Peru (Santa basin), and Bolivia (Beni basin) (Fig. S6, Table S1) containing, respectively, 1.2 million, 0.9 million and 0.1 million people who could be exposed to GLOF impacts. Interestingly, Canada and USA contain just 3 basins in the top 50 globally (Table S1) as well as the lowest ranking basin (Tyers basin, Canada), where exposure is negligible as potential GLOF runout tracks are largely unpopulated. However, Canada and USA have relatively high GLOF danger scores (0.321 and 0.059) ranking 4th and 6th respectively, mainly because they host a large number of basins with generally high GLOF lake condition scores, highlighting the importance of spatial scale in these analyses.


Document: Results
Section: Discussion
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With an increase in interest surrounding GLOFs over the last few decades, a clear geographical disparity has emerged between where GLOFs are occurring and the hotspots of research44,45. Between 1990


Document: Results
Section: Discussion
Type: figure
Figure

Image /page/2/Figure/2 description: A world map, labeled (a), displaying several major mountain regions highlighted in different colors, with corresponding pie charts showing the percentage distribution of these regions by country. A legend in the bottom right corner identifies the regions: Alps (red), Andes (blue), HMA (green), PNW (purple), HAOC (orange), and Global (white). The PNW region in North America is split between the USA (65%) and Canada (CAN, 35%). The Alps in Europe are distributed among Italy (ITA, 39%), Switzerland (CHE, 33%), France (FRA, 18%), and Austria (AUT, 10%). The Andes in South America are broken down into Peru (PER, 52%), Bolivia (BOL, 16%), Colombia (COL, 13%), Chile (CHL, 9%), Ecuador (ECU, 7%), and Argentina (ARG, 3%). The large HMA region in Asia is distributed as follows: India (IND, 32%), Pakistan (PAK, 23%), China (CHN, 12%), Kyrgyzstan (KGZ, 10%), Nepal (NPL, 9%), Kazakhstan (KAZ, 6%), Afghanistan (AFG, 4%), Uzbekistan (UZB, 2%), Bhutan (BTN, 1%), Tajikistan (TJK, 1%), with Myanmar (MMR) and Mongolia (MNG) at 0%. The HAOC region, covering high-arctic and oceanic areas, is distributed across Russia (RUS, 68%), Georgia (GEO, 25%), New Zealand (NZL, 5%), Greenland (GRL, 2%), with Iceland (ISL), Sweden (SWE), and Norway (NOR) at 0%. Inset boxes provide magnified views of the PNW, Andes, Alps, and HMA regions. A key in the bottom left shows three concentric circles representing 5%, 15%, and 50%.


Document: Results
Section: Discussion
Type: figure
Figure

Image /page/2/Figure/3 description: A bar chart, labeled (b), showing the population exposed to Glacial Lake Outburst Floods (GLOF) for various countries. The x-axis lists countries by their three-letter abbreviations. The left y-axis represents the 'Population exposed to GLOF (% national total)' from 0 to 16, corresponding to white bars with gray outlines. The right y-axis represents the 'Population exposed to GLOF (millions)' from 0.00 to 3.00, corresponding to solid colored bars. The data for each country is approximately as follows, with the first value being the percentage and the second being the population in millions: AFG (2.2%, 0.5M green), ARG (0.1%, 0.2M red), AUT (3.5%, 0.5M blue), BOL (12.3%, 0.3M purple), BTN (8.5%, 0.8M red), CAN (0.2%, 0.5M purple), CHE (3.8%, 0.8M red), CHL (1.8%, 0.4M blue), CHN (0.1%, 1.2M green), COL (0.1%, 0.4M blue), ECU (0.5%, 0.3M blue), FRA (0.2%, 0.4M red), GEO (0.5%, 0.1M orange), GRL (0.1%, negligible millions), IND (0.2%, 2.8M green), ISL (0.1%, negligible millions), ITA (0.5%, 0.9M red), KAZ (0.7%, 0.6M green), KGZ (15.8%, 0.7M green), MNG (0.1%, negligible millions), NOR (0.1%, negligible millions), NPL (2.5%, 0.8M green), NZL (0.1%, negligible millions), PAK (0.3%, 2.1M green), PER (3.8%, 1.4M blue), RUS (0.1%, negligible millions), SWE (0.1%, 0.05M orange), TJK (0.3%, 0.2M green), USA (0.1%, 0.6M purple), UZB (0.1%, 0.2M green).


Document: Results
Section: Discussion
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**Fig. 1** | **Global distribution of GLOF exposure.** a Global distribution of glacial basins, colour-coded according to mountain range, with 'High Arctic and Outlying Countries' (HAOC) representing all basins outside of the four main ranges in this study (Alps, Andes, High Mountains Asia (HMA) and Pacific North West (PNW)). Pie charts show the proportion of exposed population as individual country

contributions to the mountain range total, with pie charts sized according to percentage contribution to the 2020 global total. **b** Grey bars show exposed population as a percentage of the national total (left axis). Coloured bars show the total exposed population per country (right axis).

and 2015, Iceland, the North American Cordillera and Hindu-Kush-Karakoram were the most prominent GLOF research hotspots with 180, 144, and 142 published research items, respectively44 (Fig. S3). Since 2015, however, the Himalayas have emerged as the primary research focus, accounting for 36% of the studies undertaken between 2017 and 202145. As such, these 'hotspot' regions are often cited as having the highest GLOF danger. While true in part, our results also indicate that as of 2020, the potential for large GLOF impacts is also high across the Andes (Fig. 3), and as a nation, danger in Peru is third highest globally (Fig. 3b).

Over the last two decades, glaciers across the Andes have undergone rapid deglaciation in response to climate changes46,47 leading to the growth of many large glacial lakes and consequently a growth in overall GLOF lake conditions (Fig. 4); the number of glacial lakes across the region increased by 93% compared to just 37% in HMA across the period. Concurrent with this increase, populations living in close proximity to glacial lakes have grown (Fig. 4), increasing the overall exposure to GLOF (Fig. 2); since 1941 the population in Huaraz, Peru alone has increased by >100,00048. At the same time, regional

vulnerability remains high as a result of deep-rooted corruption and poor standards of living (Fig. S4). Comparative to other regions, the number of GLOF research items across the Andes are few; less than 8% of the research conducted between 1979 and 2021 were undertaken in this region (<100 items)44,45 (Fig. S3). We suggest this data sparsity across the Andes is perhaps preventing meaningful assessments of actual GLOF risk in the region and urgently requires attention, particularly given the second- and third-most dangerous basins are found in this region, and the region as a whole is ranked second for GLOF danger globally (Fig S6).


Document: Results
Section: Discussion
Type: text

Fig . S4 ) . Comparative to other regions , the number of GLOF research items across the Andes are few ; less than 8 % of the research conducted between 1979 and 2021 were undertaken in this region ( < 100 items )  44 , 45  ( Fig . S3 ) . We suggest this data sparsity across the Andes is perhaps preventing meaningful assessments of actual GLOF risk in the region and urgently requires attention , particularly given the second - and third - most dangerous basins are found in this region , and the region as a whole is ranked second for GLOF danger globally ( Fig S6 ) .

GLOFs can have exceptional discharges and runout distances, reaching >120 km downstream49,50. Here, we have considered anyone living within 1 km of likely GLOF runout tracks up to a maximum distance of 50 km from the glacial lake to be at risk of either direct (e.g., death or injury) or indirect (e.g., loss of land, damaged infrastructure) impacts. However, peak discharge attenuates rapidly from the flood source31, meaning that impacts are generally greatest with increasing proximity to a glacial lake. We show populations in HMA (Fig. 2), and particularly those in Pakistan (Fig. S2), are living closest to glacial lakes.


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Section: Discussion
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Figure

Image /page/3/Figure/2 description: A figure with two charts, (a) and (b), showing the spatial distribution of the population exposed to Glacial Lake Outburst Floods (GLOF).


Document: Results
Section: Discussion
Type: text

Chart (a) is a bar chart titled "Spatial distribution of population exposed to GLOF". The y-axis is "Total global population exposed to GLOF (millions)" from 0.00 to 2.50. The x-axis is "Distance from glacial lake (km)" from 5 to 50. For each distance, there are colored bars representing different mountain ranges and a light gray bar for the global total. The legend indicates: Alps (red), Andes (blue), HMA (green), PNW (purple), HAOC (orange), and Global (light gray). The green bars (HMA) are the tallest among the mountain ranges at all distances, peaking at over 1 million people. The global total peaks at around 2 million people at a distance of 35 km. Two vertical dashed lines mark the "Average GLOF runout in the Cordillera Blanca" (approx. 14 km) and "Average GLOF runout in HMA" (approx. 24 km).

Chart (b) is a pie chart titled "Mountain range population exposed to GLOF". It shows the percentage distribution of the exposed population among the mountain ranges: HMA (green) is 62%, Andes (blue) is 17%, Alps (red) is 15%, PNW (purple) is 5%, and HAOC (orange) is 1%.

**Fig. 2** | **Global spatial distribution of exposure.** a Spatial distribution of exposure within GLOF runout tracks up to 50 km from a glacial lake, at 5 km intervals at the global and mountain range scale. **b** Total contribution of mountain range to the

global total exposed population. Countries are coloured according to mountain range. *HMA* High Mountains Asia, *PNW* Pacific North West, *HAOC* High Arctic and Outlying Countries.


Document: Results
Section: Discussion
Type: text

( red ) is 15 % , PNW ( purple ) is 5 % , and HAOC ( orange ) is 1 % . * * Fig . 2 * * | * * Global spatial distribution of exposure . * * a Spatial distribution of exposure within GLOF runout tracks up to 50 km from a glacial lake , at 5 km intervals at the global and mountain range scale . * * b * * Total contribution of mountain range to the global total exposed population . Countries are coloured according to mountain range . * HMA * High Mountains Asia , * PNW * Pacific North West , * HAOC * High Arctic and Outlying Countries .

With the expansion of agriculture, development of new HEP sites (and with increasing proximity to glacial lakes), and growth of the tourism sector expected to increase in this region over the next few decades, it follows that exposure is only likely to increase as people move to higher elevations to support the aforementioned development, as has been observed in other mountain regions globally24,51-53. The characteristic rapid onset and high discharge of GLOFs means there is often insufficient time to effectively warn downstream populations and for effective action to be taken, particularly for populations located within ~10–15 km of the source lake54,55. Improvements are urgently needed to Early Warning Systems (EWS) alongside evacuation drills, plus other forms of community outreach that are sympathetic to potential social and cultural barriers, to enable more rapid warnings and emergency action in these highly exposed areas. Across HMA resources for mitigation are often limited28, and residents' lack of awareness, or lack of means to affect change, inhibits their ability to prepare for, and recover from, potential GLOF disasters sourced from remote glacial lakes56. Thus, analysing the spatial distribution of exposure as presented here not only highlights where advances are needed (e.g., EWS) but could also allow for more effective mitigation strategies (e.g., land zoning, education) to be implemented. Similar to our findings for the Andes, while Pakistan is a hotspot of GLOF danger, there is a comparative lack of published research focusing on this country and despite large-scale investment (>US\$30 million) in GLOF vulnerability projects from the United Nations Development Programme57. We suggest the area should be targeted for more detailed research.

Glaciers are exhibiting negative mass balance in nearly all glaciated regions of the world5, and over the past three decades the number, area and volume of glacial lakes have increased rapidly30. Our data show that countries with the largest, or most numerous, glacial lakes do not always possess a high GLOF danger. Instead, our results show that it is the exposed population that greatly elevates the potential impact of GLOFs globally (Fig. 4), particularly across HMA and the Andes (Fig. 1). For instance, Greenland has the highest number and area of glacial lakes of any nation in this study, thus has the highest hazard score (1.000) yet no people reside along likely GLOF runout tracks giving it a danger score of zero. Documenting changes in glacial lakes and highlighting areas where GLOF lake conditions may be


Document: Results
Section: Discussion
Type: text

countries with the largest , or most numerous , glacial lakes do not always possess a high GLOF danger . Instead , our results show that it is the exposed population that greatly elevates the potential impact of GLOFs globally ( Fig . 4 ) , particularly across HMA and the Andes ( Fig . 1 ) . For instance , Greenland has the highest number and area of glacial lakes of any nation in this study , thus has the highest hazard score ( 1 . 000 ) yet no people reside along likely GLOF runout tracks giving it a danger score of zero . Documenting changes in glacial lakes and highlighting areas where GLOF lake conditions may be

increasing, while valuable, does not therefore provide an accurate indication in terms of danger trajectories, since contemporaneous changes in population exposure may more than offset changes in lake conditions (Fig. 4). Furthermore, our data begins to highlight the degree to which natural disasters impact people; two outburst events affecting the same number of people with the same material impact (e.g., a footbridge or road washed away) can have fundamentally different consequences depending on the social, political, cultural and economic context of the country, or even catchment, in which they occur24,28,58-60. This highlights the crucial role of exposure and vulnerability in determining the impact of GLOFs. While hazard assessments dominate GLOF studies61, exposure and vulnerability assessments remain relatively unexplored topics that urgently need addressing, particularly in developing countries62 where GLOF danger is generally highest (Fig. 3).

How GLOF danger might change in the future remains subject to debate. As glaciers continue to recede existing glacial lakes will expand, and many new lakes will form39, altering the spatial pattern of GLOF lake conditions63. At the same time, we will see spatiotemporal changes in populations and their vulnerability as people, goods and services migrate in response to various socioeconomic drivers, and development related to the growth of tourism, HEP and agriculture continues to expand into higher elevations closer to glacial lakes and other forms of natural hazard52. We have shown the most dangerous basins, mainly found across HMA and the Andes do not always host the most, or the largest, glacial lakes, and rather it is the high number of people and the reduced capacity of those people to cope with disaster that plays a key role in determining overall GLOF danger (Fig. 3). This finding highlights the need for a more holistic approach to GLOF risk assessment, where each component of hazard, exposure, and vulnerability are accounted for. We highlight the value of globalscale spatial danger analysis (Fig. 3) and envisage our findings to be the starting point for more targeted risk assessments at the nationaland basin-scale. Our findings are important, as they not only identify countries and basins that rank highly in terms of GLOF danger, which can allow for more targeted GLOF risk management, but also regions where more research is urgently needed to understand risk at a fundamental level. In particular, we highlight the Andes as an under-


Document: Results
Section: Discussion
Type: figure
Figure

Image /page/4/Figure/2 description: A figure titled "Global GLOF danger" is presented in two parts, (a) and (b).


Document: Results
Section: Discussion
Type: text

Part (a) displays four maps showing the spatial distribution of GLOF (Glacial Lake Outburst Flood) danger at a basin scale. A color-coded legend indicates the GLOF Danger Rank, ranging from HIGH (red, rank 1-125) to LOW (dark blue, rank 1025+). The intermediate colors are shades of orange and light blue. The four maps are:
(i) Pacific North West: Covering Alaska, western Canada, and the northwestern USA, showing high danger in red along the southern coast of Alaska and British Columbia, with lower danger areas in blue further inland and north.
(ii) Andes: Showing high danger (red and orange) along the mountain range through Peru, Bolivia, Chile, and Argentina.
(iii) High Mountain Asia: Displaying a large area including the Hindu Kush, Karakoram, and Himalayas, with widespread high danger (red and orange). Lower danger areas (blue) are also present.
(iv) European Alps: Covering parts of France, Switzerland, Italy, and Austria, with a mix of danger levels, including high-danger red areas in the central Alps.

Part (b) is a scatter plot showing the final normalized scores for various countries related to GLOF. The y-axis represents "Normalised scores" from 0 to 1, and the x-axis lists countries by their three-letter codes. The legend indicates four metrics: Hazard (red triangle), Exposure (orange square), Vulnerability (blue circle), and Danger (black 'x').
Key data points include:
- CHN (China): Hazard ~0.35, Exposure ~0.4, Vulnerability ~0.9, Danger ~0.85.
- PAK (Pakistan): Hazard ~0.05, Exposure ~0.05, Vulnerability ~0.8, Danger ~0.9.
- IND (India): Hazard ~0.65, Exposure ~0.05, Vulnerability ~0.7, Danger ~0.25.
- USA: Hazard ~0.1, Exposure ~0.2, Vulnerability ~0.4, Danger ~0.05.
- CHE (Switzerland): Hazard ~0.1, Exposure ~0.2, Vulnerability ~0.75, Danger ~0.25.
Other countries listed include PER, CAN, CHL, ITA, NPL, COL, KGZ, RUS, AFG, BOL, KAZ, ARG, TJK, BTN, FRA, AUT, NZL, NOR, ECU, MNG, GEO, SWE, ISL, GRL, and UZB.

**Fig. 3** | **Global GLOF danger.** a Spatial distribution of GLOF danger at basin scale from high (red) to low (blue) risk. **b** Final normalised scores of GLOF lake conditions ('hazard'), exposure, vulnerability, and danger for each country, ordered from highest danger score (left) to lowest (right).

studied hotspot of GLOF danger and suggest that the region is targeted for more detailed study. While we show the global picture of contemporary GLOF danger, it remains unclear how this danger is changing temporally and whether such changes are being driven by

changes in lake conditions, damage potential, or some combination. Thus work is required to evaluate temporal changes in lake conditions, exposure and vulnerability in order to determine the relative roles in each for GLOF danger.


Document: Results
Section: Discussion
Type: figure
Figure

Image /page/5/Figure/2 description: A bubble scatter plot, labeled (a), showing the relationship between 'Population growth (% a⁻¹)' on the x-axis and 'Glacial lake area change (% a⁻¹)' on the y-axis. The x-axis ranges from -2.00 to 5.00, and the y-axis ranges from -6.00 to 12.00. Each bubble represents a country, identified by a three-letter code. The size of the bubble indicates the 'Population exposed to GLOF (x 10⁶)', with a key in the top right showing concentric circles for 0.1, 1.0, 2.0, and 3.0 million people. The color of the bubble represents a geographical region, as defined by a legend in the bottom right: red for Alps, blue for Andes, green for HMA, purple for PNW, and orange for HAOC. Most countries, particularly those in the HMA and Andes regions like India (IND), Pakistan (PAK), Peru (PER), and Nepal (NPL), are located in the upper-right quadrant, indicating both positive population growth and increasing glacial lake area. Sweden (SWE) shows the highest glacial lake area change at around 10%, while Nepal (NPL) shows one of the highest population growth rates at around 4%. India (IND) and Pakistan (PAK) have the largest bubbles, indicating the highest populations exposed to GLOF.


Document: Results
Section: Discussion
Type: figure
Figure

Image /page/5/Figure/3 description: A bubble chart, labeled (b), plotting 'Glacial lake area change (% a⁻¹)' on the y-axis versus 'Population growth (% a⁻¹)' on the x-axis. The y-axis ranges from -6.00 to 12.00, and the x-axis ranges from -2.00 to 5.00. Each bubble represents a country, identified by a three-letter code. The size of the bubble corresponds to the 'Population exposed to GLOF (% national total)', with a legend in the top right showing concentric circles for 5, 25, 35, and 50 percent. The color of the bubbles indicates the geographical region, as per the legend in the bottom right: Red for Alps (e.g., CHE, ITA), Blue for Andes (e.g., PER, CHL), Green for HMA (e.g., NPL, IND), Purple for PNW (e.g., USA, CAN), and Orange for HAOC (e.g., SWE, GEO). For example, Sweden (SWE) is near the top with a high glacial lake area change of about 10%, while Switzerland (CHE) has a large negative change of about -2.2% and a very large bubble, indicating high population exposure. Nepal (NPL) has a high population growth of nearly 4%.


Document: Results
Section: Discussion
Type: text

**Fig. 4** | **Rate of change in glacial lake area and total population.** Rates of population change between 1990 and 2020 and glacial lake area change between 1990 and 2018 as **a** absolute population exposed to GLOFs and **b** percentage of national population exposed to GLOFs in each country. Countries are colour coded

according to Mountain Range. While our study only considers contemporary GLOF danger, this highlights that variable changes in population and lake conditions may lead to very different danger scores in the near future. *HMA* High Mountains Asia, *PNW* Pacific North West, *HAOC* High Arctic and Outlying Countries.


Document: Results
Section: Methods > GLOF lake conditions
Type: text

Within natural hazard research, hazard is a critical component of risk and is defined as a function of the probability and intensity of an event, i.e., the likelihood that an event will occur from a given site based on intrinsic properties and dynamic characteristics of that site combined with the overall magnitude of the event52. Thus, the probability of a GLOF occurring at a given point in time is dependent on specific local conditions, including, but not limited to; potential topographic triggers (ice/rock/snow avalanche etc), lake-dam geometries, and lake area/volume etc10,29,33,39. Further, the likelihood of lake failure will almost certainly vary temporally. Attempts to quantify the probability of GLOFs have been undertaken at regional-scale using simple proxies for the likelihood of landslide and/or ice avalanches into lakes39,53. However, to be applied globally, these approaches require globally consistent, high-resolution DEMs, which are known to suffer from considerable artefact issues in high mountain regions where GLOFs originate64. Therefore, quantifying the probability of failure is inherently difficult at a global scale. Thus, here, we take a consequencebased approach and focus on quantifying only the intensity of a potential GLOF. We do this by using the total lake area as a proxy for intensity, where larger lakes have the potential to produce larger, more intense GLOFs. Previous regional-scale work39 has sought to use lake volume as a proxy for GLOF intensity by applying simple area-volume relationships to convert mapped lake area to assumed lake volume. However, for our study any area-volume relationship would need to be globally consistent, scaling all lake hazard values consistently, and thus we prefer to use simply mapped lake area instead.

As such, we treat the probability of failure as unknown and instead focus on quantifying the impacts or effects on the potentially affected population. Consequently, we prefer the term GLOF lake conditions to GLOF hazard, with our scoring system instead highlighting conditions that may yield more intense GLOFs should a failure occur. When combined with damage potential, the basins with the largest potential impacts can then be targeted for more detailed local studies to ascertain the probability of a GLOF occurring in the first place, allowing those with the highest potential losses to be prioritised for more local

studies. While probability is a key element of risk, we note that basins with high potential impact would still be considered comparatively high risk if the probability of failure was found to be low, while basins with low potential impacts would only suffer marginal increases in comparative risk if the probability of failure was found to be high.


Document: Results
Section: Methods > GLOF lake conditions
Type: text

may yield more intense GLOFs should a failure occur . When combined with damage potential , the basins with the largest potential impacts can then be targeted for more detailed local studies to ascertain the probability of a GLOF occurring in the first place , allowing those with the highest potential losses to be prioritised for more local studies . While probability is a key element of risk , we note that basins with high potential impact would still be considered comparatively high risk if the probability of failure was found to be low , while basins with low potential impacts would only suffer marginal increases in comparative risk if the probability of failure was found to be high .

We use the Level 4 Global Water Resource Zones shapefiles65 and the most recently available global inventory of glacial lakes30 to identify 1089 basins containing glacial lakes. We note that these Water Resource Zones do not represent true river catchments, instead showing regions that contain several associated rivers flowing into a lake or ocean, with Level 4 representing rivers that have no tributaries larger than 100 km2. This can cause strange effects, particularly in large coastal or plains areas, such as in Chilean Patagonia (Fig. 3a). However, to our knowledge there is no suitable global dataset of river catchments, and regional and national datasets are too inconsistently derived for our globally-focussed study. We group basins into four main mountain ranges; European Alps, Andes, High Mountain Asia (HMA) and Pacific Northwest (PNW), with the remaining 131 (12%) basins outside of these ranges referred to as 'High Arctic and Outlying Countries'. We then extract the raw number and area of glacial lakes per basin/country/region to act as proxies for potential GLOF intensity, before performing a linear transformation function to produce a normalised value for each indicator (Eq. 1);

$$y_{N/A} = \frac{(X)}{(Max)} \tag{1}$$

where x is the absolute number/area of glacial lakes per basin/country/ region, Max is the maximum number/area of glacial lakes found out of all basins/countries/regions, and y is the normalised value of glacial lake number/area per basin/country/region. Individual normalized values of glacial lake number ( $y_N$ ) and area ( $y_A$ ) are then multiplied to produce a singular score between 0 and 1, with higher values relating to lake conditions with the potential for more intense GLOFs.

Finally, we consider the potential downstream spatial extent of GLOFs by considering the expected reach. Runout distances of GLOFs primarily vary as a function of outburst volume and stream


Document: Results
Section: Methods > GLOF lake conditions
Type: text

/ region , Max is the maximum number / area of glacial lakes found out of all basins / countries / regions , and y is the normalised value of glacial lake number / area per basin / country / region . Individual normalized values of glacial lake number ( $ y_N $ ) and area ( $ y_A $ ) are then multiplied to produce a singular score between 0 and 1 , with higher values relating to lake conditions with the potential for more intense GLOFs . Finally , we consider the potential downstream spatial extent of GLOFs by considering the expected reach . Runout distances of GLOFs primarily vary as a function of outburst volume and stream

gradient, as well as other factors such as bed roughness, sediment concentration etc66. Thus, defining a runout distance from which to assess exposed population on a global scale is difficult. Previous research29 set a runout cut-off distance of 50 km, to facilitate a standardized comparison between glacial lakes. Their 50 km threshold is consistent with a number of observed runout distances of past GLOFs, such as at Dig Tsho in 198567, Chilleon Valley in 201568 and Chorabari in 201469. Comparisons of likely GLOF discharges with that of meteorological floods70 suggest the majority (50%) of likely GLOFs that exceed the 100-year meteorological flood discharge do so to only ~20 km downstream, with 1% theoretically reaching >85 km31. However, with lake sizes increasing due to climate change, runout of future GLOFs may well exceed that of those previously observed due to the larger volume of water potentially involved. Nevertheless, although we recognise runout distances vary considerably, with some GLOF events showing runout length >200 km50, considering such distances at a global scale could lead to large overestimations of downstream impacts in many locations29. So, following the approach of Dubey & Goyal (ref. 29) we use a cut off distance of 50 km, which should encapsulate the majority of runouts globally and provide a conservative estimate of potential GLOF reach accounting for potentially longer runout GLOFs in the future, while avoiding large overestimations by using observed but rare extreme runout distances. Using a 50 km cut off distance also accounts for issues arising from our use of Water Resource Zones, as only the region within 50 km of a glacial lake are assessed. In coastal and plains areas where the Water Resource Zones can incorporate areas without glacial lakes upstream, this ensures that only the area and population downstream and in proximity to glacial lakes are included in our calculations.


Document: Results
Section: Methods > Exposure
Type: text

GLOF runout pathways tend to follow river channels28,71, so impact increases with proximity to the channel72. Thus, similar to previous approaches73 we further constrain our potential GLOF footprint to estimate exposed populations by applying a 1 km buffer either side of any main river channel65 with a glacial lake in its upper reaches, up to a distance of 50 km (Fig. S5f). We used the 2020 Gridded Population of the World version 4 (GPWv4)74 to sum the population count per 1 km2 cell within this buffer, obtaining exposed population (Fig. S5h). We recognise that a 1km buffer is a crude estimate for identifying potential GLOF impact zones; exposed population is likely overestimated in the upper reaches where steeper elevations and narrow river valleys likely mean populations within even 100 m of a river channel may in fact be far above the impacted zone, while in the lower reaches where valleys are flatter and wider, exposed population is likely underestimated. However, as the overall impact of a GLOF wanes with distance from the river channel72,73, and given the resolution of the population data used74, at a global scale a 1 km buffer will provide a conservative but consistent estimate of the potentially exposed population. These areas of concern can then be targeted for further, more detailed analysis using more complex GLOF runout modelling and higher resolution population data to refine our initial estimates. We use a linear transformation function to produce a normalised value of exposure for each basin (Eq. 2);

$$E = \frac{(P)}{(\text{Max})} \tag{2}$$

Where E is the normalised exposure score, P is the total exposed population per basin/country/region, and Max refers to the maximum exposed population per basin/country/region respectively. To add further granularity, we split the 50 km buffer into 5 km intervals and summed the population within these intervals, to determine how population is distributed along these likely GLOF runout tracks.


Document: Results
Section: Methods > Vulnerability
Type: text

Many factors influence human vulnerability to natural hazards75–77, and vet, due in part to the absence of sufficient data, few studies have considered the temporal trend in vulnerability78. Since the implementation of the Millennium Development Goals and the succeeding Sustainable Development Goals, there has been a vast improvement in the amount, and quality, of vulnerability data available. Here we combine qualitative information obtained from the Corruption Perception Index (CPI) at national-scale and Human Development Index (HDI) at sub-national level (first internal administrative level, e.g., state or province) with a national-scale Social Vulnerability Index (SVI) to provide a proxy for GLOF vulnerability. At a global scale, corruption and human development are indicative of population fragility79-81 with higher levels of corruption and lower levels of development individually associated with larger impacts. The CPI scores and ranks countries/territories based on how corrupt a country's public sector is perceived to be by experts and business executives. It is a composite index comprised through 13 data sources and is the most widely used indicator of corruption worldwide. The HDI is a summary measure of three key dimensions of human development; health, education, and standard of living82, and is comprised of normalised indices of: life expectancy, expected years of schooling, mean years of school and Gross National Income (GNI) per capita. Both the CPI and HDI have been successfully used in previous natural hazard risk assessments51,83.

While both the CPI and HDI provide a useful metric for assessing the development of a country/territory83, they do not reflect on many factors that influence social vulnerability75. Thus, to assess the coping capacity of downstream communities and the ability of the affected nation to effectively respond to the event, a SVI was also calculated. Drawing upon an existing flood vulnerability assessment84, the SVI used in this study initially analysed 9 indicators (Table S2) that either reduce or enhance a population's and nation's capacity to cope with a GLOF disaster. To avoid double counting, we performed a correlation study (matric-plot and correlation-matrix) to ensure variables were independent from other indicators as well as those used to calculate the HDI and CPI. To keep the sample size valid, preference was given to variables with the lowest number of missing variables. As a result, four variables from the SVI were not included when calculating the final vulnerability score; percentage of safe drinking water and percentage of good sanitation as well as percentage illiterate population and percentage unemployment. The former two were highlighted both for double counting and lack of datapoints, and the latter two for double counting with data used to calculate the HDI. Consequently, the final SVI score was based on 5 unique indicators (Eq. 3);

$$SVI = \frac{\left(\frac{\text{reducing indicators}}{\text{enhancing indicators}}\right)}{5}$$
 (3)


Document: Results
Section: Methods > Vulnerability
Type: text

from the SVI were not included when calculating the final vulnerability score ; percentage of safe drinking water and percentage of good sanitation as well as percentage illiterate population and percentage unemployment . The former two were highlighted both for double counting and lack of datapoints , and the latter two for double counting with data used to calculate the HDI . Consequently , the final SVI score was based on 5 unique indicators ( Eq . 3 ) ; $ $ SVI = \ frac { \ left ( \ frac { \ text { reducing indicators } } { \ text { enhancing indicators } } \ right ) } { 5 } $ $ ( 3 )

We acknowledge that proxy values of vulnerability at nationaland sub-national-scale will hide more granular variations within countries. However, vulnerability data at finer resolution is largely absent globally and therefore we argue our approach provides the highest globally consistent resolution currently available for a globalscale study. Furthermore, we note that while the vulnerability of the immediately exposed population is critical to understanding the eventual impacts from a disaster, the capacity of the country as a whole to adequately respond to the disaster is also an important factor. As such, our vulnerability indicators attempt to capture both the physical vulnerability of the directly exposed populations, and the capacity of the country/region as a whole to cope with the event.

Finally, we note that the relative importance of each indicator on social vulnerability will change with location, with studies often assigning weights using an analytic hierarchy process and expert knowledge to fit the specific context of the study.84 Given the global scale of this study, an 'equal weighting' approach was selected with the

understanding that the outputs should be taken as a baseline value, and exact values per country may vary. In this study, all three indicators (HDI, CPI, and SVI) are normalised and combined with equal weighting (Eq. 3) to produce a single proxy for vulnerability (Eq. 4). Final values range between 0 and 1, where 1 equates to the highest vulnerability. No scores of absolute 0 were recorded.

Vulnerability =
$$1 - [HDIx(1 - CPI)x SVI]$$
 (4)


Document: Results
Section: Methods > GLOF danger
Type: text

The normalised results of all three parameters (GLOF lake conditions, exposure, and vulnerability) were then combined to produce a semi-quantitative metric for GLOF danger (Eq. 5). Here, we prefer the term GLOF danger over GLOF risk due to the lack of probability in our GLOF lake conditions score. Basins were then ranked from highest (1) to lowest (1089) danger to identify hotspots of GLOF danger.

GLOF danger =
$$[Lake Conditions \times Exposure \times Vulnerability]$$
 (5)


Document: s41748-021-00230-9
Section: Abstract
Type: text

Climate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60 °C per decade, which is higher than the mean global warming rate of 0.74 °C per 100 years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.

**Keywords** Climate change · Glacier recession · Glacial lake expansion · GLOFs · Risk management

Syed Towseef Ahmad
Syedtowseef.scholar@kashmiruniversity.net

Rayees Ahmed rayeesrashid84@gmail.com

Gowhar Farooq Wani gowhernaz@gmail.com

Mehebub Sahana mehebub.sahana@manchester.ac.uk

Harmeet Singh harmeetgeo@gmail.com

Pervez Ahmed pervezahmeduok@gmail.com

- Department of Geography and Disaster Management, School of Earth and Environmental Sciences, University of Kashmir, Srinagar 190 006, India
- School of Environment, Education and Development, University of Manchester, Manchester, England


Document: s41748-021-00230-9
Section: Abstract
Type: figure
Figure

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Document: s41748-021-00230-9
Section: 1 Introduction
Type: text

Climate change from the past few decades has significantly impacted the lifecycle of glaciers in the high-mountainous Himalayan region. The fragile Himalayan Mountains are sensitive to even minor changes in the climatic system (Lama et al. 2009). The temperature in the Himalayas during the past three decades has shown a constant increase (Shrestha et al. 2010; Dash et al. 2007; Wang et al. 2013, 2017; Khattak et al. 2011; Ali et al. 2018; Almazroui et al. 2020) causing glacier recession (Yongping 2004; Gardelle et al. 2011; Banerjee and Shankar 2013; Bolch et al. 2012a, b; Bajracharya et al. 2011; Cogley 2016; Sakai and Fujita 2017). Consequent to this climate change-induced glacier recession, glacial lakes have shown a significant increase both in terms of number and size. This has made them susceptible to Glacial Lake Outburst Floods (GLOFs) (Nie et al. 2013; Worni et al. 2012; Veh et al. 2020) with wide recognition (Yamada and Sharma 1993; Worni et al. 2013; Liu et al. 2014). Global warming is the major reason behind the changing climate and is believed to be the biggest threat to the living world. The impact of such warming has been mostly observed over the high mountain ice packs of the earth. Retreating of mountain glaciers and the development of glacial lakes has been reflected as one of the key factors for global warming. Therefore, glaciers in the high mountain regions serve as an important indicator for the monitoring and assessment of climate change (Haeberli et al. 1999; IPCC 1992; Kääb et al. 2012). In the context of rising temperatures, most of the glaciers in the Himalayan region have been receding at a greater pace, leading to increase in the volume of the glacial lakes and a concomitant rise of GLOF events. (Bolch et al. 2011a, b; Brahmbhatt et al. 2017; Bajaracharya et al. 2007, 2008; Mir et al. 2017). Melt water from these glaciers makes an essential contribution to the development and expansion of glacial lakes in the Himalayas (Fujita et al. 2008; Harrison et al. 2018; Bolch et al. 2008). The Himalayan region which contains the largest number of glaciers outside the Polar region (Yao et al. 2012, 2014) is focus of this paper. Glacial lakes are water bodies existing in a sufficient amount influenced by the presence of glaciers and or retreating processes of a glacier (Jain et al. 2015; Fitzsimons and Howarth 2018). The sudden discharging of such lakes caused due to the ice/ moraine dam breach releases enormous volume of water and debris in a devastating manner is known as Glacial Lake Outburst Floods (GLOF's) (Cenderelli et al. 2001; worni et al. 2013).

The GLOF events are likely to cause huge consequences in terms of human and infrastructural loss in the downstream regions (Emmer et al. 2018; Nie et al. 2013; Quincey et al. 2005; Yao et al. 2012). Therefore, it is


Document: s41748-021-00230-9
Section: 1 Introduction
Type: figure
Figure

Image /page/1/Picture/4 description: A paragraph of text discussing the study of glaciers and glacial lakes in the Himalayas. The text highlights the difficulty of in-situ observation and proposes remote sensing, GIS, and hydrodynamic models as alternatives. The paragraph includes several citations, which are shown in blue text. The text reads: 'important to understand the behavior of the glaciers, consequent lake development and expansion in the Himalayan region under changing climate to mitigate risks to protect the society living downstream. In the Himalayas, complete in situ observation to all the glacial lakes seems impractical due to remote locations, erratic weather conditions and rough terrain. However, the combined approaches of Remote sensing, GIS and hydrodynamic models act as a viable alternative to identify and monitor such lakes regular intervals (Huggel et al. 2003; Huggel et al. 2004; Komori, 2008; Bajracharya and Mool 2009; Bolch et al. 2011a, b; Govindha 2010; Raj et al. 2013; Worni et al. 2013; Klimes et al. 2014).'


Document: s41748-021-00230-9
Section: 1 Introduction
Type: text

Climate change and glacier research has traversed a long path with some path-breaking studies that give details about paleoclimate and future climate scenarios as well as keep a check on present situation. Climate crisis and water-related disasters is a world problem, It a serious challenge for developing and developed countries that are responsible for high carbon emissions. With the progress in earth, atmospheric and environmental sciences, use of ultramodern (geospatial and computer) technology and conventional (in situ) measurements, our approach towards assessing and addressing challenges posed by natural or human-induced processes that triggers disasters like GLOFs has undergone tremendous changes. The purpose of this study was to critically analyze the recent research on the subject concerning Himalayan region. To identify issues, underlying causes, gaps in knowledge, track shift in approach and provide some new insights into the management of GLOFs in the region with an intention to guide future research on the subject.

The next sections of the paper include materials and methods with description of study region and selection criteria for previous studies; analysis of findings from literature, GLOF events in the past, lake mapping and classification; discussion with a synthesis of similarities and dissimilarities on the subject and concluding remarks at the end that encapsulates this study with some suggestions to guide future research.


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.1 Description of Study Area
Type: text

The Himalayan mountain system, is extended over 2500 km from south of the Indus Valley to Nanga Parbat in the west and Namcha Barwa in the east. Himalayas are the loftiest and geologically young mountain ranges in the world as compared to the Alps and Appalachians. These mountain ranges are crescent shaped with a projecting southward convexity, which borders the whole northern margins of the Indian sub-continent (Roy and Purohit 2018). It spreads

2 Materials and Methods
2.1 Description of Study Area


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.1 Description of Study Area
Type: figure
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Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.1 Description of Study Area
Type: text

across the five Asian countries, namely India, China, Pakistan, Nepal and Bhutan, housing around 52.7 million people. Due to its vast size, huge altitudinal variation and complex topography, the Himalayas experience variable climate.

The Himalayan region including great ranges of central Asia is home to thousands of glaciers and glacial lakes and contains third largest snow and ice deposits in the world (Fig. 1). There are about 15,000 glaciers and 9000 glacial lakes in the Himalayan region (Mool 2005). The region contains permanent fields of snow, and during the winter season, most of the high-elevation regions receive snowfall (Kumar et al. 2005).


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies
Type: text

The climate-driven glacier recession and subsequent glacial lake expansion have increased the occurrences of Glacial Lake Outburst Floods (GLOFs) in the high glacierised basins of Himalayas. In this context, an extensive systematic

review of peer-reviewed literature was conducted with a focus on glacial lake expansion and GLOFs in the Himalayan region. About 100 research articles and review papers, book chapters and institutional reports were collected from the internet-based databases like Web of Science, Google Scholar and ScienceDirect. The keywords, such as "glaciers", "glacial lakes", glacial lakes in the Himalayas", "glacial lake expansion or assessment", "GLOFs in the Himalayas" were used to extract the research articles that refer to the period 2001-2020. Out of which only 89 most relevant research items were shortlisted and later 76 were used. Old research on the subject was used to build the foundation and background while as the recent studies were utilized to develop current state-of-the-art and knowledge. Research articles that were not administered through a half, full or double-blind peer-review process were not considered for this study. The snowball technique was used with the intention that no original contributions on glacier and glacial lake studies concerning the region is left out.


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies
Type: figure
Figure

Image /page/2/Figure/7 description: A location map of the Himalayan region, presented as a satellite image. The map is titled "Fig. 1. Location map of the study area." The Himalayan Region is outlined in yellow and subdivided into three parts: 'a' Western Himalayas, 'b' Central Himalayas, and 'c' Eastern Himalayas, which are also indicated by pink labels. The map shows surrounding geographical features, including the Arabian Sea to the southwest and the Bay of Bengal to the southeast. A legend in the bottom-left corner explains the symbols. The map includes a scale bar in kilometers (0, 95, 190, 380), a compass rose in the top-left, and is marked with latitude and longitude lines. In the top-right corner, an inset world map shows the location of the study area, highlighted with a red outline.


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies
Type: text

Fig. 1 Location map of the study area


Document: s41748-021-00230-9
Section: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies
Type: figure
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Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas
Type: text

Global average temperatures have increased by about 1 °C since the dawn of pre-industrial times (IPCC 1992). In India alone, the mean annual temperature has increased by 0. 56 °C during 1901–2009 (Attri and Tyagi, 2010) with average temperatures expected to rise between 3.5 and 5.5 °C by 2100 (Lal 2002). Regional warming in Himalayas has been observed between 0.15 and 0.60 °C per decade, which is higher than the mean global warming rate of 0.74° C per 100 years (Shrestha et al. 2010; Negi et al. 2018; Bhutiyani et al. 2007). The warming rates are not solely attributed to the natural variability but are also a result of anthropogenic causes.

3 Findings
3.1 Climate Change in the Himalayas


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.1 Western Himalayas
Type: text

Western Himalayas receive precipitation (rainfall and snowfall) from westerlies and the southwest monsoon in the winter and summer season, respectively. Changes in temperature and precipitation are key indicators of climate change. The total observed increase in mean temperature for Western Himalaya from 1991 to 2015 was 0.65 °C. (Negi et al. 2018) (Fig. 2a). Snowfall for the same period has decreased with increase in rainfall. (Negi et al. 2018) (Fig. 2b). The glacier mass loss in the region has been observed as 13% from 1962 to 2000 with the possibility of glacial lake development (Kulkarni et al. 2014).

3 Findings
3.1 Climate Change in the Himalayas
3.1.1 Western Himalayas


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
Type: text

Kattle and Yao (2013) analyzed three decades (1980–2009) of data on annual maximum, minimum and average temperatures in the central Himalayas. Spatial analyses of the average temperatures show a warming trend with a dramatic increase in temperature is observed in the latest decade. The

3 Findings
3.1 Climate Change in the Himalayas
3.1.2 Central Himalayas


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
Type: figure
Figure

Image /page/3/Figure/9 description: A combination bar and line chart titled "Precipitation Change in Western Himalaya", labeled as figure (a). The x-axis shows the years from 1991 to 2015. The y-axis is labeled "Precipitation (mm)" and ranges from 0 to 1000. The legend indicates that black bars represent Rainfall and a red line with circular markers represents Snowfall. The chart shows a general downward trend for snowfall, indicated by a dashed red trendline, with significant annual fluctuations. The highest snowfall was in 1992, over 900 mm, and the lowest was in 1995, under 200 mm. The rainfall, represented by the black bars, shows a general upward trend, indicated by a dashed black trendline, increasing from less than 100 mm per year in the early 1990s to a peak of over 250 mm in 2015.


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
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Figure

Image /page/3/Figure/10 description: A line graph titled "Temperature anomalies" shows the mean temperature anomaly from 1990 to 2015. The y-axis, labeled "Mean Temperature anomaly," ranges from -1 to 1. The x-axis represents the years from 1990 to 2015. There are two data series plotted. The first, labeled "Western Himalaya," is a black line with circular markers, showing significant fluctuations with a general upward trend, starting around -0.6 in 1990 and ending around 0.4 in 2015. The second series, labeled "Global," is a red line with circular markers, which also shows an upward trend but with less variability, starting around 0.4 in 1990 and ending close to 0.9 in 2015. Both series are accompanied by a dashed trend line indicating a positive slope. The label "(b)" is centered below the graph.


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
Type: text

Fig. 2 Precipitation (a) and temperature (b) changes in Western Himalayas. Source: Adopted from Negi et al. (2018)

Fig. 3 Mean annual temperature and rainfall changes in the Central Himalayas. Source: Adopted from (Mohandas et al. 2015)


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
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Figure

Image /page/3/Figure/13 description: A dual-axis line graph showing the mean annual rainfall and mean annual temperature for the period 2009-2011. The x-axis displays the months of the year from January to December. The left y-axis, labeled "Mean annual rainfall (mm)", ranges from 0 to 400. The right y-axis, labeled "Mean annual temperature (°C)", ranges from 0 to 30. A solid line represents the mean annual rainfall, which starts low, peaks sharply in July at approximately 375 mm, and then decreases rapidly. A dashed line represents the mean annual temperature, which gradually rises from about 10°C in January to a peak of about 24.5°C in June, and then gradually declines.


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
Type: figure
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Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas
Type: text

southern slope of the central Himalayas shows variability in temperatures, especially for the minimum temperature due natural and anthropogenic causes. In Nepal, an increasing trend of temperature has been observed with an increase in mean annual temperature by 0.06 °C per year (Shrestha et al. 2010). The maximum temperature increased by 0.7 °C (Mohandas et al. 2015) (Fig. 3).


Document: s41748-021-00230-9
Section: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.3 Eastern Himalayas
Type: text

Eastern Himalayas are experiencing widespread warming and the rate is generally greater than 0.01 °C per year. Spatial distribution of annual and seasonal temperature trends shows that most of the region is undergoing warming trends as the annual mean temperature is increasing at the rate of about 0.01 °C/year (Li et al. 2016) (Fig. 4). Dash et al. (2007) observed that maximum temperature increased in the last 100 years over the North-Eastern region of India by about 1 °C during winter and post-monsoon months.

Therefore, based on the review of hydro-climatic trends and changes in observed temperatures and precipitation as highlighted through the present study, we have a strong evidence of warming over the entire Himalayan region. However, the warming rates vary across the sub-regions and seasons.

3 Findings
3.1 Climate Change in the Himalayas
3.1.3 Eastern Himalayas


Document: s41748-021-00230-9
Section: 3 Findings > 3.2 Synthesis of Existing Literature
Type: text

Glacial lake expansion in the various parts of the Himalayas has been investigated with the help of satellite imageries, topographic maps and in-situ measurements (Nie et al. 2013, Ageta et al. 2000; Wang et al. 2015; Yamada 1993; Khadka et al. 2018; Komori et al. 2008). The Inventory of glaciers and glacial lakes in Nepal prepared by International Centre for Integrated Mountain Development (ICIMOD) has revealed that glacial lakes are increasing in number, size and volume due to glacier recession (Mool et al. 2001).

Similarly, the glacial lakes in Central Himalayas have shown a significant increase in area and number from 1990 to 2010. The number has increased by 123 and the total area of glacial lakes expanded by 28.81 km² (17.11%) as studied by (Nie et al. 2013). Wang et al. (2016) analyzed remote sensing data and long-term climate variables to examine the hydrological response of lakes in Nam Co Basin. The results show that the number of new formed glacier lakes increased by 36% and the area of glacier lakes increased by 36.7% (0.97 km²) from 1991 to 2011. According to Shrestha et al. (2017), in 2010 there were a total of 2168 glacial lakes with a total area of 127.61 km² and average size of 0.06 km² in the Koshi basin.

The number and area of the glacial lakes increased consistently over the study period from 1160 and 94.44 km² in 1977–2168 and 127.61 km² in 2010, with an overall growth rate of 86.9% and 35.1%, respectively. Khadka et al. (2018) employed open access Landsat imagery to map and analyze the spatio-temporal dynamics of glacial lakes in Nepal from 1977 to 2017. The results revealed that the total area of glacial lakes in Nepal expanded by 25%. The investigation of glacial lakes in the Chandra basin of Himalayas as studied by Prakash and Nagarajan (2018) shows an increase in the total number and area of glacial lakes in Chandra basin from 28 to 46 and area from 1.91 to 3.26 km².

The number of glacial lakes in Sikkim Himalayas has increased from 425 to 466 during 1975 and 2017 showing a discernible increase of about 9%. The total glacial lake area has also showed a significant increase of 24%, it has increased from 25.17 to 31.24 km² (Shukla et al. 2018). As reported by Begam et al. (2019) the glacial lakes in the central and eastern Himalayas have been identified and mapped, with a total surface area of 37.9 km², these glacial lakes (moraine-dammed) as a whole were observed to have expanded by 43.6% from 1990 to 2015 using multi-temporal Landsat data.

During 1964–2017, the total glacial lake area in the Poiqu River basin of central Himalaya has increased by

Fig. 4 Annual mean temperature and precipitation changes in Eastern Himalayas (1998–2013). Source: Adopted from Li et al. (2016)

3 Findings
3.2 Synthesis of Existing Literature


Document: s41748-021-00230-9
Section: 3 Findings > 3.2 Synthesis of Existing Literature
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Figure

Image /page/4/Figure/13 description: A dual-axis line graph showing annual precipitation and annual mean temperature from the year 1998 to 2013. The x-axis represents the year. The left y-axis represents annual precipitation in millimeters (mm), ranging from 500 to 1100. The right y-axis represents annual mean temperature in degrees Celsius (°C), ranging from 8.5 to 10.5. There are two data series plotted. The first, labeled "Precipitation," is a solid line with solid black circles. The second, labeled "Temperature," is a dashed line with open circles. A linear trendline is shown for each series. For precipitation, the trendline equation is y = 0.5007x + 836.97 with an R² value of 0.0004. For temperature, the trendline equation is y = 0.0297x + 9.0871 with an R² value of 0.1335. The precipitation data shows significant fluctuation, with a peak near 1100 mm in 2010 and a low point just above 500 mm in 2009. The temperature data also fluctuates, with a general upward trend, peaking above 10°C in 2009.


Document: s41748-021-00230-9
Section: 3 Findings > 3.2 Synthesis of Existing Literature
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110% and glaciers retreated with an average rate of 1.4 km2 a – 1 between 1975 and 2015 (Zhang et al. 2019). In the three river basins that form the Ganga river, the total area of glacial lakes has increased from 179 square kilometers in 2000 to 195 square kilometers in 2015 (UNDP, Nepal). The growth of glacial lakes in the Bhutan Himalayas has continued over a 20–70 years of study period, growing at a rate of less than 70 m per year in length and less than  $0.04~\rm km^2$  per year in area (Komori et al. 2008). The glacial lake area change in the various parts of Himalayas is reflected in Table 1.


Document: s41748-021-00230-9
Section: 3 Findings > 3.3 GLOF Events in the Himalayas
Type: text

Glacial Lake Outburst Floods (GLOFs) have emerged as a serious hazard from the past few decades due to increase in the human settlements, anthropogenic activities and unplanned infrastructure development in to the fragile ecosystem of high mountain regions, which were not earlier inhibited (Khanal et al. 2015). Himalayan region contains approximately 9000 glacial lakes and around 200 lakes which are potentially hazardous resulting in about 40 GLOF events from the last four decades (Yamada and Sharma 1993; Ives et al. 2010). The onrushing water from the 1994 Lugge Tsho outburst flood devastated the number of villages downstream, such as Chozoz, Thanza, Tenchey and Punkha (Watnabe et al. 1996). In 1929 GLOF event resulted due to the outburst from Ice-dammed lake (Chog Kumdan) in Shyok basin of Ladakh region, affected about 48 villages in the downstream regions (Gunn 1930). On 17th of June 2013 Chorabari GLOF event, resulted due the dam breach has caused widespread damage to the region (Allen et al. 2016). Similar events have been witnessed in the Tibetan Himalaya Cirenamco (1981), Ghulkin GLOF (1981) in Karakoram Himalayas, (Dig Tsho (1985) and Tampokhari (1998)

3 Findings
3.3 GLOF Events in the Himalayas


Document: s41748-021-00230-9
Section: 3 Findings > 3.3 GLOF Events in the Himalayas
Type: table
Table: Table 1 Expansion of glacial lakes in the various parts of Himalayas

| References                  | Time period | Change in area (%) | Study area                                           |
|-----------------------------|-------------|--------------------|------------------------------------------------------|
| Khadka et al. (2018)        | 1977–2017   | 25                 | Nepal                                                |
| Chen et al. (2007a)         | 1986–2001   | 11                 | Poiqu basin Central Himalayas                        |
| Nie et al. (2013)           | 1990–2010   | 17                 | Central Himalayas                                    |
| Shukla et al. (2018)        | 1975–2017   | 24                 | Central Himalayas                                    |
| Wang et al. (2016)          | 1991–2001   | 36.7               | Namco basin                                          |
| Wang et al. (2013)          | 1979–2009   | 173                | Central Tibet                                        |
| Shrestha et al. (2017)      | 1975–2010   | 31.5               | Koshi basin                                          |
| Zhang et al. (2015)         | 1990–2010   | 23                 | Entire Third Pole                                    |
| Debnath et al. (2018)       | 1988–2014   | 8.38               | Kangchengayo- Pauhunri,Massif, Sikkim Himalaya |
| Prakash and Nagrajan (2018) | 2000–2014   | 41.41              | Chandra basin                                        |
| Gardelle et al. (2011)      | 1990–2009   | 20–60              | Nepal and Bhutan                                     |
| Begam et al. (2019)         | 1990–2015   | 43.6               | Central and Eastern Himalayas                        |

References
Time period
Change in area (%)
Study area


Document: s41748-021-00230-9
Section: 3 Findings > 3.3 GLOF Events in the Himalayas
Type: text

in Nepal Himalayas has resulted in to the extensive human loss and property downstream (Vuichard and Zimmermann 1987; Richardson and Quincey 2009; Xu 1988).

Besides, this similar destructive GLOF events have claimed large number of lives and caused enormous damage in various parts of the world, particularly Andes, European Alps, Canadian Cordillera and central Himalayan region (Huggel et al. 2003; Clague and Evans 2000; Mergili et al. 2011; Emmer and Cochachin 2013). In 1981, a GLOF event in Nepal damaged Friendship bridge of Nepal-China Highway and Koshi power station with heavy economic losses (Bajracharya et al. 2006).

A GLOF event that occurred during August 2000 damaged 98 bridges in the Tibetan Plateau and destroyed around 10,000 houses accounts about 75 million US dollars in terms of financial losses (Shen 2004). The major GLOF events that have been witnessed in the various parts of the Himalayan region are reflected in the Table 2.


Document: s41748-021-00230-9
Section: 3 Findings > 3.4 Mapping of Glacial Lakes
Type: text

Mapping of glacial lakes is essential to analyze the spatio-temporal distribution of the glacial lakes, which is perquisite for the management of potentially dangerous glacial lakes. Glacial lake mapping has been carried out by various researchers in the different parts of Himalayas using automated and semi-automated techniques, such as Normalized Difference Water Index (NDWI) (Prakash and Nagrajan 2018; Worni et al. 2012; Bolch et al. 2008; Zhang et al. 2020), Band ratioing (Wessels et al. 2002; Bhambri et al. 2015), supervised and unsupervised classification (Zhang et al. 2020), object-oriented mapping and manual mapping (on-screen digitization) (Bhambri et al. 2018; Zhang et al. 2015; Nie et al. 2017). All these methods and techniques have been extensively used in the


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Section: 3 Findings > 3.4 Mapping of Glacial Lakes
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Document: s41748-021-00230-9
Section: 3 Findings > 3.4 Mapping of Glacial Lakes
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Table: Table 2 Major GLOF events in the Himalayan region

| No | Year | Lake                 | Catchment/basin/ mountain range | Source                                                                     | Country affected |
|----|------|----------------------|------------------------------------|----------------------------------------------------------------------------|------------------|
| 1  | 1929 | Chog Kumdan          | Shyok river                        | Gunn (1930)                                                                | India            |
| 2  | 1981 | Cirenamco            | Tibetan Himalayas                  | Vuichard and Zimmermann (1987)                                             | China            |
| 3  | 1985 | Dig Tsho             | Nepal Himalayas                    | Xu (1988)                                                                  | Nepal            |
| 4  | 1998 | Tampokhari           | Nepal Himalayas                    | Yamada and Sharma (1993)                                                   | Nepal            |
| 5  | 1994 | Luggey Tsho          | Bhutan Himalayas                   | Watanbe and Rothacher (1996)                                               | Bhutan           |
| 7  | 1964 | Gelhaipuco           | PumQu/Arun                         | Richardson and Quincey (2009), Bajracharya et al. (2006)                   | China and Nepal  |
| 8  | 1977 | Nare                 | Koshi                              | Yamada (1993), Ghimire (2004)                                              | Nepal            |
| 9  | 1980 | Nagma Pokhari        | Tamor                              | Raj and Kumar (2012)                                                       | Nepal            |
| 10 | 1981 | Zhangzangbo Boqu     | Sun Koshi                          | Xu (1988), Shrestha et al. (2010a), Chen et al. (2013), Wang et al. (2018) | China and Nepal  |
| 11 | 1998 | Sabai Tsho           | Khumbu Himal                       | Komori et al. (2012), Dwivedi (1999), Kattelmann (2003)                    | Nepal            |
| 12 | 2008 | Ghulkin Glacial lake | Kara                               | Hussain et al. (2021)                                                      | Pakistan         |
| 13 | 2000 | Glacial lake         | Yigeong                            | Zhu and Li (2011)                                                          | China and India  |
| 14 | 2005 | Glacial lake         | Parechu                            | Zhu and Li (2011)                                                          | China and India  |
| 15 | 2013 | Chorabari            | Mandakani basin                    | Allen et al. (2016)                                                        | India            |

No
Year
Lake
Catchment/basin/ mountain range
Source
Country affected


Document: s41748-021-00230-9
Section: 3 Findings > 3.4 Mapping of Glacial Lakes
Type: text

past to extract the glacial lakes/water bodies in the Himalayan region (Shukla et al. 2018; Kumar et al. 2020; Mal et al. 2020; Ageta et al. 2000; Wang et al. 2015; Yamada 1993; Khadka et al. 2018). However, due to some uncertainties created by atmospheric and physical processes like mountain shadow, soil moisture, vegetation noise, cloud cover, built-up land noise and snow cover, they are sometimes misclassified as glacial lakes due to the spectral mixing (Zhang et al. 2015). In this context Xu 2006 has modified the Normalized Difference Water Index of Mc Feeters (1996), which has been extensively used by various researchers according to their purpose of study (Chen et al. 2021; Du et al. 2016; Sarp and Ozcelik 2017; Acharya et al. 2018). The Modified Normalized Difference Water Index (MNDWI) utilizes Green and Short-Wave Infrared (SWIR) bands to enhance open water features by efficiently diminishing and removing vegetation, soil and built-up land noise (Eq. 1). Accordingly, MNDWI seems to be more suitable for enhancing and extracting information of water region with a background dominated by vegetation, soil and built-up land noise.

$$MNDWI = \frac{pG - pSWIR}{pG + pSWIR},$$
(1)

where pGand pSWIR bands are the reflectance in Green and Short-wave infrared, respectively.

A hybrid methodology which comprises the following five steps is much useful for the mapping of glacial lakes and water bodies.

- Automated extraction of glacial lake layer using techniques like NDWI, MNDWI, Band ratioing and supervised/unsupervised classification.
- Selecting the minimum threshold (based on the resolution of the data used)
- Manual editing or on-screen digitization
- Check and overlay with High-resolution Google earth images
- In situ field measurements

Post-checking and editing is almost necessary and suggested for all the automated methods and techniques for glacial lake mapping (Mergili et al. 2013). Therefore, automated techniques coupled with manual editing and check overlay with high-resolution Google earth images seems to be best method for the glacial lake mapping (Mool et al. 2001,2003). In addition to that, field-based observations to some of the bench mark glacial lakes may help to validate the results yielded from the remote sensing-based glacial lake mapping. Besides that, Digital Elevation Model based (DEM) derived slope, aspect and shaded relief values can be used to remove misclassifications of water bodies from topographic effects in the rugged terrain of Himalayan region. The field investigations that are essential part of the process are useful to validate the lake attributes and topographic parameters to generate the final database.


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Document: s41748-021-00230-9
Section: 3 Findings > 3.5 Classification of Glacial Lakes
Type: text

Classification of glacial lakes plays an important role to know the location and to understand the origin and evolution of glacial lakes. Glacier proximity, moraines, outwash plains and other geomorphological features should be taken into consideration at the time of classification of glacial lakes (Bhambri et al. 2015). Although, numerous approaches of glacial lake classification (Scheffers and Kelletat 2016; Cao et al. 2016; Mool et al. 2011; Wang et al. 2016; Yao et al. 2018; Wu et al. 2011) are available in the literature. However, as of now, there has been no internationally accepted method for the classification system of the glacial lakes.

Some researchers and organizations have proposed various classification systems of glacial lakes as per their own understanding and purpose of research. In the Inventory of glacial lakes in Uttarakhand, Govindha et al. (2016) have classified glacial lakes into 6 major types which include proglacial lakes, moraine-dammed lakes, blocked lakes, erosion lakes, cirque lakes and supra glacial lakes based on the methodology suggested by Mool and Bajracharya (2003) and Chen et al. (2007a, b). The schematic representation of different types of glacial lakes as given by Govindha & Kumar (2016) is depicted in (Fig. 5).

Pradeep et al. (2001) have also classified glacial lakes into 5 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, Supraglacial Lake and subglacial lake. Bhambri et al. (2015) classified glacial lakes based on glacier process in to four major types as ice dammed, moraine-dammed lake, glacier erosion lake and other

glacial lake. A detailed classification system which comprises 3 major classes and 10 sub-classes has been proposed by Wu et al. (2011).

Based on the hydrologic relationships between glacial lakes and their feeding glacier, Jain and Mir (2019) classified glacial lakes into 5 types as supraglacial lake, pro-glacial lake, periglacial lake, glacier fed lake and non-glacier fed lake. Yi and Cui (1995) in the glacial lake study of Altay Mountains suggested multiple classification schema. As per thermal and mechanical differences the glacial lake formation, they were classified as ice-blocked lake, moraine-dammed lake, glacial erosion lake, glacial thaw lake and glacial composite lake. Furthermore, glacial lakes were divided into ice-water lake and non-icewater lake based on the water supply of glacial lakes. Rai and Mishra (2017) have categorized Glacier lakes into six types, namely blocked lake, moraine-dammed lakes, erosion lakes, cirque lakes, pro-glacial lakes and supra glacial lake. Wang et al. (2010) classified glacial lake into 7 types which include ice-blocked lake, moraine-dammed lake, glacial erosion lake, Cirque Lake, Supraglacial Lake, landslide-dammed lake and glacial valley lake. ICIMOD, 2018 has classified morphologically glacial lakes into three major types and divided them into 10 sub-classes. A complete classification schema proposed by Yao et al. 2018, which is based on the past research studies and principal of systematic, normalization, operability and scalability has incorporated almost all the types of glacial lakes in his classification system (Table 3; Fig. 6).

3 Findings
3.5 Classification of Glacial Lakes


Document: s41748-021-00230-9
Section: 3 Findings > 3.5 Classification of Glacial Lakes
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glacial lake . Wang et al . ( 2010 ) classified glacial lake into 7 types which include ice - blocked lake , moraine - dammed lake , glacial erosion lake , Cirque Lake , Supraglacial Lake , landslide - dammed lake and glacial valley lake . ICIMOD , 2018 has classified morphologically glacial lakes into three major types and divided them into 10 sub - classes . A complete classification schema proposed by Yao et al . 2018 , which is based on the past research studies and principal of systematic , normalization , operability and scalability has incorporated almost all the types of glacial lakes in his classification system ( Table 3 ; Fig . 6 ) .

Fig. 5 Types of glacial lakes: (EL) Erosion Lake, (CL) Cirque Lake, (MDL) Moraine-dammed lake (BL) Blocked lake, (SL) Supra glacial lake and (PL) Proglacial lake. Source: Adapted from (Govindha & Kumar 2016)


Document: s41748-021-00230-9
Section: 3 Findings > 3.5 Classification of Glacial Lakes
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Figure

Image /page/7/Picture/9 description: A diagram illustrating different types of glacial lakes, each with a corresponding abbreviation. In the lower left, a cross-section labeled 'CL' shows a 'Lake' within a 'Cirque', with a 'Zone of Plucking' and a 'Zone of Abrasion' indicated. The upper and right portions of the image display several types of lakes formed by glaciers. 'EL' shows a lake at the end of a glacier flowing into a river. 'SL' depicts lakes on the surface of a glacier. Two diagrams labeled 'PL / MDL' show a proglacial lake at the terminus of a glacier. Two diagrams labeled 'BL' illustrate lakes formed at the base of a glacier, one showing calving into the lake. Each of these diagrams includes a 'GLACIER', a 'Lake', and a 'River'.


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Section: 3 Findings > 3.5 Classification of Glacial Lakes
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Document: s41748-021-00230-9
Section: 3 Findings > 3.5 Classification of Glacial Lakes
Type: table
Table: Table 3 Classification of glacial lakes

| Class                | Sub-class                     | Description                                                 |
|----------------------|-------------------------------|-------------------------------------------------------------|
| Glacial Erosion lake | Cirque lake                   | The lake in one Cirque                                      |
|                      | Glacial valley lake           | The lake in U-shaped valley by glaciation                   |
|                      | Other glacial erosion lake    | The lake formed by glacier erosion but not                  |
| Moraine-dammed lake  | End-moraine-dammed lake       | The lake between the end moraine ridge and glacier terminus |
|                      | Lateral-moraine-dammed lake   | The lake beside the lateral moraine ridge                   |
|                      | Moraine thaw lake             | The lake on the moraine ridge                               |
| Ice-blocked lake     | Advanced glacier-blocked lake | The lake blocked by advanced glacier                        |
|                      | Other glacier-blocked lake    | The lake with the dam being glacier ice                     |
| Supraglacial lake    |                               | The lake on the surface of glacier                          |
| Subglacial lake      |                               | The lake within the glacier or over the glacier bed         |
| Other glacial lake   |                               | The lake blocked by landslide, avalanche, debris flow etc., |


Document: s41748-021-00230-9
Section: 3 Findings > 3.5 Classification of Glacial Lakes
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Source: Compiled from (Yao et al. 2018)


Document: s41748-021-00230-9
Section: 4 Discussion
Type: text

Glacial lakes in the Himalayan region are increasing and expanding at an accelerated rate with increase in the number of potentially hazardous lakes. There is a strong evidence in the scholarly literature that the climate change-induced glacial retreat has resulted into the formation, development and expansion of different types of glacial lakes in the study region. Expansion rate of pro-glacial lakes connected to the glacier and moraine-dammed lakes is faster than other type of lakes. As reported by Begam et al. (2019), the glacial lakes in the central and eastern Himalayas have a total surface area of 37.9 km2. These moraine-dammed glacial lakes were observed to have expanded by 43.6% from 1990 to 2015. The study was conducted using multi-temporal Landsat data. This gives us an indication that a limited number of such studies have been conducted using high-resolution satellite data. Use of high-resolution data will give better results. The growth of glacial lakes in the Bhutan Himalayas has continued over a 20–70 years of study period, growing at a rate of less than 70 m per year in length and less than 0.04 km2 per year in area (Komori et al. 2008). According to the glacial lake inventory prepared by Shrestha et al. (2017), there were a total of 2168 glacial lakes with a total area of 127.61 km2 and average size of 0.06 km2 in the Koshi basin in 2010. The number and area of the glacial lakes increased consistently over the study period from 1160 and 94.44 km2 in 1977–2168 and 127.61 km2 in 2010, with an overall growth rate of 86.9% and 35.1%, respectively. The results indicate that the total surface area of glacial lakes in Nepal Himalayas expanded by 25% between 1987 and 2017 as studied by Khadka et al. 2018. The number of glacial lakes in the Sikkim Himalayas has increased from 425 to 466 during 1975 and 2017 showing a discernible increase of about 9%. The total glacial lake area has also showed a significant increase of ~24%, it has increased from 25.17 to 31.24 km2 (Shukla et al. 2018). Dubey and Goyal (2020) of the Indian Institute of Technology Indore, India, employed satellite data, DEM and flood models to analyze the hazard risk of 329 glacial lakes in the Indian Himalayas. The glacial lakes in the region have increased by 16% during 1993–2018. Furthermore, they estimated that about 22% of these glacial lakes possess high hazard risk both in terms of GLOF event and downstream impacts. Although this is not an exhaustive study covering all the research studies carried out so far in the study region, but on the basis of the abovecited literature, it is quite evident that glacial lakes in the Himalayan region are expanding and the formation of new glacial lakes has shown a considerable increase particularly in the form of supra glacial lakes.

The rate of growth of such glacial lakes may increase in the future due to the recession of the glaciers caused by the changing climate. The consequent changes may cause catastrophic events which can damage the people and infrastructure downstream.


Document: s41748-021-00230-9
Section: 5 Concluding Remarks
Type: text

High-mountain areas are severely affected by climate change. The present study highlighted sequentially the glacial lake changes and prioritization of risk management for potential GLOFs by investigating into major GLOF events in the Himalayan region using existing literature. Glacial lakes in the Himalayan region are increasing in number and area; hence GLOF events are becoming common in the high-glaciered basins of Himalaya. The continued recession of glaciers under changing climate may lead to the drastic increase in glacial lake area which could be possible cause for GLOFs in the region. The consequences arising from such situations are inevitable, particularly due to less availability of adequate data pertaining to intensity of rainfall, landslide locations, and outburburst potential of glacial lakes. The inferences drawn from the study suggest that there


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Section: 5 Concluding Remarks
Type: figure
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Document: s41748-021-00230-9
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Figure

Image /page/9/Figure/1 description: A composite figure displaying various types of glacial lakes through satellite imagery from Google Earth and Landsat. The figure is organized into four rows, each showcasing different lake formations.


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Top row: On the left, a Google Earth image shows a 'Cirque lake' in a mountainous, snowy region. On the right, a Landsat TM 5-4-3 band combination image displays the larger area, including 'Akkol Lake' and 'Akkol Glacier'.

Second row: This row presents three images. The first is a Landsat OLI 5-4-3 band combination showing an 'End moraine-dammed lake'. The middle image is from Google Earth, pointing out the 'End moraine'. The right image is a Landsat TM 4-3-2 band combination showing a 'Lateral moraine-dammed lake' near the 'Ngozumpa Glacier'.

Third row: This row also contains three images. The left image is a wide-view Landsat OLI 5-4-3 band combination. The middle Google Earth image is a zoomed-in view identifying an 'end moraine-dammed lake', a 'lateral moraine-dammed lake', and a 'moraine thaw lake'. The right image is a Landsat OLI 5-4-3 band combination showing an 'Ice-blocked lake'.

Bottom row: This row consists of two images. The left is a wide-view Landsat OLI 5-4-3 band combination of the 'Rongbuk Glacier'. The right image is a magnified view showing a 'Supraglacial lake' on the glacier's surface.

Fig. 6 Types of glacial lakes on Landsat Images on bands 5, 4 and 3 and Google earth images. Source: Compiled from (Yao et al. 2018)


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