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Article

Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response

by
Alton C. Byers
1,*,
Sonam Rinzin
2,
Elizabeth Byers
3 and
Sonam Wangchuk
4
1
Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, CO 80309, USA
2
School of Geography, Politics and Sociology, Newcastle University, Newcastle NE1 7RU, UK
3
Appalachian Ecology, Elkins, WV 26241, USA
4
International Centre for Integrated Mountain Development, Lalitpur 44700, Nepal
*
Author to whom correspondence should be addressed.
Water 2025, 17(10), 1457; https://doi.org/10.3390/w17101457
Submission received: 7 March 2025 / Revised: 2 May 2025 / Accepted: 8 May 2025 / Published: 12 May 2025
(This article belongs to the Special Issue Study of Hydrological Mechanisms: Floods and Landslides)
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Abstract

During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 106 m3. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers.

1. Introduction

Glacial lake outburst floods (GLOFs) have become a recognized natural hazard within the Nepal Himalaya during the past several decades [1,2]. The lakes are formed when meltwater fills the trough depressions left behind by receding glaciers, with the lake water contained by unconsolidated terminal moraines, landslides, or ice-debris avalanches [3]. Glacial lakes can contain volumes of many millions of cubic meters of water, with floods most commonly triggered by snow or ice-debris avalanches falling directly into the lake and creating surge waves capable of breaching the unconsolidated terminal moraine dams. Downstream damage can, and has, included extensive infrastructural and agricultural damage in addition to the loss of human life [3,4]. Nepal has experienced at least 26 known GLOF events since 1977 [5], although the number is most likely much higher because of under-reporting from remote regions. There is also evidence to suggest that GLOFs in Nepal are increasing in frequency and magnitude as a result of continued warming trends [5] and the de-stabilization of cryospheric landscapes in general [3].
Likewise, 21 potentially dangerous glacial lakes have been identified in Nepal [5], although the actual number is most likely higher because of the 24 transboundary lakes, of unknown danger levels, located north of the border in the Tibet Autonomous Region of China. There is also growing evidence to suggest that even small glacial lakes less than 0.01 km2 in size, rarely included in glacial lake risk assessments, can be dangerous given the right combination of cascading processes [3,4,5].
Supraglacial meltwater ponds tend to form at the lower altitudes near a glacier’s terminal moraine, and over time these can coalesce to form a single lake that grows in area and depth with each year [1,3,6]. A classic example is that of the Imja glacial lake in the Sagarmatha National Park and Buffer Zone of eastern Nepal, whose development since the early 1960s has been well documented through time-lapse satellite imagery [7,8]. The lake grew from a few meltwater ponds in 1962 to a >1 km2 lake containing more than 90 million cubic meters of water in 2014 [8,9].
In general, debris-covered glaciers in highland Nepal tend to form glacial lakes if their slope gradients are less than 2°, but experience drainage processes if the slope is greater than 2° [10]. In the latter case, water-filled englacial conduits, or ice caves, can form within the glacier that have been known to be the source of small- to medium-scale floods when triggered by the sudden release of water from surficial meltwater ponds or conduits located further up the glacier [10,11]. Glacier floods can thus be the result of breached glacial lakes, the release of water from sub-surface caves, cascading events initiated by high altitude rock breakage, heavy rainfall, and earthquakes [3,12].
In the following paper, we examine the recent and progressive development of eight meltwater ponds located near the terminus of the Kanchenjunga glacier in eastern Nepal. During a research expedition to the Kanchenjunga Conservation Area (KCA) between April and June 2024, considerable concern was expressed by local communities about the ponds and the possibility of the eventual formation of a large and potentially dangerous glacial lake. Their concern was based on real and direct experience, as at least eight GLOFs have occurred in the region within living memory that have been the cause of major disruption of life, livelihoods, and landscapes [13]. In an effort to assist local communities while furthering our understanding of glacial lake formation processes, we decided to undertake a study to model the risks associated with a potentially large glacial lake at this site. We examined the possible flood types and triggers, the differing scenarios that could produce an actual GLOF, prospective downstream impacts in the event of floods of differing magnitudes, and the development of preventative and/or mitigation opportunities of prospective use to local communities. A third objective was to develop a simple yet effective model of glacial lake growth, development, and potential hazards that could be of use to local communities throughout the Himalaya-Hindu Kush region.

2. Setting

The Kanchenjunga Conservation Area (KCA) (Figure 1) is located in eastern Nepal in the Taplejung District. Established in 1997 as Nepal’s first community-managed protected area, KCA covers an area of 2035 km2 and is known for its high biodiversity, cultural diversity, and rugged glacial landscapes [14]. Vegetation ranges from subtropical evergreen broadleaf forests of the warm temperate zone; the hemlock/oak/maple/rhododendron formations of the cool temperate zone; fir/juniper/larch/rhododendron of the subalpine zone; and dwarf rhododendron/shrub juniper of the alpine zone [13,14]. The region is sparsely inhabited by the Limbu, Rai, Tamang, Gurung, Magar, Chhetri, and Sherpa ethnic groups, likely totaling no more than 200 full-time residents which have decreased in recent years because of outmigration, globalization, and changing traditional lifestyles [14,15,16]. The Taplejung District was relatively roadless until about 10 years ago, when, similar to most regions of Nepal, an unprecedented level of road building commenced [17,18,19]. Most construction in Taplejung has been associated with various hydropower projects, which are freely encroaching into the KCA protected area [14].
The KCA is drained by the Tamor River and its tributaries, the Ghunsa Khola and Simbuwa Khola, each fed by glaciers at their uppermost watersheds. Valleys are typically U-shaped in the upper, high-altitude valleys that transform to V-shaped valleys at an altitude of about 3500 m. Some older glacial landforms persist as low as 2800 m, e.g., now-vegetated lateral and terminal moraines [20].
Debris-covered valley and clean ice glaciers cover an area of 488 ± 29 km2 [21] although the current glacial area has most likely diminished since this study was conducted. Glacial retreat within the region has been documented since the 1850s [22,23], with one study showing a retreat rate of 50% for 57 glaciers between 1958 and 1992 [20]. There is new evidence to suggest that glacial melt, ablation, and retreat rates have accelerated within the past 5–10 years, notably in the form of glacier ablation, recession, and the formation of new meltwater ponds near the terminus of glaciers [24].
In 2023, a total of 140 glacial lakes were identified within the KCA by [25], 76 of which were supra-glacial lakes and 27 of which were proglacial lakes. Their total area was determined to cover 3.37 km2 with a combined water storage capacity of 76.55 × 106 m3. Two of the glacial lakes were considered to be candidates for possibly catastrophic GLOFs [25]. The KCA region has experienced between six and eight GLOFs since 1921 [13], two of which (in 1921 and 1980) resulted in considerable damage to villages, property, and landscapes. Watanabe [26] warned of landslides and the future development of glacial lakes as threats to ecotourism development. Additionally, changes in high altitude permafrost integrity [27,28] may be triggering an increase in massive ice-debris flows [24] that are also cause for concern. In 2024, local communities expressed concern about the expansion of a new proglacial lake developing at the terminus of the Kanchenjunga glacier, and fear over the possibility of a GLOF that could damage downstream villages, agricultural fields, and forests.

3. Materials and Methods

Informal interviews with local informants provided the impetus for a more detailed study of the lower Kanchenjunga glacier and the recent (i.e., 5–10 years) development of coalescing supraglacial ponds (27.7726, 88.0187) at its debris-covered terminus, located near the seasonal grazing pastures of Ramdang (Figure 1). In order to illustrate the recent development and evolution of the new ponds, a time series satellite composite images acquired by Sentinel-2 Multispectral Instrument between September and October was developed, with the exception of 2024, where the images were acquired between April and May (Figure 2; see also Supplementary Materials).
As the climate continues to warm, it is highly likely that a supraglacial lake will form as current meltwater ponds coalesce to fill the lowermost, low-gradient portion of the Kanchenjunga glacier. The area of the expanded lake will most likely be constrained by the higher-gradient glacier above, terminal moraines on either side, and a low-relief terminal complex/outlet below, with a hypothetical area of approximately 0.82 km2 (Figure 3).
Detailed bathymetry surveys are essential for accurate estimates of GLOF hydrodynamic characteristics and risk assessment of glacial lakes. However, such surveys are difficult to obtain, as glacial lakes in the Nepal Himalaya are generally located in remote and poorly accessible areas [29]. Moreover, measuring the volume of a future Kanchenjunga glacial lake is not possible since the lake is only now in an actively forming stage. Worldwide, however, glacial lake volume–area curves generally conform to a power–law relationship indicating that glacial lake basins approximate hemispheric or cone shapes, with slightly different geometries distinguished for five different types of glacial lakes [30]. The volume of the future Kanchenjunga proglacial lake is estimated based on an upward-opening parabola as V = 44.47A1.5, or 33 × 106 m3 (Figure 3). Predicted bathymetry depths of the proglacial lake are estimated using the ice thickness method, in which eroded over-deepening of the glacier bed and corresponding ice thickness are inferred from surface slope by parameterizing basal shear stress as a function of elevation range [31,32]. As glaciers retreat, over-deepened portions of the glacier bed may fill with water and form new lakes, such as the lakes now forming on the Kanchenjunga glacier terminus.
Assuming that large mass movements could potentially trigger an outburst flood [33], mass movement trajectory data [34] were used to map probable ice, debris, and/or rock avalanches that could enter the lake (Figure 3). The r.avaflow model (version 3) was then used to simulate a range of GLOF cascade scenarios [35]. r.avaflow is a physically-based model which can illustrate the full cascade and interaction processes involved in GLOFs, starting from the mass movement entering the lake, the interaction of mass and lake water, erosion and breaching of the moraine dam, and downstream flow propagation [35]. Because of this capability, r.avaflow has been widely used in reconstructing well-documented past events [36,37] and predicting flow characteristics of possible future GLOF events for predictive hazard and risk assessment in the Himalayan region [38,39], making it suitable for modeling initial GLOF process chain in this study. We modeled the GLOF by considering a mass movement (grain density: 1800 kg/m3) of varying magnitudes entering the lake (fluid part), with the resulting waves causing moraine dam erosion and dam failure. An entrainment coefficient (CE = −6.35 kg⁻1) was used to control moraine dam erosion [35,40]. Other key input parameters include the basal friction angle (φ) and internal friction angle (δ), which govern the flow’s rheology. For the initial stages dominated by the solid phase (mass movement, lake impact, and moraine erosion), we set φ = 25° and δ = 10°. For the downstream process from the moraine, we set φ = 25° and δ = 1° to model the flow as a water-saturated debris flow [38,39].
GLOF occurrence from specific basins and individual lakes is a rare event which does not permit the calculation of flood frequency and return periods [41]. We therefore followed the established approach of assessing GLOF risk by considering different magnitude scenarios including the worst-case scenario [38,42], which is an important consideration in the face of uncertainty stemming from future ongoing climate change.
Four different GLOF cascade scenarios were modeled, assuming each could be triggered by mass movements of varying magnitudes. The small, medium, and large scenarios were based upon earlier volume estimates of avalanches between 2.7 × 106 to 6.7 × 106 m3. Our worst-case scenario avalanche was based upon the 3 October, 2023 South Lhonak glacial lake outburst flood in Sikkim, which was known to have been triggered by frozen moraine collapse of up to 14.7 × 106 m3 of debris [36]:
  • Small (mass movement volume: 3 × 106 m3);
  • Medium (mass movement volume: 5 × 106 m3);
  • Large (mass movement volume: 7 × 106 m3);
  • Worst-case scenario (mass movement volume: 15× 106 m3).
Although r.avaflow is an effective tool for modeling the complete process chain of GLOFs, its application to simulating long run-out distances (over 100 km) is highly challenging and impractical due to the substantial computational time and resources required. Because of its free access and computational efficiency, we used the HEC-RAS 2D to model the prospective downstream impacts of a GLOF from a future Kanchenjunga proglacial lake. The HEC-RAS 2D model is computationally affordable and offers user-friendly GLOF modeling options [43]. It has a large community of users and contains extensive, freely accessible resources, and has been widely used for modeling GLOFs in the Himalaya [42,44].
The main input and initial conditions in HEC-RAS 2D modeling are terrain information, boundary conditions, and Manning’s roughness number (n). The domain for the HEC-RAS 2D model was established by drawing a 1 km buffer on either side of the river centerline up to 150 km downstream of the lake. Within this model domain, a computational mesh with a grid resolution of 20 × 20 m2 was generated. HMA-8m DEM, the highest resolution open-access DEM available over the study area, was used as a source of terrain information for the model domain [45,46]. In areas where the HMA-8m DEM had spurious or incomplete coverage, we used the ALOS Global Digital Surface Model (AW3D30) [47], which we co-registered with the HMA-8m DEM to ensure consistency [45]. The upstream boundary condition was established by drawing a profile line just below the Kanchenjunga terminal moraine while the downstream boundary condition was placed at the distal end of the model domain (~30 km downstream of the lake). The hydrograph generated by the r.avaflow model, representing the impact of each scenario of mass movement, was then applied as upstream boundary conditions (Figure 4). The downstream boundary condition was assumed as normal depth at the energy slope of 0.01, which is acquired by taking the slope profile 500 m along the river centerline upstream of the downstream boundary conditions. We modelled all scenarios of GLOFs by maintaining the default value of Manning’s n (0.06) [43,45].
To assess the impact of different GLOF scenarios from potential future Kanchenjunga glacial lakes, we analyzed flood inundation extent, flow depth, velocity, arrival time, and peak discharge. Within the model domain, we assigned a pixel value of 1 for areas impacted by only one scenario, 2 for two scenarios, 3 for three scenarios, and 4 for areas impacted by all four scenarios. Using high-resolution Google Earth Imagery, we mapped bridges and buildings that intersect with flood inundation extent and provided detailed hydrodynamic analysis for the area where the maximum number of buildings is affected.

4. Results

Figure 2 shows the evolution of supraglacial ponds near the terminus of Kanchenjunga glacier from September–October 2016 to April–May 2024. As shown in Figure 2 and Figure 3, and as per discussions with trekking guides, lodge owners, and local people, the commencement of supraglacial lake development began sometime around 2016. At that time, the number of very small glacial lakes totaled 16 with a cumulative area of only 0.037 km2.
Assuming that the future glacial lake will be formed by filling the low-gradient area beneath the present-day glacier and using the empirical equation, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 106 m3 (Figure 3). When impacted by the mass movement of different magnitude scenarios (small, medium, and large), the resulting GLOFs produce peak discharges ranging from 11,000 to 22,000 m3 s−1. Under the worst-case scenario where a mass movement of 15 × 106 m3 enters the lake, the peak discharge increases more than threefold, reaching up to 70,000 m3 s−1 (Figure 4).
These GLOFs travel downstream as far as 280 km in the worst-case scenario and at least 120 km downstream even with the small scenario flood. The GLOF waves propagate with mean flow depths ranging from 7 to 16 m and velocities between 5 and 7 m s⁻1. Under the worst-case scenario, the mean depth increased to 21 m and velocity increased to 9 m s−1.
The modeled GLOFs would inundate downstream areas ranging from 5 km2 (small scenario) to 21.5 km2. In the small-magnitude scenario, the floodwater remains largely confined within the existing river channel and would affect only 16 buildings (Table 1). Under the medium scenario, these impacted buildings increased to 45, which increased further to 89 and 90 under large and worst-case scenarios. Similarly, 30 bridges are within the flood extent in the small scenario, increasing to 40 and 41, respectively, in the medium and large scenarios and 44 in the worst-case scenario (Table 1). Among downstream communities, Kampachen (6 km downstream) and Ghunsa (16 km downstream) experience the greatest building inundation which constitutes about 90% of the total inundated buildings (Table 1 and Figure 4). In Kampachen, 7 buildings are inundated under the small scenario, 14 under the medium scenario, and 20 and 21 under the large and worst-case scenarios, respectively. Here, mean GLOF flow depths range from 3 to 9 m, velocities range from 7 to 10 m s⁻1, and flood arrival times range between 5 and 10 min. In Ghunsa, no buildings are inundated under the small scenario, 14 under the medium scenario, and 54 under both large and worst-case scenarios (Table 1). Floodwaters arrive within 20 to 25 min, with mean flow depths of 6 to 11 m and velocities of 7 to 8 m s⁻1 (Figure 4).

5. Discussion

This study’s modeling results suggest that a future and theoretical GLOF from the Kanchenjunga glacier terminus, even a small magnitude scenario flood from a glacial lake situated at the Kanchenjunga glacier terminus, could cause considerable downstream damage to the villages of Kampachen and Ghunsa in the form of structural damages, economic losses, displacement, and long-term recovery challenges (Figure 4). Additional damage to agricultural fields, trails, forests, and the tourism industry would also most likely result, as well as the possible loss of both human and livestock life.
The above scenario is based upon the development of a 0.82 km2 proglacial lake as the result of existing supraglacial pools merging, the concurrent melting of bottom ice, and retention of the low-relief terminal moraine complex to contain the lake. As with many other glacial lakes, the terminal moraine complex would be susceptible to breaching through core ice melt, terminal moraine collapse, and/or through the creation of a rockfall-induced surge wave [12,48]. Likewise, landslides could also dam current glacial discharge, creating a large and potentially dangerous lake. Finally, in the best-case scenario, meltwater generated by glacier melt might exit continuously into the Ghunsa River below, faster than it can accumulate in the form of a large or dangerous lake, and any potential hazard would be averted.
In addition to the flood-triggering rockfall scenarios mentioned above, a range of other prospective low frequency/high magnitude events also exist that could trigger a small- to large-scale glacier flood [41]. They include (a) an englacial conduit and/or surficial glacial lake flood from the upper Kanchenjunga, Ramdang, Lhonak, or Chhyatungding glaciers into the lake [1,10,11], (b) massive ice-debris avalanches cascading into the glacial lake, such as the event which occurred in the nearby Nupchu valley in 2022 [3,38], (c) permafrost-related rock wall failure and associated cascading processes [3,27], and (d) other as yet unknown combinations of factors. Modeling GLOFs of varying magnitudes as a result of these mechanisms is not practical. However, we believe that our assumption of different flood magnitude scenarios reasonably represents future possibilities, including those exacerbated by the impacts of continued climate change [38,41,49,50]. For example, our small magnitude scenarios are similar to those experienced in the 2009 Tshojo glacier outburst event in Bhutan which caused widespread panic and the evacuation of vulnerable populations [51]. Likewise, our worst-case scenarios could parallel recent catastrophic events such as the 2023 flood of South Lhonak lake in Sikkim, which caused far-reaching and devastating impacts to downstream settlements and hydropower installations [36].
In summary, results from the study suggest that local people are justified in their concern over the future risks associated with the formation of a large and potentially dangerous glacial lake at the Kanchenjunga glacier terminus. Several recommendations that could help to reduce, mitigate, or even prevent a future hazardous event from the glacier, and/or within the KCA region in general, are provided below:
  • Closely monitor the lower Kanchenjunga glacier and its continued changes during the coming decade. In collaboration with members of the KCA Management Council, regular field- and satellite-based monitoring of changes in the lower Kanchenjunga glacier should be conducted by Kathmandu-based entities such as the Department of Meteorology and Hydrology (DHM), the International Centre for Integrated Mountain Development (ICIMOD), and Kathmandu University;
  • Develop inexpensive, locally appropriate, user-friendly early warning systems. Cell phone coverage, for example, is a simple, inexpensive alternative to sophisticated early warning systems that should be developed for the Ghunsa and other valleys in the KCA. During the 2013 Kosi River flood near Pokhara, hundreds of lives were saved when upstream witnesses called their family and friends downstream to warn them of the coming flood [52]. As a beginning, cell service throughout the entire KCA should continue to be developed for all villages and seasonal settlements as soon as possible, while other types of early warning systems are being assessed;
  • Encourage the development of zoning policies that prohibit the construction of lodges and other infrastructure in high-risk floodplain regions. Where the infrastructure already exists, villages should be encouraged to install gabions (rock-filled wire cages) along susceptible river channel/village interfaces to divert the flow of water during a flood event. Gabions have been used with considerable success by susceptible tourist villages in the Sagarmatha (Mt. Everest) National Park to divert flood waters from englacial conduit flood events [11];
  • Develop and implement local and national planning and training programs targeted at permafrost-related floods and other highland-lowland hazards. Between 2010 and 2015, several organizations conducted glacier hazard training programs for the Sagarmatha (Mt. Everest) National Park that are applicable to other high-altitude regions of Nepal [53]. Such training could be provided to residents of the KCA, as GLOFs and other glacier hazards represent a clear and growing threat to much of the population;
  • Develop mechanisms whereby villagers in remote locations can notify authorities in Kathmandu about their cryospheric hazard concerns, requesting suitable analyses and timely feedback regarding any current or future dangers. In particular, continued refinement of the models presented in this paper is encouraged in order to develop a rapid, reliable, and inexpensive glacial lake analytical tool capable of providing insights and recommendations to affected communities in a timely manner.

6. Conclusions

The project’s modeling results suggest that the merging and deepening of existing meltwater ponds on the Kanchenjunga glacier terminus could result in formation of a proglacial lake with a potential volume of be 33 × 106 m3. When impacted by the mass movement of different magnitude scenarios (small, medium and large), the resulting GLOFs could produce peak discharges ranging from 11,000 to 22,000 m3 s−1 and increase up to 70,000 m3 s−1 under the worst-case scenario. The floodwaters would impact bridges and infrastructure within the small, medium-, and large-scale flood scenarios, primarily in the villages of Kampachen and Ghunsa some 6 and 16 km downstream, respectively. Impacts would increase significantly in both villages for the worst-case scenarios.
While it is not possible to predict with total confidence whether or not a large proglacial lake will form on the low-gradient Kanchenjunga glacier terminus, the predictive models presented here show some promise of providing isolated and vulnerable communities with the information they need to make informed decisions for dealing with new glacial lake development. Continued model refinement is recommended in the interests of developing and providing rapid and robust predictive tools to vulnerable communities located in remote regions throughout highland Nepal. Given the rapid and recent development of numerous coalescing meltwater ponds in this area, and the concern expressed by local communities, the lower Kanchenjunga glacier region discussed in this paper should at the very least be regularly monitored over the coming decade, while mechanisms for effective and appropriate early warning systems are being developed.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17101457/s1.

Author Contributions

Conceptualization, A.C.B. and E.B.; methodology, S.R.; software, S.R.; validation, E.B.; formal analysis, S.R.; investigation, A.C.B., E.B., S.R. and S.W.; resources, A.C.B.; data curation, S.R.; writing—original draft preparation, A.C.B. and S.R.; writing—review and editing, E.B., S.R. and S.W.; visualization, A.C.B.; supervision, A.C.B.; project administration, A.C.B.; funding acquisition, A.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created.

Acknowledgments

We would like to extend our thanks to Ram Avtar, Graduate School of Environmental Science, Hokkaido University, Hokkaido, Japan for helpful suggestions regarding methods for the estimate of future glacial lake depth and volume. Thanks also to Mohan B. Chand, Department of Environmental Science and Engineering, Kathmandu University for his kind provision of Figure 1.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
KCAKanchenjunga Conservation Area
GLOFGlacial Lake Outburst Flood
DHMDepartment of Hydrology and Meteorology
ICIMODInternational Centre for Integrated Mountain Development
HEC-RASHydrologic Engineering Center River Analysis System
ALOSAdvanced Land Observing Satellite
HMAHigh Mountain Asia 8 m
DEMDigital Elevation Model

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Figure 1. Kanchenjunga Conservation Area, eastern Nepal. The study site is encircled in red southwest of the Kanchenjunga glacier, near the Ramdang seasonal pasture.
Figure 1. Kanchenjunga Conservation Area, eastern Nepal. The study site is encircled in red southwest of the Kanchenjunga glacier, near the Ramdang seasonal pasture.
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Figure 2. Evolution of supraglacial lakes on debris-covered glacier (27.7726, 88.0187) at Kanchenjunga. Time series satellite composite images were acquired by Sentinel-2 Multispectral Instrument between September and October, with the exception of the April–May 2024 images. Two large and distinct lakes which expanded rapidly in 2022 and continued to grow in 2023 and 2024 are labeled as 1 and 2, for locational comparison with the oblique view taken on 25 May 2024 (oblique photograph by A.C. Byers).
Figure 2. Evolution of supraglacial lakes on debris-covered glacier (27.7726, 88.0187) at Kanchenjunga. Time series satellite composite images were acquired by Sentinel-2 Multispectral Instrument between September and October, with the exception of the April–May 2024 images. Two large and distinct lakes which expanded rapidly in 2022 and continued to grow in 2023 and 2024 are labeled as 1 and 2, for locational comparison with the oblique view taken on 25 May 2024 (oblique photograph by A.C. Byers).
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Figure 3. Predicted future Kanchenjunga proglacial lake extent and depth, frontal moraine, and potential avalanche location, which were used as input for modeling the GLOF cascade in r.avaflow. The background image is from Google Earth Imagery (imagery date: 25 December 2020).
Figure 3. Predicted future Kanchenjunga proglacial lake extent and depth, frontal moraine, and potential avalanche location, which were used as input for modeling the GLOF cascade in r.avaflow. The background image is from Google Earth Imagery (imagery date: 25 December 2020).
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Figure 4. (A) Flood extent under various scenarios: areas impacted by all four GLOF scenarios are labelled as 4, those affected by three scenarios as 3, by two scenarios as 2, and by only one scenario as 1. (B) Hydrograph generated from GLOF modeling in r.avaflow and used as the input flow data for modeling each GLOF scenario in HEC-RAS. Flow depth and velocity at Kampachen (C,D) and Ghunsa (E,F). Discharge rate and time of peak, and flow arrival at Kampachen (G) and Ghunsa (H) under different scenarios.
Figure 4. (A) Flood extent under various scenarios: areas impacted by all four GLOF scenarios are labelled as 4, those affected by three scenarios as 3, by two scenarios as 2, and by only one scenario as 1. (B) Hydrograph generated from GLOF modeling in r.avaflow and used as the input flow data for modeling each GLOF scenario in HEC-RAS. Flow depth and velocity at Kampachen (C,D) and Ghunsa (E,F). Discharge rate and time of peak, and flow arrival at Kampachen (G) and Ghunsa (H) under different scenarios.
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Table 1. Different GLOF scenarios and their downstream impact for small, medium, large, and worst-case scenarios. KC = Kampachen, GS = Ghunsa.
Table 1. Different GLOF scenarios and their downstream impact for small, medium, large, and worst-case scenarios. KC = Kampachen, GS = Ghunsa.
GLOF ScenarioFlow Depth (m)Flow Velocity
(m s−1)		Building
(Count)		Bridge
(Count)
AllKCGSAllKCGSAllKCGS
Small533574167030
Medium94457445141840
Large116757689205441
Worst case14911610890215444
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Byers, A.C.; Rinzin, S.; Byers, E.; Wangchuk, S. Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response. Water 2025, 17, 1457. https://doi.org/10.3390/w17101457

AMA Style

Byers AC, Rinzin S, Byers E, Wangchuk S. Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response. Water. 2025; 17(10):1457. https://doi.org/10.3390/w17101457

Chicago/Turabian Style

Byers, Alton C., Sonam Rinzin, Elizabeth Byers, and Sonam Wangchuk. 2025. "Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response" Water 17, no. 10: 1457. https://doi.org/10.3390/w17101457

APA Style

Byers, A. C., Rinzin, S., Byers, E., & Wangchuk, S. (2025). Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response. Water, 17(10), 1457. https://doi.org/10.3390/w17101457

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