Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters
Type: text

Table 3 highlights various physical parameters that can be quantified using remote sensing technologies, detailing their measurement techniques, the specific remote sensing tools utilized, and their relevance in GLOF studies. The estimation of lake area and volume is foundational in assessing the potential impact of GLOF events [6,25]. Satellite optical imagery, particularly from Landsat and Sentinel-2, is typically used to delineate lake boundaries [32,98]. These measurements are critical for applying empirical relationships that estimate lake volume based on observed surface area [102,103]. Further refinement is achieved through the integration of Digital Elevation Models (DEMs), which provide a three-dimensional perspective of the lake basin and aid in more precise volume calculations [104]. This data is essential for modelling how lakes might respond under different flood scenarios and for understanding the dynamics of water release during GLOF events.

Quantifying glacier thinning and volume loss is essential for understanding mass balance and predicting future lake formation [27]. Digital Elevation Model (DEM) differencing, which compares multi-temporal elevation datasets, is the primary method for measuring surface elevation changes [25]. Satellite-derived DEMs, such as ASTER (30 m) and TanDEM-X (12 m), offer broad coverage, while airborne LiDAR and UAV photogrammetry provide sub-meter vertical accuracy for localized studies [104,105]. ICESat-2's laser altimetry, for instance, measures ice sheet elevation changes with millimeter precision, offering critical insights into glacier health. However, DEM accuracy varies with terrain complexity, and co-registration errors can inflate uncertainty in volume calculations [6,9]. In short, DEM differencing delivers unrivalled quantitative insight into glacier mass balance, yet its reliability is contingent on meticulous co-registration and terrain-specific error modelling.

The delineation of glacial lake boundaries and estimation of lake volume are central to GLOF risk assessment. Spectral indices like NDWI and Modified NDWI (MNDWI) are widely used to map lake boundaries in optical imagery [54,106]. UAV-derived bathymetry and LiDAR improve volume estimates by incorporating depth measurements and reducing reliance on area-based approximations [107]. Monitoring lake water levels and ice/snow cover provides insights into seasonal variability and potential triggers for GLOFs [108]. Radar altimetry (e.g., Sentinel-3, ICESat-2) tracks water level fluctuations with centimeter-scale precision, detecting rapid changes indicative of instability [58]. Optical sensors like MODIS and Sentinel-2 monitor ice and snow cover using indices such as NDSI, which identify meltwater sources [30,106]. However, atmospheric interference and debris cover can degrade accuracy, necessitating complementary thermal or SAR data for validation. Thus, fusing optical, radar, and UAV observations yields robust lake-volume and stability metrics, although persistent atmospheric noise and surface debris continue to challenge classification accuracy.

Surface temperature, measured using thermal infrared (TIR) bands on Landsat-8 and MODIS, helps identify melt hotspots and subglacial water pathways [96,109]. Water turbidity and sediment load, estimated through spectral analysis of red and near-infrared

Geosciences 2025, 15, 211 23 of 36


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters
Type: text

, which identify meltwater sources [ 30 , 106 ] . However , atmospheric interference and debris cover can degrade accuracy , necessitating complementary thermal or SAR data for validation . Thus , fusing optical , radar , and UAV observations yields robust lake - volume and stability metrics , although persistent atmospheric noise and surface debris continue to challenge classification accuracy . Surface temperature , measured using thermal infrared ( TIR ) bands on Landsat - 8 and MODIS , helps identify melt hotspots and subglacial water pathways [ 96 , 109 ] . Water turbidity and sediment load , estimated through spectral analysis of red and near - infrared Geosciences 2025 , 15 , 211 23 of 36

bands, signal erosion processes that weaken moraine dams [44]. For instance, PlanetScope's high revisit frequency enables near-real-time monitoring of sediment plumes in HMA lakes, though accuracy depends on water clarity and sun-glint effects [110]. Collectively, TIR and high-revisit optical sensors allow near-real-time tracking of thermal and sediment anomalies, yet their performance is moderated by water clarity and solar-reflection artefacts.

Ice thickness, a critical factor in glacier stability, is measured indirectly using ground-penetrating radar (GPR) or inferred from surface velocity models [107]. Satellite altimetry (e.g., ICESat-2) and SAR penetration (L-band) enhance large-scale assessments, though field validation remains essential [8,111]. Understanding the topography of glaciers and lakes involves measuring changes in surface elevation and overall glacier volume [112]. Techniques such as DEM differencing and Interferometric Synthetic Aperture Radar (InSAR) are crucial in these assessments [24]. Data from platforms like the Shuttle Radar Topography Mission (SRTM) and TanDEM-X provide detailed elevation information that is vital for tracking glacier retreat and detecting thinning ice areas, which can indicate potential vulnerabilities in glacier dam stability [104]. These methods provide indispensable data for understanding glacier stability and potential dam vulnerabilities, though they often require indirect measurements and field validation.

Each of these parameters benefits significantly from the integration of remote sensing data, which provides a continuous, detailed, and accessible means of monitoring changes in the glacial environment. The capabilities of modern remote sensing technologies to deliver timely data across large, often inaccessible areas make them indispensable tools in the ongoing effort to mitigate the risks associated with GLOFs.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics
Type: text

An important aspect of GLOF risk assessment is the continuous monitoring of glacier dynamics, as changes in glacier behavior directly influence the stability of ice- and moraine-dammed lakes. Understanding the temporal evolution of glaciers, particularly their flow, deformation, and mass balance, provides essential insights into the stability of glacier-fed lakes and the potential for dam failure [113]. Glacier dynamics encompass processes such as ice flow velocity, surface deformation, terminus retreat, and mass balance, all of which provide critical insights into the likelihood of dam failure and subsequent flooding [40,114,115]. Remote sensing technologies, including Synthetic Aperture Radar (SAR), optical imagery, and UAV photogrammetry, have emerged as powerful tools for tracking these dynamic processes across vast and inaccessible mountain regions.

The key parameters and monitoring methods for glacier dynamics are summarized in Table 4. Glacier flow velocity, a key indicator of ice dynamics, is measured using Interferometric SAR (InSAR) and optical feature tracking. It is crucial for predicting ice calving events and glacier surges, both of which can destabilize glacial lakes [110]. Surface deformation, including subsidence or bulging of moraine dams, can be monitored using Differential InSAR (DInSAR) and Persistent Scatterer InSAR (PSInSAR), as well as high-resolution UAV photogrammetry [64]. These techniques quantify vertical and horizontal displacements with sub-centimeter precision, identifying areas of stress accumulation.

Time series analyses using satellite imagery have also proven invaluable. By constructing multi-temporal datasets, researchers can track the progression of glacier retreat and correlate these changes with corresponding variations in lake area. Such analyses not only reveal trends in glacier mass loss but also help establish thresholds beyond which the risk of GLOF increases significantly [98,102].


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics
Type: table
Table

| <b>Table 4.</b> Glacier dynamics p | parameters and | monitoring methods. |
|------------------------------------|----------------|---------------------|
|------------------------------------|----------------|---------------------|

Table 4. Glacier dynamics p
parameters and
monitoring methods.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics
Type: table
Table

| Parameter             | Monitoring Method                                  | Remote Sensing Tools                   | Importance in GLOF Studies                                                   | Data Requirements                                                                  |
|-----------------------|----------------------------------------------------|----------------------------------------|---------------------------------------------------------------------------------|------------------------------------------------------------------------------------|
| Glacier Flow Velocity | InSAR, optical feature tracking                 | Sentinel-1, Landsat-8, PlanetScope  | Predicts ice calving events and glacier surges that destabilize lakes. | Multi-temporal SAR data (6–12-day intervals), cloud-free optical imagery. |
| Surface Deformation   | InSAR (DInSAR, PSInSAR), UAV photogrammetry  | Sentinel-1, TerraSAR-X, UAVs        | Detects subsidence or bulging in moraine dams, signaling instability.  | High-frequency SAR acquisitions, UAV campaigns during stable weather.     |
| Terminus Retreat      | Optical time series analysis                    | Landsat-8, Sentinel-2                  | Tracks glacier retreat linked to lake expansion and ice-dam formation. | Multi-decadal optical imagery (16–30 m resolution).                          |
| Mass Balance          | DEM differencing, gravimetry                    | ICESat-2, GRACE-FO                     | Quantifies ice loss/gain to predict lake volume changes.                  | High-accuracy DEMs (LiDAR/InSAR), GRACE-FO gravity data.                     |
| Crevasse Formation    | UAV photogrammetry, high-res optical imagery | UAVs, WorldView                        | Identifies stress zones on glaciers prone to collapse.                    | Sub-meter resolution imagery, repeat UAV surveys.                            |
| Subglacial Hydrology  | Ground-penetrating radar (GPR)                  | UAV-mounted GPR                        | Maps subglacial drainage systems that may trigger outbursts.              | High-resolution radar data, ice-penetrating frequencies.                     |
| Terrain Stability     | InSAR (DInSAR), optical feature tracking        | Sentinel-1, TerraSAR-X, PlanetScope | Monitors slope instability and moraine dam movement.                      | Regular SAR acquisitions (6–12 days), optical time series.                   |

Parameter
Monitoring Method
Remote Sensing Tools
Importance in GLOF Studies
Data Requirements


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics
Type: text

Another important parameter, glacier mass balance, which is the difference between accumulation and ablation, is estimated using DEM differencing and gravimetry. It provides a comprehensive assessment of ice loss or gain [6,66]. DEM differencing compares elevation changes over time, while gravimetry measures changes in the Earth's gravitational field due to ice mass variations. These measurements are essential for predicting changes in lake volume and understanding the overall health of the glacier.

Crevasse formation, a marker of glacier stress, is mapped using UAV photogrammetry and high-resolution optical imagery (e.g., PlanetScope or WorldView). Sub-meter resolution datasets capture crack density and orientation, identifying zones prone to ice collapse [110]. Subglacial hydrology, mapped using ground-penetrating radar, reveals subglacial drainage systems that may trigger outbursts. Finally, InSAR and optical feature tracking monitor terrain stability, which assesses slope instability and moraine dam movement.

Monitoring glacier dynamics through remote sensing provides a comprehensive understanding of the processes driving GLOF hazards. Techniques like InSAR, optical tracking, and UAV photogrammetry offer high-precision, multi-scale insights into ice flow, deformation, and dam stability. When integrated with hydrodynamic models, these data empower stakeholders to predict outburst scenarios, prioritize mitigation efforts, and implement timely interventions. As climate change accelerates glacier retreat, advancing these monitoring frameworks will be vital for safeguarding vulnerable communities in high-mountain regions.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research
Type: text

The complexities inherent in GLOF research, characterized by dynamic glacial environments and intricate spatial–temporal interactions, necessitate the adoption of advanced

Geosciences **2025**, 15, 211 25 of 36

analytical techniques. Machine learning (ML) has emerged as a transformative tool in this domain, offering unprecedented capabilities for data processing, pattern recognition, and predictive modelling [4,36]. By leveraging the power of algorithms and computational resources, ML techniques are enhancing our understanding of GLOF mechanisms and improving the accuracy of risk assessments.

Table 5 details the specific applications, strengths, limitations, and data requirements of each machine learning technique employed in GLOF studies. This table serves as a critical resource for understanding how different machine learning approaches are applied to remote sensing data to address various challenges in GLOF risk assessment.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research
Type: table
Table: Table 5. Machine learning techniques in GLOF research.

| Technique                                                       | Application                                                                                | Strengths                                                                         | Limitations                                                                          | Data Requirements                                                                                |
|-----------------------------------------------------------------|--------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|
| Supervised Classification (e.g., Random Forest, SVM)   | Glacier/lake mapping, landslide detection, land cover classification                 | High accuracy with labeled data; handles multi-source inputs (spectral, DEMs)     | Requires large labeled datasets; performance depends on feature engineering | Labeled optical/SAR imagery, DEMs, slope/aspect layers                                     |
| Unsupervised Clustering (e.g., K-means, ISODATA)                | Identifying melt patterns, lake expansion trends, terrain deformation clusters | No labels needed; discovers hidden patterns in data                         | Clusters may lack physical interpretability; sensitive to initialization | Multi-temporal Sentinel-1/2 data, InSAR coherence maps                                     |
| Deep Learning (e.g., CNNs, U-Net, Transformers)           | Automated lake/ice segmentation, change detection, glacier flow modeling                   | Learns hierarchical features; excels in complex spatial-temporal tasks   | Computationally intensive; prone to overfitting without large datasets               | High-resolution optical/SAR imagery, DEMs, annotated labels                                |
| Time Series Analysis (e.g., LSTM, ARIMA)                     | Predicting lake level fluctuations, glacier melt rates, anomaly detection         | Captures temporal dependencies; robust for forecasting                      | Requires long, continuous time series data; sensitive to missing values     | Historical Landsat/MODIS data, climate variables (temperature, precipitation)                    |
| Object-Based Image Analysis (OBIA) with ML                | Mapping debris-covered ice, unstable slopes, moraine dam cracks                   | Combines spectral, spatial, and contextual features; reduces noise          | Parameter tuning is complex; computationally demanding                      | High-resolution imagery (e.g., WorldView), DEMs, ancillary data (e.g., geology maps) |
| Anomaly Detection (e.g., Autoencoders, Isolation Forests) | Detecting sudden lake drainage, abnormal glacier velocity, seismic triggers       | Identifies outliers without prior knowledge; adaptable to rare events    | High false-positive rate; thresholds require calibration                       | Multi-temporal SAR/optical data, InSAR deformation maps, seismic records                |
| Ensemble Learning (e.g., XGBoost, Stacking)               | Improving GLOF risk prediction, integrating multi-sensor data                              | Reduces overfitting; combines model strengths for higher accuracy        | Computationally expensive; requires diverse base models                              | Multi-source data (optical, SAR, DEMs, climate)                                            |
| Geospatial ML (e.g., Graph Neural Networks)               | Modeling spatial interactions (e.g., lake-glacier-terrain dynamics)               | Captures large-scale spatial dependencies; integrates heterogeneous data | Demands domain expertise; resource-intensive                                   | Spatially referenced data (imagery, DEMs, hydrological models)                             |

Technique
Application
Strengths
Limitations
Data Requirements


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research
Type: text

Supervised classification techniques like Random Forest and Support Vector Machines (SVM) are employed extensively in glacier and lake mapping, landslide detection, and land cover classification [4,116]. These models are particularly valued for their ability to handle multi-source inputs and provide high accuracy when trained with adequately labeled data. They rely on a range of data, including optical and SAR imagery, digital elevation models (DEMs), and additional geographical layers, such as slope and aspect,

Geosciences **2025**, 15, 211 26 of 36

which help in accurately delineating and classifying different surface features relevant to GLOF dynamics. However, their performance hinges on extensive labeled training data, which can be labor-intensive to acquire in remote glacial environments.

Unsupervised clustering methods such as K-means and ISODATA play a crucial role in identifying melt patterns, lake expansion trends, and clusters of terrain deformation without the need for labeled datasets [117]. These techniques analyze multi-temporal satellite data to discover hidden patterns and changes over time, providing insights into the gradual changes that might not be immediately apparent. A key limitation is their sensitivity to initialization parameters, and resulting clusters may lack direct physical interpretability, requiring domain expertise to contextualize outputs.

Deep learning algorithms, including convolutional neural networks (CNNs), U-Net, and transformers, are at the cutting edge of automating complex tasks such as lake and ice segmentation, change detection, and glacier flow modelling [59,118]. These algorithms excel at learning hierarchical features from large datasets, making them exceptionally good at handling spatial-temporal tasks that are common in high-resolution remote sensing data analysis. However, these models are computationally intensive and prone to overfitting without large annotated datasets—a challenge in data-scarce regions.

Time series analysis using models like long short-term memory networks (LSTM), artificial neural network (ANN), or autoregressive integrated moving average (ARIMA) models are employed to predict lake level fluctuations, glacier melt rates, and detect anomalies. These models are adept at capturing temporal dependencies, making them particularly useful for forecasting based on historical data [119]. Their effectiveness diminishes with fragmented or incomplete time series data, underscoring the need for continuous monitoring systems [50].

Object-based image analysis (OBIA) integrated with machine learning combines spectral, spatial, and contextual information to map complex phenomena such as debris-covered ice or moraine dam cracks [120]. This approach helps in reducing noise and improving the precision of feature extraction in cluttered or heterogeneous environments. However, its computational demands and complex parameter tuning limit scalability [121]. Anomaly detection techniques such as autoencoders and isolation forests are crucial for detecting unusual events like sudden lake drainage, abnormal glacier velocities, or seismic activities that could potentially trigger a GLOF. These techniques are designed to identify outliers and anomalies in datasets without needing prior labeling of what constitutes a normal or an anomalous event [122]. Ensemble learning methods like XGBoost and model stacking are used to integrate findings from multiple models, thereby reducing overfitting and enhancing the reliability of predictions [123]. These methods combine the strengths of various models to improve overall prediction accuracy for GLOF risk assessment.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research
Type: text

and complex parameter tuning limit scalability [ 121 ] . Anomaly detection techniques such as autoencoders and isolation forests are crucial for detecting unusual events like sudden lake drainage , abnormal glacier velocities , or seismic activities that could potentially trigger a GLOF . These techniques are designed to identify outliers and anomalies in datasets without needing prior labeling of what constitutes a normal or an anomalous event [ 122 ] . Ensemble learning methods like XGBoost and model stacking are used to integrate findings from multiple models , thereby reducing overfitting and enhancing the reliability of predictions [ 123 ] . These methods combine the strengths of various models to improve overall prediction accuracy for GLOF risk assessment .

Geospatial machine learning approaches, including graph neural networks, are leveraged to model complex spatial interactions among various elements like lakes, glaciers, and terrain. These models are capable of processing large-scale spatial data and integrating diverse datasets, providing a comprehensive view of the environmental factors influencing GLOF risks [124].

The integration of these techniques offers a holistic framework for GLOF risk assessment. For example, combining supervised classification for lake mapping with LSTM-based forecasts and anomaly detection creates a multi-layered early-warning system


Document: 1. Introduction
Section: 4. Risk Assessment Models for GLOFs
Type: text

GLOFs pose significant threats to downstream communities, infrastructure, and ecosystems in high-mountain regions. Risk assessment models are critical tools for quantifying these hazards, predicting potential impacts, and guiding mitigation strategies [125]. Mod-

Geosciences **2025**, 15, 211 27 of 36

ern GLOF risk assessment methodologies have evolved to incorporate detailed physical parameters and glacier dynamics data obtained through remote sensing, enhancing the precision and reliability of hazard evaluations. These models can be categorized into three broad approaches: quantitative (e.g., hydrodynamic simulations), semi-quantitative (e.g., statistical frameworks), and qualitative (e.g., multi-criteria decision systems), each addressing distinct aspects of GLOF risk. Traditional approaches often relied on empirical observations, but contemporary models utilize sophisticated techniques to quantify and predict GLOF hazards [72]. Table 6 provides a comparative analysis of key model types employed in GLOF risk assessment, detailing their specific input parameters, the role of remote sensing data in parameter acquisition, and models' respective strengths and limitations.


Document: 1. Introduction
Section: 4. Risk Assessment Models for GLOFs
Type: table
Table: Table 6. Comparative framework for GLOF risk assessment.

| Model Type                                | Input Parameters                                                                                | Remote Sensing Data for Parameter Acquisition                                                                                | Strengths                                                                                | Limitations                                                                                |
|-------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| Hydrodynamic Models (e.g., HEC-RAS) | - Lake volume - Channel geometry - Topography/DEM - Flow roughness - Upstream discharge                             | - Satellite altimetry, optical imagery, and DEMs for lake volume - DEMs and high-resolution imagery for channel geometry - DEMs for topography - Land cover classification (optical/SAR) for roughness | - Detailed flood wave propagation - Accurate inundation extent mapping - Supports infrastructure planning | - Highly sensitive to DEM accuracy - Requires calibration with field data - Limited in steep/vegetated terrain   |
| Statistical Models                        | - Historical GLOF frequency - Lake area changes - Glacier retreat rates - Climate variables                         | - Historical optical/SAR imagery and GLOF databases - Time series satellite data (e.g., Landsat, Sentinel) for lake/glacier dynamics - Reanalysis climate data                                                  | - Quantifies probabilistic risk - Low computational cost - Identifies historical trends                   | - Relies on past events (fails for novel triggers) - Limited in data-scarce regions - Ignores physical processes |
| Multi-Criteria Decision Models         | - Lake expansion rate - Dam type - Proximity to infrastructure - Avalanche/snowpack risk - Population density | - Time series optical/SAR for lake monitoring - DEMs and settlement maps (e.g., nighttime lights) - Snow cover maps (optical/radar) for avalanche risk                                                       | - Holistic risk prioritization - Flexible integration of socio-environmental factors                         | - Subjective weight allocation - Requires expert validation - Qualitative outputs                                   |


Document: 1. Introduction
Section: 4. Risk Assessment Models for GLOFs
Type: text

As a quantitative approach, hydrodynamic models, such as HEC-RAS, are designed to simulate water flow dynamics and predict flood propagation pathways [88]. They are widely used to map inundation extents and assess downstream impacts by integrating parameters such as lake volume, channel geometry, topography (derived from DEMs), flow roughness, and upstream discharge [119]. Satellite altimetry and optical imagery provide critical inputs for estimating lake volume, while DEMs and land cover classification (using optical/SAR data) define channel geometry and flow resistance. Though these models offer high accuracy in infrastructure planning, their reliability hinges on DEM precision and field calibration, with reduced efficacy in steep or densely vegetated terrain [57].

Statistical models represent a semi-quantitative approach, prioritizing probabilistic risk assessment by leveraging historical GLOF events, lake area changes, glacier retreat rates, and climate variables (e.g., temperature, precipitation) [126]. These models identify trends and recurrence intervals using time series optical/SAR imagery (e.g., Landsat,

Geosciences 2025, 15, 211 28 of 36

Sentinel) and reanalysis climate data. While computationally efficient and valuable for identifying historical patterns, they are inherently limited by their reliance on past events, failing to account for novel triggers like unprecedented rainfall or ice avalanches in a warming climate.

Multi-criteria decision models fall under a qualitative framework, adopting a holistic approach to prioritize risk by synthesizing diverse factors such as lake expansion rates, dam type, proximity to infrastructure, avalanche/snowpack risk, and population density [91,127,128]. Remote sensing supports these models through time series optical/SAR imagery for lake monitoring, DEMs for avalanche-prone slopes, and night-time lights data for mapping settlements. Although they excel in integrating socioenvironmental factors, their subjective criteria weighting and qualitative outputs necessitate expert validation [129].

Contemporary GLOF risk frameworks increasingly combine these models to offset individual limitations. For instance, statistical models identify high-probability lakes while, hydrodynamic models map downstream impacts [125,126]. Integrating remote sensing data into these models improves input accuracy and enhances their predictive capabilities, making them essential tools for addressing the hazards posed by GLOFs [127,130]. This dynamic interplay between advanced modelling techniques and cutting-edge remote sensing technologies underpins modern strategies for disaster risk reduction in vulnerable mountainous areas.


Document: 1. Introduction
Section: 5. Challenges and Limitations
Type: text

The integration of remote sensing technologies and advanced modelling frameworks has significantly advanced GLOF research. However, several challenges and limitations persist, hindering the accuracy, reliability, and practical implementation of these tools in risk assessment and mitigation.

The accuracy and resolution of remote sensing data play a crucial role in the reliability of GLOF risk assessments [62,130]. High-resolution data, such as those from high-resolution commercial satellites or UAVs, can provide detailed information on lake morphology and dam conditions. However, these data sources may be limited in terms of temporal coverage or spatial extent, making it challenging to monitor changes over time or across large regions. On the other hand, moderate-resolution data from satellites like Landsat or Sentinel may offer broader coverage and longer temporal records but may lack the level of detail needed for detailed assessments [131]. Additionally, atmospheric conditions, such as cloud cover or haze, can affect the quality. Moreover, integrating data from multiple sources and sensors involves significant challenges in terms of data compatibility, calibration, and processing [132]. This integration is critical for creating accurate models, but can be technically complex and resource-intensive for optical remote sensing data, limiting their effectiveness in certain regions or time periods.

Predicting GLOF events and their impacts is subject to various uncertainties, including uncertainties in model parameters, input data, and model structure. For example, hydrodynamic models used to simulate flood propagation may be sensitive to uncertainties in lake bathymetry, channel roughness, or precipitation patterns [37,88]. Statistical models, while useful for probabilistic risk assessment, fail to account for novel triggers such as unprecedented rainfall or cascading hazards (e.g., ice avalanches triggered by seismic activity), limiting their predictive power in a warming climate [126]. Furthermore, machine learning frameworks face challenges in generalizing across regions due to data scarcity and variability in glacier dynamics, often overfitting to localized conditions [124]. These uncertainties can lead to discrepancies between model predictions and observed events, highlighting the need for careful validation and calibration of models.

Geosciences 2025, 15, 211 29 of 36

The impacts of climate change are altering glacier behaviors and lake formations at unpredictable rates, which complicates the ability to use past data to predict future events [133]. This rapid environmental change challenges the ability of current models to forecast GLOFs accurately. The geological settings of glacial lakes vary widely, and the hydrological processes involved in lake formation and drainage are complex and not fully understood [134]. These uncertainties make it difficult to generalize findings from one region to another or to extrapolate results from small-scale studies to larger regions.


Document: 1. Introduction
Section: 5. Challenges and Limitations
Type: text

need for careful validation and calibration of models . Geosciences 2025 , 15 , 211 29 of 36 The impacts of climate change are altering glacier behaviors and lake formations at unpredictable rates , which complicates the ability to use past data to predict future events [ 133 ] . This rapid environmental change challenges the ability of current models to forecast GLOFs accurately . The geological settings of glacial lakes vary widely , and the hydrological processes involved in lake formation and drainage are complex and not fully understood [ 134 ] . These uncertainties make it difficult to generalize findings from one region to another or to extrapolate results from small - scale studies to larger regions .

The implementation of advanced remote sensing technologies and models remains constrained by logistical, financial, and institutional barriers. High-mountain regions, particularly in developing countries like Nepal or Pakistan, lack the infrastructure for real-time data transmission and processing, delaying early warnings [29,30,135]. High-resolution satellite imagery (e.g., WorldView, Pleiades) and UAV campaigns are costly, limiting their adoption in resource-strapped regions. Even when data are available, translating them into actionable policies requires interdisciplinary collaboration between glaciologists, policy-makers, and local communities—a coordination often hampered by communication gaps or competing priorities. Additionally, field validation efforts are hindered by the remoteness and harsh conditions of glacial environments, risking incomplete or outdated ground-truth data. These challenges underscore the need for continued innovation in remote sensing technologies, robust validation protocols, and inclusive governance frameworks to bridge the gap between scientific advancements and on-the-ground risk reduction.


Document: 1. Introduction
Section: 6. Future Directions
Type: text

As GLOF hazards intensify under climate change, advancing remote sensing technologies and interdisciplinary frameworks will be critical to enhancing risk assessment and mitigation. Therefore, it is important to focus on innovative research and technological advancements that can improve predictions, enhance monitoring, and streamline mitigation efforts.

The field of remote sensing is continually evolving, with new technologies and techniques emerging regularly. For GLOF risk assessment, future research could explore the use of higher resolution imaging systems, such as CubeSats or advanced UAV platforms, to capture more detailed information on lake morphology and dam conditions. Advances in hyperspectral imaging and quantum sensors could improve spectral discrimination of turbid water and debris-covered ice, reducing classification errors. The incorporation of emerging technologies such as unmanned aerial vehicles (UAVs), ground-penetrating radar (GPR), and more sophisticated LiDAR systems can fill gaps left by traditional remote sensing methods, especially in inaccessible or cloud-prone areas. Crucially, future efforts should converge on establishing an integrated air-ground-space GLOF early warning and prediction system. This system would leverage the real-time data transmission capabilities enabled by Internet of Things (IoT) sensors, particularly through edge computing, to bypass traditional latency issues. Furthermore, the integration of real-time data processing and analysis capabilities could enable more timely detection of potential GLOF events, allowing for faster response and mitigation efforts. Specifically, Artificial Intelligence (AI), particularly transformer-based models and federated learning systems, holds potential for automating early warning systems by synthesizing multi-source data (optical, SAR, DEMs) to predict outburst triggers like rapid lake expansion or moraine subsidence. These AI and IoT technologies are paramount for enhancing the accuracy and timeliness of risk prevention.

To enhance the accuracy and reliability of GLOF risk assessments, future research could focus on improving the integration of remote sensing data with dynamic risk models. This could involve the development of advanced machine learning algorithms to better capture

Geosciences 2025, 15, 211 30 of 36

the complex relationships between environmental factors and GLOF events. Additionally, the use of enhanced simulation techniques, such as coupled hydrological-geotechnical models, could provide more realistic representations of GLOF processes and their impacts.

As climate change remains a critical driver of glacial changes, establishing long-term environmental monitoring programs will provide essential data to understand and anticipate future changes in glacial behavior and lake dynamics. Research should also focus on developing sustainable development strategies that minimize environmental impacts and enhance the adaptive capacity of communities living in high-risk regions.

Advancements in technology and modelling have the potential to significantly influence policy-making and practical hazard management strategies for GLOFs. For example, improved monitoring capabilities could lead to the development of more effective early warning systems, while enhanced risk assessment models could inform the design of targeted mitigation measures. Future research could explore how these advancements can be translated into actionable policies and management practices, particularly in the context of climate change adaptation and sustainable development.


Document: 1. Introduction
Section: 7. Conclusions
Type: text

This review has systematically explored the evolving landscape of remote sensing technologies and their applications in monitoring and risk assessment of GLOFs, a growing threat in high-mountain regions worldwide. We have highlighted the progression from traditional methods to the current era of advanced satellite and UAV-based monitoring, emphasizing the critical role of multi-sensor data fusion and machine learning in enhancing the accuracy and efficiency of GLOF studies. The integration of these technologies not only improves the precision of risk assessments but also supports the development of early warning systems that are crucial for safeguarding vulnerable communities.

Despite technological advancements, challenges such as data integration, model calibration, and the need for high-resolution data continue to constrain the full potential of remote sensing applications. Moreover, environmental and climatic variability introduces additional complexities to modeling efforts.

Future efforts should focus on refining remote sensing techniques, enhancing predictive modeling through machine learning, and translating these advancements into actionable policies and management practices. The scientific community, policymakers, and local stakeholders must work together to build resilience and mitigate the impacts of GLOFs, ensuring the safety and sustainability of high-mountain regions.

As climate change continues to drive glacier retreat and increase the frequency and intensity of GLOFs, the insights from this review call for sustained research, innovation, and collaboration. Collaboration among the scientific community, policymakers, and local stakeholders is imperative to enhance the resilience of mountainous regions against GLOF hazards. By integrating scientific advancements with policy and community-based actions, we can more effectively mitigate the impacts of GLOFs and ensure the sustainability of these high-risk regions.

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

**Funding:** This research was funded by Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number BR21882365.

Data Availability Statement: Data are contained within this article.

Geosciences 2025, 15, 211 31 of 36

**Acknowledgments:** We are grateful to all the authors of the articles that were discussed in this review.

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


Document: Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment
Type: text

Abstract: Glacial lake outburst floods (GLOF) evolve into debris flows by erosion and sediment entrainment while propagating down a valley, which highly increases peak discharge and volume and causes destructive damage downstream. This study focuses on GLOF hazard assessment in the Bhote Koshi Basin (BKB), where was highly developed glacial lakes and was intensely affected by the Gorkha earthquake. A new 2016 glacial lake inventory was established, and six unreported GLOF events were identified with geomorphic outburst evidence from GaoFen-1 satellite images and Google Earth. A new method was proposed to assess GLOF hazard, in which large numbers of landslides triggered by earthquake were considered to enter into outburst floods enlarge the discharge and volume of debris flow in the downstream. Four GLOF hazard classes were derived according to glacial lake outburst potential and a flow magnitude assessment matrix, in which 11 glacial lakes were identified to have very high hazard and 24 to have high hazard. The GLOF hazard in BKB increased after the earthquake due to landslide deposits, which increased by  $216.03 \times 10^6$  m3, and provides abundant deposits for outburst floods to evolve into debris flows. We suggest that in regional GLOF hazard assessment, small glacial lakes should not be overlooked for landslide deposit entrainment along a flood route that would increase the peak discharge, especially in earthquake-affected areas where large numbers of landslides were triggered.

**Keywords:** glacial lake outburst flood (GLOF); debris flow; Bhote Koshi; landslides; Gorkha earthquake; hazard assessment

data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md


Document: 1. Introduction
Section: 1. Introduction
Type: text

Many studies have demonstrated that most glaciers are retreating because of global warming and that the meltwater makes an important contribution to the development of glacial lakes in the Himalayas [1–5]. The sudden emptying of these lakes due to dam overflow and moraine or ice dam failure releases large volumes of water and sediment in destructive events called glacial lake outburst floods (GLOF) [6,7]. In the Himalaya region, at least 62 GLOF have been reported, which caused catastrophic destruction and fatalities in downstream regions [8–12]. The peak flood discharge can easily attain tens of thousands of m³/s and travel more than 100 km away [8,13]. Given their high magnitude discharge and long runout distance characteristics, the GLOF impact is sometimes transboundary, especially in the Himalayas. More than 10 GLOF events originated in Tibet, and the catastrophic floods killed hundreds of people and destroyed much infrastructure downstream, causing enormous damage in Nepal and India [14,15]. As a result, GLOF hazard assessment is receiving increased attention from researchers and governments.

Water 2020, 12, 464 2 of 20

In previous GLOF hazard assessment studies, only glacial lakes with an area  $>10^5$  m2 or volume  $>10^6$  m3 were considered to be risky of outburst [15,16]. However, some small outbursts occurred in the high mountain regions, but are often ignored due to the limited scale of the events or difficult access [17]. Veh [18] detected 10 previously unreported GLOF from Landsat time series in a study area covering only 10% of the Hindu Kush Himalayan region. In addition, glacial lake outburst floods are highly unsteady flows characterized by pronounced changes as they propagate down to the valley [13]. The outburst flood can change from a normal flood to a hyperconcentrated flow or debris flow [19,20], and the volumes and peak discharges can increase several to ten times owing to erosion, slides from lateral slopes, sediment entrainment and bulking process along the flow path [21,22]. As an example, in Norway, a glacial lake outburst flood developed into a debris flow due to erosion and blockage, and the volume increased nearly ten times from 25,000 to 240,000 m3 [23]. Sediment can be entrained by scouring unconsolidated deposits of the channel bed, or eroding landslide and collapse of lateral slopes [24,25]. In the seismic belts, large numbers of weak structures and broken rocks are developed along the active fracture zone, and the soils become looser after an earthquake [26]. Studies showed that large earthquakes, such as the Chi-chi earthquake and Wenchuan earthquake, trigger many collapses and landslides, resulting in an increase in loose deposits [27,28]. Although rare reported GLOF events in the Himalaya are directly triggered by earthquakes [29], the loose deposits and landslides induced by earthquakes may affect the magnitude and impact of GLOF. Therefore, it is necessary to build a GLOF hazard assessment model, considering small glacial lakes and the scenario of glacial lake outburst debris flows after earthquakes, especially in areas where many collapses and landslides have developed along the channels.


Document: 1. Introduction
Section: 1. Introduction
Type: text

after an earthquake [ 26 ] . Studies showed that large earthquakes , such as the Chi - chi earthquake and Wenchuan earthquake , trigger many collapses and landslides , resulting in an increase in loose deposits [ 27 , 28 ] . Although rare reported GLOF events in the Himalaya are directly triggered by earthquakes [ 29 ] , the loose deposits and landslides induced by earthquakes may affect the magnitude and impact of GLOF . Therefore , it is necessary to build a GLOF hazard assessment model , considering small glacial lakes and the scenario of glacial lake outburst debris flows after earthquakes , especially in areas where many collapses and landslides have developed along the channels .

The Bhote Koshi Basin across China and Nepal, is a highspot area of glacial lakes and GLOF events (Wang and Jiao, 2015). Four glacial lakes have experienced six GLOF events since 1935 (Figure 1). Taraco Lake failed on 28 August 1935, and the GLOF damaged more than 10 hm2 of wheat fields (Lv, 1999); The Jialongco GLOF occurred on 23 May and 29 June 2002, which caused 7.5 million yuanin economic losses in Nyalam County (Chen et al., 2007). The Cinrenmaco Lake experienced two GLOF events, first in 1964 and second on 10 July 1981. The GLOF in 1981 had the most destructive effects, in which more than 200 people were killed in Nepal, and the total losses were estimated at approximately three million dollars [14,30]. The latest GLOF event occurred on the night of 5 July 2016, which was caused by Gongbatongshaco (GBTSC) Lake in the Zhangzangbo Valley. GBTSC is a small moraine-dammed lake, with a surface area of  $1.7 \times 10^4$  m2 and it was almost empty after the outburst. Although it only released  $1.1 \times 10^5$  m3 of water, the peak discharge reached 2400 m3/s at Khukundol, 30 km downstream from the lake, due to severe erosion and sediment entrainment [31]. The GLOF caused severe damage downstream of Bhote Koshi, damaging 77 houses, 3 bridges and the Araniko Highway, and destroying the intake dam of the Upper Bhote Koshi Hydropower Project in Nepal (Figure 1). The 2016 GLOF damage sits within the area affected by the Gorkha earthquake (magnitude M 7.8 in 2015), where extensive landslides and rockfalls were triggered on the slopes, and some landslide deposits even blocked the river [32,33]. Therefore, the small glacial lake GBTSC GLOF caused a serious disaster, which caused us to reconsider the small-lake induced GLOF hazard after

The aims of this study are: (1) to establish a detailed glacial lake inventory of BKB after the Gorkha earthquake, based on high resolution remote sensing satellite images; (2) to evaluate the GLOF hazard of BKB considering the scenario that outburst floods evolve into debris flows due to erosion and entrainment of loose solids.

Water 2020, 12, 464 3 of 20


Document: 1. Introduction
Section: 1. Introduction
Type: figure
Figure

Image /page/2/Figure/1 description: A figure composed of four photographs labeled a, b, c, and d, illustrating the effects of a glacial lake outburst flood. Panel 'a' shows a rocky basin with a small lake, with a blue dashed line indicating the much higher 'Lake surface before outburst' and a person circled and labeled 'Man' for scale. Panel 'b' displays a wide mountain landscape with snow-capped peaks, showing the 'Breach' in the moraine from which the lake burst and the resulting large 'Debris fan' of rocks and sediment below. Panel 'c' depicts a river valley downstream, where a village is situated next to the river, with signs of landslides and erosion on the steep hillsides. Panel 'd' shows an aerial view of a concrete dam structure being overwhelmed and damaged by a powerful, muddy flood carrying significant debris.


Document: 1. Introduction
Section: 1. Introduction
Type: figure
Figure: Figure 1. Photographs of Gongbatongshaco (GBTSC) Lake and the glacial lake outburst floods (GLOF) damage caused downstream: (a) GBTSC Lake after outburst, (b) the breach and debris fan in front of the lake, (c) landslides and river bank collapse triggered by GLOF near Friendship Bridge and (d) the destroyed dam of Upper Bhote Koshi Hydroelectric Project in Nepal.

**Figure 1.** Photographs of Gongbatongshaco (GBTSC) Lake and the glacial lake outburst floods (GLOF) damage caused downstream: (a) GBTSC Lake after outburst, (b) the breach and debris fan in front of the lake, (c) landslides and river bank collapse triggered by GLOF near Friendship Bridge and (d) the destroyed dam of Upper Bhote Koshi Hydroelectric Project in Nepal.


Document: 1. Introduction
Section: 2. Study Area
Type: text

The study site is located in the central Himalayas and covers latitudes  $27^{\circ}37'-28^{\circ}31'$  N and longitudes  $85^{\circ}40'-86^{\circ}20'$  E with an area of 3406 km² (Figure 2). Bhote Koshi, which is also called Poiqu in China, is a transboundary river with a length of 143 km. It originates in the Bangbulei Mountains in northern Nyalam County, China, flows into Nepal, and at last feeds into the Ganges River. The Araniko Highway, built along the Bhote Koshi Valley, is a key trade and transport route between China and Nepal. Approximately 200,000 people live in the watershed, among which only 2.7% of them live in China, and the infrastructure in this region is particularly vulnerable [34].

The Bhote Koshi Basin stretches across the Higher Himalaya and Lower Himalaya, and the South Tibetan Detachment System (STDS) and the Main Central Thrust (MCT) pass through it. The basin is strongly affected by seismic activity. According to the statistics of the United States Geological Survey (USGS) earthquake records (http://earthquake.usgs.gov/earthquakes), there were 213 earthquakes (magnitude larger than M 4.5) in the area of 150 km² around BKB from 1983 to 2016, including a M 8.3 earthquake, three earthquakes larger than M 7.0 and 79 earthquakes equal or larger than M 5.0. The latest large earthquake, M 7.8, on 25 April 2015 and its largest aftershock (M 7.3) on 12 May 2015, produced severe impact in the study area. The epicenter of the major aftershock was only 19 km southeast of Kodari. The lower part from Zhangzangbo Valley to Dolalghat, which is approximately half of the region, was located in seismic intensity zones VIII and VII, and the upper part was in the VI zone according to the seismic intensity of the Gorkha earthquake provided by the USGS National Earthquake Information Center (Figure 2).

The elevation ranges from the highest peak of Mt. Shishapangma at 8012 m to the lowest point of 591 m in Dolalghat, Nepal. Given the large relief, the landforms are different from north to south. In the north and central parts of the basin are alpine regions and gorges, while the valley becomes broader in the south. The climate also varies considerably from south to north. The Himalayan southern slope region of the basin is affected by the Indian monsoon and experiences high precipitation levels. Meanwhile, due to blockage by the Himalayan range, the warm, moist air from the Indian monsoon

Water 2020, 12, 464 4 of 20

can hardly reach the northern part of the basin. According to monthly data obtained from the Nyalam meteorological station (3810 m a.s.l.) and the Zhangmu meteorological station (2250 m a.s.l.), the mean annual temperature ranges from 3.8  $^{\circ}$ C to 10.1  $^{\circ}$ C, and the mean annual precipitation ranges from 643.4 mm in the north to 2820.6 mm in the south.


Document: 1. Introduction
Section: 2. Study Area
Type: figure
Figure

Image /page/3/Figure/2 description: A detailed topographical and geological map titled "Figure 2. Study area and GLOF events in Bhotekoshi Basin." The map covers a region from 85°40'E to 86°20'E longitude and 27°40'N to 28°30'N latitude. It displays elevation using a color scale, ranging from 591 to 8012 meters above sea level (m a.s.l.). Key features are identified in a comprehensive legend. These include an earthquake epicenter marked with a yellow star (M7.3), GLOF (Glacial Lake Outburst Flood) events marked with orange circles at locations like Jialongco and Taraco, settlements like Kodari and Barhabise, rivers such as Bhotekoshi and Poiqu, faults shown as red lines, and the national boundary. The map also delineates glaciers, glacial lakes, the Araniko Highway, and zones of seismic intensity labeled with Roman numerals V, VI, VII, and VIII. A north arrow and a scale bar for 0 to 20 km are included. An inset map in the bottom right corner places the study area within the Central Himalaya, showing its relation to India, Nepal, and China, and marking additional earthquake epicenters.


Document: 1. Introduction
Section: 2. Study Area
Type: figure
Figure: Figure 2. Study area and GLOF events in Bhote Koshi Basin.

Figure 2. Study area and GLOF events in Bhote Koshi Basin.


Document: 1. Introduction
Section: 2. Study Area
Type: text

Water 2020, 12, 464 5 of 20


Document: 1. Introduction
Section: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping
Type: text

Glacial lake and landslide identification were based on GaoFen-1 (GF-1) satellite images. Twenty-two GF-1 images obtained from the China Center for Resources Satellite Data and Application (http://www.cresda.com/CN/) were Level-1A products, with cloud coverage less than 20% (Table 1). Seven images were used to map glacial lakes and fifteen images were used to map landslides preand post-earthquake. Geometric correction and image sharpening were conducted in ENVI 5.2 before mapping in ArcGIS 10.2. The resolution of pan sharpened images was 2 m. This high-quality imagery available allowed us to recognize glacial lakes as small as 0.01 km2. Manual visual interpretation was used to identify glacial lakes and landslides.


Document: 1. Introduction
Section: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping
Type: table
Table

| Data Usage                           | Sensor | Product ID | Date              | Cloud (%) |
|--------------------------------------|--------|------------|-------------------|-----------|
| Glacial lake mapping              | PMS1   | 2056413    | 20 December 2016  | 13        |
|                                      | PMS1   | 2056415    | 20 December 2016  | 4         |
|                                      | PMS1   | 2056414    | 20 December 2016  | 3         |
|                                      | PMS1   | 1929663    | 1 November 2016   | 13        |
|                                      | PMS1   | 1929664    | 1 November 2016   | 16        |
|                                      | PMS1   | 1929665    | 1 November 2016   | 6         |
|                                      | PMS2   | 1524197    | 14 April 2016     | 15        |
| Landslide mapping post-earthquake | PMS2   | 1242505    | 13 December 2015  | 11        |
|                                      | PMS2   | 1242506    | 13 December 2015  | 1         |
|                                      | PMS2   | 820531     | 22 May 2015       | 1         |
|                                      | PMS1   | 827786     | 23 May 2015       | 3         |
|                                      | PMS2   | 1062062    | 26 May 2015       | 14        |
|                                      | PMS2   | 1062061    | 26 September 2015 | 0         |
|                                      | PMS2   | 1242505    | 13 December 2015  | 11        |
|                                      | PMS1   | 1251892    | 17 December 2015  | 3         |
| Landslide mapping pre-earthquake  | PMS2   | 751296     | 11 April 2015     | 0         |
|                                      | PMS2   | 598009     | 19 January 2015   | 12        |
|                                      | PMS2   | 507470     | 9 December 2014   | 1         |
|                                      | PMS3   | 507469     | 9 December 2014   | 19        |
|                                      | PMS1   | 646048     | 22 September 2014 | 16        |
|                                      | PMS1   | 232717     | 22 May 2014       | 2         |
|                                      | PMS1   | 142225     | 30 December 2013  | 9         |

Data Usage
Sensor
Product ID
Date
Cloud (%)


Document: 1. Introduction
Section: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping
Type: text

Table 1. GaoFen-1 images used in this study.

All glacial lakes were verified and modified against Google Earth to see if there are some misinterpretations of the results due to the effect of terrain shadow. The characteristics and surrounding information of all lakes (larger than 0.01 km²) were measured or estimated, aided by Topography Mission digital elevation model (SRTM DEM) (30 m) and Google Earth. These data compose a complete inventory and provide a basis for identifying dangerous glacial lakes. The inventory of the database consisted of 17 parameters, and some important attributes are explained as follows:

- (a) Name: some glacial lakes were annotated according to the topographical map of 1978.
- (b) Longitude and latitude: the central location of a glacial lake was calculated automatically in ArcGIS based on WGS84 coordinates.
- (c) Elevation (m a.s.l.): the central elevation of a glacial lake was derived from the DEM.
- (d) Dam type: moraine dam, ice dam and bedrock dam, which was specified based on remote sensing images and the topography map (1:100,000; produced in 1978).
- (e) Area (km2): the glacial lake surface area was calculated automatically in ArcGIS 10.2, based on UTM projection zone 48 on a WGS84 ellipsoid.
- (f) Dam width (m): these values were estimated using Google Earth.

Water 2020, 12, 464 6 of 20

(g) Volume (m³): each glacial lake's volume was estimated using Equation (1), which was established between lake areas and volumes of lake water based on data from 33 Himalayan glacial lakes measured in the field [34],

$$V_{\rm gl} = 0.0578 A_{\rm gl}^{1.4683} \tag{1}$$

where,  $A_{gl}$  is glacial lake area.

- (h) Estimated freeboard values (1, or 0): the height of the freeboard is difficult to measure by remote sensing but is a crucial parameter that influences dam failure. Here, we estimated whether the height was larger than only a few meters (the value was 1) or indeed close to zero (the value was 0) [35], so it is a semiquantitative parameter.
- (i) Potential triggering impacts: whether the mass movement around a glacial lake can enter into the lake, such as rockfalls (R), landslides (L), ice and glacier avalanches (IGA), debris flows (DF) or flood from a lake situated upstream (ULF). If there is no mass movement, the value was null. This was identified based on Google Earth and the slope maps derived from the DEM, so it is also a semiquantitative parameter.
- (j) Distance to mother glacier (m): the distance between the back edge of a glacial lake to the mother glacier. If they are in contact, the value was 0; if there is no glacier around the lake, the value was set to null.
- (k) Distance to the nearest settlement (m): the drainage distance from the glacial lake dam to the nearest major settlement was measured using ArcGIS 10.2.
- (l) Drainage gradient (°): the average drainage gradient was estimated by a DEM-derived drainage map.


Document: 1. Introduction
Section: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping
Type: text

so it is also a semiquantitative parameter . - ( j ) Distance to mother glacier ( m ) : the distance between the back edge of a glacial lake to the mother glacier . If they are in contact , the value was 0 ; if there is no glacier around the lake , the value was set to null . - ( k ) Distance to the nearest settlement ( m ) : the drainage distance from the glacial lake dam to the nearest major settlement was measured using ArcGIS 10 . 2 . - ( l ) Drainage gradient ( ° ) : the average drainage gradient was estimated by a DEM - derived drainage map .

In this study, the term landslide refers to mass movement of a slope, including rockfalls, slope failure and soil slides. Most landslides can be easily identified by visual inspection for vegetation loss or deposits. If there is no vegetation in some areas, the morphology needs careful attention. Landslides are classified as pre-earthquake and post-earthquake landslides. The landslide volume (V) was estimated using a power-law landslide area–volume empirical formula (Equation (2)):

$$V_{\rm s} = \alpha A_{\rm s}^{\gamma} \tag{2}$$

where  $A_s$  is landslide area,  $\alpha$  and  $\gamma$  are empirically calibrated scaling parameters derived from mixed soil and bedrock landslides in the Himalayas;  $\alpha$  is 0.257, and  $\gamma$  is 1.36 [36].


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment
Type: text

Glacial lake outburst hazard assessment includes two steps, glacial lake outburst potential assessment and flow magnitude assessment. First, a qualitative method was used to identify glacial lake outburst potential; then, the outburst flow characteristics were determined, flood or debris flow according to loose matter along the flow path and the channel gradient, and then the magnitude at the nearest settlement was calculated. Finally, the GLOF hazard was derived by the glacial lake outburst probability and flow magnitude based on a matrix diagram, which has been widely used in flood, landslide and rock fall hazard assessments [7,37]. GLOF hazards in BKB were divided into four classes: "Very High", "High", "Medium" and "Low". The process of GLOF hazard assessment is summarized in Figure 3.

Water 2020, 12, 464 7 of 20


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment
Type: figure
Figure

Image /page/6/Figure/1 description: A flowchart illustrating the process for a glacier lake outburst hazard assessment. The process starts with two main inputs: 'GF-1 images Google Earth' and 'SRTM DEM'. The flowchart is divided into two main parallel paths that converge at the end. The left path determines the 'Qualitative outburst potential assessment'. It starts with 'Mapping glacial lakes' from the GF-1 images, leading to a 'Glacial lake inventory' and then 'Identifying glacial lake outburst potential'. This identification is based on five criteria: 'Dam type' (Bedrock dam vs. Moraine/ice-dam), 'Distance to glacial lake' (>1000, 500-1000, <500), 'Dam width crest' (>60, <60), 'Freeboard' (>1, ~0), and 'Rockfalls/landslide/debris flow/flood from upstream' (No, Yes). Based on these, a 'Qualitative outburst potential assessment' is made, categorized as Low, Medium, or High. The right path determines the 'Flow magnitude assessment'. It uses 'Mapping landslides' from GF-1 images to create a 'Landslides inventory'. It also uses the 'SRTM DEM' to determine 'Slope' and a 'Stream map', which are combined to find the 'Drainage gradient'. Two decision points follow: 'Landslide in downstream?' and 'Gradient > 8°?'. If the answer to both is 'No', it results in 'Flood discharge'. If the answer to either is 'Yes', it results in 'Debris flow discharge'. Both discharge types lead to a 'Flow magnitude assessment', categorized as <200, 200-500, or >500. Finally, the 'Qualitative outburst potential assessment' (relabeled as 'Outburst probability' with levels Low, Medium, High) and the 'Flow magnitude assessment' (with levels Low, Medium, High) are combined in a hazard matrix to produce the final 'Glacier lake outburst hazard assessment'. The matrix shows the resulting hazard level: Low, Medium, High, or Very High. For example, a High outburst probability and a High flow magnitude result in a Very High hazard. The entire flowchart uses a color code: blue for low risk, yellow for medium risk, and red/dark red for high/very high risk.


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment
Type: figure
Figure: Figure 3. Flow chart illustrating the process used for creating the glacial lake inventory of Bhote Koshi Basin in 2016, classifying glacial lake outburst potential and outburst risk assessment.

**Figure 3.** Flow chart illustrating the process used for creating the glacial lake inventory of Bhote Koshi Basin in 2016, classifying glacial lake outburst potential and outburst risk assessment.


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.1. Glacial Lake Outburst Potential Assessment
Type: text

Many criteria and schemes, derived from GLOF experiences all around the world, have been proposed to assess and identify potentially dangerous glacial lakes based on GLOF [38–42]. Here, a qualitative assessment method is proposed to identify the potentially dangerous glacial lakes from three criteria. The first one is potential triggering impacts, such as rockfalls, landslides, snow and ice avalanches, debris flows and flood from a lake situated upstream [43]. Such mass movement entering the lakes trigger displacement waves that subsequently overtop and erode the dams is the most common cause of dam failure in the Himalaya [8,17,44–46]. Steep glacier surfaces that are in contact with or close to a lake are prone to ice avalanches [47–49]. In addition, steep topography is also likely to cause rockfalls and landslides, and as a glacier retreats, much glacial debris remains, which may start a debris flow under heavy rainfall or intense glacier melting [8,38,50]. The second one is dam stability. Studies show most of the GLOF events in the Himalayas are caused by moraine-or ice dam failures, and the bedrock dams are the more stable with low outburst probability [8,51]. The dam width crest is an indicator for the susceptibility of a dam to fail [52]. The third one is freeboard, which is considered a crucial parameter that influences whether a potential impact wave overtops the dam [7,49].

Water 2020, 12, 464 8 of 20

Five key indicators were selected to identify glacial lake outburst potential according to the three criteria. These parameters of each glacial lake were easily obtained from the 2016 glacial lake inventory database. The critical values for assessment are given in Figure 3. The key indicator was defined with qualitative probabilities high, medium and low, and considered independently. The overall potential is not the mean of the individual indicators. A high-potential outburst glacial lake must satisfy three criteria that are high, a low-potential lake has two or three low criteria and no high criteria and the rest are medium-potential lakes. Finally, three potential degrees were classified as high, medium and low.


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment
Type: text

The flow magnitude is highly dependent on the peak discharge at the breach and the channel condition [53]. The peak discharge depends on the lake volume and the breach geometry [38]. For a rapid hazard assessment, complex breach processes and flow behavior are beyond the scope. In this paper, the worst breach scenario was assumed, i.e., a full breach that empties the glacial lake water completely. The maximum discharge ( $Q_p$ ) was estimated using the empirical formula (Equation (3)):

$$Q_p = 2V_{ql}/t \tag{3}$$

where  $V_{\rm gl}$  is the glacial lake volume, and t is the drainage duration in seconds, which is assumed to be 1000 s [54].

The outburst flood peak discharge increases due to erosion and entrained sediments. Thus, we first needed to judge whether an outburst flood would develop into a debris flow. The average channel gradient and unconsolidated deposits along the channel are key factors that affect whether an outburst flood evolves into a debris flow [13,55]. Erosion is found to occur where the channel gradients exceed 8° [38] and abundant unconsolidated deposits are distributed in the channel and on the slopes [56]. Channel gradients were calculated based on drainage maps derived from the DEM. The unconsolidated deposits include moraine deposits, fluvial and glaciofluvial sediments and landslide deposits. The maximum eroded sediment volume per unit channel length varies from ten to hundreds of cubic meters due to local and regional differences in geology, topography and hydrology of torrent catchments [38,54]. Therefore, it was hard to set a value certain of sediment depth or volume eroded by flood in different channels or basins. A rough assessment was used to estimate the flow magnitude to the nearest settlement. Flood peak discharge was estimated using an empirical equation (Equation (4)) [57,58]:

$$Q_{\rm pl} = \frac{W}{\frac{W}{Q_p} + \frac{L}{VK}} \tag{4}$$

where  $Q_p$  is the flood peak discharge m3/s; W is the capacity of the lake, m3;  $Q_p$  is the peak discharge at the breach, m3/s; L is the distance from the glacial lake dam, m; and VK is an empirical coefficient equal to 3.13 for rivers on plains, 7.15 for mountain rivers and 4.76 for rivers flowing through terrain with intermediate relief [59], which here we set the value as 7.15.

For an outburst debris flow, the water source is the outburst flood. Therefore, the peak discharge of the debris flow consists of outburst flood discharge and soil particle flow. Blocking was not considered here, so the debris flow peak discharge ( $Q_{df}$ ) can be calculated [60]:

$$Q_{\rm df} = (1 + \varphi)Q_{vl} \tag{5}$$

where  $\varphi$  is the increase coefficient of debris flow peak discharge, which can be calculated by:


Document: 1. Introduction
Section: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment
Type: text

] , which here we set the value as 7 . 15 . For an outburst debris flow , the water source is the outburst flood . Therefore , the peak discharge of the debris flow consists of outburst flood discharge and soil particle flow . Blocking was not considered here , so the debris flow peak discharge ( $ Q_ { df } $ ) can be calculated [ 60 ] : $ $ Q_ { \ rm df } = ( 1 + \ varphi ) Q_ { vl } \ tag { 5 } $ $ where $ \ varphi $ is the increase coefficient of debris flow peak discharge , which can be calculated by :

$$\varphi = (\gamma_s - \gamma_w)/(\gamma_s - \gamma_c) \tag{6}$$

where  $\gamma_s$  is the specific gravity of the solid material, g/cm3, and usually determined as 2.65 g/cm3;  $\gamma_w$  is the unit weight of water,  $\gamma_w = 1$  g/cm3;  $\gamma_c$  is the unit weight of the debris flow, g/cm3. Studies show

Water 2020, 12, 464 9 of 20

glacial lake outburst debris flow in Tibet is usually diluted flow [52], and the density is 1.3–1.8 g/cm3. For the convenience of calculation, here we set the average value of  $\gamma_c$  as the density of GBTSC outburst debris flow, 1.55 g/cm3.

According to Chinese debris flow prevention and control standards (DZT-0220-2006), a peak flow discharge of more than 200 m $^3$ /s is defined as a large hazard. However, the scale of a glacial outburst flood/debris flow is usually larger than that of a rainfall-triggered debris flow [16]. Therefore, in this paper, three flow magnitude classes were established: flow discharge <200 m $^3$ /s (low), 200–500 m $^3$ /s (medium) and >500 m $^3$ /s (high). Finally, the GLOF hazard was derived by the glacial lake outburst probability and flow magnitude based on a matrix diagram, which has been widely used in flood, landslide and rock falls hazard assessments [7,37]. GLOF hazards in BKB are divided into four classes: "Very High", "High", "Medium" and "Low".


Document: 1. Introduction
Section: 4. Results > 4.1. Glacial Lake Inventory
Type: text

A total of 122 glacial lakes larger than 0.01 km² with an area of 20.38 km² were identified based on the GF-1 images from 2016 (see Supplementary Materials). According to the dam type, 84 moraine-dammed lakes with a total area of 16.87 km² accounts for the largest number and area of all lakes. These moraine-dammed lakes are mainly distributed at 5100–5400 m a.s.l. There are 25 bedrock-dammed lakes that account for 15.3% of the area of all lakes. The average area of a bedrock-dammed lake is 0.12 km², and are mainly distributed at 4100–4700 m a.s.l. The ice-dammed lakes are the least and smallest, occupying 10.7% and 1.9% of the total number and area. The ice-dammed lakes consist of tiny and small lakes, with a mean area of 0.03 km², mainly distributed at 5000–5200 m a.s.l. (Figure 4a).


Document: 1. Introduction
Section: 4. Results > 4.1. Glacial Lake Inventory
Type: figure
Figure

Image /page/8/Figure/6 description: The image displays characteristics of a glacial lake basin in 2016 through three panels labeled a, b, and c.


Document: 1. Introduction
Section: 4. Results > 4.1. Glacial Lake Inventory
Type: text

Panel a is a topographical map showing the distribution of glacial lakes. A legend indicates the dam type and size class of the lakes. Dam types are bedrock dam (light green circles), ice dam (yellow circles), and moraine dam (dark green circles). The size classes, in square kilometers, are represented by circles of increasing size: ≤ 0.02, 0.02 - 0.1, 0.1 - 0.5, 0.5 - 1, and > 1. The map includes rivers and place names such as Tajilin, Nyalam, and Zhangmu.

Panel b is a dual-axis bar chart showing the number and area of glacial lakes by size class. The x-axis shows size classes: <0.02, 0.02 - 0.1, 0.1 - 0.5, 0.5 - 1, and >1 km². The left y-axis represents the number of lakes (grey bars), and the right y-axis represents the area in km² (teal bars). The data shows that the number of lakes is highest in the smallest size class (<0.02 km²) with approximately 56 lakes, and decreases as size increases. Conversely, the total area is greatest for the largest size class (>1 km²), at about 12 km², despite having the fewest lakes (approximately 2).

Panel c is a bar chart showing the percentage of glacial lakes by area (teal bars) and by number (grey bars) at different elevations. The x-axis shows elevation in meters, from 4000 to 5800. The y-axis shows the percentage. The distribution is concentrated at higher elevations, with the largest percentages found between 5100 m and 5400 m. The peak percentage by area is at 5200 m (around 31%), while the peak percentage by number is at 5300 m (around 14%).


Document: 1. Introduction
Section: 4. Results > 4.1. Glacial Lake Inventory
Type: figure
Figure: Figure 4. Characteristics of glacial lakes in 2016. (a) Glacial lakes distribution of different dam type and size class, (b) the number and area variation in size classes and (c) the percentage of number and area at different elevations.

**Figure 4.** Characteristics of glacial lakes in 2016. (a) Glacial lakes distribution of different dam type and size class, (b) the number and area variation in size classes and (c) the percentage of number and area at different elevations.


Document: 1. Introduction
Section: 4. Results > 4.1. Glacial Lake Inventory
Type: text

As the area of glacial lakes vary greatly, from 0.01 to  $5.29 \, \text{km}^2$ , we classify them into five size classes: tiny (A  $\leq 0.02 \, \text{km}^2$ ), small ( $0.02 < A \leq 0.1 \, \text{km}^2$ ), medium ( $0.1 < A \leq 0.5 \, \text{km}^2$ ), large ( $0.5 < A \leq 1 \, \text{km}^2$ ) and giant (A  $> 1 \, \text{km}^2$ ). The percentage of numbers and areas for each size class are shown in Figure 4b.

Water 2020, 12, 464 10 of 20

The main size class of a glacial lake is tiny, accounting for 45.9% (n = 56) of the total number, and small glacial lakes account for approximately 36.1% (n = 44). The total area of tiny and small lakes is 12.4%. Four glacial lakes are large, and three are giant lakes, occupying 69.7% of the total area. Tha mean value of lake area is  $0.17 \text{ km}^2$ , and the largest glacial lake is Galongco, with a surface area of  $5.29 \text{ km}^2$ .

The glacial lakes are distributed at elevations ranging from 4100 to 5750 m a.s.l. and are separated into different elevation classes every 100 m (Figure 4c). Most glacial lakes are located at elevations of 5000–5600 m a.s.l., accounting for 66.4% and 86.6% of the total number and area, respectively. Approximately 27.9% of glacial lakes are located below 5000 m a.s.l. and are evenly distributed in each elevation class with an average 3.5% by number. Approximately 13.9% of glacial lakes are distributed from 5200–5300 m a.s.l. and account for 28.1% of the area of all lakes. It is noticeable that the largest percentage by area is distributed at 5000–5100, which accounts for 30.55%.


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: text

The glacial lake outburst potential assessment results show that 19 glacial lakes have high outburst potential, in which all of these lakes are moraine-dammed and ice/glacier avalanche is the main potential triggering impact; 51 are medium risk, in which two are ice-dammed and nine are bedrock-dammed, and 42 lakes with an area less than 0.1 km²; 52 glacial lakes are low, in which 11 are ice-dammed and 16 are bedrock-dammed (Figure 5a). It is noticeable that 11 out of 19 high outburst potential lakes have an area less than 0.1 km², and the one bedrock-dammed lake, Gongco, with an area of 2.9 km², has low outburst potential.

4. Results
4.2. Glacial Lake Outburst Flood Hazard


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: figure
Figure

Image /page/9/Figure/5 description: The image displays two maps, labeled 'a' and 'b', of the same mountainous river basin. Both maps show elevation, rivers, and settlements, with coordinates ranging from 85°40'E to 86°20'E and 27°40'N to 28°30'N. A scale bar indicates a length of 20 km. The elevation ranges from 591 m to 8012 m. Map 'a' shows the 'Outburst potential' categorized as Low (small blue dots), Medium (yellow dots), and High (large red dots). Map 'b' shows the 'Hazard rank' categorized as Low (blue dots), Medium (orange dots), High (red dots), and Very High (large red dots). Map 'b' also includes an overlay of landslides, distinguishing between 'pre-earthquake' (yellow areas) and 'post-earthquake' (pink areas), with the latter being more extensive. Several settlements are named, including Dolalghat, Barhabise, Gumthang, Zhangmu, Quxiang, Nyalam, Gangga, and Tajilin.


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: figure
Figure: Figure 5. (a) Glacial lake outburst potential and (b) glacial lake outburst flow/debris flow hazard assessed in this study, considering landslide deposit distribution.

**Figure 5.** (a) Glacial lake outburst potential and (b) glacial lake outburst flow/debris flow hazard assessed in this study, considering landslide deposit distribution.


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: text

In this study, 1670 landslides with a total area of 18.70 km2 were identified, in which 1183, with an area of 12.18 km2, were triggered by the Gorkha earthquake (Figure 5b). These post-earthquake landslides vary in size ranging from 230 m2 to 254,474 m2. Most of the landslides are distributed in the middle and southern parts of the basin, and a large number of Gorkha earthquake-triggered landslides are concentrated in the VII region of seismic intensity. A lot of landslides were distributed in the sub-basins such as Gumthang, Deqingdang, Chongduipu, Zhangzangbo and Dianchangchanggou. Large landslides reach channels, and some small landslides are mostly located on steep slopes

disconnected from a river channel. The other 487 landslides occurred before the Gorkha earthquake and have an area of 6.51 km2. The largest mapped landslide that occurred before the earthquake is  $0.81 \text{ km}^2$ . The total landslide deposit volume is estimated at  $91.67 \times 10^6 \text{ m}^3$  before the earthquake, and the volume increased to  $216.03 \times 10^6 \text{ m}^3$  after the earthquake. Considering landslide distribution, 73 glacial lake outburst floods are highly prone to debris flow, which will increase the magnitude.


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: text

Gorkha earthquake and have an area of 6 . 51 km  2  . The largest mapped landslide that occurred before the earthquake is $ 0 . 81 \ text { km } ^ 2 $ . The total landslide deposit volume is estimated at $ 91 . 67 \ times 10 ^ 6 \ text { m } ^ 3 $ before the earthquake , and the volume increased to $ 216 . 03 \ times 10 ^ 6 \ text { m } ^ 3 $ after the earthquake . Considering landslide distribution , 73 glacial lake outburst floods are highly prone to debris flow , which will increase the magnitude .

According to the glacial lake outburst potential and flow magnitude, GLOF hazard assessment results are shown in Table 2 and Figure 5. Eleven glacial lakes are identified with very high hazard, among which seven could evolve into debris flows. Twenty-four glacial lakes are high hazard, among which 11 could evolve into debris flows. Thirty-two glacial lakes are identified with medium hazard; the other 55 glacial lakes are considered to have low to no hazard to downstream areas. Four very high-hazard glacial lakes are located in the Chongduipu gully, which presents a large threat to Nyalam County, especially the giant glacial lake Galongcuo that could generate a peak flow discharge of about 224,449 m3/s. Both Jialongcuo and Cirenmacuo have burst out twice before, and are also identified as very high hazard due to high freeboards and hanging glaciers behind the lakes. Eight glacial lakes with low-outburst probability but high-magnitude flow are considered to have high hazard. Among these lakes, Gongcuo and Darecuo are bedrock-dammed lakes and have no potential triggering impacts around the lakes, so they are considered to have a low probability of outburst. However, because of their large volumes, the outburst flows are assumed to be high. 63% (n = 22) of the very high and high glacial lakes' areas are larger than 0.1 km2, and their peak flow discharges were larger than 1000 m3/s. Ten small glacial lakes (area <0.1 km2) identified as high hazard. Three small glacial lakes, No. 16 (area 0.09 km2), No. 18 (area 0.05 km2) and No. 81 (Nongjue, area 0.07 km2), are considered very high hazard for they may cause peak debris flow discharges of 1118 m3/s, 597 m3/s and 994 m3/s at the nearest settlement, respectively.


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: table
Table: Table 2. Very high and high hazard glacial lakes.

| Id  | Name          | Longitude (°) | Latitude (°) | Elevation (m) | Dam Type | Area (km²) | Qpl (m3) | Qdf (m3) | Probability of Outburst | Flow Magnitude | Hazard    |
|-----|---------------|------------------|-----------------|------------------|----------|------------|----------|----------|----------------------------|-------------------|-----------|
| 1   | Qiezelaco     | 86.26            | 28.37           | 5532             | moraine  | 0.26       | 1967     |          | High                       | High              | Very High |
| 3   | Cawuqudenco   | 86.19            | 28.34           | 5423             | moraine  | 0.55       | 6666     |          | High                       | High              | Very High |
| 7   | Paquco        | 86.16            | 28.30           | 5307             | moraine  | 0.58       | 7950     |          | High                       | High              | Very High |
| 16  |               | 86.09            | 28.22           | 5178             | moraine  | 0.09       | 726      | 1814     | High                       | High              | Very High |
| 18  |               | 86.06            | 28.17           | 5194             | moraine  | 0.05       | 239      | 597      | High                       | High              | Very High |
| 22  | Cirenmaco     | 86.07            | 28.07           | 4633             | moraine  | 0.34       | 5087     | 12,717   | High                       | High              | Very High |
| 34  | Gangxico      | 85.87            | 28.36           | 5212             | moraine  | 4.52       | 172,879  |          | High                       | High              | Very High |
| 61  | Galongco      | 85.84            | 28.32           | 5077             | moraine  | 5.29       | 145,746  | 364,365  | High                       | High              | Very High |
| 62  |               | 85.82            | 28.30           | 5093             | moraine  | 0.27       | 1832     | 4580     | High                       | High              | Very High |
| 80  | Jialongco     | 85.85            | 28.21           | 4380             | moraine  | 0.63       | 8336     | 20,840   | High                       | High              | Very High |
| 81  | Nongjue       | 85.87            | 28.19           | 4628             | moraine  | 0.07       | 398      | 994      | High                       | High              | Very High |
| 2   | Youmojiaco    | 86.23            | 28.35           | 5337             | moraine  | 0.55       | 4881     |          | Medium                     | High              | High      |
| 6   | Gangpuco      | 86.16            | 28.32           | 5543             | moraine  | 0.22       | 2355     |          | Medium                     | High              | High      |
| 8   | Southhu       | 86.15            | 28.30           | 5343             | moraine  | 0.17       | 1227     |          | Medium                     | High              | High      |
| 9   | Taracuo       | 86.13            | 28.29           | 5257             | moraine  | 0.23       | 2186     |          | Medium                     | High              | High      |
| 10  | Tuzhuocuo     | 86.10            | 28.25           | 5201             | moraine  | 0.15       | 1309     | 3272     | Low                        | High              | High      |
| 23  |               | 86.03            | 28.07           | 4486             | bedrock  | 0.03       | 257      | 642      | Medium                     | High              | High      |
| 32  | Yinreco       | 85.89            | 28.37           | 5245             | moraine  | 0.28       | 2878     |          | Low                        | High              | High      |
| 40  | Mabiya        | 85.91            | 28.32           | 5384             | moraine  | 0.14       | 931      |          | Medium                     | High              | High      |
| 42  |               | 85.92            | 28.32           | 5345             | moraine  | 0.08       | 504      |          | Medium                     | High              | High      |
| 43  | Mulaco        | 85.93            | 28.32           | 5306             | moraine  | 0.11       | 760      |          | Medium                     | High              | High      |
| 44  | Xiahu         | 85.95            | 28.31           | 5232             | moraine  | 0.31       | 3352     |          | Medium                     | High              | High      |
| 51  | Cuonongjue    | 85.92            | 28.26           | 5095             | moraine  | 0.23       | 2353     |          | Low                        | High              | High      |
| 63  |               | 85.83            | 28.29           | 5013             | moraine  | 0.26       | 1863     | 4658     | Medium                     | High              | High      |
| 64  |               | 85.83            | 28.29           | 5050             | moraine  | 0.06       | 204      | 511      | Medium                     | High              | High      |
| 70  |               | 85.78            | 28.29           | 5418             | moraine  | 0.05       | 130      | 324      | High                       | Medium            | High      |
| 72  |               | 85.78            | 28.27           | 5309             | moraine  | 0.07       | 184      | 459      | High                       | Medium            | High      |
| 83  |               | 85.87            | 28.17           | 4712             | moraine  | 0.04       | 125      | 312      | High                       | Medium            | High      |
| 84  | Daroco        | 85.92            | 28.18           | 4366             | bedrock  | 0.48       | 10,966   | 27,414   | Low                        | High              | High      |
| 85  |               | 85.91            | 28.15           | 4486             | ice      | 0.20       | 2468     |          | Medium                     | High              | High      |
| 86  |               | 85.92            | 28.14           | 4871             | moraine  | 0.09       | 597      |          | Medium                     | High              | High      |
| 88  |               | 85.94            | 28.07           | 4524             | bedrock  | 0.06       | 391      | 977      | Low                        | High              | High      |
| 89  | Bhairab Kunda | 85.88            | 27.99           | 4102             | bedrock  | 0.06       | 304      | 760      | Low                        | High              | High      |
| 102 |               | 85.83            | 28.05           | 4250             | bedrock  | 0.07       | 210      | 524      | Low                        | High              | High      |
| 103 | Congco        | 85.87            | 28.33           | 5113             | bedrock  | 2.09       | 28936    |          | Low                        | High              | High      |

Id
Name
Longitude (°)
Latitude (°)
Elevation (m)
Dam Type
Area (km²)
Qpl (m3)
Qdf (m3)
Probability of Outburst
Flow Magnitude
Hazard


Document: 1. Introduction
Section: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard
Type: text

Note:  $Q_{\rm pl}$  is the flood peak discharge at the nearest settlement and  $Q_{\rm df}$  is the debris flow peak discharge at the nearest settlement.


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: text

The new 2016 glacial lake inventory indicates that BKB is highly developed glacial lakes. Glacial lake inventory studies have also been conducted in other regions along the Himalayas [61–64]. Glacial lake studies in the Himalayas show that the greatest numbers and areas of glacial lakes are distributed in the central Himalaya [2,7,9,10,62,65]. To compare the glacial lakes and GLOF of BKB with other regions throughout the central Himalayas, the Gyirong River Basin (GRB), which is next to BKB with a similar area was selected and the glacial lake density (glacial lake number/basin area) and lake area per basin area (total glacial lake area/basin area) were calculated (Table 3). The results show that the glacial lake density of BKB is four times that of the central Himalayas and the lake area per basin area is four times that of GRB. The basin area of GRB is larger than BKB, and the glacial lakes density is similar, while the lake per basin area varies greatly. This is due to more large and giant lakes in BKB. According to the statistics, the largest lake is less than 0.5 km² in GRB, while there are seven lakes larger than 0.5 km² with the largest being 5.29 km² in BKB. Studies show the glacial lake expansion rate reaching 0.26 km²/year in Poiqu [66], while the rate of GRB is 0.09 km²/year [63]. Glacial lake expansion is the result of glacier retreating response to climate change. That means BKB is more sensitive to climate change than GRB.

5. Discussion
5.1. Glacial Lake Inventory


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: table
Table: Table 3. Comparison of glacial lake and glacial lake outburst flood among Bhote Koshi Basin, Gyirong River Basin and Central Himalaya.

| Region                   | Basin Area (km2) | Number of Glacial Lakes | Glacial Lake Area (km2) | Glacial Lake Density | Lake per Basin Area |
|--------------------------|---------------------|----------------------------|----------------------------|-------------------------|------------------------|
| Bhote Koshi Basin        | 3406                | 122                        | 20.38                      | 0.04                    | 0.0060                 |
| Gyirong River Basin [63] | 4640                | 148                        | 7.12                       | 0.03                    | 0.0015                 |
| Central Himalaya [2]     | 280,000             | 1943                       | 203.7                      | 0.01                    | 0.0007                 |

Region
Basin Area (km2)
Number of Glacial Lakes
Glacial Lake Area (km2)
Glacial Lake Density
Lake per Basin Area


Document: 1. Introduction
Section: 5. Discussion > 5.1. Glacial Lake Inventory
Type: text

The analysis of multitemporal and high-resolution remote sensing images during the compilation of the glacial lake inventories provided a good opportunity to identify previously unreported GLOF events [67,68]. Six unreported glacial lake outburst events were found when we mapped glacial lakes from GF-1 and Google Earth. These glacial lakes have retained typical outburst geomorphic and sedimentological features, such as V-shaped breaches, debris fans and subsequent devastated channels (Figure 6). All of them were moraine-dammed lakes, and their surface areas are 0.01–0.11 km2. Two glacial lakes (Figure 6a,b) are located in Keyapu Valley and the other four (Figure 6c–f) are in Chongduipu Valley. All glacial lakes except No. 31 are fed by glaciers, and the distances to the glaciers are less than 500 m. The V-shaped breach and debris fan of glacial lake No. 86 is the largest, and its mother glacier is thick and hangs behind the lake. The surface area of the glacial lake is 0.09 km2 and the freeboard is much more than one. The rest of the outburst events were small scale and seemed to cause no downstream damage since no erosion was observed in downstream channels. The outburst flood formed a deposition fan at the intersection with the main channel, such as glacial lake No. 31, where the outburst deposition blocked the channel and formed a small lake. Some vegetation has covered the debris fan (glacial lake No. 86) and deposition fan (glacial lake No. 81). It shows that the glacial lake outburst occurred a long time ago. However, the outburst time (year) cannot be determined because of the lack of long-term and high-quality (low cloud cover or high-resolution) data. We traced theses lakes on Google Earth images, and it shows that outburst signs have existed since 1984. As we documented in the literature, we found there was a GLOF event in 1955 in Nyalam, but the record did not mention which lake burst out [30]. Four glacial lakes (Figure 6c-f) are located in the Chongduipu Basin upstream of Nyalam County. As we cannot be sure of the exact outburst time, the outburst magnitudes can be estimated only through debris fans. Glacial lake No. 86 had the largest outburst magnitude with a debris fan area of approximately 252,808 m2, and the gully downstream is highly eroded. This lake may have caused damage in downstream areas. The other three glacial lake deposits

are found at the intersections with the main channel (Chongduipu), which means their outburst floods did not propagate downstream. Therefore, we conclude that the GLOF event in 1955 was caused by glacial lake No. 86.