Document: The Google Flood Forecasting Initiative
Section: 3 Method > 3.1 Hydrologic Model
Type: text

The two architectures used for hydrologic modeling are linear models, and LSTMs. All our models currently forecast water levels (as opposed to discharge), and provide forecasts in gauged locations (i.e. locations where there is a stream gauge installed) - though work is ongoing to expand our reach beyond this. The training and validation scheme for both models is described in a sub-section after the models themselves.

Linear Model The model input includes past river stages from the target gauge (the gauge being forecast) and its upstream gauges (typically 2-5 gauges) for each time step (usually hourly). The output is a future river stage at the target gauge for a given lead time. A multiple linear regression model is trained with historical records of the above inputs and outputs. The model is optimized


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo
Type: text

using the mean square error (MSE) loss function with L2 regularization. Linear models are trained separately for each target gauge, with a separate linear model trained for all lead times up to the gauge's maximal lead time. For example, a target gauge with a selected maximal lead time of 24 hours and hourly resolution implies 24 trained linear models. Figure 2 illustrates the schema of the linear model.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo
Type: figure
Figure

Image /page/3/Figure/2 description: A diagram titled 'Linear Model' illustrates a predictive model's workflow. On the left, a series of inputs are shown, represented by red parallelograms. These inputs consist of 'upstream stage' and 'current stage' data for multiple past time steps, starting from (t-u) and continuing through to (t-1), indicated by vertical dots. These inputs are fed into a central blue vertical rectangle labeled 'linear regression'. This block also specifies 'u = 72 hour lookback'. The linear regression model produces an output, a red parallelogram labeled 'prediction (t)'. This prediction is then connected by an arrow to a smaller blue rectangle labeled 'MSE' (Mean Squared Error). Finally, an arrow from the MSE block points to another red parallelogram labeled 'target gauge stage (t)', representing the actual value to which the prediction is compared.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo
Type: text

Fig. 2. Schema of the linear model.

Long Short-Term Memory Network (LSTM) Model Building on the work described in [19, 20, 14], the LSTM model consists of two LSTMs: a sequence-to-one hindcast model and a sequence-to-sequence forecast model, where the output of the hindcast model is used as the initial state of the forecast model. The hindcast model processes data from past days sequentially, taking the following variables as inputs at each timestep: (i) Precipitation measurements through the basin, (ii) near-real-time stage, and (iii) a linear combination of stage measurements from upstream gauges over some upstream "lookback" period (typically a few days). The linear combination of stage measurements is produced by a separate linear layer with gauge-specific weights that combines a (variable) number of upstream inputs per gauge into five features that are fed as inputs into the hindcast LSTM (similar to the linear model described above).

The hindcast LSTM runs until the current time, defined to be the time of the last available measurement. The final cell state and hidden state of the hindcast LSTM are passed through a fully connected layer and the output of this "state handoff" layer is used as the initial cell state and hidden state of the forecast

LSTM. The forecast LSTM advances one step for every lead time, producing the relevant forecasts for all lead times up to the maximal one. The main goal of using a state handoff between two LSTMs is to distinguish between the different inputs that are available in real-time during hindcast and forecast - and specifically the availability of precipitation measurements.

All weights of the LSTMs (hindcast and forecast) are shared between all target gauges, i.e. they are regionally calibrated. The only gauge-specific weights are those of the linear upstream combiner layer which allows the architecture to support varying numbers of upstream gauges, travel times, sizes of tributaries, etc.

The system estimates the uncertainty of the water stage, or the time dependent distribution over the predicted stage, using a countable mixture of asymmetric Laplacians (CMAL). The parameters of this distribution are generated by feeding the hidden state of the forecast LSTM into a dedicated head layer for each forecasted time step. At each time step, the loss is calculated as the negative log-likelihood of the observed stages given the LSTM forecasts using the next maximal lead time values. It is important to note that the likelihood-based loss function is calculated only over the outputs of the forecast LSTM, and the hindcast LSTM is used only to initialize the forecast state. Since training is shared for all target gauges, the maximal lead time in the training phase is taken as the maximum of the gauge-specific values. Figure 3 illustrates the schema of the LSTM model.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo
Type: figure
Figure

Image /page/4/Figure/5 description: A diagram illustrating the architecture of an LSTM (Long Short-Term Memory) model for time-series forecasting. The model processes input data through several layers to predict future values. The flowchart begins on the left with inputs for different time steps, from (t-h) to (t-0). For each time step, the inputs are 'IMERG', 'current stage', and a stack of 'upstream stage' data. These inputs are fed into a 'fully connected (linear)' layer. The outputs from these layers are then passed to a 'hindcast LSTM (seq2one)' layer. The hidden state from the hindcast LSTM is processed by another 'fully connected' layer and then used to initialize a 'forecast LSTM (seq2seq)' layer. This forecast LSTM produces a sequence of outputs, each of which goes through a 'CMAL' block to generate a probability distribution. These distributions are compared against the 'target gauge stage' for future time steps from (t+0) to (t+k). The model's performance is evaluated using a 'Σ negative log-likelihood loss'.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo
Type: text

Fig. 3. Schema of the LSTM model.

Training and Validation The stage forecast models are trained and validated with historical data using a cross-validation scheme, in which each fold uses one year's worth of data for validation and the rest for training (i.e., 1-year leave-out). For operational use, the models were retrained on the full data set to produce the best real-time forecasts possible.

Operational Use For Alerting For each target gauge, if the maximal fore-casted river stage between the forecast's "current time" and the gauge's maximal lead time is above the predefined gauge-specific warning threshold, an alert is issued, and this maximal stage is used for inundation mapping (see below).


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model
Type: text

The two architectures used for inundation modeling are the Thresholding model, and the Manifold model, both described below. The Thresholding model produces an inundation extent map but no forecast for flood depths. The Manifold model additional produces the water depth at each pixel, but requires a digital elevation model (DEM) and requires more effort to implement and deploy at scale. The training and validation scheme for both models is described in a sub-section after the models themselves.

Thresholding Model The model assumes that each pixel in the area of interest (AOI) becomes inundated when the target gauge exceeds a (pixel-specific) threshold water stage. These thresholds are learned from the series of historic stage data at the target gauge and the corresponding state of the pixel (dry/wet) during these events. Each pixel in the inundation map is treated as a separate classification task, predicting whether the pixel will be inundated or not. We refer to the "wet" class as the positive class.

The algorithm described below identifies pixel-specific thresholds and is aimed at maximizing some F-score using an optimized global parameter called minimal ratio. An F-score [7] refers to a weighted geometric mean of precision and recall, with  $F_1$  referring to the simple harmonic mean between them and  $F_3$ giving more weight to precision over recall, for example. The algorithm below can be optimized for any choice of F-score. To achieve this, an iterative process is applied to each pixel. In each iteration, we find the threshold that maximizes the ratio of true wet events (where the water stage at the gauge is above the threshold and the pixel was wet) to false wet events (where the water stage at the gauge is above the threshold and the pixel is dry). The threshold that maximizes this ratio is the most cost-effective threshold in the sense that it provides the most true wets per false wet instance. At the first iteration all training events are considered; then, after each selection of a threshold and its respective truefalse ratio, events with stage measurements above the threshold are discarded and a new iteration starts with the remaining events. If the new true-false ratio calculated is lower than the minimal ratio parameter value, the process stops and the final threshold for the pixel is the one found in the previous iteration.

It can be shown that for every minimal ratio parameter value, no other set of pixel-specific thresholds achieves simultaneously better precision (i.e., fraction of all flooded pixels that are predicted as being flooded) and recall (i.e., fraction of all pixels that are predicted to be flooded and are really flooded); implying it is Pareto optimal. Therefore, for any F-score there exists some value of the minimal threshold parameter which finds the thresholds that optimize this F-score.

In cases where the river stage input is higher than all past stage data, the Thresholding model's output inundation map is initialized from the most severe inundation extent seen in the historical events and expanded in all directions. The expansion distance is a linear function of the difference between the forecasted stage and the stage of the highest historical event.

This Thresholding model requires almost no site-specific data like DEMs, and no manual work, making it appealing for large-scale deployment across many areas of interest in a short amount of time.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model
Type: figure
Figure: Figure 4 illustrates the schema of the Thresholding model.

Figure 4 illustrates the schema of the Thresholding model.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model
Type: figure
Figure

Image /page/6/Figure/6 description: A diagram illustrating a 'Thresholding model' for creating flood inundation maps. The process starts with 'Historic inundation maps and gauge stage data' on the left. An arrow points from a specific pixel on these maps to a timeline representing 'Historic pixel state for each flood event'. This timeline shows a sequence of 'Dry' (orange boxes) and 'Wet' (blue boxes) states. Below this, a corresponding timeline shows the 'Historic gauge stage for each flood event' with values: 2.1m, 2.8m, 3.1m, ..., 4.5m, 5.7m, 6.2m, ..., 7.3m, 8.5m. An upward arrow labeled 'Optimized stage threshold for the pixel' points from this historical data. This threshold is then used with a new 'Gauge stage' of '4.2m' to produce an 'Output flood inundation map', which is displayed at the top left of the diagram.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model
Type: text

Fig. 4. Schema of the Thresholding model.

Manifold Model The Manifold inundation model provides a machine-learning alternative to hydraulic models, by computing physically reasonable flood inundation. Its inputs are a DEM for the AOI and a target water stage. It outputs both the flood inundation extent and the inundation depth at each AOI pixel. The model is divided into two major parts, described below.

Flood Extent To Water Height Algorithm

The flood extent to water height algorithm converts a DEM and an inundation

extent map (i.e., wet/dry state for each pixel) into a water height map, which is a per-pixel water height in meters above sea level. The algorithm tries to find a physically reasonable water height map that best matches the input inundation map, where the physically reasonable requirement is defined as: (1) the water height surface must be smooth, i.e. we aim to find a water height map that does not change significantly between neighboring pixels; and, (2) the water height surface should not have a minimum or a maximum at the interior of flooded regions. This optimization problem is not differentiable, and thus cannot be easily solved directly. Instead, the following heuristic can be shown to produce an optimal solution to the above optimization problem. The algorithm identifies the boundaries of the inundated areas of the input inundation map. The water height at these boundaries is extracted from the corresponding DEM. In between these boundaries, the algorithm uses the Laplace differential equation to interpolate the water heights. The water height map is defined as a low-resolution image, where every pixel is set to be of 32x32 DEM pixels. This assures that the output map is smooth and does not contain high frequency changes, while also reducing the computational complexity of the process. In addition, outlier DEM pixels, which are pixels that cause high Laplace tension, are removed to assure that the overall function is smooth.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm
Type: text

When inferring inundation depth for a real-time gauge water stage forecast, we do not have access to the full current flood extent (as the extent-to-height algorithm above assumes). To be able to provide depth in this more challenging setting, we first perform some precomputation. We first apply the Thresholding model described above to all past events in our training data, producing an inundation extent map for each gauge stage measurement. We then apply the flood extent to water height algorithm described above to produce a water height map for each past event.

In real-time, when we receive an input water stage at the target gauge, the model simply performs per-pixel piecewise-linear interpolation between the water height maps of the training dataset to generate a new water height map corresponding to this input stage. The resulting water height map and the DEM are then used to generate: (1) an updated inundation extent map, by assigning a dry state to a pixel if its water height is lower than the DEM and a wet state otherwise; and, (2) an inundation depth map, as the difference between the water height and the DEM height for wet pixels. The use of the Thresholding model (as opposed to directly using satellite-based imagery of actual historical flood extents) as the input flood extent maps ensures that higher gauge stages always yield larger inundation extents, and thus removes unnecessary noise. When the model infers an inundation depth map for a gauge's water stage higher than all the events in the training set, it extrapolates the water height map by adding the gauge level difference to every pixel in the highest water height map computed from observed gauge stages, and uses the extrapolated water height and DEM to compute the water depth map.

4 S. Nevo
3.2 Inundation Model
Gauge Stage To Flood Depth Algorithm


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm
Type: figure
Figure: Figure 5 illustrates the schema of the Manifold model.

Figure 5 illustrates the schema of the Manifold model.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm
Type: figure
Figure

Image /page/8/Figure/2 description: A diagram illustrating a workflow, labeled the "Manifold model", for generating an "Output flood inundation map and water depth map". The process starts at the bottom with a horizontal axis representing gauge stage, with points for historic flood events at 2.1m, 3.1m, 5.7m, and 7.3m. For each historic event, a "Computed inundation map (Thresholding model)" is shown. These maps are then used to create "Physically-constrained water height maps" in the middle row, a process that also incorporates Digital Elevation Model (DEM) data. The model can then take a new input, shown as a "Gauge stage" of 4.2m, to generate an "Interpolated and smoothed water height map". This map, along with DEM data, is used to produce the final two output maps: a flood inundation map showing the extent of the water in teal, and a water depth map showing varying depths in shades of blue.


Document: The Google Flood Forecasting Initiative
Section: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm
Type: text

Fig. 5. Schema of the Manifold model.

Training and Validation The inundation models are trained and validated based on historical flood events, where flood inundation extent maps from satellite data, along with the corresponding gauge water stage measurements, are available. Similar to the stage forecast models, a 1-year leave out cross validation scheme is used for training and validation. For operational use, the models are retrained with all historical data. It should be noted that, contrary to flood inundation extent, it is much more difficult to obtain gound truth data for flood inundation depth at scale. The Manifold model is, therefore, trained and validated only on the inundation extent. However, since the Manifold model is constrained to only produce physically reasonable water height maps, accurate inundation extent metrics on the test dataset imply reasonably reliable inundation depth results (with further validation ongoing).


Document: The Google Flood Forecasting Initiative
Section: 4 Resource Requirements > 4.1 Data sets
Type: text

The system utilizes the following data sets.

**Precipitation Measurements** We currently use IMERG satellite data, though other sources can be used (as long as the models are re-trained for the new dataset).

**Precipitation Forecasts** We currently use Global Forecast System (GFS) precipitation forecasts, though other sources can be used (as long as the models are re-trained for the new dataset)

Stream Gauges Stream gauges are measurement devices that measure the water level (and sometimes estimate discharge) of the river. We use these as labels for the hydrologic model, and inputs for both the hydrologic model and the inundation model. We use both open stream gauge datasets such as GRDC, CAMELS, and Caravan - as well as proprietary datasets from the governmental hydrometeorology agencies we work with.

Elevation Maps Accurate elevation maps are crucial for the inundation model, especially when producing depth estimates. The publicly available global DEMs (e.g., NASA SRTM and MERIT) unfortunately lack the required spatial accuracy and resolution for detailed flood inundation simulation. Furthermore, they are based on data from over a decade ago, thus failing to capture the frequent topography changes caused by past floods. Consequently, we currently construct higher-resolution, up-to-date DEMs for each AOI from high resolution satellite optical imagery data in a process that is based on stereographic imaging. To keep the DEM up to date, the model is retrained annually based on fresh imagery in locations where flooding causes frequent topography changes. We currently use proprietary Google-generated elevation maps, yet one can use any reliable hydrologically-conditioned elevation maps of sufficient resolution, which depending on the basin could be anywhere between 0.5 to 30 meters.

Historical Flood Extent Maps We use the synthetic aperture radar ground range detected (SAR GRD) data from the Sentinel-1 satellite constellation to determine flood inundation maps at known timepoints and locations [24]. At any AOI, a SAR image is available once every several days, from which an inundation map was inferred using a binary classifier. Every pixel within a SAR image is classified as wet/dry via a Gaussian-mixture based classification algorithm. In order to calibrate and evaluate the classification algorithm, we have collected a dataset of Sentinel-2 multispectral images of flood events that coincide with the SAR image dates and locations. Reference Sentinel-2 flood maps were created by calculating per-pixel Normalized Difference Water Index (NDWI=(B3-B8)/(B3+B8), where B3 and B8 are green and near infrared bands, respectively) and applied a threshold of 0.


Document: The Google Flood Forecasting Initiative
Section: 4 Resource Requirements > 4.2 Computational Resources
Type: text

Training and using these models requires computational resources as well. How-ever, significant effort has gone into minimizing the computational requirements.

Training the hydrologic models takes seconds for the linear model and approximately 3 GPU hours for the LSTM (per location). Inference time is negligible for both.

Training the inundation models takes between 10 CPU minutes to 10 CPU hours depending on the AOI for both the lightweight model and the manifold model. For comparison, calibrating hydraulic models takes about 100-1200 CPU years for the same areas of interest [9].

4 Resource Requirements
4.2 Computational Resources


Document: The Google Flood Forecasting Initiative
Section: 5 Field Evaluation
Type: text

At the time of writing (early 2022), these models have been incorporated in operational systems for several years, currently covering about 360 million people across India and Bangladesh. So far, the models' performance in real-time operational systems is consistent with their cross-validated performance on historical data.


Document: The Google Flood Forecasting Initiative
Section: 5 Field Evaluation > 5.1 Hydrologic Models
Type: text

Arguably the most popular metric for evaluating the accuracy of hydrologic models is the Nash-Sutcliffe Efficiency coefficient. This metric is the  $R^2$  score of the predicted discharge, where the  $R^2$  score (or coefficient of determination) of a prediction is defined as  $1 - \frac{\sum (\hat{x}_i - x_i)^2}{var(x)}$  (with  $x_i$  representing the true value of the i'th example, and  $\hat{x}_i$  representing the prediction for the i'th example). This can be thought of as describing the percentage of target's variance explained. An  $R^2$  score of 1 means a perfect prediction (and no variance remaining), while a score of 0 means the prediction is no better than guessing the average. This trait makes this score easy to understand, regardless of the scale and behavior of the target variable. Since most of our models predict water level rather than discharge, we use the closest parallel - an  $R^2$  score over the water level predictions. Our hydrologic models have achieved an  $R^2$  score of about 0.98 in our operational systems.

A metric which is less common in the professional literature but is perhaps easier for non-hydrologists to parse is simply the average error of our predictions, in centimeters. Our hydrologic models have so far seen an average error of 8.5 centimeters, across all our operational systems.

5 Field Evaluation
5.1 Hydrologic Models


Document: The Google Flood Forecasting Initiative
Section: 5 Field Evaluation > 5.2 Inundation Models
Type: text

All of our models are evaluated at a 16x16 meter resolution. The precision of an inundation forecast is defined as the number of pixels that were both forecasted as being inundated and were indeed inundated divided by the number of pixels that were forecasted as being inundated. The recall of an inundation forecast is defined as the number of pixels that were both forecasted as being inundated and were indeed inundated over the number of pixels that were actually inundated. The f1 score of a forecast is the geometric mean of its precision and its recall.

Our inundation models have been run and evaluated at a resolution of 16x16 meters per pixel, and achieve 0.69 per-pixel f1 score on average across our operational systems.

5 Field Evaluation
5.2 Inundation Models


Document: The Google Flood Forecasting Initiative
Section: 5 Field Evaluation > 5.3 Impact Evaluation
Type: text

In addition to evaluating our systems' accuracy, we also aim to evaluate their effectiveness in driving protective action. In 2021, a randomized controlled trial

run by the Yale Economic Growth Center found that our collaboration with local NGOs to generate and distribute flood warnings in India increased the portion of people who received warnings prior to flooding events by 3x, and increased the portion of people who took protective action ahead of a flooding event by 4.4x.

5 Field Evaluation
5.3 Impact Evaluation


Document: The Google Flood Forecasting Initiative
Section: 6 Lessons Learned
Type: text

In the five years since we began this project, we have learned many lessons along the way. Here are a few highlights.

Focusing on the model is often not enough to drive real-world change. he real world is complicated, and the effectiveness of systems depends on only on the quality of your model. You have to ensure you have good (real-time) data collection systems, build a good product around your model outputs, and build partnerships with the key stakeholders that would use your systems. Especially in underserved communities, many components of the service pipeline may be flawed - and so it's critical to monitor and ensure that the full process from model to on-the-ground value works properly.

Relatedly, missing data and data errors drive the majority of operational inaccuracy. Academic benchmarks and analyses will focus exclusively on model quality, yet in the vast majority of operational systems errors in model inputs can completely dominate over model mistakes. As a result, investing in error detection, error correction, uncertainty estimation, out-of-distribution detection, review and oversight, and other similar tools is incredibly important.

Interaction with floods is incredibly diverse and requires different communication in different locations. People have different needs and expectations from flood forecasting systems. For example, in areas that rarely experience flooding, a flood with a depth of 15 centimeters can be a critical event - and people will expect a severe warning for it. However, in areas that experience frequent flooding, shallow flood waters are considered a non-event and people may be frustrated or surprised by even an informational update for such events. In extreme cases, like in some of the most flood-affected regions in Bihar, India, people ask to be warned only if and when flood waters are expected to reach waist height. Another example of such diversity is how people communicate with others about floods. In some regions, such as in much of South Asia, communities will share with their networks any information they have about upcoming floods. In others, such as parts of South America, there is stigma associated with being affected by floods, and so people won't discuss it - including critical safety information.


Document: identifying potentiarisk lakes
Type: text

- An optimality analysis was conducted to identify potentially dangerous glacial lakes in the Himalayas.
- We identified 207 glacial lakes as very high-hazard and 345 as high-hazard.
- Most of the high- or very high-hazard lakes are concentrated in the eastern and central Himalayas.
- Glacial lakes with high outburst potential have a robust hazard level, regardless of the changes in the evaluation scheme.


Document: GRAPHICAL ABSTRACT
Section: GRAPHICAL ABSTRACT
Type: figure
Figure

Image /page/0/Figure/19 description: A flowchart illustrating a three-step process for assessing Glacial Lake Outburst Flood (GLOF) hazards. The process is divided into Data, Methods, and Results. Step 1, labeled "Data," includes inputs such as Glacier inventory (RGI v6.0), Glacial lake inventory, Digital Elevation Model (DEM), and High/medium-resolution remote sensing images. Step 2, labeled "Methods," involves analyzing factors like the mean slope of the parent glacier, potential for mass movement into the lake, mean slope of the moraine dam, watershed area, and lake perimeter. These factors are then normalized to a common range of 0-1 and processed using a weighting scheme (fuzzy consistent matrix method). Step 3, labeled "Results," displays the outcomes. A pie chart shows the distribution of hazard levels: very low (dark green, 287), low (light green, 426), medium (light yellow, 405), high (orange, 345), and very high (red, 207). Below the pie chart, a diagram illustrates the components of a GLOF hazard, with a central red circle labeled "GLOF Hazard" surrounded by contributing factors like "Rock fall, landslide, or other solid mass movement," "Ice avalanches, glacier collapse," and "Dam instability."


Document: GRAPHICAL ABSTRACT
Section: ARTICLE INFO
Type: text

Article history: Received 28 June 2021 Received in revised form 15 September 2021 Accepted 15 September 2021 Available online 21 September 2021

Editor: Fernando A.L. Pacheco

Keywords: Glacial lake outburst flood (GLOF) Hazard GLOF susceptibility Weighting scheme Himalayas


Document: GRAPHICAL ABSTRACT
Section: ABSTRACT
Type: text

Glacial lakes in the Himalayas are widely distributed. Since 1900, more than 100 glacial lake outburst floods (GLOFs) have originated in the region, causing approximately 7000 deaths and considerable economic losses. Identifying potentially dangerous glacial lakes (PDGLs) is considered the first step in assessing GLOF risks. In this study, a more thorough inventory of PDGLs was presented that included numerous small-sized glacial lakes (<0.1 km2) that were generally neglected in the Himalayas for decades. Moreover, the PDGL evaluation system was improved in response to several deficiencies, such as the selection of assessment factors, which are sometimes arbitrary without a solid scientific basis. We designed an optimality experiment to select the best combination of assessment factors from 57 factors to identify PDGLs. Based on the experiments on both drained and non-drained glacial lakes in the Sunkoshi Basin, eastern Himalayas, five assessment factors were determined to be the best combination: the mean slope of the parent glacier, the potential for mass movement into the lake, the mean slope of moraine dams, the watershed area, and the lake perimeter, corresponding to the GLOF triggers for ice avalanches, rockfalls and landslides, dam instability, heavy precipitation or other liquid inflows, and lake characteristics, respectively. We then applied the best combination of assessment factors to the 1650 glacial lakes with an area greater than 0.02 km² in the Himalayas. We identified 207 glacial lakes as very high-hazard and 345 as high-hazard. It is noteworthy that in various GLOF susceptibility evaluation scenarios with different assessment factors, weighting schemes, and classification approaches, similar results for glacial lakes with high outburst potential have been obtained. The results provided here can be used as benchmark data to assess the GLOF risks for local communities.

© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

\* Corresponding authors.

E-mail addresses: weicaiwang@itpcas.ac.cn (W. Wang), anbaosheng@itpcas.ac.cn (B. An).


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

In the past few decades, glacial lakes worldwide have rapidly increased by 50% in number, area, and volume (Shugar et al., 2020). Climate warming and high mountain environmental instability have strengthened the frequency of snow/ice avalanches and landslides (Ballesteros-Canovas et al., 2018; Yao et al., 2019; Ding et al., 2021), thus causing numerous disasters in the form of glacial lake outburst floods (GLOFs, Clague and Evans, 2000; Anacona et al., 2015; Carrivick and Tweed, 2016, 2019; Wilson et al., 2018). In the Himalayas, more than 100 GLOF events have been reported since the 1900s (Komori et al., 2012; Nie et al., 2018; Zheng et al., 2021a). Approximately 50% of GLOFs are triggered by snow/ice avalanches (Emmer and Cochachin, 2013). Based on diverse dam materials, glacial lakes are generally classified into: moraine-dammed, ice-dammed, and bedrockdammed lakes (Vilímek et al., 2013). Among these, the morainedammed lakes have garnered increased attention because of their wide distribution and the frequent disasters associated with them. For example, the deadliest GLOF event was recorded on June 16, 2013, at Chorabari glacial lake, a moraine-dammed lake in the western Himalayas, which destroyed the downstream Kedarnath village and claimed more than 6000 human lives (Allen et al., 2016a; Rafig et al., 2019). Additionally, a recent GLOF event in Gongbatongshaco (moraine-dammed), the central Himalayas, occurred on July 5, 2016, resulting in transboundary impacts on both China and Nepal, causing economic losses of more than \$70 million (Cook et al., 2018; Nie et al.,

Identifying potentially dangerous glacial lakes (PDGLs) and establishing a PDGL inventory are prerequisites for conducting a GLOF risk assessment. To the best of our knowledge, more than 50 peer-reviewed papers on how to determine these possible sources of risk have been published. For example, Allen et al. (2019) used four characteristics of lakes, dams, parent glaciers, and topographic surroundings, and a flood path simulation model to assess the risk of glacial lakes on the Tibetan Plateau, determining a region of the central Himalayas as a hotspot where several high- and very high-risk glacial lakes are distributed. Various methods have also been applied to assess GLOF susceptibility in the Cordillera Blanca, Peruvian Andes, South America, and the Alps (Drenkhan et al., 2019; Emmer and Vilímek, 2013, 2014; Emmer et al., 2016; Frey et al., 2018; Huggel et al., 2002, 2004).


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

four characteristics of lakes , dams , parent glaciers , and topographic surroundings , and a flood path simulation model to assess the risk of glacial lakes on the Tibetan Plateau , determining a region of the central Himalayas as a hotspot where several high - and very high - risk glacial lakes are distributed . Various methods have also been applied to assess GLOF susceptibility in the Cordillera Blanca , Peruvian Andes , South America , and the Alps ( Drenkhan et al . , 2019 ; Emmer and Vilímek , 2013 , 2014 ; Emmer et al . , 2016 ; Frey et al . , 2018 ; Huggel et al . , 2002 , 2004 ) .

Over 1600 glacial lakes with areas larger than 0.02 km² are distributed in the Himalayas (Nie et al., 2017; Wang et al., 2020a). The GLOFs in this area are characterized by transboundary impacts, which is a significant feature. However, assessing GLOF susceptibility is limited by national borders; thus, an integrative evaluation is lacking. In addition, glacial lakes are generally smaller than precipitation- or runoffed lakes. Glacial lakes with areas greater than 0.1/1.0 km² can be considered medium/large-sized lakes (Zhang et al., 2015; Nie et al., 2017; Zhang et al., 2019). In previous assessments, the threshold of the smallest lake area used to identify PDGLs was relatively large. As a result, several small lakes have been ignored. As for the abovementioned Chorabari and Gongbatongshaco GLOFs, the areas of the lakes were only 0.04 and 0.02 km², respectively, before their outburst. Such small-sized glacial lakes may not be considered in inventories for assessing potential danger, but can cause catastrophic damage.

The performance of assessment factor selection in the GLOF susceptibility evaluation system is still disorganized and chaotic, despite the rapid, decades-long development of this knowledge. For example, there are generally several factors that can be used to refer to a GLOF trigger; among them, only a few factors are used frequently. Here, we paid extra attention to data availability, ease of extraction, and usage status when selecting the assessment factors. Similarly, the diversity of assessment factors makes selection difficult. Kougkoulos et al. (2018) noticed this problem and attempted to establish a standard or paradigm for GLOF risk assessment and recommended 13 assessment factors for evaluation work. However, scholars seem to prefer using

the factors they choose in combination with actual local conditions. For instance, in small-scale watershed studies, it is possible to obtain valuable in-situ data or high-resolution remote sensing images (such as lake volume and glacier crevasses), forcing scholars to consider using these data instead of free, medium-resolution images. Considering the deficiencies of previous scenarios, this study aimed to (i) provide a complete list of all the factors that have been used in identifying PDGLs, (ii) carry out an optimality analysis to find the best combination of assessment factors, and (iii) determine the PDGLs with an area greater than 0.02 km² in the Himalayas.


Document: GRAPHICAL ABSTRACT
Section: 2. Geographical settings
Type: text

Glaciers in the Himalayas have been experiencing an extensive recession (Kääb et al., 2012; Yao et al., 2012; Azam et al., 2018; Kumar et al., 2019), and the ice loss has accelerated in recent decades (Maurer et al., 2019). Glacial retreat and increased meltwater have resulted in the formation and expansion of glacial lakes. Glacial lakes have increased approximately 8.8% in number (from 4549 to 4950) and 14.1% in area (from 398.9 to 455.3 km²) in the Himalayas during 1990–2015 (Nie et al., 2017). The Himalayan glacial lakes are characterized by large numbers and high risks compared with other glacierized regions in the Third Pole (Fig. 1a); therefore, it is of great interest to the scientific community (Fujita et al., 2013; Linsbauer et al., 2016; Song et al., 2017; Wang et al., 2017; Cook et al., 2018; King et al., 2019; Maurer et al., 2020; Veh et al., 2020; Zhang et al., 2021).

The Himalayas are one of the global GLOF hotspots. Although controversies exist regarding the frequency of GLOFs over the past several decades, there is no doubt that the frequency of GLOFs will increase in the next few decades (Harrison et al., 2018; Veh et al., 2019; Zheng et al., 2021b). In the context of climate warming and glacial retreat, the area around lakes has become more unstable, as the steep area is likely to be closer to glacial lakes, increasing the possibility of mass movement into the lake. Furthermore, the hydrostatic pressure on moraine dams would be increased, accompanied by lake expansion, which could gradually destabilize moraine dams. Such potential threats significantly impact the evolution of glacial lakes and the development of downstream communities, prompting local governments to implement prevention and mitigation measures.


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: text

There are several comprehensive glacial lake inventories at various scales covering the Himalayan region (Zhang et al., 2015; Nie et al., 2017; Wang et al., 2020a; Shugar et al., 2020; Chen et al., 2021). We regard the available glacial lake inventories as reference data in the present study rather than using them directly. Here, we have prepared an updated glacial lake inventory, focusing on glacial lakes with the following three features: (1) mainly fed by contemporary glacier meltwater, (2) an area greater than 0.02 km², and (3) moraine-dammed lakes.

Small-sized lakes have always been neglected in previous studies on PDGL identification. However, these glacial lakes may not only cause devastating casualties and property losses but have also exhibited an increasing incidence of outbursts since the 1980s relative to medium/large-sized lakes (Fig. 2, and Supplementary materials, Table S2). Hence, we included numerous small glacial lakes in the inventory of the present study. Based on a glacial lake inventory obtained from Wang et al. (2020a), which identified glacial lakes in the Tibetan Plateau and surroundings using the Normalized Difference Water Index (NDWI) method by utilizing Landsat images (30 m), we extracted glacial lakes that fit our criteria and manually modified the lake boundaries to improve accuracy, especially for small-sized lakes. In total, there were 1650 glacial lakes in the final inventory, with a mean area of 0.175 km², and 66.4% of the lakes had an area smaller than 0.1 km² (Fig. 1b).

3. Data and methods
3.1. Glacial lake inventory


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Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: figure
Figure

Image /page/2/Figure/2 description: A figure from a scientific paper showing the distribution of glacial lakes in the Himalayan region. The figure is composed of two parts, labeled 'a' and 'b'.


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: text

Part 'a' is a map displaying a large area of South Asia, including the Himalayan mountain range. The map uses a color scale to represent elevation, ranging from -28 meters (greenish-blue) to 8806 meters (brown). Major river basins are outlined in purple and labeled, including B13 Indus, B10 Yamuna, B9 Alaknanda, B8 Ghaghara, B7 Gandak, B6 Sapta Koshi, B5 Teesta, B4 Sunkosh, B3 Manas, B2 Bhareli, B1 Subansiri, B12 Maquan, and B11 Yarlung. Blue dots represent glacial lakes, which are concentrated in the high-elevation areas. Several major cities are marked with red dots, including Islamabad, New Delhi, Kathmandu, Thimphu, Lhasa, and Dhaka. The legend also identifies symbols for rivers and glaciers.

Part 'b' is an inset graph in the top right corner. It is a histogram with a cumulative frequency curve. The x-axis is labeled 'Lake area (km²)' and ranges from 0.0 to 1.0, with a break before the value 11.23. The left y-axis, for the histogram, is 'Frequency' and goes up to 400. The right y-axis, for the cumulative curve, is 'Number' and goes up to 1600. The teal histogram bars show that the vast majority of lakes have a small area, with the highest frequency (around 400) for lakes with an area close to 0.0 km². The frequency decreases rapidly as the lake area increases. A dashed vertical line is shown at a lake area of 0.1 km². The black line represents the cumulative number of lakes, showing a steep increase for small lake areas and leveling off around 1600.

Fig. 1. The distribution of glacial lakes in the Himalayas. (a) The Himalayan region is divided into 13 different subregions based on river basin borders, namely the Subansiri (B1), Bhareli (B2), Manas (B3), Sunkoshi (B4), Teesta (B5), Sapta Koshi (B6), Gandak (B7), Ghaghara (B8), Alaknanda (B9), Yamuna (B10), Yarlung (B11), Maquan (B12), and Indus River basin (B13). Inset: the distribution of lake frequency at different size intervals.

Gandak Ghaghara Manas Subansiri Teesta Yamuna


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: figure
Figure

Image /page/2/Figure/4 description: A scatter plot with a step function overlay, showing 'Area before outburst (km²)' on the y-axis versus 'Year' on the x-axis. The x-axis ranges from before 1985 to 2020, and the y-axis ranges from -0.1 to 1.3. The data points and the step line are color-coded, transitioning from teal and dark green in the earlier years to lighter green, yellow, and finally brown in the later years. The step line starts at approximately 0.54 km² before 1985, rises to 0.78 km² around 1986, then generally trends downwards with some fluctuations, ending at 0.1 km² from 2010 to 2020. The scatter points are clustered at various levels. Notable high-value points include one at approximately 1.04 km² around 1983, another at 1.22 km² around 1995, and one at 1.03 km² around 2009. Many points, especially after 1990, are clustered below 0.3 km².


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: text

Fig. 2. Characteristics of the area of glacial lake outbursts since the 1980s. Small-sized glacial lakes have exhibited an increased frequency of outbursts since the 1980s.

 Assessment factors have been proposed or used in studies of identifying PDGLs. Every factor is described as ID, represented characteristic, name, easily measured and low error (Y), usage frequency and sources, respectively.


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: table
Table: Table 1

| ID                                                                                | Category                                                               | Assessment factor                                               | Easy                 | Frequency     | Source                                                                                |  |   |   |        |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|-----------------------------------------------------------------|----------------------|---------------|--------------------------------------------------------------------------------------------------|--|---|---|--------|
| G1                                                                                | Ice avalanches, glacier collapse                                       | Distance between lake and glacier terminus                      | Y                    | 25            | 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 19, 24, 27, 34, 35, 37, 38, 39, 40, 41, 42, 45       |  |   |   |        |
| G2                                                                                |                                                                        | Parent glacier snout steepness                                  | Y                    | 10            | 3, 6, 9, 31, 35, 38, 39, 41, 42, 45                                                              |  |   |   |        |
| G3                                                                                |                                                                        | Glacier calving                                                 |                      | 7             | 12, 32, 33, 39, 42, 44, 45                                                                       |  |   |   |        |
| G4                                                                                |                                                                        | Slope between lake and glacier                                  | Y                    | 6             | 3, 14, 34, 38, 39, 42                                                                            |  |   |   |        |
| G5                                                                                |                                                                        | Avalanche volume                                                |                      | 2             | 21, 23                                                                                |  |   |   |        |
| G6                                                                                |                                                                        | Change of parent glacier volume                                 |                      | 2             | 25, 31                                                                                |  |   |   |        |
| G7                                                                                |                                                                        | Glacier velocity                                                |                      | 2             | 25, 31                                                                                |  |   |   |        |
| G8                                                                                |                                                                        | Width of glacier calving front                                  |                      | 2             | 38, 42                                                                                |  |   |   |        |
| G9                                                                                |                                                                        | Advancing and (or) retreating glacier                           | Y                    | 1             | 45                                                                                |  |   |   |        |
| G10                                                                                |                                                                        | Lake-glacier vertical distance                                  |                      | 1             | 42                                                                                |  |   |   |        |
| G11                                                                                |                                                                        | Mean slope of parent glacier                                    | Y                    | 1             | 4                                                                                |  |   |   |        |
| R1                                                                                | Rock fall, landslide, or other solid mass movement                     | Potential for mass movement to strike a lake                    | Y                    | 25            | 1, 9, 10, 11, 13, 17, 18, 19, 20, 21, 23, 24, 25, 28, 29, 30, 31, 36, 39, 40, 41, 42, 43, 44, 45 |  |   |   |        |
| R2                                                                                |                                                                        | Regional seismic activity                                       |                      | 7             | 14, 15, 16, 32, 33, 35, 39                                                                       |  |   |   |        |
| R3                                                                                |                                                                        | Maximum slope of moraine surrounding the lake                   |                      | 2             | 35, 38                                                                                |  |   |   |        |
| R4                                                                                |                                                                        | Distance between lake and steepest slope                        | Y                    | 1             | 35                                                                                |  |   |   |        |
| R5                                                                                |                                                                        | Mean slope of lake surrounding                                  | Y                    | 1             | 38                                                                                |  |   |   |        |
| R6                                                                                |                                                                        | Steepest slope surrounding lake                                 | Y                    | 1             | 38                                                                                |  |   |   |        |
| D1                                                                                | Dam instability                                                        | Dam type                                                        | Y                    | 18            | 6, 7, 8, 13, 18, 20, 27, 28, 29, 33, 34, 35, 38, 39, 40, 42, 44, 45                              |  |   |   |        |
| D2                                                                                |                                                                        | Dam freeboard                                                   |                      | 16            | 6, 13, 14, 15, 18, 20, 25, 28, 29, 31, 33, 35, 38, 42, 44, 45                                    |  |   |   |        |
| D3                                                                                |                                                                        | Mean slope of moraine dam                                       | Y                    | 13            | 1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 39, 41, 42                                                 |  |   |   |        |
| D4                                                                                |                                                                        | Drainage type                                                   | Y                    | 12            | 7, 15, 24, 27, 28, 29, 32, 33, 37, 38, 39, 43                                                    |  |   |   |        |
| D5                                                                                |                                                                        | Dam width-height ratio                                          |                      | 10            | 9, 13, 14, 18, 20, 29, 39, 42, 44, 45                                                            |  |   |   |        |
| D6                                                                                |                                                                        | Dam width                                                       | Y                    | 10            | 4, 6, 9, 14, 18, 25, 29, 31, 38, 39                                                              |  |   |   |        |
| D7                                                                                |                                                                        | The presence of ice-cored moraine                               | Y                    | 10            | 9, 10, 21, 23, 24, 25, 31, 39, 42, 45                                                            |  |   |   |        |
| D8                                                                                |                                                                        | Ratio of freeboard to dam height                                |                      | 4             | 9, 39, 42, 44                                                                                |  |   |   |        |
| D9                                                                                |                                                                        | Dam height                                                      |                      | 3             | 25, 31, 38                                                                                |  |   |   |        |
| D10                                                                                |                                                                        | Dam texture (consolidated or unconsolidated, bedrock, or other) |                      | 3             | 7, 8, 45                                                                                |  |   |   |        |
| D11                                                                                |                                                                        | Moraine vegetation coverage                                     |                      | 2             | 42, 45                                                                                |  |   |   |        |
| D12                                                                                |                                                                        | Permafrost susceptibility                                       |                      | 2             | 31, 32                                                                                |  |   |   |        |
| D13                                                                                |                                                                        | Remedial work                                                   |                      | 2             | 37, 38                                                                                |  |   |   |        |
| D14                                                                                |                                                                        | Extent of crest                                                 |                      | 1             | 24                                                                                |  |   |   |        |
| D15                                                                                |                                                                        | Mean slope between the piping spring and the nearest lakeshore  |                      | 1             | 38                                                                                |  |   |   |        |
| H1                                                                                | Heavy precipitation, various liquid inflows                            | Unstable lake upstream                                          | Y                    | 10            | 10, 11, 21, 23, 24, 28, 29, 30, 37, 42                                                           |  |   |   |        |
| H2                                                                                |                                                                        | Parent glacier area                                             | Y                    | 8             | 3, 4, 7, 9, 16, 25, 31, 39                                                                       |  |   |   |        |

ID
Category
Assessment factor
Easy
Frequency
Source


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: table
Table: Table 1

| ID                                                                                | Category                                                               | Assessment factor                                               | Easy                 | Frequency     | Source                                                                                |  |   |   |        |
| H3                                                                                |                                                                        | Intense precipitation events                                    |                      | 4             | 14, 35, 39, 44                                                                                |  |   |   |        |
| H4                                                                                |                                                                        | High temperature events                                         |                      | 3             | 35, 39, 44                                                                                |  |   |   |        |
| H5                                                                                |                                                                        | Average annual precipitation                                    |                      | 2             | 4, 9                                                                                |  |   |   |        |
| H6                                                                                |                                                                        | The average monthly land surface temperature                    |                      | 2             | 4, 9                                                                                |  |   |   |        |
| H7                                                                                |                                                                        | Watershed area                                                  | Y                    | 3             | 1, 42, 45                                                                                |  |   |   |        |
| H8                                                                                |                                                                        | Temperature change rate                                         |                      | 1             | 4                                                                                |  |   |   |        |
| H9                                                                                |                                                                        | Mean annual air temperature                                     |                      | 1             | 32                                                                                |  |   |   |        |
| L1                                                                                | Characteristic of lake                                                 | Lake area                                                       | Y                    | 26            | 1, 2, 4, 5, 7, 8, 11, 12, 14, 15, 16, 19, 22, 24, 28, 30, 31, 32, 33, 37, 38, 39, 40, 41, 42, 45 |  |   |   |        |
| L2                                                                                |                                                                        | Rate of lake area increase                                      | Y                    | 22            | 2, 5, 7, 8, 11, 13, 14, 15, 19, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33, 34, 39, 45               |  |   |   |        |
| L3                                                                                |                                                                        | Lake volume                                                     |                      | 11            | 12, 15, 16, 25, 26, 27, 31, 33, 34, 35, 40                                                       |  |   |   |        |
| L4                                                                                |                                                                        | Lake type                                                       | Y                    | 8             | 15, 16, 26, 28, 29, 30, 32, 37                                                                   |  |   |   |        |
| L5                                                                                |                                                                        | Lake elevation                                                  | Y                    | 4             | 15, 16, 33, 37                                                                                |  |   |   |        |
| L6                                                                                |                                                                        | Past GLOF event                                                 | Y                    | 3             | 15, 37, 45                                                                                |  |   |   |        |
| L7                                                                                |                                                                        | Width of the lake                                               | Y                    | 2             | 15, 38                                                                                |  |   |   |        |
| L8                                                                                |                                                                        | Average lake depth                                              |                      | 1             | 33                                                                                |  |   |   |        |
| L9                                                                                |                                                                        | Lake perimeter                                                  | Y                    | 1             | 38                                                                                |  |   |   |        |
| L10                                                                                |                                                                        | Length of the lake                                              | Y                    | 1             | 15                                                                                |  |   |   |        |
| L11                                                                                |                                                                        | Orientation of the lake                                         | Y                    | 1             | 15                                                                                |  |   |   |        |
| I1                                                                                | Influence to downstream area                                           | Distance of settlement                                          | Y                    | 8             | 12, 15, 16, 25, 30, 31, 35, 40                                                                   |  |   |   |        |
| I2                                                                                |                                                                        | Downstream slope of dam                                         | Y                    | 4             | 4, 30, 33, 39                                                                                |  |   |   |        |
| I3                                                                                |                                                                        | I4                                                              | Flood peak discharge | Affected area |                                                                                |  | 2 | 2 | 33, 40 |
| I5                                                                                | Position of glacial lake with respect to confluence with main River | Y                                                               | 2                    | 16, 37        |                                                                                |  |   |   |        |
| Note: The source: 1: Allen et al. (2019); 2: Wang et al. (2020b); 3: Wang et al. (2011); 4: Fan et al. (2019); 5: Wang et al. (2017); 6: Liu et al. (2020); 7: Khanal et al. (2015a); 8: Wang et al. |                                                                        |                                                                 |                      |               |                                                                                |  |   |   |        |


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.1. Glacial lake inventory
Type: text

Note: The source, 1: Allen et al. (2019); 2: Wang et al. (2020b); 3: Wang et al. (2011); 4: Fan et al. (2019); 5: Wang et al. (2017); 6: Liu et al. (2020); 7: Khanal et al. (2015a); 8: Wang et al. (2012); 9: Wang et al. (2008); 10: Dubey and Goyal (2020); 11: Khadka et al. (2021); 12: Washakh et al. (2019); 13: Prakash and Nagarajan (2018); 14: Prakash and Nagarajan (2017); 15: Aggarwal et al. (2017); 16: Jha and Khare (2017); 17: Allen et al. (2016b); 18: Worni et al. (2013); 19: Aggarwal et al. (2013); 20: Deswal et al. (2020); 21: Rounce et al. (2017); 22: Begam and Sen (2019); 23: Rounce et al. (2016); 24: Khanal et al. (2015b); 25: Bolch et al. (2008); 26: Nagai et al. (2017); 27: Falatkova et al. (2019); 28: Petrakov et al. (2017); 30: Kapitsa et al. (2017); 31: Bolch et al. (2011); 32: Gruber and Mergili (2013); 33: Mergili and Schneider (2011); 34: Drenkhan et al. (2019); 35: Kougkoulos et al. (2018); 36: Frey et al. (2018); 37: Emmer et al. (2016); 38: Emmer and Vilímek (2014); 39: Emmer and Vilímek (2013); 40: Cook et al. (2016); 41: Anacona et al. (2014); 42: McKillop and Clague (2007); 43: Clague and Evans (2000); 44: Huggel et al. (2004); 45: Huggel et al. (2002).


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.2. Remote sensing data
Type: text

A glacier inventory, RGI v6.0, (RGI-Consortium, 2017), was used to identify glacial lakes with parent glaciers. Forty-four Sentinel-2A/2B images from 2020 were used to map the Himalayan glacial lakes (Supplementary materials, Table S1) and amend parent glacier boundaries for estimating several factors related to glaciers. The 10-m spatial resolution of Sentinel-2A/2B images (Level 1C) was freely downloaded from the U.S. Geological Survey (USGS) portal (https://earthexplorer.usgs.gov/), allowing us to meticulously identify PDGLs. In addition, 40 Landsat images, including 3 Landsat2 MSS images (60 m), 1 Landsat4 TM image (30 m), 28 Landsat5 TM images (30 m), and 9 Landsat8 OLI\_TIRS images (30 m), were used to delineate the area of outburst lakes before draining in the region. The SRTM data (30 m), downloaded from NASA Earth data (https://search.earthdata.nasa.gov/search), was used to estimate the slope of glaciers and areas around lakes and delineate the watershed area upstream of glacial lakes.

3. Data and methods
3.2. Remote sensing data


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.3. Optimality analysis of assessment factors
Type: text

The most important step in identifying PDGLs is the selection of assessment factors. Currently, factor selection is highly dependent on empirical judgment or employs factors that have been used in previous studies, thus lacking a solid scientific basis. Moreover, as an integrated assessment method, the best combination of factors characterize the lakes, dams, parent glaciers, and topographic surroundings.

In the present study, we obtained 57 assessment factors from 45 previous studies on the identification of PDGLs (Table 1) and then classified these factors into 6 categories based on their referential GLOF triggers. The first category consisted of 11 factors, referring to the impact of ice avalanches and glacier collapse on glacial lakes (G1–11). The second category consisted of six factors, referring to the impact of rockfalls, landslides, or other solid mass movements (R1–6). The third to sixth categories represented dam instability (D1–15), heavy precipitation or various liquid inflows (H1–9), lake characteristics (L1–11), and the impact on downstream areas (I1–5). This classification was based on previous studies (Bolch et al., 2011; Emmer and Vilímek, 2014) and is scientifically reasonable. The next step was to select the most suitable factor within the same category and find the best combination between categories.

The aforementioned assessment factors are diversified, representing the characteristics of GLOF triggers. We proposed four criteria to simplify these factors from the complicated and ensembled items (Wang et al., 2011). First, factors should be extracted from readily available data with relatively high accuracy. For example, dam freeboard (D2) requires high-resolution DEM data to ensure its accuracy, especially for small-sized lakes; thus, it was exempted from our consideration. Second, factors should be continuous-type data rather than nominal, as we hope the hazard has not only a qualitative danger level but a quantitative value. Third, factors should focus on the impacts on lake stability rather than downstream influence; hence the sixth category (I1–5) was not considered. Fourth, the factors should remain independent without redundancy. For example, the mean slope of the parent glacier (G11) and the mean slope of the surrounding lake (R5) are correlated to a

certain extent; therefore, they are not selected simultaneously. Based on the above four criteria, 17 factors were selected in the first step, namely distance between the lake and glacier terminus (G1), parent glacier snout steepness (G2), the slope between the lake and glacier (G4), lake–glacier vertical distance (G10), mean slope of parent glacier (G11), the potential for a mass movement to strike a lake (R1), mean slope of the surrounding lake (R5), mean slope of moraine dams (D3), dam width (D6), parent glacier area (H2), watershed area (H7), lake area (L1), rate of lake area increase (L2), lake elevation (L5), the width of the lake (L7), lake perimeter (L9), and length of the lake (L10).


Document: GRAPHICAL ABSTRACT
Section: 3. Data and methods > 3.3. Optimality analysis of assessment factors
Type: text

glacier ( G4 ) , lake – glacier vertical distance ( G10 ) , mean slope of parent glacier ( G11 ) , the potential for a mass movement to strike a lake ( R1 ) , mean slope of the surrounding lake ( R5 ) , mean slope of moraine dams ( D3 ) , dam width ( D6 ) , parent glacier area ( H2 ) , watershed area ( H7 ) , lake area ( L1 ) , rate of lake area increase ( L2 ) , lake elevation ( L5 ) , the width of the lake ( L7 ) , lake perimeter ( L9 ) , and length of the lake ( L10 ) .

We selected 1 factor from every category of the 17 assessment factors, except for the sixth category. A fuzzy consistent matrix (FCM) method was used to determine the category weights (Table 2). According to previous studies, ice avalanches and landslides seem to trigger more than half of the GLOFs (Emmer and Cochachin, 2013); thus, we assigned a higher weight value to them. The Sunkoshi River basin (B4) was selected as the site for the optimality experiment. It is located in the eastern Himalayas, and 170 glacial lakes are distributed in the basin, 10 of which have drained historically (Fig. 3a, b). These outburst lakes generally have a high outburst potential; therefore, the assessment results should show higher hazard indices. In addition, all factor values were normalized to a range of 0–1 to calculate the final quantitative value.

Subsequently, an optimality analysis was conducted. First, we selected the highest frequency of factors in every category to make up the initial combination (G1, R1, D3, H2, and L1) and then changed the factors in the first category in turn (G1, G2, G4, G10, and G11). The highest average hazard index of the 10 drained glacial lakes was considered the best factor in this category (Fig. 3c). Second, the factors in the other four categories were tested to obtain the best factor in each category. As a result, the best factors formed the best combination, i.e., the mean slope of the parent glacier (G11), the potential for a mass movement to strike a lake (R1), mean slope of a moraine dam (D3), watershed area (H7), and lake perimeter (L9). Finally, we applied the best combination of factors to assess GLOF susceptibility for all Himalayan glacial lakes (Table 2).


Document: GRAPHICAL ABSTRACT
Section: 4. Results > 4.1. Assessment validation
Type: text

To display qualitative results, all glacial lake hazard indices were classified into five levels using the Natural Jenks approach (Fig. 4a), namely very low, low, medium, high, and very high. This method was also used by Allen et al. (2019) and Zheng et al. (2021b) to evaluate the Tibetan Plateau glacial lakes outburst risks.

Our identified inventory comprised 72 outburst glacial lakes (Supplementary materials, Table S3). Among these, 40 lakes had an area greater than 0.1 km², while the remainder had areas lower than 0.1 km². The five selected assessment factors can better classify these large outburst lakes, with 90% of medium/large-sized lakes identified as high- or very high-hazard. Meanwhile, 69% of the small-sized lakes also fell into this classification (Fig. 4b, c). Although the hazard indices of lakes (<0.1 km²) are reduced by the lake perimeter factor, this


Document: GRAPHICAL ABSTRACT
Section: 4. Results > 4.1. Assessment validation
Type: table
Table: Table 2 Selected factors with their categories and explanations. Rank and weight of each factor are computed using the FCM method.

| ID  | Category                                              | Rank | Weight | Assessment factor                            | Explanation                                                 | Unit | Reference                                  |
|-----|-------------------------------------------------------|------|--------|----------------------------------------------|-------------------------------------------------------------|------|--------------------------------------------|
| G11 | Ice avalanches, glacier collapse                      | 5    | 0.27   | Mean slope of parent glacier                 | Mean slope of the parent glaciers                           | °    | Fan et al., 2019                           |
| R1  | Rock fall, landslide, or other solid mass movement | 4    | 0.25   | Potential for mass movement into the lake | Non-glacierized areas around the lake with a slope > 30° | km2  | Bolch et al., 2011; Rounce et al., 2016 |
| D3  | Dam stability                                         | 3    | 0.21   | Mean slope of moraine dam                    | Mean slope of moraine dam in front of the lake           | °    | Worni et al., 2013                         |
| H7  | Heavy precipitation, various types of inflows      | 2    | 0.17   | Watershed area                               | Total watershed area upstream of the lake                   | km2  | Clague and Evans, 2000                     |
| L9  | Characteristic of lake                                | 1    | 0.1    | Lake perimeter                               | Length of the lake coast                                    | km   | Emmer and Vilímek, 2014                    |

ID
Category
Rank
Weight
Assessment factor
Explanation
Unit
Reference


Document: GRAPHICAL ABSTRACT
Section: 4. Results > 4.1. Assessment validation
Type: figure
Figure

Image /page/5/Figure/2 description: A scientific figure composed of three panels labeled a, b, and c, analyzing glacial lakes in the Sunkosh River Basin. Panel a is a topographical map of the Sunkosh River Basin, showing the river system. It displays numerous glacial lakes as blue dots and specific outburst glacial lakes as red dots, labeled with IDs such as 423, 497, 517, and 522. The map's coordinates range from 27°20'N to 28°0'N latitude and 89°0'E to 90°20'E longitude. Panel b is a scatter plot titled 'Outburst glacial lake', showing the area of these specific lakes. The x-axis is 'Glacial lake ID' and the y-axis is 'Area (km²)'. Notable points include lake 423 with an area of about 1.55 km² and lake 517 with an area of about 0.7 km². A dashed line is present at an area of 0.1 km². Panel c is a bar chart showing the 'Average hazard value' for different 'Assessment factor ID's. The y-axis ranges from 0.4 to 0.8. The bars represent factors like G1, G2, R1, H7, L9, etc. The values on top of the bars are: G1 (0.671), G2 (0.671), G4 (0.698), G10 (0.652), G11 (0.704), R1 (0.704), R5 (0.678), D3 (0.704), D6 (0.616), H2 (0.704), H7 (0.714), L1 (0.714), L2 (0.706), L5 (0.71), L7 (0.723), L9 (0.726), and L10 (0.725).


Document: GRAPHICAL ABSTRACT
Section: 4. Results > 4.1. Assessment validation
Type: text

**Fig. 3.** The sensitivity experiment of assessment factors conducted in the Sunkoshi River basin (B4). (a) The distribution of both drained (red circle) and non-drained (blue circle) glacial lakes in the Sunkoshi River basin. (b) The area of the 10 drained glacial lakes in the basin. (c) The factor in the same category with the highest score is selected to reveal the best combination between categories. The spline filled with blue stripes is the final selected factor.

deviation is considered reasonable. First, our GLOF inventory included 26 recently reported events (Zheng et al., 2021a) that were not recorded by any news outlet or chronicled locally, implying that the area of the glacial lakes and flood magnitudes were very small and most likely drained before the 1970s. Second, the system error determined that small- and large-sized lakes cannot be fully identified simultaneously, and only a relative balance can be achieved.


Document: GRAPHICAL ABSTRACT
Section: 4. Results > 4.2. Potentially dangerous glacial lakes
Type: text

Among the 1650 glacial lakes in the Himalayas (>0.02 km²), 207 lakes were identified as very high-hazard lakes, 345 as high-hazard lakes, 405 as medium-hazard lakes, 426 as low-hazard lakes, and 267 as very low-hazard lakes. More high- or very high-hazard lakes are concentrated in the eastern and central Himalayas (Fig. 5) than in the west-ern Himalayas. This is in line with the characteristics of glacial lake distribution. The mean hazard index of lakes with an area lower (greater) than 0.1 km² is 0.425 (0.636), and that of lakes across the Himalayas is 0.496. At the high- and very high-hazard levels, 204 small-sized glacial lakes were identified, accounting for 37% of the total high- and very high-hazard lake numbers. This is likely the source of potential hazards that have not been identified or mentioned previously.

The Sapta Koshi River basin (B6) has the highest number of very high-hazard lakes (approximately one-third of the total), and 7 out of the 12 lakes with the highest levels are located in this basin (Supplementary materials, Table S4). These have several common features

that make them high-hazard, including a parent glacier belonging to a hanging glacier or a small valley glacier with a relatively small area, and a glacier snout and surrounding lake that are steep (Fig. 6).


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

We undertook objective screening and an optimality analysis and identified 5 of the 57 assessment factors as important for PDGL identification. Our results indicate the importance of employing an optimality experiment. This method avoids inclusion of some inappropriate or fallacious factors and drives scholars to find the best combination of assessment factors in their evaluations. For example, a large number of glacial lakes with high outburst potential are situated at relatively low altitudes. The very high-hazard lakes are distributed at an average elevation of 4875 m, and the high-hazard lakes are distributed at an average elevation of 5063 m. In contrast, the low-hazard lakes have a highest average elevation of 5222 m (Fig. 7a). The visible relationship between glacial lake outburst potential and elevation shows that the lake elevation factor is unsuitable in this identification study, as it intuitively suggests that a high lake outburst potential may correspond to a high elevation. The current optimality procedure is similar to a manual training system that forces the assessment factors to intensively express the features of drained glacial lakes. In the present study, the defined assessment factors can maximally emphasize the outburst lakes in the


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: figure
Figure

Image /page/6/Figure/2 description: A figure with three graphs labeled a, b, and c, analyzing a hazard index.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

Graph 'a' is a histogram with N=1650. The x-axis is 'Hazard index' from 0 to 1.0, and the y-axis is 'Frequency' from 0 to 40. The bars are color-coded by hazard level: dark green for 'Very low', medium green for 'Low', light green for 'Medium', orange for 'High', and red for 'Very high'. A blue curve is overlaid, showing a distribution that peaks around a hazard index of 0.45.

Graph 'b' is a scatter plot with N=40. The y-axis is 'Hazard index' and the x-axis is 'Glacial lake ID'. The teal data points represent lakes with an 'Area >= 0.1 km²'. The points are scattered between a hazard index of approximately 0.4 and 1.0.

Graph 'c' is a scatter plot with N=32. The y-axis is 'Hazard index' and the x-axis is 'Glacial lake ID'. The brownish-yellow data points represent lakes with an 'Area < 0.1 km²'. The points are scattered between a hazard index of approximately 0.2 and 1.0. Dashed horizontal lines indicate different hazard levels.

Fig. 4. Hazard index of Himalaya glacial lakes. (a) Hazard index classification based on the qualitative hazard value of 1650 glacial lakes. (b) The distribution of large-sized outburst lakes at different hazard levels. (c) The distribution of small-sized outburst lakes at different hazard levels.

Himalayas via optimality analysis and promote the identification of previously undocumented high-hazard glacial lakes.

A scientific classification approach, Natural Jenks, which is based on clustering, was employed to classify glacial lakes into disparate hazard levels. The differences between the levels could be highlighted using this approach. Assessment factors such as the potential for a mass movement into the lake, the total watershed area upstream of the lake, and the lake perimeter are more sensitive to the high GLOF susceptibility than the mean slopes of the parent glacier and moraine dams

(Fig. 7b–f). At present, the GLOF susceptibility evaluation method essentially consists of qualitative and quantitative systems. Adopting any classification approach is, in fact, unimportant in terms of the quantitative evaluation system. The quantitative system promotes the classification of glacial lakes into hazard levels after obtaining eventual scores, which determine the rank of a hazard index among all glacial lakes evaluated. If the classification approach were altered, we could still determine which glacial lake should gain more attention using these scores. A qualitative evaluation system classifies the assessment factors


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: figure
Figure

Image /page/6/Figure/7 description: A map of a mountainous region, likely the Himalayas, displaying the distribution of glacial lakes and a related assessment for various river basins. The map uses color gradients to show elevation, with green for lower areas and white for the highest peaks. Numerous blue dots represent glacial lakes, concentrated in the high-elevation areas. The map is overlaid with a grid for latitude (from 25°23'N to 35°25'N) and longitude (from 70°50'E to 90°54'E). Several large pie charts are placed over different basins, labeled with codes (B1 to B13) and names such as Indus, Yamuna, Sapta Koshi, and Yarlung. A legend in the top right corner, titled "Number of glacial lakes," indicates that the size of the pie charts corresponds to the number of lakes, with concentric circles representing <50, 100, 150, 300, and 500 lakes. A legend in the bottom left explains the colors of the pie chart segments, which represent five categories: Very low (dark green), Low (light green), Medium (pale yellow), High (orange), and Very high (red). A scale bar indicates a distance of 360 km.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

Fig. 5. The glacial lake outburst hazard across the Himalayas. The red points are the locations corresponding to the 12 highest hazard level glacial lakes.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: figure
Figure

Image /page/7/Figure/2 description: A figure from a scientific paper displaying a grid of 12 satellite images of lakes in mountainous, glacial environments. The images are arranged in three rows and four columns. Each image is labeled with a unique Lake ID. The top of the image contains citation information: "T. Zhang, W. Wang, Y. Guo et al." and "Science of the Total Environment 806 (2022) 150442". The Lake IDs for each image are as follows: Top row, from left to right: Lake ID: 1418, Lake ID: 982, Lake ID: 517, Lake ID: 1555. Middle row, from left to right: Lake ID: 860, Lake ID: 838, Lake ID: 742, Lake ID: 848. Bottom row, from left to right: Lake ID: 823, Lake ID: 837, Lake ID: 1540, Lake ID: 1140. Each image has one or more blue dots indicating the location of the lakes.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

Fig. 6. The 12 lakes with the highest hazard level in the PDGL inventory whose locations are shown in Fig. 5. For details on these glacial lakes, refer to the supplementary materials, Table S4.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: figure
Figure

Image /page/7/Figure/4 description: A figure containing six box plots, labeled (a) through (f), showing the distribution of different assessment factor values across five hazard levels: Very high, High, Medium, Low, and Very low. The boxes are color-coded from red (Very high) to orange, light green, dark green, and teal (Very low).


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

(a) Lake elevation (L5): The y-axis shows Elevation (m) from 3000 to 6000. The median elevation generally increases from the 'Very high' category (around 4800 m) to the 'Very low' category (around 5250 m).

(b) Mean slope of mother glacier (G11): The y-axis shows Slope (°) from 0 to 50. The median slope decreases from 'Very high' (around 25°) to 'Very low' (around 5°).

(c) Potential for mass movement to strike a lake (R1): The y-axis shows Area (km²) from 0 to 12. The median area and data spread decrease significantly from 'Very high' (median ~2.5 km²) to 'Very low' (median near 0 km²).

(d) Mean slope of moraine dam (D3): The y-axis shows Slope (°) from 0 to 40. The median slope generally decreases from 'Very high' (around 13°) to 'Very low' (around 7°).

(e) Watershed area (H7): The y-axis shows Area (km²) from 0 to 40. The median area and data spread decrease significantly from 'Very high' (median ~8 km²) to 'Very low' (median ~0.5 km²).

(f) Lake perimeter (L9): The y-axis shows Length (m) from 0 to 9. The median length and data spread decrease from 'Very high' (median ~2 m) to 'Very low' (median ~0.8 m).

Fig. 7. The average assessment factor values at the five hazard levels, including the (a) lake elevation, (b) mean slope of parent glacier, (c) potential for a mass movement to strike a lake, (d) mean slope of a moraine dam, (e) total watershed area upstream of the lake, and (f) lake perimeter.

(Fig. 8). The values of every factor are separated into hazard levels, and then the levels of each factor are combined to form the eventual level. The shortcoming of this classification is that it is difficult to rank glacial lakes at the same hazard level. Moreover, GLOF susceptibility evaluation is sensitive to changes in the thresholds of factor level classification (Khadka et al., 2021), and poor understanding leads to increased uncertainty in the results. On the one hand, the qualitative system can contain more assessment factors such as Boolean and discontinuous data types and is more suitable for application to small-scale studies. On the other hand, the quantitative system can be used to conduct a more robust GLOF susceptibility evaluation.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: text

levels of each factor are combined to form the eventual level . The shortcoming of this classification is that it is difficult to rank glacial lakes at the same hazard level . Moreover , GLOF susceptibility evaluation is sensitive to changes in the thresholds of factor level classification ( Khadka et al . , 2021 ) , and poor understanding leads to increased uncertainty in the results . On the one hand , the qualitative system can contain more assessment factors such as Boolean and discontinuous data types and is more suitable for application to small - scale studies . On the other hand , the quantitative system can be used to conduct a more robust GLOF susceptibility evaluation .

Furthermore, we conducted a sensitivity analysis of the weighting schemes. The analytical hierarchy process (AHP) and the equalweighting scheme were applied in this sensitivity analysis to enhance contrast. The AHP method is the most frequently used weighting scheme, and the calculated weight values are 0.503, 0.260, 0.134, 0.068, and 0.035, respectively, corresponding to the five ranks of the assessment factors in Table 2. The glacial lake hazard level is sensitive to variations in the weighting scheme. Accordingly, if the results of the equal-weighting scheme were used as background values, the original hazard level of 17% of the glacial lakes would change if the FCM method were adopted, whereas that of 44% of the glacial lakes would change upon adoption of the AHP method. Thus, compared with the AHP method, the FCM method used in the present study is less amenable to changes, but both schemes are equally efficient, accounting for 81% of the outburst events at the high- or very high-hazard level, higher than the equal-weighting scheme at 73%. This result revealed that assigning weights can increase the quality of the hazard level of drained glacial lakes, thus demonstrating a better GLOF susceptibility evaluation. On the other hand, glacial lakes at high- and very high-hazard levels were not as sensitive to changes in different weighting schemes compared with those at medium- and low-hazard levels (Table 3). In other words, weighting schemes have no decisive effect on the classification of high-hazard lakes.


Document: GRAPHICAL ABSTRACT
Section: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation
Type: table
Table: Table 3 A sensitivity analysis for different weighting schemes. The sample contains all the glacial lakes (>0.2 km2) in the Himalayas.

| Hazard level | Equal-weighting scheme (E) | FCM method (F) | AHP method (A) | Ratio of changed level (E–F, %) | Ratio of changed level (E–A, %) |
|-----------------|-------------------------------|----------------------|----------------------|---------------------------------------|---------------------------------------|
| Very high       | 221                           | 207                  | 192                  | 16                                    | 35                                    |
| High            | 304                           | 345                  | 468                  | 14                                    | 35                                    |
| Medium          | 401                           | 405                  | 360                  | 20                                    | 57                                    |
| Low             | 450                           | 426                  | 374                  | 20                                    | 51                                    |
| Very low        | 274                           | 267                  | 256                  | 12                                    | 28                                    |

Hazard level
Equal-weighting scheme (E)
FCM method (F)
AHP method (A)
Ratio of changed level (E–F, %)
Ratio of changed level (E–A, %)


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

The identification efficiency of large-sized drained glacial lakes (area  $\geq 0.1 \text{ km}^2$ ) is high, reaching 90%, and no glacial lake was assessed as low- or very low-hazard based on our method (Fig. 4b). For smallsized lakes (area < 0.1 km2), the accuracy is not as high as that of large lakes (Fig. 4c). Small glacial lakes may have shortcomings in some assessment factors, such as lake perimeter. However, lake perimeter is a necessary factor and cannot be excluded because the characteristics of lakes represent potential triggers for GLOF. As a result, we reduced the influence of this factor by giving it a lower weight value. The extracted features of drained small-sized glacial lakes have relatively lower precision than those of large-sized lakes (Fig. 4b, c), which is constrained by the resolution of remote sensing data. These deficiencies commonly cause the GLOF susceptibility evaluation system to have poor tolerance to small-sized glacial lakes. Moreover, in the assessment validation, 31% of the drained small-sized lakes were determined to have medium-, low-, or very low-hazard levels. The GLOFs originating from small-sized glacial lakes do not have a reliable outburst date or even a time range; half of these events have been reported by Zheng et al. (2021a). The latest supplementary GLOF inventory contains many small-sized drained glacial lakes that have not been reported


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

Image /page/8/Figure/8 description: A flowchart illustrating a framework for the quantitative and qualitative evaluation of Glacial Lake Outburst Flood (GLOF) risk. The framework is divided into two parallel systems that feed into a central GLOF hazard assessment, which is then combined with vulnerability and exposure to determine overall risk. The top half shows the 'Quantitative evaluation system of GLOF risk,' which uses 'Remote sensing technologies' (like satellite images and DEM) to assess factors such as 'Mean slope of parent glacier' and 'Lake perimeter' on a scale of 0 to 1. The bottom half shows the 'Qualitative evaluation system of GLOF risk,' which uses 'Field investigation' (like bathymetric surveys and monitoring) to assess the same factors on a scale from 'Very low' to 'Very high.' Both systems feed into a 'Weighting scheme' that contributes to a central 'GLOF Hazard' model. This model visualizes contributing factors like 'Ice avalanches,' 'Dam instability,' and 'Rock fall.' On the right, a Venn diagram defines 'Risk' as the intersection of 'Hazard,' 'Vulnerability,' and 'Exposure.' The GLOF Hazard is then combined with 'Downstream Exposure or/and Vulnerability' in a risk matrix. This matrix plots GLOF Hazard on the y-axis (from Very low to Very high) against exposure/vulnerability on the x-axis, with the resulting risk level color-coded from green (low risk) to red (high risk).