Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics
Type: figure
Figure

Image /page/13/Figure/6 description: A figure displaying a series of six maps that illustrate the progression of a flood over time. The maps are arranged in a 3x2 grid, showing the flood extent at different time steps: 00:30 h, 01:00 h, 01:30 h, 02:00 h, 02:30 h, and 03:00 h. A legend in the top-left panel indicates the water depth with different colors: white for 4 m, light blue for 8 m, green for 16 m, and orange-brown for 32 m. Each map is accompanied by a small inset graph in the upper right corner, plotting Q versus T with a bell-shaped curve. A vertical dashed line on this curve moves from left to right across the panels, indicating the progression of the flood hydrograph. The flood starts small at 00:30 h, expands to its maximum extent and depth around 01:30 h (when the dashed line is at the peak of the curve), and then gradually recedes in the subsequent time steps.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics
Type: text

**Fig. 8.** Flood inundation extent at selected time steps for the two-dimensional hydrodynamic reconstruction of a palaeoGLOF from Dig Tsho, Nepal Himalaya using a Total Variation Diminishing solver (ISIS 2D). Topographic grid discretisations of 4 m, 8 m, 16 m and 32 m were used. The 4 m and 8 m results are broadly comparable for all time steps, whereas use of 16 m and 32 m grids results in the inundation of sizeable areas of the valley floor otherwise unaffected by GLOF passage across the finer grids. Dashed line on inset box shows corresponding time step on the breach outflow hydrograph (for reference, this is identical to the 'optimal' hydrograph shown in Fig. 7). For scale, tick marks on main figure spaced at 200 m intervals.

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4. Contemporary modelling challenges
4.2. Hydrodynamic modelling
4.2.2. Complex flow hydraulics


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem
Type: text

In combination with downstream topographic complexity, flow bulking through the erosion and entrainment of valley floor and morainic sediment often results in the breach outflow hydrograph being largely unrepresentative of downstream flood discharges (e.g. Cenderelli and Wohl, 2003; Kershaw et al., 2005). This is especially the case for flows which rapidly transform into sediment-laden hyperconcentrated or debris flows. In investigating the failure of Neoglacial moraine dams in the Cascade Range, Oregon, O'Connor et al. (2001) estimated that GLOFs transformed from clear water to debris flows within 0.5 km of their source, with the total volume of water released believed to have comprised >25 percent sediment. This is unsurprising, given the vast quantities of unconsolidated glacial and glacio-fluvial material which comprises proximal, deglaciated terrain.

The entrainment of significant volumes of in-channel and overbank sediment has implications for flow mobility. Fundamentally, an increase in flow volume as a result of the addition of solid material will in turn increase flow velocity. However, consequent increases in flow viscosity and cohesion may act to reduce mobility (Breien et al., 2008). Experimental work by Iverson et al. (2011) revealed that flow mobility decreases with the passage and entrainment of sediment over initially dry terrain. However, if the flow overrides and entrainments sediment from initially wet terrain, flow bulking, a subsequent increase in flow speed and momentum, and the development of large positive pore pressures will result (Iverson et al., 2011). Research in the Swiss Alps by Haeberli (1983) and Huggel et al. (2002a) revealed that minimum average slopes of 11° and 2-3° are required for the sustained runout of debris flows and clear water flows, respectively, reinforcing the largely demobilising effect of substantial sediment entrainment and transport. Consequently, the presence of complex channel topography will result in periodic, transitory fluid dynamics.

From a modelling perspective, simulation of the above is highly challenging. As well as neglecting the contribution of transversal velocity to the flow momentum balance, relatively simplistic MF or MSF flow routing algorithms and the majority of 1-D hydrodynamic models are incapable of simulating non-Newtonian flow rheology. This is a fundamental characteristic of sudden onset floods, and has been documented for GLOFs (e.g. Clague and Evans, 2000; O'Connor et al., 2001; Breien et al., 2008), landslide-dammed lake outburst floods (e.g. Capra and Macías, 2002; Dunning et al., 2006; Xu et al., 2012), jökulhlaups (e.g. Carrivick, 2006, 2007), mudflows (including volcanically-triggered lahars) incorporating high concentrations of volcaniclastic material (e.g. Manville, 2004; Carrivick et al., 2009, 2010), and hyperconcentrated or debris flows caused by a rock or ice avalanche (e.g. Hubbard et al., 2007).


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem
Type: text

O ' Connor et al . , 2001 ; Breien et al . , 2008 ) , landslide - dammed lake outburst floods ( e . g . Capra and Macías , 2002 ; Dunning et al . , 2006 ; Xu et al . , 2012 ) , jökulhlaups ( e . g . Carrivick , 2006 , 2007 ) , mudflows ( including volcanically - triggered lahars ) incorporating high concentrations of volcaniclastic material ( e . g . Manville , 2004 ; Carrivick et al . , 2009 , 2010 ) , and hyperconcentrated or debris flows caused by a rock or ice avalanche ( e . g . Hubbard et al . , 2007 ) .

Entrained sediment affects flow density, viscosity and turbulence. Short-lived changes in hydraulic regime, including the direct influence of sediment on flow hydraulics (e.g. Carrivick, 2010) and concurrent channel reworking, represent major challenges for numerical modelling. Consequently, these effects commonly remain largely overlooked and unquantified (Carrivick et al., 2011). Research into flow-sediment interactions in sudden onset floods with a dam failure origin has been necessarily confined to laboratory-scale investigation (e.g. Chen and Simons, 1979; Capart and Young, 1998; Nsom, 2002; Rushmer, 2007; Carrivick, 2010; Laurent et al., 2011), where issues of scaling and the confined nature of the flow should be considered (Carrivick et al., 2011). A key finding has been the discovery that, in the early stages of the flow event, and without sufficient time to reach capacity, floods where suspension is the predominant mode of sediment transport exhibit a 'dynamic equilibrium' regime. This is manifested geomorphologically as alternating zones of erosion and deposition (Carrivick et al., 2010; Procter et al., 2010), whereby initially high rates of erosion and sediment entrainment and transport rapidly give way to a depositional regime as the transport capacity declines as a function of energy losses through boundary friction and geomorphic work (Carrivick et al., 2011). The results of numerical modelling presented by Xia et al. (2010) suggest that, at least in the initial stages of outburst flood propagation, rates of bed evolution and scour may approach or equal flow depth.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem
Type: text

exhibit a ' dynamic equilibrium ' regime . This is manifested geomorphologically as alternating zones of erosion and deposition ( Carrivick et al . , 2010 ; Procter et al . , 2010 ) , whereby initially high rates of erosion and sediment entrainment and transport rapidly give way to a depositional regime as the transport capacity declines as a function of energy losses through boundary friction and geomorphic work ( Carrivick et al . , 2011 ) . The results of numerical modelling presented by Xia et al . ( 2010 ) suggest that , at least in the initial stages of outburst flood propagation , rates of bed evolution and scour may approach or equal flow depth .

Processes of fluid flow and sediment transport are inherently linked to bed evolution. The vast majority of models currently in widespread use are only capable of routing floods over an immobile bed (Liao et al., 2007; Xia et al., 2010). The 'mobile' bed problem describes the propensity for the channel bed to undergo continuous morphological change over the course of an event, including the large-scale mobility of the underlying substrate through tractive or rolling motion. Consequently, the physical manifestation of the channel 'bed', as classically defined, becomes blurred, as a continuum develops comprising fluid, often intensely sediment-laden flow, a zone of active bed mobility and bed-flow sediment exchange, and underlying immobile substrate. A number of models capable of solving the mobile bed problem for dam break floods are reviewed in Xia et al. (2010), and are not described in detail here, however, advances in our understanding of the processes that govern this behaviour have been largely experimental (e.g. Cao et al., 2004; Simpson and Castelltort, 2006; Leal et al., 2009; Xia et al., 2010; Zhang and Duan, 2011). Key findings include the observation that flows over a mobile bed attenuate faster (Carrivick et al., 2011), and peak flow depth was greater and occurs earlier than for those over an immobile bed (Leal et al., 2009).

GLOF modelling efforts that account for the presence of a mobile bed layer in their numerical solutions are rare. Analogous studies include the work of Xia et al. (2010), who document the development and application of a 2-D morphodynamic model capable of considering the influence of flow sediment concentration and bed evolution on flow propagation, as well as the work of Carrivick et al. (2010) who utilised a 2-D fluid dynamics model, Delft-3D, to reconstruct time-varying patterns of geomorphic change produced by a crater lake break-out lahar in New Zealand. Although the model is unable to simulate changes in flow rheology as a result of varying sediment concentration, the capability of Delft-3D to handle prior specification of zones of active (i.e. alluvial) and inactive (i.e. bedrock) channel substrate and actively update cell-specific sediment quantities renders it highly suitable for the investigation of intra- and post-event channel reworking (Carrivick et al., 2010), though has yet to be applied to the simulation of GLOFs. The above findings may have significant implications for the use of purely hydrodynamic models for GLOF reconstruction and prediction, which fail to incorporate solutions of sediment erosion and transport, including spatio-temporal variations in flow rheology and hydraulics and mobile bed evolution.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation
Type: text

The various methods presently thus far belong to the 'mesh-based' category of hydrodynamic models for open channel flow. Over the past decade or so, a range of particle-based, mesh-free methods for modelling complex fluid flows have been developed. Of these, the 'smoothed-particle hydrodynamics' (SPH) approach is perhaps the most popular (e.g. Bursik et al., 2003; Cleary and Prakash, 2004; Laigle et al., 2007; Pastor et al., 2009; Liu and Liu, 2010; Huang et al., 2012; Kao and Chang, 2012). SPH is a truly mesh-free, Lagrangian, particle-based method for solving the classic mechanics of fluid flow; typically the Navier–Stokes equations. Flows are discretised into a finite number of elements, or particles, possessing predefined geometric and material properties, and inter-particle contact laws (Cleary and Prakash, 2004). Particles are not truly physical, but, when grouped, are considered as a means of representing the fluid(s) under investigation (Laigle et al., 2007).

Fully 3-D fluid dynamics, including treatments of local flow density and vertical acceleration, are simulated through inter-particle interactions, which are determined by spatial weighting or smoothing functions (Liu and Liu, 2010). When interacting *en masse*, SPH methods have been demonstrated to reproduce complex, often transient phenomena of natural fluidal flows, including supercritical, subcritical, and

transcritical flow regimes, hydraulic jumps, shock wave propagation, wave reflection and multiple wave interactions, and splashing and flow fragmentation (Chang et al., 2011; Kao and Chang, 2012), even at relatively coarse resolutions (Cleary and Prakash, 2004). In addition, dynamic free-surface behaviour (e.g. flow superelevation) is handled with ease and without the requirement for the use of surface tracking methods (commonly required by purely mesh-based models). SPH is capable of simulating rheologically complex, non-Newtonian flows with ease, including flow solidification and fluidisation behaviour, making it ideal for application to natural flows which have been shown to demonstrate transient flow rheologies.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation
Type: text

and multiple wave interactions , and splashing and flow fragmentation ( Chang et al . , 2011 ; Kao and Chang , 2012 ) , even at relatively coarse resolutions ( Cleary and Prakash , 2004 ) . In addition , dynamic free - surface behaviour ( e . g . flow superelevation ) is handled with ease and without the requirement for the use of surface tracking methods ( commonly required by purely mesh - based models ) . SPH is capable of simulating rheologically complex , non - Newtonian flows with ease , including flow solidification and fluidisation behaviour , making it ideal for application to natural flows which have been shown to demonstrate transient flow rheologies .

The above capabilities render SPH highly suited for the investigation of sudden onset, dam-break floods. To date, the use of mesh-free methods has been largely confined mainly to experimental application against standard benchmarking problems (e.g. Chang et al., 2011), although a number of real-world case studies have been investigated. These have included the routing of dam break outburst floods over DEMs of varying topographic complexity (e.g. Chang et al., 2011) and modelling hypothetical landslide runout (Huang et al., 2011, 2012), including dynamic landslide-reservoir interactions (Pastor et al., 2009). The work of Pastor et al. (2009) is broadly analogous to the direct interaction of rock or ice avalanches with moraine-dammed lake complexes, and represents a holistic, multi-component approach to modelling all of the phenomena involved, including mass movement trajectory modelling, the interaction of a landslide or avalanche with the lake body and near- and far-field wave propagation. The addition of a treatment describing subsequent wave overtopping of a natural dam structure would complete the unified simulation of all initial stages of this particular trigger mechanism, and is an approach that merits further methodological exploration. The application of mesh-free methods to the simulation of GLOFs is a natural progression from the use of the depth-averaged 1-D and 2-D methods described above. Initial concerns over issues of model validation and computational efficiency (e.g. Richards et al., 2004) appear to have been overcome in some instances, with a number of SPH codes have been shown to perform with favourable computational efficiency and reliability (e.g. Chang et al., 2011). However, examples of SPH application to 'large-scale' simulations such as the routing of dam break outburst floods through lengthy, topographically complex catchments, such as those found in high mountain regions, are currently lacking.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.3. Implications for Glacial Lake Outburst Flood modelling
Type: text

The strengths and inadequacies of the models described above must be considered by workers attempting to reconstruct palaeoGLOFs or simulate future, hypothetical outburst events. Ultimately, the choice of modelling approach will be dictated by logistical and financial constraints. From a practical perspective, a relatively simple, purely rasterand DEM-based approach will be more appealing to workers tasked with the production of first-order assessment of the hazard posed by GLOFs at the regional scale (e.g. Huggel et al., 2003; Allen et al., 2009) and particularly in remote regions where high-resolution terrain data are currently unavailable. Such models represent a low-cost and computationally-undemanding method for modelling the downstream propagation of GLOFs, favour the use of simple, empirically-derived estimations of  $Q_p$  and  $T_p$  (e.g. Table 3), and are suitable for use in combination with coarse-scale terrain data for 'first-pass' hazard assessments (e.g. Huggel et al., 2003).

Increasing model complexity is traditionally associated with increases in initial start-up costs. Although also relatively computationally undemanding, more advanced models typically require a corresponding increase in user expertise and data requirements for effective parameterisation and implementation. Many input parameters, particularly material characteristics of the moraine dam, are only obtainable through labour-intensive and logistically challenging field investigation (e.g., Xin et al., 2008; Worni et al., 2012), although the well-documented

heterogeneous nature of moraine dams (Benn and Owen, 2002; Bennett and Glasser, 2009; Hambrey et al., 2009) may hinder a single, definitive quantification of their material properties at all. Theoretically, improved representation of the varied and complex physical characteristics of breaching and flood propagation through the application of, for example, analytical or parametric dam breach models and 1-D depth-averaged hydrodynamic solvers are likely to improve the accuracy and robustness of spatial and temporal predictions of inundation and GLOF dynamics in some instances. However, the lack of a consideration of key dynamical aspects of GLOF behaviour, such as turbulent, often super- or transcritical flows, flow superelevation at channel bends, and transitory flow rheologies still serves to limit the veracity and precision of these approaches.

Higher-order modelling approaches, including the use of nextgeneration dam breach models and 2-D, 3-D, or mesh-free hydrodynamic models currently represent the most advanced, physicallyrobust approaches to GLOF modelling. Traditionally, the use of such models has required a significant degree of user expertise, though accessibility has steadily improved in recent years with the increasing use of Graphical User Interfaces (GUI) and GIS support to simplify data input, manipulation and visualization. However, this is offset somewhat by the high initial start-up costs of many codes, the majority of which are distributed as proprietary software packages. In addition, significant computational requirements render them largely unsuitable for the investigation of input parameter uncertainty and equifinality in model output through the use of Monte Carlo methods, for example. For this purpose, simpler models are at a distinct advantage until the affordability and use of high-speed parallel computing systems or highspecification desktop computers becomes more commonplace, or models become more computationally efficient.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 4. Contemporary modelling challenges > 4.4. Challenges posed by contemporary and future climatic change
Type: text

Rates of glacial recession are predicted to increase in the coming decades on a scale without any known historical precedent (IPCC, 2013). Consequently, and in addition to the challenges discussed above, we will be increasingly faced with challenges associated with modelling GLOF events and glacial and geomorphological conditions to which our range of empirical observations may no longer apply.

One of the major challenges is predicting where future glacial lakes will form. On debris-covered glaciers, a specific set of glaciological conditions which predispose a glacier to develop a network of supraglacial ponds, and ultimately a fully formed moraine-dammed glacial lake, have been identified (Reynolds, 2000; Quincey et al., 2007) and form a robust basis for identifying those glaciers which are likely to develop glacial lakes in the future. Similarly, qualitative strategies have been developed for identifying the location of subglacial overdeepenings through analysis of the characteristics of glacier surface topography, which, following glacier retreat, may serve as the focal point for meltwater collection and the development of a proglacial lake (Frey et al., 2010).

However, the rate of observed and predicted future increases in air temperature and variations in the precipitation regime of many highmountain regions are such that it remains a challenge to predict the pace of future cryospheric change, and how it relates specifically to the development of the GLOF hazard at regional and local scales. Anticipating the celerity with which various glaciological processes will operate, and the timescale required for key physical and climatological thresholds to be exceeded are hindrances to our prediction of the evolution of the GLOF hazard. A prudent approach for assessing the GLOF hazard posed by extant or future moraine-dammed lakes should be based on the regular re-analysis of glaciological, geomorphological and climatological data, with a view to identifying contemporary and future GLOF process chains, and subsequently informing the development or parameterisation of empirical and numerical dam-breach and hydrodynamic models.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: text

An appreciable degree of uncertainty surrounds the establishment of initial boundary conditions and input parameters for both reconstructive and predictive dam-breach and GLOF modelling. Few studies have undertaken detailed sensitivity analyses into the significance of this uncertainty on, for example, the form of the breach hydrograph, and subsequent implications for the assessment of the spatio-temporal evolution of the GLOF flood wave(s). In many instances, it may be the case that uncertainty associated with model parameterisation results in greater variability in model output than differences attributable to the use of, for example, 1-D, 2-D or 3-D hydrodynamic solvers. Laboratory-scale experiments (Balmforth et al., 2009) indicate that vastly different final breach widths can be reproduced from nearidentical initial breach geometries and compositions, whilst Awal et al. (2010) reported that the magnitude and timing of overtopping waves were dependent on dam geometry (specifically proximal face angle), material composition, and additional factors including ice- and rockavalanche dimensions. Similarly, in Capart's (2013) analytical dambreaching solution, peak discharge was discovered to be sensitive to the lake surface area, and highly sensitive to the initial angle of the upstream dam face, effectively highlighting the importance of quantifying such geometric descriptors of the moraine dam as precisely as possible prior to the specification of initial model boundary conditions.

With specific reference to numerical dam-breach modelling, significant uncertainty surrounds the establishment of initial conditions (e.g. dam geometry, reservoir bathymetry and hypsometry), the parameterisation of material characteristics (e.g. grain size distribution data, porosity, density, cohesion, internal angles of friction) and the establishment of suitable computational constraints (e.g. model time step and grid discretisation) (Table 4). Whilst the construction of high-resolution

DTMs of moraine dam structures (Westoby et al., 2012), their attendant lake basins (e.g. Robertson et al., 2012; Yao et al., 2012), and downstream valley topography (e.g. Bremer and Sass, 2012) using photogrammetric, laser scanning, or bathymetric surveying techniques has facilitated the extraction of metric data pertaining to, and subsequent accurate characterisation of moraine and lake geometry and down-valley topography, equally robust quantification of the aforementioned material characteristics is typically achievable only through labour-intensive and logistically-demanding fieldwork (e.g. Hanson and Cook, 2004; Osti et al., 2011; Worni et al., 2012). Furthermore, the compositional heterogeneity of moraine dams further complicates the use of a single, 'all-encompassing' material parameter ensemble for applied dam-breach simulation in a range of settings.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: text

2012 ) using photogrammetric , laser scanning , or bathymetric surveying techniques has facilitated the extraction of metric data pertaining to , and subsequent accurate characterisation of moraine and lake geometry and down - valley topography , equally robust quantification of the aforementioned material characteristics is typically achievable only through labour - intensive and logistically - demanding fieldwork ( e . g . Hanson and Cook , 2004 ; Osti et al . , 2011 ; Worni et al . , 2012 ) . Furthermore , the compositional heterogeneity of moraine dams further complicates the use of a single , ' all - encompassing ' material parameter ensemble for applied dam - breach simulation in a range of settings .

Variability in hydrodynamic model output may be attributed to model dimensionality (e.g. Alho and Aaltonen, 2008; Bohorquez and Darby, 2008), spatial resolution and quality (e.g. Sanders, 2007; Huggel et al., 2008), the spacing of successive cross-sections that represent downstream topography (e.g. Castellarin et al., 2009), as well as uncertainty surrounding the parameterisation of channel and floodplain roughness coefficients (Wohl, 1998; Hall et al., 2005, Pappenberger et al., 2005), input boundary condition data (e.g. Pappenberger et al., 2006) and stage-discharge relationships (e.g. Di Baldassarre and Claps, 2011). For event-specific flood reconstruction, systematic calibration of roughness and friction coefficients to produce 'best-fit', or 'optimal' model parameters is standard practice (e.g. Kidson et al., 2006; Cao and Carling, 2002), and may serve to inform their choice for modelling hypothetical future events (e.g. Horritt and Bates, 2002). However, the application of these calibration techniques for reconstructive and predictive GLOF modelling is highly questionable, not least because of the uniqueness of each outburst event and the appreciable unlikelihood of more than one GLOF originating from a given moraine-dammed lake. Standard


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: table
Table: Table 4 Sources of uncertainty in the GLOF model chain, the nature of their impact and practiced or proposed measures for their quantification or assessment.

|                                   | Source of uncertainty in model input (*) or output (†)         | Nature of impact or affected output variables                                                                                | Solution or measure(s) of addressing                                                                                |
|-----------------------------------|-------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Trigger                    | * Source and timing                                               | Dimensions and dynamics of overtopping waves (e.g. ice- or rock avalanche trigger); may determine whether runaway breach development is triggered                                         | Desk- or field-based glacial hazard assessment of moraine and surrounding topography; analysis of multi-temporal datasets required to quantify evolution of hazard (e.g. Reynolds Geo-Sciences, 2003; Huggel et al., 2004; Quincey et al., 2005). |
| Dam-breach modelling   | * Moraine dam geometry                                            | Dam height, crest width and distal and proximal dam face slope influence style and rate of breach development                                                                                | Fine spatial resolution characterisation and quantification of moraine-dam geometry (e.g. Westoby et al., 2012). Ideally undertaken in situ                                                                                |
|                                   | * Lake bathymetry and stage-volume relationships               | Determines reservoir pressure head and rate of inflow to breach                                                                                | Bathymetric surveying (extant lakes); photogrammetric or 'data-ready' surveying (e.g. TLS) of drained basins                                                                                |
|                                   | * Initial conditions (e.g. dam freeboard, spillway dimensions) | Influences impact of overtopping waves (freeboard); accommodation of escaping floodwaters (spillway dimensions); may ultimately determine whether runaway breach development triggered | In situ observation or analysis of fine resolution aerial or space-borne imagery                                                                                |
|                                   | * Material characteristics                                        | Rate of breach development                                                                                | In situ quantification (preferred); literature-guided parameterisation; probabilistic sampling of parameter space                                                                                |
|                                   | * Presence of buried ice in moraine                            | Rate of breach development; interaction of massive ice core with progression of breach development remains largely unquantified (e.g. Worni et al., 2012)                                 | Geophysical inspection of intact moraine dam; visual (and geophysical) analysis of breached structures.                                                                                |
| Hydrodynamic modelling | † Grid discretisation                                             | Decreasing grid resolution results in effective 'smoothing' of small-scale topographic elements; in the first instance determines direction of flow                                       | Undertake detailed sensitivity analysis to quantify impact on inundation extents and wetting-front travel times; attempt to validate results where possible (v. challenging)                                                                            |
|                                   | * Channel and valley-floor roughness coefficient(s)            | Influences effectiveness of flow conveyance, influence more important at low flows; exerts little control over flow dynamics at high flow stage and velocity                              | Literature- and field investigation-guided classification; investigate and quantify any functional relationship with topographic grid resolution                                                                                |
|                                   | † Complex flow rheology (model physicality)                    | Will influence flow velocities (and therefore wetting-front travel-time) and erosive potential of the flow                                                                                | Use of advanced hydrodynamic solvers capable of simulating sediment transport, transient flow rheology and mobile bed evolution                                                                                |
|                                   | † Model dimensionality                                            | 'Simple' codes incapable of simulating lateral transfer of flow momentum and mass. Complex flow dynamics (flow superelevation, development of flow recirculation zones etc)               | If detailed GLOF hazard assessment required, use of advanced, 2-D solvers advocated. Raster-based or 1-D solvers suitable for 'first-pass' hazard assessment                                                                                |
|                                   | † Model coupling                                                  | Model de-coupling results in loss of flow momentum and mass transfer. Importance for determining near-field flow velocities and reworking                                                 | Use fully-coupled models to simulate breach development and cascading of floodwaters (and morainic material) from lake basin to the floodplain.                                                                                |

Source of uncertainty in model input ( ) or output (†)


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: text

calibration techniques, such as the use of palaeo-stage indicators or slackwater deposits (e.g. Cenderelli and Wohl, 2003; Kershaw et al., 2005; Bohorquez and Darby, 2008) represent one possible approach to flow calibration, but are reach-specific and typically reflect only the maximum flood stage and corresponding inundation extent. Similarly, delineation of channel- and valley-floor regions subject to GLOF passage represents a straightforward method of establishing maximum flood extents. However, a widespread lack of pre-GLOF topographic datasets (for GLOF routing), combined with the inability of many widely-used hydrodynamic codes to simulate complex and highly transient flow rheologies and sediment transport dynamics complicates the use of maximum flood extents to accurately constrain palaeoGLOF dynamics.

A potential solution to the above is the adoption of probabilistic approaches to GLOF reconstruction, which embrace uncertainty in the model chain using stochastic sampling techniques and are capable of quantifying the degree of equifinality in model output. Such methods have appreciated widespread use in the distributed hydrological modelling literature (e.g. Beven and Binley, 1992; Kuczera and Parent, 1998; Lamb et al., 1998; Blazkova and Beven, 2004; Franz and Hogue, 2011), and, with limited modification, would appear to be equally suited to dam-breach reconstruction (Fig. 9) and GLOF inundation mapping (Fig. 10). As an example, quantifying the performance of dam-breach models according to their ability to reproduce the geometry of moraine breaches caused by palaeoGLOFs represents one solution to being able to identify the parameter ranges which may be deemed acceptable, or 'behavioural'. However, significant complications arise with the elucidation of a 'universal' range of input parameters for application to predictive GLOF modelling efforts given the highly site-specific nature of their derivation, and the challenges associated with identifying and reconstructing a large enough sample of GLOFs in a specific region from which to undertake a representative statistical analysis of the key modelling parameters (and their ranges) which exert the dominant influence over breach development and GLOF propagation. Specifically, and in the absence of validation of their suitability to reconstructing observed breach morphologies of other breached moraine dams, a range of tightly-constrained material characteristics that may be capable of reproducing observed breach morphologies at one site may not be readily applicable to accurately predicting the range of breach hydrographs that might arise from the failure of another. Nevertheless, the subsequent production of probability-weighted maps of inundation are arguably of more value for flood hazard and risk assessment, and the use of uncertainty-based techniques for both reconstructive and predictive GLOF modelling merits further consideration.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: figure
Figure

Image /page/17/Figure/4 description: A black and white line graph plots Q (in m³ s⁻¹) on the y-axis against T (in h) on the x-axis. The y-axis ranges from 0 to 2500 in increments of 500. The x-axis ranges from 0 to 6 in increments of 1. A solid black line starts at the origin (0,0), rises to a peak of approximately 1400 at T=2.6, and returns to zero around T=3.5. This black line is enclosed within a larger, shaded gray area which represents a range of values. The shaded area also starts at the origin, rises to a jagged peak of about 2000 around T=1.8, and then gradually decreases, extending to approximately T=5.5 before returning to zero.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: text

**Fig. 9.** Dam breach hydrographs for a reconstruction of the Dig Tsho failure. Solid black line corresponds to the 'optimal' breach outflow hydrograph, with performance conditioned on the ability of the simulation to reproduce observed geometric descriptors of post-GLOF breach morphology (including final breach depth and centreline elevation profile or slope). In contrast, grey shading represents the hydrograph envelope of all acceptable, or 'behavioural' failure simulations, reflecting a range of input parameter combinations.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: figure
Figure

Image /page/17/Figure/6 description: The image contains three maps labeled A, B, and C, which visualize data for a river or floodplain. Map A shows the "Inundation probability," with a color scale ranging from 0% (blue) to 100% (red). A blue arrow indicates the direction of flow. Map B displays inundation extents for the 5th percentile (white), 50th percentile (gray), and 95th percentile (black). Map C illustrates the "Water height (m)," with a color scale from 0 (light blue) to 13 (dark blue).


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 5. Considering uncertainty in the GLOF model chain
Type: text

**Fig. 10.** Communicating uncertainty in reconstructive mapping of the Dig Tsho GLOF. (A) inundation probability, calculated as the percentage of 'behavioural' simulations which inundate a given cell; (B) percentile-specific inundation extent, and; (C) an example of inundation extent and flow depth for the 95th percentile model. All data extracted at 02:00 h after breach initiation. For scale, tick marks spaced at 200 m intervals. Blue arrow in **A** indicates direction of flow.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 6. Conclusions
Type: text

High-magnitude outburst floods from moraine-impounded glacial lakes represent a highly-complex, multi-stage catastrophic phenomenon, capable of accomplishing significant geomorphologic reworking of channel- and valley-floor floodplain environments and posing a very real threat to infrastructure and the safety and livelihoods of communities. This review has provided a comprehensive overview of the individual 'components' that constitute a GLOF event, namely: triggering mechanism(s), moraine dam breaching, and routing of the escaping floodwaters, and the modelling approaches typically used to reconstruct or predict GLOF dynamics.

Progress in understanding of the main factors controlling moraine dam breach dynamics has been relatively slow since the emergence of the first numerical models well over two decades ago. Empirical models,

originally developed for application to the failure of artificially-constructed dams and levees, commonly appear in the geosciences literature and should not be discounted as useful tools for the production of rapid, first-pass hazard assessments. The recent advent of fully physically based advanced numerical dam-breach models heralds a significant step forward in our capability to produce robust, physically based estimates of breach outflow dynamics, including quantification of sediment evacuation rates.

The requirement to model accurately GLOF hydrodynamics at both the source and downstream is central to the production of detailed GLOF inundation maps and hazard assessment. However, the high computational burden associated with the execution of advanced 2-D and 3-D hydrodynamic codes, coupled with the relative dearth of fine-resolution topographic data required for detailed discretisation of channel and floodplain domains are still major hindrances to the widespread adoption of these models for the production of detailed hazard assessments of anticipated GLOF events.

In conclusion, we highlight the following as key modelling challenges and potential areas of future GLOF research.


Document: Modelling outburst floods from moraine-dammed glacial lakes
Section: 6. Conclusions
Type: text

dynamics , including quantification of sediment evacuation rates . The requirement to model accurately GLOF hydrodynamics at both the source and downstream is central to the production of detailed GLOF inundation maps and hazard assessment . However , the high computational burden associated with the execution of advanced 2 - D and 3 - D hydrodynamic codes , coupled with the relative dearth of fine - resolution topographic data required for detailed discretisation of channel and floodplain domains are still major hindrances to the widespread adoption of these models for the production of detailed hazard assessments of anticipated GLOF events . In conclusion , we highlight the following as key modelling challenges and potential areas of future GLOF research .

- The development of dam breach models capable of actively simulating dynamic wave behaviour in a glacial lake, such as those caused by landsliding or avalanching, and accounting for the passage of single of multiple overtopping waves in breach initiation and development;
- Further experimental and numerical investigation into the interactions between seismic activity and the geotechnical performance of moraine dams, specifically its significance in initiating dam settlement or massive structural failure, and the evolution of breach initiation via piping and intra-morainal seepage;
- The ability of dam breach models to use complex, real-world geometric data as input, such as irregular crest topography and arcuate dam planform, providing a true reflection of observed moraine dam geometry;
- Quantification of the effects of spatially continuous or discontinuous permafrost in the dam structure, including massive bodies of relict glacial ice, on patterns of breach erosion, and the development of numerical models capable of accounting for often complex, user-defined spatial variations in ice content.
- Improvements in inter-model coupling, including the preservation of breach outflow hydrodynamics, the passage of multiple overtopping flood waves or breach-flow 'pulses', and temporal variations in sediment evacuation rates from the enlarging breach;
- Quantification of the effects of model dimensionality on reconstructed or predicted flow hydraulics and patterns of inundation and geomorphologic reworking of in-channel and overbank environments, including implications for the construction of downstream hazard assessments;
- Wherever possible, the collection of dam material property and geometric data in situ, in order to constrain dam-breach model input parameters as rigorously as possible, and to reduce the extent of the uncertainty associated with numerical dam-breach model parameterisation;
- The acquisition and use of high-resolution topographic datasets, in order to make the effective use of advanced hydrodynamic models viable, including, where possible, the attainment of pre- and post-GLOF DEMs to aid quantitative analysis and interpretation of net erosion and deposition;
- The adoption of models capable of simulating spatio-temporal patterns of transitory flow rheology, including effects of flow hydraulics and capacity to perform geomorphic work, and the use of models incorporating solutions of mobile bed evolution and subsequent impacts for GLOF propagation.


Document: Monitoring earths glacial lakes from space with machine learning
Section: ABSTRACT
Type: text

The rapid worldwide formation and expansion of glacial lakes has increased the likelihood of glacial lake outburst floods, threatening lives and infrastructure, particularly in vulnerable mountain communities. Given the rapid increase in the popularity of artificial intelligence methods for remote sensing of glacial lakes, a comprehensive review is essential. We survey a decade (2015-2024) of research on glacial lake monitoring from space, with a focus on classical machine learning and deep learning approaches. We identify key trends, research gaps, and best practices for future studies. Most studies rely on optical imagery, especially Landsat-8 and Sentinel-2, while Sentinel-1 serves as a complementary radar source. However, monitoring glacial lakes in mountainous regions remains a challenge on cloudy days due to the limitations of radar and the unusability of optical data. Deep learning, particularly U-Net and DeepLab derivatives, dominates learning-based glacial lake studies but remains computationally demanding. Critical challenges involve balancing performance gains against trade-offs in data availability, computational cost, and model transferability. Geographic and methodological gaps, especially in regions experiencing rapid lake growth, underscore the need for broader spatial coverage and improved spatiotemporal model generalization. Moreover, transitioning from a focus on static seasonal mapping to frequent multi-temporal monitoring is beneficial for understanding glacial lake evolution and outburst flood hazards. Adapting emerging deep learning architectures to integrate multispectral, hyperspectral, and radar data could enhance glacial lake detection capabilities. Furthermore, thorough intermethod comparisons, benchmarking with rigorous evaluation metrics, and open-sourcing datasets and code would facilitate robust, large-scale glacial lake monitoring efforts.

data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md


Document: Monitoring earths glacial lakes from space with machine learning
Section: 1. Introduction
Type: text

Approximately 1.74% of Earth's water is stored in glaciers, ice caps, and permanent snow cover, accounting for about 68.7% of global freshwater (Shiklomanov, 1993). Glacial lakes are water bodies formed by the accumulation of meltwater in depressions created by glacier retreat, including those dammed by ice, moraines, or other glacially deposited materials (Costa and Schuster, 1987). Climate change-driven glacier mass loss is accelerating the formation and expansion of glacial lakes (NASA Decadal Survey, 2007; King et al., 2019; Shugar et al., 2020)

Globally, more than 110,000 glacial lakes have been documented, with a total mapped area of approximately 15,000 km², based on studies conducted between 2006 and 2020 (Zhang et al., 2024). Glacial lakes are integral to regional freshwater systems, storing meltwater and influencing hydrological cycles (Mingwei et al., 2025). At the same time, due to their dynamic nature, some of these lakes are sources

of Glacial Lake Outburst Flood (GLOF)s, endangering lives and critical infrastructure worldwide (Emmer et al., 2022; Taylor et al., 2023). Over 3000 GLOFs were recorded from the year 850 to 2022, while the total glacial lake area increased by approximately 22% per decade between 1990 and 2020 (Zhang et al., 2024).

Multi-temporal monitoring of glacial lakes is beneficial for assessing GLOF hazards, developing early warning systems for the protection of downstream communities, and improving water resource management (Rinzin et al., 2023; Ahmed, 2024; Emmer, 2024). However, implementing effective and scalable monitoring strategies remains challenging due to the inherent variability in glacial lake characteristics, including differences in size, shape, depth, and turbidity. Many of these lakes are small (area <0.1 km²) and remain frozen for several months, exhibiting distinctive geomorphologic traits (Carrivick and

<sup>\*</sup> Correspondence to: 4800 Oak Grove Drive, M/S 300-331, Pasadena, CA 91109, USA. E-mail address: manu.tom@jpl.nasa.gov (M. Tom).

Tweed, 2013; Costa and Schuster, 1987; Clague and Evans, 2000). Beyond these physical variations, the diversity of lake formation environments makes systematic observation difficult. These include proglacial (rock-dammed, moraine-dammed, ice-dammed), ice-marginal (glacier-blocked), supraglacial, and subglacial settings (Costa and Schuster, 1987).

In situ measurements of glacial lakes capture a wide range of parameters, including lake water level, area changes, bathymetry, temperature, precipitation, and ice cover, among others (Fujita et al., 2009; Tedesco and Steiner, 2011; Sharma et al., 2018). These measurements are often sparse, as field campaigns to glacial lakes are challenging due to their remote, high-altitude, or high-latitude locations adjacent to glaciers (Treichler et al., 2019). Consequently, satellite and airborne observations often serve as complementary, and in some cases, primary methods for large-scale and continuous monitoring. These remote sensing techniques specifically focus on the geographic extent of surface lakes. However, they provide critical insights where direct measurements are limited (Huang et al., 2018).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 1. Introduction
Type: text

et al . , 2009 ; Tedesco and Steiner , 2011 ; Sharma et al . , 2018 ) . These measurements are often sparse , as field campaigns to glacial lakes are challenging due to their remote , high - altitude , or high - latitude locations adjacent to glaciers ( Treichler et al . , 2019 ) . Consequently , satellite and airborne observations often serve as complementary , and in some cases , primary methods for large - scale and continuous monitoring . These remote sensing techniques specifically focus on the geographic extent of surface lakes . However , they provide critical insights where direct measurements are limited ( Huang et al . , 2018 ) .

Several approaches exist for mapping glacial lake extent using remote sensing. An example is Geographic Information System (GIS)-assisted manual lake boundary delineation and inventory creation methods. These methods leverage human expertise to interpret data and define boundaries from satellite observations (Ukita et al., 2011; Raj and Kumar, 2016; Petrov et al., 2017; Senese et al., 2018, etc.). However, such manual approaches are feasible only on a small scale and/or for a few time steps due to their labor intensive nature. In contrast, automated remote sensing methods offer greater scalability and enable the fusion of information from multiple data sources—including optical and radar imagery.

There has been a notable rise in the popularity of classical ML and DL methods for land cover classification applications over the past decade. These learning-based methods, in conjunction with remote sensing, have achieved significant breakthroughs across various subfields of geoscience (Karpatne et al., 2019; Camps-Valls et al., 2021; Ge et al., 2022). Such advancements also span hydrosphere (Sit et al., 2020) and cryosphere (Liu, 2021) monitoring.

Unlike traditional non-ML approaches, data-driven ML algorithms effectively learn intricate patterns from representative remote sensing datasets, enabling more accurate and efficient analysis (Maxwell et al., 2018). DL methods, while demanding in terms of training data requirements, automatically extract features – such as texture and spatial relationships – directly from data. They learn hierarchical representations and outperform classical ML methods in many fields of Earth sciences (Tuia et al., 2024; Taylor et al., 2021). However, these data-driven methods also have limitations. These include high data and computational demands, large model sizes that hinder deployment, challenges in geographical transferability, and debatable interpretability and explainability.

The potential value and widespread adoption of diverse learningbased methods underscore the need for a detailed review. A comprehensive evaluation of existing approaches, along with a synthesis of their strengths and limitations, the establishment of best practices, and the identification of key research gaps, is essential.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 1. Introduction
Type: text

They learn hierarchical representations and outperform classical ML methods in many fields of Earth sciences ( Tuia et al . , 2024 ; Taylor et al . , 2021 ) . However , these data - driven methods also have limitations . These include high data and computational demands , large model sizes that hinder deployment , challenges in geographical transferability , and debatable interpretability and explainability . The potential value and widespread adoption of diverse learningbased methods underscore the need for a detailed review . A comprehensive evaluation of existing approaches , along with a synthesis of their strengths and limitations , the establishment of best practices , and the identification of key research gaps , is essential .

Though numerous research papers in the past decade have applied ML/DL to map and monitor glacial lakes, a dedicated review remains absent. Some surveys exist on related topics—such as ML/DL for water body detection (Gautam and Singhai, 2024) and lake-water level fluctuation forecasting (Sannasi Chakravarthy et al., 2022). However, none specifically focus on remote sensing of glacial lakes. Similarly, reviews on non-ML approaches – such as bibliometric analysis of glacial lake identification (Zhengquan et al., 2023), frozen lake extraction from optical data (Jawak et al., 2015), and remote sensing applications in the mountain cryosphere (Taylor et al., 2021) – offer valuable insights. However, they do not address Artificial Intelligence (AI)-driven methodologies. The paper is structured as follows: Section 2 discusses spatiotemporal aspects in learning-based glacial lake

studies, while Section 3 provides an overview of the remote sensing data used. Section 4 explores ML/DL methodologies for studying proglacial, ice-marginal, and supraglacial lakes from space, followed by Section 5, which presents key challenges and limitations. Finally, Section 6 concludes with recommendations for future research.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring
Type: text

The first step in a glacial lake study is mapping. This involves creating an inventory or map of lakes in a study region using an underlying ML/DL model, typically based on satellite data from a single point in time. Mapping is essential for providing a snapshot of glacial lake extents and serving as a proof-of-concept. However, it alone cannot capture seasonal fluctuations, indicate long-term trends, or assess GLOF hazard potential.

The next critical step is monitoring, which builds on repeated mapping to systematically track glacial lake evolution over time. Learning-based studies such as Banerjee and Bhuiyan (2023), Lutz et al. (2023), and Sharma and Prakash (2023), among others, have reported monitoring efforts that extend beyond one-time mapping.

However, most studies (e.g., He et al., 2021; Wang et al., 2021) focus solely on mapping, prioritizing static lake characterization over understanding temporal dynamics. Effective monitoring requires repeated observations while balancing trade-offs between update frequency and various challenges. These challenges include data gaps due to cloud cover and inconsistent data acquisition, seasonal variability, and increased computational demands. A key constraint is the limited availability of suitable multi-temporal satellite imagery. This is especially challenging in high-mountain regions where frequent cloud cover and the lack of usable optical images (in winter) severely restrict observation windows.

Notably, repeated mapping – conducted as frequently as observation conditions allow – differs from multi-temporal monitoring. The latter involves a systematic analysis of temporal patterns, often designed to capture intra-annual variations and seasonal dynamics. To ensure the accuracy and reliability of new glacial lake extent products derived from learning-based algorithms, rigorous evaluation of multi-temporal monitoring outcomes is recommended before operational deployment.

Environmental factors such as cloud cover, haze, ice and snow cover, fog, evaporation, and surface reflectance - also referred to as sun glint or sun glitter - vary significantly over time in glaciated regions. These variations pose challenges for consistent glacial lake monitoring across all seasons (Mölg and Hardy, 2004; Hock, 2005). Frozen lakes, particularly supraglacial lakes or those in direct contact with glaciers or covered by snow, are difficult to distinguish spectrally from surrounding ice in optical satellite imagery. In some cases, spectral similarity makes detection nearly impossible during colder months. ML/DL methods can partially overcome this by learning spatial and contextual patterns beyond raw spectral values. Convolutional Neural Network (CNN)s extract multi-scale features such as texture, edges, and shape, while attention-based models use broader spatial context to improve classification. These approaches are effective when frozen lakes have distinct morphological boundaries. However, when both spectral and spatial cues are weak - such as under uniform snow cover - performance remains limited, regardless of model complexity.

Seasonal surface variations also affect radar-based glacial lake detection. Flat water surfaces typically exhibit low backscatter (Bauer-Marschallinger et al., 2021). Wind or thin floating ice can roughen water surfaces, increasing backscatter (Freilich and Vanhoff, 2003; Shaposhnikova et al., 2023). Snow has higher backscatter due to its heterogeneous structure (Rott, 1984). Ideally, the dataset used to train an ML/DL model should be representative of these variations to ensure robust detection across diverse environmental conditions.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring
Type: table
Table: Table 1 Examples of learning-based glacial lake studies conducted during specific seasons with favorable observation conditions.

| Publication             | Season             | Primary study site           |
|-------------------------|--------------------|------------------------------|
| Dirscherl et al. (2020) | January-February   | Antarctica                   |
| Dirscherl et al. (2021) | December-February  | Antarctica                   |
| Yuan et al. (2020)      | May-September      | Southwest Greenland          |
| Qayyum et al. (2020)    | May-November       | Hind Kush Karakoram Himalaya |
| Wang et al. (2021)      | September-November | Himalayas                    |
| Xu et al. (2023)        | Summer             | Eastern Himalaya             |
| Zhao et al. (2023)      | July-November      | High Mountain Asia           |
| Tang et al. (2024)      | June-November      | Third Pole Region            |


Document: Monitoring earths glacial lakes from space with machine learning
Section: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring
Type: text

To minimize the impact of seasonal challenges, learning-based glacial lake studies often target months with optimal data quality. For example, Wu et al. (2020) avoided July to September due to frequent cloud cover (southeastern Tibet), which hinders optical remote sensing. They also excluded January, when frozen conditions and lake shrinkage complicate the detection of lakes. As a result, they selected October and November, when melting slows, lake extents stabilize, and cloud cover decreases. Further examples of learning-based studies that targeted periods with favorable observation conditions are presented in Table 1.

Strategies constrained by seasonal conditions improve data quality, reduce cloud interference, and capture stable lake conditions. However, they neglect temporal variability and the associated risk potential, which are essential in the context of non-stationary lakes that form during the melt season or during glacier surges. To address these limitations, future studies should expand training datasets with representative reference data spanning multiple seasons and develop models that learn key intra-annual variations. Multi-sensor fusion using all available and suitable satellite sources, including complementary sensors such as Synthetic Aperture Radar (SAR), can improve temporal coverage. SAR examples include Sentinel-1 (S1) [C-band, freely available], ICEYE-X1 (X-band, commercial). Augmenting the training data set or using generative models to simulate cloud penetrating synthetic imagery may also help.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.2. Glacial lake products: types and resolutions
Type: text

ML/DL-based glacial lake products differ in type, temporal resolution, and spatial resolution. Maximum Lake Extent (MLE) products, such as those by Wang and Sugiyama (2024), delineate the largest observed lake boundary, making them valuable for GLOF hazard assessment and long-term trend analysis. While straightforward to interpret, MLE overlooks short-term lake fluctuations. In contrast, Per-Pixel Classification (PPC) products, used in studies like Yuan et al. (2020), He et al. (2021) and Thomas et al. (2023), etc., capture precise water extents. When applied over time, they support the analysis of seasonal and interannual variations. Some studies, such as Xu et al. (2023), integrate both PPC and per-lake classification.

Regarding temporal resolution, Dirscherl et al. (2021) for example, produced monthly products, while Yuan et al. (2020) generated yearly products. Higher temporal resolutions (e.g., weekly or monthly) enhance the detection of seasonal changes. Conversely, lower resolutions (e.g., yearly) prioritize computational efficiency and broader trend analysis. The required temporal resolution should align with the study objective—whether focused on long-term trend analysis or short-term event monitoring, such as rapid GLOF response.

Spatial resolution also varies based on input data. High-resolution ( $\leq$ 3 m) products (e.g., Siddique et al., 2023; Thomas et al., 2023) are essential for mapping and monitoring small glacial lakes and resolving dynamic and detailed lake boundaries. Low-resolution ( $\geq$ 30 m) products (e.g., Zhao et al., 2023; Yuan et al., 2020) are efficient for large-scale, long-term global assessments. However, they may miss small lakes or subtle changes in lake extent. Medium-resolution ( $\approx$ 10 m) products (e.g., Basit et al., 2022; Xu et al., 2023) strike a balance

between spatial detail and computational efficiency, making them well-suited for regional studies.

Balancing the need for spatial and temporal detail with resource constraints and data availability is important. We recommend selecting the appropriate product type and resolution based on the study objective.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview
Type: text

In learning-based glacial lake studies, optical satellite sensors are preferred [primarily Landsat-8 (L8) and Sentinel-2 (S2)] over radar sensors (Fig. 1). Of the 48 studies reviewed (Fig. 2), 43 (89.6%) used optical data, 16 (33.3%) used SAR data, and 12 (25%) used both (Appendix A).

Despite radar's all-weather, day-and-night imaging capabilities, optical sensors remain preferred due to their multispectral bands, which effectively capture surface water changes over time. While radar helps enhance observation frequency, optical sensors improve lake detection accuracy. The wider range of free optical data options available over the past decade – such as L8, S2, PlanetScope – compared to SAR [S1], has further reinforced this preference. Among freely available datasets, optical sensors (S2) also offer higher spatial resolution (includes 10 m bands) than SAR (S1,  $\approx\!20$  m). Furthermore, SAR requires relatively extensive pre-processing before analysis (Mullissa et al., 2021). However, optical remote sensing can be affected by cast shadows and turbidity variations. On the other hand, using SAR data in mountainous regions presents challenges due to complex terrain (more details in Section 3.1). These include geometric and radiometric distortions (Rott, 1984; Wu et al., 2021) .

L8 is the most commonly used (22 papers) sensor despite its moderate spatial (30–100 m) and temporal resolution (16 days). Most L8-based studies rely on Operational Land Imager (OLI) bands. These bands provide higher radiometric resolution (12-bit), improved signal-to-noise ratio, and narrower spectral bands compared to previous Landsat missions (Mancino et al., 2020). Few exceptions (e.g., He et al., 2021; Chen et al., 2022; Kaushik et al., 2022) incorporate Thermal InfraRed Sensor (TIRS) data.

L8 is followed by S2 (20 studies) with slightly higher spatial (10–60 m) and temporal (5 days) resolution (Fig. 1, Appendix A). PlanetScope imagery is also preferred (5 papers) due its high spatial (3 m) and temporal (daily) resolution. However, its lower spectral resolution and commercial constraints restrict its broader applicability.

Among radar sensors, S1-SAR is the primary choice (15 studies), particularly the Interferometric Wide (IW) swath Ground Range Detected (GRD) mode. Topographic data such as Digital Elevation Model (DEM) are used to distinguish lake pixels from shadows and enhance classification accuracy. No strong preference for a specific DEM is observed (Fig. 1, Appendix A).

No existing studies have incorporated Surface Water and Ocean Topography (SWOT) data (Alsdorf and Lettenmaier, 2003; Vinogradova et al., 2025). SWOT's high-resolution swath altimetry enables precise monitoring of water level changes in lakes, rivers, and reservoirs (Biancamaria et al., 2016; Getirana et al., 2024). Its unique water surface elevation data provides an unprecedented opportunity to estimate lake


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview
Type: figure
Figure

Image /page/3/Figure/2 description: A figure displaying three radar charts, each representing a different type of satellite data and its usage in publications. A note below the charts states, "Each circle represents a publication." The first chart, titled "Optical satellite imagery" in blue, shows data for sources like Sentinel-2 (4 publications), Landsat-8 (5 publications), Planetscope (1 publication), ASTER (0), Corona KH-4 (0), IRS LISS III (0), Landsat-7 (2 publications), and Landsat-Other (4 publications). The most used sources are Landsat-8, Sentinel-2, and Landsat-Other. The second chart, titled "Radar satellite imagery" in green, shows data for Sentinel-1 (4 publications), TerraSAR-X (1 publication), and GaoFen-3 (0 publications), with Sentinel-1 being the most prominent. The third chart, titled "Digital elevation model" in red, shows data for NASADEM (0), SRTM (2 publications), TanDEM-X (2 publications), ArcticDEM (1 publication), ASTER (0), Copernicus DEM (2 publications), and ALOS (0).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview
Type: text

Fig. 1. Distribution of input satellite sensors (optical, radar) and topography data used in ML and DL approaches for glacial lake studies. This plot is based on 47 publications. Cao et al. (2024) was excluded as it used Google Earth imagery, comprising a mix of images from IKONOS, QuickBird, GeoEye, WorldView, SPOT, and Pleiades. More details are in (Appendix A).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview
Type: figure
Figure

Image /page/3/Figure/4 description: A stacked bar chart illustrating the number of published works per year, categorized into Machine Learning and Deep Learning. The y-axis, labeled "Number of published works," ranges from 0 to 12. The x-axis, labeled "Year," shows data for 2015, 2018, 2020, 2021, 2022, 2023, and 2024, with a break in the axis between 2015 and 2018. A legend indicates that black dots represent individual studies, light blue bars represent Deep Learning, and light green bars represent Machine Learning. Each bar is topped with the total number of works for that year, along with the percentage and count breakdown for Deep Learning (DL) and Machine Learning (ML). The data for each year is as follows: 2015: 1 total work, 100% ML (1 study: Jain\_15). 2018: 1 total work, 100% ML (1 study: Veh\_18). 2020: 7 total works, 57% ML (4 studies: Dirscherl\_20, Halberstadt\_20, Wangchuk\_20, Zhang\_20b) and 43% DL (3 studies: Qayyum\_20, Wu\_20, Yuan\_20). 2021: 9 total works, 44% ML (4 studies: How\_21, Rinzin\_21, Thati\_21, Wendleder\_21) and 56% DL (5 studies: Chen\_21, Dirscherl\_21, He\_21, Wang\_21, Zhao\_21). 2022: 11 total works, 27% ML (3 studies: Chen\_22, Dell\_22, Hu\_22) and 73% DL (8 studies: Basit\_22, Chatterjee\_22, Jiang\_22, Kaushik\_22, Ortiz\_22, Thati\_22, Wang\_22a, Wang\_22c). 2023: 9 total works, 11% ML (1 study: Banerjee\_23) and 89% DL (8 studies: Lutz\_23, Niu\_23, Sharma\_23, Siddique\_23, Thomas\_23, Wei\_23, Xu\_23, Zhao\_23). 2024: 10 total works, 30% ML (3 studies: Mustafa\_24, Wang\_24, Wu\_24) and 70% DL (7 studies: Cao\_24, Hardie\_24, Hu\_24, Sharma\_24, Tang\_24, Xu\_24, Yin\_24). The chart shows a general upward trend in the number of publications, with Deep Learning becoming the dominant category from 2021 onwards.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview
Type: text

Fig. 2. Chronological histogram (till 2024) of published glacial lake studies (total: 48) that used satellite data and learning-based approaches. Total counts, category-wise counts, and percentages are annotated above each bar.

storage variations (Wu et al., 2022). This capability is valuable for assessing potential GLOF occurrences particularly in large supraglacial lakes in Greenland and Antarctica.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview > 3.1. Single- and multi-sensor approaches: Pros and cons
Type: text

ML/DL approaches have effectively used optical satellite data as standalone input for glacial lake studies in both mountainous (e.g., Basit et al., 2022; Siddique et al., 2023) and polar regions (e.g., Yuan et al., 2020; Halberstadt et al., 2020). However, their reliance on cloud-free conditions limits their applicability.

In contrast, radar-only methods have shown success only in non-mountainous regions, revealing a research gap. For example, Dirscherl et al. (2021) achieved high accuracy ( $F_1$  score: 0.93) in detecting supraglacial lakes in Antarctica using S1 alone. In mountainous environments, however, radar has generally been used in combination with optical imagery (e.g., Wang et al., 2021; Wu et al., 2020). While Wang et al. (2021) achieved a satisfactory Intersection-over-Union (IoU) score (0.59) in an S1-only experiment, Wu et al. (2020) did not evaluate radar data independently.

Geometric and radiometric distortions – such as foreshortening, layover, shadowing, and backscatter variability – pose significant challenges for radar remote sensing in mountainous terrain. These distortions introduce artifacts that affect both spatial structure and pixel intensities. When present in training data, the ML/DL model may learn spurious correlations – e.g., misinterpreting terrain shadow as water – leading to a corrupted decision boundary and resulting in both false positives and false negatives. Conversely, if the model is trained on clean or corrected data but applied to distorted test scenes,

its predictions may still degrade due to unseen variance in geometry or backscatter. This may also result in misclassification of actual lake pixels as background (false negatives) or surrounding terrain as lake (false positives). Novel neural network architectures optimized for SAR and enhanced pre-processing techniques are needed to mitigate these issues. However, even with these strategies, matching the accuracy of optical data will be a challenge.

Given the limitations of standalone radar data, integrating optical and SAR data has emerged as an effective strategy in mountainous regions. This approach leverages their complementary strengths to enhance classification accuracy and ensure data availability. Data-driven approaches learn rich spectral information from optical data under clear conditions and geometric features from SAR data.

Studies have demonstrated improved performance through fusion: Wu et al. (2020) observed a 4% increase in mIoU when adding S1 to L8. Additionally, Hu et al. (2024) achieved their highest IoU (0.84) when integrating S2, S1, and topographic data. Multi-sensor (including multi-mission) techniques typically rely on a primary sensor with one or more auxiliary sensors. In radar-optical fusion for high-mountain glacial lakes, optical data remains the primary input despite frequent cloud cover.

Multi-sensor fusion introduces challenges as well. First, temporal mismatches between acquisitions can complicate fusion. This was reported by Wang et al. (2021), where a 6-day gap between optical and radar data required high-level semantic fusion. Second, absolute geolocation shifts between sensors can introduce errors. For instance, Wu et al. (2020) had to address 1–2 pixel discrepancies between S1 and L8 using a mutual information-based coregistration method. Nevertheless, the benefits of multi-sensor fusion often outweigh its challenges, making it a valuable approach for glacial lake studies.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview > 3.2. Role of sensor resolution in monitoring hazardous lakes
Type: text

It is relatively more important to monitor glacial lakes that are prone to outburst floods. However, hazard potential is not determined by lake size. Numerous studies have demonstrated that GLOF impacts do not correspond to lake size and that also small to very small lakes (area <0.1 km2) can trigger devastating outburst floods (Allen et al., 2016; Vilca et al., 2021; Sattar et al., 2022; Chen et al., 2023).

Larger lakes are detected with relatively high accuracy by underlying ML/DL models. For instance, Wu et al. (2020) reported an overall IoU of 0.62 for all lakes using L8 and S1 data, which improved to 0.8 for lakes larger than  $0.1~\rm km^2$ . Notably, only 17.4% of the 8262 mapped lakes exceeded this size threshold. Most glacial lakes are typically small; for example, 85.3% of Himalayan glacial lakes are under 0.1 km² (Wang et al., 2021). In the Eastern Himalayas, the average size is even smaller at 0.053 km² (Xu et al., 2023). Given their abundance, smaller lakes (i.e. smaller than 0.1 km²) statistically experience more outbursts. Yet, their mapping and monitoring remains challenging due to detection limitations, with only few learning-based studies focusing on them. The minimum lake size considered for monitoring should be guided by the intended application.

Detecting glacial lakes smaller than 0.01 km² using S2 and S1 input data remains a challenge even for ML/DL approaches. This limitation, however, is more attributable to sensor resolution than to the methodologies themselves. To improve detection of these tiny lakes, integrating higher spatial-resolution satellite data, such as PlanetScope (e.g., Siddique et al., 2023; Xu et al., 2024), Pléiades, Worldview, etc., could be beneficial. Nevertheless, images from these sensors are costly, making them more suitable for individual lake studies or quality assessments rather than large-scale analysis.

Pan-sharpening the optical data could be useful too (Zheng et al., 2021). Additionally, combining Unmanned Aerial Vehicle (UAV) imagery with satellite data could support local-scale monitoring campaigns (Alvarez-Vanhard et al., 2021). However, scalability to country-or global-level would be extremely challenging, if not practically impossible.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 3. Remote sensing data used: An overview > 3.3. Importance of spectral indices, atmospheric correction
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Spectral indices (Montero et al., 2023) are combinations of spectral bands (e.g., ratios, normalized differences) designed to enhance specific surface properties. In the context of glacial lakes, they help highlight features such as open water, ice, or snow while reducing noise from shadows, rocks, and other interfering factors. Integrating diverse spectral indices within learning frameworks has improved glacial lake detection. This has been observed in both high mountain (e.g., Wangchuk and Bolch, 2020; Zhao et al., 2023) and polar glacier lakes (e.g., Yuan et al., 2020; Wang and Sugiyama, 2024).

Zhang et al. (2020a) found that normalized indices performed better than individual bands or band ratios. However, they also reported high correlations among indices, limiting potential accuracy gains from index combinations. In their analysis, Normalized Difference Water Index (NDWI)-G (McFeeters, 1996) outperformed other indices, including Enhanced Water Index (EWI) and NDWI-B (Huggel et al., 2002). NDWI-G [Green & Near InfraRed (NIR)] is better suited for detecting deep, clear water bodies, while NDWI-B (Blue & NIR) is more sensitive to shallow and turbid waters.

In contrast, Dirscherl et al. (2020) achieved better results with S2 bands than indices. This is likely due to atmospheric correction, which removes atmospheric effects caused by the scattering and absorption of solar radiation by atmospheric gases (Gao et al., 2009). This process prepares satellite data for remote sensing applications. Zhang et al. (2020a), on the other hand, used Top of Atmosphere (ToA) reflectance. Furthermore, among the 12 indices used, Dirscherl et al. (2020) found Tasseled Cap for Wetness (TCwet, Kauth and Thomas, 1976; Schwatke et al., 2019) and Automated Water Extraction Index (*AWEI*1nsh, Feyisa

et al., 2014) to be more important than NDWI-G. Regional differences in pixel composition within periglacial areas, notably between Antarctica (Dirscherl et al., 2020) and Asian mountain ranges (Zhang et al., 2020a), may also have contributed to these contrasting findings.

Atmospheric correction is common in data-driven glacial lake studies (Jha and Khare, 2017; Wendleder et al., 2021; He et al., 2021; Wang et al., 2022c). However, none have quantitatively assessed its direct impact on the performance of the underlying ML/DL models. This presents a significant research opportunity. At the same time, atmospheric correction has a high computational overhead, and many ML/DL models have performed well without it (Wangchuk and Bolch, 2020; Wu et al., 2020; Zhao et al., 2023; Hardie et al., 2024, etc.). Hence, an initial investigation using ToA reflectance is recommended before conducting a detailed analysis.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies
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Glacial lake studies based on remote sensing rely primarily on spectral indices and/or backscatter/reflectance thresholds. However, such approaches often require threshold adaptation for large-scale analyzes and multi-temporal monitoring (Jawak et al., 2015; Wangchuk and Bolch, 2020). While generally effective, these methods face significant challenges in mountainous environments. Complex terrain, varying weather conditions, changes in glacier dynamics, and seasonal variations in mountains contribute to notable inaccuracies in identifying glacial lakes (Bolch et al., 2011). Moreover, such methods often neglect contextual information from neighboring pixels or temporal sequences. They make decisions solely based on individual pixel values. Consequently, ML/DL approaches have become widespread.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.1. Chronological progression & methodological distribution
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The first learning-based glacial lake study (Jain et al., 2015), published a decade ago, used ASTER multispectral imagery. They used a Support Vector Machine (SVM) classifier for the semi-automatic detection of glacial lakes in the Chamkhar Chu Basin, Hindukush Himalaya, Bhutan.

Of the 48 studies surveyed (Appendix B), 31 (64.6%) employed DL approaches, while 17 (35.4%) used classical ML techniques (Fig. 2). This reflects the field's growing preference for DL methodologies. Although DL is a subset of ML, they are addressed separately in our review to highlight distinct trends.

Most studies on glacial lakes applying ML or DL were published in 2022 (11 papers), with at least seven DL articles consistently appearing each year since 2022. Fig. 2 provides a chronological overview of the literature. It includes both published papers and a book chapter (Thati et al., 2021) up to 2024. Fig. 3 illustrates the distribution of methodologies reported in these studies.

Research output grew substantially since 2020, coinciding with the introduction of the first DL-based approach (Yuan et al., 2020). This study used a CNN classifier and L8 imagery to detect supraglacial lakes in Southwest Greenland. 46 out of the 48 studies were published from 2020 onward, underscoring the growing focus on learning-based approaches in glacial lake remote sensing.

In ML/DL, classification predicts discrete class categories, while regression estimates continuous numerical values (Bishop, 2006). As of 2024, all learning-based approaches for glacial lake studies have been classification-based, with no reported use of regression models.

Among classification-based methods, CNN (Lecun et al., 1998) variants are predominant (Appendix C). U-Net (Ronneberger et al., 2015) is the most widely applied CNN architecture (Fig. 3). DeepLab (Chen et al., 2015, 2018a,b) variants (Appendix D), known for their advanced semantic segmentation capabilities, are relatively less explored. This is likely due to the additional resources required to train these relatively parameter-heavy models, which demand large, representative datasets. Among ML approaches, Random Forest (RF) and SVM are widely used (Cortes and Vapnik, 1995; Breiman, 2001; Mountrakis et al., 2011).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
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Glacial lakes are predominantly located in glaciated mountain regions, particularly at medium-to-high latitudes, as well as in the polar lowlands (Shugar et al., 2020). Over 80% of these lakes are concentrated in Greenland, the Alaska Range, Southern Andes, High Mountain Asia (HMA), and the eastern Canadian Arctic (Zhang et al., 2024). Despite this broad distribution, ML/DL-based studies on glacial lake mapping and monitoring have primarily focused on a limited subset of these regions, highlighting significant research gaps. Table E.6 (Appendix E) presents key regions with glacial lakes, notable publications that investigated them, and ML/DL studies that considered these regions as primary study sites. It also includes studies that evaluated the transferability of their ML/DL models in these regions, even if they were not the primary study sites.

HMA has received the most research attention from learning-based approaches, followed by Greenland and Antarctica (Appendix E). This geographic trend aligns with findings by Calamita et al. (2024). They reported that remote sensing is underutilized in studying lake ecosystem shifts in Europe (1%) and North America (5%). However, it is significantly more applied in Asia (23%) to monitor climate-related changes in lakes.

In contrast, several regions with significant GLOF activity remain understudied despite documented evidence. Historically, more than 60% of GLOFs have occurred in Alaska, HMA, and Iceland (Zhang et al., 2024). Northern Andean glacial lakes have also produced several GLOFs. The volume of Patagonian lakes (excluding the three largest) more than doubled from 1990–1999 to 2015–2018 (Shugar et al., 2020).

The risk associated to GLOF hazards is a key reason for this uneven geographic distribution of ML/DL-based glacial lake mapping studies. High-latitude regions have the highest number of glacial lakes, strongest lake growth rates (Shugar et al., 2020) and probably also experience most GLOF events. However, the impacts of GLOFs in terms of damage and loss – and thus GLOF risk – is much higher in lower- and mid-latitude mountain regions. This is due to both the higher concentration of people and infrastructure exposed to GLOFs, and, related, also higher vulnerabilities to GLOFs of societies in these densely populated mountain ranges (Taylor et al., 2023). This is further evidenced by the fact that even smaller lakes can cause major disasters in Andes (Vilca et al., 2021) or the Alps (Huggel et al., 2003). HMA is the only region that is a hotspot of GLOF risk and well studied in terms of ML/DL-based solutions for glacial lake mapping.

Research on the Andes remains limited, with only Wangchuk and Bolch (2020) conducting a dedicated study. Additionally, Qayyum et al. (2020) and Tang et al. (2024) included Andes in their transferability assessments. Similarly, the European Alps have been the focus of only one study. Wangchuk and Bolch (2020) examined the Andes and the Swiss Alps, however the study primarily focused on six locations in HMA.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
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Huggel et al . , 2003 ) . HMA is the only region that is a hotspot of GLOF risk and well studied in terms of ML / DL - based solutions for glacial lake mapping . Research on the Andes remains limited , with only Wangchuk and Bolch ( 2020 ) conducting a dedicated study . Additionally , Qayyum et al . ( 2020 ) and Tang et al . ( 2024 ) included Andes in their transferability assessments . Similarly , the European Alps have been the focus of only one study . Wangchuk and Bolch ( 2020 ) examined the Andes and the Swiss Alps , however the study primarily focused on six locations in HMA .

Other key regions, such as the Canadian and Russian Arctic, Scandinavia, Iceland, and New Zealand, remain unexplored using learning-based approaches, possibly due to lower risks associated with the abundant number of glacial lakes in these regions. This reveals a research opportunity not only to improve spatial coverage but also to enhance training datasets and advance algorithm development. Interestingly, the fastest-growing lakes (in terms of areal expansion) are located in Iceland, the Russian Arctic, and Scandinavia (Shugar et al., 2020). These regions offer valuable testbeds for developing and evaluating new glacial lake mapping methods, which can later be applied to high-risk regions. Comprehensive study of such regions is beneficial for building generalizable, globally robust approaches.

To further examine the spatial trends of ML/DL studies, we investigate geographical distribution of the methodologies. The reviewed approaches are categorized into six groups in Fig. 4: three for DL (U-Net, other CNNs, and other DL methods) and three for ML (RF, SVM,

and other ML methods). Each continent is represented by a distinct bar plot. Each methodology is counted only once per continent, even if multiple study sites within the same continent were analyzed. For instance, Zhao et al. (2023), who assessed over 5 sites in HMA using a CNN, is considered a single entry. However, studies that applied multiple algorithms to the same site (e.g., Halberstadt et al. (2020)) are counted separately for each methodology used. Similarly, studies applying the same algorithm in different continents (e.g., Wangchuk and Bolch (2020)) are counted separately for each continent considered.

Two DL studies (Chatterjee et al., 2022; Thomas et al., 2023) were excluded from Table E.6 (Appendix E) and Fig. 4. Although Chatterjee et al. (2022) included Lake Tibet, a glacial lake, their primary focus was on non-glacial lakes. Similarly, Thomas et al. (2023) investigated supraglacial lakes across the Arctic without specifying distinct study sites, making their inclusion in our region-clustered spatial distribution analysis difficult. However, both studies remain part of the broader methodological assessment.

DL techniques, particularly U-Net and other CNNs, are more commonly applied in HMA (Fig. 4). ML methods like RF and SVM are also more frequently used in HMA. However, they have a relatively stronger presence in Antarctica compared to DL.

Regardless of the technique, such data-driven models, if trained on small or non-representative datasets, risk overfitting. This reduces their effectiveness when applied to previously unseen regions or time periods. Therefore, ensuring spatiotemporal transferability is crucial for enhancing model robustness.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
Type: text

- clustered spatial distribution analysis difficult . However , both studies remain part of the broader methodological assessment . DL techniques , particularly U - Net and other CNNs , are more commonly applied in HMA ( Fig . 4 ) . ML methods like RF and SVM are also more frequently used in HMA . However , they have a relatively stronger presence in Antarctica compared to DL . Regardless of the technique , such data - driven models , if trained on small or non - representative datasets , risk overfitting . This reduces their effectiveness when applied to previously unseen regions or time periods . Therefore , ensuring spatiotemporal transferability is crucial for enhancing model robustness .

Many learning-based methods have been confined to their primary study sites. However, some notable exceptions assess spatial transferability by training on one continent and testing on another (Table E.6, Appendix E). For instance, Wangchuk and Bolch (2020) developed their approach for the Himalayas and tested it in the Alps and Andes. Their qualitative results from the Andes aligned with datasets from the National Water Authority of Peru. Similarly, Dirscherl et al. (2021) evaluated their model's generalizability (trained on data from Antarctica) by applying it to supraglacial lakes in Southwest Greenland.

Although the qualitative results of Dirscherl et al. (2021) were promising, large supraglacial lakes common in Greenland were underrepresented in their training dataset. This led to overfitting despite extensive data augmentation (Section 4.5.3). Fine-tuning with representative data from Greenland could improve the performance. However, this was not tested or reported. Likewise, Tang et al. (2024) trained their model on 15 mountain ranges in the Third Pole region and, after fine-tuning, demonstrated its qualitative generalization to the Patagonian Andes, Alaska, and Greenland. However, none of these studies provided quantitative validation in their transferability experiments. This highlights a key gap in assessment.

Temporal transferability is equally critical, as models must maintain performance when applied to data from the same study site across different time periods. It is less challenging in stable environments. However, it becomes significantly harder in rapidly changing conditions, requiring models to adapt to both periodic (seasonal) and non-periodic (long-term) changes. An example is Dirscherl et al. (2020), who investigated the spatiotemporal transferability of their RF model. They trained it on supraglacial lake occurrences from summer 2019 across fourteen regions. Evaluation was conducted on eight spatially independent regions from summers 2017 and 2018 across the Antarctic ice sheet. Their model achieved impressive average  $F_1$  scores of 0.997 for the non-water class and 0.86 for the water class.

Models that generalize effectively across diverse spatial and temporal contexts are essential but not yet standard practice. Future studies should prioritize comprehensive spatiotemporal transferability assessments, incorporating both quantitative and qualitative evaluations. Openly sharing datasets and ground truth annotations will further support these efforts. It will foster collaboration and enable more rigorous model development and evaluation.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
Type: figure
Figure

Image /page/6/Figure/2 description: A radial bar chart, also known as a sunburst chart, illustrates the distribution of machine learning and deep learning methods. The chart is divided into two main categories: Machine Learning (green) with 31 instances and Deep Learning (blue) with 39 instances. The Machine Learning section is subdivided into: Random Forest (12), Others (10) which includes LR, ME, NB, CRT, MD, EBT, ISODATA, LVQ, KNN, and X-MEANS, SVM (4), K-MEANS (2), C-K-MEANS, GAN, and DELSE. The Deep Learning section is subdivided into: ANN (2) and a larger category of CNN variants (37). The CNN variants category is further broken down into: U-Net (18), Deeplab (6), Others (9) which includes PSPNet, CoAtNet, YOLO, DCNN, Siamese CNN, HarDNet, FPN, Mask-R-CNN, and ACFNet, LinkNet (2), and CNN (2).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
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Fig. 3. Distribution of ML (31 methodologies) and DL (39 methodologies) approaches proposed in 48 studies on glacial lakes using satellite data. Deeplab includes its variants; methods without numbers in brackets were each used once. More details are in (Appendix C). Refer to the glossary (Appendix G) for full forms.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
Type: figure
Figure

Image /page/6/Figure/4 description: A world map displaying the geographical distribution of different machine learning (ML) and deep learning (DL) models across continents. The map is plotted on a longitude and latitude grid. Bar charts are overlaid on the continents to show the counts of different model types. A legend indicates the model types by color: orange for DL-U-Net, yellow for DL-Other-CNN, green for DL-Other, purple for ML-RF, blue for ML-SVM, and red for ML-Other. The total count for each continent is listed in parentheses. Asia has the highest count with 51 models (13 DL-U-Net, 16 DL-Other-CNN, 2 DL-Other, 7 ML-RF, 3 ML-SVM, 10 ML-Other). Antarctica has 10 models (2 DL-U-Net, 3 ML-RF, 1 ML-SVM, 4 ML-Other). North America has 7 models (3 DL-U-Net, 1 DL-Other-CNN, 2 ML-RF, 1 ML-Other). Europe and South America each have 1 model, both of which are ML-RF. Africa and Oceania have 0 models.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability
Type: text

Fig. 4. Continent-wise distribution (primary study site only) of ML/DL methodologies (six categories: 3 each for DL and ML) used in glacial lake remote sensing studies. Background map credit: https://www.naturalearthdata.com/. Refer to the glossary (Appendix G) for full forms.