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1. Greenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons

   https://doi.org/10.1029/2024EA003793  

   Dunmire, Devon; Subramanian, Aneesh C; Hossain, Emam; Gani, Md Osman; Banwell, Alison F; Younas, Hammad; Myers, Brendan (February 2025, Earth and Space Science)

   Abstract Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland‐wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper‐elevation hydrofracture limit (1,600 m), and that non‐draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2‐year ice‐sheet‐wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

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2. The fate of Greenland Ice Sheet supraglacial lakes in a warm and cool year

   https://doi.org/10.5194/egusphere-egu24-20925  

   Subramanian, Aneesh; Dunmire, Devon; Hossain, Emam; Gani, Md Osman; Banwell, Alison; Myers, Brendan (April 2024, EGU General Assembly 2024)

   Supraglacial lakes form on the surface of the Greenland Ice Sheet during the summer months and can directly impact ice sheet mass balance by removing mass via drainage and runoff or indirectly impact mass balance by influencing ice sheet dynamics. Here, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019, a cool and warm melt season respectively. We use a machine learning time series classification approach to categorize lakes into four different categories: lakes that 1) refreeze, 2) rapidly drain, 3) slowly drain, and 4) become buried lakes at the end of the melt season. We find that during the warmer 2019, not only was the number of lake drainage events higher than in 2018, but also the proportion of lakes that drained was greater. By investigating mean lake depths for these four categories, we show that drained lakes were, on average, 22% deeper than lakes that refroze or became buried lakes. Interestingly, drained lakes had approximately the same maximum depth in 2018 and 2019; however, lakes that did not drain were 29% deeper in 2018, a cooler year. Our unique two-year dataset describing the fate of every Greenland supraglacial lake provides novel insight into lake drainage and refreeze in a relatively warm and cool year, which may be increasingly relevant in a warming climate. 

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3. A comparison of supraglacial meltwater features throughout contrasting melt seasons: southwest Greenland

   https://doi.org/10.5194/tc-19-1047-2025  

   Glen, Emily; Leeson, Amber; Banwell, Alison F; Maddalena, Jennifer; Corr, Diarmuid; Atkins, Olivia; Noël, Brice; McMillan, Malcolm (January 2025, The Cryosphere)

   Abstract. Over recent decades, the Greenland Ice Sheet (GrIS) has lost mass through increased melting and solid ice discharge into the ocean. Surface meltwater features such as supraglacial lakes (SGLs), channels and slush are becoming more abundant as a result of the former and are implicated as a control on the latter when they drain. It is not yet clear, however, how these different surface hydrological features will respond to future climate changes, and it is likely that GrIS surface melting will continue to increase as the Arctic warms. Here, we use Sentinel-2 and Landsat 8 optical satellite imagery to compare the distribution and evolution of meltwater features (SGLs, channels, slush) in the Russell–Leverett glacier catchment, southwest Greenland, in relatively high (2019) and low (2018) melt years. We show that (1) supraglacial meltwater covers a greater area and extends further inland to higher elevations in 2019 than in 2018; (2) slush – generally disregarded in previous Greenland surface hydrology studies – is far more widespread in 2019 than in 2018; (3) the supraglacial channel system is more interconnected in 2019 than in 2018; (4) a greater number and larger total area of SGLs drained in 2019, although draining SGLs were, on average, deeper and more voluminous in 2018; (5) small SGLs (≤0.0495 km2) – typically disregarded in previous studies – form and drain in both melt years, although this behaviour is more prevalent in 2019; and (6) a greater proportion of SGLs refroze in 2018 compared to 2019. This analysis provides new insight into how the ice sheet responds to significant melt events, and how a changing climate may impact meltwater feature characteristics, SGL behaviour and ice dynamics in the future. 

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4. Contrasting regional variability of buried meltwater extent over 2 years across the Greenland Ice Sheet

   https://doi.org/10.5194/tc-15-2983-2021  

   Dunmire, Devon; Banwell, Alison F.; Wever, Nander; Lenaerts, Jan T.; Datta, Rajashree Tri (January 2021, The Cryosphere)

   Abstract. The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution, and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low- and high-melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and we use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56 % larger than after the 2018 melt season across the entire ice sheet. Northern Greenland has the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that different processes are responsible for buried lake formation in different regions of the ice sheet. For example, in southwest Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these meltwater features form when surface lakes partially freeze and become insulated as snowfall buries them. Conversely, in southeast Greenland, most buried lakes never appear on the surface, indicating that these features may form due to downward percolation of meltwater and/or subsurface penetration of shortwave radiation. We provide support for these processes via the use of a physics-based snow model. This study provides additional perspective on the potential role of meltwater on GrIS dynamics and mass loss. 

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5. A benchmark deep learning dataset for the classification of supraglacial lake drainage mechanism across the central-west Greenland Ice Sheet

   https://doi.org/10.31223/X58N26  

   Rines, Joshua; Lai, Ching-Yao; Abrahams, Ellianna; Shahin, Michael; Coffey, Niall; Lee, Eojin; Stevens, Laura (May 2026, EarthArXiv)

   Supraglacial lakes on the Greenland Ice Sheet drain through physically distinct pathways: hydrofracture, moulins, lateral stream routing, and crevasse-fields. Each drainage mechanism carries unique implications for ice sheet dynamics. Existing automated classifications reduce each lake's drainage behavior to a time-series of scalar values representing the observed water surface-area and classify each lake based on drainage rate (e.g., rapid vs. slow). This scalar reduction conflates physically different drainage mechanisms, which can only be determined through consideration of full spatio-temporal tracking. Here we introduce a human-benchmarked, machine learning-ready benchmark dataset that pairs full Sentinel-2 multispectral satellite imagery time series with human-expert-labels assigned for N=1679 supraglacial lakes in the central-west basin of the Greenland Ice Sheet during the 2018 (n=679) and 2019 (n=1000) melt seasons. The dataset is formatted as per-lake CF-1.8 NetCDF files each containing: six Sentinel-2 reflectance bands at 10 meter spatial resolution and daily cadence over the 153 day melt season (1 May to 30 September); a per-pixel binary cloud mask; co-registered lake water masks (both static and dynamic); and the human-assigned drainage classification labels. We accompany the dataset with a baseline deep learning classifier, demonstrating the utility of the dataset both in deep learning workflows and in extending lake drainage classification from rate-based to mechanism-based. The dataset is released through the Stanford Digital Repository under a CC BY 4.0 license, and the accompanying open-source sat-tile-stack preprocessing software under an MIT license. 

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