Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km²). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.

Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km²) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a ${\mathrm{F}}_1$-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.

The hydrological drought in Northern Italy in 2022 was, in large part, the consequence of a snow drought in the Italian Alps in the winter of 2021/22 and the resulting deficit of melt water runoff. In this communication, we assessed the snow-cover dynamics in nine Alpine Italian catchments using long time series of satellite-derived snow line elevation (SLE) measurements. We compared the SLE of the hydrological year 2021/22 to the long-term dynamics of 1985–2021. In early 2022, the SLE was located several hundred meters above the expected median values in all of the nine catchments. This resulted in deficits of snow-covered area of up to 83% in the Western Alps (catchment of Sesia, March 2022) and up to 61% in the Eastern Alps (Brenta, March 2022) compared to the long-term median. Although snow-cover data from optical satellite imagery do not contain information about snow depth and water content, in a preliminary qualitative analysis, the derived SLE dynamics show good agreement with the Standardized Snowpack Index (SSPI) which is based on the snow water equivalent (SWE). While the exact relationships between SLE, SWE, and runoff have to be explored further on the catchment basis, long-time series of SLE may have potential for use in drought early warning systems.

Supraglacial meltwater accumulation on ice shelves may have important implications for future sea level rise. Despite recent progress in the understanding of Antarctic surface hydrology, potential influences on ice shelf stability as well as links to environmental drivers remain poorly constrained. In this study, we employ state-of-the-art machine learning on Sentinel-1 synthetic aperture radar (SAR) and optical Sentinel-2 satellite imagery to provide new insight into the inter-annual and intra-annual evolution of surface hydrological features across six major Antarctic Peninsula and East Antarctic ice shelves. For the first time, we produce a high-resolution record of supraglacial lake extent dynamics for the period 2015–2021 at unprecedented 10 m spatial resolution and bi-weekly temporal scale. Through synergetic use of optical and SAR data, we obtain a more complete mapping record also enabling the delineation of buried lakes. Our results for Antarctic Peninsula ice shelves reveal below-average meltwater ponding during most of melting seasons 2015–2018 and above-average meltwater ponding throughout summer 2019–2020 and early 2020–2021 considering years 2015–2021 as a reference period. Meltwater ponding on investigated East Antarctic ice shelves was far more variable, with above-average lake extents during most 2016–2019 melting seasons and below-average lake extents during 2020–2021, considering the reference interval 2016–2021. This study is the first to investigate relationships with climate drivers both spatially and temporally including time lag analysis. The results indicate that supraglacial lake formation in 2015–2021 is coupled to the complex interplay of local, regional and large-scale environmental drivers with similar driving factors over both ice sheet regions. In particular, varying air temperature, solar radiation and wind conditions influenced supraglacial lake formation over all six ice shelves despite strong local to regional discrepancies, as revealed through pixel-based correlation analysis. Furthermore, regional climatic conditions were shown to be influenced by Southern Hemisphere atmospheric modes showing large-scale impacts on the spatio-temporal evolution of supraglacial lakes as well as on above- or below-average meltwater ponding with respect to the period 2015–2021. Finally, the local glaciological setting, including melt–albedo feedbacks and the firn air content, was revealed to strongly influence supraglacial lake distribution. Recent increases in Antarctic Peninsula surface ponding point towards a further reduction in the firn air content, implying an increased risk for ponding and hydrofracture. In addition, lateral meltwater transport was observed over both Antarctic regions with similar implications for future ice shelf stability.