Widespread distribution of supraglacial lakes around the margin of the East Antarctic Ice Sheet

Abstract

Supraglacial lakes are important to ice sheet mass balance because their development and drainage has been linked to changes in ice flow velocity and ice shelf disintegration. However, little is known about their distribution on the world’s largest ice sheet in East Antarctica. Here, we use ~5 million km2 of high-resolution satellite imagery to identify >65,000 lakes (>1,300 km2) that formed around the peak of the melt season in January 2017. Lakes occur in most marginal areas where they typically develop at low elevations (<100 m) and on low surface slopes (<1°), but they can exist 500 km inland and at elevations >1500 m. We find that lakes often cluster a few kilometres down-ice from grounding lines and ~60% (>80% by area) develop on ice shelves, including some potentially vulnerable to collapse driven by lake-induced hydro-fracturing. This suggests that parts of the ice sheet may be highly sensitive to climate warming.

Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes

“…We also compared our results with other threshold-based methods. As can be noted in Figure 9, the more conservative NDWI values such as 0.25 [13] and 0.3 [22] do not capture shallow lakes on the Nivlisen Ice Shelf, while also misclassifying cloud shadows as lakes. Our method reduces cloud shadow misclassification errors by including another thresholding method (B3-B4 > 0.07), while also allowing for shallow lake areas to be detected by using a lower NDWI threshold (0.19) ( Figure 9).…”

“…Owing to the nonlinear rise in surface melt rates under climatic warming, accurate prediction of Antarctic sea level contributions critically requires understanding supraglacial lake conditions as they relate to when and where they occur and how much meltwater they store. Most of the existing studies of lakes in Antarctica have been restricted to local and regional observations [21], and/or are limited in temporal scope [22]. No systematic continent-wide study, either spatially or temporally, has been conducted to identify supraglacial lakes and estimate their volumes.Central to quantifying supraglacial lake volumes in Antarctica is to identify and track these features from remotely-sensed data.…”

“…Supraglacial lakes appear deep blue because water attenuates red light significantly faster than blue light. Lakes are more distinct from ice at shorter wavelengths, but more distinct from snow at longer wavelengths [3][4][5][6][7]12,13,[22][23][24][25][26][27][28]; the ratio of blue and red reflectances highlights both of these properties, and therefore, is a commonly-used method to distinguish lake from non-lake areas [3,5,6,13,25,26]. However, lake identification can be confounded by a range of factors, including cloud cover, cloud shadows completely to partially obscuring lake areas, and the presence of spectrally-similar classes such as slush, blue ice, shaded rocks, and shaded snow.…”

Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach

“…In addition, the enhanced presence of supraglacial melt decreases the surface albedo of ice while increasing the absorption of incoming solar radiation, which initiates a positive feedback that further triggers surface melting [18,19]. With enhanced surface melting, the number of exposed rock similarly increases which again accelerates ice melting through a decreasing albedo [20,21]. Figure 1 shows supraglacial lakes on an Antarctic ocean-terminating outlet glacier, where the impact of supraglacial meltwater on ice dynamics is illustrated schematically.…”

Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification

“…Kingslake et al [71] were the first to note widespread surface lakes across Antarctica by manually mapping meltwater features across a temporal composite of cloud-free Landsat imagery. Stokes et al [72] also mapped lakes in East Antarctica from a composite of cloud-free Landsat imagery, and provided a minimum estimate of lake area during one month of a high-melt summer (January 2017) using a normalized difference water index threshold. Both of these spatially widespread mapping approaches employ image mosaics that merge scenes from different time periods, and therefore capture a time-integrated snapshot of lakes rather than providing detailed information about lake evolution through time.…”