We introduce an algorithm (Watta) which automatically calculates supraglacial lake bathymetry and detects potential ice layers along tracks of the ICESat-2 (Ice, Cloud, and Land Elevation Satellite) laser altimeter. Watta uses photon heights estimated by the ICESat-2 ATL03 product and extracts supraglacial lake surface, bottom, and depth corrected for refraction and (sub-)surface ice cover in addition to producing surface heights at the native resolution of the ATL03 photon cloud. These measurements are used to constrain empirical estimates of lake depth from satellite imagery, which were thus far dependent on sparse sets of in situ measurements for calibration. Imagery sources include Landsat 8 Operational Land Imager (OLI), Sentinel-2, and high-resolution Planet Labs PlanetScope and SkySat data, used here for the first time to calculate supraglacial lake depths. The Watta algorithm was developed and tested using a set of 46 lakes near Sermeq Kujalleq (Jakobshavn) glacier in western Greenland, and we use multiple imagery sources (available for 45 of these lakes) to assess the use of the red vs. green band to extrapolate depths along a profile to full lake volumes. We use Watta-derived estimates in conjunction with high-resolution imagery from both satellite-based sources (tasked over the season) and nearly simultaneous Operation IceBridge CAMBOT (Continuous Airborne Mapping By Optical Translator) imagery (on a single airborne flight) for a focused study of the drainage of a single lake over the 2019 melt season. Our results suggest that the use of multiple imagery sources (both publicly available and commercial), in combination with altimetry-based depths, can move towards capturing the evolution of supraglacial hydrology at improved spatial and temporal scales.@en

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat-2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat-2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat-2 and two from coincident Landsat-8 and Sentinel-2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat-2 produced the most accurate depths; the image-based algorithms underestimated depths (by 30%–70%). This implies that ICESat-2 depths can be used to tune image-based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.

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Surface meltwater ponding has been implicated as a major driver for recent ice shelf collapse as well as the speedup of tributary glaciers in the northeast Antarctic Peninsula. Surface melt on the NAP is impacted by the strength and frequency of westerly winds, which result in sporadic foehn flow. We estimate changes in the frequency of foehn flow and the associated impact on snow melt, density, and the percolation depth of meltwater over the period 1982–2017 using a regional climate model and passive microwave data. The first of two methods extracts spatial patterns of melt occurrence using empirical orthogonal function analysis. The second method applies the Foehn Index, introduced here to capture foehn occurrence over the full study domain. Both methods show substantial foehn-induced melt late in the melt season since 2015, resulting in compounded densification of the near-surface snow, with potential implications for future ice shelf stability.@en