Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-209-2022
https://doi.org/10.5194/essd-14-209-2022
Data description paper
 | 
24 Jan 2022
Data description paper |  | 24 Jan 2022

An inventory of supraglacial lakes and channels across the West Antarctic Ice Sheet

Diarmuid Corr, Amber Leeson, Malcolm McMillan, Ce Zhang, and Thomas Barnes

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Revised manuscript accepted for ESSD
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Cited articles

Alley, K. E., Scambos, T. A., Miller, J. Z., Long, D. G., and MacFerrin, M.: Quantifying vulnerability of Antarctic ice shelves to hydrofracture using microwave scattering properties, Remote Sens. Environ., 210, 297–306, https://doi.org/10.1016/j.rse.2018.03.025, 2018. a
Arthur, D. and Vassilvitskii, S.: k-means++: the advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, SODA '07, Society for Industrial and Applied Mathematics, USA, 1027–1035, 2007. a, b
Arthur, J. F., Stokes, C., Jamieson, S. S., Carr, J. R., and Leeson, A. A.: Recent understanding of Antarctic supraglacial lakes using satellite remote sensing, Prog. Phys. Geogr., 44, 837–869, https://doi.org/10.1177/0309133320916114, 2020. a
Banwell, A. F., MacAyeal, D. R., and Sergienko, O. V.: Breakup of the Larsen B Ice Shelf triggered by chain reaction drainage of supraglacial lakes, Geophys. Res. Lett., 40, 5872–5876, https://doi.org/10.1002/2013GL057694, 2013. a
Banwell, A. F., Caballero, M., Arnold, N. S., Glasser, N. F., Cathles, L. M., and MacAyeal, D. R.: Supraglacial lakes on the Larsen B ice shelf, Antarctica, and at Paakitsoq, West Greenland: a comparative study, Ann. Glaciol., 55, 1–8, https://doi.org/10.3189/2014AoG66A049, 2014. a
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We identify 119 km2 of meltwater area over West Antarctica in January 2017. In combination with Stokes et al., 2019, this forms the first continent-wide assessment helping to quantify the mass balance of Antarctica and its contribution to global sea level rise. We apply thresholds for meltwater classification to satellite images, mapping the extent and manually post-processing to remove false positives. Our study provides a high-fidelity dataset to train and validate machine learning methods.
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