Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5803-2023
https://doi.org/10.5194/gmd-16-5803-2023
Model description paper
 | 
19 Oct 2023
Model description paper |  | 19 Oct 2023

A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)

Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis

Related authors

Temporal evolution of the Petermann Ice Shelf estuary constrained by remote sensing observations
Michela Savignano, Alison F. Banwell, Waleed Abdalati, Robin E. Bell, Alexandra Boghosian, W. Roger Buck, Sarah E. Esenther, Emily Glazer, Adam L. LeWinter, Laurence C. Smith, and Leigh A. Stearns
EGUsphere, https://doi.org/10.5194/egusphere-2026-1396,https://doi.org/10.5194/egusphere-2026-1396, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
A framework for evaluating ice sheet altimetry uncertainty estimates
Karla Boxall, Malcolm McMillan, Alan Muir, Sarah Appleby, Sophie Dubber, Noel Gourmelen, Clare Willis, and Joe Phillips
EGUsphere, https://doi.org/10.5194/egusphere-2026-556,https://doi.org/10.5194/egusphere-2026-556, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Diverging runoff drives uncertainty in Antarctic surface mass balance projections under a high emission scenario
Benjamin Heurgue, Charles Amory, Christoph Kittel, Fredrik Boberg, Gaël Durand, Vincent Favier, Xavier Fettweis, Quentin Glaude, Heiko Goelzer, Nicolaj Hansen, Nicolas C. Jourdain, Ruth Mottram, Martin Olesen, Willem Jan Van de Berg, Michiel R. Van den Broeke, and René R. Wijngaard
EGUsphere, https://doi.org/10.5194/egusphere-2026-624,https://doi.org/10.5194/egusphere-2026-624, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
PIXAL: A Physics-Informed Explainable Machine Learning Architecture for Greenland Ice Albedo Modeling
Raf Antwerpen, Marco Tedesco, Pierre Gentine, Willem Jan van de Berg, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2025-6143,https://doi.org/10.5194/egusphere-2025-6143, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
The Modèle Atmosphérique Régional – Intelligence Artificielle (MAR-IA): surface meltwater over Greenland
Marco Tedesco, Racheet Matai, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2026-490,https://doi.org/10.5194/egusphere-2026-490, 2026
Short summary

Cited articles

Amory, C., Kittel, C., Le Toumelin, L., Agosta, C., Delhasse, A., Favier, V., and Fettweis, X.: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica, Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, 2021. 
Arnold, N., K. Richards, Willis, I., and Sharp, M.: Initial results from a distributed, physically based model of glacier hydrology, Hydrol. Processes, 12, 191–219, https://doi.org/10.1002/(SICI)1099-1085(199802)12:2<191::AIDHYP571>3.0.CO;2-C, 1998. 
Arnold, N. S.: A new approach for dealing with depressions in digital elevation models when calculating flow accumulation values, Prog. Phys. Geogr., 34, 781–809, https://doi.org/10.1177/0309133310384542, 2010. 
Banwell, A. F., Arnold, N., Willis, I., Tedesco, M., and Ahlstrom, A.: Modelling supraglacial water routing and lake filling on the Greenland Ice Sheet, J. Geophys. Res., 117, F04012, https://doi.org/10.1029/2012JF002393, 2012a. 
Banwell, A. F., Willis, I. C., Arnold, N. S., Messerli, A., Rye, C. J., and Ahlstrøm, A. P.: Calibration and validation of a high resolution surface mass balance model for Paakitsoq, west Greenland, J. Glaciol., 58, 1047–1062, https://doi.org/10.3189/2012JoG12J034, 2012b. 
Download
Short summary
We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Share