Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-645-2025
https://doi.org/10.5194/tc-19-645-2025
Research article
 | 
07 Feb 2025
Research article |  | 07 Feb 2025

Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard

Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (02 Sep 2024) by Ben Marzeion
AR by Viola Steidl on behalf of the Authors (30 Sep 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Oct 2024) by Ben Marzeion
RR by Anonymous Referee #3 (05 Nov 2024)
RR by Anonymous Referee #2 (06 Nov 2024)
RR by Anonymous Referee #4 (08 Nov 2024)
ED: Publish subject to minor revisions (review by editor) (08 Nov 2024) by Ben Marzeion
AR by Viola Steidl on behalf of the Authors (18 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (11 Dec 2024) by Ben Marzeion
AR by Viola Steidl on behalf of the Authors (13 Dec 2024)  Manuscript 
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Short summary
Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
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