The Cryosphere
The Cryosphere
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Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-645-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-645-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
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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Latest update: 05 May 2025
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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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