The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 11
Articles | Volume 20, issue 11
https://doi.org/10.5194/tc-20-6401-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-6401-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 11
Research article
 | 
11 Nov 2026
Research article |  | 11 Nov 2026

Observational data and neural network-accelerated modelling provide valuable insights for glacier thickness reconstruction in High Mountain Asia

Gillian M. A. Smith, Daniel N. Goldberg, Guillaume Jouvet, James R. Maddison, and Hamish D. Pritchard

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Short summary
We estimate the thickness of a glacier in the Himalaya, using recently available thickness measurements and a computational model which uses higher-order physics to match thickness with satellite-observed velocity. Our velocity-constrained estimate achieves similar accuracy to leading estimates, while estimates additionally constrained by thickness show increased accuracy, but limited interpolative power. We make recommendations for future measurement locations and for choosing model parameters.
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