Articles | Volume 20, issue 11
https://doi.org/10.5194/tc-20-6401-2026
https://doi.org/10.5194/tc-20-6401-2026
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

Model code and software

IGM v2.2.3 (extended) G. Jouvet et al. https://doi.org/10.5281/zenodo.18484149

Observational data and neural network-accelerated modelling provide valuable insights for glacier thickness reconstruction in High Mountain Asia: experiment files G. Smith https://doi.org/10.5281/zenodo.18495260

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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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