Articles | Volume 20, issue 10
https://doi.org/10.5194/tc-20-6045-2026
https://doi.org/10.5194/tc-20-6045-2026
Research article
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21 Oct 2026
Research article | Highlight paper |  | 21 Oct 2026

GlaUnTI: a hybrid physics–machine learning model enables transferable glacier surface mass balance estimation

Konstantin A. Maslov, Thomas Schellenberger, Claudio Persello, and Alfred Stein

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Cited articles

Aðalgeirsdóttir, G., Magnússon, E., Pálsson, F., Thorsteinsson, T., Belart, J. M. C., Jóhannesson, T., Hannesdóttir, H., Sigurðsson, O., Gunnarsson, A., Einarsson, B., Berthier, E., Schmidt, L. S., Haraldsson, H. H., and Björnsson, H.: Glacier Changes in Iceland From ∼1890 to 2019, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.523646, 2020. a
Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., and Belart, J. M. C.: Glacier change in Norway since the 1960s – an overview of mass balance, area, length and surface elevation changes, J. Glaciol., 66, 313–328, https://doi.org/10.1017/jog.2020.10, 2020. a
Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a
Bisset, R. R., Dehecq, A., Goldberg, D. N., Huss, M., Bingham, R. G., and Gourmelen, N.: Reversed Surface-Mass-Balance Gradients on Himalayan Debris-Covered Glaciers Inferred from Remote Sensing, Remote Sens., 12, https://doi.org/10.3390/rs12101563, 2020. a
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Editorial statement
This study describes a machine learning workflow that brings together a variety of surface mass balance (SMB) measurements and a SMB model to improve model skill and to evaluate the performance of the trained and untrained model. The research makes a novel contribution to glacier mass balance modelling, with the potential for transferability across diverse climatic and glaciological settings. The workflow also provides a valuable framework that could inform the wider development and application of hybrid modelling approaches to glacier evolution that remain physically grounded.
Short summary
Glaciers are melting worldwide, but direct measurements are spatially sparse. We developed a new model that combines mechanistic physical rules with machine learning to better estimate yearly gains and losses of ice mass across Europe. By learning local corrections to a physical melt model, the machine learning component improves accuracy and works well on glaciers not used for training. This will help produce more reliable glacier assessments and support future climate studies.
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