Articles | Volume 20, issue 10
https://doi.org/10.5194/tc-20-6045-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-6045-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GlaUnTI: a hybrid physics–machine learning model enables transferable glacier surface mass balance estimation
Konstantin A. Maslov
CORRESPONDING AUTHOR
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
now at: School of Geography and Planning, University of Sheffield, Sheffield, UK
Thomas Schellenberger
Department of Geosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
now at: FOSSEN AI, Oslo, Norway
Claudio Persello
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Alfred Stein
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
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Erik Schytt Mannerfelt and Thomas Schellenberger
EGUsphere, https://doi.org/10.5194/egusphere-2026-6305, https://doi.org/10.5194/egusphere-2026-6305, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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Crevasses are deep cracks in glacier ice that route water to the glacier bed and endanger travel, yet are rarely mapped regionally. We trained a machine-learning model on satellite data to map visible crevasses on all Svalbard glaciers each year from 2017 to 2025. Three in four glaciers with a clear trend lost crevasses in their lower parts, yet the total area grew, likely as thinning snow cover exposed crevasses higher up. Yearly maps can help detect glacier surges and plan safe travel routes.
Mila Koeva, Dessislava Petrova-Antonova, Sisi Zlatanova, Rob Peters, Claudio Persello, Ville Lehtola, Pirouz Nourian, Johannes Flacke, Raoul Grouls, and Alfred Stein
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., XII-4-W2-2026, 105–112, https://doi.org/10.5194/isprs-annals-XII-4-W2-2026-105-2026, https://doi.org/10.5194/isprs-annals-XII-4-W2-2026-105-2026, 2026
Jeroen Grift, Claudio Persello, and Mila Koeva
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., XI-1-2026, 329–335, https://doi.org/10.5194/isprs-annals-XI-1-2026-329-2026, https://doi.org/10.5194/isprs-annals-XI-1-2026-329-2026, 2026
Tazio Strozzi, Erik Schytt Mannerfelt, Oliver Cartus, Maurizio Santoro, Thomas Schellenberger, and Andreas Kääb
The Cryosphere, 20, 1679–1697, https://doi.org/10.5194/tc-20-1679-2026, https://doi.org/10.5194/tc-20-1679-2026, 2026
Short summary
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By analysing 30 years of satellite SAR (Synthetic Aperture Radar) data, we have found that the number of glacier surges over Svalbard has tripled since 2015. We show that this increase is unlikely to be explained solely by improvements in data quality or by random fluctuations in surge frequency, suggesting that this trend is caused by an external forcing mechanism. Given our incomplete understanding of surge initiation, the cause of the observed threefold increase remains however uncertain.
Mattia Callegari, Carlo Marin, Claudia Notarnicola, and Thomas Schellenberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-5779, https://doi.org/10.5194/egusphere-2025-5779, 2026
Short summary
Short summary
We introduce a new satellite-based approach to estimate annual glacier mass balance. Using data from 2000 to 2024, we produced yearly estimates for nearly three thousand glaciers in the Alps, Scandinavia, and Svalbard. Comparison with extensive field measurements shows strong agreement, demonstrating that this method can reliably track annual glacier changes across large regions.
Henning Åkesson, Kamilla Hauknes Sjursen, Thomas Vikhamar Schuler, Thorben Dunse, Liss Marie Andreassen, Mette Kusk Gillespie, Benjamin Aubrey Robson, Thomas Schellenberger, and Jacob Clement Yde
The Cryosphere, 19, 5871–5902, https://doi.org/10.5194/tc-19-5871-2025, https://doi.org/10.5194/tc-19-5871-2025, 2025
Short summary
Short summary
We model the historical and future evolution of the Jostedalsbreen ice cap in Norway, projecting substantial and largely irreversible mass loss for the 21st century, and that the ice cap will split into three parts. Further mass loss is in the pipeline, with a disappearance during the 22nd century under high emissions. Our study demonstrates an approach to model complex ice masses, highlights uncertainties due to precipitation, and calls for further research on long-term future glacier change.
Bedru Tareke, Paulo Silva Filho, Claudio Persello, Monika Kuffer, Raian V. Maretto, Jiong Wang, Angela Abascal, Priam Pillai, Binti Singh, Juan Manuel D'Attoli, Caroline Kabaria, Julio Pedrassoli, Patricia Brito, Peter Elias, Elio Atenógenes Villaseñor, Andrea Ramírez Santiago, Wahyu Mulyana, Jati Pratomo, Ruth Leska, Jan Streitenberger, Dennis Mwaniki, and Dana R. Thomson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 237–243, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-237-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-237-2025, 2025
F. Samadzadegan, A. Toosi, F. Dadrass Javan, and A. Stein
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 691–698, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-691-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-691-2023, 2023
A. Jafari, M. R. Delavar, and A. Stein
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 49–55, https://doi.org/10.5194/isprs-annals-V-4-2022-49-2022, https://doi.org/10.5194/isprs-annals-V-4-2022-49-2022, 2022
Tazio Strozzi, Andreas Wiesmann, Andreas Kääb, Thomas Schellenberger, and Frank Paul
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-44, https://doi.org/10.5194/essd-2022-44, 2022
Revised manuscript not accepted
Short summary
Short summary
Knowledge on surface velocity of glaciers and ice caps contributes to a better understanding of a wide range of processes related to glacier dynamics, mass change and response to climate. Based on the release of historical satellite radar data from various space agencies we compiled nearly complete mosaics of winter ice surface velocities for the 1990's over the Eastern Arctic. Compared to the present state, we observe a general increase of ice velocities along with a retreat of glacier fronts.
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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.
This study describes a machine learning workflow that brings together a variety of surface mass...
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.
Glaciers are melting worldwide, but direct measurements are spatially sparse. We developed a new...