Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-977-2023
© Author(s) 2023. 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-17-977-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2023
Research article |
| 01 Mar 2023
| 01 Mar 2023
Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, Central Asia and western Canada (1981–2019) using climate reanalyses and machine learning
Matteo Guidicelli
CORRESPONDING AUTHOR
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Matthias Huss
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Marco Gabella
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Nadine Salzmann
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos, Switzerland
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Cited
15 citations as recorded by crossref.
- Spatiotemporal mass-balance variability of Jostedalsbreen Ice Cap, Norway, revealed by a temperature-index model using Bayesian inference K. Sjursen et al. 10.1017/aog.2024.41
- A random-forest-derived 35-year snow phenology record reveals climate trends in the Yukon River Basin C. Pan et al. 10.5194/tc-19-2797-2025
- A framework for three-dimensional dynamic modeling of mountain glaciers in the Community Ice Sheet Model (CISM v2.2) S. Minallah et al. 10.5194/gmd-18-5467-2025
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network S. Kumari & A. Middey 10.1007/s10661-023-11770-0
- Glacier projections sensitivity to temperature-index model choices and calibration strategies L. Schuster et al. 10.1017/aog.2023.57
- Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. 10.1007/s11629-024-9047-4
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
- Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling 10.1109/LGRS.2024.3356160
- Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
- Performance Evaluation of High-resolution Reanalysis Datasets Over North-central British Columbia U. Goswami et al. 10.1080/07055900.2024.2308878
- Atrous spatial pyramid pooling enhanced CNN for SAR based glacier facies segmentation S. Sambyal et al. 10.1016/j.geoai.2025.100037
- Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge V. Blagovechshenskiy et al. 10.3390/w15071438
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al. 10.1007/s12040-024-02285-4
15 citations as recorded by crossref.
- Spatiotemporal mass-balance variability of Jostedalsbreen Ice Cap, Norway, revealed by a temperature-index model using Bayesian inference K. Sjursen et al. 10.1017/aog.2024.41
- A random-forest-derived 35-year snow phenology record reveals climate trends in the Yukon River Basin C. Pan et al. 10.5194/tc-19-2797-2025
- A framework for three-dimensional dynamic modeling of mountain glaciers in the Community Ice Sheet Model (CISM v2.2) S. Minallah et al. 10.5194/gmd-18-5467-2025
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network S. Kumari & A. Middey 10.1007/s10661-023-11770-0
- Glacier projections sensitivity to temperature-index model choices and calibration strategies L. Schuster et al. 10.1017/aog.2023.57
- Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. 10.1007/s11629-024-9047-4
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
- Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling 10.1109/LGRS.2024.3356160
- Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
- Performance Evaluation of High-resolution Reanalysis Datasets Over North-central British Columbia U. Goswami et al. 10.1080/07055900.2024.2308878
- Atrous spatial pyramid pooling enhanced CNN for SAR based glacier facies segmentation S. Sambyal et al. 10.1016/j.geoai.2025.100037
- Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge V. Blagovechshenskiy et al. 10.3390/w15071438
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al. 10.1007/s12040-024-02285-4
Latest update: 18 Sep 2025
Short summary
Spatio-temporal reconstruction of winter glacier mass balance is important for assessing long-term impacts of climate change. However, high-altitude regions significantly lack reliable observations, which is limiting the calibration of glaciological and hydrological models. We aim at improving knowledge on the spatio-temporal variations in winter glacier mass balance by exploring the combination of data from reanalyses and direct snow accumulation observations on glaciers with machine learning.
Spatio-temporal reconstruction of winter glacier mass balance is important for assessing...
