Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
27 Jun 2022
Model description paper |
| 27 Jun 2022
| 27 Jun 2022
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
Francesco Avanzi
CORRESPONDING AUTHOR
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Simone Gabellani
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Fabio Delogu
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Francesco Silvestro
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Edoardo Cremonese
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Umberto Morra di Cella
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Sara Ratto
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Hervé Stevenin
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
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Cited
14 citations as recorded by crossref.
- Ensemble machine learning and deep learning framework for flood susceptibility mapping in the transboundary Rapti River Basin A. Kumar et al. https://doi.org/10.1007/s12665-026-12912-6
- High-resolution satellite observations for developing advanced decision support systems for water resources management in the Po River S. Camici et al. https://doi.org/10.1016/j.jhydrol.2025.134047
- Winter snow deficit was a harbinger of summer 2022 socio-hydrologic drought in the Po Basin, Italy F. Avanzi et al. https://doi.org/10.1038/s43247-024-01222-z
- Mapping snow cover frequency at 30 m for studying seasonal variations and topographic controls on the Tibetan Plateau G. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133303
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. https://doi.org/10.3389/fsci.2023.1190191
- Revealing spring discharge variability through long-term hydrogeological monitoring: insights from case studies in the Aosta Valley M. Gizzi et al. https://doi.org/10.7343/as-2026-962
- Modelling the human impact on hydrological cycle: The MISDc v3.1 model S. Kalimisetty et al. https://doi.org/10.1016/j.envsoft.2026.107056
- Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas S. Jawak et al. https://doi.org/10.3390/rs14174403
- Enhancing the Representation of Glaciers and Ice Sheets in the ecLand Land-Surface Model: Impacts on Surface Energy Balance and Hydrology Across Scales G. Arduini et al. https://doi.org/10.5194/tc-20-1119-2026
- Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere A. Fontrodona-Bach et al. https://doi.org/10.5194/hess-30-2613-2026
- IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021) F. Avanzi et al. https://doi.org/10.5194/essd-15-639-2023
- High-resolution satellite products improve hydrological modeling in northern Italy L. Alfieri et al. https://doi.org/10.5194/hess-26-3921-2022
- Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations V. Pellet et al. https://doi.org/10.1088/2515-7620/ad7adb
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al. https://doi.org/10.5194/tc-19-4759-2025
14 citations as recorded by crossref.
- Ensemble machine learning and deep learning framework for flood susceptibility mapping in the transboundary Rapti River Basin A. Kumar et al. https://doi.org/10.1007/s12665-026-12912-6
- High-resolution satellite observations for developing advanced decision support systems for water resources management in the Po River S. Camici et al. https://doi.org/10.1016/j.jhydrol.2025.134047
- Winter snow deficit was a harbinger of summer 2022 socio-hydrologic drought in the Po Basin, Italy F. Avanzi et al. https://doi.org/10.1038/s43247-024-01222-z
- Mapping snow cover frequency at 30 m for studying seasonal variations and topographic controls on the Tibetan Plateau G. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133303
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. https://doi.org/10.3389/fsci.2023.1190191
- Revealing spring discharge variability through long-term hydrogeological monitoring: insights from case studies in the Aosta Valley M. Gizzi et al. https://doi.org/10.7343/as-2026-962
- Modelling the human impact on hydrological cycle: The MISDc v3.1 model S. Kalimisetty et al. https://doi.org/10.1016/j.envsoft.2026.107056
- Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas S. Jawak et al. https://doi.org/10.3390/rs14174403
- Enhancing the Representation of Glaciers and Ice Sheets in the ecLand Land-Surface Model: Impacts on Surface Energy Balance and Hydrology Across Scales G. Arduini et al. https://doi.org/10.5194/tc-20-1119-2026
- Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere A. Fontrodona-Bach et al. https://doi.org/10.5194/hess-30-2613-2026
- IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021) F. Avanzi et al. https://doi.org/10.5194/essd-15-639-2023
- High-resolution satellite products improve hydrological modeling in northern Italy L. Alfieri et al. https://doi.org/10.5194/hess-26-3921-2022
- Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations V. Pellet et al. https://doi.org/10.1088/2515-7620/ad7adb
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al. https://doi.org/10.5194/tc-19-4759-2025
Saved (final revised paper)
Latest update: 25 Jul 2026
Short summary
Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Knowing in real time how much snow and glacier ice has accumulated across the landscape has...
