Preprints
https://doi.org/10.5194/egusphere-2026-2851
© 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/egusphere-2026-2851
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
23 Jun 2026
| 23 Jun 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).
Brief communication: delivering a Digital-Twin-ready snow reanalysis
Abstract. We present DTE-SNOW, a Digital-Twin-ready framework for simulating the spatial and temporal dynamics of snow-water resources at 1 km resolution, based on satellite-derived precipitation, snow modeling, and the optional assimilation of Sentinel-1 snow-depth retrievals. Using test simulations over four European mountain basins (Ebro, Rhône, Po, and Inn), we show that DTE-SNOW achieves an average snow-depth bias of only a few centimeters (0.05 m when Sentinel-1 snow-depth assimilation is applied). The simulated spatial patterns successfully reproduce the topographic dependence of snow distribution, with correlations between mean annual Snow Water Equivalent (SWE) and elevation ranging from 0.63 to 0.77. Because DTE-SNOW is independent of in situ observations, it opens new opportunities toward a “SWE of everywhere” paradigm: a globally consistent estimation of snow-water resources within DestinE.
How to cite. Avanzi, F., Lievens, H., Matiu, M., Filippucci, P., Baez Villanueva, O. M., Gabellani, S., Delogu, F., Alfieri, L., Libertino, A., Quintana-Seguì, P., Miralles, D. G., Brocca, L., Massari, C., and De Lannoy, G. J. M.: Brief communication: delivering a Digital-Twin-ready snow reanalysis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-2851, 2026.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.Download & links
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Francesco Avanzi
CORRESPONDING AUTHOR
CIMA Research Foundation, Via Armando Magliotto 2, Savona, 17100, Italy
Hans Lievens
Hydro-Climate Extremes Lab (H-CEL), Department of Environment, Ghent University, Belgium
Michael Matiu
DICAM - Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, Trento, 38123, TN, Italy
Paolo Filippucci
National Research Council (CNR), Research Institute for Geo-Hydrological Protection, Perugia, 06126, Italy
Oscar M. Baez Villanueva
Hydro-Climate Extremes Lab (H-CEL), Department of Environment, Ghent University, Belgium
Simone Gabellani
CIMA Research Foundation, Via Armando Magliotto 2, Savona, 17100, Italy
Fabio Delogu
CIMA Research Foundation, Via Armando Magliotto 2, Savona, 17100, Italy
Lorenzo Alfieri
CIMA Research Foundation, Via Armando Magliotto 2, Savona, 17100, Italy
Andrea Libertino
CIMA Research Foundation, Via Armando Magliotto 2, Savona, 17100, Italy
Pere Quintana-Seguì
Observatori de l’Ebre, Ramon Llull University–CSIC, Roquetes, Spain
Diego G. Miralles
Hydro-Climate Extremes Lab (H-CEL), Department of Environment, Ghent University, Belgium
Luca Brocca
National Research Council (CNR), Research Institute for Geo-Hydrological Protection, Perugia, 06126, Italy
Christian Massari
National Research Council (CNR), Research Institute for Geo-Hydrological Protection, Perugia, 06126, Italy
Gabriëlle J. M. De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
Short summary
We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
We developed a new system to map and monitor snow and stored water across large mountain regions...