Superconducting Gravimeter Observations Show That a Satellite‐Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
Year: 2024
Type: article
Abstract: The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.
Source: Geophysical Research Letters
DOI: 10.1029/2024gl112483
Institutions: BOKU University, Centre National de la Recherche Scientifique, Université Toulouse III - Paul Sabatier, Centre National d'Études Spatiales, Centre d'Études Spatiales de la Biosphère, Institut de Recherche pour le Développement, Université de Toulouse, GFZ Helmholtz Centre for Geosciences, Technische Universität Berlin, University of Augsburg, Instituto Pirenaico de Ecología, WSL Institute for Snow and Avalanche Research SLF
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,900
OpenAlex ID: W4405628080