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

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.

Authors

Institutions

Publication Details

Journal
Geophysical Research Letters
Published
2024-12-20
DOI
https://doi.org/10.1029/2024gl112483
Citations
4
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
1.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

César Deschamps‐Berger, Christian Voigt, Michael Lehning, Simon Gascoin et al.
4 citations
Geophysical Research Letters
Cryospheric studies and observations
1.51
article

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

César Deschamps‐Berger, Christian Voigt, Michael Lehning, Simon Gascoin, Till Rehm, Karsten Schulz, Paul Schattan, Franziska Koch, Karl‐Friedrich Wetzel, Korbinian Achmüller
article en
4 citations

Abstract

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.

Geophysical Research LettersVol. 51(24)
Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), University of Augsburg (DE), Centre d'Études Spatiales de la Biosphère (FR), GFZ Helmholtz Centre for Geosciences (DE), Institut de Recherche pour le Développement (FR), Technische Universität Berlin (DE), BOKU University (AT)
Deutsche Forschungsgemeinschaft, Austrian Science Fund
Clean water and sanitation
Openalex Percentile: Top 18%
Cryospheric studies and observations
1.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.