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

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. Plain Language Summary This study addresses the challenge of accurately computing the amount of water stored in snow (known as snow water equivalent or SWE) in mountainous areas, which is important for managing water resources. Typically, there are no tools that can measure SWE across large areas in complex high alpine surroundings, only at specific points. However, at Mt. Zugspitze at the border of Germany and Austria, a special device called a superconducting gravimeter can detect changes in gravity caused by the snow, providing a way to estimate SWE over large areas. We used data from this gravimeter to test two versions of a snow model called Alpine3D. In the first version, the model relied only on weather station data. In the second version, we improved the model by using satellite images to adjust the amount and spatial distribution of precipitation (snowfall) in the model. The results showed that the model gets more accurate by using satellite data to predict SWE changes during the melting season. This finding suggests that satellite images could be a useful tool for analyzing SWE in mountainous regions with limited infrastructure.