Signals of a superconducting gravimeter at the high-alpine Mt. Zugspitze show that a satellite-derived snow depth image improves the simulation of the snow water equivalent evolution

Estimating the amount of snow, its evolution and spatiotemporal distribution in complex high-alpine terrain is currently considered as one of the most important challenges in alpine hydrology and water resources management. This is predominantly caused by the lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in vast regions with 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. An introduction into the novel sensor setup will be given including the sensitivity of the integrative gravimetric signal regarding the spatially distributed snowpack and the cryo-hydro-gravimetric signal changes. 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.