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.

中文翻译：

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超导重力仪观测表明，卫星衍生的雪深图像改进了对高山遗址雪水等效演变的模拟


缺乏关于山区集水区雪水当量 （SWE） 时空变化的准确信息仍然是雪水文学和水资源管理中的一个关键问题。这部分是因为没有传感器来测量超出局部尺度的 SWE。在德国楚格峰，超导重力仪感应季节性雪的重力效应，反映了 SWE 在几公里尺度半径内的时间演变。我们使用这个新的观测结果来评估 Alpine3D 分布式雪模型的两种配置。在默认运行中，模型强制使用气象站数据。在第二次运行中，我们根据卫星观测 （Pléiades） 得出的 8 m 分辨率雪深图像应用了降水校正。积雪深度图像极大地改善了融化季节积雪重力效应的模拟。这一结果表明，卫星观测可以增强基础设施有限的山区的 SWE 分析。