Abstract
Snow Water Equivalent (SWE) represents the water stored in the snowpack and is the most important variable from a hydrological point of view. Thanks to the recent launch of the Sentinel missions in the framework of the Copernicus program, it is possible to monitor the snow evolution with better spatial and temporal resolution. This represents an advantage when dealing with the snow accumulation and melting dynamics that are driven by complex topography. In detail, temporally sparse acquisitions from Sentinel-2 or Landsat missions combined with daily acquired low-resolution acquisitions e.g., from MODIS allow the monitor of the snow cover area (SCA) evolution (Premier et al., 2021). The availability of several years of these acquisitions is useful to carry on historical analyses on the snow patterns that repeat within a catchment due to the topography and meteorology of the study area. On the other hand, the Synthetic Aperture Radar (SAR) mounted on board of the Sentinel-1 missions has shown to be of great interest for monitoring the snowpack melting phases (Marin et al., 2020). We investigate how the combination of all this information together with a parsimonious use of in-situ measurements and eventually a simple model to estimate the potential melting (e.g., a degree day model) allows to reconstruct SWE reanalysis time-series for the last 10 years. Moreover, in this work we aim exploiting the snow depletion curves (SDC) to predict in near real time the total SWE of a given catchment. When evaluated against a reference product (i.e., Airborne Snow Observatory), the method shows a bias of -40 mm and a RMSE of 216 mm for a catchment of 970 km2 in Sierra Nevada (CA).