Evaluation of high-resolution snowpack simulations from global datasets and comparison with Sentinel-1 snow depth retrievals in the Sierra Nevada, USA

Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba

Data sets

DEM - Global and European Digital Elevation Model|Copernicus Data Space Ecosystem Copernicus https://doi.org/10.5270/ESA-c5d3d65

Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2019: Globe (V3.0.1) M. Buchhorn et al. https://doi.org/10.5281/zenodo.3939050

ASO L4 Lidar Snow Water Equivalent 50 m UTM Grid (ASO_50M_SWE, Version 1) T. Painter https://doi.org/10.5067/M4TUH28NHL4Z

Sentinel-1 snow depth data C-SNOW https://ees.kuleuven.be/project/c-snow

Model code and software

The ERA-SnowModel Pipeline Laura Sourp and Simon Gascoin https://src.koda.cnrs.fr/laura.sourp.1/era_snowmodel_pipeline

Short summary

Accurate knowledge of the spatial distribution of snow masses across landscapes is important for water management in mountain catchments. We present a new tool for estimating snow water resources without ground measurements. We evaluate the output of this tool using accurate airborne measurements in the Sierra Nevada and find that it provides realistic estimates of snow mass and snow depth at the catchment scale.