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

Spatial distribution of mountain snow water equivalent (SWE) is key information for water management. We implement a tool to simulate snowpack properties at high resolution (100 m) by sourcing only global datasets of climate, land cover and elevation. The meteorological data are obtained from ERA5 which makes the method applicable in near real time (5 day latency). We evaluate the output using 49 SWE maps derived from airborne lidar surveys in the Sierra Nevada. We find a very good agreement at the catchment scale using uncalibrated lapse rates. Larger biases at the model grid scale are especially evident at high elevation but do not alter the catchment-scale snow mass accuracy. We additionally compare the simulated snow depth to Sentinel-1 snow depth retrievals and find a similar accuracy with respect to synchronous airborne lidar surveys. However, Sentinel-1 snow depth products are temporally sparse and often masked during the melt season and do not provide SWE.