Preprints
https://doi.org/10.5194/egusphere-2024-791
https://doi.org/10.5194/egusphere-2024-791
22 Apr 2024
 | 22 Apr 2024
Status: this preprint is open for discussion.

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

Abstract. 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba

Status: open (until 17 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-791', Jeff Dozier, 09 May 2024 reply
  • RC2: 'Comment on egusphere-2024-791', Anonymous Referee #2, 13 May 2024 reply
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba

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Short summary
An accurate knowledge of the spatial distribution of the snow mass across the landscape is important for water management in mountain catchments. We present a new tool to estimate the 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 the snow mass and snow depth at the catchment scale.