Preprints
https://doi.org/10.5194/egusphere-2025-3396
https://doi.org/10.5194/egusphere-2025-3396
22 Jul 2025
 | 22 Jul 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Enhancing simulations of snowpack properties in land surface models with the Soil, Vegetation and Snow scheme v2.0 (SVS2)

Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair

Abstract. Snow microstructure—characterized by density, grain size, grain shape and arrangement—fundamentally determines snowpack macroscopic properties. Despite this critical role, many land surface models (LSMs) lack explicit representation of snow microstructure. This limitation has become increasingly critical as future spaceborne missions for snow water equivalent measurement demand advanced modelling systems capable of accurately estimating snowpack properties, including microstructure, across diverse climatic and vegetation regions. The Soil Vegetation and Snow (SVS) LSM, used by Environment and Climate Change Canada for operational land surface and hydrological predictions, has been substantially upgraded to address these challenges. SVS version 2.0 (SVS2) incorporates the detailed multilayer Crocus snowpack model, enabling distinct simulations of snowpack evolution in both open terrain and forested areas within each grid cell. Crocus within SVS2 has been upgraded from its original alpine design with three major enhancements to handle Canada’s varied snowpack conditions: an advanced albedo parameterization that accounts for spatial variability in light-absorbing particle deposition, new physical parameterizations tailored to Arctic snow characteristics, and a refined canopy model for forest environments. Significant improvements in simulations of near-surface density predictions are evident along a latitudinal transect from southern Quebec to the Canadian Arctic, while challenges remain in simulation of density and specific surface area in basal snow layers. SVS2 achieved substantial gains in snow melt-out timing accuracy, reducing prediction errors by over 50 % compared to the alpine Crocus version and surpassing two established snow reference products (ERA5-Land and ERA5-Crocus). These enhancements position SVS2 as a substantial advancement for future operational snow modeling applications across Canada.

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Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair

Status: open (until 16 Sep 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2025-3396 - No compliance with the policy of the journal', Juan Antonio Añel, 28 Jul 2025 reply
    • AC1: 'Reply on CEC1', Vincent Vionnet, 29 Jul 2025 reply
      • AC2: 'Reply on AC1', Vincent Vionnet, 07 Aug 2025 reply
        • CEC2: 'Reply on AC2', Juan Antonio Añel, 07 Aug 2025 reply
          • AC3: 'Reply on CEC2', Vincent Vionnet, 08 Aug 2025 reply
  • RC1: 'Comment on egusphere-2025-3396', Anonymous Referee #1, 19 Aug 2025 reply
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair

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Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
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