The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 7
Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-3081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 7
Research article
 | 
04 Jul 2024
Research article |  | 04 Jul 2024

Analyzing the sensitivity of a blowing snow model (SnowPappus) to precipitation forcing, blowing snow, and spatial resolution

Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont

Data sets

Pleiades snow dataset from stereo-images A. Haddjeri et al. https://doi.org/10.5281/zenodo.10037253

RGE ALTI® IGN® https://geoservices.ign.fr/rgealti

BD Forêt® V2 IGN® https://geoservices.ign.fr/bdforet

BD TOPO® IGN® https://geoservices.ign.fr/bdtopo

Global Land Ice Measurements from Space glacier database GLIMS and NSIDC https://doi.org/10.7265/N5V98602

Theia Snow Theia Snow https://doi.org/10.24400/329360/F7Q52MNK

The S2M meteorological and snow cover reanalysis in the French mountainous areas (1958-present) CNRM/Centre d'Etudes de la Neige https://doi.org/10.25326/37#v2020.2

Model code and software

Supplementary to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme": SURFEX codes and dependancies Matthieu Baron et al. https://doi.org/10.5281/zenodo.7687821

WhiteboxTools Open Core Whitebox Geospatial Inc. https://www.whiteboxgeo.com/manual/wbt_book/available_tools/geomorphometric_analysis.html#Geomorphons

esmf-org/esmf: ESMF 8.6.1 (v8.6.1) Gerhard Theurich et al. https://doi.org/10.5281/zenodo.11205527

GDAL E. Rouault et al. https://doi.org/10.5281/zenodo.8340595

louisletoumelin/neural_network_and_devine: le_toumelin_et_al_2024 (le_toumelin_2024) Louis Le Toumelin https://doi.org/10.5281/zenodo.10594274

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Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
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