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The Cryosphere An interactive open-access journal of the European Geosciences Union
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Preprints
https://doi.org/10.5194/tc-2020-377
https://doi.org/10.5194/tc-2020-377

  05 Jan 2021

05 Jan 2021

Review status: this preprint is currently under review for the journal TC.

A seasonal algorithm of the snow-covered area fraction for mountainous terrain

Nora Helbig1, Michael Schirmer1, Jan Magnusson2, Flavia Mäder1,3, Alec van Herwijnen1, Louis Quéno1, Yves Bühler1, Jeff S. Deems4, and Simon Gascoin5 Nora Helbig et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Statkraft AS, Oslo, Norway
  • 3Institute of Geography, University of Bern, Bern, Switzerland
  • 4National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA
  • 5Centre d’Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31401 Toulouse, France

Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is therefore an essential model parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation-ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires computing subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. For a spatiotemporal evaluation of modelled fSCA we compiled three independent fSCA data sets. Evaluating modelled 1 km fSCA seasonally with fSCA derived from airborne-acquired fine-scale HS data, satellite- as well as terrestrial camera-derived fSCA showed overall normalized root mean square errors of respectively 9 %, 20 % and 22 %, and represented seasonal trends well. The overall good model performance suggests that the seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.

Nora Helbig et al.

Status: open (until 02 Mar 2021)

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Nora Helbig et al.

Nora Helbig et al.

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Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
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