Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-2545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2020
Research article |
| 15 May 2020
| 15 May 2020
Snow processes in mountain forests: interception modeling for coarse-scale applications
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
David Moeser
USGS New Mexico Water Science Center, Albuquerque, NM, USA
Michaela Teich
Austrian Federal Research Centre for Forests, Natural Hazards and Landscape (BFW), Innsbruck, Austria
Department of Wildland Resources, Utah State University, Logan, UT, USA
Laure Vincent
Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Yves Lejeune
Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Jean-Emmanuel Sicart
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) – UMR 5001, 38000 Grenoble, France
Jean-Matthieu Monnet
Université Grenoble Alpes, INRAE, LESSEM, 38402 St-Martin-d'Hères, France
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Short summary
Snow retained in the forest canopy (snow interception) drives spatial variability of the subcanopy snow accumulation. As such, accurately describing snow interception in models is of importance for various applications such as hydrological, weather, and climate predictions. We developed descriptions for the spatial mean and variability of snow interception. An independent evaluation demonstrated that the novel models can be applied in coarse land surface model grid cells.
Snow retained in the forest canopy (snow interception) drives spatial variability of the...
