Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Discussion papers
https://doi.org/10.5194/hess-2019-348
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-348
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Aug 2019

Submitted as: research article | 23 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Snow processes in mountain forests: Interception modeling for coarse-scale applications

Nora Helbig1, Dave Moeser2, Michaela Teich3,4, Laure Vincent5, Yves Lejeune5, Jean-Emmanuel Sicart6, and Jean-Matthieu Monnet7 Nora Helbig et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2USGS, New Mexico Water Science Center, Albuquerque, USA
  • 3Austrian Research Centre for Forests (BFW), Innsbruck, Austria
  • 4Department of Wildland Resources, Utah State University, Logan, UT, USA
  • 5University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, France
  • 6Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) – UMR 5001, F-38000 Grenoble, France
  • 7Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France

Abstract. Snow interception by forest canopy drives the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and non-forested areas at a variety of scales. Snow intercepted by forest canopy can also drastically change the surface albedo. As such, accuratelly modelling snow interception is of importance for various model applications such as hydrological, weather and climate predictions. Due to difficulties in direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered validation of snow interception models in different snow climates, forest types and at various spatial scales and has reduced accurate representation of snow interception in coarse-scale models. We present two novel models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in a spruce forest in the Swiss Alps. Besides open area snow fall, subgrid model input parameters include the standard deviation of the DSM (digital surface models) and the sky view factor, both of which can be easily pre-computed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, U.S. and the French Alps compared well to modelled snow interception with a NRMSE for the spatial mean of lower equal ≤ 10 % and NRMSE of the standard deviation of lower equal ≤ 13 %. Our results suggest that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM of the forest is available to derive subgrid forest parameters.

Nora Helbig et al.
Interactive discussion
Status: open (until 23 Oct 2019)
Status: open (until 23 Oct 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Nora Helbig et al.
Nora Helbig et al.
Viewed  
Total article views: 198 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
152 42 4 198 3 3
  • HTML: 152
  • PDF: 42
  • XML: 4
  • Total: 198
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 23 Aug 2019)
Cumulative views and downloads (calculated since 23 Aug 2019)
Viewed (geographical distribution)  
Total article views: 135 (including HTML, PDF, and XML) Thereof 133 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 Sep 2019
Publications Copernicus
Download
Short summary
Snow retained in forest canopy (snow interception) drives spatial variability of sub canopy 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 forest canopy (snow interception) drives spatial variability of sub canopy snow...
Citation