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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 17, 3005-3021, 2013
https://doi.org/10.5194/hess-17-3005-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Aug 2013

Research article | 01 Aug 2013

Statistical modelling of the snow depth distribution in open alpine terrain

T. Grünewald2,1, J. Stötter3, J. W. Pomeroy4, R. Dadic6,5, I. Moreno Baños7, J. Marturià7, M. Spross3, C. Hopkinson8, P. Burlando5, and M. Lehning2,1 T. Grünewald et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 35, 7260 Davos, Switzerland
  • 2Cryos, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, GRAO 402 – Station 2, 1015 Lausanne, Switzerland
  • 3Institute of Geography, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria
  • 4Centre for Hydrology, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada
  • 5Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
  • 6Antarctic Research Centre, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand
  • 7Institut Geològic de Catalunya, C/Balmes 209–211, 08006 Barcelona, Spain
  • 8Department of Geography, University of Lethbridge, 4401 University Drive West, Lethbridge, Alberta, T1K 3M4, Canada

Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

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