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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 6
The Cryosphere, 6, 1323–1337, 2012
https://doi.org/10.5194/tc-6-1323-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 6, 1323–1337, 2012
https://doi.org/10.5194/tc-6-1323-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Nov 2012

Research article | 13 Nov 2012

Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

T. M. Saloranta T. M. Saloranta
  • Section for glaciers, snow and ice, Hydrology department, Norwegian water resources and energy directorate (NVE), Postboks 5091 Majorstua, 0301 Oslo, Norway

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

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