Back to Search Start Over

Distribution of snow accumulation on the Svartisen ice cap, Norway, assessed by a model of orographic precipitation

Authors :
Philippe Crochet
Idar Barstad
Miriam Jackson
Tómas Jóhannesson
Regine Hock
Thomas V. Schuler
Source :
Hydrological Processes. 22:3998-4008
Publication Year :
2008
Publisher :
Wiley, 2008.

Abstract

We apply a linear model of orographic precipitation (LT model) to estimate snow accumulation on the western Svartisen ice cap (220 km 2 ) in northern Norway. This model combines 3D airflow dynamics with simple parameterizations of cloud physics. The model is forced by large-scale atmospheric input variables taken from the ECMWF Re-analysis (ERA-40) of the European Center for Medium Range Weather Forecast (ECMWF), and the model parameters are kept constant for the entire simulation period, after optimization. The domain covers a 120 x 125 km area surrounding the ice cap. The model is run using a 1-km resolution digital elevation model, and 6-h time steps over the period from 1958 to 2002. Precipitation data from surrounding meteorological stations and winter glacier mass balance measurements on several glaciers within the region are used to evaluate the model results. Precipitation obtained from the LT model agrees well with observations from precipitation gauges and there is also fair agreement between model results and specific winter mass balance observations on the ice cap if these are corrected for winter rain. The LT model reproduces well the spatial pattern of winter accumulation across the ice cap as well as the area-averaged winter mass balances of several other glaciers in the region. This indicates that it is a useful tool in providing high-resolution, deterministic estimates of precipitation in complex terrain as required for distributed hydrological and/or glacier mass balance modelling of unmeasured areas.

Details

ISSN :
10991085 and 08856087
Volume :
22
Database :
OpenAIRE
Journal :
Hydrological Processes
Accession number :
edsair.doi...........2d77940ec6809e38f57614a328c221a5
Full Text :
https://doi.org/10.1002/hyp.7073