Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
The Cryosphere, 11, 1647-1664, 2017
https://doi.org/10.5194/tc-11-1647-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
11 Jul 2017
Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment
Emmy E. Stigter1, Niko Wanders2, Tuomo M. Saloranta3, Joseph M. Shea4,5, Marc F. P. Bierkens1,6, and Walter W. Immerzeel1 1Department of Physical Geography, Utrecht University, Utrecht, the Netherlands
2Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
3Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway
4International Centre for Integrated Mountain Development, Kathmandu, Nepal
5Centre for Hydrology, University of Saskatchewan, Saskatchewan, Canada
6Deltares, Utrecht, the Netherlands
Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Citation: Stigter, E. E., Wanders, N., Saloranta, T. M., Shea, J. M., Bierkens, M. F. P., and Immerzeel, W. W.: Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment, The Cryosphere, 11, 1647-1664, https://doi.org/10.5194/tc-11-1647-2017, 2017.
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