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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 9
Hydrol. Earth Syst. Sci., 20, 3895–3905, 2016
https://doi.org/10.5194/hess-20-3895-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 3895–3905, 2016
https://doi.org/10.5194/hess-20-3895-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Sep 2016

Research article | 22 Sep 2016

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Nena Griessinger1,2, Jan Seibert2, Jan Magnusson3, and Tobias Jonas1 Nena Griessinger et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Department of Geography, University of Zurich, Zurich, Switzerland
  • 3Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway

Abstract. In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

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In Alpine catchments, snowmelt is a major contribution to runoff. In this study, we address the question of whether the performance of a hydrological model can be enhanced by integrating data from an external snow monitoring system. To this end, a hydrological model was driven with snowmelt input from snow models of different complexities. Best performance was obtained with a snow model, which utilized data assimilation, in particular for catchments at higher elevations and for snow-rich years.
In Alpine catchments, snowmelt is a major contribution to runoff. In this study, we address the...
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