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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 3
Hydrol. Earth Syst. Sci., 12, 943–957, 2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 12, 943–957, 2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  25 Jun 2008

25 Jun 2008

Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

A. H. te Linde1,2, J. C. J. H. Aerts1, R. T. W. L. Hurkmans3, and M. Eberle4 A. H. te Linde et al.
  • 1Institute for Environmental Studies (IVM), Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands
  • 2Deltares, Rotterdamseweg 185, 2629 HD Delft, The Netherlands
  • 3Hydrology and Quantitative Water Management, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands
  • 4Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, D-56068 Koblenz, Germany

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In addition, it is argued that Land Surface Models (LSMs) carry the potential to accurately estimate hydrological partitioning, because they solve the coupled water and energy balance. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed land surface model VIC (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

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