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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 11
Adv. Geosci., 11, 117–122, 2007
https://doi.org/10.5194/adgeo-11-117-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Adv. Geosci., 11, 117–122, 2007
https://doi.org/10.5194/adgeo-11-117-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  20 Jun 2007

20 Jun 2007

Identifying and reducing model structure uncertainty based on analysis of parameter interaction

Y. Wang, J. Dietrich, F. Voss, and M. Pahlow Y. Wang et al.
  • Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr-University Bochum, 44801 Bochum, Germany

Abstract. Multi-objective optimization algorithms are widely used for the calibration of conceptual hydrological models. Such algorithms yield a set of Pareto-optimal solutions, reflecting the model structure uncertainty. In this study, a multi-objective optimization strategy is suggested, which aims at reducing the model structure uncertainty by considering parameter interaction within Pareto-optimal solutions. The approach has been used to develop a nested setup of a rainfall-runoff model, which is integrated in a probabilistic meso-/macroscale flood forecasting system. The optimization strategy aided in determining the best combination of a lumped (computationally efficient in operational real time forecasting) and a semi-distributed parameterization of the hydrological model. First results are shown for two subbasins of the Mulde catchment in Germany. The different phenomena of parameter interaction were analysed in this case study to reduce the model structure uncertainties.

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