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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 6
Hydrol. Earth Syst. Sci., 12, 1273–1283, 2008
https://doi.org/10.5194/hess-12-1273-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 12, 1273–1283, 2008
https://doi.org/10.5194/hess-12-1273-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Nov 2008

28 Nov 2008

Robust estimation of hydrological model parameters

A. Bárdossy and S. K. Singh A. Bárdossy and S. K. Singh
  • Institute of Hydraulic Engineering, University of Stuttgart, Stuttgart 70569, Germany

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

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