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www.hydrol-earth-syst-sci-discuss.net/6/729/2009/
doi:10.5194/hessd-6-729-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin
1UNESCO-IHE Institute for Water Education, Delft, The Netherlands
2Deltares, Delft, The Netherlands
3Water Resources Section, Delft University of Technology, Delft, The Netherlands
4Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
5Centre for Limnology, Indonesian Institute of Sciences, Cibinong, Indonesia
Abstract. One of the challenges in river flow simulation modelling is increasing the accuracy of forecasts. This paper explores the complementary use of data-driven models, e.g. artificial neural networks (ANN) to improve the flow simulation accuracy of a semi-distributed process based model. The IHMS-HBV model of the Meuse river basin is used in this research. Two schemes are tested. The first one explores the replacement of sub-basin models by data-driven models. The second scheme is based on the replacement of the Muskingum-Cunge routing model, which integrates the multiple sub-basin models, by an ANN. The results showed that: (1) after a step-wise spatial replacement of sub-basin conceptual models by ANNs it is possible to increase the accuracy of the overall basin model; (2) there are time periods when low and high flow conditions are better represented by ANNs; and (3) the improvement in terms of RMSE obtained by using of ANNs is greater than that when using sub-basin replacements. It can be concluded that the presented two schemes based on the analysis of seasonal and spatial weakness of the process based models can improve performance of the process based models in the context of operational flow forecasting.
Discussion Paper (PDF, 2306 KB) Interactive Discussion (Closed, 6 Comments) Final Revised Paper (HESS)
Citation: Corzo, G., Solomatine, D., Hidayat, de Wit, M., Werner, M., Uhlenbrook, S., and Price, R.: Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin, Hydrol. Earth Syst. Sci. Discuss., 6, 729-766, doi:10.5194/hessd-6-729-2009, 2009. Bibtex EndNote Reference Manager XML
