Articles | Volume 13, issue 9
Hydrol. Earth Syst. Sci., 13, 1619–1634, 2009
Hydrol. Earth Syst. Sci., 13, 1619–1634, 2009

  11 Sep 2009

11 Sep 2009

Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin

G. A. Corzo1, D. P. Solomatine1,3, Hidayat1,5, M. de Wit2, M. Werner1,2, S. Uhlenbrook1,4, and R. K. Price1,3 G. A. Corzo et al.
  • 1Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
  • 2Deltares (Delft|Hydraulics), Rotterdamseweg 185, Delft, The Netherlands
  • 3Water Resources Section, Delft University of Technology, Delft, The Netherlands
  • 4Vrije Universiteit Amsterdam, Faculteit der Aardwetenschappen, 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 show 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 ANN for routing is greater than that when using sub-basin replacements. It can be concluded that the presented two schemes can improve the performance of process-based models in the context of flow forecasting.