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

• Published in: Hydrology and earth system sciences 2009-01, Vol.13 (9), p.1619-1634
• Main Authors: Corzo, G.A, Solomatine, D.P, Hidayat, Wit, M. de, Werner, M, Uhlenbrook, S, Price, R.K
• Format: Article
• Language: English
• Subjects: Freshwater; hydrologic models; IHMS-HBV model; mathematical models; Meuse River; neural networks; simulation models; stream flow; watershed hydrology; watersheds
• ISSN: 1027-5606; 1607-7938; 1607-7938
• DOI: 10.5194/hess-13-1619-2009

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.