Calibration and verification of a hydrological model using event data
Main Article Content
Abstract
Abstract. The topic of calibration and verification of rainfall-runoff model has been subject of many researches. However, most of the researches using the continuous data for this task, while in the conditions ofVietnam, it is difficult to collect the sub-day continuous data. This leads to the need for methods that can calibrate and verify the model parameters from the event data. This paper introduces such a method. Idea of the method is to combine the auto-calibration and trial-and-error methods. Auto-calibration is executed to locate the optima sets of parameters for individual storm event by using the shuffled complex evolution algorithm. Then, the trial-and-error method will attempt to find the most suitable parameters for all of the events in the ranges defined by the parameters in the auto-calibration step. The method was applied to calibrate and verify MIKE-NAM model parameters with the case study of Ben Hai river basin. Because the searching space of parameters is narrowed, it is much easier and quick to find the best overall parameters than the traditional trial-and-error method.
Keywords: Rainfall-runoff, event data, auto- calibration, trial-and-error, searching space.References
[2] J.C. Refsgaard, B. Storm, Construction, calibration and validation of hydrological models, Distributed hydrological modelling. Dordrecht, Netherlands. Kluwer Academic Publishers, 1996.
[3] H.V. Gupta, S. Sorooshian, P.O.Yapo, Toward improved calibration of hydrological models: multiple and noncommensurable measures of information, Water Resour. Res. 34 (4), 1998, 751.
[4] G. Lindstro¨m, A simple automatic calibration routine for the HBV model, Nordic Hydrol. 28 (3), 1997, 153.
[5] S.Y. Liong, S.T. Khu, W.T. Chan, Construction of multiobjective function response surface with genetic algorithm and neural network. In: Proceedings of the International Conference on Water Resources and Environmental Research, 29–31 October, Kyoto, Japan, vol. II, 1996, pp. 31–38.
[6] S.Y. Liong, S.T. Khu, W.T. Chan, Derivation of Pareto front with accelerated convergence genetic algorithm, ACGA. In: Babovic, V., Larsen, L.C. (Eds.). Hydroinformatics’98, Balkema, Rotterdam, The Netherlands, 1998, pp. 889–896.
[7] P.O. Yapo, H.V. Gupta, S. Sorooshian, Multi-objective global optimization for hydrological models. J. Hydrol. 204 (1998) 83.
[8] DHI Water & Environment, 2004. MIKE 11 Reference Manual.
[9] S. Shamsudin, N. Hashim, Rainfall runoff simulation using MIKE11 NAM. Jurnal kejuruteraan awam, Journal of civil engineering, vol. 15, No. 2, 2002.
Madsen, H., 2000. Automatic calibrating of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology 235 (2000) 276.