A CONCEPTUAL RAINFALL-RUNOFF MODEL CONSIDERING SEASONAL VARIATION

초록

Among various deterministic rainfall-runoff models, the tank model which is a typical conceptual rainfall-runoff model os often perferred for its simple concepts. On the other hand, it requires much time and effort to obtain better results owing to the calibration of too many parameters in the model. Therefor, the demand for applying an automatic calibration method has been increased. In this study, three optimization algorithms are tested for automatic calibration: one nonlinear programmng algorithm (Powell's method) and two meta-heuristic algorithms: GA(Genetic Algorithm) and HS(Harmony Search). The achievement of the prwerful heuristic optimization algorithms enables researchers to focus on other aspects of the tank model than parameter calibration. The seasonal tank model is devised from the concept that seasonally different watershed response could reflected by seasonally different parameter valuse. Powerful optimization tool enables parameter calibration of the seasonal tank model that has 40 parameters, much increased number in comparison with 16 parameters of the non-seasonal tank model. In comparison, the seasonal tank model shows smaller SSQLs than those of the non-seasonal tnak model. The seasonal tank model could be a successful alternative rainfall-runoff simualtion model for its accuracy and convenience.