Application of Generalized Pattern Search Algorithm in Parameter Optimization of Hydrological Models
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Abstract:
Parameter optimization plays a key role in the research of hydrological model. The model performance is closely related to the determination of parameters. In this paper, the Generalized Pattern Search (GPS) algorithm was analyzed, and then its application in the parameter optimization of SIMHYD model was assessed based on the observed hydrological and meteorological data in two catchments, including one located in China with an area of 2000 km2 and the other one located in Australia with an area of 760 km2. The results indicated that (1) the Nash-Sutcliffe efficiency (NSE) coefficients are higher than 0.7 and the absolute Water Balance Errors (WBE) are lower than 6% for the catchment (Ⅰ) in both model calibration and validation, and the RMSEs are 2.24 mm/d and 2.21 mm/d in model calibration and validation, respectively; and (2) for the catchment (Ⅱ), NSEs are also higher than 0.7 and the absolute WBEs are less than 5% in both model calibration and validation, and the RMSEs are 0.11mm/d and 0.13mm/d in model calibration and validation, respectively. The GPS algorithm has a global convergence and stability and high computational efficiency and accuracy, therefore it is suitable for parameter optimization of the conceptual hydrological models.
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