Integration of a statistical emulator approach with the SCE-UA method for parameter optimization of a hydrological model

Parameter optimization of a hydrological model is an indispensable process within model development and application. The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process, resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve. The classical approaches to global parameter optimization are usually characterized by being time consuming, and having a high computation cost. For this reason, an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed, and applied within this study to optimize hydrological model parameter estimation. Meta-modeling was used to determine the optimization range for all parameters, following which the SCE-UA method was applied to achieve global parameter optimization. The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model. In this study, the daily distributed time-variant gain model (DTVGM) applied to the Huaihe River Basin, China, was chosen as a case study. The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance. The case study shows that the integrated method can efficiently complete the multi-parameter optimization process, and also demonstrates that the method is a powerful tool for efficient parameter optimization.