Parameter optimization of distributed hydrological model with a modified dynamically dimensioned search algorithm

A modified version of the dynamically dimensioned search (MDDS) is introduced for automatic calibration of watershed simulation models. The distinguishing feature of the MDDS is that the algorithm makes full use of sensitivity information in the optimization procedure. The Latin hypercube one-factor-at-a-time (LH-OAT) technique is used to calculate the sensitivity information of every parameter in the model. The performance of the MDDS is compared to that of the dynamically dimensioned search (DDS), the DDS identifying only the most sensitive parameters, and the shuffled complex evolution (SCE) method, respectively, for calibration of the easy distributed hydrological model (EasyDHM). The comparisons range from 500 to 5000 model evaluations per optimization trial. The results show the following: the MDDS algorithm outperforms the DDS algorithm, the DDS algorithm identifying the most sensitive parameters, and the SCE algorithm within a specified maximum number of function evaluations (fewer than 5000); the MDDS algorithm shows robustness compared with the DDS algorithm when the maximum number of model evaluations is less than 2500; the advantages of the MDDS algorithm are more obvious for a high-dimensional distributed hydrological model, such as the EasyDHM model; and the optimization results from the MDDS algorithm are not very sensitive to either the variance (between 0.3 and 1) for randn′ used in the MDDS algorithm or the number of strata used in the Latin hypercube (LH) sampling.