Performance of the dynamically dimensioned search algorithm: influence of parameter initialization strategy when calibrating a physically based hydrological model

Abstract

Abstract This study presents an evaluation of the performance of the dynamically dimensioned search (DDS) algorithm when calibrating the hydrological component of the Visualizing Ecosystems for Land Management Assessments (VELMA) ecohydrological model. Two calibration strategies were tested for the initial parameter values: (1) a ‘high-cost strategy’, where 100 sets of initial parameter values were randomly chosen within the overall parameter space, and (2) a ‘low-cost strategy’, where a unique set of initial parameter values was derived from the available field data. Both strategies were tested for six different values of the maximum number of model evaluations ranging between 100 and 10,000. Results revealed that DDS is able to converge rapidly to a good parameter calibration solution of the VELMA hydrological component regardless of the parameter initialization strategy used. The accuracy and convergence efficiency of the DDS algorithm were, however, slightly better for the low-cost strategy. This study suggests that initializing the parameter values of complex physically based models using information on the watershed characteristics can increase the efficiency of the automatic calibration procedures.