Effective and efficient global optimization for conceptual rainfall‐runoff models

TLDR

Calibrating conceptual rainfall‑runoff models is difficult because automatic methods often fail to find unique optimal parameters, making sensitivity analyses unreliable. The study evaluates three global search procedures on the SIXPAR rainfall‑runoff model. The authors compare three global search methods and introduce the shuffled complex evolution (SCE‑UA) algorithm for optimizing SIXPAR. The study shows that the SIXPAR model has a severe multiple‑optima problem, local search methods rarely succeed, and the SCE‑UA algorithm reliably finds the global optimum efficiently.

Abstract

The successful application of a conceptual rainfall‐runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters associated with a given calibration data set can be found, it is difficult to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. Results are presented that establish clearly the nature of the multiple optima problem for the research CRR model SIXPAR. These results suggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. Next, the performance of three existing global search procedures are evaluated on the model SIXPAR. Finally, a powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE‐UA) method, which was able to consistently locate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem.