Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration

Automatic calibration routines for hydrologic models with multiple objective capabilities are becoming increasingly popular due to advances in computational power, population‐based optimization techniques, and the recognition that a single performance measure such as the root‐mean‐square error is no longer sufficient to characterize the complex behavior of the catchment. However, as more objective functions are included in the calibration, the number of Pareto‐optimal solutions as well as the number of “near” Pareto‐optimal parameter sets increases. The calibration problem quickly becomes a decision‐making problem. In the practical sense, users of automatic calibration routines have to face the task of selecting a set of suitable model parameters from the numerous Pareto‐optimal sets. A new method of automatic calibration is proposed, which combines an effective optimization routine, based on multiobjective genetic algorithms, and Pareto preference ordering. In this case, Pareto‐optimal points that are also Pareto‐optimal in different subspace combinations of the objective functions space are preferred. The proposed method is used to calibrate the MIKE11/NAM rainfall‐runoff model for a Danish catchment. The results indicated that the method is able to sieve through the numerous Pareto‐optimal solutions and select a small number of preferred solutions. This is extremely useful to modelers who typically are required to provide the best estimated parameter sets with good overall model performance.