Pros and cons of various efficiency criteria for evaluating hydrological models

Hydrological models are commonly applied to investigate impacts of climate change and variability on hydrology. Confidence in hydrological predictions is linked to the model’s performance in reproducing observed variable under consideration. Judgment of a model’s quality is challenged by the differences which exist among the various efficiency criteria. Some of the efficiency criteria commonly applied for assessment of hydrological models include Nash-Sutcliffe Efficiency, coefficient of determination and Kling and Gupta Efficiency. However, a plethora of recently introduced "goodness-of-fit" metrics exists including, for instance, the revised R-squared and Liu efficiency. Studies on comparative analysis of the various efficiency criteria applied to hydrological modeling considering both the old and recently introduced "goodness-of-fit" metrics are lacking. In this study, several efficiency criteria are compared with respect to the computation difficulty, and speed of computation while considering various sample sizes. The pros and cons of the various (both old and new) efficiency criteria are given. The results of this study show the need for modelers to gain insights into the impact of each "goodness-of-fit" metric on model performance assessment before making hydrological predictions for planning predictive adaptations to the impacts of climate change or variability on hydrology.