On the perception of morphodynamic model skill

Abstract The quality of morphodynamic predictions is generally expressed by an overall grid-point based skill score, which measures the relative accuracy of a morphological prediction over a prediction of zero morphological change, using the mean-squared error (MSE) as the accuracy measure. Through a generic ranking for morphodynamic model predictions, this MSE based skill score (MSESS) aims at making model performance comparable across different prediction situations (geographical locations, forcing conditions, time periods, internal dynamics). The implicit assumptions underlying this approach are that the MSE is an appropriate measure of correspondence for morphological predictions and that the accuracy of the initial bed as the reference correctly reflects the inherent difficulty or ease of prediction situations. This paper presents a thorough analysis of the perception of model skill through the MSE skill score. Using synthetic examples, an example from literature and a long-yearly Delft3D model simulation, we demonstrate that unexpected skill may be reported due to a violation of either of the above assumptions. It is shown that the accuracy of the reference fails to reflect the relative difficulty of prediction situations with a different morphological development prior to the evaluation time (for instance trend, cyclic/seasonal, episodic, speed of the development). We further demonstrate that the MSESS tends to favor model results that underestimate the variance of cumulative bed changes, a feature inherited from the MSE. As a consequence of these limitations, the MSESS may report a relative ranking of predictions not matching the intuitive judgment of experts. Guidelines are suggested for how to adjust calibration and validation procedures to be more in line with a morphologist's expert judgment.