Optimal sediment transport for morphodynamic model validation

Although commonly used for the validation of morphological predictions, point-wise accuracy metrics, such as the root-mean-squared error (RMSE), are not well suited to demonstrate the quality of a high-variability prediction; in the presence of (often inevitable) location errors, the comparison of depth values per grid point tends to favour...

Scale-selective Validation of Morphodynamic Models

Although it is generally acknowledged that the practical predictability at smaller scales may be limited, output of high-resolution morphodynamic area models is mostly presented at the resolution of the computational grid. The so- presented fields typically are realistic looking, but not necessarily of similar quality at all spatial scales....

Displacement-based error metrics for morphodynamic models

The accuracy of morphological predictions is generally measured by an overall point-wise metric, such as the mean-squared difference between pairs of predicted and observed bed levels. Unfortunately, point-wise accuracy metrics tend to favour featureless predictions over predictions whose features are (slightly) misplaced. From the perspective...

Displacement based error metric for morphodynamic models (abstract)

The quality of a morphological prediction is often expressed by an overall grid-point based skill score based on the Mean Squared Error (MSE) between the predicted and observed bed levels (Sutherland et al., 2004). Although the MSE is a good measure of the overall error between model and observations, it tends to penalize rather than reward the...