Calibrating and assessing uncertainty in coastal numerical models

As the methodology and philosophy of GLUE is well established in other fields, this paper presents a practical case study to explore the strengths and weaknesses of the method when applied to a relatively complex coastal numerical model (XBeach). The results obtained are compared to a previously reported and more 'standard' model calibration undertaken within the context of a coastal storm early warning system. While the GLUE method requires orders of magnitude more computational power, it is shown that its use in place of the more common one-at-a-time 'trial-and-error' approach to model calibration, provides: a significant improvement in predictive skill; a more rigorous evaluation of the model sensitivity to parameters; the ability to identify distinct differences in the XBeach model performance dependent on dune impact processes; and additional analysis including the quantification of parameter-induced uncertainty.