Calibrating and assessing uncertainty in coastal numerical models

Abstract Advanced numerical models used to predict coastal change at a variety of time and spatial scales often contain many free parameters that require calibration to the available field data. At present, little guidance (beyond the adoption of the default values provided) is available in the field of coastal engineering to inform the selection of best-fit parameter values. Common calibration techniques can often lack a rigorous quantification of model sensitivity to parameters and parameter-induced model uncertainty. Here we employ the Generalised Likelihood Uncertainty Estimation (GLUE) method to address these issues. The GLUE method uses Monte Carlo sampling to assess the skill of many different combinations of model parameters when compared to observational data. As a rigorous modelling framework, the GLUE method provides a series of standard tools that assist the modeller to analyse model sensitivity, undertake parameter optimisation and quantify parameter-induced uncertainty. In addition, new tools are presented here to identify where unique calibrated parameter sets are required for different observational data (e.g., should the calibrated parameter set differ between alongshore locations at a site) and investigate the convergence of GLUE estimated optimum parameter values over increasing numbers of Monte Carlo samples. 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.