The accurate prediction of a barrier island response to storms is challenging because of the complex interaction between hydro- and morphodynamic processes that changes at different stages during an event. Assessment of the predictive skill is further complicated because of uncertainty in the hydraulic forcing, initial conditions, and the parameterization of processes. To evaluate these uncertainties, we investigated the morphological change that occurred during two Atlantic hurricane events on two barrier islands at Matanzas (Florida) and Fire Island (New York) with differing topographies and forcing conditions. We used the morphodynamic model XBeach with hydrodynamic forcing extracted from a regional coupled D-Flow FM/SWAN model. The XBeach model was initialized with a spatially varying roughness map derived from a land cover classification map generated with supervised conditional-random-field classification. The model was supplemented with a dynamic roughness module recognizing that, under extreme conditions, vegetation can be washed away or buried by sediment. For the Fire Island case, the modeled spatial extent of roughness reduction as a proxy for vegetation removal during the storm was accurate. For both the Fire Island and Matanzas cases, the model predicted erosion and deposition volumes and dune-crest lowering well. The occurrence of breach formation was also predicted by the model, but the exact location of these breaches did not match observations. Variations of 10% in boundary conditions (surge, wave direction, significant wave height, and bay water levels) produced regime shifts in modeled barrier island response. These results not only stress the critical role of boundary conditions in morphodynamic model skill, but also show the limitations of single deterministic model runs in forecasting impact.
van der Lugt, M. A., Quataert, E., van Dongeren, A., van Ormondt, M., & Sherwood, C. R. (2019). Morphodynamic modeling of the response of two barrier islands to Atlantic hurricane forcing. Estuarine, Coastal and Shelf Science, 229. https://doi.org/10.1016/j.ecss.2019.106404