Coastal dunes serve as the primary defence mechanism against coastal storms for many coastal communities around the world. Vegetation plays a role in increasing dune resiliency as it enables dune growth, however not enough is known about its effects during storms. Research has shown that the current climate crisis will increase the intensity of coastal storms as the sea level rises and global temperatures increase. It is therefore of utmost importance to understand the effects of vegetation on coastal dunes. Particularly with current trends of nature-based solutions to plant vegetation in dune restoration projects.

In this master thesis, the impact of Hurricane Ian on two barrier islands is analyzed and modelled. The primary focus of the study was to investigate how dune vegetation influenced the erosional effects of the storm, with a particular emphasis on enhancing the existing methodologies for incorporating vegetation into morphological models such as XBeach. The research findings derived from the data analysis revealed that the most resilient dunes are high, broad and have a dense vegetation coverage. Moreover, the model outcomes highlight the substantial improvement in predictive accuracy achieved by integrating vegetation as a bed roughness coefficient within the XBeach model. Adding vegetation to the model directly influences current velocities, but does not affect water levels, wave heights, or infragravity waves. The primary influence of vegetation becomes pronounced when an island is inundated or breached, significantly reducing the currents and sediment transport caused by water level gradients. An additional effect of vegetation was observed in a comparative analysis between vegetated and unvegetated models. This analysis highlighted a delay in dune crest lowering when vegetation was present, showcasing the importance of vegetation in shaping the response of barrier islands during storm events.

Numerical modelling can help understand the complex processes that shape barrier islands during storms. This research emphasized the necessity of including vegetation in XBeach models to enhance their predictive capabilities. The best predictions occur when high-resolution bathymetric data is combined with land use land cover (LULC) data to include vegetation as a constant bed roughness parameter. Furthermore, reducing the land classes to four different ones based on dune vegetation zones improves the results of the model and facilitates the calibration of bed friction coefficients.

The most effective models applied in this study demonstrated impressive skill, ranging from good to excellent, accurately predicting breaches in the precise areas they occurred for both islands under investigation. This research contributes to the continued improvements of modelling with XBeach and provides a detailed method of analyzing the effects of dune vegetation on dune erosion to determine the impact of Hurricanes in coastal dunes.

Many coral reef islands are low-lying, which in combination with population growth, sea level rise and possibly more frequent extreme weather events is likely to result in increased coastal risk (e.g. Storlazzi et al., 2015). On smaller scales of O(10 km) wave-driven coastal inundation can be accurately predicted with advanced models such as XBeach (Roelvink et al., 2009), at already high computational costs. For larger scales, larger number of islands, for scenario modelling, and for implementation in early warning systems, computationally faster methods are needed. Reduced physics models, which neglect some of the processes (e.g. non-hydrostatic pressure gradient term and viscosity), are a potential solution. However, their accuracy and the best method to force them has not been established. In this research we propose a new methodology to model wave-driven flooding on coral reef-lined coasts. A look-up-table (LUT), composed of XBeach model runs, is combined with a reduced-physics model, SFINCS (Leijnse et al., 2021), to achieve high accuracy predictions at limited computational expense. The LUT consists of pre-run 1D XBeach simulations for several reef profiles from Scott et al. (2020), forced with different offshore wave and water level conditions. Wave conditions close to the shore as predicted by the LUT are used to force SFINCS which then simulates the wave runup, overtopping and flooding. These are forced in SFINCS using random wave timeseries from an interpolated parameterized wave spectrum following Athif (2020). The accuracy of the method is investigated for 6 distinctive cross-shore profiles from Scott et al. (2020), for two wave scenarios (gentle swell and stormy conditions). Results of complete XBeach simulations are compared to LUT-SFINCS simulations with different boundary forcing locations. The sensitivity analysis shows that the most optimal boundary location to initialize the SFINCS model is at a water depth of 0.5 m, shore-ward of the reef edge. Interpolation of the forcing conditions at the boundary is investigated with 9 different interpolation methods. Results reveal that the most accurate method to interpolate spectral parameters (the amount of high frequency wave energy, the amount of low frequency wave energy and the frequency peak of high frequency part of the spectrum) and wave setup at the boundary is the Inverse Distance Weighting method with a power of -2. Errors introduced by the interpolated parameterized boundary conditions lead to runup estimation errors of 10-20% on average with the maximal errors up to 60%. Simulating wave runup with XBeach LUT – SFINCS couple leads to about 50-times higher computational speed compared to the XBeach model simulating hydrodynamic processes along the entire reef profile.