Coastal flooding is often driven by the combined action of storm surges and waves, which co-occur along more than half of the world’s coastlines. Vegetation such as mangroves and salt marshes provides natural protection in these events by attenuating surges, dissipating wave energy, and reducing wave setup. While advanced process-based models (e.g. XBeach) can capture such interactions, their high computational demands limit their use for large-scale or scenario-based simulations. In contrast, reduced-physics models (e.g. SFINCS–SnapWave) enable efficient simulations under combined coastal forcing, but their ability to represent vegetation–hydrodynamic interactions remains limited. This thesis improves the SFINCS–SnapWave model by incorporating vegetation-induced drag due to nonlinear wave shape (F_v,w). Model performance was evaluated against laboratory flume data and benchmarked with the process-based XBeach surfbeat model (XBeach-SB). Results show that adding F_v,w substantially improves predictions of mean water levels in vegetated foreshores, reducing setup errors at the landward end by an average of a factor of six compared to the baseline model across all scenarios. This improvement arises because F_v,w represents a vegetation drag component that counteracts wave-breaking forcing, thereby correcting the excessive setup otherwise produced by dissipation-only formulations. Significant wave height predictions remained accurate, and computational efficiency was preserved due to the empirical wave-shape approach. These findings underline the importance of vegetation in wave-driven flooding. Further improvements—such as incorporating mean-flow vegetation drag and validating against field-scale data—are recommended to extend model reliability. With these extensions, the enhanced SFINCS–SnapWave has the potential to serve as a robust and efficient tool for simulating multi-driver flooding in vegetated coasts.