Advancements in coastal flood modeling with LISFLOOD-FP: incorporating the dynamic of waves and dune failure

Coastal inundation presents a substantial risk to human lives and economic assets, driving the need for effective mitigation strategies. Recently, Nature-Based Solutions (NBS) have emerged as sustainable and adaptive alternatives to traditional "gray" infrastructure for coastal hazard management. Accurate modeling of coastal flooding and its interactions with NBS is essential for effective risk assessment, but challenges remain due to data limitations and modeling uncertainties.

Flood modeling techniques range from simplistic bathtub models to advanced hydromorphodynamic approaches. Simplified dynamic models, such as LISFLOOD-FP, which solve shallow water equations for floodplain processes, offer a practical balance between computational efficiency and accuracy.

This study enhanced LISFLOOD-FP model's ability to simulate coastal flooding by incorporating wave contributions (setup and swash), and their interactions with protective features like temporary dunes, along potential erosion and failures. These advancements were tested in Cesenatico, a coastal town in Emilia-Romagna, Italy, where seasonal dunes are constructed each winter as temporary defenses against flooding.

The enhanced model was validated using two storm events: the 2015 Saint Agatha Storm, which breached dunes and caused extensive flooding, and the 2022 Denise Storm, during which intact dunes mitigated flood impacts. The enhanced LISFLOOD-FP model significantly improved flood simulations, particularly for the 2022 event, accurately reproducing flooded areas in the presence of temporary dunes. These findings underscore the model's ability to capture the protective effects of NBS and highlight the importance of appropriately sizing such defenses.

The study also underscores the critical impact of data uncertainty on coastal flood modeling. Specifically, the lack of detailed topographic data on the location and dimensions of temporary dunes introduces significant uncertainty, with small variations in dune height—on the scale of centimeters—potentially determining whether dunes collapse or resist storm impacts. This uncertainty is compounded by the scarcity of observational flood maps, which limits rigorous model validation and reliability assessments.

This work represents a significant step toward developing a digital twin of coastal NBS, providing a robust framework for coastal management. Digital twins enable the exploration of "what-if" scenarios, optimization of defenses, evaluation of strategies, and generation of probabilistic flood forecasts, marking an important advancement in sustainable, science-driven coastal resilience planning.