Nearshore Observations and Modeling: Synergy for Coastal Flooding Prediction

Abstract

Coastal inundation has recently started to require significant attention worldwide. The increasing frequency and intensity of extreme events (sea storms, tsunami waves) are highly stressing coastal environments by endangering a large number of residential areas, ecosystems, and tourist facilities, and also leading to potential environmental risks. Predicting such events and the generated coastal flooding is thus of paramount importance and can be accomplished by exploiting the potential of different tools. An example is the combination of remote sensors, like marine radars, with numerical models. Specifically, while instruments like X-band radars are able to precisely reconstruct both wave field and bathymetry up to some kilometers off the coast, wave-resolving Boussinesq-type models can reproduce the wave propagation in the nearshore area and the consequent coastal flooding. Hence, starting from baseline simulations of wave propagation and the conversion of water elevation results into radar images, the present work illustrates the reconstruction of coastal data (wave field and seabed depth) using a specifically suited data processing method, named the “Local Method”, and the use of such coastal data to run numerical simulations of coastal inundation in different scenarios. Such scenarios were built using two different European beaches, i.e., Senigallia (Italy) and Oostende (Belgium), and three different directional spreading values to evaluate the performances in cases of either long- or short-crested waves. Both baseline and inundation simulations were run using the FUNWAVE-TVD solver. The overall validation of the methodology, in terms of maximum inundation, shows its good performance, especially in cases of short-crested wind waves. Furthermore, the application on Oostende Beach demonstrates that the present methodology might work using only open-access tools, providing an easy investigation of coastal inundation and potential low-cost integration into early warning systems.