This work aimed at testing the capability of the numerical model SWASH to be implemented in the prototype of the overtopping and flooding forecast system HIDRALERTA for Ericeira harbour. In contrast to the neural network NN_OVERTOPPING2, which is currently implemented in HIDRALERTA, SWASH is able to estimate the flood extension and wave propagation along the domain, which makes it a possible improvement to NN_OVERTOPPING2. The one-dimensional version of the SWASH model was implemented to simulate overtopping at two different profiles (antifer and tetrapods) and calibrated for three storms in 2019 by comparing the simulated overtopping discharge to NN_OVERTOPPING2 results. For the calibration, the Manning coefficient was used to represent the friction of the armour layer. Then, for operational purposes, four expressions to calculate the Manning coefficient were developed based on: the relative crest freeboard, the wave steepness, the incident wave angle and the type of armour layer. The expressions showed small errors between the calculated and calibrated Manning coefficients and highlighted the importance of the incident wave angle to obtain an accurate calibration. Despite an underestimation of the overtopping discharge in some cases, the SWASH model was found to provide overall good results when applied with calculated Manning coefficients and suitable to be implemented in HIDRALERTA.

O HIDRALERTA é um sistema de avaliação, previsão e alerta do risco da ocorrência de situações de emergência associadas aos efeitos da agitação marítima (galgamento, inundação e navios amarrados) em zonas costeiras e portuárias. Sendo um sistema de alerta, é fundamental que funcione corretamente, quer em situações correntes (apenas emitindo alertas quando tal se justifique), quer em situações de tempestade. Neste trabalho são apresentados os desenvolvimentos efetuados até à data na validação do sistema em situações de tempestade, com a sua aplicação na simulação do furacão Lorenzo e das depressões Elsa e Fabien. Da análise efetuada pode concluir-se que os alertas emitidos pelo sistema estiveram em concordância com os danos/ocorrências reportadas localmente, o que contribui para reforçar a confiança no sistema que está a ser desenvolvido. A característica diferenciadora deste trabalho é que a avaliação do risco é efetuada com base nos parâmetros específicos do risco que se pretende prever, como caudais de galgamento ou as forças nas amarrações, e não apenas em parâmetros meteorológicos e oceanográficos.

Coastal flooding, particularly due to wave-induced flooding or overtopping occurs when runup surpass coastal defences, often occurring during high tides and surges. To protect coastal communities, early warning systems (EWSs) are essential, providing crucial information to anticipate and alert for hazardous situations. EWSs are vital for coastal planning, disaster risk reduction, and climate change adaptation, offering timely and accurate information to save lives and prevent economic and environmental losses. Despite the high benefit-to-cost ratio demonstrated in economic analyses, there are few examples of EWSs for coastal flooding worldwide (e.g., Garzon et al., (2023); Harley et al., (2016) and Stockdon et al., (2023) ). The goal of this study was to develop, test, and implement an operational wave-induced flooding early warning system for sandy beaches supported by machine learning techniques.

The IPCC’s 5th Assessment Report projects rising global temperatures and sea levels along with more frequent extreme weather events. Coastal areas, including Macaronesia, are highly vulnerable to these impacts, with Atlantic storms causing substantial socio-economic losses. This study investigates public perception of the rising coastal flood risks due to climate change in Garachico (Tenerife, Canary Islands) and Praia da Vitória (Terceira, Azores), as well as local willingness to adopt adaptation measures. Public surveys were conducted in both localities and these showed that Garachico’s population feels better informed and supported by government efforts, displaying greater resilience than Praia da Vitória’s population. This research underscores the importance of flexible, community-specific adaptation strategies to address varying levels of risk awareness, even in coastal areas that face similar climate-related threats.