Storms impact coastal areas often causing damages and losses at occupied areas. On a scenario of increasing human occupation at coastal zones and under climate change conditions (including sea level rise and increasing frequency of extreme sea levels), the consequences of storms are expected to be amplified if no adaptation or further management actions are implemented. The selection of the best possible coastal management measures, considering both costs and effectiveness, will be mandatory in the future, in order to optimise resources. This work analyses the performance of risk reduction measures (beach nourishment and receptors - house and infrastructures - removal), using a decision support system comprised by a morphodynamic numerical model (XBeach) and a Bayesian network based on the source-pathway-receptor concept. The effectiveness of the risk reduction measures is then assessed by a simple index expressing the consequences to the receptors. The approach was tested at Faro Beach by evaluating its performance for a particular storm, Emma (Feb/March 2018), which fiercely impacted the southern coast of Portugal. The output results from the modelling were compared to field observations of the actual damages caused by the storm. The combined use of both measures or the solely use of the nourishment would avoid almost all observed impacts from this storm. The work is pioneer on demonstrating the use of a decision support system for coastal regions validated against observed impacts for a high-energy storm event. The methodology and the proposed index are adaptable to any sandy coastal region and can be used to test (and improve) management options at a broad number of coastal areas worldwide, minimizing implementation costs and reducing the risk to the occupation and to the people.
Keywords: Decision support systems; Management; Occupation; Risk reduction; Storm impacts.
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