Effectiveness assessment of risk reduction measures at coastal areas using a decision support system: Findings from Emma storm

Abstract

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 2 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, minimising implementation costs and reducing the risk to the occupation and to the people.

Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal

“…In that regard, the publication peaks highlight the application of AI technologies to mitigate social and climate issues, instead of looking for industrial solutions, contrary to the trends today [41][42][43][44]. In light of the above, the social and climate issues to which AI has offered relevant solutions, and which, in turn, are of interest to Portuguese policy, are related to population aging [45], climate change [46,47], green energies [48], and water resource management/planning [49][50][51][52][53]. Most of these articles have used decision-making applications, enhanced by engineering tools and computational technologies, in the fields of environmental sciences, agricultural and biological sciences, energy, and social sciences (Figure 3).…”

Simulating Destructive and Constructive Morphodynamic Processes in Steep Beaches

“…The study concentrates on Faro Beach (Praia de Faro), an inhabited stretch in the central part of Ancão Peninsula, the westmost barrier of Ria Formosa (in S. Portugal; Figure 1), that in early March of 2018 was fiercely impacted by storm Emma, the most recent severe storm in the area, causing strong erosion, overwash in low-lying zones and property damages [13]. Occupation in Faro Beach is recent (since the 1930s) and relatively small (623 buildings, of which 378 lie in the central part).…”

“…Even though the settlement has experienced repeated storm-induced damages in infrastructure and private buildings, residents seem to underestimate the related risks [24]. In fact, the major coastal hazards in the area have been related to the action of high-energy storms, inducing erosion and overwash [13,24], while the related risks for buildings and infrastructure over this barrier stretch have been assessed as very high [25,26].…”

“…At the same time, current state-of-the-art coastal morphodynamic models (e.g., XBeach) are capable of accurately reproducing storm-induced beach erosion, but much work is needed to improve the accuracy of modelled beach response during calm periods, as physical processes governing recovery are poorly understood [9]. Process-based models have mainly been used to study the effect of a single (or a few) storm sequence(s) on beach profiles (short-term influence) [9] (e.g., [12]), or as part of risk reduction measure decision support tools [13]. When accounted for, beach profile post-storm recovery is usually modelled using semi-empirical equilibrium profile models [14][15][16][17].…”

Probabilistic characterisation of coastal storm-induced risks using Bayesian Networks

“…As the impact of successive storms separated by relatively short fair-weather periods may be different to that of single events depending on storm characteristics and initial beach configuration (e.g. Dissanayake et al 2015;Eichentopf et al 2020), we retained these storms in the analysis. Thus, to properly account for their potential effects, all existing identified https://doi.org/10.5194/nhess-2020-99 Preprint.…”