Pluvial floods can cause severe socio-economic impacts on coastal urban areas like the Metropolitan Area of Barcelona. This study combined the development of high-resolution flood maps, based on a large-scale coupled 1D/2D model and empirical functions, to quantify direct economic damage to buildings and determine risk to pedestrians and vehicles. Importantly, the flood model included a network of 36 municipalities and covered 636 km2. Three scenarios were considered: single-hazard (extreme precipitation), multi-hazard (coincident extreme precipitation and storm surge), and adaptation (implementation of resilience measures). In total, 20 rain events were applied for each scenario: 5 were historic design storms, while 15 considered the effect of climate change (60 simulations in total). By the end of the century, results show potential increases in expected annual damage of up to 36%, from €139.8 M to €190.3 M. Risk for pedestrians could increase by 25% (494 ha to 620 ha) and for vehicles by 26% (59 km to 75 km) in the T10 single-hazard scenario. In the multi-hazard case, the socio-economic impacts are approximately 5% higher, while the adaptation simulations considering sustainable urban drainage systems show reductions between 6 and 18%. The metropolitan results were compared and validated with a previous assessment done in the City of Barcelona. Based on these results, urban planners, emergency responders, and public administrations can develop effective adaptation measures based on cost–benefit analyses for current and future climate scenarios. Compared to previous studies, this approach adapts existing urban-scale methodologies to regional-scale flood risk assessment.
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Àlex de la Cruz-Coronas
International Center for Numerical Methods in Engineering
Beniamino Russo
AQU Catalunya
Sofia Pacho-Gómez
FC Barcelona
Sustainability
Universitat Politècnica de Catalunya
FC Barcelona
International Center for Numerical Methods in Engineering
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Cruz-Coronas et al. (Mon,) studied this question.
synapsesocial.com/papers/69fbe3aa164b5133a91a2f7d — DOI: https://doi.org/10.3390/su18094530