Continental Portuguese Territory Flood Susceptibility Index – contribution for a vulnerability index

Abstract

Abstract. This work defines a national flood susceptibility index for the Portuguese continental territory, by proposing the aggregation of different variables which represent natural conditions for permeability, runoff and accumulation. This index is part of the national vulnerability index developed in the scope of Flood Maps in Climate Change Scenarios (CIRAC) project, supported by the Portuguese Association of Insurers (APS). This approach expands on previous works by trying to bridge the gap between different floods mechanisms (e.g. progressive and flash floods) occurring at different spatial scales in the Portuguese territory through: (a) selecting homogeneously processed datasets, (b) aggregating their values to better translate the spatially continuous and cumulative influence in floods at multiple spatial scales. Results show a good ability to capture, in the higher susceptibility classes, different flood types: progressive floods and flash floods. Lower values are usually related to: mountainous areas, low water accumulation potential and more permeable soils. Validation with independent flood datasets confirmed these index characteristics, although some overestimation can be seen in the southern region of Alentejo where, due to a dense hydrographic network and an overall low slope, floods are not as frequent as a result of lower precipitation mean values. Future work will focus on: (i) including extreme precipitation datasets to represent the triggering factor, (ii) improving representation of smaller and stepper basins, (iii) optimizing variable weight definition process, (iii) developing more robust independent flood validation datasets.

Comparing flood mortality in Portugal and Greece (Western and Eastern Mediterranean)

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Assessment of stream flood susceptibility: a cross‐analysis between model results and flood losses

“…A database of historical records of flood losses was compiled for the Águeda basin in order to validate the different combinations of weights (Reis, ; Jacinto et al , ).…”

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Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms

“…On a global scale, oods constitute the major natural hazards causing the greatest incidence of damaged property as measured by the number of material damages caused by this phenomenon (Jacinto et al, 2015). The hazards associated with oods therefore pose a great challenge for each and every one of the authorities concerned with the ood risk management activity (Luino et al, 2018).…”