Evaluation of flood risk drivers as a tool to define municipal risk profiles

This work aims to compute a flood risk index (FRI) for the 278 Portuguese municipalities, designed to rank and characterize the drivers of fluvial flooding-related disasters (Santos et al., 2020). FRI is the product of hazard, exposure and vulnerability scores, where each factor is raised to 1/3, a solution also applied by the INFORM risk index to increase the dispersion of index values.Hazard considers two variables: flood susceptibility (SUSCF), and the weather and climate events index (WCE) translating the frequency of the rainfall events that may generate peak flows. SUSCF is the product of stream flood susceptibility (SFS) (Santos et al., 2019) and the main flood-prone areas (MFPA). SFS considers flow accumulation, slope angle and relative permeability, accounting for the cumulative effect of these factors along the entire basins’ area. MPFA results from overlaying areas with slope angle ≤ 2º and areas with Height Above Nearest Drainage ≤ 2, only when they were topologically connected to streams with SFS > 5.Exposure considers three variables: population density (PD), road density (RD) and the average degree of imperviousness (ADI). PD (inhab./km2) is based on the 2011 Census. RD (km/km2) is calculated from the OpenStreetMap© data. ADI is the municipal average value of the layer “IMD - Imperviousness Degree 2012 – 20 m resolution”, from the Copernicus Land Monitoring Service.Vulnerability (V) is the product of criticality and support capability, where the latter acts by attenuating criticality, according to the methodology presented by Tavares et al. (2018) to assess social vulnerability.The six core variables were scaled to the range [0, 1] following the max-min method. The respective weights were tested and selected according to the scientific literature, correlation and reliability tests.Ward’s clustering classification was used to define seven clusters of municipalities, differing in the scores of hazard, exposure and vulnerability. While it is suggested that municipalities in some clusters would require interventions to reduce hazard, others should invest on medium to long-term measures that address high exposure and vulnerability. The results obtained with this methodological approach contribute to the diversification of flood risk management strategies. Acknowledgements:This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide‑Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT through the project with the reference CEEIND/00268/2017. References:Santos, P.P., Pereira, S., Zêzere, J.L., Tavares, A.O., Reis, E., Garcia, R.A.C., Oliveira, S.C., 2019. A comprehensive approach to understanding flood risk drivers at the municipal level. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.110127Santos, P.P., Reis, E., Pereira, S., Santos, M., 2019. A flood susceptibility model at the national scale based on multicriteria analysis. Sci. Total Environ. 667, 325–337. https://doi.org/10.1016/j.scitotenv.2019.02.328Tavares, A.O., Barros, J.L., Mendes, J.M., Santos, P.P., Pereira, S., 2018. Decennial comparison of changes in social vulnerability: A municipal analysis in support of risk management. Int. J. Disaster Risk Reduct. 31, 679–690. https://doi.org/10.1016/J.IJDRR.2018.07.009