HAO-CHE HOYa-Ting ChangYuan-Shun Chang2025-05-062025-05-062025-04-01https://www.scopus.com/record/display.uri?eid=2-s2.0-86000502521&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/728949The formulation of effective flood control strategies to enhance climate resilience necessitates the quantification of factors related to mitigation and adaptation. This study introduces a flood resilience index (FRI) that incorporates not only the physical risks of flooding but also socioeconomic conditions and time-varying factors. A variety of rainfall scenarios were considered in assessing the various elements of the FRI curve, including the hazard, nadir, and the rate at which resilience changes. The findings indicate that FRI impacts are directly influenced by rainfall intensity, while the slope of the falling limb is affected by rainfall patterns. The curve shape is defined by the perspective of the nadir. Furthermore, the evolution curves of the FRI and the hazard exhibit a phase difference due to the interaction among hazard, exposure, and sensitivity factors. These results provide crucial insights into the optimal timing of disaster relief and mitigation strategies. Under high-hazard conditions, disaster mitigation measures within adaptation pathways must focus on reducing the impact of physical flooding, while considering socioeconomic factors that significantly influence the rising limb of the FRI curve to mitigate disaster risks. In mild and moderate hazard scenarios, relief efforts should be guided by an understanding of the dominant hazard-related factors (e.g., flood depth and area) and socioeconomic stability. The proposed dynamic risk map has the potential to facilitate more effective disaster risk management and enhance overall resilience.Disaster risk managementDynamic risk mapFlood modelingFlood resilience indexSocioeconomic factors[SDGs]SDG2[SDGs]SDG3[SDGs]SDG6[SDGs]SDG9[SDGs]SDG11[SDGs]SDG13[SDGs]SDG14journal article10.1016/j.ijdrr.2025.105387