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According to Global Assessment Report on Disaster Risk Reduction [GAR, 2022], under current climate conditions, the world number of disasters per year will increase by 40% by 2030. The Intergovernmental Panel on Climate Change [IPCC, 2021] highlights that human-induced climate change is already affecting the frequency of extreme weather and climate events, such as floods, heat waves, droughts, and cyclones. For this reason, in the coming years, impacts caused by natural disasters will become central in economic, social, and ecological domains. In this interdisciplinary and dynamic context where compound events will no longer be extraordinary phenomena, it appears essential to understand and investigate how a close natural disaster can occur in altered and dynamic conditions dissimilar from the standard ones and how this can influence and modify the consequences of such natural disasters.In this context, resilience is a key element. White1 explored community resilience as the capability to anticipate risk, limit impact, and recover rapidly from a natural disaster. According to the latter definition, resilience might become a valuable parameter to analyse post-event restoration. Several researchers have focused on introducing quantitative metrics to assess resilience (Bruneau2, Cimellaro3, and Reed4 ). The main priority is comparing quantitative resilience measures to obtain a first step towards analysing the recovery phase of a flood event.In our context, resilience is defined according to the residual functionality of infrastructure or community, where functionality only refers to physical damage and not to a possible failure or interruption of service facilities. Therefore, the post-event recovery stage strongly affects residual functionality and resilience. A quantitative evaluation relies on calculating characteristic metrics such as the area under the restoration curve. Damage ratio, interarrival time, and recovery curve shape are the core of sensitivity analysis. The aim is to investigate how the interaction between these factors influences and modifies the system's resilience. Including the post-disaster restoration phase, by introducing dynamic vulnerability, allows to improve risk assessment and to provide decision-makers with a complete overview of impacts induced by compound events.[1] White, R. K., Edwards, W. C., Farrar, A., & Plodinec, M. J. (2015). A Practical Approach to Building Resilience in America’s Communities. American Behavioral Scientist, 59(2), 200-219.[2] Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; von Winterfeldti, D. A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. Earthq. Spectra 2003, 19, 733–752.[3] Cimellaro G, Reinhorn A, Bruneau M. Framework for analytical quantification of disaster resilience. Eng Struct 2010;32:3639–49.[4] Reed, D.A.; Kapur, K.C.; Christie, R.D. Methodology for Assessing the Resilience of Networked Infrastructure. IEEE Syst. J. 2009, 3, 174–180. |
