A demand-supply framework for evaluating the effect of resource and service constraints on community disaster resilience

Abstract

Tools that quantify community disaster resilience are essential for informed decision-making on community disaster resilience improvement measures. One of the major research gaps in quantifying community disaster resilience are community disaster recovery simulations. Such simulations enable an insight into factors that enable a rapid and efficient community disaster recovery and vice versa. The iRe-CoDeS framework presented in this paper, simulates community disaster recovery as a time-stepping loop, where at each time step the interplay of demand and supply of community components for various resources and services dictates components’ ability to operate and recover. Disaster resilience of a community is then quantified using a multi-dimensional metric, where each dimension represents the unmet demand of a community regarding a certain resource or a service, labelled Lack of Resilience (LoR). This paper presents how such a demand/supply approach can be applied to account for resource and service constraints, impeding factors, that prolong component recovery and thus decrease community disaster resilience. Housing resilience of North–East San Francisco exposed to a Mw7.2 earthquake on the San Andreas Fault is quantified to illustrate the proposed approach. rWhale application framework recently developed at the NHERI SimCenter is used for this purpose, presenting how such a regional simulation on the effect of natural disasters on communities can be extended using the iRe-CoDeS framework to simulate community disaster recovery and quantify community disaster resilience. It is shown that housing resilience quantification results obtained in the case study focused on a part of San Francisco are in accordance with the existing estimates of housing resilience. The evolution of the post-disaster community-level supply and demand for recovery resources and services is obtained, identifying how and when the unmet demand for these resources and services impedes community recovery. Lastly, the effect of community’s ability to mobilize resources and services needed for its recovery on its disaster resilience is investigated.