The interdependent networked community resilience modeling environment(IN-CORE)

In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households,social institutions,and local economy.Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis,it is necessary but not sufficient for community resilience planning.While there have been several studies on hurricane risk and recovery assessment at the building-and community-level,few studies have focused on the nexus of coupled physical and social disruptions,particularly when char-acterizing recovery in the face of coastal multi-hazards.Therefore,this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards(e.g.,wind,storm surge,wave)by considering the physical damage and functionality of the built environment along with the population dynamics over time.Specifically,high-resolution fragility models of buildings,and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment.Beyond simulat-ing recovery by tracking infrastructure network performance metrics,such as access to essential facilities,this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery.The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities,along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter.Furthermore,the methodology presented here provides a foundation for resilience-informed decisions for coastal communities.

Estimating long-term K-12 student homelessness after a catastrophic flood disaster

Despite efforts to end homelessness in the United States,student homelessness is gradually growing over the past decade.Homelessness creates physical and psychological disadvantages for students and often disrupts school access.Research suggests that students who experience prolonged dislocation and school disruption after a dis-aster are primarily from low-income households and under-resourced areas.This study develops a framework to predict post-disaster trajectories for kindergarten through high school(K-12)students faced with a major disaster;the framework includes an estimation on the households with children who recover and those who experience long-term homelessness.Using the National Center for Education Statistics school attendance boundaries,resi-dential housing inventory,and U.S.Census data,the framework first identifies students within school boundaries and links schools to students to housing.The framework then estimates dislocation induced by the disaster sce-nario and tracks the stage of post-disaster housing for each dislocated student.The recovery of dislocated students is predicted using a multi-state Markov chain model,which captures the sequences that households transition through the four stages of post-disaster housing(i.e.,emergency shelter,temporary shelter,temporary housing,and permanent housing)based on the social vulnerability of the household.Finally,the framework predicts the number of students experiencing long-term homelessness and maps the students back to their pre-disaster school.The proposed framework is exemplified for the case of Hurricane Matthew-induced flooding in Lumberton,North Carolina.Findings highlight the disparate outcomes households with children face after major disasters and can be used to aid decision-making to reduce future disaster impacts on students.