LAUSR

Resiliency of communities prone to natural hazards can be enhanced through the use of risk-informed decision-making tools. These tools can provide community decision makers key information, thereby providing them the ability to consider an array of mitigation and/or recovery strategies. The Center for Risk-Based Community Resilience Planning, headquartered at Colorado State University in Fort Collins, Colorado, developed an Interdependent Networked Community Resilience (IN-CORE) computational environment. The purpose of developing this computational environment is to build a decision-support system, for professional risk planners and emergency responders, but even more focused on allowing researchers to explore community resilience science. The eventual goal was being to integrate a broad range of scientific, engineering and observational data to produce a detailed assessment of the potential impact of natural and man-made hazards for risk mitigation, planning and recovery purposes. The developing computational environment will be capable of simulating the effects from different natural hazards on the physical and socioeconomic sectors of a community, accounting for interdependencies between the sectors. However, in order to validate this computational tool, hindcasting of a real event was deemed necessary. Therefore, in this study, the community of Joplin, Missouri in the USA, which was hit by an EF-5 tornado on May 22, 2011, is modeled in the IN-CORE v1.0 computational environment. An explanation of the algorithm used within IN-CORE is also provided. This tornado was the costliest and deadliest single tornado in the USA in the last half century. Using IN-CORE, by uploading a detailed topological dataset of the community and the estimated tornado path combined with recently developed physics-based tornado fragilities, the damage caused by the tornado to all buildings in the city of Joplin was estimated. The results were compared with the damage reported from field studies following the event. This damage assessment was done using three hypothetical idealized tornado scenarios, and results show very good correlation with observed damage which will provide useful information to decision makers for community resilience planning.