Hindcasting Community-Level Damage to the Interdependent Buildings and Electric Power Network after the 2011 Joplin, Missouri, Tornado

Abstract

Tornados are common natural hazards that occur in the United States and result in social and economic loss. Resiliency of communities prone to tornadoes can be enhanced through the use of risk-informed decision-making tools. These tools can provide community decision-makers with key information, thereby allowing them the ability to consider an array of mitigation and/or recovery strategies of relevant sectors in a community, including physical infrastructure, social and economic sectors. This study focuses on the community of Joplin, Missouri, which was struck by an EF-5 tornado on May 22, 2011. This tornado was the costliest and deadliest single tornado in the United States in the last 50 years. Initially, the damage caused by the tornado to the Electric Power Network (EPN) of the city is assessed by using a detailed topological data set obtained from the electric power company, combined with a spatial wind speed model and component fragilities. Many factors including the type of the electric poles, age, city growth rate, and so forth, were considered in this assessment. The results were compared with the damage reported from field studies following the event. Using the predicted damages to the poles, probabilities of power loss for each individual building in the city were calculated. A weighted cellular automata (CA) technique was used to estimate the service area of substations and the path that the electric power must travel to arrive at demand nodes. In addition, the effects of damage to the electric substations and transmission lines were considered. The results were compared with power loss reported by home and business owners to the electric power company. Combining the power loss probabilities with the probabilities of damage to buildings in Geographic Information Systems (GIS) format, combined EPN-building probabilities of damage are presented. Such information can be used by decision makers for community resilience planning, and improvement.