Effect of Residential Building Wind Retrofits on Social and Economic Community-Level Resilience Metrics

Abstract

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially small footprint. A single high-intensity tornado can result in high casualty rates and catastrophic economic and social consequences, particularly for small- to medium-size communities. Comprehensive community resilience assessment and improvement requires the analyst to develop a model of interacting physical, social, and economic systems and to measure outcomes that result from specific decisions made. These outcomes often are in the form of metrics such as the number of people injured or the number of households and/or businesses without water, but it has been recognized that most community resilience metrics have socioeconomic characteristics. In this study, for the first time, a fully quantitative interacting model is used to examine the effect of a tornado damaging physical infrastructure (buildings and electrical power network) and the effects on the population and the local economy for a real community. Then, three residential building retrofit strategies are considered as alternatives to improve community resilience, and the metrics for the physical, economic, and social sectors are computed. An illustrative example is presented for the 2011 Joplin tornado in a new open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE), with a computable general equilibrium (CGE) economics model that computes household income, employment, and domestic supply before and after the tornado. Detailed demographic data was allocated to each structure to enable the calculation of resilience metrics related to population dislocation impacts from the tornado. The results of these analyses stemming from building damage estimation have a logical trend, but the substantial contribution of this work is that, for the first time, the effect of retrofit strategies for tornado loading can be quantified in terms of their effects on socioeconomic metrics.