Community Resilience-Focused Technical Investigation of the 2016 Lumberton, North Carolina, Flood: An Interdisciplinary Approach

Abstract

In early October 2016, Hurricane Matthew crossed North Carolina as a Category 1 storm, with some areas receiving 0.38–0.46 m (15–18 in.) of rainfall on already saturated soil. The NIST-funded Center for Risk-Based Community Resilience Planning teamed with researchers from NIST’s Engineering Laboratory (Disaster and Failure Studies Program, Community Resilience Group, and the Applied Economics Office) to conduct a field study focused on the impacts of the Lumber River flooding in Lumberton, North Carolina. Lumberton is a racially and ethnically diverse community with higher than average poverty and unemployment rates, a typical civil infrastructure for a city of 22,000 residents, and a city council form of government. The field data described in this paper are from the first wave in an ongoing longitudinal research project documenting the impacts and subsequent recovery processes following the 2016 riverine flooding in Lumberton. The initial data collection for this longitudinal community resilience-focused field study had two major objectives: (1) document initial conditions after the flood for the longitudinal study of Lumberton’s recovery, with a focus on improving flood-damage and population-dislocation models; and (2) develop a multidisciplinary protocol providing a quantitative linkage between engineering-based flood damage assessments and social science-based household interviews that capture socioeconomic conditions (e.g., social vulnerabilities related to race, ethnicity, income, tenancy status, and education levels). This type of interdisciplinary longitudinal research is critical to better understand community processes in the face of disasters and ultimately provide data and inform best practices for enhancing resilience to natural hazards in US communities. This paper describes the development and implementation of this interdisciplinary effort and offers an example of combining an engineering assessment of flood damage to residential structures and social science data to model household dislocation. Dislocation probabilities were primarily driven by flooding damage but also varied significantly among Lumberton’s racial/ethnic populations and by tenure.