Full Session with Abstracts
Flood is one of the costliest yet also most common natural disasters in the United States, claiming lives, disrupting businesses, straining the financial agencies that respond, often leaving cities destroyed with families being dislocated for days, months, or permanently. Flood damage prediction can support risk-informed decision when used in combination with research on community resilience planning strategies, flood impacts, and recovery. In October 2016, Hurricane Matthew brought devastating flooding, strong winds and moderate storm surge to North Carolina, with substantial flooding occurring throughout many communities in NC. The city of Lumberton, NC was selected as a case study for flood damage prediction on the community scale. Flooding resulting from Hurricane Matthew’s rainfall devastated Lumberton with damage along its commercial corridor and in several neighborhoods such that nearly 500 low-income renters were dislocated and required temporary housing. Modeling resilience requires studying a community as a whole and not just the directly affected area; essentially considering indirect effects of the damage and subsequent disruptions including taking into account the socio-economics of the area. A detailed GIS database for the city buildings had been prepared for community modeling purposes. Then, a physics-based 2D unsteady flow finite element flood model was developed in HEC-RAS to numerically simulate the flood event and evaluate the inundation level for each building in Lumberton using real-time streamflow data for the Lumber River obtained from the USGS database. Fragility curves for each building archetype and the building’s flood height obtained from the 2D flood simulation were used to evaluate the probability of exceeding a certain damage state for each building. This community-level damage prediction is then validated using data collected following Hurricane Matthew in 2016. The first time point of data collection provided a baseline for ongoing assessment of the community through a broader longitudinal resilience-focused field study.