Flood Performance and Dislocation Assessment for Lumberton Homes after Hurricane Matthew
In order to better understand community resilience following a disaster, a multidisciplinary research team from the Center of Excellence (CoE) for Risk Based Community Resilience Planning and the National Institute of Standards and Technology (NIST) jointly conducted a series of longitudinal field studies in the U.S. city of Lumberton, North Carolina following major flooding from Hurricane Matthew (2016) Damage surveys on structures and interviews with households were conducted during the first field study to explore physical, economic, and social impacts of major riverine flooding on this small, tri racial community. This paper is focused on damage to housing and subsequent household dislocation. Empirical damage fragilities were developed for residential buildings using a comprehensive set of engineering damage inspection data collected by the team. Multi variate models were developed to assess the consequences of physical damage to housing units 13 International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26 30, 2019 2 for household dislocation, including socio demographic factors. The goal was not to develop the definitive model of household dislocation, but rather to show how engineering and social science data can be combined to better understand the broader social impacts of disasters in this case, household dislocation. This study may help inform assessments of flood damage and dislocation patterns for other U.S. communities as a function of construction, social, and economic makeup. Performance based engineering (PBE) especially as it relates to recovery of buildings and infrastructure functions, is a promising tool for enhancing a community's resilience following a disaster. Community level resilience goals, such as no more than 5% of households dislocate following a specific hazard event with a specified probability of nonexceedance, can be used to define performance objectives in a PBE framework. However, in order to better achieve resilience goals, PBE frameworks need to capture the relationship between physical damage and social impacts during the response and recovery phases of a disaster (van de Lindt et al. 2018; Koliou et al. 2018) This paper summarizes research that is part of a longitudinal study documenting the impacts of Hurricane Matthew on Lumberton, North Carolina in an effort to better understand that relationship. Two sequential field studies for Lumberton were jointly conducted by interdisciplinary teams of researchers from the Center of Excellence for Risk Based Community Resilience Planning (Center) and the National Institute of Standards and Technology (NIST) The field studies were performed approximately 2 months (in 2017) and 15 months (in 2018) after the 2016 Hurricane Matthew caused catastrophic flooding in Lumberton. Engineering damage evaluations and structured household interviews were performed during the 2017 field study to collect data on physical damage, utility service disruption, household dislocation, work and school disruption, and household socio economic and demographic characteristics. Multi variate models were developed to link physical damage of housing units to household dislocation (i.e. a household vacating their housing unit) controlling for socio demographic characteristics and tenure. In addition, household demographics were combined with damage assessments to better understand the distributional consequences of flood damage among Lumberton's racial/ethnic groups. The outcomes from this study provide new insight in understanding and quantifying the consequences of housing damage for diverse racial/ethnic households in Lumberton, the impact of damage on the dislocation of households from their residences, and the effect of social factors such as race/ethnicity and tenure status on this dislocation. Findings are used to support recommendations on establishing communitylevel performance goals grounded in stronger measurements that link physical damage to social impacts. 1. HURRICANE MATTHEW'S IMPACT ON LUMBERTON Lumberton is a small, racially and ethnically diverse in land community in Robeson County, North Carolina with a population size of 21,542 residents (U.S. Census, 2010) Lumberton's racial/ethnic composition consists of 39% nonHispanic White, 36.7% non Hispanic Black, and 12.7% American Indian, 4.8% Hispanic, and 6.7% non Hispanic other (U.S. Census, 2010) Lumberton also has a substantial portion of its community, 34.8% living at or below poverty levels (U.S. Census, 2010) In October 2016, the Lumber River, which runs through the middle of Lumberton, experienced historic flooding due to Hurricane Matthew. Stream gage data show a large rain event in early October, prior to Hurricane Matthew, which led to increased flooding and extremely saturated soils. The Lumber River crested at nearly 6.7 m (22 ft) above the gage datum, which is 2.74 m (9 ft) higher than the 13 International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26 30, 2019 3 National Weather Service flood threshold (USGS, 2017) A two staged random, non proportional stratified cluster sample was pulled for housing units in Lumberton, consisting of 568 housing units. Census blocks were selected to identify a set of housing units through a two stage process. During the first stage, a set of census blocks were randomly selected in flooded areas with a probability proportion to size based on the number of housing units per block. Census blocks were selected non proportionally in high probability flooded areas at a ratio of 3:1 compared to blocks located in low probability flooded areas. The second stage consisted of randomly selecting a fixed number of housing units in each block. Standardized engineering damage surveys were performed on the housing units, and standardized social science surveys were conducted with households occupying these housing units (or with neighbors, landlords, or managers in cases that the housing unit was not occupied) about potential household dislocation, utility disruption and socio demographic characteristics. The damage surveys documented information regarding the building type, dimensions, structural system, and flood and damage information, such as the high water mark location and evidence of damaged interior items (see Figure 1) The household dislocation survey documented information about the occupancy status of the housing unit, and when available, asked households about the duration and reason for their dislocation, as well as information on utility outage and household socio demographics. Since the goal was to understand the consequences of direct damage and utility disruption for household dislocation along with other forms of social impacts, housing units in areas with a low probability of flooding were included in the sample. In total, 568 housing units were visited as part of our surveys; 402 sampled units completed damage assessments (no assessments were made in areas clearly not suffering from flooding) complete household data was collected for approximately 300 households, with additional data about households gathered from neighbors or apartment managers. The following sections describe the data and subsequent findings. The full report with a detailed summary of the city, the impact of Hurricane Matthew, the field study and findings is provided in van de Lindt et al. (2018) Figure 1: Assessing damage to residential buildings in Lumberton: (a) measured high water mark and (b) gutted interior. 2. EMPIRICAL FLOOD DAMAGE MODELS Using results from the damage surveys, this section presents a flood performance assessment of Lumberton homes using empirical damage fragility curves. Table 1 summarizes the key characteristics of the 402 buildings in the dataset. Most of these homes, singleand multi family, were light frame wood structures (many with brick veneer) of typical maintenance for their age, and typically one to two stories in height. Almost two thirds had crawlspaces, while the 13 International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26 30, 2019 4 remaining homes were built with slab on grade foundations. The field data showed that flood levels reached up to around 122 cm [48 in] above the first floor elevation (i.e. the threshold of the front or back door of the home) corresponding to approximately 195 cm [77 in] and 127 cm [50 in] above the grade level for buildings with crawlspaces and for buildings with slabs on grade, respectively. The damaged structures were classified into five discrete damage states, ranging from DS0 (no damage) to DS4 (complete damage) to identify their overall damage level based on the physical condition of the building (see Table 2) Most homes were classified as DS0 to DS2 (see Figure 2) indicating 'slight' to 'major' damage, particularly to the contents of these structures, but no substantial internal or structural impacts to the residence. The flood depths corresponding to the damage state for a given foundation type are highly variable (i.e. have a large value of standard deviation, ranging from 13 cm [5 in] to 43 cm [17 in] Significant variability in flood depth was observed in flood damage state estimates for the residential buildings. Therefore, a fragility model was used to characterize the uncertainty in the damage evaluations. This approach estimates the probability of being in or exceeding a given damage state as a function of flood depth. In engineering applications, a lognormal distribution is often used to allow values to remain positive, eliminating the need for statistical manipulation (Ellingwood, 2001; Li and Ellingwood, 2006; Porter et al. 2007; Deniz et al. 2017a; Deniz et al. 2017b) Lognormal distributions were considered to be appropriate for characterizing exceedance proba