Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics

An Integrated Approach for Urban Pluvial Flood Risk Assessment at Catchment Level

With the rapid development of urbanization and global climate change, urban pluvial floods have occurred more frequently in urban areas. Despite of the increasing urban pluvial flood risk, there is still a lack of comprehensive understanding of the physical and social influencing factors on the process. To fill this knowledge gap, this paper proposes a novel approach to calculate the comprehensive urban pluvial flooding risk index (PFRI) and investigates the interplay impacts from different components at catchment level. To be more specific, PFRI is determined by two components, Exposure Index (EI) and Social Vulnerability Index (SoVI). EI is evaluated based on two indicators, the depression-based Topographic Control Index (TCI) and impervious area ratio. SoVI is measured based on a set of demographic and socio-economic indicators. Our results demonstrated the spatial heterogeneity of urban pluvial flood exposure and social vulnerability, as well as the composite flooding risk across the study area. Our catchment-based urban pluvial flooding risk assessment method can provide a comprehensive understanding of urban flooding and promote the formulation of effective flood mitigation strategies from the catchment perspective.

Shifting landscapes of risk: Quantifying pluvial flood vulnerability beyond the regulated floodplain

Floods are recognized as the costliest type of natural hazard both worldwide and in the United States, with projected increases in frequency and magnitude in the absence of effective adaptation strategies. In the fall of 2018, Hurricane Florence made landfall in southeastern North Carolina, USA, bringing record rainfall and resulting in widespread inundation that impacted many areas outside of the federally designated Special Flood Hazard Area (SFHA). Much of this flooding was from inland pluvial inundation, which is an understudied component of coastal risk and vulnerability assessments primarily due to the scarcity of infrastructure data and historically lower flooding recurrence rates. This has resulted in severe damages in areas that residents and local officials considered at low risk from flooding. Using nearly-coincident high-spatial, high-temporal resolution CubeSat satellite imagery, we quantified the areal extent of post-Hurricane Florence floodwater within and beyond the 100-year floodplain (SFHA) and the proportion of residential structures exposed to flooding within an eight-county study area. We propose a novel approach to estimate flood risk resulting from this singular event (termed an actualized risk index) when compared to a published empirical model of vulnerability. We show that 24.3% of detected floodwater was outside the 100-year floodplain, 43.4% of exposed residential structures are outside the 100-year floodplain, and communities of highest vulnerability are not only along the coast but also inland along the Cape Fear, Northeast Cape Fear, Trenton, and Neuse Rivers. This suggests that the SFHA may not adequately show the spatial distribution of pluvial flood risk in riverine areas, and that misunderstanding of this risk has led to a pattern of development in which houses have a higher than expected risk of flooding. Moreover, this additional flood risk may disproportionately affect lower-income residents of these largely rural areas. These results have important implications in light of recent policy guidance in southeastern USA states that mandate that predictive coastal vulnerability assessments to sea level rise be conducted relative to 100-year SFHA zones.

Coastal Migration Index for Coastal Flooding Events Increased by Sea Level Rise due to Climate Change: Mexico and Cuba Case Studies

This paper presents a coastal migration index (CMI) useful for decision-making in the current scenario of sea-level rise (SLR) due to climate change. The CMI includes coastal human population density, degree of urbanization, and coastal-flooding penetration. Quantitative and qualitative statistical techniques and the geographic information system ArcGIS View 9.0 were used. Further, a panel of fifteen international experts in coastal management issues was consulted to establish and validate the CMI. Results led to three index components based on 22 indicators. CMI was applied in the state of Tamaulipas, Mexico and in Santiago de Cuba province, Cuba. According to CMI estimates, the risk levels associated with SLR for human settlements analyzed in Mexico and Cuba were 5.3% and 11.0%, respectively. The most severely affected communities will require resettlement. Meanwhile, the CMI determined that 15.8% of the Mexican territory studied will be able to withstand the effects of SLR through the management of engineering works that will protect human settlements. The CMI determined that 79.0%, in the case of Tamaulipas, as well as 89.0% of the Cuban territory, will not require new policies or guidelines to promote conservation and protection of coastal natural resources. Lastly, the method used allowed for creation of a CMI stoplight map useful to coastal decision-makers to adopt sound management actions.

Integrating culture and molecular quantification of microbial contaminants into a predictive modeling framework in a low-lying, tidally-influenced coastal watershed

Examinations of stormwater delivery in the context of tidal inundation are lacking. Along the coastal plains of the southeastern United States, tidal inundation is increasing in frequency and severity, often with dramatic adverse impacts on timely stormwater discharge, coastal flooding hazards, and even "sunny day flooding". Therefore, a comprehensive study was conducted to examine tidally-influenced stormwater outfalls discharging to Taylor's Creek, an estuary proximal to Beaufort, NC used regularly for recreation and tourism. Over a wide range of meteorological conditions, water samples were collected and analyzed for fecal indicator bacteria (FIB, used for water quality management) and previously published quantitative microbial source tracking (qMST) markers. Nineteen sampling events were conducted from July 2017-June 2018 with samples classified according to tidal state and defined as either inundated, receding, or transition. A first-of-its-kind multiple linear regression model was developed to predict concentrations of Enterococcus sp. by tidal cycle, salinity and antecedent rainfall. We demonstrated that the majority of variability associated with the concentration of Enterococcus sp. could be predicted by E. coli concentration and tidal phase. FIB concentrations were significantly (<0.05) influenced by tide with higher concentrations observed in samples collected during receding (low) tides (EC: log 3.12 MPN/100 mL; ENT: 2.67 MPN/100 mL) compared to those collected during inundated (high) (EC: log 2.62 MPN/100 mL; ENT: 2.11 MPN/100 mL) or transition (EC: log 2.74 MPN/100 mL; ENT: 2.53 MPN/100 mL) tidal periods. Salinity, was also found to significantly (<0.05) correlate with Enterococcus sp. concentrations during inundated (high) tidal conditions (sal: 17 ppt; ENT: 2.04 MPN/100 mL). Tide, not precipitation, was shown to be a significant driver in explaining the variability in Enterococcus sp. concentrations. Precipitation has previously been shown to be a driver of Enterococcus sp. concentrations, but our project demonstrates the need for tidal parameters to be included in the future development of water quality monitoring programs.

Residential flood vulnerability along the developed North Carolina, USA coast: High resolution social and physical data for decision support

This article presents an ArcGIS geodatabase of socio-demographic and physical characteristics derived from recent high resolution data sources to construct measures of population vulnerability to inundation in the 28 counties of coastal North Carolina, U.S.A. as presented in Pricope et al., 2019. The region is simultaneously densely populated, low-lying and exposed to recurrent inundation related to storms and incremental sea level rise. The data presented here can be used as a decision support tool in coastal planning, emergency management preparedness, designing adaptation strategies and developing strategies for coastal resilience. The socio-demographic data (population and housing) was derived from 228 tables at the block-group level of geography from the 2010 U.S. Census Bureau. These data were statistically analyzed, using Principal Component Analysis, to identify key factors and then used to construct a Social Vulnerability Index (SOVI) at the block-group level of geography which highlighted regions where socio-demographic characteristics such as family structure, race, housing (primarily owner vs. renter-occupied), special needs populations (e.g. elderly and group living), and household/family size play an overwhelmingly important role in determining community vulnerability from a social perspective. An index of physical exposure was developed using the National Flood Hazards Maps (available from North Carolina's Flood Risk Information System and FEMA) along with a novel building inventory dataset available from the North Carolina Department of Public Safety that contains the Finished-Floor Elevation of every structure in the state. We took advantage of the unprecedented high spatial resolution nature of the building inventory dataset to calculate an index of physical vulnerability to inundation of every block group in the 28 coastal counties relative to Base Flood elevations and identified hotspots where this intersection predisposes people to an increased risk of flooding. Here, we present the final derived dataset containing the social, physical and an integrative measure of vulnerability to flooding that can be used at multiple scales of analysis, starting with the regional, county, local, and neighborhood to identify areas of priority intervention for risk-reduction in coastal planning and emergency management preparedness as well as forward-looking adaptation strategies.