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The article discusses how water utilities can work with FEMA for reimbursement of costs after declared disaster incidents. It emphasizes the importance of long-term disaster preparedness, documentation, and collaboration across utility departments. The process involves applying for funds through FEMA's Public Assistance Program, meeting eligibility criteria, and documenting damages for reimbursement. The article provides detailed guidance on the phases of emergency management, including mitigation, preparedness, response, and recovery, and offers case studies and resources for utilities navigating the FEMA reimbursement process. [Extracted from the article]

We recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system's hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain. Plain Language Summary: This study addresses the phenomenon of hurricane‐induced compound flooding, which arises when inland waters and storm surges coincide at the land‐ocean boundary. We've devised a hydrological‐ocean model that effectively covers such events. This model, enhanced with new algorithms, was tested using Hurricane Florence (2018) data, showing marked improvements in predicting water levels and tide effects. Our research delineates and quantifies the complex interplay between different flooding sources during such events. Key findings include the determination that both inland flooding and storm surges contributed equally to the flooding in the Cape Fear River Estuary. However, the overlapping impact of these processes, termed the "compound effect," was limited in its influence on peak flood levels, mainly due to the time gap between storm surge and inland flooding peaks. Another crucial discovery was the "nonlinear effect," which accounts for discrepancies in predicted flood levels. This effect tended to decrease flood levels in river channels but increased them on floodplains. Key Points: A local inertial equation and a diagonal flow algorithm were introduced to a newly developed dynamically coupled hydrological‐ocean modelStrong compound effect between hydrological and ocean processes occurred between their peaksThe nonlinear effect reduced the flood peaks in the river channels while amplifying them on the floodplains [ABSTRACT FROM AUTHOR]

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States. [ABSTRACT FROM AUTHOR]

The article explores the impact of climate change on how environmental history research is conducted. It describes ways in which historians have addressed conceptual and methodological challenges in studying the past due to climate change and biodiversity collapse, including collaborations with archaeologists, earth scientists and geographers, among others. It discusses links between historians' interest in studying human relationships with non-human life and natural science research.

Bluff landforms, sought-after for housing and development, present a hazard management challenge due to erosional processes despite the perceived safety of elevated land. This study focuses on the Neuse River Estuary in North Carolina, exploring coastal risk perception and erosion. A survey protocol was developed, and questionnaires targeted 246 residents with property within 100 m of the shoreline and 54 land use professionals in Craven County. To explore the connection of individual experiences with erosion, we use openly accessible LiDAR to quantify bluff retreat and erosion between 2014 and 2020, a period that encompasses Hurricane Florence in 2018. Our findings show: 1) survey results reveal a consensus among residents, with 90% observing alterations attributed to storm impacts, 2) preferences for addressing erosion lean towards structural measures, aligning with existing protection strategies (e.g., bulkheads, rip-rap) as opposed to nature-based solutions, 3) bluff tops are eroding at a higher median rate of -0.59 m per year, compared to -0.19 m per year for high sediment banks, and 4) an overall net volume decrease of approximately -1.89 cubic meters per year for Craven County, slightly more than the overall net loss for the Neuse River Estuary, which was -1.74 cubic meters per year. The findings underscore the need for a cohesive bluff erosion management plan, emphasizing the complexity of challenges and the importance of a holistic approach that combines technical studies with effective risk communication. [ABSTRACT FROM AUTHOR]

A debris slide and subsequent fire on September 18, 2018, destroyed a bank building and damaged property in Warrensville, North Carolina. The slide pushed a propane tank and an electrical generator against the bank. In that collision, an electrical arc caused the propane to explode, which produced a fireball. Employees in the bank narrowly escaped with minor injuries, and slide debris buried part of an unoccupied drive-through lane. North Carolina Geological Survey geologists responded to a request from the Ashe County Emergency Manager and investigated the slide area to assess slope stability for public safety. Aided by uncrewed aerial system imagery, we documented slope and debris slide features and geologic materials and constructed a geologic cross section through the 1,400 m2 debris slide. We concluded that record above-normal precipitation followed by rainfall from the remnants of Hurricane Florence during September 15-18, 2018, induced the slide, and that geologic conditions and hillside modifications over prior decades had predisposed the slope to failure. Colluvial deposits derived from sheared amphibolite bedrock, and curved trees within and adjacent to the 2018 slide indicated preexisting marginally stable hillslope conditions. The 2018 slide included the area of a 2000 slide that involved a cut slope constructed during earlier site development. Stability analyses showed that the described conditions led to an unstable slope at the time of the 2018 slide. A septic leach field involved in the 2018 slide was damaged; however, it is uncertain if it was a causal factor in the slide. [ABSTRACT FROM AUTHOR]

Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land‐sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF‐Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling. Plain Language Summary: Hurricane Florence hit the southeastern United States in September 2018, causing historic rainfall and flooding. Floodwaters carrying contaminants traveled down rivers to the ocean, causing harmful algal blooms, beach closures, and other impacts. In the Cape Fear River Basin, North Carolina's largest watershed, open‐air lagoons used to store waste from industrial hog farming were breached, causing sewage to flow into the ocean in a coherent feature called a 'river plume' that moved in different patterns. Flooding impacts are increasing, but the movement of contaminants in river plumes following extreme events is not well understood. We used a numerical ocean model to simulate the Cape Fear River plume carrying a dye representing contaminants following Hurricane Florence. The plume initially traveled west hugging the coast due to Earth's rotation, alternately spreading east or offshore due to changes in the direction of winds blowing on the ocean. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches far down the coast, days to weeks after the storm. We also simulated several scenarios with different river discharge and winds. This information could help guide managers on when to perform water quality sampling and issue swimming or shellfishing advisories. Key Points: Pathways of land‐sourced contaminants in the aftermath of Hurricane Florence are investigated with an ocean model with hydrology sourceThe Cape Fear River plume had a westward coastal current and intermittent offshore storage modulated by wind direction and river dischargeBeaches 100 km west saw river‐sourced water quality hazards with delayed onset and long duration, from days to weeks after the storm [ABSTRACT FROM AUTHOR]

The trajectories of recovery and non-recovery following a disaster are well-documented, but the mechanisms of post-disaster adaptation remain poorly understood. Rooted in social cognitive theory and the transactional model of stress and coping, this study longitudinally investigated the reciprocal relations among coping self-efficacy (CSE), coping behaviors (approach and avoidant), and posttraumatic stress symptoms (PTSS) among highly exposed hurricane survivors. 261 Hurricane Florence survivors completed measures of hurricane-related CSE, coping behaviors, and hurricane-related PTSS across three timepoints, beginning 5–8.5 months after Hurricane Florence. Random-intercept cross-lagged panel models investigated the relations among study variables. Reciprocal, cross-lagged relations were identified between higher CSE and approach coping from T2 to T3. The lagged relations between approach coping at T1 and T2 were significant, as well as between avoidant coping at T2 and T3. Significant cross-sectional relations were also present for CSE, coping behaviors, and PTSS at T3. Results provide partial support for the positive feedback loop involving CSE and approach coping, but not for the negative feedback loop involving avoidant coping. CSE may be an important mechanism in longer-term disaster recovery, in part by increasing use of approach coping. [ABSTRACT FROM AUTHOR]

Vertical eddy diffusivity (VED) in the planetary boundary layer (PBL) has a significant impact on forecasts of tropical cyclone (TC) structure and intensity. VED uncertainties in PBL parameterizations can be partly attributed to the model's inability to represent roll vortices (RV). In this study, RV effects on turbulent fluxes derived from a large eddy simulation (LES) by Li et al. (Geophys. Res. Lett., 2021,48, e2020GL090703) are added to the VED parameterization of the PBL scheme within the operational Hurricane Weather Research and Forecasting (HWRF) model. RV contribution to VED is parameterized through a coefficient and varies with the RV intensity and velocity scale. A modification over land has also been implemented. This modified VED parameterization is compared with the original wind-speed-dependent VED scheme in HWRF. Retrospective HWRF forecasts of Hurricanes Florence (2018) and Laura (2020) are analyzed to evaluate the impacts of the modified VED scheme on landfalling hurricane forecasts. Results show that the modified PBL scheme with the RV effect leads to an improvement in 10-m maximum wind speed forecasts of 14%-31%, with a neutral to positive improvement for track forecasts. Improved wind structure and precipitation forecasts against observations are also noted with the modified PBL scheme. Further diagnoses indicate that the revised PBL scheme enhances moist entropy in the boundary layer over land, leading to improved TC intensity prediction compared to the original scheme. [ABSTRACT FROM AUTHOR]

Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots. Plain Language Summary: In the wake of the continuing threat of urban flooding following landfalling hurricanes, understanding the possible interplay between the urban landscape and hurricane rainfall is an emerging research area. Prior studies have shown that the micro‐climate of the urban regions can modify rain over the city centers and periphery. However, most urban rainfall modification studies have considered thunderstorms and local convective storms in developing this understanding. However, an intriguing question is whether the urban land surface has any feedback on rainfall due to large systems such as hurricanes. This question was addressed here using a state‐of‐the‐art weather model considering three different representations of urban surfaces. The analysis showed that the simulated hurricane rainfall corresponding to Florence (2018) and Harvey (2017) is sensitive to the choice of the urban model physics used. The results suggest that the model better simulates the environmental conditions and spatial distribution of rainfall using single‐layer urban physics. Key Points: Quantitative Precipitation Forecasts over urban areas from landfalling hurricanes are sensitive to urban representation in Weather Research and Forecasting (WRF) simulationsThe consideration of urban physics in the WRF model simulation helped improve the urban rainfall simulationsRepresenting urban morphology helped simulated heavy rain hotpots and simpler physics better simulated regional rains [ABSTRACT FROM AUTHOR]

Keywords: Constraints; disasters; hurricanes; liminality; social media; uncertainty EN Constraints disasters hurricanes liminality social media uncertainty 300 321 22 05/16/23 20231101 NES 231101 1. Past hurricane experiences were less useful than they would have been for a more typical hurricane, as many noted in their social media posts, e.g., " ... We've had hurricanes before, but not like this one - so I don't know at all what to expect, really." The monologic series of social media posts enable study of those affected over the duration of the hazard and better understand social media expression especially from voices that are otherwise overcome by the highly public and therefore "louder" experiences of disaster. First, to isolate the people who might have experienced both flash flood and tornado hazards during Hurricane Florence, we collected all tweets from 8 September to 20 September 2018 containing both a tornado-related keyword ("tornado", "funnel cloud") and a flash flood-related keyword ("flood", "flash flood", "storm surge"), including keywords as hashtags (e.g., "#flood"). [Extracted from the article]

Those affected by catastrophic events like hurricanes are burdened with the task of preparing for and responding to the threats of harm in addition to dealing with the emotionally taxing process of consuming and sharing disaster-related information. However, little is known about how threats and emotions during natural disasters impact media usage for information seeking and sharing. This study examined the relationship between the perceived threat of disasters (including disaster severity and involvement recognition), negative emotions, and information seeking and forwarding/sharing via different types of media. We surveyed over 600 adults in U.S. counties impacted by Hurricane Florence in 2018. Our findings show that negative crisis emotions mediated the relationship between threat appraisals and information seeking and sharing behaviors. In our discussion, we suggest how disaster/emergency communication professionals can prepare and respond to disasters by knowing how emotions influence individuals' communicative behaviors. [ABSTRACT FROM AUTHOR]

In many regions of the world, including the United States, human and animal fecal genetic markers have been found in flood waters. In this study, we use high‐resolution whole genomic sequencing to examine the origin and distribution of Salmonella enterica after the 2018 Hurricane Florence flooding. We specifically asked whether S. enterica isolated from water samples collected near swine farms in North Carolina shortly after Hurricane Florence had evidence of swine origin. To investigate this, we isolated and fully sequenced 18 independent S. enterica strains from 10 locations (five flooded and five unflooded). We found that all strains have extremely similar chromosomes with only five single nucleotide polymorphisms (SNPs) and possessed two plasmids assigned bioinformatically to the incompatibility groups IncFIB and IncFII. The chromosomal core genome and the IncFIB plasmid are most closely related to environmental Salmonella strains isolated previously from the southeastern US. In contrast, the IncFII plasmid was found in environmental S. enterica strains whose genomes were more divergent, suggesting the IncFII plasmid is more promiscuous than the IncFIB type. We identified 65 antibiotic resistance genes (ARGs) in each of our 18 S. enterica isolates. All ARGs were located on the Salmonella chromosome, similar to other previously characterized environmental isolates. All isolates with different SNPs were resistant to a panel of commonly used antibiotics. These results highlight the importance of environmental sources of antibiotic‐resistant S. enterica after extreme flood events. Plain Language Summary: Coastal flooding is increasing in frequency due to climate change. It cripples civil infrastructure serving humans and damages many livestock facilities. It is usually assumed that flooding of wastewater, septic systems, and/or livestock manure transmits antibiotic‐resistant bacteria and antibiotic resistance genes to the surrounding environment. However, conclusive identification of the source of the microbial contaminants has not been reported. Moreover, the above assumption rules out environmental reservoirs as a potential source for spread. Here, we report that antibiotic‐resistant Salmonella enterica, isolated from water samples collected near swine farms after Hurricane Florence, were not from animals or manure. Instead, they were from an environmental reservoir. Our findings were based on analyzing chromosomes and plasmids independently and collectively using long‐read high‐fidelity whole‐genome sequences. Knowledge of potential pathogen sources can help mitigate the spread of pathogenic bacteria after hurricanes to minimize the impact of floods on human health. Key Points: Salmonella enterica isolated from water samples collected near swine farms after Hurricane Florence were from an environmental reservoirIn relation to the chromosome, we also found that one mobile genetic element is more promiscuous than the otherEnvironmental S. enterica were less likely than swine‐associated S. enterica to have antibiotic resistance genes present in plasmids [ABSTRACT FROM AUTHOR]

Hurricanes with high winds can generate strong ocean circulation, storm surges, and large surface waves that impact coastal regions. In 2018, Hurricane Florence crossed the narrow continental shelf and made landfall in Onslow Bay, North Carolina, USA, a 160 km curved embayment rimmed by a chain of barrier islands with inlets and narrow back-barrier lagoons. This storm had significant impacts to Bear Island, an undeveloped barrier island, including overwash and shoreline recession of the beach/dune system of up to 40 m. The objective of this study is to apply a flexible mesh numerical model to investigate wave generation by wind over the ocean, and the fine-scale response of flow and storm surge in shallow nearshore areas including inlets and back-barrier estuaries. The hydrodynamic conditions surrounding four different barrier island systems that received different hurricane wind and wave forcing relative to the cyclone eye are investigated. The highest total water levels around the barrier islands are driven by the combined tide, storm surge and waves, resulting in complex circulation patterns related to the network of shallow channels in the back-barrier environment. [ABSTRACT FROM AUTHOR]

Also, the consistency of fake news with people's political orientation increased credibility bias and sharing bias, and credibility bias increased sharing bias. They then use Twitter data from Hurricane Harvey in 2017 and Hurricane Florence in 2018 to examine the bidirectional relationships between the diffusion of falsehoods and their correction messages. The online generation and dissemination of false information (e.g., through Facebook, Twitter, Snapchat and other Internet media), commonly referred to as "fake news", has garnered immense public attention following the 2016 Brexit referendum, three US elections, the 2019 Indian lynchings, and the 2019 rise in polio cases in Pakistan. Kelvin King, Bin Wang, Diego Esobari, and Tamer Oraby use a combination of analytical modeling and data from Twitter to examine the effects of actively combatting fake news by providing correct information to dispel specific fake news stories. [Extracted from the article]

Hurricane Florence made landfall over the Carolinas 14 September 2018, bringing over 30 inches of rainfall. What remains understudied is the possible storm re‐intensification by wet and warm antecedent soil moisture (ASM), known as the Brown Ocean Effect (BOE). This study investigates this effect with two approaches: (a) two satellite‐based soil moisture (SM) data and (b) model simulation. The averaged Cyclone Global Navigation System and Soil Moisture Active Passive SM enables examination of land‐atmosphere interaction at a sub‐daily scale. Both observations and simulation results manifest positive feedback between ASM and rainfall intensity, with 3 days prior to landfall being the typical antecedent time scale. Wet (dry) ASM lead to intense (light) and concentrated (widespread) rains. We also found that soil temperature can modulate the BOE. This study aims to advance our understanding of land‐atmosphere feedback and calls to acquire accurate antecedent land states to enhance forecast skills. Plain Language Summary: Antecedent wet and warm soil conditions can maintain or re‐intensify storms via vertical mixing of water vapor, a phenomenon called the Brown Ocean Effect (BOE). Previous studies investigating the existence of BOE have considered model simulations by perturbing antecedent soil moisture. Given the uncertainties in weather models, it is important to cross‐validate the soil‐rainfall feedback by both observations and simulations. It is also critical to understand the time scale of such feedback. The growing number of remote sensing soil moisture (SM) and weather radar rainfall products at various resolutions and spatial‐temporal sampling offer unprecedented opportunities to examine this effect. Our results show consistent positive soil‐rainfall feedback from both observations and simulations. Wet antecedent soils promote intense yet concentrated rains, while dry antecedent soils cause light and widespread rains. Meanwhile, soil temperature was also found to play an important role in mediating the feedback, with colder soils even leading to a negative correlation between wet antecedent soils and extreme rainfall rates. We advocate accurate representation of antecedent land surface states combined with assimilation of remote sensing SM products into models to enhance tropical cyclone forecasts. Key Points: We hypothesize the wet antecedent soil moisture re‐intensified Hurricane FlorenceWe found that Hurricane Florence was re‐intensified by wet antecedent soils, leading to intense and concentrated rainfallWe found that cold antecedent soils impair the Brown Ocean effect in Hurricane Florence [ABSTRACT FROM AUTHOR]

Newspaper sentiment and framing have the power to represent and inform public opinion on a variety of important issues. This study examines local news articles after Hurricane Florence struck North Carolina in the United States in September 2018 to understand the framing efforts undertaken by the outlets that produced these reports, as well as their impact on news sentiment towards the flood recovery efforts of the Federal Emergency Management Agency (FEMA). The results indicate that while most articles published in the wake of Florence have a neutral sentiment, there are a significant number of both positively and negatively coded articles that illuminate important information about how the public engaged with and comprehended the role of FEMA during recovery from the disaster, and how the media chose to cover its involvement. Such scrutiny will continue to inform how public, private, and government actors understand FEMA's role and whether it achieves its goals in the future. [ABSTRACT FROM AUTHOR]

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution. [ABSTRACT FROM AUTHOR]

Today's regional natural hazards loss models rarely incorporate changes in a region's built environment over time, and thus likely misestimate a region's natural hazard risk. Of the existing natural hazard loss models that incorporate changes in the built environment, none are developed at an adequately granular spatiotemporal scale that is appropriate for regional (multi-county) natural hazards loss modeling. This work presents the new Housing Inventory Projection (HIP) method for estimating changes in a region's housing inventory for natural hazards loss modeling purposes. The method includes two modules: (1) the Regional Annual County-Level Housing module, which estimates the annual number of housing units per county over a multi-county region and multi-decadal projection period, and (2) the Single-family Location Estimation module, which estimates the likely location of future single-family housing units across a subcounty grid space. While the HIP method can be applied over a range of spatiotemporal scales, we present a case study that estimates the number of single-family houses per 1 km2 grid cell in the state of North Carolina for each year from 2020 to 2049. We then used these projections to estimate how a future housing stock would experience a Hurricane Florence-type event. Future housing projections suggest that between 2020 and 2049, nearly 2900 new houses will be built, each year, in areas that experienced at least two feet of flooding following Hurricane Florence. [ABSTRACT FROM AUTHOR]

The GOAT Our editorial team is indebted to Tom Rankin for his service and tenure as an exceptional faculty editor of Southern Cultures. B This extraordinary b Snapshot: Climate issue marks the beginning of a year and more of contemplation - and celebration - of the thirtieth anniversary of the founding of Southern Cultures and the Center for the Study of the American South in 1993. The ef-4 rated tornadoes - ef-5 is the highest intensity ranking of a tornado - that tore through the Mississippi Delta and northern Alabama in late March 2023, as we were editing this issue, charted wind speeds near two hundred miles per hour. [Extracted from the article]

The severity of hurricanes, and thus the associated impacts, is changing over time. One of the understudied threats from damage caused by hurricanes is the potential for cross-contamination of water bodies with pathogens in coastal agricultural regions. Using microbiological data collected after hurricanes Florence and Michael, this study shows a dichotomy in the presence of pathogens in coastal North Carolina and Florida. Salmonella typhimurium was abundant in water samples collected in the regions dominated by swine farms. A drastic decrease in Enterococcus spp. in Carolinas is indicative of pathogen removal with flooding waters. Except for the abundance presence of Salmonella typhimurium, no significant changes in pathogens were observed after Hurricane Michael in the Florida panhandle. We argue that a comprehensive assessment of pathogens must be included in decision-making activities in the immediate aftermath of hurricanes to build resilience against risks of pathogenic exposure in rural agricultural and human populations in vulnerable locations. [ABSTRACT FROM AUTHOR]

The extensive agricultural feedlot operations in the Neuse River Watershed (NRW) in North Carolina result in high nutrient loading, particularly of ammonium (NH4+). In September 2018, Hurricane Florence devastated large portions of the NRW, creating a unique opportunity to study the impact of such hydrological events on the biogeochemistry of riverine and riparian sediments. The high NH4+ concentrations, naturally acidic conditions, and elevated levels of ferric iron [Fe(III)] in Neuse River sediments and soils provide an ideal environment for Acidimicrobium sp. A6 (referred to hereon as A6), a bacterium capable of conducting the Feammox process in which NH4+ is oxidized while iron is reduced. A6 was observed in all sediment samples obtained from the Neuse River, and it is therefore predicted that this process may be an important mechanism for NH4+ removal in this river system. Incubations of NRW samples indicate that the NH4+ oxidation potential via the Feammox process in the NRW is comparable with aerobic NH4+ oxidation by heterotrophic microorganisms. Given the high demand for Fe(III) by the Feammox process, it has been unclear how such a process may occur in sedimentary environments where ferric iron [Fe(III)] might be depleted. The results presented here show that a major hydrologic storm event can result in an increase in Fe(III) and in an increase in the abundance of Fe-reducing bacteria, including Acidimicrobium sp. A6. These findings indicate that major hydrologic storm events may, via the delivery of Fe(III), be capable of enhancing Feammox activity in riverine sediments that favor the Feammox process. [ABSTRACT FROM AUTHOR]

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms. [ABSTRACT FROM AUTHOR]

Floods are among the most destructive natural hazards in the world, posing numerous risks to societies and economies globally. Accurately understanding and modeling floods driven by extreme rainfall events has long been a challenging task in the domains of hydrologic science and engineering. Unusual catchment responses to flooding cause great difficulty in predicting the variability and magnitude of floods, as well as proposing solutions to manage large volumes of overland flow. The usage of nature-based solutions (NBSs) has proved to be effective in the mitigation of flood peak rate and volume in urban or coastal areas, yet it is still not widely implemented due to limited knowledge and testing compared to traditional engineering solutions. This research examined an integrated hydrological and hydraulic modeling system to understand the response of an at-risk watershed system to flooding and evaluate the efficacy of NBS measures. Using the Hydrologic Engineering Center Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS) software, an integrated hydrologic–hydraulic model was developed for Hurricane Matthew- (2016) and Florence-driven (2018) floods across the Little Pee Dee–Lumber River watershed, North and South Carolina (the Carolinas), US. The focus was on Nichols, a small town that has disproportionately been impacted by flooding during these two hurricane events. The present article proposes a methodology for selecting, modeling, and evaluating the performance of NBS measures within a catchment, which can be extended to other case studies. Different NBS measures, including flood storage ponds, riparian reforestation, and afforestation in croplands, were designed, modeled, and evaluated. Hurricane Matthew's flooding event was used for evaluating the NBS scenarios given its high simulation accuracy in flood inundation compared to the less accurate results obtained for Hurricane Florence. The scenario comparison evidenced that large-scale natural interventions, such as afforestation in croplands, can reduce the inundated area in Nichols by 8 % to 18 %. On the contrary, the smaller-scale interventions such as riparian reforestation and flood storage ponds showed a negligible effect of only 1 % on flood mitigation. [ABSTRACT FROM AUTHOR]

Saltwater intrusion is a pervasive threat to coastal ecosystems. Common management strategies entail the installment of engineered flow control structures, though there is a dearth of work on their prevalence across the landscape and how different structures impact salinity under various hydrologic conditions. We manually classified more than 900 structures with most either being culvert pipes or riser structures. We then investigated how these structures impacted salinity in surface waters after the landfall of Hurricane Florence in 2018 and during the Summer of 2020. To accomplish this, we combined longitudinal and depth surveys, with long-term monitoring of salinity up and downstream of three flow control structures. Our results reveal that a flow control structure's ability to exclude or trap saltwater from/in upstream environments depends on its position inland and design. Engineered structures were effective at excluding saltwater, while unintentional structures sometimes trapped saltwater in upstream environments. This work sheds light on important factors land managers should consider when putting in place new structures for freshwater management purposes. [ABSTRACT FROM AUTHOR]

Self‐regulation shift theory (SRST) argues that most individuals are able to successfully recover from trauma via engagement in self‐regulation processes as well as the effective utilization of internal and environmental resources. However, a minority of individuals may instead experience a self‐determination violation as a result of their self‐regulatory capacity being overwhelmed. This self‐determination violation is marked by chaotic and shifting adjustment, maladaptive regulation attempts, and, ultimately, a shift to an impaired self‐state and the development of persistent psychopathology, such as posttraumatic stress disorder (PTSD). The current study utilized nonlinear dynamic system (NDS) analysis to identify adjustment trajectory dynamics among rural hurricane survivors in North Carolina (N = 131) who completed daily ecological momentary assessments (EMAs) regarding their distress (i.e., negative mood and PTSD symptoms), regulation efforts (e.g., coping), and appraisals (e.g., coping self‐efficacy) over a 6‐week period. Four adjustment trajectories were identified, including two largely adaptive trajectories (69.0% and 5.7%), a less stable adjustment trajectory (6.9%), and a fourth trajectory (18.4%) marked by shifting adjustment states and more frequent maladaptive regulation and negative appraisals, suggesting possible self‐determination violation. Consistent with this possibility, this final trajectory was also associated with more severe PTSD symptoms relative to the other three trajectories at enrollment and 6‐month follow‐up. Future work should utilize NDS to model posttrauma adjustment dynamics from within a SRST framework to identify patterns of positive and negative adjustment dynamics at different time points in the trauma recovery process. [ABSTRACT FROM AUTHOR]

This study focuses on the role of land surface moisture in generating asymmetrical precipitation surrounding a nearly stationary Hurricane Florence (2018) during landfall. Previous idealized modeling studies have suggested that atmospheric stability varies surrounding a tropical cyclone (TC) during landfall, with the atmosphere destabilizing off-shore and stabilizing on-shore. However, this finding has not been studied using a real modeling framework. Here, we produce high-resolution numerical simulations to examine the variations in precipitation and atmospheric stability surrounding Hurricane Florence. In addition to a control simulation (CTRL), two additional simulations are performed by altering the land surface cover to be moister (WETX) or drier (DRYX) compared with the CTRL. In the experiment, the altered land surface affects the equivalent potential temperature within the boundary layer. Due to changes in moisture, there are consistent but minor impacts on the spatial patterns of moist static instability. This study found that rainbands in the inner core and distant rainband regions responded differently to changes in land surface moisture. Within the inner core region of the TC, WETX produced more precipitation that was more symmetrical compared with DRYX. In DRYX, there was increased moist static instability in the outer rainband region over water and decreased moist static instability in the outer rainband region over land, which may have contributed to the enhanced precipitation asymmetries. Still, both experiments produced asymmetrical precipitation distributions, suggesting that alterations to land surface moisture had a minor impact on the precipitation asymmetries in Hurricane Florence. We conclude that precipitation asymmetries are primarily dynamically driven by weak to moderate vertical wind shear and asymmetries in moisture flux convergence. [ABSTRACT FROM AUTHOR]

Direct damage from flooding at residential properties has typically been categorized as insured, with liabilities accruing to insurers, or uninsured, with costs accruing to property owners. However, residential flooding can also expose lenders and local governments to financial risk, though the distribution of this risk is not well understood. Flood losses are not limited to direct damages, but also include indirect effects such as decreases in property values, which can be substantial, though are rarely well quantified. The combination of direct damage and property value decrease influences rates of mortgage default and property abandonment in the wake of a flood, creating financial risk. In this research, property‐level data on sales, mortgages, and insurance claims are used in combination with machine learning techniques and geostatistical methods to provide estimates of flood losses that are then utilized to evaluate the risk of default and abandonment in eastern North Carolina following Hurricane Florence (2018). Within the study area, Hurricane Florence generated $366M in observed insured damages and an estimated $1.77B in combined uninsured damages and property value decreases. Property owners, lenders, and local governments were exposed to an additional $562M in potential losses due to increased rates of default and abandonment. Areas with lower pre‐flood property values were exposed to greater risk than areas with higher valued properties. Results suggest more highly resolved estimates of a flooding event's systemic financial risk may be useful in developing improved flood resilience strategies. Plain Language Summary: The financial impacts of flooding are complex and their distribution across different groups is difficult to quantify. Traditionally, the focus has been on estimating damages that directly impact insurers and property owners, but lenders and local governments can also be affected. Following a flood, uninsured damage and reductions in property value can combine to reduce a property owner's equity, hampering their ability to borrow money and recover from the flood. This can lead to mortgage default or even property abandonment, resulting in financial consequences for the property owner, the mortgage lender, and/or the local government. This research estimates uninsured damage and property value changes throughout eastern North Carolina following Hurricane Florence via a novel machine learning approach, using data on the physical characteristics of residential properties, insurance claims, property sales, and mortgages. Results indicate that uninsured damage and property value decreases combined to be substantial and this combination significantly increased risk of mortgage default and/or abandonment. Lower valued properties experienced higher rates of default and abandonment than high valued properties, with risk varying widely across communities. This type of analysis allows for property‐level assistance to be targeted toward the most vulnerable. Key Points: Estimates of uninsured flood damage and property value decrease can be used to predict rates of mortgage default and abandonmentFinancial risk, previously not well quantified, thus expands beyond property owners and insurers to include lenders and local governmentsA new analytical approach estimates $562M in financial risk from Hurricane Florence with disproportionate impact at low valued properties [ABSTRACT FROM AUTHOR]

After a catastrophic flood, the pace of residential cleanup is an important precondition of community resilience. When the process is fast, cleanup reduces additional health or economic risks by preventing structural or electrical damage from escalating. Mobilizing volunteers to complete cleanup work quickly is a challenge that the U.S. Natural Disaster Recovery Framework has assigned to the nonprofit sector. Nonprofit coordination networks--such as those convened through volunteer organizations active in disaster (VOAD) in the United States--aim to create systems of communication that improve the efficiency and equity of post-disaster cleanup. However, the process of coordination and the conditions that encourage quick action remain understudied. The aim of this paper is to identify the impacts of network coordination on the timeline of post-disaster cleanup using geospatial analytics. To do this, we apply a space-time permutation scan statistic (STPSS) to data on the speed at which organizations from the North Carolina VOAD (NC VOAD) closed requests for volunteer assistance following Hurricane Florence in 2018. STPSS results identify clusters of requests that were filled at different speeds through space and time. In total, we identified six space-time clusters indicative of coordinated cleanup. Exploration of clustered data helps to generate new questions about why coordination sometimes happened months after the disaster and suggests ways to use data exploration to inform network function and to leverage the unique capacities of individual nonprofits while also prioritizing housing resilience in socially vulnerable communities. [ABSTRACT FROM AUTHOR]

The article presents the discussion on research and policy analyses on recovery. Topics include innovations in the methods and data used for recovery research and a larger history of longitudinal studies offering additional insights that future recovery studies can build upon; and conducting longitudinal recovery studies, methods to model recovery, and evaluations of unequal recovery outcomes.

Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018 and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies. Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding. [ABSTRACT FROM AUTHOR]

Estimating the exposure of the coastal systems to natural hazards using coastal vulnerability models, which benefits from index‐based approaches and utilize information about the characteristics of the system, has become extensively adopted in the past few decades in coastal management and planning. However, the explanatory power of index‐based approaches and subjective selection of vulnerability factors are still in dispute. This study aims to introduce a stochastic coastal vulnerability model and assess its skill in characterizing and preserving simultaneous information about various comprising factors. Two common coastal vulnerability indices, Additive Coastal Vulnerability Index and Multiplicative Coastal Vulnerability Index are formed, and then their performances are compared to the proposed Probabilistic Coastal Vulnerability Index (PCVI) for the coastal counties of South Carolina. PCVI is developed based on the joint‐probability analysis of vulnerability factors using copula functions, which makes it capable of preserving the importance of multivariate information, and in turn, forms a more informative index. The performance of indices is benchmarked against post‐hazard flood maps and the cost of fatalities from Hurricane Florence (2018) and Hurricane Matthew (2016). The PCVI revealed more accurate results in terms of explaining the importance of vulnerability associated with biophysical and socio‐economic factors. The capability of PCVI to preserve multivariate vulnerability information offers a more pragmatic approach to reflect the exposure and adaptive capacity of coastal communities facing coastal hazards. Key Points: Introduction of a new Probabilistic Coastal Vulnerability Index (PCVI), which contains various socio‐economic and biophysical factorsUsing joint‐probability analysis of vulnerability factors using copula functions to preserve simultaneous information about various factorsBenchmarking PCVI against post‐hazard flood maps and the cost of fatalities from Hurricane Florence (2018) and Hurricane Matthew (2016) [ABSTRACT FROM AUTHOR]

Natural disasters represent major stressors, resulting in psychological distress and physiological responses such as increased cortisol. During pregnancy, this impacts not only maternal well‐being, but also fetal development. In 2018, Hurricane Florence caused extensive damage across the eastern United States. Studies indicated that compared to married pregnant women, unmarried pregnant women had higher risk of distress. Here we assess hair cortisol among a subsample of participants, and variations based on marital status. Methods: We analyzed multiple stress measures among 37 participants who were pregnant during Hurricane Florence. We used questionnaires modeled on previous studies to assess hardship associated with the hurricane, psychological distress, sociodemographic characteristics, social support, and food security. We analyzed cortisol concentrations in proximal and distal hair sections, representing stress around the time of the disaster (distal) and 3–4 months following the disaster (proximal). We used linear regression to test relationships between hair cortisol and self‐report stress measures, and variations based on marital status. Results: Self‐report measures of distress and hardship were similar among married and unmarried participants. Mean cortisol levels in distal and proximal sections were higher among unmarried participants. Controlling for confounding variables, hardship was not associated with hair cortisol. Distress predicted cortisol in distal sections (β =.482, p =.018), with a trend for proximal sections (β =.368, p =.055). Marital status was a significant predictor of distal (β =.388, p =.027) and proximal (β =.333, p =.047) hair cortisol, explaining 8.6%–11.7% of unique variance. Conclusions: Preexisting and intersecting risk factors likely place unmarried pregnant individuals at risk of stress during and following a disaster. [ABSTRACT FROM AUTHOR]

Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms. [ABSTRACT FROM AUTHOR]

Hurricane‐induced compound flooding is a combined result of multiple processes, including overland runoff, precipitation, and storm surge. This study presents a dynamical coupling method applied at the boundary of a processes‐based hydrological model (the hydrological modeling extension package of the Weather Research and Forecasting model) and the two‐dimensional Regional Ocean Modeling System on the platform of the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport Modeling System. The coupled model was adapted to the Cape Fear River Basin and adjacent coastal ocean in North Carolina, United States, which suffered severe losses due to the compound flood induced by Hurricane Florence in 2018. The model's robustness was evaluated via comparison against observed water levels in the watershed, estuary, and along the coast. With a series of sensitivity experiments, the contributions from different processes to the water level variations in the estuary were untangled and quantified. Based on the temporal evolution of wind, water flux, water level, and water‐level gradient, compound flooding in the estuary was categorized into four stages: (I) swelling, (II) local‐wind‐dominated, (III) transition, and (IV) overland‐runoff‐dominated. A nonlinear effect was identified between overland runoff and water level in the estuary, which indicated the estuary could serve as a buffer for surges from the ocean side by reducing the maximum surge height. Water budget analysis indicated that water in the estuary was flushed 10 times by overland runoff within 23 days after Florence's landfall. Plain Language Summary: Compound flooding refers to a phenomenon in which two or more flooding sources occur simultaneously or subsequently within a short period of time. In this study, we present a new numerical model that combines hydrological and ocean models to represent the exchange of water levels at the land‐ocean interaction zone. To test the model's robustness, we use this model to simulate the water level changes in Cape Fear River Basin and adjacent coastal ocean in North Carolina, United States, for Hurricane Florence in 2018. The comparison between observed and simulated water level prove that the new model can better resolve the changes in water elevation during a hurricane event than the traditional method where the ocean model utilized the river model's outputs as its boundary condition. We further quantify the contributions from different processes to the water level variations in the estuary. The compound flooding in the estuary was categorized into four stages: (I) swelling, (II) local‐wind‐dominated, (III) transition and (IV) overland‐runoff‐dominated. The estuary could serve as a buffer for surges from the ocean side by reducing the maximum surge height. The water in the estuary was flushed 10 times by overland runoff within 23 days after Florence's landfall. Key Points: A coupled hydrological‐ocean model was developed using hydrological modeling extension package of the Weather Research and Forecasting model (WRF‐Hydro) and two‐dimensional Regional Ocean Modeling System (ROMS 2D) through the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport modeling systemThe dynamical coupling method was applied to the interface boundary of WRF‐Hydro and ROMS 2D to realize a seamless model couplingHurricane Florence‐induced compound flooding event was investigated by analyzing the modeled water level evolution, water budget, and nonlinear effects in the Cape Fear Estuary [ABSTRACT FROM AUTHOR]

A ground‐based C‐band Shared Mobile Atmospheric Research and Teaching (SMART) polarimetric radar and the National Weather Service WSR‐88D radar near Wilmington, North Carolina simultaneously observed Hurricane Florence (2018) as it made landfall as a category 1 hurricane. The slow translation of Florence allowed for more than 30 hr of data collection before, during and after the tropical cyclone came ashore. Nearly 26 hr of three‐dimensional wind retrievals every 6–10 min were constructed from the radar observations, providing an unprecedented view of the evolution of rainbands, the inner core and the eyewall of Hurricane Florence. This article describes the radar data, the procedures used for automated quality control, data processing and the wind retrievals that have been constructed. The location of the data and wind retrieval archive is given. These data can be used to study the dynamics and rainfall of Hurricane Florence, to quantify the impact of winds on the natural and built environment, to validate numerical simulations of the tropical cyclone, and as an educational resource for courses in radar and tropical meteorology. [ABSTRACT FROM AUTHOR]

The document titled "Correction to 'Local news sentiment towards FEMA recovery efforts after Hurricane Florence in North Carolina'" addresses an error in the description of "delay" as lacking a positive or negative sentiment in the LSD, clarifying that it is associated with a negative sentiment. The authors apologize for this mistake. This correction is important for researchers studying local news sentiment towards FEMA recovery efforts after Hurricane Florence in North Carolina. [Extracted from the article]

Methylmercury is a toxin of local, regional, and global concern, with estuarine habitats possessing ecological characteristics that support conversion of inorganic mercury into this methylated form. We monitored Hg concentrations in species within the food web of the lower Cape Fear River (CFR) estuary in 2018–2020. Samples were analyzed for Hg concentrations and nitrogen isotopes (a measure of trophic level), and we found a positive relationship within this food web each year (p < 0.0001), indicating biomagnification is occurring. The highest Hg concentrations were among the upper trophic level species (Royal Terns, 4.300 ppm). While the Hg concentrations we documented are below assumed thresholds for toxic effects, we found spikes in Hg concentrations after Hurricane Florence in 2018 and with other disturbances to the CFR that resuspended bottom sediments. Continued monitoring is needed to understand the cause of annual variations, health implications, and conservation needs. • Methylation of mercury is driven by anthropogenic and ecological factors • This region has many features that drive methylation rates, posing health risks • Mercury in the Cape Fear River has never been studied on a food web scale • Large fluctuations in mercury followed Hurricane Florence [ABSTRACT FROM AUTHOR]

This study uses the Theory of Social Support to examine the impact of Hurricane Florence on the local food system during the immediate response to the disaster focusing on the social supportive nature of food and meals. Using a quick response disaster research methodology, field observations and qualitative interviews were conducted in Eastern North Carolina, in the days following Hurricane Florence in September 2018. Individuals affected by Hurricane Florence were recruited from emergency food and meal distribution sites. A codebook consisting of 66 parent and child codes based on the Theory of Social Support was applied to analyze 23 individual interviews. Sharing food with neighbors and/or relatives and utilizing post-disaster community feeding services improved the coping capacity of survivors. Results from this study contribute to the understanding of the post-disaster food environment and may help to improve food related response and recovery to meet the needs of those affected. [ABSTRACT FROM AUTHOR]