Hurricane Events Research Articles

Nitrate isotopic composition of sequential Hurricane Harvey wet deposition: Low latitude NOx sources and oxidation chemistry

Abstract Chemical characterization of hurricane wet deposition is integral to understanding how future extreme weather events will impact coastal biogeochemical cycles. Sequential Hurricane Harvey wet deposition samples (n = 7 over 20.5 h) were collected in southeastern Texas, U.S. and the isotopic composition of nitrate and water (δ15N–NO3-, δ18O–NO3- and δ18O–H2O, δ2H–H2O) and the concentrations of ammonium, anions, and dissolved organic carbon were measured. Areas of peak deposition (~1500 mm) received up to 75% of the region's typical average annual inorganic nitrogen wet deposition. After δ15N–NO3- values were corrected for potential fractionation effects, contributions of lightning, natural gas combustion, vehicle exhaust and biogenic sources to NO3− in wet deposition (43.7 ± 31.2%, 7.8 ± 6.1%, 42.6 ± 27.5% and 5.9 ± 5.3%, respectively) were determined using a Bayesian isotope mixing model. Results highlight the significance of natural-sourced nitrogen being deposited to terrestrial systems during hurricane events. The relatively low δ18O–NO3- (36.1 ± 3.0‰) emphasized the role played by hydroxyl and peroxy radicals in HNO3 formation during the tropical summer. An established global NOx oxidation model in combination with δ18O–NO3- approximates the significance of RO2 vs O3 oxidation of NO during the daytime (36.0% vs 64.0%, respectively) and results provide valuable insight to low-latitude atmospheric chemistry.

Storms and Jobs: The Effect of Hurricanes on Individuals’ Employment and Earnings over the Long Term

Hurricanes Katrina and Rita devastated the US Gulf Coast in 2005. We use job-level data to compare the evolution of earnings for affected workers in four states with workers from matched control co...

Negative indirect effects of hurricanes on recruitment of range-expanding mangroves

Disturbances often have positive, direct effects on invasions by dispersing propagules or creating environmental conditions that favor invasive species. However, disturbances that alter interactions between resident and invading species could also affect invasion success. In northeast Florida, the black mangrove Avicennia germinans is expanding into salt marshes, where it interacts with the dead litter (wrack) of the native marsh cordgrass Spartina alterniflora. From 2015-2017, we performed monthly surveys before and after 2 hurricanes in 3 marsh microhabitats (bare sediment, vegetation, wrack) to quantify mangrove propagule and seedling densities. Wrack increased propagule retention up to 10 times relative to other microhabitats. Hurricanes did not directly harm mangrove propagules or seedlings. However, storm surge relocated wrack to upland environments, which indirectly inhibited mangroves by temporarily disrupting the facilitative effects of wrack on propagule recruitment and exposing intertidal bare patches that decreased propagule retention and seedling establishment. Wrack remained absent from intertidal areas for 1-3 mo. Because hurricane season overlaps with propagule recruitment, hurricane timing and wrack return time to intertidal areas influence the degree that hurricanes disrupt wrack-mangrove interactions. We demonstrate that large-scale disturbances can negatively and indirectly affect invader recruitment by altering interactions with resident species.

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

A Two-Stage Resilience Enhancement for Distribution Systems Under Hurricane Attacks

Hurricane events can cause severe consequences to the secure supply of electricity systems. This article designs a novel two-stage approach to minimize hurricane impact on distribution networks by automatic system operation. A dynamic hurricane model is developed, which has a variational wind intensity and moving path. The article then presents a two-stage resilience enhancement scheme that considers predisaster strengthening and postcatastrophe system reconfiguration. The pre-disaster stage evaluates load importance by an improved PageRank algorithm to help deploy the strengthening scheme precisely. Then, a combined soft open point and networked microgrid strategy is applied to enhance system resilience. Load curtailment is quantified considering both power unbalancing and the impact of line overloading. To promote computational efficiency, particle swarm optimization is applied to solve the designed model. A 33-bus electricity system is employed to demonstrate the effectiveness of the proposed method. The results clearly illustrate that the impact of hurricanes on load curtailment, which can be significantly reduced by appropriate network reconfiguration strategies. This model provides system operators a powerful tool to enhance the resilience of distribution systems against extreme hurricane events, reducing load curtailment.

Vulnerability Assessment of Bridge Piers Damaged in Barge Collision to Subsequent Hurricane Events

Abstract In a hurricane event, bridges that span rivers near coasts are at risk of experiencing water rise and increased wave loads. Such riverine bridges often allow barges to pass underneath them...

Coastal Ecosystems as an Ecological Membrane

Martínez, M.L.; Silva, R.; López-Portillo, J.; Feagin, R.A., and Martínez, E. 2020. Coastal ecosystems as an ecological membrane. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 97-101. Coconut Creek (Florida), ISSN 0749-0208.A membrane is a continuous layer that separates the interior of a cell from its surrounding cells or environment. It is selectively permeable because it regulates the transport of water, ions, and organic compounds. Its primary function is protection. Relatively analogous to a cell membrane, coastal ecosystems can be considered as an ecological membrane because they regulate the bidirectional transport of materials between the terrestrial and marine ecosystems and provide protection. The components of the ecological membrane include coral reefs, seagrass beds, beaches, coastal dunes, mangroves, marshes, and coastal lagoons. The selective permeability depends on the ecosystem. Pollutants and heavy metals, carbon, and nutrients are captured by the biota and sediments of mangroves, seagrasses, and marshes, and thus decrease the impact of pollution from the watershed to the ocean. Seagrasses, mangroves, beaches, and coastal dunes mitigate the inland effects of storms and hurricanes by reducing wave energy during storm surges. We examine how coastal ecosystems act as ecological membranes by looking at the evidence available. We focus on the flow of two features: heavy metals towards the ocean and waves towards the continent. We use field data to show how heavy metals are trapped by mangroves and coastal lagoons, which are porous ports of entry and exit. Also, wave flume experiments demonstrate how wave erosion is mitigated by vegetated coastal dunes, which act as protective barriers. Management schemes can be improved if coastal ecosystems are considered as components of a dynamic ecological membrane.

The implication of the increase in storm frequency and intensity to coastal housing markets

AbstractThis paper analyzes the effects of storm frequency and intensity on housing values in Miami‐Dade County over the last three decades. We found that higher storm exposure accelerates the housing price discount caused by augmented flood risk. The adverse effects of frequency and intensity have different impacts on perception of flood probabilities. Storm frequency affects housing prices in lower flood risk areas, whereas intensity influences the market in the higher risk zone, due to the different risk perceptions between the two factors. The results shed light on how increases in storm frequency and intensity impact the dynamics of flood risk perception in different floodplains on the coastal housing markets, suggesting future directions of flood prevention policies. Our findings highlight that one additional hurricane event is associated with a housing price discount of 1.3%. This adverse impact of frequency increases to 2.0% in the 500‐year floodplain zones, while higher storm intensity reduces housing values in the 100‐year floodplain zones by 1.5%.

The potential for using local PV to meet critical loads during hurricanes

Hurricanes are a major reason for outages in the U.S. electricity sector. These outages are largely caused by damaged transmission systems. As a distributed generator technology, solar PV has the potential to supply local loads while the transmission system is repaired. However, hurricanes mean significant cloud cover, which reduces the ability of PV to contribute as a generator. In this work we quantify the ability of PV to provide energy during 18 hurricanes that made landfall in the contiguous United States from 2004 to 2017. Based on simulations using resource data from the National Solar Radiation Database, we find that PV generation never goes to zero during daytime hours for any of the hurricanes, though its potential is considerably diminished. PV generation ranges from 18 to 60% of clear sky potential during hurricanes. After the hurricane departs, the solar resource increases considerably, with PV producing at 46–100% of clear sky potential for the 72 h following a hurricane. When coupled with storage, PV is able to supply some minimum level of energy for all hours both during and after the hurricane. The amount of load that can be supplied is a function of the size of the PV and storage, the amount of solar resource available, and the duration of the hurricane event.

Hurricane Frequency and Intensity May Decrease Dispersal of Kemp’s Ridley Sea Turtle Hatchlings in the Gulf of Mexico

Environmental variability can be an important factor in the population dynamics of many species. In marine systems, for instance, whether environmental conditions facilitate or impede the movements of juvenile animals to nursery habitat can have a large influence on subsequent population abundance. Both subtle differences in the position of oceanographic features (such as meandering currents) and major disturbances (such as hurricanes) can greatly alter dispersal outcomes. Here, we use an ocean circulation model to explore seasonal and annual variation in the dispersal of post-hatchling Kemp’s ridley sea turtles (Lepidochelys kempii). We simulated the transport of 24 cohorts of young Kemp’s ridley dispersing from three main nesting areas in the western Gulf of Mexico to describe variability in transport during the main hatching season and across years. We examined whether differences in transport distance among Kemp’s ridley cohorts could be explained by hurricane events. We found that years with high numbers of hurricanes corresponded to shorter dispersal distances within the first six months. Our findings suggest that differences in dispersal among sites and the impact due to hurricanes could influence the survivorship and somatic growth rates of turtles from different nesting sites and hatching cohorts. Considering such factors in future population assessments may aid in predicting how the potential for increasing tropical storms, a phenomenon linked to climate change, could affect Kemp’s ridley and other populations of sea turtles in the Atlantic.

Juvenile fish assemblage recruitment dynamics in a mid-Atlantic estuary: before and after Hurricane Sandy

Hurricanes can have long-term effects on estuarine fauna. Understanding these effects is important as climate change may influence the severity and frequency of these storms. On 29 October 2012, Hurricane Sandy, a large storm spanning roughly 1850 km in diameter, made landfall in Brigantine, New Jersey (USA), approximately 20 km south of Barnegat Bay, during an ongoing study of the bay’s ichthyofauna, providing an opportunity to observe fish recruitment dynamics coincident with hurricane passage. The objective of this study was to measure variance in the Barnegat Bay pre-Sandy fish assemblage relative to that of 1 and 2 yr after the storm. Barnegat Bay fishes were surveyed with an extensive otter trawl study in April, June, August, and October of 2012 (pre-Sandy), 2013 (1 yr post-Sandy), and 2014 (2 yr post-Sandy). Species composition of the fish assemblage was similar across years. Analyzed structural characteristics (abundance, diversity, richness) of the fish assemblage were occasionally more likely to occur or were larger pre-Sandy and 2 yr post-Sandy relative to 1 yr post-Sandy, but this trend was inconsistent across seasons and between structural characteristics. Furthermore, odds of occurrence and length frequency distributions for many resident species and sentinel fall/winter spawners did not indicate that variance could be definitively explained as a hurricane effect. The capability of fish to relocate from areas of temporarily unsuitable habitat and annual new recruitment of larvae and juveniles to the bay likely contributed to the observed stability in the fish assemblage.

Muddling through troubled water: resilient performance of incident management teams during Hurricane Harvey

Modern communities face escalating threats from natural disasters. Thus, the resilience of incident management teams (IMTs) during adverse events becomes crucial to protect lives and physical systems. However, prior studies have only partially highlighted factors related to IMT resilience. To provide a holistic understanding of the resilience of the IMTs, this study conducted semi-structured interviews with 10 experienced IMT personnel during Hurricane Harvey. Thematic analysis revealed six characteristics of resilient IMTs during a hurricane event: (i) establishing a common operating picture, (ii) adopting and adapting plans and protocols, (iii) proactive, re-prioritizing, and unconventional decision-making, (iv) enhancing resourcefulness and redundancy, (v) learning for improved anticipation and response readiness, and (vi) inter-organisational relationship to promote IMT functions. As an empirical investigation of the resilience of the IMTs, the findings inform future endeavours for developing incident information technologies and strategies to harmonise pre-established plans with adaptive actions in the field and fostering capabilities to learn from incidents. Practitioner summary: Resilient incident management teams establish a common operating picture; effectively adopt and adapt plans and protocols; make decisions in an unconventional and anticipatory fashion; constantly re-prioritize goals and tasks; enhance resourcefulness and redundancy; continuously learn skills for improved anticipation and response readiness; and exhibit good inter-organisational coordination and planning skills.

Hurricane effects on Neotropical lizards span geographic and phylogenetic scales

Extreme climate events such as droughts, cold snaps, and hurricanes can be powerful agents of natural selection, producing acute selective pressures very different from the everyday pressures acting on organisms. However, it remains unknown whether these infrequent but severe disruptions are quickly erased by quotidian selective forces, or whether they have the potential to durably shape biodiversity patterns across regions and clades. Here, we show that hurricanes have enduring evolutionary impacts on the morphology of anoles, a diverse Neotropical lizard clade. We first demonstrate a transgenerational effect of extreme selection on toepad area for two populations struck by hurricanes in 2017. Given this short-term effect of hurricanes, we then asked whether populations and species that more frequently experienced hurricanes have larger toepads. Using 70 y of historical hurricane data, we demonstrate that, indeed, toepad area positively correlates with hurricane activity for both 12 island populations of Anolis sagrei and 188 Anolis species throughout the Neotropics. Extreme climate events are intensifying due to climate change and may represent overlooked drivers of biogeographic and large-scale biodiversity patterns.

Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data

A disaster-resilient city should address social inequity in all its forms. Complete, accurate, and up-to-the-minute situational awareness (SA) can help disaster relief organizations stabilize the dangers and prevent further losses in poor and ethnic minority neighborhoods. However, the lapse of months or years of survey data could lead to biases since retrospective studies are affected by the emotional status at the time of the survey. To address this, we aim to examine the effects of neighborhood equity on SA in a hurricane event using geotagged Twitter data. We adopted the sentiment analysis, convolutional neural network (CNN) model and latent Dirichlet allocation (LDA) model to reflect SA in Hurricane Florence. The multinomial logit regression model reveals that the tweet originated from a poor neighborhood has a higher probability of being more negative, compared to being neutral. Also, we surprisingly found that the sentiment of a tweet in a black neighborhood could be less likely to be negative. Another novel finding is that black neighborhoods could discuss the hurricane with a positive attitude while poor neighborhoods are more concerned about the work during the hurricane. This study shows that, by incorporating with aggregated sociodemographic data, geotagged Twitter data can also be used to understand disaster SA from the perspective of social equity.

Probabilistic Resilience Measurement for Rural Electric Distribution System Affected by Hurricane Events

AbstractThis paper proposes a fully probabilistic and analytical measurement framework for assessing the resilience of linear power-distribution systems affected by hurricane wind. The topology of ...

Greenhouse gas fluxes and porewater geochemistry following short-term pulses of saltwater and Fe(III) in a subtropical tidal freshwater estuarine marsh

Estuarine tidal freshwater riparian marshes experience short-term saltwater intrusions (caused by typhoons and hurricanes) and Fe(III) inputs (associated with run-off from upstream basins during rain events). However, the effects of Fe(III) inputs and of simultaneous increases of saltwater and Fe(III) on greenhouse gas (GHG) fluxes and porewater geochemistry of estuarine tidal freshwater wetlands have not yet been studied. This study applied monthly in-situ experimental manipulations of saltwater and Fe(III) additions within a tidal freshwater wetland (the Min River estuary, southeast China) dominated by Sagittaria trifolia, over two years. We examined changes in porewater geochemistry (i.e., SO42–, Cl–, NH4+, and NO3–), ecosystem GHG (i.e., CH4, CO2, and N2O) fluxes, and soil denitrification rates. Short-term saltwater intrusions did not cause a large increase in soil salinity but did decrease CH4 fluxes. Fe(III) additions also greatly decreased CH4 fluxes, however, the combined addition of saltwater and Fe(III) did not have a synergistic effect on the reduction of CH4 emissions. Further, CO2 emissions did not significantly change in response to either the individual or the combined saltwater and Fe(III) additions. Most interestingly, we found that the combined addition of saltwater and Fe(III) led to an increase in N2O fluxes of an order of magnitude compared to the control. Based on the results of sustained-flux global warming potential (SGWP) and sustained-flux global cooling potential (SGCP) models, our findings suggest that short-term pulses of both individual saltwater and the combination of saltwater and Fe(III) caused by storm tides and soil erosion following a typhoon or hurricane event could reduce the CO2-equivalent flux from subtropical estuarine tidal freshwater wetlands, although the effects may be temporary.

748 The Calm After the Storm? Burn Injury Incidence Following Hurricanes and Tropical Storms

Abstract Introduction In recent years, we have noted a pattern of a surges in burn admissions occurring in the wake of some of the larger storm events occurring in our region. Many injuries incurred in the activities related to the storms’ aftermath— accelerate related burns from burning debris, generator explosions, and electrical repair burns. It is suspected that conditions created by larger storms may increase the population-wide risk for burn injuries for some time after the storm itself. If trues, this would suggest a need for more concerted burn prevention efforts in the wake of major natural disasters. The purpose of this study was to identify whether there is indeed a predictable surge in burn injuries relationships between seasonal variation and burn injuries in one catchment area of a regional center. We hypothesize that there is an underlying association between significant storms and an increased number of burn-related injuries before and after storms. Methods Using a retrospective approach, we examined burn center admission data from 2007–2018 (N=4,637) to include burns in the week prior to and three weeks after hurricane or tropical storm events (a storm window) in our state. This dataset only examined burns in which patients were admitted greater than 20 hours to exclude any confounding follow-up visits. Results Using Poisson regression to evaluate the effect of the storm window has on the number of daily admissions, there was found to be no significant association in respect to daily case totals (p=.165) when comparing across all years in respect to storm windows. When examining data from 2017 and 2018; years in which severe storms (category 4+) hit the state of Florida, there was found to be a significant increase in the number of daily burn admissions during the storm window by about 39% (p=0.039) as compared to 2015 when no storms hit the state. Conclusions It is hypothesized that a significant association of injuries were found in later years due to the ever-increasing intensity of storms hitting the state. Public awareness should be directed to the dangers of burns and related injuries in the wake of major weather events, i.e. before and after storms. In addition, further studies should examine the relationships between injuries and weather-related events. Applicability of Research to Practice Awareness of a tendency for burn injuries and admissions to surge after major storms could be used to improve the timing and content of burn prevention response to these incidents.

Application of surrogate models in estimation of storm surge:A comparative assessment

Coastal storm surge hazard assessment has received increased attention due to major hurricane events in the last two decades. Robust hazard assessment requires accurate and efficient storm surge prediction models; however, existing numerical models are either high-fidelity but computationally demanding or low-fidelity but with real-time forecasting application. This dichotomy has prompted the development of surrogate models that leverage available synthetic/historical storm databases to build prediction models intended to better balance efficiency and accuracy. Despite numerous studies that have examined the application of various surrogate modeling methods in coastal response prediction, no study is available that compares all of the frequently used methods for storm surge prediction. Furthermore, in most of these studies, the discussion of the performance of these models is bounded to aggregated error metrics (e.g., RMSE, R). This study aims to provide a comprehensive framework for comparison and assessment of the performance of surrogate models based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Support Vector Regression (SVR) for predicting storm surge. The United States Army Corp of Engineers’ North Atlantic Coast Comprehensive Study (NACCS) database is used for developing the models at representative coastal locations. In this study, the performance of the models is explored by investigating the stability of performance across training sample sizes, identifying systematic trends in errors, assessing performance in predicting large target response quantities, and characterizing the distribution of error. The results indicate that the performance of surrogate models may be improved by use of physically-motivated parameter scaling and that the selection of a surrogate modeling method should be informed by factors such as consistency in performance under a range of target surge elevations. In particular, the results suggest that accuracy of the tested surrogate models may be a function of the target surge elevation. Furthermore, results suggest that model performance should be assessed using factors beyond aggregate error metrics because such measures (particularly when used without modification to focus on risk-significant events) may give incomplete information about performance of surrogate models.

Opportunities and Challenges for Hurricane Resilience on Agricultural and Forest Land in the U.S. Southeast and Caribbean

Three storms in the 2017 hurricane season caused $265 billion in damages in the U.S. Southeast and Caribbean, including billions in losses in the agriculture and forestry sector. Climate change projections indicate that such disastrous hurricane seasons are becoming more normal. Working land management sectors need to prepare for this future. However, few studies evaluate hurricane resilience strategies, or challenges faced by land managers surrounding hurricane events. Boundary organizations are critical to hurricane preparedness and recovery, advising land managers before hurricanes, and often supporting recovery efforts. Here, we rely on public advisors’ experiences to understand how land managers pursue hurricane resilience. Using focus groups and an online survey of three agencies in the Southeast U.S. and U.S. Caribbean (n = 607), we identify challenges faced by land managers before and after hurricanes, and the strategies they implement to minimize damage. We learn that land managers are faced with many diverse and unique challenges related to hurricanes, but that long-term planning for hurricane events is uncommon compared to shorter-term preparedness and recovery activities. Efforts towards hurricane resilience should incorporate local needs, align with other land management goals, and increase overall resilience to climate change and related stressors. The results of this research can guide state/territorial and national-level prioritizations regarding hurricane resilience, as well as identify research needs on hurricane resilience strategies.

Resilience Assessment of Interdependent Energy Systems Under Hurricanes

Energy systems, e.g., power systems and natural gas systems, are increasingly interdependent, which brings benefits under normal conditions and introduces risks under extreme weather events, e.g., hurricanes. A comprehensive method is proposed to reflect the spatial-temporal hurricane impacts on interdependent power and natural gas systems. Mechanism analysis based and data based component failure models are used to map hurricane effects on exposed component failure probabilities based on which system states are generated using Monte Carlo simulation. For system states with failed components, the integrated energy flow is developed to determine minimum joint load shedding considering inherent properties of each system and characteristics of interdependence coordinately. Operational metrics and infrastructure metrics are employed to quantify the service adequacy and network resistance of the interdependent energy systems, respectively. The modified IEEE 24-reliability test system and the 12-node natural gas system under a simulated hurricane are used to validate the effectiveness of the proposed method. Simulation results show the method can identify weak parts and quantify the performance of interdependent power and natural gas systems under hurricanes to provide information for effective and coordinative preparedness.