Hurricane Wind Research Articles

Statistical Characteristics of Droplets Formed due to the “Bag Breakup” Fragmentation Event at the Interface between Water and High-Speed Air Flow

Abstract Recent studies indicate that the dominant mechanism for generating sprays in hurricane winds is a “bag breakup” fragmentation. This fragmentation process is typically characterized by inflation and consequent bursting of short-lived objects, referred to as “bags” (sail-like pieces of water film surrounded by a rim). Both the number of spray droplets and their size distribution substantially affect the air–sea heat and momentum exchange. Due to a lack of experimental data, the early spray generation function (SGF) for the bag breakup mechanism was based on the assumed similarity with resembling processes. Here we present experimental results for the case with a single isolated bag breakup fragmentation event. These experiments revealed several differences from similar fragmentation events that control the droplet sizes, such as secondary disintegration of droplets in gaseous flows and bursting of bubbles. In contrast to the bubble bursting, the film thickness of the bag canopy is not constant but is random with lognormal distribution. Additionally, its average value does not depend on the canopy radius but is determined by the wind speed. The lognormal size distribution of the canopy droplets is observed in conjunction with the established mechanism of liquid film fragmentation. The rim fragmentation results in two types of droplets, and their size distribution has been found to be lognormal distribution. The constructed SGF is verified by comparing it with experimental data from the literature. The perspectives of transferring the results from laboratory to field environment have also been discussed. Significance Statement The “bag breakup” fragmentation is the dominant mechanism for generating spray in hurricane winds. The number and the sizes of the spray droplets substantially affect the heat transport from the ocean to the atmosphere and, thereby, the development of hurricanes. This paper presents experimental data and analysis that demonstrate how droplet formation occurs during bag breakup fragmentation. It also shows analysis of the quantity and size of droplets formed during a single fragmentation event. This work demonstrates how obtained experimental results can be applied to real field conditions in the context of hurricane prediction models.

Tropical cyclones’ acoustic reconnaissance

Acoustic reconnaissance of tropical cyclones with underwater gliders is an emerging approach for a comprehensive understanding of the oceanic conditions leading to the formation and intensification of cyclones and for aiding in wind speed forecasting. Shaw et al. (1978) showed that the underwater sound pressure level is in the linear relationship to the logarithm of the moderate local wind speed. Wilson and Makris (2006) proposed an acoustic surface source model for hurricane wind speeds. The model requires a parameterization of the winds’ contribution. The inversion of the acoustic data leads to estimation of the local wind speeds. The Gulf of Mexico acoustic dataset, collected by the bottom-anchored monitoring system during the passage of storm Barry (2019), is analyzed and correlated with the high-resolution wind speed data generated by the Weather Research and Forecast model. The efficacy of gliders outfitted with acoustic system to provide the real-time reporting of the local wind speeds is also discussed based on the flights’ recorded soundscapes. Acoustic glider reconnaissance offers a safe and cost-efficient way of contributing new type data to NOAA and US NAVY weather forecasting models. [Work supported by the Office of the Under Secretary of Defense for Research and Engineering, award# FA9550-21-1-0215.]

Personalized Vulnerability Assessment of Customized Low-Rise Wood-Frame Residential Structures under Hurricane Wind Loads: A Flexible Scenario-Based Simulation Approach

Hurricanes are one of the costliest and deadliest weather and climate disasters that impact the United States, largely affecting the residential sector. Residential structures are essential for the health and safety of the community during a hurricane event. However, homeowners typically have to rely on general information and instructions to understand their level of risks. The knowledge of structural vulnerability to hurricanes at the individual residential building level is often limited, which hinders personalized disaster preparation and mitigation decision-making. To this end, a vulnerability assessment method is proposed for individual homeowners to create customized finite-element models that quantify wind damage of low-rise wood-frame residential structures under various scenarios using ANSYS 2020 R1 parametric design language. A case study was conducted to test the proposed methodology and investigate prevailing failure modes under different structural characteristics and wind scenarios. Specifically, the impacts of roof framing features and previous exposure to environmental conditions on the potential damage of residential buildings under hurricane wind loads were studied. Analysis results, including the percentage of failed sheathing, nailed connections and framing members, are used to inform personalized mitigation recommendations. The proposed customized vulnerability assessment may increase homeowners’ situational awareness of disasters and promote rational retrofitting decisions.

Wind Fields in Category 1–3 Tropical Cyclones Are Not Fully Represented in Wind Turbine Design Standards

AbstractOffshore wind energy deployment in the U.S. is expected to increase in the years to come, with proposed wind farm sites located in regions with high risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for wind field characteristics unique to tropical cyclones. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large‐eddy simulations of three tropical cyclones (one Category 1, one Category 2, and one Category 3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of Cat‐1, Cat‐2, and Cat‐3 storms can exceed current design standards for offshore wind turbines. Winds at 90 m can be faster than outlined in the design criteria for Class I and Class T turbines for 50‐year recurrence periods. Wind speed shear across the turbine rotor layer is also larger than assumed in design specifications. Moreover, vertical variations in wind direction across the turbine rotor layer are large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines. The existence of hurricane wind field characteristics that are not represented in design standards does not imply that damage or failure will certainly occur. Engineering safety factors incorporated in the design of the turbine's blades and structural components may prevent damage from occurring.

Understanding the Effects of Wind Intensity, Forward Speed, and Wave on the Propagation of Hurricane Harvey Surges

Hurricane storm surges are influenced by wind intensity, forward speed, width and slope of the ocean bottom, central pressure, angle of approach, shape of coastal lines, local features, and storm size. A numerical experiment is conducted using the Advanced Circulation + Simulation and Simulating Waves Nearshore (ADCIRC + SWAN) coupled model for understanding the effects of wind intensity, forward speed, and wave on the storm surges caused by Hurricane Harvey. The ADCIRC + SWAN is used to simulate hurricane storm surges and waves. The wind fields of Hurricane Harvey were reconstructed from observed data, aided by a variety of methodologies and analyses conducted by Ocean Weather Inc (OWI) after the event. These reconstructed wind fields were used as the meteorological forcing in the base case in ADCIRC+SWAN to investigate the storm surges caused by the hurricane. Hurricane Harvey was the second most costly hurricane in the United States, causing severe urban flooding by dropping more than 60 inches of rainfall in Texas. The hurricane made three landfalls, with its first landfall as a Category 4 based on the Saffir–Simpson Hurricane Wind Scale (SSHWS), with wind intensities of 212.98 km/h (59 m/s). The storm surges caused by Hurricane Harvey were unique due to the slow speed, crooked tracks, triple landfalls in the USA, and excessive rain. The model’s storm surge and wave results were compared against observed data. High water marks at 21 locations and time series at 12 National Oceanic and Atmospheric Administration (NOAA) gauges were compared with the generated results. Several cases were investigated by increasing or decreasing the wind intensity or hurricane forward speed by 25% of the OWI wind and pressure data. The effects of the wave were analyzed by comparing the results obtained from ADCIRC + SWAN (with waves) and ADCIRC (without waves) models. The study found that the changes in wind intensity had the most significant effect on storm surges, followed by wave and forward speed changes. This study signifies the importance of considering these factors to enhance accuracy in predicting storm surges.

Impact of changes in sea surface temperature due to climate change on hurricane wind and storm surge hazards across US Atlantic and Gulf coast regions

Communities in US coastal regions are threatened by hurricanes more than ever, and the effect of climate change may further aggravate the risk and corresponding losses in the future. This paper investigates the potential impact of changes in sea surface temperature (SST) on hurricane wind and storm surge hazards for the Atlantic and Gulf coast regions. An empirical track model that uses SST as an input is used to account for the effect of temperature variations on hurricane intensity. The storm surge hazard is modeled using Sea, Lake, and Overland Surges from Hurricanes (SLOSH). Four projected future climate conditions based on the Intergovernmental Panel on Climate Change (IPCC) emission scenarios are adopted to capture the effect of changes in SST on future hurricane intensity. The impacts of changes in hurricane frequency and sea-level rise are also considered. The results show that the projected increase in the average SST will lead to more intense hurricanes by the end of the twenty-first century. An increase in wind speed for all the studied sites with varying degrees is observed. The most significant increase in wind speeds is observed on the northeast Atlantic coast, with some areas showing an increase of more than 60% for high return period winds under the most extreme future climate scenario. This is because a higher increase in SST is observed in such areas, which will lead to more intense hurricanes in the future. An increasing trend is also observed for the storm surge for all the study sites in the future. However, the largest increase in predicted surge heights is mainly seen in the Gulf Coast locations.

The changing demography of hurricane at‐risk areas in the United States (1970–2018)

AbstractHurricanes have proven to be one of the most deadly and costly natural hazards in the Gulf and Atlantic coast regions of the United States. Looming climate change and increasing population in coastal areas means these hazards could become even more devastating in the future. This paper first develops estimates of areas most impacted by hurricane flooding and wind damage and then assesses the generalised patterns of demographic change in those at‐risk locations, disaggregating by both race and age to account for the complex shifts in demographic composition that have occurred over the past five decades. We find evidence of racial, ethnic, and age disparities in exposure to hurricane flooding and wind impacts. Our results highlight not only the uneven burden of risk placed on those unable or unwilling to move, but also the structural privilege that enables the white population to remain in place, even in the face of increased hurricane damage risk.

Hurricane boundary layer height distribution from dropsonde composites based on the bulk Richardson number method

The distribution of hurricane boundary layer heights (HBLHs) relative to the hurricane center is investigated in a composite framework, using total 2674 dropsondes collected within four times the radius of maximum wind speed (RMW) of 61 hurricanes during 2002–2020. The HBLH is computed by the bulk Richardson number (Rib) method, which is sensitive to the critical Rib choice and more reasonable when the surface friction effect is considered. The sea surface friction velocity is derived from the 10-m wind speed through an air-sea drag relation. The results show that the HBLH is significantly low in the eye zone (0–0.5 RMW), out of which the HBLH is highest in the gale region (0.5–1.5 RMW) and decreases with the radius to the hurricane center. Similar to the hurricane wind field asymmetry, the HBLH is generally higher in the right than in the left in relation to the hurricane motion direction. ERA5 generally overestimates the HBLH, but substantially underestimates the higher HBLHs around the RMW, showing different variation of HBLHs along with the radius compared to dropsonde observations. According to linear regression analyses, the HBLH at least determined by the Rib method is importantly affected by the hurricane boundary layer dynamic factor, while has no significant correlation with the thermal factor.

Duration of extreme synoptic wind speeds for North America in a changing climate and its engineering implementation

In modern wind engineering practice, wind load effects are often defined as a function of a basic design wind speed, an aerodynamic force coefficient, several multipliers to consider surrounding terrain, topographic effect, and the directionality of the wind climate. Among these variables, the basic design wind speed is associated with an averaging time (also known as gust duration), e.g., 0.02-second, 3-second, 10-minute, or one hour. The design level aerodynamic force coefficients are defined as a specific percentile within a reference period, representing the generic time duration of wind events, i.e., wind duration, which is assumed to be one hour. A recent study showed that the hurricane wind duration could differ from this assumption. There has not been any study that investigated synoptic wind duration and its engineering implementations comprehensively. It is also unclear whether climate change impacts the synoptic wind duration. Historical records, often less than 50 years, are insufficient to calculate the wind duration for threshold wind speeds at a higher return period. Therefore, this study employed 15 long-term hourly wind speed simulations covering most of North America, each of which has 70 years in the past (1050 years in total) and 80 years into a future changing climate (1200 years in total), to investigate the wind durations in the current and future changing climate. It was found that the durations for winds of about 700-year return period are close to the traditionally assumed one hour and have limited effects on the wind loads for the strength design. However, wind durations can be much longer than one hour for the lower return period level wind speeds, which can impact the wind responses for serviceability limit state design and cladding and component (C&C) design.

Performance of asphalt shingles under simulated hurricane winds and evaluation of current installation practices

This paper investigates the wind resistance of asphalt shingles, evaluates the efficiency of their current installation methods, and addresses the lack of a system-level resistance metric from a wind engineering standpoint. The study examined the wind performance of asphalt shingles considering three installation practices recommended in high-velocity wind areas and three nailing non-conformities due to workmanship issues by conducting full-scale failure assessment tests at the NHERI Wall of Wind Experimental Facility. Roofing Application Standard (RAS) No. 115 and FEMA's recommended practices, with and without modifications, were used as recommended practices; while missing nails, over-driven nailing, and high-nailing cases were simulated as installation deviations or errors. Monoslope, gable, and hip roof decks were also used to study the contribution of roofing geometries. The results showed that asphalt shingles installed using methods recommended by the current guidelines may be vulnerable to wind flows from critical directions. Asphalt shingles, tested in the current work, experienced failure at wind speeds below their design level, especially due to cavity pressurization. Nailing non-conformities were also observed to compromise shingles' performance. Additionally, asphalt shingle failure modes, extents, and areas varied with roof geometries and the employed installation methods.

Drag Coefficient Parameterization under Hurricane Wind Conditions

The influence of small-scale processes at the ocean–atmosphere boundary layer such as spray and foam on the surface waves prediction is studied. Estimates of the effect of including the exact number of specific fragmentation “parachute” type in the spray on the resulting drag coefficient is shown. For the estimates, the numerical simulations within WAVEWATCH III wave model are performed. The parameterizations of wind input are tested within WAVEWATCH III wave model: default ST4 and ST6 parameterizations and the ST1 and ST6 parameterizations used together with the implemented drag coefficient parameterization. The proposed parameterization takes into account the presence of foam and spay. The obtained results are compared with the NDBC buoys data. The importance of small-scale processes for waves at hurricane winds prediction and the prospects for their inclusion in modern numerical wave models is shown.

Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events

Abstract. Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to hurricanes. Existing outage models use inputs including power system information, environmental parameters, and demographic parameters. This paper reviews the existing power outage models, highlighting their strengths and limitations. Existing models were developed and validated with data from a few utility companies and regions, limiting the extent of their applicability across geographies and hurricane events. Instead, we train and validate these existing outage models using power outages from multiple regions and hurricanes, including hurricanes Harvey (2017), Michael (2018), and Isaias (2020), in 1910 US cities. The dataset includes outages from 39 utility companies in Texas, 5 in Florida, 5 in New Jersey, and 11 in New York. We discuss the limited ability of state-of-the-art machine learning models to (1) make bounded outage predictions, (2) extrapolate predictions to high winds, and (3) account for physics-informed outage uncertainties at low and high winds. For example, we observe that existing models can predict outages higher than the number of customers (in 19.8 % of cities with an average overprediction ratio of 5.2) and cannot capture well the outage variance for high winds, especially above 70 m s−1. Our findings suggest that further developments are needed for power outage models for proper representation of hurricane-induced outages.

Physics-statistics-based hybrid simulation scheme of coupled nonstationary hurricane wind and wave fields for long-span floating bridges

Abstract Long-span bridges with floating towers have recently drawn great attention from the engineering community. Due to their sensitivity to the aerodynamic and hydrodynamic loads during extreme storms, accurate and efficient simulation tools for hurricane winds and waves are needed for improved understanding of the complex dynamics of the fully coupled wind–wave–structure interaction system. Conventional simulation schemes usually generate winds and waves separately, and hence cannot capture the intense wind–wave interactions under hurricanes. In this study, a physics-statistics-based hybrid simulation scheme of nonstationary hurricane wind and wave fields is presented, where the winds and waves are coupled in both large and small scales. To simulate the large-scale winds and waves, a height-resolving hurricane wind model is coupled with a parametric hurricane wave model through a dependence between sea surface roughness and surface wind speed. In the small-scale simulations, the nonstationary wind fluctuations are statistically obtained by a Hilbert-wavelet-based scheme in which the target parameters (e.g. fluctuation intensity) are estimated based on the local sea state, while the nonstationary sea surface elevations are physically acquired by solving the governing equation of the nonlinear wave evolution under the action of winds. The simulation fidelity of the proposed physics-statistics-based hybrid scheme is demonstrated by generating the coupled nonstationary wind and wave fields approaching to a hypothetical long-span bridge with floating towers under a hurricane event.

Modified parametric hurricane wind model to improve the asymmetry in the region of maximum winds

The understanding of the ocean waves during extreme hurricane conditions is important for risk analysis in coastal areas, the design of maritime works, the evaluation of impacts on ecosystems, navigation, among many other scientific and engineering applications. Although the Reanalysis databases have improved the quality of the wind fields during the occurrence of those hurricanes for which more data is available, they have been characterized by greatly underestimating the magnitude of the wind speed. In this context, parametric models emerge as an alternative capable of meeting the need for sufficient spatial and temporal resolution, but with a significantly lower computational cost. In this research, a new asymmetric parametric model is presented that presents better results, by incorporating a series of equations that allows improving the asymmetry and the magnitude of maximum wind speed around the eye of the hurricane. This new work presents a broader and more robust validation of this model proposed for the Gulf of Mexico, in which: I) the winds of multiple hurricanes are compared, with several trajectories and categories, with speed and direction records from NOAA in-situ buoys, II) The spatial patterns obtained are compared with the wind fields available in H*wind considered the best existing database, III) the wind fields are employed as forcing of the WAVEWATCH III wave model and the results are used to validated scalar parameters and directional spectrum against information from NOAA in-situ buoys, IV) a detailed analysis is made of the changes suffered by the directional spectrum of the waves when it is forced with the results of the parametric wind model, versus the spectra recorded by NOAA buoys and, V) all previous results derived from the parametric wind model are compared with the information available in the ERA5 reanalysis database and is discussed about it. The results found show that the parametric model is capable of improving the magnitude of the maximum wind speed around the eye, as well as the directional dispersion of energy in the three-dimensional spectrum of the simulated waves under hurricane conditions.

Climate change effects on loss assessment and mitigation of residential buildings due to hurricane wind

Hurricanes are considered as one of the most devastating natural hazards. Their induced risk is expected to significantly increase with the changing climate conditions. Residential buildings are specifically highly vulnerable to hurricane winds. Therefore, accurate assessment of hurricane induced losses under changing climate conditions is crucial to assist in the development of the best mitigation strategies. In this paper, a stochastic hurricane risk assessment framework accounting for several climate scenarios is developed to assess the hurricane-induced losses of residential buildings located in hurricane-prone regions. Specifically, a total of 10,000 years of synthetic storms will be generated for both ‘observed climate’ and ‘future climate’. The future climate models correspond to the worst-case scenario SSP5-8.5 and are simulated based on two global climate models. Then a physics-based wind model is coupled with the synthetic tracks to generate the hazard probabilities. To accurately capture the upper tail effects, the Gaussian kernel density estimation function is proposed in this study. The effects of the variability of climate change models on the wind hazard and its induced losses are quantitatively evaluated. The average annual regional loss is selected as the decision variable. The framework is applied to a one-story single-family wood frame residential building in three different cities, namely Atlantic City, Miami and Galveston. Three mitigation strategies are evaluated, with varying degrees of structural mitigation, by comparing the corresponding losses incurred under different climate change scenarios. It is shown that, due to the disproportionate variation of hurricane intensity and frequency over the coastal areas, the estimated annual losses with the consideration of several mitigation strategies change unevenly from one location to another. Also, the selection of inappropriate probability density function to estimate the wind distribution might underestimate the hurricane-induced loss which is mainly due to its incapability to accurately capture the upper tail ends of the wind distribution.

Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking

AbstractTropical cyclones (TCs) cause significant disruptions to infrastructure and livelihood. The scale of loss due to TCs may be mitigated by prompt and accurate advisories about TC wind speed. Current advisories are consensus based and have a time delay of about 6 hr between each new update. As part of efforts to increase the frequency of wind speed estimations without compromising on the accuracy, we curate a high‐quality hurricane image and wind speed data set and develop a machine learning (ML) model for frequent wind speed estimation. The image and wind speed pairings are consolidated from different sources. The wind speeds are interpolated hourly to curate the TC wind speed estimation data set. The data set is supplemented with metadata that eases adoption of the data set by the ML community. We have also designed a competition to garner interest and encourage community involvement to build state‐of‐the‐art ML models to estimate wind speeds from satellite images of TCs. The competition winners were able to improve the wind speed estimation by almost 50% over the benchmark model.

Energy inequality in climate hazards: Empirical evidence of social and spatial disparities in managed and hazard-induced power outages

Due to the effects of climate change and urbanization, the severity and frequency of hazard events is expected to increase. The energy sector in the United States is ever more vulnerable to extreme climatic hazards. Hurricane winds can damage electrical lines, causing hazard-induced power outages. Extreme heat and freezing temperatures can imbalance the supply and demand for energy resulting in managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts of hazard-induced and managed power outages. Research in equitable infrastructure emphasizes that certain populations, such as lower income and racial-ethnic minority households, are disproportionately impacted by disruptions in the power system. Moreover, the connected network qualities of the power system suggests an element of spatial vulnerabilities. However, little empirical evidence exists regarding the presence and extent of energy inequality. A main roadblock is the data collection process, in that outage data is often perishable and not found at granular spatial scales to allow the undertaking of a comprehensive analysis on impacts of power losses. Recognizing this important gap, this study collected and analyzed observational data related to the managed power outages during Winter Storm Uri (2021) and the hazard-induced outages during Hurricane Ida (2021). The research quantified the period of recovery at a granular spatial scale using an equitable-focused analysis to detect social and spatial inequalities through an exploratory lens. In extreme cases of power outage, census tracts of lower income and higher percentage of Hispanic population had longer median durations of recovery during Winter Storm Uri. In the hazard-induced outages of Hurricane Ida, non-coastal zip codes with lower income had a 1.00-day longer median duration of recovery and higher percentage of Black population had a 2.00-day longer median duration of recovery while coastal zip codes with higher percentage of Black population had a 1.00-day longer median of recovery. Non-coastal regions had 63% greater spatial Gini values and 16% greater value in infrastructure inequality when compared to coastal regions. The managed power outages resulted in a 3% to 19% greater value of infrastructure inequality to the hazard-induced power outages. The findings provide evidence of pervasive social and spatial inequality in power outages during climate hazards and highlight the importance of integrating equity into the manner in which utility managers and emergency planners restore power outages.

A novel framework to study community-level social and physical impacts of hurricane-induced winds through synthetic scenario analysis

Strong hurricane winds often cause severe infrastructure damage and pose social and economic consequences in coastal communities. In the context of community resilience planning, estimating such impacts can facilitate developing more risk-informed mitigation plans in the community of interest. This study presents a new framework for synthetically simulating scenario-hurricane winds using a parametric wind field model for predicting community-level building damage, direct economic loss, and social consequences. The proposed synthetic scenario approach uses historical hurricane data and adjusts its original trajectory to create synthetic change scenarios and estimates peak gust wind speed at the location of each building. In this research, a stochastic damage simulation algorithm is applied to assess the buildings’ physical damage. The algorithm assigns a damage level to each building using the corresponding damage-based fragility functions, predicted maximum gust speed at the building’s location, and a randomly generated number. The monetary loss to the building inventory due to its physical damage is determined using FEMA’s direct loss ratios and buildings’ replacement costs considering uncertainty. To assess the social impacts of the physical damage exposure, three likely post-disaster social disruptions are measured, including household dislocation, employment disruption, and school closures. The framework is demonstrated by its application to the hurricane-prone community of Onslow County, North Carolina. The novel contribution of the developed framework, aside from the introduced approach for spatial predicting hurricane-induced wind hazards, is its ability to illuminate some aspects of the social consequences of substantial physical damages to the building inventory in a coastal community due to the hurricane-induced winds. These advancements enable community planners and decision-makers to make more risk-informed decisions for improving coastal community resilience.

Quantifying the fragility of coral reefs to hurricane impacts: a case study of the Florida Keys and Puerto Rico

Ecosystems like coral reefs mitigate rising coastal flood risks, but investments into their conservation remain low relative to the investments into engineered risk-mitigation structures. One reason is that quantifying the risk-reduction benefits of coral reefs requires an estimate of their fragility to severe stresses. Engineered structures typically have associated fragility functions which predict the probability of exceeding a damage state with the increasing loading intensity imposed by a stressor, like a hurricane. Here, we propose a preliminary framework for capturing the fragility of coral reefs towards hurricanes in an analogous way to that of an engineered structure. We base our framework on Disturbance Response Monitoring data collected in the Florida Keys and Puerto Rico following hurricanes Irma and Maria. We first establish a qualitatively consistent correlation between hurricane impacts and coral mortality rates using two surveys of coral health. We focus specifically on stony coral mortality as a metric for reef damage, simplifying the effect of coral morphology into a single quantitative index at the site scale. To quantify the loading intensity of a hurricane, we propose a Hurricane Wind Exposure Time that captures spatial variations in the exposure of different coral reef sites to hurricane force winds. We ultimately derive a simple empirical fragility function for the Florida Keys and Puerto Rico to support side-by-side comparisons of the cost-effectiveness of a coral reef and engineered solutions to flood risk reduction in these regions.

The impact of ecology on labor productivity in the development of hard-to-reach areas

The structure of extreme factors in the pioneer development of hard-to-reach territories is considered. The activity of their influence on employees in the pre-production and production periods is assessed. The dynamics of the impact of factors of the pre-production period associated with the duration of the employee's move from permanent residence toplace of work and change of time zones and climatic zones. The influence of not only traditional factors during the production period - low temperatures, hurricane winds and atmospheric pressure drops, but also the impact on the body of the "polar night" and "magnetic storms" is described, indicating the correction factors for the rehabilitation time. The model of the most optimal type of workers for northern conditions and their psychophysiological characteristics are revealed. A favorable period of active work is indicated with rational indicators of overtime hours and average coefficients of decrease in labor productivity, as well as recommended cycles and modes of work and rest for the Arctic, Komi and the Tyumen region. The features of preparation for the pioneer development of the territory are highlighted, representing measures to maximize the return on labor based on the creation of future objects from aggregate-block units of full factory readiness, delivery of labor and material and technical resources only by flights, maintaining the continuity and pace of work.