Risk perception and evacuation decisions of Florida tourists under hurricane threats: a stated preference analysis

Though most hurricane evacuation studies have focused on residents, tourists are also a vulnerable population. To assess their perceptions of risk and evacuation likelihood under different hurricane conditions, we surveyed 448 tourists visiting central Florida. Respondents viewed four maps emulating track forecast cones produced by the National Hurricane Center and text information featuring variations of storm intensity, coast of landfall, centerline position relative to the survey site, time until landfall, and event duration. We performed chi-square tests to determine which hurricane conditions, and aspects of tourists such as their demographics and previous hurricane experience, most likely influenced their ratings of risk and evacuation likelihood for respondents located on Pinellas County beaches or inland near Orlando, FL. Highly rated scenarios featured a Category 4 hurricane making landfall along the Gulf Coast with the centerline passing over the sampling site. Overall, tourists that indicated the highest risk and evacuation ratings were not previously affected by a hurricane, had a trip duration of less than 6 days, and had checked for the possibility of a hurricane strike before departure. However, results for other tourist attributes differed between tourists in coastal and inland locations. We found that although somewhat knowledgeable about hurricanes, tourists misinterpreted the track forecast cone and hurricane conditions, which led to a lower perception of risk and subsequent likelihood to evacuate. Tourists, particularly those from outside of Florida, need to be better educated about the risks they face from hurricanes that make landfall.     

The perceived landfall location of evacuees from Hurricane Gustav

Hurricane evacuations in the United States are costly, chaotic, and sometimes unnecessary. Many coastal residents consider evacuation after viewing a forecasted graphic of where the storm is anticipated to make landfall. During the evacuation process, hurricane tracks commonly deviate from the forecasted landfall track and many evacuees may not pay attention to these track deviations after evacuating. Frequently, a disconnect may occur between the actual landfall track, the official forecasted track, and the perceived track of each individual as they made their evacuation decision. Specifically for evacuees, a shift in track may decrease the hazards associated with a landfalling hurricane since evacuees perceive their threat level to be high at the time of evacuation. Using survey data gathered during the evacuation from Hurricane Gustav (2008) in coastal Louisiana (USA), we calculated a type of Z-score to measure the distance error between each evacuee’s perceived landfall location and the actual landfall location from each evacuee’s home zip code. Results indicate a personal landfall bias in the direction of home zip code for evacuees of three metropolitan regions. Evacuees from the greater New Orleans area displayed the highest error, followed by evacuees from greater Lafayette. Furthermore, we validate the authenticity of the previous results by employing two additional methods of error assessment. A large regional error score might possibly be a predictor of evacuation complacency for a future hurricane of similar magnitude, although there are many other variables that must be considered.     

Bi-level optimization for risk-based regional hurricane evacuation planning

Almost all engineering evacuation models define the objective as minimizing the time required to clear the region or total travel time, thus making an implicit assumption that who will or should evacuate is known. Conservatively evacuating everyone who may be affected may be the best strategy for a given storm, but there is a growing recognition that in some places that strategy is no longer viable and in any case, may not be the best alternative by itself. Here, we introduce a new bi-level optimization that reframes the decision more broadly. The upper level develops an evacuation plan that describes, as a hurricane approaches, who should stay and who should leave and when, so as to minimize both risk and travel time. The lower level is a dynamic user equilibrium (DUE) traffic assignment model. The model includes four novel features: (1) it refocuses the decision on the objectives of minimizing both risk and travel time; (2) it allows direct comparison of more alternatives, including for the first time, sheltering-in-place; (3) it uses a hurricane-scenario-based analysis that explicitly represents the critically important uncertainty in hurricane track, intensity, and speed; and (4) it includes a new DUE algorithm that is efficient enough for full-scale hurricane evacuation applications. The model can be used both to provide an evacuation plan and to evaluate a plan’s performance in terms of risk and travel time, assuming the plan is implemented and a specified hurricane scenario then actually occurs. We demonstrate the model with a full-scale case study for Eastern North Carolina.     

A surge response function approach to coastal hazard assessment. Part 2: Quantification of spatial attributes of response functions

In response to the 2004 and 2005 hurricane seasons, surge risk assessment approaches have been re-evaluated to develop more rapid, reliable methods for predicting the risk associated with extreme hurricanes. Here, the development of dimensionless surge response functions relating surge to hurricane meteorological parameters is presented. Such response functions present an opportunity to maximize surge data usage and to improve statistical estimates of surge probability by providing a means for defining continuous probability density functions. A numerical modeling investigation was carried out for the Texas, USA coastline to develop physical scaling laws relating storm surge response with hurricane parameters including storm size, intensity, and track. It will be shown that these scaling laws successfully estimate the surge response at any arbitrary location for any arbitrary storm track within the study region. Such a prediction methodology has the potential to decrease numerical computation requirements by 75% for hurricane risk assessment studies.     

Consistency in hurricane surface wind forecasting: an improved parametric model

A parametric hurricane wind model has been developed based on the asymmetric Holland-type vortex model. The model creates a two-dimensional surface wind field based on the National Hurricane Center forecast (or observed) hurricane wind and track data. Three improvements have been made to retain consistency between the input parameters and the model output and to better resolve the asymmetric structure of the hurricane. First, in determination of the shape parameter B, the Coriolis effect is included and the range restriction is removed. It is found that ignoring the Coriolis effect can lead to an error greater than 20% in the maximum wind speed for weak but large tropical cyclones. Second, the effect of the translational velocity of a hurricane is excluded from the input of specified wind speeds before applying the Holland-type vortex to avoid exaggeration of the wind asymmetry. The translational velocity is added back in at the very end of the procedure. Third, a new method has been introduced to develop a weighted composite wind field that makes full use of all wind parameters, not just the largest available specified wind speed and its 4-quadrant radii. An idealized hurricane and two historical Gulf of Mexico hurricanes have been used to test the model. It is found that the modified parametric model leads to better agreement with field observation compared with the results from the unmodified model. This will result in better predictions of hurricane waves and storm surges.     

基桩可靠度分析的统计相关性评价方法

为了评估不同失效准则对基桩可靠度的影响,传统的方法是引入失效准则偏差系数。然而目前所定义的失效准则偏差系数使得偏差系数和基本随机变量之间具有一定的相关性,直接将偏差系数的统计参数用于基桩可靠度分析是不尽合理的。为此,基于线性回归理论提出了新的失效准则偏差系数的定义,并采用Spearman秩相关系数方法检验了偏差系数和基本随机变量之间的相关性。采用算例分析了偏差系数和基本随机变量之间的相关性对基桩可靠度的影响。结果表明,传统的失效准则偏差系数的定义使得偏差系数和基本随机变量之间具有一定的相关性,提出的通用的失效准则偏差系数能够有效地消除这种相关性。偏差系数与基本随机变量之间的相关性对偏差系数的均值具有明显的影响,但对变异系数基本没有影响。此外,偏差系数与基本随机变量间的相关性对基桩可靠指标具有明显的影响,基桩可靠度分析中应该消除这种相关性。     

Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS

Hurricanes are one of the major natural disturbances affecting human livelihoods in coastal zones worldwide. Assessing hurricane risk is an important step toward mitigating the impact of tropical storms on human life and property. This study uses NOAA’s historical tropical cyclone database (HURDAT or ‘best-track’), geographic information systems, and kernel smoothing techniques to generate spatially explicit hurricane risk maps for New England. Southern New England had the highest hurricane risk across the region for all storm intensities. Long Island, western Connecticut, western Massachusetts, and southern Cape Cod, Martha’s Vineyard, and Nantucket had high storm probabilities and wind speeds. Results from this study suggest that these locations may be of central importance for focusing risk amelioration resources along the Long Island and New England coastlines. This paper presents a simple methodology for hurricane risk assessment that could be applied to other regions where long-term spatial storm track data exist.     

Can the visualization of rip currents prevent drowning accidents? Consideration of the effect of optimism bias

Drowning accidents at beach in Japan are caused by rip currents. To reduce these accidents, a new technology that can detect rip currents and notify beachgoers by using the Internet of Things (IoT) and Artificial Intelligence (AI) was proposed. However, studies on the effect of visualizing rip currents or considering the effect of optimism bias have not been conducted. This study investigates if visualization of rip currents might help in preventing drowning accidents, while considering the effect of optimism bias. The participants were 90 Japanese beachgoers. They were asked to answer questions based on their knowledge of the beach and rip currents, their optimism bias regarding rip currents, and awareness with or without visualization. The results of the analyses suggest that despite optimism bias, the visualization of rip currents increases the tendency of beachgoers to perceive and avoid rip currents. As described above, it was found that by visualizing the rip current, beachgoers were able to perceive and avoid rip currents. In addition, an understanding of rip currents is positively related to the intent to avoid rip currents even when rip currents are visualized. Therefore, it is necessary not only to enhance the avoidance tendency by visualizing rip currents, but also to further enhance knowledge of beachgoers to deepen the understanding of rip currents including the danger associated and methods to avoid them.

     

Impact of physical parameterization schemes on numerical simulation of super cyclone Gonu

The objective of this study is to investigate in detail the sensitivity of cumulus, planetary boundary layer and explicit cloud microphysics parameterization schemes on intensity and track forecast of super cyclone Gonu (2007) using the Pennsylvania State University-National Center for Atmospheric Research Fifth-Generation Mesoscale Model (MM5). Three sets of sensitivity experiments (totally 11 experiments) are conducted to examine the impact of each of the aforementioned parameterization schemes on the storm’s track and intensity forecast. Convective parameterization schemes (CPS) include Grell (Gr), Betts–Miller (BM) and updated Kain–Fritsch (KF2); planetary boundary layer (PBL) schemes include Burk–Thompson (BT), Eta Mellor–Yamada (MY) and the Medium-Range Forecast (MRF); and cloud microphysics parameterization schemes (MPS) comprise Warm Rain (WR), Simple Ice (SI), Mixed Phase (MP), Goddard Graupel (GG), Reisner Graupel (RG) and Schultz (Sc). The model configuration for CPS and PBL experiments includes two nested domains (90- and 30-km resolution), and for MPS experiments includes three nested domains (90-, 30- and 10-km grid resolution). It is found that the forecast track and intensity of the cyclone are most sensitive to CPS compared to other physical parameterization schemes (i.e., PBL and MPS). The simulated cyclone with Gr scheme has the least forecast track error, and KF2 scheme has highest intensity. From the results, influence of cumulus convection on steering flow of the cyclone is evident. It appears that combined effect of midlatitude trough interaction, strength of the anticyclone and intensity of the storm in each of these model forecasts are responsible for the differences in respective track forecast of the cyclone. The PBL group of experiments has less influence on the track forecast of the cyclone compared to CPS. However, we do note a considerable variation in intensity forecast due to variations in PBL schemes. The MY scheme produced reasonably better forecast within the group with a sustained warm core and better surface wind fields. Finally, results from MPS set of experiments demonstrate that explicit moisture schemes have profound impact on cyclone intensity and moderate impact on cyclone track forecast. The storm produced from WR scheme is the most intensive in the group and closer to the observed strength. The possible reason attributed for this intensification is the combined effect of reduction in cooling tendencies within the storm core due to the absence of melting process and reduction of water loading in the model due to absence of frozen hydrometeors in the WR scheme. We also note a good correlation between evolution of frozen condensate and storm intensification rate among these experiments. It appears that the Sc scheme has some systematic bias and because of that we note a substantial reduction in the rain water formation in the simulated storm when compared to others within the group. In general, it is noted that all the sensitivity experiments have a tendency to unrealistically intensify the storm at the later part of the integration phase.     

Cognitive and affective responses of Florida tourists after exposure to hurricane warning messages

Tourists are particularly vulnerable to natural disasters such as hurricanes since they might be less informed and prepared than residents of disaster-prone areas. Thus, understanding how the traits of a tropical cyclone as well as specific characteristics of tourists influence affective and cognitive responses to a hurricane warning message is a critical component in disaster planning. Using scenarios that presented tropical cyclones with different relevant characteristics (such as category at landfall), tourists’ knowledge, experience with hurricanes, trip traits, and the location of the survey (coastal or inland), this study contributes to the literature on sociological issues related to natural disasters. The findings suggest that risk perceptions and fear are influenced differently by the traits of the hurricanes and tourists’ knowledge and experience. Risk is strongly influenced by the projected category of the hurricane at landfall, while fear is not as sensitive to this extremely relevant trait of cyclones. The results also suggest that the influence of risk and fear on evacuation likelihood is strong and positive. This study shows the value of studying cognitive and affective responses to uncertain events.     

带权优化方法在曲线预测模型中的应用研究

为了能够根据监测数据推测被测量变化规律及准确预测未来变化，针对常规最小二乘法拟合精度较差的情况，提出利用带权优化方法在常规非线性拟合基础上进行优化处理。通过对工程实际监测数据的计算验证表明，带权优化得到的拟合曲线，拟合精度与预测效果均有明显提高。     

Importance of soil and elevation characteristics for modeling hurricane-induced power outages

Hurricanes can severely damage the electric power system, and therefore, predicting the potential impact of an approaching hurricane is of importance for facilitating planning and storm-response activities. A data mining approach, classification and regression trees (CART), was employed to evaluate whether the inclusion of soil and topographic variables improved the predictive accuracy of the power outage models. A total of 37 soil variables and 20 topographic variables were evaluated in addition to hurricane, power system, and environmental variables. Hurricane variables, specifically the maximum wind gust and duration of strong winds, were the most important variables for predicting power outages in all models. Although the inclusion of soil and topographic variables did not significantly improve the overall accuracy of outage predictions, soil type and soil texture are useful predictors of hurricane-related power outages. Both of these variables provide information about the soil stability which, in turn, influences the likelihood of poles remaining upright and trees being uprooted. CART was also used to evaluate whether environmental variables can be used instead of power system variables. Our results demonstrated that certain land cover variables (e.g., LC21, LC22, and LC23) are reasonable proxies for the power system and can be used in a CART model, with only a minor decrease in predictive accuracy, when detailed information about the power system is not available. Therefore, CART-based power outage models can be developed in regions where detailed information on the power system is not available.     

Risk-based input�Coutput analysis of hurricane impacts on interdependent regional workforce systems

Natural disasters, like hurricanes, can damage properties and critical infrastructure systems, degrade economic productivity, and in extreme situations can cause injuries and mortalities. This paper focuses particularly on workforce disruptions in the aftermath of hurricanes. We extend the dynamic inoperability input?Coutput model (DIIM) by formulating a workforce recovery model to identify critical industry sectors. A decision analysis tool is utilized by integrating the economic loss and inoperability metrics to study the interdependent effects of various hurricane intensities on Virginia??s workforce sectors. The extended DIIM and available workforce survey data are incorporated in the decision support tool to simulate various hurricane scenarios. For a low-intensity hurricane scenario, the simulated total economic loss to Virginia??s industry sectors due to workforce absenteeism is around $410 million. Examples of critical sectors that suffer the highest losses for this scenario include: (1) miscellaneous professional, scientific, and technical services; (2) federal general government; (3) state and local government enterprises; (4) construction; and (5) administrative and support services. This paper also explores the inoperability metric, which describes the proportion in which a sector capacity is disrupted. The inoperability metric reveals a different ranking of critical sectors, such as: (1) social assistance; (2) hospitals and nursing and residential care facilities; (3) educational services; (4) federal government enterprises; and (5) federal general government. Results of the study will help identify the critical workforce sectors and can ultimately provide insights into formulating preparedness decisions to expedite disaster recovery. The model was applied to the state of Virginia but can be generalized to other regions and other disaster scenarios.     

考虑预报偏差的迭代式集合卡尔曼滤波在地下水水流数据同化中的应用

集合卡尔曼滤波（Ensemble Kalman Filter,EnKF）方法已广泛应用于地下水水流和污染物运移模拟相关问题的求解。但前人研究多建立在同化系统预报模型是准确的基础上,忽视了模型概化的不确定性。当模型概化不准确时,将导致预报偏差,可能带来错误的系统估计。因此,文章提出考虑模型预报偏差的迭代式集合卡尔曼滤波（Bias aware Ensemble Kalman Filter with Confirming Option,Bias-CEnKF）方法。以地下水水流数据同化为例,研究模型概化存在不确定条件下,边界条件、初始条件、源汇项概化不准确时新方法的有效性。结果表明,当预报模型概化不准确时,使用标准EnKF方法进行数据同化,可能会导致滤波发散,造成同化失败。Bias-CEnKF方法不仅保留了较好的同化性能,同时减小了参数、变量、偏差项非线性关系带来的不一致性。针对文章中4种情景,Bias-CEnKF同化获得的含水层渗透系数场以及水头场均接近真实场,且预报结果可靠。本研究进一步提升了模型概化不确定时EnKF方法的适用性,为实际野外复杂条件下地下水水流数据同化问题提供了可靠的方法。     

An analysis of coastal and inland fatalities in landfalling US hurricanes

Improvements in hurricane forecasts allowing for more timely evacuations from storm-surge zones are credited with reducing lethality of US landfalling hurricanes. The deadly reach of a hurricane, however, is not limited to storm-surge zones. About 80% of direct US hurricane fatalities since 1970 occurred outside of landfall counties, with most of these fatalities caused by inland flooding. We construct a geographic information system database combining the location and cause of fatalities, estimated wind speeds, and rainfall amounts for the entire track of the storm for landfalling US hurricanes between 1970 and 2007. We analyze the determinants of total fatalities and deaths due to freshwater drowning and wind. Inclusion of inland fatalities results in no downward trend in lethality over the period, in contrast to prior research. Local storm conditions significantly affect lethality, as one-inch and one-knot increases in rainfall and wind increase total fatalities by 28 and 4%. Rainfall significantly increases freshwater-drowning deaths and is insignificant for wind deaths, while the opposite relation holds for wind speed. While coastal counties do not exhibit a significantly higher amount of lethality risk versus inland counties for total or wind-driven fatalities, freshwater-drowning fatalities occur most frequently in inland counties along the center of the storm path and its outer county tiers as we have defined them.     

Agent-Based Modeling and Analysis of Hurricane Evacuation Procedures for the Florida Keys

The unique geography of the Florida Keys presents both high risk of hurricane landfall and exceptional vulnerability to the effects of a hurricane strike. Inadequate hurricane shelters in the Keys make evacuation the only option for most residents, but the sole access road can become impassable well in advance of a major storm. These extraordinary conditions create challenges for emergency managers who must ensure that appropriate emergency plans are in place and to ensure that an orderly exodus can occur without stranding large numbers of people along an evacuation route with inadequate shelter capacity. This study attempts to answer two questions: (1) What is the minimum clearance time needed to evacuate all residents participating in an evacuation of the Florida Keys in advance of a major hurricane for 92,596 people – a population size calculated based on the 2000 US Census population data, census undercounts, and the number of tourists estimated to be in the area? (2) If a hurricane makes landfall in the Keys while the evacuation is in progress, how many residents will need to be accommodated if the evacuation route becomes impassable? The authors conducted agent-based microsimulations to answer the questions. Simulation results suggest that it takes 20 h and 11 min to 20 h and 14 min to evacuate the 92,596 people. This clearance time is less than the Florida state mandated 24-h clearance time limit. If one assumes that people evacuate in a 48-h period and the traffic flow from the Keys would follow that observed in the evacuation from Hurricane Georges, then a total of 460 people may be stranded if the evacuation route becomes impassable 48 h after an evacuation order is issued. If the evacuation route becomes impassable 40 h after an evacuation order is issued, then 14,000 people may be stranded.     

Satellite microwave detected SST anomalies and hurricane intensification

Sea surface temperature (SST) from the remotely sensed infrared measurements, like the GOES, AVHRR, and MODIS, etc., show missing values of SST over the cloudy regions associated with hurricanes. While satellite microwave measurements, like the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), can provide SST even under cloudy conditions. Both satellite microwave measurements and buoy observations show SST increase in advance of significant hurricane intensification. Moreover, hurricane intensification may also be related to the location of high SST. Our results indicate pre-existing high SST anomaly (SSTA) located at the right side of the storm track for Hurricane Katrina. Numerical simulations also confirm the important impacts of SSTA location on hurricane intensification. Similar situations are also found for Hurricanes Rita and Wilma. In contrast, if there is no high SSTA at the right location, hurricane may not undergo further intensification. This may explain why not all tropical cyclones associated with warm waters can attain peak intensity (categories 4 and 5) during their life cycle, and partially explains why hurricanes do not reach the maximum potential intensity as calculated only according to the magnitude of SST.     

Impact and response of southwest Florida mangroves to the 2004 hurricane season

Although hurricane disturbance is a natural occurrence in mangrove forests, the effect of widespread human alterations on the resiliency of estuarine habitats is unknown. The resiliency of mangrove forests in southwest Florida to the 2004 hurricane season was evaluated by determining the immediate response of mangroves to a catastrophic hurricane in areas with restricted and unrestricted tidal connections. The landfall of Hurricane Charley, a category 4 storm, left pronounced disturbances to mangrove forests on southwest Florida barrier islands. A significant and negative relationship between canopy loss and distance from the eyewall was observed. While a species-specific response to the hurricane was expected, no significant differences were found among species in the size of severely impacted trees. In the region farthest from the eyewall, increases in canopy density indicated that refoliation and recovery occurred relatively quickly. There were no increases or decreases in canopy density in regions closer to the eyewall where there were complete losses of crown structures. In pre-hurricane surveys, plots located in areas of management concern (i.e., restricted connection) had significantly lower stem diameter at breast height and higher stem densities than plots with unrestricted connection. These differences partially dictated the severity of effect from the hurricane. There were also significantly lower red mangrove (Rhizophora mangle) seedling densities in plots with restricted connections. These observations suggest that delays in forest recovery are possible in severely impacted areas if either the delivery of propagules or the production of seedlings is reduced by habitat fragmentation.     

Who should be blamed? The attribution of responsibility for a city smog event in China

The frequently occurring city smog in China has become a hot topic and raised extensive discussion in the public health domain. Our study tries to explore how citizens attribute causation and responded to city smog in Hefei, Anhui Province. The data from 420 respondents generally answered our research questions. The result suggests that attribution of responsibility can be influenced by risk perception, citizens’ occupation and source reliance. Attribution of responsibility, source reliance and risk perception can affect the citizens’ protective actions.