Hypothetical Hurricane Research Articles

What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors

Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.

Enhancing hurricane risk perception and mitigation behavior through customized virtual reality

Hurricanes are one of the most destructive natural disasters, and proper preparation and mitigation are essential to enhance resilience. However, homeowners may have inaccurate risk perceptions, resulting in inadequate mitigation decision-making. Virtual reality (VR) damage visualization has the potential to communicate hurricane risk effectively and improve disaster resilience. Previous studies utilizing VR for hurricanes have primarily focused on disaster response such as evacuation, and often presented generalized visualization consistent across all users. This study presents a methodology for offering individual users a customized tool to experience the impacts of hypothetical hurricane events on their residential structures and explore the effects of various building retrofit actions in customized 3D virtual environments. The methodology was tested with 25 participants. Participants experienced structural damage and utility disruptions caused by hurricanes in their own residential homes in a customized, immersive virtual environment. Participants’ pre- and post-VR risk perceptions, as well as their choices of mitigation actions were surveyed. The results indicate that customized VR was effective in enhancing risk awareness and that participants were able to reduce their structural damages in hypothetical hurricane events by adopting various mitigation measures after the customized VR experience. The outcomes suggest that customized visualization of potential damages in virtual environments can effectively help disaster preparation and mitigation.

Impressions of preparing and intentions to prepare for a hurricane in the United States

When faced with the potential threat of an extreme weather event, such as a hurricane, people must make important decisions about how much, if it all, they will prepare for that event. Many factors may influence people's decisions to prepare or not prepare – including social factors. In three studies among predominately White, female, and affluent online samples (total N = 784), we tested whether social factors—in particular, impressions of others and impression management concerns—predicted participant's intentions to prepare for two real hurricanes and one hypothetical hurricane. We use Integrative Data Analysis (IDA) to examine pooled results from the three studies. Results revealed two important sets of findings. First, the actions people take to prepare (or not prepare) during a hurricane influenced participants impressions. For example, participants tended to view making errors in preparation (i.e., over- or under-preparing) more negatively, with under-preparing facing the highest level of scrutiny. Second, impressions (particularly impressions of others under- or over-preparing) predicted actual preparation intentions and behavior. Results have important implications for the better understanding the social nature of decision-making and for potential intervention.

Visualizing Uncertainty in Hurricane Forecasts with Animated Risk Trajectories

Abstract Hurricane forecasts are often communicated through visualizations depicting the possible future track of the storm. The cone of uncertainty (COU) is a commonly used visualization, but the graphic is prone to misinterpretation such as thinking only locations contained within the cone’s boundary are at risk. In this study, we investigated the utility of conveying hurricane forecast tracks using a set of animated icons, each representing an instance of a possible storm path. We refer to this new visualization as animated risk trajectories (ARTs). We measured nonexperts’ perception of risk when viewing simplified, hypothetical hurricane forecasts presented as ARTs or COUs. To measure perception of risk for each visualization type, we designed experiments to have participants make decisions to evacuate individual towns at varying distances from the most likely forecast path of a storm. The ARTs led to greater risk perception in areas that fell beyond the cone’s boundaries. Nonexperts’ interpretation of risk was impacted by the visual properties of the ARTs, such as the distribution of the icons, including their density, and whether the distribution was unimodal or bimodal. This supports the suggestion that ARTs can have value in communicating spatial–temporal uncertainty. Significance Statement Because of the inherent uncertainties in weather forecasts and emergency management planning, communicating hurricane risk to the public is a unique challenge for decision-makers. Our study investigated the effect of conveying uncertainty in hurricane forecast tracks using a distribution of animated icons [animated risk trajectories (ARTs)], which correspond to potential hurricane tracks that evolve over time. This visualization was compared with a simplified cone-of-uncertainty graphic, such as those used by the National Hurricane Center. We found that ARTs offered flexibility in conveying information about hurricane risk, such as the magnitude of the risk via the number of icons and the location of the risk via the distribution of icons.

Comparisons of Perceptions of Risk for Visualizations Using Animated Risk Trajectories Versus Cones of Uncertainty

Dynamic ensemble visualizations might effectively convey the future path of an approaching tropical storm, and they offer benefits over commonly used Cones of Uncertainty. This experiment examined perceptions of risk at coastal locations of varying eccentricity from the most likely forecast path of a hypothetical hurricane. Perceptions of high (need to evacuate) and moderate (need to prepare) risk from inbound storms dropped off dramatically for Cone of Uncertainty visualizations, consistent with the influence of a containment effect induced by the cone’s boundary line. In contrast, Animated Risk Trajectories offered a more continuous sense of declining risk with distance, and much higher rates of needing to prepare at larger eccentricities that while at lower risk would still be vulnerable to some potential future paths. The findings lend further support to the viability of Animated Risk

Multi-Stage Equitable Bus-Based Hurricane Evacuation Model With a Stochastic Driver Availability Component

Transportation planning before the actual landfall of a hurricane can save lives by allowing evacuees to be transferred from the affected area to shelters in a safe and timely manner. This paper aims to fill an important research gap by proposing a detailed framework for bus-based evacuation planning with fair resource allocation in dense urban areas. We deconstruct the bus-based evacuation problem into multiple stages. Firstly, we identify a subset of existing bus stops to serve as bus pickup locations during the evacuation through the use of an integer programming model. The objective is to minimize the total number of pickup locations while ensuring full coverage of the demand. Secondly, the selected bus pickup locations are assigned to shelters where safe shelters are provided by the government. An equity component was introduced in the shelter assignment stage to ensure fair evacuation resource allocation. Finally, a bus driver management model was proposed that can be used to determine the optimal crew size in bus-based evacuation planning. A hypothetical hurricane evacuation scenario in New York City was used to evaluate the performance of the proposed methodology and the impact of model parameters. The results can provide feasible decisions on identifying bus pickup locations, shelter assignment, as well as the number of drivers needed for transit-based evacuation planning. The equity component shows a noticeable increase in equity index despite it only adding a small cost to the average travel distance.

Progression of hurricane evacuation-related dynamic decision-making with information processing

Conventional evacuation studies typically do not gauge the development of participants’ certainty about evacuation-related decisions with the updates in the information provided to them. This study uses an online survey that provides three kinds of progressively varied information about the current status of a hypothetical hurricane for five days leading to its landfall and collects respondents’ certainty of their situational comprehension and evacuation-related decisions each day. Most participants (84%) made a final decision (60% evacuate) after seeing information of just one day (four days before the landfall), indicating a tendency of swift decision-making. Modeling shows that the time spent looking at information, especially uncertainty cone forecast maps, positively influences the understanding of the hurricane’s status, which in turn helps in increasing the certainty of making evacuation-related decisions, with an increasing temporal effect. This study contributes to the understanding of the public perception of information and its association with evacuation-related decision-making.

Modeling and Analysis of Optimal Strategies for Leveraging Ride-Sourcing Services in Hurricane Evacuation

In many large-scale evacuations, public agencies often have limited resources to evacuate all citizens, especially vulnerable populations such as the elderly and disabled people, and the demand for additional transportation means for evacuation can be high. The recent development of ride-sourcing companies can be leveraged in evacuations as an additional and important resource in future evacuation planning. In contrast to public transit, the availability of ride-sourcing drivers is highly dependent on the price, since surge pricing will occur when the demand is high and the supply is low. The key challenge is thus to find the balance between evacuation demand and driver supply. Based on the two-sided market theory, we propose mathematical modeling and analysis strategies that can help balance demand and supply through a pricing mechanism designed for ride-sourcing services in evacuation. A subsidy is considered in the model such that lower-income and vulnerable individuals could benefit from ride-sourcing services. A hypothetical hurricane evacuation scenario in New York City in the case study showed the feasibility of the proposed method and the applicability of subsidies for ride-sourcing services in evacuation. The methodology and results given in this research can provide useful insights for modeling on-demand ride-sourcing for future evacuation planning.

Compound Risks of Hurricane Evacuation Amid the COVID-19 Pandemic in the United States.

The 2020 Atlantic hurricane season was extremely active and included, as of early November, six hurricanes that made landfall in the United States during the global coronavirus disease 2019 (COVID‐19) pandemic. Such an event would necessitate a large‐scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID‐19 case levels. We find that hurricane evacuation increases the total number of COVID‐19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation‐induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID‐19 transmission rates. Ultimately, the number of excess COVID‐19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives. These results are relevant to disease transmission during evacuations stemming from additional climate‐related hazards such as wildfires and floods.

The ‘Michael’ Effect: Risk Perception and Behavioral Intentions Through Varying Lenses

This study sought to measure risk perception and behavior intention of residents in coastal counties in Alabama and Florida in areas affected by Hurricane Michael in October 2018. The aim was to examine individual responses to impending disasters in areas that were recently touched by Hurricane Michael. Risk Information Seeking and Processing (RISP) model and the Theory of Planned Behavior (TPB) were employed in an experiment testing how visual cues and media messages surrounding an impending hypothetical hurricane were interpreted by residents, and their reported influence on an individual’s risk perception and decision-making in the situation. An analysis of 567 respondents determined that live video was most likely to motivate respondents to prepare activities for the storms.

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

Communicating Hurricane Risk with Virtual Reality: A Pilot Project

AbstractLandfalling hurricanes in the United States can inflict extreme damage and loss of life. The latter, particularly, can be caused by a host of socioeconomic factors, including insufficient understanding of risk by individuals expected to be impacted by the storm. Thus, we test the use of an emerging technology, virtual reality (VR), to enhance the communication of real-time risk from a hurricane forecast to make landfall. In this pilot study, individuals are presented with a hypothetical scenario where a major hurricane is forecast to impact their community within 48–72 h. The survey includes two different types of warning products related to the hypothetical hurricane: static text and maps emulating those traditionally used by media outlets and local officials to communicate risk, and a VR video simulating a hurricane landfall in a residential neighborhood. We survey two groups of equal size (each n = 62), one viewing both the VR simulation and traditional products, and the other only the latter. Each group was then asked a series of Likert-scale and open-ended questions to assess the effectiveness of both products. We determine that participants viewing both the VR and traditional products are significantly more likely to take action in preparation for the hypothetical landfall than those being exposed to just the traditional products. These results demonstrate that VR can be a useful component of hurricane warning products, and further work can be done to improve the effectiveness of such products and assess how broader segments of the population can access this information.

Assessing the Multiple Impacts of Extreme Hurricanes in Southern New England, USA

The southern New England coast of the United States is particularly vulnerable to land-falling hurricanes because of its east-west orientation. The impact of two major hurricanes on the city of Providence (Rhode Island, USA) during the middle decades of the 20th century spurred the construction of the Fox Point Hurricane Barrier (FPHB) to protect the city from storm surge flooding. Although the Rhode Island/Narragansett Bay area has not experienced a major hurricane for several decades, increased coastal development along with potentially increased hurricane activity associated with climate change motivates an assessment of the impacts of a major hurricane on the region. The ocean/estuary response to an extreme hurricane is simulated using a high-resolution implementation of the ADvanced CIRCulation (ADCIRC) model coupled to the Precipitation-Runoff Modeling System (PRMS). The storm surge response in ADCIRC is first verified with a simulation of a historical hurricane that made landfall in southern New England. The storm surge and the hydrological models are then forced with winds and rainfall from a hypothetical hurricane dubbed “Rhody”, which has many of the characteristics of historical storms that have impacted the region. Rhody makes landfall just west of Narragansett Bay, and after passing north of the Bay, executes a loop to the east and the south before making a second landfall. Results are presented for three versions of Rhody, varying in the maximum wind speed at landfall. The storm surge resulting from the strongest Rhody version (weak Saffir–Simpson category five) during the first landfall exceeds 7 m in height in Providence at the north end of the Bay. This exceeds the height of the FPHB, resulting in flooding in Providence. A simulation including river inflow computed from the runoff model indicates that if the Barrier remains closed and its pumps fail (for example, because of a power outage or equipment failure), severe flooding occurs north of the FPHB due to impoundment of the river inflow. These results show that northern Narragansett Bay could be particularly vulnerable to both storm surge and rainfall-driven flooding, especially if the FPHB suffers a power outage. They also demonstrate that, for wind-driven storm surge alone under present sea level conditions, the FPHB will protect Providence for hurricanes less intense than category five.

Information seeking and evacuation decision making in hypothetical hurricane events in the Rio Grande Valley, Texas

This study analyses how information is accessed and gathered in hurricane and inland flooding prone areas. The analysis reviews different information seeking behaviours, sources of information, and the factors that influence evacuation decision-making in each hurricane category within the Rio Grande Valley, Texas. Findings suggest information seeking frequency can increase the odds of making a decision to evacuate in the each hurricane category. Similarly, active information seeking behaviour is positively associated with the evacuation decision in the event of Category 4 and 5 hurricanes. Information sources from personal judgement show negative relationship with evacuation decisions in all except in Category 5. Information sources based on family/friend show positive association with evacuation decisions in Category 4 and 5. The findings highlight the importance of active information sources such as social media, frequent interactions with friends or family members, and active information seeking behaviours in Categories 4 and 5.

Information seeking and evacuation decision making in hypothetical hurricane events in the Rio Grande Valley, Texas

This study analyses how information is accessed and gathered in hurricane and inland flooding prone areas. The analysis reviews different information seeking behaviours, sources of information, and...

Kyne–Donner Model of Authority’s Recommendation and Hurricane Evacuation Decisions: A Study of Hypothetical Hurricane Event in the Rio Grande Valley, Texas

Many previous studies identified factors influencing hurricane evacuation decisions by testing the protective action decision model (PADM). This study further examines factors that affect the trust in authority’s recommendation and evacuation decision-making in a proposed Kyne–Donner Model. The model provides an understanding of the predictive factors influencing evacuation decision-making through the mediating factor of trust in authority’s recommendation. This study takes advantage of the structural equation modeling method to simultaneously test multi-stages of the model. There are factors, namely, age, gender, education, household size, decision maker, risk area, house materials, hurricane evacuation experience, information seeking frequency, information seeking behavior, and information sources which influence trust in authority’s recommendation which, together with hurricane evacuation impediments, influence the hurricane evacuation decision. The study’s findings are consistent with the PADM and demonstrate the importance of trust in authority’s recommendation and hurricane evacuation decision-making.

Who Will Stay, Who Will Leave: Decision-Making of Residents Living in Potential Hurricane Impact Areas During a Hypothetical Hurricane Event in the Rio Grande Valley

Abstract The Rio Grande Valley (RGV) has been impacted by various hurricanes throughout history. While Hurricane evacuation has been studied extensively in other contexts, there is limited systematic research on the warning and response to a large-scale severe weather event in South Texas. This study attempts to understand the evacuation decision-making behavior of Southern residents in the event of a Hurricane making landfall over the Rio Grande Valley. The study uses an online survey questionnaire with 1060 participants: 275 students attending the University of Texas-Rio Grande Valley and the remaining 785 are members of the community. Throughout the course of the study, information was gathered about selected elements: demographic characteristics, living conditions, the following and awareness of authority recommendations, decision maker roles, reasons for not wanting to leave a disaster-prone area facing an emergency, perception of a safe place, and expected help from both, governments and non-governmental organizations (NGOs) during evacuation that could impact residents’ evacuation decision. Findings reveal there are a number of individuals who will remain in their place of residence, even in the deadliest of hurricane Category 5. The study provides complex factors influencing the nature of evacuation decision-making of individuals who are Hispanics living in a unique geographical location of RGV.

Modeling the impact of traffic incidents during hurricane evacuations using a large scale microsimulation

Traffic incidents occurring during large-scale evacuations have been suspected of causing potentially severe delays. Previous work using mesoscopic simulations suggested that while incidents may substantially extend evacuation times for those immediately impacted, overall evacuation times for all evacuees were not significantly increased. Using highly detailed, dynamic transportation micro-simulation with greater realism and accuracy, this study assessed the impact of traffic incidents on two hypothetical hurricane evacuations from a coastal city in the United States. Six evacuation scenarios were tested with up to 143,000 evacuees and 800,000 background vehicles simulated over a 24-hour period. Scenarios included from 35 to 48 traffic incidents with locations, rates, severities, and durations based on both historical values for peak period traffic and estimates based on total vehicle miles traveled per road segment. The impacts of simulated traffic incidents, assigned to match one of three severity levels, were modeled by temporary reductions in affected lane segment capacities and reductions in vehicle speeds for other lanes in the segment. Work supports findings that while traffic incidents significantly extend the travel time of those involved, the total time required for all evacuees to clear the evacuating area is only marginally impacted. The findings also show that the time incidents occur relative to current and immediately following traffic may significantly impact evacuation clearance times for comparatively short periods.

The Proof is in the Picture: The Influence of Imagery and Experience in Perceptions of Hurricane Messaging

AbstractAlthough evidence suggests that photographs can enhance persuasive messaging by offering “proof,” less research considers their utility relative to other visual forms that ostensibly convey more information but more abstractly. Drawing on communication and information processing theory, this study examines the influence of visual features and personal experience variables in a domain with urgent need to better understand their role: hurricane messaging. In a between subjects experiment, residents of New York, New Jersey, and Connecticut (N = 1052) were exposed to a hypothetical hurricane forecast accompanied by a photograph of storm surge inundating a house (indexical image), a map of projected storm surge (iconic image), or no image (control), depending on condition. Results revealed that participants in the indexical condition perceived the greatest risk overall and were more likely to mention evacuation as a behavioral intention than did those in the iconic and control conditions, controlling for individual differences (gender, state of residence, etc.). Moreover, risk perception was greatest among residents in the indexical condition reporting fewer personal impacts of hurricanes, suggesting a moderating effect of hurricane experience on risk judgment but not on behavioral intention. Consistent with a dual-process model perspective, when exposed to an image of an identifiable “victim,” participants with less direct experience may have employed an affect heuristic, resulting in heightened risk perceptions. Practically speaking, using evocative photographs as proof may be preferable to a map or text-only approach when warning public audiences of a given hazard, but ethical issues and empirical questions remain.

The Effects of Past Hurricane Experiences on Evacuation Intentions through Risk Perception and Efficacy Beliefs: A Mediation Analysis

AbstractIndividuals’ past experiences with a hazard can encompass many different aspects, which can influence how they judge and respond to a future hurricane risk. This study, which utilizes survey data from coastal residents who are at risk from hurricanes, adds to understanding of past hazard experience in two ways. First, it examines six different aspects of people’s past hurricane experiences and the relationships among them. Then, it draws on risk theories of behavioral responses to explore how these different experiences influence people’s evacuation intentions for a hypothetical hurricane as mediated through multiple dimensions of risk perception (cognitive, negative affective) and efficacy beliefs (self efficacy, response efficacy). The results suggest that people can experience emotional or otherwise severe impacts from a hurricane even if they do not have experiences with evacuation, property damage, or financial loss. The results also reveal that different past hurricane experiences operated through different combinations of mediating variables to influence evacuation intentions. Some of these processes enhanced intentions; for instance, experience with evacuation, financial loss, or emotional impacts heightened negative affective risk perceptions, which increased evacuation intentions. Other processes dampened evacuation intentions; for instance, people with past hurricane-related emotional impacts had lower self efficacy, which decreased evacuation intentions. In some cases, these enhancing and dampening processes competed. Exploring people’s different past weather experiences and the mechanisms by which they can influence future behaviors is important for more deeply understanding populations at risk and how they respond to weather threats.