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Abstract
Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. In comparison with the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% as compared with 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.
Abstract
Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. In comparison with the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% as compared with 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.
Abstract
Anticipatory actions are increasingly being taken before an extreme flood event to reduce the impacts on lives and livelihoods. Local contextualized information is required to support real-time local decisions on where and when to act and what anticipatory actions to take. This study defines an impact-based, early-warning trigger system that integrates flood forecasts with livelihood information, such as crop calendars, to target anticipatory actions better. We demonstrate the application of this trigger system using a flood case study from the Katakwi District in Uganda. First, we integrate information on the local crop cycles with the flood forecasts to define the impact-based trigger system. Second, we verify the impact-based system using historical flood impact information and then compare it with the existing hazard-based system in the context of humanitarian decisions. Study findings show that the impact-based trigger system has an improved probability of flood detection compared with the hazard-based system. There are fewer missed events in the impact-based system, while the trigger dates are similar in both systems. In a humanitarian context, the two systems trigger anticipatory actions at the same time. However, the impact-based trigger system can be further investigated in a different context (e.g., for livelihood protection) to assess the value of the local information. The impact-based system could also be a valuable tool to validate the existing hazard-based system, which builds more confidence in its use in informing anticipatory actions. The study findings, therefore, should open avenues for further dialogue on what the impact-based trigger system could mean within the broader forecast-based action landscape toward building the resilience of at-risk communities.
Abstract
Anticipatory actions are increasingly being taken before an extreme flood event to reduce the impacts on lives and livelihoods. Local contextualized information is required to support real-time local decisions on where and when to act and what anticipatory actions to take. This study defines an impact-based, early-warning trigger system that integrates flood forecasts with livelihood information, such as crop calendars, to target anticipatory actions better. We demonstrate the application of this trigger system using a flood case study from the Katakwi District in Uganda. First, we integrate information on the local crop cycles with the flood forecasts to define the impact-based trigger system. Second, we verify the impact-based system using historical flood impact information and then compare it with the existing hazard-based system in the context of humanitarian decisions. Study findings show that the impact-based trigger system has an improved probability of flood detection compared with the hazard-based system. There are fewer missed events in the impact-based system, while the trigger dates are similar in both systems. In a humanitarian context, the two systems trigger anticipatory actions at the same time. However, the impact-based trigger system can be further investigated in a different context (e.g., for livelihood protection) to assess the value of the local information. The impact-based system could also be a valuable tool to validate the existing hazard-based system, which builds more confidence in its use in informing anticipatory actions. The study findings, therefore, should open avenues for further dialogue on what the impact-based trigger system could mean within the broader forecast-based action landscape toward building the resilience of at-risk communities.
Abstract
The authors describe a tropical cyclone risk model for the Philippines using open-source methods that can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model, are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila, Philippines, yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone–caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling.
Significance Statement
Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philippines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately represent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.
Abstract
The authors describe a tropical cyclone risk model for the Philippines using open-source methods that can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model, are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila, Philippines, yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone–caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling.
Significance Statement
Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philippines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately represent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.
Abstract
Climate trends indicate that extreme heat events are becoming more common and more severe over time, requiring improved strategies to communicate heat risk and protective actions. However, there exists a disconnect in heat-related communication from experts, who commonly include heat-related jargon (i.e., technical language), to decision-makers and the general public. The use of jargon has been shown to reduce meaningful engagement with and understanding of messages written by experts. Translating technical language into comprehensible messages that encourage decision-makers to take action has been identified as a priority to enable impact-based decision support. Knowing what concepts and terms are perceived as jargon, and why, is a first step to increasing communication effectiveness. With this in mind, we focus on the mental models about extreme heat among two groups of domain experts—those trained in atmospheric science and those trained in emergency management—to identify how each group understands terms and concepts about extreme heat. We use a hybrid data collection method of open card sorting and think-aloud interviews to identify how participants conceptualize and categorize terms and concepts related to extreme heat. While we find few differences within the sorted categories, we learn that the processes leading to decisions about the importance of including, or not including, technical information differ by group. The results lead to recommendations and priorities for communicating about extreme heat.
Significance Statement
Effective communication between domain experts is a priority for informed decision-making under extreme weather conditions. As severe heat events increase in frequency and severity, the ability to communicate about heat and its impacts in a clear manner will become vital to life safety. The use of jargon-filled technical, scientific language can serve as a barrier to understanding and engagement, delaying decision-making and action. By identifying how extreme heat terms and concepts are understood among domain experts, risk communicators can determine where to focus on the development of plain language messaging, which improves decision support and decision-making.
Abstract
Climate trends indicate that extreme heat events are becoming more common and more severe over time, requiring improved strategies to communicate heat risk and protective actions. However, there exists a disconnect in heat-related communication from experts, who commonly include heat-related jargon (i.e., technical language), to decision-makers and the general public. The use of jargon has been shown to reduce meaningful engagement with and understanding of messages written by experts. Translating technical language into comprehensible messages that encourage decision-makers to take action has been identified as a priority to enable impact-based decision support. Knowing what concepts and terms are perceived as jargon, and why, is a first step to increasing communication effectiveness. With this in mind, we focus on the mental models about extreme heat among two groups of domain experts—those trained in atmospheric science and those trained in emergency management—to identify how each group understands terms and concepts about extreme heat. We use a hybrid data collection method of open card sorting and think-aloud interviews to identify how participants conceptualize and categorize terms and concepts related to extreme heat. While we find few differences within the sorted categories, we learn that the processes leading to decisions about the importance of including, or not including, technical information differ by group. The results lead to recommendations and priorities for communicating about extreme heat.
Significance Statement
Effective communication between domain experts is a priority for informed decision-making under extreme weather conditions. As severe heat events increase in frequency and severity, the ability to communicate about heat and its impacts in a clear manner will become vital to life safety. The use of jargon-filled technical, scientific language can serve as a barrier to understanding and engagement, delaying decision-making and action. By identifying how extreme heat terms and concepts are understood among domain experts, risk communicators can determine where to focus on the development of plain language messaging, which improves decision support and decision-making.
Abstract
Climate change is expected to impact individuals’ recreation choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation and alternative activities. This paper empirically investigates the relationship between weather and outdoor recreation using nationally representative data from the contiguous United States. We find that across most outdoor recreation activities, participation is lowest on the coldest days [<35°F (1.7°C)] and highest at moderately high temperatures [80°–90°F (27°–32°C)]. Notable exceptions to this trend include water sports and snow and ice sports, for which participation peaks at the highest and lowest temperatures, respectively. If individuals continue to respond to temperature changes the same way that they have in the recent past, in a future climate that has fewer cool days and more moderate and hot days, our model anticipates net participation across all outdoor recreation activities will increase by 88 million trips annually at 1°C of warming (CONUS) and by up to 401 million trips at 6°C of warming, valued between $3.2 and $15.6 billion in consumer surplus annually (2010 population). The increase in trips is driven by participation in water sports; excluding water sports from future projections decreases the consumer surplus gains by approximately 75% across all modeled degrees of warming. If individuals in northern regions respond to temperature like people in southern regions currently do (a proxy for adaptation), total outdoor recreation trips will increase by an additional 17% in comparison with no adaptation at 6°C of warming. This benefit is generally not seen at lower degrees of warming.
Significance Statement
We extend the extant literature in four key ways. First, we probe how impacts vary by region of the United States, revealing disparate impacts across geographies. Second, we analyze substitution patterns between alternative activity categories, which sheds light on the broader implications beyond participation in outdoor recreation. Third, we add depth to the future projection of climate impacts by considering how our projections might change under empirically informed scenarios of adaptation and acclimatization. Fourth, our analysis showcases one way in which climate change results in net benefits.
Abstract
Climate change is expected to impact individuals’ recreation choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation and alternative activities. This paper empirically investigates the relationship between weather and outdoor recreation using nationally representative data from the contiguous United States. We find that across most outdoor recreation activities, participation is lowest on the coldest days [<35°F (1.7°C)] and highest at moderately high temperatures [80°–90°F (27°–32°C)]. Notable exceptions to this trend include water sports and snow and ice sports, for which participation peaks at the highest and lowest temperatures, respectively. If individuals continue to respond to temperature changes the same way that they have in the recent past, in a future climate that has fewer cool days and more moderate and hot days, our model anticipates net participation across all outdoor recreation activities will increase by 88 million trips annually at 1°C of warming (CONUS) and by up to 401 million trips at 6°C of warming, valued between $3.2 and $15.6 billion in consumer surplus annually (2010 population). The increase in trips is driven by participation in water sports; excluding water sports from future projections decreases the consumer surplus gains by approximately 75% across all modeled degrees of warming. If individuals in northern regions respond to temperature like people in southern regions currently do (a proxy for adaptation), total outdoor recreation trips will increase by an additional 17% in comparison with no adaptation at 6°C of warming. This benefit is generally not seen at lower degrees of warming.
Significance Statement
We extend the extant literature in four key ways. First, we probe how impacts vary by region of the United States, revealing disparate impacts across geographies. Second, we analyze substitution patterns between alternative activity categories, which sheds light on the broader implications beyond participation in outdoor recreation. Third, we add depth to the future projection of climate impacts by considering how our projections might change under empirically informed scenarios of adaptation and acclimatization. Fourth, our analysis showcases one way in which climate change results in net benefits.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change as well as discuss uncertainties. In six USGCRP assessments, authors identified research gaps, or topics that they indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: 1) identify and categorize research gaps within a searchable database and 2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed, along with recommendations to improve future U.S. climate assessment products.
Significance Statement
The U.S. Global Change Research Program (USGCRP) regularly assesses the state of the science of climate change and its impacts in national-level reports written by federal scientists, academic researchers, practitioners, and other experts from across the country. These reports are designed to inform policy choices and decision-making by addressing scientific confidence, uncertainty, and research gaps that limit conclusions. We identified research gaps from six climate reports and organized them into a database with over 1000 entries, spanning more than 300 topic areas. Database entries were also sorted into 22 themes for searchability. The database can be used by students, researchers, and program managers to find open research questions and plan future work.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change as well as discuss uncertainties. In six USGCRP assessments, authors identified research gaps, or topics that they indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: 1) identify and categorize research gaps within a searchable database and 2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed, along with recommendations to improve future U.S. climate assessment products.
Significance Statement
The U.S. Global Change Research Program (USGCRP) regularly assesses the state of the science of climate change and its impacts in national-level reports written by federal scientists, academic researchers, practitioners, and other experts from across the country. These reports are designed to inform policy choices and decision-making by addressing scientific confidence, uncertainty, and research gaps that limit conclusions. We identified research gaps from six climate reports and organized them into a database with over 1000 entries, spanning more than 300 topic areas. Database entries were also sorted into 22 themes for searchability. The database can be used by students, researchers, and program managers to find open research questions and plan future work.
Abstract
The WxChallenge is a national forecasting competition in which participants at colleges and universities across North America attempt to accurately predict daily high and low temperatures, maximum wind speed, and precipitation accumulation at a variety of locations each year. Undergraduate students make up the majority of participants. In this study, we observed trends from 11 seasons of WxChallenge data and related them to existing literature on local forecasting contests. Normalized scores were calculated each day for any participant that submitted a forecast. On average, undergraduate scores improved with continued participation in the contest. Significant gains are made during their first year by forecasters who participated for multiple years. Duration of participation in the contest plays a role, but significant improvements in performance were also noted with higher academic standing, potentially as a result of forecasting experience gained through other curricular or extracurricular activities.
Significance Statement
One avenue that aspiring meteorologists have outside the classroom to hone their forecasting skills is through the WxChallenge, a national collegiate forecasting contest. We wanted to see whether, on average, students who participated longer in the contest outperformed those who did not forecast for as long. We found this to be true. Mean scores by students who participated for more than one year improved significantly by the end of the first year, and they outperformed those without prior experience in the WxChallenge in following years. These findings are important because they can be used as evidence to motivate students to join a forecasting contest early in their academic careers, allowing them to gain skill before pursuing a career in forecasting.
Abstract
The WxChallenge is a national forecasting competition in which participants at colleges and universities across North America attempt to accurately predict daily high and low temperatures, maximum wind speed, and precipitation accumulation at a variety of locations each year. Undergraduate students make up the majority of participants. In this study, we observed trends from 11 seasons of WxChallenge data and related them to existing literature on local forecasting contests. Normalized scores were calculated each day for any participant that submitted a forecast. On average, undergraduate scores improved with continued participation in the contest. Significant gains are made during their first year by forecasters who participated for multiple years. Duration of participation in the contest plays a role, but significant improvements in performance were also noted with higher academic standing, potentially as a result of forecasting experience gained through other curricular or extracurricular activities.
Significance Statement
One avenue that aspiring meteorologists have outside the classroom to hone their forecasting skills is through the WxChallenge, a national collegiate forecasting contest. We wanted to see whether, on average, students who participated longer in the contest outperformed those who did not forecast for as long. We found this to be true. Mean scores by students who participated for more than one year improved significantly by the end of the first year, and they outperformed those without prior experience in the WxChallenge in following years. These findings are important because they can be used as evidence to motivate students to join a forecasting contest early in their academic careers, allowing them to gain skill before pursuing a career in forecasting.
Abstract
In the eastern Corn Belt of the United States, climate change is projected to bring warmer and wetter conditions, with more variability in the seasonal timing of rainfall, creating a multitude of challenges for agricultural production. While there are multiple adaptations to reduce the vulnerability of production to a changing climate, these adaptations have varying implications for other ecosystem services such as soil health, carbon sequestration, and water quality. We explore how beliefs about and experiences with climate change might influence adaptations that vary in their provisioning of a variety of ecosystem services, and how these adaptations may vary by characteristics of the farm and farmer. Survey data were collected from 908 respondents from August through October 2019. We find only one proposed adaptation, additional tile drainage, is associated with self-reported prior negative experiences with climate change and concern about future impacts. The other proposed adaptations (i.e., cover crops, filter strips, additional fertilizer) are associated with farmer identity. The type of farmer who is likely to adapt is generally reminiscent of the type who engage in conservation practices: younger, more educated, with off-farm income and larger farms. Our results indicate that many proposed adaptations are not perceived as effective ways to mitigate specific climate-driven impacts. However, increasing tile drainage is perceived as such, and there may be a need to offset the potential negative impacts to water quality of this likely adaptation through the promotion of edge-of-field filtration practices.
Significance Statement
The purpose of this study is to better understand how beliefs about and experiences with climate change may influence adaptations that vary in their provisioning of a suite of ecosystem services. We find that those who are adapting directly to the most severe and frequent climate-exacerbated impact, heavy rain at the wrong time of year, are likely to adapt in ways that may benefit production (e.g., increasing drainage tile to ensure fields are not inundated by spring rains) but have negative feedback to society in terms of water quality. Most proposed adaptations are not perceived as effective means of increasing resilience to experienced impacts but rather as practices that are the norm among conservation-minded farmers with larger operations.
Abstract
In the eastern Corn Belt of the United States, climate change is projected to bring warmer and wetter conditions, with more variability in the seasonal timing of rainfall, creating a multitude of challenges for agricultural production. While there are multiple adaptations to reduce the vulnerability of production to a changing climate, these adaptations have varying implications for other ecosystem services such as soil health, carbon sequestration, and water quality. We explore how beliefs about and experiences with climate change might influence adaptations that vary in their provisioning of a variety of ecosystem services, and how these adaptations may vary by characteristics of the farm and farmer. Survey data were collected from 908 respondents from August through October 2019. We find only one proposed adaptation, additional tile drainage, is associated with self-reported prior negative experiences with climate change and concern about future impacts. The other proposed adaptations (i.e., cover crops, filter strips, additional fertilizer) are associated with farmer identity. The type of farmer who is likely to adapt is generally reminiscent of the type who engage in conservation practices: younger, more educated, with off-farm income and larger farms. Our results indicate that many proposed adaptations are not perceived as effective ways to mitigate specific climate-driven impacts. However, increasing tile drainage is perceived as such, and there may be a need to offset the potential negative impacts to water quality of this likely adaptation through the promotion of edge-of-field filtration practices.
Significance Statement
The purpose of this study is to better understand how beliefs about and experiences with climate change may influence adaptations that vary in their provisioning of a suite of ecosystem services. We find that those who are adapting directly to the most severe and frequent climate-exacerbated impact, heavy rain at the wrong time of year, are likely to adapt in ways that may benefit production (e.g., increasing drainage tile to ensure fields are not inundated by spring rains) but have negative feedback to society in terms of water quality. Most proposed adaptations are not perceived as effective means of increasing resilience to experienced impacts but rather as practices that are the norm among conservation-minded farmers with larger operations.
Abstract
In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States, with widespread impacts in Florida, North Carolina, South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a category-1 storm approximately 2 weeks after formation, whereas Hurricane Michael made landfall as an “unprecedented” category-5 storm just 3 days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume and search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. Data showed that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention building, especially at the national level. Taken together, the results suggest that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for research that may provide additional insights into the linkages between attention, perception, and decision-making.
Significance Statement
The purpose of this study was to better understand the factors that influence public attention to extreme weather. This is important because attention is often noted for its mediating effect on decision-making. We found that public attention was greater during Hurricane Florence, despite the fact that Hurricane Michael was an “unprecedented” category-5 storm. Taken together, this suggests that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways.
Abstract
In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States, with widespread impacts in Florida, North Carolina, South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a category-1 storm approximately 2 weeks after formation, whereas Hurricane Michael made landfall as an “unprecedented” category-5 storm just 3 days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume and search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. Data showed that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention building, especially at the national level. Taken together, the results suggest that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for research that may provide additional insights into the linkages between attention, perception, and decision-making.
Significance Statement
The purpose of this study was to better understand the factors that influence public attention to extreme weather. This is important because attention is often noted for its mediating effect on decision-making. We found that public attention was greater during Hurricane Florence, despite the fact that Hurricane Michael was an “unprecedented” category-5 storm. Taken together, this suggests that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways.
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.
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.
