Exposure has amplified rapidly over the past half century and is one of the primary drivers of increases in disaster frequency and consequences. Previous research on exposure change detection has proven limited since the geographic units of aggregation for decennial censuses, the sole measure of accurate historical population and housing counts, vary from one census to the next. To address this shortcoming, this research produces a set of gridded population and housing data for the Chicago, Illinois, region to evaluate the concept of the “expanding bull’s-eye effect.” This effect argues that “targets”—people and their built environments—of geophysical hazards are enlarging as populations grow and spread. A collection of observationally derived synthetic violent tornadoes are transposed across fine-geographic-scale population and housing unit grids at different time stamps to appraise the concept. Results reveal that intensifying and expanding development is placing more people and their possessions in the potential path of tornadoes, increasing the likelihood of tornado disasters. The research demonstrates how different development morphologies lead to varying exposure rates that contribute to the unevenness of potential weather-related disasters across the landscape. In addition, the investigation appraises the viability of using a gridded framework for assessing changes in census-derived exposure data. The creation of uniformly sized grid data on a scale smaller than counties, municipalities, and conventional census geographic units addresses two of the most critical problems assessing historical changes in disaster frequencies and magnitudes—highly variable spatial units of exposure data and the mismatch between spatial scales of population/housing data and hazards.

Tornado disasters and their potential are a product of both hazard risk and underlying physical and social vulnerabilities. This investigation appraises exposure, which is an important component and driver of vulnerability, and its interrelationship with tornado risk in the United States since the mid-twentieth century. The research demonstrates how each of these dynamic variables have evolved individually and interacted collectively to produce differences in hazard impact and disaster potential at the national, regional, and local scales. Results reveal that escalating tornado impacts are driven fundamentally by growing built-environment exposure. The increasing tornado disaster probability is not uniform across the landscape, with the mid-South region containing the greatest threat based on the juxtaposition of an immense tornado footprint risk and elevated exposure/development rates, which manifests—at least for one important impact marker—in the area’s high mortality rate. Contemporary, high-impact tornado events are utilized to emphasize how national- and regional-level changes in exposure are also apparent at the scale of the tornado. The study reveals that the disaster ingredients of risk and exposure do vary markedly across scales, and where they have increasing and greater overlap, the probability of disaster surges. These findings have broad implications for all weather and climate hazards, with both short- and long-term mitigation strategies required to reduce future impacts and to build resilience in the face of continued and amplifying development in hazard-prone regions.

Visibility-related weather hazards have significant impacts on motor vehicle operators because of decreased driver vision, reduced roadway speed, amplified speed variability, and elevated crash risk. This research presents a national analysis of fog-, smoke-, and dust storm–associated vehicular fatalities in the United States. Initially, a database of weather-related motor vehicle crash fatalities from 1994 to 2011 is constructed from National Highway Traffic Safety Administration data. Thereafter, spatiotemporal analyses of visibility-related (crashes where a vision hazard was reported at time of event) and vision-obscured (driver’s vision was recorded as obscured by weather, and a weather-related vision hazard was reported) fatal vehicular crashes are presented. Results reveal that the annual number of fatalities associated with weather-related, vision-obscured vehicular crashes is comparable to those of more notable and captivating hazards such as tornadoes, floods, tropical cyclones, and lightning. The majority of these vision-obscured crash fatalities occurred in fog, on state and U.S. numbered highways, during the cool season and during the morning commuting hours of 0500 to 0800 local time. Areas that experience the greatest frequencies of vision-obscured fatal crashes are located in the Central Valley of California, Appalachian Mountain and mid-Atlantic region, the Midwest, and along the Gulf Coast. From 2007 to 2011, 72% of all vision-obscured fatal crashes occurred when there was no National Weather Service weather-related visibility advisory in effect. The deadliest weather-related visibility hazard crashes during the period are exhibited, revealing a spectrum of environmental and geographical settings that can trigger these high-end events.

ABSTRACT:Determining the likelihood and severity of tornado disasters requires an understanding of the dynamic relationship between tornado risk and vulnerability. As population increases in the future, it is likely that tornado disaster frequency and magnitude will amplify. This study presents the Tornado Impact Monte Carlo (TorMC) model, which simulates tornado events atop a user-defined spatial domain to estimate the possible impact on people, the built-environment or other potentially vulnerable assets. Using a Monte Carlo approach, the model employs a variety of sampling techniques on observed tornado data to provide greater insight into the tornado disaster potential for a location. Simulations based on 10 000 years of significant tornado events for the relatively high-risk states of Alabama, Illinois and Oklahoma are conducted to demonstrate the model processes, and its reliability and applicability. These simulations are combined with a fine-scale (100 m), residential built-environment cost surface to illustrate the probability of housing unit impact thresholds for a contemporary year. Sample results demonstrate the ability of the model to depict successfully tornado risk, residential built-environment exposure and the probability of disaster. Additional outcomes emphasize the importance of developing versatile tools that capture better the tornado risk and vulnerability attributes in order to provide precise estimates of disaster potential. Such tools can provide emergency managers, planners, insurers and decision makers a means to advance mitigation, resilience and sustainability strategies.

An increasing number of significant and violent tornado events in the United States have been documented and mapped at extremely high resolution by government, research and private entities using remotely sensed and post-event damage surveys; however, these assessments often generate inconsistent spatial measures of tornado strength, even for the same event. This investigation assembles a portfolio of contemporary tornado events that contain spatially comprehensive damage and/or wind velocity information from a diverse set of sources. Thereafter, the relationship between land-use/cover and tornado intensity is examined in order to quantify spatial measures of damage indicator bias in post-event tornado damage surveys. A climatology of both significant and violent tornado intensity assessments is then created, promoting the generation of synthetic, or model, paths with observationally constrained damage length and width metrics by the Enhanced Fujita scale magnitude. Results from the climatology and collection of synthetic paths are compared to previous observed, empirical and theoretical assessments, revealing differences in the spatial scale of the overall tornado footprint, as well as percentage contribution of swaths by Enhanced Fujita scale magnitude. The range of synthetic paths produced may be used to assess potential tornado damages to the population, the built environment and insurance portfolios.