Natural Hazards Review

Past research has shown that households living in manufactured housing have outsized exposure and vulnerability to multiple climate-related hazards, including floods, tornadoes, and hurricanes. Yet, few studies have specifically analyzed the risk of hot and dry climate change-induced hazards, such as extreme heat and wildfires, on households living in manufactured housing. In this study, we first use national American Housing Survey data to provide background context on the adaptive capacity to these hazards among manufactured home occupants nationally. We then focus our primary analysis on California, where the prevalence of such hazards is growing substantially due to climate change. We compile a unique, census tract-level data set of occupied housing unit characteristics by tenure status, along with extreme heat and wildfire hazard measures. We also incorporate address-level locations of mobile home parks in the state. We use descriptive statistics, t-tests, and bivariate correlations to compare the exposure for households in manufactured housing with renter and owner households residing in other housing structure types. We find that households in manufactured housing face consistently higher exposure to heat and fire hazards across all exposure measures. Census tracts with higher proportions of households in manufactured housing are much more likely to be located in areas with greater historical and projected extreme heat hazards than households living in other lower-cost housing types or households in general. Households in manufactured housing are also the most likely of all housing types to be located in tracts affected by past wildfires and in tracts with high future wildfire risk. We conclude by providing recommendations for future research and policy consideration.

Households have access to real-time travel information via different media. However, the effects of predeparture information about travel delays on households’ evacuation plans (evacuate/stay, departure time, route, and destination choices) remain unknown. Household evacuation plan adaptations undoubtedly affect traffic network loading and the effectiveness of traffic-management strategies. Approximately 400 individuals provided responses to choice experiments in a behavioral intention survey allowing investigation of household hurricane evacuation plan adaptations in three travel delay settings. The data show that deciding to stay is the least frequent adaptation, while changing departure time is the most preferred adaptation. Random parameter logit models with observed heterogeneity were estimated to study how households adapt each choice in their evacuation plans in repeated choice settings. Generally, households are less likely to stay when they are informed of longer travel delays. With a 6-h delay, evacuees are more likely to evacuate a day sooner and change routes. With a 1-h delay, evacuees are more likely to evacuate earlier that day and change destinations.

Nonprofit social service organizations play key roles in disaster response and recovery efforts targeting socially vulnerable populations, particularly in the United States. Increasingly frequent and intense disasters associated with climate change may raise new challenges for nonprofits. Yet little research has explored how changing disaster patterns may impact the roles of nonprofits. With this in mind, we examine the case of Tallahassee, Florida, which experienced a 3-year surge in tropical storm activity from 2016 to 2018. Based on interviews with nonprofit staff, supplemented by document analysis and participant observation, we explore how nonprofits experienced and adapted to this intensification of storm activity. We find that nonprofits that had formerly been peripheral to disaster work—such as homeless shelters and case management agencies—took on new responsibilities during this period but struggled to gain recognition as key actors. The authors develop a four-part scheme for classifying the roles of nonprofits and apply this scheme in interpreting the diverse experiences and adaptation strategies of nonprofit social service organizations. There is a need to more fully integrate some classes of nonprofits that have not traditionally been seen as key actors in disaster systems.

Earthquake can alter flood hazard in a region by impacting the components of stormwater drainage system. A methodology is proposed in this paper to measure flood hazard alteration after an earthquake. The study mainly focuses on flood hazard alteration as a result of the direct impact of an earthquake on a minor drainage system. A minor drainage system is the stormwater collection network where the components such as pipelines and inlets are vulnerable to earth movements. Cracks and breaks are considered as the impacts of earthquake on the pipelines, whereas inlet blockage because of earthquake-generated debris is considered as the impact on the network inlets. The methodology is applied on an entire midsize city to prove its scalability. The minor drainage system of the city is comprised of 564 pipes with the total length of 40 km (25 mi). The pipes are divided into 35 subcatchments, which serve a total area of $6.2{\mathrm{km}}^2$ ($2.4{\mathrm{mi}}^2$) of the city. Several metrics for flood hazard alteration are defined and measured for the case study, which clearly shows alteration of the flood hazard after the earthquake. The metrics are presented in both spatially variable and aggregated formats. Such analysis can help communities to better prepare for possible alteration in flood hazard after an earthquake, to better plan and allocate resources for recovery of the stormwater collection network after an earthquake, and to recognize new flood hazard hot spots.

The incidence of hurricanes has increased in the United States during recent years, affecting many aspects of the lives of those who have fallen victim to them. Delays that may be due to unexpected barriers to the recovery process are a major issue for the community, governors, and other federal authorities and directly impact the length of the recovery. Their barriers’ impacts on each other can intensify their negative effects and lead to further delays. A significant step toward resolving this problem is determining exactly how posthurricane recovery barriers interact with each other and how that affects the recovery process. Therefore, the aims of this study were to (1) investigate the causal relationships among the barrier categories to timely posthurricane recovery, and (2) identify the extent to which the barrier categories and their interactions impact the timeliness of the recovery. A survey was developed to determine the impacts of the 62 barriers that were identified as detrimental to timely posthurricane recovery. A total of 44 experts and 195 other individuals (referred to herein as the public) completed the survey. The structural equation modeling (SEM) technique was used to develop two interrelated networks of the posthurricane recovery barriers, based on experts’ and the public’s perspectives and inputs, to analyze their impacts on the duration of the recovery process after hurricanes. The findings revealed some significant discrepancies between the perspectives of the two groups regarding the impacts of various barriers on the length of the recovery process. The experts considered coordination-related barriers as the foremost influences on the length of recovery, while the public group considered social-related barriers as the most influential. Both groups considered environment-related barriers to be unimportant and to have the least effect on the duration of the recovery. The results of this study will help decision-makers identify the paths that lead to significant delays in the posthurricane recovery process. They will also help postdisaster recovery planners understand the differences between the perspectives of the public and the experts so they can develop recommendations and strategies that are effective in preventing delays.

To understand the community risk from severe weather threats, two components, including weather information and community assets, are crucial. Recently, probabilistic hazard information (PHI) from the National Oceanic and Atmospheric Administration (NOAA) Forecasting a Continuum of Environmental Threats (FACETs) program has been developed to provide dynamic weather-related information between the watch and warning systems to weather forecasters, emergency management agencies, and the public. To predict community physical risks on critical infrastructure and building properties using PHI, building type information is required. This study applied a machine learning technique to predict building types using building footprint and city zoning data. We collected Oklahoma county building property data to train and test a random forest model. The result of this study showed that building footprint and city zoning data can be applied to classify multiple building types with an accuracy of 96%. The machine learning–based building classification contributed to the acquisition of building type data in the Oklahoma City, Oklahoma, metropolitan area. This geodatabase will be utilized to predict real-time critical infrastructure and building damage assessment using PHI. In addition to their importance to physical building damage assessment, the results can be utilized to develop postdisaster responses and planning.

Construction industry employment has been severely impacted by the coronavirus (COVID-19) pandemic. This paper examines the impact of the COVID-19 pandemic in the US construction labor market. This study contributes to the state of knowledge by (1) revealing how the COVID-19 pandemic impacted construction employment across different demographic groups and geographic regions in the US, and (2) identifying vulnerable demographic groups (e.g., minorities, women, older workers) in the construction workforce that are disproportionally impacted by the pandemic. Employment data from the Current Population Survey and Current Employment Statistics (CES) programs, which are administered by the Bureau of Labor Statistics, are used to assess the impact of COVID-19 on construction employment. The impact of COVID-19 is defined as a change in 12-month employment from 2019 to 2020; the study mostly focuses on April and September, 2020. Analyzing CES survey data reveals that women in the construction workforce declined by 11.9% in April 2020, when COVID-19 caused an overall 15.6% decline in the US construction labor market. The study also reveals that Black or African American communities are disproportionately impacted by job loss in the US construction industry. In September 2020, the 12-month decline in construction employment among Black or African Americans was 29.6%, compared to 5.8% for the overall decline in construction employment. States in the north of the US suffered a rapid decline in construction employment during the initial stage of the COVID-19 pandemic. Thirty states suffered a higher 12-month decline in construction average weekly earnings in September compared to April, which reveals the persistent impact of the pandemic on the industry. This study contributes to the state of practice by helping policymakers understand the pandemic’s disproportionate impact across different demographic groups and geographic regions. It is expected that this study will assist policymakers in drafting equitable recovery policies to overcome the setbacks caused by the pandemic.

Public institutions are facing natural and manmade hazards of increasing frequency and severity. While the costs of disasters can be greatly reduced when individuals prepare, successfully encouraging preparation is difficult for governments, given the low salience of such risks. We examine whether the increased salience of other types of risks can influence individual willingness to prepare for natural and manmade hazards, and whether message impact varies with recipients’ levels of trust in their source. We capitalize upon a rare policy experiment—the staged rollout of COVID-19 lockdowns in California—to assess if increases in the salience of the pandemic were associated with greater willingness to store water for earthquake-induced system outages. We find that experiences of a disaster in a different domain (public health) and higher levels of trust in message source both increase willingness to store water. This suggests that public agencies should encourage preparedness during actual emergencies, or “not let a crisis go to waste.”

With growing shoreline populations and projections of damage and losses from coastal natural hazards, such as hurricanes and nor’easters, coastal community resilience has attracted increasing attention. To ensure coastal communities return to their normal operating conditions with original functionality, many aspects, such as social impacts, economic costs, and the physical integrity and safety of building infrastructures, require consideration to better predict future damage and vulnerability, leading to improved decision-making to mitigate future threats. In this study, an interdisciplinary framework linking social, economic, and infrastructural community resilience is proposed to project structural damage, morbidities, home displacements, and economic losses. Through Monte Carlo simulation of historical weather and vulnerability data, potential physical damage scenarios are predicted. A case study of residential building hurricane vulnerability of a coastal community is presented, and the effects of elevating buildings are evaluated. The physical damage projections indicate that the elevation of houses closest to shore, which have a higher vulnerability to hurricane storm surge damage and consequent socioeconomic impacts, shows the potential to drastically reduce cumulative hurricane-induced damage, potentially leading to substantial reductions in cumulative socioeconomic hurricane-induced losses.

Tokyo is on one hand becoming increasingly vulnerable to flood disasters due to flooding of greater severity and frequency. Tokyo’s vulnerability to flood disasters has increased because of the changes in population structure, outdated infrastructures, an insufficient energy supply, the deterioration of flood control facilities, high-value economic assets, and the weakening of flood control awareness. Therefore, the study of key factors related to flood disaster resilience is important to enhance the flood resilience of the city. This study aims to conduct a systematic analysis of flood resilience regarding ecology, economics, societal effect, infrastructure, and community capacity based on the disaster resilience of place (DROP) model. An indicator system was developed incorporating 20 indicators. The resilience of Katsushika Ward to flood disasters was quantified using collected data, the coefficient of variation, technique for order preference by similarity to an ideal solution (TOPSIS) method, the DROP regional disaster resilience model, and a geographic information system. The results showed that there are deviations in the resilience values of six dimensions, among which the system resilience and social resilience values are relatively weak. However, the highest resilience value lies in eco-environmental resilience and infrastructure, which is of great significance in disaster resilience assessment in terms of the disaster resilience dimension. In order to improve the resilience of the ecological environment, the Katsushika City’s local government needs to not only facilitate the local greening rate and air quality improvement but also properly handle sewage and garbage. It also has responsibility to strengthen the flood prevention capacity of medical facilities, refuge facilities, welfare facilities, and residential buildings so as to enhance infrastructure elasticity. From the distribution point of view, the regions with the strongest and weakest resilience to flood disasters are found in the northwest and southeast regions, respectively. Therefore, it is concluded that there is spatial variability in flood disaster resilience in the northwest, southwest, and east regions. This research could definitively help determine the spatial distribution of flood resistance in a certain area, thereby helping local governments improve their ability to respond to disasters and formulate appropriate plans to improve their resilience.

This paper proposes a simple analytical scheme and associated qualitative impact scales that capture the spatially varying effects of a regional disaster. Large-scale disasters that affect many towns and cities pose particular challenges for emergency response planning. For example, disruption to transportation systems can impede regional supply chains of critical goods, thereby exacerbating the impacts suffered locally in communities. Conventional metrics of disaster severity, such as number of casualties or intensity of ground shaking, do not adequately capture how community impacts and needs may vary across the affected region, and they do not typically consider regional transportation disruption. Using a series of impact scales, the approach in this paper captures essential attributes of three broad components related to community impacts from a regional disaster—local disaster impacts in a community, regional transportation disruption to the community, and the community’s coping capacity—and aggregates them to an overall metric of community impact. The approach can be implemented with widely varying degrees of data availability, as demonstrated in two case applications. Both cases involve an M9 Cascadia subduction zone earthquake affecting a broad region of coastal British Columbia, Canada. The first application illustrates how in a pre-event planning situation, modeled results can be used to anticipate which communities are at greatest risk, and to help prioritize mitigation and emergency response planning. The second case demonstrates how in the immediate aftermath of a disaster, the approach can be used with limited information to help prioritize response and recovery activities.

Occupational safety and health (OSH) related agencies have a plethora of injury data available; however, modern data visualization and user-friendly dissemination tools are lacking in this field. This work has created a geospatial platform using geographic information systems (GIS) combined with big data analytics for the southeastern region of United States. Severe injury reports were collected and visualized through state-of-the-art spatial statistics including spatial clustering, heat maps, and hotspots to identify the most vulnerable regions due to various injury types from a safety vantage point. A special focus was also given to the construction industry, considering the hazardous nature of the industry. Statistically significant spatial clustering was observed within the study region, with Moran’s I index of 0.49. Hotspots for severe injuries were also identified with approximately 99% confidence level using Getis Ord ${\mathrm{Gi}}^{*}$ statistics. Results indicated statistically significant high-risk zones particularly around city areas with spatial injury rates (SIR) up to approximately 2.84 per county. Analysis also showed increased number of severe injuries during summer months, with approximately 1,000 injuries during the month of June and July. The construction industry accounted for 20% of all injuries, with “caught-in/between” being the highest amongst the four primary causes of severe injuries, commonly known as the “fatal four.” Finally, a web-based application was created to disseminate the results. Big data mining coupled with geospatial technology in OSH management can offer decision makers up-to-date and highly geospatial information for severe injuries that can be integrated in developing a comprehensive occupational safety surveillance plan.

The purpose of this work is to examine the immediate impacts of hurricanes on schools and document the competing priorities faced by educators as they return to their educational mission of serving students and assisting with communal recovery. The research team conducted 115 interviews with educators across 20 school districts after Hurricane Matthew in North Carolina and Hurricane Harvey in Texas. After the hurricanes, respondents described disruptions in the form of operational, personal, and displacement. Educators adapted to their shifting responsibilities by taking on new roles in managing school property, volunteering in the community, and managing relief efforts. This paper addresses a gap in the literature on the immediate impacts of hurricanes on schools by studying two storms in different states, which enables more transferable conclusions to be drawn than in prior studies. When helping schools make disaster plans, educators and emergency planners should address the anticipated disruptions, account for shifting responsibilities, and enable clear communication during the disaster and recovery process.