Natural Hazards Review

Despite recent advancements in hurricane hazard characterization and a better understanding of the vulnerabilities of the built environment to hurricane-related hazards, coastal communities still face considerable losses due to extensive damage from hurricanes. Designing and constructing the most vulnerable types of buildings, that is, residential buildings, to be considered as resilient structures can play a significant role in maintaining the functionality of these communities after strong hurricane events. To construct buildings that can maintain or recover functionality in a timely manner after a hurricane event, an organized and clear understanding of the vast extent of hurricane hazards that could disrupt the functionality of buildings is crucially important. This knowledge could enable investigators to pursue research gaps and illuminate less-understood aspects of hurricane-induced failure mechanisms to effectively limit loss of functionality in residential buildings after hurricanes. With this intention, this paper presents a review of the damage mechanisms for different kinds of hurricane-related hazards in low-rise residential buildings and tries to synthesize information in the literature, building codes, and guidelines to identify research gaps. Based on the highlighted gaps, the paper suggests future research endeavors to augment the existing knowledge regarding hurricane-induced loss of functionality through refined or new research methodologies. The paper concludes with a brief discussion on the benefits, limitations, and future directions of the performance and functionality-based design in the context of hurricane engineering and loss assessment in low-rise residential buildings.

Abstract

To present a hybrid theory of disaster prevention actions and develop a common framework, this study theoretically and systematically reviewed recent research in Japan on actions from three perspectives: definition, multidisciplinarity, and multidimensionality. First, this research validated that the definition of actions on disaster prevention and reduction should be understood in three contexts: responsibility, willingness, and habituation; the diversity of terminology; and the type of measure. Second, the multidisciplinary nature of the approach affirmed that the motivations and approaches could be organized according to the axes of individual and social models and epistemic and design sciences. Third, per the multidimensional nature of theories explaining actions, the applied theories were organized based on the four perspectives: process, motivation, internal experience, and context. Furthermore, it was suggested that there are three critical perspectives: complicity in neoliberalism, invisibility of social inequalities, and bias in reporting desirable results (expert-serving bias). Taking an approach that emphasizes interdisciplinarity and multidimensionality, this study details the background of Japanese research used to employ a theoretical approach to understand individual cognition and demonstrates the need for research to understand the political, cultural, social, and economic environments that support disaster prevention behavior, noting that this is tied to the unique Japanese context of individual will and dependence on the government. This study is significant in that it provides a map for positioning one’s research and pointing the way toward future collaboration in the direction showing definition, interdisciplinarity, and multidimensionality are important components in understanding disaster prevention behavior.

Practical Applications

This study reviewed three aspects of recent disaster research in Japan to better understand disaster prevention behavior: what it is and what areas and aspects of it are being studied. First, Japanese disaster prevention behavior can be understood in relation to “responsibility,” “will,” and “habit”; the diversity of behaviors; and the types of measures applied. Second, motivations and approaches could be amenable to organization along the axes of whether the coping subject is an individual or society and whether the science focuses on “what it is” or “what it can do.” Third, explanations of disaster prevention behaviors were found to be viewable in relation to four perspectives: process, motivation, experience, and context. Three criticisms were also found: social inequalities are less visible, many results that are preferable to the researcher are reported, and there is a risk of becoming connected to particular political policies. This study found that although many theories that explain individuals’ perceptions are used to explain their disaster prevention behavior, it is also important to understand the environment of the political, cultural, social, and economic aspects that surround the individual.

The pioneering work of Dennis Mileti and those with whom he collaborated have established a sound basis in understanding how human beings behave when encountering hazard warnings. His work greatly expanded our understanding of the social environment into which warnings are issued and provided clear guidelines on how warnings should be delivered to optimize behavioral compliance; how warning message content can be construed to motivate protective actions; and why people may fail to comply or delay actions after receipt of a warning. This paper addresses Mileti’s work in earthquake warnings, first in the context of earthquake predictions and later, as optimism waned that accurate earthquake predictions were possible, as his insights were applied to newer approaches in the form of earthquake early warnings and operational earthquake forecasting. This paper will first briefly trace the historical development of approaches to warn of a potentially damaging earthquake because it is important to place Mileti’s work in the historical context in which it was carried out. It then assesses the manner in which the current methods of earthquake warning for short- and intermediate-term time frames via earthquake early warning and operational earthquake forecasting have benefited from Mileti’s very significant observations and insights. His work in evaluating and clarifying the social environment in which warnings are issued is an important and enduring element of his legacy, has significantly influenced how hazard warnings are now formulated, and has greatly shaped the work of those of us who carry on the study of hazard warning systems.

Persons with disabilities may face longer disaster recovery trajectories, despite federal mandates for equal access and reasonable accommodations in disaster recovery services. The longer recovery trajectories for persons with disabilities may be mitigated with better access to federal disaster assistance. This study evaluated whether disaster assistance applicants with special accommodations requests were more likely to experience denials in Federal Emergency Management Agency (FEMA) assistance eligibility after Hurricane Harvey. The author relied on FEMA’s Individuals and Households Program (IHP) registrant data. The analysis found that applicants requesting special accommodations were less likely to be denied eligibility than nonrequesting applicants, controlling for storm damage, income, and flood insurance [odds ratio (OR) 0.57; 95% $\mathrm{CI}=0.51–0.63$]. However, communities with a higher prevalence of total disabilities had a slight but statistically lower probability of special accommodations requests as a proportion of total FEMA applications. At the zip code level, a 1% increase in US Census total disability was associated with a 0.11% decrease in the percent of special accommodation requests, holding factors such as levels of Harvey-related damage, poverty, population density, and race/ethnicity constant ($\mathrm{beta}=-0.11$; $p\text{-}\mathrm{value}=0.04$).

The seismic stability analysis of slopes is frequently analyzed using the method of slices based on the pseudostatic approach. However, the time response analysis is considered to be one of the most competent tools to analyze the performance of slopes under seismic motion since this method provides details about deformation and stress distribution within the slope. In this paper, a finite-element-based time response analysis was performed to examine the seismic response of an existing hill slope located in the Kavalappara (11.414° N, 76.237° E) region of Kerala, India, where the shear strength of many slopes reduces considerably due to heavy rainfall. In this regard, a synthetic acceleration time history was developed from site-specific seismic hazard analysis. Also, a pseudostatic analysis based on the finite-element method was performed to estimate the factor of safety and corresponding stability measure. The finite-element model was developed based on Mohr-Coulomb and hardening soil with small stiffness (HSsmall) constitutive models to capture the soil behavior during an earthquake. Further, Newmark’s sliding block analysis was conducted analytically to study the probable displacement after the seismic event and to validate the present finite-element analysis results. The pseudostatic analysis revealed that the slope is not safe against a pseudostatic loading of $0.12g$. The seismic response of the slope under earthquake motion was described via displacement developed due to ground shaking. The results of time response indicated a continuous forward and downward earthquake-induced movement of the slope crest. The maximum displacement observed during the seismic event was 18.84 mm. Further, a sensitivity analysis was performed by considering Newmark’s method and a few well-known empirical methods to check the variation of FS values with yield acceleration. Displacement and factor of safety values calculated using analytical methods were in good agreement with that obtained from finite-element-based time response analysis. Moreover, it has been concluded that there is potentially unstable slope condition exists due to combined groundwater variations and earthquake impact.

Abstract

While existing literature has explored how hazard experience, salience, and demographics characteristics shape threat appraisal and hazard adjustment intentions, this study expands on past studies by exploring how additional factors such as qualitative characteristics of the hazard, political ideology, and oil entanglements shape threat appraisals, coping appraisals, and adjustment intentions in response to a techna hazard. This study builds on protection motivation theory (PMT) to explore factors that shape Oklahomans’ intentions to adjust to induced seismicity using data collected from households ($n=866$) across 27 counties in Oklahoma that have experienced varying levels of seismic activity resulting from oil and gas exploration. Correlational analyses and structural equation modeling show that several variables not included in the original PMT, such as feelings of dread or negative emotions associated with earthquakes, are important predictors of intentions to adopt hazard adjustments. This study concludes with examining the effect of additional factors on adjustment intentions and risk perceptions that can help guide future earthquake risk management in identifying and taking appropriate actions that will stimulate precautionary behavior of private actors.

Practical Applications

This study builds on PMT to explore factors that shape Oklahomans’ intentions to adjust to induced seismicity using data collected from households ($n=866$) across 27 counties in Oklahoma that have experienced varying levels of seismic activity resulting from oil and gas exploration. While our results lend support to PMT hypotheses, we found that several variables not included in the original PMT, such as feelings of dread or negative emotions associated with earthquakes, are important predictors of intentions to adopt hazard adjustments. Results of this research can help guide future earthquake risk management in Oklahoma, providing insights that can be used to help residents identify and take appropriate actions to reduce their earthquake risk to reduce their risk. Local and state governments in Oklahoma should work to raise awareness of earthquake risk and use our research findings to emphasize adjustment measures that have low adoption intentions and high potential to reduce risk, and that are relatively cheap and easy to install (e.g., installing secure cabinets). Likewise, stakeholders across the state should work to eliminate financial barriers by providing subsidies or government loans for costly adjustment measures (e.g., purchasing earthquake insurance) that protect individuals and property from future earthquake hazards. Local emergency managers should also work to increase households’ familiarity and knowledge about earthquake risks and communicate the multiuse functions of many adjustment activities, which we find to be a strong predictor of adjustments.

Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure.

In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC’s social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.

This study examined the predictive ability of perceived vulnerability to disease (PVD), fear of COVID-19, and coping mechanisms on the Korean immigrants’ psychological distress level amid the pandemic. Through purposive sampling, both foreign-born and US-born Korean immigrants residing in the US above the age of 18 years were invited to an online survey. Between May and June 2020, data collection took place, which yielded the final sample of 790 participants from 42 states. An artificial neural network (ANN) was used to verify variables that predict the level of psychological distress on the participants. The model with one hidden layer holding six hidden neurons showed the best performance. The error rate was approximately 27%, and the results from the sensitivity analysis, the receiver operating characteristics (ROC) curve, showed that the area under the curve (AUC) was 0.801. The most powerful predicting variables in the neural network were resilience, PVD, and social support. Implications for practice and policy are discussed.

This study proposes and evaluates a combined soft computing method to mitigate agricultural flood damage downstream of reservoirs, in which a fuzzy inference system and evolutionary optimization are linked. A flood damage function was developed by linking a Mamdani fuzzy inference system and Hydrological Engineering Centre—River Analysis System two-dimensional (HEC RAS-2D) model. First, an expert panel proposed the fuzzy rules of flood damage to develop the damage function. Then, this function was utilized in the reservoir operation model, in which evolutionary optimization methods were applied to optimize release from the reservoir. Finally, a decision-making system consisting of the two stages of evaluation were used for selecting the best algorithm. In the first stage, the performance of the penalty functions was considered to exclude some inappropriate algorithms. Then, the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) finalized the best solution of the reservoir management. Based on the results in the case study, either the firefly algorithm or the differential evolution algorithm is the best method to mitigate flood damage. The outputs corroborated the robustness of the developed method to mitigate potential flood damage. The maximum flood damage was reduced 40% compared with the natural flow. Moreover, average flood damage and inundation duration in the simulated period were mitigated considerably. It is recommended to apply the proposed method in flood mitigation studies in which overcoming flood damage data scarcity is a challenge.

Abstract

This study explored the impact of Hurricane Michael within the Black community in Bay County and surrounding regions in the Florida Panhandle. It is imperative to investigate the challenges and resilience factors among historically marginalized populations to assist with empowering the community to rebuild and regain a sense of normalcy. Focus groups and interviews were conducted to expose victims’ lived experiences with accessing community, federal, and local resources after Hurricane Michael. Thematic network analysis was developed to present a visual representation of the psychosocial, environmental, and social justice issues encountered by the Black community following Hurricane Michael. A weblike depiction of the data yielded a global theme of psychosocial and psychological trauma; organizing themes based on resiliency, environmental, social justice, and mental health issues among adults and children; and basic themes regarding racism, discrimination, and ineffective assistance from local and federal agencies. The crisis intervention theory provides a framework for organizing pertinent resources within Black communities after severe natural hazards. Findings indicate the need for culturally competent counselors who understand the challenges and resilience of the Black community and to assist with rebuilding efforts.

Practical Applications

This study explored the impact of Hurricane Michael 2 years after landfall with 25 members of the Black community. Through qualitative data in the form of interviews and focus groups, interviewees described the racism they encountered in their recovery, as well as the mental health support they received from the Black community. Contractors routinely refused to work on their damaged houses, preferring to work on houses owned by White residents, or they did not complete jobs. Thus, 2 years later some Black families still were living with no roofs or in severely damaged homes. FEMA was of little help because they required formal deeds to prove home ownership, and Black families in that area passed their homes from generation to generation, starting with an allocation from a plantation. Interviewees reported that Black churches and school still were not rebuilt. Some Black residents and businesses left the area to rebuild elsewhere. The recommendations of this study are to (1) change FEMA rules to recognize intergenerational home ownership and provide assistance to apply; (2) rebuild Black schools, businesses, churches, and individual homes; and (3) monitor racist practices by contractors.

The behavior of cantilever sheet pile wall in an earthquake susceptible area was regulated by soil liquefaction, induced pore water pressure, and soil and wall properties. A dynamic analysis of cantilever sheet pile wall having infinite surcharge load was executed by using finite difference–based computer program subjected to the 1994 Northridge, 1989 Loma Gilroy, and 2001 Bhuj motions. The different liquefiable and nonliquefiable soil layers are simulated by properties of Pohang sand for UBCSAND and Mohr–Coulomb constitutive models, respectively. The ground water table was assumed at the ground surface. It was observed that the maximum bending moment can occur at any instant, not necessarily at the instant of maximum acceleration. In the liquefiable layer, the soil stiffness and shear strength decreased, causing the loss in acceleration amplification factor, and maximum amplification factor was obtained for the motion with the lowest bedrock-level acceleration. The bending moment in the cantilever sheet pile wall depended on the bedrock-level acceleration, whereas displacement depended on both the duration of the time history as well as bedrock-level acceleration. The soil was liquefied only in the case of the 1994 Northridge motion with no surcharge, and by placing the surcharge on the backfill, the liquefaction resistance increased. The bending moment, displacement, and settlement decreased with increasing distance of the surcharge load from the cantilever sheet pile wall. The present study was validated by the experimental results of cantilever walls in cohesionless dry soil.

The risk of damage of glass curtain walls in residential areas caused by impact of wind-borne debris has been given increasing importance. In this paper, by incorporating the numerical analysis of three-dimensional (3D) flight trajectories of wind-borne debris and computational fluid dynamics (CFD) simulation of the local wind environment in the context of buildings group, a novel impact damage prediction model for the glass curtain wall of urban buildings is proposed. To this end, the 3D flight trajectories and characteristics of plate-type debris are first studied by solving the 3D equation of motion of debris, of which the aerodynamic coefficients of plate-type debris is obtained by CFD numerical simulation. The flight characteristics of debris with different initial parameters are analyzed, showing that the properties of debris and initial conditions have significant effect on the flight trajectories and impact position of plate-type debris. Further, an impact damage prediction model for the glass curtain wall of an urban building is proposed. In the proposed model, the flight trajectory analysis of the wind-borne debris is embedded in the context of the local wind environment in a residential area obtained by CFD simulation. The damage caused by the impact of debris on the glass curtain wall of the buildings is then analyzed and illustrated by an example. The proposed model is of great significance for the wind damage analysis of urban building glass curtain walls considering local wind environment and real trajectories of debris.

Abstract

As one of the principal lifeline systems, transportation networks are crucial for evacuation during extreme weather events like hurricanes, and critical network links must remain intact. The conventional evaluation measures prioritize to achieve the maximum system efficiencies, and therefore they estimate the functional criticality of a road network using measures such as travel time increase or throughput reduction caused by a link disruption. This study asks a fundamental question on equity achievement of such measures and develops a new framework to incorporate road users’ vulnerabilities in identifying critical network links. This study introduces new evaluation measures that integrate the most vulnerable zones for evacuation prioritization based on social, environmental, and economic vulnerabilities. Results show that the critical links for the vulnerable population during an evacuation are not always identified by conventional link-based measures that emphasize overall system efficiencies. Among the links selected as critical using the throughput measure, only 25% serve socially vulnerable communities and 38% serve environmentally vulnerable populations. This highlights the importance of considering road users’ vulnerability when prioritizing resources to strengthen the links since a link disruption may cause more significant consequences for vulnerable road users. Decision-making to identify critical links and minimize the impact of disruptions remains critical to distribute resources more effectively during an emergency and support the timely and safe evacuation of vulnerable populations that should be prioritized to achieve more equitable evacuation and disaster responses. An online interactive map is developed based on the results of this study to show the exact location of the critical links and other important metrics.

Practical Applications

In general, network links that carry higher traffic volume and ensure connectivity to isolated subnetworks represent critical links because disruptions on the links would impact more people, with a greater magnitude of travel time increase from rerouting or rescheduling. However, the individuals and communities that use the infrastructure determine the importance of the road links during extreme weather events. If a certain link serves at-risk communities (e.g., lower income or older population) for hurricane evacuation, the link should be considered critical regardless of its total traffic volume or impact on travel time if disrupted. These links must be resilient to save lives within the neighborhood, facilitate the evacuation of vulnerable groups, and strengthen the region’s overall resiliency. The proposed research identifies the criticality of network links by identifying the community impacts of network disruption. In particular, this study focuses on developing a framework to determine the critical network links based on the vulnerability or importance of communities in a hurricane-prone area using three vulnerability measures and compares the results with the outputs of traditional measures.

The roll-on/roll-off (ro-ro) transport system, which plays a huge role in the transportation and economy of the Philippines, mainly uses piers to provide smooth berthing of ships. However, its structural configuration attracts different hazards causing damage, particularly to piles. Tsunamis produce devastating wave forces. This may heavily result in discontinuity of the whole port operation as there will be no available berthing structure for ships. Hence, this study was conducted to determine the structural resilience of the pile-supported piers of the Port of Batangas against tsunami loads at different variations of wave height and water depth. Structural software was used to model the piers and incorporate calculated tsunami drag forces. Furthermore, pushover analysis was performed to produce the base shear versus displacement curves needed for the calculation of the robustness index. The rapidity index, on one hand, is based on an interview with the Philippine Ports Authority - Port Management Office (PPA-PMO) of Batangas resulting in recovery rates for demolition works and pile replacement. Combining these indices using the concept of resilience triangle, values of the resilience index ranging from 0.0 to 1.0 were calculated at both longitudinal and transverse directions. As a result, the piers were categorized as highly resilient in almost 50% of the tsunami load cases. It proved that the pier with existing batter piles exhibits higher resilience than the other which is only supported by vertical piles.

This study quantitatively examines indicators frequently used to estimate community resilience and proposes a method for assessing the validity of indicators used in community resilience measurement. An array of 18 indicators and related measures were identified as being common to existing methodologies. A comprehensive and replicable method that can evaluate the reliability and validity of community resilience indicators was then employed to explore the appropriateness of indicator selection by comparing different sets of measures. Multiple internal consistency test methods, such as Cronbach’s alpha, correlation analysis, cluster analysis, and classification tree, were conducted to address varying aspects of community resilience and to estimate commonalities in indicator selections. Structural equation modeling (SEM) provided a system to investigate indicators’ validity and overall performance in a hypothetical construct of community resilience predicting a proxy of community resilience outcomes. In exploring internal consistency and external validation of indicators, the importance of conducting validation studies is highlighted. To ensure indicators are ready for use among practitioners and policymakers, the quality of indicators needs to be tested and clearly stated in the context of other indicators and empirical outcomes of community resilience.

When evacuation is necessary in a wildfire event, affected communities must be alerted and warned of the imminent danger and instructed on what to do to protect themselves. One channel available to message providers in the United States is Wireless Emergency Alerts (WEAs) disseminated via the Integrated Public Alert and Warning System (IPAWS). Recent wildfire events have shed light on the need to improve WEA strategies and messages when alerting exposed populations of imminent fire threat. The purpose of this article is to assess how, when, and where WEAs have been used in US wildfires; whether they comply with guidance set out by Mileti and Sorensen’s Warning Response Model (WRM); and whether the expansion in characters (from 90 to 360) of WEA messages has influenced compliance with the WRM. A quantitative content analysis was conducted of WEA messages sent during US wildfires from January 2020–April 2022. A total of 1,284 messages were manually coded based upon the content and style categories identified in the WRM. Descriptive analyses (and Chi-square tests) were performed to illustrate how 90-character and 360-character WEA messages differ by key content and style features. Results showed that certain content features (i.e., location, guidance, and the name of the hazard) were included more often than others (i.e., source, hazard description, hazard consequences, and timing information) and that the inclusion of most content features increased with increasing message character length. Additionally, when assessing message ‘completeness,’ the use of acronyms was prevalent in both 90- and 360-character wildfire WEAs; whereas the inclusion of URLs was linked to increased message length. Wildfire WEAs also displayed inconsistency both within and across states in their use of terminology to trigger evacuation. These findings, among others, have implications for theory highlighting a growing need to confirm that message receivers understand and can act on the messages sent, regardless of the language used. In addition, for message creators, recommendations for effective WEA messages for wildfires are provided.

Researchers have long sought to understand what drives people to prepare for natural hazards. In this paper, we extend existing theories on protection motivation and decision-making to examine the mediating roles of perceived coping efficacy in disaster preparedness. Drawing on data from a national survey of US households, we analyze multiple preparedness measures including making emergency plans, stockpiling supplies, setting aside financial savings for emergencies, and purchasing disaster insurance in a multi-hazard context. Using path analysis, we find that experiencing a disaster in the past five years increases one’s perceived coping efficacy, including self-efficacy in undertaking preparedness measures and perceived efficacy of preparedness. Moreover, heightened self-efficacy is found to increase individuals’ propensity to stockpile supplies, make emergency plans, and increase financial savings for emergencies. Perceived response efficacy is positively associated with emergency planning and savings. We also find that recent disaster experience has a direct, positive impact on making emergency plans and purchasing flood insurance. Our findings shed light on the cognitive mechanisms driving disaster preparedness behaviors and enabling factors for different preparedness choices.

Wheel loaders play an important role during emergency rescues under geologic hazard conditions, such as debris flow and landslide. The current literature shows a relative lack of research on the failure mechanism and safety threshold of emergency supplies and vehicles under debris flow. This work presents a method to quantitatively study the interaction between debris flow intensity and wheel loader damage index. The proposed method employs force analysis and finite element modeling to obtain a series of stability thresholds of wheel loaders following the change of debris flow intensity. The results show that the maximum stress of the vehicle frame under the overall impact of over 0.15 MPa exceeds 250 MPa on the side. For the engine compartment shell, the stress of a large area on the shell exceeds the tensile yield strength, and the displacement exceeds 150 mm when impacted by stones at a speed of over $10\mathrm{m}/\mathrm{s}$. The intake and exhaust system, together with the cooling system in the engine compartment, are likely to be damaged. For the fuel tank, the deformation is more than 100 mm when the impact speed of stone exceeds $12.5\mathrm{m}/\mathrm{s}$. The critical impact conditions triggering skidding, tipping, and suspending for the wheel loader are 14 KN, $16.2-29.2\mathrm{KN}$, and $2.4{\mathrm{m}}^3$, respectively. The research results can improve the accuracy of vulnerability assessment of vehicles and emergency equipment under debris flow.

Natural hazards are highly threatening to human survival and development. Over the last 4 decades, research on natural hazards has become increasingly advanced and in-depth. With the continued development of science and technology and the complex changes in the environment, reviewing research fields and exploring current research hot spots and trends have become a key issue. In this study, 25,969 publications from the Web of Science were selected and statistically analyzed using bibliometrics to extract information on authors, institutions, countries/regions, journals, disciplines, keywords, and abstracts. Research hot spots were identified by machine learning. The results showed that (1) the number and growth rate of publications related to natural hazards are continuously increasing; (2) the author Biswajeet Pradhan, the institution of Chinese Academy of Sciences, the country of US, and the journal Natural Hazards are the most prolific in each aspect, respectively, and Geosciences Multidisciplinary is the most important subject; (3) keyword analysis showed that earthquakes, landslides, and floods are the core of natural hazards research, and climate change, disaster prevention and mitigation, and emergency management have received more and more attention in recent years; and (4) among the topics obtained from latent Dirichlet allocation (LDA) models, the most frequent topics are “Mechanism and simulation” and “Risk Assessment,” and “Drought and water hazards” and “Machine learning” are developing rapidly.

Community resilience provides a paradigm guiding communities’ preparedness and mitigation efforts to counter the increasing risks of extreme events (EEs). Knowledge regarding how communities perform during and after EEs can inform proactive resilience enhancement practices. However, the EE impacts on impacted communities are influenced by multiple variables and their implicit interactions, which are difficult to be understood and modeled with conventional mathematical or statistical models. Thus, we calibrate a spatio-temporal deep learning model to capture the dynamics of impacted communities and use explainable artificial intelligence (XAI) approach to interpret the influence of communities’ social and physical properties on EE impacts. Specifically, we couple graph convolutional neural network (GCN) and long short-term memory (LSTM) to model the mobility dynamics for 666 census tracts (i.e., communities) in 14 medium-sized US cities affected by recent hurricanes. The model takes both static variables characterizing communities’ preexisting conditions and dynamic variables depicting the changing hazard exposure. The interpretation of the model predictions based on DeepLIFT shows that community resilience, inferred from the perturbation of human mobility in this research, is highly event and geography dependent. Variables including walkability, green spaces, and civil participation generally contribute to fewer mobility perturbations, i.e., resilience contributive, while automobile-oriented accessibility and social vulnerability lead to more mobility perturbations when hazard conditions are controlled. This study empirically validates the mediative role of communities’ social and physical properties on EE impacts and promotes more data-driven approaches for understanding and anticipating complex, dynamic, and place-specific community resilience.

Puerto Rico has a long and complicated history with the US, and although the archipelago is no longer a colony, the colonial relationship remains embedded in US–Puerto Rico relations. In the disaster context, this historical legacy manifests as mismatches in power and place between where recovery is planned and policies are created (the federal US) and where disaster is experienced and recovery is carried out (Puerto Rico). This research investigated how these mismatches have affected housing reconstruction in Puerto Rico since Hurricanes Irma and Maria, with a particular focus on local actors, US-affiliated nonlocal agencies, and global-scale actors. Using semistructured interviews with representatives from Puerto Rico–based reconstruction organizations with extensive knowledge of housing reconstruction, this study investigated how place and power mismatches have created barriers to the housing reconstruction process in Puerto Rico after Hurricanes Irma and Maria. Our findings reimagine an existing mismatch framework (spatial, temporal, functional, knowledge, and norms) through the lenses of power and place. In doing so, we demonstrate that power and place are more than descriptive features of disaster recovery, but are dynamic dimensions that require alignment among all involved actors to achieve desirable recovery outcomes. The paper ends with a discussion of key strategies for facilitating the integration of multiple-scale actors along the dimensions of power and place.

Debris is one of the most challenging cascading effects posed by hurricane events, causing large financial and logistical burdens to coastal communities. Moreover, disaster debris can have cascading consequences on the safety and functionality of infrastructure, inhibit community recovery efforts, and lead to public health concerns. However, existing debris predictive models have shown an unsatisfactory performance, with errors up to 50% in debris estimates, and only consider a limited set of predictive variables. Given the importance of debris in coastal community resilience, this study leveraged a convergent research strategy to propose a knowledge-informed data-driven methodology by developing a comprehensive database that expands across human-built–natural systems to inform a probabilistic data-driven model of debris volume. A Gaussian process model was used to generate the debris volume model from a debris removal database for Hurricane Ike and the human-built–natural systems predictors. Moreover, different spatial resolutions (500, 250, and 125 m) were tested to analyze their effect on the model performance. Results showed that the low-resolution (500 m) and the intermediate-resolution (250 m) models have the best performance with a normalized root-mean squared error (RMSE) of 0.49 and 0.50, respectively. These two models were then used to explore the relative variable importance of the predictive variables in the model in order to get insights on the drivers of the debris process and propose more flexible lower-dimensionality models. The influence of different predictors and the trade-offs of resolution and model performance were also discussed before demonstrating application of the model for a synthetic storm in the Galveston, Texas, region. The proposed methodology and the probabilistic estimates of debris quantities are key to develop comprehensive risk estimates of storm impacts on coastal communities and can support informed decision making and mitigation planning strategies.

The involvement of spontaneous volunteers (SVs) in disaster response represents a significant resource. However, existing emergency management plans often fail to take spontaneous volunteers into account due to negative perceptions and uncertainty about SV convergence. We developed an agent-based simulation model of spontaneous volunteer convergence to aid the disaster response planning process. The model considers a heterogeneous population of agents, each with unique attributes such as motivation, opinion, and site choice behavior. Model development was informed by the literature as well as interviews with volunteers and volunteer managers, participant observations, and discussions with practitioners. To illustrate the practical value of the model, we present a case study that addressed research questions related to volunteer reception centers and volunteer assignment policies. This transdisciplinary study bridges the gap between operations research and management science and social science, and provides a new decision aid to help improve the integration of spontaneous volunteers in disaster management plans.

Abstract

Many coastal communities around the world that are vulnerable to inundation from tsunamis or storm surges are investing significant resources into countermeasures such as seawalls, levees, and breakwaters to limit damage from such events. A potential framework for studying the relationship between coastal hazard countermeasures and their impact on community resilience is proposed in this investigation and is applied to 27 coastal cities in the Tōhoku region of Japan following the 2011 tsunami. Community resilience was quantified using a modified version of the resilience inference measurement (RIM) method in which parameters of intensity, exposure, damage, and recovery were utilized in a $k$-means clustering analysis to categorize respective cities into four different resilience groups. As a supplemental analysis to the modified RIM method, historical data representing the exposure and damage parameters from the 1896, 1933, and 2011 Tōhoku tsunamis were utilized to establish limit states (LSs) for fragility curves to demonstrate the level of response for a specific damage condition. Three limit states were selected for 2%, 20%, and 40% of dwellings in a city being destroyed for a given tsunami event. An ordinal logistic regression was performed to determine whether there was a relationship between the resilience group of each city and the maximum countermeasure height. No statistically significant correlation was found between the maximum height of a coastal hazard countermeasure and the resilience group of each city, which indicates that investing in such infrastructure does not necessarily impact overall community resilience for an extreme event such as the 2011 Tōhoku tsunami. For this scenario, the probability of damage was assessed by developing fragility curves that demonstrated larger probability of exceedance discrepancies between LSs at lower maximum tsunami runup values. Coastal hazard countermeasures therefore may still be effective in limiting damage and thereby improve community resilience for less extreme tsunami events. The results of this investigation will assist government officials and engineers in selecting coastal resilience policies and strategies to limit future impacts from coastal hazards.

Practical Applications

Coastal hazard countermeasures such as seawalls, levees, and breakwaters are being implemented by coastal communities across the globe to protect people and infrastructure from damage caused by tsunamis and storm surges. Built structures such as these have significant economic, environmental, and social impacts. A potential framework for studying the relationship between coastal hazard countermeasures and their influence on community resilience is proposed in this investigation. The framework was applied to the 2011 tsunami event that devasted coastal cities in the Tōhoku region of Japan. Most of these cities had constructed seawalls prior to this disaster. The application of this event to the proposed framework did not result in a statistically significant correlation between the maximum height of seawalls and the resilience behavior of a city, which indicates that investing in such infrastructure does not necessarily improve overall community resilience for an extreme event such as the 2011 Tōhoku tsunami. Coastal hazard countermeasures may still be effective in limiting damage for less extreme tsunami events and thereby improve community resilience for such events. The framework proposed in this report will assist government officials and engineers in selecting coastal resilience policies and strategies to limit future impacts from coastal hazards.

Abstract

The public policy innovation and adoption processes are dynamic and complex. This is no exception for the adoption of hazard mitigation policies by localities prone to natural hazards. This paper synthesizes theories about policy innovation and adoption, and literature about hazards mitigation, and proposes a theoretical framework for understanding the factors driving hazard mitigation policy adoption at the local level. Our goal is to identify the key elements and parameters of the hazard mitigation policy adoption construct as well as the relationship between them. Using the property buyout program as an example, we present case studies in the states of North Carolina and New Jersey to illustrate a proposed theoretical framework and outline the directions for future research. The case studies show promising evidence consistent with the proposed framework, covering five categories—hazard problem, social context, institutional capacity, cross-sector collaboration, and policy diffusion. In particular, as for institutional capacity, three aspects influence the uptake of buyouts, including individual capacity [e.g., geographic information system (GIS) and technical skills], organizational capacity (e.g., reducing the negative financial impact on the tax base of buyouts and encouraging an innovative culture of flood mitigation strategies), and system capacity (e.g., cooperation among local organizations). To further validate the framework, systematic research of localities with diverse characteristics of policy adopters and nonadopters is needed.

Practical Applications

Flood hazard mitigation is a policy issue with great significance for governments and thousands of flood-prone communities in the US. Local governments bear the primary responsibility of initiating actions for managing flood hazard. Our theorization exercise and the case studies on the property buyout program highlight the importance of several internal and external factors that influence decisions to pursue this hazard mitigation measure at the local level. For local floodplain managers, disaster resilience officers, and policy entrepreneurs, these findings offer pointers as to where they should focus their effort to maximize the possibility of success. Within the community, developing a comprehensive appreciation of the local flood hazard, GIS, and technical skills of local officials as well as cooperation between local organizations are essential in cultivating a culture that encourages innovative flood mitigation strategies. Externally, coordination and communication between the federal, state, and local levels, as well as learning and emulation of practices in peer communities are key for local success. In this regard, the training workshops and outreach programs of FEMA, FEMA regional offices, and its state counterparts are critical. These programs will elevate the issue on the local agenda, disseminate best practices, demystify the process, and incentivize more mitigation policy adoption.

Assessing disaster resilience is one of the important research issues for disaster risk management. The process of postdisaster recovery (PDR) involves multiple stakeholders, and their expectations and engagement are considered a key factor for the success of PDR projects. This paper is oriented toward sustainable development, combined with the demands of many stakeholders, and proposes a comprehensive recovery evaluation that considers the sustainability of community resilience. Based on internet public opinion (IPO), a novel method integrating a modified analytic hierarchy process (AHP) and hesitant fuzzy linguistics (HFL) is introduced to guarantee multiple stakeholders’ engagement. Specifically, literature research and field interviews are combined with IPO data using text mining techniques to establish evaluation indices. The evaluation results indicate that the overall recovery level of the Lushan earthquake is between fairly and slightly good. It reveals that balances between social (e.g., infrastructure reconstruction, public services and economic recovery, etc.) and individual aspects (e.g., housing allowance, life recovery, mental health, etc.) are needed for recovery improvements. Moreover, housing is regarded as the most urgent and critical task in PDR. Accordingly, increasing requirements for the protection of intangible heritages are anticipated. Finally, possible policies on recovery and poverty alleviation are recommended.

The complex situations and chain effects of urban risk factors have resulted in a focus on improving emergency capability based on the importance of the collaborative governance network. This study constructed an integrated analysis framework to adapt to China’s emergency governance situation. This study aimed to contribute to the literature on collaborative emergency governance based on the framework of target capability by exploring the combination of risk warning, organization embedding, information sharing, resource integration, and business function as major influencing factors in the capability of collaborative emergency governance. Through a qualitative comparative analysis based on 50 contingency plans in Shanghai, China, and followup interviews, the study explored the multistructure equivalent causality and equivalent cause-and-effect chain in detail. The findings reveal three configuration paths for the generation of collaborative emergency capability: business-chain resource optimization, precontrol embedded system overlay, and balanced organizational embedding types. This research provides a theoretical reference and practical understanding of using “different routes to the same goal” in urban collaborative emergency governance.

Abstract

The fast unfolding of the global COVID-19 pandemic has disproportionately affected the homeless sector by triggering tremendous challenges for individuals experiencing homelessness (IEHs) and related service agencies. This quick-response research project qualitatively collected time-sensitive data from the IEHs and service stakeholders (SSs) experiences, challenges, efforts, and suggestions during the first wave of COVID-19 in the two most populated municipalities in the province of Nova Scotia, Canada, namely, Halifax Regional Municipality and Cape Breton Regional Municipality. Through analyzing and synthesizing the standpoints from both IEHs and SSs, this technical note presents recommendations, addressing the practical challenges that IEHs have been confronting during COVID-19 and systemic issues in which homelessness is rooted. These recommendations will assist community-based agencies in improving their emergency response capacity, better serving IEHs in COVID-19 in particular, and supporting other vulnerable and marginalized populations in future extreme events in general.

Practical Applications

COVID-19 has created major challenges for people experiencing homelessness as well as for service organisations working to help. This research project interviewed people experiencing homelessness as well as service providers in the Halifax and Cape Breton Regional Municipalities of Nova Scotia, Canada. They were asked about their experiences during COVID-19, as well as challenges encountered, how they responded, and suggestions for changes needed. This article presents recommendations for changes needed to address both the challenges that people experiencing homelessness faced during COVID-19, as well as other problems of homelessness that have existed for years. These recommendations will assist service organizations to improve the ways they respond to emergencies in order to better serve people experiencing homelessness–not only during COVID-19 but also during other challenging events in the future. The recommendations include: all services working with people experiencing homeless need to work together and advocate for shelter/affordable housing, need to include the voices of people experiencing homelessness in decision-making processes, federal government needs to work with provinces when providing emergency funding supports, and need to ensure that essential information is fully accessible to all vulnerable and marginalized communities.

Flooding annually causes thousands of fatalities and billions of dollars in damage globally. Predicting future floods has become increasingly challenging due to changing urban environments and land surface conditions. Simultaneously, severe floods are likely to increase due to climate change and associated shifts in rain patterns, resulting into potentially stronger and more consequential flood events. High-water marks represent key information to be collected after flooding for advancing the understanding of flood impacts and the development of mitigation strategies. However, high-water marks often become increasingly difficult to detect with time passing after a flood event due to drying. In addition, access into flooded areas can be complicated by destroyed infrastructure, leading to significant loss of data or risk to personnel entering these recently flooded areas. Here, initial data are presented demonstrating the application of multispectral imagery in rapidly collecting and mapping high-water marks after flooding. The multispectral images were collected 3–4 weeks after the July 14, 2021, western European flood events in the town of Mayschoss, Germany, along the Ahr River. At that time, affected buildings, walls, and soil were exposed to high summer temperatures and solar radiation, as well as dust from surrounding emergency response and repair works. High-water marks were barely visible by eye. Preliminary results showed the high-water mark is significantly enhanced in the blue band (wavelength 443 to 507 nm) and can be modally isolated through linear combination of the blue band and red-edge band (wavelength 705 to 729 nm). The results illustrate the potential to apply this technique in postdisaster reconnaissance to quickly and safely map high-water levels to identify the magnitude and extent of flooding in urban areas.