Natural Hazards Review

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Substituting organic fertilizer for chemical fertilizer is widely viewed as an effective path to promoting sustainable agricultural development amidst the imminent threat of climate change. This paper investigates the effect of government intervention and market incentives on farmers’ organic fertilizer application behavior (FOFAB), using survey data from Xinjiang, China. The results show that, first, three sub-dimensional variables of government intervention, namely policy publicity, fiscal subsidies, and regulation constraints, all have a significantly positive impact on FOFAB, and that fiscal subsidy ranks first in terms of influencing the degree of the effect. Second, there is a mediating effect of market incentives on the relationship between government intervention and FOFAB via three paths, namely the market price of organic fertilizer, the cost of applying organic fertilizer, and market demand for organic food. The most significant mechanism is that fiscal subsidies have a positive influence on FOFAB by activating market demand for organic food, reducing the cost of applying organic fertilizer, and promoting the market price of organic food. In addition, the variables of family endowments have a steady and significant effect on FOFAB. Social networks did not exert a positive effect on FOFAB. Therefore, the local government should incentivize governmental intervention measures to improve the market competition mechanism, encourage more farmers to join agricultural cooperatives, and expand social networks to facilitate FOFAB.

Housing resilience planning is a challenging process that requires active participation of multisector stakeholders, including public agencies, private industries, nongovernmental organizations (NGOs), academia, and community residents. Despite the importance of multisector stakeholder collaboration and engagement, there is limited understanding of how stakeholders collaborate and engage in housing resilience planning. To address this gap, this study used social network analysis (SNA) to analyze how multisector stakeholders collaborate in producing housing resilience-focused plans, reports, and guidelines. A bipartite SNA model was built based on the data collected from 39 housing resilience planning documents in the Greater Miami and the Beaches (GM&B) region, including the City of Miami, the City of Miami Beach, and Miami-Dade County. Three network centrality measures were computed, and exponential random graph model (ERGM) analysis was performed to analyze the network. This study found that there are significant differences in the centrality measures across different stakeholder sectors. Among them, public agencies and academic stakeholders contributed more to housing resilience planning, whereas the involvement of community residents was relatively limited compared with that of the other sectors. In addition, the results suggest that more-balanced, decentralized, and intersector collaboration mechanisms may be needed to enhance housing resilience planning. The findings from this study offer knowledge of and insight into how to facilitate more-effective multisector stakeholder collaboration in planning for housing resilience.

Hazards pose significant issues for the performance of critical lifelines, and postdisaster performance of roadway networks, in particular, is crucial, as the roadways are key links used in completing infrastructure repairs. The performance of the roadways depends on an ability to quickly assess roadway facility conditions through inspection for which access along the same roadway and bridge system that is under examination is required. Thus, some restoration activities to the roadway elements may need to be completed before inspections of other roadway network elements or elements of other infrastructure lifelines can even begin. The order in which these inspection and restoration activities are taken can greatly affect the time until services from critical lifelines are restored, and the simultaneous consideration of the restoration needs of multiple lifelines given their reliance on the roadway network facilitates faster overall recovery of the communities they serve. A multistage stochastic program with decision-dependent uncertainty is proposed for jointly devising postdisaster inspection and restoration activity schedules to minimize roadway downtime and maximize opportunities for timely completion of repairs to other lifelines over the recovery period. The developed technique is illustrated in an example designed on the roadway and power distribution networks in Arlington, Virginia. Results from the numerical experiments show significant benefits of coordinating actions over multiple lifelines in bringing back services to a community impacted by a disaster event. In fact, coordination led to a greater total number of functioning elements in both roadway and power networks with no reduction in the number of functioning elements from either network over a recovery period. Additionally, a 12% gain in benefits of joint scheduling could be obtained if perfect foresight were possible, showing the potential value of perfect information.

Sea-level rise in response to climate change and global warming severely impacts coastal cities through increased soil erosion and other hazards. Therefore, simulating threats in coastal locations is critical for coastal city management and planning. The Nonlinear Autoregressive Exogenous-Neural Network (NARX-NN) was used in conjunction with the Bruun model and GIS methods to estimate the rate of sea-level rise, develop a coastal erosion model and coastal hazards maps, and simulate a sea-level increase with a maximum speed of $79.26\mathrm{mm}/\mathrm{year}$, and an average of about $25.34\mathrm{mm}/\mathrm{year}$, with a $1.48\mathrm{m}/\mathrm{year}$ average erosion rate simulated from 2013 to 2020 along Merang kechil to Kuala Marang in Terengganu state coastal areas. According to the Bruun model, the areas most vulnerable to shoreline erosion are Kuala Nerus, Pendagan Buluh, and Kuala Ibai. Batu Rakit (Reach 1) has the highest rate of coastal erosion, at 28.16%, compared to 16.5 percent in Kuala Nerus (Reach 2) and 19.1% in Pengadang Buluh (Reach 3). The findings of this study might be utilized to build new coastal hazard erosion maps in a GIS framework, which could then be used as part of Malaysia’s East Coast zone vulnerability assessment. The findings may also aid in the prioritization of conservation efforts in afflicted areas or the decision to adapt to the effects of coastal erosion. This article presents a methodological framework and an erosion management prioritization system to help coastal managers, planners, and developers identify hazardous zones and improve coastal management plans using geospatial models.

Natural disasters such as hurricanes, earthquakes, and floods cause massive damage and losses around the world. Investigating their impact on affected communities and delineating them from nondisaster forces have major scientific and policy implications. To that end, a series of econometric tests were applied to the Hurricane Resiliency Index (HRI) for six selected study areas to identify and date-stamp periods of mildly explosive behavior (MEB). Evidence of MEB signals a structural break or nonlinearity in an otherwise stationary time series. The starting and ending points of multiple MEB episodes indicate the extent to which a community recovers toward normalcy. It is found that the recovery time associated with hurricanes is much shorter than that with economic recessions. The overall severity of MBE, when considering both duration and amplitude, is most pronounced when a major hurricane strikes dense populations. The findings also highlight that the compounding effect of economic recession and hurricane poses the most serious threat to a local economy. Finally, the Hurricane Resiliency Index is shown to outperform the Federal Reserve Bank of Dallas’s Metro Business-Cycle Index in capturing such behaviors.

This paper aims to establish inherent concepts associated with urban resilience theorizing, which lacks the links to urban planning in a way useful to enhance the urban resilience of existing settlements, through a multidisciplinary approach and acceptance of the complex structure of urban resilience. Moreover, most of the literature addressing the subject refer to it through bland and perplexing terminology, making the subject of urban resilience difficult to practice in a contemporary urban planning scenario. This literature study-based paper reviews the concept of resilience and identifies its key components and its application to disaster and urban planning nexus while exploring the links between the 5 W’s of urban resilience literature, namely, What, Where, When, Why, and Who. Based on this, the paper intends to establish the synthesized urban resilience characteristic concepts to lay out the directions for cities to move toward resiliency with a link to urban planning by working on the suggested linked attributes. Hence, the concept of urban planning is explored as a means of engaging and embedding urban resilience in a transdisciplinary process. The concepts of vulnerability and adaptive coping capacities are presented, interlinking the concepts of urban planning and urban resilience and supporting the suggestive attributes through past literature. Furthermore, the paper discusses the suggestive elements connecting the theory of resilience and urban planning and suggests the components encompassing vulnerability, urban planning, coping and adaptation, and urban governance as key factors for the urban resilience framework. Urban planning and governance have the potential to reduce vulnerability and enhance coping and adaptive capacities, thereby impacting urban resilience positively. The various attributes of urban planning can be explored to assess, impact, and influence the urban resilience levels (/ or correspondingly vulnerability and adaptive/coping capacities); hence, urban planning can be a potent tool to influence, affect, and strengthen the resilience of the urban system.

The study adopts the Copula function to evaluate index data and historical flood disaster simulation samples to reduce the subjectivity of the evaluation results. A genetic algorithm is used to calculate the model parameters and predict flood hazard levels. The spatial data processing technology of the geographic information system (GIS) is employed to extract and analyze spatial data to acquire indicators. A comprehensive hazard evaluation index system containing a maximum of 1, 6, 24 h heavy rain, relative height difference, average gradient, and drainage density is established to perform detailed analysis. The complex links of the evaluation index values to flood hazard analysis are uncovered by applying this data-focused flood hazard evaluation strategy. By comparing the actual occurrence times and forecast results of flood disasters in 64 research areas of Hubei Province, we find the established model has good prediction effect and can provide data support for flood disaster early warning.

With the aim of fostering the development of robust tools to simulate the impact of natural hazards on structures, lifelines, and communities, the Natural Hazards Engineering Research Infrastructure Computational Modeling and Simulation Center gathered 60 researchers, developers, and practitioners working in natural hazards engineering (NHE) for a workshop to prioritize research questions and identify community needs for data and computational simulation capabilities. Participants used their wide-ranging expertise in earthquake, coastal, and wind hazards from engineering, planning, data sciences, and social sciences perspectives to identify five major thrusts of recommended future work, including detailed suggestions for each: (1) development of housing and household recovery models; (2) integration of existing models into flexible computational workflows; (3) investment in the collection of high-value open data; (4) commitment to sharing and utilizing high-value data; and (5) development of versatile, multidisciplinary testbed studies. Participant responses and workshop data were analyzed with the help of an ontology that the authors designed to support data classification in a broad range of NHE applications. The paper also includes observations and suggestions for planning and conducting interactive workshops of this type.

Resilient infrastructure, which better withstands, adapts, and recovers from disasters, can limit disaster impacts, such as disruptions to infrastructure services and time and efforts needed for recovery. However, in the context of a disaster, the impacts on infrastructure are not evenly distributed across different communities. Thus, we need to account for such disparities (or inequalities) when assessing infrastructure resilience. To address this need, this paper proposes a social-welfare–based infrastructure resilience assessment (SW-Infra-RA) model for quantifying the collective resilience of infrastructure serving multiple communities. This model accounts for (1) disaster inequality—the unequal distributions of disaster impacts on infrastructure across different communities; and (2) disaster vulnerability—the disaster impacts on the infrastructure of communities that suffer from the most severe impacts—both of which have impacts on the collective resilience of infrastructure. A set of hypothetical and real case studies were conducted to illustrate the use of the proposed model in quantitatively assessing infrastructure resilience. This study contributes to the body of knowledge by providing a new infrastructure resilience assessment model that accounts for disaster inequality and vulnerability. The proposed model has the potential to support the development and investment of infrastructure in a more equitable manner; it facilitates equitable resilience in future infrastructure planning and development.

The rapid recognition of rainstorm-induced emergencies (e.g., building and road inundation, facility damage, and trapped citizens) is vital to timely disaster response. One big challenge that limits the performance of emergency recognition is the data imbalance between different emergency domains. The present study aims to develop an effective cross-domain transfer learning framework for rainstorm-induced emergency recognition based on the text reports provided by citizens. The critical component of the framework is the use of joint distribution adaption (JDA) analysis embedded in a discriminative feature mapping procedure, which transfers rich knowledge learned from large-scale datasets to the learning task from small emergence data. Considering the feature incompleteness that is caused by short text length, a basic probability assignment function is constructed and applied to extract important spatial features for rainstorm emergency recognition, with an improved marginal Fisher analysis being adopted to optimize cross-domain text feature representation. The proposed scheme is validated using the empirical data of ten emergency classes from Wuhan City, China. Our experimental results show that the proposed method could significantly address data imbalance and thus help achieve high recognition performance through domain knowledge complementation. Meanwhile, the use of various spatial features is proved to be effective in tackling missing features. This scheme can be further developed into smart systems for rainstorm disaster response with reasonable performance and imbalanced sample sizes.

The decisions of whether and how to evacuate during a climate disaster are influenced by a wide range of factors, including emergency messaging, social influences, and sociodemographics. Further complexity is introduced when multiple hazards occur simultaneously, such as a flood evacuation taking place amid a viral pandemic that requires physical distancing. Such multihazard events can necessitate a nuanced navigation of competing decision-making strategies wherein a desire to follow peers is weighed against contagion risks. To better understand these trade-offs, we distributed an online survey during a COVID-19 pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. In this paper, we estimate a random parameter discrete choice model in both preference space and willingness-to-pay space. The results of our model show that the directionality and magnitude of the influence of peers’ choices of whether and how to evacuate vary widely across respondents. Overall, the decision of whether to evacuate is positively impacted by peer behavior, while the decision of how to evacuate (i.e., ride-type selection) is negatively impacted by peer influence. Furthermore, an increase in flood threat level lessens the magnitude of peer impacts. In terms of the COVID-19 pandemic impacts, respondents who perceive it to be a major health risk are more reluctant to evacuate, but this effect is mitigated by increased flood threat level. These findings have important implications for the design of tailored emergency messaging strategies and the role of shared rides in multihazard evacuations. Specifically, emphasizing or deemphasizing the severity of each threat in a multihazard scenario may assist in: (1) encouraging a reprioritization of competing risk perceptions; and (2) magnifying or neutralizing the impacts of social influence, thereby (3) nudging evacuation decision-making toward a desired outcome.

Evaluation of dike-break-induced flood risk has been a worldwide concern due to its enormous economic, environmental, and societal importance. The mitigation of flood risk in detention basins is a complex decision making process that could span multiple engineering and scientific disciplines. In this paper, an integrated dike-break-induced flood modeling, analysis, and management framework is proposed. The MIKE21-based numerical approach is first adopted to model the flood routing process in detention basins. With the physical behavior of the flood well understood, physics-informed approaches are proposed to better quantify the dike-break-induced flood risks, e.g., human life, economic and environmental losses, offering valuable information for engineers and policymakers to formulate targeted contingency plans. The Zhuhu detention basin in the Poyang Lake district in China is investigated using the proposed framework. The flood movement in the detention basin is first numerically simulated using the MIKE21-based model. Based on the results, i.e., inundation area, water depth, flow velocity, and arrival time of flood peak, the detention basin is divided into several flood disaster zones. The induced flood risks are then estimated for the different zones. Customized emergency evacuation plans are also formulated for the different flood disaster zones. The study of the Zhuhu detention basin confirms that the proposed framework effectively fuses numerical modeling, physics-informed analysis, and management of flood events, providing an integrated and enhanced decision making process for flood warning and risk mitigation in flood detention basins or at other places.

Over the last decade, engineering studies have mentioned the need for meeting societal demands of damage expectations and housing performance for major earthquakes. However, they do not refer to any systematic analysis examining such societal demands, nor have they investigated interactions of this with voluntary seismic strengthening. A questionnaire distributed to homeowners of wooden-framed houses in the Wellington region of New Zealand investigated these issues. Survey respondents expected a better seismic performance of their house after voluntarily undertaking structural strengthening (i.e., positive outcome expectancy) and expected a better seismic performance of their house, regardless of the use of strengthening, than what is accepted by current seismic codes, which aim for life safety (i.e., collapse prevention). Although this study found that experience of damage in past earthquakes was not associated with final implementation of such preparedness measures, access to strengthening information and trust toward providers of information, such as builders, were found to play a key role when adopting strengthening. This study gives insight into the role that outcome expectancy and sources of information play in voluntary structural strengthening of residential low-rise buildings and indicates a need for better engagement, such as working closer with builders as sources of information, to reduce losses in future major earthquakes.

Abstract

Flooding has been the most costly natural disaster over the last 2 decades within the US. Therefore, recent research has focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate economic exposure. For this, depth–damage functions have commonly been employed to predict the relative or absolute damage to buildings caused by different magnitudes of flooding. Although depth–damage functions, such as those adopted by the US Army Corps of Engineers, are widely available for fluvial and coastal flooding, less work has been done to develop functions for pluvial-induced flooding. Here, we use a database containing 13.5 million claims to develop pluvial depth–damage functions. For this, recently released flood hazard data are utilized to identify claims within the database that are likely related to pluvial flooding. We employed two types of regression models to fit the depth–damage functions. Secondarily, we developed an automated valuation model (AVM) to estimate building values across the state of New Jersey. These building values were then combined with flood hazard layers in order to apply the depth–damage functions and compute an aggregate annualized loss for New Jersey. The results indicated moderate agreement between the observed damage within the state of New Jersey and that computed by applying the study-developed depth–damage curves to buildings within the state using pluvial flood hazard layers. It is anticipated that the depth–damage functions developed by this research will aid future work in more accurately quantifying the economic risks associated with flooding across the US.

Practical Applications

This research included in this paper aimed to develop a practical tool for the estimation of losses from heavy rainfall–driven flood events. Although established models exist to estimate losses from river and surge flooding, rainfall-driven flood damages are not as well understood. In order to estimate these losses, we also developed a property characteristic–driven valuation model. The production of the model gives us a property value baseline to estimate the rainfall losses against our derived damage curves. Our hope is that planners, researchers, and others may use this to estimate losses to properties from extreme rainfall events, which in turn can help to inform the allocation of resources in protecting against those events.

The demand for inexpensive and reliable warning systems has increased in recent years as a result of the increase in the number and severity of flood disasters. A new generation of low-cost sensors for flood monitoring and warning is being developed by the federal government and private sectors, in some cases collaboratively. We perform a benefit-cost analysis of this new product category, (i.e., low-cost flood inundation sensors), which can readily be deployed in a wireless or internet of things network. The use of these sensors can improve the coverage and lengthen the lead time of flood warning systems. The production costs of this new technology are only a fraction of those of existing sensors with similar capability and reliability, and operating costs are modest. Benefits depend on such factors as the ability to improve lead times of warnings to reduce property damage, deaths, and injuries from floods as well as the extent of adoption of the new sensors. Our analysis indicates a benefit–cost ratio of 1.4 to 1. However, our results are based on several assumptions. Hence, we have undertaken extensive sensitivity analyses to determine that our results are robust.

Economic resilience defines a community’s ability to prevent, withstand, and quickly recover from major disruptions to its economic base. Instead of having repeated damage and need for outside assistance, resilient communities proactively protect themselves against hazards, build self-sufficiency, and become more sustainable over the long term. Within these communities, small businesses are an important driver of economic growth and employment. However, small businesses are extremely vulnerable to natural disasters: about 40–60 percent of them never reopen their doors after a disaster. To gain an insight into the vulnerability and resilience of small businesses, we collected firm-level data through an online survey of primary decision-makers of small businesses located in 2017 Hurricane Harvey impacted counties. The questions in the survey covered five broad categories: general business characteristics; finance impact; operation impact; built environment impact; and mitigation actions. The analysis shows a small variation in recovery time between industry groups. However, the contrast between firms that invested in resilience and firms that did not is significant. We then modeled the small business recovery as a stochastic process and used the survey data to select probability models and then estimate model parameters. If firms chose to invest in resilience, the mean and median times could be reduced by 57% and 8.5%, respectively. Using the baseline provided, a firm could estimate the length of recovery expected and prepare a business continuity plan accordingly. In a hurricane’s aftermath, their performance can be benchmarked against that of their peers. The findings would serve as basis for public policies towards incentivizing prestorm mitigation and resilience-building actions.

Managed retreat in the form of voluntary flood-buyout programs provides homeowners with an alternative to repairing and rebuilding residences that have sustained severe flood damage. Buyout programs are most economically efficient when groups of neighboring properties are acquired because they can then create unfragmented flood control areas and reduce the cost of providing local services. However, buyout programs in the United States often fail to acquire such efficient, unfragmented spaces, for various reasons, including long administrative timelines, the way in which buyout offers are made, desires for community cohesion, and attachments to place. Buyout programs have relied primarily on posted price mechanisms involving offers that are accepted or rejected by homeowners with little or no negotiation. In this paper, we describe four alternative strategies that have been used successfully in land-preservation agricultural–environmental contexts to increase acceptance rates and decrease fragmentation: agglomeration bonuses, reverse auctions, target constraints, and hybrid approaches. We discuss challenges that may arise during their implementation in the buyout context—transaction costs, equity and distributional impacts, unintended consequences, and social pressure—and recommend further research into the efficiency and equity of applying these strategies to residential buyout programs with the explicit goal of promoting spatial coordination.

The Sendai and Hyogo Frameworks emphasize integrating modern science with Indigenous knowledge, a bottom-up community-based disaster management approach. However, a top-down disaster management mechanism and a lack of awareness about Indigenous knowledge are part of the current reality in Taiwan. This study chose the great Cinsbu, an Atayal community in Taiwan, as a case study area, using interviews to explore traditional ecological knowledge in Indigenous people’s daily lives. The study also applies a bottom-up, community-based approach through workshops. This study’s goals are to apply Indigenous knowledge to improve community disaster planning and to work with communities to assist them in utilizing their (Indigenous) knowledge and resources. As a key to achieving disaster resilience, the study highlights three approaches based on traditional ecological knowledge: the natural environment, agriculture production, and social capital. The natural environment approach integrates modern science and traditional ecological knowledge through community risk mapping. The agriculture production approach conserves seeds and stores food using traditional methods, such as pickling and storing in traditional barns. The social capital approach establishes a disaster management organization based on social networking. Finally, the community develops hazard preparedness and disaster response measures based on traditional ecological knowledge. These measures can be integrated with existing disaster management to form a specific mechanism for Indigenous communities.

This technical note calls for an interdisciplinary research agenda to study the intersection of policy problems and extreme events. We argue that extreme events can unveil chronic problems in societies such as political polarization. We used social media data following the 2014 Soma mining disaster in Turkey as an illustrative case study. Our findings indicate that polarized sense-making during and following extreme events can hinder the effectiveness of response and recovery operations. We conclude with some recommendations for using social media data during response operations to improve disaster communications in contexts characterized by high levels of political polarization.