Natural Hazards Review

This paper builds on the group of studies that have used satellite night light data to measure the impact of natural disasters on economic development. It examines the dynamics of disasters using monthly subnational-level data for Southeast Asian countries covering the period 2012–2018. Four issues are considered. First, the paper determines the proportion of disasters that can be detected through an exogenous structural break in the night light luminosity data. Second, it identifies the factors that are associated with detection. Third, it uses the luminosity data to examine how consistent disaster recovery patterns are with theoretical scenarios. Finally, it identifies the factors associated with different recovery patterns. Findings suggest that the probability of disasters being detected using night light data is positively associated with disaster intensity. Recoveries from many, but not all, disasters do not result in a significant change in luminosity from its predicted trend. Although some disasters result in luminosity returning to a permanently lower growth path, it is more common for it to return to a higher-than-predicted trend.

Due to its near-real-time crowdsourcing nature, social media demonstrates a great potential of rapidly reflecting public opinion during emergency events. However, systematic approaches are still desired to perceive public opinion in a rapid and reliable manner through social media. This research proposes two quantitative metrics—the fraction of event-related tweets (FET) and the net positive sentiment (NPS)—to examine the intensity and direction dimensions of public opinion. While FET is modeled through normalizing population size differences, NPS is modeled through a Bayesian-based method to incorporate uncertainty from social media information. To illustrate the feasibility and applicability of the proposed FET and NPS, we studied public opinion on society reopening amid COVID-19 for the entire United States and four individual states (i.e., California, New York, Texas, and Florida). The reflected trends of public opinion have been supported by the reopening policy timeline, the number of COVID-19 cases, and the economy characteristics. This research is expected to assist policy makers in obtaining a prompt understanding of public opinion from the intensity and direction dimensions, thereby facilitating timely and responsive policy making in emergency events.

Hurricanes cause substantial inundation of transportation networks, rendering them inaccessible to emergency response vehicles. Because storm tides and heavy rainfall often co-occur during hurricanes, a reliable assessment of roadway inundation in coastal areas requires adequate representation of both flood sources. This study serially coupled a hydrodynamic storm surge model with a two-dimensional hydrodynamic model for rainfall-driven flow to quantify compound flooding in a coastal region. The output of this flood modeling approach is used as an input in an optimization algorithm to identify the required location of ambulances prior to the flood event and determine areas that should be served aerially so that patients can be transported to a critical trauma center within the golden hour or treated by other means. Application of this integrative framework was illustrated for flooding under Hurricane Irene (2011) in southeast Virginia. The compound flood modeling framework had excellent agreement with data from an overland flood gauge. The application of the ambulance positioning algorithm showed that that present ambulance stations are not adequate to completely cover the study area within the golden hour even in the absence of flooding, and three traffic analysis zones need to be served aerially or treated by other means. It also was determined that no traffic analysis zones had access to the trauma center within the golden hour if 10- or 25-cm flood depths were considered as the thresholds for ambulance access. However, after introducing additional ambulance stations, or staging stations where patients could be treated in place or transferred aerially to the trauma center, 52% and 75% of the traffic analysis zones were able to maintain access to the trauma center by ambulance under 10- and 25-cm flood thresholds, respectively. Under the 45-cm flood depth threshold, 73% of the traffic analysis zones maintained access after introducing five additional ambulance stations, but no additional staging stations were required in this case. The framework developed in this multidisciplinary study can provide a useful predictive tool for emergency managers in flood-prone coastal regions.

A rapid method for estimating the peak discharge from a breached landslide dam is developed from an analysis of 42 outburst floods. Systematic classification of the river blockages based on the landslide type, its material composition, and its travel distance is used to assess the erodibility of a dam. The blockage erodibility, along with the impoundment’s characteristics, controls the size (height and width) of a breach and the speed at which it forms, which determines the peak discharge. Three categories or types of landslide dams are identified to quantify their relative erodibility. A regression equation is developed to predict an outburst flood peak discharge based on the blockage type, the dam height, and the volume of impounded water at the time of breaching. Prediction limits are also calculated, which provide a sound statistical assessment of the possible peak outflow range. With the potential consequence known, the downstream population’s orderly evacuation can be carried out when a landslide dam failure is approaching.

Understanding how vulnerability conditions are related to disruptions in social support and trajectories of recovery after disasters is important for promoting resilience. Based on household survey data from New Jersey counties impacted by Superstorm Sandy, a hierarchical clustering method was utilized to classify recovery trajectories as well as common patterns of social support reflecting contrasting dimensions of social capital over time. Residents with a higher level of home damage relied largely on institutional sources for material and information support over the course of recovery. Younger and higher-income residents had a higher proportion of informal sources, particularly for emotional support. Patterns of social support were associated with recovery trajectories when vulnerability and disaster impact were controlled, where institutional sources for material and informational support combined with informal sources for emotional support were associated with quicker recovery trajectories. Results provide implications for bonding and bridging forms of social capital in recovery and motivate research and investments for assessing and cultivating both informal relations and institutional networks from which postdisaster social support can be mobilized.

A distributed simulation model is presented that integrates post-earthquake household decisions into quantifying the seismic resilience of communities subjected to a sequence of earthquakes. A simple multi-attribute rating technique (SMART) is used to model post-earthquake household decision making at the building level, whereas the earthquake sequence is modeled using time-dependent analysis during recovery from the first shock. Incremental dynamic analysis is used to develop fragility curves for first shock–damaged structures, which are distinguished from the conventional fragility curves of undamaged structures. A case study of a prototype community that comprises households with different socioeconomic characteristics in accordance with a typical small US community is used to show the influence that household decisions have on the overall seismic resilience of the community. The results suggest that seismic events with a larger first shock have a more severe impact on the seismic resilience of communities than events with a smaller first shock regardless of the magnitude of the subsequent shock.

Although it is largely accepted that disasters offer windows of opportunity for adoption of risk-reduction measures, the dimensions of support for hazard mitigation following a disaster are not well understood. Drawing on a survey of Upper Texas Coast residents 9 months after Hurricane Harvey (2017), this study explores public support for various types of hazard mitigation within the context of a natural hazard focusing event. Regression models estimate the association of risk perceptions, objective risk, political beliefs, and disaster experience with individual support for hazard mitigation of seven types including structural and nonstructural, household, and community levels. The findings suggest that individuals with higher risk perceptions, those who reside in areas with high flood risk, those who experienced disaster damages to their property, and those who value investment of tax dollars in disaster management are more likely to support hazard mitigation than their counterparts. This support varies across hazard mitigation type, indicating there are some factors related to the benefit gained from the specific mitigation technique that are more relevant than others.

This work investigated the factors affecting household choice of departure time during evacuations in Hurricane Matthew in 2016. Departure time estimates are needed to predict time-varying evacuation demand for use in simulation models and the development of evacuation traffic management strategies. The research team conducted a household survey after Hurricane Matthew in the Jacksonville, Florida, metropolitan area, with a total sample size of 588 respondents. Newly introduced factors were examined for significance throughout this work and were found to affect evacuation departure timing, such as uncertainty, family relationships, and cohesion. Uncertainty affects how certain the potential evacuees are about hurricane information such as hurricane impact location, whether they live in an evacuation zone, the timing of the hurricane and the evacuation destination and the route by which to get there, as well as the time needed to prepare for the evacuation. Family cohesion is related to decision-making agreement among household members and their preference to stay together in difficult situations. Such factors were poorly presented in previous literature. A Cox proportional-hazards model, a survival analysis technique used to study time till event, was used to model the evacuation departure timing, based on data from a post-Hurricane Matthew survey of Jacksonville, Florida, metropolitan area residents. The final model contained three significant variables, of which two are related to uncertainty and family cohesion. This study also used a binary logit model to examine evacuees’ retrospective preferences about whether they would have changed their evacuation timing. The preferred model contained five significant variables related to past experience, the type of evacuation order received, and the evacuation destination. This work opens several opportunities for additional studies on topics such as the stability of departure time; in addition, new factors presented here should be considered in future studies, such as certainty levels and household cohesion. Additional measures of experience could be incorporated to better understand the nuances of the experiences of different components of evacuation behavior.

Natural hazard causes severe types of damage to infrastructure systems at regular intervals, and the occurrence of such events is inevitable. The concept of resilience is adopted to make infrastructure systems more reliable, adaptable, and recoverable against natural disasters. Resilience is defined as the ability of an infrastructure to resist the impact of a disaster and bounce back to its desirable performance level after the disaster. Recovery of infrastructure, which forms a part of resiliency, is a time-dependent process in nature. In this work, a dynamic Bayesian network (BN) model is developed for the resilience assessment of housing infrastructure against flood hazards. The proposed resilience model is then implemented in the testbed of Barak Valley in North-East India. An extensive field survey is performed to collect relevant indicator data for resilience assessment and validation. To assess the recovery process, the housing infrastructure resiliency of the Barak Valley testbed is evaluated and compared between multiple flood event time periods. Lastly, the most critical indicators of the proposed dynamic BN model are identified by performing sensitivity analysis. This study will help the planner, designers, policymakers, and stakeholders to provide resilience-based decisions on flood resiliency of housing infrastructure systems.

This paper provides an improved soil classification scheme and associated spectral acceleration response curves along with the site amplification factors for the ongoing revision of IS 1893 Part-1 [BIS (Bureau of Indian Standards). 2016. Criteria for earthquake resistant design of structures. IS 1893-1. New Delhi, India: BIS]. A ground response analysis of 124 sites across India is conducted using the nonlinear procedure of DEEPSOIL. In addition to the classical geotechnical parameters $N_{\mathrm{SPT}}$, $S_u$, and plasticity index (PI), the improved soil classification scheme also uses the fundamental period of a site ($T_0$) and the average shear wave velocity to the seismic bedrock, instead of only $V_{S,30}$. New site amplification factors (i.e., $F_a$ and $F_v$) are proposed for all site classes of the improved soil classification scheme and compared with the amplification factors of other national codes. Further, elastic design response spectra are proposed for the improved soil classification scheme by considering the proposed site amplification factors (i.e., $F_a$ and $F_v$). The compatibility of the proposed elastic design response spectra with other national codes’ design spectra is further evaluated.

At 18:08 on August 4, 2020, a large explosion occurred at Hangar 12 in the Port of Beirut. The size of the explosion was equivalent to that of an earthquake with a local magnitude (${\mathrm{M}}_{\mathrm{L}}$) of 3.3 according to the USGS. As one of the largest nonmilitary explosions to ever impact an urban region, this event provides unprecedented opportunities to document explosion impacts on urban infrastructure. To facilitate this data collection, the Geotechnical Extreme Events Reconnaissance (GEER) Association coordinated a multiagency response directed toward the collection of perishable data of engineering interest. Two main categories of infrastructure systems were impacted: the Port of Beirut and the Beirut building stock. Within the Port, the explosion triggered a quay wall failure and flow slide, and strongly impacted grain silo structures that were in close proximity to Hangar 12. Within the city, historical masonry structures, older reinforced concrete structures, and modern high-rise structures were impacted. Through a combination of in-person inspections and street-view surveys, we collected data on structural performance (including damage to load-bearing elements) and building façades. Performance levels were classified according to procedures applied following earthquakes (for structural performance) and newly proposed procedures (for façades). We describe spatial distributions of these damage types and dependencies on source distance and location-to-explosion direction. We demonstrate that physical damages correlated with damage proxy maps produced by the Jet Propulsion Laboratory and the Earth Observatory of Singapore based on Copernicus Sentinel-1 satellite synthetic aperture radar data, with a stronger correlation with structural damage than with façade damage.

In April 2015, an increase in the seismic activity of the Cotopaxi volcano, one of the world’s most active volcanoes, alarmed local authorities. A potential eruption of the Cotopaxi volcano could affect over 85,000 people. Despite the extensive development of studies on international humanitarian aid, the first steps in this area of Ecuador started recently, following the disasters of recent years, and very little focus has been given to the evaluation of volcanic eruptions. This study aims to consolidate, analyze, and present, in a systematic way, an efficient evacuation plan based on a mathematical model to safeguard the integrity of the population in case of an eruption of the Cotopaxi volcano. This study proposes a multiobjective mixed integer linear programming (MILP) model to minimize the total time and cost required to evacuate people considering the logistics of rescue operations, shelters, and distribution centers’ optimal allocations. The study’s findings include an emergency evacuation plan that reduces evacuation times from 64.4 to $28.36\min /\mathrm{person}$ to safe locations, a solution that local and international governments can use to enhance contingency plans and improve the distribution of humanitarian aid for events of a similar nature.

Extreme natural hazards pose huge challenges to public administration. In the era of social media, governmental response takes the form of complex adaptive systems, applying information technology to support public organizations to interact with and adapt to each other in achieving collective performance. Using multiple sources of data on the 2016 Funing tornado, which affected Jiangsu Province, China, four mechanisms supporting the organizational use of social media (core-organization adoption, rapid message iteration, critical task orientation, and multiple channel integration) to adapt to the disaster situations are examined in the centralized public administrative context. It is found that WeChat, the most popular social media application in China, supported the timely flow of information between core organizations to execute critical tasks mainly at the initial stage of disaster response, and, in association with other information channels, improved organizational adaptation. These findings increase our understanding of governmental response to extreme natural hazards in the social media era.

Climate change increases the frequency and intensity of certain kinds of natural hazard events in alpine areas. This interdisciplinary study addresses the hypothetical possibility of relocating the residents of three alpine areas in Austria: the Sölk valleys, the Johnsbach valley, and the St. Lorenzen/Schwarzenbach valleys. Our particular focus is on these residents’ expectations about such relocations. We find that (1) many residents expect that in the next decades the state will provide them with a level of natural hazards protection, aid, and relief that allows them to continue to live in these valleys; (2) this expectation receives some legal protection but only when it is associated with fundamental rights; and (3) the expectation is morally significant, i.e., it ought to be considered in assessing the moral rightness or justness of relocation policies. These results suggest legal changes and likely extend to many other (Austrian) alpine areas as well.