Validating Resilience and Vulnerability Indices in the Context of Natural Disasters

Due to persistent and serious threats from natural disasters around the globe, many have turned to resilience and vulnerability research to guide disaster preparation, recovery, and adaptation decisions. In response, scholars and practitioners have put forth a variety of disaster indices, based on quantifiable metrics, to gauge levels of resilience and vulnerability. However, few indices are empirically validated using observed disaster impacts and, as a result, it is often unclear which index should be preferred for each decision at hand. Thus, we compare and empirically validate five of the top U.S. disaster indices, including three resilience indices and two vulnerability indices. We use observed disaster losses, fatalities, and disaster declarations from the southeastern United States to empirically validate each index. We find that disaster indices, though thoughtfully substantiated by literature and theoretically persuasive, are not all created equal. While four of the five indices perform as predicted in explaining damages, only three explain fatalities and only two explain disaster declarations as expected by theory. These results highlight the need for disaster indices to clearly state index objectives and structure underlying metrics to support validation of the results based on these goals. Further, policymakers should use index results carefully when developing regional policy or investing in resilience and vulnerability improvement projects.     

On Some Recent Definitions and Analysis Frameworks for Risk,Vulnerability, and Resilience

Recently, considerable attention has been paid to a systems‐based approach to risk, vulnerability, and resilience analysis. It is argued that risk, vulnerability, and resilience are inherently and fundamentally functions of the states of the system and its environment. Vulnerability is defined as the manifestation of the inherent states of the system that can be subjected to a natural hazard or be exploited to adversely affect that system, whereas resilience is defined as the ability of the system to withstand a major disruption within acceptable degradation parameters and to recover within an acceptable time, and composite costs, and risks. Risk, on the other hand, is probability based, defined by the probability and severity of adverse effects (i.e., the consequences). In this article, we look more closely into this approach. It is observed that the key concepts are inconsistent in the sense that the uncertainty (probability) dimension is included for the risk definition but not for vulnerability and resilience. In the article, we question the rationale for this inconsistency. The suggested approach is compared with an alternative framework that provides a logically defined structure for risk, vulnerability, and resilience, where all three concepts are incorporating the uncertainty (probability) dimension.     

Economics of Disaster Risk,Social Vulnerability,and Mental Health Resilience

We investigate the relationship between exposure to Hurricanes Katrina and/or Rita and mental health resilience by vulnerability status, with particular focus on the mental health outcomes of single mothers versus the general public. We advance a measurable notion of mental health resilience to disaster events. We also calculate the economic costs of poor mental health days added by natural disaster exposure. Negative binomial analyses show that hurricane exposure increases the expected count of poor mental health days for all persons by 18.7% (95% confidence interval [CI], 7.44–31.14%), and by 71.88% (95% CI, 39.48–211.82%) for single females with children. Monthly time‐series show that single mothers have lower event resilience, experiencing higher added mental stress. Results also show that the count of poor mental health days is sensitive to hurricane intensity, increasing by a factor of 1.06 (95% CI, 1.02–1.10) for every billion (U.S.$) dollars of damage added for all exposed persons, and by a factor of 1.08 (95% CI, 1.03–1.14) for single mothers. We estimate that single mothers, as a group, suffered over $130 million in productivity loss from added postdisaster stress and disability. Results illustrate the measurability of mental health resilience as a two‐dimensional concept of resistance capacity and recovery time. Overall, we show that natural disasters regressively tax disadvantaged population strata.     

Vulnerability Assessment of Mining Subsidence Hazards

Between 1996 and 1999, five mining subsidence events occurred in the iron-ore field in Lorraine, France, and damaged several hundred buildings. Because of the thousand hectares of undermined areas, an assessment of the vulnerability of buildings and land is necessary for risk management. Risk assessment methods changed from initial risk management decisions that took place immediately after the mining subsidence to the risk assessment studies that are currently under consideration. These changes reveal much about the complexity of the vulnerability concept and about difficulties in developing simple and relevant methods for its assessment. The objective of this article is to present this process, suggest improvements on the basis of theoretical definitions of the vulnerability, and give an operational example of vulnerability assessment in the seismic field. The vulnerability is divided into three components: weakness, stakes value, and resilience. Final improvements take into account these three components and constitute an original method of assessing the vulnerability of a city to subsidence.     

On the Complex Quantification of Risk: Systems‐Based Perspective on Terrorism

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems‐based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality‐impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: “What is the likelihood?” and “What are the consequences?” can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences.     

On the Complex Definition of Risk: A Systems-Based Approach

The premise of this article is that risk to a system, as well as its vulnerability and resilience, can be understood, defined, and quantified most effectively through a systems-based philosophical and methodological approach, and by recognizing the central role of the system states in this process. A universally agreed-upon definition of risk has been difficult to develop; one reason is that the concept is multidimensional and nuanced. It requires an understanding that risk to a system is inherently and fundamentally a function of the initiating event, the states of the system and of its environment, and the time frame. In defining risk, this article posits that: (a) the performance capabilities of a system are a function of its state vector; (b) a system's vulnerability and resilience vectors are each a function of the input (e.g., initiating event), its time of occurrence, and the states of the system; (c) the consequences are a function of the specificity and time of the event, the vector of the states, the vulnerability, and the resilience of the system; (d) the states of a system are time-dependent and commonly fraught with variability uncertainties and knowledge uncertainties; and (e) risk is a measure of the probability and severity of consequences. The above implies that modeling must evaluate consequences for each risk scenario as functions of the threat (initiating event), the vulnerability and resilience of the system, and the time of the event. This fundamentally complex modeling and analysis process cannot be performed correctly and effectively without relying on the states of the system being studied.     

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.     

Resilience Analysis of Countries under Disasters Based on Multisource Data

Disasters occur almost daily in the world. Because emergencies frequently have no precedent, are highly uncertain, and can be very destructive, improving a country's resilience is an efficient way to reduce risk. In this article, we collected more than 20,000 historical data points from disasters from 207 countries to enable us to calculate the severity of disasters and the danger they pose to countries. In addition, 6 primary indices (disaster, personal attribute, infrastructure, economics, education, and occupation) including 38 secondary influencing factors are considered in analyzing the resilience of countries. Using these data, we obtained the danger, expected number of deaths, and resilience of all 207 countries. We found that a country covering a large area is more likely to have a low resilience score. Through sensitivity analysis of all secondary indices, we found that population density, frequency of disasters, and GDP are the three most critical factors affecting resilience. Based on broad‐spectrum resilience analysis of the different continents, Oceania and South America have the highest resilience, while Asia has the lowest. Over the past 50 years, the resilience of many countries has been improved sharply, especially in developing countries. Based on our results, we analyze the comprehensive resilience and provide some optimal suggestions to efficiently improve resilience.     

Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.     

Rethinking Vulnerability Analysis and Governance with Emphasis on a Participatory Approach

This article draws on vulnerability analysis as it emerged as a complement to classical risk analysis, and it aims at exploring its ability for nurturing risk and vulnerability governance actions. An analysis of the literature on vulnerability analysis allows us to formulate a three‐fold critique: first, vulnerability analysis has been treated separately in the natural and the technological hazards fields. This separation prevents vulnerability from unleashing the full range of its potential, as it constrains appraisals into artificial categories and thus already closes down the outcomes of the analysis. Second, vulnerability analysis focused on assessment tools that are mainly quantitative, whereas qualitative appraisal is a key to assessing vulnerability in a comprehensive way and to informing policy making. Third, a systematic literature review of case studies reporting on participatory approaches to vulnerability analysis allows us to argue that participation has been important to address the above, but it remains too closed down in its approach and would benefit from embracing a more open, encompassing perspective. Therefore, we suggest rethinking vulnerability analysis as one part of a dynamic process between opening‐up and closing‐down strategies, in order to support a vulnerability governance framework.     

Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability $(V_S)$, vulnerability to property $(V_P)$, and vulnerability to the location in terms of awareness $(V_A)$. The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zones—namely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real‐life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.     

Vulnerability,Risk Perception,and Health Profile of Marginalized People Exposed to Multiple Built‐Environment Stressors in Worcester,Massachusetts: A Pilot Project

Millions of low‐income people of diverse ethnicities inhabit stressful old urban industrial neighborhoods. Yet we know little about the health impacts of built‐environment stressors and risk perceptions in such settings; we lack even basic health profiles. Difficult access is one reason (it took us 30 months to survey 80 households); the lack of multifaceted survey tools is another. We designed and implemented a pilot vulnerability assessment tool in Worcester, Massachusetts. We answer: (1) How can we assess vulnerability to multiple stressors? (2) What is the nature of complex vulnerability—including risk perceptions and health profiles? (3) How can findings be used by our wider community, and what lessons did we learn? (4) What implications arise for science and policy? We sought a holistic picture of neighborhood life. A reasonably representative sample of 80 respondents captured data for 254 people about: demographics, community concerns and resources, time‐activity patterns, health information, risk/stress perceptions, and resources/capacities for coping. Our key findings derive partly from the survey data and partly from our experience in obtaining those data. Data strongly suggest complex vulnerability dominated by psychosocial stress. Unexpected significant gender and ethnic disease disparities emerged: notably, females have twice the disease burden of males, and white females twice the burden of females of color (p < 0.01). Self‐reported depression differentiated by gender and age is illustrative. Community based participatory research (CBPR) approaches require active engagement with marginalized populations, including representatives as funded partners. Complex vulnerability necessitates holistic, participatory approaches to improve scientific understanding and societal responses.     

Rural Nevada and Climate Change: Vulnerability,Beliefs, and Risk Perception

In this article, we present the results of a study investigating the influence of vulnerability to climate change as a function of physical vulnerability, sensitivity, and adaptive capacity on climate change risk perception. In 2008/2009, we surveyed Nevada ranchers and farmers to assess their climate change‐related beliefs, and risk perceptions, political orientations, and socioeconomic characteristics. Ranchers’ and farmers’ sensitivity to climate change was measured through estimating the proportion of their household income originating from highly scarce water‐dependent agriculture to the total income. Adaptive capacity was measured as a combination of the Social Status Index and the Poverty Index. Utilizing water availability and use, and population distribution GIS databases; we assessed water resource vulnerability in Nevada by zip code as an indicator of physical vulnerability to climate change. We performed correlation tests and multiple regression analyses to examine the impact of vulnerability and its three distinct components on risk perception. We find that vulnerability is not a significant determinant of risk perception. Physical vulnerability alone also does not impact risk perception. Both sensitivity and adaptive capacity increase risk perception. While age is not a significant determinant of it, gender plays an important role in shaping risk perception. Yet, general beliefs such as political orientations and climate change‐specific beliefs such as believing in the anthropogenic causes of climate change and connecting the locally observed impacts (in this case drought) to climate change are the most prominent determinants of risk perception.     

Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners’ adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane—when their memory of the event is the strongest—it can help to substantially decrease the vulnerability of a community.     

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

Potential climate‐change‐related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual‐level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region.     

Infrastructure Vulnerability Assessment Model (I-VAM)

Quantifying vulnerability to critical infrastructure has not been adequately addressed in the literature. Thus, the purpose of this article is to present a model that quantifies vulnerability. Vulnerability is defined as a measure of system susceptibility to threat scenarios. This article asserts that vulnerability is a condition of the system and it can be quantified using the Infrastructure Vulnerability Assessment Model (I-VAM). The model is presented and then applied to a medium-sized clean water system. The model requires subject matter experts (SMEs) to establish value functions and weights, and to assess protection measures of the system. Simulation is used to account for uncertainty in measurement, aggregate expert assessment, and to yield a vulnerability (Omega) density function. Results demonstrate that I-VAM is useful to decisionmakers who prefer quantification to qualitative treatment of vulnerability. I-VAM can be used to quantify vulnerability to other infrastructures, supervisory control and data acquisition systems (SCADA), and distributed control systems (DCS).     

Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.     

A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.     

外包信息系统脆弱性评价模型研究

针对外包信息系统脆弱性评价问题,从技术脆弱性和管理脆弱性两个方面提出了信息系统脆弱评价指标体系。在此基础上,给出外包信息系统脆弱性评价流程,构建基于技术脆弱性和管理脆弱性的二维评价矩阵模型。最后通过一个制造企业的电子商务外包案例说明该评价模型的科学性和有效性。     

Global Warming Risk Perceptions in India

Few studies have focused on global warming risk perceptions among people in poor and developing countries, who are disproportionately impacted by climate change. This analysis conducts a comprehensive assessment of global warming risk perceptions in India using a national sample survey. Consistent with cultural theory, egalitarianism was positively associated with global warming risk perceptions. In addition, perceived vulnerability and resilience to extreme weather events were also two of the strongest factors associated with global warming risk perceptions. While worry was positively associated with risk perceptions, it accounted for only a small proportion of the variance, unlike studies in developed countries. Finally, the study also collected global warming affective images. The most common responses were “don't know” or “can't say” (25%), followed by “pollution” (21%), “heat” (20%), and “nature” (16%). The study finds that the predictors of global warming risk perceptions among the Indian public are both similar and different than those in developed countries, which has important implications for climate change communication in India.