International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP) (12th : 2015)

Integrated spatial community resilience decision tool unifying social vulnerability indices and relative… Francis, Royce A.; Esfandiary, Siamak 2015-07

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12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  1 Integrated Spatial Community Resilience Decision Tool Unifying Social Vulnerability Indices and Relative Sea-Level Rise Predictions Royce A. Francis* Assistant Professor, Dept. of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA Siamak Esfandiary1 Program Specialist, FEMA, Department of Homeland Security, Washington, DC, USA ABSTRACT: Infrastructure resilience and social resilience have been independently investigated, but have not yet been integrated in the evaluation of preparedness and natural hazard response investments. In this paper, we present an integrated spatial evaluation of vulnerability to relative sea level rise in the US states of Delaware, Maryland, and Virginia. This paper is motivated by the notion that there may be an interaction between community resilience measured by social indicators and community resilience measured by technical vulnerability indicators. We evaluate this hypothesis by creating a decision support tool integrating relative sea level rise (RSLR) and social vulnerability index (SoVI) for those communities delineated by county. These results will be useful to policy makers by explicitly communicating the tradeoffs between SoVI and technical vulnerability, while also illustrating the potential for prioritizing regions based on the interaction between social resilience and infrastructure resilience.                                                  1 The views expressed in this paper are those of the author and do not necessarily represent those of the United States Federal Emergency Management Agency.  1. INTRODUCTION In the United States, tension between the required investment rehabilitation of aging infrastructures and mitigating unanticipated consequences of emerging catastrophic threats is building. This tension appears because a large fraction of the built infrastructure systems require substantial investments to ensure reliable service delivery as they approach the end of their design life cycles. At the same time, increasing public awareness of their vulnerability to the consequences of infrastructure failures attributable to climate change is leading to increased urgency for policy makers to provide the necessary political and civic leadership to protect these lifeline systems. The required information for prioritizing these investments via effective civic leadership involves assessment of the technical needs and assessment of the most vulnerable populations. However, the assessment of both of these dimensions is often siloed in disciplinary communities that may not overlap. As a result, the identification of vulnerable populations may not include an assessment of the hardness of lifeline systems where they are located. This hardness may modulate the urgency with which policy makers need to address the infrastructure problem. As a result, an inefficient allocation of resources can occur since the policy makers may not be aware of the true extent of the physical infrastructure needs. On the other hand, identification of physical infrastructure 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  2 vulnerabilities in the absence of social vulnerabilities may lead to ineffective communication and prioritization of the needed investments. Ineffective communication may occur if the physical vulnerabilities are emphasized without broader contextualization within the social and economic decision context. This usually goes hand in hand with misdirected prioritization by policy makers whose short-term rationales may not match the long-term perspective demanded by infrastructure management. By synthesizing the two, decision makers—both policy makers and informed citizens—can make decisions about investment priorities with greater efficacy and a more sober view of infrastructure protection requirements. With this background in mind, the long-term goal of this research is to identify methods for measuring critical infrastructure resilience that effectively incorporates uncertainty, technical vulnerability, and social vulnerability. By combining these dimensions, resilience investments, especially those in the lifeline infrastructure systems, including buildings and structures, can be used to enhance community resilience at a more local level. In the long-term, this research will be incorporated into both computational efforts aimed at influencing design codes and standards, tabletop exercises used to facilitate emergency response planning, and continuing threat assessment and evaluation of community resilience. The specific objective of this paper is to create a geographic decision tool that integrates social vulnerability and ecological vulnerability to sea-level rise. This tool uses a geographic interface that can be used to prioritize further investigation into the specific resilience investments required in the targeted geographic region. The short-term objectives of this paper in support of enabling this long-term objective is to: • Formulate an integrated decision tool used to prioritize geographic areas requiring resilience evaluation; and, • Demonstrate application of the integrated decision tool to a sea-level rise case study. 2. BACKGROUND AND RATIONALE The United States’ public awareness of vulnerability to natural disasters increased dramatically after Hurricane Katrina. Hurricane Katrina is among the most destructive hurricanes in U.S. history. The overall destruction caused by Hurricane Katrina, which was both a powerful hurricane as well as a catastrophic flood hazard, makes it the third largest hurricane in U.S. history (Knabb et al. 2011). America’s most severe natural disasters had become steadily less deadly and more destructive of property for about a century prior to Katrina. Yet, Hurricane Katrina not only damaged far more property than any previous natural disaster ($96 billion), it was also the deadliest natural disaster (1330 deaths) in the United States since Hurricane San Felipe in 1928 (Knabb et al. 2011). Beyond the unquantifiable costs of injury and loss of life from disasters, the economic losses from natural disasters just in 2011 in the United States exceeded $55 billion, with 14 events costing more than a billion dollars in damages each (Smith and Katz 2013). It appears that destructive forces of natural disasters are strengthening and the devastation left in the wake of these disasters is worsening. More importantly, although much attention has been devoted to the assessment of vulnerability to hurricanes, much less has been devoted to the United States’ vulnerability to sea level rise (SLR). Hurricane Sandy raised awareness of this threat as it inundated New York City and Long Island subway infrastructure, induced failures in the electric power network, and led to behaviors and policy decisions causing major oil and gas shortages in the region. As researchers and practitioners debated reasonable responses to this event, it became clear that SLR attributable to climate change will exacerbate these problems if left unaddressed. 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  3 Data suggest that the cost of disasters will continue to rise, given the population’s shift toward coastal and southern regions (Smith and Katz 2013). More people will be in the way of hazards such as hurricanes and flood. This makes it increasingly important to incorporate assessment of social vulnerability into resilience preparations. Gilbert F. White and Eugene Haas had concluded in their 1975 report on the US ability to withstand and respond to natural disasters that losses and potential losses from natural hazards were rising and the nation’s vulnerability to them was increasing due to (White and Haas 1975): 1. Suburbanization with more people living in unprotected floodplains, seismic zones, and coastal locations;  2. Residential mobility with more people moving into areas where they were unfamiliar with the local hazards and ways of coping with them;  3. The increased size of corporations permitting more risk-taking behavior in terms of plant locations in highly hazardous areas because they had the capacity to absorb the loss; and  4. The increased proportion of the affordable housing stock in mobile homes It was also pointed out in this report that prior to their research very little attempt had been made to use social sciences to better understand the economic, social and political significance of extreme events. Similarly, O’Keefe (O'Keefe et al. 1976) argues that disasters were more a consequence of socio-economic vulnerability than natural factors, where human agency is the driver of vulnerability rather than physical events. In recent years there has been a noticeable change among US federal agencies in the rhetoric about hazards, shifting from disaster vulnerability to disaster resilience, where disaster resilience is considered a more proactive and positive expression of community engagement in reducing natural hazards requiring novel approaches to the design of risk-based standards (McAllister 2013).  In summary, the rationale for this research is two-fold: 1. Vulnerability to natural hazards, and ultimately the economic costs of these hazards, is principally a result of human behaviors and other social factors. The technical focus on disaster vulnerability did not necessarily incorporate these factors. The shift towards building resilience to natural hazards expressly requires proactive assessment of the ways social factors modulate the consequences of natural disasters; and, 2. The shift in population to coastal areas increases the potential losses attributable to hurricanes. At the same time, much less attention has been paid to the intersection between demographics and SLR. SLR is widely believed to amplify the consequences of hurricanes if left unaddressed. In the following sections, we present a case study introducing the inferences or insights that can be drawn from decision tools that include both technical and social vulnerability features. 3. METHODOLOGY We suggest that social vulnerability can be combined with technical vulnerabilities to identify priorities for infrastructure resilience investment by using a visual decision support tool. First, we discuss the dataset indicating technical vulnerability, and then the indicators for social vulnerability. In the results section, we show maps of our results and a prototype of the decision support tool. 3.1. Technical Vulnerability.  The first step of applying the decision support tool is to select the metric for technical vulnerability. The metric one selects depends heavily on the technical system or natural hazard under evaluation. Because we are focused on sea-level rise, we select the historical relative sea-level rise (RSLR) for the coastal counties in 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  4 Delaware (DE), Maryland (MD), and Virginia (VA) as the metric for technical vulnerability. The dataset used here has been published in Gutierrez et al. (Gutierrez, Plant, and Thieler 2011a). Gutierrez et al. used a Bayesian Belief Network to predict probability of shoreline change rates in the eastern United states using the THK99 dataset. THK99 had been compiled to evaluate the potential sea level rise vulnerability for the Atlantic coast of the United States by Thieler and Hammar-Klose (Thieler and Hammar-Klose 1999). Gutierrez et al. focus on long-term shoreline change since climate change datasets can span such long periods of time. Their objective was to determine if long-term shoreline change, can be predicted based on long-term average forcing data as input variables. Our analysis employs the RSLR as proxy to these predictions as the technical vulnerability metric to SLR. Their predictions, including Geographic Information System (GIS) shape files and database objects are freely available from the United States Geological Survey (USGS) (Gutierrez, Plant, and Thieler 2011b). RSLR was averaged for each coastal county in DE, VA, and MD, and joined to GIS shapefiles containing the Social Vulnerability Index. 3.2. Social Vulnerability.  The second step of applying the decision support is to select the metric for social vulnerability. The U.S. Army Corps of Engineers Institute of Water Resources (IWR) compared 4 different tools that are commonly used to measure social vulnerability (Dunning and Durden 2013). Based on this comparison, IWR has adopted the Social Vulnerability Index (SoVI) as its fundamental social vulnerability analysis methodology. SoVI is a comparative metric created at the Hazards and Vulnerability Research Institute of the University of South Carolina in order to measure the social vulnerability of US counties to natural hazards (Cutter et al. 2003). SoVI is synthesized from 30 socioeconomic variables obtained from the US Census Bureau. These 30 socioeconomic variables are then synthesized to produce a metric that can serve as a proxy variable for a county’s capacity to respond to disasters or invest in mitigation strategies. Because the US Census Bureau updates their data approximately every five years, there are three sets of SoVI indicators available for analysis: SoVI 2000, SoVI 2005-09, and SoVI 2006-10. This analysis employs SoVI 2006-10. In SoVI 2006-10, although 30 variables have been used to compile the metric, seven components explain 72% of the variability in the data: race and class, wealth, elderly residents, Hispanic ethnicity, special needs individuals, Native American ethnicity, and service industry employment.  Table 1. Social Vulnerability Index versus Average RSLR for Coastal DE, MD, VA Counties County Name Avg RSLR (mm/yr) SoVI National Percentile Accomack 3.26842105 0.5962 Anne Arundel 2.89473684 0.0111 Baltimore 2.46666667 0.0703 Calvert 3.075 0.0102 Dorchester 3.07941176 0.4617 Gloucester 3.53421053 0.0935 Harford 2.63928571 0.0242 Kent 2.675 0.5307 Kent 2.9 0.5307 Lancaster 3.45 0.8218 Mathews 3.6025 0.2733 Middlesex 3.53333333 0.4136 New Castle 2.9 0.0776 Northampton 3.67592593 0.875 Northumberland 3.28333333 0.6182 Queen Anne's 2.865 0.0264 Somerset 3.20673077 0.8244 St. Mary's 3.14090909 0.0312 Sussex 2.6 0.4378 Talbot 2.925 0.2275 Virginia Beach 3.91875 0.042 Worcester 2.96666667 0.7041 York 3.575 0.0089  4. RESULTS The results of this analysis are shown in Figure 1 and Figure 2 using GIS. Figure 1 shows the SoVI for each county in the study region, along with the USGS monitoring sites that have RSLR predictions. Figure 2 shows the average predicted RSLR for the counties in the study region.  12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  5  Figure 1. Social Vulnerability Index for DE, MD, and VA coastal counties, and USGS Monitored Sites. Darker colors are higher SoVI scores indicating higher vulnerability.  There does not appear to be a linear correlation between SoVI and RSLR in this case study. Figure 3, illustrates the SoVI index for each coastal county with monitoring stations, while Table 1 lists the SoVI national percentile and RSLR for each county. In Figure 3, higher values of SoVI indicate higher levels of social vulnerability, and higher values of RSLR indicate higher sea level rise and a higher likelihood of shoreline change. In Table 1, the higher the national percentile for SoVI, the higher the social vulnerability. The eight counties indicated in bold font are the 8 coastal counties with both SoVI and RSLR above the median values.   Figure 2. Average RSLR for DE, MD, and VA coastal counties and USGS Site Locations. Darker colors indicate higher RSLR. White counties do not have any USGS Site Locations.  Figure 3. Graphical Decision Support Tool plotting RSLR versus SoVI. The most suitable quadrant is the lower left, where vulnerability and RSLR are both low. In the upper right quadrant, investments needed to improve both social capacity and risk mitigation. In the lower right, risk mitigation is needed, while in the upper left, social capacity is required. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 2.5 3 3.5 4 Social'Vulnerability'(SoVI)'Index'Percen6le''Rela6ve'Sea'Level'Rise'(mm/yr)'12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  6 5. DISCUSSION These results show that the combination of social vulnerability and technical vulnerability can lead to the identification of resilience priorities that address critical needs in community resilience measurement. Moreover, the use of the proposed decision support tool can facilitate investment decision-making when making tradeoffs between mitigation of economic consequences and supporting community abilities to make preparations in advance of natural disasters.In the long-term, this research will inform both policy and technical research. The results of this research program will contribute to the evolution of increasing public involvement in community resilience preparedness. In addition, this research program may contribute to identification of metrics that can be used to measure community resilience. This decision support tool can be used to facilitate multi-criteria decision making for improving community resilience. 6. REFERENCES Cutter, S. L., Boruff, B. J., and Shirley, W. L. (2003). “Social Vulnerability to Environmental Hazards*.” Social Science Quarterly, 84(2), 242–261. Dunning, C. M., and Durden, S. E. (2013). Social Vulnerability Analysis: A Comparison of Tools. U.S. Army Engineer Institute for Water Resources, Alexandria, VA, 1–34. Gutierrez, B. T., Plant, N. G., and Thieler, E. R. (2011a). “A Bayesian network to predict coastal vulnerability to sea level rise.” Journal of Geophysical Research, 116(F2), F02009. Gutierrez, B. T., Plant, N. G., and Thieler, E. R. (2011b). A Bayesian Network to Predict Vulnerability to Sea-Level Rise: Data Report. US Geological Survey, Washington, DC, 1–17. Knabb, R. D., Rhome, J. R., and Brown, D. P. (2011). Tropical Cyclone Report: Hurricane Katrina, 23-30 August 2005., National Hurricane Center. McAllister, T. P. (2013). Developing Guidelines and Standards for Disaster Resilience of the Built Environment: A Research Needs Assessment. National Institute of Standards and Technology, 1–153. O'Keefe, P., Westgate, K., and Wisner, B. (1976). “Taking the naturalness out of natural disasters.” Nature, 260(5552), 566–567. Smith, A. B., and Katz, R. W. (2013). “US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases.” Natural Hazards, 67(2), 387–410. Thieler, E. R., and Hammar-Klose, E. S. (1999). National Assessment of Coastal Vulnerability to Sea-Level Rise: Preliminary Results for the U.S. Atlantic Coast., US Geological Survey, Woods Hole Field Center. White, G. F., and Haas, J. E. (1975). Assessment of Research on Natural Hazards. MIT Press, Cambridge.  


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