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Submitted for publication in Earthquake Spectra Measuring Improvements in the Disaster Resilience of Communities Stephanie E. Chang, M.EERI, and Masanobu Shinozuka, M.EERI Corresponding (first) author: Stephanie E. Chang Mailing address: Dept. of Geography, Box 353550, Univ. of Washington, Seattle, WA 98195 Phone: (206)616-9018 Fax: (206)543-3313 E-mail address: email@example.com Submission date for review copies: to be completed Submission date for camera-ready copy: Submitted for publication in Earthquake Spectra Measuring and Improving the Disaster Resilience of Communities Stephanie E. Chang, M.EERI, and Masanobu Shinozuka This paper proposes a conceptual and measurement framework to quantitatively assess the disaster resilience of communities. The concept of resilience suggests a much broader framework than simply reducing monetary losses. Resilience can be conceptualized along four interrelated dimensions: technical, organizational, social, and economic. Evaluating community resilience and the degree to which mitigation and preparedness activities further resilience goals requires a multidisciplinary approach and quantitative measures. This paper proposes measures that relate the impacts of potential earthquakes to multi- dimensional performance standards. The approach is demonstrated in a case study of the Memphis, Tennessee, water delivery system. An earthquake loss estimation model provides a starting point for quantifying resilience. The analysis compares two retrofit strategies and finds that only one improves resilience over the status quo. While the resilience framework can be useful for guiding mitigation and preparedness efforts, further research is needed to fully implement the concept. INTRODUCTION The policies, practices, and theories of disaster management have in recent years increasingly promoted the goal of achieving disaster-resilient communities. In the late 1990s, the Federal Emergency Management Agency (FEMA) instituted Project Impact, a program to encourage communities to better understand their risks, engage in education, and invest in mitigation activities through public-private partnerships (FEMA, 2000; Nigg et al., 2000). A synthesis of scholarship in the hazards field also emphasized fostering community resilience as a key goal in halting the rapid growth of disaster losses (Mileti, 1999). In that context, it defined a disaster-resilient community as one that “can withstand an extreme natural event with a tolerable level of losses” and one that “takes mitigation actions consistent with achieving that level of protection.” (p.5) As a step toward implementing this idea, Bruneau et al. (forthcoming) proposed a conceptual framework for defining and measuring disaster resilience. They defined resilience as “the ability of social units (e.g. organizations, communities) to mitigate hazards, contain the effects of disasters when they occur, and carry out recovery activities in ways that minimize social disruption and mitigate the effects of future earthquakes.” (p.5) More specifically, a resilient system should demonstrate three characteristics: reduced failure probabilities, reduced consequences from failures, and reduced time to recovery. __________________________ (SEC) Dept. of Geography, Box 353550, Univ. of Washington, Seattle, WA 98195-3550 (MS) University of California, Irvine, Dept. of Civil and Environmental Engineering, Irvine, CA 92697-2175 Submitted for publication in Earthquake Spectra Bruneau et al. pointed out the need for quantitative measures of the resilience concept. Quantitative measurement can help in understanding how various factors contribute to resilience and why some communities are more resilient than others. Quantitative measures would also be able to address questions such as: How disaster-resilient is a community? Is the community becoming more resilient over time? What types of risk reduction efforts can most effectively move the community towards disaster resilience? Earthquake loss estimation models, which broadly quantify the potential impacts of earthquakes on a region, provide a natural starting point for attempts to measure community resilience. These models combine extensive spatial databases (e.g., of regional soil conditions, populations, and buildings) with computational algorithms for physical damage, monetary loss, and often human casualties and economic disruption. Loss estimation models have gained substantial attention in recent years and are becoming well-established in the literature and, increasingly, in practice. A well-known example is HAZUS, FEMA’s nationally applicable loss estimation methodology and software. Earthquake Spectra devoted a theme issue to loss estimation models in 1997 (vol.13, no.4). While these models generally focus on property damage, particularly to the building stock, there have been several recent studies that emphasize lifeline infrastructure systems and how their damage could lead to regional economic disruptions (Applied Technology Council, 1991; Rose et al., 1997; Cho et al., 2001; Chang et al., 2002; Kim et al., 2002). Because loss estimation models generally produce results in terms of expected dollar losses, they do not directly measure resilience. This paper aims to develop and demonstrate a measurement framework for disaster resilience. It builds on the conceptual framework developed by Bruneau et al., as well on advances in loss estimation methodologies. It proposes a series of resilience measures and illustrates, through a case study application of the Memphis water system, how they can be used to support decision-making for strengthening community resilience. A secondary objective of the case study is to explore the extent to which loss estimation models can be used to measure resilience. This entails identifying linkages between loss estimation modeling and resilience measurement, and identifying gaps that require further research. CONCEPTUAL FRAMEWORK DIMENSIONS OF RESILIENCE Bruneau et al. (forthcoming) suggest that resilience can be conceptualized along the following four interrelated dimensions: technical, organizational, social, and economic (TOSE). Technical resilience refers to how well physical systems perform when subjected to earthquake forces. Organizational resilience refers to the ability of organizations to respond to emergencies and carry out critical functions. Social resilience refers to the capacity to reduce the negative societal consequences of loss of critical services in earthquakes. Economic resilience refers to the capacity to reduce both direct and indirect economic losses resulting from earthquakes. Of these four (TOSE) dimensions, the technical and organizational dimensions are most pertinent to the performance and resilience of critical systems such as electric power, water, hospital, and emergency response. The social and economic dimensions are most relevant to the performance and resilience of the community as a whole. This is illustrated in Figure 1. Submitted for publication in Earthquake Spectra Resilience Objectives… …at community level: Community SOCIAL – minimize casualties & social disruption ECONOMIC – minimize economic disruption Other systems …at individual system level: Hospital Electric power TECHNICAL – minimize loss system system of facilities & equipment ORGANIZATIONAL – minimize Local emerg. Water service disruption through mgmt. system system organizational response Figure 1. Relationship between resilience objectives and scale of analysis. PROPERTIES OF RESILIENCE Bruneau et al. (forthcoming) identify four properties of resilience: robustness, rapidity, redundancy, and resourcefulness (4 R’s). Robustness refers to strength, or the ability of elements, systems, and other units of analysis to withstand a given level of stress or demand without suffering degradation or loss of function. Rapidity refers to the capacity to meet priorities and achieve goals in a timely manner in order to contain losses and avoid future disruptions. Redundancy is the extent to which elements, systems, or other units of analysis exist that are substitutatible, i.e., capable of satisfying functional requirements in the event of disruption, degradation, or loss of functionality. Resourcefulness is the capacity to identify problems, establish priorities, and mobilize resources when conditions exist that threaten to disrupt some element, system, or other unit of analysis. Robustness and rapidity can be viewed as the desired ends of resilience-enhancing measures. Redundancy and resourcefulness are some of the means to these ends. MEASUREMENT FRAMEWORK Because resilience is a multi-dimensional concept, developing measures of resilience that are quantifiable, succinct, and meaningful remains a challenge. Bruneau et al. (forthcoming) offer a series of 80 illustrative measures set out in 5 tables. They relate to 5 systems (“global”, electric power, water, hospital, and response and recovery systems), the 4 dimensions of resilience (TOSE), and the 4 properties of resilience (4 R’s). For example, a social performance measure for rapidity of the hospital system might be: “all injuries treated in first day.” The measurement framework proposed here makes two significant refinements to the Bruneau et al. framework: (1) it outlines a more succinct series of resilience measures, Submitted for publication in Earthquake Spectra and (2) it reframes the measures in a probabilistic context. In addition, in the next section of this paper, the measures are applied in a case study of the Memphis water system. Figure 2 sketches the measurement framework proposed here. System performance Q is plotted over time. Note that the concept of system performance is applicable not only to infrastructure systems such as the water delivery network, but also to the community as an urban system. Example where System Performance (Q) r0 > r *, t1 < t * Without-earthquake r* r0 ke qua e arth With Time t0 t1 t* Figure 2. Resilience measurement framework. Earthquake impacts refer to a “without-earthquake” timepath. A simplification that is often made in practice is to compare pre- and post-disaster states, assuming that pre-disaster conditions are “normal” and static. The proper comparison is between “with” and “without” disaster scenarios. For some types of disruptions, such as economic disruptions that last for a long period of time (e.g., years), this distinction may be important and would require a forecast of what would have happened without the earthquake. Resilience in Figure 2 is defined by comparing loss of system performance to pre-defined performance standards of robustness (r*) and rapidity (t*). The initial loss r0 is compared with r*, an absolute level of loss which can be some pre-specified “maximum acceptable loss.” The time to full recovery t1 is compared with t*, an absolute duration of loss which can be some pre-specified “maximum acceptable disruption time.” Figure 2 illustrates the case where, in the particular scenario earthquake, the system meets the rapidity performance standard (t1<t*) but not the robustness standard (r0>r*). Figure 2 pertains to a particular earthquake scenario. Since resilience refers to a capacity for dealing with potential future events, it is necessary to introduce the probabilistic element. This consists of (1) the probability that the system will meet the performance standards r* and t* in a given earthquake i, and (2) the probability of occurrence of various seismic events. We can define resilience, as a starting point, as the probability that the system of interest will meet pre-defined performance standards A in a scenario seismic event of magnitude i, or Pr(A│i): Pr( A i ) = Pr( r0 < r * and t1 < t * ) (1) Submitted for publication in Earthquake Spectra In other words, resilience is quantified as the probability of meeting both robustness and rapidity standards in event i. Often, discussion and decision-making regarding “acceptable” performance standards can most readily take place in the context of scenario events. Beyond this, it may also be useful to define broad system resilience ZA in reference to the entire range of possible earthquake events: Z A = ∑ Pr ( A i ) ⋅ Pr(i ) (2) i Alternatively, different performance standards may be defined for different classes of events. In lifeline earthquake engineering, for example, it is quite common for different standards to be specified for a more common, lower-magnitude “operating basis earthquake” as well as a rare, higher-magnitude “design basis earthquake.” The definition of performance measures and standards is clearly central to the quantification of resilience. Ideally, these definitions should be developed in consultation with decision-makers, the public, and other potential end-users. Because both professional and lay persons are not accustomed to thinking in these terms, this may a formal investigation (e.g., a structured survey) and/or a consensus-seeking discussion. While this was beyond the scope of the current study, examples were developed (shown in Table 1) for purposes of the case study. These examples are also useful for stimulating discussion and could be used in a more formal investigation of performance measures and standards. The examples shown in Table 1 pertain to a water lifeline system and its role in broader community resilience. “Technical” and “organizational” performance standards are defined at the level of the water system. Technical performance refers to the extent of physical damage to the network, measured in this case by the number of major pumping stations lost and the percentage of pipes broken. Organizational performance refers to the extent of service disruption, measured here as the percentage of population losing water service. Note that organizational performance depends not only on the extent of physical damage, but also on network flow conditions. This in turn reflects the degree of network redundancy. Moreover, in principle, network flow should also reflect the utility agency’s degree of organizational resourcefulness. This refers to the ability of the utility to respond to the emergency by rapidly detecting damage, efficiently deploying repair crews, using shutoff valves to isolate damage, implementing mutual aid agreements to speed up repairs, and so on. For both technical and organizational performance, “robustness” measures indicate the initial post-earthquake conditions while “rapidity” measures consider the repair and restoration timeframe. As mentioned previously, the specific examples of r* and t* in Table 1 are provided for illustrative purposes only. “Social” and “economic” measures are defined at the level of the community as a whole. Social performance could refer to the population displaced from their homes – that is, forced to seek emergency shelter – due to the disaster. (Another possible measure might refer to the population needing medical attention.) Loss of water service to residences could be a main source of population displacement. A complete analysis should also consider other factors such as housing damage that could also force people to seek emergency shelter. Similarly, economic performance refers to the loss of gross regional product (GRP) due to the disaster and should consider water outage to businesses along with other sources of economic disruption. Submitted for publication in Earthquake Spectra Table 1. Example performance measures and standards. Dimension of Performance Robustness Standard Rapidity Standard Resilience Measure (r*) (t*) (unit of analysis) Technical Network physical ≤1 major pump <1 week until all (water system) condition station loses pump stations and function 99% of pipes intact Organizational Water service <5% of population <1 week until 99% of (water system) loses water service population has water service Social Population living at <5% of population <1 week until 99% of (community) home(a) displaced from population living at homes home Economic Economic activity <5% of GRP(b) lost <1 week until return (community) to 99% of GRP Notes: (a) as opposed to staying in emergency shelters or other temporary housing; (b) gross regional product. APPLICATION TO MEMPHIS WATER SYSTEM The conceptual and measurement frameworks described above were applied in a case study of the water delivery system serving Memphis, Tennessee. This application builds on prior research that developed an earthquake loss estimation model for that system (Chang, 2002; Chang et al., 2002). An important goal of the application was to gain insight on the utility of loss estimation models for measuring resilience. MEMPHIS WATER SYSTEM Memphis Light, Gas, and Water Division (MLGW) provides water to the City of Memphis and, with the exception of a few unincorporated municipalities, the remainder of Shelby County (2000 population 897,000). MLGW draws water from an underground aquifer that is accessed by wells. The water network (Figure 3) includes approximately 1370 km of buried pipe, as well as several pumping stations, elevated tanks, and booster pumps. The network is modeled here as a series of some 960 demand nodes and supply nodes, connected by 1300 links. Nine pumping stations, each of which includes both booster pumps and treatment facilities, are represented by supply nodes.1 Because the elevated tanks have very limited capacity, they are not included in the model. The demand nodes indicate how much water is normally required from the network at locations throughout the system. The links primarily represent 10-inch and larger water mains. While this constitutes only some 30 percent of the entire system (which is predominantly 6-inch distribution pipes), these larger mains would be given priority in the event of a disaster (C. Pickel, personal communications, June 3, 2003). The database includes attributes of the pipelines and pumping stations that affect seismic performance. Pipeline damage in earthquakes depends upon pipe diameter, material, and soil 1 Since the time the data was originally obtained for the model, MLGW has added a tenth pumping station in the eastern portion of the county and a few major pipelines in the northwestern part of the county to enhance network redundancy. Submitted for publication in Earthquake Spectra condition. Pumping stations are characterized by fragility curves that indicate the probability of failure for different levels of ground shaking, in this case, peak ground acceleration (PGA). The fragility curves are calibrated on estimates of seismic capacity as reported in Hwang et al. (1998). Figure 3. MLGW water network and population density in Shelby County SEISMIC RETROFITS The Memphis region is at risk from earthquakes originating in the New Madrid Seismic Zone (NMSZ) in the central United States (Johnston and Nava, 1985). Historically, the NMSZ produced three events of Richter magnitude 8.0 or greater in the winter of 1811-12. At that time, damages were relatively minor, as the region was sparsely populated. However, the earthquakes reportedly caused the Mississippi River to flow backgrounds for a time and rang church bells as far away as Boston. MLGW is well aware of the seismic risk and has been engaging in a seismic upgrading program since the late 1980s (Pickel, communications). This program has focused on the pumping stations, which are critical for maintaining the water supply. Other retrofit strategies, such as large-scale replacement of seismically vulnerable cast iron pipes, were not considered due to the great expense that would be involved. The seismic upgrading program has been partly financed by MLGW’s own budget and partly by grants from the Federal Emergency Management Agency (FEMA) on a cost-share basis. The pumping station upgrades initially involved installing backup electric power generators. Later, new facilities were also designed with seismic considerations. While some older pumping stations have been seismically retrofitted, it may be another decade or more before the retrofit program is completed. Submitted for publication in Earthquake Spectra The current study models two alternative mitigation strategies. Retrofit 1 represents an actual strategy adopted by MLGW, in which the Morton and Davis pumping stations are seismically upgraded. These two facilities are labeled with a “1” in Figure 3 above. Mitigation is represented in the model by shifting the respective fragility curves. The mitigated fragility curves were calibrated based on contractor reports of the upgrading projects.2 Retrofit 2 represents a hypothetical strategy in which two alternative pumping stations, Mallory and Sheahan, are seismically upgraded, instead of Morton and Davis.3 These two facilities are labeled “2” in Figure 3 above. They were selected on the basis of their location in densely-populated areas of the county, as well as their low seismic capacities (0.18g). In this hypothetical case, the pumping stations are retrofitted to a level typical of other facilities (0.30g). The seismic capacities for all 9 pumping stations in each scenario (i.e., no retrofit, retrofit 1, and retrofit 2) are shown in Table 2. Table 2. Pump station seismic capacities in each retrofit scenario. Pump station No retrofit(1) Retrofit 1(2) Retrofit 2(2) Mallory 0.18g 0.18g 0.30g * Sheahan 0.18g 0.18g 0.30g * Morton 0.22g 0.30g * 0.22g McCord 0.30g 0.30g 0.30g Allen 0.30g 0.30g 0.30g Lichterman 0.30g 0.30g 0.30g Palmer 0.30g 0.30g 0.30g Davis 0.30g 0.36g * 0.30g L.N.G. 0.45g 0.45g 0.45g Notes: (1) after Hwang et al. (1998); (2) * denotes pump stations retrofitted in scenario. MODELING LOSS AND RESILIENCE To evaluate resilience, this study made use of a previously developed lifeline loss estimation model that had been applied to the MLGW water system. The model is described here in overview; details can be found in Chang (2002), Chang et al. (2002), Hwang et al. (1998), and Shinozuka (1994). The left-hand side of Figure 4 shows the structure of the loss estimation model in thick lines. Beginning with a scenario earthquake, the model integrates engineering and economic analysis to produce estimates of expected economic disruption loss. 4 Monte Carlo simulation is performed to estimate physical damage, repair and restoration, hydraulic flow and water outage, and economic loss. The simulations are performed for weekly time intervals following the earthquake. Policy and decision variables 2 “Feasibility Study Report on Seismic Retrofit of Existing Buildings at Ray Morton Pumping Station for Memphis Light, Gas & Water Division, Memphis, Tennessee,” (1992) and “Memphis Light, Gas and Water Division – Davis Water Pumping Station – Phase II Seismic Vulnerability Assessment of Existing Buildings, Equipment, and Systems” (1997). 3 MLGW is currently considering upgrading these two facilities. 4 The model simulates direct losses to regional businesses. For related research on modeling indirect losses, or the “ripple effects” of direct losses, see Rose and Liao (2003). Submitted for publication in Earthquake Spectra influence outcomes through two channels: pre-disaster mitigation (which reduces damage) and post-disaster response (which reduces outage). Scenario earthquake Performance Standards Technical Mitigation Damage Resilience Restoration Population Organizational Response Outage Impacted Resilience Pop.-GIS Data Econ.-GIS Economic Economic Data Loss Resilience Expected Economic Loss Figure 4. Relationship between loss estimation model and resilience measures. Thick lines denote loss estimation model. Shaded area indicates Monte Carlo simulation portion of model. Dotted lines show linkages between loss estimation model and resilience measures. A key element of the model consists of the link between the engineering and economic portions. Results of hydraulic analysis on the intact and damaged representations of the network provide estimates of water availability at each demand node on the network. Demand nodes are associated with economic activity through spatial overlays and other manipulations using a geographic information system (GIS). Note that data on the spatial distribution of economic activity (i.e., employment by place of work) are available for spatial units known as Traffic Analysis Zones (TAZs). There are 515 TAZs in the county. The dotted lines in Figure 4 indicate the linkages between the loss estimation model and the resilience measurement. As shown in the figure, even though the loss estimation model was not designed to assess resilience, many of its intermediate results are useful for this purpose. It should be noted that these intermediate results must be disaggregated by time (i.e., week after earthquake). In addition to the model results, performance standards are needed for the resilience assessment. Technical resilience was estimated in a straightforward manner from results on physical damage to the network. The number of pumping stations rendered inoperational and the Submitted for publication in Earthquake Spectra percentage of pipes suffering breaks, as well as the time to repair the network to 99 percent, were compared to the performance standards listed in Table 1. Note that in the restoration model, pump stations are given the highest priority, followed by large-diameter transmission pipes. Organizational resilience required some additional data analysis. Recall that the performance standards pertain to the percent of the population that is without water service. Intermediate model results indicated the water available at each demand node on the network for each time period. This was spatially related to population data, which was available for the 133 census tracts in the county, using GIS overlay. Note that while this does measure population without water, it does not completely reflect organizational resilience because the loss estimation model does not explicitly model MLGW’s emergency response capability.5 It is also limited to analysis of water flow, and does not assess water quality or pressure, which are important considerations for potability and fire suppression. Social resilience is omitted in the current study because the loss estimation model did not produce outputs that indicate disruption to society along the lines of Table 1. In order to estimate social resilience, a behavioral model is needed of the propensity of households to seek temporary shelter in the event of water outage and other sources of earthquake-related disruption. Developing such a model was beyond the scope of this study. Economic resilience was estimated in a straightforward manner from results on disruption to the regional economy. The initial loss of gross regional product (GRP) and the time to return to 99 percent of baseline levels were simply compared to the performance standards in Table 1. Because total restoration times in the earthquake scenarios were relatively short, it was assumed for the sake of simplicity that the without-earthquake GRP baseline would be identical to pre-disaster levels. Note that the analysis measures economic resilience assuming that only the water system is damaged in an earthquake. A more complete analysis would involve modeling disruption from all sources in the earthquake, including building damage to businesses, housing damage (which would affect households and hence labor input), electric power outages, traffic disruptions, and so on. It would also involve modeling how businesses respond to multiple sources of disruption. RESULTS The loss estimation model was run for 200 Monte Carlo simulations for each of the 3 retrofit cases (including “no retrofit”) and 2 earthquake scenarios. The scenarios pertain to events of magnitudes 6.5 and 7.0, respectively, occuring in the New Madrid Seismic Zone with epicenter at Marked Tree, Arkansas, some 55 km northwest of downtown Memphis. The results of the loss estimation model for these two events are an expected economic loss of $42.4 million for the M 6.5 earthquake and $136.4 million for the M 7.0 earthquake (see Chang, 2002). In contrast, as discussed below, resilience results are much more informative and insightful. Table 3 shows results for technical resilience, where values in the table indicate the probability of meeting performance standards in Table 1. The probabilities refer to the 5 To the authors’ knowledge, there are currently no models of utility response capability that include such elements as damage to emergency response infrastructure (e.g., vehicles, equipment, communications), availability of personnel, existence of an effective response plan, decision-making on valving off portions of the network, etc. Submitted for publication in Earthquake Spectra percent of simulations where outcomes meet these standards. Results range from 0 (no resilience) to 100 (complete resilience). Recall that the robustness standard is that no more than 1 of the 9 pumping stations is rendered inoperational by an earthquake.6 The rapidity standard is that after one week’s repair activity, all of the pumping stations and 99 percent of the pipes are intact. Table 3 shows that Retrofit 1 does not materially improve system performance over the unretrofitted case.7 However, Retrofit 2 nearly doubles system robustness in the M 6.5 earthquake, to 23 percent. In the M 7.0 event, it increases robustness from 0 to 7 percent. Thus, Retrofit 2 appears to be much more beneficial than Retrofit 1. However, additional pumping station retrofits beyond Retrofit 2 are needed to raise the system’s technical robustness to a high level. In the M 7.0 event, rapidity is also an issue. Pipe retrofits (e.g., replacement of some of the cast iron pipe in the system to reduce expected damage) or response measures such as increased mutual aid are needed in order to improve MLGW’s capacity to make the necessary repairs rapidly in such a seismic event. Table 3. Technical resilience results (percent of simulations meeting performance standards). Resilience criteria M 6.5 M 7.0 Retrofit strategy earthquake earthquake Robustness and rapidity No retrofit 12 0 Retrofit strategy 1 10 0 Retrofit strategy 2 23 0 Robustness only No retrofit 12 0 Retrofit strategy 1 10 1 Retrofit strategy 2 23 7 Rapidity only No retrofit 100 0 Retrofit strategy 1 100 0 Retrofit strategy 2 100 0 Table 4 shows results in terms of organizational resilience. The robustness standard here is that less than 5 percent of the population lose water service in the immediate aftermath of the earthquake. The rapidity standard is that less than 1 percent of the population is still without water one week later. Since rapidity standards are met in all cases, results are dominated by robustness outcomes. Again, Retrofit 2 improves robustness to a much greater degree than Retrofit 1. In the case of the M 7.0 event, it nearly doubles robustness over the unretrofitted case, from 21 to 39 percent. Improving technical resilience, network redundancy, or organizational factors such as repair prioritization plans could further improve organizational resilience. 6 According to MLGW engineers, the system would be able to withstand the loss of one pumping station, but would have difficulty coping with the loss of more than one in a disaster (Pickel, communications). 7 Results for Retrofit 1 in the M6.5 event are actually slightly worse than results for the Unretrofitted case, but this is an artifact of the Monte Carlo simulation approach. Submitted for publication in Earthquake Spectra Table 5 shows economic resilience results. The robustness standard is that the economic disruption entail less than 5 percent of gross regional product (GRP) immediately after the disaster, and less than 1 percent a week later. Again, Retrofit 2 affords noticeable improvements in robustness over both Retrofit 1 and the unretrofitted cases. Nonetheless, overall economic resilience in Retrofit 2 is still quite low in the M 7.0 event (16 percent). Improving technical or organizational resilience, or implementing measures such as business continuity plans, could raise economic resilience. Table 4. Organizational resilience results (percent of simulations meeting performance standards). Resilience criteria M 6.5 M 7.0 Retrofit strategy earthquake earthquake Robustness and rapidity No retrofit 63 21 Retrofit strategy 1 66 26 Retrofit strategy 2 74 39 Robustness only No retrofit 63 21 Retrofit strategy 1 66 26 Retrofit strategy 2 74 39 Rapidity only No retrofit 100 100 Retrofit strategy 1 100 100 Retrofit strategy 2 100 100 Table 5. Economic resilience results (percent of simulations meeting performance standards). Resilience criteria M 6.5 M 7.0 Retrofit strategy earthquake earthquake Robustness and rapidity No retrofit 68 9 Retrofit strategy 1 72 6 Retrofit strategy 2 79 16 Robustness only No retrofit 68 25 Retrofit strategy 1 72 28 Retrofit strategy 2 79 46 Rapidity only No retrofit 100 34 Retrofit strategy 1 100 26 Retrofit strategy 2 100 30 In sum, the example application has shown how the multi-dimensional concept of resilience can be operationalized and quantified. It has demonstrated how mitigation activities can be assessed and compared on the basis of their resilience improvements. In this Submitted for publication in Earthquake Spectra example, the selection of the Mallory and Sheahan pumping stations for retrofit in strategy 2 is shown to be preferable to the Morton and Davis pumping stations in strategy 1. Nonetheless, additional measures beyond Retrofit Strategy 2 are needed to raise system and community resilience to a high level. These might involve additional pump station retrofits, pipe replacements, or such measures as improvements to emergency response planning. AREAS FOR FURTHER RESEARCH One aim of this study was to identify further research that is needed in order to fully implement the concept of community resilience for purposes of supporting disaster reduction decision-making. The example application showed that while current loss estimation models provide a useful starting point for quantifying resilience, important data gaps and modeling issues remain. Three areas for further study merit particular attention. First, research is needed into appropriate performance standards – technical, organizational, social, and economic – which are central to the concept and measurement of resilience. Such research should identify stakeholder groups and their representatives (e.g., utility managers, elected officials). Appropriate performance standards could be identified through such methods as interviews, surveys, and focus groups. Standards of robustness and rapidity should be defined in ways that can best support mitigation decision-making. Second, a new class of models is needed that integrates engineering with organizational response. Such models should focus on the delivery of infrastructure service. For a water system, this would mean the delivery of potable water supply to end users. In contrast, current loss estimation models estimate the available water supply at network nodes given conditions of water leakage. They are unable to address such questions as: How can the lifeline agency manage flows through such responses as using shutoff valves? How can it prioritize restoration to best meet post-disaster water demands? What is the quality of the water from the damaged network? New data on organizational decision-making will be required to develop such models. Third, a new class of models is needed that integrates engineering with societal impacts. Such models should focus on community-level disruptions (e.g., households being displaced from their homes). This would entail modeling how individuals and households behave in the face of disaster, and how this behavior differs across groups. These models should consider the effects of community-level mitigation, preparedness, and response activities (e.g., government deployment of water trucks to neighborhoods losing potable water). CONCLUSIONS As an objective, improving the disaster resilience of communities calls for a broader conceptual framework than simply reducing monetary losses. A resilience framework must address the capacity of social units (e.g., individuals, organizations, and communities) to prepare for and respond to disasters. A key challenge is how to measure resilience and resilience improvements. This paper has proposed and demonstrated a quantitative framework for measuring resilience. While implemented for the case of earthquake risk to the water delivery system in Memphis, the methodology is broadly applicable to other critical infrastructure systems, communities, and types of disasters. However, new research is needed in several key areas in order to fully operationalize and realize the advantages of the concept of resilience. Submitted for publication in Earthquake Spectra The Memphis case study showed that resilience assessment goes beyond traditional loss estimation and can better inform loss-reduction decision-making in some important ways. First, it quantifies not simply the losses that might be expected in potential future earthquakes, but how these losses relate to standards of acceptable performance. This addresses the question of how much – and how much more – disaster mitigation and preparedness is needed. It helps to determine whether community resilience is increasing over time. It also encourages community-based discussion of appropriate seismic performance standards for critical infrastructure systems. Second, resilience assessment focuses attention not only on the magnitude of losses, but also on the speed of recovery. This places both pre-disaster mitigation and post-disaster response within a common framework by which they can be evaluated and compared. Comparative studies can thus be conducted to assess the effectiveness of various loss reduction measures, ranging from structural retrofits to emergency response plans. Third, the approach addresses in a systematic fashion the multiple, interrelated dimensions of resilience (technical, organizational, social, and economic). This clarifies the role of different types of interventions in improving community resilience. It helps in understanding why some systems are more disaster-resilient than others. Further, it emphasizes that the seismic resilience of engineered systems (e.g., the water delivery network) is not simply an engineering problem, but has important organizational aspects and contributes significantly to the disaster resilience of the entire community. ACKNOWLEDGMENTS This work was supported in whole by the Earthquake Engineering Research Centers Program of the National Science Foundation under Award Number ECC-9701471 to the Multidisciplinary Center for Earthquake Engineering Research. The authors thank Ron Eguchi (Imagecat, Inc.) and Chris Chamberlin (University of Washington) for their valuable contributions. Information and cooperation provided by MLGW is gratefully acknowledged. REFERENCES Applied Technology Council, 1991, Seismic Vulnerability and Impact of Disruption of Lifelines in the Conterminous United States, ATC-25, Redwood City, California, 440 pp. Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. 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