Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 203
Appendix E Foundations for Monitoring Climate–Security Connections C onclusion 6.1 sets out the five types of key phenomena for which monitoring to anticipate national security risks related to climate events is needed: 1. Climate and other biophysical environment phenomena; 2. The exposures of human populations and the systems that provide food, water, health, and other essentials for life and well-being; 3. The susceptibilities of people, assets, and resources to harm from climate events; 4. The ability to cope with, respond to, and recover from shocks; and 5. The potential for outcomes of inadequate coping, response, and recovery to rise to the level of concern for U.S. national security. There is substantial variation in the degree to which the expert com- munities that study and analyze these phenomena have achieved consensus about a set of key variables that are essential for monitoring. In many cases the foundation for identifying, collecting, and analyzing basic data is still under construction. The sections in this appendix reflect the com- mittee’s judgment about some of the major data and monitoring needs and capacities related to the five types of key phenomena above. In addi- tion, as discussed in Chapter 6, there are significant challenges associated with developing and applying the analytic techniques needed to turn ever- increasing amounts of data into useful information. 203
OCR for page 204
204 CLIMATE AND SOCIAL STRESS CLIMATE AND OTHER BIOPHYSICAL ENVIRONMENT PHENOMENA The effects of human activity on the natural environment coupled with natural changes in the physical climate system make long-term monitoring of the global climate system crucial both to understanding the variability and changes in the Earth system and for providing inputs to model-based prediction schemes. As the human population continues to increase, so too will global demands on agricultural, water, and energy resources. Analyzing regional climate impacts and assessing human vulnerabilities will require high-frequency and spatially dense observations as well as information on the change and rate of change of the global climate system. Regional and national networks must be developed, particularly in regions currently experiencing an increased demand on natural resources. If sufficient obser- vations are not collected, the ramifications will be serious, including less accurate weather forecasts and an inability to monitor natural hazards. As discussed in Chapter 6, in 1998 the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Framework Convention on Climate Change (UNFCCC) established specific requirements for systematic climate observations and a sustained observing system. Those requirements included supporting research to understand more fully the causes of cli- mate change, to predict future global climate change, and to characterize extreme events important to impact assessments, adaptation, risks, and vulnerability. In 2003 the Second Adequacy Report on the Global Observ- ing System (Global Climate Observing System, 2003) concluded that while improvements had been made in the global observing system, deficiencies remained in the global coverage and quality of ocean, atmosphere, and terrestrial measurements. The report concluded that satellite observations over all domains were essential to the global observing system and that they must continue uninterrupted. Despite such urging, however, U.S. satellite observation capabilities are expected to decline by 25 percent over the next 8 to 10 years, according to a recent National Research Council (NRC) r eport (National Research Council, 2012) on NASA’s implementation of the Decadal Survey. (See further discussion below.) The Global Climate Observing System, sponsored by the World Me- teorological Organization, the United Nations (UN) Educational, Scientific and Cultural Organization, the UN Environmental Program, and the Inter- national Council for Science, is charged with advising the community on global climate observations and overseeing implementation based on UN- FCCC standards. In 2010 the organization developed a list of 50 essential climate variables (ECVs) that are possible to implement globally and whose observation could yield significant progress toward meeting the UNFCCC requirements (Global Climate Observing System, 2010). These ECVs are
OCR for page 205
APPENDIX E 205 concerned with atmospheric conditions over land, sea, and ice and include such variables as temperature, wind speed and direction, pressure, cloud properties, and carbon dioxide levels. Ocean ECVs include sea surface and subsurface temperature and salinity, ocean color, sea ice, sea level, oxygen, and nutrients. Most terrestrial observations focus on ground water, water use, snow cover, land cover, and soil moisture. The ECVs represent a consensus on a broad and comprehensive set of parameters to document Earth’s climate system. Designed primarily to char- acterize key aspects of the Earth system, they are aimed more at informing scientific analysis more than policy decisions. There have, however, been several attempts to develop indices based on the ECVs that would yield an integrated measure of climate impacts that could be more useful for policy analysis. One such index is the U.S. Climate Extremes Index (CEI). It was developed as a “monitoring and communications tool to help U.S. citizens and policymakers identify possible trends or long-term variations in a vari- ety of climate extremes indicators” (Gleason et al., 2008). The CEI is com- posed of five parameters: monthly minimum and maximum temperatures, daily precipitation, days with and without precipitation, and the Palmer Drought Severity Index (PDSI) (Palmer, 1965). Temperature is important for monitoring a variety of phenomena, including heat waves, cold waves (including freeze events such as late spring freezes), and even unusually warm or cold months and seasons that can have effects on a variety of sec- tors. Precipitation is the basic building block for monitoring precipitation deficits and excess, including drought and heavy rain events. Air pressure is important for monitoring storms, heat waves, and other events, while water vapor is important for monitoring the potential for heavy rain events and drought. The PDSI is a dryness indicator based on a combination of recent temperature and soil moisture observations. The CEI was tailored for the continental United States. Currently there is an effort to develop a CEI that would provide a more globally integrated picture of the current state of climate extremes. However, the development of such a global CEI has been hampered by a lack of data availability in many regions and for most of the ECVs. A white paper prepared for the 2011 World Climate Research Programme Open Science Conference (Trenberth et al., 2012) concluded that although the data for ocean and atmospheric ECVs have adequate global coverage over a long enough time and sufficient quality, data for terrestrial measurements are seriously deficient. It also concluded that although existing in-situ data cover most of the high-priority regions, spatial and temporal coverage could be im- proved. It called for more integration of satellite data as well as for new observations that would provide information about such areas as climate change mitigation and adaptation efforts. Data at regional and local scales on such factors as soil moisture, stream flow, and sea surface temperature
OCR for page 206
206 CLIMATE AND SOCIAL STRESS that are related to climate events, are extremely inadequate. Soil moisture, for example, is important for monitoring drought as well as for estimating the potential for flooding. Streamflow is a good large-area integrator of short- and longer-term moisture conditions for a basin, a region, or even a continent. Sea surface temperature is especially important for tracking El Niño–Southern Oscillation events. The white paper found that such data, as well as detailed hourly information on variables such as precipitation intensity, distribution, fre- quency, and amount of precipitation, are necessary for predicting extreme events on regional scales. Because there are not yet adequate data in these areas, it may be necessary to develop global indicators in an incremental fashion—for instance, by adding one or two parameters at a time or by be- ginning with specific regions that allow early development, such as Europe or Australia. Numerous analyses have documented the linkages between global cli- mate change and environmental sustainability (e.g., National Research Council, 2010a, 2010b, 2010c). One example is the way in which changes in ocean and atmospheric circulation force corresponding changes in ocean temperatures, which reach the ice shelves, resulting in land ice loss, sea level rise, and, ultimately, coastal erosion. Likewise, changes in precipitation patterns can affect the snowpack and surface hydrology, thereby affecting agricultural productivity. And increased carbon dioxide emissions absorbed by the ocean lead to increased ocean acidity, which destroys the marine organisms that provide food sources for other marine life and thus nega- tively affects fisheries. To understand these relationships it is necessary to measure a broad and diverse set of the variables that connect global climate change with human life-supporting systems such as those providing for food, energy, water, and health. Global-scale indicators and metrics based on a broad spectrum of observations can provide some advance warning of the impacts of global climate change (National Research Council, 2010b). Sea level rise is one such indicator, for example, because it is a function of oceanic, land ice, and hydrologic processes. However, to obtain better estimates and projections of rising sea levels it will be necessary to make better sustained observations of such variables as sea state, atmospheric wind speed and direction, sea-ice extent, the mass balance of mountain glaciers and ice sheets, and river discharge. The United States and other countries do not currently have national strategies for sustaining long-term environmental observations. Joint efforts in support of the UNFCCC and IPCC are encouraging international col- laboration, and many nations have recognized the need for a fully imple- mented observing system. However, funding is a major obstacle. Major gaps in satellite and in-situ observations are developing because of the loss of several key satellites, such as Cryosat, the Orbiting Carbon Observa-
OCR for page 207
APPENDIX E 207 tory, and Glory, and because of the reduced maintenance on the Tropical Ocean Atmospheric Array of ocean buoys and on land-based carbon tow- ers. Delays or cancellation of other satellites will further increase the data gap. Lack of data will severely impair the ability of climate scientists to understand and forecast changes from interactions and feedbacks in the Earth system and will limit the information available to users and decision makers. Serious consequences will include less accuracy in forecasting and in the projecting of natural hazards and extreme events; as these events af- fect a growing population that is already placing significant stress on Earth’s systems and is expanding into areas exposed to likely climate hazards, the consequences will only grow with time. The national and international efforts noted above have focused mainly on understanding and anticipating climate changes, climate events, and some of their direct consequences. The field has made progress in setting priorities, but its emphasis has not been on security issues. Such an empha- sis would require types of monitoring not mentioned above. A consider- able number of the types of monitoring relevant to security issues were identified in a previous report from the NRC, Monitoring Climate Change Impacts (National Research Council, 2010b). We note that many of the environmental variables that are important to human life-supporting sys- tems cannot be measured readily or with sufficient resolution and accuracy from satellites and instead require place-based measurement and automated interrogation and assimilation. Examples of such variables include the q uality of agricultural land, the condition of aquifers, and the ranges of key pests and pathogens. In addition, to our knowledge there has as yet been no serious priority setting among environmental measurements, looking at the issue through the lens of security analysis. Thus the priorities noted here and in the previ- ous NRC effort (National Research Council, 2010b) provide only a start- ing point toward prioritizing climate and environmental monitoring needs for security analysis purposes. Efforts to develop priorities should aim at identifying a small number of composite indices designed for the specific purposes of analysis or early warning. Progress will require additional work, which should be conducted through collaborations involving climate scientists, environmental scientists, social scientists, and security analysts. An effective monitoring system is feasible in principle, but it will require an authoritative setting of priorities, the integration of climate and social and political stress indicators, and the development of reliable protocols for international collaboration.
OCR for page 208
208 CLIMATE AND SOCIAL STRESS EXPOSURE TO POTENTIALLY DISPRUPTIVE CLIMATE EVENTS Exposure refers to the presence of people, livelihoods, environmental services and resources, infrastructure, or economic, social, or cultural as- sets in places that could be adversely affected (Intergovernmental Panel on Climate Change, 2012:3). For security analysis it is important to recognize that different climate hazards matter for different places because each popu- lation and location of interest has a particular pattern of exposures and sus- ceptibilities to harm as well as of abilities to cope and respond. Therefore, monitoring of exposures needs to include both changes in the frequency and intensity of potentially disruptive climate events and changes in the societal conditions that affect which people and things are exposed to harm from specific events and the different exposures of different segments of societies. This section focuses on the monitoring of people and things. It is, of course, equally important to monitor the hazards. Although some hazards, such as large-scale floods, are easy to monitor, others (e.g., droughts) are not presently well monitored. The following are, in our judgment, some of the monitoring requirements that are most important for estimating exposures to potentially disruptive climate events. Population Estimates on Spatially Disaggregated Scales Understanding how hazards might affect human societies requires a basic understanding of where people are located with respect to the haz- ards, and estimates of exposed populations form a fundamental building block of any vulnerability assessment. The most basic indicator is the number of people residing in a given area, sometimes called the headcount. In some cases one wants to know more demographic detail than simply the headcount, for example, the age structure, the number of orphans, the percentage of female-headed households, and so on. Such information is often available in national censuses. In some cases the most important variable to estimate is not the number of residents in an area, but the total number of people located in the area at a given time, regardless of their residence. This total number of people in an area, including both residents and nonresidents, is sometimes referred to as the ambient population. For example, many of the people exposed to the Asian tsunami in 2004 were tourists, who were not counted in the lo- cal censuses. In other cases a region may have large numbers of migratory workers whose exposure is relevant to understanding risk but who are not counted in the censuses. An example is China prior to the 2008 financial crisis, when a large number of people located in vulnerable areas along the coast were migrant workers from the interior. In many climatically fragile areas, people respond to shifting climatic
OCR for page 209
APPENDIX E 209 patterns by taking part in seasonal migration. Some massive population movements (e.g., the Hajj) also take place at times of major holidays. Such dynamics can not only affect estimates of exposure, but also influence the emergence of a hazard (e.g., infectious disease). For some hazards it may be helpful to estimate population distributions on an even finer time-scale (e.g., hourly); in some large urban areas the number of people present varies by orders of magnitude over the course of a day. There are a handful of databases that have been used to generate esti- mates of these phenomena. Of these the Gridded Population of the World (GPW), produced by Columbia University, and LandScan, produced by Oak Ridge National Laboratory, are the most widely used. Regional col- lections are also available. (For a recent review see Tatem et al., 2012.) GPW integrates national census data using the highest spatial resolution available; LandScan estimates ambient population using census data along with spatial correlates of population location. Such information is useful across the entire life cycle of hazard assess- ment, preparedness, and response. Well before an event of concern, such information is relevant for risk assessment and planning for reducing sus- ceptibility among exposed populations. As an expected event approaches, the information is relevant for preparedness and crisis response planning (evacuation, resource mobilization, etc.). And in post-crisis periods, it is relevant for damage assessment and reconstruction planning; at this stage, census-derived data are no longer relevant, and estimates must be made afresh using ground surveys and satellite imagery. The main weaknesses of the available data are the temporal lags (de- cennial censuses are inadequate for many purposes) and the lack of spatial resolution. Location and Characteristics of Critical Infrastructure Some risks are more a function of the exposure of critical infrastructure than of the exposure of populations. In such cases, it is important to moni- tor the locations and key characteristics of such infrastructure as power plants, roads, railroads, ports, telecommunications centers, hospitals, pris- ons, government buildings, and key manufacturing facilities. It is important that, in addition to monitoring, there should be an ongoing assessment of the adequacy of this infrastructure and of whether plans are in place for backup procedures if the adequacy standards are not yet met. Publicly available global databases on such infrastructure are not con- sidered adequate. Data on roads, for example, are available but are out- dated and of very low accuracy, while other infrastructure information is not available except in commercial proprietary databases (mainly from the insurance industry and large engineering firms) and from classified military
OCR for page 210
210 CLIMATE AND SOCIAL STRESS databases. There is an effort under way to compile a global exposure data base for use in earthquake modeling1 that will represent the most compre- hensive public database of this type when complete. However, because it is oriented toward modeling economic loss rather than modeling security threats, it cannot be expected to meet all needs for security assessments. Information on infrastructure exposures would be useful at all stages of the life cycle of potentially disruptive events: early on for risk assess- ment and planning, and later on for damage assessment and response prioritization. Land Use and Land Cover Patterns of land use affect vulnerability to hazards and also constitute an integral aspect of exposure. Whether a given area is devoted to commer- cial agriculture, subsistence agriculture, grazing, dense urban settlement, or wilderness will make a difference on how a given hazard affects security dynamics. For example, a prolonged drought in an area with significant subsistence agriculture and grazing will likely result in population move- ments, whereas such a response would be less likely in an area with other land uses. There are standard classification schemes for characterizing land use and land cover, and these form the basis for generating indicators. Indica- tors that characterize different types of agricultural activity and different levels of urbanization would be particularly useful. A number of global databases measure land use and land cover, with satellite imagery providing the most important input, along with significant validation from ground-based observations. There are a number of sources of global data on land cover change, including NASA’s Land-Cover/Land- Use Change Program,2 the Global Land Cover Facility at the University of Maryland,3 and the U.S. Geological Survey.4 Other sources provide land use data for specific counties or regions. For example, the Department of Global Ecology of the Carnegie Institution for Science, based at Stanford University, compiles high-resolution data on forest cover and related vari- ables for tropical regions.5 These databases are considered accurate enough for use in global mod- eling and other broad exercises, but their accuracy and precision within specific regions is low. Their precision is low enough, for example, that they 1 GED4GEM. See http://bit.ly/KCzpyj (accessed November 15, 2012). 2 See http://lcluc.umd.edu/data_information.php (accessed November 15, 2012). 3 See http://esip.umiacs.umd.edu/index.shtml (accessed November 15, 2012). 4 See http://landcover.usgs.gov/index.php (accessed November 15, 2012). 5 See http://claslite.ciw.edu; http://cao.stanford.edu (accessed November 15, 2012).
OCR for page 211
APPENDIX E 211 tend not to discriminate between subsistence and commercial agriculture or between different levels of urbanization. They also tend to have difficulty in characterizing pasture, a critical land use category for understanding climate impacts. Moreover, the databases tend to disagree widely in delin- eating urban extents. Data on land use and land cover are most useful when used for risk assessment and planning in the early stages of the life cycle of potentially disruptive events. SUSCEPTIBILITY TO HARM Susceptibility is the likelihood of immediate harm to a population, community, society, or system as the result of exposure to a climate event. Thus, susceptibility is an indicator of the extent that an event would create disruptive change in the short term in that population, community, society, or system. For security analysis, it is important to identify factors that influ- ence the general susceptibility of populations to all types of climatic shocks and stresses as well as the susceptibility to specific types of climate-induced events, such as a flood or a pandemic. This section discusses monitoring needs for measures of general susceptibility before turning to a few key areas of specific susceptibility: food, water, and health. Monitoring of General Susceptibility A number of factors influence general susceptibility, including various economic, demographic, social, and environmental conditions in a region; the form and quality of the infrastructure and the built environment (In- tergovernmental Panel on Climate Change, 2007, 2012); and the presence or a recent history of violent conflict (Barnett, 2006; Barnett and Adger, 2007; Brklacich et al., 2010). In most cases it is not only the values of the variables that are relevant but also the direction and rate of change in those variables. For example, while the level of poverty in a region is an indicator of general human well-being and therefore of susceptibility to harm, there is additional value in knowing whether the level of poverty is increasing. If it is—even if poverty in the population does not seem particularly high to start with—this would suggest that well-being is deteriorating and that the potential for harm from climatic shocks may be increasing because fi- nancial and other assets that previously would have allowed households to cope with exposure to climate shocks are being depleted and because the construction of protective infrastructure is lagging far behind and further exposing populations to risk (Parry et al., 2009). In addition to determining which types of susceptibility factors should be monitored, it is also critically important to consider the appropriate scale
OCR for page 212
212 CLIMATE AND SOCIAL STRESS or level of analysis for assessing and monitoring the susceptibility of specific populations. A susceptibility assessment may apply to an entire population or to a defined subset of a population (such as a particular ethnic group) within a politically bounded region, such as a city, state, or country, or within various types of functional regions, such as urban neighborhoods, border zones, agricultural areas, hydrologic basins, and so forth. As a general rule, susceptibility monitoring should emphasize locations that are currently or potentially of security concern, but it should also pay attention to regions where major humanitarian or other types of crises may arise or where large migration inflows or outflows may be likely. It is also important to recognize that many of the factors that influ- ence susceptibility to future climatic shocks and stress are already in flux as the result of ongoing environmental and climatic changes (Paavola, 2008) and of non-climatic processes, including globalization and urbaniza- tion (O’Brien and Leichenko, 2007; Leichenko and O’Brien, 2008). For example, rapid rates of deforestation on the hill slopes surrounding a city, which are often associated with population immigration, will exacerbate the susceptibility of a region to extreme precipitation events. Areas where susceptibility indictors are rapidly changing—especially when these changes suggest that susceptibility is increasing—also merit special attention because these are regions where social and political turmoil may be more likely, particularly if the regions have a recent history of violent conflict. Measurements of economic conditions provide an indication of the financial and material well-being within a population or region and of the capacity to withstand climate-related shocks and stresses. Regions or population subgroups with low or deteriorating levels of per capita income and financial assets, high levels of poverty, high levels of unemployment, or high levels of income inequality can be expected to be more susceptible to harm from climatic risks and hazards. Economic diversity also has an important influence on susceptibility. Regions or populations that depend on a single agricultural commodity for their livelihoods and where alterna- tive livelihood options are limited also tend to be more susceptible to harm from all types of climatic shocks and stresses, particularly those affecting their main source of livelihood. Potential indicators of economic well-being and economic diversity include per capita income, poverty rates, levels of inequality, unemployment rates (overall and by age cohort), percentage of the labor force in agriculture, and share of agricultural production by crop. Many of these basic economic variables are regularly catalogued by international organizations such as the World Bank, which maintains data bases on variables at the level of the nation-state for most countries.6 National-level information on agricultural production and crop yields is 6 See http://databank.worldbank.org/data/home.aspx (accessed November 15, 2012).
OCR for page 213
APPENDIX E 213 available from the International Food Policy Research Institute7; other data sources are discussed later. Many national governments also collect economic data at both the national and subnational levels, but for less- developed countries the availability and reliability of these data are limited, and many countries do not release their routinely collected economic data. Subnational data are also available in datasets of the Center for Interna- tional Earth Science Information Network at Columbia University; these datasets contain subnational estimates of indicators of economic well-being, including poverty, inequality, unmet basic needs, and food security, for a large number of countries.8 One important limitation of using existing data from many publicly available secondary sources is that the underlying source data, such as national population censuses, are updated relatively infrequently. These datasets provide reasonable estimates of baseline condi- tions, but they would need to be supplemented with primary data collected for specific regions of interest in order to gauge current conditions or to assess changes over shorter periods of time. Demographic and social conditions are another group of factors that influence susceptibility to harm from climatic shocks and stresses. Regions with high rates of population growth, particularly from immigration, can be expected to be more susceptible to many climate risks because, as discussed in Chapter 5, new immigrants to a region are often poorer than long-time residents, are likely to lack knowledge of local environmental conditions, and tend to live in hazard-prone areas such as flood plains. Highly urban- ized areas concentrate people and may therefore increase the disruption that would result from events experienced there; on the other hand, it may be easier in highly urbanized areas for response efforts to reach affected people. Regions with high shares of elderly residents or of young children also tend to be more susceptible to multiple hazards because these groups rely on others for their safety and well-being. Regions with low levels of education and literacy—both of which are indicators of human capital—would also be expected to be more susceptible to harm from a variety of climate events (Cavallo and Noy, 2010). In regions with high levels of gender inequality, females could be expected to be more susceptible to harm from climate events than males because of a relative lack of assets and a lack of access to resources that may be available to men. The health status of populations also influences susceptibility to harm from such hazards as food insecurity, poor water quality, and exposure to infectious disease agents. Potential demographic and social variables to monitor include popula- tion growth rate, rate of immigration, share of the population that is over 7 See http://www.ifpri.org/dataset/agro-maps-mapping-agricultural-production-systems (accessed November 15, 2012). 8 See http://sedac.ciesin.columbia.edu/theme/poverty (accessed November 15, 2012).
OCR for page 228
228 CLIMATE AND SOCIAL STRESS ratist, or independence movements), (b) the right of the current government or administration to rule (as evidenced by insurrections, insurgencies, or, in the case of opposition to authoritarian regimes, popular or democratic movements), or (c) the methods or processes by which the incumbent au- thorities came to be holding office (as evidenced by, on an increasing scale of violence, anti-government protests, demonstrations, or riots). The situ- ation most likely to lead to problems would be a combination of all three rejections by multiple groups that agree on short-term goals (even if they differ on long-term goals), including at a minimum the ouster of the cur- rent authority figures. Such conditions would be conducive to the collapse or fall of the government or administration and possibly even to a regime change in the aftermath of the disaster. As discussed in Chapter 4, the case of Myanmar after 2008’s Cyclone Nargis, where a type of slow democra- tization is currently occurring, is a possible example, depending upon the final outcome of the process. Many countries at serious risk of disasters, including several of national security interest to the U.S. government, are in the substantial and com- plicated middle area between these two ideal types of high-legitimacy and low-legitimacy regimes. Turkey, for example, is closer to the first type—a country in which a major natural disaster (e.g., a great earthquake affecting Istanbul) would likely not pose a threat to the regime (although perhaps to the incumbent authorities and possibly the government of the time). Pakistan, with a major earthquake or flood, could be closer to the second type because, although the regime survived devastating floods in 2010, the aftermath of such an event could provide the occasion for an Islamist or military usurpation of power that would change not only the authorities but also the government and, quite likely, the entire regime. Considering that in the 21st century the standard for legitimacy in most countries is some form or approximation of a democracy, it is worth con- sidering and monitoring the characteristics of a democratic political system that might make it resistant to erosion or collapse in the wake of a major disaster. That set would include (1) sustained levels of high socioeconomic development without stark inequities; (2) regularized and accepted mecha- nisms and channels to organize and express grievances; (3) low levels of violence, particularly political violence; (4) a sustained record of low public corruption; (5) full civil liberties, a free media, and regular and fair elections with peaceful turnovers of post-election positions; and (6) effective gover- nance capacities from the national to the local level, including for extreme event emergency response and recovery.23 23 An example of a regime’s stability under disaster can be seen in the situation that arose in Germany in August 2002 when devastating floods affected large areas and many cities and towns in the eastern part of the country. Interestingly, before the floods Chancellor Gerhard
OCR for page 229
APPENDIX E 229 A nation-state whose characteristics were a mirror image of these would likely have its stability threatened by a major disaster. Such a state would have: (1) low to middle levels of socioeconomic development, with stark inequities; (2) restricted, ineffective, or nonexistent mechanisms and channels to organize and express grievances; (3) high levels of violence, either currently or in the recent past; (4) sustained high levels of public corruption; (5) restricted civil liberties and media, fraudulent or non-com- petitive elections, and problematic turnovers of governing authority; and (6) ineffective or very limited governance capacities from the national to the local level, including for extreme event emergency response and recovery.24 Specific Monitoring Needs These considerations suggest some specific monitoring needs for as- sessing the short-, medium-, and long-term effects of a major disaster or a sequence of disasters on social and political stability. In addition to the four factors introduced at the beginning of the section, monitoring should also be aimed at the following baseline and social and political conditions: 1. The coping capacities of the principal groups affected directly and indirectly by the event or sequence of events and the differences in coping capacity among the principally affected groups. Some exist- ing indicators of social capital may serve as indicators of coping capacity (Aldrich, 2012). 2. The general response capacity, which can be monitored in part by observing the response capacities of responsible formal organiza- tions. Response capacity is partly a matter of budgets and supplies (e.g., emergency shelter materials, water purification equipment, and food stocks). It also encompasses logistical capacities, the availability and training of general response personnel, and access to special- ized personnel (e.g., first responders or medical personnel). Because capacity is meaningful only in relation to the costs of providing a ssistance—some of which, such as food prices, can be volatile— the costs of price-volatile supplies and equipment should also be monitored. Schröder was behind in the polls for the September 2002 elections. His opposition to the oncoming U.S. invasion of Iraq combined with an effective disaster response and then a carefully financed recovery plan brought him back electorally, and he won reelection, albeit with a much reduced majority (from 21 to 9) in the Bundestag. 24 These pre-conditions characterized Haiti when it was struck by a major earthquake in 2010, except that the United Nations and the community of nongovernmental organizations were providing most of the services normally associated with a state—and all Haitians were well aware of it.
OCR for page 230
230 CLIMATE AND SOCIAL STRESS 3. Expectations of response on the part of the general public, key socioeconomic and cultural groups, and disaster-affected popula- tions. This includes expectations of government emergency response capabilities and effectiveness as well as perceptions of government capabilities and effectiveness versus those of other domestic actors (e.g., the military or religious organizations in many countries), non- governmental organizations in general, or outside donors. Expecta- tions provide an important baseline indicator in advance of extreme events and can be measured by survey methods, perhaps as part of the more general efforts to survey public attitudes toward govern- ment discussed below. We are unaware, however, of any efforts to do such measurement systematically across countries for disaster- related issues. Perceptions of response after a disruptive event can be assessed in the same way and compared with the expectations to provide an indicator of the perceived adequacy of the response. The key dimensions of response perception include the effectiveness, transparency, and honesty of the responding organizations. 4. Surge capacity, one indicator of which is the availability of desig- nated standby personnel and the funds to support them in the event of an emergency. 5. The likelihood of effective response. A baseline assessment could be provided by examining the track records of government and other response organizations in providing support to the affected popula- tions or areas both generally and after past disruptive events, the his- tory of national governments in allowing outside resources to flow to affected areas, and the record of national and local governments in delivering normal services to affected or potentially affected areas. 6. The direct and indirect social and economic impacts of the disaster (numbers killed, injured, and made homeless as well as the numbers of those whose livelihoods have been destroyed or jeopardized). The monitoring of these impacts, which is done in the aftermath of an event, could be done by class, race, ethnicity, gender, geography, political orientation, and so on. These factors are most important to monitor at the geographic level of the event, rather than at the national level. We recognize that some of this information may be difficult to collect, particularly in countries where some of these is- sues are likely to be sensitive and governments are either reluctant to ask the questions or else to make the information public. In such cases, it may be necessary to rely on indirect measures. 7. The impacts of actual response and recovery, which can be moni- tored through a number of attitudinal variables. These variables range from attitudes about specific responses to a disaster to atti- tudes about more general political and social conditions and capaci-
OCR for page 231
APPENDIX E 231 ties. These are normally measured by public opinion surveys asking for approval ratings, but they can also be assessed by examining the treatment of the government in free mass media. The attitudinal variables that can be monitored to assess the impacts of response and recovery can include • Expectations of government disaster recovery capabilities and effectiveness by the general public, by key socioeconomic groups, and in the disaster-affected population. • Perceptions of the effectiveness of government efforts in disaster recovery compared with efforts of other actors, institutions, or donors, assessed in the general public, key socioeconomic groups, and disaster-affected populations. • Mobilization of disaster-related grievances by location, degree, type of grievance, population subgroup, and apparent purposes or goals. This monitoring should be particularly sensitive to con- centrations of grievances by region, class, race, ethnicity, religious orientation, or other pre-existing societal cleavage. • Attitudes toward incumbent authorities (i.e., the leaders or leader- ship group) from local to national levels. This can be assessed by approval ratings collected from general public samples, key socio- economic groups, and disaster-affected populations, and should be sensitive to the above sets of pre-existing societal cleavage. • Attitudes toward the government or administration, measured by approval ratings among the general public, key socioeconomic groups, and disaster-affected populations from local to national levels. This monitoring should also be sensitive to the above sets of pre-existing societal cleavage. • Attitudes about the underlying legitimacy or “rightness” of the nation, the state, the current government, and the incumbent authorities, monitored in the general public, key socioeconomic groups, and disaster-affected populations, and sensitive to the above pre-existing societal cleavages. In surveys or focus groups, legitimacy can be assessed directly by asking whether the current form of government is appropriate for solving national problems. It can be assessed indirectly by examining such indicators as pro- vision of basic health and security services. 8. Social or political instability over the longer term. Potential indica- tors include • Pre-existing levels of internal violence and political instability in the country of interest.
OCR for page 232
232 CLIMATE AND SOCIAL STRESS • Pre-existing levels of violence in neighboring countries and changes in those levels after the event or sequence of events in the country of interest. • Attitudes toward or approval of the nation, state, current govern- ment, and incumbent authorities within key institutions (e.g., the military, private-sector commercial and industrial organizations, and churches or religious organizations and movements). This is normally assessed with standard forms of political analysis, although there may also be a role for monitoring general or spe- cialized media. • Mobilization of opposition by parties, groups, or movements that may attempt to take advantage of the post-impact disaster period and the recovery period to advance their interests and agendas. This monitoring should be particularly sensitive to differences be- tween largely non-violent “in-system” opposition parties, groups, or movements versus “out-system” and more violence-prone enti- ties, especially if the latter are organized along the lines of major pre-existing societal cleavages. This is assessed by standard forms of political analysis. • The repressive capacities of the state and the degree to which these are enhanced, remain static, or are degraded by the climate event or sequence of disasters. Examples of Monitoring Resources for Coping, Response, and Recovery Response capacity. The increasing international attention in recent years to reducing disaster risks rather than simply responding when disas- ters occur has led to a number of major communication and coordination initiatives that also offer sources of information about national, regional, and international capabilities. For example, the UN International Strategy for Disaster Reduction Secretariat (UNISDR), as part of its initiative to create national platforms for disaster risk reduction (81 countries at the time of this report) is developing a database of national response capacity.25 Another UNISDR initiative is the Integrated Research on Disaster Risk’s Forensic Investigations of Disasters,26 launched in partnership with the International Social Science Council and the International Council for Science. Its goals are (1) to provide a baseline of the current state of the science in integrated research on disaster risk in order to measure the ef- fectiveness of multiple programs, (2) to identify and support a long-term 25 See http://www.unisdr.org/partners/countries (accessed November 15, 2012). 26 See http://www.irdrinternational.org/about-irdr/scientific-committee/working-group/ forensic-investigations/ (accessed November 15, 2012).
OCR for page 233
APPENDIX E 233 science agenda for the research community and funding agencies, and (3) to provide a scientific basis to support policy and practice. All of these various activities are yielding data on, or at least relevant to, national disaster re- sponse capabilities and offer relatively inexpensive monitoring possibilities for the U.S. intelligence community. Perceptions and attitudes. In addition to traditional political analysis and research, both inside and outside the government, there are a number of resources for data on public perceptions and attitudes. In particular, public opinion survey projects such as Afrobarometer,27 the mericas Ba- A rometer survey from the Latin American Public Opinion Project,28 Asian Barometer,29 and Arab Barometer30 are accumulating evidence about at- titudes toward democracy, government performance, and a range of social and political issues. Regime types and governance. The longstanding Polity project, now part of the Political Instability Task Force effort, provides data that support quantitative and comparative analysis of regime authority characteristics and transitions. The types of governing authority range from fully insti- tutionalized autocracies through mixed, or incoherent, authority regimes (termed “anocracies”) to fully institutionalized democracies. The “Polity Score” captures this regime authority spectrum on a 21-point scale ranging from –10 (hereditary monarchy) to +10 (consolidated democracy) (Polity 4 Project, 2012). Assessing the quality of governance has become a major focus for devel- opment assistance in recent years. Hundreds of datasets have emerged from donor agencies, governments, nongovernmental organizations, think tanks, and academia. (For a review of some of the “uses and abuses” of governance indicators, see Arndt and Oman, 2006.) Often used for research purposes because of their independence and freedom from member government in- fluences, the annual datasets from Transparency International and Human Rights Watch are readily accessible and include various indicators for gov- ernance. The Worldwide Governance Indicators Project, produced by a team of scholars at the Brookings Institution with funding from the World Bank, is an example of a widely cited set of indicators from the world of intergov- ernmental organizations. It reports aggregate and individual indicators for six dimensions of governance: voice and accountability, political stability and the absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. As explained on the World Bank’s 27 See http://www.afrobarometer.org (accessed November 15, 2012). 28 See http://www.vanderbilt.edu/lapop (accessed November 15, 2012). 29 See http://www.asianbarometer.org/newenglish/introduction/default.htm (accessed November 15, 2012). 30 See http://www.arabbarometer.org/about.html (accessed November 15, 2012).
OCR for page 234
234 CLIMATE AND SOCIAL STRESS website, “These aggregate indicators combine the views of a large number of enterprise, citizen, and expert survey respondents in industrial and de- veloping countries. The individual data sources underlying the aggregate indicators are drawn from a diverse variety of survey institutes, think tanks, non-governmental organizations, and international organizations” (World Bank, 2012). The data cover 213 economies for the period 1996–2010. A number of donor agencies also support national assessments. For example, as part of its Democracy, Human Rights, and Governance Program, USAID occasionally funds surveys that capture public experience with and percep- tions of corruption. Political instability and violence. A number of these resources were already noted in Chapter 5 as part of the discussion on assessing the evi- dence base for connections between climate change and security risk. The resources include the Uppsala Conflict Data Program,31 the Political Insta- bility Task Force (Box 5-1), and the Peace and Conflict Instability Ledger (Hewitt et al., 2012). A description of major datasets on international conflict and cooperation is maintained as an online appendix to the Com- pendium Project of the International Studies Association.32 Cross-cutting projects. In addition to the Political Instability Task Force, another U.S. government effort to examine a number of factors that affect coping, response, and recovery is the USAID Alert List. Since 2004 the USAID has produced an annual report that ranks countries according to (1) their current level of fragility and (2) a forecast of their risk for con- flict or political instability and then combines the two rankings to produce an assessment of the most vulnerable countries. The seventh report was produced in 2011. The report is based on quantitative analysis of unclas- sified, open source information, and although the actual list of rankings is not made public, details about the methodology and some general findings are available. For example, in 2009, 23 of the 29 most vulnerable countries were in Africa (Moore, 2010:4) The concept of “fragility” refers to the extent to which interactions between state and society produce outcomes that are (1) effective and (2) legitimate; the USAID measure assesses states on these two dimensions in- dependently, and the two assessments for a state are often very different.33 Of the 33 political, security, economic, and social indicators used to assess fragility, 16 are related to effectiveness and 17 to legitimacy. The forecasts of instability use a model developed by the Center for International De- 31 See http://www.pcr.uu.se/research/UCDP/ (accessed November 15, 2012). 32 See http://www.paulhensel.org/compendium.html (accessed June 23, 2102). 33 Principal components analysis was used to calculate weightings based on the relative influ- ence each indicator exerts on empirical relationships in the data, and those weights are then used to compute country scores.
OCR for page 235
APPENDIX E 235 velopment and Conflict Management at the University of Maryland. Its forecasts rely on a limited number of explanatory variables. In 2010 the project included an assessment of vulnerability to climate change based on a statistical model that analyzes global weather data and projects them forward. This approach permits physical climate hazards and human vul- nerability to conflict to be disaggregated from the effects of climate change (Hewitt, 2012). SUMMARY The multitude of possible sources of data and indicators that could be applied to understanding climate-security connections is potentially overwhelming. This appendix attempts to provide an overview of major data sources and monitoring projects across the phenomena of interest of this project as well as a sense of the degree of consensus across research communities about key variables and indicators. It serves to illustrate the complexity and challenges associated with one of the report’s findings: Monitoring systems will require the integration of quantitative indicators of both environmental and social phenomena with traditional security and intelligence analytic methods. REFERENCES Aldrich, D.P. 2012. The politics of natural disasters (pre-print). In Oxford bibliogra- phies in political science. Available: http://works.bepress.com/cgi/viewcontent. cgi?article=1019&context=daniel_aldrich (accessed October, 4, 2012). Arndt, C., and C. Oman. 2006. Uses and abuses of governance indicators. Paris, France: OECD, Centre for Development. Barnett, J. 2006. Climate change, insecurity, and justice. Pp. 115–130 in Fairness in adapta- tion to climate change, W.N. Adger, J. Paavola, M.J. Mace, and S. Huq, Eds. Cambridge, MA: MIT Press. Barnett, J., and W. N. Adger. 2007. Climate change, human security and violent conflict. Political Geography 26(6):639–655. Brklacich, M., M. Chazan, and H.G. Bohle. 2010. Human security, vulnerability, and global environmental change. Pp. 35–52 in Global environmental change and human security, R. Matthew, J. Barnett, B. McDonald, and K. O’Brien, Eds. Cambridge, MA: MIT Press. Brown, M.E., F. Tondel, T. Essam, J.A. Thorne, B.F. Mann, K. Leonard, B. Stabler, and G. Eilerts. 2012. Country and regional staple food price indices for improved identification of food insecurity. Global Environmental Change 22(3):784–794. Burke, R.L., K.G. Vest, A.A. Eick, J.L. Sanchez, M.C. Johns, J.A. Pavlin, R.G. Jarman, J.L. Mothershead, M. Quintana, T. Palys, M.J. Cooper, J. Guan, D. Schnabel, J. Waitumbi, A. Wilma, C. Daniels, M.L. Brown, S. Tobias, M.R. Kasper, M. Williams, J.A. Tjaden, B. Oyofo, T. Styles, P.J. Blair, A. Hawksworth, J.M. Montgomery, H. Razuri, A. Laguna- Torres, R.J. Schoepp, D.A. Norwood, V.H. MacIntosh, T. Gibbons, G.C. Gray, D.L. Blazes, K.L. Russell, and AFHSC-GEIS Influenza Surveillance Writing Group. 2011. Department of Defense influenza and other respiratory disease surveillance during the 2009 pandemic. BMC Public Health 11(Suppl. 2):S6.
OCR for page 236
236 CLIMATE AND SOCIAL STRESS Cavallo, E., and I. Noy. 2010. The economics of natural disasters. IDB Working Paper Series No. IDB-WP-124. Washington, DC: Inter-American Development Bank. Clinton, W.J. 1996. Presidential decision directive NSTC-7. Washington, DC: The White House. Dawe, D. 2009. The unimportance of “low” world grain stocks for recent world price in- creases. ESA Working Paper No. 09-01. Food and Agriculture Organization. Available: ftp://ftp.fao.org/docrep/fao/011/aj989e/aj989e.pdf (accessed August 10, 2012). Diaz-Quijano, F., and E.A. Waldman. 2012. Factors associated with dengue mortality in Latin America and the Caribbean, 1995–2009: An ecological study. American Journal of Tropi- cal Medicine and Hygiene 86(2):328–334. Famine Early Warning Systems Network. 2008. FEWS NET strategy and vision for markets and trade. Washington, DC: FEWS NET. (Updated January 2008). Available: http:// www.fews.net/docs/special/FEWSNETMarketandTradeStrategy2005-2010.pdf (accessed December 17, 2012). Fukuda, M.M., T.A. Klein, T. Kochel, T.M. Quandelacy, B.L. Smith, J. Villinski, D. Bethell, S. Tyner, Y. Se, C. Lon, D. Saunders, J. Johnson, E. Wagar, D. Walsh, M. Kasper, J.L. Sanchez, C.J. Witt, Q. Cheng, N. Waters, S.K. Shrestha, J.A. Pavlin, A.G. Lescano, P.C.F. Graf, J.H. Richardson, S. Durand, W.O. Rogers, D.L. Blazes, K.L. Russell, and AFHSC-GEIS Malaria and Vector Borne Infections Writing Group. 2011. Malaria and other vector-borne infection surveillance in the U.S. Department of Defense Armed Forces Health Surveillance Center-Global Emerging Infections Surveillance program: Review of 2009 accomplishments. BMC Public Health 11(Suppl. 2):S9. Gleason, K.L., J.H. Lawrimore, D.H. Levison, T.R. Karl, and D.J. Karoly. 2008. A revised U.S. climate extremes index. Journal of Climate 21:2,124–2,137. Gleick, P.H. (Ed.). 2011. The world’s water, Volume 7. Washington, DC: Island Press. Global Climate Observing System. 2003. The second report on the adequacy of the global observing systems for climate in support of the UNFCCC. April 2003 (GCOS-82, WMO/TD No. 1143). Available: http://www.wmo.int/pages/prog/gcos/Publications/gcos- 82_2AR.pdf (accessed October 15, 2012). Global Climate Observing System. 2010. Implementation plan for the global observing system for climate in support of the UNFCCC (2010 update). Geneva, Switzerland: World Me- teorological Organization. Available: http://www.wmo.int/pages/prog/gcos/Publications/ gcos-138.pdf (accessed August 6, 2012). Hayden, M. 2012. The dengue vector mosquito Aedes aegypti at the margins: Sensitivity of a coupled natural and human system to climate change. Briefing to the Committee on Assessing the Impacts of Climate Change on Social and Political Stresses, January 12, Washington, DC. Hewitt, J.J. 2012. 2011 alert lists: Methodological overview. Briefing to the Committee on Assessing the Impacts of Climate Change on Social and Political Stresses, March 1, Washington, DC. Hewitt, J.J., J. Wilkenfeld, and T.R. Gurr. 2012. Peace and conflict 2012. Boulder, CO: Paradigm. Intergovernmental Panel on Climate Change. 2007. Climate change 2007: Impacts, adapta- tion, and vulnerability. Cambridge, UK: Cambridge University Press. Intergovernmental Panel on Climate Change. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. Special report of working groups I and II of the Intergovernmental Panel on Climate Change. C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor and P.M. Midgley, Eds. Cambridge, UK: Cambridge University Press.
OCR for page 237
APPENDIX E 237 Johns, M.C., R.L. Burke, K.G. Vest, M. Fukuda, J.A. Pavlin, S.K. Shrestha, D. Schnabel, S. Tobias, J.A. Tjaden, J.M. Montgomery, D.J. Faix, M.R. Duffy, M.J. Cooper, J.L. Sanchez, D.L. Blazes, and AFHSC-GEIS Outbreak Response Writing Group. 2011. A growing global network’s role in outbreak response: AFHSC-GEIS, 2008–2009. BMC Public Health 11(Suppl. 2):S3. Koffi, J.K., P.A. Leighton, Y. Petcat, L. Trudel, L.R. Lindsay, F. Milford, and N.H. Ogden. 2012. Passive surveillance for I. scapularis ticks: Enhanced analysis for early detection of emerging Lyme disease risk. Journal of Medical Entomology 49:400–409. Lagi, M., K.Z. Bertrand, and Y. Bar-Yam. 2011. The food crises and political instability in North Africa and the Middle East. Available: http://arxiv.org/pdf/1108.2455v1.pdf (ac- cessed October 4, 2012). Leichenko, R.M., and K.L. O’Brien. 2008. Environmental change and globalization: Double exposures. New York: Oxford University Press. Lindgren, E., Y. Andersson, J.E. Suk, B. Sudre, and J.C. Semenza. 2012. Monitoring EU emerg- ing infectious disease risk due to climate change. Science 336:418–419. Money, N.N., R. Maves, P. Sebeny, M. Kasper, M.S. Riddle, and AFHSC-GEIS Enteric Surveil- lance Writing Group. 2011. Enteric disease surveillance under the AFHSC-GEIS: Current efforts, landscape analysis and vision forward. BMC Public Health 11(Suppl. 2):S7. Moore, F. 2010. Climate change in Africa. Testimony before the Subcommittee on African Affairs and Global Health, Committee on Foreign Affairs, United States House of Representatives, April 15. Available: http://gopher.info.usaid.gov/press/speeches/2010/ ty100415.html (accessed July 9, 2012). National Research Council. 2001. Under the weather: Climate, ecosystems, and infectious diseases. Committee on Climate, Ecosystems, Infectious Diseases, and Human Health. Washington, DC: National Academy Press. National Research Council. 2010a. Advancing the science of climate change. Panel on Advanc- ing the Science of Climate Change. Washington, DC: The National Academies Press. National Research Council. 2010b. Monitoring climate change impacts: Metrics at the inter- section of the human and earth systems. Committee on Indicators for Understanding Global Climate Change. Washington, DC: The National Academies Press. National Research Council. 2010c. Adapting to the impacts of climate change. Panel on Adapting to the Impacts of Climate Change. Washington, DC: The National Academies Press. National Research Council. 2012. Earth science and applications from space: A midterm as- sessment of NASA’s implementation of the decadal survey. Committee on the Assessment of NASA’s Earth Science Program. Washington, DC: The National Academies Press. O’Brien, K., and R. Leichenko. 2007. Human security, vulnerability, and sustainable adapta- tion. New York: United Nations Development Programme. Available: http://hdr.undp. org/en/reports/global/hdr2007-8/papers/O’Brien_Karen%20and%20Leichenko_Robin. pdf (accessed August 10, 2012). Office of the Director of National Intelligence. 2012. Global water security: Intelligence com- munity assessment. Washington, DC: Office of the Director of National Intelligence. Ogden, N.H., C. Bouchard, K. Kurtenbach, G. Margos, L.R. Lindsay, L. Trudel, S. Nguon, and F. Milord. 2010. Active and passive surveillance and phylogenetic analysis of Borrelia burgdorferi elucidate the process of Lyme disease risk emergence in Canada. Environ- mental Health Perspectives 118:909–914. Paavola, J. 2008. Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environmental Science and Policy 11:642–654. Palmer, W.C. 1965. Meteorological drought. Washington, DC: U.S. Department of Commerce.
OCR for page 238
238 CLIMATE AND SOCIAL STRESS Parry, M., N. Arnell, P. Berry, D. Dodman, S. Fankhauser, C. Hope, S. Kovats, R. Nicholls, D. Satterthwaite, R. Tiffin, and T. Wheeler. 2009. Assessing the costs of adaptation to climate change: A review of the UNFCC and other recent estimates. London, UK: Inter- national Institute for Environment and Development and Imperial College of London, Grantham Institute for Climate Change. Polity 4 Project. 2012. Polity 4 Project: Political regime characteristics and transitions, 1800–2010. Available: http://www.systemicpeace.org/polity/polity4.htm (accessed July 28, 2012). Russell, K.L., J. Rubenstein, R.L. Burke, K.G. Vest, M.C. Johns, J.L. Sanchez, W. Meyer, M. Fukuda, and D.L. Blazes. 2011. The Global Emerging Infection Surveillance and Response System (GEIS), a U.S. government tool for improved global biosurveillance: A review of 2009. BMC Public Health 11(Suppl. 2):S2. Singh, R.P., D.P. Hodson, J. Huerta-Espino, Y. Jin, P. Njau, R. Wanyera, S.A. Herrera-Foessel, and R.W. Ward. 2008. Will stem rust destroy the world’s wheat crop? Advances in Agronomy 98:271–310. Tatem, A.J., S. Adamo, N. Bharti, C. Burgert, M. de Castro, A. Dorelien, G. Fink, C. Linard, J. Mendelsohn, L. Montana, M. Montgomery, A. Nelson, A.M. Noor, D. Pindolia, G. Yetman, and D. Balk. 2012. Mapping populations at risk: Improving spatial demographic data for infectious disease modeling and metric derivation. Population Health Metrics 10(1):8. Trenberth, K.E., R. Anthes, A. Belward, O. Brown, E. Haberman, T.R. Karl, S. Running, B. Ryan, M. Tanner, and B. Wielicki. 2012. Challenges of a sustained climate observing system. Paper presented for WCRP Open Science Conference 2011, Denver, CO, October 24–28. (Revised February 15, 2012). Available: http://www.cgd.ucar.edu/cas/Trenberth/ trenberth.papers/Trenberth%20paper%20OSC%20October%202011_v13.pdf (accessed August 5, 2012). United Nations. 2012. The Millenium Development Goals report. New York: United Nations. Witt, C.J., A.L. Richards, P.M. Masuoka, D.H. Foley, A.L. Buczak, L.A. Musila, J.H. Richardson, M.G. Colacicco-Mayhugh, L.M. Rueda, T.A. Klein, A. Anyamba, J. Small, J.A. Pavlin, M. Fukuda, J. Gaydos, K.L. Russell, and AFHSC-GEIS Predictive Surveil- lance Writing Group. 2011. The AFHSC-Division of GEIS Operations Predictive Sur- veillance Program: A multidisciplinary approach for the early detection and response to disease outbreaks. BMC Public Health 11(Suppl. 2):S10. World Bank. 2012. World governance indicators. Available: http://info.worldbank.org/ governance/wgi/index.asp (accessed July 28, 2012). Wright, B.D. 2011. The economics of grain price volatility. Applied Economic Perspectives and Policy 33(1):32–58.