WWDR-4 Issues Workshop

Discussion Paper

Identifying uncertainty and defining risk in the context of the WWDR-4

Prepared for the World Water Assessment Programme

by

Kye Mesa Baroang, Molly Hellmuth, and Paul Block

International Research Institute for Climate and

Society Earth Institute

Columbia University

Identifying uncertainty and defining risk in the context of the WWDR-4 Kye Mesa Baroang, Molly Hellmuth, and Paul Block Executive Summary Water management is a process of continuous adaptation to uncertainties and responses to risks. Meeting today’s challenges and tomorrow's demands given increasing system complexities, uncertainties and risks requires new approaches. While water managers have always faced and addressed risks, mounting pressures from external drivers and the recognition of the resulting nonstationarity in water systems (as well as the systems affecting them) are leading to a changing landscape of risks and uncertainties. The nonstationary nature of water and other systems requires different techniques for assessing and managing risks. The 3rd World Water Development Report examined several drivers of change, including climate change and demographic, economic, social, environmental, governance, and technological drivers, as well as their interactions as they relate to the sustainability of water resources and systems (WWAP, 2009). These drivers of change, along with natural processes, define and shape water-related risks, such as water scarcity, water quality degradation and pollution, loss of water-related ecosystem services, and the impact of extreme hydrometeorological events. While some drivers of change might increase both water-related and other critical risks (e.g, global economic collapse), others may result in positive outcomes beyond water resources, but exacerbate water-related risks (e.g., economic growth that leads to increased water use and consumption). Understanding the uncertainties in a system is critical to characterizing risks and developing approaches to decision-making. Water resources professionals can generally conceptualize uncertainty as arising in two broad and somewhat fluid categories: natural variability (essentially inherent irreducible randomness in the physical world) and incomplete knowledge. As knowledge has increased, we have increasingly shifted our understanding of natural variability, leading to revised assumptions of climate and hydrologic stationarity and recognition of nonstationarity in these systems. This has significant implications for water resources management and decision-making. Knowledge uncertainty, which stems from a lack of understanding of system processes, insufficient data and our inability to model these systems, figures prominently in constraining our ability to characterize and manage risks. Taken in combination, the uncertainty resulting from these factors is amplified in real world contexts, complicating decision-making processes. In developing countries, in particular, uncertainty due to historically lower investments in data gives rise to sub-optimal conditions for decision-making. There is often a trade-off between "optimal" and robust solutions, and the appropriate option in a given context is typically determined by the level of risk. Poor characterization of these risks and uncertainties often leads to inefficient solutions. The suite of uncertainties arising from both water-related risks themselves and the drivers of change are further complicated by the existence of nonlinearities and possible thresholds beyond which hazard impacts become irreversible.

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While there are a wide variety of definitions of risk, water resources managers are primarily concerned with avoiding negative consequences; though taking advantage of opportunities is also a central component of risk management. Risk is often expressed as a combination of hazard and vulnerability, where hazard is defined as an event or condition with harmful effects. Probabilities can be associated with both the hazard and vulnerability components. Vulnerability is constantly evolving, and is in many ways a product of the drivers of change - that is, the likelihood that harm will be caused is based on the social, political, economic and physical conditions of the population or system experiencing the hazard. Conceptualizing the probabilistic nature of the hazard and vulnerability are essential to developing strategies that address the risk. For a given context, it may be appropriate to address the likelihood of hazard occurrence (e.g., conserve water to avoid scarcity), vulnerability to the hazard (e.g., change policies to increase community resilience to scarcity), or both. Risk and its various components are strongly shaped by individual and social perceptions. Individual perception of risk can both affect decision-making at the individual level and drive demands on water managers and decision makers to address certain risks or manage them in a given way. At the community level, social interpretation of physical hazards can be significantly shaped (and sometimes amplified) by how risks are communicated (Kasperson et al., 2003). Management of these risks requires an understanding of the current situation and recent trends, and should involve forecasting possible futures, recognizing that knowledge and information inputs are imperfect and incomplete. Decision makers must learn how to effectively integrate driver uncertainties (due to current and future conditions) when approaching planning and investment decisions. Surprisingly, there is a major gap between our current understanding of hydroclimatology and the understanding that underpins most water management decision processes. The vast majority of water managers in the world use outdated approaches (based on assumptions of stationarity in climate and other systems) to set operating policies, make operational decisions, and evaluate long-term plans. This approach is particularly vulnerable to a changing climate and other evolving drivers of change. The decision-making necessary to manage water-related risks requires methodologies for assessing and analyzing the possible risks. This can then serve as the foundation for identifying opportunities to reduce risks. Numerous approaches to assessing risks have evolved, including the development of quantitative risk analysis methods such as probabilistic risk assessment, and the more recent "democratization" of the risk analysis process that provides stakeholders a greater voice in determining relevant uncertainties and risks. Such an "analytic-deliberative" approach is similar to the methods used to engage stakeholders in integrated water resources management. The nonstationary nature of water and other systems requires different techniques for assessing and managing risks. Technical approaches to quantifying the uncertainty in nonstationary systems include importance sampling, fuzzy reasoning, and Bayesian methods. However, not all risk management approaches rely on quantifying uncertainty. Some accept the irreducible nature of some uncertainties and build off

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adaptive management practices to emphasize learning from the past and building resilience to possible change. Others, such as robustness analysis, portfolio theory, scenario analysis and "no-regrets" approaches, focus on making decisions and developing management practices that offer benefits across a wide range of possible outcomes. Regardless of the approach, risk management in water resources must consider the planning horizon and develop plans that appropriately address the investment needs and capacities across various time scales. In order to effectively and efficiently make decisions and take actions to address water-related risks, it is essential to have a framework for understanding and approaching the key risks. The following recommended components of a comprehensive definition of risk can help orient discussions of risk and management in water resources for the 4th World Water Development Report. Fundamentally, risk comprises characterization of 1) hazard occurrence and likelihood, and 2) vulnerability to the consequences of the hazard. Uncertainty arises in both predicting a hazard occurrence and knowing the vulnerability to the hazard. The uncertainties are most often characterized by assigning probabilities, when possible; risk increases if the probability of the hazard increases, the probability and/or magnitude of a hazard's consequences increase, or both. The risk assessment necessary to inform management of water-related risks should include the following components/steps: identify the possible hazard; determine the probability of hazard occurrence (to the extent possible); and characterize vulnerability of human systems to the hazard. If it is not possible to assign a quantitative probability to the hazard occurrence, other techniques should be used to characterize the hazard possibility in a way that is useful in decision-making. For example, possible hazard occurrences without associated probabilities can be outlined in scenarios that serve as inputs to a no-regrets approach that emphasizes actions which avoid harm under nearly any possible outcome. The uncertainties and probabilities associated with hazards and vulnerability should shape decisions regarding investments, planning and operations. They help determine the appropriate questions to ask and provide information to answer them (e.g., Does preventing floods of a certain severity justify a proposed investment? Is the combination of the probability of its occurrence and the likely consequences sufficient to warrant the investment? Can a given investment or intervention decrease a set of multiple risks?). While water managers have always sought to address uncertainties and risks, today’s water problems seem both more urgent and more complex than those of the past. Population is more than double what it was fifty years ago, leading to intensifying competition for water resources and increasing water stress. In addition, the frequency of hydrometeorological disasters is rising, along with financial, human and economic losses. Climate change is expected to exacerbate these impacts in many regions. At the same time, our knowledge and awareness of the importance of equity and balancing multiple needs of stakeholders is increasing. Trade-offs will inevitably have to be made amongst competing needs and users in this increasingly complex landscape. Ultimately, a comprehensive and nuanced understanding of water-related risks and the uncertainties underlying them should help water managers and policy makers determine the most effective responses in a world of changing conditions and constrained resources.

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Introduction Changes and the uncertainties associated with them are critical to our relationship with water and water resources. Some changes within human and natural systems result in negative consequences and increase risk, while others may have positive effects that reduce risk. We make decisions under uncertain conditions and try to manage these risks. Given the inherently stochastic nature of water systems and drivers, water resources professionals have essentially always been addressing issues of uncertainty and risk. However, as systems undergo fundamental changes and become increasingly complex and interwoven, it becomes even more difficult to make decisions and manage risks in water resources. As our knowledge increases and drivers of change impact water resources, assumptions of stationarity in climatic and hydrologic systems are called into question. The purpose of this paper is to help establish a common understanding and acceptable definitions of risk and uncertainty in the broader context of water resources, their use and management. After beginning with an exploration of the various elements and sources of uncertainty, we outline some of the key definitions and conceptions of risk. The paper then explores the application of uncertainty and risk in the context of water resources, with an emphasis on four water-related risks. We examine the role of key drivers identified in the 3rd World Water Development Report on these risks. This is followed by a brief history of the evolving approach to risk analysis, with references to applications in water resources and a discussion of the recent responses to addressing the uncertainties associated with nonstationarity. The paper concludes with a brief discussion of the recommended components of a comprehensive definition of risk that can help orient discussions of risk and management in water resources for the 4th World Water Development Report. General review of uncertainty Uncertainty, as a general concept, reflects an inability to describe an existing state or predict an outcome with complete accuracy. We can separate the principal components of uncertainty into 1) a lack of knowledge (for whatever reason) about the present state, and 2) a lack of knowledge regarding how a system will change in the future. These are not exclusive, of course, and uncertainty can be (and often is) driven by both elements. In general, the existence of uncertainty regarding knowledge or understanding of current conditions significantly compounds uncertainty in predicting future conditions. In water resources engineering, the primary uncertainties can be categorized as hydrologic uncertainty, hydraulic uncertainty, structural uncertainty, and economic uncertainty (Mays, 1996). More generally, water resources professionals commonly conceptualize uncertainty in two parts: natural variability (essentially inherent irreducible randomness in the physical world) and incomplete knowledge or knowledge uncertainty (NRC, 1996). The distinction between the two can be shown in flood-frequency modeling. As suggested in IACWD (1981), the frequency-curve probability distribution describes natural variability, while the curve's error bounds

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reflecting uncertainty in the parameters of the probability distribution demonstrate the knowledge uncertainty. Regardless of the application, the distinction between natural variability and knowledge uncertainty can be seen as being fluid to some degree. As knowledge increases, some natural variation may be able to be modeled, leaving the remaining uncertainty based on incomplete knowledge (NRC, 2000). However, while our scientific knowledge is constantly advancing, the current understanding of the physical universe suggests that there will always be some level of irreducible uncertainty in nature. The reduction of knowledge uncertainty can help us better characterize and understand natural variability. Improved knowledge and information has changed our conceptualization of natural variability, leading to a shift away from assumptions of climate and hydrologic stationarity to understanding their nonstationarity. The recognition that the magnitude of natural variability may change and increase as records go farther back and capture different states has lead to growing attention to increasing the historical climatic and hydrologic record, including through use of proxies (e.g., tree rings). Nonstationarity greatly complicates the management of water resources, as will be discussed in more detail later in this paper. Science is still in the very early stages of being able to characterize the external drivers of nonstationarity and its implications for water resources. Focusing on the domain of knowledge uncertainty, we can identify three sources for our inability to fully describe the current situation or predict future conditions1: 1) lack of scientific understanding of systems and process; 2) lack of data; and 3) inability to adequately model systems and processes. Lack of scientific understanding of systems and processes In many cases, we can identify patterns and correlations between physical conditions without fully understanding the physical processes that underlie the relationships. While we might be able to build from statistically determined relationships to develop a model with reasonable predictive power, uncertainty will remain (often to a high degree) if we are unable to identify and fully model the drivers in the system. For example, water resources managers may recognize increasing flood frequency and seek to determine the best strategy to address the increased floods. In choosing between storage and protection options, it is critical to understand the underlying factors driving the changes to the system (e.g., long-term climate change, land use change, etc.). Understanding the natural "noise" in the system can be particularly important if the system in not stationary, making it necessary to determine the existence and significance of trends in hydrologic processes (Frederick et al., 1997). While irreducible natural uncertainty can limit full scientific understanding, there are generally many other equally critical barriers preventing complete knowledge of a

1 The reader is encouraged to refer to Morgan, 1990; NRC, 1996; and Walker et al., 2003 for further exploration of the nature and sources of uncertainties.

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given system. In some cases, there is simply a lack of sufficient research; this may be particularly true for studies of biological, economic and social systems or processes. The limiting factor is not the lack of data or modeling capability; rather, it is the fact that the topic has not been adequately explored to the degree necessary to reduce uncertainties. Such limitations may be able to be addressed merely through increased investment of time and resources to research. Even when processes have been extensively examined, significant uncertainties might arise due to poorly understood (or unrecognized) nonlinearities within a system. Uncertainties can cascade and increase nonlinearly if various processes, each with their own set of natural variability and knowledge uncertainties, are combined. Outcomes from modeling such processes can differ dramatically depending on whether the uncertainties are considered separately or together (NRC, 2000). Even without nonlinearities, the interactions between processes and the interrelationships of their attendant uncertainties can significantly magnify system complexity. Natural and social systems are increasingly recognized as being not balanced near equilibrium, but "facing discontinuities and uncertainty from complexes or suites of synergistic stresses and shocks" (Folk et al., 2002 p.16). This is quite relevant for water systems and will be discussed in more detail in the drivers section later in this paper. Complete understanding of a system may also be constrained by the way in which it is perceived by various stakeholders or investigators. Different perceptions arising from varying "underlying mental models" can result in the concurrent presence of multiple conceptions and approaches to the same system or process (Pahl-Wostl, 2007). This can be true across cultures as well as scientific fields, and can result in significant uncertainty. For example, communities in regions affected by frequent flooding may have an approach to flood events (impacts, uncertainties, frequency, etc.) that is distinctly different from the models developed by science. The Omo people in southern Ethiopia, for example, are opposing the Gilgel Gibe system of hydropower dams because they depend on annual floods as a means of providing nutrients and raising water tables to allow crop planting (Hathaway, 2008). Assessing the full variety of approaches can be critical to gaining a full understanding of the uncertain nature of floods and their consequences. Lack of data The insufficient availability of appropriate data both contributes to and is affected by the lack of complete scientific understanding of systems and processes. Data is critical to research and learning; but it is also often necessary to understand a system enough to know what data is relevant and should be sought. Data underpins the ability of water resources managers and decision-makers to effectively understand and act on the operations, planning and investment needs. Lack of appropriate, quality data is one of the primary contributors to uncertainty in both understanding existing conditions and modeling possible futures. When using models to make decisions, managers must take into account the possibility that the model is initialized with inaccurate data or was developed based on insufficient data. Such uncertainty contributes to the need to develop more flexible management approaches.

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The principal challenge arises in trying to infer behaviour or conditions based on limited data. The NRC (2000) identifies the following elements leading to data uncertainty: "1) measurement errors, 2) inconsistent or heterogenous data sets, 3) data handling and transcription errors, and 4) nonrepresentative sampling caused by time, space, or financial limitations" (p.44). The fourth point encompasses many of the most critical barriers to gathering sufficient data, particularly for the water sector. For example, designing and planning storage reservoirs requires an estimate of the likely inflow to the system. This is almost always based on very limited data of historical conditions (e.g. precipitation and runoff over the past 40 years), which may not accurately capture the full distribution of possible outcomes due to low frequency climate variability or simply extremes with longer recurrence intervals, for example. Data availability from hydrologic observations of freshwater systems remains severely limited across the world, with fragmented and unequal distributions between and within nations (WWAP, 2009). This is particularly true in lower income countries, where the majority of the population are dependent upon natural resources (particularly availability of water) to sustain their livelihoods. Insufficient data can lead to less than optimal critical decisions regarding resource allocation. Global efforts, such as the Global Environmental Monitoring System – Water Programme, have dedicated significant resources to reducing inadequate spatial and temporal coverage of water observations and monitoring. Additionally, efforts such as the Global Runoff Data Centre have been fairly successful at increasing data collection centres globally (see Figure 1).

Figure 1. Distribution of Global Runoff Data Centre streamflow gauges Source: WWAP (2009) However, observational gaps continue to persist even when the infrastructure exists, due to limited sharing of the data (WWAP, 2009). Ultimately, addressing uncertainty by increasing data monitoring and collection requires addressing factors such as data loss from natural or social crises, limited financial and human resources, political barriers, and lack of institutional commitment to data sharing.

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Inability to adequately model systems and processes The uncertainties due to incomplete scientific knowledge and limited data described above clearly constrain the ability to understand or model a system, leading to under-optimized management and investment in water resources. However, even if one has nearly complete, accurate data and fully understands the mechanics underlying a given system, it may not be possible to perfectly model the system. For example, while the climate has been extensively researched and is well understood in many respects, it is still not well modeled due to the complexities arising from the inherent randomness. Even with a solid understanding of physical processes affecting a hydrologic system, we will not be able to model it perfectly. In hydraulic and hydrologic modeling, many uncertainties arise because simplified or idealized models and equations are necessary to describe flow conditions (Mays, 1996). Additionally, the lack of homogeneity within systems results in highly uncertain parameters. As an example, consider a hydrologist's choice of a representative soil type to model infiltration and runoff. While the selection might be accurate for much of the system, the soil will not be uniform throughout the system, causing some degree of error in the modeling. Ultimately, due to the interconnected nature of natural and physicals systems, it is fundamentally impossible to capture and model every element affecting a system; uncertainty is inevitable (NRC, 2000). Additionally, even if one could somehow develop a universal theory to capture all known processes, there is not sufficient computational ability to translate this into practical information. Walker et al., (2003) classify uncertainty as ranging in degree across four levels (Figure 2).

Figure 2 Levels of uncertainty ranging between determinism and total ignorance Source: Walker et al., (2003) Statistical and scenario uncertainty both essentially describe levels of incomplete knowledge, differing in the way in which the uncertainty can legitimately be expressed. "Recognized ignorance" in the authors' model can be viewed as the uncertainty due to the inherent natural variability in the system. Their fourth level of uncertainty, however, covers an important concept not yet addressed here. Beyond the level of unexplainable indeterminacy is where we find ourselves in total ignorance, unable to even know what uncertainty exists (these are unknown unknowns). While it is important to be aware of the existence of such factors, their absolute uncertainty leads us to almost always leave them out of any analysis. The evolving discussion of tipping points in the climate system and possible impacts on water systems can be viewed as

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transitioning through these stages - from total ignorance, to the recognition of the possible tipping points, and recently moving toward designing ways of measuring possible sensitivities given the uncertainties (Lenton et al., 2008). The use of expert elicitation to rank these uncertainties suggests the degree of difficult and uncertainty in modeling such tipping points. Natural variability and knowledge uncertainty can critically affect the way decision-makers and managers approach water resources. Uncertainties in human and natural systems mean that activities or actions can lead to beneficial or negative outcomes. In order to better understand how uncertainties affect decisions, it is necessary to explore the concept of risk and the role of uncertainties in shaping risks. Defining risk and understanding risk perception Just as uncertainty is defined differently within different fields, the concept of risk can also vary significantly based on the field and context. The following section provides a brief review of a few relevant definitions of risks and some key concepts, particularly regarding the role of uncertainty in understanding risk. The material extends beyond the water sector in order to situate later sections in the context of broader risk principles. Definitions Economist Frank Knight's work on uncertainty and risk in economics has remained highly influential in shaping how some agencies and practitioners view risk. In this conception, risk refers to a situation in which the probability of futures outcomes is measurable (Knight, 1921). This is in contrast to uncertainty, which occurs when it is not possible to measure the likelihood of outcomes. Here, risk does not apply only in cases of possible negative outcomes. This does not reflect the connotation of risk widely held by the general public or generally used by water resources engineers, who often define risk as the probability of structural or performance failure (Mays, 1996). Thus, some agencies use Knight's definition of uncertainty, but define risk in terms of the probability of a negative outcome (Yoe, 1996; NOAA, 2009). The disaster risk management community has a similar, but slightly different definition of risk. The UN International Strategy for Disaster Reduction provides a list of terminology definitions that are intended to reflect the current understanding and practical use in the field. They make the connection between probability and possible negative outcomes explicit by defining risk as "the combination of the probability of an event and its negative consequences" (UNISDR, 2009). Thus, risk increases if either the likelihood of the event increases or the event's negative consequences increase (or both). So, a flood risk increases if either nonstationarity leads to increased average flood frequency or land use changes result in more people being susceptible to flood events. Importantly, they also highlight that risks are perceived and defined differently, specifically "in popular usage the emphasis is usually placed on the concept of chance or possibility, such as in 'the risk of an accident'; whereas in technical settings the emphasis is usually placed on the consequences, in terms of 'potential losses' for some particular cause, place and period" (UNISDR, 2009).

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The definition of risk from UNDP's Bureau for Crisis Prevention and Recovery follows the latter emphasis and offers a heuristic formula. They suggest that risk is "conventionally expressed by the equation: Risk = Hazard x Vulnerability" (UNDP, 2004 p. 136). The definition clarifies that uncertainty is considered not in terms of the probability of the hazard, but rather "probability of harmful consequences . . . resulting from interactions between natural or human induced hazards and vulnerable conditions" (UNDP, 2004 p. 136). Here, vulnerability is essentially the likelihood that an event will cause harm based on the social, political, economic and physical conditions of the population or system experiencing the hazard. Thus, the uncertain and probabilistic nature of the risk is bundled into the way in which the event is experienced. A community may become more vulnerable to drought if policies decrease their resilience and increase exposure to drought consequences. In their comprehensive report on understanding risk for decision-making, the U.S. National Research Council noted that risk definitions generally include 1) identification of what could be harmed or lost; 2) determining the hazard that could cause the loss; and 3) making a judgment about the likelihood of the event occurring (NRC, 1996). This is very similar to the definition offered by the UNISDR. However, this description highlights the human element involved in conceiving what constitutes a risk, as discussed in the following section. Perception of risk While developing a common definition (or definitions) for risk is quite valuable, it is essential to understand that risk and its various components are highly shaped by individual and social perceptions. Psychologists, political scientists, sociologists and economists have constructed theories to explain how characteristics at the individual, group or social (culture) level affect how risk is understood and experienced. These characteristics might be based on economic, cognitive or social structure influences. At the individual level, studies have shown that the perception of risk from the same hazard can vary significantly between individuals. Factors that might affect risk perception include the degree to which exposure was voluntary, whether issues of fairness were involved and an individual's sense of dread or fear from the event (Marris et al., 1997). This suggests that issues of environmental justice may play a role in risk perception in water resources management. If a community believes that it has been discriminated against regarding siting of a dam or development of water supply infrastructure, it may perceive associated risks as much higher. Additionally, while there is also a high degree of variability in risk perception based on how much knowledge individuals believe scientists have regarding the risk, the individual's own knowledge of the risk appears to have little effect (Marris et al., 1997; Wildavsky and Dake, 1990). At the level of social organization, some have argued that risk perception might also be significantly affected by cultural characteristics. These characteristics include values, social structure and the level of trust or bonding experienced by members of social groups (Rayner, 1992; Tansey and O'Riordan, 1999). While somewhat contested, the concept of the possible cultural influences on risk perception strengthens the argument that risk is understood as more than only hazards and consequences.

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These theories and others have formed the basis for research regarding the social amplification of risk. The theory underlying the social amplification of risk is that the experience of risk results from the social interpretation of physical hazards and can be significantly shaped by how risks are communicated (Kasperson et al., 2003). This can be seen in the way that water quality risks are communicated, with communities often experiencing fears of heightened risk based on regulators' descriptions of safe pollutant levels (Kasperson et al., 1988). Such studies have been further supported by findings of differing levels of risk perception based on the degree to which stakeholders were engaged in water management planning (Baggett et al., 2006). Risk, uncertainty and water resources Uncertainty plays a critical role in conceptions of risk. The nature of risk requires some degree of uncertainty; an event is no longer defined as a risk if it is guaranteed either to happen or not to happen. Whether the emphasis is on the probability of the hazard occurring or the likelihood of it causing harm, uncertainty is the underlying factor. While this difference in emphasis might significantly affect the way the risk is addressed (i.e., whether hazard possibility is minimized or community vulnerability is mitigated), any effort to reduce the risk will face the various elements of uncertainty described above. We can loosely define a category of risks that concern human interactions with water. These "water-related" risks relate to water use and management, but also the impact of water-related extreme events such as floods, droughts, and landslides. There are many water-related risks, each with their own associated uncertainties. Some of the key risks associated with water use and management are 1) water scarcity, 2) water quality degradation and pollution, 3) loss of water-related ecosystem services, and 4) extreme events. These risks each fit into the conception of risk that includes a hazard (event or conditions with negative impacts) and human vulnerability. The water-related risks are largely shaped by human-caused pressures on water systems. As human society progresses, these pressures are evolving and changing, becoming the primary drivers of the changes in water resources that determine the degree to which the above risks manifest. The uncertainty in the current and future conditions of these drivers compounds the uncertainty arising from their impact on the water-related risks. As described in the 3rd edition of the World Water Development Report, the drivers can be broken down into the following general categories: 1) demographic, social and economic; 2) technological innovation; 3) policy, law and finance; and 4) climate change (WWAP, 2009). The following section explores a sample of some critical elements related to the four water-related risks and the role of external drivers in shaping these risks. Key water-related risks The relationships between the water-related risks and water use and management are complex. Water use and management affects both the likelihood of occurrence for underlying events or conditions while also impacting the vulnerability shaping each risk in some way. Additionally, as decisions and actions influence the external drivers, the

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resulting changes (whether positive or negative) also significantly impact water-related risks. Rather than explore each of these connections, this section provides illustrative examples for each risk and a brief discussion of how external drivers affect the risks and associated uncertainties. We offer a somewhat deeper treatment of the drivers in the first risk, water scarcity, with the understanding that similar concepts apply for all four water-related risks. Water scarcity The amount of freshwater available for use depends on both the supply and the demand or rate of use. When water availability is insufficient because of changes in the supply, demand or both, the resulting scarcity can create a range of significant negative impacts. It is projected that by 2025, 1.8 billion people will face absolute water scarcity and fully two-thirds of the global population could be under water stress (UN Water, 2006). Possible water scarcity consequences include shortfalls in municipal and industrial supply; decreased availability of hydropower; reductions in irrigated or rainfed agriculture; and possibly even out migration or conflict. The probability of water scarcity occurring can be affected by natural system conditions (e.g., arid climates and areas with limited groundwater) and variability in precipitation and evapotranspiration. Natural patterns, climate variability across multiple time scales (e.g., interannual and decadal), and limited information on water availability can be sources of significant uncertainty in water scarcity risks (WWAP, 2009). However, human-caused drivers of change also significantly shape water scarcity risks, both in terms of the probability of occurrence and human vulnerability to the effects. Increase in water demand has actually exceeded the increase in population growth globally, leading to significant growth in per capita water use (Figure 3). Much of this increase in per capita demand may be attributable to changes in lifestyle and consumption patterns due to social and economic transitions, particularly in poor countries undergoing rapid economic growth. The resulting water scarcity is already sizable and growing, as shown in Figure 4. While many trends are likely to exacerbate water scarcity, some of the economic trends might actually reduce water scarcity risk. For example, increased international trade has lead to net global water savings due to virtual water, the importation of water-intensive goods and services by water-scarce countries from water-abundant countries (Hoekstra and Chapagain, 2008).

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Figure 3 Maps showing the relative impact of climate change and population growth on changes in demand. Maps of change in water reuse index (∑DIA/Q) predicted under Sc1 (climate change alone), Sc2 (population and economic development only), and Sc3 (both effects). Changes in the ratio of scenario-specific ∑DIA/Q (∑DIA/Qscenario) relative to contemporary (∑DIA/Qbase) conditions are shown. A threshold of +/-20% is used to highlight areas of substantial change. Source: Vörösmarty et al., (2000)

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Figure 4 Map of water scarcity by country Source: WWAP (2009) Various policies and technological innovations might also either contribute to or mitigate water scarcity risks. This includes direct innovations, such as water conservation technologies, and policies directly related to water management, such as water supply regulation. Additionally, some technological innovation not directly related to the use or management of water resources can have a significant impact on water scarcity risk (both hazard occurrence and vulnerability). Examples discussed in the 3rd edition of the World Water Development Report include bioenergy, which could increase water scarcity by diverting water resources to crops used to produce energy, and genetically-modified strains of drought-resistant crops, which have already lead to reduced vulnerability to climate variability and water scarcity (WWAP, 2009). Similarly, policies not directly related to water resources can play a role in scarcity risks. In one well known case, groundwater extraction in parts of rural India is highly unsustainable because farmers have been provided free or highly subsidized electricity to pump the water for irrigation (Shah et al., 2006). Climate change will most likely alter patterns of climate variability, for example by shifting the probability distribution toward drier average conditions, increasing the likelihood of extremes, or both. There may also be shifts or changes in seasons and timing of streamflow (e.g. snow-dominated basins may have earlier peaks). The IPCC's technical report on water projects that climate change will result in both increased drought events over many areas and reduced water availability through decreased precipitation and increased water pollution by 2050 (IPCC, 2008). While some regions may experience increased water availability, the report concludes that the negative impacts on freshwater systems will outweigh the benefits.

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Water quality degradation and pollution The amount of water available for human use and consumption also depends on the quality of the available water. Even if water can be treated for consumption, poor water quality can increase the cost of treatment, making it unavailable for users who cannot afford the increased costs. In addition to reducing water availability, poor water quality can be a hazard for human health and affected ecosystems. Water quality can be compromised in various ways through both natural processes and human activities. For example, an extremely wet climate event can significantly increase the turbidity of mountain-fed surface water. A public drinking water supplier relying on this source may be forced to perform an expensive switch to groundwater sources until the turbidity level drops below a safe threshold. Additionally, human activities can lead to contaminants such as microbial pathogens, oxygen-consuming materials, heavy metals, pesticides and suspended sediments (WWAP, 2009). Contaminants can complicate the water quality impacts caused by natural factors, including variability in precipitation and temperature, which can affect ecosystem tolerance thresholds and result in degraded water quality (Murdoch et al., 2000). There are also many technologies that have been developed to reduce both point and non-point sources of a wide range of pollutants of surface waters (Carpenter et al., 1998). Additionally, water rights and licences can limit pollution of surface waters and groundwater. While technological innovations, policies and management practices such as these are sometimes aimed specifically at reducing water pollution, some activities increase the likelihood of water quality deterioration, including siltation, sediment loads due to river regulation, and wastewater releases from combined sewage and storm runoff systems (WWAP, 2009; Miller and Yates, 2005). The probability of some of these occurrences, and thus water quality risks are likely to increase due to impacts of climate change (IPCC, 2008). The increased probability of pollution hazards are accompanied by increased vulnerability due to demographic and socioeconomic trends. As the population increases, water needs rise and poorer communities are forced to rely on more deteriorated water sources, vulnerability grows. Loss of ecosystem services Freshwater ecosystems provide a wide range of services that support the health and survival of other natural and human systems. These can be broken down into categories such as provisioning services, regulating services, supporting services and cultural services, and include nutrient cycling, regulation of water balance, and erosion regulation, among others (MEA, 2005). When natural events or human actions modify ecosystems, some of these services can be negatively affected or lost entirely. Water management, for example, can reduce the flow of water discharging into oceans and flowing through rivers, significantly affecting erosion control, water purification, and nutrient cycling. Agriculture represents both a consumptive use and an ecosystem service. In this case, the hazard of the deteriorating ecosystem service is driven by both its own overexploitation and the increased demands for other water uses. (CAWMA, 2007). Additionally, the construction practices and land use changes associated with the

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current urbanization trends can lead to destruction of ecosystems, leading to loss of services (WWAP, 2009). Climate change may also significantly impact ecosystem services given the degree to which ecosystem health is regulated by climate factors such as temperature and precipitation. There remain significant gaps in our understanding of the full range of ecosystem services and uncertainty regarding the possible thresholds and ecosystem responses to different sources of variability and change (Schröter et al., 2005). Thus, while knowledge is increasing, we remain largely ignorant of both the probability of losing given ecosystems (and associated services) and our vulnerability to the possible consequences. Water-related extreme events Extreme water-related climate events, or hydrometeorological disasters, occur on both ends of the spectrum; too much water over a given period can result in flooding and surges, while too little water over time can lead to dangerously low river flows and drought. Climate and water-related natural disasters result in the greatest damage in terms of both humans killed or affected and economic loss (Adikari et al., 2008). Water management policies, infrastructure and technologies are often directly aimed at reducing or mitigating the impacts of these hydrometeorological disasters. Many reservoirs function as water storage to both avoid shortfalls (including during droughts, if possible) and reducing flooding events. Additionally, policies and financing can be combined to explicitly address related risks, such as the development of index insurance plans to help reservoir system users meet their needs in the case of drought (Brown and Carriquiry, 2007). However, diverting river flow can also result in increased drought or flood conditions for some downstream communities. For example, the filling of the Ataturk dam by the Turks in 1990 is notoriously famous for heightened conflict; both Syria and Iraq accused Turkey of failing to notify them of their plans, which resulted in significant downstream damage (Block and Strzepek, 2009). Additionally, failures of water management infrastructure such as dams and levies can leave systems more vulnerable to climate events, leading to more hydrometeorological disasters than would have otherwise occurred. While direct human-based factors can appreciably mitigate or exacerbate vulnerability to these events, climate variability and change also remain a critical driver, with climate change expected to increase flooding and drought probabilities over many areas (IPCC, 2008). In addition to the above factors that largely affect the likelihood of extremes occurring, external drivers are also shaping vulnerability to the hydrometeorological extremes. The increasing population combined with urbanization trends, policies and land use changes (e.g., poor communities moving into more environmentally degraded areas) are leading to increased vulnerability to water-related disasters. While technological innovation may improve the ability to mitigate or cope with disaster outcomes, many drivers are leading to increasing the number of people exposed to extremes, with a bias toward people with lower capacity to survive or recover from such disasters.

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Water-related risks: uncertainty, thresholds and irreversibility As already suggested, the uncertainties associated with the above water-related risks are considerable. There is a significant paucity of data for all these risks as well as their drivers. This lack of data constrains knowledge of existing conditions as well as the ability make projections of future changes. There are also many limitations on the ability to model the indirect drivers, such as population growth (see Figure 5) and technological innovation. Without this data the ability to manage the risks and make appropriate decisions is often severely limited. Political systems and social change lead to a constantly evolving landscape in which decisions are made. The addition of uncertainty due to limited information (from scarce data, incomplete communication to decision-makers, or ineffective tools to integrate the data into decision-making practices, for example) makes the challenges even greater. There remains great uncertainty about the effectiveness of tools and policies designed to mitigate the water-related risks, particularly given nonstationary conditions.

Figure 5 Projections of demographic trends through 2050 Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2007).

In addition to these factors, the possible existence of thresholds and irreversible consequences can dramatically increase both the risk and risk perception. If a system has a threshold limit (or tipping point) beyond which a hazard's impact increases nonlinearly (or just at a faster rate), this would clearly have a significant affect on the

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risk. Whether the focus is on hazard probability or vulnerability, the rapidly worsening consequence would increase the risk. Levies, for example, protect against flooding up to a certain level, but can fail and lead to a catastrophe if they are breached. The risk remains minimal until a certain threshold is reached, and then it grows dramatically. There is often uncertainty regarding where thresholds actually exist within a system, leading to large possible shifts in the risk distribution based on a system's sensitivity (Brugnach et al., 2003). Physical systems, in particular, might have thresholds (that are unknown or about which there is great uncertainty) that affect hazard probabilities, most often leading to increasing hazard likelihood. There is increasing concern about such thresholds in the global climate system and for many ecosystems (Keller et al., 2008). For some systems, there is a possibility that a given negative impact could be irreversible, at least on very long time scales. Examples include ecosystem loss and unsustainable groundwater extraction from "fossil aquifers" or those with very slow recharge. Irreversibilities are also often tied to considerable uncertainty, which results in great barriers to modeling and understanding the possible risks (Nachtnebel, 2002; Pindyck, 2007). The ominous nature of such irreversible outcomes may significantly shape both the actual risk and perception of the risk. The threat of these possibilities was one of the drivers of the development of the Precautionary Principle, which states that, "when human activities may lead to morally unacceptable harm that is scientifically plausible but uncertain, actions shall be taken to avoid or diminish that harm" (UNESCO, 2005). The intent of such a principle is to justify action to reduce certain possible negative consequences, regardless of how small that possibility might be. Here, the emphasis would be on the vulnerability or degree of harm (i.e., whether it is "morally unacceptable") rather than hazard likelihood. Related to the precautionary principle, are "no regrets" strategies. This is a principle strategy for managing climatic uncertainties which emphasizes actions that can be taken to provide immediate benefits, regardless of climate change, for example. In a case study in South Africa, Callaway et al., (2008) show how the implementation of water markets is a no regrets strategy, providing substantial returns no matter how the climate changes. In other words, the benefits of this project were very flexible and resilient to climate change. The sections above have described a sample of key water-related risks and discussed the role of drivers in shaping the current and future conditions affecting these risks. In order for water resources professionals to make decisions and manage the risks given the relevant uncertainties, they must be able to assess the risks in a coherent and structured manner. The following section explores ways of performing such assessments by reviewing the evolution of risk analysis methodology, with a specific focus on the techniques for addressing nonstationarity. A review of risk analysis methodology Methods of assessing and analyzing risk have existed for millennia. Some of the earliest documented risk assessment practices come from the Asipu in the Tigres-Eupphrates region around 3200BC. When residents of the area consulted the Asipu to help them make risky decisions, the Asipu would "identify the important dimensions of the problem,

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identify alternative actions, and collect data on the likely outcomes . . . of each alternative" (Covell and Mumpower, 2001 p.3). These elements essentially remained the primary components of assessing risk, whether formally or informally, until more quantitative methods evolved. Most histories of risk analysis trace the field to "its twin roots in mathematical theories of probability, and scientific methods for identifying causal links between adverse health effects and different types of hazardous activities" (Covell and Mumpower, 2001 p.7). Around the same time that Blaise Pascal was formalizing theories of probability, a Londoner named John Graunt was implementing some of the earliest recorded attempts to calculate empirical probabilities for births and deaths, ultimately evolving into demography. Some of key later advances in conceptualizing risk and theorizing how to analyze and manage it arose in economics. Frank Knight, John Maynard Keynes and Kenneth Arrow were among the leading economists working in risk in the early 1900s. Much of the work of the period focused on applying concepts in game theory, a branch of applied mathematics that seeks to capture strategic behaviour mathematically, to risk and economic decision-making. A fundamental principle of game theory is that "the true uncertainty lies in the intentions of others" (Berstein, 1998 p.232). This appears to somewhat contrast the conceptions of uncertainty previously discussed in this paper. While not generally applicable to some aspects of natural sciences, game theory offers some useful tools and approaches in the water resources context, particularly regarding the management of risk. Applications have included water allocation analysis (Young et al., 1980; Tijs and Driessen, 1986), institutional dynamics (Ostrom, 1990; Bardhan, 1993), and water pricing mechanisms (Johansson et al., 2002). Through the middle of the 20th century, risk analysis continued to develop and become increasingly quantitative. Scientists and risk professionals improved the ability to identify and measure risk and developed more formal techniques for quantitative risk analysis (QRA), which essentially entails assigning some numerical value to the risk analysis output (e.g., a probability, frequency estimate, etc.) (Covell and Mumpower, 2001). Steps in a classical approach that relies on using "best estimates" of uncertainty for collection of probabilistic risks (risk indices) include, 1) identify suitable risk indices; 2) develop a model of the activity or system being analyzed; 3) link with more detailed elements of the system and the overall risk indices; 4) estimate unknown parameters of the model; and 5) use the model to generate estimates of the risk indices" (Aven 2003, p.15). Such analyses have progressed and can be combined with some method of sensitivity analysis, reliability analysis or quantitative acceptance analysis, among others (Aven, 2003). Water resources management, particularly reservoir operations, relies heavily on integrating these elements of risk analysis into decision-making (e.g. yield-reliability curves, operating rule curves, thresholds, etc.). In the late 1960s and 1970s, growing public concern over the risks from technology to health and the environment (e.g. toxic chemicals and nuclear energy) combined with disasters in the aerospace and nuclear industries lead to a movement toward broad acceptance of probabilistic risk assessments, a form of QRA emphasizing probabilistic techniques to assess risks for decision-making (Bedford and Cooke, 2001). As various groups within the US federal government and internationally began applying such probabilistic approaches to safety and other risk assessments, the US National Academy

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of Sciences sought to standardize risk analysis methodology domestically with the 1983 release of the Risk Assessment in the Federal Government: Managing the Process. This publication, known as the "Red Book", views risk analysis as science-driven and comprising three distinct elements: research, risk assessment and risk management (NRC, 1983). The "Red Book" became a seminal work and largely shaped the approach to risk analysis both domestically and internationally, with the European Commission, in particular, emphasizing the separation of risk characterization from risk management (Lofstedt, 2003). The "Red Book" emphasis on the sole science-driven nature of risk analysis began to be questioned as it was increasingly recognized that the approach largely neglects stakeholders and the role of political, social and economic influences. International agencies such as the FAO and WHO replaced the focus on research with an emphasis on risk communication (CAC, 1997). There was increasing focus on the “democratization” of the risk analysis process, and the US and others moved to a more "analytic-deliberative" approach involving deliberations to allow those affected by the risk to help determine how to address uncertainties (NRC, 1996; Renn, 1999). This shift toward more holistic approaches to managing risk that integrate the perspectives of involved stakeholders can also be seen in the movement toward integrated water resources management (IWRM). Approaches such as the World Water Vision recognize the importance of stakeholder engagement, and have sought to encourage collaboration between stakeholders and water professionals (Cosgrove and Rijsberman, 2000). Risk analysis approaches and tools to address nonstationarity As discussed, the last few decades have witnessed increasing recognition of nonstationarities in many natural systems (largely resulting from the drivers discussed above), particularly in the context of ecosystems and the global climate system. Water resources professionals, among many others, have begun to question the traditional approaches to quantifying and analyzing risk based on historical precedents or known properties of a given system. The result has been an increasing array of techniques to analyze, assess and then manage the possible risks arising from system nonstationarity. The majority of these approaches have developed recently to address the consequences of global climate change. On the technical side of risk analysis, many have sought to move away from traditional stochastic approaches (random element-driven probabilities) to better quantify and assess the uncertainties posed by a nonstationary system through different techniques of uncertainty analysis. These include importance sampling, which weights variables based on the impact of the parameter being estimated (Lu and Zhang, 2003); fuzzy reasoning, which allows the model to use variables based on imprecise information (Bender, 2002; Ganoulis, 2004; Elshayeb, 2005); and Bayesian methods, which allow uncertainties to be reduced through updating (Wood and Rodríguez-Iturbe, 1975; Freer et al., 1996; Hobbs, 1997). Bier et al. (1999) provide a brief examination of probabilistic methods specifically designed to assess the likelihood of extreme events, such as floods, under both stationary and nonstationary conditions. However, some have argued that a stochastic approach can be used under nonstationary conditions such as climate

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change, provided it is scaled to reproduce the observed and expected trends (Koutsoyiannis, 2005). These techniques represent approaches based on trying to predict the future largely through quantifying and characterizing uncertainties. This contrasts a different set of approaches that explicitly recognizes the irreducible nature of some uncertainties and attempts to make human and other systems more robust in the face of a large range of possible outcomes (rather than focusing on identifying or quantifying that range). One such approach emphasizes learning from the past and building resilience to possible change (Dessai and van der Sluijs, 2007). This partly stems from adaptive management practices and relies on addressing uncertainty as it arises based on increased capacity to handle change. There are many options along a spectrum of uncertainty quantification, each of which can shape both the decision-making process and the techniques used to assess risks. Even when uncertainty is quite significant and leads to exceptional difficulty in predicting hazard probabilities in nonstationary systems, there are various methods that offer some level of quantitative analysis. The IPCC and others use multiple climate models to produce many simulations and create ensemble projections that convey uncertainties in the model outcomes (IPCC, 2007). These can then be used to create probability distributions of possible climate futures. Jones (2001) combines the probabilities of certain climate outcomes with stakeholder-defined thresholds for the resulting impacts. There have also been significant efforts devoted to scenario analysis for both climate change generally, and water resources specifically. Scenario analysis addresses uncertainty due to the primary drivers discussed above by assessing the risks and trade-offs that arise based on different development, investment and demographic paths (see IPCC, 2007; Alcamo et al., 2000; Liu et al., 2004). There are a number of approaches that rely less on the quantification of uncertainties. These may be particularly valuable for cases in which uncertainty is essentially unknowable or unquantifiable. For example, robustness analysis assumes uncertainties cannot be quantified using probabilities and shows where each policy option succeeds or fails within an entire "uncertainty space"; a robust option is satisfactory over the entire space (Dessai and van der Sluijs, 2007 p.44). There is thus a trade-off between optimality and robustness. Another option is diversification or portfolio theory, in which investments are made in a wide variety of possible policy solutions that diversify the risks, reducing the overall risk of the total portfolio of options (Aerts et al., 2008). Finally, the "no-regrets" approach mentioned earlier emphasizes accepting uncertainty and making decisions that will lead to positive (or at least not harmful) outcomes regardless of the changes in risk distribution (Heltberg et al., 2009). There is increasing interest in such approaches, particularly given the growing uncertainties in predicting the future of drivers and the resulting impacts on hydrologic systems. Table 1 below provides a list of some of the key frameworks for decision-making under uncertainty and the associated uncertainties they address.

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Table 1 A qualitative indication of how well each of the Frameworks for decision-making under uncertainty and each of the uncertainty assessment methods deals with each of the three uncertainty levels. ++ very good; + good +- somewhat; - bad; -- very bad Source: Dessai and van der Sluijs (2007), p.60 Ultimately, these approaches are applied within the context of decision-making to determine how best to manage water resources through planning, operations and investment. Critical financial and temporal trade-offs shape the decisions that must be made to address risks. The temporal trade-offs are largely determined by planning horizons. Water managers and decision makers must consider what operations, policy and investment solutions are appropriate partly based on the planning horizon, with the understanding that sources of uncertainty are associated with different time scales (Lu, 2009). At the relatively short time scale, managers of reservoir systems often explicitly address future uncertainties and risk by applying hedging rules in which some degree of current water shortage is accepted in order to avoid more severe shortages in future

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months under certain conditions. Such hedging rules for reservoir operations are typically developed early in the life of a reservoir and then maintained. However, given that previously discussed drivers can lead to nonstationary systems, these rules can require updating (e.g., through optimization or modeling) to reflect changing conditions such as increased demand or decreased precipitation (Tu et al., 2008). Water resources professionals often employ cost-benefit analysis techniques to manage key risks. Such techniques often integrate probabilistic outcomes and scenarios with their associated costs and benefits to determine a ratio that can support decision-making. For example, Block and Brown (2008) use future climate trends to drive a coupled hydrology-hydropower optimization model to determine projected cost-benefit ratios over 50 years. Importantly, they also address uncertainty in other drivers by evaluating the ratios' responses to varying economic and policy conditions. Cost-benefit approaches are often applied across multiple water-related risks to determine whether the quantified probabilities and vulnerabilities associated with a hazard are sufficient to justify proposed investments (See Li et al., 2009 for a review of more technical stochastic approaches to decision-making). As noted above, one effective way to address some types of uncertainty is to develop scenarios reflecting different possible futures (e.g. based on climate, investment, socioeconomic, and/or demographic trends). These scenarios can then be integrated into decision-making tools using a wide variety of approaches. In addition to using them as an input to cost-benefit analysis, scenarios can also be used to evaluate the robustness of policies or decisions. For example, while a water quality management policy would be optimality-robust if it remains nearly optimal for all scenarios, it would be deemed feasibility-robust if it essentially remains feasible for all scenarios (Watkins and McKinney, 1997). Decision makers and stakeholders can then assess relevant trade-offs and determine which type of robustness is most critical for the given context. Water resources managers and decision makers most often face multiple hazards and varying associated probabilities, uncertainties and levels of vulnerability. While the likelihood of a given hazard might be quite low, the existence of multiple low-probability hazards can increase overall risk for a system and community. Actions, policies and investments that are able to reduce risks across several hazards can significantly improve risk management efficiency. An illustrative example is found in multipurpose reservoir systems that reduce flood occurrence, provide storage for use under conditions of scarcity, and increase food and energy security. Concluding recommendations Water-related risks already capture a significant amount of attention and resources on the part of the public, the research community and decision-makers. There are a wide variety of conceptions of risk and ways of analysing, understanding, quantifying and managing them. While there is not a need for a universal definition of risk, it is important to provide a common understanding of what risk entails for water resources by outlining a set of key components based on the above discussion. The following framework should be used to provide a basic structure for defining risk and uncertainty for water resources in the context of the 4th World Water Development Report.

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Risk comprises characterization of 1) hazard occurrence and likelihood, and 2) vulnerability to the consequences of the hazard. Hazard is defined here as an event or condition with a negative impact on human systems. Vulnerability is essentially the likelihood that a hazard will cause harm based on the social, political, economic and physical conditions of the population or system experiencing the hazard. Uncertainty arises in both predicting a hazard occurrence and knowing the vulnerability to the hazard. The uncertainties are most often characterized by assigning probabilities; risk increases if 1) the probability of the hazard increases, 2) the probability and/or magnitude of a hazard's consequences increase, 3) or both. If nonstationary conditions make it difficult or impossible to quantify uncertainties and assign probabilities, techniques that characterize possible outcomes in alternative ways can be used. In order to understand how to manage water-related risks, it is necessary to characterize and assess the risks. Below are critical components of such an assessment: Identify the possible hazard. Identifying the hazard requires knowledge of what events or conditions are possible and the impact they might have on individuals, communities or systems. The emphasis here is not yet on the degree of vulnerability, but rather just a determination of what hazards might affect the community (essentially, "what's relevant and what matters"). This requires the input of stakeholders that might be impacted or have been impacted in the past and the recognition that hazards can be shaped by perception. At this stage, there is great uncertainty regarding what should actually be considered a possible hazard. While an event, such as a flood or drought of a certain severity, may have never occurred over the historical record, a future occurrence is not impossible. It is necessary to understand the limited nature of the available data, the degree to which a system is stationary, and the possible role of external drivers in leading to previously unknown hazards. Determine the probability of hazard occurrence (to the extent possible). As described above, this characterization can be quite challenging and can take many different forms. Following from the previous step, two interrelated considerations are essential in water resources: the possibility of nonstationarity and its impact on hazard occurrence; and the role of external drivers of change. At this stage, the assessment extends beyond merely determining whether the hazard is possible to the even more critical step of predicting hazard likelihood, possibly including characterizing gradations of hazard severity. For example, various degrees of water pollution might represent significantly different hazards and best be represented using distinct probability distributions. Unlike in the previous step, it is often crucial to quantify uncertainties to the extent possible and understand the role of both natural variability and knowledge uncertainties, particularly in the context of nonstationarity and possible irreversibility. If it is not possible to assign probabilities, it is still critical to characterize the hazard in a way that can be used for decision-making (e.g., by ranking possible occurrence against other events while clarifying the limitations and caveats of such an approach).

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Characterize vulnerability of human systems to the hazard. The full assessment of a risk requires analysis of how the hazard might affect a population or human system. Vulnerability exists along a spectrum and also exhibits a probabilistic nature. The social, economic and physical context shapes the likelihood that a given hazard will have certain consequences. Uncertainty at this stage arises not only from lack of knowledge regarding how systems will respond to the hazard, but also from great ambiguity in the role and future direction of external drivers in shaping the vulnerability. For example, assessing the vulnerability component of water scarcity risk requires characterizing both existing susceptibility to scarcity consequences and the likelihood of demographic changes increasing that susceptibility. Again, these can be represented as probabilities, but remembering that systems underlying vulnerabilities also exhibit nonstationarity. As with the identification of hazards, assessing vulnerability should include engaging affected stakeholders and recognition of the role of perception in defining hazard impacts and vulnerability to them. It is necessary to develop a robust understanding of risk that includes the above components in order to take action and make decisions to manage water-related risks. The uncertainties and probabilities associated with hazards and vulnerability should shape decisions regarding investments and operations. They help determine the appropriate questions to ask and provide information to answer them (e.g., Does preventing floods of a certain severity justify a proposed investment? Is the combination of the probability of its occurrence and the likely consequences sufficient to warrant the investment? Can a given investment or intervention decrease a set of multiple risks?). Understanding the risk-related gaps in knowledge can help develop investment priorities, determine the research agenda, and identify data needs. However, a comprehensive and nuanced understanding of the uncertainties underlying water-related risks should also help water managers and policy makers know when to accept a certain level of ignorance or ambiguity and focus on flexibly managing the risk to accommodate a wide spectrum of outcomes. Managers must also recognize what is and is not within their control; in the absence of any capacity to shape external drivers, water professionals must often focus on managing and mitigating their consequences for water-related risks. The techniques and methods described in the risk analysis methodology section can be applied to help formally assess and address these challenges. This paper has sought to provide a basic exploration of the key elements of risk and uncertainty for water resources along with an introduction to some approaches to risk analysis. Ultimately, in the context of the entire report, the above definition of risk and its components should help the contributors to the 4th World Water Development Report develop a common approach to exploring water-related risks and their management.

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References Adikari, Y., J. Yoshitani, N. Takemoto, and A. Chavoshian. 2008. Technical Report on the Trends of Global Water-related Disasters: a revised and updated version of 2005 report. Technical Note of PWRI No. 4088. PWRI. Aerts, J. C. J. H., W. Botzen, A. van der Veen, J. Krywkow, and S. Werners. 2008. Dealing with uncertainty in flood management through diversification. Ecology and Society 13(1): 41. Alcamo, J., T. Henrichs, T. Rösch. 2000. World Water in 2025: Global modeling and scenario analysis for the World Commission on Water for the 21st Century. Center for Environmental Systems Research, University of Kassel. van Aalst, M., M. Hellmuth, and D. Ponzi. 2007. Come Rain or Shine: Integrating Climate Risk Management into African Development Bank Group Operations. Tunis, Tunisia: African Development Bank.

Aven, T. 2003. Foundations of risk analysis: a knowledge and decision-oriented perspective. England: John Wiley & Sons. Bardhan, P. 1993. Analytics of the institutions of informal cooperation in rural development. World Development. Oxford, 21(4) p. 633-639. Baggett, S., P. Jeffrey and B. Jefferson. 2006. Risk perception in participatory planning for water reuse. Desalination, 187(1-3) p. 149-158. Bedford, T. and R. Cooke. 2001. Probabilistic Risk Analysis: Foundations and Methods. Cambridge, UK: Cambridge University Press. Bender, M.J. 2002. Fuzzy compromise approach to water resources systems planning under uncertainty. In Risk, Reliability, Uncertainty, and Robustness of Water resources Systems. eds. J.J Bogardi and Z.W. Kundzewicz. UNESCO International Hydrology Series. Cambridge, UK: Cambridge University Press. Berstein, P. L. 1998. Against the gods: the remarkable story of risk. John Wiley & Sons. Bier, V.M., Y.Y. Haimes, J.H. Lambert, N.C. Matalas, R. Zimmerman. 1999. A Survey of Approaches for Assessing and Managing the Risk of Extremes. Risk Analysis, 19(1) p. 83 – 94. Block, P. and C. Brown. 2009. Does Climate Matter? Evaluating the Effects of Climate Change on Future Ethiopian Hydropower, Planning for an uncertain future - Monitoring, integration, and adaption. Proceedings of the Third Interagency Conference on Research in the Watersheds, United States Geological Survey, R. Webb and D. Semmens, editors.

26

Block, P., and K. Strzepek. 2009. Economic Analysis of Large-scale Upstream River Basin Development on the Blue Nile in Ethiopia Considering Transient Conditions, Climate Variability, and Climate Change, Journal of Water Resources Planning and Management (in press). Brown, C., and M. Carriquiry. 2007. Managing hydroclimatological risk to water supply with option contracts and reservoir index insurance. Water Resour. Res. 43. W11423, doi:10.1029/2007WR006093. Brugnach, M., J. Bolte, G.A. Bradshaw. 2003. Determining the significance of threshold values uncertainty in rule-based classification models. Ecological Modelling. 160(1-2) p. 63-76. Callaway, J.M., D.B. Louw, and M.E. Hellmuth. 2008. Chapter 14: Benefits and Costs of Measures for Coping with Water and Climate Change: Berg River Basin, South Africa. In F. Ludwig, P. Kabat, H. van Schaik, and M. van der Valk.eds. Climate Change Adaptation in the Water Sector. London: Earthscan, p. 191-212. Carpenter S.R., N. F. Caraco, D. L. Correll, R. W. Howarth, A. N. Sharpley, V. H. Smith. 1998. Nonpoint Pollution of Surface Waters with Phosphorous and Nitrogen. Ecological Applications 8(3) p. 559-568. Codex Alimentarius Commission (CAC). 1997. Report of the Twenty-second Session of the Codex Alimentarius Commission, Geneva, 23-28 June 7 1997. (Available at: http://www.codexalimentarius.net/web/archives.jsp?year=97. ALINORM 97/37). 8 Appendix III: Definitions of risk analysis terms related to food safety. Cooke, R.M. 2009. A Brief History of Quantitative Risk Assessment. Resources 172. Washington, DC: Resources for the Future. Comprehensive Assessment of Water Management in Agriculture (CAWMA). 2007. Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture. London: Earthscan, and Colombo: International Water Management Institute. Cosgrove, W., and F. Rijsberman. 2000. World Water Vision: Making Water Everybody’s Business. London: Earthscan. Covello, V.T. and J. Mumpower. 2001. Risk Analysis and Risk Management: An Historical Perspective. In Environmental risk planning and management, eds. S. Gerrard, R. K. Turner, I. Bateman. UK: Edward Elgar Publishing Limited. Dessai, S. and J.P. van der Sluijs. 2007. Uncertainty and Climate Change Adaptation - a Scoping Study. Report NWS-E-2007-198, Department of Science Technology and Society, Copernicus Institute. Utrecht University.

27

Elshayeb Y. 2005. Overcoming uncertainties in risk analysis: trade-offs among methods of uncertainty analysis. In Comparative Risk Assessment and Environmental Decision Making, eds. I. Linkov and A.B. Ramadan. NATO Science Series 38. Netherlands: Springer. Folke, C., S. Carpenter, T. Elmqvist, L. Gunderson, C.S. Holling, et al. 2002. Resilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations. Scientific Background Paper on Resilience for the process of The World Summit on Sustainable Development on behalf of The Environmental Advisory Council to the Swedish Government. Frederick, K.D., D.C. Major, and E.Z. Stakhiv. 1997. Water resources planning principles and evaluation criteria for climate change: summary and conclusions. Climatic Change. 37. Netherlands: Kluwer. p.291-313. Freer, J., K. Beven, and B. Ambroise. 1996. Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach. Water Resources Research 32(7) p.2161–2174. Funtowicz, S.O. and J.R. Ravetz. 1990. Uncertainty and Quality in Science for Policy. Dordrecht, Netherlands: Kluwer. Ganoulis, J. 2004. Integrated risk analysis for sustainable water resources management. In Comparative Risk Assessment and Environmental Decision Making, eds. I. Linkov and A.B. Ramadan. NATO Science Series 38. Netherlands: Springer. Hathaway, T. 2008. What cost Ethiopia's dam boom? California, USA: International Rivers Network. Heltberg, R., P. B. Siegel, and S.L. Jorgensen. 2009. Addressing human vulnerability to climate change: Toward a ‘no-regrets’ approach. Global Environmental Change. 19(1) p. 89-99. Hobbs, B.F. 1997. Bayesian Methods for Analysing Climate Change and Water Resource Uncertainties. Journal of Environmental Management. 49(1) p. 53-72. Hoekstra, A. Y., and A. L. Chapagain. 2008. Globalization of Water: Sharing the Planet’s Freshwater Resources. Oxford: Blackwell Publishing. Interagency Advisory Committee on Water Data (IACWD). 1981. Guidelines for Determining Flood Flow Frequency. Hydrology Subcommittee. Bulletin #17B (Revised and corrected). Reston, Vir: U.S. Geological Survey. Intergovernmental Panel on Climate Change (IPCC). 2008. Technical Paper on Climate Change and Water. IPCCXXVIII/Doc.13, Intergovernmental Panel on Climate Change, Geneva. www.ipcc. ch/meetings/session28/doc13.pdf. Intergovernmental Panel on Climate Change (IPCC). 2008. Climate Change 2007:

28

Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: Intergovernmental Panel on Climate Change. Johansson R.C., Y. Tsur, T.L. Roe, R. Doukkali, A. Dinar. 2002. Pricing irrigation water: a review of theory and practice. Water Policy. 4(2) p. 173-199. Kasperson, R. E., O. Renn, P. Slovic, H. Brown, J. Emel, R. Goble, J.X. Kasperson, and S. Ratick. 1988. The Social Amplification of Risk: A Conceptual Framwork. Risk Analysis 8(2) p. 177-187. Kasperson J.X. R. E. Kasperson, N. Pidgeon, P. Slovic. 2003. The social amplification of risk: assessing fifteen years of research and theory. In The social amplification of risk. Eds. N. Pidgeon, R.E. Kasperson, P. Slovic. Cambridge, UK: Cambridge University Press. Keller, K., G. Yohe, M. Schlesinger. 2008. Managing the risks of climate thresholds: uncertainties and information needs. Climatic Change. 91(1-2). Knight, F.H. 1921. Risk, Uncertainty, and Profit. Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Company. Koutsoyiannis, D. 2006. Nonstationarity versus scaling in hydrology. Journal of Hydrology. 324 p. 239–254. Lenton, T.M., H. Held, E. Kriegler, J.W. Hall, W. Lucht. et al.Tipping elements in the Earth's climate system. PNAS 105(6) p. 1786-1793. Li., Y.P., G.H. Huang, and S.L. Nie. 2009. Water Resources Management and Planning under Uncertainty: an Inexact Multistage Joint-Probabilistic Programming Method. Water Resources Management. 23 p. 2515-2538. Liu, A., T. Sty, and J.A. Goodrich. 2000. Land use as a mitigation strategy for the water-quality impacts of global warming: a scenario analysis on two watersheds in the Ohio River Basin. Environmental Engineering and Policy. 2(2) p. 65-76. Lofstedt, R. E. 2003. A European Perspective on the NRC "Red Book," Risk Assessment in the Federal Government: Managing the Process. Human and ecological risk assessment 9(5) p. 1327-1335. Lu, Z. and D. Zhang. 2003. On importance sampling Monte Carlo approach to uncertainty analysis for flow and transport in porous media. Advances in water resources. 26(11) p. 1177-1188. Lu, Z. 2009. Applying Climate Information for Adaptation Decision-Making: A Guidance and Resource Document. National Communications Support Programme. Marris, C., I. Langford, T. Saunderson, and T. O'Riordan. 1997. Exploring the "psychometric paradigm": Comparisons between aggregate and individual analyses.

29

Risk Analysis 22(4) p. 665-9. Mays, L.W. 1996. The role of risk analysis in water resources engineering. Water resources update 103 p. 8-11. Morgan, M.G. and M. Henrion. 1990. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, UK: Cambridge University Press. Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and Human Well-Being: Wetlands and Water Synthesis. Washington, DC: World Resources Institute. Milly, P.C.D., J. Betancourt, M. Falkenmark, R.M. Hirsch, Z.W. Kundzewicz. 2008. Climate Change: Stationarity Is Dead: Whither Water Management? Science 319(5863) p. 573 – 574. Miller, K., and D. Yates. 2006. Climate Change and Water Resources: A Primer for Municipal Water Providers. Denver, CO: Awwa Research Foundation. Murdoch, P. S., J.S. Baron, T.L. Miller. 2000. Potential Effects of Climate Change on Surface-Water Quality in North America. J. Am. Water Resour. Assoc. 36(2) p. 347-366. Nachtnebel, H.P. 2002. Irreversibility and sustainability in water resources systems. In Risk, Reliability, Uncertainty, and Robustness of Water resources Systems. eds. J.J Bogardi and Z.W. Kundzewicz. UNESCO International Hydrology Series. Cambridge, UK: Cambridge University Press. National Oceanic and Atmospheric Administration (NOAA). 2009. Restoration Economics: Risk and Uncertainty in Environmental Restoration Programs. http://www.csc.noaa.gov/coastal/economics/riskinrest.htm. National Research Council (NRC). 1983. Risk Assessment in the Federal Government: Managing the Process. Washington, DC: National Academies Press. ___. 1996. Understanding Risk: Informing Decisions in a Democratic Society. Washington, DC: National Academies Press. ___. 2000. Risk Analysis and Uncertainty in Flood Damage Reduction Studies Committee on Risk-Based Analysis for Flood Damage Reduction. Washington, DC: National Academies Press. Ostrom E. 1990. Governing the commons: the evolution of institutions for collective action. New York: Cambridge University Press. Pahl-Wostl, C., J. Sendzimir, P. Jeffrey, J. Aerts, G. Berkamp, and K. Cross. 2007. Managing change toward adaptive water management through social learning. Ecology and Society 12(2): 30. Pindyk, R.S. 2007. Uncertainty in Environmental Economics. Review of Environmental

30

Economics and Policy 1(1) p. 45-65 Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. 2007. World Population Prospects: The 2006 Revision, Highlights. New York: United Nations. Rayner, S. 1992. Cultural Theory and Risk Analysis. In: Social Theories of Risk, eds. S. Krimsky and D. Golding. Westport: Praeger. Renn, O. 1999. A Model for an Analytic−Deliberative Process in Risk Management, Environ. Sci. Technol. 33(18) p. 3049–3055. Sanderson, W.C.; Hellmuth, M.E. and Strzepek, K.M. 2001. Botswana's Future: Modeling Population and Sustainable Development Challenges in the Era of HIV/AIDS. Executive Summary. Laxenburg, Austria: International Institute for Applied Systems Analysis. Saunders, J. F. and W. M. Lewis. 2003. Implications of climatic variability for regulatory low flows in the South Platte River basin, Colorado. J. Am. Water Resour. Assoc. 39 p. 33-45. Schröter,D., W. Cramer, R. Leemans, I.C. Prentice, M.B. Araújo. et al. 2005. Ecosystem Service Supply and Vulnerability to Global Change in Europe. Science 310(5752) p. 1333 – 1337. Shah, T., O. P. Singh, and A. Mukher. 2006. Some aspects of South Asia's groundwater irrigation economy: analyses from a survey in India, Pakistan, Nepal Terai and Bangladesh. Hydrogeology Journal. 14(3). Tansey, J. and T. O'Riordan. 1999. Cultural theory and risk: a review. Health, Risk and Society 1(1). Tijs, S.H., and T.S.H. Driessen. 1986. Game Theory and Cost Allocation Problems. Management Science. 32(8) p. 1015-1028. Tu, M.Y., N.S. Hsu, F.T.C. Tsai, W.W.G. Yeh. 2008. Optimization of Hedging Rules for Reservoir Operations. Journal of Water Resources Planning and Management. 134(1) p. 3-13. United Nations Development Report (UNDP). 2004. Reducing Disaster Risk: A Challenge for Development. New York: UNDP Bureau for Crisis Prevention and Recovery. United Nations Economic, Scientific and Cultural Organisation (UNESCO). 2005. The Precautionary Principle. Paris: UNESCO World Commission on the Ethics of Scientific Knowledge (COMEST). United Nations International Strategy for Disaster Reduction (UNISDR). 2009. UNISDR Terminology on Disaster Risk Reduction. http://www.unisdr.org/eng/terminology/terminology-2009-eng.html

31

32

UN Water. 2006. Coping with water scarcity: A strategic issue and priority for system-wide action. UN-Water Thematic Initiatives. Vörösmarty, C.J., P. Green, J. Salisbury, and R.B. Lammers. 2000. Global Water Resources: Vulnerability from Climate Change and Population Growth. Science 289(5477) p. 284-8. Walker, W. E., P. Harremoës, J. Rotmans, J. P. van der Sluijs, M. B. A. van Asselt, P. H. M. Janssen, and M. P. K. von Krauss. 2003. Defining uncertainty: a conceptual basis for uncertainty management in model based decision support. Integrated Assessment 4(1), p. 5-17. Watkins, D.W. and D.C. McKinney. 1997. Finding Robust Solutions to Water Resources Problems. Journal of Water Resources Planning and Management 123(1) p. 49-58. Wildavsky, A. and K. Dake. 1990. Theories of risk perception: Who fears what and why? Daedalus. 119 (4) p. 41-60. Wolt, J.D. and R.K.D Peterson. 2000. Agricultural Biotechnology And Societal Decision-Making: The Role Of Risk Analysis. AgBioForum 3(1). Wood, E. F., and I. Rodríguez-Iturbe. 1975. A Bayesian Approach to Analyzing Uncertainty Among Flood Frequency Models. Water Resour. Res. 11(6) p. 839–843. World Water Assessment Programme (WWAP). 2009. The United Nations World Water Development Report 3: Water in a Changing World. Paris: UNESCO, and London: Earthscan. Yoe, C.E. 1996. An introduction to risk and uncertainty in the evaluation of environmental investments. IWR Report 96-R-8, prepared for the U.S. Army Corps of Engineers, Institute for Water Resources. Alexandria, VA. Young, H.P., N. Okada, and T. Hashimoto. 1980. Cost allocation in water resources development: a case study of Sweden. Water Resources Research 18(3) p. 463-475.