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 suboptimal 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 waterrelated 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 waterrelated 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.
3
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 conditions 1 : 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
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.
1
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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 underoptimized 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 decisionmakers 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.
14
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
15
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.
16
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
17
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,
18
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. yieldreliability 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
19
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
20
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.
21
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
22
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 decisionmaking. For example, Block and Brown (2008) use future climate trends to drive a
coupled hydrology-hydropower optimization model to determine projected costbenefit ratios over 50 years. Importantly, they also address uncertainty in other drivers
by evaluating the ratios' responses to varying economic and policy conditions. Costbenefit 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 lowprobability 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.
23
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).
24
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.
25
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