2. CONCEPTS 1: A BRIEF OVERVIEW OF CLIMATE CHANGE AND THE
3.5 Uncertainty
Risk decisions are typically characterised by high levels of uncertainty, which need to be appreciated by those involved in the risk management process (Suter, 1993). In water resources, for example, the existence of various uncertainties is a major contributor to potential project, or system, failure (Yen, 2002). These uncertainties, in the exactness of the values produced and in the decisions taken, exist despite the statistical rigour with which risk estimation may have been carried out (Suter, 1993).
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Uncertainty may be defined as “the situation in which no unique and complete understanding of the system to be managed exists” (Brugnach et al., 2009).
Disregarding the uncertainties inherent in a system is one of the most critical errors in any type of risk management (McColl et al., 2000). Uncertainties can arise from various sources, which are not only technical or scientific in nature, but may also result from different perceptions and conflicting views about a particular issue (Brugnach et al., 2009).
Conceptually, the typologies of uncertainty in risk management identified by Suter (1993), McColl et al. (2000), Schulze (2003d) and Brugnach et al. (2009), can be characterised into four types (Figure 3.6), viz. natural variability, incomplete knowledge, decision-rule uncertainty and the human element.
Figure 3.6 Typology of uncertainty, developed from multiple sources (Suter, 1993;
McColl et al., 2000; Schulze, 2003d; Brugnach et al., 2009)
Natural variability arises from many inherently random factors that must be considered in a risk assessment (McColl et al., 2000). This type of uncertainty refers to the inherently unpredictable aspects of a system that are due to inherent natural variability or complex system behaviour (Brugnach et al., 2009). From a hydro-
Typology of Uncertainty Incomplete
Knowledge
Human Element
Multiple Knowledge Frames Data Uncertainty
Natural Variability
Data Uncertainty Model Uncertainty
Model Choice and Structure
Model Input
Model Parameters
Model Output
Decision-rule
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climatic hazard perspective this may include occurrences of episodic events, such as intense rainfall, which may become further complicated by demographic factors in the exposed population, such as the distributions of age and gender. In regard to this type of uncertainty, the unpredictability of the system is accepted as something that will not change in the foreseeable future (Brugnach et al., 2009), and while it can be described, no amount of additional data collection or analysis can reduce the degree of variability, nor the resulting uncertainty, found in natural processes (Suter, 1993;
McColl et al., 2000; Schulze, 2003d).
The second type of uncertainty shown in Figure 3.6, incomplete knowledge, refers to situations where the available theoretical and empirical knowledge is unable to provide sufficient understanding of things that are potentially knowable (Suter, 1993).
This can be due to several factors, including lack of, or inadequate representativeness of, data due to practical constraints (Yen, 2002), to the unreliability of the available data, to incomplete theoretical understanding of system dynamics, or to ignorance (Brugnach et al., 2009). Furthermore, incomplete knowledge includes uncertainty about the future, for example, on socio-economic development and consequent emissions of greenhouse gases, or the effectiveness of policies to mitigate these emissions (UKCIP, 2003).
A subset of incomplete knowledge is that of model uncertainty. Owing to models being simplifications of reality, their predictive accuracy is limited (McColl et al., 2000). Model uncertainties may arise from numerous factors:
• Model choice and structure – This reflects the inability of the simulation model to accurately represent the complexities of the system’s true physical behaviour (McColl et al., 2000; Yen, 2002). Furthermore, different models may give different results for the same problem, as a result of differences in their detailed structures, even though they are based on the same fundamental physics. An example is GCMs producing different responses to the same greenhouse gas forcing (UKCIP, 2003; Giorgi et al., 2008). Therefore, the choice as to which model to use introduces more uncertainty through the subjective human element. However, using several models to create an ensemble of projections may improve confidence (Giorgi et al., 2008), or at least give a better understanding of the uncertainties involved.
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• Model parameters – This reflects an inability to accurately quantify the model parameters, e.g. the behaviour of clouds and/or the strength of atmospheric convection in GCMs (Cox and Stephenson, 2007). This inability may be attributed to statistical uncertainties or to flawed experimental design (McColl et al., 2000; Yen, 2002).
• Model input and output – Uncertainties in model input often result from one or many of the sources of incomplete knowledge stated above, e.g. those regarding future greenhouse gas emissions, which form the basis for developing the parameters with which to force a GCM simulation (Hewitson et al., 2005b). These uncertainties are then propagated through the model, together with model structure and parameter uncertainties, to result in uncertain output. Furthermore, these uncertain outputs often become inputs into other models (e.g. uncertain GCM output is input into downscaling algorithms, and this output becomes input into hydrological models), further increasing the uncertainties associated with final resulting output (UKCIP, 2003; Cox and Stephenson, 2007; Giorgi et al., 2008).
Decision-rule uncertainty takes the form of vague or unsuitable operational definitions for desired outcome criteria, value parameters, and decision variables (McColl et al., 2000). These include the selection of particular types of summary statistics for outcome measures, (e.g. lifetime mortality risk versus annual mortality risk), and the choice of variables that express subjective value judgments in the form of utility functions, for example, the monetary value attributed to loss of life (McColl et al., 2000).
The last type of uncertainty shown in Figure 3.6 is that associated with what makes us human in regard to imperfections and subjectivity. Imperfections refer to human errors, which include mistakes made in the execution of risk assessment, mainly through poor quality assurance, (e.g. data recording errors, data handling and transcription errors), model input errors and any other human factors that are not accounted for in the modelling or design procedure (Suter, 1993; Yen, 2002).
The basic philosophy of risk assessment has several inherent contradictions that seriously compromise its claim to scientific consistency and objectivity (McColl et al.,
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2000). Subjectivity, referred to by Brugnach et al. (2009) as multiple knowledge frames, refers to different, sometimes conflicting, views about the best way to understand the system (Brugnach et al., 2009). Those involved in the risk management process carry their own inherent biases related to their professional training and are often unaware of their personal biases, nor do they fully realise the extent to which this can influence professional judgement (McColl et al., 2000). This kind of uncertainty can be called ambiguity and can originate from differences in, inter alia, professional backgrounds, scientific disciplines, value systems and societal positions (Brugnach et al., 2009).
Other than uncertainties associated with natural variability, uncertainties in the risk management process are, in principle, reducible given either more time, more data or improved quality assurance (McColl et al., 2000; Brugnach et al., 2009). It is important to note that many, if not all, of these types of uncertainty are present simultaneously in each stage of the risk management process.
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Following on the above review on hazards, vulnerabilities, risk and uncertainty, it needs to be reiterated that while this thesis focuses on techniques for identifying changes in hydro-climatic hazards related to impacts of projected climate change (i.e.
the initial stages of the risk management process – cf. Section 3.4), it is ubiquitously stated in the literature that the most effective way of reducing risk is to address the vulnerability side of the risk equation. This, however, can only be achieved if one can answer the question of “vulnerable to what?”, as the hazard and the potential vulnerability it exposes are inextricably linked within the context of risk.
Without adequate feedback and learning, risk management is unlikely to be effective (Smith and Petley, 2009). The uncertainties mentioned above, and hence those associated with climate change, provide justification for developing water management institutions that are more flexible and responsive to changing conditions (Frederick, 1998). Users of water resources may be protected from the impacts of climate change through the application of effective water management strategies, which would require adopting appropriate policies (Pittock, 2005). Therefore, the
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incorporation of risk management strategies within adaptive water resources management is a key element to hydro-climatic risk mitigation (Aerts and Droogers, 2009). The following chapter addresses water resources management in South Africa, with particular reference to integrated and adaptive water resources management.
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