The role of uncertainty in climate change adaptation strategies—A Danish water management example

The role of uncertainty in climate change adaptation strategies—A Danish water management example

The role of uncertainty in climate change adaptation strategies—A Danish water management example

examples of action strategies with uncertain source - Abstract

We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation.

Keywords

Climate change Adaptation Uncertainty Risk Water sectors Multi-disciplinary

1 Introduction

Climate change affects many aspects of human societies and the ecosystems on which they depend. Impacts on key sectors, such as agriculture, health, water supply, urban drainage, roads, buildings and the environment, can already be observed and are expected to increase in the future (IPCC 2007b; EU Commission 2009). The present climate projections exhibit large uncertainties arising among others from assumptions on greenhouse gas emissions, incomplete climate models and the downscaling of climate projections (IPCC 2007c). When assessing the physical impacts of climate change on water related sectors, traditional uncertainties in hydro-ecological models, such as data and parameter uncertainty and model structural uncertainty need to be addressed. For socio-economic impacts, additional uncertainties, involving aspects of costing and problem framing, need inclusion (van der Keur et al. 2008). The complete suite of uncertainties has been referred to as the uncertainty cascade (Hulme and Carter 1999; Katz 2002; Foley 2010).

Making climate change adaptation decisions is particularly difficult since they rely on uncertainties related to climate projections as well as to developments in natural systems and sectors that are affected by other uncertainties. Climate change impacts and adaptation also influence a wide range of stakeholders with different interests, making it difficult to distinguish uncertainties related to technical information stemming from different perceptions and understandings of issues that reflect stakeholder interests, perceived burdens and benefits. Decision-making in climate change adaptation deals with how, when and to what extent risks from climate events can and should be reduced, given private stakeholders’ interests and those of society at large. Uncertainties are seldom explicitly recognised and dealt with in practical climate adaptation planning (Preston et al. 2011).

Uncertainty has for many years been recognised by UN Intergovernmental Panel on Climate Change (IPCC) as crucial (IPCC 2007a), and it will receive even more attention in the forthcoming Fifth Assessment Report (AR5) (Yohe and Oppenheimer 2011). A goal of the AR5 is to apply “a common framework with associated calibrated uncertainty language that can be used to characterise findings of the assessment process” (Mastrandrea et al. 2011). According to an AR5 uncertainty guidance note, the degree of certainty of a key finding should be characterised qualitatively in terms of the confidence in the validity of a finding and the degree of agreement as well as in quantified measures of uncertainty (Mastrandrea et al. 2011). This approach has been criticised for being oversimplified and potentially leading to misleading overconfidence, because it “omits any systematic analysis of the types and levels of uncertainty and quality of the evidence, and more importantly dismisses indeterminacy and ignorance as important factors in assessing these confidence levels” (Curry 2011).

Our objectives are: (i) to outline a common uncertainty framework, including a terminology, that is generically applicable in climate change adaptation; (ii) to assess climate change related uncertainties in water related disciplines and sectors; and (iii) to evaluate strategies on how uncertainty affects climate change adaptation decision making. We have applied this framework to four water related sectors in Denmark. Given our focus we do not discuss all aspects related to adaptive management, such as resilience, adaptive capacity and social learning (Pahl-Wostl 2007; Lebel et al. 2010).

2 Uncertainty framework

2.1 Definition of uncertainty

We adopt the definition of Klauer and Brown (2003) that a person is uncertain if s/he lacks confidence about the specific outcomes of an event. This definition holds that for most technical and natural sciences, uncertainty is primarily an objective matter, whilst acknowledging that uncertainty includes subjective aspects.

2.2 Typology

Our typology, which is adapted from Walker et al. (2003), Refsgaard et al. (2007) and van der Keur et al. (2008), characterises all uncertainties according to three dimensions nature, level and source.

The nature of uncertainty can be epistemic, aleatory and ambiguity. Epistemic uncertainty is the uncertainty due to imperfect knowledge and is reducible by gaining more knowledge via research, data collection and modelling. Aleatory uncertainty, also termed ontological or stochastic uncertainty, is due to inherent variability. It can be quantified, but is stochastic and irreducible. Ambiguity results from the presence of multiple ways of understanding or interpreting a system. It can originate from differences in professional backgrounds, scientific disciplines, value systems and interests.
The level of uncertainty characterises how well the uncertainty can be described within the range from determinism to total ignorance (Fig. 1), where determinism is the ideal, non-achievable, situation where everything is known exactly and with absolute certainty. Within this range, statistical uncertainty can be described using well-known statistical terms. Scenario uncertainty, in general, cannot be described statistically but are used when possible outcomes are known but not all probabilities of such outcomes are present (Brown 2004). Qualitative uncertainty occurs when not even possible outcomes are known (Brown 2004). Recognised ignorance occurs when there is an awareness of lack of knowledge on a certain issue, but where it is not possible to categorise the uncertainty further. Total ignorance denotes a state of complete lack of awareness about imperfect knowledge.

With the information we provide about examples of action strategies with uncertain source

, We hope you can be helped and hopefully set a precedent with you . Or also you can see our other references are also others which are not less good about Mayora Booth was Popular Among the Jobseekers

, So and we thank you for visiting.
open contoh marketing : http://link.springer.com/article/10.1007/s11027-012-9366-6