Transcript Copernicus Institute Universiteit Utrecht

Copernicus Institute
Taking uncertainty on board in
decision making
The example of adaptation to climate
change
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Attitudes to risk and uncertainty
Risk = Probability x Consequence
Lay attitudes involve more than just ‘risk’
• Severity and Nature of Consequences
• Cultural Orientations (there is no single
‘public’)
• Social Amplification Effects
• Trust in Risk Managers / Science
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Nederland Later
hotspot
Major anticipated
problem areas
•Sea level
•River runoff
•Groundwater pressure
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Adaptation under what uncertainty?
• Planned adaptation
– to single scenario of anticipated climate impacts
(KNMI 2000 scenario)
no uncertainty
– to single scenario of anticipated climate impacts +
to variability
statistical uncertainty (without epistemic unc.)
– to range of scenario’s of anticipated climate impacts
(KNMI 2006 scenario’s)
scenario uncertainty
– to range of scenario’s of anticipated climate impacts
+ imaginable climate surprises (MNP Nederland
Later)
scenario uncertainty + recognized ignorance
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Decision-making frameworks
• Top down approaches
– Prevention Principle
– IPCC approach
– Risk approaches
• Bottom up approaches
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–
–
–
–
–
Precautionary Principle
Engineering safety margin
Anticipating design
Resilience
Adaptive management
Human development approaches
• Mixed approaches
– Adaptation Policy Framework
– Robust decision making
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IPCC seven step approach:
1) Define problem (study area, sectors, etc.);
2) Select method of assessment most appropriate to the
problems;
3) Test methods/conduct sensitivity analysis;
4) Select and apply climate change scenarios;
5) Assess biophysical and socio-economic impacts;
6) Assess autonomous adjustments;
7) Evaluate adaptation strategies.
(Carter et al. 1994, Parry and Carter 1998)
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Risk approach (UK-CIP)
Eight stages decision framework:
1. Identify problem and objectives
2. Establish decision-making criteria
3. Assess risk
4. Identify options
5. Appraise options
6. Make decision
7. Implement decision
8. Monitor, evaluate and review.
“The risk assessment endpoints should
help
the decision-maker define levels of risk
(probabilities and consequences or
impacts)
that are acceptable, tolerable or
unacceptable”
Flexible characteristics:
cricular
Feedback and iteration
Stages 3, 4 and 5 are tiered. (identify, screen, prioritise and
evaluate before more detailed risk assessments and options
appraisals are required.)
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No regrets
• Favour adaptation strategies which will
yield benefits (for other, less uncertain,
policy concerns) regardless of whether
or not climate impacts will occur.
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Precautionary Principle
“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.
Morally unacceptable harm refers to harm to humans or the environment that is
• threatening to human life or health, or
• serious and effectively irreversible, or
• inequitable to present or future generations, or
• imposed without adequate consideration of the human rights of those affected.
The judgment of plausibility should be grounded in scientific analysis. Analysis
should be ongoing so that chosen actions are subject to review.
Uncertainty may apply to, but need not be limited to, causality or the bounds of the
possible harm.
Actions are interventions that are undertaken before harm occurs that seek to
avoid or diminish the harm. Actions should be chosen that are proportional to
the seriousness of the potential harm, with consideration of their positive and
negative consequences, and with an assessment of the moral implications of
both action and inaction. The choice of action should be the result of a
participatory process. ”
(UNESCO COMEST 2005)
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Engineering Safety Factor / Conservative Design
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“Anticipating design”
Long term surprise scenario margin
(e.g. Greenland, WAIS)
Flexibility to build
higher dike later
Engineering
safety
margin
Stronger foundation than
needed under most plausible
scenarios
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Adaptive management
• Iterative feedback and learning based approach
• Management as experiment
• Emphasis in process and continuous learning
(trial and error, small step->evaluate->adjust)
• Flexibility, flexibility, flexibility!
• Especially useful in small scale systems
• May fail in case of surprises and discontinuities
in system response (if past experience from
which you learned is not a key to the future)
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Resilience
• If uncertainties about climate change
are large, one can still know how the
resilience of social-ecological systems
can be enhanced
• Resilience is the capacity of a system
to tolerate disturbance without
collapsing into a qualitatively different,
usually undesired, state
Principles:
•Homeostasis
•Omnivory
•High flux
•Flatness
•Buffering
•Redundancy
www.resalliance.org
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Robust decision-making
• Models are used as exploratory tools
instead of prediction
• Uncertainty ranges to be explored can
be informed by the current state of
scientific knowledge
• Adaptation decisions are assessed
against climate change uncertainties
until a robust strategy (least sensitive
to uncertainties) is identified
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Robustness exploration (Dessai, 2005)
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Problem: Dimensioning of water supply system
Additional water required (Ml/d) to maintain levels of service in
2030 under different demand scenarios as a function of regional
climate response uncertainty
AWS
-50
-25
0
Climate impacts
uncertainty (%)
25
25
20
15
10
5
0
-5
-10
-15
-20
-25
-30
25-50
50-75
-35
-40
-45
50
-50
Summer precipitation change (%)
-75--50
-50--25
-25-0
0-25
75-100
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Synthesis
Statistical
uncertainty
decision making under uncertainty frameworks
Scenario Recognized
uncertainty ignorance
& surprises
IPCC approach
+
++
--
Risk approaches
++
+
--
Engineering safety margin
++

-
Anticipating design
++
+
+
Resilience

+
++
Adaptive management
++
-
--
Prevention Principle
++

--
Precautionary Principle
+
++
++
Human development approaches

+
+
Adaptation Policy Framework
+
+
+
Robust decision making
+
++
+
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Synthesis
Uncertainty assessment methods
Statistical
uncertainty
Scenario
uncertainty
Recognized
ignorance &
surprises
Scenario analysis ("surprise-free")

++
-
Expert elicitation
+
+
+
Sensitivity analysis
+


Monte Carlo
++
-
-
Probabilistic multi model ensemble
++

+
Bayesian methods
++
-

NUSAP / Pedigree analysis
+
+
++
Fuzzy sets / imprecise probabilities
+

+
Stakeholder involvement

+
+
Quality Assurance / Quality Checklists
+
+
++
Extended peer review (review by stakeholders)

+
++
Wild cards / surprise scenarios
-
+
++
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Synthesis
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