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Transcript of Relevant, Ready, Responsive, Reliable Flood Risk Management and the U.S. Army Corps of Engineers...
Relevant, Ready, Responsive, Reliable
Flood Risk Management and the U.S. Army Corps of Engineers
Actions for Change
David Moser1, Martin Schultz2, Todd Bridges2 and Brian Harper1
US Army Corps of Engineers1. Institute for Water Resources, Alexandria, VA
2. Engineer Research and Development Center, Vicksburg, MS
4th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability
Toronto, CanadaMay 6-8, 2008
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Outline
• Twelve Actions for Change• Risk-informed decision framework (RIDF) for
Louisiana Coastal Protection and Restoration (LaCPR) Project.
• Illustrative example:– How risks, costs, and other tradeoffs are evaluated.– How risk and uncertainty is incorporated into the
decision process.– How stakeholder preferences are considered.
• How RIDF serves the Actions for Change
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Actions for Change (1-8): Systems and Risk-Based Approaches
1. Employ an integrated, systems-based approach
2. Employ risk-based concepts in planning and design
3. Continuously reassess and update policy
4. Employ dynamic independent review
5. Employ adaptive planning and engineering systems
6. Focus on sustainability
7. Review and inspect completed works
8. Assess and modify organizational behavior
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Actions for Change (9-12): Communication and Professionalism
Communication:
9. Effectively communicate risk
10. Involve the public in developing risk reduction strategies
Professionalism:
11.Manage and enhance technical expertise and professionalism
12. Invest in research
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Louisiana Coastal Protection and Restoration Project (LaCPR)
“…develop and present a full range of flood, coastal, and hurricane protection measures exclusive of normal policy considerations for south Louisiana.”
Factors not restricted to of national economic benefits and costs.
Consider full range of risks to people, cultural heritage, environment, property, and economy as well as project costs.
Five Planning Units in So. Louisiana
Charge:
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Risk-Informed Decision Framework
• Multi-Criteria Decision Analysis (MCDA)
• Identify plan that maximizes utility and optimizes the level of risk reduction given preferences
• Provides a means to:– Make tradeoffs between risks, costs, and other
decision outcomes
– Enhance transparency
– Account for aleatory and epistemic uncertainty
– Address stakeholder preferences and values
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Steps of Multi-Criteria Decision Analysis (MCDA)
1. Identify decision alternatives
2. Develop an objectives hierarchy and choose performance metrics
3. Assess preferences over objectives
4. Model performance of decision alternatives
5. Evaluate multi-attribute utility scores
6. Choose alternative that maximizes expected utility
7. Sensitivity analysis
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Illustrative Example
• Nine (9) flood risk-reduction alternatives and a no-action alternative
• Five (5) decision objectives and eight (8) performance metrics
• Three (3) uncertain planning assumptions– Stage frequency curve (ADCIRC Model)– Development pattern (employment growth rate and
population distribution)– Rate of relative sea-level rise
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Objectives Hierarchy and MetricsSuper-objectives Sub-objectives Performance Metric
Maximize human health and safety
Minimize life loss & health impacts among residents
1Risk to population within the inundation footprint (n)
Minimize economic losses from storm surge
Minimize residential and commercial property losses
2Risk to residential and commercial property ($)
Minimize disruptions to the local economy
3Risk to gross regional sales output ($)
Minimize costMinimize the cost of risk reduction projects
4 Life-cycle project costs ($)
Minimize environmental impacts
Maximize wetland acreage 5Net change in wetland acreage (acres)
Promote a sustainable ecosystem
6 Spatial integrity index
Minimize indirect impacts 7 Indirect impacts index
Minimize other social effects
Sustain cultural heritage 8Number of unique cultural centers protected (n)
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Preferences and ConsequencesC
DF
Stage
Stage frequencycurve (t)
Dam
age
Stage
Stage damage function (t)
Risk metric
mF 125.0
CD
F
mF 175.0
CDF for riskmetric (t)
(a) (b) (c)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
CD
F
Stage
CD
F
Stage
Stage frequencycurve (t)
Dam
age
Stage
Dam
age
Stage
Stage damage function (t)
Risk metric
mF 125.0
CD
F
mF 175.0
Risk metric
mF 125.0
CD
F
mF 175.0
CDF for riskmetric (t)
(a) (b) (c)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
• Assess stakeholder and decision maker’s preferences over objectives– Conduct interviews using an elicitation instrument to assess relative
importance. – Obtain relative preference weight on the sub-objective
• Model decision outcomes for each alternative in terms of performance metrics
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Multi-Attribute Utility
• Utility: An aggregate measure of relative satisfaction with modeled decision outcomes given objectives– Transform metrics to a 0 (worst) to 1 (best)– Weight transformed metrics by relative importance– Compensatory
• Six Planning Scenarios
iN
ii mVwU
1
Rate of Sea-Level Rise
(Relative to Observed)
Development Pattern None Moderate High
High employment & compact population
k = 1 k = 2 k = 3
BAU employment& dispersed population
k = 4 k = 5 k = 6
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Multi-Criteria Decision Analysis
Kkkjkj
Jj|UpUEMAXMaximize Expected Utility:
DevelopmentPattern
RelativeSea-levelRise
DecisionAlternatives
Performance Outcome (Utility score)
UncertaintyIn stage frequency
n
ii mFVwU
1
125.0
n
ii mFVwU
1
175.0
High
None
High & compact
BAU & dispersed
Alternative 1
Alt 2
Alt 3
Alt n
Alt N
Moderate
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Sensitivity of the Decision
• Choose alternative that maximizes expected utility
– Optimal level of risk reduction is the level associated with the chosen alternative given the decision maker’s preferences.
• How sensitive is the decision?– to stakeholder and decision maker’s preferences?– to the distribution of probability over sea-level rise
scenarios?
– to the pattern of development?
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Sensitivity to Preference Patterns
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8
(c)
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8 Metric Weight
1 0.202 0.353 0.204 0.055 0.056 0.057 0.058 0.05
Preference Pattern A
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8 Metric Weight
1 0.302 0.303 0.004 0.305 0.006 0.007 0.008 0.10
Preference Pattern B
(d)
(a) (b)
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8
(c)
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8 Metric Weight
1 0.202 0.353 0.204 0.055 0.056 0.057 0.058 0.05
Preference Pattern A
0.0
0.2
0.4
0.6
0.8
1.0
NoAction
A1 A2 A3 A4 A5 A6 A7 A8 A9
Decision Alternative
Exp
ecte
d U
tility
Metric 1 Metric 2 Metric 3 Metric 4
Metric 5 Metric 6 Metric 7 Metric 8 Metric Weight
1 0.302 0.303 0.004 0.305 0.006 0.007 0.008 0.10
Preference Pattern B
(d)
(a) (b)
BAU employmentDispersed population
High employmentCompact population
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Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
How sensitive is the decision to sea-level rise assumptions?
BAU Employment Growth Rate and Dispersed Population
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Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
Changing the Development Scenario alters the Landscape
High Employment Growth Rate and Compact Population
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Risk-Informed Planning and the USACE Actions for Change
1. An integrated, systems-based approach to problem solving
2. Uses risk-based concepts in planning and decision making
3. Suitable for adaptive planning and engineering systems
4. Helps planners effectively communicate risks
5. Involves stakeholders in developing risk reduction strategies and decision making
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Back-up Slides
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High Employment Growth Rate andCompact Population Distribution
Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
Low Employment Growth Rate andDispersed Population Distribution
Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
(a) (b) High Employment Growth Rate andCompact Population Distribution
Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
Low Employment Growth Rate andDispersed Population Distribution
Prob(RSLR = High)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Prob(RSLR = Moderate)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Prob(RSLR = Low)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Alternative A7Alternative A8Alternative A1Not feasible
(a) (b)
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Aleatory and Epistemic Uncertainty
• Aleatory uncertainty: – Natural variability that cannot be reduced– Example is the variability in storm surge
• Epistemic uncertainty:– Lack of knowledge, can in principle be reduced– Examples used in modeling performance outcomes
for LaCPR:• Characterization of the stage frequency curve for storm
surge• Projections of the future rate of sea-level rise• Population and employment growth rate projections
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Model Performance of Alternatives in Terms of Risk Metrics
CD
F
Stage
Stage frequencycurve (t)
Dam
age
Stage
Stage damage function (t)
Risk metric
mF 125.0
CD
F
mF 175.0
CDF for riskmetric (t)
(a) (b) (c)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
CD
F
Stage
CD
F
Stage
Stage frequencycurve (t)
Dam
age
Stage
Dam
age
Stage
Stage damage function (t)
Risk metric
mF 125.0
CD
F
mF 175.0
Risk metric
mF 125.0
CD
F
mF 175.0
CDF for riskmetric (t)
(a) (b) (c)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
Forecast ofE[damages]
Time (t)
E[d
amag
es]
(d)
• Calculate risk metrics under each plan– Integrate stage frequency curve w/ the damage function
• Characterize parameter uncertainty in the risk metrics– Integrate realizations of the stage frequency curve w/ damage
function
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Multi-attribute Utility (U )
worse)is (more
)()(
)(1
better) is (more )()(
)(
)(
mMINmMAX
mMINm
mMINmMAX
mMINm
mVi
i
i
U Multi-attribute utility scorew Preference weight on objectiveV(mi) Risk-neutral value scoreM Metric for the objectivei Index on objectives
iN
ii mVwU
1
Val
ue V
(mi)
Metric (m)
Averse
Seeking
Neutral
0
1
Value Function
BestWorst
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Six Planning Scenarios
Rate of Sea-Level Rise
(Relative to Observed)
Development Pattern None Moderate High
High employment & compact population
k = 1 k = 2 k = 3
BAU employment& dispersed population
k = 4 k = 5 k = 6
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Assess Preferences over Objectives
1. RANK OBJECTIVE 2. RATE
1 Minimize risks to residents 20
2 Minimize property losses 20
3 Minimize regional economic losses 15
4 Minimize project cost 15
5 Maximize wetland acreage 10
6 Promote sustainable ecosystem 10
7 Minimize indirect environmental impacts 5
8 Historic Properties Protected 5
100
Stakeholder and Decision Maker’s