Information Needs for Developing Robust Adaptive Strategies

Robert Lempert Director, RAND Pardee Center for Longer Range Global Policy and the Future Human Condition Workshop on Climate Science Needed to Support Robust Adaptation Decisions Georgia Tech February 6, 2014

Climate-Related Decisions Poses Both Analytic and Organizational Challenges

Public planning should be: • Objective • Subject to clear rules and procedures • Accountable to public Climate-related decisions involve: • Incomplete information from new, fast-moving, and sometimes irreducibly uncertain science • Many different interests and values • Long-time scales • Near certainty of surprise

How to make plans more robust and adaptable while preserving public accountability?

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Concept of Decision Support Focuses Attention on How (in Addition to What) Information is Used Supply and demand of scientific information may be mismatched

Decision support: • Bridges supply and demand with a focus on decision processes • Represents organized efforts to produce, disseminate, and facilitate the use of data and information to improve decisions • Includes as key elements: – Recognition that decision processes are at least as important as decision products – Co-production of knowledge between users and producers – Institutional stability – Design for learning Sarewitz and Pielke (2007 ) NRC (2009) 3

Should Climate Science Inform Exploratory, Rather Than Consolidative, Models?

• Consolidative models: – Bring together all relevant knowledge into a single package which, once validated, can be used as a surrogate for the real world – Are often used for prediction • Exploratory models: – Map assumptions onto consequences, without privileging any one set of assumptions – Cannot be validated – Can, when used with appropriate decision processes and experimental designs, provide policy-relevant information Bankes (1993) Weaver et. al. (2013 ) 4

Consolidative Models Useful for ‘Predict Then Act’ Analysis

What will future conditions be?

Under those conditions, what is best near-term decision?

How sensitive is the decision to those conditions?

• This traditional approach to risk management works well when the future – Isn’t changing fast – Isn’t hard to predict – Doesn’t generate much disagreement

Analytics aims to provide ranking of strategies, which implies consolidative models

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Predict Then Act Methods Can Backfire in Deeply Uncertain Conditions

What will future conditions be?

Under those conditions, what is best near-term decision?

How sensitive is the decision to those conditions?

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Uncertainties are underestimated

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Competing analyses can contribute to gridlock

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Misplaced concreteness can blind decisionmakers to surprise

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Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions

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In the early 1970s forecasters made projections of U.S energy use based on a century of data Gross national product (trillions of 1958 dollars) 2.2

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1.8

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0 0 1975 Scenarios Historical trend continued 20 1973 1890 1900 1973 1910 40 1950 1940 1960 1920 1929 60 80 100 1970 120 140 Energy use (10 15 Btu per year) 160 180

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Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions

In the early 1970s forecasters made projections of U.S energy use based on a century of data … they all were wrong Gross national product (trillions of 1958 dollars) 2.2

2.0

1.8

1.6

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1.2

1.0

.8

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0 0 1973 1980 1977 1890 1900 1973 20 1910 40 1920 1929 60 1950 1940 1960 80 100 1970 120 1975 Scenarios Historical trend continued 140 Energy use (10 15 Btu per year) 160 180

Deep uncertainty occurs when the parties to a decision do not know or do not agree on the likelihood of alternative futures or how actions are related to consequences

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RDM Manages Deeply Uncertain Conditions by Running the Analysis Backwards

“ RDM (Robust Decision Making) Process ” Proposed strategy Identify vulnerabilities of this strategy Develop strategy adaptations to reduce vulnerabilities

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Start with a proposed strategy Use multiple model runs to identify conditions that best distinguish futures where strategy does and does not meet its goals Identify steps that can be taken so strategy may succeed over wider range of futures

Analytics aims to provide concise summary of key tradeoffs, a useful function of exploratory models

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Flood Risk Management Study for Ho Chi Minh City Provides an Example

Over 15 years, HCMC has planned multi-billion dollar flood investments using best available projections

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Conditions have diverged from projections and the city is at significant risk

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How Can HCMC Develop This Plan When Today ’s Predictions Are No More Likely To Be Accurate?

Today, HCMC seeks an innovative, integrated flood risk management strategy

12 World Bank WPS-6465 (2013)

Simulation Model Projects Flood Risk From Estimates of Hazard, Exposure, and Vulnerability

Hazard

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Future rainfall intensity

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Height of the Saigon River Exposure

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Population in the study area

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Urban form Vulnerability

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Vulnerability of population to flood depth Risk Model Risk = Expected Number of People Affected By Floods Each year

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Model Projections Depend on Values of Six Deeply Uncertain Parameters

Rainfall Increase +0% Increase River Height 20 cm Population 7.4 M + 35% 100 cm 19.1 M Urban Form Growth in Outskirts Poverty Rate 2.4 % Vulnerability Not Vulnerable Growth in Center 25 % Very Vulnerable

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Traditional Planning Asks “What Will The Future Bring?”

Best Estimate Future Risk Model Infrastructure performance under best estimate future Risk to the Non-Poor

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We Run HCMC ’s Infrastructure Plan Through 1000 Different Combinations of Conditions

Risk Model Alternative Future Future What factors best distinguish this region, where risk is reduced for both poor and non-poor?

Infrastructure Under Alternative Future

Risk to the Non-Poor

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1. Under What Future Conditions Is HCMC ’s Infrastructure Vulnerable?

Infrastructure fails to reduce risk when: > 6% increase in rainfall intensity, > 45 cm increase in river height, OR > 5% poverty rate 6% 45cm 45cm rise

Infrastructure

5% In this region, risk is reduced for both poor and non-poor 6% increase

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2. Are Those Conditions Sufficiently Likely To Warrant Improving HCMC ’s Plan?

NOAA SLR + SCFC Subsidence 75 cm

Even stakeholders with diverse views can agree that its worth considering improvements to current plan

45cm rise

Infrastructure

MONRE SLR Estimate 30 cm 6% increase IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

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We Consider A Range of Options for Integrated Flood Risk Management

“Soft Options” 2. Raise Homes 3. Relocate Areas 1. Rely on current infrastructure 4. Manage Groundwater 5. Capture Rain Water

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How Will Adding “Soft” Options Improve Our Strategy?

NOAA SLR + SCFC Subsidence (75 cm) Elevate (23%, 55cm) Infrastructure MONRE SLR Estimate 30 cm + IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

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Rainwater (10%, 100cm)

What Are Tradeoffs Between Robustness And Cost?

Relocate (17%, 100cm) Stakeholders debate about how much robustness they can afford (which is much more useful than debating what the future will be) NOAA SLR + SCFC Subsidence (75 cm) Groundwater (7%, 55cm) x Infrastructure Elevate (23%, 55cm) MONRE SLR Estimate 30 cm IPCC SREX Mid Value (20%) IPCC SREX High Value (35%) High Cost Low Cost

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RDM Approach Also Used to Help Develop New Plans for Managing Colorado River

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2012 Bureau of Reclamation study, in collaboration with seven states and other users: Assessed future water supply and demand imbalances over the next 50 years Developed and evaluated opportunities for resolving imbalances

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RDM Embeds Analytics in a “Deliberation with Analysis ” Decision Support Process

Participatory Scoping Key products include: 1. Scenarios that illuminate vulnerabilities of plans 2. New or modified plans that address these vulnerabilities 3. Tradeoff curves that help decision makers choose robust strategies Tradeoff Analysis New Options Case Generation Scenario Exploration and Discovery Deliberation Analysis Deliberation with Analysis Robust Strategy Scenarios that Illuminate Vulnerabilities

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Decision Structuring: Work with Decision Stakeholders to Define Objectives/Parameters

Deliberation with Stakeholders 1. Decision Structuring

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Metrics that reflect decision makers ’ goals

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Management strategies (levers) considered to pursue goals

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Uncertain factors that may affect ability to reach goals Relationships among metrics, levers, and uncertainties Information needed to organize simulation modeling

Also called “XLRM”

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Case Generation: Evaluate Strategy in Each of Many Plausible Futures

Simulating Futures

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Strategy Plausible assumptions

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Potential outcomes 2. Case Generation 100s/1000s of cases Large database of simulations model results (each element shows performance of a strategy in one future)

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Scenario Generation: Mine the Database of Cases to Identify Policy-Relevant Scenarios

1. Indicate policy-relevant cases in database of simulation results 2. Statistical analysis finds low dimensional clusters with high density of these cases 3. Scenario Discovery Uncertain input variable 1 3. Clusters represent scenarios and driving forces of interest to decisionmakers Scenarios that illuminate vulnerabilities of proposed strategy

Strategy successful Strategy less successful

Parameter 1

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Tradeoff Analysis: Help Decisionmakers to Compare Tradeoff Among Strategies

Visualization helps decisionmakers compare strategies 4. Tradeoff Analysis Robust strategy or information to enable decision makers to make more robust strategy

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Analysis with Colorado System Simulations Reveal Key Vulnerabilities

Baseline strategy + 24,000 climate projections

• Recent historic • Paleo records • Model projections • Paleo-adjusted model projections Several demand projections

+ Reclamation ’s Colorado River Simulation System Upper Basin Shortages Lee Ferry Deficits Elevation Below 1,000 ’ ’ Lower Basin Shortages (2-year) Lower Basin Shortages (5-year) Remaining Demand Above Apportionment Percent Traces Percent Years

28 RAND, RR-242-BOR

Used Climate Information to Assess Vulnerabilities of Current Management Plans

Current plans fall short if:

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Temperature greater than 2

°

F

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Any decrease in precipitation

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Analysis Can Support Deliberation Regarding Near- and Longer-Term Actions

Common Options Strategy Contingent Actions Initial Actions Initial Actions (dependent on beliefs)

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What about probabilities?

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RDM Considers Sets of Alterative Probability Distributions

Expected value of strategy

s

for distribution r (x) is given by

EV

= ò r ( ) ( )

dx

HARD 1.Choosing what strategies to consider 2.Choosing what futures to consider ( )

s

future

x

in some 4.Knowing – and convincing other people that you know – the true probability distribution EASY r ( ) above

Thus RDM considers many probability distributions over the set of futures x - NOT a uniform distribution

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Some Strategies Are Robust Over a Wide Range of Probability Estimates

This chart:

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Shows expected cost to taxpayers from re-authorizing U.S. Terrorism Risk Insurance Act

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Quoted on floor of US Senate by a proponent

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Called “insidious” by opponents

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Usefully informed Congressional debate CBO, Treasury Assumption

RAND, MG-679-CTRMP 33

Observations

• Predict-the-act approaches promote restricted view of demand for climate information – Generally seek to aggregate information into consolidative models aimed at prediction • “Backwards” analyses like RDM effectively employ a much wider range of products from climate science – Integrated models of entire system (exploratory and consolidative) – Models of individual components of full system – Scientific understanding not well-embodied in models – Any probabilistic information, however imprecise • RDM creates demand for decision support methods and tools for managing and summarizing large and diverse sets of information in a decision-relevant context.

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More Information

http://www.rand.org/international/pardee/ http://www.rand.org/methods/rdmlab.html

Thank you!

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Flexible Options Can Prove Effective

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Research Portfolio Includes Developing and Evaluating Improved Tools

Analytic tools to facilitate RDM

Exploratory modeling methods run computer simulation models many times to create a database that links a wide range of assumptions to their consequences Scenario Discovery methods uses cluster analysis on these databases of model results to simply characterize the future conditions where a the proposed strategy does not meet its goals

Tools to represent information Tools to draw meaning from information Users

Tool development efforts include: 1.Facilitating design of strategies with “pareto-satisficing surfaces” 2.Higher dimensional scenario discovery 3.Adaptive sampling 38

Portfolio Analysis Reveals Key Tradeoffs

Lee Ferry Deficit Vulnerability Lake Mead Vulnerability

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“XLRM” Framework for the Colorado River Basin Study

Uncertain Factors (X)

Demand Conditions (6) Supply Conditions (4) • • • • Observed Resampled (103 traces) Paleo Resampled (1,244 traces) Paleo Conditioned (500 traces) Downscaled GCM Projected (112 traces) System Operations Conditions (2)

Relationships or Models (R)

Colorado River Simulation System (CRSS)

Options and Strategies (L)

Options for demand reduction and supply augmentation (40) Portfolios of many options designed to adjust over time in response to new information (4) • Near-term actions • Signposts • Contingent actions

Performance Metrics (M)

• Water delivery (5) • Electric power (3), Recreation (11), Ecological (5), Water quality (1), and Flood control (1)

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Colorado Basin Study Followed This Process

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Start with proposed policy and its goals Interactive visualizations

• Deliberate: Participants to decision define objections, options, and other parameters • Analysis:

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Identify futures where policy fails to meet its goals

Participants work with experts to generate and interpret decision relevant information

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Evaluate whether new policies are worth adopting Revised instructions 3.

Identify policies that address these vulnerabilities

Dozens of workshops with many stakeholders over course of study

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