Risk and Reward

1. Riskand Reward Casey Brown Associate Professor of Civil and Environmental Engineering University of Massachusetts UMass Hydrosystems Research Group

2. Uncertainty = Risk + Opportunity? Risk = an expected value Risk = product of the consequences of a hazard and the probability that the hazard will occur - Risk = expected loss For example, Risk = flood damage X probability of flood Risk = $100,000,000 X 0.01 = $1M In some fields, risk = probability of negative event Risk of being hit by asteroid = 10-9

3. Uncertainty = Risk + Opportunity? • Opportunity = product of the consequences of an event and the probability that the event will occur • Opportunity = expected loss or gain

4. Risks and Opportunities Opportunity

5. Uncertainty: Pigs, Ducks, Skunks Unkunk = an unknown unknown related: surprises; black swans Kunk = a known unknown Skunk = a known that stinks (Taleb, 2007) (Klemes, 2002)

6. Emission Scenarios General Circulation Models (GCMs) Geography Department, U. Oregon Hydrologic Model Water System Performance Under Future Climate Scenarios Downscaling Greene County, PA Department of Econ. Development Water Resources System Model Wisconsin Valley Improvement Company

7. Decision Frameworks for Climate Change • How will the science improve decisions? • Usual mode of engagement: Prediction - centric • Science reduces the uncertainty affecting the decision • E.g., Science: the most likely future condition is A • Decision – under Future A, Option 1 is my best choice • Mode of engagement under climate change • Science characterizes uncertainty (may increase) • E.g., Science: here is a wide range of possible futures, and we’re not sure they delimit the true range • Decision – um … UMass Hydrosystems Research Group

9. Decision-centric Climate Science “Decision Scaling”, Brown and Wilby, 2012 (EOS) • Focus on identifying the vulnerabilities of the system • Identify climate changes that are problematic • Evaluate options to improve robustness to such climate changes Mean Mean Post-bias correction error in two climatic periods Post-bias correction error in two climatic periods Standard Deviation Standard Deviation Cov(P, T) Cov(P, T)

10. The Summary • Inherent, irreducible uncertainties of climate system • Requires a shift of emphasis from “reduce uncertainty” to risk reduction • Decision-based approaches allow specification of the information that is actually needed (maybe less than you think!) • GCMs provide information that can be useful for managing risks when treated appropriately • When using uncertain information (climate change, seasonal forecast), must manage the residual risk

11. Risk and Development Upside and downside

12. Per Capita GDP vs Latitude (Sachs, 2001)

13. Rainfall Variability and GDP Bubble Size = GDP per capita (Blue = low interannual variability of rainfall) Monthly Rainfall Variability Mean Annual Rainfall

14. Rainfall Variability and GDP Bubble Size = GDP per capita (Blue = low interannual variability of rainfall) Wealthy nations share a small window of favorable climate (low variability; moderate rainfall) Monthly Rainfall Variability Mean Annual Rainfall

15. Hydroclimate risk to economic growth in sub-Saharan Africa Casey Brown · Robyn Meeks · Kenneth Hunu· Winston Yu Climactic Change 2011 • Hydroclimate variability is the dominant and negative climate effect on economic growth • 10% increase in drought area causes a 40% reduction in annual growth in SSA • Globally, 10% increase in drought area causes a 30% reduction in annual growth

16. Risks and Opportunities Opportunity

17. Growth with 10% reduction in drought effect Status Quo Growth

18. Flood risk management An example of risk estimation and management

19. Flood Control Storage

20. Overflowing Dam: limits of control

21. Flood Risk Estimation and “Nonstationarity” • Traditional approaches based on stationarity– the past represents the future • Synthetic streamflows and critical period analysis • Flood risk estimated from historical record = “100 year flood” • Fixed water allocation • Recognition of Temporal Structure in the hydrologic record • ENSO, PDO and extended departures from long term mean • Flood risk and rainfall totals vary between years and decades • Monitoring, forecasting, early warning systems • Recognition of climate change and Nonstationarity • Klemes (1974): “… by assuming nonstationarity we acknowledge nonexistence of preset limits and directions … unpredictability… and subscribe to philosophical indeterminism” • Emphasis on diagnosing change and its implications • Growing recognition of limited ability to predict the future • Are our risk management strategies resilient to an uncertain future?

23. Example application: Iowa River Spillway use: -1993 -2008 -2013 Are floods increasing? June 2008 floods Proposed adaptations: Raise Levees Reservoir re-operation

24. R. Vogel

25. Trend in historic record Stream gage

26. Integrated Uncertainties Steinschneideret al.: The integrated effects of climate and hydrologic uncertainty on future flood risk assessments, in preparation.

27. Exceedance Probability 0.01

28. Exceedance Probability 0.01

29. Is the physical uncertainty the easy part? Peak Flow Exceedance Probability “Nonstationarity” Damage Function “Optimal” Flood Risk Reduction Plan “the wicked” Rittel and Webber (1973) Actual Flood Risk Reduction Decision GB Shaw: “Every profession is a conspiracy against the laity”

30. Risk Management Evaluating alternatives

31. Benefit Cost Analysis of Risk Management Benefits – reduction in risk (avoided expected losses) • change of probability or consequence • Notice, this is an expected value Costs – the costs of reducing risks (can be nonfinancial) Decision: Find the alternative that yields maximum benefit/cost ratio

32. Optimal Flood Risk Management Lund (2002): where s = flood stage p(s) = probability of given flood stage XP = Permanent flood control measure XO = option flood control measure cP, cO = costs of measures D = damage function based on flood stage, flood control measures

33. Fort Hood: Water Supply and Flood Risk Lake Belton Facts Capacity: 1,357 MCM ~60% Flood Storage ~40% Water Supply Drainage: 9,220 km2

34. Typical Reservoir Storage Allocation

36. Existing Conservation Pool – Robust Performance

37. Alternative 1

38. Alternative 4

39. Best Performing Alternatives for given climate change

40. International upper great lakes study

41. International Upper Great Lakes Study • 20% of world’s freshwater • 40 million people affected • Multiple Objectives: • Ecosystem • Navigation • Recreation • Hydroelectricity Production • Coastal real estate

43. Great Lakes water levels still remain far below average, official says Mar 4, 2013    Great Lakes in the news: Deep trouble: Great Lakes water levels fall to economically perilous lows Climate change lowering Great Lakes levels, retired Army Corps expert tells Bay City crowd Great Lakes levels up slightly, but 'boaters are going to be shocked' • By Jim Lynch • The Detroit News

44. Great Lakes “System” http://mff.dsisd.net/mff/Images/GreatLakeProfiles2.jpg

47. Lake Superior historic monthly water levels Historic Range = 1.2 m

48. Climate Change Projections of Net Basin Supply - Lake Superior, 2050

49. Problem Select a lake regulation plan that satisfies multiple stakeholder objectives for the next 30 years

50. Challenges • System not well understood • Multi-million investment in climate science yielded greater uncertainty • Future highly uncertain (deep or severe) • Multiple competing objectives with non-additive costs and benefits • Stakeholders would not agree on scenarios • “True Believers” vs “Skeptics” • Decision to last 20-30 years