What are the obstacles to use of climate information in water management?

1. What are the obstacles to use of climate information in water management? Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington 2011 University of Oklahoma International WaTERConference October 24, 2011

2. Outline • The hydrology of the U.S. (and especially the Western U.S.) is changing • Understanding and predicting the nature of hydrologic change • Unanswered scientific issues • Barriers to including climate information in water resources planning

3. 1. The hydrology of the U.S. is changing

4. from Mote et al, BAMS 2005

5. From Stewart et al, 2005

6. Positive + Negative Soil Moisture Annual Trends • Positive trends for ~45% of CONUS (1482 grid cells) • Negative trends for ~3% of model domain (99 grid cells)

7. Continental U.S. weighted contribution of upper 10th percentile to annual precipitation, 1910-96 From Karl and Knight, 1998

8. Trends in annual precipitation maxima in 100 largest U.S. urban areas, 1950-2009 from Mishra and Lettenmaier, GRL 2011

9. Number of statistically significant increasing and decreasing trends in U.S. streamflow (of 395 stations) by quantile (from Lins and Slack, 1999)

10. 2. Understanding and predicting the nature of hydrologic change

11. Climate Scenarios Performance Measures Downscaling Global climate simulations, next ~100 yrs Delta Precip, Temp Reliability of System Objectives Water Management Model Hydrologic Model (VIC) DamReleases, Regulated Streamflow Natural Streamflow

12. bias-corrected climate scenario from NCDC observations month m month m raw climate scenario from PCM historical run Bias Correction Note: future scenario temperature trend (relative to control run) removed before, and replaced after, bias-correction step.

13. monthly PCM anomaly (T42) interpolated to VIC scale VIC-scale monthly simulation observed mean fields (1/8-1/4 degree) Spatial Downscaling

14. 2a. Example 1: Columbia River basin hydropower and fish flow reliability

15. BAU 3-run average historical (1950-99) control (2000-2048) PCM Business-as-Usual scenarios Columbia River Basin (Basin Averages)

19. 2b. Example 2: Hydrology and water resources implications – Colorado River system

20. Lake Mead – 2/2010 02, 2000 110 ft 02, 2006 30 ft 02, 2010 02, 2009

21. Timeseries Annual Average PCM Projected Colorado R. Temperature ctrl. avg. hist. avg. Period 1 2010-2039 Period 2 2040-2069Period 3 2070-2098

22. Timeseries Annual Average PCM Projected Colorado R. Precipitation hist. avg. ctrl. avg. Period 1 2010-2039 Period 2 2040-2069Period 3 2070-2098

23. Annual Average Hydrograph Simulated Historic (1950-1999)Period 1 (2010-2039)Control (static 1995 climate)Period 2 (2040-2069)Period 3 (2070-2098)

24. Natural Flow at Lee Ferry, AZ allocated20.3 BCM Currently used 16.3 BCM

26. Total Basin Storage

27. Annual Releases to the Lower Basin target release

28. Annual Releases to Mexico target release

29. Annual Hydropower Production

30. 2c. Example 3: Hydrology and water resources implications – Yakima River basin, Washington

31. Focus Watersheds • Columbia River • Washington portion • Puget Sound • Green River • Snohomish River • Cedar River • Tolt River • Yakima River

32. Yakima River Basin management model Unregulated Regulated • Basin shifts from snow to more rain dominant

33. Yakima River Basin water management effects

34. 3. Unanswered scientific issues

35. Much of the uncertainty in the projections comes from uncertainty in the global climate models (especially precipitation) But what about the hydrologic uncertainties? There are many,but three in particular: 1) Uncertainty in hydrologic model predictions of the precipitation and temperature sensitivities of annual runoff 2) Uncertainties in the sensitivity of floods to changes in precipitation 3) Uncertainties in the coupled interaction of atmospheric circulation and river runoff in topographically complex areas

36. Precipitation Elasticities of runoff percent change in annual runoff per percent increase in precipitation Q ref-1% - Qref precip elasticity = Qref 1% precip elast, Colorado basin reference precipitation (100% = historic)

37. Number of statistically significant increasing and decreasing trends in U.S. streamflow (of 395 stations) by quantile (from Lins and Slack, 1999)

38. Sensitivity of projected change in runoff to spatial resolution from Seager et al, Science, 2007

39. 4. Barriers to including climate change information in water resources planning

40. Stationarity—the idea that natural systems fluctuate within an unchanging envelope of variability—is a foundational concept that permeates training and practice in water-resource engineering. In view of the magnitude and ubiquity of the hydroclimatic change apparently now under way, however, we assert that stationarity is dead and should no longer serve as a central, default assumption in water-resource risk assessment and planning.

41. Some responses from the water management community: • It (climate change) is just a manifestation of natural variability, hence for planning purposes, just requires properly capturing the stochastic properties of the historical record • Climate model output is too uncertain to provide useful information for water resources planning and/or decision making • Most water resources decision making is tied to a particular historical record (so-called critical period planning). This has legal implications (standard of practice). • “Existing planning protocols already incorporate uncertainty”

42. “Synthetic hydrology” c. 1970 Figure adapted from Mandelbrot and Wallis (1969)

43. Ensembles of Colorado River (Lees Ferry) temperature, precipitation, and discharge for IPCC A2 and B1 scenarios (left), and 50-year segments of tree ring reconstructions of Colorado Discharge (from Woodhouse et al, 2006)

44. Climate change scenarios Seager et al. 2007 19 GCMs, A1B scenario Christensen and Lettenmaier, 2007 11 GCMs, A1B scenario Non union 8 GCMs, A1B scenario Data provided by Qiuhong Tang

45. Hybrid Climate Change Perturbations New time series value = 19000 Objective: Combine the time series behavior of an observed precipitation, temperature, or streamflow record with changes in probability distributions associated with climate change. Value from observed time series = 10000 visual courtesy Alan Hamlet

46. Observed and Climate Change Adjusted Naturalized Streamflow Time Series for the Snake River at Ice Harbor KAF KAF Blue = Observed time series Red = Climate change time series visual courtesy Alan Hamlet

47. Conclusions • There is a disconnect between the climate science and water management communities. They are aware of climate projections, and may be using them informally, but formally, most decisions are still based on analysis of historical observations. • There is a need to update and extend the work in planning under uncertainty (e.g., the Harvard Water Program of the 1960s) for nonstationary environments. • Dealing with (lack of) consistency in climate projections (periodic updates) is one key aspect of the problem.