1. Modeling regional consequences of climate variability and change Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington University of Washington Program on Climate Change Summer Institute Issues in regional climate modeling and evaluating impacts June 19, 2003 Leavenworth, WA

2. Questions Can we distinguish between human-caused and natural variability in streamflow records? Can we distinguish climate influences from human (land use change) influences on the land surface branch of the hydrologic cycle? How can the sensitivities and vulnerabilities of water resource systems to climate change best be assessed? What information is needed from climate models (and at what spatial and temporal resolution) to project the impacts of climate change on hydrology and water resources? What information do water resource managers need to incorporate the effects of climate change into planning and design? On what timeline and under what circumstances do socioeconomic changes matter for climate impacts projections? What are the implications of climate prediction uncertainty for water resources planning? What are the distinguishing features of water resources issues in the Pacific Northwest?

3. 1) Can we distinguish between human- caused and natural variability in streamflow records?

4. a) The future as indicated by climate models  Increasing T -> increased atmospheric moisture -> increased P  Hence increased risk of hydrologic extremes

5. source: Ziegler et al, J. Clim, 2003

6. A widely advanced hypothesis regarding acceleration of the global water cycle: “According to model predictions, the most significant manifestation of climate change would be an acceleration of the global water cycle, leading to … a general exacerbation of extreme hydrologic regimes, floods and droughts” (NASA Global Water and Energy Cycle solicitation, 2000). “There is evidence that suggests that the global hydrologic cycle may be intensifying, leading to an increase in the frequency of extremes” (Hornberger et al, USGCRP water cycle science plan) Climate models generally project an acceleration in the rate of global water cycling and an increase in global precipitation … (Morel, GEWEX News, 2001)

7. b) The situation as indicated by observations over the last ~ century Increased in mean and “extreme” P over much of continental U.S. except winter But no apparent changes in floods (although many upward trends in low flows over much of the country)

9. (from Lins and Slack, 1999)

11. “Since 1910, precipitation has increased by about 10% across the contiguous United States. The increase in precipitation is reflected primarily in the heavy and extreme daily precipitation events. For example, over half (53%) of the total increase of precipitation is due to positive trends in the upper 10 percentiles of the precipitation distribution.” (Karl and Knight, BAMS, 1998)

12. from Karl and Knight, 1998 Percent contribution of upper 10 th percentile daily precipitation to annual total, averaged over U.S.

13. Groisman et al (2001) In three of five regions of the eastern two-thirds of the contiguous U.S., a significant increase in the frequency of “very heavy” precipitation events (> 101.6 mm/day) occurred during the 20th century. The return period of “very heavy” precipitation events changed during the past century in the Midwest from 10 to 7 years, in the South from 4 to 2.7 years, and in the Northeast from 26 to 11 years.

14. c) Is there any relationship between trends in heavy precipitation and (lack of) trends in floods?

15. Source: Groisman et al, BAMS, 2001

17. “In the Eastern half of the United States we found a significant relationship between the frequency of heavy precipitation and high streamflow events both annually and during the months of maximum streamflow. An increase of spring heavy precipitation events over the eastern United States indicates with high probability that during the 20th century an increase of high streamflow conditions has also occurred.” Groisman et al, BAMS, 2001

18. Conclusions Results of U.S. studies seemingly inconsistent, but based on statistical analysis, number of trends in annual maximum flood is barely larger than would be expected by chance (and probably not field significant) Natural variability is large enough to obscure fairly large changes, suggests aggregation and/or compositing approaches (but these tend to complicate interpretation) Some of the apparent inconsistencies may well have to do with attempts to perform “simple” time series type approaches to a complicated nonlinear process (issues e.g. with spatial scale of precipitation-runoff interactions and their variability with season, antecedent conditions, temporal signature of extreme precipitation, and surface conditions Is it really possible to make more progress on the problem without a dynamic modeling approach?

19. 2) Can we distinguish climate influences from human (land use change) influences on the land surface branch of the hydrologic cycle?

20. Source: National Institute of Public Health and the Environment (RIVM, Netherlands) and the Center for Sustainability and the Global Environment (SAGE, University of Wisconsin). Estimated 1850 and 1990 global land cover

21. Early Conifer Middle Conifer Late Conifer Early Deciduous Middle Deciduous Late Deciduous Brush Agriculture Water Historical (1900) Current (1990) Columbia River basin estimated 1900 and 1990 vegetation cover (from ICBEMP)

27. 3) How sensitive is the climate system to land surface feedbacks?

30. Production of a hydrologic data set for the continental U.S. Using VIC land surface model, simulation run for 50 years at 3-hour time step Input Time series of spatial data One terabyte of output archived

31. Predictability due to Soil Moisture Widespread predictability at 0 lead (1½ month) Little predictability in zones where winter runoff is high For summer runoff, significant predictability up to 3 seasons

32. Predictability due to Soil Moisture Widespread predictability at 0 lead (1½ month) Little predictability in zones where winter runoff is high For summer runoff, significant predictability up to 3 seasons

33. Exploratory Work on Teleconnection between SST and Soil Moisture Study Domain and Datasets Sea surface temperature: Extended Reconstruction of Global Sea Surface Temperature data set based on COADS data. (1847-1997) developed by T.M. Smith and R.W. Reynolds, NCDC. The original data resolution is 2ºlongitude, 2 º latitude. It was interpolated into 0.5 º resolution (The ocean domain is chosen according to the Bin Yu and J.M. Wallace’s paper, 2000, J. Climate, 13, 2794-2800) Soil Moisture: VIC retrospective land surface dataset (1950-1997). The original data with 1/8 degree resolution is aggregated into 0.5 º resolution.

34. Soil Moisture Predictability by Persistence and SST PCs The highest variance explained is more than 90%. For June, over 40% of the variance is explained over most of the study domain, including Mexico.

35. Introducing SST PCs benefits long-time lead predictability (of June soil moisture), but no significant benefits for less than 6-month lead time predictability. SST and Persistence Persistence

36. 4) How can the sensitivities and vulnerabilities of water resource systems to climate change best be assessed?

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

38. Weak links Chain of models (accumulation of errors) Extrapolation of (hydrology model) parameterizations beyond tested range Climate (and hydrology) model biases Downscaling issues Improper characterization of management environment and objectives

39. 5) What information is needed from climate models (and at what spatial and temporal resolution) to project the impacts of climate change on hydrology and water resources?

40. Precipitation, precipitation, and precipitation (and then temperature, humidity, wind, surface solar and longwave radiation, and other forcing variables) Quantifiable accuracy at the native resolution of the climate model (not clear that the push for higher and higher resolution is helping us) Better understanding of the interaction of topography and future change

41. 6) What information do water resource managers need to incorporate the effects of climate change into planning and design?

42. The joint (in space and time) probability distribution of future streamflows In practice, we only get at this via simulation given surface climate forcings and a hydrology model, hence see previous question But, perhaps some method of quantifying the uncertainty across models (in a method other than multiple scenarios, which leads to throwing up hands and saying that it’s all too uncertain to be useful Note that the default (used by essentially all water resources planners for large systems) is to base planning on an historic set of observed streamflows – typically of length around 50 years

43. 7) On what timeline and under what circumstances do socioeconomic changes matter for climate impacts projections?

44. Lesser of economic planning horizon (typically around 40 years depending on discount rate) and institutional planning horizon (typically 10-20 years) Longer in cases where decisions require or would result in irreversible commitments

45. 8) What are the implications of climate prediction uncertainty for water resources planning?

46. In an ideal world, none – planners deal with uncertainty all the time, it just needs to be couched in terms they are used to dealing with (e.g., natural variability is not very well characterized in an observation record of length ~50 years) In practice, lots – climate prediction uncertainty is used as an excuse to do nothing (in fairness, absence of planning methods that aren’t focused on using longest length of historic record available, and standards of professional practice, play a role as well)

47. 9) What are the distinguishing features of water resources issues in the Pacific Northwest?

48. Relatively low natural interannual variability Mediterranean climate leads to extended low flow period in late summer and early fall Strong effects of snow on seasonal hydrographs, especially in Columbia basin interior (mixed and some rainfall dominant basins on the west side) Relatively small (relative to mean annual flow) reservoir storage, meaning it is operated mostly for seasonal, rather than interannual carryover Large role of hydropower (greater than anywhere else in the U.S., dominates reservoir operation in the Columbia basin ESA listing of salmonids (major effects on reservoir operation), and other environmental considerations especially affect low flow management