1. Understanding hydrologic change: Diagnosis, attribution, and prediction Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington for Department of Atmospheric Sciences University of Illinois Champagne Urbana March 11, 2009

2. Outline of this talk Hydrologic change as a grand challenge to the community Some examples of hydrologic change associated with land cover, climate, water management Observational evidence Confounding of change agents -- diagnostic studies Predicting hydrologic change – examples –Reconstructing Land cover and climate change in the Puget Sound basin –Predicting climate change impacts in the Columbia River basin –Colorado River hydrologic sensitivity to climate change Unanswered questions and challenges

3. What are the “grand challenges” in hydrology? From Science (2006) 125 th Anniversary issue (of eight in Environmental Sciences): Hydrologic forecasting – floods, droughts, and contamination From the CUAHSI Science and Implementation Plan (2007): … a more comprehensive and … systematic understanding of continental water dynamics … From the USGCRP Water Cycle Study Group, 2001 (Hornberger Report): [understanding] the causes of water cycle variations on global and regional scales, to what extent [they] are predictable, [and] how … water and nutrient cycles [are] linked?

4. Important problems all, but I will argue instead (in addition) that understanding hydrologic change should rise to the level of a grand challenge to the community.

5. Basic premise Humans have greatly affected the land surface water cycle through –Land cover change –Climate change –Water management While climate change has received the most attention, other change agents may well be more significant

6. Landslides in Stillman Creek Drainage, upper Chehalis River Basin, WA, December, 2007 Visual courtesy Steve Ringman, The Seattle Times

7. Tmin at selected Puget Sound basin stations, 1916-2003

8. Water management and Hydrologic change Columbia River at the Dalles, OR

9. Observational studies

10. Observed streamflow trends, 1944- 93 (from Lins and Slack, GRL, 1999) Number of trends Location of trends

11. Arctic River Stream Discharge Trends Discharge to Arctic Ocean from six largest Eurasian rivers is increasing, 1936 to 1998: +128 km 3 /yr (~7% increase) Most significant trends during the winter (low- flow) season Discharge, km 3 /yr Annual trend for the 6 largest rivers Peterson et al. 2002 J F M A M J J A S O N D 10 20 30 40 Discharge, m 3 /s GRDC Monthly Means Ob’ 1950 1960 1970 1980 Discharge, km 3 Winter Trend, Ob’ Visual courtesy Jennifer Adam

12. from Mote et al, BAMS 2005

13. From Stewart et al, 2005

14. Model Runoff Annual Trends 1925-2003 period selected to account for model initialization effects Positive trends dominate (~28% of model domain vs ~1% negative trends) Positive + Negative Drought trends in the continental U.S. – from Andreadis and Lettenmaier (GRL, 2006)

15. HCN Streamflow Trends Trend direction and significance in streamflow data from HCN have general agreement with model-based trends Subset of stations was used (period 1925-2003) Positive (Negative) trend at 109 (19) stations

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

17. Diagnostic studies – attempting to unravel confounding effects

18. The Distributed Hydrology-Soil- Vegetation Model (DHSVM)

20. Explicit Representation of Downslope Moisture Redistribution Lumped Conceptual (Processes parameterized)

21. DHSVM Snow Accumulation and Melt Model

22. Model residuals analysis (hydrologic model simulation minus observed discharge, where model is run with fixed vegetation inputs, and essentially serves as a control) From Bowling et al, WRR, 2000

23. Time series of key variables (obs.) All variables have been normalized (fractionalized) by dividing by the CCSM3-FV control run mean over first 300 yrs. Necessary for the multivariate detection and attribution (D&A), so have same variance in each variable (the “units problem”). Visual courtesy Tim Barnett, SIO

24. Ensemble signal strength & significance (conclusion: as much as 60% of observed change is attributable to anthropogenic causes) Fingerprint Signal Strength Significance Visual courtesy Tim Barnett, SIO

25. Annual Air Temperature/Stream Flow Correlation -15 -10 -5 0 Air Temperature,  C T/Q Correlation 0.4 0.2 0.0 -0.2 -0.4 COLD: no T control on Q THRESHOLD: T control through permafrost melt WARM: T control through Evapotranspiration (+) Correlation (-) Correlation Adam et al (2007): Diagnosing causes of Arctic river discharge trends

26. Temperature Trends, 99% Precipitation Trends, 99% mm/year  C/year Lena Yenisey Ob’ Secondary Basins

27. Stream Flow Trends, 99% Lena Ob’ Yenisey Trend, mm/year 1940 1960 1980 2000

28. Precipitation Trends (for periods with stream flow 99%) Lena Yenisey Trend, mm/year 1940 1960 1980 2000 Ob’

29. Diagnosing hydrologic change: The Puget Sound basin as a case study

30. Topography of the Puget Sound basin

31. The role of changing land cover – 1880 v. 2002 1880 2002

32. Tmin at selected Puget Sound basin stations, 1916-2003

33. Tax at selected Puget Sound basin stations, 1916-2003

34. Precipitation at selected Puget Sound basin stations, 1916-2003

35. The Distributed Hydrology-Soil- Vegetation Model (DHSVM)

36. Study Areas Puget Sound basin, Washington State, USA Temperate marine climate, Precipitation falls in October – March Snow in the highland, rare snow in the lowland Targeted sub-basins

37. Model Calibration

38. Land cover change effects by region

39. Predicted temperature change effects on seasonal streamflow by region

40. Comparison of temperature change and land cover change effects on annual maximum flows, selected upland and lowland basins

41. Washington State Climate Change Impacts Assessment Mandated by 2007 State Legislature; to be focused on: Public health, Coastal zone Forestry Stormwater Infrastructure Water supply Salmon and ecosystems Energy

43. Global Climate Models 2 different emissions scenarios 20 models using A1B (medium scenario) 19 models using B1 (low scenario) Downscaled to regional projections of P and T for the 2020s, 2040s, 2080s Hydrologic Models Projections of future changes in snowpack, streamflow, soil moisture, etc. Energy Water ManagementForestsAgriculture Salmon Infrastructure

44. Large Scale Model (VIC) ~12mi 2 per cell Hydrologic Simulations Fine Scale Model (DHSVM) ~6 acres per cell

45. Climate Change Projections (using “delta method”) 39 Climate Change Scenarios - each is a monthly timeseries of P and T from 2000-2099 3 chosen projection windows 200020502100 Mean  P &  T for 2020s (2010-2039) Mean  P &  T for 2040s (2030-2059) Mean  P &  T for 2080s (2070-2099) Historical daily timeseries (1916-2006) perturbed by mean monthly  P &  T (same mean  P and  T applied to each day in a given month) New daily timeseries which incorporates historical daily patterns and future projections of precipitation and temperature

46. Focus Watersheds Columbia River –Washington portion Puget Sound –Green River –Snohomish River –Cedar River –Tolt River Yakima River

47. Elsner, M.M. et al. 2009: Implications of 21 st Century climate change for the hydrology of Washington State (in review) Low Medium

48. Rain dominant watershed Monthly Streamflow Projections Transient rain-snow watershed Snowmelt dominant watershed

49. Cedar River - inflow to Chester Morse Reservoir Weekly Streamflow Projections Yakima River at Parker

50. Precipitation in fall-winter, water demand in summer Water management systems: Seattle - municipal, fish Tacoma - municipal, flood control Everett - municipal, hydropower Reservoir capacities small relative to annual flow Case study 1: Puget Sound Basin

51. Puget Sound Basin Variations in impacts within and between systems (A1B) Seattle, M&I and environmental flows Tacoma, flood control, more constrained storage Everett, hydropower, more interannual variability TacomaEverettSeattle

52. M&I reliability measures, differ for all systems Current demand, reliability little impact from future change (A1B) Tacoma, water allocations closer to current system capacity Everett, largest system capacity Note: simulations prior to adaptations Puget Sound Basin municipal supply - current demand 1% 7%0%

53. Case study 2: Yakima River Basin Irrigated crops largest agriculture value in the state Precipitation (fall-winter), growing season (spring-summer) Five USBR reservoirs with storage capacity of ~1 million acre-ft, ~30% unregulated annual runoff Snowpack sixth reservoir Water-short years impact water entitlements

54. Yakima Basin Methods

55. Yakima River Basin Unregulated

56. Yakima River Basin Unregulated Basin shifts from snow to more rain dominant

57. Yakima River Basin Basin shifts from snow to more rain dominant management model UnregulatedRegulated

58. Yakima River Basin Basin shifts from snow to more rain dominant Water prorating, junior water users receive 75% of allocation

59. Yakima River Basin Basin shifts from snow to more rain dominant Water prorating, junior water users receive 75% of allocation Junior irrigators less than 75% prorating (current operations): 14% historically 32% in 2020s A1B (15% to 54% range of ensemble members) 36% in 2040s A1B 77% in 2080s A1B historical 2020s2080s

60. Crop Model - Apple Yields Yields decline from historic by 20% to 25% (2020s) and 40% to 50% (2080s)

61. Shifts in energy production and demand

62. WACCIA 12 Feb 2009 more winter flooding Models project more winter flooding in sensitive “transient runoff” river basins that are common in the Cascades, Likely reducing survival rates for incubating eggs and rearing Salmon and Ecosystems

63. WACCIA 12 Feb 2009 Summer base flows are projected to drop substantially duration of the summer low flow season is also projected to increase in snowmelt and transient runoff rivers Summer base flows are projected to drop substantially (5 to 50%) for most streams in western WA and the Cascades, and the duration of the summer low flow season is also projected to increase in snowmelt and transient runoff rivers, reduceing rearing habitat

64. WACCIA 12 Feb 2009

65. Annual Releases to the Lower Basin target release RUNOFF SENSITIVITY OF COLORADO RIVER DISCHARGE TO CLIMATE CHANGE

66. Christensen and Lettenmaier (HESSD, 2007) – multimodel ensemble analysis with 11 IPCC AR4 models (downscaled as in C&L, 2004)

67. Magnitude and Consistency of Model-Projected Changes in Annual Runoff by Water Resources Region, 2041-2060 Median change in annual runoff from 24 numerical experiments (color scale) and fraction of 24 experiments producing common direction of change (inset numerical values). +25% +10% +5% +2% -2% -5% -10% -25% Decrease Increase (After Milly, P.C.D., K.A. Dunne, A.V. Vecchia, Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, 347-350, 2005.) 96% 75% 67% 62% 87% 71% 67% 62% 58% 67% 62% 58% 67% 100%

68. from Seager et al, Science, 2007

69. Question: Why such a large discrepancy in projected Colorado River flow changes? ~6=7% annual flow reduction in Christensen and Lettenmaier (2007) 10-25% by Milly et al (2005) > 35% by Seager et al (2007) Understanding the hydrologic sensitivities

70. Dooge (1992; 1999): For temperature, it’s more convenient to think in terms of sensitivity (v. elasticity) where Ψ P is elasticity of runoff with respect to precipitation

71. VIC NOAHSAC Unconditional histograms of 1/8 degree grid cell precipitation elasticities from model runs of three land surface models (VIC, NOAH, and SAC) over Colorado Basin for 20 years, 1985-2005

72. ModelPrecipitation -Elasticity Temp- sensitivity (Tmin & Tmax ) %/ 0 C Temp- sensitivity ( Tmax) %/ 0 C Flow @ Lees Ferry (MACF) VIC1.9-2.2-3.315.43 NOAH1.81-2.85-3.9317.43 SAC1.77-2.65-4.1015.76 Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC, NOAH, and SAC models

73. VIC Precipitation elasticity histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

74. Spatial distribution of precipitation elasticities Censored spatial distribution of annual runoff

75. Composite seasonal water cycle, by quartile of the precipitation elasticity distribution

76. Temperature sensitivity (equal change in Tmin and Tmax) histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

77. Spatial distribution of temperature sensitivities (equal changes in Tmin and Tmax) Censored spatial distribution of annual runoff

78. Composite seasonal water cycle, by quartile of the temperature sensitivity (equal change in Tmin and Tmax) distribution

79. So is there, or is there not, a dichotomy between the various estimates of mid-century Colorado River runoff changes? Replotted from Seager et al (2007)

80. b) On the other hand, from Seager et al (2007), very roughly, mid-century ΔP  -18%, so for = 1.5-1.9, and temperature sensitivity  -0.02 - -0.03, and ΔT  2 o C, ΔQ  35% (vs > 50% + from GCM multimodel average) a) Lowest mid-century estimate (Christensen and Lettenmaier, 2007) is based on a precipitation downscaling method that yields smaller mid-century precipitation changes. Adjusting for this difference nearly doubles the projected change to around 10% by mid century – not far from Milly et al (2005), but still well below Seager et al (2007)

81. More important, though, is the question: In the context of hydrologic sensitivities to (global) climate change, does the land surface hydrology matter, or does it just passively respond to changes in the atmospheric circulation? i.e., in the long-term mean, VIMFC  P-E  Q, so do we really need to know anything about the land surface to determine the runoff sensitivity (from coupled models)? OR is the coupled system sensitive to the spatial variability in the processes that control runoff generation (and hence ET), and in turn, are there critical controls on the hydrologic sensitivities that are not (and cannot, due to resolution constraints) be represented in current coupled models?

82. The answer … … Probably lies in high resolution, coupled land- atmosphere simulations, that resolve areas producing most runoff, and their role in modulating (or exacerbating) regional scale sensitivities.

83. Concluding thoughts Ample evidence that the hydrology of the U.S. (and elsewhere) is changing Diagnosis remains a challenge (and inherently involves modeling and model assumptions) Stationarity assumption (which is the underpinning of most water planning and management decisions) is no longer defensible (especially in the western U.S.) What to replace it with remains an open question Prediction approaches inherently involve a chain of models; uncertainty in the predictions is a key issue for decision makers Interactions between large scale and local land hydrological processes remain a key question