Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Hydrologic change: What do we, and don’t we know? Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Symposium in honor of 40 years of research by Professor Enda O’Connell University of Newcastle Newcastle upon Tyne March 26, 2009 A perspective on the evolution of hydrology over ~40 years The end of the era of major dam construction 13,382dams, Visual courtesy Hiroshi Ishidaira, Yamanashi University Reservoir construction has slowed post ~1970 800 . 700 Number of Reservoirs 600 500 Australia/New Zealand Africa Asia Europe Central and South America North America 400 300 200 100 0 Up to 1901- 1911- 1921- 1931- 1941- 1951- 1961- 1971- 1981- 19901900 1910 1920 1930 1940 1950 1960 1970 1980 1990 1998 visual courtesy Peter Gleick Arguably, the challenge of the 70s was to characterize hydrologic variability, with an implicit assumption of stationarity (or at least quasi-stationarity) 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. What are the challenges of the 2000s? • From Science (2006) 125th 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? 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. Agents of hydrologic change, and examples –Land cover change –Climate change –Water management Landslides in Stillman Creek Drainage, upper Chehalis River Basin, WA, December, 2007 Visual courtesy Steve Ringman, The Seattle Times Water management and Hydrologic change Columbia River at the Dalles, OR Historic Naturalized Flow Estimated Range of Naturalized Flow With 2040’s Warming Regulated Flow Figure 1: mean seasonal hydrographs of the Columbia River prior to (blue) and after the completion of reservoirs that now have storage capacity equal to about one-third of the river’s mean annual flow (red), and the projected range of impacts on naturalized flows predicted to result from a range of global warming scenarios over the next century. Climate change scenarios IPCC Data and Distribution Center, hydrologic simulations courtesy of A. Hamlet, University of Washington. from Mote et al, BAMS 2005 From Stewart et al, 2005 Arctic River Stream Discharge Trends Discharge, km3/yr Discharge, km3 Peterson et al. 2002 Visual courtesy Jennifer Adam Winter Trend, Ob’ 1950 Discharge, m3/s • Discharge to Arctic Ocean from six largest Eurasian rivers is increasing, 1936 to 1998: +128 km3/yr (~7% increase) • Most significant trends during the winter (lowflow) season Annual trend for the 6 largest rivers 40 30 1960 Monthly Means Ob’ 1970 1980 GRDC 20 10 J F M A M J J A S O N D About 50% of the 400 sites show an increase in annual minimum flow from 1941-70 to 1971-99 Minimum flow Increase No change Decrease Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002 About 50% of the 400 sites show an increase in annual median flow from 1941-71 to 1971-99 Median flow Increase No change Decrease Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002 About 10% of the 400 sites show an increase in annual maximum flow from 1941-71 to 1971-99 Maximum flow Increase No change Decrease Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002 USGS streamgage annual flood peak records used in study (all >=100 years) Visual courtesy Bob Hirsch Are floods correlated with Water Year? Negative 17 6 5 2 All sites = 0.1 = 0.05 = 0.01 Which sites Broad (GA) significant at Logan (UT) = 0.01 ? Positive 19 7 7 5 Red Lake (MN) Red (MN/ND) Pembina (ND) Minnesota (MN) Arkansas (KS) Visual courtesy Bob Hirsch Predicting hydrologic change: The Puget Sound basin as a case study The role of changing land cover – 1880 v. 2002 1880 2002 Tmin at selected Puget Sound basin stations, 1916-2003 The Distributed Hydrology-SoilVegetation Model (DHSVM) Land cover change effects on seasonal streamflow for eastern (Cascade) upland gages Land cover change effects on seasonal streamflow at selected eastern lowland (Greater Seattle area) gages Predicted temperature change effects on seasonal streamflow at eastern (Cascade) upland gages Predicted temperature change effects on seasonal streamflow at selected eastern lowland gages (greater Seattle area) 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). 58% +10% 67% 62% 58% 96% +2% 62% 62% 71% 87% -2% 75% 100% 67% 67% 67% -5% -10% -25% (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.) Decrease 87% +5% Increase +25% RUNOFF SENSITIVITY OF COLORADO RIVER DISCHARGE TO CLIMATE CHANGE Figure 9 Annual Releases to the Lower Basin 14 1.2 Average Annual Release to Lower Basin (BCM/YR) Probability release to Lower Basin meets or exceeds target (probability) 12 1 target release 10 8 0.6 6 0.4 4 0.2 2 0 0 Historical Control Period 1 Period 2 Period 3 Probability BCM / YR. 0.8 Keeping score: where do we do (at least passably) well? Detecting change (statistical tools are reasonably well adapted to the problems) Predicting change (albeit with a conditional chain of models) And where do we do fall short? Attribution of hydrologic change; and Providing meaningful estimates of uncertainty of future projections (i.e., how uncertain are our model sensitivities)? 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 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 Example of ensemble method 9000 cfs 7200 Week 22 5400 3600 1800 0 1 3 5 7 9 11 13 15 17 19 21 ensemble rank for the 2020s • • • • • • Historical (1917-2006), weekly averages start Oct 1 2020s ensembles of 20 A1B and 19 B1, delta method produce 90 years with a climate resembling 2005 to 2035 2020s composite of A1B and B1 (2005-2035) 2040s composite of A1B and B1 (2025-2055) 2080s composite of A1B and B1 (2065-2095) Probability distributions at specified time