Towards assessment and prediction of hydrologic change Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington European Geosciences Union Session HS 1.1: New instrumentation and data analysis techniques for a developing hydrolgy Vienna April 22, 2009

A perspective on the evolution of hydrology over ~40 years

13,382dams, The end of the era of major dam construction これは構築したデータベースを用いて、ダムの経年変化を示したものです。 これより、欧米を中心に次第にアジアへと広まっていることがわかります。 Visual courtesy Hiroshi Ishidaira, Yamanashi University

Reservoir construction has slowed post ~1970 The focus on new “infrastructure” is slowing due to economic, political, and environmental factors. 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.

Some evolving problems in hydrology: Land cover change Climate change Hydrologic extremes

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

from Mote et al, BAMS 2005

From Stewart et al, 2005

Arctic River Stream Discharge Trends Discharge, km3/yr Annual trend for the 6 largest rivers Peterson et al. 2002 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 (low-flow) season 1950 1960 1970 1980 Discharge, km3 Winter Trend, Ob’ In 2002, Peterson et al. published a paper in Science in which they showed that the combined riverine input of the six largest northern eurasian basins has increased by approximately 7% since the 1930’s. Furthermore, a paper published by Bowling et al. in 2000 demonstrated that the winter flows had the most significant trends, which is shown here for the Ob’ river. The bottom figure shows the hydrograph, or mean monthly flows, at the outlet of the Ob’ basin. The shape of this hydrograph is typical for northern rivers in which there are strong contrasts between seasons – with very high flows during the late spring and summer and very low flows during the cold season. Therefore, the most significant changes are occurring during the season with the least flow. The purpose of this study is to investigate the causes of these stream flow changes. J F M A M J J A S O N D 10 20 30 40 Discharge, m3/s GRDC Monthly Means Ob’ Visual courtesy Jennifer Adam

About 50% of the 400 sites show an increase in annual minimum flow from 1941-70 to 1971-99 Locations of sites with significant (based on t-test) up or down change in annual minimum flow 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 Location of sites with significant (based on t-test) up or down change in annual median flow 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 Location of sites with significant (based on t-change) change in annual maximum flow. 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 Positive All sites 17 19  = 0.1 6 7  = 0.05 5  = 0.01 2 Which sites significant at  = 0.01 ? Broad (GA) Logan (UT) Red Lake (MN) Red (MN/ND) Pembina (ND) Minnesota (MN) Arkansas (KS) Visual courtesy Bob Hirsch

Hydrologic extremes – is it time for a new look at an old problem (e.g., Weitzmann et al, “fat tailed” distributions in climatic extremes “Condition of separation”, replotted from Matalas et al, WRR, 1975

Predicting hydrologic change: The Puget Sound basin as a case study

The role of changing land cover – 1880 v. 2002

Tmin at selected Puget Sound basin stations, 1916-2003

The Distributed Hydrology-Soil-Vegetation 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). +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% 58% 100% Slide 1 shows a subset of the information on slide 2. The notes are the same for both slides. The Water Resources Regions are colored according to the projected percent change in mean annual runoff for the period 2041-2060, relative to the period 1901-1970. These projections are model-estimated changes associated with hypothetical ("SRES A1B" scenario) changes in climate forcing. The printed value inside each region of the map (slide 2 only) is the majority percentage of the 24 experiments that are in agreement on the direction (increase or decrease) of change; for example, the value of 67 in the Texas Gulf region indicates that 67% (i.e., 16) of the 24 experiments projected a decrease in runoff. Actual future changes in runoff can be expected to differ from these projections, primarily because of departures of actual forcing from the SRES A1B scenario, errors in the models' representation of runoff response to climate forcing, and unforced variability ("randomness") of the climate system. The majority percentages (slide 2 only) should not be read as probabilities, but rather as a combined measure of two factors: the degree of agreement among models and the modeled strength of the forced runoff change relative to modeled internal variability of the climate system. In Water Resources Regions for which a strong majority of experiments agree on the direction of change (Alaska, Upper Colorado, Lower Colorado, and Great Basin), the models suggest that the forced runoff change by 2041-2060 will be large compared to unforced runoff variability. In other Regions, either (1) the forced runoff change will not be large relative to the model estimate of unforced variability, or (2) the forced runoff change will be large relative to unforced variability, but this fact is obscured by substantial differences in model errors from one model to the next. The color of a Region is determined only by changes in runoff produced inside the Region. However, where a downstream Region (e.g., the Lower Colorado or the Lower Mississippi) receives streamflow from one or more upstream Regions, the streamflow through the downstream Region will be affected by runoff changes in both the downstream and the upstream Regions. Thus, increasing runoff in the Upper Mississippi, Ohio, and Tennessee Regions implies increasing flow of the Mississippi River through the Lower Mississippi Region, even though the projected runoff change in the Lower Mississippi Region is small. The figure is based on figure 4 of Milly et al. (2005); that reference documents the computations in detail. The computational differences from the published figures are (1) the geographic scope here is limited to the United States; (2) instead of depicting changes in point values of runoff, this figure depicts only changes in areal averages of runoff over Water Resources Regions of the U.S. Water Resources Council; (3) the composite across experiments is formed from the median instead of the mean. The projected changes are median values over a set of 24 climate-model experiments conducted on 12 climate models. The number of experiments exceeds the number of models, because the experiment was run more than once on some models. The 12 models used were the subset of 23 (IPCC AR4) candidate models that best reproduced the global pattern of observed time-mean streamflow during the 20th Century. Reference: Milly, P.C.D., K.A. Dunne, and A.V. Vecchia, 2005, Global pattern of trends in streamflow and water availability in a changing climate, Nature, v. 438, p. 347-350. 28

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

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)? More generally, a strong probabilitistic framework for interpreting change (nonstationary statistics)

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 Ensemble ranking Example of ensemble method 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