Sensitivity of Colorado Stream Flows to Climate Change Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Ninth SAHRA Annual Meeting Tucson September 23, 2009

Outline of this talk 1.Review of recent studies 2.Understanding the hydrologic sensitivities 3.Unanswered questions

Magnitude and Consistency of Model-Projected Changes in Annual Runoff by Water Resources Region, 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, , 2005.) 96% 75% 67% 62% 87% 71% 67% 62% 58% 67% 62% 58% 67% 100%

from Seager et al, Science, 2007 Means, replotted for Colorado River basin

Christensen et al, Climatic Change, 2004

Time series Annual Average Period Period Period hist. avg. ctrl. avg. PCM Projected Colorado R. Temperature Hydrology and water management implications

hist. avg. ctrl. avg. PCM Projected Colorado R. Precipitation Timeseries Annual Average Period Period Period

Annual Average Hydrograph Simulated Historic ( )Period 1 ( ) Control (static 1995 climate)Period 2 ( ) Period 3 ( )

Storage Reservoirs Run of River Reservoirs CRRM Basin storage aggregated into 4 storage reservoirs –Lake Powell and Lake Mead have 85% of basin storage Reservoir evaporation = f(reservoir surface area, mean monthly temperature) Hydropower = f(release, reservoir elevation) Monthly timestep Historic Streamflows to Validate Projected Inflows to assess future performance of system

Total Basin Storage

Annual Releases to the Lower Basin target release

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

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)

 Wood et al (2002; 2004) downscaling method removes bias by mapping from PDF of GCM output to PDF of observations on a monthly basis  PDFs are estimated for each grid cell and month of the year  This same mapping is then applied to the future climate run.  The method does not attempt to preserve GCM inferred differences in precipitation.  There is in general no reason to assume that the GCM precipitation changes are applicable to higher spatial resolutions Diagnosis

 All precipitation values were rescaled so as to match GCM changes on an annual basis  This resulted in a change (reduction) in mean annual precipitation for from 1.9% (CL2007) to 2.6% for A2 emissions scenario (closest to A1B used in M2005 and S2007)  The associated annual mean runoff reduction (Imperial Dam, averaged over 11 GCMs) changed from 5.9 to 10.0%  This is within (although at the lower end of) the range reported in M2005  Note that M2005 and S2007 use the A1B IPCC emissions scenario, vs A2 scenario used by CL2007  M2007 and S2007 use (partially) different GCM runs and procedures (M2005 count multiple ensembles from a single GCM as separate runs CL2007 Re-runs

Understanding the hydrologic sensitivities

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

Inferred runoff elasticities wrt precipitation for major Colorado River tributaries, using method of Sankarasubramanian and Vogel (2001) Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography

ModelPrecipitation -Elasticity Temp- sensitivity (Tmin & Tmax ) %/ 0 C Temp- sensitivity ( Tmax) %/ 0 C Lees Ferry (MACF) VIC NOAH SAC Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC, NOAH, and SAC models

Spatial distribution of precipitation elasticities Censored spatial distribution of annual runoff

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

Composite seasonal water cycle, by quartile of the runoff elasticity distribution

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

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

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

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)

b) On the other hand, from Seager et al (2007), very roughly, mid-century ΔP  -18%, so for = , and temperature sensitivity  , and ΔT  2 o C, ΔQ  40% (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)

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?

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.