1. RECENT FINDINGS ON RUNOFF SENSITIVITY OF COLORADO RIVER DISCHARGE TO CLIMATE CHANGE
Dennis P. Lettenmaier*
Department of Civil and Environmental Engineering
University of Washington
Reconciling Colorado River Flows Stakeholder Meeting
Southern Nevada Water Authority
Las Vegas, NV
November 14, 2008
*with contributions from Hugo Hidalgo (SIO), Tazebe Beyenne (UW), and Kostas Andreadis (UW)

2. Outline of this talk The history of conflicting estimates
Understanding the hydrologic sensitivities
Unanswered questions

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

5. PCM Projected Colorado R. Temperature

6. PCM Projected Colorado R. Precipitation

8. 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)

9. 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%
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 , relative to the period 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 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

10. from Seager et al, Science, 2007

11. Diagnosis
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

12. CL2007 Re-runs
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 

13. Understanding the hydrologic sensitivities

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

15. 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

16. 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,
VIC
NOAH
SAC

17. Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC, NOAH, and SAC models
Model
Precipitation-Elasticity
Temp-sensitivity
(Tmin & Tmax ) %/ 0C
Temp-sensitivity ( Tmax) %/ 0C
Lees Ferry
(MACF)
VIC
1.9
-2.2
-3.3
15.43
NOAH
1.81
-2.85
-3.93
17.43
SAC
1.77
-2.65
-4.10
15.76

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

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

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

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

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

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

24. Temperature sensitivity (Tmin fixed) histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

25. Spatial distribution of temperature sensitivities (Tmin fixed)
Censored spatial distribution of annual runoff

26. Composite seasonal water cycle, by quartile of the temperature sensitivity (fixed Tmin) distribution

27. 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)

28. 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)
b) On the other hand, from Seager et al (2007), very roughly, mid-century ΔP  -18%, so for = , and temperature sensitivity  , and ΔT  2 oC, ΔQ  35% (vs > 50% + from GCM multimodel average)

29. 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?

30. 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.