Atmospheric Greenhouse Gas Stabilization Targets: Implications for hydrology and water management Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Presentation for NRC Committee on Stabilization Targets for Atmospheric Greenhouse Gas Concentrations Washington D.C. September 16, 2009

The question: What will be the hydrologic/water management effects of given levels of GHG concentrations? Complications: Land hydrology (river runoff for sake of this discussion) depends on precipitation, and variables (net radiation, temperature) that affect evapotranspiration, not directly on GHG concentrations (aside from CO 2 fertilization effect on ET) Some key studies have shown ongoing effects of climate change (cleanest studies are generally for snow-dominant hydrology, e.g., western U.S., and appear to be related mostly to temperature change) Many studies of hydrologic effects of given (mostly P, T) scenarios Fewer studies have evaluated water management implications of altered hydrology Much less work on hydrologic sensitivities to given change in forcings, essentially none have framed water management issues in this context

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 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 J F M A M J J A S O N D Discharge, m 3 /s GRDC Monthly Means Ob’ Discharge, km 3 Winter Trend, Ob’ Visual courtesy Jennifer Adam

Minimum flow Increase No change Decrease About 50% of the 400 sites show an increase in annual minimum flow from to Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002

About 50% of the 400 sites show an increase in annual median flow from to 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 to Maximum flow Increase No change Decrease Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002

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%

Model Runoff Annual Trends 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)

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 ) Positive (Negative) trend at 109 (19) stations

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

Historical ( ), weekly averages start Oct s ensembles of 20 A1B and 19 B1, delta method produce 90 years with a climate resembling 2005 to s composite of A1B and B1 ( ) 2040s composite of A1B and B1 ( ) 2080s composite of A1B and B1 ( ) Probability distributions at specified time Example of ensemble method Ensemble ranking

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

Annual Releases to Mexico target release

Annual Hydropower Production

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

Annual streamflow sensitivities to precipitation and temperature

Dooge (1992; 1999): where and ′

(Budyko curve) Special cases: a)AE = constant: Ψ P = P/Q (inverse of runoff ratio) b)P/PE large (e.g., tundra): Ψ P = 1 c)P/PE small (desert): depends on Φ’(0) (but Ψ P ~ 3 for some forms)

Precipitation sensitivity is straightforward Evapotranspiration, however, depends on net radiation and vapor pressure deficit (among other variables), whereas (air) temperature is the more commonly observed variable Air temperature in turn, affects (or is affected by): downward solar and (net) longwave radiation sensible and latent heat fluxes ground heat flux snowmelt timing (and energy fluxes) Hence, it may be more useful to consider temperature sensitivity

Ψ P over the continental U.S. (from Sankarasubramanian and Vogel, WRR, 2001)

ModelPrecipitation Elasticity Temp- sensitivity (Tmin & Tmax ) %/ o C Temp- sensitivity ( Tmax) %/ o C Lees Ferry (MAF) VIC Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC model

River Yakima Basin Ψ P (obs) Ψ P (mod) α T (1) α T (2) Bumping River Tieton River Kachess River Yakima at Parker Puget Sound Basin Cedar River E Green River A Tolt River Summary of precipitation elasticities and temperatures sensitivities for Yakima River and Puget Sound rivers, WA

Sensitivity of mountain snowpack to termperature change (from Casola et al, 2009)

Estimating Sensitivity – Geometric Approach (Casola et al. 2008) Sensitivity ( ) is defined: can be estimated by comparing a Base climate to a +1ºC Warmer climate:  SWE = (  T) Where SWE is the basin-integrated SWE. SWE can be estimated from a function representing the vertical profile of SWE (S(z)) AND a function representing the distribution of area with elevation (A(z))

Geometric Approach Elevation S(z) increasing SWE Snow base = 600m Assume: a linearly increasing profile for S(z) Snow top = 3000m

Geometric Approach Elevation S(z) increasing SWE Snow base = 600m Assume: a linearly increasing profile for S(z) Snow top = 3000m Also assume: a moist adiabatic lapse rate (  = -6.5ºC/km) and that the effect of warming raises the S(z) by:  z=-  T/ 

Estimating S(z) Elevation S(z) increasing SWE Snow base = 600m Snow top = 3000m Elevation zz S(z) increasing SWE Old Snow base = 600m New Snow base = 750m Snow top = 3000m

A(z) Hypsometric Curve Obtaining A(z) Probability A(z) is the derivative of the Hypsometric Curve

Elevation (m) SWE Volume (S(z) x A(z)) Estimating Outer Curve = Base Climate Inner Curve = +1ºC Climate

-7.5 ºC/km -6.5 ºC/km -5.5 ºC/km -4.5 ºC/km 700 m 22%25%29%35% 600 m 20%23%27%33% 500 m 19%21%25%30% Sensitivity of Lapse Rate Base of Snowpack Increasing  (3-5% per ºC/km) Increasing (2-3% per 100 m)

Bow Glacier, Alberta 1897 and 2002 (from Schindler and Donahue, 2006) South Saskatchewan River May-Aug flows (from Schindler and Donahue, 2006); first year normalized to 100 Receding glaciers and low flows

Figure 3: Global regions where glacier melt is estimate to make up at least 5 percent of seasonal low flow

13,382dams, The end of the era of major dam construction Visual courtesy Hiroshi Ishidaira, Yamanashi University

Challenges for this study Emissions  concentrations  physical variables (P, T) Snow-dominant systems most studied and understood, but are they the most important? Evidence for changes in hydrologic extremes in observation record isn’t consistent with projections, and potentially has large impacts on infrastructure (natural variability issues?) Need to recognize that water resources impacts may differ from hydrologic (e.g., change in streamflow seasonality makes little difference to lower CO basin water deliveries, but is critical in CA and PNW) Need a basis for continental (and ideally global) integration