atmospheric greenhouse gas stabilization targets: implications for hydrology and water management...

Atmospheric Greenhouse Gas Stabilization Targets: Implications for hydrology and water management

Dennis P. LettenmaierDepartment 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 CO2 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 andHydrologic change

Regulated Flow

Historic Naturalized Flow

Estimated Range of Naturalized FlowWith 2040’s Warming

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.

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 km3/yr (~7% increase)

• Most significant trends during the winter (low-flow) season

Dis

cha

rge,

km

3/y

r Annual trend for the 6 largest rivers

Peterson et al. 2002

J F M A M J J A S O N D

10

20

30

40

Dis

cha

rge,

m3/s

GRDCMonthly Means Ob’

1950 1960 1970 1980

Dis

cha

rge,

km

3

Winter Trend, Ob’

Visual courtesy Jennifer Adam

Minimum flowIncreaseNo changeDecrease

About 50% of the 400 sites show an increase in annual minimum flow from 1941-70 to 1971-99

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 flowIncreaseNo changeDecrease

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 flowIncreaseNo changeDecrease

Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002

Magnitude and Consistency of Model-Projected Changesin 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%

Dec

reas

eIn

crea

se

(After Milly, P.C.D., K.A. Dunne, A.V. Vecchia, Global pattern of trends in streamflow andwater availability in a changing climate, Nature, 438, 347-350, 2005.)

96%

75%67%

62%87%

87%

71%

67%62%

58%

67%

62%58%

67%100%

Model Runoff Annual Trends

• 1925-2003 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 1925-2003)

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 (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

Example of ensemble method

Week 22

0

1800

3600

5400

7200

9000

1 3 5 7 9 11 13 15 17 19 21

ensemble rank for the 2020s

cfs

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

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

2001

2007

2013

2019

2025

2031

2037

2043

2049

2055

2061

2067

2073

2079

2085

2091

2097

YEAR

(mm

/day

)

AVG_PRECIP

EVAP

P - E 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: ΨP = P/Q (inverse of runoff ratio)

b) P/PE large (e.g., tundra): ΨP = 1

c) P/PE small (desert): depends on Φ’(0) (but ΨP ~ 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

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

Model PrecipitationElasticity

Temp-sensitivity (Tmin & Tmax ) %/ oC

Temp-sensitivity ( Tmax) %/ oC

Flow @ Lees Ferry (MAF)

VIC 2.4 -5.9 -10.8 15.43

Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC model

River

Yakima Basin ΨP (obs) ΨP (mod) αT(1) αT(2)

Bumping River 1.4 1.9 -5.8 -9.8

Tieton River 1.4 1.6 -2.4 -6.3

Kachess River 1.2 1.7 -3.7 -6.4

Yakima at Parker 1.3 1.6 -2.8 -5.2

Puget Sound Basin

Cedar River E 1.4 1.4 -1.1 -3.0

Green River A 1.4 1.6 -2.4 -5.6

Tolt River 1.1 1.2 -0.7 -1.5

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)

( ) ( )SWE S z A z dz

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))

= WARM BASE

BASE

SWE SWE

SWE

Geometric ApproachEle

vati

on

S(z)increasing SWE

Snow base = 600m

Assume: a linearly increasing profile for S(z) Snow top

= 3000m

Geometric ApproachEle

vati

on

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)Ele

vati

on

S(z)increasing SWE

Snow base = 600m

Snow top = 3000m

Ele

vati

on

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

Ele

vati

on

(m

)

SWE Volume (S(z) x A(z))

Estimating

Outer Curve = Base Climate

Inner Curve = +1ºC Climate

red area 23% Apr.1 SWE/ C

outer curve area

-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 o

f S

now

pack

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