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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