Transcript No Slide Title - University of Washington

HYDROLOGY IN AN ERA OF GLOBAL
CHANGE: THE COLORADO RIVER BASIN
AS A CASE STUDY
Borland Lecture in Hydrology
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Hydrology Days 2008
Colorado State University
Fort Collins, CO
March 26, 2008
Outline of this talk
1.
2.
3.
4.
The hydrologic challenge of global change
Projections for the Colorado River basin
Understanding the hydrologic sensitivities
Unanswered questions
1. The hydrologic
challenge of global
change
From Stewart et al, 2005
The culprit: Changes in winter temperatures
(especially temperature minima)
Western Washington station temperatures, 1916-2003
Winter daily minima
Winter daily maxima
Western Washington station temperatures, 1916-2003
Spring daily minima
Spring daily maxima
Time series of key variables (obs.)
All variables have been
normalized (fractionalized) by
dividing by the CCSM3-FV
control run mean over first
300 yrs.
Necessary for the multivariate
detection and attribution
(D&A), so have same
variance in each variable (the
“units problem”).
Visual courtesy
Tim Barnett, SIO
Ensemble signal strength & significance
(conclusion: as much as 60% of observed change is attributable to
anthropogenic causes)
Fingerprint
Signal Strength
Significance
Visual courtesy Tim
Barnett, SIO
2) Projections for the Colorado River basin
Prediction and assessment approach
Climate
Scenarios
Global climate
simulations, next
~100 yrs
Hydrologic
Model (VIC)
Natural
Streamflow
Downscaling
Delta
Precip,
Temp
Performance
Measures
Reliability
of System
Objectives
Reservoir
Model
DamReleases,
Regulated
Streamflow
Accelerated Climate
Prediction Initiative
(ACPI)
Based on
NCAR/DOE Parallel
Climate Model at ~
2.8 degrees lat-long)
Bias Correction
bias-corrected climate scenario
month m
raw climate scenario
from NCDC observations
month m
from PCM historical run
Note: future scenario temperature trend (relative to control run)
removed before, and replaced after, bias-correction step.
Downscaling
monthly PCM
anomaly (T42)
interpolated to
VIC scale
VIC-scale
monthly simulation
observed
mean fields
(1/8-1/4 degree)
Bias Correction and Downscaling
Approach
climate model scenario  hydrologic model  snowpack
meteorological outputs
inputs
runoff
streamflow
•2.8 (T42)/0.5 degree
resolution
•monthly total P, avg. T
• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax
Climate Change Scenarios
PCM Simulations (~ 3 degrees lat-long)
Historical
B06.22 (greenhouse CO2+aerosols forcing)
1870-2000
Climate Control
B06.45 (CO2+aerosols at 1995 levels)
1995-2048
Climate Change
Climate Change
Climate Change
B06.44 (BAU6, future scenario forcing)
B06.46 (BAU6, future scenario forcing)
B06.47 (BAU6, future scenario forcing)
1995-2099
1995-2099
1995-2099
PNNL Regional Climate Model (RCM)
Simulations (~ ¾ degree lat-long)
Climate Control
B06.45 derived-subset
1995-2015
Climate Change
B06.44 derived-subset
2040-2060
Future streamflows
• 3 ensembles averaged
• summarized into 3 periods;
» Period 1
» Period 2
» Period 3
2010 - 2039
2040 - 2070
2070 - 2098
Hydrology and water management implications
PCM Projected Colorado R. Temperature
Time series
Annual Average
ctrl. avg.
hist. avg.
Period 1 2010-2039
Period 2 2040-2069
Period 3 2070-2098
PCM Projected Colorado R. Precipitation
Timeseries
Annual Average
hist. avg.
ctrl. avg.
Period 1 2010-2039
Period 2 2040-2069
Period 3 2070-2098
Annual Average Hydrograph
Simulated Historic (1950-1999)
Control (static 1995 climate)
Period 1 (2010-2039)
Period 2 (2040-2069)
Period 3 (2070-2098)
Projected Spatial Change in Runoff
90 %
86 %
82 %
83 %
April 1 Snow Water Equivalent
CRRM
•
Historic Streamflows to Validate
•
Projected Inflows to assess future performance
of system
•
Monthly timestep
•
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)
Storage Reservoirs
Run of River Reservoirs
Natural Flow at Lee Ferry, AZ
Natural Flow at Lee Ferry Stream Gage
30
Annual Flow (BCM)
25
allocated
20.3 BCM
20
15
Currently used
16.3 BCM
10
5
0
1900
1910
1920
1930
Annual Flow
1940
1950
10 Year Average
1960
1970
1980
Running Average
1990
2000
Total Basin Storage
Figure 8
70
Minimum
60
Average
Maximum
Storage, BCM
50
40
30
20
10
0
Historical
Control
Period 1
Period 2
Period 3
Annual Releases to the Lower Basin
Figure 9
14
1.2
Average Annual Release to Lower Basin (BCM/YR)
Probability release to Lower Basin meets or exceeds target (probability)
12
1
target release
10
8
0.6
6
0.4
4
0.2
2
0
0
Historical
Control
Period 1
Period 2
Period 3
Probability
BCM / YR.
0.8
Annual Releases to Mexico
Figure 10
1.2
Average Annual Release to Mexico
(BCM/YR)
3
Probability release to Mexico meets or
exceeds target (probability)
BCM / YR.
2.5
1
0.8
2
target release
0.6
1.5
0.4
1
0.2
0.5
0
0
Historical
Control
Period 1
Period 2
Period 3
Probability
3.5
Annual Hydropower Production
Figure 12
18000
Minimum
16000
Average
Energy, GW - hr
14000
Maximum
12000
10000
8000
6000
4000
2000
0
Historical
Control
Period 1
Period 2
Period 3
Uncontrolled Spills
Figure 13
Probability of Spill / Spill in BCM
1.6
1.4
1.2
Historical
Control
Period 1
Period 2
1
Period 3
0.8
0.6
0.4
0.2
0
Probability a year will have one or more spills
Average spill amount
Deliveries to CAP & MWD
Figure 11
1.4
BCM / YR. / Probability
1.2
1
0.8
0.6
0.4
probability of CAP shrtg
if CAP shrtg, avg. amount
probability of MWD shrtg
If MWD shrtg, avg. amount
0.2
(probability)
(BCM /YR.)
(probability)
(BCM /YR.)
0
Historical
Control
Period 1
Period 2
Period 3
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% annual flow reduction in Christensen and Lettenmaier
(2007)
• 10-25% by Milly et al (2005)
• > 35% by Seager et al (2007)
Magnitude and Consistency of Model-Projected Changes
in 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).
58%
+10%
67%
62%
58%
96%
+2%
62%
62%
71%
87%
-2%
75%
100%
67%
67%
67%
-5%
-10%
-25%
(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, 347-350, 2005.)
Decrease
87%
+5%
Increase
+25%
from Seager et al, Science, 2007
3. Understanding the hydrologic sensitivities
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
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
Bivariate
Precipitationtemperature
sensitivities
inferred from
naturalized
Colorado River
streamflows at
Lees Ferry, and
from simulated
Lees Ferry flows
observed
simulated
Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography
Precipitation
elasticity ΨP as
a function of
mean
accumulated
snow depth
Source: Sankarasubramanian and Vogel, WRR, 2001
Unconditional histograms of 1/8 degree grid cell precipitation
elasticities from model runs for 20 years, ~1985-2005
VIC
NOAH
SAC
Summary of precipitation elasticities and
temperatures sensitivities for Colorado River at
Lees Ferry for VIC, NOAH, and SAC models
Model
Precipitation Temp-Elasticity
sensitivity
(Tmin &
Tmax ) %/
0C
Tempsensitivity
( Tmax)
%/ 0C
Flow @
Lees
Ferry
(MACF)
VIC
1.9
-2.2
-3.3
15.43
NOAH 1.81
-2.85
-3.93
17.43
SAC
-2.65
-4.10
15.76
1.77
VIC
Precipitation
elasticity
histograms, all
grid cells and
25% of grid cells
producing most
(~73%) of
runoff
Spatial distribution
of precipitation
elasticities
Censored
spatial
distribution of
annual 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
Censored spatial
distribution of
annual runoff
Spatial distribution of
temperature
sensitivities (equal
changes in Tmin and
Tmax)
Composite seasonal water cycle, by quartile of the temperature
sensitivity (equal change in Tmin and Tmax) distribution
Temperature
sensitivity (Tmin
fixed) histograms,
all grid cells and
25% of grid cells
producing most
(~73%) of runoff
Censored spatial
distribution of
annual runoff
Spatial distribution
of temperature
sensitivities (Tmin
fixed)
Composite seasonal water cycle, by quartile of the
temperature sensitivity (fixed Tmin) distribution
Precipitation elasticity vs Q
Scatterplots of
elasticities and
temperature
sensitivities vs mean
annual runoff
Temp sensitivity 2 vs Q
Temp sensitivity 1 vs Q
4. Unanswered questions
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)
a) Lowest mid-century estimate (Christensen and
Lettenmaier, 2007) is based on a precipitation
downscaling method that yields smaller midcentury precipitation changes (by about a factor of
two on multimodel average). Adjusting for this
difference doubles the projected change to around
12% 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
= 1.5-1.9,
and temperature sensitivity  -0.02 - -0.03, and ΔT 
2 oC, ΔQ  35% (vs > 50% + from GCM multimodel
average)
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 landatmosphere simulations, that resolve areas
producing most runoff, and their role in modulating
(or exacerbating) regional scale sensitivities.