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53(334"''15#
Human-Induced Changes
in the Hydrology of the Western
United States
Tim P. Barnett,
1
* David W. Pierce,
1
Hugo G. Hidalgo,
1
Celine Bonfils,
2
Benjamin D. Santer,
2
Tapash Das,
1
Govindasamy Bala,
2
Andrew W. Wood,
3
Toru Nozawa,
4
Arthur A. Mirin,
2
Daniel R. Cayan,
1,5
Michael D. Dettinger
1,5
Observations have shown that the hydrological cycle of the western United States changed
significantly over the last half of the 20th century. We present a regional, multivariable climate
change detection and attribution study, using a high-resolution hydrologic model forced by global
climate models, focusing on the changes that have already affected this primarily arid region
with a large and growing population. The results show that up to 60% of the climate-related trends
of river flow, winter air temperature, and snow pack between 1950 and 1999 are human-induced.
These results are robust to perturbation of study variates and methods. They portend, in
conjunction with previous work, a coming crisis in water supply for the western United States.
W
ater is perhaps the most precious nat-
ural commodity in the western United
States. Numerous studies indicate the
hydrology of this region is changing in ways that
will have a negative impact on the region (1–3).
Between 1950 and 1999 there was a shift in the
character of mountain precipitation, with more
winter precipitation falling as rain instead of snow
(2, 4, 5), earlier snow melt (4, 6), and associated
changes in river flow (7–10). In the latter case,
the river flow experiences relative increases in
the spring and relative decreases in the summer
months. These effects go along with a warming
over most of the region that has exacerbated
these drier summer conditions (5, 8, 11).
The west naturally undergoes multidecadal
fluctuations between wet and dry periods (12).
If drying from natural climate variability is the
cause of the current changes, a subsequent wet
period will likely restore the hydrological cycle
to its former state. But global and regional cli-
mate models forced by anthropogenic pollutants
suggest that human influences could have caused
the shifts in hydrology (2, 13–15). If so, these
changes are highly likely to accelerate, making
modifications to the water infrastructure of the
western United States a virtual necessity.
Here, we demonstrate statistically that the
majority of the observed low-frequency changes
in the hydrological cycle (river flow, tempera-
ture, and snow pack) over the western United
States from 1950 to 1999 are due to human-
caused climate changes from greenhouse gases
and aerosols. This result is obtained by evaluat-
ing a combination of global climate and regional
hydrologic models, together with sophisticated
data analysis. We use a multivariable detection
and attribution (D&A) methodology (16–18)to
show that the simultaneous hydroclimatic changes
observed already differ significantly in length
and strength from trends expected as a result of
natural variability (detection) and differ in the
specific ways expected of human-induced ef-
fects (attribution). Focusing on the hydrological
cycle allows us to assess the origins of the most
relevant climate change impacts in this water-
limited region.
We investigated simultaneous changes from
1950 to 1999 (19) in snow pack (snow water
equivalent or SWE), the timing of runoff of the
major western rivers, and average January through
March daily minimum temperature (JFM T
min
)
in the mountainous regions of the western United
States (20). These three variates arguably are
among the most important metrics of the west-
ern hydrological cycle. By using the multivaria-
ble approach, we obtain a greater signal-to-noise
(S/N) ratio than from univariate D&A alone (see
below).
The SWE data are normalized by October-
to-March precipitation (P) to reduce variability
from heavy- or light-precipitation years. Observed
SWE/P and temperature were averaged over each
of nine western mountainous regions (Fig. 1) to
reduce small-spatial-scale weather noise. The
river flow variate is the center of timing (CT), the
day of the year on which one-half of the total
water flow for the year has occurred, computed
from naturalized flow in the Columbia, Colorado,
and Sacramento/San Joaquin rivers. CT tends to
decrease with warming because of earlier spring
melting.
Selected observations from these regions and
variables are displayed in Fig. 2, showing the
trends noted above, along with substantial re-
gional differences and “weather noise.” SWE/P
trends in the nine regions vary from –2.4 to –7.9%
per decade, except in the southern Sierra Nevada
where the trend is slightly positive. The JFM
T
min
trends are all positive and range from 0.28°
to 0.43°C per decade, whereas the river CT
1
Scripps Institution of Oceanography, University of California,
San Diego, La Jolla, CA 92093, USA.
2
Lawrence Livermore
National Laboratory, Livermore, CA 94550, USA.
3
Land Surface
Hydrology Research Group, Civil and Environmental Engi-
neering, University of Washington, Seattle, WA 98195, USA.
4
National Institute for Environmental Studies, 16-2, Onogawa,
Tsukuba, Ibaraki 305-8506, Japan.
5
U.S. Geological Survey, La
Jolla,CA92093,USA.
*To whom correspondence should be addressed. E-mail:
tbarnett-ul@ucsd.edu
Fig. 1. Map showing averaging regions over which SWE/P and JFM T
min
were determined. The
hatching shows the approximate outline of the three main drainage basins used in this study.
22 FEBRUARY 2008 VOL 319 SCIENCE www.sciencemag.org
1080
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This article is a U.S. government work, and is not subject to copyright in the United States.
arrives from 0.3 to 1.7 days per decade earlier.
The challenge in D&A analysis is to determine
whether a specific, predetermined signal repre-
senting the response to external forcing is present
in these observations.
We compared the observations with results
from a regional hydrologic model forced by glob-
al climate model runs. One of the global mod-
els, the Parallel Climate Model (PCM) (21), has
been used previously in hydrological studies in
the western United States (22) and realistically
portrays important features of observed climate
and the amplitude of natural internal variability.
The second climate model, the anthropogenical-
ly forced medium-resolution Model for Interdis-
ciplinary Research on Climate (MIROC) (23–25),
was selected from the current Intergovernmental
Panel on Climate Change (IPCC) AR4 set of
global runs (26) because it had available many
20th-century ensemble members with daily data,
and because it offered a high degree of realism
in representing the Pacific Decadal Oscillation
(PDO). W e used the anthropogenically forced
versions of these models to obtain an estimate
for the expected signal not confounded by other
forcing mechanisms. The models provided mul-
tiple realizations (10 for MIROC, 4 for PCM) of
the historical response of the climate system to
anthropogenic forcing. The daily output from
these coarse-horizontal-resolution model results
was downscaled to a 1/8° × 1/8° latitude-longitude
grid by two different statistical methods [Bias Cor-
rection and Spatial Disaggregation (BCSD) (27)
and Constructed Analogues (CA) (28)]. The down-
scaled temperature and precipitation data were
supplied as input to the Variable Infiltration
Capacity (VIC) hydrological model (15, 27, 29)
to obtain river flow and SWE/P.
We used the downscaled model results to
estimate an anthropogenic “fingerprint” for the
PCM and MIROC models (30). The fingerprint
describes the joint variability of SWE/P, JFM
T
min
, and river flow (Fig. 3) (20). The model
fingerprints are very similar despite the different
external forcings used (20, 26). The results show
that warmer temperatures accompany decreases
in SWE/P and decreases in CT of major western
river systems. The sign of each variable is a mono-
pole, indicating a coherent regional-scale signal
over the western United States.
The temporal component of the fingerprint
(not shown) is well represented by a simple trend.
This implies that the fingerprint primarily cap-
tures the spatial expression of long-term changes,
and not shorter-period climate modes (such as El
Niño–Southern Oscillation or the PDO).
The signal strength is calculated as the least-
squares linear trend of the projection of a data
set (model or observations) onto the fingerprint
(20). The upper panel of Fig. 4 shows the en-
semble mean signals for our various model runs
and the observations (20). The observations show
a positive signal indistinguishable from the PCM
and MIROC anthropogenically forced runs. These
signals exclude zero at the 95% confidence in-
terval, thus achieving “detection.”
We used 1600 years of downscaled control
run data from two different global models (20)
to estimate the probability that the observed sig-
nal could be due to natural, internal variability
(Fig. 4, lower panel). The observed signal falls
outside the range expected from natural variabil-
ity with high confidence (P < 0.01). In separate
analyses for PCM and MIROC, the likelihood
that the model signal arises from natural internal
variability is between 0.01 and 0.001 (20). The
different downscaling methods have little impact
on these results. We conclude that natural inter-
nal climate variability alone cannot explain either
the observed or simulated changes in SWE/P,
JFM T
min
, and CT in response to anthropogenic
forcing.
PCM simulations forced solely by the com-
bined impacts of observed solar variability and
volcanic activity (Sol/Vol, Fig. 4) show a signal
with sign opposite to that observed. We con-
clude that solar and volcanic forcing also fail to
explain the observed hydrological changes.
Might anthropogenically induced precipitation
changes account for our results? This is unlikely
because our variables were chosen to minimize
sensitivity to precipitation fluctuations. However,
previous work has identified an anthropogenic
effect on global-scale changes in precipitation
(31). We conducted a univariate D&A analysis
on precipitation, comparing the fingerprint ob-
tained from the anthropogenic runs to the con-
trol runs and observations. The results (Fig. 4,
lower panel) show that the observed changes in
precipitation over the nine western U.S. moun-
tain regions are indistinguishable from natural
variability . W e found the same for model precipi-
tation (not shown). We conclude that although
Fig. 3. Fingerprints from the multi-
variate analysis of PCM and MIROC.
Fig. 2. Observed time series of se-
lected variables (expressed as unit
normal deviates) used in the mul-
tivariate detection and attribution
analysis. Taken in isolation, seven
of nine SWE/P, seven of nine JFM
T
min
, and one of the three river flow
variables have statistically signifi-
cant trends.
www.sciencemag.org SCIENCE VOL 319 22 FEBRUARY 2008 1081
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precipitation may be affected by anthropogenic
forcing on larger scales or in other regions, or in
this region in the future, it cannot explain the
strong changes in western U.S. hydrology from
1950 to 1999.
Finally, the observations are consistent with
the anthropogenic model runs. The observed
signal is stronger than found in either model, but
the differences are not statistically significant.
The ensemble mean signal strength from PCM
is 60% of the observed signal strength; that is,
PCM estimates that three-fifths of the projected
trend can be ascribed to human effects. The two
downscaling methods give somewhat different
signal strengths (Fig. 4), but the attribution holds
no matter which is chosen. We conclude that
application of a rigorous, multi variable D&A
methodology shows a detectable and attributable
signature of human effects on western hydrology.
We examined the time evolution of signal
and noise by projecting the observations (signal)
and control run data (noise) onto the multivar-
iable fingerprint, then fitting linear trends of in-
creasing length L to the resulting projected time
series. This enabled us to calculate a S/N ratio
as a function of L (from 10 to 50 years); Fig. 5
shows that the S/N ratio rises above the 5% sig-
nificance threshold no later than 1986. This
result is robust to uncertainties in the model
fingerprint, model-based noise estimates, and
statistical downscaling method (20). We also
repeated the D&A analysis without areal weight-
ing and found that it made no difference in our
conclusions.
The variables examined here covary in a phys-
ically and internally consistent way: An increase
in minimum temperature is associated with less
SWE/P and earlier runoff. Quantitatively , we also
compared the S/N obtained from separate analy-
ses of each variable with that obtained for the
full multivariable problem (20). For fixed choices
of fingerprint, noise, and downscaling (32), the
S/N values from the separate SWE/P, JFM T
min
,
and CT analyses were 2.90, 2.95, and 1.85, re-
spectively, all significant at about the 0.05 level
or above. The multivariable analysis had a S/N
of 3.62, and so it has quantitative value as well as
providing a test of whether SWE/P, JFM T
min
,
and CT covary in a physically consistent way .
Our results are robust with respect to un-
certainties in model estimates of anthropogenic
climate fingerprints and natural variability, down-
scaling method, and the choice of univariate or
multivariate D&A analysis. Estimates of natural
variability used for significance testing agree
well with those derived from paleo proxies (20).
The analyses show with high confidence that
the majority of the detrimental changes already
seen in western U.S. hydrology are caused by
human-induced effects. PCM, which has the
most realistic signal strength, shows that human
effects account for 60% of the observed 1950–
1999 trend in signal strength. MIROC accounts
for 35% of the trend. On the basis of Fig. 4 (up-
perpanel)andthediscussionofMIROCin(20),
the PCM number seems more reliable.
Our results are not good news for those liv-
inginthewesternUnitedStates.Thescenariofor
how western hydrology will continue to change
has already been published using one of the mod-
els used here [PCM (2)] as well as in other re-
cent studies of western U.S. hydrology [e.g., (15)].
It foretells water shortages, lack of storage ca-
pability to meet seasonally changing river flow ,
transfers of water from agriculture to urban uses,
and other critical impacts. Because PCM per-
forms so well in replicating the complex signals
of the last half of the 20th century , we have
every reason to believe its projections and to act
on them in the immediate future.
References and Notes
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3. B. Udall, G. Bates, Intermountain West Climate Summary,
Western Water Assessment, January 2007 (available from
University of Colorado).
4. A. F. Hamlet, P. W. Mote, M. P. Clark, D. P. Lettenmaier,
J. Clim. 18, 4545 (2005).
5. N. Knowles, M. D. Dettinger, D. R. Cayan, J. Clim. 19,
4545 (2006).
6. P. W. Mote, A. F. Hamlet, M. P. Clark, D. P. Lettenmaier,
Bull. Am. Meteorol. Soc. 86, 39 (2005).
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8. D. R. Cayan, S. Kammerdiener, M. D. Dettinger, J. Caprio,
D. Peterson, Bull. Am. Meteorol. Soc. 82, 399 (2001).
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1136 (2005).
10. S. K. Regonda, B. Rajagopalan, M. Clark, J. Pitlick,
J. Clim. 18, 372 (2005).
11. P. Y. Groisman et al., J. Hydrometeorol. 5, 64 (2003).
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Adjusting to Hydroclimatic Variability (National Academy
of Sciences, Washington, DC, 2007).
13. P. C. D. Milly, K. A. Dunne, A. V. Vecchia, Nature 438,
347 (2005).
14. R. Seager et al., Science 316, 1181 (2007); published
online 4 April 2007 (10.1126/science.1139601).
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Discuss. 3, 1 (2006).
16. T. P. Barnett, M. Schlesinger, J. Geophys. Res. 92, 14772
(1987).
17. B. D. Santer et al., Clim. Dyn. 12, 77 (1995).
18. R. Schnur, K. I. Hasselmann, Clim. Dyn. 24, 45 (2005).
19. Note that this period excludes the large-scale changes in
runoff, precipitation, and water storage that have occurred
in the southwest, especially the Colorado River drainage,
since 2000. We do not claim that the large changes since
2000 are necessarily the result of human-induced warming.
20. See supporting material on Science Online.
21. W. Washington et al., Clim. Dyn. 16, 755 (2000).
22. T. P. Barnett et al., Clim. Change 62, 1 (2004).
23. K-1 Model Developers, K-1 Coupled Model (MIROC)
Description (K-1 Technical Report 1), H. Hasumi,
S. Emori, Eds. (Center for Climate System Research,
University of Tokyo, 2004).
24. T. Nozawa et al., Geophys. Res. Lett. 32, L20719 (2005).
25. T. Nozawa et al., MIROC, CGER’s Supercomputer
Monograph Report 12 (Center for Global Environmental
Research, National Institute for Environmental Studies,
Tsukuba, Japan, 2007).
-0.05 0.00 0.05 0.10 0.15
95
99
1
5
10
25
50
75
90
95
99
99.9
99.99
Signal strength
Percentile
PCM
(BCSD)
PCM
(BCSD)
PCM
(CA)
PCM
(CA)
MIROC
MIROC
Sol/Vol
Sol/Vol
Obs
Obs(Precip)
Fig. 4. Ensemble average signal strength (upper
panel; standard deviations of the fingerprint’s
principal component per decade) and percentile
rank of ensemble mean signal strength for the
indicated model runs with respect to the combined
(CCSM3-FV and PCM) control run (lower panel). Per-
centile values were calculated by Monte Carlo
resampling of the control run taking into account
N, the varying number of ensemble members. PCM
(BCSD) and PCM (CA): PCM runs with anthropogenic
forcing, with two different downscaling methods as
described in the text (N = 4). MIROC: MIROC runs
with anthropogenic forcing (N = 10). Sol/Vol: PCM
runs with only solar and volcanic forcing included
(N = 2). The cross shows the signal strength ob-
tained from the observations (N =1).Forcom-
parison purposes, also shown is the observed signal
strength from a separate analysis of precipitation
changes over the nine mountain regions (dia-
mond). Values outside the hatched and cross-
hatched regions are significant at the 0.01 and
0.05 levels, respectively.
Fig. 5. Time-dependent S/N esti-
mates for two different estimates
of natural variability. The x axis
is the last year of L-length linear
trend in the signal estimate.
1986 detection
1983 detection
1%
5%
CCSM3-FV noise (normalization); PCM noise (sig. test)
PCM noise (normalization); CCSM3-FV noise (sig. test)
Last year of L-length linear trend in signal
Signal-to-noise ratio
1960 1970 1980 1990
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