Transcript Hydrologic change: What do we, and don`t we know?

Hydrologic change: What do we, and don’t
we know?
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Symposium in honor of 40 years of research by Professor Enda
O’Connell
University of Newcastle
Newcastle upon Tyne
March 26, 2009
A perspective on the evolution of
hydrology over ~40 years
The end of the era of major dam construction
13,382dams,
Visual courtesy Hiroshi Ishidaira, Yamanashi University
Reservoir construction has slowed post ~1970
800
.
700
Number of Reservoirs
600
500
Australia/New Zealand
Africa
Asia
Europe
Central and South America
North America
400
300
200
100
0
Up to 1901- 1911- 1921- 1931- 1941- 1951- 1961- 1971- 1981- 19901900 1910 1920 1930 1940 1950 1960 1970 1980 1990 1998
visual courtesy Peter Gleick
Arguably, the challenge of the 70s was to
characterize hydrologic variability, with
an implicit assumption of stationarity
(or at least quasi-stationarity)
Stationarity—the idea that natural systems fluctuate within an
unchanging envelope of variability—is a foundational concept
that permeates training and practice in water-resource
engineering.
In view of the magnitude and ubiquity of the hydroclimatic
change apparently now under way, however, we assert that
stationarity is dead and should no longer serve as a central,
default assumption in water-resource risk assessment and
planning.
What are the challenges of the 2000s?
• From Science (2006) 125th Anniversary issue (of eight in
Environmental Sciences): Hydrologic forecasting – floods,
droughts, and contamination
• From the CUAHSI Science and Implementation Plan (2007): …
a more comprehensive and … systematic understanding of
continental water dynamics …
• From the USGCRP Water Cycle Study Group, 2001
(Hornberger Report): [understanding] the causes of water cycle
variations on global and regional scales, to what extent [they]
are predictable, [and] how … water and nutrient cycles [are]
linked?
Important problems all, but I will argue instead (in addition) that
understanding hydrologic change should rise to the level of a
grand challenge to the community.
Agents of hydrologic change, and
examples
–Land cover change
–Climate change
–Water management
Landslides in
Stillman Creek
Drainage,
upper
Chehalis River
Basin, WA,
December,
2007
Visual courtesy Steve
Ringman, The Seattle
Times
Water management and
Hydrologic change
Columbia River at the Dalles, OR
Historic Naturalized Flow
Estimated Range of
Naturalized Flow
With 2040’s Warming
Regulated Flow
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.
from Mote et al, BAMS 2005
From Stewart et al, 2005
Arctic River Stream Discharge
Trends
Discharge, km3/yr
Discharge, km3
Peterson et al. 2002
Visual courtesy
Jennifer Adam
Winter Trend, Ob’
1950
Discharge, m3/s
• 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 (lowflow) season
Annual trend for the 6
largest rivers
40
30
1960
Monthly
Means
Ob’
1970
1980
GRDC
20
10
J F M A M J J A S O N D
About 50% of the 400 sites show an
increase in annual minimum flow from
1941-70 to 1971-99
Minimum flow
Increase
No change
Decrease
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 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
1941-71 to 1971-99
Maximum flow
Increase
No change
Decrease
Visual courtesy Bob Hirsch, figure from McCabe & Wolock, GRL, 2002
USGS streamgage annual flood peak
records used in study (all >=100 years)
Visual courtesy Bob Hirsch
Are floods correlated with Water Year?
Negative
17
6
5
2
All sites
 = 0.1
 = 0.05
 = 0.01
Which sites Broad (GA)
significant at Logan (UT)
 = 0.01 ?
Positive
19
7
7
5
Red Lake (MN)
Red (MN/ND)
Pembina (ND)
Minnesota (MN)
Arkansas (KS)
Visual courtesy Bob Hirsch
Predicting hydrologic change: The
Puget Sound basin as a case study
The role of changing land cover – 1880 v. 2002
1880
2002
Tmin at
selected
Puget
Sound basin
stations,
1916-2003
The Distributed Hydrology-SoilVegetation Model (DHSVM)
Land cover
change
effects on
seasonal
streamflow
for eastern
(Cascade)
upland
gages
Land cover
change
effects on
seasonal
streamflow
at selected
eastern
lowland
(Greater
Seattle
area) gages
Predicted
temperature
change
effects on
seasonal
streamflow at
eastern
(Cascade)
upland gages
Predicted
temperature
change
effects on
seasonal
streamflow at
selected
eastern
lowland gages
(greater
Seattle area)
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%
RUNOFF SENSITIVITY OF COLORADO
RIVER DISCHARGE TO CLIMATE CHANGE
Figure 9
Annual Releases to the Lower Basin
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
Keeping score: where do we do (at least
passably) well?

Detecting change (statistical tools are reasonably
well adapted to the problems)

Predicting change (albeit with a conditional chain of
models)
And where do we do fall short?

Attribution of hydrologic change; and

Providing meaningful estimates of uncertainty of future
projections (i.e., how uncertain are our model
sensitivities)?
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
Example of ensemble method
9000
cfs
7200
Week 22
5400
3600
1800
0
1
3
5
7
9 11 13 15 17 19 21
ensemble rank for the 2020s
•
•
•
•
•
•
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