Download What are the “grand challenges” in hydrology?

Some thoughts on predicting hydrologic
futures: The role of model sensitivity
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Berkekey Catchment Science Symposium 2008
UC Berkeley
December 14, 2008
Outline of this talk
• The role of hydrology in Earth system science
• What are the grand challenges in hydrology?
• Understanding hydrologic change examples:
– Land cover and land use change
– Climate change
– Water management
• Do we have a framework for evaluating our
ability to predict change?
The role of hydrology in Earth
system science
“Where is the water, where is it going and
coming from and at what rate, and what
controls its movement and that of the
constituents that move with it?”
What are the “grand challenges” in
hydrology?
• 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.
Basic premise
• Humans have greatly affected the land
surface water cycle through
– Land cover change
– Climate change
– Water management
• While climate change has received the most
attention, other change agents may well be more
significant
Landslides in
Stillman Creek
Drainage,
upper
Chehalis River
Basin, WA,
December,
2007
Visual courtesy Steve
Ringman, The Seattle
Times
1. Hydrologic effects of land use and land cover change
Background: Cropland expansion
Percentage
of global
land area:
3
14
Ramankutty and Foley, Global Biogeochem. Cycles, 1999
Clearcutting in the Pacfic Northwest
Visuals from Osborne (2001) and
Sightline Institute
How well
do we
predict
the
hydrologic
signature
of land
cover
change?
Source: Van Shaar et al,
Hydrological Processes, 2002
Source: Van Shaar et al,
Hydrological Processes, 2002
Source: Van Shaar et al, Hydrological
Processes, 2002
2. Hydrologic sensitivity to climate change
The role of changing climate, 1950-2000
source: Mote et al (2005)
Tmin at
selected
Puget
Sound basin
stations,
1916-2003
Tmax at
selected
Puget Sound
basin
stations,
1916-2003
from Seager et al, Science, 2007
Postmortem: Christensen and Lettenmaier (HESSD,
2007) – multimodel ensemble analysis with 11 IPCC
AR4 models (downscaled as in C&L, 2004)
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%
71%
58%
96%
62%
62%
87%
-2%
75%
100%
+2%
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%
Dooge (1992; 1999):
where ΨP is elasticity of runoff with respect to precipitation
For temperature, it’s more convenient to
think in terms of sensitivity (v. elasticity)
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
Summary of precipitation elasticities and
temperatures sensitivities for Colorado River at
Lees Ferry for VIC, NOAH, and SAC models
Model
Precipitation TempTemp-Elasticity
sensitivity
sensitivity (
(Tmin &
Tmax) %/
Tmax ) %/ 0C 0C
Flow @
Lees
Ferry
(MACF)
VIC
1.9
-2.2
-3.3
15.43
NOAH
1.81
-2.85
-3.93
17.43
SAC
1.77
-2.65
-4.10
15.76
~9
?
Hoerling ~2
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 (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
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
3. Hydrologic effects of water management structures
Global Reservoir Database
Location (lat./lon.), Storage capacity, Area of water surface,
Purpose of dam, Year of construction, …
13,382dams,
Visual courtesy of Kuni Takeuchi
Global Water System Project
IGBP – IHDP – WCRP - Diversitas
Human modification
of hydrological systems
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.
What protocols do we have to evaluate our
ability to predict hydrologic change?
Klemes (Hyd Sci. J., 1986) argues for testing based on
a) split sample (SS), at the same site
b) Differential split sample (DSS), where model is calibrated to
“pre” condition (e.g., pre-cutting), appropriate model
characteristics (e.g., change in LAI) are adjusted, and model
predictions are tested against “post” data
c) Proxy basin test (PB), where model is transferred from one
basin, and applied to the PB without direct calibation there (but
using parameter transfer algorithms that may include other
basins)
d) Proxy basin differential split sample (PC-DSS), transfer from
one (or more) basins and from pre to post period.
Refsgaard and Knudsen (WRR, 1996) apply this construct
Some challenges
The framework is a bit specific to streamflow prediction (and
calibration protocols, etc.)
Signal to noise issues often preclude evaluation of model
performance over the relatively short time periods for which data
(especially DSS variations) exist, yet modest long-term changes can
have substantial practical effects (e.g., the Lake Mead example)
Data problems (especially the case in the DSS variants), and
confounding of nonstationarity with the SS protocol (can be addressed
by sample design variations, e.g., “shuffled deck” rather than split
sample
Where good DSS data sets are available (e.g., H.J. Andrews), there
often is a mismatch in spatial scale, and the magnitude of the
disturbance signature
Opportunistic DSS data sets often don’t include observations of key
variables, observation periods too short, etc (e.g., Entiat Experimental
Basins)
Conclusions
•We need to understand hydrologic sensitivities – to vegetation and
climate change – better. There is a compelling motivation to do so both
both on a scientific basis, and to address societal needs.
•The uncertainties in predicting sensitivities to processes driven by
temperature and/or evaporative demand changes seem to be greater
than those related to precipitation change, even though in the climate
world, prediction of precipitaiton change is generally considered more
difficult than temperature
•Although some hydrological consequences of water management are
essentially deterministic, others are not, and we do not have a unified
approach to addressing these issues – the history is much more one of
case studies. Until and unless this can be done, development of unified
approaches to predicting hydrologic change associated with water
management will be impeded.