HYDROLOGY IN AN ERA OF GLOBAL
CHANGE*
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Duke University
Center on Global Change Seminar Series
April 24, 2008
*initially presented as the AMS Horton Lecture, Jan., 2008
With thanks to the University of Washington
Land Surface
Hydrology Group
UW LAND SURFACE
HYDROLOGY
RESEARCH GROUP 2008
Hulin Gao
Amanda Tan
Fransisco Munoz
Ted Bohn
Tazebe Beyenne
Kristian Mickelson Shrad Shukla
Alan Hamlet
Quihong Tang
Mergia Sonessa
Dennis Lettenmaier
Ben Livneh
Andrew Wood
Kostas Andreadis
Chunmei Zhu
Lan Cuo
John Yearsley Nathalie Voisin
Xiaogong Shi
Elizabeth Clark
And especially:
Kostas Andreadis (UW)
Tazebe Beyenne (UW)
Elizabeth Clark (UW)
Lan Cuo (UW)
Mariza Costa-Cabral (Hydrology Futures, Seattle)
Ingjerd Haddeland (Norwegian Water Resources and
Energy Directorate)
Hugo Hidalgo (Scripps Institution of Oceanography)
Ben Livneh (UW)
Ramiro Saurral and Vicente Barros (University of
Buenos Aires)
Amanda Tan (UW)
Robert E. Horton (1875-1945)
• Published 100-200 papers (no known bibliography)
• Best known for 1933 Trans AGU paper “The role of
infiltration in the hydrologic cycle”
• However, much of his early work (e.g., MWR, 1905) dealt
with snow hydrology
• 24 papers appeared in MWR, earliest in May 1905, last in
Apr. 1934
• Last papers appeared shortly before his death, e.g.
“Erosional development of streams” (Trans GSA, 1945)
• Comments in Science (Dec. 10, 1937) “Hydrology research”:
All hydrologic phenomena are in reality physical phenomena
and are governed by the fundamental laws of physics. Many
otherwise excellent hydrologic researches have suffered from
lack of adequate consideration of the physical processes
involved and from the failure to use mathematical methods.
Water balance of the continental U.S., from
“Hydrologic interrelations between lands and oceans,”
Robert E. Horton, Trans AGU, 1943.
Aggregated Maurer et al. (2002) data vs Horton (1943)
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 sensitivities
to global change should rise to the level of a grand
challenge to the community.
In an era of
global
change …
•
What are the
impacts of land use
and land cover
change on river
basin hydrology?
•
What is the
climatic sensitivity
of runoff?
•
What are the
impacts of water
management on the
water cycle?
1. Land cover/land use change effects
Global
cropland
expansion,
1700-1992
(from
Ramankutty
and Foley,
Global
Biogeochem.
Cycles, 1999)
Do we understand the sensitivities?
Case study 1: Vegetation and climate change
effects on streamflow in the Uruguay River basin
1990s land cover (U MD)
Global Potential Vegetation
(Ramankutty and Foley)
Forest/Woodland
Uruguay River basin
land cover change –
potential vegetation
vs 1990s
Shrubland/grassland
Cropland
Simulated and
observed
streamflows,
Uruguay River
at Concordia,
Uruguay –
calibration
(1995-99) and
verification
(1990-94).
Visual courtesy Ramiro
Saurral and Vicente Barros,
University of Buenos Aires
Simulated and observed mean monthly flows at
Concordia, 1990-99 for ~1990 land cover, and sensitivity
to land cover change (forest type 7; grassland type 10)
Visual courtesy Vicente Barros and Ramiro Saurral, University of Buenos Aires
Predicted and
observed Concordia
discharge, decade
of 1960s (upper) and
1990s (lower), both
simulations using
1990s vegetation,
and consistent
observing network
for two decades.
Visual courtesy Vicente
Barros and Ramiro Saurral,
University of Buenos Aires
Case study 2: Land cover change in the Mekong
River basin
EXPANSION OF
RICE PADDIES
10 km
The broad low land
along the Mun River
was drained for more
irrigated rice. The
1946
interfluves of tributaries of the Mun and
Chi were converted to
(bunded) rainfed rice.
AREA OF DETAIL
1984
100 km
From: Fukui et al., Global Environ. Res. 3 (2), 2000.
Predicted streamflow trends
•In the dry season (Nov-Apr),
cultivation is limited, and ET from
cropland is far less than from forest.
The simulated change from forest to
cropland agrees with observations for
1962-2000 (~120% increase).
Mainstem
wet
season
•In the wet season (May-Oct),
simulated evapo-transpiration from
bunded rice paddies is large but does
not quite reach that of forest.
dry
season
Mun-Chi
sub-basin
Chiang Vient. Muk. S P O
Saen
downstream distance
Chiang Saen
Yasothon (Chi)
Chi
Chi
Mun
Mun
dry
season
wet
season
Rasi
Salai (Mun)
Ubon
downstream distance
Yasothon
(Chi river)
Rasi Salai
(Mun river)
Ubon
Junction
Vientiane
Mukdahan
Pakse
Stung Treng (S)
Phnom Penh (P)
Outlet (O)
OBSERVED STREAMFLOW TRENDS:
Percent Change in Monthly Flows Per Year in 1962-2000
(based on the Mann-Kendall test for trends)
4
Chiang Saen
Vientiane minus Chiang Saen
Mukdahan minus Vientiane
Pakse minus Mukdahan
Ubon
Yasothon
Stung Treng minus Pakse
Trend Slope as %
of Month's Average
3
2
1
Streamflows from
Northeast
Thailand show
fast-rising trends
in the dry season
months (Winter).
0
jan
-1
-2
-3
-4
feb mar
apr may jun
jul
aug sep
oct
nov dec
Streamflows from
Laos show
decreasing
trends in the dry
season months
(Winter).
Chi River (Yasothon): A ~3% increase per year in dry-season streamflow leads
to a ~120% increase (more than a doubling) in the 40 years from 1962 to 2000.
Case study 3: Land cover change in an urbanizing
catchment, Mercer Creek, WA
Mercer Creek (~31.1 km2) land
cover, 1882 and 2002
1882
2002
Mercer Creek annual flows 1955-2006, and double
mass curve
2. What is the climatic elasticity of runoff?
19-model GCM average, Colorado
River basin, annual values 2001-2100
Replotted from Seager
et al., Science, 2007
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
Two approaches to estimating
sensitivities:
a) From observations (with inherent record length,
and perhaps stationarity complications) and
b) From models (with inherent model dependence)
ΨP over the continental U.S. (from
Sankarasubramanian and Vogel, WRR, 2001)
Precipitation elasticity
ΨP as a function of
Budyko humidity
index over the
continental U.S.
•Upper plot:
Hydrologic regions 1,
3, 12 (New England,
SE, Texas)
•Lower plot:
Hydrologic regions 10
and 17 (Missouri and
Pacific NW)
Source: Sankarasubramanian and Vogel, WRR, 2001
Precipitation
elasticity ΨP as
a function of
mean
accumulated
snow depth
Source: Sankarasubramanian and Vogel, WRR, 2001
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
Bivariate
Precipitationtemperature
sensitivities
inferred from
naturalized
Colorado River
streamflows at
Lees Ferry, annual
and winter T
Observed –
annual T
Observed –
winter T
Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography
Bivariate
Precipitationtemperature
elasticities inferred
from naturalized
Colorado River
streamflows at Lees
Ferry, and from
simulated Lees Ferry
flows
Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography
Precipitation elasticity
as a function of
precipitation difference
(T = 0) from Colorado
River at Lees Ferry
naturalized annual
flows, 1905-2006.
Upper plot unsmoothed,
lower smoothed.
Annual basin
precipitation elasticity
from VIC model (20year simulation), with
+10% precipitation
increase (~1.9 for
basin at outlet)
Elasticity
Runoff sensitivity to 1o C
increase in Tmin and Tmax
(downward solar radiation
constant)
Runoff from cells
with negative
sensitivity
Runoff from
cells with
negative
sensitivity
Spatial distribution of
runoff sensitivity to 1o
C increase in Tmin and
Tmax (downward solar
radiation constant)
Basin aggregate:
2.2% per oC
Runoff sensitivity to 2o C
increase in Tmax and no
increase in Tmin (changes
both vpd and downward
solar radiation)
Basin aggregate:
3.3% per oC
So is there, or is there not, a dichotomy?
Very roughly, mid-century ΔP  18%, so for
= 1.51.9, and temperature sensitivity  0.02-0.03, and ΔT  2
oC, ΔQ  35% (vs > 50% + from GCM)
More important, though, is the question: does the
land surface hydrology matter, or does the land
surface 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?
3. What are the impacts of water management
on the water cycle?
Construction of dams has
vastly altered the water
cycle by:
•Altering the seasonal cycle, and
annual amount of discharge (6 major
global rivers, including the
Colorado, no longer flow at their
mouths)
~1900
•Increasing the time of travel
through the channel system
•Changing the quality of rivers, and
constituents and physical
characteristics of continental river
discharge
•Transporting water within and
between rivers basins, and altering
its partitioning (usually meaning
increased evapotranspiration)
2000
Reservoir construction has slowed.
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
All reservoirs larger than 0.1 km3
Some examples
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.
Colorado River basin
Irrigation water
requirements
Evapotranspiration
increase
mm
0 100 200
Changes in latent
heat fluxes
Wm-2
Percent
0 50 100
Changes in sensible Changes in surface
heat fluxes
temperatures
0 10 20
°C
Wm-2
-30 -20 -10 0
-1.5 -1.0 -0.5 0
•
Figure: Results for three peak irrigation months (Jun, Jul, Aug), averaged
over the 20-year simulation period.
•
Max changes in one cell during the summer: Evapotranspiration increases
from 24 to 231 mm, latent heat decreases by 63 W m-2, and daily averaged
surface temperature decreases 2.1 °C
•
Mean annual “natural” runoff and evapotranspiration: 42.3 and 335 mm
•
Mean annual “irrigated” runoff and evapotranspiration: 26.5 and 350 mm
Colorado River basin – modelled effects of
irrigation on moisture and energy fluxes
Irrigation water
requirements
Evapotranspiration
increase
mm
0 100 200
●
●
●
●
Changes in latent
heat fluxes
Wm-2
Percent
0 50 100
Changes in sensible Changes in surface
heat fluxes
temperatures
0 10 20
°C
Wm-2
-30 -20 -10 0
-1.5 -1.0 -0.5 0
Figure: Results for three peak irrigation months (Jun, Jul, Aug), averaged over
the 20-year simulation period.
Max changes in one cell during the summer: Evapotranspiration increases from
24 to 231 mm, latent heat decreases by 63 W m-2, and daily averaged surface
temperature decreases 2.1 °C
Mean annual “natural” runoff and evapotranspiration: 42.3 and 335 mm
Mean annual “irrigated” runoff and evapotranspiration: 26.5 and 350 mm
Our typical approach to modeling water management
effects within the land hydrological cycle
Atmospheric forcing
(gridded observations, or
downscaled from weather
or climate model)
Hydrology Model
Water
Management
Model
Some thoughts on the
institutional setting
• International programs
The role of WCRP (and especially GEWEX)
and the need for reinvention
• Funding agencies
The impact of decisions by program managers,
and the need for more community
involvement in the setting of priorities
“The most general problem is … the transition
from a qualitative to a quantitative science ..”
(Horton, “The field, scope, and status of the science
of hydrology,” Trans. AGU, 1931)
Conclusions
•We need to understand hydrologic sensitivities – to
vegetation and climate change – better. There is a
compelling motivation to do so both from a scientific and
societal need basis.
•We need a more scientific approach to understanding
the feedbacks and implications of water management
and anthropogenic perturbations on the water cycle
•The time has come to rethink international programs
related to land hydrology, and related U.S. funding
priorities and mechanisms