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![Page 1: The evolution of hydrology in an interdisciplinary earth science setting Dennis P. Lettenmaier Department of Civil and Environmental Engineering University.](https://reader036.fdocuments.net/reader036/viewer/2022062322/5697bf881a28abf838c89413/html5/thumbnails/1.jpg)
The evolution of hydrology in an interdisciplinary earth science setting
Dennis P. LettenmaierDepartment of Civil and Environmental
EngineeringUniversity of Washington
AMS Walter Orr Roberts LectureSan Diego
January 19, 2004
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Outline
1) Walter Orr Roberts
2) Background – history of hydrology
3) Recent evolution; “hydrology at the interface”
Land-atmosphere interactions
Land-ocean interactionsHydrology in the context of global environmental change
4) Doing interdisciplinary research: issues, successes, and failures
5) Emerging opportunities6) Issues and roadblocks
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Walter Orr Roberts 1915-1990• Director, High Altitude Observatory (Climax, CO) through
1960• First Director, NCAR, 1960-68• UCAR President, 1968-73• Director, UN Program on Food, Climate, and the World’s
Future 1973-81• Among many honors and awards (10 honorary
doctorates!) he was known for:– Worked to maintain scientific contacts with Soviet Union during
Cold War– Held together HAO through difficult times, staff once worked a
month without pay– Preferred title “Superintendent” of HAO to “Director” as more
modest– “Walked with kings and never lost the common touch” (John
Eddy, Journal American Astronomical Society, 1991)
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2) History of Hydrology (a condensed version)
•Two pathways:
Water development (engineering, e.g., dams and distribution systems)
Scientific understanding (genesis of runoff; water cycle)
•Former evolved largely absent scientific understanding (but impressive structures as far back as almost 5000 years in Middle East
•Headway modest on latter until 18th Century, but linkage to atmosphere was always key (which it was not in engineering pathway)
•Engineering considerations began to dominate science in late 19th and 20th Century with formation of USGS and U.S. stream gauging network (hydrologic design could proceed on basis of knowledge of streamflow alone
•With first computers in 1950s came watershed models (e.g. Stanford Watershed Model), but linkages to atmosphere were minimal (model forcings typically precipitation and PET; no explicit vegetation)
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Sadd-el-Kafara Dam, Egypt, 2600 BC (photo from Schnitter,1994)
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“No hydrological or meteorological instrument has received attention so consistently and for such a long period as the rain gauge” (Biswas, 1970)
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“As a result of rapid growth in the 1880's, the U.S. population began to branch westward into the drier regions of the country, leaving the usually dependable waterways of the East far behind. In 1889, the first U.S. stream-gaging station was established on the Rio Grande near Embudo, New Mexico. By 1895, discharge measurements were being made by the USGS in at least 27 states throughout the country.”
Delaware River Basin Commission, A brief history of stream gauges
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“There is no reliable mathematical process by which the best value for the quota, or the necessary size of the reservoir can be calculated, since these figures depend not only on the mean discharge but on the exact sequent of high and low years which may occur in the future” (A.D. Butcher, 1938, quoted in Hurst et al, 1965)
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Stochastic hydrology – the underlying problem (from Hurst et al, 1965)
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Colorado River Natural Flow at Lee Ferry, AZ
Currently used 16.3 BCM
allocated20.3 BCM
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Hydrologic simulation modeling – the Stanford Watershed model (per Steve Gorelik and Keith Loague)
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Status of hydrologic research ~1980
• Stochastic (or “synthetic”) hydrology – attempting to reproduce statistics of observed streamflow time series (ostensibly for reservoir sizing)
• Conceptual hydrologic modeling – for reproduction of streamflow records (typically at daily or subdaily – “storm” time steps) for streamflow forecasting, and design/analysis where precipitation record lengths exceed those for streamflow
• Hillslope/small catchment scale observation and modeling
• Groundwater
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13,382dams,
Hydrology post ~1980: the “push”
Visual courtesy Hiroshi Ishidaira, Yamanashi University
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0
100
200
300
400
500
600
700
800
Up to1900
1901-1910
1911-1920
1921-1930
1931-1940
1941-1950
1951-1960
1961-1970
1971-1980
1981-1990
1990-1998
Nu
mb
er
of
Re
se
rvo
irs
.
Australia/New Zealand
Africa
Asia
Europe
Central and South America
North America
Reservoir construction has slowed.
visual courtesy Peter Gleick
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from Arnell (1999)
Hydrology post ~1980: the “pull”
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From Bowling and Lettenmaier, 1997
Hydrology post ~1980: the “pull” (cont.)
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3) Recent history; “hydrology at the interface”
“There are no interesting problems in hydrology. They are all at the interface”
Eric Wood
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a) Land-atmosphere interactions
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from Lynn et al, 1995
The land surface matters …
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From Lynn et al (1995)
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From Avissar et al, 2002
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The land surface matters …
From Koster and Suarez, 1995
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Bias Score (Oct-Nov-Mar)
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1 2 3 4 5 6 7 8
Forecast Day
Bia
s S
core P1 (2.54 mm/day)
P1 (12.7 mm/day)
P2 (2.54 mm/day)
P2 (12.7 mm/day)
Bias Score (Dec-Jan-Feb)
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1 2 3 4 5 6 7 8
Forecast Day
Bia
s S
core P1 (2.54 mm/day)
P1 (12.7 mm/day)
P2 (2.54 mm/day)
P2 (12.7 mm/day)
Effects of Land Surface Initialization on Extended Range Weather Forecast Skill (from Qian and Leung, 2005)
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ISLSCP Field Experiment Design
Land-atmosphere interactions – the observational basis
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BOREAS study designBOREAS IFC Strategy
BOREAS (BOReal Ecosystem-Atmosphere Study) – 1994-96
Visuals from Running et al (1999) and Margolis and Ryan (1997)
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0
1
0 1W /W c
P EQ
W c
W
dW /dt=P-E-QE=βep
Q=0, W <W c
P-E, W =W c
β
Land-atmosphere interactions – model evolution
The Manabe-Budyko bucket (Manabe, 1969)
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SiB (Simple Biosphere model) – per Sellers et al (1986)
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Source: Maurer et al, 2002
VIC long-term monthly streamflow simulations, selected large continental U.S. rivers
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b) Land-ocean interactions
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Trend = 7.3 km3/year
Su et al. 2005
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www.clivar.org
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Arctic Ocean Freshwater Budget (HadCM3 Results)
Cattle and Cresswell, 2000
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Riv
er
Run
off
P-E
Tot
al
Inpu
ts
Sea
Ice
Exp
ort
Oce
an
Exp
ort
Tot
al
Exp
orts
Net
E
xpor
t
Fre
shw
ater
Tra
nspo
rt (
km3
yr-
1)
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From Aagaard and Carmack (1989), per Eugeny Karabanov
Discharge of the major Arctic rivers
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Carleton et al. (1990)
Local SST ( e.g. Gulf of California) has influence on the monsoon
Fig.9 Correlation of SSTs and Arizona summer rainfall in wet years
Remote SST in equatorial Pacific in El Nino (La Nina) tends to be associated with dry (wet) monsoon
Fig. 19. Maps of the composite seasonal (JJAS) precipitation anomalies (mm day−1) for El Nino (La Nina).
Higgins et al, 1999
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Higher (lower) winter precipitation & spring snowpack
More (less) spring soil moisture
Weak (strong) monsoon
Lower (higher)early summer surface temperature
WetDry
WarmCold
×
××
Winter Precipitation - Monsoon Rainfall Land Surface feedback hypothesis (Zhu et al, 2004)
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c) Hydrology in the context of global environmental change
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-60
-30
0
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-60
-30
0
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-150 -120 -90 -60 -30 0 30 60 90 120 150
Global Scale Hydrologic Prediction
from Nijssen et al, 2001
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Seasonal Evapotranspiration (1980-1993)
from Nijssen et al, 2001
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GCM Predicted Climate ChangeChange in precipitation and temperature for selected basins
GFDL_CGCMCCCMA-CGCM1
HCCPR-CM2CCSR-CGCM
HCCPR-CM3CSIRO-CGCM
MPI-ECHAM4DOE-PCM3
2025 2045 2095
-40 -30 -20 -10
0 10 20 30 40 Amazon Amur Mackenzie
-40 -30 -20 -10
0 10 20 30 40 Mekong Mississippi Severnaya Dvina
-40 -30 -20 -10
0 10 20 30 40
0 1 2 3 4 5 6 7 8
Xi
0 1 2 3 4 5 6 7 8
Yellow
0 1 2 3 4 5 6 7 8 9
Yenisei
Change in temperature (C)
Chang
e in p
reci
pit
ati
on
(%
)
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Simulated Streamflow 2025
Baseline HCCPR-CM2 HCCPR-CM3
MPI-ECHAM4 DOE-PCM3
0
100000
200000
300000 Amazon
0
5000
10000
15000
20000
25000 Amur
0
10000
20000
30000 MacKenzie
0
10000
20000
30000
40000 Mekong
0
1000020000
30000
40000
50000 Mississippi
0
5000
10000
15000
20000 Severnaya Dvina
0
5000
10000
15000 Xi
J F M A M J J A S O N D0
1000
2000
3000
4000 Yellow
J F M A M J J A S O N D0
25000
50000
75000
100000 Yenisei
J F M A M J J A S O N D
m3/s
m3/s
m3/s
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Simulated Streamflow2045
Baseline HCCPR-CM2 HCCPR-CM3
MPI-ECHAM4 DOE-PCM3
0
100000
200000
300000 Amazon
05000
10000150002000025000 Amur
0
10000
20000
30000 MacKenzie
0
10000
20000
30000
40000 Mekong
01000020000300004000050000 Mississippi
0
5000
10000
15000
20000 Severnaya Dvina
0
5000
10000
15000 Xi
J F M A M J J A S O N D0
1000
2000
3000
4000 Yellow
J F M A M J J A S O N D0
25000
50000
75000
100000 Yenisei
J F M A M J J A S O N D
m3/s
m3/s
m3/s
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Glen Canyon Dam, USGS 1984
Mechanisms by which humans are
affecting the GWS
reservoirs, withdrawal, transfers, resulting in stop-flow events, changes in nutrient and sediment fluxes etc.
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Global Water System Project
IGBP – IHDP – WCRP - Diversitas
Human modificationof hydrological systems
Regulated Flow
Historic Naturalized Flow
Estimated Range of Naturalized FlowWith 2040’s Warming
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.
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Global Water System Project
IGBP – IHDP – WCRP - Diversitas
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(http://hydro.iis.u-tokyo.ac.jp/GW/result)
Global Runoff & Water useGlobal Runoff & Water use
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a) Latent heat b) Sensible heat c) Surface temperature
Wm-2 Wm-2 °C0 10 20 -30 -20 -10 0 -1.5 -1.0 -0.5 0
Colorado River Basin
Mekong River Basin
Simulated changes in latent and sensible heat, and surface temperature, due to irrigation
from Haddeland et al, 2005
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Simulated effects of irrigation and reservoirs on discharge of Colorado and Mekong Rivers
from Haddeland et al, 2005
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Visual from Palmieri, NAS Sackler symposium, 2004
The social context: water storage per person globally
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HUMAN COMPONENTSe.g. water related institutions,
water engineering works,water use sectors
WATER CYCLING
PHYSICAL COMPONENTS
e.g. moisture transport, precipitation,
river discharge, water storage volumes
BIOLOGICAL & BIOGEOCHEMICAL
COMPONENTSe.g. species richness,
habitat quality,water quality
The Global Water System
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Table courtesy Peter Gleick
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4) Doing interdisciplinary research – successes and failures
a)The EOS IDS experience
b)RISAs, and the UW Climate Impacts Group
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EOS/IDS• Attempt to “build” the science community to support Mission
to Planet Earth, an interdisciplinary effort by construct• Long-term, (reasonably) stable funding, initial projects 10
years, starting in late 1980s• (At least) two models: a) primarily single institution, b)
multi-institution “best players” (problems with both)• Overall success of IDS: community building (e.g. IWG
meetings twice/year), and developing a global perspective linked to remote sensing. Shortcoming: productivity per dollar (or scientist) inversely proportional to n (generally true of most large projects)
• IDS is now “just another” research program (3-year grants, smaller teams) – IDS vision arguably died with launch of Terra and Aqua
• Hard to separate lots of “good stuff” produced by IDS investigators from progress in interdisciplinary science (not even clear what the evaluation criteria are, or should be, beyond getting different communities to talk to each other)
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The Miles Recipe cont’d
• All participants must have at least some interest in end-to-end integration, including the human dimensions.
• At least two people in the team must have as their primary responsibility “seeing the problem whole” and facilitating interconnections when and where needed.
• All must be involved in interactions with stakeholders to some extent.
• Stir until done
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5) Emerging opportunities
1) Continuing trends (e.g., dynamic vegetation, linked biogeochemistry, land-atmosphere interactions at weather to climate time scales) – and numerous “second tier” issues on which progress in these areas depend
2) Linked aspects of human behavior and the water cycle at regional to continental (and perhaps global) scales
3) Prediction of interactions between hydrological and biological (terrestrial and aquatic) systems, e.g., in the context of biodiversity and species extinction
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6) Issues and roadblocks
1) Social dynamics and Inefficiencies of large projects
2) Career evaluation criteria (multi-authored publications, journal preferences, etc.)
3) Disciplinary structure of funding sources (influence of research funding can’t be overemphasized)
4) Inertia, inappropriateness of research results, and various other obstacles to implementation of research advances (one aspect of human dimensions research)
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Thanks to:
• AMS, for providing a professional home for research on land-atmosphere interactions
• The following individuals, for material used in this talk:– Jenny Adam– Ingjerd Haddeland– Jordan Lanini– Ruby Leung– Ed Miles– Andrea Ray – Steve Running– Amy Snover– Anne Steinemann– Eric Wood – Chunmei Zhu– and many others …