Transcript Cynthia Rosenzweig & Max Campos

Agriculture and Water Resources
Cynthia Rosenzweig and Max Campos
AIACC Trieste Project Development Workshop
[email protected]
Linking Regional Water Supplies
and Water Demands
Availability of water
for agriculture in
the coming
decades depends
not only on
changing climate,
but also on
population,
economic
development, and
technology
Water Availability:
Five International Case Studies
Rosenzweig et al., 1999, 2001
Linking a suite of models in order to improve projections of
water availability, by taking potential changes in both
water supply and demand into account.
SCENARIOS
GCMs
variability
CLIMATE
Precip.,
Temp.
Solar Rad.
WATBAL
Streamflow
PET
CERES
Crop water
demand
CROPWAT
Regional
irrigation
SCENARIOS
Population, Development,
Technology
WEAP
Evaluation
Planning
• Runoff, water
demands, and
water system
reliability
• Environmental
stress due to
human use of
water resources
• Crop yields based
on consistent
projections of
changes in water
supply and
demand
Maize production in 1998
Argentina
Brazil
China
Population (millions)
2020
Rest
1600
Hungary &
Romania
USA
1400
1200
Soybean production in 1998
Argentina
1000
1995
Low
800
High
600
Rest
Brazil
400
200
USA
China
Hungary &
Romania
(<0.01%)
0
Brazil
China
US
Cynthia Rosenzweig1, David C. Major1, Kenneth Strzepek2,
Ana Iglesias1, David Yates2, Alyssa Holt2, and Daniel Hillel1
SCENARIOS
GCMs
variability
Crop yields and
water demands
are estimated with
process based crop
models (calibrated and
validated).
The ratios (Kc)
between simulated and
actual crop ET are
used to estimate
regional water demand
with CROPWAT.
Irrigation demand is
adjusted by a regional
irrigation efficiency.
Daily
climate
(34 sites)
Process
models
CERES
SOYGRO
Monthly
climate
(27 water
regions)
REGIONAL
DATABASES
Crops
Soils
Yields
Management
Yields
Irrigation
Phenology
PET, ETc
Kc
Empirical
model
CROPWAT
CLIMATE
CHANGE
EFFECTS
Phenology
CO2
Kc
Net irrigation
all crops
Spatial
database
Crop areas
Irrig. efficiency
TOTAL
IRRIGATION
DEMAND
Crop water demand model interactions
Water supply calculated using WATBAL
PET calculation by Priestley-Taylor (ensuring consistency with the
crop models
WATBAL is run for 50 yrs of climate change and variability
scenarios, using SAMS WG.
Modeled vs. observed monthly runoff
for the Titsza water region.
Schematic of WATBAL processes
Evapotranspiration
Effective
precipitation
1.80
1.60
mm/day
1.40
Surface
runoff
1.00
0.80
0.60
0.40
0.20
Baseflow
Kaczmarek, 1993; Yates, 1996
Ken Strzepek, Univ. of Colorado, Boulder
Modeled
Observed
Oct-86
Oct-85
Oct-84
Oct-83
Oct-82
Oct-81
0.00
Oct-80
Sub-surface
runoff
Oct-79
Soil moisture zone
Relative
depth
1.20
R2= 0.55
Ann. avg mod. = 208 mm
Ann. avg obs. = 213 mm
Working with Multiple Models:
Consistency at different Spatial Scales
Harbin (China)
6
ET0 (mm/day)
5
HARA
4
w bHARA
3
Dier Songhua Jian
2
Nen Jian
Songhua Jian
1
0
0
50
100
150
200
250
300
350
Day of Year
Grand Island (Nebraska)
8
ET0 (mm/day)
7
6
5
GNEA
4
w bGNEA
3
Low er Missouri
2
1
0
0
50
100
150
200
Day of Year
250
300
350
Balance of water
supply and demand
is undertaken in the Water
Evaluation and Planning
(WEAP) model.
Population and GDP
drivers are used to
calculate future industrial,
municipal, and domestic
water use, and forecast
increases in irrigation
areas. (UN population
forecasts and economic
forecasts of The
Netherlands Central
Planning Bureau.)
WEAP schematic for the water
regions in the US Corn Belt
Stockholm Environment Institute, 1997
Boston, MA
Annual Runoff (m3x1011)
Change in annual runoff and water reliability for the
2020s with change climate scenarios
6
4
2
0
Annual Reliability (%)
Danube Argentina
Brazil
China
USA
Brazil
China
US
100
90
80
70
60
50
Danube
Argentina
Current
MPI
GFDL
GISS
Key Water Resource Results
100
Possible decadal
surprises
60
40
Demand met
Reliability
20
0
1990 2000
2010
2020
2040
2040
2050
Year
400
Change in
seasonality
Runoff (cfs)
Percentage
80
Current
GFDL
MPI
HC
300
200
100
50
O
N
D
J
F
M A M
Months
J
J
A
S
Strzepek et al., 1999
Projected change in environmental stress for the
Danube water regions
Reference 1995
Reference 2010
GISS 2010
Reference 2020
GISS 2020
High stress
Medium stress
Low stress
No stress
 Demand to supply ratio (environmental stress)
measures degree of economic development and
impacts on ecosystems.
 If the demand to supply ratio is low, then there is
ample water for ecosystem services.
Crop Coefficients
Corn
Adaptation:
Optimizing crop
varieties
P1
P1
Juvenile phase (growing degree
days base 8 C from emergence
to end of the juvenile phase)
P2
P2
P5
P5
Photoperiod sensitivity
G2
G2
G5
G5
Potential kernel number
Grain filling duration (growing
degree days base 8 form silking
to physiological maturity)
Potential kernel weight (growth
rate)
Testing adaptation with crop models
Irrigation Demand mm/ha Base Climate
Effect of Cultivar and Planting Date
350
300
250
day 100
day 110
day 120
day 130
200
150
100
50
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16
Cultivars
Nitrogen Leaching (kg/ha) Base Climate
Effect of Cultivar and Planting Date
16
15.9
15.8
15.7
day 130
15.6
15.5
15.4
15.3
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16
Cultivars
Nitrogen Leached: Effect of Precipitation
Growing Season Precipitation
493.8
4
sowing to flowering
150
420
440
460
480
500
growing season precipitation
Nitrogen Leached
200
floweing to maturity
250
15.7
.
100
469.3
50
434
0
400
47.8
47.8
1
1
48.1
48.1
2
2
47.9
47.9
3
3
50.4
50.4
4
483.1
Corn Growing Season
4
14.1
3
12.6
2
10.9
1
0
5
10
nitrogen leached
15
20