Agricultural and Forest Meteorology 117 (2003) 97–122
Integrated assessment of Hadley Center (HadCM2) climate-change
impacts on agricultural productivity and irrigation water supply in
the conterminous United States
Part II. Regional agricultural production in 2030 and 2095
R. César Izaurralde a,∗ , Norman J. Rosenberg a , Robert A. Brown b ,
Allison M. Thomson a
a
Joint Global Change Research Institute, Pacific Northwest National Laboratory, University of Maryland,
8400 Baltimore Avenue, College Park, MD 20740, USA
b Independent Project Analysis, 11150 Sunset Hills Road, Suite 30, Reston, VA 20190, USA
Received 22 January 2003; accepted 23 January 2003
Abstract
A national assessment (NA) evaluated the potential consequences of climate change and variability on the agriculture,
water resources, as well as other economic and natural resource sectors in the United States. As part of this process, we
used scenarios of the HadCM2 GCM and the EPIC agroecosystem model to evaluate climate-change impacts on crop yields
and ecosystem processes. Baseline climate data were obtained from national records for 1961–1990. The scenario runs for
2025–2034 and 2090–2099 were extracted from a HadCM2 run. EPIC was run on 204 representative farms under current
climate and two 10-year periods centered on 2030 and 2095, each at CO2 concentrations of 365 and 560 ppm. Crops were
simulated under both dryland and irrigated management, with irrigation water supply estimates taken from the HUMUS
simulations in Paper 1. Texas, New Mexico, Colorado, Utah, Arizona, and California are projected to experience significant
temperature increases by 2030. Slight cooling is expected by 2030 in Alabama, Florida, Maine, Montana, Idaho, and Utah.
Larger areas are projected to experience increased warming by 2095. Uniform precipitation increases are expected by 2030
in the northeast. These increases are predicted to expand to the eastern half of the country by 2095. Regionally, dryland
corn yields could increase, decrease or remain unchanged under the two scenarios. EPIC simulated yield increases for the
Great Lakes, Corn Belt and Northeast regions. Simulated yields of irrigated corn were predicted to increase in almost all
regions. Soybean yields could decrease in the Northern and Southern Plains, the Corn Belt, Delta, Appalachian, and Southeast
regions and increase in the Lakes and Northeast regions. Simulated wheat yields exhibited upward trends under scenarios of
climate-change. Evapotranspiration in dryland corn is expected to increase in both future periods while water-use efficiency
will decrease. National corn production in 2030 and 2095 could be affected by changes in three major producing regions. In
2030, corn production could increase in the Corn Belt and Lakes regions but decrease in the Northern Plains leading to an
overall decrease in national production. National wheat production is expected to increase during both future periods. A proxy
indicator was developed to provide a sense of where in the country, and when water would be available to satisfy change in
∗ Corresponding author. Present address: Battelle Pacific Northwest National Laboratory, 8400 Baltimore Ave., Suite 201, College Park,
MD 20740, USA. Fax: +1-3013146760.
E-mail address: cesar.izaurralde@pnl.gov (R.C. Izaurralde).
0168-1923/03/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S0168-1923(03)00024-8
98
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
irrigation demand for corn and alfalfa production as these are influenced by the HadCM2 scenarios and CO2 -fertilization.
Irrigation requirement by irrigated crops declines under these scenarios as transpiration is suppressed.
© 2003 Elsevier Science B.V. All rights reserved.
Keywords: Erosion productivity impact calculator (EPIC); Corn; Wheat; Alfalfa; Crop yield; Evapotranspiration; Runoff
1. Introduction
1.1. Background and objectives of the US national
assessment
A national assessment (NA) of the US recently examined the possible impacts of climatic change and
variability on a broad range of social, economic, and
natural-resource sectors such as human health, agriculture, forests, water and coastal areas. The assessment
was mandated by law (1990 Global Change Research
Act, P.L. 101–606) and carried out in public–private
partnership following guidance established by the US
Global Change Research Program. The National Assessment Synthesis Team (1998) was charged with the
regional and national assessment of climatic change
impacts on agriculture including the economic implications of these impacts and strategies for adaptation
to them. Air temperature and precipitation changes
projected by the Canadian Climate Model (CCM)
and the Hadley Center Model (HadCM2) were made
available to the modeling teams that participated in
the assessment. Researchers at the Pacific Northwest
National Laboratory (PNNL) modeled crop yields and
crop production at two times during the 21st century
using climate-change projections of the HadCM2.
The Erosion Productivity Impact Calculator (recently
renamed Environmental Policy Integrated Climate)
model was used to simulate crop yields together
with a host of other agroecosystem processes (EPIC;
Williams, 1995).
1.2. Review of literature
The use of the climate-change scenario, built from
results of general circulation model (GCM) runs, has
been at the core of climatic change assessments on
agricultural and water resources for the past 20 years
(Rosenberg, 1992). For example, runs of the Goddard Institute for Space Studies GCM were used to
study the distribution of wheat (Triticum aestivum L.)
growing regions across North America under the then
current climate and that that would occur when CO2
concentration reaches double its pre-industrial level
(2× CO2 ) (Rosenzweig, 1985). The northern expansion of wheat growing regions in Canada emerged as
one clear conclusion of the study. These early projections, however, had coarse spatial resolution, relied on
empirical procedures to calculate crop phenology and
lacked information about the CO2 -fertilization effect
on crop growth (Kimball, 1983).
Gradually, investigators began using crop and
agroecosystem models to assess how agricultural production could be affected were climate to change according to GCM projections (e.g. Rosenzweig, 1989;
Easterling et al., 1992; Mearns et al., 1992; Brown
and Rosenberg, 1999; Guereña et al., 2001). This approach has proven useful because also it yields insight
into some of the extended impacts of climate-change
on ecosystem processes such soil water storage
(Mearns et al., 1992), runoff (Rosenberg et al., 2003,
companion paper), soil erosion (Favis-Mortlock and
Boardman, 1995), and soil organic matter cycling
(Post et al., 1996; Schimel et al., 2000). Algorithms
to account for the effects of elevated CO2 on photosynthesis and transpiration (documented for example by Kimball, 1983; Acock, 1990; Mauney et al.,
1994) have also been incorporated and tested in models such as EPIC (Stockle et al., 1992a,b), CERES
(Tubiello et al., 1995, 1999) and ecosys (Grant et al.,
1995a,b).
There has been a rich diversity of approaches used
for modeling future climate-change and sea level rise
(Harvey, 2000). While all GCMs are based on sound
physical principles, which are believed to be invariant (e.g. conservation of mass), they often differ in
the simplifying assumptions (e.g. empirical relations)
and parameterization approaches (i.e. scaling) used
to make them computationally efficient. Not surprisingly then, GCMs also compute future climates that
differ in the spatial distribution and intensity of climatic changes (Harvey, 2000). Thus, assessments of
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
climate-change impacts on agricultural production
frequently use output of more than one GCM to drive
ecosystem models (Adams et al., 1990; Mearns et al.,
1992; Rosenzweig and Parry, 1994; Brown and
Rosenberg, 1999).
Brown and Rosenberg (1999), for example, used
EPIC and regionalized scenarios from three GCMs
(GISS; UKTR (United Kingdom Meteorological Transient, now HadCM2); and BMRC (Australian Bureau
of Meteorological Research Center)) to simulate the
impacts of climate-change on the potential productivity of corn (Zea mays L.) and winter wheat in the
US. Little if any change in wheat and corn production was predicted in a future with only a 1 ◦ C increase in global mean temperatures (GMT) and in the
absence of a CO2 -fertilization effect on crop growth.
Regardless of the GCM scenario used, an enhancement in atmospheric [CO2 ] from the near current 365
to 560 and 750 ppm increased yields of winter wheat
(a C3 crop) but not those of corn (a C4 crop). Yields
fell by as much as 70% below baseline (1961–1990)
were modeled when GMT increased by 5 ◦ C. While
the magnitude of yield changes simulated may vary,
there was agreement as to the general direction of these
changes.
1.3. Research objectives
Here, we present our contribution to the US national
assessment with a study of possible climate-change
impacts on US agriculture using projections of the
HadCM2 GCM for two periods during the 21st century. The same HadCM2 scenario and time periods
were used by Rosenberg et al. (2003) (Paper I) to
study climate-change impacts on US water resources.
Those results are incorporated here to provide a
measure of irrigation water supply as we detail the
climate-change impacts on crop yields, yield variability, incidence of various stress factors on yield and
on evapotranspiration and national crop production.
These results and those of others (Tubiello et al.,
2000; Paustian et al., 2000) have been integrated into
the NA agricultural sector report (Reilly et al., 2003).
In addition in this paper, we assess whether the change
in water supplies modeled in Paper I of this series
strengthen or weaken the potential for irrigation of the
corn and alfalfa (Medicago sativa L.) crops across the
country.
99
2. Materials and methods
2.1. Climatic data
We followed general guidelines established by the
National Assessment Synthesis Team (1998) of using
historical and scenario-driven approaches for designing and conducting the simulation runs. Historical data
for the baseline runs were obtained from databases
that reside in the Hydrological Unit Model for the
United States (HUMUS) (Srinivasan et al., 1993) for
the period 1961–1990. These data originate from daily
records of temperature and precipitation from the national cooperative network of meteorological stations
(Reek et al., 1992). The historical data in HUMUS are
resolved at the eight-digit USGS basin scale.
Climatic data for the scenario runs were obtained
from the National Center for Atmospheric Research
(NCAR). The data included results of a computer run
with the HadCM2 GCM for the period 1994–2100.
This transient GCM run was generated assuming a 1%
per year increase in CO2 concentrations together with
changes brought about by aerosol effects on albedo. As
part of the VEMAP project (VEMAP Members, 1995),
model run results were downscaled to a half-degree
spatial resolution. The procedures followed for downscaling included methods to account for the effects of
topography on temperature and precipitation.
2.2. Simulation model description
We used a set of “representative farms” like those
of Easterling et al. (1992) to quantitatively describe soil–climate-management conditions prevailing within each of the 204 four-digit Hydrological
Unit Area (HUA) basins of the conterminous US.
The EPIC model (Williams, 1995) was used to simulate climate-change impacts on grain yields of corn,
wheat, and soybean (Glycine max [L.] Merr.) as well
as hay yields of alfalfa. Corn and alfalfa were simulated both with and without irrigation. Irrigation in
EPIC is applied automatically based on soil water
deficits, and it is assumed there is no limit to irrigation
supply. EPIC simulates crop growth on a daily time
step and requires inputs of the maximum and minimum daily temperature, precipitation, solar radiation
and wind speed as well as monthly statistics such as
the standard deviation of maximum and minimum
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temperatures and number of days with precipitation.
Crop growth is simulated by calculating the potential
daily photosynthetic production of biomass. The daily
potential growth is decreased by stresses caused by
shortages of radiation, water and nutrients, by temperature extremes, and by inadequate soil aeration.
The value of the most severe stress calculated daily is
used to constrain biomass accumulation, root growth,
harvest index and crop yield.
EPIC was modified by Stockle et al. (1992a) to account for the effects of CO2 concentration and vapor pressure deficit on radiation use efficiency (RUE,
g MJ−1 ) and evapotranspiration (ET). Elevated CO2
increases photosynthesis in C3 plants but the effect
appears to be small in C4 plants. Elevated CO2 also reduces evapotranspiration in both C3 and C4 plants due
to significant reductions in stomatal conductance and,
consequently, transpiration. A non-linear equation was
developed in EPIC to express the RUE response of major plant species to increasing CO2 concentrations following experimental evidence summarized by Kimball
(1983). Their analysis showed that crop yields could
probably increase by about 33% with a doubling of
atmospheric CO2 concentration with a 99% confidence in response ranging from 24 to 43%. Since
not all crops would respond equally to CO2 enrichment, Stockle et al. (1992b) modeled this response as
a function of crop type. The expected biomass/yield
increase when doubling the atmospheric CO2 concentration in a stress free environment was 33% for
wheat, 24% for soybean and 10% for corn (Stockle
et al., 1992b). Radiation use efficiency is also affected
by vapor pressure deficit (VPD, kPa). Because VPD
varies from day to day and from region to region, RUE
is adjusted daily according to the VPD value calculated or measured for that day (Stockle et al., 1992a).
While EPIC has not been specifically tested against
observations from CO2 enrichment experiments, the
theoretical responses modeled in EPIC appear to be
consistent with recent results arising from FACE type
experiments (Amthor, 2001).
Plants growing in CO2 enriched environments
show up to a 40% decrease in stomatal conductance
(Morison, 1987). This reduction in stomatal conductance leads to a reduction in transpiration. EPIC
currently has five equations that can be used to calculate ET but the Penman–Monteith method is the only
one that is used to account for the effects of VPD and
CO2 concentration on leaf resistance and ET (Stockle
et al., 1992a). Maximum stomatal conductance (the
inverse of stomatal resistance) values used in EPIC
follow those reported by Korner et al. (1979). In
EPIC, stomatal conductance decreases linearly with
VPD once a threshold value from maximum conductance is reached (Stockle et al., 1992a). EPIC uses
a function that makes leaf conductance decrease linearly between 330 and 660 ppm. Thus, the application
of these corrections on a daily basis lead to realistic
simulation of CO2 concentration and VPD effects on
leaf conductance and ET.
Crop yields in EPIC are estimated by multiplying
above-ground biomass at maturity by a harvest index
(HI, proportion of the total biomass in the harvested
organ). EPIC calculates the HI on a daily basis incorporating the effects of water, temperature and nutrient
stresses on the plant.
2.3. Validation of the EPIC model
Previous studies have validated EPIC crop yields
in the United States. Kiniry et al. (1990) concluded
that EPIC was able to reproduce actual yields of corn,
wheat and soybean under a variety of management and
climate conditions. Rosenberg et al. (1992) found that
EPIC-simulated yields in the central US compared favorably with historical county yields (USDA National
Agricultural Statistics Service), yields from agronomic
experiments and yields estimated by local agricultural
experts. In a regional study, Easterling et al. (1996)
found that EPIC simulations of representative farms
using climate and soils data on a 0.5 grid scale explained 65% of the variation in corn yields in eastern
Iowa and 54% of the variation in wheat in western
Kansas for the period 1984–1992. In addition, Brown
and Rosenberg (1999) compared EPIC yields of corn,
sorghum, soybean and wheat with NASS yields and
yields from agronomic experiments and found that
EPIC overestimated historical yields slightly and more
closely approximated the yields from agronomic experiments.
Other variables in EPIC have also undergone validation. Chung et al. (1999) performed EPIC simulations in southeast Iowa and found that EPIC was able
to replicate the long-term environmental changes related to different tillage systems. A series of papers by
Roloff et al. (1998a,b,c) compared EPIC simulations
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
to historical conditions in Canada and found that it
reliably simulates a range of long-term environmental
variables, however EPIC was not as accurate in simulating interannual variability of crop yields.
For this study, the EPIC model results were compared with results from the crop models Century,
CERES (Maize and Wheat) and DNDC under baseline and climate-change conditions (Paustian et al.,
2000). The models were found to produce similar
yield results for corn and wheat with the HadCM2
climate-change predictions, tending toward constant
or increased yields for corn and wheat. The results of
the comparison suggest that all four models consistently represent regional patterns of crop productivity.
2.4. Input data and scenario runs
Climatic data for the scenario runs were developed
as follow. The baseline scenario used the historical
records for the period 1961–1990. Two HadCM2 scenarios for the periods 2025–2034 (H1) and 2090–2099
(H2) were built by extracting the data from the 8 mm
tapes and reformatting these into EPIC daily input
files. The meteorological data extracted corresponded
to grid cells located as close as possible to each HUA
centroid. Each scenario was run under two conditions
of atmospheric CO2 stabilization: 365 and 560 ppm.
These concentrations were chosen to represent, respectively, the lack of and the manifestation of a
CO2 -fertilization effect. The total number of runs was
4896 runs (204 farms × 4 crops × 3 scenarios × 2
CO2 levels). The output data extracted for analyses
included: crop yield, standard deviation of crop yield,
potential evapotranspiration, actual evapotranspiration, runoff, irrigation water and number of stress
days (water, temperature and nutrient) affecting crop
production.
2.5. Analysis of simulation results
Analyses of variance on all output variables were
conducted at the regional level using the USDA Economic Research Service (USDA-ERS) production regions as the basis for aggregation. Farms within each
of 10 regions served as ANOVA replicates. Further
analyses were conducted to evaluate the sensitivity
of crop productivity to climatic change at the basin
scale using annual yields as replicates. Additionally,
101
we present an analysis of the demand for irrigation
water by corn and alfalfa crops and the supply of irrigation water projected by the HUMUS model (Paper
1) to be available for each representative farms.
3. Results and discussion
3.1. Climate-change scenarios
The changes in temperature and precipitation predicted by HadCM2 runs for the conterminous US
are more moderate than those predicted by the CCM
GCM (National Assessment Synthesis Team, 2000).
Warming predicted by the HadCM2 model during
the 21st century is 2.8 ◦ C while for the CCM model
it reaches 5.0 ◦ C. The HadCM2 predictions fit midway in the warming range (1.7–5.0 ◦ C) estimated
by a set of widely used GCMs (NAST, 2000). In
both, the projected increases in average temperatures
would develop more because of increases in minimum than maximum temperatures (Karl et al., 1993).
Regional and seasonal climate changes predicted by
the HadCM2 runs are presented and discussed in
detail by Rosenberg et al. (2003) in Paper I. Here,
we present a brief discussion of these changes in the
context of the 10 agricultural regions to be considered below (Fig. 1 and Table 1). By 2030, increases
in minimum temperature are expected to be largest
in the Mountain, Southern Plains and Delta regions
(Table 1). Temperature increases are minimal in the
Northeast while an intermediate warming occurs in
the rest of the country. A much more pronounced and
extended warming is projected for 2095.
The distribution of projected changes in annual
precipitation by 2030 presents two distinct patterns.
When aggregated over large regions (Table 1) precipitation deviations across the country are mostly
positive (4–126 mm) except in the Delta and Southeast regions where average decreases of −46 and
−21 mm, respectively, are expected. A considerably
wetter future is predicted to prevail by the end of the
21st century (Table 1). The eastern half of the country
exhibits consistent and extensive increases in precipitation ranging from 154 to 288 mm. The western half
reveals a consistent pattern of precipitation increases
but these increases are generally smaller than those
projected for the eastern half of the country.
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R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Fig. 1. USDA economics research service agricultural regions (in color) drawn over the USGS four-digit basins (boundaries delineated in
white).
and CO2 -fertilization effects. A notable exception occurred in the Pacific region (Washington, Oregon and
California) where the climate-change main effect was
not significant in the majority of the crop-irrigation
combinations. Another region with a rather low sensitivity was the Southeast. The algorithms established
for crop growth made corn, soybean and wheat very
sensitive to the simulated changes in climate and CO2
atmospheric concentration.
3.2. Regional analysis of crop productivity
Analyses of variance of crop yields simulated in
each of the 10 US agricultural regions are presented
in Table 2. The statistical model fitted considered
two main effects (two CO2 and three climate-change
scenario levels) and a simple interaction. The results
suggest that most regions and crop-irrigation combinations were sensitive to the simulated climatic change
Table 1
Regional distribution of air temperature and precipitation under baseline conditions and deviations from baseline projected by the HadCM2
model
CO2 /scenario
Region
Pacific
Mountain
Northern
Plains
Southern
Plains
Lakes
Corn Belt
Delta
Northeast
Appalachian
Southeast
Maximum daily air temperature (◦ C)
(1) Ba
19.0
18.0
1.1
0.9
(2) H1b
2.9
2.7
(3) H2c
16.0
1.3
2.7
24.7
1.3
3.1
12.3
0.9
2.4
16.4
0.9
2.0
23.8
1.2
2.2
13.5
0.5
2.0
20.0
0.7
1.7
25.0
1.0
2.0
Minimum daily air temperature (◦ C)
(1) B
5.4
1.3
(2) H1
1.1
1.9
(3) H2
3.5
4.5
1.9
1.4
3.6
10.5
1.6
3.7
0.1
1.4
3.7
4.5
1.2
3.2
10.9
1.6
3.1
1.9
0.3
2.5
6.6
1.4
3.2
12.0
0.9
2.4
Precipitation (mm)
(1) B
799
(2) H1
44
(3) H2
164
a
339
71
120
562
13
79
727
4
88
792
71
219
941
87
254
EPIC simulation driven by baseline climate conditions.
EPIC simulation driven by HadCM2 model prediction of 2035 climate.
c EPIC simulation driven by HadCM2 model prediction of 2090 climate.
b
1383
−46
194
1054
126
288
1215
64
275
1354
−21
154
Table 2
Statistical significance of the effects of three climate-change scenarios (SCEN), two CO2 levels (CO2 ) and their interactions on US regional yields of corn, soybean, winter
wheat and alfalfa
Region
Pacific
(P > F)
Mountain
(P > F)
Lakes
(P > F)
Southern
Plains
(P > F)
Northern
Plains
(P > F)
Corn Belt
(P > F)
Delta
(P > F)
Northeast
(P > F)
Appalachian
(P > F)
Southeast
(P > F)
Corn—dryland
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.365
0.473
0.994
147.2
NS
NS
NS
0.004
0.004
0.898
115.2
∗
∗
NS
0.002
0.002
0.945
38.8
∗
∗
NS
0.003
0.009
0.936
42.1
∗
∗
NS
0.001
0.000
0.740
14.9
∗
∗
NS
0.000
0.000
0.743
8.7
∗
∗
NS
0.077
0.684
0.714
21.1
∗
NS
NS
0.016
0.006
0.929
23.7
∗
∗
NS
0.011
0.668
0.970
20.4
∗
NS
NS
0.094
0.432
0.979
31.8
∗
NS
NS
Corn—irrigated
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.380
0.172
0.994
46.3
NS
NS
NS
0.021
0.000
0.947
28.8
∗
∗
NS
0.000
0.000
0.871
7.6
∗
∗
NS
0.005
0.000
0.984
14.6
∗
∗
NS
0.013
0.000
0.940
13.9
∗
∗
NS
0.000
0.000
0.910
6.4
∗
∗
NS
0.337
0.138
0.989
24.7
NS
NS
NS
0.049
0.000
0.980
22.6
∗
∗
NS
0.052
0.694
0.994
21.6
∗
NS
NS
0.142
0.692
0.995
31.0
NS
NS
NS
Soybean
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.063
0.627
0.987
79.1
∗
NS
NS
0.001
0.026
0.949
75.0
∗
∗
NS
0.000
0.000
0.975
30.9
∗
∗
NS
0.000
0.000
0.503
25.6
∗
∗
NS
0.000
0.000
0.842
21.1
∗
∗
NS
0.000
0.000
0.943
10.3
∗
∗
NS
0.000
0.000
0.795
16.6
∗
∗
NS
0.003
0.012
0.955
31.7
∗
∗
NS
0.000
0.155
0.993
21.5
∗
NS
NS
0.005
0.058
0.994
26.7
∗
∗
NS
Wheat
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.048
0.559
0.997
48.9
∗
NS
NS
0.000
0.000
0.873
55.8
∗
∗
NS
0.000
0.161
0.560
23.5
∗
NS
NS
0.000
0.082
0.931
33.5
∗
∗
NS
0.000
0.000
0.341
8.2
∗
∗
NS
0.000
0.000
0.678
15.8
∗
∗
NS
0.000
0.000
0.898
16.5
∗
∗
NS
0.000
0.000
0.699
21.5
∗
∗
NS
0.000
0.000
0.796
17.8
∗
∗
NS
0.002
0.309
0.974
27.6
∗
NS
NS
Alfalfa—dryland
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.095
0.567
0.983
77.0
∗
NS
NS
0.001
0.073
0.965
25.2
∗
∗
NS
0.000
0.104
0.963
57.6
∗
NS
NS
0.000
0.000
0.846
23.5
∗
∗
NS
0.000
0.105
0.974
29.8
∗
NS
NS
Alfalfa—irrigated
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.000
0.000
0.809
15.2
∗
∗
NS
0.000
0.000
0.597
14.1
∗
∗
NS
0.000
0.000
0.812
15.0
∗
∗
NS
0.000
0.000
0.712
11.6
∗
∗
NS
0.000
0.000
0.833
11.8
∗
∗
NS
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Effect
Corn and alfalfa are simulated with and without irrigation. The coefficient of variation (C.V.) of each experiment is also given. Asterisk (*) denotes significance at 5% or less probability. NS means not significant.
103
104
Table 3
Statistical significance of the effects of three climate-change scenarios (SCEN), two CO2 levels (CO2 ) and their interactions on the standard deviations of US regional yields
of corn, soybean, winter wheat and alfalfa
Region
Pacific
(P > F)
Mountain
(P > F)
Lakes
(P > F)
Southern
Plains
(P > F)
Northern
Plains
(P > F)
Delta
(P > F)
Corn
Belt
(P > F)
Northeast
(P > F)
Appalachian
(P > F)
Southeast
(P > F)
Corn—dryland
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.317
0.040
0.835
112.5
NS
∗
NS
0.001
0.000
0.993
78.3
∗
∗
NS
0.571
0.000
0.722
36.5
NS
∗
NS
0.485
0.016
0.794
35.2
NS
∗
NS
0.985
0.000
0.859
47.2
NS
∗
NS
0.795
0.000
0.631
31.2
NS
∗
NS
0.875
0.008
0.875
42.2
NS
∗
NS
0.943
0.000
0.944
44.6
NS
∗
NS
0.938
0.065
0.929
44.5
NS
∗
NS
0.727
0.000
0.941
33.6
NS
∗
NS
Corn—irrigated
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.602
0.044
0.996
66.3
NS
∗
NS
0.432
0.052
0.981
65.1
NS
∗
NS
0.582
0.641
0.998
57.3
NS
NS
NS
0.556
0.912
0.993
46.7
NS
NS
NS
0.564
0.000
0.986
55.7
NS
∗
NS
0.283
0.000
0.970
29.5
NS
∗
NS
0.569
0.740
0.991
43.4
NS
NS
NS
0.408
0.000
0.997
45.1
NS
∗
NS
0.606
0.928
0.996
53.8
NS
NS
NS
0.436
0.641
0.989
42.8
NS
NS
NS
Soybean
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.040
0.002
0.799
60.0
∗
∗
NS
0.001
0.002
0.929
63.4
∗
∗
NS
0.019
0.002
0.695
34.4
∗
∗
NS
0.007
0.000
0.899
31.5
∗
∗
NS
0.031
0.000
0.783
30.3
∗
∗
NS
0.002
0.000
0.768
20.0
∗
∗
NS
0.246
0.009
0.978
43.8
NS
∗
NS
0.015
0.000
0.919
31.4
∗
∗
NS
0.051
0.000
0.903
27.1
∗
∗
NS
0.077
0.000
0.866
29.2
∗
∗
NS
Wheat
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.108
0.001
1.000
56.5
NS
∗
NS
0.000
0.235
0.986
55.5
∗
NS
NS
0.000
0.000
0.607
24.0
∗
∗
NS
0.003
0.763
0.745
29.7
∗
NS
NS
0.017
0.000
0.997
27.0
∗
∗
NS
0.001
0.000
0.739
26.0
∗
∗
NS
0.024
0.117
0.952
28.0
∗
NS
NS
0.013
0.000
0.922
32.3
∗
∗
NS
0.022
0.165
0.943
35.0
∗
NS
NS
0.001
0.000
0.897
22.4
∗
∗
NS
Alfalfa—dryland
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.010
0.124
0.949
58.1
∗
NS
NS
0.000
0.014
0.943
47.5
∗
∗
NS
0.005
0.398
0.950
34.2
∗
NS
NS
0.004
0.001
0.852
24.0
∗
∗
NS
0.024
0.000
0.776
29.2
∗
∗
NS
Alfalfa—irrigated
CO2
SCEN
CO2 × SCEN
C.V. (%)
0.165
0.638
0.988
53.1
NS
NS
NS
0.044
0.439
0.994
69.7
∗
NS
NS
0.040
0.000
0.903
41.3
∗
∗
NS
0.002
0.002
0.966
24.0
∗
∗
NS
0.032
0.006
0.989
31.5
∗
∗
NS
Corn and alfalfa are simulated with and without irrigation. The coefficient of variation (C.V.) of each experiment is also given. Asterisk (*) denotes significance at 5% or less probability. NS means not significant.
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Effect
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
The year-to-year variability within each study period was evaluated by conducting ANOVA on the regional standard deviations of crop yields as dependent
variables of climate-change and [CO2 ] (Table 3). Corn
yield variability was sensitive to the warming induced
by climatic change but not to a CO2 -enriched atmosphere. The other three crops modeled, all of C3 -type
photosynthesis pathway, produced standard deviations that were sensitive—in most regions—to both
climatic-change scenario and the CO2 -fertilization
effect.
The lack of significant interactions between the
climatic changes and CO2 effects (Tables 2 and 3)
indicates that the modeled effects were additive, thus
allowing for the interpretation of results based on
105
main effect means. Based on this analysis, yields of
dryland corn in the two future scenarios are expected
to remain unchanged, to increase or to decrease in
different regions of the country (Table 4). For example, the statistical analysis conducted lacked power to
detect differences in the Pacific region that extends
from Washington to California. Apparently, the high
variability induced by the scenario × CO2 × farm interactions precluded the detection of significant yield
changes due to the modeled effects. Regional patterns
of dryland corn yields and associated yield changes
are illustrated in Fig. 2.
The Mountain west is another large region with low
inherent productivity for corn. On average, dryland
corn yields are predicted to decrease by 45% below
Table 4
Simulated yields of dryland and irrigated corn under baseline climate (B) and the HadCM2 projections in 2030 (H1) and 2095 (H2), each
at two CO2 concentration levels (365 and 560 ppm)
CO2 /scenario
Region (Mg ha−1 )
Pacific
Main effect means
Dryland
(1) 365
1.39
(2) 560
1.80
(1) B
1.61
(2) H1
1.25
(3) H2
1.93
Irrigated
(1) 365
(2) 560
(1) B
(2) H1
(3) H2
5.80
6.27
5.59
5.77
6.75
Treatment means
Dryland
B-365
1.38
B-560
1.83
H1-365
1.08
H1-560
1.42
H2-365
1.71
H2-560
2.15
Irrigated
B-365
B-560
H1-365
H1-560
H2-365
H2-560
a
5.39
5.79
5.53
6.01
6.48
7.02
Mountain
Northern
Plains
Southern
Plains
Lakes
Corn Belt
Delta
Northeast
Appalachian
Southeast
aa
a
a
a
a
0.75
1.13
1.19
0.65
0.97
b
a
a
b
a
3.73
4.72
5.02
3.65
4.00
b
a
a
b
b
4.69
5.88
6.13
5.02
4.72
b
a
a
b
b
5.30
5.86
4.76
5.62
6.37
b
a
c
b
a
6.30
6.87
6.29
6.64
6.81
b
a
b
a
a
5.98
6.54
6.41
6.29
6.08
b
a
a
a
a
4.56
5.04
4.35
4.97
5.08
b
a
b
a
a
6.11
6.79
6.43
6.32
6.61
b
a
a
a
a
5.38
6.08
6.05
5.73
5.40
b
a
a
a
a
a
a
b
ab
a
5.22
5.67
4.65
5.76
5.92
b
a
b
a
a
6.14
6.60
5.86
6.77
6.48
b
a
c
a
b
7.41
7.97
7.96
8.11
7.00
b
a
a
a
b
5.63
6.04
4.82
6.03
6.65
b
a
c
b
a
6.51
6.98
6.37
6.94
6.93
b
a
b
a
a
6.34
6.70
6.30
7.06
6.21
a
a
ab
a
b
4.78
5.17
4.41
5.23
5.29
b
a
b
a
a
6.47
7.04
6.58
6.87
6.82
b
a
a
a
a
5.68
6.30
6.08
6.15
5.74
a
a
a
a
a
a
a
a
a
a
a
0.98
1.40
0.51
0.80
0.76
1.18
bc
a
d
cd
cd
ab
4.60
5.44
3.11
4.20
3.48
4.51
ab
a
d
bc
cd
b
5.55
6.70
4.33
5.70
4.20
5.23
b
a
c
ab
c
bc
4.57
4.95
5.30
5.94
6.04
6.69
d
cd
c
b
b
a
6.05
6.53
6.31
6.98
6.53
7.09
c
b
cb
a
b
a
6.26
6.55
5.84
6.74
5.84
6.32
ab
ab
b
a
b
ab
4.16
4.54
4.70
5.24
4.81
5.35
d
cd
bcd
ab
abc
a
6.13
6.73
5.94
6.70
6.27
6.95
bc
ab
c
ab
abc
a
5.76
6.35
5.34
6.13
5.04
5.76
ab
a
ab
ab
b
ab
b
ab
ab
ab
ab
a
4.47
4.82
5.53
6.00
5.67
6.17
c
c
b
ab
ab
a
5.66
6.05
6.52
7.02
6.24
6.72
d
c
b
a
c
b
7.70
8.22
7.81
8.41
6.72
7.27
bc
ab
bc
a
d
cd
4.65
4.98
5.82
6.24
6.42
6.89
d
d
c
bc
b
a
6.15
6.58
6.69
7.20
6.68
7.17
c
b
b
a
b
a
6.14
6.45
6.84
7.28
6.03
6.38
b
ab
ab
a
b
ab
4.24
4.57
5.02
5.44
5.08
5.50
c
bc
ab
a
ab
a
6.32
6.85
6.57
7.17
6.52
7.11
b
ab
ab
a
ab
ab
5.80
6.36
5.82
6.48
5.42
6.06
a
a
a
a
a
a
Means within a column and section followed by the same letter are not significantly different at the 10% level of probability.
106
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Fig. 2. Simulated baseline yields of dryland corn and changes from baseline in 2030 and 2095 under the HadCM2 climate scenarios.
baseline during the first future period but to recover
in the 2095 period. Significant yield reductions are
also expected in both future periods in the Northern
and Southern Plains, with these reductions somewhat
offset by the CO2 -fertilization effect (see treatment
means in Table 4).
Different results, however, were simulated for the
northeastern regions of the country. Yield increases
were predicted in future production of dryland corn
in the Lakes, Corn Belt and Northeast regions of the
US (Table 4). These yield increases would most likely
arise from a reduced incidence of low-temperature
stress on corn growing under warmer than current
conditions. Overall, corn yields modeled in traditional
dryland growing regions (Fig. 2) with HadCM2 scenarios were less affected than yields simulated by
Brown and Rosenberg (1999) using a suite of three
other GCMs (BMRC, GISS, and UKTR). Absent a
CO2 -fertilization effect, yields projected using the
HadCM2 scenarios would increase on average 3–6%
while with the three GCMs, yields would remain
unchanged or decrease by ∼20%. With a CO2 effect present, average yields with HadCM2 scenarios
would increase up to 16–18% from baseline while
the set of three GCMs scenarios would make yields
recover to within baseline levels. The more favorable results observed under the HadCM2 scenario
would occur because of a combination of increased
precipitation and water-use efficiency (Tables 1
and 8). Assuming enough water is available for irrigation, our results suggest that yields of irrigated
corn would increase in almost all regions of the country including those where losses had been predicted
under dryland conditions (Table 4).
The analysis becomes more complex in the
case of soybean production (Table 5). Were the
CO2 -fertilization effect not operative, yields would
decrease significantly in the Northern and Southern
Plains, the Corn Belt, Delta, Appalachian, and Southeast regions. Yields would increase only in the Lakes
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
107
Table 5
Simulated soybean yields under baseline climate (B) and the HadCM2 projections in 2030 (H1) and 2095 (H2), each at two CO2
concentration levels (365 and 560 ppm)
CO2 /scenario
Region (Mg ha−1 )
Pacific
Main
(1)
(2)
(1)
(2)
(3)
Mountain
Northern
Plains
Southern
Plains
Lakes
Corn Belt
Delta
Northeast
Appalachian
Southeast
effect means
365
0.62
560
0.82
B
0.70
H1
0.67
H2
0.79
ba
a
a
a
a
0.44
0.60
0.57
0.43
0.55
b
a
a
b
a
1.25
1.60
1.71
1.27
1.29
b
a
a
b
b
1.21
1.55
1.80
1.26
1.09
b
a
a
b
c
1.54
1.83
1.48
1.67
1.91
b
a
c
b
a
1.88
2.20
2.15
1.99
1.98
b
a
a
b
b
1.52
1.78
1.88
1.65
1.42
b
a
a
b
c
1.29
1.53
1.30
1.36
1.57
b
a
b
b
a
1.73
2.05
2.00
1.86
1.82
b
a
a
ab
b
1.42
1.69
1.71
1.51
1.44
b
a
a
b
b
Treatment means
B-365
0.61
B-560
0.80
H1-365
0.57
H1-560
0.76
H2-365
0.68
H2-560
0.91
ab
ab
b
ab
ab
a
0.49
0.66
0.36
0.50
0.46
0.64
bc
a
c
b
bc
a
1.52
1.90
1.10
1.45
1.12
1.46
b
a
c
b
c
b
1.58
2.01
1.10
1.43
0.96
1.21
b
a
cd
b
d
c
1.36
1.59
1.52
1.82
1.75
2.07
e
cd
de
b
bc
a
1.99
2.31
1.82
2.16
1.83
2.13
c
a
d
b
d
b
1.75
2.01
1.50
1.81
1.32
1.53
b
a
cd
b
d
c
1.20
1.40
1.24
1.48
1.44
1.70
d
bcd
cd
b
bc
a
1.84
2.16
1.69
2.02
1.67
1.98
bc
a
c
ab
c
ab
1.58
1.84
1.37
1.65
1.31
1.58
ab
a
bc
a
c
abc
a
Means within a column and section followed by the same letter are not significantly different at the 10% level of probability.
and Northeast regions of the US. The simulated yield
trends in the Pacific and Mountain regions are either
not significant or unclear.
Simulated winter wheat yields generally increase
across the US under scenarios of climate-change
(Table 6). However, the Northern and Southern
Plains—currently the two largest wheat producing
areas—appear to be quite sensitive to projected cli-
mate changes (Fig. 3). Absent the CO2 -fertilization effect, large areas would experience yield losses (Fig. 3).
In 2030, yields would decrease by ∼6% across the
plains (Table 6). In 2095, while yields would decrease
in the Southern Plains they would increase in the
Northern Plains. Were the CO2 -fertilization effect to
manifest itself fully, yields would increase in all parts
of the country (Table 6). Overall, our results with
Table 6
Simulated winter wheat yields under baseline climate (B) and the HadCM2 projections in 2030 (H1) and 2095 (H2), each at two CO2
concentration levels (365 and 560 ppm)
CO2 /scenario
Region (Mg ha−1 )
Pacific
Main
(1)
(2)
(1)
(2)
(3)
Mountain
Northern
Plains
Southern
Plains
Lakes
Corn Belt
Delta
Northeast
Appalachian
Southeast
effect means
365
3.62
560
4.37
B
3.72
H1
4.07
H2
4.20
ba
a
a
a
a
2.00
2.68
2.14
2.06
2.81
b
a
b
b
a
3.06
3.93
3.40
3.37
3.71
b
a
ab
b
a
3.54
4.43
4.18
4.15
3.61
b
a
a
a
b
3.71
4.39
3.32
4.33
4.51
b
a
c
b
a
3.57
4.23
3.11
4.17
4.41
b
a
c
b
a
4.31
5.11
4.05
5.10
4.97
b
a
b
a
a
3.63
4.34
3.10
4.19
4.66
b
a
c
b
a
3.97
4.74
3.54
4.59
4.93
b
a
c
b
a
3.48
4.25
3.60
4.00
4.00
b
a
a
a
a
Treatment means
B-365
3.37
B-560
4.08
H1-365
3.68
H1-560
4.45
H2-365
3.81
H2-560
4.59
b
ab
ab
a
ab
a
1.84
2.44
1.74
2.38
2.42
3.21
c
b
c
b
b
a
3.09
3.71
2.90
3.85
3.20
4.21
c
b
c
ab
c
a
3.75
4.61
3.65
4.66
3.21
4.02
bc
a
bc
a
c
ab
3.05
3.59
3.96
4.70
4.13
4.89
d
c
b
a
b
a
2.85
3.37
3.81
4.53
4.04
4.79
d
c
b
a
b
a
3.71
4.40
4.65
5.54
4.56
5.39
c
b
b
a
b
a
2.83
3.37
3.82
4.57
4.24
5.08
e
d
c
b
bc
a
3.23
3.86
4.18
5.01
4.50
5.37
d
c
bc
a
b
a
3.25
3.96
3.58
4.42
3.61
4.39
c
ab
bc
a
bc
a
a
Means within a column and section followed by the same letter are not significantly different at the 10% level of probability.
108
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Fig. 3. Simulated baseline yields of winter wheat and changes from baseline in 2030 and 2095 under the HadCM2 climate scenarios.
winter wheat and with dryland corn were more favorable than those obtained by Brown and Rosenberg
(1999) using a set of three GCMs. Our results with
the HadCM2 are consistent with those obtained by
Tubiello et al. (2000) who also used the HadCM2.
Alfalfa hay crops simulated for the five regions detailed in Table 7 also exhibited contrasting responses
to the projected climate changes. Modeled dryland hay
production in the Pacific region was not sensitive to
the projected changes in temperature and precipitation. In contrast, the significant increases in dryland
hay yield projected for the Southern Plains and Corn
Belt regions accompanying changes in climatic variables are attributed to climate–soil interactions.
3.3. Changes in evapotranspiration, runoff and
plant stress in dryland corn
Evapotranspiration in dryland corn is expected
to increase in both future periods in response to
the warming projected by HadCM2 runs (Table 8).
The largest increases by 2030 would register in the
east (91–210 mm) while those in the west would be
more modest (32–81 mm). By 2095, the increases in
evapotranspiration in the west would be much more
pronounced than in the east, more than doubling in
the Pacific region. We surmise that these differential
changes in evapotranspiration could arise from dissimilar patterns of relative changes in precipitation between regions. A current atmosphere enriched in CO2
would reduce evapotranspiration of dryland corn by as
little as 2 mm in the Mountain region and by as much
as 31 mm in the Delta region. In western regions, the
presence of a CO2 -fertilization effect would reduce
by 8% the projected increases in evapotranspiration
while in eastern regions this relative reduction in evapotranspiration would be as much as 14% (Table 8).
Water-use efficiency (WUE), economic crop yield
per unit of water consumed in evapotranspiration, expresses the effectiveness with which a crop might be
able to respond to changes in climate and CO2 concentration. The range in WUE under current climate
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
109
Table 7
Simulated dryland and irrigated alfalfa yields under baseline climate (B) and the HadCM2 projections in 2030 (H1) and 2095 (H2), each
at two CO2 concentration levels (365 and 560 ppm)
Scenario
Regiona (Mg ha−1 )
Pacific
Main effect means
Dryland
(1) 365
(2) 560
(1) B
(2) H1
(3) H2
Irrigated
(1) 365
(2) 560
(1) B
(2) H1
(3) H2
Treatment means
Dryland
B-365
B-560
H1-365
H1-560
H2-365
H2-560
Irrigated
B-365
B-560
H1-365
H1-560
H2-365
H2-560
a
Mountain
Northern Plains
Southern Plains
Corn Belt
4.67
6.00
4.93
5.16
5.92
b
a
a
a
a
3.26
4.25
3.54
3.57
4.15
b
a
b
b
a
5.14
6.66
6.30
5.42
5.99
b
a
a
b
ab
7.63
9.22
7.48
8.64
9.15
b
a
c
b
a
8.51
10.14
8.34
9.54
10.10
b
a
c
b
a
11.58
13.68
11.63
13.00
13.24
b
a
b
a
a
10.35
12.14
9.97
11.85
11.91
b
a
b
a
a
8.91
10.47
8.36
10.17
10.55
b
a
b
a
a
8.49
9.89
7.75
9.83
10.00
b
a
b
a
a
9.10
10.58
8.63
10.38
10.52
b
a
b
a
a
4.34
5.52
4.51
5.81
5.15
6.68
b
ab
ab
ab
ab
a
3.08
4.00
3.09
4.05
3.60
4.69
c
ab
c
ab
bc
a
5.57
7.02
4.68
6.16
5.17
6.81
bc
a
c
ab
c
a
6.85
8.11
7.76
9.53
8.27
10.03
c
b
b
a
b
a
7.66
9.03
8.64
10.44
9.23
10.97
c
b
b
a
b
a
10.70
12.57
11.92
14.08
12.11
14.37
d
bc
cd
ab
cd
a
9.21
10.73
10.90
12.80
10.94
12.87
c
b
b
a
b
a
7.71
9.01
9.35
10.98
9.69
11.41
c
b
b
a
b
a
7.17
8.32
9.07
10.59
9.23
10.77
d
c
bc
a
b
a
7.99
9.27
9.58
11.17
9.72
11.31
c
b
b
a
b
a
Means within a column and section followed by the same letter are not significantly different at the 10% level of probability.
varied more than threefold from 3.2 kg ha−1 mm−1 in
the Mountain region to 10.9 kg ha−1 mm−1 in the Appalachian region (Table 8). Under climate-change and
no change in crop cultivars, corn is expected to use
water less efficiently than at present. The decreases
in WUE by 2030 absent the CO2 -fertilization effect
would range from 1.6 to 3.5 kg ha−1 mm−1 . A CO2 effect would effectively halve (0.7–1.5 kg ha−1 mm−1 )
the modeled decreases in WUE. A similar interpretation can be derived from the data for the 2095 period.
Our analysis assumes that modeled changes in leaf
stomatal resistance and consequent changes in transpiration and WUE will extend to the regional scale.
Jarvis and McNaughton (1986) argued, however, that
because of adjustments in humidity profiles that would
take place within the planetary boundary layer changes
in stomatal resistance predicted at leaf scale might
have little influence at the regional scale. A partial
stomatal closure would reduce transpiration rate making the planetary boundary layer drier. This, in turn,
would increase the vapor concentration gradient between leaves and atmosphere, thereby negating any
effect caused by changes in stomatal opening. A recent review by Kimball et al. (1999) of experimental
and modeling evidence bearing on this question seems,
however, to suggest that stomata may exert greater
control on regional ET than Jarvis and McNaughton
(1986) had previously asserted.
110
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Table 8
Regional distribution of evapotranspiration, water-use efficiency and runoff under baseline conditions and their deviations from baseline
induced by climate and CO2 concentration changes, singly and in combination
CO2 /scenario
Region
Pacific
Mountain
Northern
Plains
Evapotranspiration (mm) under baseline climate
B-365
318
307
501
Southern
Plains
Lakes
568
446
Deviations in evapotranspiration (mm) under climate and CO2 scenarios
B-560
−7
−2
−16
−17
−23
H1-365
81
32
49
60
210
H1-560
75
30
43
53
183
H2-365
131
77
98
89
247
H2-560
124
75
90
74
218
Water-use efficiency (kg ha−1 mm−1 ) under baseline climate
B-365
4.3
3.2
9.2
9.8
(kg ha−1
Water-use efficiency
B-560
5.9
H1-365
2.7
H1-560
3.6
H2-365
3.8
H2-560
4.9
mm−1 )
4.6
1.5
2.4
2.0
3.1
Runoff (mm) under baseline climate
B-365
153
21
Corn Belt
Delta
Northeast
Appalachian
Southeast
581
705
454
564
533
−27
201
171
226
193
−31
173
152
206
174
−18
191
170
211
190
−20
161
140
186
163
−19
91
71
100
79
10.2
10.4
8.9
9.2
10.9
10.8
under climate and CO2 scenarios
11.2
12.2
11.7
5.7
6.9
8.1
7.7
9.2
9.4
5.8
6.4
8.7
7.6
8.1
10.1
11.8
8.1
9.3
8.1
9.2
9.7
6.7
7.9
6.4
7.2
10.4
7.3
8.4
7.2
8.3
12.4
8.2
9.5
8.4
9.6
12.4
8.6
10.1
8.0
9.4
35
86
Deviations in runoff (mm) under climate and CO2 scenarios
B-560
1
0
3
4
H1-365
−3
20
−15
−33
H1-560
−3
20
−14
−31
H2-365
21
16
−4
−5
H2-560
22
16
−3
−1
The regional pattern of change in runoff is more
complex. Surface runoff could increase or decrease
across regions within a given period and do the same
across periods within a region (Table 8). The expected
general trend, however, is for runoff to decrease from
baseline during the 2030 period and to increase during
the 2095 period. Runoff appears to be only slightly
sensitive to changes in atmospheric CO2 concentrations. These findings agree with modeling work by
Hulme et al. (1999) for projected changes in hydrology in Europe using HadCM2 runs. Rosenberg et al.
(2003) (Paper I) discuss in detail the climate-change
and CO2 impacts on runoff as assessed by the same
HadCM2 scenarios and the hydrology model HUMUS
(Srinivasan et al., 1993).
Our analysis also indicates that corn growing in
the climate of 2030 would experience more days of
water stress than under current climatic conditions
120
156
384
230
181
108
2
−38
−34
−18
−14
5
−17
−11
30
38
10
−105
−99
32
44
2
−10
−7
24
28
3
−30
−26
26
30
1
−16
−15
18
20
(Table 9). As it does with other plant water-related
variables, the CO2 -fertilization effect would moderate
the increase in water stress days by one to two thirds.
The widespread increases in precipitation projected
for the end of the 21st century lessen the increase in
water stress days or even reduce them slightly below
baseline. The most noticeable changes in number of
temperature stress days occur in the Pacific, Mountain, Lakes, and Appalachian regions where decreases
range from 2 to 13 days. Increases in temperature
stress days, where they occur, are few in number.
3.4. Changes in regional and national potential
production of dryland corn
While changes in future yields provide insight as
to how crops would respond to potential changes in
climate, they provide little information in terms of
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
111
Table 9
Regional distribution of water and temperature stress days under baseline conditions and their deviations from baseline induced by climate
and CO2 concentration changes, singly and in combination
CO2 /scenario
Region
Pacific
Northern
Plains
Southern
Plains
45.7
−4.9
8.1
4.2
−1.6
−5.3
15.0
−5.7
16.9
10.9
9.6
4.4
19.4
−5.6
12.4
6.0
5.5
0.4
2.6
−1.2
5.1
2.5
3.4
0.9
6.1
−0.8
3.2
0.8
0.7
−0.5
4.9
−1.4
12.2
7.8
4.3
1.3
7.7
−1.0
2.9
1.1
2.4
0.8
20.9
−2.0
4.9
2.1
1.1
−1.2
21.5
−1.8
11.7
8.2
8.9
6.1
Temperature stress days (days)
B-365
36.4
34.7
B-560
1.2
1.1
H1-365
−5.0
−3.3
H1-560
−4.1
−2.4
H2-365
−13.1
−6.6
H2-560
−12.6
−6.0
20.7
0.9
0.6
1.7
−0.8
0.1
13.6
0.3
1.0
1.6
2.9
3.8
45.0
0.3
−2.8
−1.9
−12.2
−11.8
16.1
0.3
4.6
5.0
1.7
2.1
4.8
−0.2
2.5
3.0
3.7
4.2
28.3
0.1
1.3
1.6
−1.2
−1.0
17.8
0.1
−6.0
−5.8
−8.9
−8.7
8.4
0.0
0.5
0.5
−0.5
−0.5
Water stress days (days)
B-365
44.2
B-560
−4.8
H1-365
5.9
H1-560
1.7
H2-365
1.8
H2-560
−2.3
Mountain
aggregate production at the regional or national levels. We attempt here to provide such analysis by
“recreating” the national production of dryland corn
under current and future climates with simulated
yield results together with recent information on area
planted to corn and winter wheat and subsequently
harvested. Data on size of areas planted and harvested
were obtained from the USDA Economics and Statistics System (USDA-ESS) (http://www.usda.mannlib.
cornell.edu/) for the years 1998–1999 for corn
(Table 10) and for 1996–1998 for winter wheat
(Table 11). The state-by-state information was aggregated to the regional level. Regional production
for the different climate scenarios was calculated by
multiplying the value of simulated yield by the harvested area. National production of corn and wheat
was calculated by adding the regional values. The
simulated national dryland corn yield was estimated
by a weighted-average procedure using harvested
area as the weighting factor. The estimated simulated
national corn production for the entire US of about
152,400,000 Mg was 64% of the average reported
for the period 1996–1998 and 80% when compared
to that for 1988–1992 (Table 10). One reason for
the difference in estimates arising from the simulated national corn production and that reported by
USDA-ESS is that the latter includes production of
Lakes
Corn Belt
Delta
Northeast
Appalachian
Southeast
both dryland and irrigated corn. Another reason is that
the simulated values for the three largest producing
regions were noticeably lower than those obtained by
aggregating county-level data. Nevertheless, there was
a strong relationship between simulated and reported
regional corn production (y = 1.428x; r 2 = 0.983∗ ;
n = 8). This comparison excluded data for the Pacific and Mountain regions because it was assumed
that corn in those regions is produced largely under
irrigation. Similarly, a simulated national corn yield
of 5.32 Mg ha−1 was 73% of the USDA-ESS national
average for the period 1996–1998 and 76% when
compared with that for 1988–1992. The three largest
corn-producing regions are the Corn Belt, Lakes and
Northern Plains (Table 10).
We recognize the complexity in attempting to recreate national and regional patterns of crop production
with simulation models and databases. Nonetheless,
these results appear to set a realistic baseline from
which to project future changes in corn production.
The data in Table 10 suggest that of the three most important corn producing regions, two would be affected
positively (Corn Belt and Lakes) and one negatively
(Northern Plains). The negative impact during the period centered at 2030—absent the CO2 -fertilization
effect—would offset any increase in the other major
regions and cause an overall decrease in the national
112
Table 10
Changes in regional/national production potentials of dryland corn associated with Hadley 2030 (H1) and Hadley 2095 (H2) scenarios
Region
Pacific
Dryland corn yields (Mg ha−1 )
B-365
1.38
B-560
1.83
H1-365
1.08
H1-560
1.42
H2-365
1.71
H2-560
2.15
US
Mountain
0.98
1.40
0.51
0.80
0.76
1.18
Northern
Plains
4.60
5.44
3.11
4.20
3.48
4.51
Southern
Plains
4.57
4.95
5.30
5.94
6.04
6.69
Corn
Belt
Delta
Northeast
Appalachian
Southeast
6.05
6.53
6.31
6.98
6.53
7.09
6.26
6.55
5.84
6.74
5.84
6.32
4.16
4.54
4.70
5.24
4.81
5.35
6.13
6.73
5.94
6.70
6.27
6.95
5.76
6.35
5.34
6.13
5.04
5.76
5.32
5.88
5.20
5.98
5.52
6.21
1998–1999 area (×103 ha) planted to corn and harvested and the ratio of harvested/planted
Planted
333
720
6644
1002
5304
14160
Harvested
185
387
6051
843
4590
13632
Harvested/planted
0.56
0.54
0.91
0.84
0.87
0.96
487
440
0.90
1475
1125
0.76
1361
1056
0.78
456
318
0.70
31943
28629
0.89
Regional and national dryland corn production (×103 Mg)
B-365
256
379
27823
B-560
340
544
32939
H1-365
200
196
18800
H1-560
263
310
25426
H2-365
317
295
21055
H2-560
399
458
27305
5.55
6.70
4.33
5.70
4.20
5.23
Lakes
4677
5650
3652
4807
3541
4407
20967
22700
24318
27250
27709
30731
82535
89045
85971
95159
89045
96644
2757
2885
2572
2969
2570
2783
4683
5104
5292
5896
5415
6017
6469
7105
6275
7078
6621
7333
1833
2021
1699
1952
1603
1832
152379
168332
148973
171109
158171
177909
Regional and national production deviations from baseline (×103 Mg)
B-560
84
164
5116
973
H1-365
−56
−183
−9023
−1025
H1-560
8
−70
−2397
129
H2-365
61
−85
−6768
−1136
H2-560
143
79
−518
−270
1734
3351
6284
6743
9764
6509
3435
12623
6509
14109
127
−186
211
−187
26
421
609
1213
732
1334
637
−194
609
152
864
188
−135
119
−230
−1
15953
−3406
18730
5792
25530
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Scenario /
area (ha)
Table 11
Changes in regional/national production potentials of dryland winter wheat associated with Hadley 2030 (H1) and Hadley 2095 (H2) scenarios
Region
Pacific
US
Mountain
Dryland winter wheat yields (Mg ha−1 )
B-365
3.37
B-560
4.08
H1-365
3.68
H1-560
4.45
H2-365
3.81
H2-560
4.59
1.84
2.44
1.74
2.38
2.42
3.21
Northern Plains
3.09
3.71
2.9
3.85
3.2
4.21
Southern Plains
3.05
3.59
3.96
4.7
4.13
4.89
Corn Belt
Delta
Northeast
Appalachian
Southeast
2.85
3.37
3.81
4.53
4.04
4.79
3.71
4.4
4.65
5.54
4.56
5.39
2.83
3.37
3.82
4.57
4.24
5.08
3.23
3.86
4.18
5.01
4.5
5.37
3.25
3.96
3.58
4.42
3.61
4.39
3.19
3.88
3.33
4.22
3.43
4.30
1996–1998 area (×103 ha) planted to winter wheat and harvested and the ratio of harvested/planted
Planted
1480
2551
6137
5196
326
1930
Harvested
1397
2220
5391
3536
304
1707
Harvested/planted
0.94
0.87
0.88
0.68
0.93
0.89
558
520
0.93
276
265
0.96
919
723
0.79
315
273
0.87
19687
16335
0.83
Regional and national winter wheat production (×103 Mg)
B-365
4705
4094
16731
B-560
5696
5429
20088
H1-365
5137
3871
15702
H1-560
6212
5295
20846
H2-365
5319
5384
17326
H2-560
6408
7142
22795
3.75
4.61
3.65
4.66
3.21
4.02
Lakes
13256
16296
12903
16473
11347
14211
928
1092
1205
1430
1256
1488
4905
5800
6557
7796
6953
8244
1923
2280
2410
2871
2363
2793
749
892
1011
1210
1122
1345
2335
2791
3022
3622
3253
3882
887
1080
977
1206
985
1197
50512
61444
52795
66961
55310
69505
Regional and national production deviations from baseline (×103 Mg)
B-560
991
1335
3357
3040
H1-365
433
−222
−1029
−353
H1-560
1508
1201
4115
3217
H2-365
614
1290
596
−1909
H2-560
1703
3048
6064
954
164
277
502
329
560
895
1652
2891
2048
3339
358
487
948
441
871
143
262
461
373
596
455
687
1287
918
1547
194
90
319
98
311
10932
2283
16449
4798
18993
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Scenario/
area (ha)
113
114
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
production of corn. Again, the yield changes and the
area under corn in these three regions would dominate
the changes predicted by the end of the 21st century,
this time with a moderate increase in production. Further analysis would be required to include the impacts
on production of possible shifts in producing areas induced by climatic change.
Similarly for winter wheat, the four largest simulated producing regions were the Northern and Southern Plains together with the Corn Belt and Pacific
regions (Table 11). The national simulated estimate of
50,512,000 Mg was 9% greater than the national average reported for 1996–1998. It is likely that these
improvements in production estimates arise because
wheat in the US is grown mostly under dryland conditions. The relationship between simulated and reported
production for all 10 regions was also strong (y =
0.825x; r2 = 0.811∗ ; n = 10). Regional wheat production was overestimated in the Northern and Southern Plains while it was underestimated in the Pacific,
Mountain and Corn Belt regions.
The data in Table 11 reveal the Northern and Southern Plains as two regions where production during the
21st century could decrease or increase, depending
on how the CO2 -fertilization effect ultimately affects
wheat growth. The space–time domain with a chance
for a surprise is the Southern Plains in 2095. At the
national level, production of dryland winter wheat is
likely to increase in both future periods. As with corn,
further analysis would be required to ascertain how
shifts in production areas could affect total national
wheat production.
4. Assessing changes in irrigation supply and
demand
4.1. Connecting water supply and irrigation
demand
Two of the four crops dealt with in this paper—
corn and alfalfa—were simulated under both dryland
and irrigated conditions. Table 4 shows that simulated irrigated yields of corn generally exceed simulated dryland yields under the baseline climate with no
CO2 -fertilization effect (B-365, Table 4). This benefit
is evident in the drier portions of the country (southern
and northern Plains) and quite substantial in the Pacific
and Mountain regions. The advantage accruing to corn
irrigation is small in the remainder of the country and
even slightly negative in the Delta region. Dryland and
irrigated yields of alfalfa are simulated only for five of
the 10 regions defined by USDA (Fig. 1). Under baseline climate with no CO2 -effect (B-365, Table 7) alfalfa yields are substantially increased by irrigation in
the Pacific and Mountain regions and moderately increased in the Northern and Southern Plains and Corn
Belt.
Here, we couple HUMUS model simulated water
yields from Paper I and irrigation demand data from
the EPIC model analyses reported in this paper to determine how the HadCM2 climates of 2030 and 2095
and the CO2 -fertilization effect would alter the opportunity for irrigating corn and alfalfa. By extension
these results convey a notion about prospects for crop
irrigation generally throughout the United States under the projected conditions of change.
Our proxy measure of the relationship between water supply and demand in this study is very simple.
We calculate change in the water demand/supply situation using the quantity (WY − IRR), where WY, the
water yield, is the annual average runoff + lateral flow
in each of the 204 four-digit basins and IRR is the total amount of water applied during the crop growing
season to each irrigated crop on each of the representative farms in the same 204 four-digit basins. WY is
calculated by aggregating water yields simulated by
HUMUS at the eight-digit basin scale to the four-digit
scale. The simulation is made assuming that the vegetation currently dominant in each eight-digit basin
covers it entirely. IRR is calculated assuming that the
crop root zone is fully replenished whenever 50 mm of
soil moisture has been withdrawn. Thus, the amount
of irrigation applied is a function of evapotranspiration
rate and growing season length. The four-digit basin
is the mapping unit used in Figs. 4 and 5 for corn and
alfalfa, respectively.
WY is a surrogate for, but not a true measure of,
water available for irrigation. It can be thought of as an
indicator of the volume of water flowing into streams
in a particular basin that could be withdrawn for irrigation or other uses. For example, in the basin in southwestern Texas shown in Fig. 4a in red, about 500 mm
more water is needed to irrigate corn than the same
unit of land would contribute to streamflow, were it
under the vegetative cover that currently predominates
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
115
Fig. 4. (a) Difference in water supply and demand, in mm, simulated with the HUMUS and EPIC models, respectively, under baseline
climate conditions for irrigated corn; (b–e) supply/demand ratio (Rs/d ) of changes in quantity (WY − IRR) under a given scenario over
that under baseline climate conditions.
116
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Fig. 5. (a) Difference in water supply and demand, in mm, simulated with the HUMUS and EPIC models, respectively, under baseline
climate conditions for irrigated alfalfa; (b–e) supply/demand ratio (Rs/d ) of changes in quantity (WY − IRR) under a given scenario over
that under baseline climate conditions.
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
in that basin. Stated another way: 1 ha of irrigated corn
will require about 500 mm more than the water yield of
an adjacent hectare under the predominant vegetation.
Thus, as we use it (WY − IRR) provides a qualitative
notion of how changes in water supply and crop water demand due to a given scenario of climate-change
would alter the area of land that can be sustained under irrigation.
Fig. 4a shows that under current climate and land
use conditions the quantity (WY − IRR) is positive
in sign for corn over the entire eastern US from the
Atlantic to Louisiana and eastern Texas in the south
to Minnesota in the north. Coastal Washington and
Oregon, northern California and northern Idaho are
also regions in which WY exceeds IRR demand of
corn. In the Great Plains from Montana to southern
Texas irrigation of corn requires 100–300 mm more
water than the land can supply. In the Great Basin, and
particularly in the Southwest, demand exceeds supply
by as much as 600 mm.
Fig. 4b–e show a measure of supply/demand relations through the ratio Rs/d defined by:
Rs/d =
(WY − IRR)scenario
|(WY − IRR)baseline |
Negative values of Rs/d indicate worsening and positive values improvement of the water supply/demand
situation for corn irrigation.
4.2. Changing potential for irrigation
In 2030, despite rising temperatures and because of
increased rainfall (see Paper I), much of the Eastern
Seaboard and New England show up to 50% improvement in water balance (Fig. 4b). This is true too of the
Pacific Northwest coast with even greater improvement in coastal Northern California, parts of Idaho,
Montana and interior Oregon. The remainder of the
country, however, shows a general worsening of the
water balance, modestly so in the East and parts of the
intermountain West and severely in several four-digit
basins in the Plains region. CO2 -fertilization is inoperative in Fig. 4b.
Despite still greater increases in temperature in 2095
(more severe in the West than the East) large increases
in precipitation improve water balance over almost the
entire eastern US (Fig. 4d, no CO2 -fertilization). There
is a general improvement along the Pacific coast and
117
interior California and in Arizona and the Great Basin.
Improvement in water balance occurs in the eastern
Great Plains from Minnesota to northern Texas.
The CO2 -fertilization effect on water balance for
corn irrigation is demonstrated for 2030 and 2095
in Fig. 4c and e, respectively. In 2030, the number of basins in the East and West in which water
balance improves increases greatly in response to
CO2 -fertilization. In the central portions of the country worsening of the water balance is alleviated somewhat. The impact of CO2 -fertilization is even more
dramatic in 2095 with virtually all of the country except the Great Plains showing improvement in water
balance with respect to the baseline situation.
The state of supply versus demand for water to irrigate alfalfa is shown in Fig. 5 for only those portions of the country in which that crop is normally
irrigated. Under baseline conditions (Fig. 5a) WY exceeds IRR only in the Lake States, Upper Mississippi basin, and in coastal Washington, Oregon and
northern California. In the remainder of the country yearly irrigation demand exceeds supply by ∼200
to more than 600 mm. The effects of climate-change
and CO2 -fertilization are shown in Fig. 5b–e. The
HadCM2 scenarios of climate-change for 2030 worsen
the supply versus demand situation for irrigated alfalfa in almost all of the country except for a few
basins in the western states of Washington, Oregon,
Idaho, Montana, New Mexico, Arizona and Colorado.
The supply/demand situation worsens particularly in a
basin in the northern Central Valley of California and
three adjacent basins in northeastern Kansas, southeastern Nebraska and western Iowa.
The greater rainfall in 2095 improves the supply/demand situation in the Lake states and Iowa and
portions of the Southwest (Fig. 5d). The excess of
water yield over irrigation demand declines in much
of the Pacific Northwest; in one basin centered in
northern Idaho the balance reverses sign.
As in the case of corn, CO2 -fertilization improves
the water balance in almost every basin, both in 2030
and 2095. In 2030 areas of deficit around the Great
Lakes, the Great Basin and Pacific Northwest become
areas of surplus. In 2095 (Fig. 5e), CO2 -fertilization
causes water yields to exceed alfalfa irrigation demand
still more strongly in the Lakes region and Pacific
Northwest and lessens deficits virtually everywhere
else.
118
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
The effects of CO2 -fertilization on Rs/d for both
corn and alfalfa in much of the West are consistent with
theory and result from the way in which both EPIC
and HUMUS treat the CO2 -fertilization effect. In the
algorithms developed by Stockle et al. (1992a,b) used
in these models, rates of photosynthesis, biomass and
leaf area increase with increasing [CO2 ] in C3 plants
(alfalfa, for example) and transpiration is suppressed
through partial stomatal closure in both C3 and C4
plants (corn, for example). However, Rosenberg et al.
(1990) and Brown and Rosenberg (1997) have shown
in sensitivity studies that the additional leaf area resulting from CO2 -fertilization can offset much of the
transpiration suppression that results from reduced
stomatal conductance.
Tables 12 and 13 illustrate the mechanisms by which
CO2 -fertilization alters the water supply/demand situation for irrigation of corn and alfalfa, respectively.
In these tables one four-digit basin represents each
MWRR in which the crop is grown. With CO2 concentration at 365 ppm WY is increased in 12 (2030) and
16 (2095) of the 18 MWRRs. In those basins showing
gains WY increases still further with CO2 -fertilization
(CO2 concentration = 560 ppm), losses are reduced
or converted to small gains in the other basins. Note
that WY values are identical in Tables 12 and 13. Response to CO2 is greater in the densely vegetated regions of the country but is, of course, affected by the
uneven distribution of climate-change as well.
With respect to the baseline, the higher temperatures and the regionally variable pattern of change
in precipitation projected by the HadCM2 model
for 2030 increases irrigation demand for corn in
17 of the 18 representative basins and reduces it in
one (Table 12). Despite the higher temperatures, the
general increase in precipitation in 2095 leads to irrigation demands smaller than in 2030. Under the
irrigation conditions simulated in this study, transpiration is virtually unrestricted. Thus, in both time periods CO2 -fertilization significantly reduces irrigation
demand.
Results for alfalfa irrigation differ only in that the
quantities of irrigation water involved are greater than
for corn. This is especially true in the more southerly
Table 12
Water yield and irrigation requirement for corn under baseline, climate-change, and CO2 -fertilization scenarios
MWRR
New Eng.
Mid-Atl.
S.Atl.-Gulf
Great Lakes
Ohio
Tenn.
U. Miss.
L. Miss.
Souris-R.-R
Missouri
Ark-W.-R.
TX Gulf
Rio Grande
U. Colorado
L. Colorado
Great Basin
Pacific NW
California
a
Four-digit
basin
0107
0205
0305
0408
0512
0603
0708
0805
0902
1012
1103
1209
1306
1406
1507
1604
1702
1804
Scenario.
Variable.
c CO (ppm).
2
b
Baselinea
Water yield
(mm)b
584
518
526
302
455
804
296
604
56
33
81
151
18
29
32
42
186
398
HadCM2 (2030)a
Irrigationb
(mm)
46
69
13
107
102
111
80
52
102
206
228
300
278
313
593
492
380
267
HadCM2 (2095)a
Delta water
yieldb (mm)
Delta irrigationb
(mm)
Delta water
yieldb (mm)
Delta irrigationb
(mm)
365c
365c
365c
365c
42
74
39
61
63
55
51
−8
−18
−9
8
−2
−11
9
18
22
106
21
560c
56
89
55
74
78
72
65
4
−13
−7
14
10
−11
11
20
23
112
23
54
61
50
74
87
154
106
235
133
119
144
119
237
183
−7
67
174
406
560c
31
17
31
31
46
115
63
174
85
52
72
61
146
109
−96
−17
70
304
139
196
187
160
211
310
186
198
13
−10
25
17
−2
50
89
42
54
150
560c
154
211
204
173
226
328
199
212
21
−9
33
32
0
53
90
43
59
153
57
54
17
85
57
67
78
107
119
104
98
98
237
163
−81
31
163
352
560c
37
19
11
46
13
30
41
44
69
37
33
22
159
91
−148
−51
70
258
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
119
Table 13
Water yield and irrigation requirement for alfalfa under baseline, climate-change, and CO2 -fertilization scenarios
MWRR
Great Lakes
Ohio
U. Miss.
Souris-R.-R.
Missouri
Ark.-W.-R.
Rio Grande
U. Colorado
L. Colorado
Great Basin
Pacific NW
California
Four-digit
basin
0408
0512
0708
0902
1012
1103
1306
1406
1507
1604
1702
1804
Baselinea
Water yield
(mm)b
302
455
296
56
33
81
18
29
32
42
186
398
HadCM2 (2030)a
Irrigation
(mm)b
181
154
154
204
289
578
804
567
1613
759
622
498
HadCM2 (2095)a
Delta water
yield (mm)b
Delta irrigation
(mm)b
Delta water
yield (mm)b
Delta irrigation
(mm)b
365c
365c
365c
365c
560c
148
83
163
319
300
433
719
341
19
261
531
431
70
26
74
226
206
294
528
222
−181
106
369
315
61
63
51
−18
−9
8
−11
9
18
22
106
21
560c
74
78
65
−13
−7
14
−11
11
20
23
112
23
133
94
196
313
226
407
550
280
185
113
393
361
560c
59
37
143
228
150
250
367
178
0
−22
246
252
160
211
186
13
−10
25
−2
50
89
42
54
150
560c
173
226
199
21
−9
33
0
53
90
43
59
153
a
Scenario.
Variable.
c CO (ppm).
2
b
and more arid MWRRs and is partly explained by the
fact that alfalfa is a perennial crop with a considerably
longer growing season than that of corn. For the same
reasons the climate-change scenarios have a greater
quantitative impact, raising irrigation demands by as
much as 550 and 719 mm in the Rio Grande basin in
2030 and 2095, respectively, with no CO2 -fertilization
(365 ppm). The CO2 -fertilization effect (560 ppm) reduces the increased irrigation demand sharply in most
basins; in a few (e.g. Great Basin in 2030 and L. Colorado in 2095) reducing irrigation requirement to below baseline.
The simulations reported above support the expectation that suppression of transpiration will increase
the availability of water to runoff the land or penetrate
to depth and will, thereby, increase water yields. Further, the suppression of transpiration in irrigated crops
should decrease irrigation requirement. Both effects
contribute to an improvement in the supply/demand
situation for crop irrigation.
5. Summary and conclusions
Scenarios of climatic change produced by the
HadCM2 GCM for periods centered on 2030 and
2095 were used in the agro-ecosystem model EPIC
to assess the impacts on yield of four major crops for
the conterminous US. The effects of CO2 -fertilization
alone were also simulated as were its interactions
with climate-change. Most regions of the country
and crop × irrigation combinations were sensitive to
the simulated climatic changes and CO2 -fertilization
effects. Corn yield variability was sensitive to the
warming induced by climatic change but not to the
direct effects of CO2 enrichment. The simulations
predicted regional increases, decreases and invariance
in yields of dryland corn under climate projected for
2030 and 2095. Yield increases were predicted for the
Lakes, Corn Belt and Northeast regions of the US.
Dryland corn growing in 2030 would experience more
days with water stress than under current climate.
Evapotranspiration in dryland corn is expected
to increase in response to the warming projected
by HadCM2 for both future periods. Accordingly,
water-use efficiency of dryland corn is predicted to
decrease by 2–4 kg ha−1 mm−1 under climate-change.
Corn production is likely to change significantly in
2030 and 2095 in the three most important current
producing regions of the country. While in 2030 corn
production is expected to increase in the Corn Belt
and Lakes regions and to decrease in the Northern
120
R.C. Izaurralde et al. / Agricultural and Forest Meteorology 117 (2003) 97–122
Plains, the overall impact would be a decrease in national production. Corn yield changes predicted for
these three regions by the end of the 21st century
could lead to a moderate increase in national production. Increases in irrigated corn yields were predicted
in almost all regions of the country. Soybean yields
are anticipated to decrease in the Northern and Southern Plains, the Corn Belt, Delta, Appalachian, and
Southeast regions. Soybean yields should increase in
the Lakes and Northeast regions. Winter wheat yields
and national production increase consistently across
the US in both future periods. Alfalfa production was
sensitive to climate-change in the Southern Plains and
Corn Belt but not in the Pacific region. The climate
scenarios significantly affect both water yield and
irrigation demand in corn and alfalfa.
The balance of water supply and demand for
corn irrigation could be affected by the geographical
differences in temperature and precipitation changes
predicted for the two future periods. In general, and
absent a CO2 -fertilization effect, the water balance in
2030 derived from the HadCM2 projections improves
in the East, deteriorates throughout the Great Plains
while it remains stable or even improves in the West.
The climate effects on the balance of supply and demand for irrigation would be more favorable in 2095
than in 2030. A CO2 -fertilization effect improves the
water balance across the nation in both time periods.
Absent a CO2 -fertilization effect, the balance of supply and demand for alfalfa irrigation could deteriorate
along a NE-SW swath from the northern Plains to
New Mexico but improve in the Pacific Northwest.
The water balance for irrigated alfalfa does not improve as widely as it does for corn between 2030 and
2095. This could probably be ascribed to the high
transpiration demands and growth characteristics of
the perennial alfalfa crop. As in the case of irrigated
corn, the water balance for irrigated alfalfa improves
with the CO2 -fertilization effect.
Acknowledgements
We gratefully acknowledge the help of Dr. Nan
Rosenbloon and Dr. Benjamin Feltzer, National Center for Atmospheric Research (Boulder, CO); Dr.
Verel Benson of USDA-ARS (Temple, TX); and
Dr. Jimmy Williams of the Texas A&M University
Blacklands Research Station (Temple, TX). We also
thank an anonymous reviewer for identifying issues
requiring clarification and for suggesting improvements in the manuscript. This research was supported
by the US Department of Energy under Contract
DE-AC06-76RLO 1830.
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