Climate data
RZWQM2 cannot directly use projected daily weather data to predict crop yield
since, some predicted future daily weather data include frost days in June, which, in
most years, would terminate corn growth according to RZWQM2 simulation.
Therefore, future climate data was generated by superimposing monthly differences of
all the climate components on historical weather data. To obtain the differences, we
generated historical (1971-1999, centered in 1985) and future (2038-2070, centered
around 2055) daily weather data for the experiment site using all 6 climate models.
Using these two sets of data, 70 years apart, we calculated the difference between
historical and future weather data on a monthly basis. However, as observed weather
data for the study site was only available from 1989 to 2009 (centered in 2000), there
was only 55 years apart from 2038-2070. Therefore, monthly differences were
adjusted by a factor of 55/70 (Table 9).
Changes in weather variables were calculated by subtracting the monthly
averages of simulated historical weather data from the monthly average of simulated
future climate data. Then each month’s temperature increase (°C), was added to the
daily minimum and maximum temperatures of the observed historical weather data
set. Likewise, each month’s percent change (%) in precipitation, solar radiation,
relative humidity, and wind speed served to change the daily variables in the
corresponding month. The CO2 was assumed to increase to 548 ppm (centered around
2055 for future period, interpolated between CO2 for 2050 and 2060) from the current
369 ppm (centered around 2000). This was based on the A1B climate change scenario
(IPCC online document). This approach will retain the same weather pattern as the
observed period. The storm track changes in future are not considered in this
approach.
RZWQM2 Model Overview
The Root Zone Water Quality Model (RZWQM2) is a comprehensive model
describing the physical and chemical processes occurring in the crop root zone that
affects water quality and crop growth (Ahuja et al., 2000). A modified Green-Ampt
approach served to calculate infiltration from precipitation, irrigation, or snowmelt.
The Richards equation, assuming plant uptake as a sink, served to calculate water
redistribution in the soil profile. The steady-state Hooghoudt equation served to
calculate subsurface drainage flux. RZWQM2 incorporates a state-of-the-art model
for carbon and nitrogen cycling in soil profile (OMNI; Shaffer et al., 2000), as well as
the DSSAT (decision support system for agrotechnology transfer) crop growth model
(Jones et al. 2003), enhancing RZWQM2’s capacity to describe crop development, as
well as water and nutrient uptake. The extended Shuttleworth-Wallace equation
served to calculate PET (Potential Evapotranspiration), and took into account the
effect of surface crop residue dynamics on aerodynamics and energy fluxes (Farahani
and DeCoursey 2000). Management practices, e.g., planting date, fertilization, manure
and irrigation, were simulated in DSSAT (Rojas and Ahuja 2000).
A radiation use efficiency (RUE) approach was used in RZWQM2 to calculate net
biomass production. Curvilinear multipliers were used to empirically model the
effects of elevated CO2 on RUE (Allen et al. 1987; Peart et al. 1989). A modified
Michaelis–Menten equation with a y-intercept was used to fit the effects of CO2 on
crops:
𝑅𝑈𝐸 =
𝑅𝑈𝐸𝑚 ∙CO2
CO2 +𝐾𝑚
+ 𝑅𝑈𝐸𝑖
(1)
where, RUEm is the asymptotic response limit of (RUE−RUEi) at high CO2, RUEi is
the intercept on the y-axis when CO2= 0, and Km is the value of CO2 when
(RUE − RUEi) = 0.5 RUEm. The effects of elevated CO2 potential transpiration were
calculated according to the Shuttleworth–Wallace equation (Allen 1990; Rogers et al.
1983).
Table 1 Climate changes obtained from different climate models for ‘What if’ option in
RZWQM2.
BL_M1
BL_M2
BL_M3
BL_M4
BL_M5
BL_M6
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
T
3.1
1.8
1.4
2.8
1.9
0.6
3.2
2.6
2.9
1.6
1.7
1.7
WS
100.9
98.0
99.3
97.0
98.5
107.0
99.5
99.6
101.6
98.9
101.7
100.1
SWR
97.3
99.2
99.5
97.6
100.4
101.7
100.5
101.1
97.7
95.9
97.5
94.2
RH
105.3
103.0
108.9
96.5
99.6
99.6
95.2
96.7
104.1
108.1
104.9
113.5
P
116.5
94.8
109.2
116.7
98.6
105.9
86.3
95.1
134.7
117.1
99.0
107.9
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
T
2.8
3.5
2.7
1.9
1.5
2.2
2.8
3.4
3.3
2.6
1.6
2.3
WS
99.8
97.3
101.4
103.0
101.9
99.4
98.0
97.3
98.7
98.2
101.5
99.9
SWR
98.0
100.6
95.2
98.1
99.8
99.5
101.3
102.2
102.0
100.0
96.3
97.0
RH
97.6
94.0
99.1
99.9
98.8
99.6
98.3
95.1
85.5
95.4
100.9
104.4
P
130.5
87.4
106.7
128.1
113.3
102.0
100.6
76.7
87.0
95.7
113.9
108.5
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
T
2.6
2.0
1.5
1.2
1.9
2.6
2.8
3.2
3.1
2.2
1.8
1.7
WS
99.8
96.4
99.9
99.8
102.8
98.1
99.0
99.5
104.3
100.2
102.6
97.1
SWR
94.0
97.1
99.4
100.1
100.8
105.6
105.9
104.3
102.1
99.1
96.7
96.1
RH
99.1
99.5
96.2
96.6
97.5
95.4
91.0
91.1
94.3
99.5
99.7
99.4
P
100.9
123.3
92.9
122.5
117.9
80.9
92.5
94.8
95.1
139.8
117.2
100.2
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
T
2.7
3.1
1.4
1.8
1.7
1.7
2.4
3.4
2.8
3.1
1.7
2.0
WS
99.4
95.7
98.0
97.8
100.3
99.5
101.7
100.4
99.3
99.1
101.5
97.6
SWR
95.8
98.1
95.7
101.7
102.3
99.4
104.9
107.4
106.5
106.8
99.8
97.9
RH
103.4
102.4
100.4
101.5
101.4
100.1
96.9
90.4
93.9
91.0
98.7
101.4
P
119.2
101.3
109.8
105.4
110.9
125.5
91.7
80.8
98.8
91.0
107.9
105.2
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
T
2.0
1.7
0.7
0.3
1.6
2.6
2.5
2.6
2.0
1.1
1.4
3.4
WS
98.4
96.3
98.1
98.8
100.6
98.3
97.3
99.9
99.2
98.0
98.5
100.9
SWR
96.8
98.9
100.5
96.2
98.1
103.7
102.7
102.8
100.8
99.2
97.7
102.4
RH
102.0
102.0
100.1
100.9
101.7
99.8
97.0
98.4
98.4
102.4
102.5
103.2
P
97.7
118.9
93.3
117.8
119.6
103.5
103.6
97.3
108.1
110.2
124.8
134.3
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
T
3.5
2.0
1.5
1.5
1.0
-0.4
1.9
2.2
2.4
2.3
3.5
3.2
WS
94.7
96.5
102.4
96.2
98.4
99.8
102.7
103.0
102.2
100.3
98.8
98.1
SWR
94.7
97.0
98.3
97.7
97.3
97.9
99.8
99.6
98.7
96.0
97.8
96.0
RH
101.7
98.3
99.8
103.4
110.1
108.4
98.0
98.7
102.1
104.1
95.9
97.8
P
113.4
106.1
103.1
110.8
116.9
123.1
90.9
99.2
130.4
131.2
79.3
102.4
eCO2
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
151.9
(oC);
Note: T, Temperature
WS, Wind speed (%); SWR, Short Wave Radiation(%); RH, Relative
Humidity(%); P, precipitation (%). eCO2, elevated Carbon Dioxide (%); BL, baseline; BL_M1,
future scenario from CRCM_ccsm; BL_M2, future scenario from CRCM_cgcm3; BL_M3, future
scenario from HRM3_hadcm3; BL_M4, future scenario from RCM3_cgcm3; BL_M5, future
scenario from RCM3_gfdl; BL_M6, future scenario from WRFG_ccsm
Table 2 Impacts of Isolated weather variables on water balance.
AVG_T
AVG_P
AVG_CO2
AVG_RH
AVG_WS
AVG_SWR
AE
(cm)
AT
(cm)
PE
(cm)
PT
(cm)
AET
(cm)
PET
(cm)
Drainage WUEsoybean
(cm)
(kg m-3)
WUEcorn
(kg m-3)
20.0
18.4
18.3
18.0
18.3
18.3
25.1
25.9
23.9
27.8
26.0
26.1
34.5
29.9
29.0
29.9
29.8
29.9
27.1
27.7
25.1
30.1
27.7
27.9
45.1
44.3
42.2
45.8
44.3
44.4
61.6
57.6
54.1
60.0
57.5
57.8
28.9
32.9
31.1
27.4
29.0
28.8
1.65
2.09
2.34
2.01
2.11
2.14
0.69
0.71
0.94
0.68
0.71
0.72
Note: BL, baseline, AVG, averaged over 6 combined future scenarios; AVG_T, averaged impacts
of temperature; AVG_P, averaged impacts of precipitation; AVG_CO2, averaged impacts of CO2;
AVG_RH, averaged impacts of relative humidity; AVG_WS, averaged impacts of wind speed;
AVG_SWR, averaged impacts of short wave radiation; AE, actual evaporation; AT, actual
transpiration; PE, potential evaporation; PT, potential transpiration; AET, actual
evapotranspiration; PET, potential evapotranspiration.
Table 3 Impacts of Isolated weather variables on N dynamics across six models.
AVG_T
AVG_P
AVG_CO2
AVG_RH
AVG_WS
AVG_SWR
Deni
(kg N ha-1)
Mine
(kg N ha-1)
Immo
(kg N ha-1)
N loss in
drainage
(kg N ha-1)
FWANC
(mg L-1)
20.3
20.6
26.0
20.7
21.1
21.6
111.3
113.4
125.8
114.1
113.7
114.8
11.3
11.9
13.1
11.9
12.0
12.1
40.1
37.8
35.4
34.4
34.0
33.9
14.0
11.9
11.8
13.1
12.2
12.2
Note: AVG, averaged over 6 combined future scenarios; AVG_T, averaged impacts of temperature;
AVG_P, averaged impacts of precipitation; AVG_CO2, averaged impacts of CO2; AVG_RH,
averaged impacts of relative humidity; AVG_WS, averaged impacts of wind speed; AVG_SWR,
averaged impacts of short wave radiation; Deni, denitrification; Mine, mineralization; Immo,
Immobilization; FWANC, Flow-Weighted Average NO3-N concentration.
Table 4 KS Test of cumulative distribution functions (CDFs) for soybean and corn under different
scenarios.
D value for
soybean
BL_M1
BL_M2
BL_M3
BL_M4
BL_M5
BL_M6
0.9(SD)
0.9(SD)
0.8(SD)
0.95(SD)
0.95(SD)
0.9(SD)
D value
for corn
0.35(NSD)
0.45(SD)
0.4(NSD)
0.4(NSD)
0.3(NSD)
0.3(NSD)
Note: SD, significantly different; NSD, not significantly different; BL_M1, future scenario with
differences calculated from CRCM_ccsm; BL_M2, future scenario with differences calculated
from CRCM_cgcm3; BL_M3, future scenario with differences calculated from HRM3_hadcm3;
BL_M4, future scenario with differences calculated from RCM3_cgcm3; BL_M5, future scenario
with differences calculated from RCM3_gfdl; BL_M6, future scenario with differences calculated
from WRFG_ccsm
Table 5 Average number of days when Tmax>34oC and Tmin<8 oC and average temperature from
June to September and days to maturity for different scenarios.
Average number of days
scenarios
Tmax>34oC
Tmin
BL
1.7
BL_M1
<8 oC
T
TAVG
Jun
Jul
Aug
Sep
11.1
20.5
22.3
21.0
16.7
10.1
5.6
21.1
25.5
23.6
BL_M2
12.0
4.6
22.7
25.1
BL_M3
12.4
4.7
23.1
BL_M4
10.1
5.3
BL_M5
10.1
BL_M6
6.1
AVG
10.1
Days of maturity
soybean
corn
20.1
112.5
133.3
19.6
22.5
106.5
118.8
24.4
20.0
23.1
104.9
116.7
25.1
24.2
19.8
23.1
104.3
115.8
22.2
24.7
24.4
19.5
22.7
105.4
118.0
6.3
23.1
24.8
23.6
18.7
22.6
105.1
118.9
7.2
20.1
24.2
23.2
19.1
21.7
108.9
125.2
5.6
22.1
24.9
23.9
19.5
22.6
105.9
118.9
Note: AVG, averaged over 6 combined future scenarios; T, temperature; TAVG, average
temperature from June to September; BL, baseline; BL_M1, future scenario with differences
calculated from CRCM_ccsm; BL_M2, future scenario with differences calculated from
CRCM_cgcm3; BL_M3, future scenario with differences calculated from HRM3_hadcm3;
BL_M4, future scenario with differences calculated from RCM3_cgcm3; BL_M5, future scenario
with differences calculated from RCM3_gfdl; BL_M6, future scenario with differences calculated
from WRFG_ccsm.
Figure 1 Effects of increase of annual averaged precipitation on increase of tile drainage under six
climate models from 2045 to 2064.
Increase of tile drainage (cm)
7
y=0.099+0.888*x
R2=0.995
6
5
BL_M1
BL_M2
BL_M3
BL_M4
BL_M5
BL_M6
4
3
2
1.5
3.0
4.5
6.0
7.5
Increase of precipitation (cm)
Note: BL_M1, future scenario from CRCM_ccsm; BL_M2, future scenario from CRCM_cgcm3;
BL_M3, future scenario from HRM3_hadcm3; BL_M4, future scenario from RCM3_cgcm3;
BL_M5, future scenario from RCM3_gfdl; BL_M6, future scenario from WRFG_ccsm
Figure 2 Effects of increase of annual averaged temperature on decrease in maturity days under six
climate models from 2045 to 2060: A) soybean; B) corn.
9.0
7.5
18
y=-1.127+3.09*x
R2=0.9
6.0
BL_M1
BL_M2
BL_M3
BL_M4
BL_M5
BL_M6
4.5
Decrease in maturity days (days)
Decrease in maturity days (days)
A) Soybean
B) Corn
y=-1.515+6.324*x
R2=0.958
15
BL_M1
BL_M2
BL_M3
BL_M4
BL_M5
BL_M6
12
9
3.0
1.6
2.0
2.4
Temperature (oC)
2.8
3.2
1.6
2.0
2.4
2.8
3.2
Temperature (oC)
Note: BL_M1, future scenario from CRCM_ccsm; BL_M2, future scenario from CRCM_cgcm3;
BL_M3, future scenario from HRM3_hadcm3; BL_M4, future scenario from RCM3_cgcm3;
BL_M5, future scenario from RCM3_gfdl; BL_M6, future scenario from WRFG_ccsm
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