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Sensitivity Analysis of the Factors Influencing the Environmental Impact of
Biofuel Production in the Upper Mississippi River Basin
Tong Zhai1, Paul R. Hummel2, Anthony S. Donigian, Jr.3, David A. Wells3, Roberta
Parry5
1
AQUA TERRA Consultants, 150 E. Ponce de Leon Ave., Suite 355 Decatur, GA
30030; PH (404) 378-8337 ext 5; FAX (404) 378-8332; email: [email protected]
2
AQUA TERRA Consultants, 150 E. Ponce de Leon Ave., Suite 355 Decatur, GA
30030; PH (404) 378-8337 ext 2; FAX (404) 378-8332; email:
[email protected]
3
AQUA TERRA Consultants, 2685 Marine Way, Suite 1314 Mountain View, CA
94043; PH (650) 962-1864; FAX (650) 962-0706; email: [email protected]
4
U.S. EPA, 1200 Pennsylvania Ave., N. W. Washington, DC 20460; PH (202) 5660387; email: [email protected]
5
U.S. EPA, 1200 Pennsylvania Ave., N. W. Washington, DC 20460; PH (202) 5640508; email: [email protected]
ABSTRACT
The U.S. biofuel industry, namely the conversion of ethanol from corn, has increased
the demand for corn as a result of government initiatives to increase alternative fuel
productions to 35 billion gallons annually by 2017. Currently, a third of the corn crop
grown nationally is used for ethanol production and this is expected to increase
significantly in the foreseeable future.
Growing corn traditionally requires high inputs of water and nutrients. The inevitable
increase of environmental impacts resulting from increased corn production has been
the focus of a US EPA-funded modeling study. The objective has been to identify the
significant factors under various climate and management conditions that are most
influential in controlling the environmental impacts. These factors can then serve as
quantitative indicators to guide policy and management practices to minimize
negative effects on the environment.
This work focuses on the Upper Mississippi River Basin (UMRB), which is central to
the nation’s corn production. UMRB is one of the major sources of excess nutrient
loadings into the Mississippi River and considered a leading contributor to the
hypoxia problem in the Gulf of Mexico.
The Soil and Water Assessment Tool (SWAT) model was applied to simulate the
hydrologic and water quality impacts of increased corn production in the UMRB area.
UMRB SWAT model sensitivity analyses were performed for a variety of different
climate and management scenarios. The results indicate that changes in rainfall and
temperature are the most influential factors for all hydrologic and nutrient outputs.
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When the management practice of removing corn stover (a process to yield more
ethanol producing material) was incorporated in the model, it generally reduced the
nutrient loss and increased erosion. Nutrient management practices did not produce
large impacts or changes in nutrient loss at the basin scale.
INTRODUCTION
This work was a continuation of a previous modeling effort in conjunction with EPA,
Modeling Water Quality Impacts of Corn Production for Ethanol in the Upper
Mississippi River Basin (UMRB, Figure 1) (US EPA, 2008). During that effort, the
Soil and Water Assessment Tool (SWAT) model was used to simulate crop
production, stream flow, and nutrient and sediment losses from the UMRB under
increased corn production to meet projected ethanol demands. A baseline model was
developed to represent current conditions as of 2005, which was used to conduct
sensitivity analysis on a number of important meteorological and management related
factors. The goal of this effort was to further understand the model characteristics and
sensitivities to parameters and input forcing functions that control the model’s
response in its output.
Figure 1 Upper Mississippi River Basin with 8-digit HUCs (Hydrologic Unit Codes)
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UMRB Hydrologic Modeling
During the previous modeling effort, the SWAT model was setup on 131 subwatersheds (8-digit Hydrologic Unit Code (HUC)) for the entire UMRB using the 2001
National Land Cover Data (NLCD) and Cropland Data Layer (CDL, 2004-06) for the
land use coverage and the USDA-NRCS’s STATSGO for the soils data. In addition,
information from the Conservation Tillage Information Center (CTIC) and USDA
Census of Agriculture 2002/1997 was used to identify the cropping rotation and
management practices for the agricultural land areas in these same 131 sub-watersheds.
Based on the management information at this level, each sub-watershed was assigned
appropriate management and tillage practices. The model was set up to run using 19602001 weather data, a 42-year time period, available from NRCS climatic data center,
which is a derivative of National Oceanic and Atmospheric Administration-National
Weather Service-National Climate Data Center, NOAA-NWS-NCDC datasets. In
addition the weather data has been spatially interpolated to assign one distinct weather
station to each sub-basin.
The 42-year SWAT model run was performed and the results analyzed to establish
runoff, sediment, nitrogen, and phosphorous loadings from each of the 131 8-digit
HUC sub-watersheds. Additionally, results were summarized for the larger 4-digit
HUC sub-basins, at the total outflow from the UMRB, and at the various USGS gage
sites distributed along the Mississippi River main stem. These results provided the
baseline scenario model values to which the future alternatives are compared. As part
of this baseline model an average corn yield of 140.7 bu/ac was established to
approximate yields for the year 2005.
The UMRB SWAT model was calibrated for flow and water quality data from 13
USGS gages on the main stem of the Mississippi River, spatially distributed from the
upper reaches in Minnesota and Wisconsin to the UMRB outlet below Grafton, Illinois.
The calibrated simulation results showed the model adequately simulated streamflow at
yearly (R2: 0.79 ~ 0.99) and monthly (R2: 0.31 ~ 0.81) timesteps. It also simulated the
observed trends in total N (R2: 0.59 ~ 0.81) and total P (R2: 0.14 ~ 0.65) loads in the
stream outflow from the UMRB at the 13 gages. More detailed validation and
calibration work using the same geospatial dataset were done on subwatersheds within
the UMRB, such as the Raccoon River watershed (Jha et al., 2006). It was shown that
the SWAT model simulated both streamflow and nutrient loading well, comparing to
observed data.
UMRB SWAT Sensitivity Analyses
Sensitivity analysis (SA) for SWAT model has been carried out by many others while
focusing on different aspects of its application. Holvoet et al. (2005) conducted the
classic one-at-a-time (OAT) sensitivity analysis for SWAT model hydrology
parameters, paying special attention to the transport of pesticide. The work identified
dominant hydrological parameters and pesticide cycling parameters that formed the
basis for management practices leading to reduction of pesticide loss to the river
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system. Ficklin et al. (2009) conducted the sensitivity assessment of SWAT model on
a highly agricultural watershed and found that “atmospheric CO2, temperature and
precipitation change have significant effects on water yield, evapotranspiration,
irrigation water use, and streamflow”. Jha et al. (2006) studied the climate change
sensitivity of the UMRB using the SWAT model. They focused on streamflow only
and used climate projections by seven global climate models. Their results indicated
that the UMRB hydrology, as simulated by SWAT, is very sensitive to climate
disturbance. Similar efforts on climate change assessment in the UMRB were also
done by others using global climate models, while focusing on streamflow responses
(Takle et al., 2009).
The focus of this project was to examine the sensitivity of the UMRB SWAT model’s
predicted hydrology, nutrient and sediment loading, and corn production outcome to
changes in management and climate related factors. The analysis was done within the
context of assessing the impacts of increased corn production in the UMRB to meet
future ethanol demands.
Four input factors were considered in this sensitivity analysis; two related to
management practices (auto fertilization and corn residue removal) and two related to
meteorological (precipitation and temperature) changes. These input factors and their
variations are shown in Table 1.
Each level of the input factors was simulated in a one-at-a-time fashion. Hence,
excluding the baseline case, eight simulations were run for the entire UMRB (131 HUC8 sub watersheds) area (i.e. four scenarios times two input levels).
Total annual water yield, nitrogen, phosphorous, and sediment loadings summed across
all 131 HUC-8 sub watersheds within the UMRB were calculated from SWAT’s longterm average annual outputs. Total annual corn yield, N, P fertilizer applied, and N, P
fertilizer uptake by corn crop were also calculated. These total annual values were
compared between each scenario and the original baseline. Percent changes in annual
outputs were calculated using Equation 1.
Percent Change =
OIFL  Obaseline
 100
Obaseline
Equation 1
where OIFL is an output value for a given input factor level (IFL), Obaseline is the
corresponding output value for the baseline.
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Table 1: Summary of Input Changes for Sensitivity Analysis Scenarios
Input Factors
(abbrev.)
Auto fertilization
nitrogen stress
factor
(AFNSTRS)
Auto fertilization
maximum
nitrogen
application rate
(AFRate)
Auto fertilization
maximum yearly
nitrogen
application rate
(AFRate)
Levels of
change from
Baseline
Range in
Percent change
of input factors
+27%, -15%
42%
+27%, -15%
42%
+27%, -15%
42%
Auto fertilization
application
efficiency (AFEff)
+27%, -15%
42%
Corn residue
removal (RS)
+91%, 0%
91%
Daily air
temperature
(TMP)
+26%, -26%
52%
Daily precipitation
(PCP)
+20%, -20%
40%
Description
Increase (+) by 27% and
decrease (-) by 15% the autofertilization nitrogen stress
threshold; Default value: 0.75
Increase (+) by 27% and
decrease (-) by 15% the autofertilization maximum nitrogen
application rate; Default value:
200 kg N /ha
Increase (+) by 27% and
decrease (-) by 15% the autofertilization maximum yearly
nitrogen application rate;
Default value: 300 kg N /ha
Increase (+) by 27% and
decrease (-) by 15% the autofertilization application
efficiency; Default value: 1.3
Increase (+) crop harvest index
of corn by 91%, from 0.47 to
0.9; 0% means no residue
removal;
Increase (+) and decrease (-)
daily temperature by 2 degree C
across UMRB;
Increase (+) by 20% and
decrease (-) by 20% the daily
precipitation across UMRB;
A sensitivity index (SI) was calculated as the ratio of the average absolute percent
change in a model output from two simulations (for the two levels of a given input
factor as shown in Table 1) to the average absolute percent change in the corresponding
input factor (Equation 2).
SI =
Percentage _ Change _ in _ Output _ Value
 100
Percentage _ Change _ in _ InputFactorLevel
Equation 2
Such a formulation essentially quantifies the relative change in model output due to a
relative change in a given input factor. Values of SI near 100% indicate a 1:1 sensitivity,
with the model producing a result in direct proportion to the input parameter change (i.e.
a 10% change in input factor produces a 10% change in model results). In a similar
manner, values of SI near 200% indicate a sensitivity of 1:2, (10% input change
produces a 20% output change), whereas a SI of 20% indicates relative model
insensitivity, where a 10% change in the input produces only a 2% change in the output
item on average.
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SENSITIVITY ANALYSIS RESULTS
The sensitivity results are displayed in the 9 “tornado” diagrams (Figures 2 to 10)
showing the differential influence of the four input factors on the nine model outputs.
The sample legend shown here provides insight into the layout of the tornado diagrams:
Legend
- 20%
+ 20%
Base
Parameter X
(275)
275 is the
Sensitivity
Index
±20%: percent change
in Parameter X
Percent Change in model output from Baseline output
Details of the tornado diagrams’ layout are:




Input factors are placed on the left axis, ranked by their SI values in descending
order from top to bottom. These SI values are displayed as values in parenthesis
with input factors’ labels along the vertical ordinates.
The horizontal x-axis shows the percent changes in an output value (bars).
The zero percentage point on x-axis corresponds to the baseline value.
Within the figures, the underscored percentage value at the end of each bar is the
corresponding percent change in the input factor (Table 1).
The sensitivity of model outputs to the changes in the four input factors is discussed
below.
Precipitation
The change in precipitation had the greatest impact on water quality related outputs. As
the driving force in the overall hydrology of the watershed, precipitation exerts a strong
positive causal effect on water yield, total N and P loadings, and sediment loading from
the UMRB area (Figures 2 to 5). This is consistent with most hydrologic models in that
rainfall, and thus water flow, drives the transport of nutrients and sediment. The work
done by Jha et al. (2006) on climate change sensitivity assessment on UMRB found
similar responses to rainfall changes.
As seen in Figures 6 to 10, the impact of precipitation change is much less significant to
corn growth, plant nutrient uptake, and fertilizer applied. Due to the fact that the SWAT
model’s auto-fertilization scheme compensates the nutrient loss from the soil nutrient
pool, the impact on crop growth due to rainfall change is dampened. It follows that the
impact from rainfall change to nutrient uptake becomes less pronounced, because
nutrient uptake is demand driven via plant growth.
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Temperature
Compared to precipitation, temperature variations also have significant impact on the
hydrologic cycle, though to a somewhat lower extent. It is noted that there is an
inverse relationship between water yield and temperature change (Figure 2), which is
expected. It follows that nutrient and sediment loss will also be negatively correlated
with temperature change as they are dependent on overland runoff volume (Figures 3
to 5).
As noted above, crop growth is modeled via heat unit accumulation that expectedly
makes temperature the single most influential factor in the model’s plant growth
simulation (i.e., fertilizer applied (Figures 6 and 8), nutrient uptake (Figures 7 and 9),
and crop yield (Figure 10)). It is noted that the corn yield decreases regardless of
temperature change direction. It does show reduction in temperature lowers the yield
more than increase in temperature. It can be argued that temperature decrease reduces
growth through slower heat unit accumulation, while temperature increase can slow
growth due to excessive heat above optimal growth temperatures. Additionally,
temperature increase can negatively impact soil water availability, reducing potential
growth.
The soil nutrient needs through fertilizer application (Figures 6 and 8) seem to agree
with the change in crop growth (Figure 10) due to the same temperature change. The
negative impact on crop growth due to temperature changes (regardless of direction)
leads to less nutrient demand from the soil nutrient pool, hence reduction in fertilizer
application.
Auto Fertilizer Application
All three of the auto fertilization parameters have minimum impact on water yield
(Figure 2) and sediment load (Figure 5). They impact hydrology and sediment loading
indirectly through plant growth related processes such as canopy interception of rainfall,
land cover by plant biomass or residue. Auto fertilization generally influences soil
nutrient availability, which is the basis for the dynamic nutrient stress factor that is used
to modify the potential growth of the plant. From Figure 10, it is shown that their impact
on corn yield is limited, which portends their limited roles in the SWAT model’s
hydrology simulation.
Auto fertilizer application has a more direct impact on N and P loadings (Figures 3 and
4). Increases in the three parameters leads to increased nutrient loading and vice versa,
but the changes are all quite small, mostly less than 5%. AFEff has a slightly greater
impact than AFRate and AFNSTRS. This seems to indicate that direct modification of
the target nutrient demand (AFEff) is more effective than changing application rate
ceilings (AFRate) or triggering threshold (AFNSTRS) under the conditions presented
herein.
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By a similar token, increase in the three auto fertilization parameters leads to increases
in N and P fertilizer applied and uptake, and vice versa (Figures 6 to 9). Again, AFEff
has a greater impact than AFRate and AFNSTRS, except for P uptake (Figure 9). As
stated above, the impact from the three auto fertilization parameters on corn yield are
limited (Figure 10). The nutrient stress threshold (AFNSTRS) has greater impact than
AFEff and AFRate for corn yield.
Overall, auto fertilization parameters have limited impact on all model results examined.
Changes in model outputs in either direction did not exceed 10% in all cases, with the
higher values produced for fertilizer applied.
Corn Residue Removal
Corn residue removal noticeably reduces N, P stream loadings (Figures 3 and 4)
while increasing sediment loads (Figure 5). It also reduces total water yield slightly
(Figure 2).
As residue is the main source of fresh soil organic N and P, the removal of residue
leads to higher N, P fertilizer input during the corn growing seasons. Due to the
SWAT model’s inability to explicitly account for residue removal apart from
economic yield, its impact on corn yield is not considered in this discussion. This
made the interpretation of the simulated decrease in plant N, P uptake (Figures 7 and
9) more difficult.
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(275) PCP
Nitrogen Load
Baseline: 15.61 lbs/ac
-20%
(208) PCP
+20%
+26%
(2) RS
-26%
+91%
Scenarios
(52) TMP
Scenarios
Water Yield
Baseline: 9.87 inches
0%
+27% -15%
(0.17) AFEff
-20%
+20%
(20) AFEff
-15%
+27%
(16) TMP
+26%
-26%
(11) RS
+91%
0%
(0.13) AFNSTRS
-15% +27%
(7) AFRate
-15%
+27%
(0.13) AFRate
+27% -15%
(5) AFNSTRS
-15%
+27%
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
-80
-70
-60
-50
Percent difference from base scenario (%)
-30
-20
-10
0
10
20
40
50
60
70
80
Figure 3: Sensitivity of N loading to the four input factors.
Phosphorous Load
Sediment Load
Baseline: 1.45 lbs/ac
Baseline: 3.53 tons/ac
-20%
30
Percent difference from base scenario (%)
Figure 2: Sensitivity of water yield to the four input factors.
(215) PCP
-40
+20%
(316) PCP
-20%
+20%
+26%
(6) AFEff
-26%
-15%
+27%
+91%
(3.8) RS
(71) TMP
Scenarios
Scenarios
(38) TMP
0%
(4) RS
(2) AFNSTRS
-15% +27%
(0.087) AFNSTRS
-50
-40
-30
-20
-10
0
10
20
30
40
50
Percent difference from base scenario (%)
Figure 4: Sensitivity of P Load to the four input factors
60
70
80
+91%
-15% +27%
(0.14) AFRate
-60
0%
-15% +27%
-15% +27%
-70
-26%
(0.52) AFEff
(2) AFRate
-80
+26%
+27% -15%
-80 -70 -60 -50 -40 -30 -20 -10
0
10
20
30
40
50
60
70
80
Percent difference from base scenario (%)
Figure 5: Sensitivity of sediment load to the four input factors
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Baseline:207.76 lbs/ac
-26%
+26%
(59) AFEff
Scenarios
N Uptake
Baseline: 140.39 lbs/ac
-15%
+27%
(27) PCP
-20%
+20%
(21) AFRate
-15%
+27%
-15%
(17) AFNSTRS
+27%
0%
(8) RS
-80
-70
-60
-50
-40
-30
-20
-10
Scenarios
(64) TMP
N Fertilizer Applied
10
20
-15%
(21) TMP
+26%
(15) AFRate
(12) AFNSTRS
40
50
60
70
80
-70
-60 -50
(36) TMP
+27%
+20%
(21) AFRate
-15%
+27%
(17) AFNSTRS
-15%
(8) RS
0%
-20
-10
Scenarios
Scenarios
-20
-10
0
10
+27%
+91%
0
10
20
30
40
20
30
40
50
60
70
80
Figure 7: Sensitivity of plant N uptake to the four input
+26%
-40 -30
-30
Percent difference from base scenario (%)
P Uptake
-20%
-50
-40
Baseline: 25.47 lbs/ac
(27) PCP
-60
-20%
Baseline:21.06 lbs/ac
-15%
-70
0%
P Fertilizer Applied
-26%
-80
+27%
+20%
-80
Figure 6: Sensitivity of applied N fertilizer to the four input factors
factors
(59) AFEff
+27%
+91%
Percent difference from base scenario (%)
(64) TMP
-26%
-15%
(0.4) PCP
30
+27%
-15%
(4) RS
+91%
0
(36) AFEff
50
60
Percent difference from base scenario (%)
Figure 8: Sensitivity of applied P fertilizer to the four input
factors
70
80
+26%
-26%
(9) AFNSTRS
-15%
(6) PCP
-20%
+20%
(5) AFEff
-15%
+27%
+27%
(1) AFRate
-15% +27%
(0.33) RS
0% +91%
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
Percent difference from base scenario (%)
Figure 9: Sensitivity of plant P uptake to the four input factors
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Corn Yield
Baseline:140.7 bu/ac
-26%
Scenarios
(23) TMP
+26%
(9) AFNSTRS
-15%
+27%
(6) PCP
-20%
+20%
(5) AFEff
-15%
+27%
(0.78) AFRate
-15%
+27%
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
Percent difference from base scenario (%)
Figure 10: Sensitivity of corn yield to the four input factors.
SUMMARY
Overall, precipitation and temperature were the most influential factors to water quality
related model outputs. Temperature change is negatively correlated with the changes in
the water quality related model outputs, while they are positively correlated with
precipitation changes. Temperature has the most influence in plant growth related
outputs, namely, corn yield, N, P fertilizer application and plant N, P uptake. Auto
fertilization has limited impact on all model outputs considered. Generally, it has greater
impact on nutrient budget and corn growth than on hydrology and sediment loading. All
three parameters related to auto fertilization have positive correlation with all of the
outputs except water yield and sediment loading. Corn residue removal also has greater
impact on nutrient budget related outputs than on others such as water yield and corn
yield.
The findings from this study indicated that future climate change could greatly
influence water resource availability and NPS pollution potential from increased corn
production in the UMRB. Corn stover removal should be carried out carefully in a
location-specific fashion so as to minimize agricultural NPS pollution. Best
management practices such as nutrient management should be evaluated at local scale
rather than the scale of the entire UMRB.
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REFERENCES
Ficklin, D.L., Yuzhou Luo, E. Luedeling, and Minghua Zhang, 2009 Climate change
sensitivity assessment of a highly agricultural watershed using SWAT, Journal of
Hydrology, v374(1-2):16-29.
Holvoet, K., A. van Griensven, P. Seuntjens, and P.A. Vanrolleghem, 2005
Sensitivity analysis for hydrology and pesticide supply towards the river in
SWAT, Physics and Chemistry of the Earth, Parts A/B/C, v30(8-10): 518-526.
Jha, M., J.G. Arnold, P.W. Gassman, F. Giorgi, and R.R. Gu, 2006 Climate change
sensitivity assessment on Upper Mississippi river Basin streamflows using SWAT,
Journal of the American Water Resources Association (JAWRA), v42(4):9971015.
Takle, E.S., M. Jha, and E. Lu, 2009: Climate change and streamflow in the Upper
Mississippi River Basin. Chapter 12. In S.C. Pryor, ed., Understanding Climate
Change: Climate variability, predictability and change in the Midwestern USA.
Indiana University Press.
US EPA, 2008. Modeling Water Quality Impacts of Corn Production for Ethanol in
the Upper Mississippi River Basin. U.S. Environmental Protection Agency,
Office of Water, Washington, DC.
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