Environmental Assessment ~~~ Modeling ~~~ Water Resources 

Sensitivity Analysis of Water Quality Impacts of Corn Production 

for Ethanol in the Upper Mississippi River Basin 

Prepared by 

AQUA TERRA Consultants 

Mountain View, CA 94043 

Decatur, GA 30030 

Under Work Assignments 3-32 and 3-33 

 of US EPA Contract No. EP-C-06-029 

Submitted to 

Dr. David Wells, 

US EPA Office of Water 

Washington, DC 20460 

January 22, 2010 
CONTENTS 

 Page 

Figures............................................................................................................................... iii 

Tables ................................................................................................................................ iii 

1.0 Introduction................................................................................................................. 1 

 1.1 Background and Objectives. ............................................................................. 1 

 1.2 UMRB SWAT Model Overview.......................................................................... 2 

 1.3 UMRB SWAT Sensitivity Analyses.................................................................... 3 

2.0 Sensitivity Analysis .................................................................................................... 4 

 2.1 UMRB SWAT Model.......................................................................................... 4

 2.2 Method .............................................................................................................. 4 

3.0 Sensitivity Analysis Results ...................................................................................... 7 

 3.1 Precipitation....................................................................................................... 8 

 3.2 Temperature...................................................................................................... 8 

 3.3 Auto Fertilizer Application.................................................................................. 9 

 3.4 Corn Residue Removal ..................................................................................... 9 

 3.5 Summary........................................................................................................... 10 

4.0 References................................................................................................................... 16 

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FIGURES 

Figure 1.1 Upper Mississippi River Basin with 8-digit HUCs (Hydrologic Unit Codes). 1 

Figure 3.1 Sensitivity of water yield to the four input factors ........................................ 11 

Figure 3.2 Sensitivity of N loading to the four input factors .......................................... 11 

Figure 3.3 Sensitivity of P Load to the four input factors .............................................. 12 

Figure 3.4 Sensitivity of sediment load to the four input factors ................................... 12 

Figure 3.5 Sensitivity of N fertilizer application to the four input factors ....................... 13 

Figure 3.6 Sensitivity of plant N uptake to the four input factors .................................. 13 

Figure 3.7 Sensitivity of applied P fertilizer to the four input factors............................. 14 

Figure 3.8 Sensitivity of plant P uptake to the four input factors................................... 14 

Figure 3.9 Sensitivity of corn yield to the four input factors ......................................... 15 

TABLES 

Table 2.1 Summary of Input Changes for Sensitivity Analysis Scenarios................... 4

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SECTION 1.0 

INTRODUCTION

1.1 BACKGROUND AND OBJECTIVES

This work is a continuation of the previous modeling effort under Work Assignments 2-32 

and 2-33 (EPA Contract EP-C-06-029), Modeling Water Quality Impacts of Corn Production 

for Ethanol in the Upper Mississippi River Basin (UMRB, Figure 1.1) (US EPA, 2008). 

During the previous modeling effort, the Soil and Water Assessment Tool (SWAT) model 

was used to simulate the UMRB area. The model was established to mimic the dynamic 

linkage between corn production and the resulting stream flow and nutrient and sediment 

loadings. A baseline model was developed to represent current conditions. Projected 

ethanol goals were used to determine future corn area increases in 2010, 2015, 2020, and 

2022. The model was run for these future time points and resulting flow and pollutant loads 

were compared to the baseline conditions. 

This work used the baseline UMRB SWAT model to conduct sensitivity analysis on a 

number of important meteorological and management related factors. The goal was to 

further understand the model characteristics and sensitivities to parameters and input 

forcing functions that control the model response for key environmental indicators. 

Figure 1.1: Upper Mississippi River Basin with 8-digit HUCs (Hydrologic Unit Codes) 

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1.2 UMRB SWAT MODEL OVERVIEW 

1.2.1 UMRB Hydrologic Modeling 

The SWAT model was used for this study and the previous modeling effort. SWAT is a 

semi distributed and continuous simulation model that has gained both national and 

international acceptance as an efficient and reliable watershed modeling tool. Its 

widespread use has been demonstrated through hundreds of SWAT-related papers in the 

open technical literature, presentations at international SWAT conferences, and its 

inclusion in the US EPA’s Better Assessment Science Integrating Point and Nonpoint 

Sources (BASINS) (US EPA, 2007) system. 

The SWAT model’s application in the UMRB watersheds was documented in detail in the 

final report for the previous phase’s modeling project (US EPA, 2008). Generally, the 

SWAT model was shown to simulate monthly stream flow well in the region. Its 

comprehensive modeling algorithms for plant growth and various agricultural production 

systems, along with its extensive input database, provide a sound framework to evaluate 

biofuel production and its environmental impact in the UMRB in an integrated fashion. 

During the previous modeling effort, the SWAT model was setup on 131 sub-watersheds 

(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) as the land use coverage for 

the baseline scenario; the USDA-NRCS STATSGO provided the soils data for the entire 

analyses. 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 by 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 setup to run using 1960-

2001 weather data, a 42-year time period, available from NRCS climatic data center, which 

is a derivative of National Oceanic and Atmospheric Adminstration-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 original UMRB modeling report provides complete details of the model 

setup. 

The 42-year SWAT model runs were performed and the results analyzed to establish 

runoff, sediment, nitrogen, and phosphorous loadings from each of the 131 8-digit HUC 

sub-watersheds and the larger 4-digit sub-basins, along with the total outflow from the 

UMRB and at the various USGS gage sites distributed along the Mississippi River 

mainstem. These results provided the baseline scenario model values to which the future 

alternatives are compared. 

The UMRB SWAT model was calibrated for flow and water quality data from 13 USGS 

gages on the mainstem 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 to 0.99) and monthly (R2: 0.31 to 0.81) timesteps. It also simulated the observed 

trends in total N (R2

: 0.59 to 0.81) and total P (R2: 0.14 to 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., 

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2007). It was shown that the SWAT model simulated both streamflow and nutrient loading 

well, comparing to observed data. 

1.2.2 UMRB Crop Growth Modeling

In the previous effort, SWAT scenarios were developed to simulate the increase in corn 

production due to increased biofuel production goals for the years 2010, 2015, 2020, and 

2022. Detailed description for corn yield target calculations based on the biofuel production 

goal projection was given in the final report for the previous study. 

The baseline SWAT model was established using data from the year 2005. Its target 

national average corn yield of 150 bushels per acre (bu/ac) was established as the desired 

average baseline yield. The SWAT simulated baseline average yield for the UMRB was 

140.7bu/ac, which was considered acceptable due to the significant portion of the study 

area falling in the Northern U.S. 

The SWAT baseline model can be used to analyze a wide range of various scenarios 

representing changes in management practices, nutrient inputs (e.g. increased fertilizer 

applications), and climate conditions. Overall, the SWAT UMRB model has been calibrated 

for streamflow and nutrient loadings across the UMRB area along the main stem of its 

drainage network. The model was considered adequate in reproducing long-term trends in 

crop growth, hydrological and environmental indicators. 

1.3 UMRB SWAT Sensitivity Analyses 

Basin-scale hydrologic modeling using models such as SWAT has long been employed 

to obtain holistic assessment of a watershed’s hydrologic characteristics and 

interrelations among key forcing factors. The analysis of the influence from these forcing 

factors can yield practical guidelines for better managing a watershed and anticipating 

potential changes to water resources and nonpoint source (NPS) pollution. 

Parameter sensitivity analysis for the SWAT model has been carried out by many 

researchers 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 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. 

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SECTION 2.0 

SENSITIVITY ANALYSIS

2.1 UMRB SWAT MODEL 

As described earlier, the baseline condition was defined to represent UMRB conditions 

corresponding to the approximate time frame of 2003-2006. The calibration of the SWAT 

model using land use and corn yield levels from this time frame established a baseline 

model for hydrology (water yield from land and streamflow), nutrient loss (total N and total P 

loadings from land and reaches), and sediment loss (loadings from land and reaches) in 

the UMRB. This baseline SWAT model for the UMRB was used for the sensitivity analysis 

performed in this effort. 

2.2 METHOD

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 

below in Table 1. 

Table 2.1: Summary of Input Changes for Sensitivity Analysis Scenarios 

Input Factors 

(abbrev.) 

Levels of change 

from Baseline 

Range in Percent 

change of input 

factors 

Description 

Auto fertilization 

nitrogen stress 

factor (AFNSTRS) 

+27%, -15% 42% 

Increase (+) by 27% and 

decrease (-) by 15% the autofertilization nitrogen stress 

threshold; Default value: 0.75 

Auto fertilization 

maximum nitrogen 

application rate 

(AFRate) 

+27%, -15% 42% 

Increase (+) by 27% and 

decrease (-) by 15% the autofertilization maximum nitrogen 

application rate; Default value: 

200 kg N /ha 

Auto fertilization 

maximum yearly 

nitrogen 

application rate 

(AFRate) 

+27%, -15% 42% 

Increase (+) by 27% and 

decrease (-) by 15% the autofertilization maximum yearly 

nitrogen application rate; Default 

value: 300 kg N /ha 

Auto fertilization 

application 

efficiency (AFEff) 

+27%, -15% 42% 

Increase (+) by 27% and 

decrease (-) by 15% the autofertilization application efficiency; 

Default value: 1.3 

Corn residue 

removal (RS) +91%, 0% 91% 

Increase (+) crop harvest index of 

corn by 91%, from 0.47 to 0.9; 0% 

means no residue removal; 

Daily air 

temperature 

(TMP) 

+26%, -26% 52% 

Increase (+) and decrease (-) 

daily temperature by 2 degree C 

across UMRB; 

Daily precipitation 

(PCP) +20%, -20% 40% 

Increase (+) by 20% and 

decrease (-) by 20% the daily 

precipitation across UMRB; 

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With the exception of the corn residue removal scenario, the range of input changes 

attempted to represent physically realistic extremes in the input factor values in an effort to 

see the full range of model response. 

Corn crop fertilization was simulated with the SWAT model’s auto-fertilization schedule; the 

fertilization scenario involved changes to four (4) model parameters, listed in Table 2.1. The 

SWAT model initiates auto-fertilization whenever the potential plant growth falls below a 

certain threshold. This threshold is defined by the input value of the nitrogen stress 

threshold parameter (AFNSTRS). AFNSTRS is a fraction of the potential plant growth 

under ideal conditions (no nutrient shortage, for example) during the growing season. 

When plant growth falls under this threshold, auto fertilization is triggered. The default value 

of AFNSTRS is 0.75, or 75% of ideal crop growth; it is increased by 27% to 0.95 and 

decreased by 15% to 0.6375 for the sensitivity runs. The auto fertilization operation also 

allows the user to specify the type of fertilizer, the fraction of total fertilizer applied to the soil 

surface, the maximum annual and per application fertilization rates, and the application 

efficiency. Elemental nitrogen and phosphorous is chosen as the fertilizer. The per 

application fertilization rate and yearly fertilization rate are by default 200 kg/ha (178.6 

lb/ac) and 300 kg/ha (267.9 lb/ac), respectively. These two parameters are increased or 

decreased in parallel from their default values; thus, the changes made to these two 

parameters are treated as a single parameter change and labeled as AFRate. 

The fertilizer application efficiency (AFEff) is a fraction value that modifies the target nutrient 

demand, which is determined by the amount of nutrient removed at harvest. When it is 

greater than 1, the model will apply extra fertilizer to compensate for nutrient loss due to 

surface runoff; when it is less than 1, only a fraction of the target nutrient demand is met. 

AFEff is by default 1.3. It is increased by 27% to a value of 1.65 and decreased by 15% to a 

value of 1.1. As the timing of auto fertilization is determined solely by AFNSTRS, the same 

percentage changes made to AFNSTRS were used to vary the AFRate and AFEff 

parameters to achieve consistency in evaluating their impact. 

The UMRB SWAT model used precipitation and temperature data from 131 weather 

stations, with one for each of its 131 subwatersheds. The daily temperature 

increase/decrease of 2 degrees C was applied to all temperature data in all weather 

stations. Then, a subwatershed area-weighted average percentage increase/decrease in 

annual average temperature across the entire UMRB was calculated, i.e. ±26%. The daily 

precipitation values were increased/decreased uniformly by 20% across all 131 weather 

stations in similar fashion, without changing storm intensity. The choice of these climate 

changes was based on global climate model (GCM) prediction for the UMRB region (Takel 

et al., 2005 and 2009) and previous SWAT sensitivity analysis (Jha et al., 2006). 

For corn residue removal, the corn Harvest Index was increased from the original 0.47 to 

0.9 to simulate residue removal for the procurement of additional biofuel production 

material. SWAT’s Harvest Index (HI) is the ratio of harvested biomass (considered as 

economic yield, i.e. corn grain in this case) to the total crop biomass at the time of harvest. 

Thus, by increasing HI, the SWAT model will remove more than the original proportion of 

plant biomass that is considered as economic yield (i.e. additional corn residue is 

removed). 

The SWAT model for the current UMRB land use scenario (without corn area increase) 

was used in the series of sensitivity analysis simulations and is referred to as the baseline 

hereinafter. Each level of the input factors was simulated in a one-at-a-time fashion. Hence, 

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excluding the baseline case, eight simulations were run for the entire UMRB (131 HUC-8 

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 long-term 

average annual outputs. Also, total annual corn yield, N, P fertilizer applied, and N, P 

fertilizer uptake by corn crop were also calculated from SWAT’s long-term average annual 

outputs. These total annual values were compared between each scenario and the original 

baseline. Percent changes were calculated for these total annual values using Equation 1. 

Percent Change = ∗100 −

baseline

IFL baseline

O

OO

 Equation 1 

where OIFL is an output value for a given input factor level (IFL), Obaseline is the 

corresponding output value for the baseline. 

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 = 100 ___

____ ∗

Percentage inChange InputFactorLevel

Percentage OutputinChange Value 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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SECTION 3.0 

SENSITIVITY ANALYSIS RESULTS

The sensitivity results are displayed in the 9 “tornado” diagrams (Figures 3.1 to 3.9) 

showing the differential influence of the four input factors on the nine model outputs. These 

figures, displayed at the end of this section, are as follows: 

Figure 3.1 Sensitivity of water yield to the four input factors 

Figure 3.2 Sensitivity of N loading to the four input factors 

Figure 3.3 Sensitivity of P Load to the four input factors 

Figure 3.4 Sensitivity of sediment load to the four input factors 

Figure 3.5 Sensitivity of N fertilizer application to the four input factors 

Figure 3.6 Sensitivity of plant N uptake to the four input factors 

Figure 3.7 Sensitivity of applied P fertilizer to the four input factors 

Figure 3.8 Sensitivity of plant P uptake to the four input factors 

Figure 3.9 Sensitivity of corn yield to the four input factors 

The sample legend shown here provides insight into the layout of the tornado diagrams: 

Parameter X

(275) 

275 is the 

Sensitivity 

Index (SI)

Base

Percent Change in model output from Baseline output

- 20% + 20%

±20%: percent change Legend in Parameter X 

Along with the sample legend, details of the tornado diagrams include: 

• Input factors are placed on the left axis, ranked by their Sensitivity Index (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 (gray bar). 

• The zero percentage point on x-axis corresponds to the baseline value. 

• Within the figures, the underscored percentage value at the end of every bar is 

the corresponding percent change in the input factor (Table 2.1). 

The sensitivity of model outputs to the changes in the four input factors is discussed below. 

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3.1 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. As seen in Figures 3.1 to 3.4, the percent change in water quality related outputs is 

about the same below and above their baseline levels, corresponding to the ±20% change 

in rainfall volume; however, the sensitivity is clearly non-linear – a 20% change in 

precipitation produces more than a 40% change in water yield, nutrient loads, and sediment 

loads. 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 3.5 to 3.9, the impact of precipitation change is much less significant to 

corn growth, plant nutrient uptake, and fertilizer applied. Plant growth in SWAT is 

temperature regulated via the cumulative seasonal heat unit, on which potential growth is 

determined. This potential growth is further modified by soil water and nutrient availability, 

which are indirectly influenced by rainfall volume and infiltration. 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. 

Similar efforts on climate change assessment in the UMRB done by Jha et al (2006) and 

Takle et al. (2009) employed multiple global climate models’ projected precipitation 

changes in the UMRB. It is noted that the ±20% change in precipitation used in this 

sensitivity analysis is within the projected range of change by the various global climate 

models. This range is also consistent with the range expectations (±50% to ±20%) by the 

U.S. Global Climate Change Program’s summary for the Midwest region (US 

GCRP/CCSP, 2003). 

3.2 Temperature 

Compared to precipitation, temperature variations also have significant impact on the 

hydrologic cycle, though at a somewhat lower extent. It is noted that there is an inverse 

relationship between water yield and temperature change (Figure 3.1), which is 

expected. Increasing temperature will increase evapotranspiration, thus reducing water 

flow on the land. It follows that nutrient and sediment loss will also be negatively 

correlated with temperature change as they are dependent on overland runoff volume 

(Figures 3.2 to 3.4). 

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 3.5 and 3.7), nutrient uptake (Figures 3.6 and 

3.8), and crop yield (Figure 3.9)). It is noted that the corn yield decreases regardless of 

temperature change direction. It does show reduction in temperature lowers the yield 

more than temperature increase. 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. 

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The soil nutrient needs through fertilizer application (Figures 3.5 and 3.7) seem to agree 

with the change in crop growth (Figure 3.9) 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. 

The ±2 degree C change is at the lower end of the change spectrum specified in the 

U.S. Global Climate Change Program’s summary for the Midwest region (Midwest 

section of the National Assessments (2003). This is also the smallest variation used by 

Jha et al. (2006) in their climate assessment work for the UMRB. 

3.3 Auto Fertilizer Application 

All three of the auto fertilization parameters have minimum impact on water yield (Figure 

3.1) and sediment load (Figure 3.4). 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 3.9, 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 3.2 and 

3.3). 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. 

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 3.5 to 3.8). Again, AFEff has a 

greater impact than AFRate and AFNSTRS, except for P uptake (Figure 3.8). As stated 

above, the impact from the three auto fertilization parameters on corn yield are limited 

(Figure 3.9). 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. 

3.4 Corn Residue Removal 

Corn residue removal noticeably reduces N, P stream loadings (Figures 3.2 and 3.3) 

while increasing sediment loads (Figure 3.4). It also reduces total water yield slightly 

(Figure 3.1). 

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 3.6 and 3.8) more difficult. 

Extensive field research has been conducted on the impact of residue as land cover. 

Dam et al. (2005) experimented on tillage impacts on soil property and residue cover 

over an 11-year period. They concluded that there were no significant differences among 

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different tillage and residue cover regimes in terms of long-term mean dry matter corn 

yields and that it was climatic-related differences that had a greater influence on dry 

matter corn yields. Blanco-Canqui and Lal (2007) described their experiment on soil and 

crop response to harvesting corn residues for biofuel production. They found that the 

impact from corn residue removal on soil productivity and corn yield heavily depended 

on soil type and terrain. Significant impact was evident only on sloping and erosion 

prone soils. The simulated impact due to residue removal as presented here agrees with 

these experimental findings. The overall impact due to residue removal across the entire 

UMRB will be moderate. More detailed study focusing on local soil and topography could 

yield more location-specific estimates from such changes. 

3.5 Summary 

This project conducted a sensitivity assessment of the existing UMRB SWAT model 

established for the time period of 2003 to 2006. Four main input factors related to climate 

conditions and management options were varied in a one-at-a-time fashion and 

corresponding water quality and crop growth related model outputs were analyzed. To 

compare the impacts of the changes, a Sensitivity Index was calculated, defined as the 

relative percent change in a model output per one percent change in an input factor. 

Overall, precipitation and temperature were the most influential factors to water quality 

related model outputs, namely, water yield, total N, P loadings, and sediment loading. 

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. 

It is noted that the complex interaction among inter-dependent processes in the SWAT 

model can make it difficult to interpret a single-factor-only sensitivity index as presented. 

This was especially true for crop nutrient budget related outputs. Further analysis of the 

effects from combining multiple changes is warranted. 

It is also noted that both the temperature increase and decrease implemented here resulted 

in reduced nutrient uptake and crop yield, possibly leading to the conclusion that the 

baseline model is being run with optimal temperature conditions. Additional assessment of 

smaller temperature changes would provide clearer understanding of the impacts of this 

input parameter. 

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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Water Yield

Baseline: 9.87 inches 

-15%

+27%

-15%

0%

-26%

+20%

+27%

-15%

+27%

+91%

+26%

-20%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(0.13) AFRate

(0.13) AFNSTRS

(0.17) AFEff

(2) RS

(52) TMP

(275) PCP

Scenarios

Percent difference from base scenario (%)

Figure 3.1: Sensitivity of water yield to the four input factors. 

Nitrogen Load

Baseline: 15.61 lbs/ac

+20%

+27%

+27%

+27%

0%

-26%

-20%

-15%

-15%

-15%

+91%

+26%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(5) AFNSTRS

(7) AFRate

(11) RS

(16) TMP

(20) AFEff

(208) PCP

Scenarios

Percent difference from base scenario (%)

Figure 3.2: Sensitivity of N loading to the four input factors. 

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Phosphorous Load

Baseline: 1.45 lbs/ac

+20%

-26%

+27%

0%

+27%

+27%

-20%

+26%

-15%

+91%

-15%

-15%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(2) AFNSTRS

(2) AFRate

(3.8) RS

(6) AFEff

(38) TMP

(215) PCP

Scenarios

Percent difference from base scenario (%)

Figure 3.3: Sensitivity of P Load to the four input factors 

Sediment Load

Baseline: 3.53 tons/ac

+91%

+27%

+27%

-15%

-26%

+20%

0%

-15%

-15%

+27%

+26%

-20%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(0.087) AFNSTRS

(0.14) AFRate

(0.52) AFEff

(4) RS

(71) TMP

(316) PCP

Scenarios

Percent difference from base scenario (%)

Figure 3.4: Sensitivity of sediment load to the four input factors 

 

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N Fertilizer Applied

Baseline: 140.39 lbs/ac

-26%

-15%

0%

-15%

-15%

-20%

+26%

+27%

+91%

+27%

+27%

+20%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(8) RS

(17) AFNSTRS

(21) AFRate

(27) PCP

(59) AFEff

(64) TMP

Scenarios

Percent difference from base scenario (%)

Figure 3.5: Sensitivity of N fertilizer application to the four input factors 

N Uptake

Baseline:207.76 lbs/ac

-15%

+26%

-15%

-15%

+91%

+20%

+27%

-26%

+27%

+27%

0%

-20%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(0.4) PCP

(4) RS

(12) AFNSTRS

(15) AFRate

(21) TMP

(36) AFEff

Scenarios

Percent difference from base scenario (%)

Figure 3.6: Sensitivity of plant N uptake to the four input factors. 

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P Fertilizer Applied

Baseline:21.06 lbs/ac

-26%

-15%

0%

-15%

-15%

-20%

+26%

+27%

+91%

+27%

+27%

+20%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(8) RS

(17) AFNSTRS

(21) AFRate

(27) PCP

(59) AFEff

(64) TMP

Scenarios

Percent difference from base scenario (%)

Figure 3.7: Sensitivity of applied P fertilizer to the four input factors 

P Uptake

Baseline: 25.47 lbs/ac

-26%

+27%

+20%

+27%

+27%

+91%

+26%

-15%

-20%

-15%

-15%

0%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(0.33) RS

(1) AFRate

(5) AFEff

(6) PCP

(9) AFNSTRS

(36) TMP

Scenarios

Percent difference from base scenario (%)

Figure 3.8: Sensitivity of plant P uptake to the four input factors 

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Corn Yield

Baseline:140.7 bu/ac

-15%

-20%

-15%

-15%

-26%

+27%

+20%

+27%

+27%

+26%

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

(0.78) AFRate

(5) AFEff

(6) PCP

(9) AFNSTRS

(23) TMP

Scenarios

Percent difference from base scenario (%)

Figure 3.9: Sensitivity of corn yield to the four input factors. 

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REFERENCES

Blanco-Canqui, H. and R. Lal, 2007 Soil and crop response to harvesting corn residues 

for biofuel production, Geoderma, v141(3-4):355-362. 

Dam, R.F., B.B. Mehdi, M.S.E. Burgess, C.A. Madramootoo, G.R. Mehuys, and I.R. 

Callum 2005 Soil bulk density and crop yield under eleven consecutive years of corn 

with different tillage and residue practices in a sandy loam soil in central Canada, 

Soil and Tillage Research, v84(1):41-53. 

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):997-1015. 

US GCRP/CCSP, 2003. US National Assessment of the Potential Consequences of 

Climate Variability and Change, Mega-Region: Midwest. Last updated October 11, 

2003. URL: http://www.usgcrp.gov/usgcrp/nacc/midwest.htm <accessed on January 

12, 2010>. 

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, 2007. Better Assessment Science Integrating point and Nonpoint Sources -- 

BASINS Version 4.0. EPA-823-C-07-001. U.S. Environmental Protection Agency, 

Office of Water, Washington, DC. Available at: 

http://www.epa.gov/waterscience/basins/

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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