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

A Q U A T E R R A C o n s u l t a n t s ii

### 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

A Q U A T E R R A C o n s u l t a n t s iii

### 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)

A Q U A T E R R A C o n s u l t a n t s 1

### 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 th e entire UMRB using th e 2001 National Land Cover Data (NLCD) and Cropland Data Layer (CDL, 2004-06) as the land use coverage for the baseline scenario; the US DA-NRCS STATSGO provided t he soils data for the entire analyses. In addition, information from the C onservation 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 ti me 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 (R 2 

### : 0.79 to 0.99) and monthly (R 2 

### : 0.31 to 0.81) timesteps. It also simulated the observed trends in total N (R 2 

### : 0.59 to 0.81) and total P (R 2 

### : 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.,

A Q U A T E R R A C o n s u l t a n t s 2

### 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 managem ent practices, nutrient inputs (e.g. increas ed 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 model s 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) conduct ed the sensitivity assessment of SWAT model on a highly agricultural watershed and found that “atmospheric CO 2 

### , 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 fo cused 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 st reamflow responses (Takle et al., 2009).

The focus of this project was to examine t he 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 increas ed corn production in the UMRB to meet

future ethanol demands.

A Q U A T E R R A C o n s u l t a n t s 3

### 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% anddecrease (-) by 15% the autofertilization nitrogen stress threshold; Default value: 0.75

Auto fertilization maximum nitrogen application rate (AFRate) +27%, -15% 42% Increase (+) by 27% anddecrease (-) 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% anddecrease (-) 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% anddecrease (-) by 15% the autofertilization applic ation 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 sensitivit y runs. The auto fertiliz ation 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 compens ate 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,

A Q U A T E R R A C o n s u l t a n t s 5

### 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 baseline IFL



### O OO Equation 1

where O IFL 

### is an output value for a given input factor level (IFL), O baseline 

### 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

___

____ ∗ rLevel InputFacto inChange Percentage Value OutputinChange Percentage 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

in Parameter X 

# Legend

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 vari ations 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.

A Q U A T E R R A C o n s u l t a n t s 8

### 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 temper ature 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 ge nerally 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 applicat ion 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

A Q U A T E R R A C o n s u l t a n t s 9

### 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 so il 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.

A Q U A T E R R A C o n s u l t a n t s 10

### 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) PCPScenarios

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) PCPScenarios

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) PCPScenarios

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) PCPScenarios

Percent difference from base scenario (%)



### Figure 

### 3. 4: Sensitivity of sediment load to the four input factors

A Q U A T E R R A C o n s u l t a n t s 12

### 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) TMPScenarios

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) AFEffScenarios

Percent difference from base scenario (%)



### Figure 

### 3. 6: Sensitivity of plant N uptake to the four input factors.

A Q U A T E R R A C o n s u l t a n t s 13P 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) TMPScenarios

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) TMPScenarios

Percent difference from base scenario (%)



### Figure 

### 3. 8: Sensitivity of plant P uptake to the four input factors

A Q U A T E R R A C o n s u l t a n t s 14Corn 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) TMPScenarios

Percent difference from base scenario (%)



### Figure 

### 3. 9: Sensitivity of corn yield to the four input factors.

A Q U A T E R R A C o n s u l t a n t s 15

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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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