Environmental Assessment ~~~ Modeling ~~~ Water Resources 

Model Calibration of Water Quality Impacts of Corn Production 

for Ethanol in the Raccoon River Basin 

Prepared by 

AQUA TERRA Consultants 

Mountain View, CA 94043 

Decatur, GA 30030 

With 

Texas A & M University 

Spatial Sciences Laboratory (TAMU-SSL) 

College Station, TX 77843 

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 13, 2010 
CONTENTS 

 Page 

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

Tables ................................................................................................................................ iv 

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

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

 1.2 Study Area......................................................................................................... 2 

 1.3 Selection Criteria............................................................................................... 2 

2.0 Model Setup and Calibration...................................................................................... 6 

 2.1 Model Setup ...................................................................................................... 6

 2.2 Observed Data .................................................................................................. 6 

 2.3 Model Calibration .............................................................................................. 7 

3.0 Model Results.............................................................................................................. 13 

 3.1 Baseline and Future Scenario Development..................................................... 13 

 3.2 Scenario Results ............................................................................................... 13 

 3.3 Summary........................................................................................................... 15 

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

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FIGURES 

Figure 1.1 The Raccoon River watershed and its location within the UMRB .................. 1 

Figure 1.2 Raccoon River watershed showing impaired stream segments (orange 

highlights) and biorefinery locations............................................................... 3 

Figure 1.3 Observed flow and water quality stations in the Raccoon River watershed, with 

Redfield and Van Meter calibration sites identified ........................................ 5 

Figure 2.1 Simulated vs observed annual streamflow rate (cfs), total nitrogen loading rate 

(tons/d), total phosphorous loading rate (tons/d), sediment loading rate 

(tons/d) ........................................................................................................... 10 

Figure 2.2 Simulated vs observed monthly streamflow rate (cfs), total nitrogen loading 

rate (tons/d), total phosphorous loading rate (tons/d), sediment loading rate 

(tons/d) ........................................................................................................... 11 

TABLES 

Table 2.1 Summary of observed data using in the Raccoon River model ..................... 7 

Table 2.2 SWAT parameter calibration adjustments in the Raccoon River model......... 8 

Table 2.3 Model calibration statistics for monthly and yearly simulation results ............ 12 

Table 3.1 Corn yield, corn area, and ethanol produced for Raccoon River Scenarios .. 13 

Table 3.2 Comparison of average annual total nitrogen loads....................................... 14 

Table 3.3 Comparison of average annual total phosphorous loads ............................... 14 

Table 3.4 Comparison of average annual sediment outflows and streamflows ............. 14 

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SECTION 1.0 

INTRODUCTION 

1.1 BACKGROUND AND OBJECTIVES

The production of biofuels has become an important industry in the US and has seen a dramatic 

expansion in production capacity, with continued growth expected. In 2006, the U.S. produced 

approximately 4.9 billion gallons of ethanol, mostly from corn kernels. Of the potential crops for 

biofuel production, corn has the highest rates of application of fertilizers and pesticides, leading to 

the concern that higher corn production could lead to increased loading of nutrients, pesticides, and 

sediment to waterways, including major rivers and estuaries. 

Previous efforts on these work assignments focused on modeling and assessing the potential 

impacts on surface waters resulting from the expected increase in corn production in the Upper 

Mississippi River Basin (UMRB). This study sought to continue that effort by performing a more 

detailed analysis of a distinct watershed within the UMRB. As shown in Figure 1.1, the Raccoon 

River in central Iowa was selected as the case study watershed. 

 

Figure 1.1 The Raccoon River watershed and its location within the UMRB

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This work is a follow-on effort to a previous study described in the report Modeling Water Quality 

Impacts of Corn Production for Ethanol in the Upper Mississippi River Basin (US EPA, 2008). 

Much of the background information for this study (model set-up, scenario development, etc.) is 

found in that report and is frequently referenced in this one. 

1.2 STUDY AREA

The Raccoon River watershed encompasses an area of roughly 2.3 million acres, or 3625 square 

miles, in central Iowa. It is comprised of two 8-digit Hydrologic Unit Codes (HUCs). The northern 

portion, HUC 07100006, contains the North Raccoon branch and the main Raccoon River. The 

southern portion, HUC 07100007, contains the Middle and South branches which flow into the 

main Raccoon River at the HUC outlet. The Raccoon watershed drains out of the northern HUC at 

the river’s confluence with the Des Moines River at the city of Des Moines, Iowa. The watershed 

includes most of Calhoun, Carrol, Greene, and Guthrie counties along with portions of Buena Vista, 

Pocahontas, Sac, Webster, Dallas and Polk counties. 

The area is dominated by agriculture (nearly 88%), with 42% of the watershed producing corn and 

33% producing soybeans, mostly in rotation. A smaller fraction (13%) of the watershed is made up 

of other agricultural land uses including hay and row crops. Urban areas make up roughly 8% of 

the watershed, with most of it in and around the city of Des Moines near the watershed’s outlet. 

Forest (2%) and water/wetlands (2%) make up the remaining portions of the Raccoon River 

watershed. 

1.3 SELECTION CRITERIA 

In selecting the case study watershed, a variety of attributes were desired. These included: 

• A percentage of corn area representative of the UMRB 

• Stream segments included in the 303d list of impaired waterways due to high nutrient 

levels 

• Biorefinery plants 

• Drinking water intakes 

• Observed streamflow and water quality data 

As described here, the Raccoon River is well-suited to meet these requirements. 

The Raccoon River watershed contains a substantial amount (42%) of corn area. Though this 

percentage is toward the high end for subwatersheds in the UMRB, it is not the highest, as 

numerous UMRB subwatersheds approach a corn area of 50%. Furthermore, review of the model 

runs for the entire UMRB shows that the projected corn area increase for the Raccoon watershed 

is in line with (< 15% different) the increase projected for the entire UMRB. Finally, the average 

corn yield for the watershed is also representative of the UMRB (< 10% different). 

The Iowa Department of Natural Resources’ 303d list of impaired waters includes numerous 

stream segments from the Raccoon River. Details of these impairments may be found in the Iowa 

DNR TMDL report “Water Quality Improvement Plan for Raccoon River, Iowa”, which can be found 

at http://www.iowadnr.gov/water/watershed/tmdl/files/final/raccoon08tmdl.pdf (Schilling and Wolter, 

2008). In particular, the two stream segments, from the confluence of the North and South 

branches to the watershed’s outlet (highlighted in Figure 1.2), were listed in 2006 for having >25% 

of samples exceed the maximum contaminant limit (MCL) for nitrate concentration 

(http://www.igsb.uiowa.edu/wqm/wqa/303d.html#2006) (Iowa Department of Natural Resources, 

2006). 

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Figure 1.2 Raccoon River watershed showing impaired stream segments (orange highlights) 

and biorefinery locations 

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Additionally, four instances of exceeding allowable nitrate concentration were reported between 

2000 and 2002, causing a stream segment (also highlighted in Figure 1.2 on the Middle Raccoon 

branch) to be added to the 303d list. According to Iowa’s 303d list for 2008 

(http://www.igsb.uiowa.edu/wqm/wqa/303d.html#2008) (Iowa Department of Natural Resources, 

2008), these segments remain on the list due to continued exceedance of nitrate limits. 

As shown in Figure 1.2, the Raccoon River watershed contains numerous biorefinery plants spread 

throughout the area. Biorefinery locations shown here were taken from Renewable Fuel 

Association’s table of Ethanol Biorefinery Locations (http://www.ethanolrfa.org/industry/locations/) 

(Renewable Fuels Association, 2008). 

As part of the UMRB and by itself, the Raccoon River has been the focus of numerous modeling 

studies. Also, there is a substantial amount of observed data throughout the watershed collected 

mostly by the U.S. Geological Survey (USGS) (see Figure 1.3). A description of the observed data 

used in this effort is found in Section 2.2. 

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Figure 1.3 Observed flow and water quality stations in the Raccoon River watershed, with 

Redfield and Van Meter calibration sites identified 

Van Meter 

Redfield 
SECTION 2.0 

SETUP AND CALIBRATION

2.1 MODEL SETUP

Initial setup of the Raccoon River model was performed using the same SWAT model parameter 

set as the UMRB model previously developed. A distinct model of the Raccoon River was 

developed by extracting the SWAT input files from the UMRB model for the two HUC-8s that 

comprise the Raccoon watershed. This means that watershed characteristics and conditions for 

the Raccoon River model are the same as those in the UMRB, including land use, soil, and slope 

data, tillage and cropping practices, and fertilizer and manure application rates. 

Using the same SWAT input files also means that the same Hydrologic Response Units (HRU) 

used in the UMRB are used in the Raccoon River model. HRUs are the basic building blocks of 

SWAT, where simulation of the processes being modeled is performed. HRUs represent unique 

combinations of land use, soil characteristics, and management within each sub-basin being 

modeled. 

For consistency, other assumptions remained the same from the UMRB model to the Raccoon 

River model. The same meteorological data were applied and the model was run for the same 

period of time, from 1960 to 2001. As in the UMRB model, the Raccoon River model sought to 

model baseline conditions using a target year of 2005. Baseline corn yields were assumed to 

remain the same, thus the average yield of 148.9 bushels per acre (bu/ac) for the Raccoon River 

duplicated the average yield of the same two HUC-8s from the UMRB model. 

2.2 OBSERVED DATA

Along with streamflow and water quality data reported at USGS gaging stations, additional water 

quality data were also available through a long-term water quality study in the Raccoon River at the 

Van Meter USGS gage, as described by Schilling and Lutz (2004). For the purpose of this study, 

model output was only reported at the outlet of each HUC-8. Thus, specific attention was paid to 

the observed data nearest these points, as indicated in Figure 1.3. In the northern HUC, the Van 

Meter gage (05484500) provides the long-term daily stream flow data for the simulation duration 

(1960 to 2001). However, water quality data are only available on a sparse set of dates between 

1975 and 1993. In the southern HUC, the Redfield gage (05484000) also provides long-term daily 

streamflow data for the simulation duration, but no water quality data are available at this station. 

Data available for use in this study are summarized in Table 2.1 below. 

The weather data (precipitation and temperature) for the simulation duration were derived from the 

long term records at weather stations at Guthrie, IA and Fort Dodge, IA. 

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Table 2.1: Summary of observed data used in the Raccoon River model 

Station ID Station Name Period of Record HUC Drainage Area 

square miles 

05484000 

South 

Raccoon 

River at 

Redfield, IA 

3/4/1940 - present (streamflow) 

No water quality data 07100007 994 

05484500 

North 

Raccoon 

River at Van 

Meter, IA 

10/1/1915 - present (streamflow) 

1975 to 2005 (water quality) 07100006 3441 

2.3 MODEL CALIBRATION

Given the scope and scale of the UMRB modeling effort, comparison of model results to observed 

data was carried out only along the mainstem of the Mississippi River. Calibration was limited to 

crop yield and nutrient uptake values to ensure that yield levels under current and projected future 

conditions were an accurate reflection of planned ethanol production targets for each scenario. 

In this study, a more detailed calibration of the model was conducted. The Raccoon River 

Watershed (comprised of two HUC8 watersheds, i.e. 0700006 and 0700007) was extracted from 

the original UMRB SWAT simulation set up to allow in-depth calibration. Simulated streamflow and 

water quality (including total nitrogen, total phosphorous, and sediment loadings) were calibrated 

against observed data at both monthly and yearly time steps. Streamflow was calibrated in both the 

north and south Raccoon River outlets, at Redfield and Van Meter, respectively. As described 

above, water quality data were only available near the Raccoon River Watershed’s outlet at Van 

Meter, IA. Thus, calibration for nutrient and sediment was carried out only at the north Raccoon 

River outlet at Van Meter, which is the final outlet for the two HUC8 watersheds. 

The calibration process was carried out by varying several SWAT hydrologic calibration 

parameters within their acceptable ranges. Using the original input data and parameterization of 

the UMRB baseline scenario (from Phase I and II of the biofuel project) for the Raccoon 

watersheds, simulation results were compared to observed data. Adjustments to SWAT 

parameters were made to improve on these initial comparisons. Given that most of the SWAT land 

surface parameters were established from databases used to set up the UMRB model, it was not 

deemed practical to include them in the calibration adjustments. Thus, calibration adjustments 

were focused on in-stream parameters as these were simply set to default values during model set 

up. The SWAT parameters shown in Table 3.1 were modified to obtain a closer fit of the simulated 

outputs to the observed data at the two calibration locations (Redfield and Van Meter). 

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Table 2.2: SWAT parameter calibration adjustments in the Raccoon River model 

Parameter Description Default 

Value 

Calibrated 

Value 

SPCON 

Linear parameter for calculating the maximum amount 

of sediment that can be reentrained during channel 

sediment routing (unitless) 

0.008 0.01 

SPEXP Exponent parameter for calculating sediment 

reentrained in channel sediment routing (unitless) 1 1.50 

CH_EROD Channel erodability factor (cm/h/Pa) 0 0.04 

CH_COV Channel cover factor (unitless) 0 0.45 

PRF Peak rate adjustment factor for sediment routing in the 

main channel (unitless) 1.0 0.90 

BC4 Rate constant for mineralization of organic P to 

dissolved P in the reach at 20 C (day-1) 0.35 0.45 

RS5 Organic phosphorous settling rate in the reach at 20 C 

(day -1) 0.01 0.025 

BC1 Rate constant for biological oxidation of NH4 to NO2 in 

the reach at 20 C (day -1) 0.55 0.99 

BC2 Rate constant for biological oxidation of NO2 to NO3 in 

the reach at 20 C (day -1) 1.1 1.15 

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The simulated SWAT model outputs for streamflow (cfs), total nitrogen loading (tons/d), total 

phosphorous loading (tons/d), and sediment loading (tons/d) were compared to the observed data. 

They were compared at both yearly (Figure 3.1) and monthly (Figure 3.2) time steps. The 

comparison statistics are listed in Table 3.2. There are extended gaps in observed data for nitrogen 

and phosphorous loadings for the simulation duration, as well as in the year of 1973 for sediment 

loading data. These gaps in the observed data are shown in Figures 3.1 and 3.2 where only 

simulated values are displayed. 

It is evident that the model simulation generally follows the observed trend in the four outputs as 

shown by the relatively high R2

 values. However, for nutrient and sediment loading rates, there are 

large discrepancies from the observed (low Nash-Sutcliffe [NS], high Percent Bias [PB], and high 

Root Mean Square Error [RMSE]). 

Streamflow was best simulated by the SWAT model as evidenced by the high R2

, low RMSE, and 

the NS values close to unity. Conversely, sediment load showed the least fit between simulated 

and observed data. Large gaps in the observed monthly nutrient and sediment loading data made 

the calibration effort especially challenging. From Figure 3.1, the model underpredicts nutrient and 

sediment loads most noticeably during the years of 1978 and 1979. The model significantly 

underpredicts sediment loading in 1998 and 1999. The monthly results (Figure 3.2) seem to 

indicate that the large discrepancy between the simulated and the observed data concentrated in a 

few months during those years. For example, in March and April of 1978, the model significantly 

underpredicts nutrients and sediment loading, so is true in March of 1979, June of 1998, and July 

of 1999. It is noted that there is 8% urban land with some point sources, the model has not 

considered any of the point sources as well as any animal production sites such as lagoon and 

manure management in an explicit manner. In addition, atmospheric load is not explicitly 

considered. These could in part explain the under-prediction by the model. Finally, the observed 

data were obtained from grab samples and extrapolated to monthly load using statistical 

procedures (Schilling and Lutz, 2004), which can also introduce experimental errors that contribute 

to the discrepancy between the simulated and observed loads. 

This calibration effort indicates that the Raccoon River SWAT model satisfactorily simulates the 

observed streamflow dynamics in long-term simulations. However, nutrient and sediment loading 

simulations would benefit from more intensive calibration. 

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Figure 2.1: Simulated vs observed annual streamflow rate (cfs), total nitrogen loading rate 

(tons/d), total phosphorous loading rate (tons/d), sediment loading rate (tons/d). 

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Figure 2.2: Simulated vs observed monthly streamflow rate (cfs), total nitrogen loading rate 

(tons/d), total phosphorous loading rate (tons/d), sediment loading rate (tons/d). 

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Table 2.3: Model calibration statistics for monthly and yearly simulation results 

Monthly R2 NS PB RMSE 

Streamflow 0.886 0.998 15.8 37 

Total N Load 0.765 0.646 41.1 42447 

Total P Load 0.604 0.315 45.1 9179 

Sediment 0.587 0.354 24.3 9509 

Yearly R2 NS PB RMSE 

Streamflow 0.934 1.000 16.5 17 

Total N Load 0.934 0.472 39.8 24657 

Total P Load 0.903 0.485 37.4 2224 

Sediment 0.398 0.069 24.4 3880 

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SECTION 3.0 

MODEL RESULTS

This section first describes the development of the baseline conditions and the model scenario 

changes for the increased ethanol production targets for future time horizons. Following that is 

a comparison and discussion of the results for the baseline and future scenario model runs. 

3.1 BASELINE AND FUTURE SCENARIO DEVELOPMENT

A primary objective of this study was to perform a more detailed analysis than the original study on 

a subwatershed with the UMRB. To coordinate the two efforts, the baseline and future scenario 

conditions from the original study were applied to this study. A summary of corn yields, corn areas, 

and ethanol production for the model scenarios that were run is shown in Table 3.1 below. 

Further details about the development of the baseline and future scenarios can be found in the 

original UMRB study report (US EPA, 2008). However, it is important to note some of the key 

assumptions built into the scenarios: 

• A 1.23% annual corn yield increase, based on USDA projections (USDA, 2008) 

• 12% of existing (baseline) corn is used for ethanol production 

• 100% of added corn in future scenarios is used for ethanol production 

• A moisture content of 20% when converting corn grain mass to bushels 

• A conversion factor of 2.7 gallons per bushel (G/bu) when estimating ethanol production 

Table 3.1: Corn yield, corn area, and ethanol produced for Raccoon River scenarios 

Corn Area, ac Scenario Average Yield, 

bu/ac Total Ethanol 

Ethanol 

Produced, 

10^6 Gallons/Yr 

Baseline 148.9 971,600 116,600 46.9 

2010 157.7 1,320,000 464,900 197.9 

2015 165.4 1,399,000 543,900 242.9 

2020 176.6 1,367,500 512,500 244.4 

2022 180.1 1,349,000 492,900 239.7 

3.2 SCENARIO RESULTS

The Raccoon River model was executed for the available input climate record, 1960-2001, with the 

model run under conditions of the increased corn production and yields noted above. Separate 

model runs were performed for each of the four projection years, and the model results were 

analyzed for comparison across the scenarios. In viewing these results note that the “Unit Land 

Load” is the rate at which the nutrient entered the waterways and the “Total Land Load” is the total 

amount of nutrient that entered the waterways. The “Total Outflow” is the total amount of nutrient 

that left the UMRB, with the difference between it and the “Total Land Load” being used to 

determine the percentage of nutrient “Removed by Assimilation”. Load and outflow values are 

from all land uses, not just corn areas. The “Corn Area Increase” and “Outflow Change” 

percentages represent the change from baseline conditions for each year. 

As shown in Table 3.2, all future scenario runs showed increased nitrogen loads. However, 

scenario runs beyond 2010 showed decreasing total nitrogen outflow with the 2022 scenario 

showing less than a 2% increase over the baseline. A detailed analysis of the scenario results 

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included review and analysis of nitrogen application and plant uptake for all scenarios. This 

showed that the decreasing of nitrogen loads can be attributed to the increased yield production 

of future scenarios, resulting in greater plant uptake of nitrogen. In addition, as noted above, 

less corn acreage was needed for the 2020 and 2022 scenarios due to the increased yield 

values. 

Table 3.2: Comparison of average annual total nitrogen loads 

Scenario 

Corn 

Area, 

million ac 

Unit 

Land 

Load, 

lbs/ac 

Total Land 

Load, 

million lbs 

Total 

Outflow, 

million 

lbs 

Removed 

by 

Assimilation, 

% 

Corn Area 

Increase, 

% 

Outflow 

Change, 

% 

Baseline 0.971 13.80 32.0 24.1 24.9

2010 1.320 15.06 35.0 26.3 24.8 35.9 9.1

2015 1.399 14.93 34.7 26.1 24.8 44.0 8.3

2020 1.368 14.36 33.3 25.1 24.8 40.7 4.2

2022 1.349 14.16 32.9 24.7 24.8 38.8 2.5

As shown in Table 3.3 all future scenario runs showed decreased phosphorous loads. One 

reason for this is that phosphorous is sediment-associated and the future scenario runs also 

simulated decreased sediment loads (see Table 3.4). A second reason is the same issues seen 

in decreasing nitrogen loads (increase in plant uptake and decrease in corn area over time) 

applying to phosphorous results. 

Table 3.3: Comparison of average annual total phosphorous loads 

Scenario 

Corn 

Area, 

million ac 

Unit 

Load, 

lbs/ac 

Total 

Load, 

million lbs 

Outflow, 

million 

lbs 

Removed 

by 

Assimilation, 

% 

Corn Area 

Increase, 

% 

Outflow 

Change, 

% 

Baseline 0.971 0.918 2.13 1.73 19

2010 1.320 0.872 2.02 1.64 19.2 35.9 -5.2

2015 1.399 0.844 1.96 1.58 19.4 44.0 -8.7

2020 1.368 0.846 1.96 1.58 19.4 40.7 -8.7

2022 1.349 0.849 1.97 1.59 24.7 38.8 -8.1

Table 3.4 shows results for both flow and sediment, with flow showing relatively small 

decreases from baseline conditions and sediment showing more significant decreases. This is 

primarily due to the corn being modeled as a well-managed crop in terms of sediment loss, 

resulting in the corn stover remaining on the fields following harvest and reducing erosion loads. 

As mentioned above, this decrease also contributed to the decreased phosphorous loads in 

future scenarios. 

Table 3.4: Comparison of average annual sediment outflows and streamflows 

Scenario Corn Area, 

Million ac 

Sediment, 

Million tons

Flow, 

cfs 

Sediment 

Change, %

Flow 

Change, % 

Baseline 0.971 1.016 1665

2010 1.320 1.005 1664 -1.1 -0.1 

2015 1.399 0.994 1656 -2.1 -0.5 

2020 1.368 0.993 1649 -2.2 -1.0 

2022 1.349 0.994 1648 -2.1 -1.0 

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The decrease in sediment and phosphorous loads for future runs in the Raccoon River model is 

noteworthy given that Phase I/II results showed increases in the corresponding loads for the 

entire UMRB. A review of the Phase I/II results was made to assess the frequency at which 

UMRB subwatersheds (i.e. HUC-8s) showed decreases in sediment and phosphorous loads. 

Initial analysis revealed roughly one-third of the UMRB subwatersheds exhibited a decrease in 

phosphorous load and slightly more than one half of the subwatersheds exhibited a decrease in 

sediment load. These subwatersheds contained a significant fraction of corn area (>25%), 

though it is likely that other factors contributed to these results. This issue will be analyzed and 

discussed further in the Phase III/IV scenario report. 

3.3 SUMMARY 

The Raccoon River SWAT model results for the baseline biofuel scenario (representing production 

demand of 2003 to 2006) were further examined and improved through a calibration process. 

Observed long-term streamflow, nutrient (total nitrogen and total phosphorus loading), and 

sediment data were compared to the simulation results at both monthly and annual time steps. 

Streamflow values were in excellent agreement with the observed data while nutrient and sediment 

results did not agree with the observed data nearly as well. There were several extremely large 

nutrient and sediment loading events during the early and late periods of the simulation where 

streamflow was not excessively large. A more intensive calibration of the nutrient and sediment 

loads would be beneficial to the Raccoon River model. This could be implemented by refining the 

scale of the model, resulting in a greater number of subwatersheds. This would then allow for a 

greater number of calibrations sites, making use of the numerous observed data locations spread 

throughout the study area. 

Using the revised parameter set from the baseline calibration, the Raccoon River SWAT model 

was then run for the same future ethanol goals as the original UMRB study. As in the original 

study, nitrogen loads increased for the future scenario runs, though at a greater rate. Future 

phosphorous loads decreased in the Raccoon River model, where they had shown minor 

increases in the UMRB model. As in the UMRB model, there was minimal change in streamflow. 

However, there was a greater decrease in sediment load in the Raccoon River model than in the 

UMRB. This greater decrease is the likely cause for the change from increase to decrease in 

phosphorous loading.

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SECTION 4.0 

REFERENCES 

Iowa Department of Natural Resources: Iowa section 303(d) Impaired Waters Listings (2006). 

Accessed January 11, 2009, from Iowa DNR web site: 

http://www.igsb.uiowa.edu/wqm/wqa/303d.html#2006

Iowa Department of Natural Resources: Iowa section 303(d) Impaired Waters Listings (2008). 

Accessed January 11, 2009, from Iowa DNR web site: 

http://www.igsb.uiowa.edu/wqm/wqa/303d.html#2008

Renewable Fuels Association: Biorefinery Locations (2008). Accessed April, 2008, from 

Renewable Fuels Association web site: http://www.ethanolrfa.org/industry/locations/

Schilling, K.E. and Lutz D.S. 2004. Relation of nitrate concentrations and baseflow in the 

Raccoon River, Iowa. Journal of the American Water Resources Association. 40:889-900 

Schilling, K.E. and Wolter C.F. 2008. Water Quality Improvement Plan for Raccoon River, Iowa: 

Total Maximum Daily Load for Nitrate and Escherichia coli. Iowa Department of Natural 

Resources Watershed Improvement Section. Available at: 

http://www.iowadnr.gov/water/watershed/tmdl/files/final/raccoon08tmdl.pdf

USDA, 2008. USDA Projections to 2017. OCE-2008-1. U.S. Department of Agriculture, Office 

of the Chief Economist, World Agricultural Outlook Board. Available at: 

http://www.ers.usda.gov/Publications/OCE081/ 

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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