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, 2010CONTENTS

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

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

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 sedim ent outflows and streamflows ............. 14

A Q U A T E R R A C o n s u l t a n t s iiiSECTION 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). Th is 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 select ed as the case study watershed.

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

A Q U A T E R R A C o n s u l t a n t s 1This 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 encom passes an area of roughly 2.3 m illion 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 07 100007, contains the Mi ddle 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).

A Q U A T E R R A C o n s u l t a n t s 2Figure 1.2 Raccoon River watershed showing impaired stream segments (orange highlights) and biorefinery locations

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

A Q U A T E R R A C o n s u l t a n t s 4A Q U A T E R R A C o n s u l t a n t s 5 Figure 1.3 Observed flow and water quality stations in the Raccoon River watershed, with

Redfield and Van Meter calibration sites identified Van Meter RedfieldSECTION 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 th at watershed characteri stics 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 file s also means that the same Hy drologic 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 sa me 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.

A Q U A T E R R A C o n s u l t a n t s 6Table 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).

A Q U A T E R R A C o n s u l t a n t s 7Table 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

A Q U A T E R R A C o n s u l t a n t s 8The 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 R 2 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 R 2 , low RMSE, and the NS values close to unity. Conversely, sedime nt 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 experi mental errors th at 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.

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

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

A Q U A T E R R A C o n s u l t a n t s 11Table 2.3 : Model calibration statistics for monthly and yearly simulation results

Monthly R 2 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 R 2 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

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

A Q U A T E R R A C o n s u l t a n t s 13included 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 contri buted 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

A Q U A T E R R A C o n s u l t a n t s 14The 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.

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

A Q U A T E R R A C o n s u l t a n t s 16