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Wabash River Watershed Water Quality Trading Feasibility Study

Final Report

September 2011

Prepared for U.S. Environmental Protection Agency Targeted Watershed Grant WS-00E71501-0

Prepared by Conservation Technology Information Center

With support from Tetra Tech, Inc. Kieser & Associates, LLC

CTIC would like to thank our project partners for their support:

Agri Drain Corporation Duke Energy Indiana Association of Soil and Water Conservation Districts Indiana Farm Bureau Indiana Soybean Alliance Purdue University Extension Contents

1. Introduction ...... 1 1.1 What is a WQT Market Feasibility Analysis? ...... 1 1.2 What is the Purpose of the Wabash River Watershed WQT Market Feasibility Analysis? ...... 2 1.3 What Does This Report Contain? ...... 3 2. Understanding the Wabash River Watershed ...... 3 2.1 Relationship of the Wabash River Watershed to the Gulf of Mexico ...... 3 2.2 Overview of Nutrient Sources and Loadings in the Wabash River Watershed ...... 4 3. Feasibility Analysis Summary ...... 5 3.1 Drivers and Incentives for Trading ...... 5 3.2 Suitable Pollutants for Trading ...... 8 3.3 Watershed Considerations ...... 10 3.4g Timin ...... 10 3.5 Geographic Scope for Trading Analysis ...... 11 3.6 Potential Credit Buyers and Sellers ...... 15 3.7 Potential Credit Demand ...... 20 3.8 Potential Credit Supply ...... 43 3.9 Potential stakeholder participation ...... 58 4. Putting It All Together – Market Analysis and Trading Considerations ...... 60 4.1 Pollutant Loads ...... 60 4.2 Regulatory Drivers ...... 60 4.3 Trade Ratios ...... 60 4.4 Baselines ...... 71 4.5 Supply Side Credit Generation ...... 73 4.6 Differences in Control Costs ...... 78 4.7 Other Trading Considerations ...... 85 5. Next Steps for Water Quality Trading in the Wabash River Watershed ...... 91 5.1 Outreach and Education ...... 91 5.2 Prioritized Subwatershed for Future Analysis ...... 92 5.3 Trading Program Frameworks ...... 93 5.4 Conclusion ...... 94 Appendix A: Letter to Illinois EPA from USEPA ...... 95 Appendix B: Wabash River Watershed TMDL Reduction Summaries and Wasteload Allocations (WLAs) ...... Appendix C: Characterization of Wabash River Nutrient Loads ...... Appendix D: Compilation of Nonpoint Source Analysis Technical Memos ...... Appendix E: Point Source Survey Results ...... Cited References ......

Final Report – September 2011 Page i Tables Table 1. Nutrient Breakpoints by Ecoregion Under Consideration by IDEM (from Selvaratnam, S. and J. Frey 2011) ...... 6 Table 2. Summary of Nutrient Criteria Development Progress for U.S. EPA, Ohio, Minnesota, and Wisconsin ...... 7 Table 3. Eight‐digit HUCs in the Wabash River Watershed across Indiana and Illinois...... 14 Table 4. Number of facilities with NPDES permits in each 8 digit HUC of the Wabash River watershed ...... 15 Table 5. Tippecanoe County’s 2007 Conservation Tillage Data1...... 19 Table 6. Estimated existing nutrient loads from permitted NPDES facilities in the Wabash River watershed...... 20 Table 7. Changes in pollutant loads and resulting credit demand under different TN permit effluent scenarios ...... 23 Table 8. Changes in pollutant loads and resulting credit demand under different TP permit effluent scenarios ...... 26 Table 9. Summary of CWNS information for all facilities in the Wabash River watershed...... 29 Table 10. Indicators associated with advanced treatment facilities in the Wabash River watershed...... 29 Table 11. Summary of treatment type by HUC...... 30 Table 12. Summary of 2008 CWNS permit information for facilities in the Tippecanoe and Driftwood watersheds...... 31 Table 13. Permit limit summaries for facilities in the Driftwood and Tippecanoe watersheds...... 32 Table 14. Summary of facility type and flows for WWTPs included in Driftwood and Tippecanoe nutrient removal analysis ...... 33 Table 15. Summary of ENR treatment levels and assumptions for WWTP upgrade simulations ...... 34 Table 16. Color coding of ENR treatment levels for Tables 17‐23 ...... 35 Table 17. 0.05 MGD activated sludge ENR upgrade options and costs ...... 36 Table 18. 0.05 MGD lagoon ENR upgrade options and costs ...... 36 Table 19. 0.3 MGD activated sludge ENR upgrade options and costs ...... 37 Table 20. 0.3 MGD lagoon ENR upgrade options and costs ...... 38 Table 21. 0.75 MGD activated sludge ENR upgrade options and costs ...... 39 Table 22. 2.5 MGD trickling filter ENR upgrade options and costs ...... 40 Table 23. 5 MGD activated sludge ENR upgrade options and costs ...... 42 Table 24. Total Nitrogen Exported at the Mouth of 8‐Digit HUC Watersheds, Independent of Upstream Watershed Loading (USGS, 1997)...... 44 Table 25. Total Phosphorus Exported at the Mouth of 8‐Digit HUC Watersheds, Independent of Upstream Watershed Loading (USGS, 1997)...... 45 Table 26. SPARROW model estimates of agricultural NPS loading ...... 46 Table 27. General Guidelines for Interpreting NO3‐N Concentrations in Tile Drainage Water1. (Purdue University, 2005) ...... 50 Table 28. Filterstrip Treatment Efficiency Results at the Subwatershed Scale, in Percent, Driftwood Watershed...... 51 Table 29. Filterstrip Treatment Efficiency Results at the Subwatershed Scale, in Percent, Tippecanoe Watershed...... 51 Table 30. Filterstrip Treatment Efficiency Results in Percent, Field Scale Results, Driftwood Watershed...... 52

Final Report – September 2011 Page ii Table 31. Cover Crop Treatment Efficiency Results in Percent at the Subwatershed level, Driftwood Watershed...... 53 Table 32. Cover Crop Treatment Efficiency Results in Percent at the Subwatershed level, Tippecanoe Watershed...... 53 Table 33. Cover Crop Treatment Efficiency Results in Percent, Driftwood Subwatersheds...... 54 Table 34. Cover Crop Treatment Efficiency Results in Percent, Tippecanoe Subwatersheds...... 54 Table 35. No‐till Residue Management Treatment Efficiency Percents at the Subwatershed level, Driftwood Watershed...... 55 Table 36. No‐till Residue Management Treatment Efficiency Percents at the Subwatershed level, Tippecanoe Watershed...... 55 Table 37. No‐till Residue Management Treatment Efficiency Results in Percent, Driftwood Watershed...... 56 Table 38. Annual Nutrient Load Reduction Potential, Wabash River Watershed 8‐digit HUC Subwatersheds. (Assuming a 10 and 25 Percent Participation of Agricultural Row Cropped Acres, 20 Percent Reductions, and 40 lbs TN/acre and 3 lbs TP/acre Loading Rates.) ...... 57 Table 39. Estimates of phosphorus bioavialability fractions for specific source categories...... 65 Table 40. Current and Potential Future Point and Nonpoint Source Baselines in the Wabash River watershed ...... 71 Table 41. 2011 Indiana EQIP General Eligible Practices ...... 75 Table 42. Summary of Credit Production per Acre of BMP ...... 75 Table 43. Summary of BMP Credit Production and Annualized Life Cycle Cost per Acre ...... 76 Table 44. Summary of Annualized Life Cycle Cost per Credit: Filter Strips (Prime Land) ...... 76 Table 45. Summary of Annualized Life Cycle Cost per Credit: Filter Strips (Marginal Land) ...... 77 Table 46. Summary of Annualized Life Cycle Cost per Credit: Cover Crops ...... 77 Table 47. Summary of Annualized Life Cycle Cost per Credit: Residue Management ...... 78 Table 48. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Small (<.3 MGD) Facilities by Treatment Level ...... 79 Table 49. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Medium (.3 MGD – 5 MGD) Facilities by Treatment Level ...... 79 Table 50. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Large (>5 MGD) Facilities by Treatment Level ...... 80 Table 51. Resulting demand and supply factoring in estimated cost margins for each TP permit effluent scenario ...... 81 Table 52. Resulting demand and supply factoring in cost margins for each TN permit effluent scenario ...... 83 Figures Figure 1. Location of the Wabash River watershed in relation to the MARB and the hypoxic zone in the Gulf of Mexico...... 4 Figure 2. Location and size of major reservoirs located in the Wabash River watershed ...... 13 Figure 3. Location of karst features in the Wabash River watershed ...... 13 Figure 4. NPDES permitted facilities by size and subwatershed in the Wabash River watershed...... 16 Figure 5. 2009 Landuse Map of the Wabash Watershed (MRLC, 2009) ...... 18 Figure 6. Number of Beef Cattle per Subwatershed within the Wabash‐Patoka Watershed...... 47 Figure 7. Number of Dairy Animals per Subwatershed within the Wabash‐Patoka watershed...... 48

Final Report – September 2011 Page iii

Final Report – September 2011 Page iv 1. Introduction In 2008, the U.S. Environmental Protection Agency (EPA) awarded a Targeted Watershed Grant to the Conservation Technology Information Center (CTIC) to conduct a water quality trading (WQT) market feasibility analysis for the Wabash River watershed. The 2008 Targeted Watershed Program funded ten projects focusing on water quality trading or other market‐based water quality projects to reduce nitrogen, phosphorus, sediment, or other pollutant loadings that cause hypoxia in the Gulf of Mexico. The projects are located in the three Mississippi River sub‐basins with the highest nutrient loads contributing to hypoxia in the Northern Gulf of Mexico: the Ohio River, the Upper Mississippi River, and the Lower Mississippi River. The Wabash River watershed is a major tributary to the Ohio River. CTIC partnered with Kieser & Associates, LLC and Tetra Tech, Inc. (Tt) to conduct a market feasibility analysis to determine if the necessary conditions exist in the Wabash River watershed to support the development and implementation of a viable, sustainable water quality trading program involving agricultural nonpoint sources and permitted point sources. This report summarizes the approach and the findings of the Wabash River Watershed WQT market feasibility analysis.

1.1 What is a WQT Market Feasibility Analysis? Although there might be an interest to conduct WQT in a particular watershed, certain factors need to be present to make it a viable, sustainable program. A WQT feasibility analysis is a process for collecting and analyzing the data and information needed to determine if the technical and economic factors exist to support trading between potential sources. The very basic factors needed to support WQT are as follows: • Well‐defined sources and amounts of pollution. WQT requires an understanding of pollutant sources. In the case of the Wabash River watershed, nitrogen and phosphorus are the pollutants of concern. Sources generating nutrient loads are potential buyers and sellers in a water quality trading approach. Collecting information to characterize the type of sources and the associated nutrient loads from each source help to determine if there will be an adequate supply and demand for tradable credits. • Regulatory drivers and incentives. Without regulatory drivers or some type of incentive, sources wouldn’t feel compelled to consider and, ultimately, participate in WQT. The most compelling drivers for WQT are those related to regulatory requirements. In most cases, this is a more stringent permit effluent limit based on a more stringent water quality standard. In other cases, it could be a watershed pollutant reduction goal that might not have a regulatory component, but provides other type of incentives to meet this goal (e.g., avoidance of a total maximum daily load). • Difference in control costs among sources. Sources with high pollutant control costs will have an economic motivation to seek out tradable credits from other sources that are able to control pollutants to meet requirements at a lower cost. It is this difference in control costs among sources that will determine which sources might participate as buyers and which sources might have the ability to participate as sellers. WQT feasibility is largely driven by economics, both actual and perceived costs (e.g., transaction costs and risk factors).

Final Report – September 2011 Page 1 A WQT market feasibility analysis has two components: 1) a pollutant suitability analysis and 2) an economic suitability analysis. • The pollutant suitability analysis includes information on pollutant type and form, geographic scope, potential buyers and sellers, potential water quality trading credit supply and demand, potential trade ratios to account for pollutant fate and transport as well as uncertainty, issues related to avoiding localized areas of excessive pollutant loading (i.e., hotspots), and duration of water quality trading credits. • The economic suitability analysis includes information on potential buyers’ willingness‐to‐pay for water quality credits, potential sellers’ price for generating water quality credits, effect of trade ratios on the cost of water quality trading credits, and the potential costs of involving stakeholders in designing and implementing a water quality trading program.

Information from each of these components provides insight as to where WQT might encounter barriers in a particular watershed and what type of trading framework might be most appropriate based on the sources with the greatest potential for participation.

A WQT market feasibility analysis is not intended to provide definitive answers about how WQT should work in a particular watershed, only if the conditions are ripe to support such an effort. WQT program design and implementation requires coordination and facilitation with watershed stakeholders to ensure the program integrates well with other efforts. What the product of a WQT market feasibility analysis can do, however, is give watershed stakeholders a starting place and a foundation when moving into the design phase. The analysis can also identify where watershed stakeholders will potentially have to do additional research to obtain detailed, watershed‐specific information that could affect WQT success. This might mean holding focus groups with point sources and nonpoint sources to better understand attitudes, perceptions, and concerns. It might also mean public meetings with watershed residents and organizations that have perceptions and opinions about how to meet water quality goals.

1.2 What is the Purpose of the Wabash River Watershed WQT Market Feasibility Analysis? The purpose of the WQT market feasibility analysis for the Wabash River watershed is to conduct a preliminary assessment for the potential of viable, sustainable trading to meet water quality goals. This analysis is an initial assessment focused on using mostly existing data examined through the lens of pollutant suitability and economic suitability. The purpose of this analysis was not to collect new data, but to identify where additional data and information might be needed to support further WQT feasibility assessment activities and future WQT program development. The goal is to create a foundation for future work that, over time, watershed stakeholders contribute to more and more. Ultimately, this WQT market feasibility analysis is intended to characterize the watershed for purposes of trading, identify existing data gaps, and make recommendations about WQT feasibility where the data can support these types of recommendations. Where data are not available, the Project Team has identified next steps and additional data needs to move water quality trading in the Wabash River watershed forward.

Through the Wabash River watershed WQT market feasibility analysis, the Project Team led by CTIC reviewed existing watershed data and information available through ongoing Wabash River watershed and Ohio River basin projects, such as the Wabash River Total Maximum Daily Load (TMDL) and TMDLs for subwatersheds in the Wabash River, such as Limberlost Creek, the Little Wabash River, and the

Final Report – September 2011 Page 2 South Fork Wildcat Creek. Information and data on point sources in the watershed were also obtained from the Indiana Department of Environmental Management (IDEM) and the Illinois Environmental Protection Agency (IEPA), and information from nonpoint source loading estimates were obtained from the U.S. Geological Survey (USGS).

1.3 What Does This Report Contain? The Wabash River watershed WQT market feasibility analysis report contains the following: • Section 1: Understanding the Wabash River Watershed. An overview of the Wabash River watershed as it relates to the Gulf of Mexico hypoxia issue. • Section 2: Feasibility Analysis Summary. This section provides a discussion of the information used in the pollutant suitability and economic suitability analysis – the two components of the overall WQT market feasibility analysis. • Section 3: Putting It All Together: Market Analysis and Trading Considerations. This section synthesizes the information provided in Section Two to provide an analysis of the overall market potential for water quality trading in the Wabash River watershed. This section also addresses other trading considerations that will affect the market. • Section 4: Next Steps for Water Quality Trading in the Wabash River Watershed. This section identifies data needs and additional analyses to move the concept of water quality trading forward in the Wabash River watershed. • Appendices. The appendices to the report include the Wabash River TMDL and detailed technical memos generated by the Project Team to inform different components of the WQT market feasibility analysis process.

2. Understanding the Wabash River Watershed Characterizing the physical, chemical, and biological attributes of a watershed is an important first step in assessing the feasibility of WQT. This section provides a brief overview of the Wabash River watershed as it relates to the Gulf of Mexico hypoxia issue.

2.1 Relationship of the Wabash River Watershed to the Gulf of Mexico The area that drains to the Gulf of Mexico is referred to as the Mississippi‐Atchafalaya River Basin (MARB). This basin drains 1,245,000 square miles across 31 states and is the focus of the efforts to manage nutrients causing hypoxia in the Gulf of Mexico. Within the MARB, the Wabash River watershed covers approximately 33,000 square miles, draining portions of Indiana and Illinois. Figure 1 shows the location of the Wabash River watershed in relation to the MARB that drains to the Gulf of Mexico.

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Figure 1. Location of the Wabash River watershed in relation to the MARB and the hypoxic zone in the Gulf of Mexico.

While the Wabash River watershed is only 2 percent of the total area of the MARB, it delivers a relatively significant nutrient load to the Gulf of Mexico via the Ohio River – one of the three sub‐basins that deliver the highest nutrient loading to the Gulf. According to USGS SPARROW modeling, the Wabash River watershed contributes approximately 9,994 tons of total phosphorus and 139,278 tons of total nitrogen to the Gulf of Mexico each year. Reducing the nutrient load from the Wabash River watershed will contribute to both local water quality improvements and help reduce the load contributing to the Gulf of Mexico’s hypoxic zone.

2.2 Overview of Nutrient Sources and Loadings in the Wabash River Watershed In 2006, the IEPA and IDEM developed the 2006 Wabash River Nutrient and Pathogen TMDL report to address water quality impairments in the Wabash River watershed. Information from Indiana and Illinois 2002, 2004, and 2006 Clean Water Act (CWA) Section 303(d) listings demonstrate that several segments in the Wabash River watershed are impaired for nutrients, among other pollutants. Review of the data and comparison to state nutrient targets led to a determination that nutrient TMDLs should be developed for all segments of the Wabash River from the Indiana/Ohio state line to the confluence of the Wabash and Vermilion Rivers.

The TMDL addresses nutrient sources that discharge directly to the mainstem of the Wabash River, including point sources permitted under the National Pollutant Discharge Elimination System (NPDES)

Final Report – September 2011 Page 4 program, subwatersheds, and significant tributaries. Permitted NPDES point sources identified in the TMDL include industrial facilities, power plants, wastewater treatment facilities, municipal separate storm sewer systems (MS4s), combined sewer overflows, and confined animal feeding operations (CAFOs).

Although a detailed source analysis was not conducted for the subwatersheds, the TMDL does provide an analysis of land use/land cover in the subwatersheds and tributaries that drain directly to the Wabash River. Agriculture is the most significant land use in the area that drains directly to the Wabash River (82 percent), as well as the tributaries (53 – 97 percent). The TMDL concluded that nonpoint sources, including agriculture, contribute the largest loads of TP and nitrate to the Wabash, but that point sources can have an important impact during low flow periods. The WWTP loads therefore need to be reduced to meet the in‐stream 0.30 mg/L benchmark.

In summary, the Wabash River watershed contains several segments that are impaired for nutrients due to a variety of point and nonpoint sources. This basic understanding of water quality issues in the Wabash River watershed provides a basic foundation for a more in‐depth water quality trading market feasibility analysis, presented in Section Two.

3. Feasibility Analysis Summary This section summarizes the information compiled through the pollutant suitability analysis and the economic suitability analysis. This information provides the basis for the water quality trading market analysis and considerations presented in Section Three.

3.1 Drivers and Incentives for Trading Sources considering WQT generally do so because of more stringent permit effluent limits resulting from changes to water quality standards or implementation of wasteload allocations (WLAs) in approved TMDLs. These are considered regulatory drivers for WQT. In addition to regulatory drivers, sources might consider WQT as a result of other incentives, such as the desire to avoid more stringent permit limits before the development of a TMDL or to meet a pollutant reduction goal established through a watershed management plan. A brief discussion of regulatory drivers and other incentives in the Wabash River watershed is provided below.

3.1.1 Water Quality Standards State’s water quality standards drive NPDES permit effluent limits and TMDLs; therefore, water quality standards are the ultimate regulatory driver for WQT. If water quality standards result in NPDES permit effluent limits that pose a technical or financial burden for sources, the standards can serve as a driver for WQT.

To date, Indiana has not adopted numeric water quality criteria for nutrients to protect aquatic life uses. As stated in the 2006 TMDL, Indiana uses draft nutrient benchmarks: • Total phosphorus should not exceed 0.3 mg/L. • Nitrate + nitrite should not exceed 10 mg/L. • Dissolved oxygen should not be below the water quality standard of 4.0 mg/L and should not consistently be close to the standard (i.e., in the range of 4.0 to 5.0 mg/L). Values should also

Final Report – September 2011 Page 5 not be consistently higher than 12 mg/L and average daily values should be at least 5.0 mg/L per calendar day. • No pH values should be less than 6.0 or greater than 9.0. pH should also not be consistently close to the standard (i.e., 8.7 or higher). • Algae growth should not be “excessive” based on field observations by trained staff.

IDEM considers a segment to be impaired for nutrients when two or more of these benchmarks are exceeded based on a review of all recent data. It is anticipated that IDEM will adopt numeric nutrient criteria in the near future. IDEM is working with USGS to develop numeric nutrient criteria that take into account the relationship between stressors and the biological community. Table 1 presents possible numeric nutrient criteria presented by IDEM at the U.S. EPA Nutrient TMDL Workshop in February 2011. The numeric nutrient criteria development effort is still ongoing and these are preliminary values used during the development of the feasibility study.

Table 1. Nutrient Breakpoints by Ecoregion Under Consideration by IDEM (from Selvaratnam, S. and J. Frey 2011) Ecoregion Nutrient Breakpoints Glacial North Central/West Plains Low (oligotrophic) TN < 0.60 mg/L None TP < 0.035 mg/L None High (eutrophic) TN > 1.2 mg/L TN > 1.7 mg/L TP > 0.14 mg/L TP > 0.13 mg/L

Once Indiana adopts numeric nutrient criteria, IDEM will issue NPDES permits with more stringent water quality‐based effluent limits to meet the new criteria. In Illinois, the state has adopted the following criteria: 0.05 mg/L TP for lakes and 10 mg/L TN for rivers. In addition, U.S. EPA has strongly encouraged IEPA to develop additional nutrient criteria.

Progress in developing nutrient criteria in other U.S. EPA Region 5 states provides context for the direction of future nutrient criteria. Table 2 presents a summary of nutrient criteria development progress for EPA (EPA, 2000a, EPA 2000b and EPA, 2000c), Ohio (Ohio EPA, 2011), Minnesota (MPCA, 2010a, 2010b) and Wisconsin (WI DNR, 2010).

Final Report – September 2011 Page 6 Table 2. Summary of Nutrient Criteria Development Progress for U.S. EPA, Ohio, Minnesota, and Wisconsin Authority Status Phosphorus Criteria Nitrogen Criteria EPA Aggregated VI Total VII Total IX Total VI VII IX Level IV Nutrient Guidance Nitrogen Nitrogen Nitrogen 76.25 ug/L 33 ug/L 36.56 ug/L Ecoregions 2.18 ug/L 0.54 ug/L 69 ug/L Ohio EPA Developing TMDL derived Site Specific Water Quality Standards Minnesota South River Central River 4-day chronic toxicity standard for Pollution Control Developing Nutrient Region Nutrient Region Nitrite + Nitrate of 4.9 mg/L Agency 150 ug/L 100 ug/L Wisconsin Promulgated Department of NR 102 Large Rivers Small Rivers

Natural (Phosphorus 100 ug/L 75 ug/L Resources Rule)

3.1.2 NPDES Permit Effluent Limits As stated under the water quality standards discussion, numeric nutrient criteria directly affects NPDES permit limits for nitrogen and phosphorus. To date, IDEM issues NPDES permits that do not contain water quality‐based effluent limits for nutrients. This will change when IDEM adopts numeric nutrient criteria, as discussed above. For now, IDEM requires NPDES permitted facilities to conduct discharge monitoring as a way to generate better data on total phosphorus and total nitrogen concentrations. This information will help IDEM in developing future water quality‐based effluent limits for nutrients to meet impending numeric nutrient criteria.

A recent review of Illinois NPDES permits conducted by U.S. EPA Region 5 showed that reviewed NPDES permits did not contain nutrient effluent limitations. As a result of the findings of this review, U.S. EPA Region 5 issued a letter in January 2011 that directs Illinois EPA to establish nutrient effluent limitations when it makes the determination that a nutrient discharge will cause an excursion beyond Illinois’ existing narrative nutrient criteria. Appendix A contains a copy of the January 2011 letter from U.S. EPA Region 5 to Illinois EPA about the matter of developing nutrient effluent limitations for nutrient discharges from permitted point sources.

3.1.3 2006 Wabash River TMDLs The 2006 Wabash River TMDLs contain WLAs for point sources and load allocations (LAs) for nonpoint sources to address nutrient impairments. The TMDL assigns total phosphorus WLAs to point sources. The TP WLAs have not yet been incorporated into NPDES permits because facilities are first required to conduct monitoring to determine their actual discharge concentrations. For tributaries and subwatersheds draining directly to the Wabash River, the TMDL assigns a 4 percent reduction in phosphorus loads and no reductions in nitrate. The 4 percent phosphorus load reduction under the TMDL might serve as a nonpoint source baseline – the amount a nonpoint source seller has to reduce by before becoming eligible to sell credits to buyers in need of credits. A list of other TMDLs and associated WLAs for Wabash River subwatersheds is found in Appendix B.

Final Report – September 2011 Page 7 3.1.4 Watershed Management Planning IDEM’s Watershed Management Planning Checklist provides watershed organizations with a framework to develop a Section 319 approvable watershed management plan. Several watershed management plans for subwatersheds of the Wabash River watershed are available through IDEM’s watershed management planning website. Many of these plans contain nutrient reduction goals. For example, the watershed management plan for the Upper Tippecanoe River contains a 20 percent nutrient load reduction goal to be achieved by 2010 and the watershed management plan for the Lower Eel River contains a 10 percent nutrient load reduction goal. Not every subwatershed in the Wabash River watershed has an IDEM‐approved watershed management plan, but for those that do, the nutrient load reduction goals established for nonpoint sources can play a role in promoting participation in WQT. In addition, the nutrient load reduction goals quantified in watershed management plans might also serve as a nonpoint source baseline for nonpoint sources in specific watersheds that want to participate in trading as credit sellers.

3.1.5 Gulf Hypoxia Action Plan In 2008, the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force released the 2008 Action Plan – a national strategy to control hypoxia in the Gulf of Mexico and improve water quality in the MARB. The 2008 Action Plan calls for a dual 45 percent reduction in the riverine total nitrogen load and in the riverine total phosphorus load. This dual nutrient reduction loading goal is not an enforceable goal, but it does provide an overarching target for sources in the Mississippi River Basin – which includes the Wabash River watershed – to strive for to help reduce the hypoxic zone in the Gulf of Mexico.

3.1.6 Summary: Drivers and Incentives for Trading In summary, a few point source regulatory drivers for WQT are emerging based on TMDLs. Many waters do not currently have nutrient drivers within the Wabash River watershed and likely will not until IDEM adopts numeric nutrient criteria and IEPA adopts more stringent and encompassing criteria. It is likely that IDEM will adopt numeric nutrient criteria in the near future, which will trigger the need to update existing NPDES permits with water quality‐based effluent limits for nutrients. When permitted facilities are required to comply with new, more stringent nutrient permit limits, there will be a more tangible regulatory driver for WQT in the Wabash River watershed. The WQT market feasibility analysis for the Wabash River watershed makes the assumption that new numeric nutrient criteria will become a reality in Indiana and act as a driver for sources to consider WQT as a potential implementation tool to achieve more stringent permit limits. If numeric nutrient criteria are not adopted in Indiana and NPDES permits are not re‐issued with more stringent nutrient effluent limits, point sources in the Wabash River watershed would not have a sufficient regulatory driver for WQT.

3.2 Suitable Pollutants for Trading Nitrogen and phosphorus are considered appropriate pollutants for trading under U.S. EPA’s 2003 Water Quality Trading Policy and the U.S. EPA Water Quality Trading Toolkit for Permit Writers. Nutrients are relatively persistent in river environments and the focus of the Gulf of Mexico hypoxia issue. Local eutrophication issues such as dissolved oxygen impairments and nuisance algal blooms require TMDLs and are fueling the consideration of statewide nutrient criteria. Therefore, phosphorus and nitrogen forms are the focus of the Wabash River watershed WQT market feasibility analysis. At a very basic level, this means a focus on total nitrogen and total phosphorus. Difficulties in determining loading of

Final Report – September 2011 Page 8 reactive nutrient forms exist because of the sizeable variability in concentrations of soluble reactive forms across very short time periods. This has resulted in water quality monitoring programs relying heavily on TN and TP when estimating loads. However, bioavailability of the nutrients discharged by each source can be an important aspect of offsetting. For example, if a reticent nutrient form is traded for a source’s load which is substantially bioavailable the water quality impacts may not be addressed.

In addition, pollutant parameter suitability considerations for WQT include determining at what concentration a nutrient form has acutely toxic properties or quickly manifests other stresses. Lastly, the consideration of persistence is important. A parameter that is quickly attenuated is not a viable offset for impacts further downstream. Discharges of sizeable concentrations of ammonia can create acute toxicity concentrations. Ammonia can also consume high levels of oxygen as the form is converted into NO2 and the NO3. Because of these interactions WQT to address ammonia effluent limits is not appropriate. However, nitrogen in the form of ammonia is a tradable nutrient form when it is present in concentration levels characteristic of healthy ecosystems and not causing the described impacts. In these settings ammonia is cycling normally through the DIN or TN succession. Persistence, or the fate and transport of nutrients is an important consideration when setting eligible boundaries for trading transactions. For both of the nutrients adequate provisions can be included in the program boundaries and location factor to address quicker attenuation rates in headwater streams and downstream persistence. A brief discussion on the considerations related to the different forms of nitrogen and phosphorus in the context of WQT in the Wabash River watershed is provided below.

3.2.1 Nitrogen Considerations The 2006 Wabash River TMDL established loading limits for TN, which eliminates the need to consider other forms of nitrogen (e.g., nitrate, organic nitrogen) in the Wabash River watershed. While TN will allow all sources to trade with each other because it represents a stable pollutant that provides an equivalent trading relationship, there are some considerations to keep in mind about the impact from different forms of nitrogen. In general, there is greater environmental benefit to removing the more bioavailable forms of nitrogen, which include dissolved organic nitrogen and dissolved inorganic nitrogen (nitrate, nitrite, and ammonia). For example, the Gulf of Mexico 2008 Hypoxia Action Plan states that nitrogen composition should be emphasized in nutrient reduction strategies. Nitrate is the most important form fueling the primary production that leads to hypoxia development in the spring (April, May, and June). Between 2001 and 2005, total annual nitrogen loads to the Gulf of Mexico declined, but in the critical spring months, the reduction in total nitrogen load is from nitrogen forms other than nitrate. Research conducted by the USGS (1997) suggests that the relationship between nitrate concentration and flow might be due to nitrate leached from soil and the unsaturated zone during high flow conditions. In addition, agricultural tile drainage might also contribute to increased nitrate levels during high flows. As a result, WQT in the Wabash River watershed should place a priority on reducing nonpoint sources of DIN and to a lesser extent on bioavailable forms of DON.

3.2.2 Phosphorus Considerations Similar to TN, the 2006 Wabash River TMDL uses TP, a form of phosphorus that eliminates the need to consider other forms of phosphorus (e.g., soluble phosphorus) in the Wabash River watershed. Using TP allows for an equivalent trading relationship among sources with a phosphorus contribution. However, there are differences in the type of phosphorus associated with point and nonpoint sources. As a result, the potential effect of phosphorus from these sources on the Wabash River watershed will vary. Agricultural nonpoint sources discharge primarily the non‐soluble, sediment‐attached form of

Final Report – September 2011 Page 9 phosphorus. Point sources, wastewater treatment facilities in particular, discharge primarily soluble forms of phosphorus. The bioavailability of the TP from each discharger will be considered in Section 4.3 of this report for both the Wabash River and the Gulf of Mexico.

3.3 Watershed Considerations Nutrient water quality standards are emerging in states across the nation (e.g., Wisconsin and Florida) that have phosphorus criteria levels for selected regions at or below 0.1 mg/L TP. Nitrogen water quality standards, to a much lesser extent in the Midwest, are being considered that would substantially reduce stream concentrations to single digits as compared to the 10 mg/l nitrate drinking water standard. These new criteria could create a setting where it is common for numerous impaired waters to be listed and waters achieving water quality attainment may have limited available capacity for future waste loads. Federal regulations (i.e., 40 CFR 122.44(d) and 40 CFR 122.4(i)) pertaining to NPDES permit requirements expressly prevent a permit effluent limit from “causing or contributing” to water quality violations. These requirements apply to WQT as well. A trade cannot create a local “hot spot” (area of impairment) in one water body because it is protecting another. However, WQT can allow upstream buyers of credits to purchase downstream generated credits if the stream reaches between the two participants is in compliance with water quality standards. In large streams the buyer’s discharge may not significantly alter the stream’s nutrient concentration. In these setting where the stream is in compliance a new discharger could purchase downstream generated credits to comply with waste load allocation requirements of the system.

If the stream’s concentrations are above water quality standards or a permittee’s discharge is substantial to the point where a local violation would be caused, then the appropriate WQT framework would require generated credits upstream of the buyer’s discharge. This assumes the presence of ample credit supply exists upstream of the buyer and that the credit supply will remain persistent within the stream to the point of the buyer’s discharge. Because of these considerations, an understanding of the fate and transport of nutrients in rivers and streams is emerging as a critical issue for water quality standards, estuary protection and effluent limit setting programs. WQT is no different. Fortunately, WQT goals are set by the effluent limit requirements of the permit application and WQT eligibility requirements can be created to address upstream generated nutrient credit attenuation concerns. Tools like the USGS SPARROW model for the entire Mississippi River watershed are being developed at finer resolutions to allow for an understanding of scale requirements that can be used to set boundary conditions for WQT (e.g. a 10‐digit HUC evaluation provided replacing the current 8‐digit HUC results).

3.3.1 Summary: Suitable Pollutants for Trading In summary, both total nitrogen and total phosphorus are suitable pollutants for trading in the Wabash River watershed. However, WQT in the Wabash River watershed should focus on strategies to target sources of nitrate and account for the differences in the soluble and non‐soluble forms of phosphorus associated with point sources and nonpoint sources. The fate and transport of nutrients within the Wabash River and downstream to the Gulf of Mexico also need to be taken into account.

3.4 Timing WQT frameworks are required to be contemporaneous with NPDES permit effluent limit requirements. NPS generated credits are both episodic in nature and can have a seasonal variation with more credits being generated in one temperature regime or vegetative growth cycle than another (e.g., spring versus

Final Report – September 2011 Page 10 winter or pre‐crop canopy versus full canopy). NPDES permit effluent limits are assigned to respond to critical periods. The WQT framework must also respond with contemporaneous credit generation for these effluent limits.

The critical period noted in the 2006 Wabash River TMDLs for nutrients include both high flow periods (such as spring runoff) when nutrient loads are high, as well as low flow summer periods when the assimilative capacity of the river is reduced. The critical period in the Gulf of Mexico for the hypoxia issue is late spring/early summer (e.g., April, May, June). Permit effluent limit setting processes for far‐ field drivers will have to consider other watershed characteristics in addition to critical time periods. For instance, internal loading or recycling of nutrients and transport time for nutrients become relevant considerations when setting protection limits for water bodies.

Where possible, seasonal or annual critical periods for NPDES permit effluent limits should be accompanied by adequate supporting justification. For example, in the Chesapeake Bay trading framework U.S. EPA accepted an annual credit generation window and in Wisconsin the DNR Water Quality Trading Framework allows “banking” of NPS credits to be used within the year generated. Where the critical period is more constrained (e.g., one month) WQT must be structured to contemporaneously generate credits or not be an eligible option.

3.4.1 Summary: Timing Spring is a critical time period for both the Wabash River watershed and within the Gulf of Mexico. A Wabash River trading framework will therefore need to focus on reducing the load of nutrients during this period.

3.5 Geographic Scope for Trading Analysis Understanding how the geographic scope of the watershed could affect the viability and sustainability of trading is a key aspect of a WQT market feasibility analysis. Geographic scope of the watershed influences important factors such as pollutant fate and transport, which in turn affects credit supply and demand through the application of trade ratios.

For purposes of the Wabash River watershed WQT market feasibility analysis, the Project Team focused on the Indiana and Illinois portions of the Wabash River watershed. Beginning in the State of Ohio, the Wabash River watershed covers 32,950 square miles extending across most of the State of Indiana (24,320 square miles) and with significant parts of the State of Illinois. Through the pollutant suitability analysis portion of the WQT market feasibility analysis, the Project Team considered conditions throughout the entire Wabash River watershed that could affect trading and then focused in on subwatersheds for a more detailed analysis. In considering the entire watershed, the Project Team took into consideration factors such as the location of major lakes and reservoirs that could act as a pollutant sink and affect fate and transport. Figure 2 shows the location and size of reservoirs located throughout the Wabash River watershed. If water quality trading were to take place in the Wabash River watershed, the design of the trading program would have to take these reservoirs into account through delivery and location trade ratios (see Section 4.3).

Other physical features to consider are the karst regions of the watershed, which total approximately 263,500 acres as shown in Figure 3. Similar to reservoirs, karst features can affect the fate and transport of pollutants and they can also affect runoff rates. Because karst features are limited to the southeast portion of the Wabash River watershed, restrictions on trading due to karst would likely only affect

Final Report – September 2011 Page 11 credit suppliers located in this area. A water quality trading program design should include guidance on how to identify karst features and tailor eligibility requirements accordingly.

Other geographic considerations include land use practices that change hydrology, chemistry, and biology of a watershed. In the Wabash River watershed, coal mining is of particular interest. Surface coal mines are found in the southeastern portion of the Wabash River watershed. Subwatersheds with coal mining activities might have lower pH levels due to mine runoff that could affect the bioavailability of nutrients. This could affect potential trading activities in close proximity to these areas because understanding fate and transport of pollutants would be challenging and difficult to account for in program design (e.g., credit estimation and trade ratios).

Final Report – September 2011 Page 12 Figure 2. Location and size of major reservoirs located in the Wabash River watershed

Figure 3. Location of karst features in the Wabash River watershed

In addition to looking at the characteristics across the entire Wabash River watershed that could affect water quality trading, the Project Team looked at subwatersheds to help in the more detailed analysis necessary to understand the factors affecting potential credit supply and demand. The 8‐digit Hydrologic Unit Codes (HUCs) found in the Wabash River watershed are listed in Table 3.

Final Report – September 2011 Page 13 Table 3. Eight‐digit HUCs in the Wabash River Watershed across Indiana and Illinois. BASIN SUBBASIN HUC_8 SQ. MILES Wabash Salamonie 05120102 552 Wabash Mississinewa 05120103 842 Wabash Eel 05120104 829 Wabash Middle Wabash-Deer 05120105 652 Wabash Tippecanoe 05120106 1,960 Wabash Wildcat 05120107 813 Wabash Middle Wabash-Little Vermilion 05120108 2,287 Wabash Vermilion 05120109 1,439 Wabash Sugar 05120110 808 Wabash Middle Wabash-Busseron 05120111 2,019 Wabash Embarras 05120112 2,442 Wabash Lower Wabash 05120113 1,321 Wabash Little Wabash 05120114 2,148 Wabash Skillet 05120115 1,049 Patoka-White Upper White 05120201 2,754 Patoka-White Lower White 05120202 1,675 Patoka-White Eel 05120203 1,195 Patoka-White Driftwood 05120204 1,154 Patoka-White Flatrock-Haw 05120205 586 Patoka-White Upper East Fork White 05120206 811 Patoka-White Muscatatuck 05120207 1,143 Patoka-White Lower East Fork White 05120208 2,025 Patoka-White Patoka 05120209 859 Total 32,950

No pre‐existing watershed model exists for all of the subwatersheds within the Wabash River watershed. Given the size of the Wabash River watershed, conducting detailed modeling for all subwatersheds was not within the scope of this WQT market feasibility analysis. Instead, the Project Team conducted an analysis using the Soil and Water Assessment Tool (SWAT) of two subwatersheds to facilitate a smaller‐scale quantitative assessment. The Project Team evaluated factors related to nonpoint source credit suppliers (e.g., agricultural producers) to identify two subwatersheds that would provide characteristics that could be extrapolated to the rest of the Wabash River watershed. The Project Team examined a variety of information such as land use, location of flow gages, cropland data, animal counts, location within the watershed (i.e., headwater or not), and location of karst features. Based on this analysis, the Project Team selected the Tippecanoe and Driftwood subwatersheds for more targeted assessment in the feasibility analysis (the locations of the Tippecanoe and Driftwood subwatersheds are shown in Figures 2 and 3).

3.5.1 Summary: Geographic Scope for Trading Analysis In summary, the entire Wabash River watershed appears appropriate for WQT as long as trading program design takes into account notable features. These features include karst areas, major lakes and reservoirs, and other land uses that change the natural hydrology. These features could affect nutrient fate and transport, but could be addressed in WQT program design.

Final Report – September 2011 Page 14 3.6 Potential Credit Buyers and Sellers In WQT, potential credit buyers are sources that need to reduce a pollutant load to comply with a regulatory requirement and credit sellers are sources that have the means to supply a unit of pollutant load reduction by over‐controlling a pollutant load. Depending on the type of water quality trading program, a potential credit buyer is typically a facility covered by a NPDES permit. Potential credit sellers could be either other NPDES permitted point sources that over‐control a pollutant discharge or eligible nonpoint sources that generate pollutant load reductions through best management practice implementation. For the purposes of the Wabash River watershed WQT market feasibility analysis, the primary focus is on NPDES permitted facilities as potential credit buyers and agricultural nonpoint sources as potential credit sellers.

3.6.1 NPDES Permitted Facilities as Potential Credit Buyers According to information provided by IEPA and IDEM, there are 943 facilities with NPDES permits within the Wabash River watershed. Table 4 provides a list of the number of NPDES permitted facilities by 8‐digit HUC.

Table 4. Number of facilities with NPDES permits in each 8 digit HUC of the Wabash River watershed # of All 8 Digit HUC Permits Large Medium Small 5120101 62 8 15 39 5120102 19 1 7 11 5120103 39 2 21 16 5120104 25 2 8 15 5120105 11 7 4 5120106 48 2 18 28 5120107 29 3 10 16 5120108 23 4 11 8 5120109 51 8 13 30 5120110 10 2 8 5120111 54 9 22 23 5120112 61 5 20 36 5120113 28 1 10 17 5120114 43 6 12 25 5120115 16 1 5 10 5120201 157 16 62 79 5120202 42 5 15 22 5120203 30 2 13 15 5120204 54 4 20 30 5120205 14 2 7 5 5120206 23 1 11 11 5120207 35 1 19 15 5120208 52 2 22 28 5120209 17 1 7 9 Total 943 86 357 500

Final Report – September 2011 Page 15 According to Table 4, the most NPDES permits are located in HUC 5120201, which has a total of 157. This HUC has the most facilities across all size categories. Small facilities are the predominant size category in the Wabash River watershed, with more than 50 percent of facilities falling in this category.

Figure 4. NPDES permitted facilities by size and subwatershed in the Wabash River watershed.

Final Report – September 2011 Page 16 Section 3.6 provides an analysis of the potential credit demand that these facilities might generate through more stringent permit effluent limits. Once a facility has met the nutrient reduction requirements it is possible for this facility to provide further reduction and generate credits for sale to other facilities.

3.6.2 Agricultural Nonpoint Sources as Potential Credit Sellers WQT frameworks are created to fit the local setting. This includes working with the watershed characteristics to achieve and protect beneficial uses and water quality standards as well as advance watershed management goals. The nutrients in NPS runoff can be adequately quantified and adjusted for equivalence and location at the field scale and be transferable as credits to offset permitted wastewater discharges. However, WQT must fit within the socio‐political structure. WQT has the opportunity to accelerate implementation for TMDLs, provide a mechanism for future growth in capped watersheds, and provide interim compliance leverage in cases of variance request or compliance schedule relief. To realize some of these benefits, the appropriate policies and perspectives must be in place. See the discussion on Baselines in Section 4.4.

Land use in the Wabash River watershed includes subregions dominated by forest, urban and agricultural coverages (Figure 5). Forested land use yields relatively light nutrient loading when compared to urban and agricultural land management (Reckhow and Chapra, 1983 and Dodd et al., 1992). The riverine geomorphology includes stable channels, incising channels and enhanced drainage features like channelized streams and ditches. These factors affect nutrient transport pathways and attenuation rates.

Final Report – September 2011 Page 17

Figure 5. 2009 Landuse Map of the Wabash Watershed (MRLC, 2009)

Final Report – September 2011 Page 18 The crop coverage map illustrates how much Indiana and Illinois benefit from abundant agricultural resources. Currently these resources are being managed in economically productive livestock and grain commodity production operations. The prevalence of corn‐soybean rotations is typical of Midwest states. In addition, livestock density across the watershed is relatively high, but variable in terms of animal type and concentration. A consequence of this production and the physical features of the watershed is that the Wabash River watershed is the highest nutrient loading watershed in the Ohio River Basin (USGS, 2008). This high level of loading sets the stage for reduction opportunities. Current conservation practice adoption varies across the State of Indiana. For example the 2007 CTIC crop residue transect survey results for counties within the Tippecanoe watershed are provided in Table 5. The county variation ranges from 46 percent in some type of corn conservation tillage practices up to 91 percent.

Table 5. Tippecanoe County’s 2007 Conservation Tillage Data1 County (rank) No-Till Mulch Till Reduced Till Conventional Till Corn Data Pulaski (19) 21 48 23 9 Kosciusko (24) 26 18 27 29 Fulton (33) 21 20 39 21 White (67) 5 19 23 54 Average corn 18.25 26.25 28 28.25 Soybean Data White (26) 52 23 13 11 Kosciusko (29) 66 16 10 8 Pulaski (35) 68 25 7 1 Fulton (38) 67 24 8 1 Average soybean 63.25 22 9.5 5.25 1 Indiana State Department of Agriculture Conservation Tillage Data available at: http://www.in.gov/isda/2354.htm

Based on the variable loading rates and variable adoption of conservation practices, the Wabash River watershed consists of both regions containing more than ample credit supply opportunities and those with reduced opportunity. However, within every subwatershed of the Wabash River there exists a number of individual sites that contain the key characteristics desired to supply credits: 1) implementation of a BMP will substantially reduce current NPS loading 2) the site is located in close proximity to the Wabash River or one of its tributaries 3) a willing land owner.

These prerequisites are necessary to supply an adequate volume of credits at an economical price. In low volume regions WQT may be limited to individual permits needing assistance with difficult or costly compliance attainment issues. In the regions with ample ability to supply credits a larger program could be available where many buyers and sellers participate. Section 3.7 describes the methods to preliminarily assess the entire Wabash. A higher resolution of assessment at the local level is advised as part of the WQT framework development process, should WQT programs in the Watershed be pursued. To evaluate and quantify the regional potential at a higher resolution stakeholder input is required. One objective would be to gather farm data regarding operational practices of nutrient and conservation management. This information can be both distinctly individualized and considered confidential by the producer and Farm Bill public programs. Producers may choose to divulge historic practices once

Final Report – September 2011 Page 19 funding opportunities are present, but often remain silent when requested for data until they are comfortable with the program and the individuals running it.

Section 3.7 provides an analysis of the potential credit supply from these agricultural nonpoint sources located in the Wabash River watershed.

3.7 Potential Credit Demand Determining the potential credit demand from NPDES permitted facilities in the Wabash River watershed requires an understanding of the estimated pollutant load reductions necessary to meet more stringent permit limits, as well as the type of existing treatment and control technology upgrade options that are available to facilities to meet more stringent permit limits. This section examines both factors that play a role in generating a demand for credits.

3.7.1 Estimates of Existing Pollutant Loads and Pollutant Load Reductions Under Three Permit Limit Scenarios The Project Team developed a technical memorandum entitled Characterization of Wabash River Nutrient Loads as part of the WQT market feasibility analysis to help estimate potential credit demand. This memorandum is found in Appendix C. This technical memo estimates the existing TN and TP loads under current permit limits and then examines the necessary pollutant load reductions to achieve more stringent permit limits in the future.

Table 6 summarizes estimated existing nutrient loads from the 943 NPDES permitted facilities in the Wabash River watershed. The estimated existing loads are categorized by size of facility in the categories of small, medium, large. Small facilities are those permitted to discharge no more than 0.3 million gallons per day (MGD). Medium‐sized facilities are those with permitted discharges that range between 0.3 to 5 MGD. Large facilities discharge more than 5 MGD.

Table 6. Estimated existing nutrient loads from permitted NPDES facilities in the Wabash River watershed. Estimated Existing Loads1 Total Phosphorus Total Nitrogen Facility Size (# Daily Load Annual Load Daily Load Annual Load HUC_8 of facilities) (lbs/day) (Tons) (lbs/day) (Tons) Large (8) 381 70 2,069 378 Medium (15) 104 19 798 146 05120101 Small (39) 14 3 85 16 Total (62) 499 91 2,952 539 Large (1) 8 2 63 12 Medium (7) 39 7 317 58 05120102 Small (11) 3 1 22 4 Total (19) 51 9 402 73 Large (2) 64 12 1,348 246 Medium (21) 79 14 669 122 05120103 Small (16) 6 1 46 8 Total (39) 148 27 2,063 376

Final Report – September 2011 Page 20 Estimated Existing Loads1 Total Phosphorus Total Nitrogen Facility Size (# Daily Load Annual Load Daily Load Annual Load HUC_8 of facilities) (lbs/day) (Tons) (lbs/day) (Tons) Large (2) 42 8 418 76 Medium (8) 58 11 357 65 05120104 Small (15) 4 1 30 5 Total (25) 104 19 805 147 Medium (7) 31 6 236 43 05120105 Small (4) 2 0 12 2 Total (11) 33 6 248 45 Large (2) 7 1 73 13 Medium (18) 99 18 736 134 05120106 Small (28) 7 1 71 13 Total (48) 113 21 880 161 Large (3) 276 50 2,065 377 Medium (10) 40 7 325 59 05120107 Small (16) 11 2 53 10 Total (29) 326 60 2,443 446 Large (4) 3,878 708 27,162 4,957 Medium (11) 114 21 652 119 05120108 Small (8) 3 1 24 4 Total (23) 3,995 729 27,837 5,080 Large (8) 1,029 188 5,245 957 Medium (13) 98 18 463 85 05120109 Small (30) 4 1 35 6 Total (51) 1,130 206 5,743 1,048 Medium (2) 5 1 36 7 05120110 Small (8) 3 1 24 4 Total (10) 8 1 60 11 Large (9) 2,749 502 21,571 3,937 Medium (22) 251 46 1,330 243 05120111 Small (23) 5 1 43 8 Total (54) 3,005 548 22,943 4,187 Large (5) 536 98 2,259 412 Medium (20) 103 19 622 113 05120112 Small (36) 8 1 65 12 Total (61) 647 118 2,946 538 Large (1) 68 12 510 93 Medium (10) 63 12 409 75 05120113 Small (17) 5 1 31 6 Total (28) 137 25 950 173 Large (6) 3,155 576 22,613 4,127 Medium (12) 128 23 581 106 05120114 Small (25) 9 2 59 11 Total (43) 3,292 601 23,254 4,244 Large (1) 30 6 135 25 Medium (5) 19 3 85 15 05120115 Small (10) 6 1 28 5 Total (16) 55 10 248 45

Final Report – September 2011 Page 21 Estimated Existing Loads1 Total Phosphorus Total Nitrogen Facility Size (# Daily Load Annual Load Daily Load Annual Load HUC_8 of facilities) (lbs/day) (Tons) (lbs/day) (Tons) Large (16) 3,878 708 28,752 5,247 Medium (62) 315 58 3,065 559 05120201 Small (79) 17 3 147 27 Total (157) 4,210 768 31,964 5,834 Large (5) 1,240 226 9,522 1,738 Medium (15) 139 25 993 181 05120202 Small (22) 11 2 61 11 Total (42) 1,390 254 10,576 1,930 Large (2) 49 9 366 67 Medium (13) 54 10 338 62 05120203 Small (15) 3 1 21 4 Total (30) 106 19 725 132 Large (4) 221 40 1,660 303 Medium (20) 84 15 579 106 05120204 Small (30) 5 1 40 7 Total (54) 311 57 2,279 416 Large (2) 153 28 1,041 190 Medium (7) 36 7 272 50 05120205 Small (5) 1 0 7 1 Total (14) 190 35 1,320 241 Large (1) 37 7 571 104 Medium (11) 16 3 165 30 05120206 Small (11) 5 1 37 7 Total (23) 57 10 772 141 Large (1) 53 10 234 43 Medium (19) 93 17 676 123 05120207 Small (15) 9 2 58 11 Total (35) 155 28 968 177 Large (2) 71 13 1,557 284 Medium (22) 78 14 768 140 05120208 Small (28) 5 1 42 8 Total (52) 154 28 2,367 432 Large (1) 36 7 272 50 Medium (7) 48 9 285 52 05120209 Small (9) 3 1 17 3 Total (17) 88 16 574 105 1Existing loads based on flow and TN data reported in the Integrated Compliance Information System (ICIS). TN concentrations were not reported for the vast majority of facilities and therefore were estimated based on reported BOD values. See Characterization of Wabash River Nutrient Loads in Appendix C for details. Estimating existing loads for the different facility size categories in each 8‐digit HUC helps to determine the potential change in pollutant loads when different permit effluent limits are considered. As discussed in Section 3.1, the Project Team has made assumptions about likely future permit effluent limits that would result from numeric nutrient criteria based on trends in other Midwest states. For purposes of the WQT market feasibility analysis, the Project Team developed scenarios using different assumed permit effluent limit values. For TN, the assumed values are 3 mg/L, 5 mg/L, and 8 mg/L. For TP, the assumed values are 0.3 mg/L and 0.5 mg/L.

Final Report – September 2011 Page 22 Table 7 summarizes the change in pollutant loads for TN under each permit effluent limit scenario. In addition, this table shows the associated pollutant load reduction that each facility size category is estimated to need to achieve the more stringent permit effluent limits. This is assumed to be the potential credit demand for each facility size category.

Table 7. Changes in pollutant loads and resulting credit demand under different TN permit effluent scenarios Annual Load (Tons) Annual Load Reduction Estimated Loads Assuming Facility Size Discharge Value (# of Existing of 3 of 5 of 8 HUC_8 facilities) Loads mg/L mg/L mg/L (Tons) @ 3 (Tons) @ 5 (Tons) @ 8 Large (8) 377.5 102.0 147.4 215.4 275.5 230.1 162.1 Medium (15) 145.6 31.9 50.9 79.3 113.7 94.7 66.3 05120101 Small (39) 15.6 6.9 8.3 10.3 8.7 7.3 5.3 Total (62) 538.7 140.8 206.5 305.0 397.9 332.2 233.7 Large (1) 11.6 2.3 3.9 6.2 9.3 7.7 5.4 Medium (7) 57.9 11.5 19.2 30.7 46.3 38.7 27.2 05120102 Small (11) 4.0 2.0 2.2 2.6 2.0 1.7 1.3 Total (19) 73.4 15.8 25.3 39.5 57.6 48.1 33.9 Large (2) 246.0 49.2 82.0 131.2 196.8 164.0 114.8 Medium (21) 122.1 26.3 42.0 65.6 95.7 80.0 56.5 05120103 Small (16) 8.3 2.5 3.5 4.9 5.8 4.9 3.4 Total (39) 376.4 78.0 127.5 201.7 298.4 248.9 174.7 Large (2) 76.2 76.2 76.2 76.2 0.0 0.0 0.0 Medium (8) 65.1 13.5 21.7 34.0 51.6 43.4 31.1 05120104 Small (15) 5.5 1.6 2.3 3.2 3.9 3.2 2.3 Total (25) 146.8 91.3 100.2 113.5 55.5 46.7 33.4 Medium (7) 43.1 9.2 14.9 23.3 33.9 28.3 19.8 05120105 Small (4) 2.2 0.8 1.0 1.4 1.4 1.1 0.8 Total (11) 45.3 10.0 15.9 24.7 35.3 29.4 20.6 Large (2) 13.3 13.3 13.3 13.3 0.0 0.0 0.0 05120106 Medium (18) 134.3 27.3 44.6 70.6 107.0 89.6 63.7 (Tippecanoe) Small (28) 13.0 4.1 5.6 7.8 8.9 7.4 5.2 Total (48) 160.6 44.8 63.5 91.7 115.8 97.0 68.8 Large (3) 376.8 76.3 126.4 201.5 300.6 250.5 175.3 Medium (10) 59.4 11.8 19.6 31.3 47.6 39.8 28.0 05120107 Small (16) 9.7 2.3 3.5 5.4 7.4 6.2 4.3 Total (29) 445.9 90.3 149.5 238.2 355.6 296.4 207.7 Large (4) 4957.0 990.4 1650.6 2641.0 3966.6 3306.4 2316.1 Medium (11) 118.9 21.7 35.9 57.2 97.2 83.0 61.7 05120108 Small (8) 4.3 2.2 2.6 3.1 2.1 1.8 1.2 Total (23) 5080.2 1014.3 1689.0 2701.2 4066.0 3391.2 2379.0 Large (8) 957.1 184.9 294.1 458.0 772.2 663.0 499.1 Medium (13) 84.5 20.2 28.2 40.4 64.4 56.3 44.2 05120109 Small (30) 6.5 6.0 6.0 6.1 0.5 0.4 0.3 Total (51) 1048.1 211.0 328.4 504.5 837.1 719.7 543.6

Final Report – September 2011 Page 23 Annual Load (Tons) Annual Load Reduction Estimated Loads Assuming Facility Size Discharge Value (# of Existing of 3 of 5 of 8 HUC_8 facilities) Loads mg/L mg/L mg/L (Tons) @ 3 (Tons) @ 5 (Tons) @ 8 Medium (2) 6.6 1.3 2.2 3.5 5.3 4.4 3.1 05120110 Small (8) 4.3 1.5 2.0 2.7 2.8 2.3 1.6 Total (10) 10.9 2.9 4.2 6.2 8.1 6.7 4.7 Large (9) 3936.7 1354.7 1785.1 2430.6 2582.0 2151.7 1506.2 Medium (22) 242.6 43.4 71.1 112.6 199.2 171.6 130.0 05120111 Small (23) 7.8 5.1 5.5 6.2 2.7 2.3 1.6 Total (54) 4187.1 1403.2 1861.6 2549.3 2784.0 2325.5 1637.8 Large (5) 412.3 62.9 104.8 167.6 349.4 307.5 244.7 Medium (20) 113.5 34.0 44.7 60.8 79.5 68.8 52.7 05120112 Small (36) 11.9 9.9 10.1 10.5 2.0 1.7 1.4 Total (61) 537.7 106.8 159.6 238.9 430.9 378.0 298.8 Large (1) 93.1 18.6 31.0 49.6 74.5 62.1 43.4 Medium (10) 74.7 15.0 24.2 38.0 59.6 50.5 36.7 05120113 Small (17) 5.7 2.7 3.1 3.6 3.0 2.6 2.0 Total (28) 173.4 36.3 58.3 91.2 137.1 115.1 82.1 Large (6) 4126.9 812.8 1354.6 2167.4 3314.2 2772.3 1959.6 Medium (12) 106.1 16.6 27.4 43.4 89.4 78.7 62.6 05120114 Small (25) 10.8 6.7 7.2 8.1 4.1 3.6 2.8 Total (43) 4243.8 836.1 1389.2 2218.8 3407.7 2854.6 2025.0 Large (1) 24.6 3.7 6.1 9.8 20.9 18.4 14.7 Medium (5) 15.4 2.3 3.9 6.2 13.1 11.6 9.3 05120115 Small (10) 5.2 1.5 2.0 2.6 3.7 3.2 2.6 Total (16) 45.2 7.5 12.0 18.6 37.7 33.2 26.6 Large (16) 5247.2 1179.0 1853.3 2864.8 4068.2 3393.9 2382.4 Medium (62) 559.4 125.7 197.1 304.2 433.7 362.3 255.2 05120201 Small (79) 26.8 10.1 12.9 17.0 16.8 14.0 9.8 Total (157) 5833.5 1314.7 2063.3 3186.1 4518.8 3770.2 2647.4 Large (5) 1737.8 475.9 686.2 1001.6 1261.9 1051.6 736.1 Medium (15) 181.2 29.1 47.8 75.8 152.1 133.5 105.4 05120202 Small (22) 11.2 3.1 4.3 6.0 8.1 6.9 5.1 Total (42) 1930.2 508.0 738.2 1083.5 1422.1 1192.0 846.7 Large (2) 66.8 13.4 22.3 35.6 53.4 44.5 31.2 Medium (13) 61.6 11.6 18.8 29.7 50.1 42.8 31.9 05120203 Small (15) 3.9 1.1 1.5 2.2 2.8 2.3 1.6 Total (30) 132.3 26.0 42.6 67.6 106.3 89.7 64.7 Large (4) 303.0 60.6 101.0 161.6 242.4 202.0 141.4 05120204 Medium (20) 105.7 22.0 35.5 55.8 83.7 70.2 49.9 (Driftwood) Small (30) 7.3 3.1 3.8 4.8 4.2 3.5 2.5 Total (54) 416.0 85.7 140.3 222.2 330.3 275.7 193.8 Large (2) 189.9 37.4 62.4 99.8 152.5 127.5 90.1 Medium (7) 49.6 11.4 17.7 27.3 38.2 31.8 22.3 05120205 Small (5) 1.3 1.0 1.0 1.1 0.4 0.3 0.2 Total (14) 240.8 49.8 81.2 128.3 191.1 159.7 112.6

Final Report – September 2011 Page 24 Annual Load (Tons) Annual Load Reduction Estimated Loads Assuming Facility Size Discharge Value (# of Existing of 3 of 5 of 8 HUC_8 facilities) Loads mg/L mg/L mg/L (Tons) @ 3 (Tons) @ 5 (Tons) @ 8 Large (1) 104.2 20.8 34.7 55.6 83.3 69.4 48.6 Medium (11) 30.1 9.6 13.0 18.1 20.5 17.1 12.0 05120206 Small (11) 6.7 2.0 2.8 4.0 4.7 3.9 2.7 Total (23) 141.0 32.4 50.5 77.7 108.6 90.5 63.3 Large (1) 42.8 6.4 10.7 17.1 36.4 32.1 25.7 Medium (19) 123.3 30.8 45.9 68.5 92.6 77.5 54.8 05120207 Small (15) 10.6 2.4 3.6 5.4 8.2 6.9 5.1 Total (35) 176.7 39.6 60.2 91.1 137.1 116.5 85.6 Large (2) 284.1 58.9 96.4 152.7 225.2 187.7 131.4 Medium (22) 140.2 29.5 47.5 74.5 110.7 92.7 65.7 05120208 Small (28) 7.7 3.2 4.0 5.1 4.4 3.7 2.6 Total (52) 432.0 91.6 147.9 232.3 340.4 284.1 199.7 Large (1) 49.6 9.9 16.5 26.5 39.7 33.1 23.1 Medium (7) 52.1 10.1 16.8 26.8 41.9 35.3 25.3 05120209 Small (9) 3.1 0.7 1.0 1.5 2.3 2.0 1.6 Total (17) 104.7 20.8 34.4 54.7 83.9 70.4 50.1

As shown by Table 7, large facilities in the Wabash River watershed have the most significant credit demand for TN under more stringent permit effluent limitations. However, there are significantly more small facilities compared to medium and large facilities and cumulatively they will also generate a significant credit demand. A portion of small rural facilities might experience difficulties such as being understaffed or not able to generate financial resources necessary to meet new restrictive nutrient requirements within the first permit period (typical of most compliance schedules requirements).

Table 8 summarizes the change in pollutant loads for TP under each permit effluent limit scenario. This table also shows the associated pollutant load reduction that each facility size category is estimated to need to achieve the more stringent permit effluent limits. This is assumed to be the potential credit demand for each facility size category for TP.

Final Report – September 2011 Page 25 Table 8. Changes in pollutant loads and resulting credit demand under different TP permit effluent scenarios Loads Loads Assuming Assuming Estimated Discharge Discharge Existing Value of 0.3 Value of 0.5 Loads mg/L mg/L Annual Load Annual Load Annual Load Annual Load Annual Load Reductions Reductions HUC_8 Facility Size (Tons) (Tons) (Tons) (Tons) @ 0.3 (Tons) @ 0.5 Large (8) 69.5 10.2 14.4 59.3 55.1 Medium (15) 19.0 3.2 5.1 15.8 13.9 05120101 Small (39) 2.5 0.7 0.8 1.8 1.7 Total (62) 91.0 14.1 20.3 77.0 70.7 Large (1) 1.5 0.2 0.4 1.3 1.2 Medium (7) 7.1 1.2 1.9 5.9 5.2 05120102 Small (11) 0.6 0.2 0.2 0.4 0.4 Total (19) 9.3 1.6 2.5 7.7 6.7 Large (2) 11.7 4.9 8.2 6.7 3.5 Medium (21) 14.3 2.6 4.2 11.7 10.2 05120103 Small (16) 1.0 0.2 0.3 0.8 0.7 Total (39) 27.0 7.8 12.7 19.2 14.3 Large (2) 7.6 7.6 7.6 0.0 0.0 Medium (8) 10.6 1.3 2.2 9.2 8.4 05120104 Small (15) 0.7 0.2 0.2 0.6 0.5 Total (25) 18.9 9.1 10.0 9.8 8.9 Medium (7) 5.7 0.9 1.5 4.8 4.2 05120105 Small (4) 0.3 0.1 0.1 0.2 0.2 Total (11) 6.0 1.0 1.6 5.0 4.4 Large (2) 1.3 1.3 1.3 0.0 0.0 05120106 Medium (18) 18.0 2.7 4.5 15.3 13.5 (Tippecanoe) Small (28) 1.2 0.4 0.5 0.8 0.7 Total (48) 20.6 4.5 6.3 16.1 14.3 Large (3) 50.4 7.8 12.8 42.6 37.6 Medium (10) 7.2 1.2 1.9 6.1 5.3 05120107 Small (16) 2.0 0.2 0.4 1.7 1.6 Total (29) 59.6 9.2 15.1 50.4 44.5 Large (4) 707.7 99.0 165.1 608.6 542.6 Medium (11) 20.9 2.2 3.6 18.7 17.3 05120108 Small (8) 0.5 0.2 0.3 0.3 0.3 Total (23) 729.0 101.4 168.9 627.6 560.1 Large (8) 187.7 18.5 29.4 169.2 158.3 Medium (13) 17.9 2.0 2.8 15.8 15.0 05120109 Small (30) 0.7 0.6 0.6 0.1 0.1 Total (51) 206.3 21.1 32.8 185.2 173.5 Medium (2) 0.9 0.1 0.2 0.8 0.7 05120110 Small (8) 0.5 0.2 0.2 0.4 0.3 Total (10) 1.4 0.3 0.4 1.1 1.0 Large (9) 501.7 135.9 178.9 365.8 322.8 05120111 Medium (22) 45.8 4.3 7.1 41.4 38.7

Final Report – September 2011 Page 26 Loads Loads Assuming Assuming Estimated Discharge Discharge Existing Value of 0.3 Value of 0.5 Loads mg/L mg/L Annual Load Annual Load Annual Load Annual Load Annual Load Reductions Reductions HUC_8 Facility Size (Tons) (Tons) (Tons) (Tons) @ 0.3 (Tons) @ 0.5 Small (23) 0.9 0.5 0.6 0.4 0.3 Total (54) 548.4 140.8 186.6 407.6 361.8 Large (5) 97.8 6.3 10.5 91.6 87.4 Medium (20) 18.8 3.4 4.5 15.4 14.3 05120112 Small (36) 1.5 1.0 1.0 0.5 0.5 Total (61) 118.1 10.7 16.0 107.5 102.2 Large (1) 12.4 1.9 3.1 10.5 9.3 Medium (10) 11.6 1.5 2.4 10.1 9.2 05120113 Small (17) 0.9 0.3 0.3 0.7 0.6 Total (28) 24.9 3.6 5.8 21.3 19.1 Large (6) 575.8 81.3 135.5 494.5 440.3 Medium (12) 23.3 1.7 2.7 21.7 20.6 05120114 Small (25) 1.6 0.7 0.7 0.9 0.9 Total (43) 600.7 83.6 138.9 517.1 461.8 Large (1) 5.5 0.4 0.6 5.2 4.9 Medium (5) 3.5 0.2 0.4 3.2 3.1 05120115 Small (10) 1.0 0.2 0.2 0.9 0.8 Total (16) 10.0 0.8 1.2 9.3 8.8 Large (16) 707.7 117.9 185.3 589.8 522.4 Medium (62) 57.6 12.6 19.2 45.0 38.4 05120201 Small (79) 3.1 1.0 1.3 2.0 1.8 Total (157) 768.3 131.5 205.8 636.9 562.6 Large (5) 226.4 47.6 68.6 178.8 157.7 Medium (15) 25.3 2.9 4.8 22.4 20.5 05120202 Small (22) 2.0 0.3 0.4 1.7 1.6 Total (42) 253.6 50.8 73.8 202.8 179.8 Large (2) 8.9 1.3 2.2 7.6 6.7 Medium (13) 9.8 1.2 1.9 8.6 7.9 05120203 Small (15) 0.6 0.1 0.2 0.5 0.5 Total (30) 19.3 2.6 4.3 16.7 15.0 Large (4) 40.4 6.1 10.1 34.3 30.3 05120204 Medium (20) 15.4 2.2 3.6 13.2 11.8 (Driftwood) Small (30) 0.9 0.3 0.4 0.6 0.5 Total (54) 56.7 8.6 14.0 48.1 42.7 Large (2) 27.9 3.7 6.2 24.2 21.7 Medium (7) 6.6 1.1 1.8 5.4 4.8 05120205 Small (5) 0.2 0.1 0.1 0.1 0.0 Total (14) 34.6 5.0 8.1 29.7 26.5 Large (1) 6.7 2.1 3.5 4.6 3.2 Medium (11) 2.9 1.0 1.2 2.0 1.7 05120206 Small (11) 0.9 0.2 0.3 0.7 0.6 Total (23) 10.5 3.2 4.9 7.2 5.5

Final Report – September 2011 Page 27 Loads Loads Assuming Assuming Estimated Discharge Discharge Existing Value of 0.3 Value of 0.5 Loads mg/L mg/L Annual Load Annual Load Annual Load Annual Load Annual Load Reductions Reductions HUC_8 Facility Size (Tons) (Tons) (Tons) (Tons) @ 0.3 (Tons) @ 0.5 Large (1) 9.6 0.6 1.1 9.0 8.6 Medium (19) 17.0 3.1 4.6 13.9 12.4 05120207 Small (15) 1.7 0.2 0.4 1.5 1.3 Total (35) 28.3 4.0 6.0 24.3 22.3 Large (2) 12.9 5.9 9.6 7.1 3.3 Medium (22) 14.3 2.9 4.7 11.4 9.6 05120208 Small (28) 0.9 0.3 0.4 0.6 0.5 Total (52) 28.2 9.1 14.7 19.1 13.5 Large (1) 6.6 1.0 1.7 5.6 5.0 Medium (7) 8.7 1.0 1.7 7.7 7.1 05120209 Small (9) 0.6 0.1 0.1 0.5 0.5 Total (17) 16.0 2.1 3.4 13.9 12.5

3.7.2 Existing Wastewater Treatment and Estimated Upgrade Costs Estimating potential credit demand goes beyond estimating the necessary pollutant load reductions to meet the TN and TP permit effluent limitation scenarios. It also involves understanding the existing type of treatment and which facilities might choose to upgrade their control technologies to meet the more stringent permit effluent limitations. The Project Team conducted an analysis of the type of wastewater treatment and the cost to upgrade. This analysis is part of the Characterization of Wabash River Nutrient Loads technical memorandum found in Appendix C.

The level of new pollutant control measures needed to meet nutrient reductions specified by TMDLs or other regulatory drivers will be dependent upon each treatment plant’s current operations and the cost associated with the most likely control measure (e.g., biological phosphorus removal). Information on the type of wastewater treatment used by plants within the Wabash River watershed was obtained from the Clean Watersheds Needs Survey (CWNS) for the entire watershed. For the Driftwood and Tippecanoe subwatersheds, the Project Team supplemented CWNS information with data from a review of actual NPDES permits.

Information from the CWNS places facilities under the category of Secondary Wastewater Treatment or the category of Advanced Wastewater Treatment. Secondary treatment typically requires a treatment level that produces an effluent quality of less than 30 mg/L of both BOD5 and total suspended solids (secondary treatment levels required for some lagoon systems may be less stringent). In addition, the secondary treatment must remove 85 percent of BOD5 and total suspended solids from the influent wastewater. A facility is considered to have Advanced Wastewater Treatment if its permit includes one or more of the following: BOD less than 20 mg/L; Nitrogen Removal; Phosphorous Removal; Ammonia Removal; Metal Removal; Synthetic Organic Removal.

Table 9 summarizes the CWNS information for all of the facilities in the Wabash River watershed. It indicates that 21 percent of the facilities have advanced treatment, 14 percent are known to have secondary treatment, and no information is available for 65 percent of the facilities. Because the CWNS

Final Report – September 2011 Page 28 focuses on larger facilities, and because smaller facilities are less likely to have advanced treatment, it is probable that the majority of the facilities with no information do not use advanced treatment.

Table 9. Summary of CWNS information for all facilities in the Wabash River watershed. Treatment Level Number of Facilities Percent Advanced Treatment 211 21% Secondary 144 14% No Information 666 65% Total Facilities in Watershed 1021 100% Note: the number of facilities in the CWNS (1021) differs from the number obtained by combining the data from IDEM and IEPA (943). The source of the discrepancy is unknown but may be due to the timing of when the two data sets were created.

Table 10 summarizes the indicators of advanced treatment and shows that only a small proportion (10%) of the facilities have limits for phosphorus and nitrogen (not including ammonia).

Table 10. Indicators associated with advanced treatment facilities in the Wabash River watershed. Advance Indicators Number of Facilities Percent BOD 164 77.7% Nitrogen 1 0.5% BOD, Nitrogen 1 0.5% BOD, Phosphorus 2 0.9% BOD, Ammonia 26 12.3% BOD, Phosphorus, Ammonia 17 8.1% Total 211 100.0%

Table 11 summarizes the type of treatment by HUC and indicates that HUCs 05120201, 05120202, and 05120111 have the most facilities with advanced treatment. The cities of Terra Haute, Bloomington, Indianapolis, Anderson, and Muncie are located in these HUCs.

Final Report – September 2011 Page 29 Table 11. Summary of treatment type by HUC. No CWNS HUC 8 Advanced Treatment Secondary Treatment Information 05120101 8 5 49 05120102 3 2 14 05120103 9 4 26 05120104 3 4 18 05120105 3 2 6 05120106 8 5 35 (Tippecanoe) 05120107 5 4 20 05120108 8 3 12 05120109 7 7 37 05120110 2 1 7 05120111 13 5 36 05120112 8 10 43 05120113 4 8 16 05120114 6 12 25 05120115 1 8 7 05120201 33 9 115 05120202 13 4 25 05120203 8 1 21 05120204 (Driftwood) 8 4 42 05120205 5 0 9 05120206 4 1 18 05120207 5 2 28 05120208 10 6 36 05120209 2 3 12 Total 176 110 657

A more detailed analysis was performed to determine the type of treatment for facilities in the Tippecanoe and Driftwood watersheds (which are being modeled in SWAT to support the feasibility study).

The CWNS information for the 92 facilities in these two watersheds is shown in Table 12. Seventeen of the facilities have advanced treatment, nine have secondary treatment, and treatment type was not reported for 66 facilities.

Final Report – September 2011 Page 30 Table 12. Summary of 2008 CWNS permit information for facilities in the Tippecanoe and Driftwood watersheds. Permit WWTP or Present Treatment Number of Watershed Type Other Level Present Advance Indicators Facilities Major Other No Info No Info 2 BOD (Biochemical Oxygen Major WWTP Advanced Treatment 2 Demand) Minor Other No Info No Info 23 Minor Other Secondary No Info 1 Tippecanoe Ammonia Removal, BOD Minor WWTP Advanced Treatment 1 (05120106) (Biochemical Oxygen Demand) BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 5 Demand) BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 1 Demand), Ammonia Removal Minor WWTP No Info No Info 3 Minor WWTP Secondary No Info 4 Major Other No Info No Info 1 BOD (Biochemical Oxygen Major WWTP Advanced Treatment 3 Demand) Major WWTP Secondary No Info 1 Minor Other No Info No Info 34 Driftwood BOD (Biochemical Oxygen (05120204) Minor WWTP Advanced Treatment 4 Demand) BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 1 Demand), Ammonia Removal Minor WWTP No Info No Info 3 Minor WWTP Secondary No Info 3

Of the 92 facilities, the Project Team was able to obtain permits for the 31 facilities that are WWTP within the watershed. These permits indicate that the following specific types of treatment methods are used within the watershed (either individually or in combination with one another): • trickling filters • activated sludge (including extended aeration and oxidation) • discharge waste stabilization lagoons

Out of these only the activated sludge systems can effectively be retrofit for biological nitrogen removal. Therefore, the trickling filter and lagoon treatment processes will require an expansion to reduce nutrient in their effluent.

All 31 facilities are required to report CBOD5 and ammonia values, 21 of the facilities have permitted ammonia limits, and only one facility has a phosphorus limit (Table 13).

Based on the lack of TN or TP limits in the available permits, and the types of treatment processes specified in the CWNS and the collected permits, it appears that almost none of the facilities in the Driftwood and Tippecanoe are targeting the treatment of TN or TP. The facilities that are treating for ammonia (via nitrification) likely do not include technology for denitrification or phosphorus removal.

Final Report – September 2011 Page 31 Upgrades would therefore be necessary to provide enhanced nutrient removal (ENR) for TN and TP removal. Table 13. Permit limit summaries for facilities in the Driftwood and Tippecanoe watersheds. Permit Limit No Designation in CWNS Secondary Advanced Design Flow (MGD) 0.024 to 0.2 0.08 to 5.13 0.08 to 5.13 Monthly Average CBOD5 – Summer (mg/L) 12 to 25 20 to 25 10 to 25 Monthly Average CBOD5 – Winter (mg/L) 12 to 25 25 15 to 30 Weekly Average CBOD5 – Summer (mg/L) 18 to 40 30 to 40 15 to 40 Weekly Average CBOD5 – Winter (mg/L) 18 to 40 40 23 to 45 Monthly Average Ammonia – Summer (mg/L) 1.3 to 9.4 1.5 to 9.6 1.1 to 8.6 Monthly Average Ammonia – Winter (mg/L) 2 to 10.8 2.2 to 10.4 1.6 to 11 Weekly Average Ammonia – Summer (mg/L) 2 to 14.1 2.2 to 14.4 1.6 to 12.9 Weekly Average Ammonia – Winter (mg/L) 3 to 16.2 3.3 to 15.6 2.4 to 16.5 Phosphorus Limit (mg/L) N/A N/A 1

To understand potential credit demand, it is necessary to understand the estimated costs for upgrading technology to provide ENR for TN and TP removal. This section of the report provides estimated costs for upgrading permitted WWTPs in the Driftwood and Tippecanoe watersheds to enhanced nutrient ENR for reducing TN and TP effluent loads. The costs are based only on cited literature and the information available in the CWNS and the permits and are intended solely to inform the feasibility study. Actual upgrade costs vary widely, depending on a great number of factors, including: • actual target effluent concentrations for nitrogen and phosphorus; • existing facilities’ suitability for various types of upgrades; • various wastewater characteristics, including influent TP and TN concentrations, influent rbCOD:TP and BOD:TN ratios, alkalinity levels, actual flow and constituent concentrations and their hourly, daily, and seasonal variations; • various operating characteristics, including ambient temperatures, mixed liquor characteristics, and plant configuration and control methods; • local labor, material, and operational costs which may vary significantly over time; and

• financing terms.

The cost estimates are intended only as a general guide for order‐of‐magnitude cost ranges for various upgrade options. More detailed, plant‐specific analyses are necessary to determine whether WQT would be a more cost‐effective option for reducing nutrient loading. In particular, WQT may be advantageous for: • long‐term compliance and cost savings; • short‐term compliance for the useful life of the facility or when other regulatory requirements will be better understood in regards to needed upgrades; • variance or compliance schedule justification; • small or difficult to upgrade facilities; and • future growth in fully capped watersheds.

Final Report – September 2011 Page 32 The analysis of permits showed that WWTPs in the Driftwood and Tippecanoe subwatersheds span a variety of process types for permitted flow rates ranging from about 24,000 gallons per day (gpd) to 5.0 million gallons per day (MGD). For the purposes of this evaluation, the secondary (i.e., biological) treatment process identified in the permits for each of these plants was used to categorize the WWTP as either an “activated sludge” system or a “lagoon/trickling filter” system. The reason that this simple categorization was chosen is because activated sludge systems are generally relatively simple to convert to biological nutrient removal (BNR) systems (by adding anaerobic and/or anoxic reactors), while lagoons and trickling filters generally require more extensive secondary treatment system modifications to be upgraded to BNR. A number of specific treatment process were listed in the permits for WWTPs categorized as “activated sludge” systems, including “extended aeration”, “oxidation ditch”, “waste stabilization”, “sequencing batch reactor”, and “activated sludge” or “conventional activated sludge”. The numbers of facilities in each of these two main categories across a range of flow rates are summarized in Table 14.

Based on this summary of the characteristics of WWTPs in the target watersheds, seven generic WWTPs (design flow/system type) were used to evaluate the range of potential costs that may be required to upgrade WWTPs in the Driftwood and Tippecanoe watersheds to nitrogen removal, phosphorus removal, or both. The generic WWTPs simulated in the cost analysis are also identified in Table 14. The design flow of each simulated plant is roughly equal to the average flow rate for the WWTPs in a given flow range.

Table 14. Summary of facility type and flows for WWTPs included in Driftwood and Tippecanoe nutrient removal analysis Activated Sludge (AS) Lagoon/Trickling Filter (TF) Flow Range # Total Flow Simulation # Total Flow (MGD) Facilities (MGD) Plant Facilities (MGD) Simulation Plant <0.1 5 0.26 0.05 MGD AS 4 0.21 0.05 MGD Lagoon 0.1 to 0.5 9 2.35 0.3 MGD AS 5 1.29 0.3 MGD Lagoon 0.5 to 1.0 2 1.59 0.75 MGD AS 0 0.00 -- 1.0 to 4.0 0 0.00 -- 2 5.15 2.5 MGD TF >4.0 4 21.13 5.0 MGD AS 0 0.00 --

In addition to the variety of process types and flow ranges identified in Table 14, for each simulation plant a number of different nutrient removal upgrade options were evaluated. In general, selection of the specific nutrient removal upgrade options simulated was based on the availability of cost information for those options, as found in the literature. Based on the review of relevant literature and previous nutrient removal experience, the Project Team selected two generic levels of treatment for nitrogen removal and two levels of treatment for phosphorus removal for the cost analysis. These levels are as follows: • TN1. The “low enhanced nutrient removal (ENR)” treatment level for nitrogen (or TN1) can be met by adding anoxic reactors (along with nitrified mixed liquor recycle lines) prior to the existing secondary treatment process (“pre‐anoxic”) or adding post‐secondary anoxic treatment (typically using filters supplemented with an external carbon source), for denitrification. Land application of effluent was also designated as a potential option for the TN1 treatment level, with nitrogen removal attributed to both nitrification/denitrification processes and vegetative

Final Report – September 2011 Page 33 uptake and sequestration (Crites and Tchobanoglous 1998). These processes have been documented to meet a TN of 10 mg/l reliably, but may be designed to meet lower TN levels. • TN2. The “high ENR” treatment level for nitrogen (or TN2) requires both pre‐ and post‐ secondary anoxic reactors and has been demonstrated as capable of achieving effluent TN concentrations below 5 mg/l (typically, 2‐3 mg/l). • TP1. The “low ENR” level for phosphorus (TP1), which requires an effluent TP of 1 mg/l or less, can be met using enhanced biological phosphorus removal (EBPR), typically involving the addition of anaerobic “selector” reactors prior to the secondary treatment unit, or using alum, which is typically dosed between the secondary treatment process and the secondary clarifier (but potentially in other configurations), for precipitating phosphorus. • TP2. The “high ENR” treatment level for phosphorus (TP2) requires either multi‐point alum addition or EBPR with single‐ or multi‐point alum addition and enhanced solids removal processes can be used to reach TP levels below 0.5 mg/l, often down to 0.1 mg/l or lower. Land application systems are also well documented to be able to meet TP2 treatment levels.

These treatment levels are summarized in Table 15, along with the assumptions for influent and baseline (i.e., effluent levels in the absence of ENR) concentrations. For the purposes of the cost analysis, the Project Team assumed a baseline TN concentration of 25 mg/l and TP concentration of 4 mg/l, the midpoints of the ranges shown in Table 15. In the cost calculations, these represent the assumed average effluent concentrations for the WWTPs prior to implementing an ENR process.

Another important implicit assumption in all of the cost calculations is that existing secondary treatment processes are nitrifying or can be made to nitrify – that is, they are sufficient to convert the majority of influent organic nitrogen and ammonia to nitrate, such that denitrification retrofits would be effective upgrade options. It is important to note that, even under a WQT approach to meeting TN reductions, participating WWTPs would still need to meet existing or revised water quality based effluent discharge limits for ammonia, a potentially mobile and toxic wastewater constituent. Under a WQT approach, nitrifying WWTPs would continue to discharge TN in the form of nitrate at non‐toxic levels.

Table 15. Summary of ENR treatment levels and assumptions for WWTP upgrade simulations TN TP Treatment Lagoon/ Level Effluent AS options TF options Effluent AS options Lagoon/TF options None (influent) 25-35 mg/l 4-8 mg/l Baseline (no 20-30 mg/l 2-6 mg/l ENR) 5-10 mg/l Pre- or post- Post-anoxic 0.5-1 mg/l EBPR or single- EBPR replacement or anoxic retrofit replacement point alum retrofit single-point alum Low ENR (1) or land or land retrofit application application <5 mg/l Pre-/Post- Post-anoxic <0.5 mg/l EBPR and/or EBPR replacement anoxic retrofit replacement multi-point alum and/or multi-point alum High ENR (2)1 retrofit or land retrofit or land application application 1 Enhanced solids removal also generally required for the high ENR process upgrades, particularly for TP removal

Final Report – September 2011 Page 34 Table 16 summarizes the characteristics of all applicable permutations of the TN and TP treatment levels as used in the cost analysis and provides a guide to the color coded rows in Table 17 through Table 23, which present the results of the upgrade cost analyses.

Table 16. Color coding of ENR treatment levels for Tables 17‐23 Treatment Level Effluent TN Effluent TP Color Coding TN1 5-10 mg/l -- Pink TP1 -- 0.5-1 mg/l Blue TN2 <5 mg/l -- Tan TP2 -- <0.5 mg/l Olive TN1/TP1 5-10 mg/l 0.5-1 mg/l Green TN1/TP2 5-10 mg/l <0.5 mg/l Purple TN2/TP2 <5 mg/l <0.5 mg/l Aqua

Note that the calculations for cost per pound (cost/#) removed that are summarized in Table 17 through Table 23 use the actual effluent TN and TP treatment levels indicated in the cost reference cited, while also assuming that the design/permitted flows are the actual plant flows (this assumption has little bearing on capital costs, but does affect O&M cost estimates). To be consistent with the basis used in U.S. EPA’s nutrient removal reference document (U.S. EPA 2008), the Project Team converted all costs to annual costs assuming 20‐year financing terms and a 6 percent interest rate. Additionally, all costs in Table 17 through Table 23 are presented in 2011 dollars using the latest ENR construction cost index (9011, March 2011) as a basis for adjusting the costs generated from the various sources indicated in the tables. Finally, note that where costs per pound removed are presented in Table 17 through Table 23, total costs are used in all calculations. Accordingly, it is difficult to compare the cost per pound of TP removed for a TN1/TP1 system with the cost per pound of TP removed for a TP1 system, for example, since the former number includes costs necessary for TN removal in addition to TP removal, while the latter number would be only associated with TP removal.

Table 17 and Table 18 provide estimated costs for 0.05 MGD activated sludge and lagoon upgrade options, respectively. Note that the cost estimates for the single‐point alum addition upgrade option may be biased high, as the capital costs cited by Keplinger, et al. (2003) were significantly higher than those cited in comparable references addressing single‐point alum treatment (i.e., as summarized in Table 19 and Table 21).

Final Report – September 2011 Page 35 Table 17. 0.05 MGD activated sludge ENR upgrade options and costs

Annual Treatment Level Cost Cost $/# Upgraded Process ($/yr) TN TP $/# TN $/# TP TN+TP Reference MLE – added anoxic zone 36,074 10 mg/l 2 mg/l 15.80 118.51 13.94 Foess (1998)1 Single-point alum addition 90,296 0.75 mg/l 182.54 182.54 Keplinger (2003)2 MLE + denitrification filters 59,636 6 mg/l 1 mg/l 20.53 130.01 17.73 Foess (1998)3 Land app. – spray irrigation 152,167 10 mg/l 0.1 mg/l 66.65 256.35 52.90 Buchanan (2010)4 Land app. – drip irrigation 82,891 10 mg/l 0.1 mg/l 36.35 139.79 28.85 Buchanan (2010)4 1 Used present worth costs for 50,000 gpd anoxic tank for MLE upgrade retrofit system (option R1) 2 Used average capital and O&M costs for Iredell (0.25 MGD), Valley Mills (0.81 MGD), and Hico (0.87 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used present worth costs for 50,000 gpd deep bed denitrification filter upgrade retrofit system (option R2) 4 Used model-simulated costs for 50,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

Table 18. 0.05 MGD lagoon ENR upgrade options and costs

Annual Treatment Level Cost Cost $/# Upgraded Process ($/yr) TN TP $/# TN $/# TP TN+TP Reference New MLE system 181,133 10 mg/l 2 mg/l 79.34 595.03 70.00 Foess (1998)1 Single-point alum addition 90,296 0.75 mg/l 182.54 182.54 Keplinger (2003)2 New MLE + denitrification 203,661 6 mg/l 1 mg/l 70.42 446.02 60.82 Foess (1998)3 filters Land app. – spray 152,167 10 mg/l 0.1 mg/l 66.65 256.35 52.90 Buchanan (2010)4 irrigation Land app. – drip irrigation 82,981 10 mg/l 0.1 mg/l 36.35 139.79 28.85 Buchanan (2010)4 1 Used present worth costs for 50,000 gpd MLE process system (option 1) 2 Used average capital and O&M costs for Iredell (0.025 MGD), Valley Mills (0.081 MGD), and Hico (0.087 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used present worth costs for 50,000 gpd MLE and deep-bed filtration process system (option 6) 4 Used model-simulated costs for 50,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

The results presented in Table 17 and Table 18 indicate that for 0.05 MGD activated sludge systems, secondary treatment upgrades and alum addition would typically be most cost effective, while comparably‐sized lagoon systems may be cost‐effectively upgraded for ENR via land application.

Table 19 and Table 20 provides estimated costs for 0.3 MGD activated sludge and lagoon upgrade options, respectively, with average costs for each treatment level provided where more than one option is presented. As indicated above, the average cost estimates for the single‐point alum addition upgrade option may be biased high, as the capital costs cited by Keplinger, et al. (2003) were significantly higher than those cited by CH2M‐Hill (2010) and others for single‐point alum treatment.

Final Report – September 2011 Page 36 Table 19. 0.3 MGD activated sludge ENR upgrade options and costs Treatment Level Cost Annual $/# Upgraded Process Cost ($/yr) TN TP $/# TN $/# TP TN+TP Reference Single-point alum addition 214,146 1 mg/l 78.16 78.16 Keplinger (2003)2 Single-point alum addition 10,552 1 mg/l 3.85 3.85 CH2M Hill (2010)3 TP1 AVERAGE 112,349 41.01 41.01 Not specified 154,196 3 mg/l 7.67 7.67 Colorado (2010)4 Multi-point alum addition 157,473 0.1 mg/l 44.21 44.21 CH2M Hill (2010)3 Not specified 269,758 0.1 mg/l 75.74 75.74 Colorado (2010)4 TP2 AVERAGE 213,616 59.98 59.98 Not specified 212,0771 10 mg/l 1 mg/l 15.48 77.41 12.90 U.S. EPA (2007)5 Not specified 469,8841 6 mg/l 0.8 mg/l 27.08 160.79 23.18 M&E (2008)6 TN1/TP1 AVERAGE 340,9811 21.28 119.10 18.04 Land app. – spray irrigation 910,143 10 mg/l 0.1 mg/l 66.44 255.54 52.73 Buchanan (2010)7 Land app. – drip irrigation 483,498 10 mg/l 0.1 mg/l 35.30 135.75 28.01 Buchanan (2010)7 TN1/TP2 AVERAGE 696,821 50.87 195.65 40.37 1 Capital costs only. O&M costs not included. 2 Used average capital and O&M costs for Valley Mills (0.081 MGD), Hico (0.087 MGD), and Clifton (0.328 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used average total life cycle costs for Oakley (0.25 MGD), Coalville (0.35 MGD), and Fairview (0.375 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for 0.1 MGD plant upgrades from Table 6 of paper, normalized as unit costs ($/gpd capacity) 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 7 Used model-simulated costs for 300,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

Final Report – September 2011 Page 37 Table 20. 0.3 MGD lagoon ENR upgrade options and costs Treatment Level Cost Upgraded Process Annual Cost TN TP $/# TN $/# TP $/# TN+TP Reference CH2M Hill New MLE system 352,708 3 mg/l 25.75 25.75 (2010)2 CH2M Hill Single-point alum 80,433 1 mg/l 29.36 29.36 (2010)2 Single-point alum 214,146 1 mg/l 78.16 78.16 Keplinger (2003)3 TP1 AVERAGE 147,290 53.76 53.76 CH2M Hill Multi-point alum + filters 307,001 0.1 mg/l 86.20 86.20 (2010)2 Not specified 269,758 0.1 mg/l 75.74 75.74 Colorado (2010)4 TP2 AVERAGE 288,380 80.97 80.97 Not specified 212,0771 10 mg/l 1 mg/l 15.48 77.41 12.90 U.S. EPA (2007)5 Not specified 469,884 6 mg/l 0.8 mg/l 27.08 160.79 23.18 M&E (2008)6 TN1/TP1 AVERAGE 340,9811 119.10 18.04 Buchanan Land application - spray 910,143 10 mg/l 0.1 mg/l 66.44 255.54 52.73 (2010)7 Buchanan Land application - drip 483,498 10 mg/l 0.1 mg/l 35.30 135.75 28.01 (2010)7 TN1/TP2 AVERAGE 696,821 195.65 40.37 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for 0.55 MGD lagoon retrofits, normalized as unit costs ($/gpd capacity) 3 Used average capital and O&M costs for Valley Mills (0.081 MGD), Hico (0.087 MGD), and Clifton (0.328 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for 0.1 MGD plant upgrades from Table 6 of paper, normalized as unit costs ($/gpd capacity) 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 7 Used model-simulated costs for 300,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

The results presented in Table 19 and Table 20 indicate that at these relatively low design flows, land application may be a cost‐effective ENR upgrade option, compared with more traditional secondary treatment upgrades or multi‐point alum addition, as needed to achieve comparably low TP levels.

Table 21 provides estimated costs for 0.75 MGD activated sludge upgrade options, with average costs for each treatment level provided where more than one option is presented. Because the costs cited by Keplinger (2003) were significantly higher than those cited by CH2M‐Hill (2010) and U.S. EPA (2008) for single‐point alum treatment, the Keplinger data was not used in the average cost calculation for TP1 treatment level upgrade options.

Final Report – September 2011 Page 38 Table 21. 0.75 MGD activated sludge ENR upgrade options and costs Treatment Level Cost Annual Cost $/# Upgraded Process ($/yr) TN TP $/# TN $/# TP TN+TP Reference MLE – added anoxic zone 72,554 10 mg/l 2.12 2.12 CH2M Hill (2010)3 Single-point alum addition 390,779 1 mg/l 57.05 57.05 Keplinger (2003)4 EBPR or single-point alum 29,232 1 mg/l 4.27 4.27 CH2M Hill (2010)3 addition Fermenter addition 28,942 0.5 mg/l 3.62 3.62 U.S. EPA (2008)5 Single-point alum addition 60,853 0.5 mg/l 7.62 7.62 U.S. EPA (2008)5 Fermenter and filter 60,358 0.5 mg/l 7.55 7.55 U.S. EPA (2008)5 addition TP1 AVERAGE 44,8461 5.77 5.77 Phased Isolation Ditch 69,016 3 mg/l 1.37 1.37 U.S. EPA (2008)5 retrofit MLE retrofit 111,316 3 mg/l 2.22 2.22 U.S. EPA (2008)5 Step-feed retrofit 111,316 3 mg/l 2.22 2.22 U.S. EPA (2008)5 Denitrification filter retrofit 230,053 3 mg/l 4.58 4.58 U.S. EPA (2008)5 TN2 AVERAGE 130,425 2.60 2.60 EBPR + multi-stage alum + 166,445 0.1 mg/l 18.69 18.69 CH2M Hill (2010)3 filters Fermenter, filter, and alum 118,242 0.1 mg/l 13.28 13.28 U.S. EPA (2008)5 addition Multi-point alum and filter 134,321 0.1 mg/l 15.09 15.09 U.S. EPA (2008)5 addition TP2 AVERAGE 139,669 15.69 15.69 Not specified 530,0912 10 mg/l 1 mg/l 15.48 77.39 12.90 U.S. EPA (2007)6 Not specified 557,904 6 mg/l 0.8 mg/l 12.86 76.36 11.01 M&E (2008)7 TN1/TP1 AVERAGE 543,9982 14.17 76.88 11.96 Land application - spray 2,275,356 10 mg/l 0.1 mg/l 66.44 255.54 52.73 Buchanan (2010)8 Land application - drip 1,169,997 10 mg/l 0.1 mg/l 34.16 131.40 27.11 Buchanan (2010)8 TN1/TP2 AVERAGE 1,722,677 50.30 193.47 39.92 Phased Isolation Ditch 238,958 3 mg/l 0.1 mg/l 4.76 26.84 4.04 U.S. EPA (2008)5 retrofit 5-stage act. sludge + alum 271,611 3 mg/l 0.1 mg/l 5.41 30.50 4.59 U.S. EPA (2008)5 retrofit Alum addition + 352,253 3 mg/l 0.1 mg/l 7.01 39.56 5.96 U.S. EPA (2008)5 denitrification filter TN2/TP2 AVERAGE 287,607 5.73 32.30 4.86 1 Average does not include Keplinger data 2 Capital costs only. O&M costs not included. 3 Used average total life cycle costs for Fairview (0.375 MGD), Moroni (0.9 MGD), Hyrum City (1.3 MGD), and Tremonton (1.9 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for Hico (0.087 MGD), Clifton (0.328 MGD), and Meridian (0.36 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 5 Used extrapolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 6 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 7 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 8 Used model-simulated costs for 750,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

Final Report – September 2011 Page 39

The results presented in Table 21 indicate that at these somewhat higher design flows, traditional secondary treatment upgrades or alum addition are more cost effective than land application for achieving very low TP levels.

Table 22 provides estimated costs for 2.5 MGD trickling filter upgrade options, with average costs for each treatment level provided where more than one option is presented. For this higher flow rate, we assumed that land application would no longer be a viable upgrade option.

Table 22. 2.5 MGD trickling filter ENR upgrade options and costs

Annual Treatment Level Cost Cost $/# Upgraded Process ($/yr) TN TP $/# TN $/# TP TN+TP Reference CH2M Hill New MLE system 163,852 10 mg/l 1.44 1.44 (2010)2 New phased isolation ditch 376,001 5 mg/l 2.47 2.47 U.S. EPA (2008)3 system New MLE system 1,009,265 5 mg/l 6.63 6.63 U.S. EPA (2008)3 New SBR system 1,128,002 5 mg/l 7.41 7.41 U.S. EPA (2008)3 New 4-stage bardenpho 1,385,265 5 mg/l 9.10 9.10 U.S. EPA (2008)3 system TN1 AVERAGE 812,477 5.41 5.41 New A/O system 811,098 1 mg/l 35.53 35.53 U.S. EPA (2008)3 CH2M Hill Single-point alum addition 144,841 1 mg/l 6.34 6.34 (2010)2 Single-point alum addition 168,211 0.5 mg/l 6.32 6.32 U.S. EPA (2008)4 New A/O w/fermenters 841,054 0.5 mg/l 31.58 31.58 U.S. EPA (2008)3 New A/O w/fermenters + 989,475 0.5 mg/l 37.15 37.15 U.S. EPA (2008)3 filters New mod UCT w/fermenters 1,504,002 0.5 mg/l 56.47 56.47 U.S. EPA (2008)3 + filters New 5-stage bardenpho 1,553,476 0.5 mg/l 58.32 58.32 U.S. EPA (2008)3 w/filters TP1 AVERAGE 858,880 33.10 33.10 New denitrification filters 662,948 3 mg/l 3.96 3.96 U.S. EPA (2008)4 New A/O w/fermenters + 1,137,896 0.1 mg/l 38.34 38.34 U.S. EPA (2008)3 filters + alum Multi-stage alum addition CH2M Hill 1,307,813 0.1 mg/l 44.06 44.06 with filters (2010)2 New A/O with fermenters, 1,137,490 0.1 mg/l 38.33 38.33 U.S. EPA (2008)3 filters, alum Multi-point alum addition with 395,790 0.1 mg/l 13.34 13.34 U.S. EPA (2008)4 filters TP2 AVERAGE 994,747 33.52 33.52 Jiang et al New A/O system 3,860,366 10 mg/l 1 mg/l 33.82 169.09 28.18 (2004)5 Not specified 441,4891 10 mg/l 1 mg/l 3.87 19.34 3.22 U.S. EPA (2007)6 Not specified 1,039,337 6 mg/l 0.8 mg/l 7.19 42.68 6.15 M&E (2008)7 New 3-stage UCT system 1,345,686 5 mg/l 1 mg/l 8.84 58.94 7.69 U.S. EPA (2008)3

Final Report – September 2011 Page 40 Annual Treatment Level Cost Cost $/# Upgraded Process ($/yr) TN TP $/# TN $/# TP TN+TP Reference New step feed AS system 900,422 5 mg/l 1 mg/l 5.92 39.44 5.14 U.S. EPA (2008)3 New 5-stage Bardenpho 1,434,739 5 mg/l 0.5 mg/l 9.43 53.86 8.02 U.S. EPA (2008)3 TN1/TP1 AVERAGE 1,503,673 11.51 63.89 9.73 Jiang et al New A/A/O with alum + filters 5,301,614 3.5 mg/l 0.1 mg/l 32.40 178.63 27.43 (2004)5 Alum addition with 989,475 3 mg/l 0.1 mg/l 5.91 33.34 5.02 U.S. EPA (2008)4 denitrification filters New phased isolation 722,317 3 mg/l 0.1 mg/l 4.31 24.34 3.66 U.S. EPA (2008)3 ditch+alum+filters New SBR + alum + fiters 1,236,844 3 mg/l 0.1 mg/l 7.39 41.67 6.28 U.S. EPA (2008)3 New 5-stage Bardenpho + 1,682,108 3 mg/l 0.1 mg/l 10.05 56.67 8.53 U.S. EPA (2008)3 alum + filters TN2/TP2 AVERAGE 1,986,472 12.01 66.93 10.18 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for Tremonton (1.9 MGD), Snyderville (2.4 MGD), and Magna (3.3 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 3 Used interpolated life cycle costs per MG treated for expansion options, normalized to $/gpd capacity 4 Used interpolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 5 Interpolated between 1 MGD and 10 MGD de novo plant options 6 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >1.0-10.0 MGD ($1,742,000/mgd capacity) 7 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD)

The results presented in Table 22 indicate that for replacement systems associated with trickling filter upgrades at these higher design flows, the additional costs associated with higher levels of ENR (e.g., TN1/TP1‐>TN2/TP2) are relatively modest.

Table 23 provides estimated costs for 5 MGD activated sludge upgrade options, with average costs for each treatment level provided where more than one option is presented. As for the 2.5 MGD option, for this higher flow rate, we assumed that land application would no longer be a viable upgrade option.

Final Report – September 2011 Page 41 Table 23. 5 MGD activated sludge ENR upgrade options and costs Treatment Level Cost Annual $/# Upgraded Process Cost TN TP $/# TN $/# TP TN+TP Reference CH2M Hill MLE – added anoxic zone 457,869 10 mg/l 2.01 2.01 (2010)2 CH2M Hill Single-point alum addition 271,677 1 mg/l 5.95 5.95 (2010)2 Fermenter addition 118,737 0.5 mg/l 2.23 2.23 U.S. EPA (2008)3 Single-point alum addition 237,474 0.5 mg/l 4.46 4.46 U.S. EPA (2008)3 Fermenter addition with filters 316,632 0.5 mg/l 5.94 5.94 U.S. EPA (2008)3 TP1 AVERAGE 236,130 4.65 4.65 Phased isolation ditch retrofit 336,422 3 mg/l 1.00 1.00 U.S. EPA (2008)3 MLE retrofit 554,106 3 mg/l 1.65 1.65 U.S. EPA (2008)3 Step feed retrofit 554,106 3 mg/l 1.65 1.65 U.S. EPA (2008)3 Denitrification filters 1,048,844 3 mg/l 3.13 3.13 U.S. EPA (2008)3 TN2 AVERAGE 623,370 1.86 1.86 EBPR + multi-point alum addition CH2M Hill 1,721,904 0.1 mg/l 29.01 29.01 and filters (2010)2 Fermenter addition with alum and 504,632 0.1 mg/l 8.50 8.50 U.S. EPA (2008)3 filters Multi-point alum addition with 653,054 0.1 mg/l 11.00 11.00 U.S. EPA (2008)3 filters TP2 AVERAGE 959,863 16.17 16.17 A/O retrofit + alum addition 518,769 10 mg/l 1 mg/l 2.27 11.36 1.89 Jiang et al(2005)4 Not specified 882,9781 10 mg/l 1 mg/l 3.87 19.34 3.22 U.S. EPA (2007)5 Not specified 1,699,014 6 mg/l 0.8 mg/l 5.88 34.88 5.03 M&E (2008)6 TN1/TP1 AVERAGE 1,033,587 4.01 21.86 3.38 Jiang et al A/A/O system + alum + filters 2,383,485 3.5 mg/l 0.1 mg/l 7.28 40.15 6.17 (2005)4 PID retrofit 1,068,633 3 mg/l 0.1 mg/l 3.19 18.00 2.71 U.S. EPA (2008)3 5-stage w/chem P 1,286,318 3 mg/l 0.1 mg/l 3.84 21.67 3.26 U.S. EPA (2008)3 Alum addition w/denitrification 1,484,213 3 mg/l 0.1 mg/l 4.43 25.00 3.77 U.S. EPA (2008)3 filters TN2/TP2 AVERAGE 1,555,662 4.69 26.21 3.98 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for Payson (4.5 MGD), Brigham (6 MGD), and Spanish Fork (6 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 3 Used interpolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 4 Interpolated between 1 MGD and 10 MGD retrofit plant options 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >1.0-10.0 MGD ($1,742,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD)

The results presented in Table 23 indicate that for a 5 MGD activated sludge upgrade, the cost increase associated with meeting a TP2 standard versus TP1 are significant, although the potential cost increase associated with meeting high versus low ENR combined TN/TP standards are less pronounced.

Final Report – September 2011 Page 42 3.8 Potential Credit Supply Estimation of potential credit supply from agricultural reductions was assessed using two models: SPARROW and SWAT. The SPARROW loads are based on those published by USGS whereas the Project Team set up and calibrated the SWAT model for the Tippecanoe and Driftwood River watersheds. Evaluation of both models was required to increase the resolution of watershed assessment procedures at scale. Results from the SPARROW model are provided in Table 24 and Table 25 for TN and TP, respectively. SPARROW model estimates of agricultural NPS loading are provided in Table 26.

The SPARROW model estimates for the Wabash River watershed indicate a substantial amount of TN and TP are generated within and exported at the mouth of each 8‐Digit HUC. Each HUC estimate is independent of the upstream loading passing through the HUC. The estimated loading from all land use categories and all HUCs is 923,700 pounds TN and 87,900 pounds TP per year.

The estimated cumulative agricultural loading that was generated by the model within each 8‐Digit HUC and exported at the mouth is 762,200 pounds of TN and 79,500 pounds of TP per year. The maximum standard error (combined fertilizer and manure related standard errors) is 5 percent for TN and 26 percent for TP. The average maximum standard error estimate is 2 percent for TN and 9 percent for TP.

The use of average results does not provide sufficient resolution or information on spatial variability. WQT program frameworks target optimum sites that generate economical transactions. Using an average pound per acre estimate helps illustrate the limited usefulness of the results. The entire watershed has approximately 21,088,000 acres. Using these figures, the average loading from agricultural land uses in the watershed is 0.04 and 0.002 pounds per acre for TN and TP, respectively. Assuming an average BMP reduction of 20 percent, it would take approximately 125 acres to generate 1 pound of TN load reduction. A finer resolution can capture the spatial variability. Targeting fields that have higher reduction capability has the potential to provide more economical transactions. In addition, the SPARROW model estimates already include reductions attributable to attenuation dynamics within each 8‐Digit HUC.

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Table 24. Total Nitrogen Exported at the Mouth of 8‐Digit HUC Watersheds, Independent of Upstream Watershed Loading (USGS, 1997). 1997 SPARROW Estimated Total Nitrogen Exported Average Watershed Subwatershed HUC Sq. Miles Acres (Pounds) Pounds/Acre Wabash Upper Wabash 5,120,101 1,589 1,016,960 57,591 0.06 Wabash Salamonie 5,120,102 552 353,280 9,357 0.03 Wabash Mississinewa 5,120,103 842 538,880 24,311 0.05 Wabash Eel 5,120,104 829 530,560 14,527 0.03 Wabash Middle Wabash-Deer 5,120,105 652 417,280 30,828 0.07 Wabash Tippecanoe 5,120,106 1,960 1,254,400 46,838 0.04 Wabash Wildcat 5,120,107 813 520,320 29,088 0.06 Wabash Middle Wabash-Little Vermilion 5,120,108 2,287 1,463,680 73,067 0.05 Wabash Vermilion 5,120,109 1,439 920,960 37,090 0.04 Wabash Sugar 5,120,110 808 517,120 20,979 0.04 Wabash Middle Wabash-Busseron 5,120,111 2,019 1,292,160 61,710 0.05 Wabash Embarras 5,120,112 2,442 1,562,880 82,977 0.05 Wabash Lower Wabash 5,120,113 1,321 845,440 65,194 0.08 Wabash Little Wabash 5,120,114 2,148 1,374,720 48,280 0.04 Wabash Skillet 5,120,115 1,049 671,360 17,211 0.03 Patoka-White Upper White 5,120,201 2,754 1,762,560 66,439 0.04 Patoka-White Lower White 5,120,202 1,675 1,072,000 48,900 0.05 Patoka-White Eel 5,120,203 1,195 764,800 16,309 0.02 Patoka-White Driftwood 5,120,204 1,154 738,560 36,800 0.05 Patoka-White Flatrock-Haw 5,120,205 586 375,040 23,203 0.06 Patoka-White Upper East Fork White 5,120,206 811 519,040 26,845 0.05 Patoka-White Muscatatuck 5,120,207 1,143 731,520 25,199 0.03 Patoka-White Lower East Fork White 5,120,208 2,025 1,296,000 36,035 0.03 Patoka-White Patoka 5,120,209 859 549,760 24,873 0.05 Total 32,950 21,088,000 923,652 0.04

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Table 25. Total Phosphorus Exported at the Mouth of 8‐Digit HUC Watersheds, Independent of Upstream Watershed Loading (USGS, 1997). 1997 SPARROW Total Estimated Total Phosphorus Phosphorus Exported Average Watershed Subwatershed HUC Sq. Miles Acres (Pounds) Pounds/Acre Wabash Upper Wabash 5120,101 1,589 1,016,960 5,571 0.0028 Wabash Salamonie 5120102 552 353,280 843 0.0011 Wabash Mississinewa 5120103 842 538,880 2,293 0.0020 Wabash Eel 5120104 829 530,560 1,503 0.0015 Wabash Middle Wabash-Deer 5120105 652 417,280 3,326 0.0042 Wabash Tippecanoe 5120106 1,960 1,254,400 3,338 0.0012 Wabash Wildcat 5120107 813 520,320 3,413 0.0035 Wabash Middle Wabash-Little Vermilion 5120108 2,287 1,463,680 5,474 0.0012 Wabash Vermilion 5120109 1,439 920,960 2,711 0.0007 Wabash Sugar 5120110 808 517,120 2,261 0.0020 Wabash Middle Wabash-Busseron 5120111 2,019 1,292,160 4,594 0.0009 Wabash Embarras 5120112 2,442 1,562,880 6,300 0.0015 Wabash Lower Wabash 5120113 1,321 845,440 5,758 0.0019 Wabash Little Wabash 5120114 2,148 1,374,720 4,357 0.0013 Wabash Skillet 5120115 1,049 671,360 1,204 0.0006 Patoka-White Upper White 5120201 2,754 1,762,560 5,130 0.0010 Patoka-White Lower White 5120202 1,675 1,072,000 4,353 0.0018 Patoka-White Eel 5120203 1,195 764,800 1,507 0.0008 Patoka-White Driftwood 5120204 1,154 738,560 3,070 0.0015 Patoka-White Flatrock-Haw 5120205 586 375,040 2,290 0.0026 Patoka-White Upper East Fork White 5120206 811 519,040 2,627 0.0024 Patoka-White Muscatatuck 5120207 1,143 731,520 2,606 0.0015 Patoka-White Lower East Fork White 5120208 2,025 1,296,000 2,669 0.0011 Patoka-White Patoka 5120209 859 549,760 2,252 0.0022 Total 32,950 21,088,000 79,452 0.0411

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Table 26. SPARROW model estimates of agricultural NPS loading Agricultural Total Agricultural Total Nitrogen Total Nitrogen Phosphorus Total Phosphorus Exported Maximum Exported Maximum Watershed Subwatershed HUC (Pounds) Standard Error (Pounds) Standard Error Wabash Upper Wabash 5120101 49,815 1% 5,571 4% Wabash Salamonie 5120102 8,098 5% 843 26% Wabash Mississinewa 5120103 21,120 2% 2,293 9% Wabash Eel 5120104 12,503 3% 1,503 14% Wabash Middle Wabash-Deer 5120105 26,686 1% 3,326 6% Wabash Tippecanoe 5120106 38,985 1% 3,338 7% Wabash Wildcat 5120107 25,415 1% 3,413 6% Wabash Middle Wabash-Little Vermilion 5120108 58,534 1% 5,474 5% Wabash Vermilion 5120109 30,400 4% 2,711 15% Wabash Sugar 5120110 18,428 2% 2,261 10% Wabash Middle Wabash-Busseron 5120111 52,001 2% 4,594 7% Wabash Embarras 5120112 69,343 1% 6,300 4% Wabash Lower Wabash 5120113 55,613 2% 5,758 5% Wabash Little Wabash 5120114 42,586 1% 4,357 5% Wabash Skillet 5120115 14,542 5% 1,204 23% Patoka-White Upper White 5120201 48,112 1% 5,130 6% Patoka-White Lower White 5120202 39,288 1% 4,353 5% Patoka-White Eel 5120203 13,672 4% 1,507 16% Patoka-White Driftwood 5120204 31,043 2% 3,070 8% Patoka-White Flatrock-Haw 5120205 20,024 2% 2,290 10% Patoka-White Upper East Fork White 5120206 22,596 2% 2,627 8% Patoka-White Muscatatuck 5120207 18,745 2% 2,606 9% Patoka-White Lower East Fork White 5120208 24,266 1% 2,669 8% Patoka-White Patoka 5120209 20,584 2% 2,252 10% Total 762,200 79,452

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The Wabash River watershed has significant variability in characteristics that promote or reduce NPS nutrient loading. For example, the variation in land use and vegetative cover is illustrated in Figure 5. Additionally, variations in Indiana animal livestock density estimated using 2007 Indiana Agricultural receipts are presented for beef cattle in Figure 6 and dairy cattle in Figure 7.

Figure 6. Number of Beef Cattle per Subwatershed within the Wabash‐Patoka Watershed.

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Figure 7. Number of Dairy Animals per Subwatershed within the Wabash‐Patoka watershed.

Metrological conditions also vary significantly across the watershed, with the range of average annual precipitation for 1961 to 1990 generally increases traveling from north to south by approximately 10 inches per year (Oregon Climate Service, 1995). According to a USGS study the average runoff from 1975‐2004 increases in a general pattern from north to south ranging from 10 to 18 inches per year (USGS, 2008). The ability for an agricultural field to supply nutrient credits is dependent on these and other physical, chemical and biological characteristics.

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To illustrate the benefits of using a smaller scale model for estimating nutrient loads, a limited SWAT 2009 model was developed for the Driftwood (05120204) and Tippecanoe (05120106) watersheds. The model was set up for agricultural land use, forestry and urban runoff were estimated beginning with default values. Another constraint is that point source data was not readily available. The model was then calibrated and validated using hydrology records from USGS stations across a 13‐year period (1997‐ 2009) with a 3‐year equilibration period. The calibration was completed using the limited water quality data from STORET and USGS and a weight of evidence approach based on other regional model results. The SWAT model calibration and validation results are provided in Appendix D. The model is limited by lack of sufficient operational practice information (e.g., nutrient application rates and methods), lack of point source data and lack of water quality data for calibration. However, the model and resulting outputs are considered sufficient for this preliminary evaluation.

Three scenarios for BMPs were created to test each BMP independently for the ability to reduce TN and TP. The three BMPs that were tested are as follows: 1) no‐till residue management, 2) filter strips, and 3) cover crops. These three BMPs are not the only practices that can be used to generate nutrient load reductions. These BMPs were selected based on several factors: 1) the three practices provide a range of nutrient load reduction results, 2) the SWAT 2009 model construct manages operational BMPs easier than structural and/or bank stabilization BMPs, and 3) the input data for gully corrections and bank stabilization was not readily available. The NRCS1 definition of each BMP is provided below (Indiana state office of the NRCS, electronic Field Office Tech Guide. Accessed April 12, 2011 at http://efotg.sc.egov.usda.gov//efotg_locator.aspx): Filter strips: A strip or area of herbaceous vegetation that removes contaminants from overland flow. Cover Crops: Grasses, legumes, forbs, or other herbaceous plants established for seasonal cover and other conservation purposes. No‐till Residue: Managing the amount, orientation and distribution of crop and other plant residues on the soil surface year‐round, while growing crops in narrow slots, or tilled or residue free strips in soil previously untilled by full‐width inversion implements.

A fourth BMP, nutrient management, was considered. Individual field data was not available to determine the range of nutrients currently applied and the percent of fields within each range. Therefore a nutrient management scenario was not fully completed. Nutrient management is critical to successful systems of BMP implementation as discussed in the Final Report of the Lake Erie Millennium Network Synthesis Team (Lake Erie Millennium Network Synthesis Team, 2010). In this final report the findings indicate:

There is no agronomic benefit to applying P fertilizer when STP levels reach 60 mg/kg Mehlich 3 P. Considering this benchmark, the occurrence of soil samples exceeding 60 mg/kg Mehlich 3 P was < 20% for 19 counties, 20 to 40% for 28 counties, and > 40% in 4 counties. Across the fifty counties, STP levels that are >60 mg/kg occur 30% of the time. (p. 10)

And,

Large additions of fertilizer or manure may change the soil mechanisms controlling P mobility by overwhelming a soil’s ability to moderate P solubility resulting in a dominant P mineral phase,

1 Indiana State office of the NRCS, electronic Field Office Technical Guide. Accessed April 12, 2011 at http://efotg.sc.egov.usda.gov//efotg_locator.aspx

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from the amendment (fertilizer/manure), controlling P solubility. This is an important finding because it suggests that management of soils with a low to moderate STP may need to be considered differently than soils with high STP. It may be misleading to lump them together and attempt to predict runoff P at low to moderate STP levels using models developed where sites with very high STP are included, because the mechanisms controlling P solubility (transport risk) are different. (p. 8)

The presence of high phosphorus applications (manure and/or fertilizer) occur 30 percent of the time in Ohio. A similar expectation might be made for Indiana. Nitrogen variability is demonstrated in a Purdue University Extension Agronomy Guide (Purdue University, 2005).

Table 27. General Guidelines for Interpreting NO3‐N Concentrations in Tile Drainage Water1. (Purdue University, 2005) NO3 –N Concentration (ppm) Interpretation < 5 Native grassland, CRP land, alfalfa, managed pastures Row crop production on mineral soil without N fertilizer Row crop production with N applied at 45 lbs/acre below economically optimum 5-10 N Rate2 Row crop production with successful winter crop to “trap” N 10 - 20 Row crop production with N applied at optimum N rate Soybeans Row crop production where: • N applied exceeds crop need • N applied not synchronized with crop need > 20 • Environmental conditions limit crop production and N fertilizer use efficiency • Environmental conditions favor greater than normal mineralization of soil organic matter 1 General guidelines for interpreting NO3-N concentrations in tile drainage water. The interpretation is derived from numerous studies conducted throughout the cornbelt and highlights land management strategies commonly found in association with a concentration measured in tile as the tile leaves the edge of field. 2 Economically optimum N rate is the rate that maximizes the return on investment in N fertilizer and therefore may be slightly lower than the N rate that maximizes crop yield.

However, the rates of manure and fertilizer applications within a subwatershed are not well documented in public records. Information like phosphorus soil testing is not available on a field‐by‐field basis, this is considered confidential information by many programs including those in the Federal Farm Bill. Pragmatic estimation of the TN and TP reductions from nutrient management without ranges of current practices prevents this practice from being accurately estimated.

3.8.1 Filterstrip Treatment Efficiency Results Table 28 for the Driftwood subwatershed, and Table 29 for the Tippecanoe subwatershed, provide reduction results for implementing filter strips on the edges of row cropped lands.

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Table 28. Filterstrip Treatment Efficiency Results at the Subwatershed Scale, in Percent, Driftwood Watershed. Sediment Subwatershed (in short tons) TP (in lbs) TN (in lbs) 3 Baseline 63,614.45 98,693.56 870,782.44 Filterstrip 45,438.97 74,860.31 702,915.79 % change -28.57 -24.15 -19.28 8 Baseline 125,460.07 278,662.35 2,467,305.30 Filterstrip 101,835.68 231,564.60 2,168,099.61 % change -18.83 -16.90 -12.13 23 Baseline 49,719.55 110,996.66 1,325,192.57 Filterstrip 39,227.43 90,869.64 1,096,201.60 % change -21.10 -18.13 -17.28

Table 29. Filterstrip Treatment Efficiency Results at the Subwatershed Scale, in Percent, Tippecanoe Watershed. Sediment (in Subwatershed short tons) TP (in lbs) TN (in lbs) 15 Baseline 8,413 30,564 364,300 Filterstrip 5,978 22,871 289,526 % change -28.9 -25.2 -20.5 28 Baseline 8,533 39,186 2,220,708 Filterstrip 6,677 30,921 1,849,657 % change -21.8 -21.1 -16.7 34 Baseline 46,706 83,358 873,397 Filterstrip 35,049 64,522 702,124 % change -25.0 -22.6 -19.6

To further refine the analysis the Project Team focused on the range of variability as much as the average results. Using field runoff projections for filter strips a percent reduction available from the BMP was completed in three Driftwood subwatersheds. This watershed was selected to evaluate at the field level due it having larger variability in the subwatershed results. This estimate still includes some use of averaging. The SWAT model groups common parcels that have like soils and land use. These groupings are referred to as hydrologic resource units (HRU). The groupings range from hundreds to thousands of acres each. For agricultural row cropping the HRU categorization considers the soil characteristics, crop rotation make up, cropping tillage implements and timing of passes and the nutrient application rates and methods. The variability in these physical and cultural settings, in addition to climate and other factors described above, introduces a range of uncertainty in credit estimation at the watershed scale. To overcome this, implement passes selected to simulate no‐till, mulch till and conventional tillage settings were based on the Indiana State Department of Agriculture conservation tillage data2 were analyzed. SWAT model scenarios for the corn‐soybean rotations assessed BMP treatment efficiencies. In the Driftwood subwatersheds only the highest residue rates (simulated by no‐till) or the lowest residue rates (simulated by conventional moldboard plow implement passes) exist.

Filter strip treatment efficiencies for nitrogen and phosphorus are listed in Table 30. NPS load reductions are in the range of 18 to 23 percent for nitrogen and 19 to 31 percent reduction of phosphorus. The

2 Indiana State Department of Agriculture (ISDA) ‐ 2007 Conservation Tillage Data by county‐ Available at: http://www.in.gov/isda/2354.htm

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filter strip average nitrogen reduction is 20.7 percent with a standard deviation of 1.3 percent. For phosphorus the average reduction is 25.3 percent with a standard deviation of 3 percent. An area weighted mean in reduction is 19.6 and 22.7 percent respectively for nitrogen and phosphorus. Therefore, a conservative treatment efficiency value would be 20 percent for nitrogen and 22 percent for phosphorus.

Table 30. Filterstrip Treatment Efficiency Results in Percent, Field Scale Results, Driftwood Watershed. Phosphorus Corn – Soybean Nitrogen Percent Percent Tillage Practice Subwatershed HRU (acres) Reduction Reduction No-till Corn & Drilled 1 1 (3,113) 21 24 Soybeans 1 3 (1,637) 21 25 4 45 (1039) 22 27 9 107 (3011) 21 25 Conventional Tillage 1 2 (564) 23 31 of Corn & Soybeans 1 4 (3,630) 20 24 4 44 (1,864) 20 24 4 48 (1,316) 21 27 Conventional Till 1 5 (960) 22 30 Corn & No-till Drilled 4 47 (1,844) 21 25 Soybeans 4 49 (3,423) 20 23 9 108 (15,664) 18 19 9 109 (4,108) 19 25

3.8.2 Cover Crop Treatment Efficiency Results Fall rye cover crop plantings were simulated across corn‐soybean rotation row crops in subwatersheds 3, 8 and 23 in the Driftwood Watershed and 19, 30 and 34 in the Tippecanoe Watershed. In Table 31, the reduction results for the Driftwood subwatersheds are provided. Comparing these to the Tippecanoe subwatershed results, provided in Table 32, indicates a larger range of variability than that found in the filter strip investigation. The substantial variability in treatment efficiency can be partially explained by the similarity of residue management and cover cropping. The cover crop leaves residue in the field over winter periods. In fields with high residue management the reductions are usually lower. But the previous use of high residue tillage practices does not fully explain the variability. This is evident in the variability that exists within Tables 32 and 33. Specifically, the variability found in the conventional tillage corn no‐till drill soybean category indicates the influence other factors have on the performance of cover cropping. Soil types, rate and timing of nutrients and year‐to‐year variability can all add to the variability in performance. Having optimum weather conditions allows the cover crop roots to keep nutrients closer to the surface. However, in different years wet weather or poor timing can lead to the nutrients leaching past the root zone prior to the uptake by the cover crop. Table 10 demonstrates an overall higher treatment efficiency gained by cover crops in the Tippecanoe subwatersheds. However, the variability still exists.

Table 33 indicates the range of variability in the Driftwood subwatersheds is from 6 to 47 percent for nitrogen reductions. The average is 25.7 percent with a standard deviation of 13.3 percent. The area weighted mean is 18.7 percent reduction in nitrogen. For phosphorus reductions the range is from 8 to

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53 percent with an average of 22.6 percent and a standard deviation of 12.6 percent. The phosphorus area weighted mean is 28 percent.

Table 34 indicates the range of variability in the Tippecanoe subwatersheds is from 12 to 53 percent for nitrogen reductions. The average is 32.3 percent with a standard deviation of 11.3 percent. The area weighted mean is 34.1 percent reduction in nitrogen. For phosphorus reductions the range is from 5 to 48 percent with an average of 23.6 percent and a standard deviation of 12.3 percent. The phosphorus area weighted mean is 26.6 percent.

Selecting a common value to use across the Wabash River watershed (intended for this project only) requires being conservative when assessing these watershed tables. A conservative treatment efficiency to use for cover crops would be 12 percent for nitrogen and 10 percent for phosphorus in the Driftwood and 21 percent for nitrogen and 10 percent for phosphorus in the Tippecanoe. Therefore, a conservative overall Wabash River watershed treatment efficiency factor for cover crops can be 12 percent for nitrogen and 10 percent for phosphorus. This estimate is for extrapolation purposes only. It is evident that there are settings where the average results greatly exceed these low estimates. As such all 8‐digit HUC watersheds can be expected to have a fraction of row cropped acres that will generate load reductions substantially greater than these estimates used for a conservative credit supply calculation.

Table 31. Cover Crop Treatment Efficiency Results in Percent at the Subwatershed level, Driftwood Watershed. Sediment Subwatershed (in short tons) TP (in lbs) TN (in lbs) 3 Baseline 47,523.19 46,417.47 606,466.57 Cover crop 35,965.97 36,931.85 456,404.85 % change -24.32 -20.44 -24.74 8 Baseline 53,811.47 57,150.39 982,454.42 Cover crop 41,817.83 47,331.51 970,886.99 % change -22.29 -17.18 -1.18 23 Baseline 170,780.14 623,550.71 7,812,374.37 Cover crop 165,272.36 612,790.28 7,788,234.26 % change -3.23 -1.73 -0.31

Table 32. Cover Crop Treatment Efficiency Results in Percent at the Subwatershed level, Tippecanoe Watershed. Sediment Subwatershed (in short tons) TP (in lbs) TN (in lbs) 19 Baseline 124,082 867,469 14,102,017 Covercrop 117,937 850,727 13,451,474 % change -5.0 -1.9 -4.6 30 Baseline 13,303 109,721 2,379,021 Covercrop 10,644 83,631 1,401,823 % change -20.0 -23.8 -41.1 34 Baseline 46,706 83,358 873,397 Covercrop 30,093 56,904 542,834 % change -35.6 -31.7 -37.8

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Table 33. Cover Crop Treatment Efficiency Results in Percent, Driftwood Subwatersheds. Phosphorus Corn – Soybean Nitrogen Percent Percent Tillage Practice Subwatershed HRU (acres) Reduction Reduction No-till Corn & Drilled 3 39 (2980) 11 10 Soybeans 8 103 (1,363) 6 8 Conventional Tillage 8 102 (8,031) 8 53 of Corn & Soybeans 23 227 (607) 47 33 23 228 (438) 25 15 23 229 (643) 47 33 23 230 (345) 36 30 Conventional Till 3 38 (3801) 28 19 Corn & No-till Drilled 3 40 (3,439) 22 18 Soybeans 3 41 (3,311) 25 18 23 231 (578) 26 18 23 232 (1,168) 27 16

Table 34. Cover Crop Treatment Efficiency Results in Percent, Tippecanoe Subwatersheds. Phosphorus Corn – Soybean Nitrogen Percent Percent Tillage Practice Subwatershed HRU (acres) Reduction Reduction No-till Corn & Drilled 19 212 (16,793) 31 19 Soybeans 19 213 (10,574) 23 15 19 216 (6,370) 12 5 30 291 (17,525) 20 22 34 330 (2,105) 21 16 34 336 (4,463) 23 9 Conventional Tillage 19 217 (3,171) 35 19 of Corn & Soybeans 30 292 (6,546) 21 19 30 293 (11,438) 34 14 34 331 (4,674) 35 22 34 334 (1,896) 34 22 34 335 (2,459) 36 24 34 337 (3,673) 33 22 Conventional Till 19 214 (5,850) 42 41 Corn & No-till Drilled 19 215 (8,409) 50 40 Soybeans 30 290 (23,325) 53 48 34 332 (7,983) 46 44 34 333 (4,516) 45 44

3.8.3 No­till Residue Treatment Efficiency Results The subwatershed treatment efficiency results for no‐till residue management in the Driftwood subwatersheds are given in Table 35 and Table 36. Many forms of residue management exist. The no‐till, ridge‐till and strip‐till management systems all leave substantially more residue on the field than mulch till (typically chisel plowing once and minimal disc passes) or conventional moldboard plow. Moldboard plowing, a form of conventional tillage, typically leaves less than 5 percent residue on the field.

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Table 35. No‐till Residue Management Treatment Efficiency Percents at the Subwatershed level, Driftwood Watershed. Sediment (in Subwatershed short tons) TP (in lbs) TN (in lbs) 3 Baseline 47,523.19 46,417.47 606,466.57 Residue 44,038.62 41,952.67 651,088.95 % change -7.33 -9.62 7.36 8 Baseline 53,811.47 57,150.39 982,454.42 Residue 47,686.51 51,264.50 1,086,082.03 % change -11.38 -10.30 10.55 23 Baseline 170,780.14 623,550.71 7,812,374.37 Residue 168,340.21 617,180.62 7,958,735.41 % change -1.43 -1.02 1.87

Table 36. No‐till Residue Management Treatment Efficiency Percents at the Subwatershed level, Tippecanoe Watershed. Sediment Subwatershed (in short tons) TP (in lbs) TN (in lbs) 19 Baseline 124,082 867,469 14,102,017 Covercrop 117,937 850,727 13,451,474 % change -5.0 -1.9 -4.6 30 Baseline 13,303 109,721 2,379,021 Covercrop 10,644 83,631 1,401,823 % change -20.0 -23.8 -41.1 34 Baseline 46,706 83,358 873,397 Covercrop 30,093 56,904 542,834 % change -35.6 -31.7 -37.8

Residue management alters the hydrologic pathway of the precipitation. The residue on the field traps more rainfall and snowmelt on the field slightly increasing the infiltration. An increase in nitrogen loading is observed. High residue management also is shown to increase nitrogen loading in both watersheds. Nitrogen is a soluble parameter and increased infiltration associated with high residue tillage can increase the drainage tile loading of nitrates (Pennsylvania State University, 1996).

The results of evaluating the range of phosphorus reductions at the HRU level in the Driftwood subwatersheds are provided in Table 37. The field delivery to streams ranges from 11 to 15 percent. The average reduction is 12.6 percent with a standard deviation of 1.2 percent. The area weighted mean is 10.3 percent. A conservative estimate for phosphorus reductions would be 10 percent.

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Table 37. No‐till Residue Management Treatment Efficiency Results in Percent, Driftwood Watershed. Nitrogen Phosphorus Corn – Soybean Reduction Reduction Tillage Practice Subwatershed HRU (acres) (Percent) (Percent) Conventional Tillage 8 102 (8,031) -7 11 of Corn & Soybeans 23 227 (607) -2 13 23 228 (438) -3 12 23 229 (643) -1 11 23 230 (345) 0 15 Conventional Till 3 38 (3801) -9 12 Corn & No-till Drilled 3 40 (3,439) -7 12 Soybeans 3 41 (3,311) -2 13 8 104 (9,953) -10 13 8 105 (1,845) -5 14 23 231 (578) -5 12 23 232 (1,168) -5 13

3.8.4 Estimation of the Watershed Potential Reductions in Nitrogen and Phosphorus Agricultural credit supply can be estimated using the nutrient reduction estimates, number of row cropped acres and a typical loading rate. The 2008 National Land Cover Data Set was used to supply estimates of corn and soybean acres within each 8‐Digit HUC. This data is provided in Table 38. The number of producers that are willing to participate further reduces the number of acres enrolled. A low end range of estimates is provided based on 10, 25 and 35 percent producer involvement. This limitation is applied for three main reasons: 1) not every producer is willing to participate in WQT programs, 2) not every acre in the watershed is capable of generating the load rates identified from this analysis, and 3) the lower estimate of participation better reflects a mature WQT program selection process targeting sites that provide higher credits per dollar.

Using the SWAT model estimates of current per acre loading, confirmed by comparisons with regional studies (USGS, 1997), (Smith, 2008) the TN loading for row crop agriculture can be conservatively estimated at 30 lbs TN /acre (33.6 kg TN /ha) and 3 lbs TP/acre (3.4 kg TP/ha).

Estimation of the volume of credits that can now be generated based on the pound per acre reduction potential. The three BMPs evaluated demonstrate that a BMP exists that will provide substantial watershed reduction potential by using only a fraction of the row cropped land within the watershed. The following estimates have not assigned a baseline condition or a trade ratio. Therefore, these are reported to provide the potential to supply agricultural NPS credits. The full trade ratio and baseline discussion will follow in later sections. The BMP evaluation summary of credits (i.e., pounds per acre without considering baselines or trade ratios) is as follows:

• Filter strips: Approximately 240 TN credits and 26.4 TP credits. A filter strip acre serves 40 acres of row crop (SWAT 2009 default application). The nitrogen reduction credit is based on 30 lbs TN/acre of runoff at 20 percent treatment efficiency serving 40 acres. The phosphorus reduction

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credit is based on 3 lbs TP/acre runoff loading treated at a 22 percent efficiency serving 40 acres. Table 38. Annual Nutrient Load Reduction Potential, Wabash River Watershed 8‐digit HUC Subwatersheds. (Assuming a 10 and 25 Percent Participation of Agricultural Row Cropped Acres, 20 Percent Reductions, and 40 lbs TN/acre and 3 lbs TP/acre Loading Rates.) Assuming 20% Removal Assuming 20% Removal and 10% Producer and 25% Producer Participation Participation Total Wabash River Corn and Watershed Bean TN TP TN TP 8-digit HUCs Corn1 Soybeans1 Acres Reduction Reduction Reduction Reduction Patoka 60,230 48,101 108,331 64,999 6,500 162,497 16,250 Eel 157,185 173,854 331,039 198,623 19,862 496,559 49,656 Mississinewa 145,656 187,385 333,041 199,825 19,982 499,562 49,956 Tippecanoe 507,145 310,764 817,909 490,745 49,075 1,226,864 122,686 Middle Wabash-Little 495,346 407,851 903,197 541,918 54,192 1,354,796 135,480 Vermilion Sugar 181,053 168,888 349,941 209,965 20,996 524,912 52,491 Embarras 507,303 493,372 1,000,675 600,405 60,041 1,501,013 150,101 Upper East Fork 119,568 135,259 254,827 152,896 15,290 382,241 38,224 White Lower White 158,550 173,803 332,353 199,412 19,941 498,530 49,853 Middle Wabash-Deer 183,536 115,514 299,050 179,430 17,943 448,575 44,858 Little Wabash 287,852 369,529 657,381 394,429 39,443 986,072 98,607 Upper White 347,017 402,364 749,381 449,629 44,963 1,124,072 112,407 Wildcat 197,126 168,787 365,913 219,548 21,955 548,870 54,887 Lower East Fork 85,544 81,640 167,184 100,310 10,031 250,776 25,078 White Eel 144,053 148,398 292,451 175,471 17,547 438,677 43,868 Vermilion 380,112 317,559 697,671 418,603 41,860 1,046,507 104,651 Upper Wabash 272,719 319,122 591,841 355,105 35,510 887,762 88,776 Muscatatuck 79,822 112,163 191,985 115,191 11,519 287,978 28,798 Flatrock-Haw 133,588 133,083 266,671 64,999 6,500 162,497 16,250 Middle Wabash- 333,446 286,934 620,380 198,623 19,862 496,559 49,656 Busseron Skillet 101,355 154,546 255,901 199,825 19,982 499,562 49,956 Lower Wabash 232,214 183,031 415,245 490,745 49,075 1,226,864 122,686 Salamonie 95,113 131,535 226,648 541,918 54,192 1,354,796 135,480 Driftwood 196,508 218,206 414,714 209,965 20,996 524,912 52,491 12008 National Land Cover Data Set

• Cover Crops: Approximately 3.6 TN credits and 0.3 TP credits. The nitrogen reduction credit is based on 30 lbs of TN/acre runoff treated at 12 percent efficiency. The phosphorus reduction credit is based on 3 lb TP/acre treated at 10 percent efficiency. • No‐till Residue: Approximately 0.3 TP credits. One acre of moldboard plow conversion to no‐till residue management practices increases nitrogen loading to the watershed. However, in phosphorus limited freshwaters this practice provides similar results to that of implementing

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cover cropping. The phosphorus reduction credit is based on 3 lb TP/acre treated at 10 percent efficiency.

Potential agricultural credit supply is provided in Table 38. The table is based on availability of BMPs that supply a 20 percent reduction of the 30 lbs TN/acre rate and a 20 percent reduction of the 3 lbs TP/acre. The acreage considered in the estimate of supply is limited to 10 or 25 percent of corn and soybean acres in the watershed.

In summary, the amount of potential to reduce agricultural row crop nonpoint source nutrient loading is ample. The forecasted potential for nutrient reductions is based on very conservative estimates. The conservative assumptions include: • The ability to generate 20 percent reductions across the watershed is predicated by: 1. The BMPs options available are many times greater than those assessed 2. The variability within each BMP assessed indicates that even the poorer performing BMPs have substantial opportunities to outperform the conservative estimates if placed in the right setting 3. Field scale calculations, based on site‐specific information, combined with a targeting framework in the WQT program will prioritize site selection • Agricultural land management other than corn and soybean acres can be used to generate credits • Estimate of nutrient runoff loading rates agrees well with regional research • Low producer participation levels used to generate cumulative numbers

The expected availability of load reduction to generate WQT credits is higher than Table 14 indicates.

3.9 Potential stakeholder participation

3.9.1 Water Quality Trading Feedback: Regulated Point Sources On September 21, 2010, CTIC presented information on the project to approximately 60 participants at the Indiana Water Environment Seminar. With assistance from the Project Team, CTIC developed a survey to assess those wastewater treatment plant representatives’ knowledge, perceptions and opinions on water quality trading.

On Jun 14, 2011, CTIC sent emails to 24 of those present at the seminar, asking each to respond to the survey electronically.

On July 11, 2011, CTIC contacted these same 24 waste water treatment plant representatives with a reminder to complete the online survey. Twenty‐four percent of those surveyed responded. Appendix E contains the survey questions and responses.

Duke Energy submitted the following statement regarding water quality trading: Duke Energy endorses the concept of water quality trading. This tool can be an important option for nutrient standard compliance in the future. Municipalities, utilities, other dischargers, and agriculture could potentially benefit greatly with this program by keeping costs low and improving water quality and the environment. Duke

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Energy will continue to be engaged by offering expertise and monitoring the progress of the issues associated with water quality trading.

3.9.2 Water Quality Trading Feedback: Farmer Focus Group On March 25, 2011, CTIC held a farmer focus group meeting in Greenfield, IN which lies in the Driftwood River watershed. Fifteen farmers, agribusiness, agriculture media and watershed group representatives attended. Jim Klang of Kieser and Associates, LLC presented information on water quality trading and fielded questions from the group.

Questions asked at the focus group and the associated answers are presented below.

Q: How will water quality be measured if farm ground is split between two watershed boundaries?

A: This will be specified by those developing the program.

Q: Many farmers have at least some best management practices in place. How can the farmer benefit from those practices previously implemented?

A: This will be specified by those developing the program.

Q: Would pollution control agencies require/drive a program such as this? What is the Indiana Department of Environmental Management’s incentive to support a program like this?

A: The federal Environmental Protection encourages states to develop nutrient standards for water quality. Some states have approved nutrient management standards, such as Wisconsin, which has approved nutrient standard for lakes. The Indiana Department of Environmental Management has not developed nutrient management standards, nor has the Illinois Environmental Protection Agency.

Q: How will urban pollutants be addressed? At what watershed scale (hydrologic unit code) can these programs work?

A: This will be specified by those developing the program. The program should be built to avoid “hot spots.”

Q: If farmers are upstream of the regulated facility, can they sell credits?

A: Most likely. This will be specified by those developing the program.

Q: Is there a program such as this in place in Indiana?

A: Not at this time.

Q: What are the regulated facilities expecting with regard to a program like this? Are they ready to talk about a program like this in real terms?

A: Many regulatory agencies are watching how existing programs function, because they expect nutrient standards will be imposed in the future.

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Questions posed to the audience:

Q: Are you comfortable with the environmental protection aspect of a program such as this?

A: It depends on the accuracy of the models that measure water quality results.

Q: Who are you comfortable with coming onto your land to check best management practice performance?

A: Crop consultants or USDA Natural Resources Conservation Service representatives.

Q: Would you be comfortable with a requirement that directs management practice inspection?

A: If the inspection process is transparent. For example, the farmer needs to know exactly what will be assessed. Farmer costs related to inspections should be known.

4. Putting It All Together – Market Analysis and Trading Considerations This section synthesizes the information presented in Section Two and provides a summary of findings and recommendations related to overall WQT market feasibility in the Wabash River watershed.

4.1 Pollutant Loads The timing of upgrades, design capacity of the facility, and treatment technologies selected are important factors that determine the economic and performance capability for point source entities to become credit generators. Therefore, while there is a strong potential for point source to point source trading to be effective in the Wabash River watershed a more detailed assessment of options based on the newly required effluent limits will be necessary.

4.2 Regulatory Drivers As discussed in Section 3.1.1., Indiana’s current water quality standards and nutrient permit limits provide a regulatory incentive for trading based only on TMDLs developed to protect the narrative water quality standards. However, the regulatory forecast for numeric nutrient criteria indicates that nutrient criteria will emerge within the next decade. These effluent limits will likely affect permit limits once these new standards are approved. Knowing these regulatory changes are likely to occur within the next 3‐5 years, stakeholders do have a strong reason to consider water quality trading now as a potential tool for achieving permit limits in the future.

4.3 Trade Ratios A load reduction from one source, at a remote location, must provide equal or greater environmental protection for the water resource. A watershed understanding aids the WQT managers when developing a program. For example, an understanding of the natural nutrient attenuation that occurs on the land and in the streams, lakes and rivers prior to reaching the protected water resource allows appropriate factors to be considered. A trade ratio refers to an explicit factor that is applied to either or both the

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buyer and seller. According to the Water Quality Trading Toolkit for Permit Writers development of trade ratios should consider the following elements when being developed: • Location Factors and/or Delivery Ratios: address the differences in attenuation of nutrients when discharges occur at spatially different points. A location factor addresses the attenuation of the nutrients being traded between the buyer’s or seller’s discharge point and the downstream water resource being protected. A delivery ratio addresses the attenuation that occurs between the buyer’s and seller’s discharge points when the seller is upstream. • Equivalency Factors: address the differences in environmental stress that slight differences in discharged pollutant forms or interaction between multiple stressors have on a water resource. For instance, in nutrient trading a buyer may discharge a higher level of bioavailable phosphorus forms than the agricultural nonpoint source runoff discharge being offered as an offset. Other programs trading to relieve impairments with multiple stressors develop ratios for each stressor based on how the parameters interact within that specific watershed setting. • Uncertainty Factors: address the introduced variability, errors and lack of understanding that WQT programs work with on a daily basis. Uncertainty occurs from many sources. A few main components that introduce uncertainty into WQT transactions are: 1) from analytical errors when collecting and testing water quality samples, 2) from stochastic variability in discharger loading, climatic events and nonpoint source settings, 3) credit estimation tools, such as models, that introduce simplifications of the real world, and 4) errors in watershed understandings within the current day best available science. • Policy Factors: address the socio‐political elements watershed and WQT decisions makers implement to address equity issues, incentivize good behavior, advance watershed goals or provide disincentives for less desirable practices.

Addressing these components can be done by assigning one or more factors to the buyer and others to the seller or as a block in one trade ratio. The Ohio EPA Water Quality Trading Rules3 require point source to point source trading to use a ratio where “one pound of pollutant reduction equals one pound of water quality credit for that pollutant”. For nonpoint source generated credits for point source discharges the trade ratio must be: 1) When there is not an approved TMDL, be calculated using a trading ratio where two pounds of pollutant reduction equals one pound of water quality credit for that pollutant; or

2) When there is an approved TMDL, be calculated using a trading ratio where three pounds of pollutant reduction equals one pound of water quality credit for that pollutant.

The rules also allow the director to consider or impose other alternative trade ratios based on watershed, habitat restoration or other considerations.

The draft Water Quality Trading Rules in Minnesota4 quantified phosphorus based risk trade ratios. The Minnesota Pollution Control Agency (MPCA) defined the risk trade ratio as a factor that addresses the total of all risks associated with trading. The point source to point source trades when dealing with an upstream seller are to use a one credit sold to 1.1 credit purchased ratio. For downstream sellers the

3 Ohio EPA Division of Surface Water OAC Chapter 3745‐5 Water Quality Trading; available at http://www.epa.ohio.gov/dsw/rules/3745_5.aspx 4 Minnesota Pollution Control Agency Draft Water Quality Trading Rules and Statement of Needs and Reasonableness; available at: http://www.pca.state.mn.us/index.php/water/water‐permits‐and‐rules/water‐rulemaking/water‐quality‐trading‐rule‐ development.html#draftrule

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ratio increases requiring 1.4 credits to be purchased. For nonpoint source generated credits the risk trade ratio is one credit sold to 2.5 credits purchased. In addition, every trade shall include an environmental factor (a policy factor) where the buyer must purchase an additional ten percent of credits that are not available for use.

These states are examples of regulatory authorities that have evaluated their watershed settings and the potential beneficial use of WQT for achieving the water quality protection goals. The state then created the more simplified method of a combined trade ratio to roll out watershed implementation when using WQT.

4.3.1 Location Factors and Delivery Ratios The location factors and delivery ratios are watershed specific attenuation coefficients. The need to use a location factor and/or delivery ratio also depends on the methods used to define the environmental credit value. Upland delivery ratios may be necessary if the load reduction estimation tools do not predict an edge of field result or the fields being credited are not adjacent to waterbodies. Channel attenuation addressed by delivery ratios (assimilation losses between upstream credit generators and downstream buyers) may not be required if the WQT program addresses losses via a location factor for both the buyer and seller. Because an actual WQT framework is not set the location factor method will be used in the feasibility evaluation.

This section explains the method used to determine the WQT location factor for each watershed. The location factor is determined by applying values of nutrient loading predicted by the USGS SPARROW model results (USGS, 2009). The model estimates the fraction of incremental nutrient load delivered to the Gulf from upstream watersheds. Incremental loading is defined as the amount of Nitrogen/Phosphorus generated in an individual watershed that arrives at the Gulf of Mexico. This percentage can be used to determine location factors for each watershed and can assist programs attempting to protect both the Gulf of Mexico and upstream waters.

As water travels downstream, interaction with the surroundings causes nutrients to be naturally removed from the stream. Water entering streams near the Gulf has less time to interact with its surroundings than water entering farther upstream. In most cases, the percentage of nutrients that reach the Gulf is higher for water that enters streams near the Gulf compared to water that enters upstream watersheds. However, the SPARROW model sometimes predicts that a downstream watershed has a lower percentage of delivered incremental loads than an upstream watershed. This could be caused by error in the model or individual watershed characteristics, including lakes, wetland impoundments, and poor hydrologic connectivity in the local watershed. Such characteristics allow more nutrients to be assimilated than would otherwise occur in the stream channel. In order to conservatively represent the most restrictive watershed, all of the best‐fit lines were adjusted down to include the lowest incremental watershed results. This reduced the risk of creating nutrient hotspots by applying a more conservative location factor that did not overestimate natural attenuation in any watershed. According to Bill Franz5, U.S. EPA Region V, project manager for the SPARROW modeling, U.S. EPA contracts with USGS a 12‐digit HUC output from SPARROW will be available in 2011. This will allow finer resolution to be applied using this technique.

Figures 1‐17 graphically show how the SPARROW data was adjusted to determine the location factor. The percentage values provided by the SPARROW model are fitted with a trend line (y1) that best

5 Bill Franz, personal communication, May 25, 2010, Chicago, IL

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represents the linear nature of the data. If a point is below the best fit line, it is being credited with more natural nutrient attenuation than is actually predicted in that watershed. To correct this characteristic for the mainstem of the watershed and its larger tributaries, the trend line is adjusted down to the lowest point to assure an individual watershed‘s percentage is not over‐estimated. The resulting best fit line (y2) provides a conservative estimate of a watershed’s nutrient load delivery.

Within an 8‐digit HUC the attenuation factor in subwatersheds is already addressed by the channel process based estimates of SWAT. Prior to the SPARROW update a linear interpolation of the current SPARROW model results could be used. Or to calculate location factors using a step wise process, factors for the subwatersheds can be determined by running the watershed assessment model with multiple scenarios. The scenarios are designed to evaluate the change in loading at the mouth given a change in loading at each subwatershed (entered one at a time).

Review of the SPARROW data indicates the maximum phosphorus attenuation rate between Wabash 8‐digit HUC watersheds is 49 percent; total nitrogen maximum is 44 percent. Both of these maximums occur when the Salamonie River loading travels to the confluence of the Wabash River with the Ohio River. More commonly, headwaters to confluence based loading losses are approximately 20 percent. Based on these tables, for the purposes of evaluating feasibility the near‐field trading will apply a 10 percent location factor and far‐field transactions are assigned a 25 percent factor. This reflects the midrange of possible values for each situation.

4.3.2 Equivalency Factors Equivalency factors address a generated credit reduction providing the same level of stressor impact relief on the water body being protected. This includes addressing potential differences in nutrient bioavailability. This feasibility study provides an in depth review of the potential for differences between buyer and seller’s discharged nutrients and the characteristics affecting nutrient bioavailability in each.

Phosphorus: While it is possible for a WQT program to require statistical sampling of discharges to determine their bioavailability this may not be cost effective or necessary. Instead an understanding of the forms of phosphorus discharged and the percentages within the total phosphorus discharged can inform the bioavailable estimate. NPS runoff may be assessed using this same breakdown. Addressing fresh water eutrophication in the Midwest is a concern of many states. The Minnesota legislature pursued a desire to better understand the stressors that lead to eutrophication by commissioning a report entitled “Detailed Assessment of Phosphorus Sources to Minnesota Watersheds”. The oversight task was assigned to the Minnesota Pollution Control Agency. This study contained a technical memorandum that defines the expected variability of phosphorus bioavailability found in different sources (Barr, 2004). The memorandum results are summarized in Table 1 below.

Bioavailability equivalence can be determined by dividing the bioavailability of the two sources to achieve a ratio. The denominator of the ratio is the equivalence fraction of the buyer discharge. This ratio can then be used to provide the WQT program with an equivalence discount factor. Table 1 indicates the most likely estimate of phosphorus bioavailability of a source, as well as, the range of expected variability. Combining the finding in Table 39 creates a list of probable equivalency factors. The equivalency factors for various trades are provided below:

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• Point source to point source domestic WWTPs trades is 85.5 / 85.5 or 1.0 with the likely range of variability in these transactions is expected to be plus or minus 10 percent • Equivalence factor in point‐point domestic WWTP selling to industrial WWTP trades is 85.5 / 88 or 0.97 with a likely range of variability of approximately 15 percent • The equivalence factor in domestic point source and agricultural nonpoint sources (fertilizer based) trades are 58 / 85.5 or 0.68 with a range of expected variability of approximately 20 percent • The equivalence factor in industrial point sources and agricultural nonpoint sources (fertile based) is 58 / 88 or 0.66 with a likely range of variability of approximately 25 percent

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Table 39. Estimates of phosphorus bioavialability fractions for specific source categories.

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For agricultural row cropping sites that have a historic manure application rate greater than agronomic recommended rates forms of phosphorus have higher fractions of bioavailability. The recommended factors are: • Domestic wastewater point source with ‐ agricultural nonpoint; 80 / 85.5 or 0.94 with a likely variability of 10 percent • Industrial point source and agricultural nonpoint sources; 80 / 88 or 0.91 with a likely variability of 25 percent

The range of variability is also presented. However, in a WQT program that has multiple buyers and sellers the range of likely variability of the program can be expected to decrease. The weighted average of bioavailability fractions from a source type will begin to converge towards the most likely value provided by the thorough review of research studies.

Nitrogen: Bioavailability of Total Nitrogen (TN) can also be characterized by understanding the forms of nitrogen in the discharges. TN consists of dissolved and particulate nitrogen forms. However, nitrogen forms are further subdivided into inorganic and organic forms of dissolved and organic nitrogen. The dissolved inorganic nitrogen (DIN) forms (NO2, NO3 and NH4) are 100 percent bioavailable (Berman and Chava, 1999). However, independent study findings regarding the bioavailability of organic nitrogen, dissolved inorganic nitrogen (DON) and particulate organic nitrogen (PON) result in a wide range of results. The predictability of bioavailability ranges becomes more difficult due to the aquatic life response varying substantially from setting to setting. This variability may be, in part, due to how bioavailability testing is based on algal bioassays (Seitzinger et al., 2002). DON in freshwater riverine systems was historically thought to be only available to bacterial uptake rather than direct algal uptake. When considering the Wabash and Mississippi River settings research is further complicated by the limited laboratory or bioassay testing methods being applied – these laboratory tests are run over a three week incubation period (Urgun‐Demirtas et al, 2008)(Berman and Chava, 1999). The nutrient spiraling occurring in the Mississippi Basin can occur over a substantially longer time period. These systems have trading options both longer and shorter than this lab period.

Research indicates that humic systems release more of the DON than previously thought, when studied across a summer period. Up to 20 percent of the DON can be photoammoniafied (Bushaw, 1996 and Dagg and Breed, 2003). Near field trading programs may trap DON and PON for a larger periods of time (e.g., when summer low flow pools develop) exposing the DON and PON to photochemical breakdown, zooplankton grazing and bacterial uptake resulting in NH4‐N or NO3release. Summarizing these studies, nonpoint source DIN is assumed to be 100 percent bioavailable while DON and PON collectively can be conservatively estimated at 20 percent bioavailable across the summer period. This is a conservative estimate as it does not account for the bacterial and zooplankton uptake.

Nonpoint Source Dominated Stream Considerations Research indicates a broad range of ratios comparing stream total nitrogen to DON. Seitzinger et al. indicated a literature review ranging from 10 to 80 percent (Seitzinger et al, 2002). Assessing the cropping and pasture runoff forms must consider the pathway of the nitrogen loading. Surface runoff is generally made up of a high fraction of organic nitrogen while tile water and groundwater recharge to the stream consists primarily of DIN. A reach’s unique loading ratio of tile and surface runoff determine the bioavailability. According to Goolsby et al. (USGS, 1999) the Wabash River mainstem has a 63

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percent DIN and 37 percent DON make up from 1980 to 1996. Combining these two assessments, the stream fractions of inorganic and organic and bioavailability of each, the total nitrogen bioavailability of crop and pasture sources can be conservatively estimated (i.e., the crop ratio of DON to TN will be lower than the River mainstem fraction. DIN bioavailability (63 percent times 100 percent bioavailable) plus organic nitrogen bioavailability (37 percent times 20 percent bioavailable) equals approximately 70 percent of the total nitrogen that is bioavailable in the system.

Wastewater Effluent Nitrogen Bioavailability Determining the bioavailability of WWTP nitrogen must also be done. Assessing the same forms of nitrogen (e.g., particulate and dissolved and further subdivided into inorganic and organic) the inorganic fractions are assumed to be 100 percent bioavailable. Literature indicates from review of data from WWTPs that secondary effluent WWTPs that denitrify have DON percentages around 10% of the total nitrogen discharged (Pehlivanoglu, 2004). However, advanced treatment with low nitrogen below 3 mg/l increases the fraction of DON in the total nitrogen to be 40 to 50 percent of total nitrogen (Chandran, 2010). These values indicate approximately 92 percent of the discharged secondary effluent is bioavailable. A conservative assumption for the secondary plants would therefore be 90 percent bioavailable. BNR facilities would have a 60 percent bioavailable fraction.

4.3.3 Uncertainty Factors The introduced uncertainty must be addressed to ensure WQT programs are as protective of the environment as onsite upgrades at the treatment facility. Methods to address the uncertainty include assigning a high conservative margin of safety to assure all factors are addressed. However, this can be costly and may eliminate the economic value of trading. Another approach is to assess the range of variability of the crediting method itself. This includes assessing variability in the input factors, the possible errors or variability possible when running the crediting estimation method, and the trade ratio components.

With today’s computing power a WQT program can run a jackknifing or Monte Carlo statistical test relatively inexpensively. Jackknifing tests inform program managers by removing the variability in key inputs one at a time and checking for response sensitivity. Monte Carlo assessments assign a range of variability and standard deviation for each input and begin simulation runs using expected values for inputs. Then large series of simulations (e.g., 10,000) are run using a randomly generated list of inputs using the defined range of variability for each as limiting conditions. These tests inform the WQT managers on the range of and probability of canceling or compounding errors. These tests can also be used with a sensitivity analysis to inform a manager which inputs need the most attention to detail. If a credit estimation result is very sensitive to a single parameter and field testing of that parameter is inexpensive (e.g., $15 soil phosphorus test) then a field protocol can be instituted that will decrease the introduced uncertainty. Otherwise a margin of safety is provided to address the introduced uncertainty.

It is important to consider that WQT is not a watershed diagnostic tool, it is a flexible compliance alternative. As such the methods used to set NPDES permit limits prior to WQT implementation can be used to inform the WQT program. Assumptions made in TMDLs, existing watershed planning work and modeling all can be used to guide the decisions for addressing uncertainty. WQT programs may experience a bias that the results of WQT must be iron clad, when the NPDES permit process itself is based on numerous assumptions or simplifications reflecting the best available science. As such to maintain cost‐effective alternatives the WQT program managers should work to minimize the use of

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overly conservative uncertainty factors. This includes recognition of the conservative assumptions used in the credit estimation method itself. For instance the Region V model or STEP‐L field reduction calculators used in the Midwest only credit sediment attached nutrients. These models do not credit soluble nutrient loading reductions. This can be used as an implicit margin of safety within the program.

For this feasibility assessment the point source loading is based on monitoring. Even though analytical and collection errors may exist, this is the compliance attainment regulatory measurement method. As such no uncertainty factor is necessary. Nonpoint source credit generation experiences possible introduced uncertainty in the estimates of field loading and reduction.

Direct margins of safety are necessary for the credits estimates of base loading and potential for reduction: • As discussed in Section 3.7, the estimates of loading from SPARROW are extremely well justified, a 10 percent factor is to be used for this input component when developing the uncertainty factor. • The BMP reduction estimates are based on averages within the subwatersheds; this is an implicit margin of safety. However, limitations in input data availability require the consideration of additional margins of safety. The evaluation will apply an additional XX percent uncertainty factor for this component due to modeling limitations.

The prior factors are considered as direct margins of safety applied to loading predictions. The following components are discussed recognizing that for trade ratio applications the margin of safety is being applied to a discount factor. These trade ratio factors are multipliers of the load reduction estimate. As such a margin of safety on the discount factor must consider the introduced error or uncertainty, and the magnitude of the reduction in credit value the factor applies. For example, a discount factor is applied linearly within the crediting method. This factor introduces variability with a maximum range of 25 percent. The expected value of the discount factor is 20 percent. Therefore, the maximum introduced variability is 25 percent of 20 percent; an uncertainty factor value of 5 percent can be considered sufficient. • The bioavailability factors are based on conservative estimates from peer reviewed literature. These factors have a typical maximum range of plus or minus 10 percent variability from the expected value. The bioavailability factors for nonpoint source trading introduce a maximum discount of 38 percent. Ten percent of this discount is approximately 4 percent. • Location factors developed are based on the SPARROW model (USGS, 2008) for far‐field estimates and on the implicit algorithms of the SWAT model results already assigned an uncertainty factor above. The far field location factor estimates used in this study are based on Table 2 for TN and Table 3 for TP. The SPARROW model is based on regressions calibrated with water quality and quantity monitoring station data. The range of the 95 percent confidence interval improves as the setting location moves closer in proximity to the Ohio River. As such, the maximum margin of safety for this method is 38 percent for nitrogen and 56 percent for TP. If the watersheds are closer to the Ohio River the results are typically around 10 to 20 percent for TN and 10 to 15 percent for TP. Therefore, for far field trading the median value of 26 percent rounded down to 25 percent for TN and 20 percent for TP is recommended. The reduction from maximum to median is justifiable based on the natural tendency for buyers to look for cost effective solutions. For near field trading, these estimates are based on linear reductions and the fact that Wabash subwatersheds are hydrologically interconnected.

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Therefore, the 10 percent discount factor is recognized to have a maximum rounded value of 50 uncertainty requiring a 5 percent uncertainty factor. • Policy Factors are socio‐politically derived and applied after the crediting estimation method. As such no uncertainty factor is required.

The final determination of uncertainty factors considers the individual component factors and solves for a reasonable overall factor considering implicit conservative assumptions and canceling errors. To apply these factors in an easy to use format the results below are discussed by round up to the next five percent mark. Resulting uncertainty factor estimates are as follows: • Loading estimates: 10 percent for SPARROW estimations • Loading estimates: 30 percent for SWAT model estimations • Bioavailability factors: 5 percent • Location factors: 25 percent for far‐field and a 5 percent factor for near‐field

To account for the probability of self canceling errors using a statistical analysis would require a larger population of credit generation sites than available in this preliminary assessment. The feasibility assessment will use conservative factors to evaluate potential, not to recommend final factors. It is recommended that a multidiscipline advisory committee made up of local experts be used to establish and vet the WQT program framework if chosen. The expected values used in the credit estimation process will depend on the credit estimation tools selected. This credit supply assessment applied methods/tools which are not typical of credit estimation techniques used in the field. Watershed models assess larger areas and inform supply volume potential and attenuation concerns. Field models provide better resolution necessary to factor in site‐specific characteristics in order to optimize compliance offset opportunities.

A conservative uncertainty factor based on lack of recognition of self canceling errors and using a compounding uncertainty values the near field trading uncertainty factor at 50 percent and the far‐field uncertainty factor at 70 percent.

As stated above the uncertainty with point source to point source trading will only be affected by bioavailability factors (nonexistent for municipal plants trading with municipal plants) and location factor uncertainty of 5 percent for near field and 25 percent for far field.

4.3.4 Policy Factors Policy factors can include net benefit factors assuring that when allowing the flexible compliance alternative of WQT there is an increased reduction in the pollutant parameter being traded. Other policy factors may be considered by program managers to address equity issues such as cost, incentivize BMPs that provide ancillary benefits such as habitat or target a specific subwatershed over another. In addition, policy factors have been used to bring buyers to the table early in the program as in the Great Miami WQT program6. For this feasibility study a net benefit factor for the water resource will be applied. As illustrated by the MPCA draft WQT rules, a commonly used net benefit factor is ten percent.

6 Miami Conservancy District. Great Miami River Watershed Water Quality Credit Trading Program Operations Manual, available at: http://www.miamiconservancy.org/water/documents/TradingProgramOperationManualFeb8b2005secondversion.pdf

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The final trade ratio used on the credit supply side will be: • Bioavailability discount factor for phosphorous: 32 percent for sites without heavy manure applications; 6 percent for sites with historically heavy manure application issues. • Bioavailability discount factors for nitrogen: 22 percent for municipal systems achieving secondary treatment requirements (equals one minus the ratio of 70 percent for NPS bioavailability divided by 90 percent bioavailability or secondary effluent) • Near field location factors: 10 percent • Far field location factors: 25 percent • Near field uncertainty factors: 50 percent • Far field uncertainty factors: 70 percent

4.3.5 Supply Trade Ratios The point source ‐ nonpoint source trade ratio for supply calculations results in a cumulative discount factor of: • Near field trading: o 92 percent for non‐manure related phosphorus locations, and o 70 percent for phosphorus sites with historically heavy manure applications o 82 percent for all nitrogen applications The above near field factors are rounded up to require two pounds of credit purchased for every one pound discharged. The estimated load reduction value will be divided by two. • Far field trading: o 127 percent for phosphorus non‐manure related locations o 101 percent for phosphorus credits generated at sites with historically high manure applications o 117 percent for all nitrogen applications The above far field factors are rounded up to require 2.3 pounds purchased for every pound offset. The estimated load reduction value will be divided by 2.3.

The point source to point source feasibility assessment will be based on: • Near field trading will be 15 percent • Far field trading will be 38 percent (rounded up to 40 percent)

The estimated supply side value will be divided by 1.15 and 1.4 respectively.

4.3.6 Buyer Trade Ratios The buyer must apply the net benefit policy factor to the purchases of credits. Therefore, every pound of nutrient discharge being offset will require 1.1 pounds of credit purchases.

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4.4 Baselines Determining when a source is eligible to generate and use credits is an important element of water quality trading. The point at which a source is eligible to participate is referred to as a baseline. Different baselines exist for credit buyers and sellers. According to U.S. EPA’s Water Quality Trading Toolkit for NPDES Permit Writers, baselines are the requirements that a source would be subject to in the absence of water quality trading. For point sources that want to generate and sell credits, the baseline is the point source’s most stringent NPDES permit limit. This can be a water quality‐based effluent limit (WQBEL). Point sources that want to buy credits to meet a WQBEL must first achieve a level of treatment equal to their categorical technology‐based effluent limits (TBEL). U.S. EPA does not allow trading to meet a TBEL. For nonpoint sources that want to generate and sell credits, methods to establish baselines vary depending on existing rules (i.e., state or local statutes and ordinances) and whether an approved TMDL with a load allocation exists. In the absence of an approved TMDL with a load allocation, nonpoint sources that want to sell credits must first meet state or local requirements. If no state or local requirements exist, a nonpoint source can use existing practices as its applicable baseline (e.g., based on a 3‐year history the producer must install an additional best management practice to generate a pollutant load reduction). Baselines have the potential to affect credit supply within a water quality trading market. Rigorous baselines for credit sellers could limit the number of credits available or price the reduction credits beyond the buyer’s range of willingness to pay.

In the Wabash River watershed, there are different potential baselines to consider given the current TMDL, the anticipated changes to water quality standards – and thus, NPDES permit effluent limits – and Gulf of Mexico hypoxia goals. Table 40 summarizes the current load allocations and which will be the required future baselines for nonpoint sources in the Wabash River watershed under Indiana’s existing nutrient benchmarks and potential new numeric nutrient criteria.

Table 40. Current and Potential Future Point and Nonpoint Source Baselines in the Wabash River watershed Baselines Type of Under Current Nutrient Benchmarks Potential Under New WQSs Hypoxia Source TMDL Permits Pre-Attainment Post-Attainment Goals TN TP TN TP TN TP TN TP TN TP Credit Sellers Point No 4% Monitoring Monitoring Permit Permit 3/5/8 0.3 mg/L 45% 45% Source reductions compliance compliance mg/L schedule schedule Nonpoint No 4% N/A; N/A; Interim Interim New New 45% 45% Source reduction consider consider baselines baselines TMDL TMDL approved approved targets targets WMPs WMPs Credit Buyers Point No 4% Monitoring Monitoring Permit Permit 3/5/8 0.3 mg/L 45% 45% Source reductions compliance compliance mg/L schedule schedule

Under the current nutrient benchmarks, the 2006 Wabash River watershed TMDL identifies a four percent reduction in TP and no reductions in TN for tributaries and subwatersheds discharging to the mainstem of the Wabash River. The permits issued to point sources under the current nutrient

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benchmarks contain monitoring requirements for TP and TN; therefore, there is no WQBEL to serve as a point source baseline for water quality trading in the Wabash.

Assuming that there will be changes to Indiana’s WQSs that will result in numeric nutrient criteria, baselines for point sources will change dramatically. Once IDEM adopts new numeric nutrient criteria, this will trigger a change in NPDES permit effluent limits. IDEM will have to reissue NPDES permits to contain WQBELs to meet the new numeric nutrient criteria. The Project Team assumes that new numeric nutrient criteria will result in WQBELs ranging from 3, 5, or 8 mg/L for TN and 1, 0.5, or 0.3 mg/L for TP. Any point sources that want to generate credits will first have to meet this more stringent baseline. It is assumed that reissued NPDES permits will contain a compliance schedule that requires permittees to meet the new WQBELs within the permit period.

Although nonpoint sources are not permitted, changes to Indiana’s WQSs could affect the baseline for nonpoint source sellers, as well. A change in WQSs could trigger the need to modify TMDL targets used to generate the allocations in the 2006 Wabash River watershed TMDL. This would lead to changes in load allocations and nonpoint source targets used in Section 319 watershed management plans. Both of these changes could alter the baseline for nonpoint source sellers.

The 2008 Gulf of Mexico Hypoxia Action Plan provides an additional reduction goal for point sources and nonpoint sources. These reduction goals may serve as a baseline for nonpoint source sellers. How this particular 45 percent reduction goal is implemented, however, could affect the nature of the nonpoint source baseline. It is important to remember that trading offers watershed managers an opportunity to accelerate early implementation of effluent limits and nonpoint source BMPs. A baseline that seems onerous for nonpoint source sellers could deter participation in trading and prevent sources from achieving early water quality improvements. Tailoring the baseline for nonpoint sources in a way that guarantees water quality benefits and promotes participation could create a win‐win situation for the Wabash River watershed and the achievement of hypoxia goals. The Wisconsin DNR prepared a WQT framework that recognizes that portions of their required agricultural NPS management requirements are limited by cost share funding resources (WI DNR 2011). The WQT framework recommends WQT funding to be considered an equivalent source of funding. The Ag producer then can generate interim credits for five years to bring the field down to a Phosphorus Index (PI) rating of 6 and long‐term credits for reductions beyond the PI rating of 6. For example, a site generates 18 credits per acre to reduce from a PI of 10 down to a PI of 1. The site receives 8 interim credits for five years, and then these credits are no longer eligible for use. The remaining 10 credits can be used indefinitely.

Likewise, IDEM could create a Gulf of Mexico state strategy that provides a time line for interim measurable milestones. The 45 percent goal could be divided into five nine year periods with an additional nine percent reduction goal added each period. The eligibility to generate WQT credits could then use the current interim measurable milestone as a baseline for generation credits. If the first nine years of compliance with the measurable milestone was eligible to generate WQT credits, agricultural producers would be afforded another incentive to protect the Gulf of Mexico.

For trading to take place in some watershed, regulators need to consider adoption thresholds of current requirements and decide if interim milestones are an option. If decision makers prevent WQT credit generation prior to complete attainment of TMDL load allocations (i.e., without allowing for interim measurable milestone goals) then the utility of WQT to advance TMDL implementation will be dampened.

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WQT programs across the nation use several alternatives for setting baseline requirements: • A three or five year cropping history • TMDL load allocation requirements • Local rules and ordinances requiring conservation measures

These baseline considerations are described in narrative and numeric forms. Examples include threshold measures like Phosphorus Index performance in Wisconsin or TMDL implementation plans that describe approved BMP lists.

To create a WQT framework within the Wabash River watershed, a significant effort will need to be invested in identifying appropriate and equitable eligibility requirements. These requirements will need to be vetted and tested for integration into existing programs and policies. For the purpose of this feasibility study, the policies limiting baseline requirement are assumed to be moderately restrictive to show potential supply of credits using a reasonably expected outcome of policy considerations.

4.5 Supply Side Credit Generation Evaluating implementation and opportunity costs of BMPs is critical for determining the potential economic benefits of WQT and conveying these benefits to wastewater treatment facility representatives and farmers. This section provides the results of an annual payment analysis for three different BMPs. The three BMPs assessed are cover crops, residue management, and filter strips. Each BMP has a different life cycle, each with associated opportunity costs, establishment costs, operation and maintenance (O&M) schedules and replacement costs. To overcome the difference in schedules and pricing a Life Cycle Cost (LCC) analysis was used. The LCC analysis begins with a present worth calculation which provides a present day equivalent cost for each BMP. The present worth analysis considers all expenditures made, including: 1) current investments, 2) annual payments, and 3) one time future payments. This present worth analysis was performed using a three percent inflation factor and a 20‐year BMP implementation period, including replacement costs if BMP design life is less than 20 years. The LCC analysis then converts the present worth into a twenty‐year annual payment assuming a five percent discount factor.

4.5.1 Determination of Costs The BMP establishment costs are gathered from the USDA NRCS Indiana payment schedules for the 2011 Environmental Quality Incentives Program (EQIP) program. This farm bill program provides 50 percent of the establishment cost of the BMP and an O&M payment for agricultural conservation. The EQIP payment schedule is provided on a BMP unit cost basis. The cost information for the three BMPs evaluated here are presented on an acre unit basis.

WQT trading is a market based program. The price paid for a credit will be determined by what the market will bear. The buyer desires the lowest cost available but only has control of the maximum payment that will be made. The seller considers the value of the BMP not only for installation cost reimbursement, but also for production goals, quality of life and future opportunities that may be lost if the land is tied up in a contract.

These factors were considered in a report completed for the Great Miami WQT program. A payment comparison between EQIP and WQT was performed by Kieser & Associates, LLC (Kieser & Associates, 2008) for several BMPs common to both programs in the area. The findings indicate that the WQT

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payments are very comparable with the EQIP payments. Still, the market pricing for this robust trading program has ample supply of producers interested in using the WQT program which includes a reverse auction prioritization of proposals. The reverse auction method sets a window of time that the round of BMP proposals will be accepted in. The proposals are submitted and prioritized on a dollar per combined nitrogen and phosphorus credit basis. The lowest unit price proposals are awarded up to the round cap on investment costs. Because the payments made by EQIP and WQT are so comparable, other reasons to participate in WQT programs exist. In addition, a few producers were noted that entered into a WQT contract below the EQIP equivalent payment because the producer desired the BMP for more than monetary reasons. The Kieser report lists several considerations why a producer would select WQT programs instead of EQIP. WQT payments are sometimes more attractive to a producer as transactions can be set up to have more flexibility in the added value uses of the land. For example, WQT provides flexible contract lengths. This gives the farmer the ability to enter and leave the market on their terms. Another benefit is that a producer can sign up a few or all acres into the BMP, while certain EQIP practices specify maximum land enrollment caps for farmers to receive payments. Some BMP’s payment schedules in EQIP end after three years. This period is considered sufficient for transition into practices like No‐till. In WQT a high residue practice can be signed up for a much longer‐term payment because the focus is on nutrient reduction. Vegetative seed mix requirements are less stringent than those specified by EQIP and allow more flexibility in both planting and uses. For instance a farmer participating in WQT can utilize other added value uses like haying to subsidize BMP costs.

Based on these considerations the economic feasibility determination for WQT in the Wabash watershed will consider a range of annual payments. This will enable the evaluation of market conditions to occur by informing the reader regarding the range and breakpoints to indicate where an adequate supply of nutrient credits exist. The BMP cost schedule will include several scenarios of payments: • 50 percent of establishment, plus O&M (equivalent to EQIP payment schedules) • 100 percent of establishment costs, plus O&M • 100 percent of establishment costs, O&M plus an opportunity cost of $4 per bushel corn • 100 percent of establishment costs, O&M plus an opportunity cost of $8 per bushel corn

Costs of lost opportunities are assessed in this analysis by determining the net profit from corn row cropping. Revenue lost is determined assuming a yield of 161 bushels per acre (bu/ac) for corn rotation on fields with average soil productivity (Purdue University, 2011). Corn costs of $4 and $8 per bushel were used to illustrate the fluctuating corn market in recent years (current corn costs of 7.70 $/bu have nearly doubled from 2 years ago). The opportunity cost was evaluated on a per acre basis using 2011 forecasted corn production input costs (Purdue University, 2011).

In addition to these assessments the cost analysis includes a discussion of BMPs installed on marginal riparian lands. It is only in the last decade that many producers have been able to use yield monitors on combines to consider marginal lands differently. Using scales at a grain coop will only provide a producer with an acre average yield. A yield monitor on a combine provides producers and technical service providers real time results. The producer can witness reductions in yield on much smaller strips of land. The marginal land’s yield loss can be due to many reasons; saturated soils, compaction, poor fertility or erosion. The cost analysis for riparian marginal lands in this assessment assumes a 25 percent reduction in yield (121 bu/ac). Based on 2011 input costs, a yield of 121 bushels/acre of corn that sells for $4/bushel will not recoup the cost of the investment. Therefore, investing the land into an alternative crop like supplemental haying, as part of a WQT program, could be more profitable.

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4.5.2 Present Worth Cost Analysis Using establishment costs, O&M costs, and opportunity costs, the present worth cost of each BMP is determined. Costs are analyzed over a 20‐year period, using an inflation rate of 3 percent to project future annual costs. Consistent with costs and production yields discussed above, present worth costs are on a per‐acre basis. Present values from all cost categories are calculated and added in order to determine the total present value.

4.5.3 Life Cycle Cost Analysis The life cycle cost (LCC) analysis for the BMP scenarios provides an estimate of the average annual cost of each BMP. The LCC of each BMP was determined by annualizing the total present worth cost. Considerations in calculating the LCC include installation, replacement, operation and maintenance, and opportunity costs. Installation and O&M costs were referenced from the NRCS 2011 EQIP Payment Schedule7 and from the IN Average Annual Cost Calculator8. A summary of these costs is provided in Table 41. Credit production per acre of BMP was calculated using SWAT model results, BMP treatment efficiencies, and the number of acres treated per acre of BMP. A summary of credits produced per acre of BMP can be seen in

Table 42. An inflation rate of 3 percent and discount factor of 5 percent was included in the annualized life cycle cost. The cost per credit of each BMP was then determined by multiplying the BMP cost per acre by the credits generated per acre. A summary of credits generated per acre and the cost per acre of each BMP scenario can be seen in Table 43.

Table 41. 2011 Indiana EQIP General Eligible Practices EQIP Payment Actual Cost Life Span O&M Yield Loss BMP Scenario [$/ac] [$/ac] [yr] [%] [bu/ac] Filter strips (Prime land) 351 702 10 2% 161 Filter strips (Marginal land) 351 702 10 2% 121 Cover Crop 31 62 1 1% -8 Residue Management 21 42 1 0% 6

Table 42. Summary of Credit Production per Acre of BMP Treatment Acres Lbs / Acre Efficiency Served per Credits / Acre of BMP BMP Scenario TN TP TN TPBMP Acre TN TP Combined Filter Strips (Prime) 30 3 0.2 0.22 40 240 26.4 266.4 Filter Strips (Marginal) 30 3 0.2 0.22 40 240 26.4 266.4 Cover crops 30 3 0.12 0.1 1 3.6 0.3 3.9 Residue Management 3 0.1 1 0.3

7 Available at the Indiana NRCS website: http://www.in.nrcs.usda.gov/programs/eqip/2011_EQIP_General_Practice_Details.pdf 8 Available at the Indiana NRCS website: http://efotg.sc.egov.usda.gov//efotg_locator.aspx

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Table 43. Summary of BMP Credit Production and Annualized Life Cycle Cost per Acre Cost / Acre Credits / Acre of BMP No Opportunity Cost Opportunity Cost Included 50% (EQIP BMP Scenario TN TP Combined Equivalent) Full Cost $4.00 / bu $6.00 / bu $8.00 / bu Filter Strips 240 26.4 266.4 $59.29 $118.58 $291.79 $704.29 $1,116.79 (Prime) Filter Strips 240 26.4 266.4 $59.29 $118.58 $85.54 $394.92 $704.29 (Marginal) Cover Crops 3.6 0.3 3.9 $42.60 $85.19 $44.20 $23.70 $3.21 Residue 0.3 $28.59 $57.17 $103.29 $126.35 $149.41 Management 3% inflation rate 5% discount factor Land use not included in LCC analysis Indiana EQIP payment used as 50% of establishment costs O&M costs referenced from Indiana EQIP

4.5.4 Filter Strips Filter strips utilizing warm season grasses have an EQIP payment rate of $351 per acre, corresponding with an actual establishment cost of $702. Filter strip opportunity costs have been analyzed under the following two scenarios: 1) installation on prime farm land (161 bu/ac) and 2) installation on marginal farmland with an associated 25 percent reduction in yield (121 bu/ac). Filter strips have a life span of 10 years and therefore are replaced once during the 20 year time period. They also have a yearly 2% O&M cost. When analyzed assuming installation on prime farm land, the per acre life cycle cost is in excess of $1000 when corn prices are relatively high (See Table 43). However, a filter strip as evaluated by the SWAT model reduces nutrients from 40 acres of land. Therefore, the volume of credits generated will offset the apparently high annual costs. Installation of filter strips on marginal farmland could be more appealing for farmers as planting corn on marginally productive land can result in income losses due to lower yields and the same variable and overhead costs. A summary of annualized life cycle costs per credit for filter strips installed on prime and marginal farm lands can be seen in Table 44 and Table 45 respectively.

Table 44. Summary of Annualized Life Cycle Cost per Credit: Filter Strips (Prime Land) Cost / Credit Payment Scenario TN TP Combined 50% Cost (EQIP Equivalent) $0.25 $2.25 $0.22 Full Cost $0.49 $4.49 $0.45 $4 / bu $1.22 $11.05 $1.10 Full + Opportunity Cost $6 / bu $2.93 $26.68 $2.64 $8 / bu $4.65 $42.30 $4.19 A filterstrip serves 40 acres of row crop (SWAT2009 default application). The nitrogen credited reduction is based on the 16 lbs TN/acre at 20 percent treatment efficiency of 40 acres. The phosphorus credited reduction is based on a 4 lbs TP/acre loading treated at a 22 percent efficiency over 40 acres.

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Table 45. Summary of Annualized Life Cycle Cost per Credit: Filter Strips (Marginal Land) Cost / Credit Payment Scenario TN TP Combined 50% Cost (EQIP Equivalent) $0.25 $2.25 $0.22 Full Cost $0.49 $4.49 $0.45 $4 / bu $0.36 $3.24 $0.32 Full + Opportunity Cost $6 / bu $1.65 $14.96 $1.48 $8 / bu $2.93 $26.68 $2.64 A filterstrip serves 40 acres of row crop (SWAT2009 default application). The nitrogen credited reduction is based on the 16 lbs TN/acre at 20 percent treatment efficiency of 40 acres. The phosphorus credited reduction is based on a 4 lbs TP/acre loading treated at a 22 percent efficiency over 40 acres.

4.5.5 Cover Crops Cover crops have an EQIP payment rate of $31 per acre, corresponding to an assumed establishment cost of $62 per acre (actual costs vary from site to site). For corn production, implementation of cover crop practices is projected to increase crop yield by 8 bushels/acre (Mannering 2007). Therefore, there is no opportunity cost associated with cover crops when used in continuous corn scenarios. Rather, there is a net gain in crop yields and a subsequent increase in commodity revenue. Cover crops have a life span of 1 year and therefore include a yearly replacement cost, as well as a yearly 1 percent O&M cost. Due to the increase in corn yield, cover crops are an economically viable BMP and therefore an attractive and practical choice for farmers. When analyzed using a relatively high commodity price ($8/bu), the increased corn yield nearly pays for the entire practice cost, yielding an annualized life cycle cost of $3.21 per acre. A summary of annualized life cycle costs per credit for cover crops can be seen in Table 46.

Table 46. Summary of Annualized Life Cycle Cost per Credit: Cover Crops Cost / Credit Payment Scenario TN TP Combined 50% Cost (EQIP Equivalent) $11.83 $141.99 $10.92 Full Cost $23.66 $283.97 $21.84 $4 / bu $12.28 $147.34 $11.33 Full + Opportunity Cost $6 / bu $6.58 $79.01 $6.08 $8 / bu $0.89 $10.69 $0.82 The nitrogen credited reduction is based on 16 lbs of TN/acre treated at 12 percent efficiency. The phosphorus credited reduction is based on 4 lb TP/acre treated at 10 percent efficiency.

4.5.6 Residue Management No‐till and strip‐till residue management has an EQIP payment rate of $21 per acre, corresponding to an actual establishment cost of $42 per acre. No‐till corn has been found to have a 4 percent lower yield than conventional tillage yields. However, no‐till corn grown in rotation with soybeans was found to have comparable yields to that of conventional tillage (Purdue 2011). Assuming that the crop is continuous corn, a 4 percent reduction in the 161 bu/ac yield would correspond with a 6 bu/ac yield loss. This practice has a life span of 1 year, requiring a yearly replacement cost. However, there is no

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additional O&M cost associated with the practice. A summary of annualized life cycle costs per credit for residue management can be seen in Table 47.

Table 47. Summary of Annualized Life Cycle Cost per Credit: Residue Management Cost / Credit Payment Scenario TN TP Combined 50% Cost (EQIP Equivalent) $95.29 Full Cost $190.57 $4 / bu $344.30 Full + Opportunity Cost $6 / bu $421.17 $8 / bu $498.03 One acre of moldboard plow conversion to no-till residue management practices increases nitrogen loading to the watershed. However, in phosphorus limited freshwaters this practice provides similar results to that of implementing cover cropping. The phosphorus credited reduction is based on 4 lb TP/acre treated at 10 percent efficiency.

4.6 Differences in Control Costs Economic incentive is a key factor that influences whether WQT is likely to generate participation in a watershed. Without adequate economic incentive, there is no market‐based driver for buyers and sellers to engage in a trade. Section 3.6 discusses potential credit demand, including the associated estimated cost per pound of TN and TP reduction through technology upgrades. This information becomes more meaningful when compared to the NPS BMP estimated costs presented in Section 4.5. Tables 48–50 present this cost comparison by size and treatment level.

The information under the median estimated upgrade costs aggregates information found in the upgrade options and cost tables in Section 3.6. It is important to note that the facility size categories of small, medium, and large do not directly align with the size categories used in the upgrade option and cost tables. This is also true for treatment levels. The Project Team used the size ranges and treatment levels most closely related. As a result, the TN treatment level of 8 mg/L in the tables below reflects the upgrade costs associated with a 10 mg/L treatment level used to develop the upgrade estimated costs in Section 3.6.

For purposes of comparison, the NPS BMP estimated costs associated with cover crops and filter strips were used to show a range of BMP costs. This information comes from the annualized life cycle cost per credit tables presented in Section 4.5.

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Table 48. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Small (<.3 MGD) Facilities by Treatment Level Potential Cost Median NPS BMP Estimated Cost Per $/lb Margin at a 2:1 Median Estimated Reduction (Min-Max) Trade Ratio Upgrade Costs $/lb $/credit1 Reduction (Min- (Minimum Cost of Treatment Level Max) Cover Crop Filter Strips Supply2) TP $198.07 $175.97 0.3 mg/L ($139.79-$256.35) $141.99 $11.05 ($193.57) $182.54 ($10.69-$283.97) ($2.25-$42.30) $160.44 0.5 mg/L (sole value) ($178.04) TN 3 mg/L N/A N/A $45.48 $23.38 5 mg/L $11.83 $1.22 ($20.53-$70.42) ($40.98) ($0.89-$23.66) ($0.25-$4.65) $51.50 $29.40 8 mg/L ($15.80-$79.34) ($47.00) TN+TP TN1 (5-10 mg/l) TP2 $40.88 $18.78 (<0.5 mg/l) ($28.85-$52.90) $10.92 $1.10 ($36.38) TN2 (<5 mg/l) ($0.82-$21.84) ($0.22-$4.19) N/A N/A TP2 (<0.5 mg/l) 1Potential cost margin determined by doubling the lowest NPS BMP median cost value and subtracting this from the median per pound estimated cost of the WWTPs. (This reflects a 2:1 trade ratio applied in the WQT program) 2Minimum cost of supply reflects the minimum NPS reduction cost doubled to reflect a 2:1 trade ratio subtracted from the median per pound estimated cost of the WWTPs.

Table 49. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Medium (.3 MGD – 5 MGD) Facilities by Treatment Level Potential Cost Median NPS BMP Estimated Cost Per $/lb Margin at a 2:1 Median Estimated Reduction (Min-Max) Trade Ratio Upgrade Costs $/lb $/credit1 Reduction (Min- (Minimum Cost of Treatment Level Max) Cover Crop Filter Strips Supply2) TP 0.3 mg/L $39.56 $17.46 ($13.28-$255.54) $141.99 $11.05 ($35.06) 0.5 mg/L $39.92 ($10.69-$283.97) ($2.25-$42.30) $17.82 ($3.62-$160.79) ($35.42) TN 3 mg/L $5.66 $3.22 ($1.37-$32.40) ($5.16) 5 mg/L $8.13 $11.83 $1.22 $5.69 ($2.47-$27.08) ($0.89-$23.66) ($0.25-$4.65) ($7.63) 8 mg/L $24.65 $22.21 ($1.44-$66.44) ($24.15)

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Potential Cost Median NPS BMP Estimated Cost Per $/lb Margin at a 2:1 Median Estimated Reduction (Min-Max) Trade Ratio Upgrade Costs $/lb $/credit1 Reduction (Min- (Minimum Cost of Treatment Level Max) Cover Crop Filter Strips Supply2) TN+TP TN1 (5-10 mg/l) TP2 $28.01 $25.81 (<0.5 mg/l) ($27.11-$52.73) $10.92 $1.10 ($27.57) TN2 (<5 mg/l) $5.49 ($0.82-$21.84) ($0.22-$4.19) $3.29 TP2 (<0.5 mg/l) ($3.66-$27.43) ($5.05) 1Potential cost margin determined by doubling the lowest NPS BMP median cost value and subtracting this from the median per pound estimated cost of the WWTPs. (This reflects a 2:1 trade ratio applied in the WQT program) 2Minimum cost of supply reflects the minimum NPS reduction cost doubled to reflect a 2:1 trade ratio subtracted from the median per pound estimated cost of the WWTPs.

Table 50. Comparison of Potential Estimated Upgrade Costs and NPS BMP Costs for Large (>5 MGD) Facilities by Treatment Level Potential Cost Median NPS BMP Estimated Cost Per $/lb Margin at a 2:1 Median Estimated Reduction (Min-Max) Trade Ratio Upgrade Costs $/lb $/credit1 Reduction (Min- (Minimum Cost of Treatment Level Max) Cover Crop Filter Strips Supply2) TP 0.3 mg/L $21.67 $0.43 ($8.50-$40.15) $141.99 $11.05 ($17.17) 0.5 mg/L $5.20 ($10.69-$283.97) ($2.25-$42.30) -$16.90 ($2.23-$34.88) ($0.70) TN 3 mg/L $3.16 $0.72 ($1.00-$4.43) ($2.66) 5 mg/L $5.88 $11.83 $1.22 $3.44 (sole value) ($0.89-$23.66) ($0.25-$4.65) ($5.38) 8 mg/L $2.27 -$0.17 ($2.01-$3.87) ($1.77) TN+TP TN1 (5-10 mg/l) TP2 N/A N/A (<0.5 mg/l) $10.92 $1.10 TN2 (<5 mg/l) $3.52 ($0.82-$21.84) ($0.22-$4.19) $1.32 TP2 (<0.5 mg/l) ($2.71-$6.17) ($3.08) 1Potential cost margin determined by doubling the lowest NPS BMP median cost value and subtracting this from the median per pound estimated cost of the WWTPs. (This reflects a 2:1 trade ratio applied in the WQT program) 2Minimum cost of supply reflects the minimum NPS reduction cost doubled to reflect a 2:1 trade ratio subtracted from the median per pound estimated cost of the WWTPs.

As shown in the Tables 48–50, facilities within the small category (<.3 MGD), have the greatest median estimated upgrade costs across all TN and TP treatment levels. When these median estimated upgrade costs are compared to NPS BMP median estimated cost per pound reduction, it is clear that the potential cost margin is significant, particularly for potential TP treatment levels. These tables also show

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that there are medium and large facilities that could benefit from efficient WQT to meet more stringent permit effluent limits.

To evaluate where WQT might effectively assist with cost effective nutrient reductions within the Wabash River watershed it is helpful to look across the subwatersheds. An evaluation of potential demand comparisons to the potential supply provides a reasonable maximum number of entities that can benefit from participating in WQT. Table 51 and Table 52 compare point source demand and NPS supply for TP and TN by subwatershed.

Table 51. Resulting demand and supply factoring in estimated cost margins for each TP permit effluent scenario Estimated Credit Supply @ Annual Load Annual Load 20% Adoption and 2:1 Trade Reductions (Tons) Reductions (Tons) Ratio HUC_8 Facility Size @ 0.3 @ 0.5 (Tons) Medium (15) 15.8 13.9 05120101 Small (39) 1.8 1.7 Total (54) 17.6 15.6 22.2 Medium (7) 5.9 5.2 05120102 Small (11) 0.4 0.4 Total (18) 6.3 5.6 33.9 Medium (21) 11.7 10.2 05120103 Small (16) 0.8 0.7 Total (37) 12.5 10.9 12.5 Medium (8) 9.2 8.4 05120104 Small (15) 0.6 0.5 Total (23) 9.8 8.9 12.4 Medium (7) 4.8 4.2 05120105 Small (4) 0.2 0.2 Total (11) 5.0 4.4 11.2 Medium (18) 15.3 13.5 05120106 Small (28) 0.8 0.7 (Tippecanoe) Total (46) 16.1 14.2 30.7 Medium (10) 6.1 5.3 05120107 Small (16) 1.7 1.6 Total (26) 7.8 6.9 13.7 Medium (11) 18.7 17.3 05120108 Small (8) 0.3 0.3 Total (19) 19.0 17.6 33.9 Medium (13) 15.8 15.0 05120109 Small (30) 0.1 0.1 Total (43) 15.9 15.1 26.2 Medium (2) 0.8 0.7 05120110 Small (8) 0.4 0.3 Total (10) 1.1 1.0 13.1 Medium (22) 41.4 38.7 05120111 Small (23) 0.4 0.3 Total (45) 41.8 39.0 12.4

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Estimated Credit Supply @ Annual Load Annual Load 20% Adoption and 2:1 Trade Reductions (Tons) Reductions (Tons) Ratio HUC_8 Facility Size @ 0.3 @ 0.5 (Tons) Medium (20) 15.4 14.3 05120112 Small (36) 0.5 0.5 Total (56) 15.9 14.8 37.5 Medium (10) 10.1 9.2 05120113 Small (17) 0.7 0.6 Total (27) 10.8 9.8 30.7 Medium (12) 21.7 20.6 05120114 Small (25) 0.9 0.9 Total (37) 22.6 21.5 24.7 Medium (5) 3.2 3.1 05120115 Small (10) 0.9 0.8 Total (15) 4.1 3.9 12.5 Medium (62) 45.0 38.4 05120201 Small (79) 2.0 1.8 Total (141) 47.0 40.2 28.1 Medium (15) 22.4 20.5 05120202 Small (22) 1.7 1.6 Total (37) 24.1 22.1 12.5 Medium (13) 8.6 7.9 05120203 Small (15) 0.5 0.5 Total (28) 9.1 8.3 11.0 Medium (20) 13.2 11.8 05120204 Small (30) 0.6 0.5 (Driftwood) Total (50) 13.8 12.3 13.1 Medium (7) 5.4 4.8 05120205 Small (5) 0.1 0.0 Total (12) 5.6 4.8 4.1 Medium (11) 2.0 1.7 05120206 Small (11) 0.7 0.6 Total (22) 2.7 2.3 9.6 Medium (19) 13.9 12.4 05120207 Small (15) 1.5 1.3 Total (34) 15.4 13.7 7.2 Medium (22) 11.4 9.6 05120208 Small (28) 0.6 0.5 Total (50) 12.0 10.1 6.3 Medium (7) 7.7 7.1 05120209 Small (9) 0.5 0.5 Total (16) 8.2 7.6 4.1

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Table 52. Resulting demand and supply factoring in cost margins for each TN permit effluent scenario

Facility Size Annual Load Reduction Estimated Credit Supply @ 20% (# of (Tons) @ 3 (Tons) @ 5 (Tons) @ 8 Adoption and 2:1 Trade Ratio HUC_8 facilities) mg/L mg/L mg/L (Tons) Medium (15) 113.7 94.7 66.3 221.9 05120101 Small (39) 8.7 7.3 5.3 Total (54) 122.4 102.0 71.6 Medium (7) 46.3 38.7 27.2 05120102 Small (11) 2.0 1.7 1.3 Total (18) 48.3 40.4 28.5 338.7 Medium (21) 95.7 80.0 56.5 05120103 Small (16) 5.8 4.9 3.4 Total (37) 101.5 84.9 59.9 124.9 Medium (8) 51.6 43.4 31.1 05120104 Small (15) 3.9 3.2 2.3 124.1 Total (23) 55.5 46.6 33.4 Medium (7) 33.9 28.3 19.8 05120105 Small (4) 1.4 1.1 0.8 Total (11) 35.3 29.4 20.6 112.1 Medium (18) 107.0 89.6 63.7 05120106 Small (28) 8.9 7.4 5.2 (Tippecanoe) Total (46) 115.9 97.0 68.9 306.7 Medium (10) 47.6 39.8 28.0 05120107 Small (16) 7.4 6.2 4.3 137.2 Total (26) 55.0 46.0 32.3 Medium (11) 97.2 83.0 61.7 05120108 Small (8) 2.1 1.8 1.2 338.7 Total (19) 99.3 84.8 62.9 Medium (13) 64.4 56.3 44.2 05120109 Small (30) 0.5 0.4 0.3 261.6 Total (43) 64.9 56.7 44.5 Medium (2) 5.3 4.4 3.1 05120110 Small (8) 2.8 2.3 1.6 131.2 Total (10) 8.1 6.7 4.7 Medium (22) 199.2 171.6 130.0 05120111 Small (23) 2.7 2.3 1.6 124.1 Total (45) 201.9 173.9 131.6 Medium (20) 79.5 68.8 52.7 375.3 05120112 Small (36) 2.0 1.7 1.4

Total (56) 81.5 70.5 54.1 Medium (10) 59.6 50.5 36.7 05120113 Small (17) 3.0 2.6 2.0 Total (27) 62.6 53.1 38.7 306.7 Medium (12) 89.4 78.7 62.6 05120114 Small (25) 4.1 3.6 2.8 246.5 Total (37) 93.5 82.3 65.4

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Facility Size Annual Load Reduction Estimated Credit Supply @ 20% (# of (Tons) @ 3 (Tons) @ 5 (Tons) @ 8 Adoption and 2:1 Trade Ratio HUC_8 facilities) mg/L mg/L mg/L (Tons) Medium (5) 13.1 11.6 9.3 05120115 Small (10) 3.7 3.2 2.6 Total (15) 16.8 14.8 11.9 124.9 Medium (62) 433.7 362.3 255.2 05120201 Small (79) 16.8 14.0 9.8 Total (141) 450.5 376.3 265.0 281.0 Medium (15) 152.1 133.5 105.4 05120202 Small (22) 8.1 6.9 5.1 Total (37) 160.2 140.4 110.5 124.6 Medium (13) 50.1 42.8 31.9 05120203 Small (15) 2.8 2.3 1.6 Total (28) 52.9 45.1 33.5 109.7 Medium (20) 83.7 70.2 49.9 05120204 Small (30) 4.2 3.5 2.5 (Driftwood) Total (50) 87.9 73.7 52.4 131.2 Medium (7) 38.2 31.8 22.3 05120205 Small (5) 0.4 0.3 0.2 Total (12) 38.6 32.1 22.5 40.6 Medium (11) 20.5 17.1 12.0 05120206 Small (11) 4.7 3.9 2.7 Total (22) 25.2 21.0 14.7 95.6 Medium (19) 92.6 77.5 54.8 05120207 Small (15) 8.2 6.9 5.1 Total (34) 100.8 84.4 59.9 72.0 Medium (22) 110.7 92.7 65.7 05120208 Small (28) 4.4 3.7 2.6 Total (50) 115.1 96.4 68.3 62.7 Medium (7) 41.9 35.3 25.3 05120209 Small (9) 2.3 2.0 1.6 Total (16) 44.2 37.3 26.9 40.6

The values in Tables 50 and 51 are based on a 2:1 trading ratio and 20 percent farmer participation. The tables illustrate adequate NPS capacity to supply all 357 small facilities with TP and TN credits. The table also highlights subwatersheds in red where the NPS credit supply estimates are insufficient for all of the remaining medium size facility potential demand. However, WQT could still benefit numerous medium size entities. For example, an analysis of the TP credits remaining after the small facility demand has been met suggests that 404 of the 466 medium sized facilities could be supplied with an adequate number of TP credits. Similarly, for the most restrictive TN effluent limit, adequate credit supply could still benefit 433 of the 466 facilities.

These results do not reflect the facilities that would not participate or who would be eliminated from WQT participation due to a variety of issues (e.g., variability in costs between facilities, remaining useful life of a facility, risk averse management styles, or location of facility in a headwaters area). In addition these tables do not estimate the potential benefits from combining point source credit supply with

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nonpoint source supply. To do these assessments spatial analysis of locations (both point source and nonpoint source) would have to be done.

4.7 Other Trading Considerations The preliminary findings in this report indicate WQT for TN and TP would both be environmentally protective and economically viable for many entities within the Wabash watershed. Additional benefits of trading when compared with investing in on‐site technology include: • Providing longer planning and construction schedules: Long‐term WQT would provide substantial economic opportunity for some entities. For point sources with tight compliance schedules that prevent adequate planning time or that delay upgrades to realize the full useful life of the existing facility, WQT becomes a means to address short‐term needs for nutrient reductions. In addition, when appropriate, WQT provides leverage for facilities engaging in NPDES variance requests. In these settings, WQT can provide a level of nutrient reduction that demonstrates the permittee’s goodwill. • Allows facilities to consider partial treatment options: The economic analysis indicates many sites will be able to achieve many nutrient effluent limits by upgrading with BNR technologies. However, the assessment did not comprehensively detail other economically beneficial settings due to lack of site‐specific information. Certain circumstances will occur where BNR treatment is insufficient to achieve compliance goals. In this setting a financial assessment of the cost of the additional treatment units required for the new restrictive limits (e.g., chemical precipitants and/or filters) versus the use of WQT credits may be performed. The assessment may find that a combination of treatment technology and WQT credits is the most cost effective. An added benefit of using a combined compliance approach is that these facilities need to purchase fewer credits than facilities using WQT to meet their entire nutrient reduction requirement. • Additional environmental gains: NPS BMPs used in WQT credit supply provide other pollutant reductions and produce other ancillary environmental benefits. Examples are riparian buffers and introducing perennial cash crops into the field rotation. Both BMPs would provide nutrient credit generation. The added benefits of these BMPs are reductions in sediment and potentially bacteria loading. Other benefits in physical parameters would be produced by introducing water storage back into the watershed, which would result in hydrograph dampening. Temperature gains from the shading effects of some BMPs could benefit aquatic life and increase dissolved oxygen concentrations in the water. • Compliance program managers can use WQT programs: Compliance excursions can be managed by requiring the violator to purchase a sum of WQT credits. • Third party purchasers retiring credits: Entities who wish to retire a portion of the watershed’s nutrient loading could be allowed access to credit purchases. • Options for watershed management: WQT provides additional opportunities within a watershed to target difficult settings. This can be accomplished by building incentives into the WQT program that give preferences to BMPs that increase habitat, target subwatersheds or reward other desired attributes. • Allowing for future growth in fully allocated watersheds: Managing future growth issues in a fully allocated watershed is on the horizon if not already here in many regions in the U.S. WQT can be used to relieve some of the pressure water resource protection places on expanding or existing NPDES dischargers and new entities wishing to operate in the watershed. Offsetting

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loads through WQT can work hand‐in‐hand with technology advances and reuse alternatives. Having more alternatives in the tool box provides for individual choices and flexibility to overcome site‐specific challenges. • Flexible Contracts: WQT provides more flexibility when compared with some public conservation funding programs like EQIP and other cost‐share opportunities. WQT focuses on measureable water quality improvements compared to other conservation programs which can be holistic in nature. Examples of flexibility include length of contract, no caps on the number of acres that can be enrolled, less restrictive seed mixtures and value added opportunities. Value added opportunities are illustrated by a filter strip example. WQT contracts can reduce restrictions on cash cropping when the grass is used for hay. The net return from haying of buffers is an opportunity cost that is not removed. Other examples are stacking of ecosystem service payments from events such as selling hunting rights or biomass production. • Accelerating implementation schedules: By introducing alternative funding to NPS entities and reducing the cost of compliance for permittees, implementation plans and other water quality efforts benefit from WQT. WQT also attracts additional adopters due to its increased contracting flexibility regarding implementation restrictions and issues of timing.

4.7.1 Attributes to Consider to Improve the Potential for Success WQT programs operate at the cross roads of two busy thoroughfares. WQT can be viewed simultaneously as a market‐based program and an environmental protection program. When assessed using market‐based criteria, the trading program must address stability issues common to other marketplaces. When assessed using environmental criteria, WQT must contain provisions that provide assurances that water quality protection is first and foremost the objective of the program.

The following list outlines program considerations and options that take into account the four main stakeholder categories engaged in WQT program operations or appraisals. The four stakeholder groups in the Wabash watershed are: • Buyers of credits: NPDES permit representatives • Sellers of credits: Either NPDES permit representatives or agricultural producers • Regulatory agencies: The delegated Clean Water Act officials • Concerned third parties: Entities concerned about environmental and/or farm sustainability

Each of these audiences views the development and operation of a WQT program and weighs the progress against their list of critical elements. Some of the elements of one stakeholder group overlap with other groups. Some critical elements are unique to the group’s self‐interest. For instance, credit pricing can cause tension between buyers and sellers. The simplest example of this is when buyers desire the lowest price per credit possible versus sellers which desire the highest payment for BMP implementation as possible. Buyers control the maximum purchase price and sellers control the minimum purchase price. A marketplace is successful when it produces a product for sale that is priced (including a profit margin) within the buyer’s willingness to pay range. This simple example touches upon two more perceptions where internal conflicts arise between some stakeholder groups. The first is the perception held by a few that nobody should profit from protecting the environment. The second is that an environmental commoditization is not holistic. In other words, a market based on phosphorus credit transactions promotes NPS reductions above or at the expense of habitat creation.

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These concerns, and others, can be fully addressed or reasonably managed by identifying the concerns germane to the setting and structuring the WQT program framework to include the correct provisions that will balance stakeholder interests that are vital to success. Fortunately, WQT does not require the impossible goal of appealing to all. Instead, WQT development must recognize which issues create veto circumstances (e.g., regulatory authority denying program elements), a reduction in participation (e.g., a buyer’s mistrust of the negotiation process), or introduction of controversy (e.g., a third party successfully creating doubt around the program’s ability to protect the water resource). The following two sections identify concerns and management options that can be considered during development of a WQT framework to provide for environmental protection and market stability.

4.7.2 Environmental Protection Environmental protection is a prerequisite of NPDES permits. NPDES WQT permits can be expected to receive a more than fair share level of scrutiny. As explained previously, no NPDES permit can cause or contribute to a water quality violation. The ability for a WQT program to provide for environmental protection affects how every stakeholder will accept or reject the program. Using an information protocol and a weight of evidence process, WQT program managers can navigate through this uncharted frontier. The following list identifies the information protocol questions and provides suggestions on how they may be addressed: • What are the water quality goals that the trading program will execute? o WQT is a NPDES permit compliance option. As such, the minimum goal is to comply with the permit effluent limits assigned to the discharge under conventional treatment circumstances. o WQT can be a means to accelerate other environmental efforts. WQT can, up to a certain point, target hard to reach areas, advance habitat protection and assist with pollutant reductions in other parameters than those being traded. Pricing is critical in a market‐based system. If too many watershed management goals are placed on WQT programs the market price become the limiting element.

The WQT program managers must be objective and keep in mind that WQT will not work in all watersheds. A small watershed scale program must answer the questions: 1) Are there sufficient supply options for the demand? and 2) Will water quality violations occur? Larger watershed programs need to address the same questions but may decide to restrict the program to certain subwatersheds depending on the findings. The weight of evidence approach can assist with identifying these potentially deal‐ breaking constraints.

The weight of evidence approach (or, sometimes referred to as multiple lines of evidence) is explained in the U.S. EPA guidance manual for biotic impairments entitled CADDIS: The Causal Analysis/Diagnosis Decision Information System (U.S. EPA, 2000). The process fosters appropriate decision making using multiple reasonable opinions to form a consensus. A weight of evidence approach applies the best available science to the setting and makes decisions based upon the current understanding. Decisions can be made based on three different outcomes. The first is a causal linkage based on hard evidence that environmental protection is provided. For instance, the credit equation in point source to point source trading is based on monitored results. The second outcome is proof that the environmental protection is not provided by hard science. For instance, monitoring indicates that use of credits generated by downstream sources will contribute to a local water quality violation that exists between the buyer and seller. Finally, the third outcome is when the watershed understanding is incomplete but

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a reasonable resolution exists. For instance, a lack of adequate monitoring to assess the resource conditions and partition sources and source reduction capability can be overcome when using standard NPS tools and reasonable margins of safety. If the outcome of the element being assessed does not result in any of these three findings, the team must revisit the topic and data seeking a means to resolve the gap in watershed understanding.

Fortunately an increased level of watershed understanding is emerging. More and more data sets are available to use in this process. Data sets include water quality and quantity monitoring, land use data, modeling, benchmarking salient WQT programs and reviewing local conservation office conservation assessment tools. Sources of information include federal and state agencies like U.S. EPA, NRCS, USGS, IDEM, IEPA, DNR and research from land grant colleges such as Purdue University and University of Illinois.

The weight of evidence process will assist with identifying and prioritizing concerns. The process also advances use of the best available science to address these concerns. The weight of evidence context can include: • Keeping decisions local • Supporting the local decisions with the use of an interdisciplinary team of experts • Creating a transparent communication process to provide access to the stakeholders • Involve the regulating agencies and • Identifying and involving champions from each stakeholder category (preferably selecting individuals that have a history of building unity and being reasonable).

The weight of evidence process is based on a logic decision tree. Sometimes the flow path can be iterative. It supports decision making using the following steps: • Identifying each information protocol question • Prioritizing the questions and issues • Assessing the available data on the topic (monitoring and benchmarking) • Create and review a list of options to address the question at hand • Determine if the current data validates the suggested option (resulting in moving on to the next issue) or rejects it (resulting in the selection of another option) • When the evaluation is inconclusive, use of a “reasonableness test” begins o The multiple discipline expert panel reviews the findings and gaps in understanding o Recommend an affordable direction (e.g., developing a credit estimation method based on watershed modeling or use of acceptable currently used standard methods like NRCS NPS assessment tools) o Testing and identifying the uncertainty in the selected option o Determining adequate margins of safety that provide for both the protection goals and economic viability using an objective judgment on reasonableness

It may be important to support this process through efforts to educate the public on the issues at hand, communicate the findings and solicit comments and feedback.

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By keeping the decisions local, involving the regulatory staff and inviting in regional experts, a pragmatic solution can evolve when perfect science and understanding do not exist. In these circumstances the WQT program can operate simultaneously with collection and evaluation of more data and periodically considering investing in better tools as part of a planned adaptive management approach. The WQT program should be integrating these assessments with other watershed programs so that the coordination becomes mutually beneficial.

4.7.3 Market Stability Market stability can be thought of as a three legged stool. One leg is long‐term demand for the product. The second is the ability to supply the product at an adequate price. And the third is confidence in the product being what it claims to be. Anyone who has purchased a large ticket item like a used car or home understands there is a long‐term risk with that investment. WQT trading also has a level of risk and liability with its investment. Objectively recognizing this aspect of market‐based systems allows methods for management of risk to be introduced into the program framework. Fortunately, WQT markets are not free market economies and operate under a regulated or quasi regulated marketplace structure. This fact allows for risk and liability to be controlled to a certain extent.

The following concerted efforts bring market stability to WQT programs and should be considered when selecting the trading program frameworks described in Section 5.3. • Keeping the decisions local: This element enhances the local stakeholder involvement and influence when assessing watershed management, additional conservation issues and equity.

• Transparent decision making: This element allows for opportunities for trust in the process to develop between all engaged stakeholders.

• WQT programs take time to develop: Having the necessary administrative infrastructure in place early is paramount to success. In situations where NPDES permit limits have tight compliance schedules the permittee cannot afford to wait for a WQT program to develop. Considering the time it takes to construct BMPs and establish vegetation, acquiring adequate levels of credits for compliance could take some time. Having a sufficient compliance schedule to allow for NPS BMP implementation could preclude using of a portion of the compliance schedule window to develop the program. Likewise, the program experiences the most efficient use when it is developed slightly ahead of demand and does not exist too long without use and support.

• Including adaptive management at scheduled times: This process provides for two critical issues. The first is acknowledgement of the program’s limitations and the program provides a means to address them over time. The second issue is structuring change at predictable intervals so that entities are comfortable with large, long‐term investments in the marketplace.

• Use of locally developed infrastructure: This point leverages existing networks that have already been developed. Business relationships develop trust and confidence in the partnership typically across several years. Conservation decision making is no different. By engaging the current conservation service providers, a producer’s anxiety can be reduced.

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• Recognition of the complexity of WQT and the need for simplicity: WQT trading is a complex program that crosses over multiple disciplines. The protection of water quality must be provided at a level of sufficient rigor, yet the delivery of the program must be simple. The program must be simple enough to not take up too much time but at the same time it must be complete enough to explain the process at a level that is transparent. One successful way to manage this is poll the community of buyers and seller and find out who they trust to operate the program. This consideration overlaps well with the last item. If there is confidence established in the local service providers for both buyer and seller, then the program can leverage that confidence by training the trainer to deliver the program marketing. Guiding questions include: o What is the level of capability that local service providers have to operate credit estimation tools? o What level of compensation is a fair service provider’s fee? The consideration and answers to these questions will allow for opportunities to create market frameworks that minimize transaction fees.

• Identify the regulatory agency concerns: Regulatory agencies have complete veto authority over the use of WQT in the permits they issue. Finding out the concerns the agency has is essential to program success. Limitations in accepting WQT programs include: a) lack of trust in the science, b) lack of resources available within the agency to adequately assess all of the program elements, and c) having confidence the reporting and compliance program will work. By identifying early which of these and other critical issues are of concern to agency staff, effective problem solving can begin. Identifying appropriate and objective partners can be a potential solution (e.g., MCD uses both Ohio DNR and county SWCD staff to evaluate and audit agricultural BMP implementation for Ohio EPA review) (MCD, 2001)(Ohio DNR, 2011).

4.7.4 Water Quality Trading Framework Options for Compliance Mechanisms Managing NPDES permit compliance is riskier when working with NPS for at least two substantial reasons. First, land use conservation measures experience episodic and catastrophic events that may render a BMP or management measure useless or inadequate for a period of time. Secondly, participants are liable for their operation but turn the evaluation and control of success over to other parties. Existing programs have confronted these issues and developed the following compliance assurance mechanisms: • Replacement/correction windows: o Ohio EPA rules section 3745‐5‐12 (Ohio EPA, 2007) allows a ninety day correction window for which crediting contracts are not revoked if reported in a timely manner. o Pennsylvania Department of Environmental Protection (PADEP) plans to exercise enforcement discretion with respect to permittees in the year which credits are determined to be invalid, as long as (1) the credit failure is not due to negligence or willfulness on the part of the permittee and (2) the permittee replaces the credits for future compliance periods (PADEP, 2008).

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o West Virginia trading guidelines allow a period where buyers may purchase contemporaneous credits after the averaging period if it is discovered the compliance measures were deficient.

• Insurance pools: MCD (MCD, 2001) and (PADEP) (PADEP, 2008) provide for market stability by creating insurance pools of back up credits.

• Broadening the compliance period: WI DNR and the Chesapeake Bay programs have extended the NPS averaging period to be based on annual average credit results.

Benchmarking other successful WQT programs can provide examples of market stabilizing mechanisms like these for the WQT framework decision makers.

5. Next Steps for Water Quality Trading in the Wabash River Watershed As stated in the introduction, a WQT feasibility study provides insight as to where WQT might encounter barriers in a particular watershed and what type of trading framework might be most appropriate based on the sources with the greatest potential for participation. It is an initial step in investigating the potential for WQT success in a watershed. Based on the information compiled and analyzed for the Wabash River watershed, the Project Team has developed recommendations on next steps for moving WQT beyond the feasibility assessment phase. These next steps include conducting more in‐depth outreach and education with stakeholders, prioritizing subwatersheds for future analysis, and exploring the potential WQT frameworks that could be effective in the Wabash River watershed. These recommendations are discussed in more detail below.

5.1 Outreach and Education Understanding the attitudes and perceptions of key stakeholders towards WQT is essential to determining the potential success of this water quality management tool. This feasibility analysis obtained informal input from regulators, point sources, and agricultural landowners through personal communications, focus groups, and online surveys. However, much more in‐depth outreach and education would be necessary with these key stakeholder groups to move WQT forward in the Wabash River watershed.

5.1.1 Regulators Trades involving NPDES point sources would require support from IDEM, specifically staff involved in water quality standards and assessment, NPDES permitting, and enforcement. As a result, it is imperative to engage IDEM in discussions about moving the concept of WQT in the Wabash River watershed beyond the feasibility phase. Outreach to IDEM could include presenting the findings of the WQT feasibility analysis, discussing concerns about the use of WQT now and in the future, and highlighting potential water quality benefits. IDEM holds the key to the drivers necessary for WQT (e.g., numeric nutrient criteria, more stringent NPDES permit effluent limits, modified TMDL targets). Without the agency’s support, it will prove challenging to move WQT forward toward program development and implementation.

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5.1.2 Point sources Even with the appropriate drivers in place, WQT will not take off in the Wabash River watershed without support from regulated point sources. Concerns about aspects of WQT such as risk management and transaction costs could limit participation. Outreach to point sources within the Wabash River watershed should present the findings of the WQT feasibility analysis, but also illicit feedback on point sources’ perspectives of WQT and potential barriers to participation. Point source involvement in WQT program design and development will also help to generate support and trust in the process and could lead to greater participation.

5.1.3 Agricultural landowners Without adequate credit supply, WQT would not be feasible. An adequate credit supply for point source‐to‐nonpoint source trading will depend on participation from agricultural landowners. Issues related to WQT such as credit verification, certification, and liability often serve as barriers to agricultural landowner participation. Through outreach and education, agricultural landowners can gain a better understanding of how WQT works and their role in the process. Identifying the factors that would influence the agricultural community’s willingness to participate and crafting solutions to existing challenges would be a key goal of outreach to this stakeholder group.

5.2 Prioritized Subwatershed for Future Analysis The pollutant and economic suitability findings indicate an ample credit supply and demand exists for WQT in each subwatershed within the Wabash River watershed. There is sufficient technical support and economic incentives to invest in WQT in the future as demand drivers emerge. Watershed managers deciding to develop a WQT program can review the findings of this report and fit the findings to the scale of the proposed WQT program. Decision makers would need to vet the economics and supply capabilities based upon better site‐specific data and conditions. Current policies and rules and physical characteristics of the local watershed also need to be taken into account. These important steps enhance the ability of a WQT program to integrate with other watershed activities. These steps can be informed by the outreach and education process described in Section 5.1. However, WQT legal and technical complexities go beyond the outreach efforts and need to address the program framework details using pragmatic solutions tailored to the watershed.

WQT does not have to engage every member of the watershed to be beneficial for advancing watershed nutrient reduction goals. However, an entity’s ability to block or advance WQT efforts can vary. Entities with the ability to either stop or host WQT efforts include the regulatory agencies, current conservation service providers and third parties likely to generate lawsuits or contentious comments during the NPDES permit public review periods. Likewise, an assessment of the likely number of buyers willing to participate can be used to select and justify the level of funding necessary for the program set up.

The following summary of elements can be used to inform decision makers where sufficient synergy for each program principle may exist: • Regulatory agency o Concerns, enthusiasm or hesitancies from agency o Ability to allocate resources to participate in a program

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o Whether limitations on agency resources will be an issue (e.g. can this be alleviated by assigning WQT administration roles to other governmental or non‐governmental units, such as NRCS?) o Other identified procedural limitations • Demand side o Level of WQT educational efforts and awareness in the watershed o The number of entities interested in a WQT program o The trading scale desired (e.g., one permit, a small second or third order watershed or a larger watershed/basin) o Expected timing of the new nutrient effluent restrictions (e.g., will WQT program development be completed in time to meet the buyers’ compliance schedules?) o Third party entities that can be trusted to find, aggregate and document NPS credits o The current decision support tools/procedures for legal requirements, tracking and reporting and their availability for WQT programs • Supply side o Indications of the farmers’ willingness to participate o Producers’ trust for sharing information to implement the program (e.g., identifying farm options, divulging site data, recommending pricing, inspecting and reporting) o Availability of decision support tools for crediting and administration • Third party involvement o Interest and/or concerns with WQT activities o Identifying available experts and champions o The basis of any concerns o Ways in which concerns can be reasonably addressed • Desired development structure o Preferred framework organization o Identify position roles and responsibilities o Find entity or organization that could/should host the program

These considerations of socio‐political of a best fit are provided to assist program decision makers in selecting high potential watersheds as they advance into the next phase of trading development.

5.3 Trading Program Frameworks Moving from the WQT feasibility analysis phase to the WQT program design phase involves determining the type of trading program framework that would work in the Wabash River watershed. At a very basic level, WQT can involve point‐to‐point source trades or point source‐to‐nonpoint source trades. But within those two broad categories, there are different framework options for implementing trading. U.S. EPA describes the different types of WQT frameworks that are options for Wabash River watershed stakeholders and IDEM to consider. These frameworks are briefly summarized below. More information can be found in U.S. EPA’s Water Quality Trading Toolkit for NPDES Permit Writers.

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5.3.1 Point­to­Point Source WQT Frameworks According to U.S. EPA, point‐to‐point source trading is “relatively straightforward, easily measurable, and directly enforceable (U.S. EPA 2007).” Because trading for both partners is reflected in NPDES permits, regulators tend to feel a higher level of comfort with this type of trading. Under this category, trading can include single point‐to‐point source trades, multiple facility point source trading, or a point source credit exchange.

5.3.2 Point­to­Nonpoint Source WQT Frameworks Under this category of trading, point sources are often able to meet more stringent permit effluent limits at a relatively cheaper cost. U.S. EPA states that for this category of WQT, “extra care should be taken to ensure that nonpoint source load reduction uncertainty is addressed (U.S. EPA 2007).” The WQT feasibility analysis for the Wabash River watershed addresses the issue of uncertainty in the discussion on trade ratios in Section 4.3. Point‐to‐nonpoint source WQT frameworks include: • Single point source‐nonpoint source trades; where the permittee can find the NPS credits or use a middleman/broker to provide credits from a site (includes the use of electronic tools to find trades • Single or multiple point source‐nonpoint source trades using an aggregator; where use of the middleman collects and sells cumulative larger blocks of credits to the buyer • Nonpoint source credit exchange, where the exchange host purchases credits from one or more sites to dispense among buyers

5.4 Conclusion The WQT pollutant and economic feasibility study findings indicate that the Wabash River watershed has a high potential for WQT to provide substantial economic and environmental benefits in the future. The beneficial attributes found in this report include the ability to provide small to medium wastewater facilities with economical and relevant compliance options to address future nutrient effluent limit requirements. This can be done using WQT which provides opportunities to enhance the holistic conservation efforts associated with implementation of nutrient reduction goals. When considering implementing a WQT program, managers are encouraged to review this report and make final programmatic decisions based upon the site‐specific information and scale of the specific location. Program set up expenses for WQT can be expected to be more affordable when the program is developed to provide services for multiple entities. Lastly, program development phase can be expected to take a couple of years to complete. The program development phase can be coordinated with the future nutrient water quality criteria development processes and state Gulf of Mexico hypoxia action strategies. Until these demand drivers emerge, the use of WQT programs will most likely be limited to watersheds with a narrative based nutrient TMDL.

Final Report – September 2011 Page 94

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Buchanan, J.R. 2010. Wastewater Planning Model, Version 1.0. Cost of Individual and Small Community Wastewater Management Systems. Water Environment Research Foundation (WERF) project no. DEC2R08, Performance and Costs for Decentralized Unit Processes.

CH2M Hill. 2010. Statewide Nutrient Removal Cost Impact Study. Prepared for Utah Division of Water Quality. October 2010.

Chandran, K., 2010. Methylotrophic microbial ecology and Kinetics. WERF Opportunistic Research Project. WERF presentation 2010.

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Foess, G. W., P. Steinbrecher, K. Williams, and G.S. Garrett. 1998.Cost and Performance Evaluation of BNR Processes. Florida Water Resources Journal. December 1998.

Indiana State Department of Agriculture. 2007. Tillage Data. Accessed at: http://www.in.gov/isda/2354.htm.

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Jiang, F., M.B. Beck, R.G. Cummings, K. Rowles, and D. Russell. 2005. Estimation of Costs of Phosphorus Removal in Wastewater Treatment Facilities: Adaptation of Existing Facilities. Water Policy Working Paper #2005‐011. February 2005.

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Keplinger, K.O., A.M. Tanter, and J.B. Houser. 2003. Economic and Environmental Implications of Phosphorus Control at North Bosque River Wastewater Treatment Plants. Texas Institute for Applied Environmental Research. TR0312. July 2003.

Kieser & Associates. 2008. An Economic Comparison of the USDA NRCS Environmental Quality Incentives Program Payments and Water Quality Credit Trading in the Great Miami River Watershed of Ohio, December 31, 2008. Accessed August 18, 2011 at: http://www.envtn.org/uploads/EQIP_WQT_GMR2008.pdf.

Lake Erie Millennium Synthesis Team. 2010. Lake Erie Nutrient Loading and Harmful Algal Blooms: Research Findings and Management Implications. Accessed July 20, 2011 at: http://ohiodnr.com/LinkClick.aspx?fileticket=WUiuJ5mY2jo%3D&tabid=9273.

Mannering, J. V., D.R. Griffith, and K.D. Johnson. 2007. Winter Cover Crops: Their Value and Management. Department of Agronomy, Purdue University Cooperative Extension Service. Accessed August 4, 2010. Available online at: http://www.agry.purdue.edu/ext/forages/publications/ay247.htm.

M&E (Metcalf and Eddy). 2008. Chesapeake Bay Tributary Strategy Compliance Cost Study. November 2008.

Miami Conservancy District. 2001. Great Miami River Watershed Water Quality Credit Trading Program Operations Manual, Draft Version 3, April 2011 Update. Accessed at: http://www.miamiconservancy.org/water/documents/TradingProgramOperationManualFeb8b200 5secondversion.pdf.

MPCA (Minnesota Pollution Control Agency). 2010a. Minnesota Nutrient Criteria Development for Rivers DRAFT. November, 2010. Accessed April 26, 2011 at: http://www.pca.state.mn.us/index.php/water/water‐permits‐and‐rules/water‐ rulemaking/proposed‐water‐quality‐standards‐rule‐revision.html.

MPCA. 2010b. Aquatic Life Water Quality Standards, Technical Support Document for Nitrate, November 12, 2010. Accessed April 26, 2011 at: http://www.pca.state.mn.us/index.php/water/water‐permits‐ and‐rules/water‐rulemaking/proposed‐water‐quality‐standards‐rule‐revision.html.

MRLC. 2009. National Land Cover Dataset. Multi‐Resolution Land Characteristics Consortium. Available at http://www.mrlc.gov/.

Ohio EPA (Ohio Environmental Protection Agency). 2007. OAC Chapter 3745‐5 Water Quality Trading Rules. Ohio Environmental Protection Agency, Division of Surface Water. Accessed at: http://www.epa.state.oh.us/dsw/rules/3745_5.aspx).

Ohio EPA. 2011. Nutrient Standards: Where will they lead? Webcast presented by the Ohio Water Environment Association. February 24, 2011. Accessed May 17, 2011 at: http://www.ohiowea.org/.../OWEA_Webinar_Nutrient_Removal_2_24_2011.

Ohio DNR (Department of Natural Resources). 2011. Estimating Load Reductions for Agricultural and Urban BMPs. Accessed on August 5, 2011 at: http://www.ohiodnr.com/soilandwater/programs/agpollutionabate/default/tabid/8856/Default.as px.

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PADEP (Pennsylvania Department of Environmental Protection). 2008. Final Trading of Nutrient and Sediment Reduction Credits – Policy and Guidelines. Document 392‐0900‐001.

PADEP, 2010. PA Bulletin, Doc. No. 10‐1927a. Use of offsets and tradable credits from pollution reduction activities in the Chesapeake Bay Watershed. Title 25. Part 1. Subpart C. Article II. Chapter 96.8.

Pehlivanoglu, E., Sedlak, D.L., 2004. Bioavailability of wastewater‐derived organic nitrogen to the alga Selenastrum Capricornutum. Water Research 38: 3189–3196.

Pennsylvania State University. 1996. Nutrient Management in Conservation Tillage Systems. Conservation Tillage Series Number Four. Accessed July 20, 2011 at: http://cropsoil.psu.edu/extension/ct/conservation‐tillage‐4.

Purdue University. 2005. Agronomy Guide AY‐318‐W: Interpreting Nitrate Concentration in Tile Drainage Water, January 2005. Accessed June 3, 2011 at: http://www.extension.purdue.edu/extmedia/AY/AY‐318‐W.pdf.

Purdue University. 2011. Purdue Crop Costs and Return Guide: October 2010 Estimates. Purdue Extension, Purdue University.

Reckhow, K.H., and S.C. Chapra. 1983. Engineering approaches for lake management. Volume 1: Data analysis and empirical modeling. Butterworth Publishers. U.S. Environmental Protection Agency.

Seitzinger, S.P, R.W. Sanders, and R. Styles. 2002. Bioavailability of DON from natural and anthropogenic sources to estuarine plankton. Limnol. Oceanogr. 47(2): 353–366.

Selvaratnam, S. and J. Frey. 2011. Nutrient Targets for Development of Biologically Based Total Maximum Daily Loads for the Upper Midwest. In Proceedings of the U.S. EPA Nutrient TMDL Workshop, New Orleans, Louisiana, February 15‐17, 2011.

Smith, D.R., S.J. Livingston, B.W. Zuercher, M. Larose, G.C. Heathman, and C. Huang. 2008. Nutrient losses from row crop agriculture in Indiana. Journal of Soil and Water Conservation 63(6): 396‐409.

Urgun‐Demirtas, M., C. Sattayatewa, and K.R. Pagilla. 2008 Bioavailability of dissolved organic nitrogen in treated effluents. Water environment research; 80(5):397‐406.

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USGS (United States Geological Survey). 1997. Regional interpretation of water‐quality monitoring data. Smith, Richard, Schwarz, Gregory, Alexander, Richard. Water Resources Research, VOL. 33, NO. 12, Pages 2781–2798, December 1997.

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Final Report – September 2011 Page 98 Appendix A: Letter to Illinois EPA from USEPA

Obtained by InsideEPA.com Obtained by InsideEPA.com Obtained by InsideEPA.com Obtained by InsideEPA.com Obtained by InsideEPA.com (This page left intentionally blank.) Appendix B: Wabash River Watershed TMDL Reduction Summaries and Wasteload Allocations (WLAs)

Appendix B: Wabash River Watershed TMDL Reduction Summaries and Wasteload Allocations (WLAs)

Point Source State TMDL Date Approved TMDL Segment Pollutants LA Reductions Reductions Illinois Altamont New Reservoir Oct-04 Altamont New Reservoir TP 84% N/A Illinois Busseron TMDL Revised on 9/22/08 not yet approved Sulpher, Kettle, and Robbins CreTP 40 to 82% N/A Illinois Charleston SCR Sep-03 Charleston SCR TP 87% 0 Illinois Fox River Sep-04 Olney East Fork Lake TP 75% N/A Illinois Fox River Sep-04 Borah Lake TP 75% N/A Illinois Fox River Sep-04 Fox River Low DO (Reduct 70-75% 0 Illinois Fox River Sep-04 Olney East Fork Lake Low DO (Reduct 45% N/A Indiana Limberlost Creek TMDL Jul-07 All Total-N 25% 0% Indiana Limberlost Creek TMDL Jul-07 All Total-P 90% 0% Illinois Little Vermillion River/Georgetown Lake TMDL 9/2005 and 10/2006 Georgetown Lake TMDL TP 0% 46% Illinois Little Wabash River Sep-07 Lake Mattoon TP 91% 0 Illinois Little Wabash River Sep-07 Paradise Lake TP 88% 0 Illinois Little Wabash River Sep-07 Lake Sara TP 81% 0 Illinois Little Wabash River II Sep-08 Newton Lake TP 61% 0 Illinois Oakland/WaLnut Point Lakes Sep-05 Lake Oakland TP 80% 0 Illinois Oakland/WaLnut Point Lakes Oct-07 Walnut Point Lake TP 72% Illinois Salt Fork Vermillion River Oct-07 Salt Fork Vermillion River Nitrate 9-44% Illinois Salt Fork Vermillion River Oct-07 Salt Fork Vermillion River Nitrate 38% Illinois Salt Fork Vermillion River Oct-07 Salt Fork Vermillion River Nitrate 29% Illinois Skillet Fork Sep-07 Sam Dale Lake TP 62% 0%

Illinois Skillet Fork Sep-07 StephenWayne City A. ForbesSide Channel LAKE TP 51% 0% Illinois Skillet Fork Sep-07 Reservoir TP 56% 0% Indiana South Fork Wildcat Creek TMDL Jul-08 All Nitrate + Nitrite 0% 0% Indiana South Fork Wildcat Creek TMDL Jul-08 All TP 42% 0% Illinois Sugar Creek/Paris Twin Lakes Sep-05 Paris Twin Lake West TP 75% Illinois Sugar Creek/Paris Twin Lakes Sep-05 Paris Twin Lake East TP 75% Illinois Sugar Creek/Paris Twin Lakes Apr-08 Sugar Creek Low DO N/A N/A Illinois Vermillion River-N. Fork/Vermillion Lake Dec-06 North Fork Vermillion River Nitrate 48% 0% Illinois Vermillion River-N. Fork/Vermillion Lake Dec-06 Lake Vermillion TN 34% 0% Illinois Vermillion River-N. Fork/Vermillion Lake Dec-06 Lake Vermillion TP 77% 0% Illinois Vermillion River-N. Fork/Vermillion Lake Dec-06 Hoopston Branch Low DO N/A N/A Indiana Wabash River TMDL Sep-06 All Nitrate 0% 0% Indiana Wabash River TMDL Sep-06 All TP 5% 97% (This page left intentionally blank.) Appendix C: Characterization of Wabash River Nutrient Loads

To: Christa Jones, CTIC From: Kevin Kratt, Elizabeth Hansen, Victor A. D'Amato, and Kellie DuBay, Tetra Tech cc: Jim Klang, Kieser & Associates Subject: Characterization of Wabash River Nutrient Loads Date: February 16, 2011

1 INTRODUCTION

The purpose of this memorandum is to document Tetra Tech’s characterization of the sources of nutrients within the Wabash River watershed. The characterization of point sources was made using effluent monitoring data reported by facilities to the Illinois Environmental Protection Agency (IEPA) and the Indiana Department of Environmental Management (IDEM). Information on type of treatment was also obtained from the Clean Watersheds Needs Survey (CWNS) and from a review of selected permits. The characterization of nonpoint sources was made using SPAtially Referenced Regression on Watershed Attributes (SPARROW) loads previously estimated by the U.S. Geological Survey (USGS).

The purpose of the characterization of nutrient sources is to support a study of the feasibility of a nutrient trading program within the watershed. More refined aspects of the point and nonpoint source loading estimates will be made, as needed, to support the feasibility study. A map of the Wabash River watershed, including land uses, is shown in Figure 1.

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Figure 1. Land use within the Wabash River watershed.

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2 CHARACTERIZATION OF POINT SOURCE LOADS

According to information provided by IEPA and IDEM there are 943 facilities with National Pollutant Discharge Elimination System (NPDES) permits within the Wabash River watershed1 (Figure 2 and Table 1). Most (777) of these facilities have individual rather than general permits. Effluent monitoring data reported by each facility were retrieved by Tetra Tech in cooperation with IEPA and IDEM. IEPA sent compiled discharge monitoring reports (DMR) and the Indiana data were acquired from the Integrated Compliance Information System (ICIS). These two datasets were merged into a master database for the parameters of interest (flow, biochemical oxygen demand (BOD), nitrogen (various forms), and phosphorus) and the data for each facility is summarized in Appendix A. The available period of record varies by facility but generally covers 2000 through 2009.

Considerable gaps existed in the available flow and nutrient data:

• 117 facilities had no reported flow data • 413 facilities had no ammonia data • 376 facilities had no BOD data • 934 had no non-ammonia nitrogen data • 862 facilities had no phosphorus data

1 Note that there are possible facilities with NPDES permits in HUC 05120101 located in Ohio that are not included in this analysis.

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Figure 2. Facilities with NPDES permits in the Wabash River watershed.

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Table 1. Number of facilities with NPDES permits in each 8 digit HUC of the Wabash River watershed 8 Digit HUC # of All Permits 05120101 62 05120102 19 05120103 39 05120104 25 05120105 11 05120106 48 05120107 29 05120108 23 05120109 51 05120110 10 05120111 54 05120112 61 05120113 28 05120114 43 05120115 16 05120201 157 05120202 42 05120203 30 05120204 54 05120205 14 05120206 23 05120207 35 05120208 52 05120209 17 Total 943

A number of assumptions had to be made to address the data gaps in the available effluent monitoring data. These assumptions are described below:

• Effluent data included values from both intake structures as well as external outfalls. Data flagged as being from intake structures were not included in the analysis. • Flow data were reported in multiple units. o Flows reported as monthly totals were converted to daily by dividing by the number of days in the month. o Flows reported as gallons per minute records were converted to million gallons per day (MGD). o Flows without reported units were assumed to be MGD. o Flow values orders of magnitude greater than a facilities design flow were removed from the database as units could not be assumed and data entry errors likely occurred. • A constant flow of 0.1375 MGD was assigned to the 117 facilities that did not have flow data. This value is one-half of the average reported minimum for the other 853 stations with reported flows.

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• Maximum monthly reported flows were not included in the analysis to avoid any bias in the results from such values.

This analysis yielded a total average daily flow of 1,518 MGD from the 943 facilities in the watershed, or 1.6 MGD per facility. The number of facilities and total discharge volumes by eight digit HUC is presented in Table 2.

Table 2. Summary of DMR Flow Data by 8 Digit HUC within the Wabash River Watershed 8 Digit # NPDES Sum of Average Daily Flows (MGD) Annual Estimated Flow (MG) HUC Facilities 05120101 62 35 12,943 05120102 19 4 1,331 05120103 39 17 6,387 05120104 25 29 10,411 05120105 11 2 843 05120106 48 12 4,236 05120107 29 21 7,667 05120108 23 222 81,106 05120109 51 50 18,256 05120110 10 1 263 05120111 54 388 141,631 05120112 61 26 9,628 05120113 28 8 3,037 05120114 43 184 67,051 05120115 16 2 633 05120201 157 309 112,757 05120202 42 129 47,086 05120203 30 6 2,121 05120204 54 19 6,991 05120205 14 11 4,083 05120206 23 8 2,801 05120207 35 10 3,507 05120208 52 21 7,685 05120209 17 5 1,683 Total 943 1,518 554,136

The available flow data were multiplied by available concentration data to estimate annual nutrient loads within the watershed. As with the flow data, a number of assumptions had to be made to address the considerable data gaps. These assumptions are described below:

• The reported water quality data included both loads (e.g., pounds per month) and concentrations (e.g., milligrams per liter). Furthermore, the data were expressed on both a daily and a monthly basis. For consistency, only concentrations were used in the loading analysis. Annual loads were

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calculated by multiplying average daily flows by average daily concentrations and appropriate conversion factors. • Facilities were categorized as Wastewater or Industrial facilities based on their permit IDs. Facilities that did not report BOD, TN, TP, TN, or ammonia were also assumed to be Industrial facilities. • For wastewater facilities, existing BOD and nutrient concentrations were used as an indicator of treatment performance to set effluent concentrations for unreported parameters based on the following assumptions: o If the maximum reported BOD value was below 15 mg/L or unknown, TP effluent concentration was estimated to be 2 mg/L, TN concentration was estimated to be 15 mg/L, and NH3 effluent concentration was estimated to be 0.5 mg/L. o If the maximum reported BOD value was between 15 and 20 mg/L, TP effluent concentration was estimated to be 4 mg/L, TN concentration was estimated to be 18 mg/L, and NH3 effluent concentration was estimated to be 1 mg/L. o If the maximum reported BOD value was above 25 mg/L, TP effluent concentration was estimated to be 4.5 mg/L, TN concentration was estimated to be 20 mg/L, and NH3 effluent concentration was estimated to be 1.5 mg/L. o If no BOD data were provided, BOD concentrations were assumed based on the existing TN, NH3, and TP values above. • For Industrial facilities, the following effluent concentrations were assigned for unreported parameters: o TP 0.2 mg/L; TN 2 mg/L; NH3 0.1 mg/L; BOD 1 mg/L

The resulting load estimates are provided, by eight digit HUC, in Table 3 to Table 6 and Figure 3 to Figure 6.

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Table 3. Estimated Ammonia Loads Daily Load 8 Digit HUC (lb/day) Annual Load (tons) Annual Load (kg) 05120101 165 30 30,635 05120102 35 6 6,435 05120103 54 10 9,967 05120104 59 11 11,019 05120105 14 3 2,671 05120106 72 13 13,378 05120107 65 12 11,976 05120108 1,150 210 213,162 05120109 254 46 47,036 05120110 12 2 2,153 05120111 2,544 464 471,695 05120112 156 29 28,958 05120113 54 10 10,012 05120114 849 155 157,350 05120115 39 7 7,208 05120201 1,148 210 212,962 05120202 415 76 76,911 05120203 30 5 5,582 05120204 129 23 23,866 05120205 171 31 31,621 05120206 53 10 9,745 05120207 385 70 71,338 05120208 101 19 18,798 05120209 116 21 21,578 Total 8,068 1,472 1,496,055

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Figure 3. Estimated Annual Ammonia Loads (tons)

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Table 4. Estimated BOD Loads

8 Digit HUC Daily Load (lb/day) Annual Load (tons) Annual Load (kg) 05120101 1,192 218 221,077 05120102 861 157 159,649 05120103 520 95 96,402 05120104 404 74 74,852 05120105 60 11 11,054 05120106 438 80 81,217 05120107 951 174 176,382 05120108 14,287 2,607 2,649,141 05120109 5,704 1,041 1,057,600 05120110 28 5 5,200 05120111 14,830 2,707 2,749,987 05120112 5,286 965 980,122 05120113 440 80 81,654 05120114 17,182 3,136 3,185,944 05120115 715 131 132,599 05120201 11,243 2,052 2,084,758 05120202 8,239 1,504 1,527,710 05120203 320 58 59,253 05120204 802 146 148,642 05120205 588 107 109,033 05120206 205 37 38,041 05120207 2,905 530 538,596 05120208 706 129 130,853 05120209 574 105 106,435 Total 88,477 16,147 16,406,201

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Figure 4. Estimated Annual BOD Loads (tons)

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Table 5. Estimated TN Loads

8 Digit HUC Daily Load (lb/day) Annual Load (tons) Annual Load (kg) 05120101 2,952 539 547,363 05120102 402 73 74,588 05120103 2,063 376 382,463 05120104 805 147 149,184 05120105 248 45 46,032 05120106 880 161 163,168 05120107 2,443 446 453,058 05120108 27,837 5,080 5,161,759 05120109 5,743 1,048 1,064,944 05120110 60 11 11,115 05120111 22,943 4,187 4,254,327 05120112 2,946 538 546,291 05120113 950 173 176,173 05120114 23,254 4,244 4,311,925 05120115 248 45 45,898 05120201 31,964 5,834 5,927,123 05120202 10,576 1,930 1,961,159 05120203 725 132 134,414 05120204 2,279 416 422,663 05120205 1,320 241 244,705 05120206 772 141 143,243 05120207 968 177 179,507 05120208 2,367 432 438,907 05120209 574 105 106,422 Total 145,320 26,521 26,946,431

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Figure 5. Estimated Annual TN Loads (Tons)

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Table 6. Estimated TP Loads

8 Digit HUC Daily Load (lb/day) Annual Load (tons) Annual Load (kg) 05120101 499 91 92,497 05120102 51 9 9,423 05120103 148 27 27,477 05120104 104 19 19,204 05120105 33 6 6,103 05120106 113 21 20,895 05120107 326 60 60,523 05120108 3,995 729 740,743 05120109 1,130 206 209,625 05120110 8 1 1,453 05120111 3,005 548 557,157 05120112 647 118 120,029 05120113 137 25 25,335 05120114 3,292 601 610,355 05120115 55 10 10,188 05120201 4,210 768 780,673 05120202 1,390 254 257,697 05120203 106 19 19,612 05120204 311 57 57,633 05120205 190 35 35,186 05120206 57 10 10,654 05120207 155 28 28,754 05120208 154 28 28,638 05120209 88 16 16,228 Total 20,202 3,687 3,746,082

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Figure 6. Estimated Annual Phosphorus Load (tons)

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3 TYPE OF WASTEWATER TREATMENT AND COSTS TO UPGRADE

The level of new pollutant control measures needed to meet nutrient reductions specified by TMDLs or other regulatory drivers will be dependent upon each treatment plant’s current operations and the cost associated with the most likely control measure (e.g., biological phosphorus removal). Information on the type of wastewater treatment used by plants within the Basin was obtained in two ways:

1. Information for the entire basin was based on the Clean Watersheds Needs Survey CWNS 2. Information for the Driftwood and Tippecanoe watersheds was based on CWNS information and a review of actual permits

3.1 CWNS Review

CWNS was a collaborative effort between 47 states, the District of Columbia, U.S. territories and EPA. From February 2008 through April 2009, states, the District of Columbia, and U.S. territories collected and provided data for a report that included summaries of current and planned wastewater treatment plant practices. The report details expected capital costs of projects and was intended to provide Congress the means to inform legislation. The report was also intended to provide data to state environmental agencies, legislatures, and governor’s offices to help administer environmental programs. Finally, the data were intended for academia and industry to help with water quality research and technology support.

Information from the CWNS places facilities into one of the following two categories:

Secondary Wastewater Treatment Secondary treatment typically requires a treatment level that produces an effluent quality of less than 30 mg/L of both BOD5 and total suspended solids (secondary treatment levels required for some lagoon systems may be less stringent). In addition, the secondary treatment must remove 85 percent of BOD5 and total suspended solids from the influent wastewater.

Advanced Wastewater Treatment A facility is considered to have Advanced Wastewater Treatment if its permit includes one or more of the following: BOD less than 20 mg/L; Nitrogen Removal; Phosphorous Removal; Ammonia Removal; Metal Removal; Synthetic Organic Removal.

Table 7 summarizes the CWNS information for all of the facilities in the Wabash River Basin. It indicates that 21 percent of the facilities have advanced treatment, 14 percent are known to have secondary treatment, and no information is available for 65 percent of the facilities. Because the CWNS focuses on larger facilities, and because smaller facilities are less likely to have advanced treatment, it is probable that the majority of the facilities with no information do not use advanced treatment.

Table 7. Summary of CWNS information for all facilities in the Wabash River Basin. Treatment Level Number of Facilities Percent Advanced Treatment 211 21% Secondary 144 14% No Information 666 65% Total Facilities in Watershed 1021 100%

Table 8 summarizes the indicators of advanced treatment and shows that only a small proportion (10%) of the facilities have limits for phosphorus and nitrogen (not including ammonia).

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Table 8. Indicators associated with advanced treatment facilities in the Wabash River Basin. Advance Indicators Number of Facilities Percent BOD 164 77.7% Nitrogen 1 0.5% BOD, Nitrogen 1 0.5% BOD, Phosphorus 2 0.9% BOD, Ammonia 26 12.3% BOD, Phosphorus, Ammonia 17 8.1% Total 211 100.0%

Table 9 summarizes the type of treatment by HUC and indicates that HUCs 05120201, 05120202, and 05120111 have the most facilities with advanced treatment. The cities of Terra Haute, Bloomington, Indianapolis, Anderson, and Muncie are located in these HUCs.

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Table 9. Summary of treatment type by HUC. No CWNS HUC 8 Advanced Treatment Secondary Treatment Information 05120101 8 5 49 05120102 3 2 14 05120103 9 4 26 05120104 3 4 18 05120105 3 2 6 05120106 8 5 35 05120107 5 4 20 05120108 8 3 12 05120109 7 7 37 05120110 2 1 7 05120111 13 5 36 05120112 8 10 43 05120113 4 8 16 05120114 6 12 25 05120115 1 8 7 05120201 33 9 115 05120202 13 4 25 05120203 8 1 21 05120204 8 4 42 05120205 5 0 9 05120206 4 1 18 05120207 5 2 28 05120208 10 6 36 05120209 2 3 12 Total 176 110 657

3.2 Facilities in the Tippecanoe and Driftwood Watersheds

A more detailed analysis was performed to determine the type of treatment for facilities in the Tippecanoe and Driftwood watersheds (which are being modeled in SWAT to support the feasibility study). The CWNS information for the 92 facilities in these two watersheds is shown in Table 10. Seventeen of the facilities have advanced treatment, nine have secondary treatment, and treatment type was not reported for 66 facilities.

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Table 10. Summary of 2008 CWNS permit information for facilities in the Tippecanoe and Driftwood watersheds. Permit WWTP Present Treatment Number of Watershed Present Advance Indicators Type or Other Level Facilities Major Other No Info No Info 2 BOD (Biochemical Oxygen Major WWTP Advanced Treatment 2 Demand) Minor Other No Info No Info 23 Minor Other Secondary No Info 1 Ammonia Removal, BOD Tippecanoe Minor WWTP Advanced Treatment 1 (Biochemical Oxygen Demand)

BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 5 Demand) BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 1 Demand), Ammonia Removal Minor WWTP No Info No Info 3 Minor WWTP Secondary No Info 4 Major Other No Info No Info 1 BOD (Biochemical Oxygen Major WWTP Advanced Treatment 3 Demand) Major WWTP Secondary No Info 1 Minor Other No Info No Info 34 Driftwood BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 4 Demand) BOD (Biochemical Oxygen Minor WWTP Advanced Treatment 1 Demand), Ammonia Removal Minor WWTP No Info No Info 3 Minor WWTP Secondary No Info 3

Of the 92 facilities, Tetra Tech was able to obtain permits for the 31 facilities that are WWTP with the watershed. These permits indicate that the following specific types of treatment methods are used within the watershed (either individually or in combination with one another):

ƒ trickling filters ƒ activated sludge (including extended aeration and oxidation) ƒ discharge waste stabilization lagoons

Out of these only the activated sludge systems can effectively be retrofit for biological nitrogen removal. Therefore, the trickling filter and lagoon treatment processes will require an expansion to reduce nutrient in their effluent.

All 31 facilities are required to report CBOD5 and ammonia values, 21 of the facilities have permitted ammonia limits, and only one facility has a phosphorus limit (Table 11).

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Table 11. Permit limit summaries for facilities in the Driftwood and Tippecanoe watersheds. Permit Limit No Designation in CWNS Secondary Advanced Design Flow (MGD) 0.024 to 0.2 0.08 to 5.13 0.08 to 5.13 Monthly Average CBOD5 – Summer (mg/L) 12 to 25 20 to 25 10 to 25 Monthly Average CBOD5 – Winter (mg/L) 12 to 25 25 15 to 30 Weekly Average CBOD5 – Summer (mg/L) 18 to 40 30 to 40 15 to 40 Weekly Average CBOD5 – Winter (mg/L) 18 to 40 40 23 to 45 Monthly Average Ammonia – Summer (mg/L) 1.3 to 9.4 1.5 to 9.6 1.1 to 8.6 Monthly Average Ammonia – Winter (mg/L) 2 to 10.8 2.2 to 10.4 1.6 to 11 Weekly Average Ammonia – Summer (mg/L) 2 to 14.1 2.2 to 14.4 1.6 to 12.9 Weekly Average Ammonia – Winter (mg/L) 3 to 16.2 3.3 to 15.6 2.4 to 16.5 Phosphorus Limit (mg/L) N/A N/A 1

3.2.1 Costs to Upgrade

This section of the report provides estimated costs for upgrading permitted WWTPs in the Driftwood and Tippecanoe watersheds to enhanced nutrient removal (ENR) for reducing TN and TP effluent loads. The costs are based only on cited literature and the information available in the CWNS and the permits and are intended solely to inform the feasibility study. Actual upgrade costs vary widely, depending on a great number of factors, including:

• actual target effluent concentrations for nitrogen and phosphorus; • existing facilities’ suitability for various types of upgrades; • various wastewater characteristics, including influent TP and TN concentrations, influent rbCOD:TP and BOD:TN ratios, alkalinity levels, actual flow and constituent concentrations and their hourly, daily, and seasonal variations; • various operating characteristics, including ambient temperatures, mixed liquor characteristics, and plant configuration and control methods; • local labor, material, and operational costs which may vary significantly over time; and • financing terms.

Accordingly, the reader should use the results presented herein only as a general guide for order-of- magnitude cost ranges for various upgrade options. More detailed, plant-specific analyses should be conducted to determine whether water quality trading (WQT) may be a better, more cost-effective option for reducing nutrient loading. In particular, WQT may be advantageous for:

• long-term compliance and cost savings; • short-term compliance for the useful life of the facility or when other regulatory requirements will be better understood in regards to needed upgrades; • variance or compliance schedule justification; • small or difficult to upgrade facilities; and • future growth in fully capped watersheds.

As previously indicated, permits for 31 wastewater treatment plants (WWTPs) within the Driftwood and Tippecanoe watersheds were collected and analyzed for details about their permitted flow rates, system types, and effluent limits. Based on the lack of TN or TP limits in the available permits, and the types of

20 treatment processes specified in the CWNS and the collected permits, it appears that almost none of the facilities in the Driftwood and Tippecanoe are targeting the treatment of TN or TP. The facilities that are treating for ammonia (via nitrification) likely do not include technology for denitrification or phosphorus removal. Upgrades would therefore be necessary to provide ENR for TN and TP removal.

The analysis of permits showed that WWTPs in these watersheds span a variety of process types for permitted flow rates ranging from about 24,000 gallons per day (gpd) to 5.0 million gallons per day (MGD). For the purposes of this evaluation, the secondary (i.e., biological) treatment process identified in the permits for each of these plants was used to categorize the WWTP as either an “activated sludge” system or a “lagoon/trickling filter” system. The reason that this simple categorization was chosen is because activated sludge systems are generally relatively simple to convert to biological nutrient removal (BNR) systems (by adding anaerobic and/or anoxic reactors), while lagoons and trickling filters generally require more extensive secondary treatment system modifications to be upgraded to BNR. A number of specific treatment process were listed in the permits for WWTPs categorized as “activated sludge” systems, including “extended aeration”, “oxidation ditch”, “waste stabilization”, “sequencing batch reactor”, and “activated sludge” or “conventional activated sludge”. The numbers of facilities in each of these two main categories across a range of flow rates are summarized in Table 12.

Based on this summary of the characteristics of WWTPs in the target watersheds, seven generic WWTPs (design flow/system type) were used to evaluate the range of potential costs that may be required to upgrade WWTPs in the Driftwood and Tippecanoe watersheds to nitrogen removal, phosphorus removal, or both. The generic WWTPs simulated in the cost analysis are also identified in Table 12. The design flow of each simulated plant is roughly equal to the average flow rate for the WWTPs in a given flow range.

Table 12. Summary of facility type and flows for WWTPs included in Driftwood and Tippecanoe nutrient removal analysis Flow Range Activated Sludge (AS) Lagoon/Trickling Filter (TF) (MGD) # Total Flow Simulation # Total Flow Simulation Plant Facilities (MGD) Plant Facilities (MGD) <0.1 5 0.26 0.05 MGD AS 4 0.21 0.05 MGD Lagoon 0.1 to 0.5 9 2.35 0.3 MGD AS 5 1.29 0.3 MGD Lagoon 0.5 to 1.0 2 1.59 0.75 MGD AS 0 0.00 -- 1.0 to 4.0 0 0.00 -- 2 5.15 2.5 MGD TF >4.0 4 21.13 5.0 MGD AS 0 0.00 --

In addition to the variety of process types and flow ranges identified in Table 12, for each simulation plant a number of different nutrient removal upgrade options were evaluated. In general, our selection of the specific nutrient removal upgrade options simulated was based on the availability of cost information for those options, as found in the literature. Based on our review of relevant literature and previous nutrient removal experience, we selected two generic levels of treatment for nitrogen removal and two levels of treatment for phosphorus removal to aid in communicating the results of the cost analysis.

The “low enhanced nutrient removal (ENR)” treatment level for nitrogen (or TN1) can be met by adding anoxic reactors (along with nitrified mixed liquor recycle lines) prior to the existing secondary treatment process (“pre-anoxic”) or adding post-secondary anoxic treatment (typically using filters supplemented with an external carbon source), for denitrification. Land application of effluent was also designated as a potential option for the TN1 treatment level, with nitrogen removal attributed to both nitrification/denitrification processes and vegetative uptake and sequestration (Crites and Tchobanoglous 1998). These processes have been documented to meet a TN of 10 mg/l reliably, but may be designed to

21 meet lower TN levels. The “high ENR” treatment level for nitrogen (or TN2) requires both pre- and post- secondary anoxic reactors and has been demonstrated as capable of achieving effluent TN concentrations below 5 mg/l (typically, 2-3 mg/l). The “low ENR” level for phosphorus (TP1), which requires an effluent TP of 1 mg/l or less, can be met using enhanced biological phosphorus removal (EBPR), typically involving the addition of anaerobic “selector” reactors prior to the secondary treatment unit, or using alum, which is typically dosed between the secondary treatment process and the secondary clarifier (but potentially in other configurations), for precipitating phosphorus. The “high ENR” treatment level for phosphorus (TP2) requires either multi-point alum addition or EBPR with single- or multi-point alum addition and enhanced solids removal processes can be used to reach TP levels below 0.5 mg/l, often down to 0.1 mg/l or lower. Land application systems are also well documented to be able to meet TP2 treatment levels.

These treatment levels are summarized in Table 13, along with our assumptions for influent and baseline (i.e., effluent levels in the absence of ENR) concentrations. For the purposes of the cost analysis, we assumed a baseline TN concentration of 25 mg/l and TP concentration of 4 mg/l, the midpoints of the ranges shown in Table 13. In the cost calculations, these represent the assumed average effluent concentrations for the WWTPs prior to implementing an ENR process.

Another important implicit assumption in all of the cost calculations is that existing secondary treatment processes are nitrifying or can be made to nitrify – that is, they are sufficient to convert the majority of influent organic nitrogen and ammonia to nitrate, such that denitrification retrofits would be effective upgrade options. It is important to note that, even under a WQT approach to meeting TN reductions, participating WWTPs would still need to meet existing or revised water quality based effluent discharge limits for ammonia, a potentially mobile and toxic wastewater constituent. Under a WQT approach, nitrifying WWTPs would continue to discharge TN in the form of nitrate at non-toxic levels.

Table 13. Summary of ENR treatment levels and assumptions for WWTP upgrade simulations TN TP Treatment Level Effluent AS options Lagoon/TF Effluent AS options Lagoon/TF options options None (influent) 25-35 mg/l 4-8 mg/l Baseline (no ENR) 20-30 mg/l 2-6 mg/l Low ENR (1) 5-10 mg/l Pre- or post- Post-anoxic 0.5-1 mg/l EBPR or single- EBPR replacement or anoxic retrofit or replacement or point alum retrofit single-point alum retrofit land application land application High ENR (2)1 <5 mg/l Pre-/Post-anoxic Post-anoxic <0.5 mg/l EBPR and/or multi- EBPR replacement and/or retrofit replacement point alum retrofit multi-point alum retrofit or or land application land application 1 Enhanced solids removal also generally required for the high ENR process upgrades, particularly for TP removal

Table 14 summarizes the characteristics of all applicable permutations of the TN and TP treatment levels as used in the cost analysis and provides a guide to the color coded rows in Table 15 through Table 21 , which present the results of the upgrade cost analyses.

Note that the calculations for cost per pound (cost/#) removed that are summarized in Table 15 through Table 21 use the actual effluent TN and TP treatment levels indicated in the cost reference cited, while also assuming that the design/permitted flows are the actual plant flows (this assumption has little bearing on capital costs, but does affect O&M cost estimates). To be consistent with the basis used in EPA’s nutrient removal reference document (US EPA 2008), all costs were converted to annual costs assuming 20-year financing terms and a 6% interest rate. Additionally, all costs presented in Table 15 through Table 21 are presented in 2011 dollars using the latest ENR construction cost index (9011, March 2011) as a basis for adjusting the costs generated from the various sources indicated in the tables. Finally, note that where costs per pound removed are presented in Table 15 through Table 21, total costs are used in all calculations. Accordingly, it is difficult to compare the cost per pound of TP removed for a TN1/TP1

22 system with the cost per pound of TP removed for a TP1 system, for example, since the former number includes costs necessary for TN removal in addition to TP removal, while the latter number would be only associated with TP removal.

Table 14. Color coding of ENR treatment levels for Tables 4-10 Treatment Effluent Effluent Color Level TN TP Coding TN1 5-10 mg/l -- Pink 0.5-1 TP1 -- Blue mg/l TN2 <5 mg/l -- Tan TP2 -- <0.5 mg/l Olive 0.5-1 TN1/TP1 5-10 mg/l Green mg/l TN1/TP2 5-10 mg/l <0.5 mg/l Purple TN2/TP2 <5 mg/l <0.5 mg/l Aqua

Table 15 and Table 16 provide estimated costs for 0.05 MGD activated sludge and lagoon upgrade options, respectively. Note that the cost estimates for the single-point alum addition upgrade option may be biased high, as the capital costs cited by Keplinger, et al. (2003) were significantly higher than those cited in comparable references addressing single-point alum treatment (i.e., as summarized in Table 17 and Table 19).

Table 15. 0.05 MGD activated sludge ENR upgrade options and costs Annual Treatment Level Cost Upgraded Process Cost $/# Reference TN TP $/# TN $/# TP ($/yr) TN+TP MLE – added anoxic 36,074 10 mg/l 2 mg/l 15.80 118.51 13.94 Foess (1998)1 zone Single-point alum Keplinger 90,296 0.75 mg/l 182.54 182.54 addition (2003)2 MLE + denitrification 59,636 6 mg/l 1 mg/l 20.53 130.01 17.73 Foess (1998)3 filters Land app. – spray Buchanan 152,167 10 mg/l 0.1 mg/l 66.65 256.35 52.90 irrigation (2010)4 Land app. – drip Buchanan 82,891 10 mg/l 0.1 mg/l 36.35 139.79 28.85 irrigation (2010)4 1 Used present worth costs for 50,000 gpd anoxic tank for MLE upgrade retrofit system (option R1) 2 Used average capital and O&M costs for Iredell (0.25 MGD), Valley Mills (0.81 MGD), and Hico (0.87 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used present worth costs for 50,000 gpd deep bed denitrification filter upgrade retrofit system (option R2) 4 Used model-simulated costs for 50,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

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Table 16. 0.05 MGD lagoon ENR upgrade options and costs Annual Treatment Level Cost Upgraded Process Cost $/# Reference TN TP $/# TN $/# TP ($/yr) TN+TP New MLE system 181,133 10 mg/l 2 mg/l 79.34 595.03 70.00 Foess (1998)1 0.75 Keplinger Single-point alum addition 90,296 182.54 182.54 mg/l (2003)2 New MLE + denitrification 203,661 6 mg/l 1 mg/l 70.42 446.02 60.82 Foess (1998)3 filters Buchanan Land app. – spray irrigation 152,167 10 mg/l 0.1 mg/l 66.65 256.35 52.90 (2010)4 Buchanan Land app. – drip irrigation 82,981 10 mg/l 0.1 mg/l 36.35 139.79 28.85 (2010)4 1 Used present worth costs for 50,000 gpd MLE process system (option 1) 2 Used average capital and O&M costs for Iredell (0.025 MGD), Valley Mills (0.081 MGD), and Hico (0.087 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used present worth costs for 50,000 gpd MLE and deep-bed filtration process system (option 6) 4 Used model-simulated costs for 50,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

The results presented in Table 15 and Table 16 indicate that for 0.05 MGD activated sludge systems, secondary treatment upgrades and alum addition would typically be most cost effective, while comparably-sized lagoon systems may be cost-effectively upgraded for ENR via land application.

Table 17and Table 18 provides estimated costs for 0.3 MGD activated sludge and lagoon upgrade options, respectively, with average costs for each treatment level provided where more than one option is presented. As indicated above, the average cost estimates for the single-point alum addition upgrade option may be biased high, as the capital costs cited by Keplinger, et al. (2003) were significantly higher than those cited by CH2M-Hill (2010) and others for single-point alum treatment.

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Table 17. 0.3 MGD activated sludge ENR upgrade options and costs Annual Treatment Level Cost Upgraded Process Cost $/# Reference TN TP $/# TN $/# TP ($/yr) TN+TP Single-point alum Keplinger 214,146 1 mg/l 78.16 78.16 addition (2003)2 Single-point alum CH2M Hill 10,552 1 mg/l 3.85 3.85 addition (2010)3 TP1 AVERAGE 112,349 41.01 41.01 Colorado Not specified 154,196 3 mg/l 7.67 7.67 4 (2010) CH2M Hill Multi-point alum addition 157,473 0.1 mg/l 44.21 44.21 (2010)3 Colorado Not specified 269,758 0.1 mg/l 75.74 75.74 (2010)4 TP2 AVERAGE 213,616 59.98 59.98 Not specified 212,0771 10 mg/l 1 mg/l 15.48 77.41 12.90 EPA (2007)5 Not specified 469,8841 6 mg/l 0.8 mg/l 27.08 160.79 23.18 M&E (2009)6 TN1/TP1 AVERAGE 340,9811 21.28 119.10 18.04 Land app. – spray Buchanan 910,143 10 mg/l 0.1 mg/l 66.44 255.54 52.73 irrigation (2010)7 Land app. – drip Buchanan 483,498 10 mg/l 0.1 mg/l 35.30 135.75 28.01 irrigation (2010)7 TN1/TP2 AVERAGE 696,821 50.87 195.65 40.37 1 Capital costs only. O&M costs not included. 2 Used average capital and O&M costs for Valley Mills (0.081 MGD), Hico (0.087 MGD), and Clifton (0.328 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 3 Used average total life cycle costs for Oakley (0.25 MGD), Coalville (0.35 MGD), and Fairview (0.375 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for 0.1 MGD plant upgrades from Table 6 of paper, normalized as unit costs ($/gpd capacity) 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 7 Used model-simulated costs for 300,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

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Table 18. 0.3 MGD lagoon ENR upgrade options and costs Treatment Level Cost Annual Upgraded Process $/# Reference Cost TN TP $/# TN $/# TP TN+TP CH2M Hill New MLE system 352,708 3 mg/l 25.75 25.75 (2010)2 CH2M Hill Single-point alum 80,433 1 mg/l 29.36 29.36 (2010)2 Keplinger Single-point alum 214,146 1 mg/l 78.16 78.16 (2003)3 TP1 AVERAGE 147,290 53.76 53.76 Multi-point alum + CH2M Hill 307,001 0.1 mg/l 86.20 86.20 filters (2010)2 Colorado Not specified 269,758 0.1 mg/l 75.74 75.74 (2010)4 TP2 AVERAGE 288,380 80.97 80.97 Not specified 212,0771 10 mg/l 1 mg/l 15.48 77.41 12.90 EPA (2007)5 Not specified 469,884 6 mg/l 0.8 mg/l 27.08 160.79 23.18 M&E (2009)6 TN1/TP1 AVERAGE 340,9811 119.10 18.04 Land application - Buchanan 910,143 10 mg/l 0.1 mg/l 66.44 255.54 52.73 spray (2010)7 Buchanan Land application - drip 483,498 10 mg/l 0.1 mg/l 35.30 135.75 28.01 (2010)7 TN1/TP2 AVERAGE 696,821 195.65 40.37 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for 0.55 MGD lagoon retrofits, normalized as unit costs ($/gpd capacity) 3 Used average capital and O&M costs for Valley Mills (0.081 MGD), Hico (0.087 MGD), and Clifton (0.328 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for 0.1 MGD plant upgrades from Table 6 of paper, normalized as unit costs ($/gpd capacity) 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 7 Used model-simulated costs for 300,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

The results presented in Table 17 and Table 18 indicate that at these relatively low design flows, land application may be a cost-effective ENR upgrade option, compared with more traditional secondary treatment upgrades or multi-point alum addition, as needed to achieve comparably low TP levels.

Table 19 provides estimated costs for 0.75 MGD activated sludge upgrade options, with average costs for each treatment level provided where more than one option is presented. Because the costs cited by Keplinger (2003) were significantly higher than those cited by CH2M-Hill (2010) and US EPA (2008) for single-point alum treatment, the Keplinger data was not used in the average cost calculation for TP1 treatment level upgrade options.

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Table 19. 0.75 MGD activated sludge ENR upgrade options and costs Annual Cost Treatment Level Cost Upgraded Process Reference ($/yr) TN TP $/# TN $/# TP $/# TN+TP MLE – added anoxic CH2M Hill 72,554 10 mg/l 2.12 2.12 zone (2010)3 Single-point alum Keplinger 390,779 1 mg/l 57.05 57.05 addition (2003)4 EBPR or single-point CH2M Hill 29,232 1 mg/l 4.27 4.27 alum addition (2010)3 Fermenter addition 28,942 0.5 mg/l 3.62 3.62 EPA (2008)5 Single-point alum 60,853 0.5 mg/l 7.62 7.62 EPA (2008)5 addition Fermenter and filter 60,358 0.5 mg/l 7.55 7.55 EPA (2008)5 addition TP1 AVERAGE 44,8461 5.77 5.77 Phased Isolation Ditch 69,016 3 mg/l 1.37 1.37 EPA (2008)5 retrofit MLE retrofit 111,316 3 mg/l 2.22 2.22 EPA (2008)5 Step-feed retrofit 111,316 3 mg/l 2.22 2.22 EPA (2008)5 Denitrification filter 230,053 3 mg/l 4.58 4.58 EPA (2008)5 retrofit TN2 AVERAGE 130,425 2.60 2.60 EBPR + multi-stage CH2M Hill 166,445 0.1 mg/l 18.69 18.69 alum + filters (2010)3 Fermenter, filter, and 118,242 0.1 mg/l 13.28 13.28 EPA (2008)5 alum addition Multi-point alum and 134,321 0.1 mg/l 15.09 15.09 EPA (2008)5 filter addition TP2 AVERAGE 139,669 15.69 15.69 Not specified 530,0912 10 mg/l 1 mg/l 15.48 77.39 12.90 EPA (2007)6 Not specified 557,904 6 mg/l 0.8 mg/l 12.86 76.36 11.01 M&E (2009)7 TN1/TP1 AVERAGE 543,9982 14.17 76.88 11.96 Buchanan Land application - spray 2,275,356 10 mg/l 0.1 mg/l 66.44 255.54 52.73 (2010)8 Buchanan Land application - drip 1,169,997 10 mg/l 0.1 mg/l 34.16 131.40 27.11 (2010)8 TN1/TP2 AVERAGE 1,722,677 50.30 193.47 39.92 Phased Isolation Ditch 238,958 3 mg/l 0.1 mg/l 4.76 26.84 4.04 EPA (2008)5 retrofit 5-stage act. sludge + 271,611 3 mg/l 0.1 mg/l 5.41 30.50 4.59 EPA (2008)5 alum retrofit Alum addition + 352,253 3 mg/l 0.1 mg/l 7.01 39.56 5.96 EPA (2008)5 denitrification filter TN2/TP2 AVERAGE 287,607 5.73 32.30 4.86 1 Average does not include Keplinger data 2 Capital costs only. O&M costs not included. 3 Used average total life cycle costs for Fairview (0.375 MGD), Moroni (0.9 MGD), Hyrum City (1.3 MGD), and Tremonton (1.9 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 4 Used average capital and O&M costs for Hico (0.087 MGD), Clifton (0.328 MGD), and Meridian (0.36 MGD) plant single-point alum addition upgrades, normalized as unit costs ($/gpd capacity) 5 Used extrapolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 6 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >0.1-1.0 MGD ($6,972,000/mgd capacity) 7 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD) 8 Used model-simulated costs for 750,000 gpd systems installed in loam soils (per NRCS Web Soil Survey data for area) and added capital costs for land acquisition (assuming $5,000/acre) and a 25% allowance for various professional fees, both of which are not included in the model

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The results presented in Table 19 indicate that at these somewhat higher design flows, traditional secondary treatment upgrades or alum addition are more cost effective than land application for achieving very low TP levels.

Table 20 provides estimated costs for 2.5 MGD trickling filter upgrade options, with average costs for each treatment level provided where more than one option is presented. For this higher flow rate, we assumed that land application would no longer be a viable upgrade option.

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Table 20. 2.5 MGD trickling filter ENR upgrade options and costs Annual Treatment Level Cost Upgraded Process Cost $/# $/# Reference TN TP $/# TP ($/yr) TN TN+TP CH2M Hill New MLE system 163,852 10 mg/l 1.44 1.44 (2010)2 New phased isolation ditch 376,001 5 mg/l 2.47 2.47 EPA (2008)3 system New MLE system 1,009,265 5 mg/l 6.63 6.63 EPA (2008)3 New SBR system 1,128,002 5 mg/l 7.41 7.41 EPA (2008)3 New 4-stage bardenpho 1,385,265 5 mg/l 9.10 9.10 EPA (2008)3 system TN1 AVERAGE 812,477 5.41 5.41 New A/O system 811,098 1 mg/l 35.53 35.53 EPA (2008)3 CH2M Hill Single-point alum addition 144,841 1 mg/l 6.34 6.34 (2010)2 0.5 Single-point alum addition 168,211 6.32 6.32 EPA (2008)4 mg/l 0.5 New A/O w/fermenters 841,054 31.58 31.58 EPA (2008)3 mg/l New A/O w/fermenters + 0.5 989,475 37.15 37.15 EPA (2008)3 filters mg/l New mod UCT 0.5 1,504,002 56.47 56.47 EPA (2008)3 w/fermenters + filters mg/l New 5-stage bardenpho 0.5 1,553,476 58.32 58.32 EPA (2008)3 w/filters mg/l TP1 AVERAGE 858,880 33.10 33.10 New denitrification filters 662,948 3 mg/l 3.96 3.96 EPA (2008)4 New A/O w/fermenters + 0.1 1,137,896 38.34 38.34 EPA (2008)3 filters + alum mg/l Multi-stage alum addition 0.1 CH2M Hill 1,307,813 44.06 44.06 with filters mg/l (2010)2 New A/O with fermenters, 0.1 1,137,490 38.33 38.33 EPA (2008)3 filters, alum mg/l Multi-point alum addition 0.1 395,790 13.34 13.34 EPA (2008)4 with filters mg/l TP2 AVERAGE 994,747 33.52 33.52 New A/O system 3,860,366 10 mg/l 1 mg/l 33.82 169.09 28.18 Jiang (2004)5 Not specified 441,4891 10 mg/l 1 mg/l 3.87 19.34 3.22 EPA (2007)6 0.8 Not specified 1,039,337 6 mg/l 7.19 42.68 6.15 M&E (2009)7 mg/l New 3-stage UCT system 1,345,686 5 mg/l 1 mg/l 8.84 58.94 7.69 EPA (2008)3 New step feed AS system 900,422 5 mg/l 1 mg/l 5.92 39.44 5.14 EPA (2008)3 0.5 New 5-stage Bardenpho 1,434,739 5 mg/l 9.43 53.86 8.02 EPA (2008)3 mg/l TN1/TP1 AVERAGE 1,503,673 11.51 63.89 9.73 New A/A/O with alum + 3.5 0.1 5,301,614 32.40 178.63 27.43 Jiang (2004)5 filters mg/l mg/l Alum addition with 0.1 989,475 3 mg/l 5.91 33.34 5.02 EPA (2008)4 denitrification filters mg/l New phased isolation 0.1 722,317 3 mg/l 4.31 24.34 3.66 EPA (2008)3 ditch+alum+filters mg/l 0.1 New SBR + alum + fiters 1,236,844 3 mg/l 7.39 41.67 6.28 EPA (2008)3 mg/l

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Annual Treatment Level Cost Upgraded Process Cost $/# $/# Reference TN TP $/# TP ($/yr) TN TN+TP New 5-stage Bardenpho + 0.1 1,682,108 3 mg/l 10.05 56.67 8.53 EPA (2008)3 alum + filters mg/l TN2/TP2 AVERAGE 1,986,472 12.01 66.93 10.18 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for Tremonton (1.9 MGD), Snyderville (2.4 MGD), and Magna (3.3 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 3 Used interpolated life cycle costs per MG treated for expansion options, normalized to $/gpd capacity 4 Used interpolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 5 Interpolated between 1 MGD and 10 MGD de novo plant options 6 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >1.0-10.0 MGD ($1,742,000/mgd capacity) 7 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD)

The results presented in Table 20 indicate that for replacement systems associated with trickling filter upgrades at these higher design flows, the additional costs associated with higher levels of ENR (e.g., TN1/TP1->TN2/TP2) are relatively modest.

Table 21 provides estimated costs for 5 MGD activated sludge upgrade options, with average costs for each treatment level provided where more than one option is presented. As for the 2.5 MGD option, for this higher flow rate, we assumed that land application would no longer be a viable upgrade option.

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Table 21. 5 MGD activated sludge ENR upgrade options and costs Treatment Level Cost Annual Upgraded Process $/# $/# $/# Reference Cost TN TP TN TP TN+TP CH2M Hill MLE – added anoxic zone 457,869 10 mg/l 2.01 2.01 (2010)2 CH2M Hill Single-point alum addition 271,677 1 mg/l 5.95 5.95 (2010)2 0.5 Fermenter addition 118,737 2.23 2.23 EPA (2008)3 mg/l 0.5 Single-point alum addition 237,474 4.46 4.46 EPA (2008)3 mg/l 0.5 Fermenter addition with filters 316,632 5.94 5.94 EPA (2008)3 mg/l TP1 AVERAGE 236,130 4.65 4.65 Phased isolation ditch retrofit 336,422 3 mg/l 1.00 1.00 EPA (2008)3 MLE retrofit 554,106 3 mg/l 1.65 1.65 EPA (2008)3 Step feed retrofit 554,106 3 mg/l 1.65 1.65 EPA (2008)3 Denitrification filters 1,048,844 3 mg/l 3.13 3.13 EPA (2008)3 TN2 AVERAGE 623,370 1.86 1.86 EBPR + multi-point alum 0.1 CH2M Hill 1,721,904 29.01 29.01 addition and filters mg/l (2010)2 Fermenter addition with alum 0.1 504,632 8.50 8.50 EPA (2008)3 and filters mg/l Multi-point alum addition with 0.1 653,054 11.00 11.00 EPA (2008)3 filters mg/l TP2 AVERAGE 959,863 16.17 16.17 A/O retrofit + alum addition 518,769 10 mg/l 1 mg/l 2.27 11.36 1.89 Jiang (2005)4 Not specified 882,9781 10 mg/l 1 mg/l 3.87 19.34 3.22 EPA (2007)5 0.8 Not specified 1,699,014 6 mg/l 5.88 34.88 5.03 M&E (2009)6 mg/l TN1/TP1 AVERAGE 1,033,587 4.01 21.86 3.38 3.5 0.1 A/A/O system + alum + filters 2,383,485 7.28 40.15 6.17 Jiang (2005)4 mg/l mg/l 0.1 PID retrofit 1,068,633 3 mg/l 3.19 18.00 2.71 EPA (2008)3 mg/l 0.1 5-stage w/chem P 1,286,318 3 mg/l 3.84 21.67 3.26 EPA (2008)3 mg/l Alum addition w/denitrification 0.1 1,484,213 3 mg/l 4.43 25.00 3.77 EPA (2008)3 filters mg/l TN2/TP2 AVERAGE 1,555,662 4.69 26.21 3.98 1 Capital costs only. O&M costs not included. 2 Used average total life cycle costs for Payson (4.5 MGD), Brigham (6 MGD), and Spanish Fork (6 MGD) activated sludge plant upgrades, normalized as unit costs ($/gpd capacity) 3 Used interpolated life cycle costs per MG treated for retrofit options, normalized to $/gpd capacity 4 Interpolated between 1 MGD and 10 MGD retrofit plant options 5 Used average unit capital costs for BNR upgrades based on MD and CT WWTPs for flow range of >1.0-10.0 MGD ($1,742,000/mgd capacity) 6 Used capital cost equation for BNR upgrades in PA for WWTPs <10 MGD (cost, $M = 2.643 +2.156MGD)

The results presented in Table 21 indicate that for a 5 MGD activated sludge upgrade, the cost increase associated with meeting a TP2 standard versus TP1 are significant, although the potential cost increase associated with meeting high versus low ENR combined TN/TP standards are less pronounced.

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4 CHARACTERIZATION OF NONPOINT SOURCE LOADS

USGS used the SPARROW model to estimate delivered nutrient yields to the Gulf of Mexico (Robertson et. al, 2009). SPARROW is a GIS-based watershed model that uses a hybrid statistical ⁄mechanistic approach to estimate nutrient sources, transport, and transformation in terrestrial and aquatic ecosystems of watersheds under long-term steady state conditions (Smith et al., 1997; Alexander et al., 2008). Given a specification of nutrient sources, the model is used to estimate nutrient delivery to streams from subsurface and overland flow (‘‘land-to-water’’ delivery) in relation to landscape properties, including climate, soils, topography, drainage density, and artificial drainage. The results of the USGS SPARROW application for the eight digit HUCs in the Wabash River watershed are reported in Figure 7 and Figure 8 and Table 7 and Table 8. (Results are only available for TP and TN).

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Figure 7 SPARROW Nitrogen Delivered Incremental Load (ton/year)

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Table 22 Summary of SPARROW Results for Total Nitrogen in the Wabash River Watershed. Percent of Total Nitrogen Total Nitrogen Lower 90% CI Upper 90% CI Fraction of Delivered STATES Delivered Total Nitrogen Delivered for Delivered for Delivered Incremental Incremental include in Incremental Incremental Incremental Incremental Incremental Load Delivered yield from HUC HUC8 NAME the HUC8 Area (km2) Load (kg) Yield (kg/km2) Yield (kg/km2) Yield (kg/km2) Yield (kg/km2) to the Gulf urban areas 5120101 Upper Wabash IN OH 4,606 9,942,288 3,058 2,159 790 4,550 0.71 3.63 5120102 Salamonie IN 1,173 2,382,205 2,788 2,031 767 4,571 0.73 2.18 5120103 Mississinewa IN OH 2,154 4,015,573 2,720 1,864 728 4,050 0.69 5.71 5120104 Eel IN 1,927 3,101,180 2,043 1,609 575 3,244 0.79 5.35 5120105 Middle Wabash-Deer IN 1,828 3,509,733 2,485 1,920 729 4,376 0.77 3.69 5120106 Tippecanoe IN 5,191 7,744,421 2,014 1,492 465 3,124 0.74 3.68 5120107 Wildcat IN 1,898 4,592,930 3,219 2,420 950 4,533 0.75 6.57 Middle Wabash-Little 5120108 Vermilion IL IN 5,588 9,453,485 2,085 1,692 675 3,285 0.81 5.07 5120109 Vermilion IL IN 3,718 7,256,760 2,598 1,952 715 4,208 0.75 5.81 5120110 Sugar IN 2,047 4,179,970 2,623 2,042 739 4,036 0.78 3.38 Middle Wabash- 5120111 Busseron IL IN 5,383 8,864,405 1,906 1,647 633 3,386 0.86 6.19 5120112 Embarras IL IN 6,297 11,304,234 2,264 1,795 623 3,996 0.79 2.61 5120113 Lower Wabash IL IN KY 3,303 7,452,239 2,473 2,256 915 4,889 0.91 2.00 5120114 Little Wabash IL IN 6,008 8,533,342 1,737 1,420 457 3,012 0.82 2.75 5120115 Skillet IL 2,207 2,384,098 1,303 1,080 345 2,362 0.83 1.88 5120201 Upper White IN 7,265 12,686,096 2,411 1,746 684 3,703 0.72 27.52 5120202 Lower White IN 4,298 6,600,367 1,780 1,536 568 3,133 0.86 5.43 5120203 Eel IN 3,033 3,877,723 1,703 1,279 477 2,781 0.75 4.15 5120204 Driftwood IN 2,872 5,331,381 2,670 1,856 692 3,744 0.70 8.40 5120205 Flatrock-Haw IN 1,101 2,299,259 2,854 2,088 691 4,587 0.73 3.25 Upper East Fork 5120206 White IN 2,246 4,915,341 2,813 2,188 744 4,785 0.78 4.54 5120207 Muscatatuck IN 2,944 3,748,317 1,843 1,273 364 2,854 0.69 4.84 Lower East Fork 5120208 White IN 4,944 4,587,912 1,113 928 317 2,062 0.83 8.07 5120209 Patoka IN 2,255 2,750,127 1,466 1,220 455 2,612 0.83 5.45

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Figure 8 SPARROW Phosphorus Delivered Incremental Load (ton/year)

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Table 23 Summary of SPARROW Results for Total Phosphorus in the Wabash River Watershed. Percent of Total Total Delivered Phosphorus Total Phosphorus Lower 90% CI Upper 90% CI Fraction of Incremental STATES Delivered Phosphorus Delivered for Delivered for Delivered Incremental yield from include in Incremental Incremental Incremental Incremental Incremental Load Delivered urban areas HUC HUC8 NAME the HUC8 Area (km2) Load (kg) Yield (kg/km2) Yield (kg/km2) Yield (kg/km2) Yield (kg/km2) to the Gulf based on 5120102 Salamonie IN 1,173 127,838 245 109 20 220 0.44 3.18 5120103 Mississinewa IN OH 2,154 178,652 172 83 13 172 0.48 11.57 5120104 Eel IN 1,927 279,154 171 145 28 316 0.85 8.21 Middle Wabash- 5120105 Deer IN 1,828 227,556 149 124 23 297 0.83 7.87 5120106 Tippecanoe IN 5,191 366,631 108 71 17 158 0.65 8.95 5120107 Wildcat IN 1,898 291,081 187 153 25 369 0.82 14.44 Middle Wabash- 5120108 Little Vermilion IL IN 5,588 533,594 120 95 19 158 0.80 11.81 5120109 Vermilion IL IN 3,718 527,013 173 142 34 417 0.82 11.24 5120110 Sugar IN 2,047 291,531 171 142 26 287 0.83 6.69 Middle Wabash- 5120111 Busseron IL IN 5,383 510,086 107 95 22 249 0.88 14.30 5120112 Embarras IL IN 6,297 866,466 162 138 25 325 0.85 4.71 5120113 Lower Wabash IL IN KY 3,303 404,433 131 122 30 285 0.93 4.86 5120114 Little Wabash IL IN 6,008 730,396 141 122 28 241 0.86 4.30 5120115 Skillet IL 2,207 180,753 94 82 16 164 0.87 3.37 5120201 Upper White IN 7,265 852,713 167 117 25 238 0.70 49.93 5120202 Lower White IN 4,298 513,648 135 120 29 209 0.88 9.28 5120203 Eel IN 3,033 242,980 115 80 18 132 0.70 7.78 5120204 Driftwood IN 2,872 318,669 148 111 15 197 0.75 19.22 5120205 Flatrock-Haw IN 1,101 120,762 136 110 22 271 0.81 8.70 Upper East Fork 5120206 White IN 2,246 356,744 193 159 40 393 0.82 8.48 5120207 Muscatatuck IN 2,944 325,146 143 110 22 265 0.77 7.97 Lower East Fork 5120208 White IN 4,944 463,784 114 94 19 199 0.82 10.03 5120209 Patoka IN 2,255 273,273 142 121 16 266 0.85 7.30 5120101 Upper Wabash IN OH 4,606 1,171,286 377 254 49 528 0.68 4.07

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5 COMPARISON OF POINT SOURCE AND SPARROW LOADING ESTIMATES

Table 9 lists the loading estimates from the point source estimates summarized in Section 2 and the SPARROW loading estimates summarized in Section 3. This information can be used to support additional analysis of the feasibility of setting up a nutrient trading program within the watershed.

Table 24 Comparison of point source and SPARROW loading estimates. Total Phosphorus (tons/year) Total Nitrogen (tons/year) 8 Digit HUC Point Sources SPARROW Point Sources SPARROW 05120101 91 1,153 539 9,785 05120102 9 126 73 2,345 05120103 27 176 376 3,952 05120104 19 275 147 3,052 05120105 6 224 45 3,454 05120106 21 361 161 7,622 05120107 60 286 446 4,520 05120108 729 525 5,080 9,304 05120109 206 519 1,048 7,142 05120110 1 287 11 4,114 05120111 548 502 4,187 8,724 05120112 118 853 538 11,126 05120113 25 398 173 7,335 05120114 601 719 4,244 8,399 05120115 10 178 45 2,346 05120201 768 839 5,834 12,486 05120202 254 506 1,930 6,496 05120203 19 239 132 3,816 05120204 57 314 416 5,247 05120205 35 119 241 2,263 05120206 10 351 141 4,838 05120207 28 320 177 3,689 05120208 28 456 432 4,515 05120209 16 269 105 2,707 Total 3,687 9,994 26,521 139,278

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6 REFERENCES

Alexander, R.B., R.A. Smith, G.E. Schwarz, E.W. Boyer, J.V. Nolan, and J.W. Brakebill, 2008. Differences in Phosphorus and Nitrogen Delivery to the Gulf of Mexico From the Mississippi River Basin. Environmental Science and Technology 42:822-830.

Buchanan, J.R. 2010. Wastewater Planning Model, Version 1.0. Cost of Individual and Small Community Wastewater Management Systems. Water Environment Research Foundation (WERF) project no. DEC2R08, Performance and Costs for Decentralized Unit Processes.

CH2M Hill. 2010. Statewide Nutrient Removal Cost Impact Study. Prepared for Utah Division of Water Quality. October 2010.

Colorado Water Quality Control Division. 2010. Technologies, Performance and Costs for Wastewater Nutrient Removal. September 2, 2010.

Crites, R.W., G. Tchobanoglous. 1998. Small and Decentralized Wastewater Management Systems. McGraw-Hill pub.

Foess, G. W., P. Steinbrecher, K. Williams, G.S. Garrett. 1998. Cost and Performance Evaluation of BNR Processes. Florida Water Resources Journal. December 1998.

Jiang, F., M.B. Beck, R.G. Cummings, K. Rowles, D. Russell. 2004. Estimation of Costs of Phosphorus Removal in Wastewater Treatment Facilities: Construction De Novo. Water Policy Working Paper #2004-010. June 2004.

Jiang, F., M.B. Beck, R.G. Cummings, K. Rowles, D. Russell. 2005. Estimation of Costs of Phosphorus Removal in Wastewater Treatment Facilities: Adaptation of Existing Facilities. Water Policy Working Paper #2005-011. February 2005.

Keplinger, K.O., A.M. Tanter, J.B. Houser. 2003. Economic and Environmental Implications of Phosphorus Control at North Bosque River Wastewater Treatment Plants. Texas Institute for Applied Environmental Research. TR0312. July 2003.

Metcalf and Eddy. 2008. Chesapeake Bay Tributary Strategy Compliance Cost Study. November 2008.

Robertson, D.M., G.E. Schwarz, D.A. Saad, and R. B. Alexander. 2009. Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds. Journal of the American Water Resources Association. 45:534-549.

Smith, R.A., G.E. Schwarz, and R.B. Alexander, 1997. Regional Interpretation of Water-Quality Monitoring Data. Water Resources Research 33:2781-2798.

US EPA. 2006. Land Treatment of Municipal Wastewater Effluents. Land Remediation and Pollution Control Division National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency. Cincinnati, Ohio EPA/625/R-06/016. September 2006

US EPA. 2007. Biological Nutrient Removal Processes and Costs. United States Environmental Protection Agency, Office of Water, Washington, D.C. EPA-823-R-07-002. June 2007.

38

US EPA. 2008. Municipal Nutrient Removal Technologies Reference Document, Volume 1 – Technical Report. United States Environmental Protection Agency, Office of Wastewater Management, Washington, D.C. EPA-823-R-08-006. September 2008.

39

APPENDIX A – SUMMARY OF AVAILABLE POINT SOURCE DATA

Table A-1. Available flow data for each NPDES facility (MGD) 8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120101 IN0002623 12/31/2001 12/31/2009 24 0.96 0.06 2.49 05120101 IN0003484 9/30/1999 4/30/2009 130 0.02 0.00 0.11 05120101 IN0004596 6/30/1998 12/31/2009 137 0.07 0.06 0.09 05120101 IN0004839 11/30/1998 12/31/2009 246 0.34 0.00 0.98 05120101 IN0020206 9/30/1998 12/31/2009 134 0.14 0.03 0.28 05120101 IN0020745 4/30/1998 12/31/2009 138 0.45 0.15 0.79 05120101 IN0021440 7/31/2000 12/31/2009 111 0.21 0.11 0.34 05120101 IN0022411 12/31/2000 12/31/2009 108 2.18 0.79 5.03 05120101 IN0023132 12/31/1997 12/31/2009 141 4.56 1.68 8.20 05120101 IN0023736 8/31/1998 12/31/2009 136 0.22 0.07 0.46 05120101 IN0024112 9/30/2000 7/31/2008 1 0.12 0.12 0.12 05120101 IN0024741 8/31/1999 12/31/2009 121 2.69 1.44 4.65 05120101 IN0024902 10/31/1999 12/31/2009 121 0.85 0.24 1.48 05120101 IN0025321 1/31/1999 6/30/2003 36 0.01 0.00 0.01 05120101 IN0030635 11/30/2000 12/31/2009 108 0.00 0.00 0.01 05120101 IN0031364 1/31/1996 12/31/2009 166 0.00 0.00 0.01 05120101 IN0031411 8/31/1997 12/31/2009 9 0.00 0.00 0.00 05120101 IN0031453 1/31/1996 12/31/2009 157 0.00 0.00 0.01 05120101 IN0031739 6/30/2000 12/31/2009 98 0.00 0.00 0.02 05120101 IN0031763 2/28/2002 12/31/2009 94 0.03 0.01 0.25 05120101 IN0032328 2/29/2000 12/31/2009 187 4.58 2.60 8.68 05120101 IN0034444 8/31/1997 12/31/2009 138 0.32 0.00 25.00 05120101 IN0035378 12/31/1995 12/31/2009 236 2.50 1.59 5.12 05120101 IN0037001 6/30/1998 12/31/2009 137 0.15 0.01 0.37 05120101 IN0037427 5/31/2001 12/31/2009 85 0.01 0.00 0.03 05120101 IN0039063 6/30/2002 12/31/2004 25 0.06 0.02 0.06 05120101 IN0039357 10/31/1998 12/31/2009 127 0.23 0.09 4.49 05120101 IN0042391 11/30/1995 12/31/2009 167 1.05 0.09 1.64 05120101 IN0044130 11/30/1996 12/31/2009 103 11.13 0.00 87.00 05120101 IN0045357 11/30/1998 12/31/2009 114 0.01 0.00 0.01 05120101 IN0050211 10/31/1998 12/31/2009 133 0.04 0.01 0.67 05120101 IN0050971 10/31/1997 12/31/2009 141 0.01 0.00 0.01 05120101 IN0051098 10/31/1997 12/31/2009 142 0.01 0.00 0.11 05120101 IN0051187 6/30/2000 12/31/2009 113 0.01 0.00 0.03 05120101 IN0051861 7/31/2000 12/31/2009 112 0.00 0.00 0.01 05120101 IN0053121 1/31/2003 5/31/2004 14 0.00 0.00 0.01 05120101 IN0053147 9/30/1998 12/31/2009 126 0.01 0.00 0.02 05120101 IN0054127 7/31/2000 12/31/2009 112 0.00 0.00 0.01 05120101 IN0055158 7/31/1998 12/31/2009 89 0.75 0.00 50.00 05120101 IN0059048 12/31/1996 12/31/2009 202 0.03 0.00 0.12 05120101 IN0059218 11/30/2001 12/31/2009 95 0.00 0.00 0.03 05120101 IN0059510 9/30/1997 12/31/2009 144 0.05 0.01 0.43 05120101 IN0059757 7/31/1998 7/31/2007 107 0.00 0.00 0.01 05120101 ING250038 1/31/1997 12/31/2009 146 0.02 0.01 0.07 05120101 INP000042 12/31/1998 12/31/2009 98 0.06 0.03 0.42

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8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120101 INP000057 10/31/1996 12/31/2009 146 0.04 0.00 5.00 05120101 INP000071 11/30/1996 12/31/2009 143 0.02 0.00 0.06 05120101 INP000094 7/31/1997 12/31/2009 137 0.01 0.00 0.04 05120101 INP000129 9/30/1997 7/31/2006 66 0.06 0.00 0.87 05120101 INP000143 5/31/1996 12/31/2009 136 0.02 0.00 2.59 05120101 INP000169 12/31/1997 3/31/2005 13 0.00 0.00 0.00 05120101 INP000181 8/31/1998 10/31/2006 17 0.05 0.02 0.08 05120102 IN0003891 2/28/1998 12/31/2002 45 0.04 0.00 0.29 05120102 IN0020095 4/30/1998 12/31/2009 140 1.47 0.90 3.45 05120102 IN0020117 10/31/2000 12/31/2009 100 0.35 0.04 1.16 05120102 IN0020559 5/31/2000 12/31/2009 83 0.51 0.00 28.00 05120102 IN0024791 12/31/1998 12/31/2009 131 0.36 0.19 0.64 05120102 IN0031721 6/30/2000 5/31/2009 99 0.01 0.00 0.03 05120102 IN0037583 4/30/2000 12/31/2009 116 0.01 0.00 0.06 05120102 IN0040495 12/31/1998 12/31/2009 130 0.07 0.02 0.57 05120102 IN0041637 10/31/2000 12/31/2009 106 0.00 0.00 0.00 05120102 IN0046078 12/31/1998 12/31/2009 14 0.06 0.00 0.28 05120102 IN0057410 1/31/1999 12/31/2009 107 0.01 0.00 1.00 05120102 IN0058963 6/30/1997 12/31/2009 140 0.01 0.00 0.08 05120102 IN0060437 6/30/1999 12/31/2009 31 0.22 0.01 0.78 05120102 ING250056 11/30/1998 12/31/2009 4 0.02 0.00 0.06 05120102 INP000160 3/31/1998 12/31/2009 124 0.04 0.02 0.53 05120102 INP000190 10/31/1998 12/31/2009 122 0.06 0.02 0.09 05120103 IN0001961 1/31/1995 12/31/2006 166 0.21 0.00 0.70 05120103 IN0002372 3/31/1999 12/31/2009 144 0.16 0.00 0.58 05120103 IN0003450 5/31/1999 8/31/2004 57 0.09 0.00 1.18 05120103 IN0005002 1/31/1999 12/31/2009 150 0.05 0.00 0.42 05120103 IN0020001 12/31/1998 12/31/2009 129 0.12 0.04 0.91 05120103 IN0020371 12/31/1996 12/31/2009 167 0.11 0.06 0.25 05120103 IN0020982 1/31/1996 12/31/2009 167 0.86 0.31 2.53 05120103 IN0021105 9/30/1996 12/31/2009 156 0.54 0.23 0.93 05120103 IN0021491 12/31/1998 12/31/2009 132 0.58 0.20 1.11 05120103 IN0021628 12/31/1997 12/31/2009 144 1.41 0.73 1.97 05120103 IN0021652 11/30/1998 12/31/2009 132 0.26 0.02 0.71 05120103 IN0022136 9/30/1998 12/31/2009 135 0.33 0.10 0.65 05120103 IN0022985 9/30/1998 12/31/2009 134 1.36 0.56 29.85 05120103 IN0024279 8/31/2001 12/31/2009 79 0.01 0.00 0.02 05120103 IN0024406 8/31/1998 12/31/2009 133 0.29 0.08 0.81 05120103 IN0025585 4/30/1996 12/31/2009 164 9.41 4.30 19.20 05120103 IN0029815 11/30/2000 12/31/2009 155 0.03 0.00 0.08 05120103 IN0030015 11/30/2000 12/31/2009 98 0.02 0.00 0.40 05120103 IN0030643 12/31/1998 12/31/2009 109 0.01 0.00 0.04 05120103 IN0031372 3/31/1998 12/31/2009 135 0.01 0.00 0.11 05120103 IN0032948 10/31/2003 11/30/2004 11 0.01 0.00 0.06 05120103 IN0036978 5/31/1999 12/31/2009 121 0.40 0.04 0.83 05120103 IN0038016 8/31/1998 12/31/2009 128 0.07 0.00 0.74 05120103 IN0038962 1/31/1999 12/31/2009 129 0.02 0.00 0.55 05120103 IN0040321 6/30/1998 12/31/2009 57 0.13 0.03 0.26

41

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120103 IN0041173 7/31/2000 12/31/2009 113 0.00 0.00 0.01 05120103 IN0041319 8/31/1998 12/31/2009 125 0.23 0.04 0.69 05120103 IN0044423 5/31/2001 12/31/2009 101 0.04 0.00 0.43 05120103 IN0048267 3/31/2001 12/31/2009 75 0.00 0.00 0.02 05120103 IN0049832 7/31/1996 12/31/2009 161 0.01 0.00 0.03 05120103 IN0055271 2/28/1997 12/31/2009 210 0.00 0.00 0.02 05120103 IN0059889 3/31/1998 12/31/2009 110 0.00 0.00 0.02 05120103 ING250017 10/31/2000 12/31/2009 215 0.03 0.00 0.24 05120103 INP000082 1/31/1997 12/31/2009 135 0.21 0.00 0.32 05120103 INP000100 7/31/1997 2/28/2007 123 0.03 0.00 0.34 05120103 INP000144 6/30/1996 12/31/2009 144 0.03 0.00 0.23 05120103 INP000201 8/31/1998 12/31/2009 123 0.16 0.01 1.01 05120104 IN0001244 1/31/2000 10/31/2004 38 0.04 0.00 0.26 05120104 IN0003697 1/31/1995 12/31/2009 176 1.17 0.09 5.04 05120104 IN0020567 9/30/1998 12/31/2009 133 0.23 0.13 0.39 05120104 IN0020753 8/31/2000 12/31/2009 112 0.01 0.00 0.03 05120104 IN0021113 2/28/1999 12/31/2009 130 0.28 0.12 0.53 05120104 IN0022624 6/30/1996 12/31/2009 161 1.48 0.86 2.72 05120104 IN0025453 1/31/1995 12/31/2009 140 0.21 0.03 0.36 05120104 IN0030627 11/30/2000 2/28/2007 75 0.01 0.00 0.02 05120104 IN0031208 7/31/2000 12/31/2009 112 0.01 0.00 0.10 05120104 IN0031445 5/31/2000 8/31/2007 49 0.00 0.00 0.02 05120104 IN0031798 10/31/2000 12/31/2009 109 0.01 0.00 0.06 05120104 IN0037729 9/30/1996 12/31/2009 151 0.02 0.01 0.27 05120104 IN0039934 12/31/1997 12/31/2009 144 0.04 0.03 0.27 05120104 IN0040533 9/30/2001 12/31/2009 99 0.04 0.02 0.09 05120104 IN0040649 2/28/1997 12/31/2009 65 0.52 0.05 3.68 05120104 IN0041246 11/30/1996 12/31/2009 151 23.85 1.00 43.01 05120104 IN0043893 4/30/1998 12/31/2009 139 0.02 0.00 0.05 05120104 IN0046931 3/31/1999 12/31/2009 359 0.16 0.00 0.48 05120104 IN0053783 12/31/1996 12/31/2009 130 0.01 0.00 0.05 05120104 IN0055166 8/31/1997 12/31/2009 120 0.19 0.00 0.82 05120104 INP000088 7/31/1997 12/31/2009 136 0.02 0.00 0.19 05120104 INP000091 8/31/1996 12/31/2009 146 0.04 0.02 0.07 05120104 INP000107 7/31/1999 12/31/2009 108 0.02 0.00 1.03 05120104 INP000178 7/31/1999 12/31/2009 91 0.01 0.00 0.01 05120105 IN0002089 1/31/2001 12/31/2009 178 0.21 0.00 2.32 05120105 IN0020141 3/31/1999 12/31/2009 127 0.47 0.14 1.38 05120105 IN0020354 12/31/1998 12/31/2009 131 0.13 0.01 0.40 05120105 IN0021199 1/31/1999 12/31/2009 130 0.21 0.08 0.58 05120105 IN0021377 6/30/2006 12/31/2009 41 1.02 0.74 1.32 05120105 IN0030562 12/31/1998 12/31/2009 103 0.07 0.01 0.16 05120105 IN0034461 10/31/2000 12/31/2009 108 0.01 0.00 0.07 05120105 IN0052370 10/31/1998 12/31/2009 133 0.03 0.01 0.63 05120105 IN0059471 10/31/1997 12/31/2009 131 0.02 0.00 0.42 05120105 INP000011 6/30/1998 7/31/2005 66 0.01 0.00 0.03 05120106 IN0003263 9/30/1998 3/31/2008 48 2.79 0.40 11.40 05120106 IN0003387 1/31/1995 12/31/2009 467 0.64 0.00 3.60

42

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120106 IN0003662 8/31/1997 12/31/2009 79 1.58 0.01 61.51 05120106 IN0004278 4/30/1996 12/31/2009 163 0.04 0.02 0.06 05120106 IN0004847 8/31/1999 12/31/2009 126 0.02 0.00 0.24 05120106 IN0020541 5/31/1997 12/31/2009 149 0.23 0.12 0.48 05120106 IN0021288 8/31/1998 12/31/2009 130 0.30 0.18 0.49 05120106 IN0021580 7/31/1997 12/31/2009 148 0.22 0.07 0.48 05120106 IN0021661 12/31/1996 12/31/2009 155 1.09 0.63 1.74 05120106 IN0022438 7/31/1999 12/31/2009 67 0.33 0.14 0.88 05120106 IN0024805 7/31/1997 12/31/2009 148 1.93 0.76 3.24 05120106 IN0025208 8/31/2000 12/31/2009 100 0.03 0.01 0.19 05120106 IN0025232 9/30/1998 12/31/2009 155 0.26 0.00 4.70 05120106 IN0030031 10/31/2000 12/31/2009 110 0.00 0.00 0.01 05120106 IN0030571 1/31/1995 12/31/2009 62 0.11 0.04 0.48 05120106 IN0030881 5/31/2001 12/31/2009 102 0.03 0.00 0.10 05120106 IN0030911 7/31/1997 12/31/2009 135 0.03 0.00 0.14 05120106 IN0036943 9/30/2000 12/31/2009 110 0.01 0.01 0.02 05120106 IN0037044 11/30/2000 12/31/2009 107 0.01 0.00 0.41 05120106 IN0039870 2/28/1999 12/31/2009 89 0.06 0.00 0.41 05120106 IN0040002 4/30/2006 12/31/2009 44 0.06 0.03 0.10 05120106 IN0040347 1/31/1999 12/31/2009 193 0.07 0.01 0.86 05120106 IN0040797 10/31/2000 12/31/2009 107 0.12 0.02 0.25 05120106 IN0041726 2/28/1997 12/31/2009 151 0.02 0.00 0.19 05120106 IN0041742 4/30/1998 12/31/2009 123 0.06 0.02 0.78 05120106 IN0042501 2/28/1999 9/30/2007 69 0.00 0.00 0.00 05120106 IN0045578 5/31/2000 12/31/2009 174 0.44 0.27 0.70 05120106 IN0048411 4/30/1998 12/31/2009 87 0.02 0.00 0.27 05120106 IN0050326 9/30/2000 12/31/2009 110 0.01 0.00 0.19 05120106 IN0050652 12/31/1997 12/31/2009 89 0.00 0.00 0.02 05120106 IN0052078 2/29/2000 12/31/2009 68 0.08 0.01 0.23 05120106 IN0054445 6/30/2001 12/31/2009 71 0.01 0.00 0.01 05120106 IN0054704 9/30/1997 12/31/2009 147 0.04 0.03 0.40 05120106 IN0056456 2/28/2001 12/31/2009 156 0.01 0.00 0.05 05120106 IN0057185 8/31/1999 9/30/2004 52 0.00 0.00 0.00 05120106 IN0058327 10/31/2000 12/31/2009 83 0.12 0.01 0.75 05120106 IN0059081 12/31/1996 12/31/2009 7 0.00 0.00 0.00 05120106 IN0060101 7/31/1998 12/31/2009 60 0.10 0.00 1.58 05120106 IN0060887 10/31/2000 12/31/2009 102 0.13 0.00 1.61 05120106 ING250036 1/31/1997 12/31/2009 148 0.05 0.01 0.10 05120106 INP000018 2/28/2002 11/30/2005 13 0.01 0.01 0.01 05120106 INP000074 12/31/1996 12/31/2009 135 0.03 0.00 0.07 05120106 INP000113 5/31/1997 12/31/2009 137 0.09 0.06 0.16 05120106 INP000142 6/30/1996 12/31/2009 150 0.01 0.00 0.07 05120106 INP000152 12/31/1996 12/31/2009 137 0.02 0.00 0.13 05120106 INP000164 1/31/1998 12/31/2009 129 0.15 0.11 0.21 05120107 IN0001830 1/31/1995 12/31/2009 178 1.44 0.08 8.44 05120107 IN0020532 9/30/2000 12/31/2009 107 0.19 0.12 0.32 05120107 IN0020907 9/30/1999 12/31/2009 121 0.18 0.09 0.35 05120107 IN0021091 6/30/1996 12/31/2009 168 0.42 0.00 1.59

43

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120107 IN0022934 8/31/1999 12/31/2009 124 3.72 2.27 6.64 05120107 IN0023353 10/31/2005 12/31/2009 49 0.01 0.00 0.03 05120107 IN0031801 8/31/2000 12/31/2009 107 0.04 0.02 0.26 05120107 IN0031844 6/30/1999 12/31/2009 124 0.06 0.01 0.55 05120107 IN0031976 12/31/1998 12/31/2009 131 0.09 0.02 0.24 05120107 IN0032875 9/30/2000 12/31/2009 185 12.72 4.57 27.29 05120107 IN0036935 5/31/2000 12/31/2009 111 0.01 0.00 0.03 05120107 IN0037214 3/31/2001 12/31/2009 101 0.01 0.00 0.03 05120107 IN0038768 7/31/1996 12/31/2009 146 0.07 0.00 1.12 05120107 IN0038784 2/28/1998 12/31/2009 129 0.02 0.00 0.19 05120107 IN0039497 6/30/1999 12/31/2009 125 0.04 0.01 0.90 05120107 IN0039799 12/31/1998 12/31/2009 124 0.07 0.01 0.30 05120107 IN0040355 9/30/1998 12/31/2009 112 0.09 0.01 0.40 05120107 IN0040762 9/30/1998 12/31/2009 131 0.11 0.04 0.24 05120107 IN0041131 5/31/1995 8/31/2007 242 0.01 0.00 0.13 05120107 IN0041866 5/31/2000 12/31/2009 104 0.04 0.02 0.25 05120107 IN0041912 7/31/1995 12/31/2009 164 0.01 0.00 0.08 05120107 IN0044245 4/30/2000 12/31/2009 116 0.13 0.03 0.50 05120107 IN0044652 5/31/2000 12/31/2009 114 0.02 0.00 0.20 05120107 IN0051624 4/30/2000 12/31/2009 111 1.30 0.00 1.75 05120107 IN0055697 8/31/1996 12/31/2009 218 0.01 0.00 0.04 05120107 IN0055921 2/28/2001 12/31/2009 104 0.02 0.01 0.23 05120107 IN0058173 7/31/2000 12/31/2009 111 0.01 0.00 0.12 05120107 INP000151 8/31/1996 12/31/2009 125 0.00 0.00 0.02 05120108 IL0020966 1/31/2000 8/31/2009 431 0.14 0.04 0.86 05120108 IL0022322 1/31/1999 8/31/2009 502 1.14 0.06 15.00 05120108 IN0002763 1/31/1995 12/31/2009 533 192.00 0.00 809.30 05120108 IN0003506 5/31/1997 12/31/2009 345 0.19 0.00 0.97 05120108 IN0003859 12/31/2000 12/31/2009 201 0.13 0.00 0.43 05120108 IN0020052 12/31/1997 12/31/2009 141 0.11 0.05 0.52 05120108 IN0021164 11/30/1996 12/31/2009 154 0.01 0.00 0.02 05120108 IN0022608 6/30/1996 12/31/2009 160 0.72 0.36 1.86 05120108 IN0023990 12/31/1998 12/31/2009 130 0.17 0.06 1.14 05120108 IN0024716 2/28/1997 12/31/2009 150 0.41 0.19 0.75 05120108 IN0024821 6/30/1999 12/31/2009 136 7.88 0.00 12.60 05120108 IN0032468 1/31/1995 12/31/2009 277 15.73 11.60 94.80 05120108 IN0036447 1/31/1995 12/31/2009 177 1.45 0.66 2.02 05120108 IN0038334 1/31/2000 12/31/2009 117 0.07 0.03 0.52 05120108 IN0038971 12/31/1998 12/31/2009 127 0.01 0.00 0.02 05120108 IN0039705 9/30/1996 12/31/2009 157 0.03 0.02 0.06 05120108 IN0039756 11/30/1998 12/31/2009 132 0.06 0.04 0.11 05120108 IN0043273 4/30/2001 12/31/2009 103 1.17 0.51 2.02 05120108 IN0050253 8/31/1997 12/31/2009 143 0.23 0.03 0.61 05120109 IL0004057 4/30/2003 8/31/2009 184 1.07 0.10 8.69 05120109 IL0004138 2/28/1999 8/31/2009 74 0.01 0.00 0.13 05120109 IL0004162 1/31/1999 8/31/2009 256 0.04 0.03 0.05 05120109 IL0004235 4/30/2004 8/31/2009 79 1.66 0.71 1.98 05120109 IL0004456 11/30/1999 8/31/2009 336 0.00 0.00 0.07

44

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120109 IL0004545 1/31/1999 8/31/2009 143 0.05 0.00 0.86 05120109 IL0004618 4/30/2003 8/31/2009 120 0.01 0.01 0.01 05120109 IL0020788 11/30/1999 8/31/2009 471 11.07 0.72 31.80 05120109 IL0022128 5/31/2000 8/31/2009 448 3.45 1.53 7.07 05120109 IL0022250 4/30/2004 8/31/2009 22 0.05 0.01 0.07 05120109 IL0023086 8/31/2000 8/31/2009 355 0.66 0.25 1.70 05120109 IL0023108 1/31/2000 8/31/2009 454 0.37 0.10 1.70 05120109 IL0023205 1/31/2000 8/31/2009 460 0.96 0.47 1.59 05120109 IL0024619 1/31/1999 8/31/2009 26 0.00 0.00 0.00 05120109 IL0024830 5/31/2002 8/31/2009 352 1.68 0.46 10.49 05120109 IL0027278 10/31/1999 8/31/2009 424 0.00 0.00 0.03 05120109 IL0031429 1/31/1999 8/31/2009 516 0.56 0.03 3.01 05120109 IL0031500 11/30/2000 8/31/2009 448 18.51 0.00 47.54 05120109 IL0031721 7/31/2000 8/31/2009 439 1.14 0.17 5.16 05120109 IL0047902 10/31/2000 8/31/2009 202 0.00 0.00 0.00 05120109 IL0048062 10/31/1999 8/31/2009 440 0.02 0.00 0.45 05120109 IL0050741 1/31/1999 8/31/2009 476 0.00 0.00 0.01 05120109 IL0051781 4/30/2004 8/31/2009 110 0.00 0.00 0.00 05120109 IL0055301 5/31/2002 8/31/2009 296 0.00 0.00 0.03 05120109 IL0055751 5/31/2002 8/31/2009 299 0.00 0.00 0.00 05120109 IL0063002 1/31/1999 8/31/2009 335 0.51 0.09 0.96 05120109 IL0067156 7/31/2001 8/31/2009 214 0.00 0.00 0.01 05120109 IL0067601 1/31/1999 8/31/2009 300 0.07 0.00 0.36 05120109 IL0069388 1/31/1999 8/31/2009 238 0.03 0.03 0.04 05120109 IL0071021 10/31/2001 8/31/2009 14 5.00 1.67 16.40 05120109 IL0075167 9/30/2001 8/31/2009 20 0.00 0.00 0.01 05120109 ILG580060 3/31/2003 8/31/2009 304 0.07 0.02 1.07 05120109 ILG580132 1/31/1999 8/31/2009 414 0.12 0.02 1.13 05120109 ILG580214 1/31/2003 8/31/2009 312 0.15 0.01 0.60 05120109 ILG580216 1/31/2003 8/31/2009 320 0.83 0.02 210.00 05120109 ILG580247 6/30/2003 8/31/2009 296 0.09 0.03 0.69 05120109 ILG640002 1/31/1999 8/31/2009 162 0.00 0.00 0.00 05120109 ILG640101 1/31/1999 8/31/2009 222 0.00 0.00 0.01 05120110 IN0020443 4/30/1998 12/31/2009 140 0.14 0.04 0.39 05120110 IN0020630 5/31/2000 12/31/2009 108 0.11 0.05 0.30 05120110 IN0024589 12/31/1998 12/31/2009 170 0.15 0.01 0.67 05120110 IN0034428 12/31/1997 12/31/2009 141 0.01 0.00 0.13 05120110 IN0041157 9/30/2000 12/31/2009 101 0.02 0.00 0.03 05120110 IN0046396 9/30/2000 12/31/2009 111 0.01 0.00 0.11 05120110 IN0057347 9/30/1998 12/31/2009 117 0.00 0.00 0.02 05120110 IN0059153 12/31/1996 12/31/2009 152 0.14 0.11 0.16 05120110 INP000132 2/28/2002 12/31/2003 22 0.00 0.00 0.01 05120111 IL0000116 1/31/1999 8/31/2009 1222 50.91 0.00 392.00 05120111 IL0004065 4/30/2004 8/31/2009 148 0.19 0.00 1.21 05120111 IL0004073 1/31/1999 8/31/2009 105 2.77 0.80 14.50 05120111 IL0004120 5/31/1999 8/31/2009 744 34.78 0.00 146.00 05120111 IL0021377 2/28/2002 8/31/2009 364 2.40 0.84 4.18 05120111 IL0028126 1/31/1999 8/31/2009 481 0.14 0.03 0.82

45

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120111 IL0029254 7/31/2002 8/31/2009 344 0.11 0.02 0.70 05120111 IL0030732 12/31/1999 12/31/2009 948 2.14 0.80 4.60 05120111 IL0053945 1/31/2001 12/31/2009 308 0.00 0.00 0.00 05120111 IL0063274 1/31/2002 12/31/2009 190 0.00 0.00 0.01 05120111 IL0068365 11/30/2002 8/31/2009 301 1.54 0.15 6.60 05120111 ILG551036 1/31/1999 8/31/2009 356 0.01 0.00 0.09 05120111 ILG580034 1/31/1999 8/31/2009 464 0.14 0.02 1.64 05120111 ILG640107 1/31/1999 12/31/2009 448 0.01 0.00 1.60 05120111 ILG640172 1/31/1999 12/31/2009 446 0.08 0.00 1.06 05120111 IN0001601 6/30/1998 12/31/2009 138 0.10 0.00 0.32 05120111 IN0002119 11/30/1998 12/31/2009 86 0.01 0.00 0.19 05120111 IN0002810 1/31/1995 12/31/2009 707 126.66 0.00 1306.80 05120111 IN0003026 1/31/1995 12/31/2009 174 0.87 0.00 1.55 05120111 IN0003328 3/31/2001 12/31/2006 76 0.18 0.09 0.35 05120111 IN0020389 9/30/2004 12/31/2009 59 0.26 0.08 0.48 05120111 IN0020800 6/30/2000 12/31/2009 174 0.16 0.05 0.53 05120111 IN0021148 4/30/1999 12/31/2009 127 0.24 0.06 2.66 05120111 IN0024554 10/31/1999 12/31/2009 140 1.58 0.50 3.54 05120111 IN0025224 12/31/1998 12/31/2009 123 0.09 0.02 1.98 05120111 IN0025607 4/30/1999 12/31/2009 127 11.30 6.10 19.80 05120111 IN0030228 7/31/1997 12/31/2009 142 0.55 0.00 28.00 05120111 IN0030678 9/30/2000 3/31/2005 35 0.00 0.00 0.02 05120111 IN0031909 9/30/2000 12/31/2009 102 0.00 0.00 0.05 05120111 IN0039322 6/30/1998 12/31/2009 133 0.11 0.03 0.64 05120111 IN0039829 11/30/1998 12/31/2009 136 0.03 0.01 0.09 05120111 IN0039837 1/31/1999 12/31/2009 128 0.46 0.26 0.70 05120111 IN0040134 3/31/1999 12/31/2009 124 0.09 0.03 0.28 05120111 IN0040835 9/30/1998 12/31/2009 120 0.01 0.00 0.02 05120111 IN0041084 7/31/1997 12/31/2009 146 0.01 0.00 0.42 05120111 IN0041092 3/31/1999 12/31/2009 128 0.00 0.00 0.02 05120111 IN0046809 6/30/1998 12/31/2009 45 0.02 0.00 0.04 05120111 IN0050296 11/30/1997 12/31/2009 424 145.31 0.00 483.80 05120111 IN0055085 11/30/1996 12/31/2009 155 0.23 0.11 0.55 05120111 IN0056154 12/31/1996 12/31/2009 155 0.01 0.00 0.13 05120111 IN0061328 3/31/2002 12/31/2009 10 2.92 0.00 29.20 05120111 IN0109592 10/31/2000 12/31/2009 108 0.12 0.01 1.66 05120111 INP000149 6/30/1996 12/31/2009 148 0.02 0.01 0.07 05120111 INP000161 8/31/1997 12/31/2009 127 0.09 0.02 0.96 05120112 IL0004049 5/31/1999 8/31/2009 206 0.49 0.00 3.98 05120112 IL0004219 11/30/1999 8/31/2009 228 1.17 0.00 11.48 05120112 IL0004375 1/31/1999 8/31/2009 542 0.14 0.00 0.90 05120112 IL0021644 12/31/2000 12/31/2009 879 4.02 0.55 27.50 05120112 IL0026107 7/31/2001 8/31/2009 384 0.60 0.20 1.69 05120112 IL0027499 1/31/1999 8/31/2009 437 0.69 0.09 3.26 05120112 IL0029467 6/30/1999 8/31/2009 491 1.48 0.47 4.20 05120112 IL0029831 12/31/2000 12/31/2009 1075 6.51 0.11 62.20 05120112 IL0030121 3/31/2000 12/31/2009 928 1.00 0.05 6.48 05120112 IL0031453 4/30/2004 12/31/2009 534 0.28 0.05 3.01

46

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120112 IL0035084 1/31/1999 12/31/2009 1082 0.48 0.00 0.99 05120112 IL0042757 10/31/1999 12/31/2009 772 0.01 0.00 0.12 05120112 IL0047210 1/31/1999 12/31/2009 258 0.07 0.00 0.80 05120112 IL0049212 9/30/2000 12/31/2009 880 0.01 0.00 0.10 05120112 IL0049361 5/31/1999 12/31/2009 806 0.00 0.00 0.03 05120112 IL0051209 7/31/2001 12/31/2009 556 0.00 0.00 0.07 05120112 IL0051250 4/30/2000 12/31/2009 154 0.00 0.00 0.02 05120112 IL0051829 2/28/2001 12/31/2009 524 0.00 0.00 0.03 05120112 IL0051837 6/30/2002 8/31/2009 334 0.01 0.00 0.06 05120112 IL0051900 2/28/2002 12/31/2009 160 0.03 0.02 0.05 05120112 IL0055417 1/31/1999 12/31/2009 4 0.00 0.00 0.00 05120112 IL0055948 1/31/2003 12/31/2009 328 0.07 0.01 1.06 05120112 IL0059005 9/30/2002 12/31/2009 682 0.35 0.06 0.83 05120112 IL0060119 8/31/1999 12/31/2009 836 0.01 0.00 0.01 05120112 IL0060585 1/31/1999 12/31/2009 12 0.44 0.38 0.50 05120112 IL0063096 12/31/2000 12/31/2009 840 0.06 0.00 0.80 05120112 IL0066974 2/28/2001 8/31/2009 412 0.01 0.00 0.10 05120112 IL0071617 1/31/1999 8/31/2009 510 1.05 0.00 6.83 05120112 IL0073610 9/30/1999 8/31/2009 407 0.13 0.02 0.75 05120112 ILG580001 1/31/2003 8/31/2009 284 0.13 0.02 0.82 05120112 ILG580058 1/31/1999 8/31/2009 387 0.03 0.00 0.05 05120112 ILG580065 1/31/1999 8/31/2009 478 0.33 0.01 4.44 05120112 ILG580092 1/31/1999 8/31/2009 444 0.02 0.00 0.08 05120112 ILG580118 1/31/2003 8/31/2009 256 0.17 0.02 0.22 05120112 ILG580158 4/30/2003 8/31/2009 264 0.18 0.02 0.85 05120112 ILG580224 5/31/2000 12/31/2009 690 0.03 0.00 0.04 05120112 ILG582001 3/31/2003 12/31/2009 596 0.14 0.00 1.20 05120112 ILG640103 1/31/1999 12/31/2009 444 0.05 0.01 0.78 05120112 ILG640153 1/31/1999 12/31/2009 374 0.42 0.00 1.00 05120112 ILG840095 1/31/1999 12/31/2009 195 2.89 2.89 2.89 05120113 IL0023477 3/31/2002 12/31/2009 724 0.30 0.06 0.95 05120113 IL0049174 1/31/1999 12/31/2009 432 0.01 0.00 0.06 05120113 IL0069973 1/31/1999 12/31/2009 837 0.00 0.00 0.04 05120113 IL0071111 11/30/2002 12/31/2009 672 0.01 0.00 0.09 05120113 ILG580112 4/30/2003 12/31/2009 507 0.06 0.00 0.80 05120113 ILG580170 4/30/2003 12/31/2009 624 0.04 0.01 0.07 05120113 ILG580188 2/28/2003 12/31/2009 496 0.08 0.00 0.90 05120113 ILG580201 1/31/2003 12/31/2009 472 0.01 0.00 0.18 05120113 ILG580206 1/31/2003 12/31/2009 464 0.06 0.00 0.75 05120113 ILG580208 1/31/2003 12/31/2009 586 0.02 0.01 0.02 05120113 IN0020699 10/31/1998 12/31/2009 132 0.11 0.05 0.41 05120113 IN0024392 12/31/1997 12/31/2009 131 1.99 0.98 3.75 05120113 IN0025798 2/28/2001 12/31/2009 106 0.01 0.00 0.01 05120113 IN0030406 12/31/1997 12/31/2009 86 0.01 0.00 0.40 05120113 IN0031020 3/31/1999 12/31/2009 129 4.07 1.78 12.46 05120113 IN0037249 5/31/2001 12/31/2009 90 0.00 0.00 0.03 05120113 IN0038288 12/31/1998 12/31/2009 130 0.17 0.04 0.46 05120113 IN0040517 7/31/2005 12/31/2009 51 0.24 0.09 0.47

47

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120113 IN0041734 5/31/1998 12/31/2009 126 0.02 0.00 0.04 05120113 IN0045837 3/31/2001 12/31/2009 101 0.00 0.00 0.01 05120113 IN0045845 4/30/1999 12/31/2009 124 0.00 0.00 0.01 05120113 IN0056626 7/31/1999 3/31/2003 23 0.00 0.00 0.06 05120113 IN0058556 3/31/2001 12/31/2009 102 0.42 0.04 0.87 05120114 IL0020273 1/31/2001 12/31/2009 840 1.30 0.42 2.90 05120114 IL0020605 11/30/1999 12/31/2009 948 1.26 0.40 27.56 05120114 IL0020974 1/31/1999 12/31/2009 986 0.21 0.03 0.79 05120114 IL0025429 11/30/1999 12/31/2009 598 0.01 0.00 0.13 05120114 IL0025500 10/31/1999 12/31/2009 940 0.04 0.00 0.16 05120114 IL0027910 10/31/1999 12/31/2009 956 1.86 0.50 5.26 05120114 IL0028622 1/31/1999 12/31/2009 1108 3.38 0.03 22.40 05120114 IL0030091 12/31/2000 12/31/2009 849 0.43 0.07 2.61 05120114 IL0044792 11/30/1999 12/31/2009 914 0.12 0.01 0.89 05120114 IL0047244 1/31/1999 12/31/2009 512 0.00 0.00 0.05 05120114 IL0047481 7/31/2001 12/31/2009 200 0.01 0.00 0.08 05120114 IL0048755 10/31/1999 12/31/2009 956 2.61 0.99 6.87 05120114 IL0049191 1/31/1999 12/31/2009 1800 168.35 0.00 806.40 05120114 IL0055093 1/31/1999 12/31/2009 275 0.01 0.00 0.03 05120114 IL0055701 1/31/1999 12/31/2009 904 0.00 0.00 0.05 05120114 IL0060208 1/31/2003 12/31/2009 516 0.01 0.00 0.10 05120114 IL0069931 1/31/1999 12/31/2009 484 0.00 0.00 0.01 05120114 IL0071374 8/31/2002 12/31/2009 676 0.02 0.01 0.04 05120114 IL0075922 4/30/2002 12/31/2009 300 0.03 0.00 0.15 05120114 ILG580024 1/31/2003 12/31/2009 632 0.06 0.00 0.30 05120114 ILG580056 1/31/1999 12/31/2009 976 0.07 0.01 0.32 05120114 ILG580070 2/28/2003 12/31/2009 542 0.05 0.00 0.24 05120114 ILG580076 4/30/2003 12/31/2009 468 0.10 0.00 0.70 05120114 ILG580133 2/28/2003 12/31/2009 648 0.53 0.11 3.46 05120114 ILG580152 1/31/1999 12/31/2009 970 0.42 0.01 8.84 05120114 ILG580159 1/31/1999 12/31/2009 724 0.05 0.00 0.43 05120114 ILG580197 1/31/2003 12/31/2009 656 0.07 0.02 0.98 05120114 ILG580211 4/30/2003 12/31/2009 596 0.57 0.06 1.50 05120114 ILG640048 1/31/1999 12/31/2009 578 0.19 0.07 0.43 05120115 IL0004294 1/31/1999 12/31/2009 452 0.01 0.00 0.08 05120115 IL0024643 1/31/2000 12/31/2009 96 0.00 0.00 0.00 05120115 IL0046957 12/31/2000 12/31/2009 838 0.25 0.01 1.93 05120115 IL0054496 12/31/2002 12/31/2009 368 0.04 0.00 0.22 05120115 IL0068977 11/30/1999 12/31/2009 68 0.01 0.00 0.07 05120115 IL0073903 6/30/2000 12/31/2009 40 0.00 0.00 0.01 05120115 ILG580029 1/31/1999 12/31/2009 184 0.81 0.01 33.90 05120115 ILG580080 1/31/2003 12/31/2009 620 0.04 0.00 0.54 05120115 ILG580105 1/31/1999 12/31/2009 864 0.03 0.01 0.05 05120115 ILG580108 1/31/1999 12/31/2009 868 0.03 0.02 0.28 05120115 ILG580129 1/31/2003 12/31/2009 580 0.05 0.00 0.71 05120115 ILG580146 1/31/2003 12/31/2009 656 0.03 0.01 0.06 05120115 ILG580195 2/28/2003 12/31/2009 360 0.05 0.00 0.68 05120115 ILG580220 7/31/2000 12/31/2009 894 0.11 0.08 0.90

48

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120201 IN0001236 1/31/1995 12/31/2009 176 0.34 0.00 1.01 05120201 IN0001341 1/31/1995 12/31/2009 770 0.58 0.00 2.47 05120201 IN0001783 1/31/1995 12/31/2009 282 0.44 0.01 3.45 05120201 IN0001791 1/31/1995 12/31/2009 124 0.45 0.03 2.05 05120201 IN0001813 1/31/1995 12/31/2009 621 1.32 0.00 31.09 05120201 IN0003310 1/31/1995 12/31/2009 276 3.42 0.13 11.07 05120201 IN0004146 6/30/1998 9/30/2003 58 0.02 0.00 0.08 05120201 IN0004677 1/31/1995 12/31/2009 351 16.30 0.03 47.65 05120201 IN0004685 1/31/1995 12/31/2009 553 35.14 0.01 137.46 05120201 IN0020028 12/31/1998 12/31/2009 131 0.23 0.11 0.50 05120201 IN0020044 8/31/1997 12/31/2009 147 1.25 0.59 3.10 05120201 IN0020079 12/31/1997 12/31/2009 142 1.12 0.57 2.26 05120201 IN0020087 11/30/1998 12/31/2009 183 0.25 0.13 1.72 05120201 IN0020150 7/31/2000 12/31/2009 113 1.11 0.62 2.29 05120201 IN0020168 6/30/1999 12/31/2009 125 3.60 2.10 6.85 05120201 IN0020303 9/30/1998 12/31/2009 135 1.55 0.87 3.52 05120201 IN0020338 1/31/2006 12/31/2009 46 0.31 0.02 3.08 05120201 IN0020401 7/31/1998 12/31/2009 137 0.43 0.10 1.08 05120201 IN0020729 6/30/1999 12/31/2009 125 0.16 0.07 0.32 05120201 IN0020770 3/31/1998 12/31/2009 134 0.57 0.26 1.08 05120201 IN0020796 12/31/1997 12/31/2009 134 0.13 0.01 0.40 05120201 IN0020958 9/30/1999 12/31/2009 121 0.61 0.04 1.01 05120201 IN0021024 7/31/2000 12/31/2009 175 1.51 0.36 3.10 05120201 IN0021202 6/30/1996 12/31/2009 161 2.96 0.00 5.77 05120201 IN0021245 7/31/1998 12/31/2009 137 2.33 1.06 4.85 05120201 IN0021334 11/30/1996 12/31/2009 156 0.29 0.14 0.82 05120201 IN0021351 11/30/1997 12/31/2009 30 0.12 0.00 1.16 05120201 IN0021474 8/31/1997 12/31/2009 148 1.29 0.66 2.31 05120201 IN0021512 4/30/1997 12/31/2009 133 0.18 0.05 0.61 05120201 IN0022012 3/31/1998 12/31/2009 38 0.08 0.02 0.16 05120201 IN0022080 9/30/2001 10/31/2005 38 0.01 0.00 0.02 05120201 IN0022101 10/31/1997 12/31/2009 124 0.01 0.00 0.01 05120201 IN0022306 12/31/1998 12/31/2009 132 0.50 0.01 47.00 05120201 IN0022314 12/31/1998 12/31/2009 131 1.54 0.08 19.69 05120201 IN0022497 4/30/1998 12/31/2009 140 9.35 6.50 15.60 05120201 IN0022586 5/31/2001 12/31/2009 103 0.58 0.28 1.14 05120201 IN0023183 1/31/1995 12/31/2009 284 97.14 56.50 186.00 05120201 IN0023825 11/30/1998 12/31/2009 194 1.28 0.01 2.69 05120201 IN0024970 3/31/1999 12/31/2009 126 0.02 0.01 0.04 05120201 IN0025151 9/30/1998 12/31/2009 131 0.01 0.00 0.31 05120201 IN0025364 1/31/2003 10/31/2003 7 0.01 0.01 0.02 05120201 IN0025372 10/31/1998 12/31/2009 127 0.04 0.02 0.64 05120201 IN0025402 9/30/2001 9/30/2005 31 0.14 0.07 0.81 05120201 IN0030023 10/31/2000 12/31/2009 109 0.01 0.01 0.05 05120201 IN0030830 11/30/2000 8/31/2004 37 0.00 0.00 0.01 05120201 IN0030902 10/31/2000 12/31/2009 108 0.10 0.01 7.00 05120201 IN0031071 6/30/1998 12/31/2009 137 0.24 0.03 0.53 05120201 IN0031135 11/30/2000 12/31/2009 106 0.01 0.00 0.06

49

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120201 IN0031356 3/31/1998 12/31/2009 116 0.01 0.00 0.04 05120201 IN0031526 8/31/1997 3/31/2006 99 0.01 0.00 0.03 05120201 IN0031569 9/30/2000 12/31/2009 89 0.02 0.00 0.40 05120201 IN0031640 1/31/2001 12/31/2009 106 0.00 0.00 0.01 05120201 IN0031712 9/30/2000 12/31/2009 110 0.01 0.00 0.20 05120201 IN0031925 2/29/2000 12/31/2009 76 0.01 0.00 0.01 05120201 IN0031933 9/30/2000 5/31/2005 40 0.03 0.00 0.61 05120201 IN0031950 1/31/1995 2/29/2008 238 78.61 42.00 158.60 05120201 IN0032476 1/31/1995 12/31/2009 358 17.83 9.20 38.70 05120201 IN0032719 10/31/2004 12/31/2009 45 3.54 1.30 6.87 05120201 IN0032905 2/28/1999 6/30/2007 97 0.04 0.03 0.04 05120201 IN0036587 11/30/1999 12/31/2009 161 0.20 0.00 1.20 05120201 IN0036820 6/30/1998 12/31/2009 143 0.31 0.01 2.27 05120201 IN0036951 1/31/2004 12/31/2009 71 1.04 0.70 1.76 05120201 IN0037133 10/31/1998 2/28/2003 19 0.01 0.00 0.01 05120201 IN0037184 4/30/2003 10/31/2003 4 0.02 0.01 0.02 05120201 IN0038059 3/31/1998 12/31/2009 115 0.17 0.00 0.45 05120201 IN0038407 7/31/2000 1/31/2004 36 0.02 0.02 0.15 05120201 IN0038598 4/30/2000 12/31/2009 115 0.04 0.02 0.42 05120201 IN0038857 9/30/1998 12/31/2009 132 0.01 0.00 0.01 05120201 IN0038881 11/30/1996 1/31/2008 186 0.01 0.00 0.04 05120201 IN0039471 5/31/2000 12/31/2009 86 0.01 0.00 0.18 05120201 IN0039772 9/30/1997 12/31/2009 144 0.23 0.11 0.48 05120201 IN0040088 4/30/1999 12/31/2009 123 0.03 0.02 0.23 05120201 IN0040479 3/31/1998 12/31/2009 141 0.06 0.02 0.16 05120201 IN0040681 7/31/1999 12/31/2009 123 0.15 0.05 0.65 05120201 IN0041548 4/30/1998 12/31/2009 1 0.04 0.04 0.04 05120201 IN0041815 1/31/1999 12/31/2009 211 0.77 0.00 4.60 05120201 IN0041971 7/31/1995 11/30/2006 133 0.01 0.00 0.34 05120201 IN0043281 5/31/1998 4/30/2006 80 0.09 0.00 0.24 05120201 IN0043559 12/31/1997 12/31/2009 112 0.01 0.00 0.02 05120201 IN0043974 3/31/2001 4/30/2004 29 0.03 0.02 0.05 05120201 IN0044971 1/31/1998 12/31/2009 124 0.07 0.00 0.51 05120201 IN0045209 1/31/1999 12/31/2009 302 0.18 0.00 1.19 05120201 IN0045446 7/31/2000 2/28/2007 76 0.01 0.00 0.11 05120201 IN0049026 8/31/1996 12/31/2009 159 1.87 1.01 3.59 05120201 IN0049361 5/31/1998 12/31/2009 139 0.13 0.07 0.23 05120201 IN0049581 7/31/1998 12/31/2009 135 0.07 0.03 0.42 05120201 IN0049794 2/28/1997 12/31/2009 150 0.07 0.01 0.42 05120201 IN0050024 5/31/1997 12/31/2009 69 0.06 0.03 0.08 05120201 IN0050164 4/30/2000 12/31/2009 87 0.01 0.01 0.10 05120201 IN0050393 7/31/1999 12/31/2009 117 1.67 0.12 3.49 05120201 IN0051365 4/30/1999 12/31/2009 90 0.23 0.00 0.98 05120201 IN0052256 12/31/2000 12/31/2009 107 0.04 0.01 0.08 05120201 IN0053627 9/30/2000 7/31/2007 81 0.02 0.00 0.26 05120201 IN0054666 1/31/1999 12/31/2009 48 0.07 0.00 0.63 05120201 IN0054771 3/31/2001 12/31/2008 76 0.11 0.05 0.21 05120201 IN0054887 11/30/1998 12/31/2009 95 0.42 0.00 3.60

50

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120201 IN0054917 8/31/1998 12/31/2009 135 0.17 0.02 0.45 05120201 IN0055280 10/31/1998 12/31/2009 133 0.01 0.00 0.60 05120201 IN0055484 4/30/1999 12/31/2009 127 4.91 0.60 7.70 05120201 IN0055760 4/30/1997 12/31/2009 152 0.95 0.22 2.70 05120201 IN0056375 4/30/2003 9/30/2005 11 0.00 0.00 0.01 05120201 IN0056979 3/31/1999 12/31/2009 3 0.21 0.00 0.38 05120201 IN0057487 5/31/1999 12/31/2009 127 0.02 0.00 0.05 05120201 IN0057495 9/30/1998 11/30/2003 60 0.04 0.01 0.31 05120201 IN0057614 9/30/2004 12/31/2009 62 1.40 0.14 1.95 05120201 IN0057720 1/31/2000 12/31/2009 85 0.26 0.11 0.35 05120201 IN0058645 6/30/2001 12/31/2009 32 0.04 0.03 0.04 05120201 IN0059072 11/30/1996 12/31/2009 135 0.00 0.00 0.01 05120201 IN0059170 11/30/1996 12/31/2009 120 0.13 0.00 3.38 05120201 IN0059315 5/31/1997 12/31/2009 146 0.12 0.03 0.96 05120201 IN0059340 10/31/2002 2/28/2003 1 0.03 0.03 0.03 05120201 IN0059358 6/30/1997 12/31/2009 1 0.01 0.01 0.01 05120201 IN0059366 4/30/1997 12/31/2009 61 0.00 0.00 0.02 05120201 IN0059544 7/31/1997 12/31/2009 130 0.84 0.03 1.95 05120201 IN0059943 3/31/1998 12/31/2009 138 0.00 0.00 0.02 05120201 IN0060496 9/30/1999 1/31/2006 24 0.00 0.00 0.01 05120201 IN0060551 9/30/2000 12/31/2009 46 0.02 0.01 0.17 05120201 IN0060640 7/31/2000 12/31/2009 95 0.08 0.03 0.16 05120201 IN0061301 7/31/2001 12/31/2009 97 0.01 0.00 0.05 05120201 IN0109762 4/30/2000 12/31/2009 115 0.01 0.00 0.04 05120201 IN0109967 8/31/1999 12/31/2009 17 0.00 0.00 0.01 05120201 ING080003 10/31/1999 5/31/2004 44 0.00 0.00 0.00 05120201 ING080054 12/31/2000 9/30/2004 20 0.00 0.00 0.00 05120201 ING080082 8/31/2003 1/31/2006 13 0.07 0.03 0.11 05120201 ING080087 11/30/1997 12/31/2002 42 0.00 0.00 0.02 05120201 ING080094 9/30/1998 1/31/2004 7 0.00 0.00 0.00 05120201 ING080102 2/28/1999 12/31/2009 118 0.11 0.00 9.60 05120201 ING080109 8/31/1999 10/31/2003 33 0.01 0.00 0.03 05120201 ING080114 11/30/1999 12/31/2009 81 0.00 0.00 0.01 05120201 ING080124 6/30/2006 12/31/2009 38 0.00 0.00 0.01 05120201 ING080128 7/31/2000 12/31/2009 101 0.00 0.00 0.03 05120201 ING080141 6/30/2001 1/31/2005 35 0.00 0.00 0.00 05120201 ING080142 7/31/2001 9/30/2007 18 0.00 0.00 0.01 05120201 ING250058 2/28/1999 12/31/2009 122 0.03 0.00 0.35 05120201 ING250061 4/30/1999 11/30/2003 48 0.06 0.01 0.08 05120201 INP000025 1/31/2001 12/31/2009 95 0.01 0.00 0.02 05120201 INP000041 2/28/1997 12/31/2009 140 0.02 0.00 0.22 05120201 INP000076 10/31/1996 12/31/2009 145 0.00 0.00 0.01 05120201 INP000081 11/30/1996 2/28/2007 93 0.00 0.00 0.00 05120201 INP000089 12/31/1997 9/30/2009 112 0.07 0.01 0.82 05120201 INP000099 10/31/1996 12/31/2009 142 0.11 0.01 1.34 05120201 INP000106 10/31/1998 12/31/2009 121 0.00 0.00 0.01 05120201 INP000123 7/31/1997 6/30/2002 51 0.01 0.00 0.01 05120201 INP000126 8/31/1997 12/31/2009 134 0.22 0.03 0.46

51

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120201 INP000158 8/31/1997 12/31/2009 135 0.00 0.00 0.00 05120201 INP000208 7/31/1999 9/30/2005 51 0.00 0.00 0.00 05120202 IN0001082 11/30/1999 12/31/2009 13 0.09 0.05 0.10 05120202 IN0002887 1/31/1995 12/31/2009 266 61.74 0.00 494.28 05120202 IN0003611 2/28/1998 12/31/2009 88 0.01 0.01 0.06 05120202 IN0004391 1/31/1995 12/31/2009 622 52.65 0.08 223.96 05120202 IN0020192 9/30/1998 12/31/2009 133 0.40 0.05 0.84 05120202 IN0020214 1/31/1999 12/31/2009 131 0.16 0.02 0.31 05120202 IN0020575 6/30/1998 12/31/2009 136 1.25 0.49 3.00 05120202 IN0021083 3/31/2001 12/31/2009 105 1.24 0.71 2.67 05120202 IN0022373 12/31/1998 12/31/2009 130 0.37 0.03 3.37 05120202 IN0023639 10/31/1998 12/31/2009 90 0.26 0.05 1.10 05120202 IN0023795 12/31/1998 12/31/2009 126 0.04 0.01 0.08 05120202 IN0024325 12/31/1999 12/31/2009 184 0.51 0.00 1.27 05120202 IN0025658 8/31/1998 12/31/2009 130 3.65 2.30 7.70 05120202 IN0031470 5/31/2000 12/31/2009 106 0.00 0.00 0.04 05120202 IN0034932 4/30/1999 12/31/2009 83 0.13 0.03 0.20 05120202 IN0035726 12/31/1998 12/31/2009 129 2.95 1.25 5.80 05120202 IN0036790 7/31/2000 12/31/2009 86 0.02 0.00 0.38 05120202 IN0038296 7/31/2000 12/31/2009 103 0.06 0.02 1.01 05120202 IN0038415 9/30/2000 12/31/2009 111 0.01 0.00 0.06 05120202 IN0038466 3/31/2000 12/31/2009 57 0.00 0.00 0.01 05120202 IN0039110 7/31/2000 12/31/2009 53 0.00 0.00 0.01 05120202 IN0039276 2/28/1998 12/31/2009 133 0.71 0.24 8.80 05120202 IN0039985 7/31/1998 12/31/2009 136 0.06 0.04 0.13 05120202 IN0040801 9/30/2002 12/31/2009 84 0.15 0.09 0.30 05120202 IN0042650 3/31/2001 12/31/2009 103 0.03 0.01 0.06 05120202 IN0043737 10/31/1997 12/31/2009 86 0.01 0.01 0.01 05120202 IN0043753 6/30/1998 12/31/2009 130 0.03 0.00 0.64 05120202 IN0049883 3/31/1998 12/31/2009 127 0.00 0.00 0.05 05120202 IN0049891 8/31/1998 6/30/2003 57 0.00 0.00 0.02 05120202 IN0053384 3/31/1999 12/31/2009 107 0.21 0.00 1.71 05120202 IN0058416 11/30/2000 12/31/2009 106 0.01 0.00 0.04 05120202 IN0058742 4/30/2001 12/31/2009 28 0.12 0.00 0.90 05120202 IN0059641 10/31/1997 12/31/2009 287 1.00 0.00 3.02 05120202 IN0059871 1/31/1998 12/31/2009 140 0.00 0.00 0.02 05120202 IN0060143 9/30/1998 12/31/2009 1 0.00 0.00 0.00 05120202 IN0060577 4/30/2000 12/31/2009 110 0.01 0.01 0.05 05120202 ING080134 1/31/2001 12/31/2009 73 0.00 0.00 0.01 05120202 INP000176 12/31/1997 12/31/2009 130 0.72 0.53 0.91 05120202 INP000200 9/30/1998 12/31/2009 15 0.00 0.00 0.00 05120203 IN0001279 10/31/1998 12/31/2009 135 0.82 0.00 2.30 05120203 IN0001848 4/30/1999 12/31/2009 126 0.04 0.01 0.13 05120203 IN0021008 6/30/1999 12/31/2009 123 0.30 0.10 0.76 05120203 IN0021032 1/31/1995 12/31/2009 288 1.87 0.28 6.20 05120203 IN0021318 5/31/1998 12/31/2009 132 1.06 0.04 97.00 05120203 IN0022616 12/31/1999 12/31/2009 156 0.38 0.17 0.98 05120203 IN0030279 5/31/2001 12/31/2009 58 0.01 0.00 0.02

52

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120203 IN0030724 12/31/1997 12/31/2009 111 0.00 0.00 0.00 05120203 IN0030783 11/30/1999 12/31/2009 102 0.00 0.00 0.01 05120203 IN0031518 7/31/1997 11/30/2006 76 0.01 0.00 0.06 05120203 IN0031747 5/31/2000 12/31/2009 114 0.00 0.00 0.01 05120203 IN0035173 2/29/2000 12/31/2009 117 0.13 0.06 0.71 05120203 IN0037401 6/30/2005 12/31/2009 50 0.00 0.00 0.01 05120203 IN0039292 5/31/1999 12/31/2009 95 0.14 0.02 0.60 05120203 IN0039861 4/30/2003 12/31/2009 79 0.16 0.06 0.37 05120203 IN0040436 10/31/1998 12/31/2009 132 0.04 0.02 0.35 05120203 IN0040941 8/31/1998 12/31/2009 151 0.07 0.01 2.60 05120203 IN0043877 9/30/1997 12/31/2009 146 0.07 0.01 0.16 05120203 IN0045527 8/31/1999 12/31/2009 122 0.28 0.14 0.61 05120203 IN0047074 1/31/2001 12/31/2009 106 0.00 0.00 0.01 05120203 IN0050695 11/30/2000 12/31/2009 109 0.02 0.00 0.48 05120203 IN0058459 12/31/1995 12/31/2009 10 0.14 0.07 0.38 05120203 IN0059765 1/31/1998 12/31/2009 77 0.00 0.00 0.01 05120203 IN0059846 3/31/1998 12/31/2009 47 0.01 0.01 0.03 05120203 IN0059986 4/30/1998 12/31/2009 96 0.04 0.00 0.36 05120203 IN0060429 9/30/1999 12/31/2009 2 0.01 0.00 0.01 05120203 IN0109606 4/30/2000 12/31/2009 47 0.05 0.01 0.27 05120203 INP000012 2/28/2003 3/31/2005 11 0.01 0.01 0.01 05120203 INP000037 6/30/1995 12/31/2009 47 0.08 0.00 0.79 05120203 INP000156 7/31/2002 8/31/2005 19 0.06 0.04 0.08 05120204 IN0001350 1/31/1995 12/31/2009 269 0.30 0.00 3.11 05120204 IN0002925 6/30/2001 12/31/2009 99 0.22 0.12 0.36 05120204 IN0020109 12/31/1998 12/31/2009 131 3.38 1.65 8.06 05120204 IN0020966 8/31/1998 12/31/2009 136 0.47 0.25 1.08 05120204 IN0021181 12/31/1996 12/31/2009 154 3.98 2.08 6.77 05120204 IN0021415 10/31/1998 12/31/2009 132 0.46 0.00 0.92 05120204 IN0023841 6/30/1997 12/31/2009 142 0.26 0.06 0.66 05120204 IN0024503 4/30/1998 12/31/2009 118 0.41 0.00 5.72 05120204 IN0024937 9/30/1998 12/31/2009 131 0.17 0.05 0.93 05120204 IN0025356 4/30/1998 12/31/2009 82 0.00 0.00 0.05 05120204 IN0025437 4/30/2005 12/31/2009 55 0.02 0.00 0.12 05120204 IN0030040 2/28/1998 12/31/2009 139 0.03 0.00 0.55 05120204 IN0031399 3/31/2001 12/31/2009 104 0.00 0.00 0.08 05120204 IN0031593 3/31/2006 12/31/2009 44 0.01 0.00 0.02 05120204 IN0031879 5/31/2000 12/31/2009 99 0.03 0.02 0.03 05120204 IN0032867 11/30/1999 12/31/2009 121 5.49 2.60 9.72 05120204 IN0037389 6/30/1998 12/31/2009 133 0.00 0.00 0.02 05120204 IN0038873 4/30/1997 12/31/2009 201 0.02 0.00 0.14 05120204 IN0040151 6/30/1999 12/31/2009 124 0.08 0.01 0.51 05120204 IN0040177 10/31/1998 12/31/2009 122 0.37 0.18 0.83 05120204 IN0041181 7/31/2000 12/31/2009 113 0.04 0.01 0.49 05120204 IN0041777 6/30/1999 12/31/2009 57 0.01 0.00 0.07 05120204 IN0042064 2/28/2003 5/31/2004 15 0.02 0.01 0.03 05120204 IN0042358 6/30/1997 12/31/2009 149 0.13 0.05 0.45 05120204 IN0042366 8/31/2000 12/31/2009 112 1.37 0.16 2.96

53

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120204 IN0043966 12/31/1998 12/31/2009 58 0.03 0.00 0.23 05120204 IN0044661 8/31/1998 12/31/2009 135 0.04 0.01 1.30 05120204 IN0045063 6/30/2001 10/31/2007 71 0.01 0.01 0.25 05120204 IN0045284 5/31/2000 12/31/2009 227 0.20 0.00 0.64 05120204 IN0046060 3/31/1999 12/31/2009 8 0.05 0.00 0.09 05120204 IN0047490 1/31/2001 12/31/2009 105 0.01 0.00 0.23 05120204 IN0048011 4/30/2006 12/31/2009 32 0.00 0.00 0.01 05120204 IN0049689 2/28/1997 12/31/2009 201 0.00 0.00 0.02 05120204 IN0050148 5/31/2001 12/31/2009 102 0.20 0.01 3.31 05120204 IN0051691 6/30/1999 12/31/2009 124 0.03 0.03 0.10 05120204 IN0053538 7/31/2000 12/31/2009 61 0.11 0.00 0.47 05120204 IN0054186 10/31/2000 1/31/2004 20 0.01 0.00 0.01 05120204 IN0056383 12/31/1996 12/31/2009 214 0.00 0.00 0.01 05120204 IN0058009 12/31/1999 12/31/2009 120 0.08 0.02 0.80 05120204 IN0059307 5/31/1997 12/31/2009 43 0.20 0.00 0.32 05120204 IN0060623 4/30/2000 12/31/2009 80 0.01 0.00 0.03 05120204 IN0060828 11/30/2000 7/31/2008 5 0.00 0.00 0.00 05120204 IN0109479 7/31/2000 12/31/2009 110 0.00 0.00 0.02 05120204 IN0109541 2/28/1998 5/31/2007 131 0.07 0.00 0.36 05120204 IN0109797 1/31/1995 6/30/2009 34 0.01 0.00 0.02 05120204 INP000053 1/31/1995 7/31/1995 7 0.02 0.00 0.02 05120204 INP000078 10/31/1996 12/31/2009 126 0.01 0.00 0.21 05120204 INP000147 5/31/1996 12/31/2009 149 0.03 0.01 0.07 05120204 INP000163 9/30/1998 12/31/2009 118 0.15 0.02 0.52 05120204 INP000179 5/31/1998 12/31/2009 35 0.03 0.00 0.19 05120205 IN0021253 9/30/1999 12/31/2009 99 0.34 0.03 1.52 05120205 IN0021270 1/31/1995 12/31/2009 288 1.48 0.49 3.74 05120205 IN0031551 7/31/1997 12/31/2009 241 0.01 0.00 0.68 05120205 IN0032573 1/31/1999 12/31/2009 128 7.61 4.80 11.50 05120205 IN0039632 2/28/1998 12/31/2009 142 0.01 0.00 0.03 05120205 IN0040398 10/31/1998 12/31/2009 129 0.08 0.02 0.80 05120205 IN0049166 6/30/1998 9/30/2003 60 0.01 0.01 0.01 05120205 IN0053546 11/30/2000 12/31/2009 104 0.01 0.00 0.14 05120205 IN0054844 3/31/1999 12/31/2009 88 0.34 0.01 0.87 05120205 IN0055131 9/30/2001 12/31/2009 41 0.18 0.14 0.30 05120205 IN0109746 12/31/1998 12/31/2009 56 0.59 0.01 11.60 05120205 ING080039 6/30/2000 3/31/2007 27 0.25 0.00 0.87 05120206 IN0002461 12/31/1998 12/31/2009 44 0.93 0.01 1.69 05120206 IN0003719 11/30/1999 12/31/2009 158 0.13 0.02 0.40 05120206 IN0021075 9/30/1998 12/31/2009 131 0.06 0.02 0.26 05120206 IN0022454 9/30/1998 12/31/2009 134 0.36 0.20 0.80 05120206 IN0024473 4/30/2006 12/31/2009 44 4.56 1.56 10.51 05120206 IN0024830 12/31/1997 12/31/2009 141 0.34 0.01 1.38 05120206 IN0032140 10/31/1998 12/31/2009 134 0.02 0.01 0.20 05120206 IN0037559 12/31/2000 10/31/2003 16 0.03 0.01 0.04 05120206 IN0037567 11/30/2000 12/31/2009 58 0.00 0.00 0.02 05120206 IN0038938 8/31/2000 12/31/2009 109 0.02 0.00 0.04 05120206 IN0039349 6/30/1999 12/31/2009 125 0.07 0.02 0.52

54

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120206 IN0043095 5/31/2000 12/31/2009 112 0.01 0.00 0.06 05120206 IN0043681 11/30/2000 12/31/2009 105 0.02 0.00 0.67 05120206 IN0051683 11/30/2002 12/31/2009 78 0.06 0.00 0.50 05120206 IN0053091 6/30/1998 12/31/2009 136 0.17 0.01 1.92 05120206 IN0053937 8/31/1999 12/31/2009 127 0.07 0.01 0.66 05120206 IN0055891 8/31/1999 12/31/2009 121 0.03 0.00 0.06 05120206 IN0057363 12/31/1998 12/31/2009 103 0.06 0.00 0.15 05120206 IN0059625 9/30/1997 12/31/2009 143 0.03 0.00 4.10 05120206 IN0060674 8/31/2000 12/31/2009 105 0.03 0.00 0.08 05120206 IN0061115 4/30/2001 11/30/2004 22 0.40 0.04 0.60 05120207 IN0001864 11/30/1999 12/31/2009 96 0.01 0.00 0.07 05120207 IN0004740 8/31/1999 12/31/2009 123 0.06 0.00 0.35 05120207 IN0020451 5/31/1997 12/31/2009 150 1.63 0.89 2.86 05120207 IN0021156 2/28/1999 12/31/2009 124 0.37 0.01 0.91 05120207 IN0021911 7/31/1999 12/31/2009 123 1.04 0.40 1.84 05120207 IN0022683 6/30/1999 12/31/2009 123 0.25 0.12 0.29 05120207 IN0022802 10/31/1998 12/31/2009 163 0.08 0.01 1.10 05120207 IN0024210 4/30/1998 12/31/2009 133 0.12 0.03 1.50 05120207 IN0025135 3/31/1999 12/31/2009 128 0.88 0.32 1.78 05120207 IN0025836 9/30/1998 10/31/2009 62 0.01 0.00 0.03 05120207 IN0035734 6/30/2003 3/31/2008 8 0.95 0.53 2.13 05120207 IN0035742 6/30/1998 3/31/2008 97 0.10 0.00 0.63 05120207 IN0037745 10/31/1997 12/31/2009 27 0.20 0.05 0.57 05120207 IN0037851 1/31/1995 6/30/2009 87 0.01 0.00 0.07 05120207 IN0037885 6/30/1998 3/31/2008 43 0.74 0.01 1.80 05120207 IN0038539 6/30/1998 12/31/2009 138 0.07 0.01 0.59 05120207 IN0038865 7/31/2000 9/30/2009 89 0.08 0.00 0.48 05120207 IN0042013 8/31/1999 6/30/2008 26 0.02 0.00 0.06 05120207 IN0043478 4/30/1999 12/31/2009 36 0.01 0.01 0.02 05120207 IN0044962 6/30/1998 6/30/2006 33 0.50 0.08 0.96 05120207 IN0048763 12/31/1997 12/31/2009 65 0.31 0.00 0.70 05120207 IN0051055 8/31/1998 12/31/2009 9 0.02 0.01 0.04 05120207 IN0051331 12/31/1997 12/31/2009 119 0.01 0.00 0.19 05120207 IN0052230 7/31/1996 12/31/2009 53 0.09 0.00 4.23 05120207 IN0055832 2/28/1998 12/31/2009 141 0.00 0.00 0.03 05120207 IN0056049 6/30/2001 12/31/2009 102 0.29 0.13 0.74 05120207 IN0056103 1/31/1995 8/31/2007 136 0.01 0.00 0.04 05120207 IN0057789 4/30/2001 12/31/2009 93 0.03 0.00 0.40 05120207 IN0109703 11/30/1996 12/31/2009 95 1.40 0.01 39.00 05120207 ING080127 4/30/2000 12/31/2009 82 0.01 0.00 0.02 05120207 ING340019 7/31/2001 12/31/2009 73 0.09 0.00 4.00 05120207 INP000154 2/28/1998 6/30/2009 247 0.01 0.00 0.03 05120207 INP000204 10/31/1998 12/31/2009 121 0.08 0.02 0.16 05120207 INP000230 4/30/2002 12/31/2009 80 0.00 0.00 0.02 05120208 IN0001775 5/31/2000 12/31/2009 176 1.05 0.00 14.92 05120208 IN0001911 3/31/1998 12/31/2009 104 0.12 0.05 0.34 05120208 IN0003247 2/28/1998 12/31/2009 140 0.03 0.02 0.07 05120208 IN0003573 1/31/1995 12/31/2009 213 0.31 0.03 0.71

55

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120208 IN0003646 1/31/1995 12/31/2009 416 0.05 0.00 0.94 05120208 IN0003905 2/28/1999 8/31/2003 43 0.01 0.01 0.04 05120208 IN0004901 4/30/1999 12/31/2009 154 0.08 0.00 0.90 05120208 IN0021539 4/30/2000 12/31/2009 768 0.15 0.00 1.27 05120208 IN0021601 9/30/1998 12/31/2009 135 0.15 0.05 3.80 05120208 IN0022489 9/30/1998 12/31/2009 101 0.11 0.00 1.68 05120208 IN0022951 2/28/1999 12/31/2009 128 0.67 0.35 1.55 05120208 IN0023744 3/31/1999 12/31/2009 84 0.08 0.00 0.54 05120208 IN0023787 9/30/1998 12/31/2009 135 0.51 0.28 0.98 05120208 IN0023876 12/31/1997 12/31/2009 142 0.28 0.03 0.57 05120208 IN0023981 9/30/1998 12/31/2009 135 0.18 0.07 0.90 05120208 IN0024023 7/31/1997 12/31/2009 144 0.51 0.19 0.97 05120208 IN0024953 6/30/2000 12/31/2009 64 0.01 0.00 0.02 05120208 IN0025623 12/31/1998 12/31/2009 132 1.98 1.04 3.35 05120208 IN0030163 6/30/1998 12/31/2009 59 0.01 0.00 0.07 05120208 IN0030236 12/31/1997 12/31/2009 143 0.04 0.02 0.08 05120208 IN0030325 12/31/2000 12/31/2009 81 0.03 0.00 0.68 05120208 IN0030350 7/31/2000 9/30/2004 21 0.00 0.00 0.00 05120208 IN0031577 5/31/1998 12/31/2009 118 0.01 0.00 0.02 05120208 IN0035718 1/31/1995 12/31/2009 289 12.32 5.97 31.10 05120208 IN0036854 1/31/1997 12/31/2009 154 0.00 0.00 0.02 05120208 IN0037281 4/30/1996 12/31/2009 59 0.04 0.00 1.34 05120208 IN0038326 10/31/2000 12/31/2009 93 0.00 0.00 0.01 05120208 IN0038920 12/31/1996 12/31/2009 142 0.01 0.00 0.03 05120208 IN0039241 6/30/2004 12/31/2009 66 0.50 0.26 1.00 05120208 IN0040631 9/30/1998 12/31/2009 82 0.22 0.02 0.95 05120208 IN0041009 9/30/2001 12/31/2009 70 0.00 0.00 0.01 05120208 IN0042617 11/30/2000 12/31/2009 68 0.00 0.00 0.03 05120208 IN0043729 12/31/1997 12/31/2009 141 0.06 0.02 0.11 05120208 IN0043818 1/31/1998 12/31/2009 140 0.02 0.02 0.03 05120208 IN0044211 9/30/1998 12/31/2009 53 0.03 0.00 0.37 05120208 IN0045187 10/31/1997 12/31/2009 145 0.12 0.05 0.24 05120208 IN0048453 12/31/1997 12/31/2009 24 0.30 0.00 1.34 05120208 IN0050105 8/31/1998 12/31/2009 130 0.02 0.00 0.04 05120208 IN0052086 3/31/1998 12/31/2009 48 0.01 0.00 0.01 05120208 IN0052949 7/31/1999 12/31/2009 125 0.05 0.01 1.11 05120208 IN0053741 4/30/1999 12/31/2009 1 0.00 0.00 0.00 05120208 IN0055077 5/31/2001 7/31/2005 10 0.00 0.00 0.00 05120208 IN0055824 7/31/1996 12/31/2009 177 0.00 0.00 0.02 05120208 IN0057321 9/30/1995 12/31/2009 107 0.00 0.00 0.01 05120208 IN0060526 12/31/1999 12/31/2009 110 0.01 0.00 0.01 05120208 IN0060810 9/30/2000 12/31/2009 87 0.40 0.04 1.56 05120208 ING080132 10/31/2000 12/31/2009 71 0.00 0.00 0.02 05120208 INP000174 6/30/1998 12/31/2009 97 0.00 0.00 0.02 05120209 IN0003093 6/30/1998 12/31/2009 119 0.03 0.00 0.23 05120209 IN0003808 5/31/2000 12/31/2009 114 0.45 0.21 0.86 05120209 IN0020648 9/30/1999 12/31/2009 121 0.30 0.10 0.71 05120209 IN0020834 6/30/2001 12/31/2009 100 2.17 0.00 5.10

56

8 Digit HUC NPDES ID Start Date End Date Count Average Minimum Maximum 05120209 IN0023124 6/30/1996 12/31/2009 162 1.03 0.40 2.79 05120209 IN0031704 2/28/2001 12/31/2009 104 0.01 0.00 0.07 05120209 IN0040789 9/30/1997 12/31/2009 123 0.06 0.00 0.46 05120209 IN0042536 1/31/1999 12/31/2009 40 0.07 0.00 0.24 05120209 IN0046035 6/30/1999 12/31/2009 16 0.06 0.00 0.40 05120209 IN0046361 9/30/1998 12/31/2009 24 0.11 0.03 0.32 05120209 IN0047465 11/30/2001 5/31/2003 15 0.00 0.00 0.00 05120209 IN0052698 11/30/1998 12/31/2009 133 0.23 0.07 0.57 05120209 IN0053163 8/31/2000 12/31/2009 112 0.00 0.00 0.01 05120209 IN0055204 9/30/2001 8/31/2003 19 0.00 0.00 0.00 05120209 IN0056260 3/31/2003 10/31/2003 2 0.00 0.00 0.00 05120209 IN0058866 6/30/1996 12/31/2009 36 0.05 0.01 0.06 05120209 INP000127 10/31/1999 12/31/2009 217 0.03 0.00 0.08

57

Table A-2. Available BOD data for each NPDES facility (mg/L) 8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0020796 05120201 12/31/1997 12/31/2009 134 3.12 0.90 11.20 IN0020800 05120111 6/30/2000 12/31/2009 115 2.67 1.20 7.20 IN0020834 05120209 6/30/2001 12/31/2009 101 7.09 4.00 25.00 IN0020907 05120107 9/30/1999 12/31/2009 122 3.79 2.00 7.00 IN0020958 05120201 9/30/1999 12/31/2009 124 1.85 1.00 3.00 IN0020966 05120204 8/31/1998 12/31/2009 137 2.79 1.70 10.30 IN0020982 05120103 1/31/1996 12/31/2009 168 4.13 1.00 9.00 IN0021008 05120203 6/30/1999 12/31/2009 125 2.75 1.40 6.60 IN0021024 05120201 7/31/2000 12/31/2009 114 2.68 1.10 5.10 IN0021032 05120203 1/31/1995 12/31/2009 180 2.61 0.41 14.00 IN0021075 05120206 9/30/1998 12/31/2009 136 5.03 0.70 34.10 IN0021083 05120202 3/31/2001 12/31/2009 106 2.70 2.00 5.50 IN0021091 05120107 6/30/1996 12/31/2009 168 3.37 0.20 13.00 IN0021105 05120103 9/30/1996 12/31/2009 158 2.30 1.00 23.00 IN0021113 05120104 2/28/1999 12/31/2009 131 2.57 1.00 10.00 IN0021148 05120111 4/30/1999 12/31/2009 128 5.49 1.48 14.10 IN0021156 05120207 2/28/1999 12/31/2009 124 3.88 0.60 10.54 IN0021164 05120108 11/30/1996 12/31/2009 157 7.19 2.40 16.70 IN0021181 05120204 12/31/1996 12/31/2009 154 3.09 1.20 9.30 IN0021199 05120105 1/31/1999 12/31/2009 130 3.90 0.05 10.40 IN0021202 05120201 6/30/1996 12/31/2009 163 3.25 1.00 11.20 IN0021245 05120201 7/31/1998 12/31/2009 138 4.20 2.00 9.00 IN0021253 05120205 9/30/1999 12/31/2009 101 12.38 0.66 67.20 IN0021270 05120205 1/31/1995 12/31/2009 179 2.26 0.80 7.00 IN0021288 05120106 8/31/1998 12/31/2009 134 4.02 1.09 9.40 IN0021318 05120203 5/31/1998 12/31/2009 133 5.02 1.30 35.30 IN0021334 05120201 11/30/1996 12/31/2009 158 2.36 0.21 7.18 IN0021351 05120201 11/30/1997 12/31/2009 27 8.52 2.80 30.00 IN0021377 05120105 6/30/2006 12/31/2009 43 3.20 2.00 8.10 IN0021415 05120204 10/31/1998 12/31/2009 134 2.54 0.10 14.01 IN0021440 05120101 7/31/2000 12/31/2009 112 5.19 1.90 14.00 IN0021474 05120201 8/31/1997 12/31/2009 149 2.68 1.00 14.00 IN0021491 05120103 12/31/1998 12/31/2009 133 4.17 1.00 8.00 IN0021512 05120201 4/30/1997 12/31/2009 153 21.51 9.00 43.00 IN0021539 05120208 4/30/2000 12/31/2009 130 1.42 0.30 7.90 IN0021580 05120106 7/31/1997 12/31/2009 149 2.37 0.40 8.30 IN0021601 05120208 9/30/1998 12/31/2009 136 3.12 0.60 17.90 IN0021628 05120103 12/31/1997 12/31/2009 145 3.57 1.30 23.30 IN0021652 05120103 11/30/1998 12/31/2009 133 2.76 1.00 7.00 IN0021661 05120106 12/31/1996 12/31/2009 157 6.35 2.00 25.70 IN0021911 05120207 7/31/1999 12/31/2009 126 10.39 2.00 50.90 IN0022012 05120201 3/31/1998 12/31/2009 46 5.31 2.00 10.90 IN0022080 05120201 9/30/2001 10/31/2005 38 7.82 0.90 19.60 IN0022101 05120201 10/31/1997 12/31/2009 146 7.69 2.00 28.00 IN0022136 05120103 9/30/1998 12/31/2009 136 2.83 1.14 11.63 IN0022306 05120201 12/31/1998 12/31/2009 123 13.01 2.00 88.50 IN0022314 05120201 12/31/1998 12/31/2009 132 2.55 0.20 12.18 IN0022373 05120202 12/31/1998 12/31/2009 130 6.74 2.10 29.30

58

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0022411 05120101 12/31/2000 12/31/2009 108 2.19 0.80 4.80 IN0022438 05120106 7/31/1999 12/31/2009 28 16.08 4.00 40.00 IN0022454 05120206 9/30/1998 12/31/2009 136 4.36 1.70 8.80 IN0022489 05120208 9/30/1998 12/31/2009 87 15.17 4.25 53.00 IN0022497 05120201 4/30/1998 12/31/2009 141 5.31 2.00 11.00 IN0022586 05120201 5/31/2001 12/31/2009 104 3.60 1.00 7.80 IN0022608 05120108 6/30/1996 12/31/2009 163 5.78 0.00 23.83 IN0022616 05120203 12/31/1998 12/31/2009 165 2.63 1.10 7.70 IN0022624 05120104 6/30/1996 12/31/2009 163 4.71 1.80 10.00 IN0022683 05120207 6/30/1999 12/31/2009 124 3.62 1.00 7.10 IN0022802 05120207 10/31/1998 12/31/2009 105 8.94 2.90 21.80 IN0022934 05120107 8/31/1999 12/31/2009 125 2.80 0.00 6.60 IN0022951 05120208 2/28/1999 12/31/2009 130 4.13 1.90 19.00 IN0022985 05120103 9/30/1998 12/31/2009 136 5.53 2.00 10.85 IN0023124 05120209 6/30/1996 12/31/2009 163 2.59 0.20 11.70 IN0023132 05120101 12/31/1997 12/31/2009 145 11.66 1.50 65.20 IN0023183 05120201 1/31/1995 12/31/2009 203 4.40 1.00 22.00 IN0023353 05120107 10/31/2005 12/31/2009 51 5.83 2.00 24.00 IN0023639 05120202 10/31/1998 12/31/2009 58 14.84 0.16 52.00 IN0023736 05120101 8/31/1998 12/31/2009 137 2.42 1.40 3.60 IN0023744 05120208 3/31/1999 12/31/2009 85 22.29 11.00 49.00 IN0023787 05120208 9/30/1998 12/31/2009 136 3.53 2.00 10.00 IN0023795 05120202 11/30/1998 12/31/2009 134 4.08 1.13 9.60 IN0023825 05120201 11/30/1998 12/31/2009 197 4.48 0.00 11.00 IN0023841 05120204 6/30/1997 12/31/2009 150 17.57 3.00 48.40 IN0023876 05120208 12/31/1997 12/31/2009 143 2.79 2.00 9.70 IN0023981 05120208 9/30/1998 12/31/2009 136 2.79 1.00 8.30 IN0023990 05120108 12/31/1998 12/31/2009 130 4.17 1.00 14.20 IN0024023 05120208 7/31/1997 12/31/2009 145 4.59 2.10 14.90 IN0024112 05120101 9/30/2000 7/31/2008 1 3.00 3.00 3.00 IN0024210 05120207 4/30/1998 12/31/2009 134 2.08 0.90 4.20 IN0024279 05120103 8/31/2001 12/31/2009 82 2.29 0.90 8.50 IN0024325 05120202 12/31/1999 12/31/2009 183 6.04 2.00 11.10 IN0024392 05120113 12/31/1997 12/31/2009 145 1.98 1.10 4.60 IN0024406 05120103 8/31/1998 12/31/2009 134 3.53 1.40 12.00 IN0024473 05120206 4/30/2006 12/31/2009 45 2.62 1.80 4.10 IN0024503 05120204 4/30/1998 12/31/2009 111 6.31 0.00 26.05 IN0024554 05120111 10/31/1999 12/31/2009 146 19.36 0.00 222.00 IN0024589 05120110 12/31/1998 12/31/2009 122 11.52 0.09 63.00 IN0024716 05120108 2/28/1997 12/31/2009 152 2.63 0.60 18.00 IN0024741 05120101 8/31/1999 12/31/2009 123 3.56 0.00 11.00 IN0024791 05120102 12/31/1998 12/31/2009 133 2.89 1.00 7.50 IN0024805 05120106 7/31/1997 12/31/2009 149 8.42 1.90 59.00 IN0024821 05120108 6/30/1999 12/31/2009 136 10.27 1.10 273.00 IN0024830 05120206 12/31/1997 12/31/2009 106 11.03 2.00 26.00 IN0024902 05120101 10/31/1999 12/31/2009 123 4.16 2.00 19.60 IN0024937 05120204 9/30/1998 12/31/2009 132 3.87 0.67 16.92 IN0024953 05120208 6/30/2000 12/31/2009 64 3.03 1.00 26.20

59

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0024970 05120201 3/31/1999 12/31/2009 128 4.48 1.20 7.40 IN0025135 05120207 3/31/1999 12/31/2009 130 9.64 1.00 54.10 IN0025151 05120201 9/30/1998 12/31/2009 131 6.13 1.50 13.00 IN0025208 05120106 8/31/2000 12/31/2009 102 4.64 2.00 16.40 IN0025224 05120111 12/31/1998 12/31/2009 124 5.84 2.00 16.00 IN0025232 05120106 8/31/1998 12/31/2009 112 14.78 0.06 35.10 IN0025356 05120204 4/30/1998 12/31/2009 82 6.24 2.10 35.00 IN0025364 05120201 1/31/2003 11/30/2003 7 10.68 3.50 26.00 IN0025372 05120201 10/31/1998 12/31/2009 129 3.60 1.00 12.90 IN0025402 05120201 9/30/2001 9/30/2005 31 22.66 6.50 60.00 IN0025437 05120204 4/30/2005 12/31/2009 56 4.16 1.00 15.20 IN0025585 05120103 4/30/1996 12/31/2009 164 2.09 1.00 23.00 IN0025607 05120111 4/30/1999 12/31/2009 129 6.38 2.00 40.00 IN0025623 05120208 12/31/1998 12/31/2009 132 6.01 2.30 19.00 IN0025658 05120202 8/31/1998 12/31/2009 136 3.09 1.00 28.00 IN0025798 05120113 2/28/2001 12/31/2009 107 3.40 1.40 6.40 IN0029815 05120103 11/30/2000 12/31/2009 110 2.20 0.50 5.80 IN0030015 05120103 11/30/2000 12/31/2009 110 4.99 1.70 24.20 IN0030023 05120201 10/31/2000 12/31/2009 111 4.81 2.00 10.10 IN0030031 05120106 10/31/2000 12/31/2009 111 4.49 2.20 8.30 IN0030040 05120204 2/28/1998 12/31/2009 139 3.17 1.00 10.00 IN0030163 05120208 6/30/1998 12/31/2009 59 4.32 2.80 7.00 IN0030228 05120111 7/31/1997 12/31/2009 146 3.18 0.70 16.00 IN0030236 05120208 12/31/1997 12/31/2009 145 3.18 1.00 8.30 IN0030279 05120203 5/31/2001 12/31/2009 59 3.14 0.40 9.10 IN0030325 05120208 12/31/2000 12/31/2009 105 4.73 0.90 97.00 IN0030350 05120208 7/31/2000 9/30/2004 21 1.32 0.35 3.40 IN0030406 05120113 12/31/1997 12/31/2009 88 2.10 0.40 11.00 IN0030562 05120105 12/31/1998 12/31/2009 105 10.15 3.90 22.30 IN0030571 05120106 1/31/1995 12/31/2009 64 2.14 1.10 6.20 IN0030627 05120104 11/30/2000 2/28/2007 75 4.22 0.66 10.65 IN0030635 05120101 11/30/2000 12/31/2009 110 3.21 0.71 8.49 IN0030643 05120103 12/31/1998 12/31/2009 111 3.10 1.00 10.00 IN0030678 05120111 9/30/2000 3/31/2005 35 7.47 2.00 17.40 IN0030724 05120203 12/31/1997 12/31/2009 112 3.92 2.00 6.00 IN0030783 05120203 11/30/1999 12/31/2009 103 7.75 3.00 20.00 IN0030830 05120201 11/30/2000 8/31/2004 37 8.00 4.00 35.10 IN0030881 05120106 5/31/2001 12/31/2009 104 5.27 2.00 28.90 IN0030902 05120201 10/31/2000 12/31/2009 110 11.79 1.20 222.30 IN0030911 05120106 7/31/1997 12/31/2009 95 3.80 2.00 14.70 IN0031020 05120113 3/31/1999 12/31/2009 129 3.71 1.00 8.90 IN0031071 05120201 6/30/1998 12/31/2009 138 1.71 0.50 2.10 IN0031135 05120201 11/30/2000 12/31/2009 109 3.72 1.20 16.50 IN0031208 05120104 7/31/2000 12/31/2009 113 4.50 0.20 22.70 IN0031356 05120201 3/31/1998 12/31/2009 117 3.08 0.18 7.20 IN0031364 05120101 1/31/1996 12/31/2009 168 7.14 1.60 23.40 IN0031372 05120103 3/31/1998 12/31/2009 136 6.23 0.40 18.50 IN0031399 05120204 3/31/2001 12/31/2009 105 3.81 1.86 14.40

60

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0031411 05120101 8/31/1997 12/31/2009 9 4.03 2.70 5.90 IN0031445 05120104 5/31/2000 8/31/2007 49 7.27 3.50 10.00 IN0031453 05120101 1/31/1996 12/31/2009 157 6.89 1.40 29.20 IN0031470 05120202 5/31/2000 12/31/2009 108 7.28 0.20 66.00 IN0031518 05120203 7/31/1997 11/30/2006 76 7.85 0.00 50.00 IN0031526 05120201 8/31/1997 3/31/2006 99 8.46 0.01 82.00 IN0031551 05120205 7/31/1997 12/31/2009 137 4.64 1.30 19.70 IN0031569 05120201 9/30/2000 12/31/2009 91 5.24 0.90 54.00 IN0031577 05120208 5/31/1998 12/31/2009 122 12.23 2.10 58.50 IN0031593 05120204 3/31/2006 12/31/2009 46 3.11 2.00 9.70 IN0031640 05120201 1/31/2001 12/31/2009 106 5.64 1.90 26.60 IN0031704 05120209 2/28/2001 12/31/2009 105 3.11 1.00 8.00 IN0031712 05120201 9/30/2000 12/31/2009 111 3.70 1.90 11.40 IN0031721 05120102 6/30/2000 5/31/2009 98 2.83 0.00 10.70 IN0031739 05120101 6/30/2000 12/31/2009 101 4.06 1.00 22.00 IN0031747 05120203 5/31/2000 12/31/2009 115 7.41 3.00 18.00 IN0031763 05120101 2/28/2002 12/31/2009 94 5.75 2.30 11.00 IN0031798 05120104 10/31/2000 12/31/2009 110 6.03 1.20 23.60 IN0031801 05120107 8/31/2000 12/31/2009 113 3.22 1.00 14.92 IN0031844 05120107 6/30/1999 12/31/2009 127 7.20 0.68 30.90 IN0031879 05120204 5/31/2000 12/31/2009 113 12.03 2.00 36.60 IN0031909 05120111 9/30/2000 12/31/2009 103 9.25 0.60 74.00 IN0031925 05120201 2/29/2000 12/31/2009 113 9.94 0.70 77.50 IN0031933 05120201 9/30/2000 5/31/2005 46 5.05 0.01 13.00 IN0031950 05120201 1/31/1995 2/29/2008 155 4.33 2.00 15.00 IN0031976 05120107 12/31/1998 12/31/2009 132 6.11 2.00 30.00 IN0032140 05120206 10/31/1998 12/31/2009 134 4.96 1.00 45.10 IN0032328 05120101 2/29/2000 12/31/2009 118 3.07 2.00 6.70 IN0032468 05120108 1/31/1995 12/31/2009 180 9.38 2.40 22.20 IN0032476 05120201 1/31/1995 12/31/2009 180 2.86 0.94 10.00 IN0032573 05120205 1/31/1999 12/31/2009 132 6.76 2.00 13.00 IN0032719 05120201 10/31/2004 12/31/2009 48 17.12 1.80 66.70 IN0032867 05120204 11/30/1999 12/31/2009 122 4.75 2.00 12.00 IN0032875 05120107 9/30/2000 12/31/2009 112 4.72 2.44 8.40 IN0034428 05120110 12/31/1997 12/31/2009 142 7.78 0.25 22.00 IN0034444 05120101 8/31/1997 12/31/2009 136 4.83 1.00 15.40 IN0034461 05120105 10/31/2000 12/31/2009 111 2.20 0.12 13.80 IN0034932 05120202 4/30/1999 12/31/2009 75 22.10 12.00 34.00 IN0035173 05120203 2/29/2000 12/31/2009 119 2.93 1.30 10.60 IN0035378 05120101 12/31/1995 12/31/2009 168 5.90 2.10 15.10 IN0035718 05120208 1/31/1995 12/31/2009 177 3.21 1.00 10.00 IN0035726 05120202 12/31/1998 12/31/2009 133 2.97 1.00 8.00 IL0004294 05120115 1/31/2000 10/31/2009 146 6.09 3.00 110.00 IL0020273 05120114 1/31/2001 12/31/2009 303 29.12 0.10 150.00 IL0020605 05120114 1/31/2000 12/31/2009 363 21.79 0.41 152.67 IL0020974 05120114 1/31/2000 12/31/2009 364 11.60 1.10 190.00 IL0021644 05120112 12/31/2000 12/31/2009 356 35.51 1.00 343.00 IL0023477 05120113 3/31/2002 12/31/2009 275 44.75 1.00 181.00

61

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IL0024643 05120115 1/31/2001 3/31/2004 48 4.54 1.00 30.20 IL0025429 05120114 4/30/2000 12/31/2009 171 89.46 2.00 752.00 IL0025500 05120114 1/31/2000 12/31/2009 353 12.72 0.30 90.00 IL0027910 05120114 1/31/2000 12/31/2009 357 24.77 0.55 134.20 IL0028622 05120114 1/31/2000 12/31/2009 403 72.23 2.00 571.00 IL0029831 05120112 12/31/2000 12/31/2009 366 47.00 1.00 934.00 IL0030091 05120114 12/31/2000 12/31/2009 330 34.59 1.00 257.00 IL0030121 05120112 3/31/2000 12/31/2009 354 41.65 2.00 181.00 IL0030732 05120111 1/31/2000 12/31/2009 360 47.27 1.00 203.00 IL0031453 05120112 4/30/2004 12/31/2009 209 47.02 5.00 316.00 IL0035084 05120112 1/31/2000 12/31/2009 542 20.24 1.00 75.00 IL0042757 05120112 1/31/2000 12/31/2009 281 35.85 1.00 206.00 IL0044792 05120114 1/31/2000 12/31/2009 312 52.95 0.75 470.00 IL0046957 05120115 12/31/2000 12/31/2009 273 75.13 2.00 809.00 IL0047244 05120114 5/31/2000 5/31/2009 30 11.13 1.00 25.00 IL0047481 05120114 7/31/2001 7/31/2009 66 14.23 0.30 78.00 IL0048755 05120114 1/31/2000 12/31/2009 360 93.23 0.70 480.00 IL0049174 05120113 4/30/2000 12/31/2009 102 197.40 5.00 6650.00 IL0049191 05120114 1/31/2000 12/31/2009 348 7.86 1.60 53.00 IL0049212 05120112 9/30/2000 12/31/2009 336 25.60 2.00 83.00 IL0049361 05120112 1/31/2000 10/31/2009 290 60.62 1.00 1060.00 IL0051209 05120112 7/31/2001 12/31/2009 178 37.05 0.00 191.00 IL0051250 05120112 4/30/2000 10/31/2009 50 1.60 1.00 10.00 IL0051829 05120112 2/28/2001 8/31/2009 190 26.86 1.00 186.00 IL0053945 05120111 4/30/2001 10/31/2009 105 408.70 1.00 4086.00 IL0054496 05120115 12/31/2005 12/31/2009 98 80.59 0.10 1125.00 IL0055701 05120114 1/31/2000 12/31/2009 329 69.59 1.00 610.00 IL0059005 05120112 9/30/2002 12/31/2009 239 44.08 0.57 267.67 IL0060119 05120112 1/31/2000 12/31/2009 281 44.44 0.70 372.00 IL0060208 05120114 1/31/2003 12/31/2009 165 69.61 2.00 390.00 IL0063096 05120112 12/31/2000 12/31/2009 323 55.41 0.22 640.00 IL0063274 05120111 1/31/2002 12/31/2008 80 10.09 1.00 62.00 IL0068977 05120115 3/31/2000 4/30/2007 21 11.90 1.00 131.00 IL0069973 05120113 2/29/2000 12/31/2009 295 88.20 0.00 2635.00 IL0071111 05120113 11/30/2002 12/31/2009 253 66.09 2.00 375.00 IL0071374 05120114 8/31/2002 12/31/2009 227 22.24 0.05 390.00 IL0073903 05120115 11/30/2001 7/31/2007 12 2.08 1.00 9.00 IL0075922 05120114 5/31/2002 12/31/2009 184 21.13 4.00 93.00 ILG580024 05120114 1/31/2003 12/31/2009 240 64.91 6.00 370.00 ILG580029 05120115 1/31/2000 1/31/2004 57 59.59 1.33 310.00 ILG580056 05120114 1/31/2000 12/31/2009 356 64.79 1.00 450.00 ILG580070 05120114 2/28/2003 12/31/2009 196 74.27 1.90 293.00 ILG580076 05120114 4/30/2003 12/31/2008 143 55.34 1.00 250.00 ILG580080 05120115 1/31/2003 12/31/2009 235 44.37 1.00 201.00 ILG580105 05120115 1/31/2000 12/31/2009 329 45.45 1.50 277.00 ILG580108 05120115 1/31/2000 12/31/2009 330 51.33 2.00 294.00 ILG580112 05120113 4/30/2003 12/31/2009 167 55.87 2.00 723.00 ILG580129 05120115 1/31/2003 12/31/2009 173 52.95 1.00 281.00

62

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) ILG580133 05120114 2/28/2003 12/31/2009 225 74.34 4.00 335.00 ILG580146 05120115 1/31/2003 12/31/2009 252 105.24 0.48 773.00 ILG580152 05120114 1/31/2000 12/31/2009 335 41.74 3.40 270.00 ILG580159 05120114 1/31/2000 12/31/2009 262 29.05 1.50 150.00 ILG580170 05120113 4/30/2003 12/31/2009 203 25.91 0.33 500.00 ILG580188 05120113 2/28/2003 5/31/2008 186 45.42 1.00 266.00 ILG580195 05120115 2/28/2003 12/31/2009 132 31.46 1.90 139.00 ILG580197 05120114 1/31/2003 12/31/2009 251 36.60 5.00 370.00 ILG580201 05120113 1/31/2003 11/30/2009 174 32.02 1.00 144.00 ILG580206 05120113 1/31/2003 10/31/2009 144 55.06 1.00 265.00 ILG580208 05120113 1/31/2003 11/30/2009 235 38.28 1.00 225.00 ILG580211 05120114 4/30/2003 12/31/2009 225 47.34 2.00 202.00 ILG580220 05120115 7/31/2000 12/31/2009 320 25.06 1.40 296.00 ILG580224 05120112 5/31/2000 12/31/2009 222 45.51 2.00 274.00 ILG582001 05120112 3/31/2003 12/31/2009 189 59.39 3.00 271.00 IN0001082 05120202 11/30/2005 12/31/2009 14 17.66 4.00 32.00 IN0001244 05120104 1/31/2000 9/30/2000 7 3.34 2.00 9.75 IN0001279 05120203 10/31/1998 12/31/2004 16 23.19 10.00 57.00 IN0001601 05120111 6/30/1998 12/31/2009 139 4.30 0.90 18.78 IN0001775 05120208 7/31/2007 12/31/2009 2 2.25 2.00 2.50 IN0002372 05120103 11/30/2007 12/31/2009 26 20.28 8.07 41.10 IN0002763 05120108 1/31/1995 12/31/2009 179 7.32 0.02 43.00 IN0002810 05120111 1/31/1995 12/31/2009 180 6.07 0.20 34.90 IN0002887 05120202 1/31/2006 12/31/2009 45 6.35 4.00 27.00 IN0002925 05120204 6/30/2001 12/31/2009 103 4.13 1.70 8.50 IN0003026 05120111 3/31/2005 12/31/2009 52 119.18 5.55 423.00 IN0003328 05120111 3/31/2001 12/31/2006 76 50.60 16.33 340.10 IN0003387 05120106 1/31/1995 12/31/2009 180 4.14 0.20 54.75 IN0003506 05120108 5/31/1997 12/31/2009 152 4.64 2.00 52.00 IN0003573 05120208 4/30/2004 12/31/2009 69 7.66 1.00 64.00 IN0003646 05120208 1/31/1995 12/31/2009 173 4.59 0.00 13.00 IN0003808 05120209 5/31/2000 12/31/2009 114 20.19 1.00 930.00 IN0004839 05120101 11/30/1998 12/31/2009 218 3.88 0.57 19.42 IN0004901 05120208 9/30/2003 9/30/2008 38 5.29 2.00 36.00 IN0005002 05120103 1/31/1999 12/31/2009 17 230.34 2.00 1146.00 IN0020001 05120103 12/31/1998 12/31/2009 132 2.37 1.00 5.40 IN0020028 05120201 12/31/1998 12/31/2009 133 2.21 1.00 7.00 IN0020044 05120201 8/31/1997 12/31/2009 148 1.91 0.60 4.00 IN0020052 05120108 12/31/1997 12/31/2009 144 4.89 2.30 8.90 IN0020079 05120201 12/31/1997 12/31/2009 145 3.59 1.60 6.40 IN0020087 05120201 11/30/1998 12/31/2009 133 4.56 1.00 13.12 IN0020095 05120102 4/30/1998 12/31/2009 140 3.31 1.00 11.30 IN0020109 05120204 12/31/1998 12/31/2009 132 2.89 0.70 9.80 IN0020117 05120102 10/31/2000 12/31/2009 101 12.34 6.70 23.10 IN0020141 05120105 3/31/1999 12/31/2009 129 1.86 1.00 5.00 IN0020150 05120201 7/31/2000 12/31/2009 114 2.63 1.70 4.60 IN0020168 05120201 6/30/1999 12/31/2009 126 3.25 1.40 8.20 IN0020192 05120202 9/30/1998 12/31/2009 135 4.07 1.90 6.80

63

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0020206 05120101 9/30/1998 12/31/2009 134 10.07 4.10 90.00 IN0020214 05120202 1/31/1999 12/31/2009 132 2.74 1.00 7.45 IN0020303 05120201 9/30/1998 12/31/2009 136 12.13 1.70 78.00 IN0020338 05120201 1/31/2006 12/31/2009 47 6.71 1.90 31.00 IN0020354 05120105 12/31/1998 12/31/2009 133 5.75 1.20 13.50 IN0020371 05120103 12/31/1996 12/31/2009 168 4.25 0.30 14.10 IN0020389 05120111 9/30/2004 12/31/2009 61 4.02 1.70 8.00 IN0020401 05120201 7/31/1998 12/31/2009 138 2.39 0.80 6.80 IN0020443 05120110 4/30/1998 12/31/2009 140 4.90 1.65 18.80 IN0020451 05120207 5/31/1997 12/31/2009 152 3.76 2.00 8.00 IN0020532 05120107 9/30/2000 12/31/2009 108 3.63 1.18 12.60 IN0020541 05120106 5/31/1997 12/31/2009 150 2.63 0.80 7.70 IN0020559 05120102 5/31/2000 12/31/2009 86 13.91 2.00 29.00 IN0020567 05120104 9/30/1998 12/31/2009 135 2.27 2.00 6.00 IN0020575 05120202 6/30/1998 12/31/2009 138 4.54 3.00 8.80 IN0020630 05120110 5/31/2000 12/31/2009 116 3.61 0.60 9.40 IN0020648 05120209 9/30/1999 12/31/2009 121 1.40 1.00 6.33 IN0020699 05120113 10/31/1998 12/31/2009 133 25.62 1.50 205.00 IN0020729 05120201 6/30/1999 12/31/2009 126 16.25 9.11 47.30 IN0020745 05120101 4/30/1998 12/31/2009 138 2.58 1.70 5.20 IN0020753 05120104 8/31/2000 12/31/2009 113 9.21 2.00 41.00 IN0020770 05120201 3/31/1998 12/31/2009 140 3.45 1.00 10.00 IN0036587 05120201 9/30/2000 12/31/2009 65 17.44 0.50 156.60 IN0036790 05120202 7/31/2000 12/31/2009 87 10.82 2.50 152.00 IN0036820 05120201 6/30/1998 12/31/2009 89 21.48 1.97 339.00 IN0036854 05120208 1/31/1997 12/31/2009 155 3.21 0.03 22.00 IN0036935 05120107 5/31/2000 12/31/2009 113 3.87 1.00 26.00 IN0036943 05120106 9/30/2000 12/31/2009 111 3.70 2.50 9.90 IN0036951 05120201 1/31/2004 12/31/2009 72 4.22 1.40 8.80 IN0036978 05120103 5/31/1999 12/31/2009 127 2.28 0.40 9.00 IN0037001 05120101 6/30/1998 12/31/2009 139 3.37 1.10 25.90 IN0037044 05120106 11/30/2000 12/31/2009 110 6.11 1.60 18.50 IN0037133 05120201 10/31/1998 2/28/2003 22 20.17 3.50 72.00 IN0037184 05120201 4/30/2003 10/31/2003 4 11.98 3.60 33.00 IN0037214 05120107 3/31/2001 12/31/2009 104 4.65 2.60 14.40 IN0037249 05120113 5/31/2001 12/31/2009 92 3.10 0.00 12.20 IN0037281 05120208 4/30/1996 12/31/2009 60 19.84 0.04 365.00 IN0037389 05120204 6/30/1998 12/31/2009 134 4.19 1.40 42.60 IN0037401 05120203 6/30/2005 12/31/2009 51 3.59 2.00 6.80 IN0037427 05120101 5/31/2001 12/31/2009 87 2.96 0.90 12.50 IN0037559 05120206 12/31/2000 10/31/2003 33 3.23 2.00 5.95 IN0037567 05120206 11/30/2000 12/31/2009 60 4.86 2.00 25.00 IN0037583 05120102 4/30/2000 12/31/2009 115 3.25 0.17 12.70 IN0037605 05120202 1/31/2001 12/31/2009 0 5.50 IN0037729 05120104 9/30/1996 12/31/2009 156 7.49 1.20 30.00 IN0037851 05120207 1/31/1995 6/30/2009 45 38.08 3.00 132.00 IN0038016 05120103 8/31/1998 12/31/2009 129 11.02 2.60 57.00 IN0038059 05120201 3/31/1998 12/31/2009 117 9.65 1.38 130.00

64

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0038288 05120113 12/31/1998 12/31/2009 131 2.18 0.40 10.40 IN0038296 05120202 7/31/2000 12/31/2009 105 4.47 0.49 13.00 IN0038326 05120208 10/31/2000 12/31/2009 95 3.77 1.30 7.70 IN0038334 05120108 1/31/2000 12/31/2009 119 6.06 3.00 13.50 IN0038407 05120201 7/31/2000 1/31/2004 40 13.29 2.70 42.00 IN0038415 05120202 9/30/2000 12/31/2009 112 11.51 3.00 49.00 IN0038466 05120202 3/31/2000 12/31/2009 58 5.98 2.00 16.00 IN0038539 05120207 6/30/1998 12/31/2009 138 2.94 0.60 11.60 IN0038598 05120201 4/30/2000 12/31/2009 117 3.40 1.60 14.20 IN0038768 05120107 7/31/1996 12/31/2009 158 13.52 2.60 47.70 IN0038784 05120107 2/28/1998 12/31/2009 138 9.23 1.00 51.50 IN0038857 05120201 9/30/1998 12/31/2009 135 3.89 0.90 36.00 IN0038865 05120207 7/31/2000 9/30/2009 89 41.02 2.30 252.40 IN0038873 05120204 4/30/1997 12/31/2009 148 3.82 1.02 9.50 IN0038881 05120201 11/30/1996 1/31/2008 129 2.82 0.14 13.00 IN0038920 05120208 12/31/1996 12/31/2009 143 4.50 1.50 16.00 IN0038938 05120206 8/31/2000 12/31/2009 109 5.08 3.60 7.20 IN0038962 05120103 1/31/1999 12/31/2009 131 4.37 1.00 18.60 IN0038971 05120108 12/31/1998 12/31/2009 132 4.88 2.50 12.00 IN0039110 05120202 7/31/2000 12/31/2009 53 2.40 1.50 7.75 IN0039241 05120208 6/30/2004 12/31/2009 67 2.38 1.00 4.40 IN0039276 05120202 2/28/1998 12/31/2009 134 8.03 2.00 36.00 IN0039292 05120203 5/31/1999 12/31/2009 50 11.53 2.00 62.60 IN0039322 05120111 6/30/1998 12/31/2009 137 3.31 0.90 13.50 IN0039349 05120206 6/30/1999 12/31/2009 127 2.59 0.50 11.30 IN0039357 05120101 10/31/1998 12/31/2009 129 3.22 0.40 9.40 IN0039471 05120201 5/31/2000 12/31/2009 87 17.14 2.00 444.30 IN0039497 05120107 6/30/1999 12/31/2009 127 6.63 3.00 25.70 IN0039632 05120205 2/28/1998 12/31/2009 143 5.18 1.00 51.30 IN0039705 05120108 9/30/1996 12/31/2009 159 6.72 1.60 15.20 IN0039756 05120108 11/30/1998 12/31/2009 134 1.62 1.10 2.50 IN0039772 05120201 9/30/1997 12/31/2009 145 8.09 1.29 65.70 IN0039799 05120107 12/31/1998 12/31/2009 126 9.29 0.20 20.70 IN0039829 05120111 11/30/1998 12/31/2009 85 10.26 1.90 24.40 IN0039837 05120111 1/31/1999 12/31/2009 131 2.01 2.00 3.00 IN0039861 05120203 4/30/2003 12/31/2009 81 2.96 2.50 8.00 IN0039870 05120106 2/28/1999 12/31/2009 52 15.25 6.30 30.00 IN0039934 05120104 12/31/1997 12/31/2009 145 3.61 1.30 13.70 IN0039985 05120202 7/31/1998 12/31/2009 138 4.49 2.10 15.00 IN0040002 05120106 4/30/2006 12/31/2009 45 3.11 1.80 5.90 IN0040088 05120201 4/30/1999 12/31/2009 126 4.73 2.00 13.60 IN0040134 05120111 3/31/1999 12/31/2009 129 4.15 0.02 28.50 IN0040151 05120204 6/30/1999 12/31/2009 126 5.67 0.83 23.90 IN0040177 05120204 10/31/1998 12/31/2009 123 8.69 4.70 15.00 IN0040321 05120103 6/30/1998 12/31/2009 40 13.06 4.00 24.00 IN0040347 05120106 1/31/1999 12/31/2009 130 15.17 4.80 35.60 IN0040355 05120107 9/30/1998 12/31/2009 62 14.05 5.00 22.00 IN0040398 05120205 10/31/1998 12/31/2009 135 4.50 1.40 11.27

65

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0040436 05120203 10/31/1998 12/31/2009 134 2.80 0.61 11.40 IN0040479 05120201 3/31/1998 12/31/2009 141 2.58 0.99 18.20 IN0040495 05120102 12/31/1998 12/31/2009 131 4.27 2.00 8.80 IN0040517 05120113 7/31/2005 12/31/2009 50 4.33 2.00 20.00 IN0040533 05120104 9/30/2001 12/31/2009 100 3.78 0.70 15.90 IN0040631 05120208 9/30/1998 12/31/2009 50 27.89 6.10 225.00 IN0040649 05120104 2/28/1997 12/31/2009 36 20.93 10.90 57.50 IN0040681 05120201 7/31/1999 12/31/2009 126 4.97 1.00 69.00 IN0040762 05120107 9/30/1998 12/31/2009 134 2.80 1.00 9.00 IN0040789 05120209 9/30/1997 12/31/2009 126 9.71 2.20 32.00 IN0040797 05120106 10/31/2000 12/31/2009 109 3.71 1.70 7.30 IN0040801 05120202 9/30/2002 12/31/2009 163 45.47 2.00 178.00 IN0040835 05120111 9/30/1998 12/31/2009 117 8.84 3.00 22.00 IN0040941 05120203 8/31/1998 12/31/2009 137 16.19 2.40 47.50 IN0041009 05120208 9/30/2001 12/31/2009 70 3.20 2.00 10.70 IN0041084 05120111 7/31/1997 12/31/2009 149 10.41 0.90 28.00 IN0041092 05120111 3/31/1999 12/31/2009 130 11.91 0.80 30.00 IN0041131 05120107 5/31/1995 8/31/2007 123 7.25 1.00 113.00 IN0041157 05120110 9/30/2000 12/31/2009 102 4.51 1.20 19.40 IN0041173 05120103 7/31/2000 12/31/2009 114 14.57 2.00 70.00 IN0041181 05120204 7/31/2000 12/31/2009 114 5.05 0.90 15.80 IN0041190 05120111 11/30/1999 10/31/2009 0 5.50 IN0041637 05120102 10/31/2000 12/31/2009 108 3.19 2.00 27.00 IN0041726 05120106 2/28/1997 12/31/2009 153 4.02 0.07 20.00 IN0041734 05120113 5/31/1998 12/31/2009 139 6.46 0.20 83.00 IN0041742 05120106 4/30/1998 12/31/2009 125 3.58 0.00 11.40 IN0041777 05120204 6/30/1999 12/31/2009 59 6.69 1.00 116.00 IN0041866 05120107 5/31/2000 12/31/2009 108 4.91 2.00 13.20 IN0041912 05120107 7/31/1995 12/31/2009 169 6.99 0.30 30.00 IN0041971 05120201 7/31/1995 11/30/2006 133 4.38 2.20 8.20 IN0042013 05120207 8/31/1999 6/30/2008 15 9.51 2.10 21.00 IN0042064 05120204 2/28/2003 5/31/2004 15 5.81 2.40 10.60 IN0042358 05120204 6/30/1997 12/31/2009 150 3.35 1.00 8.80 IN0042366 05120204 8/31/2000 12/31/2009 113 14.38 4.00 39.00 IN0042391 05120101 11/30/1995 12/31/2009 170 3.97 2.20 33.40 IN0042617 05120208 11/30/2000 12/31/2009 68 6.46 0.01 37.50 IN0042650 05120202 3/31/2001 12/31/2009 106 5.51 2.00 10.00 IN0043095 05120206 5/31/2000 12/31/2009 114 3.49 0.30 41.20 IN0043273 05120108 4/30/2001 12/31/2009 105 4.50 3.00 10.14 IN0043281 05120201 5/31/1998 4/30/2006 99 9.91 1.60 45.00 IN0043559 05120201 12/31/1997 12/31/2009 144 6.95 3.60 18.20 IN0043681 05120206 11/30/2000 12/31/2009 103 5.34 0.19 17.00 IN0043877 05120203 9/30/1997 12/31/2009 147 4.43 2.30 9.00 IN0043893 05120104 4/30/1998 12/31/2009 141 4.73 1.10 26.10 IN0043966 05120204 12/31/1998 12/31/2009 58 9.64 2.00 53.00 IN0043974 05120201 3/31/2001 4/30/2004 29 5.50 0.75 17.00 IN0044211 05120208 9/30/1998 12/31/2009 38 9.10 0.00 22.20 IN0044423 05120103 5/31/2001 12/31/2009 104 8.50 3.30 24.30

66

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0044652 05120107 5/31/2000 12/31/2009 116 10.62 0.20 35.50 IN0044661 05120204 8/31/1998 12/31/2009 137 3.21 0.80 13.30 IN0045004 05120101 7/31/1999 4/30/2009 0 5.50 IN0045063 05120204 6/30/2001 10/31/2007 73 2.55 0.70 7.00 IN0045187 05120208 10/31/1997 12/31/2009 146 5.72 2.20 21.00 IN0045357 05120101 11/30/1998 12/31/2009 134 4.76 0.50 25.70 IN0045446 05120201 7/31/2000 2/28/2007 76 7.40 2.50 23.00 IN0045527 05120203 8/31/1999 12/31/2009 124 3.55 1.30 6.30 IN0045837 05120113 3/31/2001 12/31/2009 102 2.00 0.03 14.20 IN0045845 05120113 4/30/1999 12/31/2009 124 3.72 1.00 8.00 IN0045870 05120110 2/28/2002 4/30/2003 0 5.50 IN0046361 05120209 9/30/1998 12/31/2009 12 7.10 2.00 13.50 IN0046396 05120110 9/30/2000 12/31/2009 111 9.18 3.80 19.80 IN0047074 05120203 1/31/2001 12/31/2009 108 6.92 1.30 17.00 IN0047465 05120209 11/30/2001 5/31/2003 7 34.49 17.50 56.30 IN0047490 05120204 1/31/2001 12/31/2009 108 5.75 4.00 13.70 IN0048011 05120204 4/30/2006 12/31/2009 28 5.22 1.30 8.40 IN0048267 05120103 3/31/2001 12/31/2009 78 6.16 0.01 18.10 IN0048453 05120208 12/31/1997 12/31/2009 18 14.16 0.18 36.00 IN0048763 05120207 12/31/1997 12/31/2009 59 22.13 7.00 66.45 IN0049026 05120201 8/31/1996 12/31/2009 161 2.99 1.60 31.00 IN0049361 05120201 5/31/1998 12/31/2009 138 4.49 1.30 16.50 IN0049581 05120201 7/31/1998 12/31/2009 138 6.23 1.20 64.20 IN0049689 05120204 2/28/1997 12/31/2009 148 4.36 0.90 23.60 IN0049794 05120201 2/28/1997 12/31/2009 152 4.81 1.40 18.00 IN0049832 05120103 7/31/1996 12/31/2009 162 5.95 1.50 13.50 IN0049883 05120202 3/31/1998 12/31/2009 122 4.04 0.75 20.70 IN0049891 05120202 8/31/1998 6/30/2003 55 3.22 0.01 12.00 IN0050105 05120208 8/31/1998 12/31/2009 132 6.55 2.00 25.80 IN0050148 05120204 5/31/2001 12/31/2009 102 3.83 0.71 73.35 IN0050211 05120101 10/31/1998 12/31/2009 135 6.18 0.70 58.00 IN0050253 05120108 8/31/1997 12/31/2009 146 2.59 1.00 8.00 IN0050296 05120111 11/30/1997 12/31/2009 146 3.66 1.00 10.00 IN0050326 05120106 9/30/2000 12/31/2009 112 3.93 2.50 17.00 IN0050652 05120106 12/31/1997 12/31/2009 83 10.33 0.02 193.00 IN0050695 05120203 11/30/2000 12/31/2009 110 6.06 2.00 19.20 IN0050971 05120101 10/31/1997 12/31/2009 146 3.35 0.36 12.30 IN0051055 05120207 8/31/1998 12/31/2009 9 15.19 6.70 51.00 IN0051098 05120101 10/31/1997 12/31/2009 144 5.22 0.23 50.00 IN0051187 05120101 6/30/2000 12/31/2009 115 2.90 2.00 24.00 IN0051331 05120207 12/31/1997 12/31/2009 119 3.83 1.10 20.30 IN0051624 05120107 4/30/2000 12/31/2009 116 13.58 3.00 46.00 IN0051683 05120206 11/30/2002 12/31/2009 84 6.36 1.80 23.00 IN0051691 05120204 6/30/1999 12/31/2009 126 2.70 0.30 8.10 IN0051861 05120101 7/31/2000 12/31/2009 114 4.77 0.70 11.00 IN0052078 05120106 5/31/2000 12/31/2009 47 9.74 3.00 20.00 IN0052230 05120207 7/31/1996 12/31/2009 54 7.61 0.00 44.00 IN0052256 05120201 12/31/2000 12/31/2009 109 4.22 0.80 23.80

67

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0052370 05120105 10/31/1998 12/31/2009 135 5.17 3.30 14.00 IN0052698 05120209 11/30/1998 12/31/2009 134 7.63 1.70 65.30 IN0052949 05120208 7/31/1999 12/31/2009 126 4.59 0.00 7.20 IN0053091 05120206 6/30/1998 12/31/2009 139 13.39 4.00 40.50 IN0053121 05120101 1/31/2003 5/31/2004 14 4.90 1.25 11.52 IN0053147 05120101 9/30/1998 12/31/2009 131 3.29 1.45 6.80 IN0053163 05120209 8/31/2000 12/31/2009 113 4.76 1.00 17.00 IN0053546 05120205 11/30/2000 12/31/2009 106 6.22 3.80 29.00 IN0053627 05120201 9/30/2000 7/31/2007 82 5.14 1.70 11.40 IN0053741 05120208 4/30/1999 12/31/2009 1 6.80 6.80 6.80 IN0053783 05120104 12/31/1996 12/31/2009 87 12.20 2.00 73.00 IN0053899 05120202 3/31/1999 12/31/2009 0 5.50 IN0054127 05120101 7/31/2000 12/31/2009 112 2.53 0.40 19.40 IN0054186 05120204 10/31/2000 1/31/2004 19 6.20 2.50 14.10 IN0054445 05120106 6/30/2001 12/31/2009 71 6.42 3.00 10.60 IN0054704 05120106 9/30/1997 12/31/2009 147 4.39 1.20 70.67 IN0054771 05120201 3/31/2001 12/31/2008 76 2.76 1.30 6.20 IN0055077 05120208 5/31/2001 7/31/2005 10 4.42 2.00 5.00 IN0055085 05120111 11/30/1996 12/31/2009 158 5.73 2.50 13.30 IN0055131 05120205 9/30/2001 12/31/2009 45 3.62 1.50 7.40 IN0055158 05120101 7/31/1998 12/31/2009 48 17.36 0.88 42.50 IN0055166 05120104 8/31/1997 12/31/2009 93 14.08 3.40 28.80 IN0055271 05120103 2/28/1997 12/31/2009 154 4.13 1.00 12.10 IN0055280 05120201 10/31/1998 12/31/2009 135 3.63 0.80 10.00 IN0055484 05120201 4/30/1999 12/31/2009 129 4.99 2.20 9.40 IN0055654 05120201 1/31/1999 5/31/2007 0 5.50 IN0055697 05120107 8/31/1996 12/31/2009 160 3.16 2.00 8.60 IN0055760 05120201 4/30/1997 12/31/2009 153 2.26 1.10 4.60 IN0055824 05120208 7/31/1996 12/31/2009 179 3.40 0.05 16.00 IN0055832 05120207 2/28/1998 12/31/2009 143 8.77 1.00 22.70 IN0055891 05120206 8/31/1999 12/31/2009 123 5.87 2.00 54.00 IN0055921 05120107 2/28/2001 12/31/2009 106 6.10 0.20 21.30 IN0056049 05120207 6/30/2001 12/31/2009 103 7.14 2.40 44.20 IN0056103 05120207 1/31/1995 8/31/2007 70 8.07 2.20 43.00 IN0056154 05120111 12/31/1996 12/31/2009 157 8.75 2.00 27.00 IN0056260 05120209 3/31/2003 10/31/2003 2 5.00 5.00 5.00 IN0056375 05120201 4/30/2003 9/30/2005 11 7.63 4.00 14.70 IN0056383 05120204 12/31/1996 12/31/2009 157 3.36 1.00 11.10 IN0056456 05120106 2/28/2001 12/31/2009 94 4.37 0.40 21.50 IN0057321 05120208 9/30/1995 12/31/2009 161 6.34 2.00 23.60 IN0057347 05120110 9/30/1998 12/31/2009 117 5.28 2.30 26.10 IN0057363 05120206 12/31/1998 12/31/2009 58 10.28 2.00 32.00 IN0057487 05120201 5/31/1999 12/31/2009 127 6.83 1.00 28.30 IN0057495 05120201 9/30/1998 11/30/2003 60 5.86 2.00 29.10 IN0057614 05120201 9/30/2004 12/31/2009 64 5.09 2.30 10.10 IN0058009 05120204 12/31/1999 12/31/2009 121 3.94 1.70 19.60 IN0058173 05120107 7/31/2000 12/31/2009 112 8.23 0.70 318.40 IN0058327 05120106 10/31/2000 12/31/2009 60 19.09 8.50 34.80

68

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0058416 05120202 11/30/2000 12/31/2009 109 5.21 0.10 20.00 IN0058866 05120209 6/30/1996 12/31/2009 33 23.45 0.05 406.00 IN0058963 05120102 6/30/1997 12/31/2009 110 13.37 3.30 35.80 IN0059048 05120101 12/31/1996 12/31/2009 104 18.97 0.00 110.00 IN0059072 05120201 11/30/1996 12/31/2009 140 4.21 1.20 11.10 IN0059218 05120101 11/30/2001 12/31/2009 91 4.82 1.60 18.90 IN0059340 05120201 5/31/2002 2/28/2003 7 5.00 5.00 5.00 IN0059544 05120201 7/31/1997 12/31/2009 136 4.57 1.30 21.15 IN0059625 05120206 9/30/1997 12/31/2009 143 10.18 0.00 62.30 IN0059757 05120101 7/31/1998 7/31/2007 107 5.71 1.90 57.50 IN0059765 05120203 1/31/1998 12/31/2009 76 6.83 1.00 49.00 IN0059871 05120202 1/31/1998 12/31/2009 142 8.71 1.00 158.40 IN0059889 05120103 3/31/1998 12/31/2009 114 5.54 0.37 25.20 IN0059943 05120201 3/31/1998 12/31/2009 137 5.36 0.20 19.75 IN0059986 05120203 4/30/1998 12/31/2009 76 14.49 0.00 35.80 IN0060101 05120106 7/31/1998 12/31/2009 54 16.22 0.00 34.20 IN0060437 05120102 6/30/1999 12/31/2009 21 11.34 2.70 36.20 IN0060526 05120208 12/31/1999 12/31/2009 112 4.13 2.00 10.50 IN0060577 05120202 4/30/2000 12/31/2009 111 2.14 1.80 8.20 IN0060640 05120201 7/31/2000 12/31/2009 97 3.14 0.90 20.20 IN0060674 05120206 8/31/2000 12/31/2009 106 5.15 1.40 24.50 IN0060887 05120106 10/31/2000 12/31/2009 104 3.15 0.91 21.00 IN0061301 05120201 7/31/2001 12/31/2009 99 5.86 0.02 55.00 IN0109479 05120204 11/30/2000 12/31/2009 91 6.23 2.00 15.00 IN0109592 05120111 10/31/2000 12/31/2009 109 16.79 4.10 35.30 IN0109703 05120207 11/30/1996 12/31/2009 135 71.92 6.00 280.00 IN0109746 05120205 12/31/1998 12/31/2009 53 15.96 4.95 40.00 IN0109762 05120201 4/30/2000 12/31/2009 116 4.09 1.00 18.20 IN0109797 05120204 1/31/1995 6/30/2009 36 27.51 4.00 84.00 IN0109967 05120201 8/31/1999 12/31/2009 19 4.80 1.60 9.25 INP000126 05120201 8/31/1997 12/31/2009 148 703.98 8.00 2931.25 INP000127 05120209 10/31/1999 12/31/2009 242 1160.98 353.00 4258.00 INP000160 05120102 2/29/2008 12/31/2009 20 860.78 343.00 3201.00 INP000176 05120202 12/31/1997 12/31/2009 144 239.26 13.40 570.00 INP000181 05120101 8/31/1998 10/31/2006 17 39.29 4.50 99.00 INP000190 05120102 10/31/1998 12/31/2009 134 779.22 217.00 2228.00 INP000204 05120207 10/31/1998 12/31/2009 135 2380.71 501.00 9221.00

69

Table A-3 Available ammonia data for each NPDES facility (mg/L) 8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IL0004073 05120111 7/31/2000 10/31/2008 68 0.28 0.01 3.35 IL0004219 05120112 11/30/1999 7/31/2009 123 0.77 0.20 9.00 IL0004294 05120115 1/31/1999 10/31/2009 312 0.72 0.04 5.00 IL0020273 05120114 1/31/2001 12/31/2009 408 0.68 0.01 2.70 IL0020605 05120114 12/31/1999 9/30/2009 462 0.45 0.03 4.60 IL0020788 05120109 11/30/1999 4/30/2009 260 0.20 0.03 6.30 IL0020966 05120108 1/31/2000 3/31/2009 239 1.49 0.04 9.75 IL0021377 05120111 2/28/2002 5/31/2009 176 0.07 0.01 0.60 IL0021644 05120112 12/31/2000 9/30/2009 414 0.52 0.05 6.80 IL0022128 05120109 5/31/2000 5/31/2009 218 0.73 0.05 5.14 IL0022322 05120108 1/31/1999 8/31/2009 268 0.93 0.01 7.20 IL0023086 05120109 8/31/2000 5/31/2009 86 4.32 1.60 9.80 IL0023108 05120109 1/31/2000 8/31/2009 217 0.80 0.10 19.00 IL0023205 05120109 1/31/2000 5/31/2009 240 0.61 0.07 4.00 IL0024643 05120115 1/31/2001 3/31/2004 96 2.18 0.03 10.90 IL0024830 05120109 5/31/2002 6/30/2009 185 0.92 0.02 31.20 IL0026107 05120112 9/30/2001 5/31/2009 200 0.35 0.01 7.90 IL0027278 05120109 5/31/2000 6/30/2009 222 3.00 0.02 25.20 IL0027910 05120114 10/31/1999 9/30/2009 468 0.33 0.03 2.00 IL0028126 05120111 1/31/1999 3/31/2009 239 4.96 0.25 15.00 IL0028622 05120114 1/31/1999 9/30/2009 482 0.69 0.01 7.08 IL0029467 05120112 6/30/1999 2/28/2009 117 0.77 0.05 5.50 IL0029831 05120112 12/31/2000 6/30/2009 412 0.55 0.01 3.30 IL0030091 05120114 9/30/2006 12/31/2009 152 19.78 8.00 38.00 IL0030121 05120112 3/31/2000 7/31/2009 282 6.71 0.02 25.00 IL0030732 05120111 12/31/1999 6/30/2009 458 0.83 0.15 3.10 IL0031429 05120109 3/31/2004 6/30/2009 64 3.45 0.17 31.00 IL0031453 05120112 4/30/2004 6/30/2009 262 3.92 0.30 293.00 IL0031500 05120109 11/30/2000 6/30/2009 234 0.82 0.06 11.20 IL0031721 05120109 7/31/2000 6/30/2009 244 1.65 0.17 15.70 IL0035084 05120112 1/31/1999 4/30/2009 654 1.52 0.09 20.00 IL0042757 05120112 10/31/1999 7/31/2009 366 1.40 0.02 27.60 IL0046957 05120115 12/31/2000 12/31/2009 372 11.44 0.05 45.50 IL0047481 05120114 7/31/2001 7/31/2009 88 0.06 0.02 0.27 IL0047902 05120109 10/31/2000 5/31/2009 87 1.33 0.04 16.40 IL0048062 05120109 10/31/1999 6/30/2009 229 0.44 0.04 2.50 IL0048755 05120114 10/31/1999 5/31/2009 389 0.44 0.02 4.80 IL0049174 05120113 1/31/1999 5/31/1999 10 1.05 0.47 1.97 IL0049212 05120112 9/30/2000 6/30/2009 432 1.98 0.10 16.00 IL0049361 05120112 5/31/1999 8/31/2009 421 11.03 0.07 66.50 IL0050741 05120109 1/31/1999 6/30/2009 247 0.55 0.02 2.30 IL0051209 05120112 9/30/2001 12/31/2009 160 0.38 0.00 1.70 IL0051250 05120112 4/30/2000 8/31/2005 92 1.10 0.01 9.26 IL0051781 05120109 4/30/2004 10/31/2005 15 0.56 0.01 2.70 IL0051829 05120112 3/31/2001 4/30/2009 218 3.18 0.05 12.30 IL0053945 05120111 4/30/2001 8/31/2006 108 1.89 0.20 12.90 IL0054496 05120115 12/31/2005 12/31/2009 131 0.20 0.10 1.00 IL0055301 05120109 5/31/2002 2/28/2009 110 1.41 0.01 14.50

70

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IL0055701 05120114 1/31/1999 7/31/2009 448 0.96 0.01 8.65 IL0055751 05120109 5/31/2002 4/30/2009 114 1.03 0.04 15.50 IL0059005 05120112 9/30/2002 12/31/2009 288 0.23 0.02 1.13 IL0060119 05120112 8/31/1999 12/31/2009 378 0.68 0.00 3.00 IL0063096 05120112 12/31/2000 8/31/2009 398 3.26 0.01 15.00 IL0063274 05120111 12/31/2007 12/31/2008 10 3.00 1.00 6.50 IL0066974 05120112 2/28/2001 6/30/2009 204 5.38 0.20 15.20 IL0067156 05120109 9/30/2001 12/31/2006 94 1.38 0.01 8.00 IL0067601 05120109 11/30/2004 4/30/2005 10 2.38 0.17 5.18 IL0068977 05120115 3/31/2000 4/30/2007 12 0.34 0.01 1.00 IL0071111 05120113 11/30/2002 6/30/2009 312 2.17 0.07 13.80 IL0071617 05120112 1/31/1999 5/31/2009 253 0.58 0.01 6.40 IL0073610 05120112 2/28/2001 6/30/2009 202 0.45 0.19 13.00 IL0073903 05120115 11/30/2001 5/31/2007 14 0.34 0.01 0.79 IL0075922 05120114 6/30/2007 12/31/2009 40 1.61 0.20 7.10 IN0001082 05120202 11/30/1999 12/31/2009 13 3.63 0.03 10.20 IN0001244 05120104 1/31/2000 9/30/2000 7 0.13 0.10 0.17 IN0001350 05120204 1/31/2004 12/31/2009 25 0.97 0.01 2.06 IN0001813 05120201 9/30/2004 12/31/2009 126 0.61 0.14 1.72 IN0002810 05120111 1/31/1995 12/31/2009 297 1.79 0.04 14.70 IN0002925 05120204 6/30/2001 12/31/2009 102 0.40 0.10 2.60 IN0003026 05120111 3/31/2005 12/31/2009 52 6.44 0.20 16.49 IN0003328 05120111 3/31/2001 12/31/2006 75 9.75 2.30 26.60 IN0003387 05120106 1/31/1995 12/31/2009 179 0.16 0.00 1.50 IN0003573 05120208 4/30/2004 12/31/2009 68 0.34 0.04 2.00 IN0003646 05120208 1/31/1995 12/31/2009 172 1.03 0.00 10.00 IN0003808 05120209 5/31/2000 12/31/2009 114 18.63 0.06 982.00 IN0004839 05120101 11/30/1998 12/31/2009 164 0.97 0.09 10.33 IN0005002 05120103 1/31/1999 12/31/2009 17 0.70 0.05 2.74 IN0020001 05120103 12/31/1998 12/31/2009 126 0.38 0.01 8.46 IN0020028 05120201 12/31/1998 12/31/2009 131 0.20 0.04 0.82 IN0020044 05120201 8/31/1997 12/31/2009 147 0.36 0.10 1.50 IN0020052 05120108 12/31/1997 12/31/2009 143 1.00 0.20 2.20 IN0020079 05120201 12/31/1997 12/31/2009 142 0.23 0.02 2.50 IN0020087 05120201 11/30/1998 12/31/2009 131 0.61 0.00 10.70 IN0020095 05120102 4/30/1998 12/31/2009 140 0.66 0.05 9.50 IN0020109 05120204 12/31/1998 12/31/2009 131 0.38 0.07 1.38 IN0020117 05120102 4/30/2001 12/31/2009 79 4.03 0.12 16.10 IN0020141 05120105 3/31/1999 12/31/2009 127 0.53 0.10 19.45 IN0020150 05120201 7/31/2000 12/31/2009 113 0.12 0.02 0.78 IN0020168 05120201 6/30/1999 12/31/2009 125 0.66 0.04 7.40 IN0020192 05120202 9/30/2004 12/31/2009 63 1.74 0.04 22.90 IN0020206 05120101 9/30/1998 11/30/2009 80 1.05 0.66 2.10 IN0020214 05120202 1/31/1999 12/31/2009 130 0.22 0.00 2.30 IN0020303 05120201 4/30/2007 12/31/2009 32 0.17 0.07 1.44 IN0020338 05120201 1/31/2006 12/31/2009 45 1.66 0.06 7.79 IN0020354 05120105 12/31/1998 12/31/2009 131 0.49 0.00 2.70 IN0020371 05120103 12/31/1996 12/31/2009 167 0.51 0.03 4.10

71

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0020389 05120111 9/30/2004 12/31/2009 61 0.77 0.08 2.03 IN0020401 05120201 7/31/1998 12/31/2009 137 0.06 0.01 0.79 IN0020443 05120110 4/30/1998 12/31/2009 140 0.64 0.00 7.20 IN0020451 05120207 5/31/1997 12/31/2009 151 0.20 0.01 3.51 IN0020532 05120107 9/30/2000 12/31/2009 107 0.93 0.10 2.50 IN0020541 05120106 5/31/1997 12/31/2009 149 0.15 0.01 1.50 IN0020559 05120102 5/31/2003 12/31/2009 54 1.55 0.00 8.00 IN0020567 05120104 7/31/2008 12/31/2009 16 0.32 0.01 2.00 IN0020575 05120202 6/30/1998 12/31/2009 136 0.17 0.05 0.90 IN0020630 05120110 5/31/2000 12/31/2009 114 0.79 0.02 7.30 IN0020648 05120209 9/30/1999 12/31/2009 120 0.36 0.04 9.00 IN0020699 05120113 5/31/2009 12/31/2009 5 0.43 0.00 1.60 IN0020745 05120101 4/30/1998 12/31/2009 138 0.23 0.05 1.70 IN0020753 05120104 6/30/2005 12/31/2009 54 1.67 0.07 19.61 IN0020770 05120201 3/31/1998 12/31/2009 139 0.21 0.06 0.61 IN0020796 05120201 12/31/1997 12/31/2009 133 0.20 0.08 0.49 IN0020800 05120111 6/30/2000 12/31/2009 114 0.31 0.01 1.50 IN0020834 05120209 6/30/2001 12/31/2009 102 1.45 0.19 6.59 IN0020907 05120107 9/30/1999 12/31/2009 122 0.40 0.00 2.20 IN0020958 05120201 9/30/1999 12/31/2009 121 0.80 0.00 6.20 IN0020966 05120204 8/31/1998 12/31/2009 136 0.35 0.02 1.05 IN0020982 05120103 1/31/1999 12/31/2009 131 0.58 0.10 4.90 IN0021008 05120203 6/30/1999 12/31/2009 123 1.41 0.00 5.90 IN0021024 05120201 7/31/2000 12/31/2009 113 0.18 0.01 1.32 IN0021032 05120203 4/30/2004 12/31/2009 67 0.17 0.03 1.12 IN0021075 05120206 9/30/1998 12/31/2009 134 3.22 0.01 30.29 IN0021083 05120202 3/31/2001 12/31/2009 105 0.10 0.00 0.50 IN0021091 05120107 6/30/1996 12/31/2009 162 1.30 0.09 6.25 IN0021105 05120103 9/30/1996 12/31/2009 157 0.70 0.14 2.90 IN0021113 05120104 2/28/1999 12/31/2009 129 0.11 0.03 1.10 IN0021148 05120111 4/30/1999 12/31/2009 127 0.75 0.00 8.33 IN0021156 05120207 2/28/1999 12/31/2009 123 1.61 0.10 24.91 IN0021164 05120108 11/30/1996 12/31/2009 156 0.31 0.03 1.00 IN0021181 05120204 12/31/1996 12/31/2009 155 0.53 0.00 7.50 IN0021199 05120105 1/31/1999 12/31/2009 129 0.28 0.01 11.00 IN0021202 05120201 6/30/1996 12/31/2009 161 0.52 0.10 2.90 IN0021245 05120201 7/31/1998 12/31/2009 137 0.49 0.04 4.88 IN0021253 05120205 9/30/1999 12/31/2009 98 2.92 0.05 23.83 IN0021270 05120205 1/31/1995 12/31/2009 178 0.22 0.01 3.45 IN0021288 05120106 8/31/1998 12/31/2009 132 0.11 0.01 0.60 IN0021318 05120203 5/31/1998 12/31/2009 126 0.35 0.02 3.90 IN0021334 05120201 11/30/1996 12/31/2009 157 0.54 0.00 4.30 IN0021351 05120201 4/30/2003 12/31/2009 19 1.05 0.05 7.30 IN0021377 05120105 6/30/2006 12/31/2009 41 0.93 0.13 4.93 IN0021415 05120204 10/31/1998 12/31/2009 134 0.31 0.00 1.80 IN0021440 05120101 7/31/2000 12/31/2009 111 1.23 0.20 15.00 IN0021474 05120201 8/31/1997 12/31/2009 148 0.24 0.10 3.60 IN0021491 05120103 12/31/1998 12/31/2009 132 1.27 0.05 12.90

72

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0021512 05120201 12/31/2001 12/31/2009 92 2.31 0.10 15.00 IN0021539 05120208 4/30/2000 12/31/2009 229 2.35 0.00 208.89 IN0021580 05120106 7/31/1997 12/31/2009 148 0.70 0.00 3.10 IN0021601 05120208 9/30/1998 12/31/2009 135 1.10 0.01 19.20 IN0021628 05120103 12/31/1997 12/31/2009 144 0.23 0.01 3.87 IN0021652 05120103 11/30/1998 12/31/2009 132 0.23 0.00 4.38 IN0021661 05120106 12/31/1996 12/31/2009 156 0.79 0.11 12.50 IN0021911 05120207 7/31/1999 12/31/2009 123 0.58 0.05 5.30 IN0022012 05120201 3/31/2001 12/31/2009 9 4.17 0.40 10.60 IN0022080 05120201 9/30/2001 10/31/2005 38 0.42 0.10 1.40 IN0022101 05120201 10/31/1999 12/31/2009 119 1.12 0.05 10.93 IN0022136 05120103 9/30/1998 12/31/2009 127 0.15 0.00 1.60 IN0022306 05120201 3/31/2004 12/31/2009 65 4.18 0.25 12.40 IN0022314 05120201 12/31/1998 12/31/2009 131 0.77 0.00 7.50 IN0022373 05120202 12/31/1998 12/31/2009 127 0.93 0.03 6.40 IN0022411 05120101 12/31/2000 12/31/2009 108 0.40 0.01 3.70 IN0022438 05120106 7/31/1999 12/31/2009 26 4.38 0.08 21.50 IN0022454 05120206 8/31/2006 12/31/2009 40 0.90 0.16 3.61 IN0022489 05120208 9/30/1998 12/31/2009 32 5.85 0.10 50.93 IN0022497 05120201 4/30/1998 12/31/2009 140 0.54 0.10 3.50 IN0022586 05120201 5/31/2001 12/31/2009 103 0.18 0.03 0.65 IN0022608 05120108 10/31/2006 12/31/2009 31 9.15 0.20 52.90 IN0022616 05120203 12/31/1998 12/31/2009 168 0.56 0.07 3.10 IN0022624 05120104 6/30/1996 12/31/2009 161 0.81 0.03 7.73 IN0022683 05120207 6/30/1999 12/31/2009 123 0.04 0.00 0.10 IN0022802 05120207 10/31/1998 12/31/2009 102 1.47 0.05 12.00 IN0022934 05120107 8/31/1999 12/31/2009 124 0.40 0.07 1.90 IN0022951 05120208 2/28/1999 12/31/2009 127 0.21 0.06 0.64 IN0022985 05120103 9/30/1998 12/31/2009 134 0.31 0.18 0.99 IN0023124 05120209 7/31/1997 12/31/2009 148 0.27 0.07 1.74 IN0023132 05120101 12/31/1997 12/31/2009 143 0.95 0.09 33.10 IN0023183 05120201 1/31/1995 12/31/2009 201 0.57 0.10 6.40 IN0023353 05120107 10/31/2005 12/31/2009 49 2.69 0.13 14.53 IN0023639 05120202 10/31/1998 12/31/2009 56 3.10 0.05 86.00 IN0023736 05120101 8/31/1998 12/31/2009 136 0.65 0.10 6.60 IN0023744 05120208 1/31/2005 12/31/2009 40 6.03 0.05 166.00 IN0023787 05120208 9/30/1998 12/31/2009 135 0.22 0.10 2.40 IN0023795 05120202 11/30/1998 12/31/2009 132 0.24 0.00 1.73 IN0023825 05120201 11/30/1998 12/31/2009 194 0.45 0.10 6.00 IN0023841 05120204 10/31/2002 12/31/2009 85 4.06 0.02 17.30 IN0023876 05120208 12/31/1997 12/31/2009 142 0.22 0.01 3.09 IN0023981 05120208 9/30/1998 12/31/2009 135 0.33 0.02 4.08 IN0023990 05120108 12/31/1998 12/31/2009 130 0.60 0.02 24.00 IN0024023 05120208 7/31/1997 12/31/2009 144 0.56 0.10 2.30 IN0024112 05120101 9/30/2000 7/31/2008 0 1.00 IN0024210 05120207 4/30/1998 12/31/2009 133 0.53 0.02 2.00 IN0024279 05120103 8/31/2001 12/31/2009 82 2.33 0.01 17.90 IN0024325 05120202 1/31/2000 12/31/2009 131 1.07 0.19 2.80

73

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0024392 05120113 12/31/1997 12/31/2009 144 0.14 0.01 1.00 IN0024406 05120103 8/31/1998 12/31/2009 133 0.61 0.02 4.80 IN0024473 05120206 4/30/2006 12/31/2009 38 0.67 0.10 2.20 IN0024503 05120204 4/30/1998 12/31/2009 66 0.79 0.10 12.50 IN0024554 05120111 10/31/1999 12/31/2009 144 3.68 0.03 11.30 IN0024589 05120110 12/31/1998 12/31/2009 120 5.28 0.10 29.00 IN0024716 05120108 5/31/1997 12/31/2009 144 1.09 0.08 9.76 IN0024741 05120101 8/31/1999 12/31/2009 122 0.50 0.02 7.00 IN0024791 05120102 5/31/1999 12/31/2009 97 0.46 0.01 4.46 IN0024805 05120106 7/31/1997 12/31/2009 149 1.06 0.04 14.90 IN0024821 05120108 6/30/1999 12/31/2009 135 0.37 0.00 4.24 IN0024830 05120206 12/31/1997 12/31/2009 65 4.41 0.04 16.00 IN0024902 05120101 10/31/1999 12/31/2009 122 0.38 0.01 3.20 IN0024937 05120204 8/31/2004 12/31/2009 42 0.15 0.02 0.90 IN0024953 05120208 6/30/2000 12/31/2009 64 2.11 0.05 30.40 IN0024970 05120201 4/30/2001 12/31/2009 102 0.53 0.06 1.16 IN0025135 05120207 3/31/1999 12/31/2009 128 0.81 0.08 3.70 IN0025151 05120201 9/30/2001 12/31/2009 96 2.47 0.01 22.30 IN0025208 05120106 4/30/2006 12/31/2009 42 3.45 0.23 26.00 IN0025224 05120111 12/31/1998 12/31/2009 123 0.42 0.04 2.33 IN0025232 05120106 8/31/1998 12/31/2009 84 3.20 0.00 14.01 IN0025356 05120204 4/30/1998 12/31/2009 81 5.53 0.01 42.20 IN0025372 05120201 6/30/2007 12/31/2009 29 1.82 0.20 6.00 IN0025402 05120201 9/30/2001 9/30/2005 31 10.09 5.00 16.40 IN0025437 05120204 4/30/2005 12/31/2009 55 1.23 0.02 14.90 IN0025585 05120103 4/30/1996 12/31/2009 164 0.27 0.05 1.10 IN0025607 05120111 4/30/1999 12/31/2009 127 1.08 0.10 36.37 IN0025623 05120208 1/31/2009 12/31/2009 11 0.66 0.16 1.65 IN0025658 05120202 8/31/1998 12/31/2009 135 0.54 0.03 7.97 IN0025798 05120113 3/31/2002 12/31/2009 93 0.37 0.00 2.02 IN0029815 05120103 11/30/2000 12/31/2009 109 0.18 0.04 1.30 IN0030015 05120103 11/30/2000 12/31/2009 108 0.48 0.02 3.30 IN0030023 05120201 1/31/2003 12/31/2009 82 0.46 0.10 10.10 IN0030031 05120106 10/31/2000 12/31/2009 110 0.34 0.13 2.30 IN0030040 05120204 2/28/2001 12/31/2009 105 0.28 0.01 1.20 IN0030163 05120208 6/30/1998 12/31/2009 59 3.71 0.01 18.20 IN0030228 05120111 7/31/1997 12/31/2009 143 1.28 0.00 14.90 IN0030236 05120208 12/31/1997 12/31/2009 143 0.54 0.10 4.70 IN0030279 05120203 5/31/2001 12/31/2009 58 0.15 0.02 0.68 IN0030325 05120208 12/31/2000 12/31/2009 102 2.60 0.05 47.00 IN0030350 05120208 7/31/2000 9/30/2004 21 0.40 0.04 1.06 IN0030406 05120113 1/31/2008 12/31/2009 9 0.51 0.00 2.50 IN0030562 05120105 12/31/1998 12/31/2009 103 5.07 0.54 16.00 IN0030571 05120106 8/31/2004 12/31/2009 61 0.28 0.11 3.20 IN0030627 05120104 3/31/2002 2/28/2007 59 0.15 0.02 0.96 IN0030635 05120101 7/31/2005 12/31/2009 53 0.16 0.02 0.86 IN0030643 05120103 12/31/2001 12/31/2009 79 1.29 0.17 8.70 IN0030678 05120111 9/30/2000 3/31/2005 31 2.88 0.19 8.70

74

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0030724 05120203 12/31/1997 12/31/2009 111 0.53 0.02 2.30 IN0030783 05120203 7/31/2000 12/31/2009 95 0.58 0.06 3.50 IN0030830 05120201 11/30/2000 8/31/2004 37 1.79 0.20 9.10 IN0030881 05120106 5/31/2001 12/31/2009 100 1.41 0.02 11.95 IN0030902 05120201 10/31/2000 12/31/2009 108 4.51 0.04 25.80 IN0030911 05120106 6/30/1999 12/31/2009 81 0.71 0.06 5.50 IN0031020 05120113 11/30/2004 12/31/2009 61 1.21 0.20 5.39 IN0031071 05120201 6/30/1998 12/31/2009 137 0.27 0.00 1.40 IN0031135 05120201 3/31/2002 12/31/2009 92 0.39 0.04 2.20 IN0031208 05120104 7/31/2003 12/31/2009 76 0.47 0.01 3.90 IN0031356 05120201 3/31/2001 12/31/2009 80 8.55 0.32 49.00 IN0031364 05120101 1/31/1999 12/31/2009 130 3.53 0.07 35.50 IN0031372 05120103 3/31/1998 12/31/2009 99 1.54 0.10 12.90 IN0031399 05120204 3/31/2001 12/31/2009 103 1.27 0.03 5.62 IN0031411 05120101 8/31/1997 12/31/2009 8 1.24 0.86 1.48 IN0031445 05120104 5/31/2000 8/31/2007 49 0.41 0.08 1.03 IN0031453 05120101 1/31/1999 12/31/2009 122 2.83 0.08 68.70 IN0031470 05120202 5/31/2000 12/31/2009 106 1.94 0.00 35.80 IN0031518 05120203 7/31/2000 11/30/2006 39 13.36 0.01 173.00 IN0031551 05120205 7/31/2000 12/31/2009 104 0.31 0.01 1.78 IN0031569 05120201 9/30/2000 12/31/2009 89 2.72 0.10 16.00 IN0031577 05120208 5/31/2000 12/31/2009 103 1.30 0.10 14.50 IN0031593 05120204 3/31/2006 12/31/2009 44 1.87 0.04 12.80 IN0031640 05120201 1/31/2001 12/31/2009 105 2.88 0.06 45.30 IN0031704 05120209 2/28/2001 12/31/2009 104 0.19 0.02 1.68 IN0031712 05120201 7/31/2001 12/31/2009 100 1.03 0.10 7.58 IN0031721 05120102 6/30/2000 5/31/2009 97 0.55 0.03 11.70 IN0031739 05120101 6/30/2000 12/31/2009 98 0.68 0.02 7.80 IN0031747 05120203 5/31/2000 12/31/2009 113 0.35 0.01 1.32 IN0031763 05120101 2/28/2002 12/31/2009 94 0.66 0.00 6.50 IN0031798 05120104 4/30/2002 12/31/2009 91 0.35 0.03 1.60 IN0031801 05120107 8/31/2003 12/31/2009 74 1.04 0.02 8.10 IN0031844 05120107 6/30/2002 12/31/2009 89 1.39 0.10 10.10 IN0031879 05120204 7/31/2005 12/31/2009 52 12.46 0.13 118.00 IN0031909 05120111 9/30/2003 12/31/2009 70 2.06 0.20 12.60 IN0031925 05120201 1/31/2003 12/31/2009 80 4.25 0.02 39.00 IN0031933 05120201 9/30/2003 5/31/2005 11 3.90 0.98 9.10 IN0031950 05120201 1/31/1995 2/29/2008 155 0.50 0.10 5.00 IN0031976 05120107 12/31/2006 12/31/2009 36 0.51 0.00 3.10 IN0032140 05120206 10/31/2000 12/31/2009 110 1.11 0.01 14.30 IN0032328 05120101 2/29/2000 12/31/2009 117 0.90 0.10 5.00 IN0032468 05120108 5/31/2003 12/31/2009 79 1.15 0.07 6.50 IN0032476 05120201 1/31/1995 12/31/2009 179 0.17 0.01 7.10 IN0032573 05120205 1/31/1999 12/31/2009 130 2.33 0.70 5.60 IN0032719 05120201 10/31/2004 12/31/2009 46 0.50 0.10 4.10 IN0032867 05120204 11/30/1999 12/31/2009 121 0.63 0.03 4.87 IN0032875 05120107 9/30/2000 12/31/2009 110 0.17 0.04 1.40 IN0034428 05120110 12/31/2000 12/31/2009 105 27.61 0.00 2500.00

75

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0034444 05120101 8/31/2000 12/31/2009 103 4.33 0.01 61.80 IN0034461 05120105 10/31/2003 12/31/2009 73 1.04 0.03 7.10 IN0034932 05120202 4/30/1999 12/31/2009 74 2.13 0.07 14.50 IN0035173 05120203 2/29/2000 12/31/2009 117 0.30 0.06 3.89 IN0035378 05120101 12/31/1995 12/31/2009 166 0.78 0.20 3.10 IN0035718 05120208 1/31/1995 12/31/2009 179 0.37 0.05 3.60 IN0035726 05120202 12/31/1998 12/31/2009 132 0.27 0.09 1.30 IN0036447 05120108 10/31/2001 12/31/2009 98 7.69 1.60 111.00 IN0036587 05120201 9/30/2000 12/31/2009 58 0.63 0.05 18.20 IN0036790 05120202 7/31/2003 12/31/2009 55 5.45 0.02 29.90 IN0036820 05120201 6/30/1998 12/31/2009 36 6.89 0.03 87.00 IN0036854 05120208 2/28/2001 12/31/2009 105 0.25 0.01 1.78 IN0036935 05120107 5/31/2000 12/31/2009 112 0.91 0.04 4.90 IN0036943 05120106 9/30/2000 12/31/2009 109 0.14 0.03 2.55 IN0036951 05120201 1/31/2004 12/31/2009 71 0.25 0.10 0.88 IN0036978 05120103 5/31/1999 12/31/2009 126 0.36 0.03 11.20 IN0037001 05120101 6/30/1998 12/31/2009 137 0.66 0.06 4.10 IN0037044 05120106 11/30/2003 12/31/2009 72 2.93 0.07 38.90 IN0037184 05120201 4/30/2003 10/31/2003 4 11.11 1.10 25.00 IN0037214 05120107 4/30/2006 12/31/2009 44 0.79 0.16 4.00 IN0037249 05120113 5/31/2001 12/31/2009 90 0.42 0.00 13.50 IN0037281 05120208 4/30/2007 12/31/2009 13 0.41 0.03 1.44 IN0037389 05120204 4/30/1999 12/31/2009 124 1.40 0.02 20.80 IN0037401 05120203 6/30/2005 12/31/2009 50 0.51 0.10 2.00 IN0037427 05120101 6/30/2006 12/31/2009 23 1.42 0.13 5.20 IN0037559 05120206 12/31/2000 10/31/2003 33 0.74 0.30 1.08 IN0037567 05120206 11/30/2000 12/31/2009 60 1.28 0.15 8.70 IN0037583 05120102 4/30/2003 12/31/2009 78 0.42 0.01 7.00 IN0037729 05120104 8/31/2004 12/31/2009 62 1.14 0.15 5.70 IN0038016 05120103 8/31/1998 12/31/2009 128 1.26 0.00 10.10 IN0038288 05120113 12/31/1998 12/31/2009 130 0.27 0.01 3.00 IN0038296 05120202 7/31/2000 12/31/2009 104 0.75 0.00 12.78 IN0038326 05120208 10/31/2000 12/31/2009 94 0.50 0.02 2.71 IN0038334 05120108 1/31/2000 12/31/2009 117 0.61 0.33 1.13 IN0038407 05120201 7/31/2000 1/31/2004 40 12.96 3.70 29.00 IN0038415 05120202 9/30/2002 12/31/2009 87 1.33 0.09 12.20 IN0038466 05120202 3/31/2003 12/31/2009 57 0.41 0.00 3.10 IN0038539 05120207 6/30/1998 12/31/2009 138 0.55 0.00 7.10 IN0038598 05120201 4/30/2000 12/31/2009 115 1.14 0.10 12.20 IN0038768 05120107 12/31/2006 12/31/2009 34 3.14 0.47 7.42 IN0038784 05120107 2/28/2001 12/31/2009 101 2.60 0.02 15.30 IN0038857 05120201 9/30/2001 12/31/2009 97 1.43 0.08 10.75 IN0038865 05120207 7/31/2003 9/30/2009 53 23.64 1.96 71.43 IN0038873 05120204 4/30/1999 12/31/2009 126 0.73 0.07 8.05 IN0038881 05120201 9/30/2004 1/31/2008 36 0.97 0.23 3.03 IN0038920 05120208 12/31/1999 12/31/2009 119 0.12 0.00 4.90 IN0038938 05120206 8/31/2003 12/31/2009 69 0.57 0.10 1.70 IN0038962 05120103 1/31/1999 12/31/2009 129 1.50 0.09 11.10

76

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0038971 05120108 12/31/1998 12/31/2009 130 0.67 0.18 8.60 IN0039110 05120202 7/31/2000 12/31/2009 53 0.22 0.04 1.59 IN0039241 05120208 6/30/2004 12/31/2009 66 0.21 0.03 0.90 IN0039276 05120202 2/28/1998 12/31/2009 133 0.97 0.05 5.78 IN0039292 05120203 5/31/1999 12/31/2009 47 4.43 0.12 15.30 IN0039322 05120111 6/30/1998 12/31/2009 134 2.00 0.02 30.10 IN0039349 05120206 6/30/1999 12/31/2009 125 0.31 0.00 4.72 IN0039357 05120101 10/31/1998 12/31/2009 128 0.87 0.01 6.48 IN0039471 05120201 5/31/2000 12/31/2009 85 3.32 0.06 24.50 IN0039497 05120107 6/30/2002 12/31/2009 88 0.50 0.02 4.90 IN0039632 05120205 2/29/2000 12/31/2009 118 0.72 0.00 12.02 IN0039705 05120108 9/30/1996 12/31/2009 158 0.60 0.02 20.10 IN0039756 05120108 11/30/1998 12/31/2009 131 0.32 0.10 0.60 IN0039772 05120201 5/31/2002 12/31/2009 84 2.78 0.12 27.52 IN0039799 05120107 12/31/1998 12/31/2009 124 6.31 0.04 332.00 IN0039829 05120111 11/30/1998 12/31/2009 85 11.45 0.18 25.00 IN0039837 05120111 1/31/1999 12/31/2009 128 0.19 0.10 0.20 IN0039861 05120203 4/30/2003 12/31/2009 79 0.97 0.49 8.21 IN0039870 05120106 2/28/1999 12/31/2009 53 7.91 0.14 22.30 IN0039934 05120104 8/31/2008 12/31/2009 16 0.38 0.12 0.90 IN0039985 05120202 7/31/1998 12/31/2009 137 0.43 0.03 1.50 IN0040002 05120106 4/30/2006 12/31/2009 44 0.54 0.05 5.13 IN0040088 05120201 7/31/2005 12/31/2009 53 0.51 0.10 4.00 IN0040134 05120111 3/31/1999 12/31/2009 127 0.96 0.00 24.87 IN0040151 05120204 6/30/1999 12/31/2009 124 4.89 0.10 23.60 IN0040177 05120204 7/31/2008 12/31/2009 17 0.54 0.01 1.80 IN0040321 05120103 6/30/1998 12/31/2009 19 2.44 0.10 9.84 IN0040347 05120106 1/31/1999 12/31/2009 126 6.24 0.06 45.00 IN0040355 05120107 9/30/1998 12/31/2009 61 5.47 0.00 23.40 IN0040398 05120205 10/31/1998 12/31/2009 132 0.63 0.00 2.09 IN0040436 05120203 10/31/1998 12/31/2009 132 0.25 0.06 1.00 IN0040479 05120201 3/31/1998 12/31/2009 141 0.99 0.05 3.40 IN0040495 05120102 12/31/1998 12/31/2009 130 1.10 0.10 3.90 IN0040517 05120113 1/31/2006 12/31/2009 45 0.50 0.00 11.00 IN0040533 05120104 7/31/2006 12/31/2009 35 1.23 0.10 7.20 IN0040631 05120208 9/30/1998 12/31/2009 49 6.98 1.00 13.00 IN0040649 05120104 11/30/2001 12/31/2009 19 5.38 0.55 15.50 IN0040681 05120201 7/31/1999 12/31/2009 124 2.94 0.27 21.30 IN0040762 05120107 9/30/1998 12/31/2009 132 0.43 0.04 9.10 IN0040789 05120209 6/30/2002 12/31/2009 84 5.66 0.00 20.30 IN0040797 05120106 10/31/2000 12/31/2009 108 0.42 0.02 2.00 IN0040801 05120202 9/30/2002 12/31/2009 166 12.50 0.03 39.80 IN0040835 05120111 9/30/2001 12/31/2009 84 0.68 0.14 4.00 IN0040941 05120203 4/30/2003 12/31/2009 79 5.31 0.10 17.50 IN0041009 05120208 9/30/2001 12/31/2009 70 1.28 0.02 27.80 IN0041084 05120111 7/31/1999 12/31/2009 122 0.51 0.03 3.30 IN0041092 05120111 9/30/1999 12/31/2009 122 0.59 0.04 3.20 IN0041131 05120107 9/30/2003 8/31/2007 27 0.37 0.10 3.28

77

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0041157 05120110 9/30/2000 12/31/2009 101 0.90 0.01 15.80 IN0041173 05120103 7/31/2005 12/31/2009 53 8.82 0.08 39.50 IN0041181 05120204 7/31/2003 12/31/2009 77 1.51 0.03 10.40 IN0041637 05120102 10/31/2000 12/31/2009 106 1.12 0.06 10.80 IN0041726 05120106 12/31/2006 12/31/2009 31 3.54 0.26 15.30 IN0041734 05120113 5/31/2001 12/31/2009 103 1.17 0.01 18.80 IN0041742 05120106 4/30/1998 12/31/2009 123 1.60 0.01 33.00 IN0041777 05120204 6/30/1999 12/31/2009 56 0.41 0.01 4.30 IN0041866 05120107 5/31/2000 12/31/2009 105 0.78 0.10 3.40 IN0041912 05120107 7/31/1998 12/31/2009 135 3.29 0.30 18.87 IN0041971 05120201 11/30/2004 11/30/2006 21 0.30 0.09 1.50 IN0042013 05120207 8/31/1999 6/30/2008 15 2.08 0.09 8.50 IN0042358 05120204 6/30/1997 12/31/2009 149 0.36 0.01 2.40 IN0042366 05120204 8/31/2000 12/31/2009 112 3.17 0.20 13.90 IN0042391 05120101 11/30/1995 12/31/2009 168 0.51 0.06 7.40 IN0042617 05120208 11/30/2000 12/31/2009 68 2.93 0.03 20.40 IN0042650 05120202 3/31/2001 12/31/2009 103 0.73 0.10 2.30 IN0043095 05120206 3/31/2003 12/31/2009 79 1.31 0.01 22.85 IN0043273 05120108 4/30/2001 12/31/2009 103 0.64 0.49 1.18 IN0043281 05120201 8/31/1999 4/30/2006 70 0.91 0.14 7.00 IN0043559 05120201 12/31/2000 12/31/2009 106 1.56 0.20 13.00 IN0043681 05120206 11/30/2003 12/31/2009 69 0.46 0.01 3.82 IN0043877 05120203 9/30/1997 12/31/2009 146 0.21 0.00 0.80 IN0043893 05120104 10/31/2008 12/31/2009 11 1.71 0.10 6.00 IN0043966 05120204 12/31/2001 12/31/2009 45 2.25 0.10 14.60 IN0043974 05120201 3/31/2001 4/30/2004 29 1.41 0.02 14.50 IN0044211 05120208 9/30/1998 12/31/2009 36 2.18 0.00 9.20 IN0044245 05120107 4/30/2000 12/31/2009 115 0.63 0.10 1.24 IN0044423 05120103 8/31/2006 12/31/2009 30 3.33 0.26 8.70 IN0044652 05120107 6/30/2005 12/31/2009 53 9.64 0.38 41.75 IN0044661 05120204 1/31/2008 12/31/2009 22 0.41 0.14 0.96 IN0045063 05120204 6/30/2004 10/31/2007 36 0.40 0.01 1.70 IN0045187 05120208 8/31/2007 12/31/2009 28 0.67 0.00 7.10 IN0045357 05120101 11/30/2000 12/31/2009 109 2.33 0.02 18.83 IN0045446 05120201 5/31/2001 2/28/2007 43 6.63 0.22 31.20 IN0045527 05120203 8/31/1999 12/31/2009 122 0.17 0.05 2.30 IN0045837 05120113 3/31/2004 12/31/2009 68 1.10 0.00 21.30 IN0045845 05120113 4/30/1999 12/31/2009 123 0.43 0.14 2.10 IN0046361 05120209 9/30/1998 12/31/2009 8 3.63 0.01 16.00 IN0046396 05120110 9/30/2000 12/31/2009 111 0.80 0.02 1.60 IN0047074 05120203 1/31/2001 12/31/2009 105 0.40 0.01 4.17 IN0047465 05120209 11/30/2001 5/31/2003 7 10.30 5.40 14.80 IN0047490 05120204 1/31/2004 12/31/2009 70 2.14 0.10 23.50 IN0048011 05120204 4/30/2006 12/31/2009 28 23.77 0.70 67.20 IN0048267 05120103 5/31/2006 12/31/2009 33 1.93 0.01 7.20 IN0048453 05120208 11/30/2002 12/31/2009 7 3.34 1.17 6.70 IN0048763 05120207 3/31/1998 12/31/2009 56 10.18 1.45 20.70 IN0049026 05120201 8/31/1996 12/31/2009 159 0.19 0.03 3.88

78

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0049361 05120201 6/30/2008 12/31/2009 18 4.67 1.27 13.33 IN0049581 05120201 7/31/1998 12/31/2009 136 1.31 0.10 13.50 IN0049689 05120204 2/28/1997 12/31/2009 146 0.71 0.01 14.30 IN0049794 05120201 2/28/1997 12/31/2009 151 1.69 0.08 19.40 IN0049832 05120103 7/31/2006 12/31/2009 41 0.86 0.07 5.50 IN0049883 05120202 8/31/1998 12/31/2009 117 7.02 0.04 72.10 IN0049891 05120202 8/31/2001 6/30/2003 18 1.90 0.04 5.27 IN0050105 05120208 7/31/2003 12/31/2009 75 6.62 0.02 35.20 IN0050148 05120204 5/31/2001 12/31/2009 99 1.77 0.11 14.90 IN0050211 05120101 12/31/2005 12/31/2009 47 3.67 0.10 20.20 IN0050253 05120108 8/31/1997 12/31/2009 143 0.30 0.01 9.97 IN0050296 05120111 11/30/1999 12/31/2009 118 0.26 0.10 0.80 IN0050326 05120106 9/30/2000 12/31/2009 110 0.19 0.00 2.78 IN0050652 05120106 11/30/2007 12/31/2009 14 0.60 0.00 2.18 IN0050695 05120203 11/30/2002 12/31/2009 84 0.84 0.03 12.22 IN0050971 05120101 10/31/1999 12/31/2009 116 0.46 0.01 2.50 IN0051055 05120207 8/31/2001 12/31/2009 7 5.49 0.45 16.00 IN0051098 05120101 6/30/2008 12/31/2009 17 15.20 9.00 25.00 IN0051187 05120101 9/30/2005 12/31/2009 51 0.30 0.02 2.20 IN0051331 05120207 12/31/2000 12/31/2009 90 0.75 0.01 20.06 IN0051683 05120206 11/30/2002 12/31/2009 82 1.10 0.02 8.24 IN0051691 05120204 6/30/2001 12/31/2009 100 1.61 0.03 6.80 IN0051861 05120101 7/31/2002 12/31/2009 88 0.28 0.01 2.78 IN0052078 05120106 5/31/2000 8/31/2005 23 0.75 0.40 1.10 IN0052230 05120207 5/31/2005 12/31/2009 10 14.60 0.01 59.20 IN0052256 05120201 12/31/2000 12/31/2009 107 0.35 0.01 3.83 IN0052370 05120105 12/31/1999 12/31/2009 120 0.49 0.10 4.80 IN0052698 05120209 11/30/1998 12/31/2009 133 4.86 0.10 35.90 IN0052949 05120208 7/31/1999 12/31/2009 125 0.88 0.10 3.30 IN0053091 05120206 6/30/2001 12/31/2009 100 3.30 0.12 30.43 IN0053147 05120101 9/30/2001 12/31/2009 97 0.59 0.01 5.90 IN0053163 05120209 8/31/2002 12/31/2009 88 0.57 0.08 5.11 IN0053546 05120205 11/30/2003 12/31/2009 70 3.15 0.22 28.60 IN0053627 05120201 9/30/2000 7/31/2007 82 1.12 0.20 7.90 IN0053741 05120208 4/30/2002 12/31/2009 1 0.31 0.31 0.31 IN0053783 05120104 1/31/2008 12/31/2009 15 5.13 0.14 11.40 IN0053937 05120206 8/31/1999 12/31/2009 32 0.39 0.03 5.00 IN0054127 05120101 7/31/2003 12/31/2009 62 0.85 0.05 4.97 IN0054445 05120106 6/30/2001 12/31/2009 71 0.65 0.09 1.27 IN0054704 05120106 9/30/1997 12/31/2009 146 1.64 0.04 26.25 IN0054771 05120201 3/31/2001 12/31/2008 76 0.38 0.08 2.00 IN0055085 05120111 8/31/2006 12/31/2009 34 0.59 0.04 3.70 IN0055131 05120205 9/30/2001 12/31/2009 41 0.28 0.05 1.24 IN0055158 05120101 7/31/1998 12/31/2009 46 1.27 0.00 10.09 IN0055166 05120104 5/31/1999 12/31/2009 78 1.43 0.02 7.68 IN0055271 05120103 2/28/1997 12/31/2009 153 0.91 0.07 9.80 IN0055280 05120201 10/31/1998 12/31/2009 133 0.38 0.05 1.85 IN0055484 05120201 4/30/1999 12/31/2009 127 0.74 0.00 8.40

79

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0055697 05120107 8/31/1996 12/31/2009 159 0.33 0.02 1.58 IN0055760 05120201 4/30/1997 12/31/2009 152 0.33 0.01 5.00 IN0055824 05120208 7/31/1996 12/31/2009 180 0.60 0.00 14.29 IN0055832 05120207 2/28/1998 12/31/2009 142 0.81 0.01 1.90 IN0055891 05120206 8/31/1999 12/31/2009 121 0.68 0.02 42.00 IN0055921 05120107 2/28/2001 12/31/2009 104 1.18 0.00 21.30 IN0056049 05120207 6/30/2001 12/31/2009 102 2.21 0.12 17.50 IN0056154 05120111 8/31/2004 12/31/2009 64 1.05 0.14 13.40 IN0056375 05120201 4/30/2003 9/30/2005 11 4.97 0.56 13.80 IN0056383 05120204 11/30/2004 12/31/2009 60 0.54 0.03 1.40 IN0056456 05120106 2/28/2001 12/31/2009 95 2.17 0.09 23.90 IN0057321 05120208 10/31/1997 12/31/2009 141 0.51 0.00 10.90 IN0057347 05120110 1/31/2009 12/31/2009 4 4.97 0.20 15.13 IN0057363 05120206 12/31/1998 12/31/2009 57 2.89 0.01 18.10 IN0057487 05120201 5/31/2002 12/31/2009 91 1.00 0.10 5.47 IN0057495 05120201 9/30/1998 11/30/2003 59 2.40 0.10 28.80 IN0057614 05120201 9/30/2004 12/31/2009 62 0.24 0.03 1.79 IN0057720 05120201 1/31/2000 12/31/2009 89 0.20 0.00 1.94 IN0058009 05120204 12/31/1999 12/31/2009 120 0.67 0.10 3.50 IN0058173 05120107 7/31/2000 12/31/2009 109 1.92 0.10 12.20 IN0058327 05120106 10/31/2000 12/31/2009 59 3.10 0.05 13.90 IN0058416 05120202 11/30/2000 12/31/2009 107 0.50 0.07 6.70 IN0058963 05120102 3/31/2002 12/31/2009 82 2.95 0.04 11.88 IN0059048 05120101 12/31/1996 12/31/2009 34 23.23 5.90 52.75 IN0059072 05120201 11/30/1996 12/31/2009 136 0.40 0.03 5.90 IN0059218 05120101 11/30/2004 12/31/2009 55 1.55 0.10 15.40 IN0059544 05120201 7/31/1997 12/31/2009 135 0.34 0.04 2.03 IN0059625 05120206 9/30/1997 12/31/2009 143 2.38 0.01 30.09 IN0059757 05120101 7/31/1998 7/31/2007 107 0.81 0.10 11.40 IN0059765 05120203 1/31/1998 12/31/2009 77 1.69 0.04 13.80 IN0059871 05120202 1/31/1998 12/31/2009 141 2.34 0.03 22.40 IN0059889 05120103 3/31/1998 12/31/2009 112 3.98 0.25 27.80 IN0059943 05120201 3/31/1998 12/31/2009 137 0.80 0.01 8.20 IN0059986 05120203 4/30/1998 12/31/2009 35 0.76 0.05 5.27 IN0060101 05120106 7/31/1998 12/31/2009 19 3.16 0.10 11.00 IN0060437 05120102 6/30/1999 12/31/2009 19 2.28 0.12 11.54 IN0060526 05120208 12/31/1999 12/31/2009 111 1.01 0.03 15.10 IN0060577 05120202 4/30/2000 12/31/2009 108 0.13 0.00 3.80 IN0060640 05120201 7/31/2000 12/31/2009 95 0.10 0.00 1.10 IN0060674 05120206 8/31/2000 12/31/2009 105 0.94 0.00 8.80 IN0060887 05120106 10/31/2000 12/31/2009 101 2.78 0.05 26.14 IN0061301 05120201 7/31/2001 12/31/2009 97 1.03 0.00 10.00 IN0109479 05120204 7/31/2000 12/31/2009 156 1.00 0.01 6.23 IN0109592 05120111 10/31/2002 12/31/2009 84 8.44 0.70 22.50 IN0109606 05120203 4/30/2000 12/31/2009 47 0.55 0.03 2.92 IN0109703 05120207 11/30/2001 12/31/2009 64 25.74 0.05 200.00 IN0109746 05120205 12/31/1998 12/31/2009 52 2.01 0.04 12.10 IN0109762 05120201 4/30/2000 12/31/2009 114 0.83 0.01 9.86

80

8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0109797 05120204 6/30/2006 6/30/2009 26 7.01 0.28 14.40 IN0109967 05120201 8/31/2002 12/31/2009 17 1.95 0.16 7.03 INP000200 05120202 9/30/1998 1/31/2003 48 4701.31 38.70 9815.00

81

Table A-4. Available Total Nitrogen data for each NPDES facility 8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0001775 05120208 5/31/2000 12/31/2009 3 1.63 0.50 2.10 IN0001830 05120107 6/30/2002 6/30/2007 3 0.51 0.40 0.61 IN0003573 05120208 4/30/2004 1/31/2009 7 1.83 0.50 3.00 IN0036587 05120201 12/31/2007 12/31/2009 4 0.34 0.07 0.68 IN0050296 05120111 3/31/1999 12/31/2009 6 1.96 1.00 5.30 IN0059471 05120105 1/31/2003 9/30/2008 24 2.93 1.10 6.30 IN0060551 05120201 9/30/2001 8/31/2006 2 1.42 0.94 1.90 IN0109541 05120204 12/31/2003 3/31/2007 1 2.32 2.32 2.32 INP000176 05120202 12/31/1997 12/31/2009 142 43.56 21.00 55.70

82

Table A-5 Available phosphorus data for each NPDES facility (mg/l) 8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IL0026107 05120112 9/30/2001 8/31/2009 192 1.04 0.14 6.00 IL0030121 05120112 3/31/2007 12/31/2009 64 7.93 2.10 24.00 IL0059005 05120112 9/30/2002 12/31/2009 288 0.89 0.26 2.00 IN0001775 05120208 5/31/2000 12/31/2009 3 0.14 0.02 0.32 IN0001830 05120107 6/30/2002 6/30/2007 3 0.12 0.10 0.17 IN0003573 05120208 4/30/2004 12/31/2007 5 0.07 0.03 0.10 IN0020389 05120111 9/30/2004 12/31/2009 61 2.08 0.70 15.80 IN0020541 05120106 8/31/2002 2/28/2007 51 0.98 0.30 2.00 IN0020648 05120209 6/30/2009 12/31/2009 6 3.22 1.90 4.40 IN0020770 05120201 3/31/1998 12/31/2009 140 0.62 0.15 2.37 IN0020796 05120201 10/31/2002 12/31/2009 82 0.53 0.01 2.40 IN0020958 05120201 9/30/1999 12/31/2009 122 0.20 0.01 0.60 IN0021091 05120107 6/30/1996 12/31/2009 164 0.44 0.10 7.00 IN0021105 05120103 9/30/1996 12/31/2009 157 0.58 0.20 1.10 IN0021113 05120104 7/31/2008 12/31/2009 17 3.55 1.80 4.60 IN0021202 05120201 10/31/1996 4/30/2003 79 2.41 0.60 9.40 IN0021334 05120201 11/30/1996 12/31/2009 157 0.38 0.00 1.50 IN0021474 05120201 8/31/1997 12/31/2009 148 0.40 0.05 1.69 IN0021580 05120106 7/31/1997 12/31/2009 148 0.46 0.01 1.00 IN0021652 05120103 11/30/1998 12/31/2009 34 1.21 0.00 2.30 IN0022373 05120202 11/30/2004 12/31/2009 61 0.89 0.34 2.40 IN0022411 05120101 12/31/2000 12/31/2009 108 0.40 0.17 0.70 IN0022454 05120206 9/30/1998 12/31/2009 135 0.31 0.05 1.01 IN0022586 05120201 5/31/2001 12/31/2009 103 0.47 0.20 0.80 IN0022616 05120203 12/31/1998 12/31/2009 168 0.50 0.10 1.00 IN0022985 05120103 9/30/1998 12/31/2009 134 1.19 0.03 91.60 IN0023124 05120209 4/30/2009 12/31/2009 7 2.09 1.12 5.40 IN0023132 05120101 5/31/2008 12/31/2009 16 5.56 1.87 7.60 IN0023736 05120101 1/31/1999 12/31/2009 129 0.76 0.16 1.20 IN0023787 05120208 9/30/1998 11/30/2009 45 0.96 0.09 1.90 IN0023876 05120208 12/31/1997 12/31/2009 141 0.49 0.20 2.60 IN0024279 05120103 8/31/2001 12/31/2009 79 3.22 0.20 17.10 IN0024473 05120206 5/31/2006 11/30/2009 24 0.96 0.40 2.40 IN0024791 05120102 6/30/2005 12/31/2009 54 0.64 0.05 1.00 IN0024953 05120208 6/30/2000 12/31/2009 63 0.80 0.10 2.00 IN0025232 05120106 11/30/2005 12/31/2009 20 0.99 0.03 4.17 IN0025585 05120103 4/30/1996 12/31/2009 164 0.64 0.31 1.10 IN0025623 05120208 5/31/1999 10/31/2009 66 0.69 0.20 1.07 IN0029815 05120103 11/30/2000 12/31/2009 109 0.50 0.14 1.10 IN0030163 05120208 6/30/1998 12/31/2009 59 0.21 0.05 0.52 IN0030279 05120203 5/31/2001 12/31/2009 58 0.54 0.02 1.00 IN0030350 05120208 7/31/2000 9/30/2004 21 10.20 4.80 13.70 IN0030724 05120203 12/31/1997 12/31/2009 109 0.73 0.02 4.00 IN0030881 05120106 5/31/2001 12/31/2009 103 1.49 0.20 12.40 IN0031071 05120201 6/30/1998 12/31/2009 134 0.41 0.06 1.62 IN0031976 05120107 9/30/2008 12/31/2009 15 6.37 4.70 10.80 IN0032468 05120108 8/31/2008 12/31/2009 16 3.83 1.85 4.88 IN0032573 05120205 5/31/1999 10/31/2009 65 2.10 0.53 4.66

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8 Digit Average Minimum Maximum NPDES ID HUC Start Date End Date Count (mg/L) (mg/L) (mg/L) IN0035718 05120208 5/31/1995 10/31/2009 90 0.68 0.07 1.60 IN0036587 05120201 12/31/2007 12/31/2009 4 0.57 0.10 1.10 IN0036943 05120106 9/30/2000 12/31/2009 107 0.42 0.01 3.30 IN0036951 05120201 1/31/2004 12/31/2009 71 0.61 0.29 0.89 IN0036978 05120103 5/31/1999 12/31/2009 125 0.46 0.02 1.32 IN0038326 05120208 10/31/2000 12/31/2009 95 0.87 0.22 1.07 IN0038539 05120207 6/30/1998 12/31/2009 138 0.45 0.10 2.80 IN0039110 05120202 7/31/2000 12/31/2009 53 0.65 0.10 1.41 IN0039357 05120101 8/31/2003 12/31/2009 66 2.39 0.03 9.70 IN0040797 05120106 10/31/2000 8/31/2005 53 2.32 0.87 4.60 IN0041637 05120102 10/31/2000 12/31/2009 106 0.85 0.03 6.80 IN0041866 05120107 5/31/2000 12/31/2009 104 1.10 0.06 6.20 IN0041912 05120107 10/31/2004 12/31/2009 40 2.06 1.20 6.20 IN0043559 05120201 12/31/2000 12/31/2009 104 1.07 0.20 7.00 IN0049026 05120201 8/31/1996 12/31/2009 159 0.67 0.24 2.40 IN0050296 05120111 3/31/1999 12/31/2009 6 0.20 0.10 0.27 IN0050326 05120106 9/30/2000 12/31/2009 109 0.61 0.03 2.50 IN0052078 05120106 2/29/2000 8/31/2005 23 0.57 0.38 0.83 IN0054445 05120106 6/30/2001 12/31/2009 71 0.67 0.05 2.10 IN0054771 05120201 3/31/2001 12/31/2008 76 0.51 0.20 1.10 IN0055158 05120101 12/31/2008 12/31/2009 1 3.20 3.20 3.20 IN0055280 05120201 10/31/1998 9/30/2008 118 0.76 0.01 25.00 IN0055760 05120201 4/30/1997 12/31/2009 152 0.50 0.10 3.90 IN0056375 05120201 4/30/2003 9/30/2005 11 7.03 0.50 15.30 IN0056456 05120106 2/28/2001 12/31/2009 95 1.47 0.01 7.33 IN0059471 05120105 1/31/2003 9/30/2008 24 0.46 0.14 0.93 IN0059544 05120201 7/31/1997 12/31/2009 135 0.42 0.10 2.50 IN0059871 05120202 1/31/1998 12/31/2009 140 1.48 0.20 27.90 IN0060551 05120201 9/30/2001 8/31/2006 2 0.26 0.22 0.30 IN0060640 05120201 8/31/2007 12/31/2009 27 1.06 0.01 4.20 IN0060887 05120106 10/31/2000 12/31/2009 101 0.84 0.11 7.12 IN0109541 05120204 12/31/2003 3/31/2007 1 0.30 0.30 0.30 INP000113 05120106 5/31/1997 12/31/2009 150 0.24 0.05 10.00

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Appendix D: Compilation of Nonpoint Source Analysis Technical Memos

Kieser & Associates MEMORANDUM Environmental Science and Engineering

To: Christa Jones, CTIC Date: 04/01/2010 Kevin Kratt, Tetra Tech Kellie Dubay, Tetra Tech Elizebeth Hansen, Tetra Tech

From: Jim Klang cc: Project File Laurence Picq

Re: Crop Rotations modeled in SWAT for Tippecanoe and Driftwood Watersheds

This memorandum presents the crop rotations suggested for the SWAT modeling of the Tippecanoe and Driftwood Watersheds. These rotations have been compiled based on information provided by Christa Jones, CTIC, as well as data available from Indiana State Department of Agriculture (ISDA) and USDA Agricultural Census websites. In particular, the 2007 Conservation Tillage Data by county1 (ISDA 2007) were used to estimate tillage practices within both watersheds. Data from the 2007 Agricultural Census2 were used to estimate the percentage of rotations with corn silage and manure applications. TIPPECANOE WATERSHED

Table 1: Percentage of tillage practices per county (ISDA 2007)

County (rank) No-Till Mulch Till Reduced Till Conventional Till Corn Data Pulaski (19) 21 48 23 9 Kosciuko (24) 26 18 27 29 Fulton (33) 21 20 39 21 White (67) 5 19 23 54 Average corn 18.25 26.25 28 28.25 Soybean Data White (26) 52 23 13 11 Kosciusko (29) 66 16 10 8 Pulaski (35) 68 25 7 1 Fulton (38) 67 24 8 1 Average soybean 63.25 22 9.5 5.25

1 Available at: http://www.in.gov/isda/2354.htm 2 Available at: http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/County_Profiles/Indiana/

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Table 2: Acreage of main crops per county (USDA 2007).

County Corn for grain Corn for silage Popcorn Wheat for grain Soybean for (acres) (acres) (acres) (acres) bean (% of total corn) (% of total corn) (% of total corn) (acres) Pulaski 109,518 (85%) 2,209 (2%) 16,622 (13%) 71,127 Kosciuko 108,230 (97%) 3,689 (3%) 4,714 79,437 Fulton 90,215 (97%) 2,361 (3%) 2,018 59,288 White 190,457 (96%) 8,238 (4%) 3,192 91,761

Approximately 1/3 of the rotations are considered to be corn/corn rotations. Approximately 2% of the rotations (20,000 acres) are modeled as corn/soybean/winter wheat, and another 2% are considered to be corn silage/soybean rotations.

The crop rotations will be modeled using the 5 most common rotations as follows:  Continuous no-till corn/soybean (#1): 195,456 acres (18% of corn HRUs+22% of soybean HRUs)  Conventional Tillage Corn/soybean (#2): 223,372 acres (28% of corn HRUs+13% of soybean HRUs), including about 10,000 acres as corn silage/soybean  Corn/no-till drill soybean (#3): 401,009 (63% of soybean HRUs+26% of corn HRUs)  Corn/soybean/winter wheat(#4): 20,028 acres (2% of soybean HRUs+2% of corn HRUs)  Conventional Tillage Corn/corn 161,530 acres (26% of corn HRUs), (#5): including about 10,000 acres of corn silage/corn Silage with manure application

The HRUs receiving the rotations above will be selected randomly among the Corn or Soybean HRUs, although the total acreage for each rotation will remain as close as possible to the totals listed above.

In the Tippecanoe watershed, about 3.5% about the total acreage of cropland receive manure applications (USDA 20073). This corresponds approximately to the area receiving the corn/soybean/winter wheat rotation.

The following questions remain outstanding to finalize crop rotation data input into SWAT: - Confirm planting and harvesting dates for all crops. - Confirm timing, type and application rates of manure and other fertilizers.

3 2007 Census by Watershed. Available at: http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Watersheds/index.asp

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Table 3: HRUs breakdown per crop in SWAT.

Area % of total Number of Min/Max HRU size (acres) Crop (acres) watershed HRUs Corn 621,271 49.51 100 63.5/29,354 Pasture 56,214 4.48 36 108/5,575 Soybean 380,124 30.29 102 133/17,377

Crop rotations modeled in SWAT for Tippecanoe Watershed NOTE: Cells highlighted in blue represent commonly used values and are subject to change if more specific, local data are available.

1. Continuous No-till Corn/Soybean APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME 1 4 30 Plant CORN

1 5 2 Fertilizer CORN 93.2 10-34-10 Anhydrous 1 6 1 Fertilizer CORN 120 Ammonia

1 11 5 Harvest & Kill CORN

2 5 10 Plant SOYBEAN

2 10 5 Harvest & Kill SOYBEAN

2. Conventional Tillage Corn/Soybean (no manure application) APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 1 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN

1 11 15 Tillage CHISEL PLOW

2 5 5 Tillage ONE-WAY (DISK TILLER)

2 5 5 Tillage FIELD CULTIVATOR

2 5 10 Plant SOYBEAN

2 10 5 Harvest & Kill SOYBEAN

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3. Corn/No-till drill soybean (no manure application) APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage ONE-WAY (DISK TILLER)

1 4 30 Plant CORN

1 5 1 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN No implement in SWAT for 2 5 10 Plant SOYBEAN drilling

2 10 5 Harvest & Kill SOYBEAN

1 11 1 Tillage CHISEL PLOW

4. Corn/Soybean/Winterwheat with manure

APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME ONE-WAY (DISK 1 4 28 Tillage TILLER)

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 5 Fertilizer CORN 93.2 10-34-10 Harvest & 1 11 5 Kill CORN

1 11 15 Tillage CHISEL PLOW

2 5 10 Plant SOYBEAN Harvest & 2 10 5 Kill SOYBEAN WINTER No implement in SWAT 2 10 5 Plant WHEAT for drilling Harvest & WINTER 3 7 25 Kill WHEAT 18,000 gal/ac 3 8 15 Fertilizer (1) Swine –Fresh Manure

3 10 15 Tillage CHISEL PLOW

(1) Estimating Manure application rates – PennState Cooperative Extensive

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5. Corn/Corn APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage ONE-WAY (DISK TILLER)

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 5 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN

1 11 10 Tillage CHISEL PLOW GT15FT Anhydrous 2 4 26 Fertilizer 120 Ammonia

2 4 28 Tillage ONE-WAY (DISK TILLER)

2 4 28 Tillage FIELD CULTIVATOR

2 4 30 Plant CORN

2 5 5 Fertilizer CORN 93.2 10-34-10

2 11 1 Harvest & Kill CORN

2 11 15 Tillage CHISEL PLOW Swine –Fresh 2 11 15 Fertilizer 18,000 gal/ac (1) Manure

DRIFTWOOD WATERSHED

Table 4: Percentage of tillage practices per county (ISDA 2007)

County (rank) No-Till Mulch Till Reduced Till Conventional Till Corn Data Shelby (1) 47 23 30 0 Henry (2) 59 17 23 0 Hancock (17) 38 5 53 3 Johnson (75) 10 5 28 57 Rush(15) 30 18 44 8 Average corn 36.8 13.6 35.6 13.6 Soybean Data Shelby (1) 95 2 3 1 Henry (7) 90 6 3 0 Hancock (13) 95 2 2 1 Johnson (60) 56 25 15 4 Rush(4) 83 6 8 4 Average soybean 83.8 8.2 6.2 2

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Table 5: Acreage of main crops per county (USDA 2007).

County Corn for grain Corn for silage Wheat for grain Soybean for (acres) (acres) (acres) bean (acres) (% of total corn) (% of total corn) Shelby 100,745 (100%) 2,219 87,289 Henry 77,977(98%) 1,651 (2%) 3,423 65,123 Hancock 78,485 (99.8%) 128 (0.2%) 4,619 71,235 Johnson 68,822 (98%) 1,217 (2%) 3,196 49,993 Rush 107,095 (98%) 1,909 (2%) 3,850 82,344

About 1/5 of the corn/soybean rotations are considered to be corn/corn rotations. Approximately 2% of the corn/soybean rotations (10,500 acres) are considered corn/soybean/winterwheat, and another 2% are considered to be corn silage/soybean rotations.

The corn/soybean crop rotations will be modeled using the 5 most common rotations as follows:  Continuous no-till corn/soybean (#6): 100,004 acres (40% of corn HRUs)  Conventional Tillage Corn/soybean (#7): 76,570 acres (24% of corn HRUs+6% of soybean HRUs).  Corn/no-till drill soybean (#8): 289,052 acres (92% of soybean HRUs+14% of corn HRUs), including 10,500 acres in corn silage/soybean.  Corn/soybean/winter wheat(#9): 10,523 acres (2% of soybean HRUs+2% of corn HRUs)  Conventional Tillage Corn/corn (#10): 50,002 acres (20% of corn HRUs)

The HRUs receiving the rotations above will be selected randomly among the Corn or Soybean HRUs, although the total acreage for each rotation will remain as close as possible to the totals listed above.

In the Driftwood watershed, about 5% about the total acreage of cropland receive manure applications (USDA 20074). This corresponds approximately to the area receiving the corn/soybean/winterwheat and corn silage/soybean rotations.

Table 6: HRUs breakdown per crop in SWAT.

Area % of total Number of Min/Max HRU size (acres) Crop (acres) watershed HRUs Corn 250,009 33.64 63 164/35,087 Pasture 86,794 11.68 56 137/7,856 Soybean 276,142 37.16 67 33/26,894

4 2007 Census by Watershed. Available at: http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Watersheds/index.asp

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The following questions remain outstanding to complete crop rotation input into SWAT: - Confirm planting and harvesting dates for all crops. - Confirm tillage implements and timing of operations - Confirm timing, type and application rates of manure and other fertilizers.

CROP ROTATIONS AS MODELED IN SWAT

6. Continuous No-till Corn/Soybean APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME 1 4 30 Plant CORN

1 5 2 Fertilizer CORN 93.2 10-34-10 Anhydrous 1 6 1 Fertilizer CORN 120 Ammonia

1 11 5 Harvest & Kill CORN

2 5 10 Plant SOYBEAN

2 10 5 Harvest & Kill SOYBEAN

7. Conventional Tillage Corn/Soybean (no manure application) APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 1 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN

1 11 15 Tillage CHISEL PLOW

2 5 5 Tillage ONE-WAY (DISK TILLER)

2 5 5 Tillage FIELD CULTIVATOR

2 5 10 Plant SOYBEAN

2 10 5 Harvest & Kill SOYBEAN

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8. Corn/No-till soybean (no manure application) APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage ONE-WAY (DISK TILLER)

1 4 30 Plant CORN

1 5 1 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN

2 5 10 Plant SOYBEAN

2 10 5 Harvest & Kill SOYBEAN

1 11 1 Tillage CHISEL PLOW

9. Corn/Corn APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME Anhydrous 1 4 26 Fertilizer 120 Ammonia

1 4 28 Tillage ONE-WAY (DISK TILLER)

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 5 Fertilizer CORN 93.2 10-34-10

1 11 5 Harvest & Kill CORN

1 11 10 Tillage CHISEL PLOW GT15FT Anhydrous 2 4 26 Fertilizer 120 Ammonia

2 4 28 Tillage ONE-WAY (DISK TILLER)

2 4 28 Tillage FIELD CULTIVATOR

2 4 30 Plant CORN

2 5 5 Fertilizer CORN 93.2 10-34-10

2 11 1 Harvest & Kill CORN

2 11 15 Tillage CHISEL PLOW Swine –Fresh 2 11 15 Fertilizer 18,000 gal/ac (1) Manure

(1) Estimating Manure application rates – PennState Cooperative Extensive

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10. Corn/Soybean/Winterwheat with manure

APPLICATION RATE FERTILIZER IMPLEMENT

YR MTH DAY OPERATION CROP (lbs/ac) NAME NAME ONE-WAY (DISK 1 4 28 Tillage TILLER)

1 4 28 Tillage FIELD CULTIVATOR

1 4 30 Plant CORN

1 5 5 Fertilizer CORN 93.2 10-34-10 Harvest & 1 11 5 Kill CORN

1 11 15 Tillage CHISEL PLOW

2 5 10 Plant SOYBEAN Harvest & 2 10 5 Kill SOYBEAN

WINTER No implement in SWAT 2 10 5 Plant WHEAT for drilling

Harvest & WINTER 3 7 25 Kill WHEAT 18,000 gal/ac 3 8 15 Fertilizer (1) Swine –Fresh Manure

3 10 15 Tillage CHISEL PLOW

(1) Estimating Manure application rates – Penn State Cooperative Extensive

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Kieser & Associates , L L C MEMORANDUM Environmental Science and Engineering LLCLLCLLC

To: Christa Jones, CTIC Date: September 24, 2010 Kellie Dubay, Tetra Tech Kevin Kratt, Tetra Tech Elizabeth Hansen, Tetra Tech

From: Jim Klang, K&A cc: project files

Re: Example Location Factors based on SPARROW model results

This memo presents one method to determine the water quality trading (WQT) location factor for each watershed. The location factor in this example is determined by applying values of nutrient loading predicted by the USGS SPARROW model1. The USGS developed model estimates the fraction of incremental nutrient load delivered to the Gulf from upstream watersheds. Currently USGS has published 8-digit Hydrologic Unit Code (HUC) results on line. Incremental loading is defined as the amount of Nitrogen/Phosphorus generated in an individual watershed that arrives at the Gulf of Mexico. This percentage can be used to determine location factors based on projected attenuation for and between each watershed. In programs attempting to protect both the Gulf of Mexico and upstream waters where there may be limited sampling data this method can assist their early efforts. However, the SPARROW standard error estimates should be considered when setting the appropriate uncertainty factor for the program.

As water travels downstream, interaction with the surroundings causes nutrients to be naturally removed from the stream. Water entering streams near the Gulf has less time to interact with its surroundings than water entering farther upstream. In most cases, the percentage of nutrients that reach the Gulf is higher for water that enters deeper rivers and those near the Gulf compared to water that enter shallower upstream watersheds. However, the SPARROW model sometimes predicts that a downstream watershed has a lower percentage of delivered incremental loads than an upstream watershed. This could be caused by modeling error or individual watershed characteristics, including lakes, wetland impoundments, and poor hydrologic connectivity in the local watershed. Such characteristics allow more nutrients to be assimilated than would otherwise occur in the stream channel. In order to conservatively represent the most restrictive watershed, all of the best-fit lines were adjusted down to include the watershed with the least incremental loading. This reduced the risk of creating nutrient hotspots by applying a more conservative location factor that did not overestimate natural attenuation in any watershed.

1 Dale M. Robertson, “ Incorporating Uncertainty into the Ranking of SPARROW Model Nutrient Yields from the Mississippi/Atchafalaya River Basin Watersheds,” Oct 6, 2009, Figures 1-17 graphically show how the SPARROW data was adjusted to determine the location factor. The percentage values provided by the SPARROW model are fitted with a trend line (y1) that best represents the linear nature of the data. If a point is below the best fit line, it is being credited with more natural nutrient attenuation than is actually predicted in that watershed. The trend line is adjusted down to the lowest point to ensure an individual watershed„s percentage is not over-estimated. The resulting best fit line (y2) provides a conservative estimate of a watershed‟s nutrient load delivery. Local WQT programs can use the difference between the resulting Gulf of Mexico based watershed location factors to determine a regional location factor for the trade.

The current version of the SPARROW model uses data from each 8-digit HUC in a watershed. This can be a limiting aspect of this example. However, according to Bill Franz2, the EPA Region 5 Project Manager for SPARROW modeling funding grants, an updated version of the SPARROW model will be available in 2011. The new version will provide finer resolution by incorporating a 12-digit HUC system.

2 Bill Franz, personal communication, May 25, 2010, Chicago, IL Upper Wabash River Pathway 1

Table 1-N Fraction of Incremental Nitrogen Nitrogen WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted Upper 5120101 Wabash 0.71 0.71 Middle Wabash- 5120105 Deer 0.77 0.76 Middle Wabash - Little 5120108 Vermillion 0.81 0.81 Middle Wabash - 5120111 Busseron 0.86 0.86 Lower 5120113 Wabash 0.91 0.91 Figure 1-N

Table 1-P Fraction of Incremental Phosphorus Phosphorus WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted Upper 5120101 Wabash 0.68 0.68 Middle Wabash- 5120105 Deer 0.83 0.73 Middle Wabash - Little 5120108 Vermillion 0.80 0.79 Middle Wabash - 5120111 Busseron 0.88 0.84 Lower 5120113 Wabash 0.93 0.90 Figure 1-P

Salamonie River Pathway 2

Table 2-N Fraction of Adjusted Incremental WQT Nitrogen Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120102 Salamonie 0.73 0.66 Upper 5120101 Wabash 0.71 0.71 Middle Wabash- 5120105 Deer 0.77 0.75 Middle Wabash - Little 5120108 Vermillion 0.81 0.79 Middle Wabash - 5120111 Busseron 0.86 0.83 Lower 5120113 Wabash 0.91 0.87 Figure 2-N

Table 2-P Fraction of Phosphorus Incremental WQT Phosphorus Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted

5120102 Salamonie 0.44 0.44 Upper 5120101 Wabash 0.68 0.53 Middle Wabash- 5120105 Deer 0.83 0.62 Middle Wabash - Little 5120108 Vermillion 0.80 0.70 Middle Wabash - 5120111 Busseron 0.88 0.79 Lower 5120113 Wabash 0.93 0.88 Figure 2-P Mississinew River Pathway 3

Table 3-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120103 Mississinew 0.69 0.66 Upper 5120101 Wabash 0.71 0.71 Middle Wabash- 5120105 Deer 0.77 0.75 Middle Wabash - Little 5120108 Vermillion 0.81 0.79 Middle Wabash - 5120111 Busseron 0.86 0.83 Lower 5120113 Wabash 0.91 0.87 Figure 3-N

Table 3-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120103 Mississinew 0.48 0.48 Upper 5120101 Wabash 0.68 0.56 Middle Wabash- 5120105 Deer 0.83 0.64 Middle Wabash - Little 5120108 Vermillion 0.80 0.72 Middle Wabash - 5120111 Busseron 0.88 0.81 Lower 5120113 Wabash 0.93 0.89 Figure 3-P

Eel River (104) Pathway 4

Table 4-N Fraction of Adjusted Incremental WQT Nitrogen Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120104 Eel 0.79 0.74 Middle Wabash- 5120105 Deer 0.77 0.77 Middle Wabash - Little 5120108 Vermillion 0.81 0.81 Middle Wabash - 5120111 Busseron 0.86 0.84

Lower 5120113 Wabash 0.91 0.87 Figure 4-N

Table 4-P Fraction of Adjusted Incremental WQT Phosphorus Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120104 Eel 0.85 0.75 Middle Wabash- 5120105 Deer 0.83 0.78 Middle Wabash - Little 5120108 Vermillion 0.80 0.80 Middle Wabash - 5120111 Busseron 0.88 0.82 Lower 5120113 Wabash 0.93 0.84 Figure 4-P

Tippecanoe River Pathway 5

Table 5-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120106 Tippecanoe 0.74 0.72 Middle Wabash- 5120105 Deer 0.77 0.77 Middle Wabash - Little 5120108 Vermillion 0.81 0.81 Middle Wabash - 5120111 Busseron 0.86 0.85 Lower 5120113 Wabash 0.91 0.90 Figure 5-N

Table 5-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120106 Tippecanoe 0.65 0.65 Middle Wabash- 5120105 Deer 0.83 0.71 Middle Wabash - Little 5120108 Vermillion 0.80 0.77 Middle Wabash - 5120111 Busseron 0.88 0.83 Lower 5120113 Wabash 0.93 0.89 Figure 5-P

Wildcat River Pathway 6

Table 6-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120107 Wildcat 0.75 0.73 Middle Wabash- 5120105 Deer 0.77 0.77 Middle Wabash - Little 5120108 Vermillion 0.81 0.81 Middle Wabash - 5120111 Busseron 0.86 0.85 Lower 5120113 Wabash 0.91 0.89 Figure 6-N

Table 6-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120107 Wildcat 0.82 0.74 Middle Wabash- 5120105 Deer 0.83 0.77 Middle Wabash - Little 5120108 Vermillion 0.80 0.80 Middle Wabash - 5120111 Busseron 0.88 0.82 Lower 5120113 Wabash 0.93 0.85 Figure 6-P

Vermillion River Pathway 7

Table 7-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120109 Vermillion 0.75 0.75 Middle Wabash - Little 5120108 Vermillion 0.81 0.80 Middle Wabash - 5120111 Busseron 0.86 0.86

Lower 5120113 Wabash 0.91 0.91 Figure 7-N

Table 7-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120109 Vermillion 0.82 0.76

Middle Wabash - Little 5120108 Vermillion 0.80 0.80 Middle Wabash - 5120111 Busseron 0.88 0.84

Lower 5120113 Wabash 0.93 0.88 Figure 7-P Sugar River Pathway 8

Table 8-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120110 Sugar 0.78 0.77 Middle Wabash - Little 5120108 Vermillion 0.81 0.81 Middle Wabash - 5120111 Busseron 0.86 0.86

Lower 5120113 Wabash 0.91 0.90 Figure 8-N

Table 8-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120110 Sugar 0.83 0.76 Middle Wabash - Little 5120108 Vermillion 0.80 0.80 Middle Wabash - 5120111 Busseron 0.88 0.83

Lower 5120113 Wabash 0.93 0.87 Figure 8-P

Embarras River Pathway 9

Table 9-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120112 Embarras 0.79 0.79 Middle Wabash - 5120111 Busseron 0.86 0.85

Lower 5120113 Wabash 0.91 0.91 Figure 9-N

Table 9-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120112 Embarras 0.85 0.84 Middle Wabash - 5120111 Busseron 0.88 0.88 Lower 5120113 Wabash 0.93 0.92 Figure 9-P

Upper White Pathway 10

Table 10-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

Upper 5120201 White 0.72 0.72 Lower 5120202 White 0.86 0.82

Lower 5120113 Wabash 0.91 0.91 Figure 10-N

Table 10-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

Upper 5120201 White 0.70 0.70

Lower 5120202 White 0.88 0.82

Lower 5120113 Wabash 0.93 0.93 Figure 10-P

Eel River (203) Pathway 11

Table 11-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120203 Eel 0.75 0.75 Lower 5120202 White 0.86 0.83

Lower 5120113 Wabash 0.91 0.91 Figure 11-N

Table 11-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120203 Eel 0.70 0.70 Lower 5120202 White 0.88 0.81 Lower 5120113 Wabash 0.93 0.93 Figure 11-P

Driftwood River Pathway 12

Table 12-N Fraction of Incremental Nitrogen Nitrogen WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted

5120204 Driftwood 0.70 0.70 Upper East 5120206 Fork White 0.78 0.75 Lower East 5120208 Fork White 0.83 0.80

5120202 Lower White 0.86 0.85 Lower 5120113 Wabash 0.91 0.90 Figure 12-N

Table 12-P Fraction of Incremental Phosphorus Phosphorus WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted

5120204 Driftwood 0.75 0.74 Upper East Fork 5120206 White 0.82 0.78 Lower East Fork 5120208 White 0.82 0.82 Lower 5120202 White 0.88 0.86 Lower 5120113 Wabash 0.93 0.91 Figure 12-P Flatrock-Haw River Pathway 13

Table 13-N Fraction of Incremental Nitrogen Nitrogen WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted Flatrock- 5120205 Haw 0.73 0.73 Upper East Fork 5120206 White 0.78 0.77 Lower East Fork 5120208 White 0.83 0.82 Lower 5120202 White 0.86 0.86 Lower 5120113 Wabash 0.91 0.91 Figure 13-N

Table 13-P Fraction of Incremental Phosphorus Phosphorus WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted Flatrock- 5120205 Haw 0.81 0.76 Upper East Fork 5120206 White 0.82 0.79 Lower East Fork 5120208 White 0.82 0.82 Lower 5120202 White 0.88 0.85 Lower 5120113 Wabash 0.93 0.88 Figure 13-P

Muscatatuck River Pathway 14

Table 14-N Fraction of Incremental Nitrogen Nitrogen WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted

5120207 Muscatatuck 0.69 0.69 Upper East 5120206 Fork White 0.78 0.74 Lower East 5120208 Fork White 0.83 0.80

5120202 Lower White 0.86 0.85 Lower 5120113 Wabash 0.91 0.90 Figure 14-N

Table 14-P Fraction of Incremental Phosphorus Phosphorus WQT Load Location Delivered to Factor as HUC8 Name the Gulf Adjusted

5120207 Muscatatuck 0.77 0.75 Upper East 5120206 Fork White 0.82 0.78 Lower East 5120208 Fork White 0.82 0.82

5120202 Lower White 0.88 0.86 Lower 5120113 Wabash 0.93 0.90 Figure 14-P

Patoka River Pathway 15

Table 15-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120209 Patoka 0.83 0.83 Lower 5120113 Wabash 0.91 0.91 Figure 15-N

Table 15-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120209 Patoka 0.85 0.85 Lower 5120113 Wabash 0.93 0.93 Figure 15-P

Skillet River Pathway 16

Table 16-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

5120115 Skillet 0.83 0.78 Little 5120114 Wabash 0.82 0.82

Lower 5120113 Wabash 0.91 0.86 Figure 16-N

Table 16-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

5120115 Skillet 0.87 0.83 Little 5120114 Wabash 0.86 0.86 Lower 5120113 Wabash 0.93 0.89 Figure 16-P

Little Wabash River Pathway 17

Table 17-N Fraction of Incremental Adjusted Nitrogen WQT Load Location Delivered to Factor for HUC8 Name the Gulf Nitrogen

Little 5120114 Wabash 0.82 0.82 Lower 5120113 Wabash 0.91 0.91 Figure 17-N

Table 17-P Fraction of Incremental Adjusted Phosphorus WQT Load Location Delivered to Factor for HUC8 Name the Gulf Phosphorus

Little 5120114 Wabash 0.86 0.86 Lower 5120113 Wabash 0.93 0.93 Figure 17-P

Kieser & Associates , L L C MEMORANDUM Environmental Science and Engineering LLCLLCLLC

To: Christa Jones, CTIC Date: September 24, 2010 Kellie Dubay, Tetra Tech Kevin Kratt, Tetra Tech Elizabeth Hansen, Tetra Tech

From: Jim Klang, K&A cc: project files Laurence Picq, K&A

Re: SWAT flow, sediment and nutrient calibration and validation.

A SWAT model was developed for the Driftwood (USGS 8-digit HUC, 05120204) and Tippecanoe (USGS 8-digit HUC, 05120106) watersheds. The model was run for 13 years (1997- 2009) including a 3-year equilibration period. Flow calibration was done on a monthly basis for the years 2000 to 2004. The model was validated for the period 2005-2009 to test the strength of the model’s predictions. Simulated flow results were evaluated with commonly used statistical performance measures: Nash-Sutcliffe Efficiency Index1 and coefficient of determination r2. The model could not be calibrated for sediment and nutrients because of a lack of adequate monitoring data. Results were compared to available literature values.

The calibration methodology and results for both watersheds are presented below.

A- FLOW CALIBRATION

1. Driftwood Watershed

Flow calibration was conducted for the two USGS gages within the Driftwood watershed with the most recent available flow records: Big Blue River at Shelbyville at the mouth of subbasin 14, and Sugar Creek near Edinburgh at the mouth of subbasin 23. No gage was available at the mouth of the watershed (Figure 1).

The Sugar Creek gage drains 41% of the watershed while the Big Blue River gage drains 36% of the watershed. Parameters changed during calibration were applied to all subbasins except for one groundwater parameter which was made to vary for each gaged section of the watershed (parameters are listed in Appendix A).

1 The Nash-Sutcliffe Efficiency Index (NSE) is a widely used goodness-of-fit measure. NSE values above 0.5 are generally accepted as adequate (Santhi et.al, 2001)

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Figure 1: USGS and STORET gages in the Driftwood Watershed

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Overall, simulated flows at both gages were consistent with observed data (Table 1), with the NSE value at or above 0.50. Simulated and observed flow hydrographs are presented in Appendix B.

Table 1: Results of SWAT calibration and validation for average monthly flow.

Average monthly flow (m3/sec) Observed Simulated % difference Nash-Sutcliffe R2 Sub 23 15.05 14.54 -3.4 0.50 0.56 Calibration Sub 14 13.76 12.89 -6.3 0.58 0.71 Sub 23 22.21 20.35 -8.4 0.67 0.63 Validation Sub 14 19.68 20.41 3.7 0.66 0.70

A flow duration curve2 (Figure 2) was also used to assess whether simulated flow patterns were representative of observed flow patterns. Figure 2 suggests a reasonable comparison between observed data and model simulations during most flow patterns. However, during dry periods, the model tends to underestimate flows.

1000.00

Simulated Daily Flows Observed Daily Flows 100.00

10.00 m3/sec 1.00 0 10 20 30 40 50 60 70 80 90 100 % exceedance

0.10

0.01

Figure 2: Flow duration curve for gage at Sugar Creek.

2 A flow duration curve relates flow values (y axis) to the percent of time those values have been met or exceeded (x axis) (US EPA, 2007).

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In addition, an annual cumulative flow comparison between simulated flows and observed flows from the USGS monitoring stations were completed. The Driftwood comparisons were based on the two subwatersheds used in the calibration process. Results for typical years are given for subwatershed 14 and 23 below in Figures 3 and 4. These figures suggest a reasonable comparison between observed data and model simulations during most flow patterns. However, the referenced underestimation during dry periods is reflected here also.

700 2003 Cumulative Flow Comparison; 600 Subwatershed 23 Observed cumulative discharge 500 (m3) Simulated cumulative discharge 400 (m3)

300

200 Discharge (Million Discharge(Million m3)

100

0 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1

Figure 3: Annual cumulative flow comparison for subwatershed 23

700 2003 Cumulative Flow Comparison; 600 Subwatershed 14

500 Observed cumulative discharge (m3) 400 Simulated cumulative discharge (m3) 300

200 Discharge (Million Discharge(Million m3)

100

0 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1

Figure 4: Annual cumulative flow comparison for subwatershed 14

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2. Tippecanoe Watershed

Flow calibration was conducted close to the mouth of the watershed at USGS gage Tippecanoe River near Delphi (mouth of subbasin 33) (Figure 5). Available information about reservoirs, lakes and wetlands areas and storage volumes was entered into SWAT to improve simulation of hydrologic processes in the watershed.

Figure 5: USGS and STORET gages in the Tippecanoe watershed.

Overall, simulated flows are consistent with observed data, although some discrepancies in flow events occur during the validation period (Table 2). These discrepancies could be related to reservoir management practices and to limitations in the climate data used.

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Table 2: Results of SWAT calibration and validation for average monthly flow.

Average monthly flow (m3/sec) Observed Simulated % difference Nash-Sutcliffe R2 Calibration 51.97 49.53 -4.7 0.55 0.86 Validation 65.77 65.56 -0.3 0.49 0.81

A flow duration curve (Figure 6) was also used to assess whether simulated flow patterns were representative of observed flow patterns. Figure 6 suggests a small variation between observed data and model simulations during moist and dry conditions; this difference is most likely related to the difficulty of modeling accurately lake, reservoir and wetlands storage volumes. The duration curve is considered adequate for this feasibility study; within the context of a trading program which includes setting up appropriate boundary conditions that need to be established at major lakes/reservoirs to minimize the discrepancies and introduced uncertainty.

1000.00

Observed Daily Flows Simulated Daily Flows

100.00 m3/sec

10.00

1.00 0 10 20 30 40 50 60 70 80 90 100 % exceedance Figure 6: Flow duration curve for Tippecanoe River gage at Delphi.

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In addition, an annual cumulative flow comparison between simulated flows and observed flows from the USGS monitoring station was completed. The Tippecanoe comparisons are based on the subwatershed used in the calibration process. However, reservoir release events can distort modeling processes. It is often prudent to set a boundary condition above and below large lakes and reservoirs for Water Quality Trading Programs at scale to remove any uncertainty introduced from flow and/or sediment and nutrient attenuation. The result of the cumulative flow comparison for a typical year is given below (Figure 7). The figure suggests a reasonable comparison between observed data and model simulations during most flow patterns. However, there may be an additional reservoir related affect in later months of the year.

2,500 2003 Cumulative Flow Comparison; Tippecanoe Watershed

2,000

) 3

1,500 Observed cumulative discharge (m3) Simulated cumulative discharge (m3)

1,000 Discharge (Million Discharge(Million m

500

0 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1

Figure 7: Annual cumulative flow comparison for Tippecanoe Watershed

B- SEDIMENT AND NUTRIENT CALIBRATION

Limited monitoring data from STORET stations were available for calibration for both the Driftwood and Tippecanoe watersheds. A detailed analysis (See Appendix C) showed that monitoring data were not representative of loading conditions and therefore could not be used for an adequate sediment and nutrient calibration.

In order to estimate the accuracy of the model, simulated sediment and nutrient loads (Table 3) were compared to USGS SPARROW data3 (Table 4) as well as local and regional averages

3 SPARROW (Spatially Referenced Regression On Watershed attributes) is a statistical model developed by USGS for the entire United States. SPARROW data are available at: http://water.usgs.gov/nawqa/sparrow/.

Kieser & Associates, LLC Page 7 found in the literature (Table 5). A limited number of nutrient parameters were changed to bring simulated values within the range of referenced values.

Simulated sediment loadings were generally in the low end of the range for Tippecanoe and in the high end for Driftwood compared to loading values presented in Bracmort et al (2006) and Illinois EPA (1999). The low sediment loading value for Tippecanoe may be explained by the large number of lakes, reservoirs and wetlands present in the watershed. These larger impoundments provide sediment trapping and deposition. Simulated total phosphorus and total nitrogen loads were within the range of literature for both watersheds, although simulated TN load in Tippecanoe appeared to be relatively larger than the SPARROW value. The SPARROW modeling concept included calibrating the whole Mississippi River Basin on known USGS flow and chemistry data, estimating wastewater treatment plant data based on know effluent concentrations of a subset of plants that are distributed across the basin at a ratio determined by population, fertilizer and livestock input assumptions are in part based on the US Agricultural Census data. The remaining nonpoint source non-agricultural inputs also use an averaging technique supplemented by the chemical monitoring station data. As a whole these methods are calibrated and predict adequately across a larger basin. When comparing data from SPARROW with the SWAT model data the reader should consider the standard error estimates for individual sources with the SPARROW prediction. This model’s nitrogen point source predictions within the Driftwood’s watershed are 136 percent of the estimated mean value; 15, 19 and 39 percent are the standard error estimates for livestock, atmospheric and non-agricultural respectively. The non-fertilizer categories represent explains approximately 29 percent of the nitrogen loading. The total Driftwood watershed load has an estimated standard error of approximately 3 percent, however the source partitioning within the total load can introduce significant uncertainty regarding kg/ha estimates.

The same is true within the Tippecanoe watershed with a total watershed nitrogen yield standard error of 4 percent and a point source, livestock, atmospheric and non-agriculture source predicting standard error of 562, 14, 17 and 79 percent respectively. The non-fertilizer categories explain approximately 34 percent of the watershed’s yield.

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Table 3: SWAT simulated sediment and nutrient loads at the mouth of the Driftwood and Tippecanoe watersheds.

Sediment Organic P Mineral P TP Organic N NO3 Year (metric TN (Kg/year) (kg/year) (kg/year) (kg/year) (kg/yr) (kg/year) tonne/year) Driftwood Watershed 2000 152,749 80,405 517,230 597,635 1,035,550 2,817,720 3,853,270 2001 107,844 41,985 443,603 485,588 630,820 2,868,480 3,499,300 2002 164,760 49,771 529,845 579,616 741,629 11,820,590 12,562,219 2003 187,175 67,612 677,241 744,853 912,464 3,442,800 4,355,264 2004 103,594 42,677 399,378 442,055 600,945 4,430,960 5,031,905 2005 219,566 89,198 733,243 822,441 1,241,720 8,572,120 9,813,840 2006 243,918 88,230 750,540 838,770 1,277,520 9,662,200 10,939,720 2007 131,264 60,955 406,696 467,651 789,735 6,117,721 6,907,456 2008 462,519 118,400 1,039,426 1,157,826 1,733,690 15,300,590 17,034,280 2009 199,105 53,733 517,213 570,946 786,370 3,848,570 4,634,940 Yearly 197,249 69,297 601,442 670,738 975,044 6,888,175 7,863,219 average Tippecanoe Watershed 2000 61,520 39,175 222,030 261,205 379,958 7,760,800 307,990 2001 104,014 74,551 479,266 553,817 820,121 11,313,500 672,999 2002 46,321 31,213 225,028 256,241 328,790 8,864,720 5,774 2003 143,066 220,834 458,670 679,504 2,110,546 12,589,140 0 2004 109,870 37,097 339,843 376,940 452,806 10,863,400 0 2005 77,045 59,374 367,205 426,579 613,892 9,377,400 0 2006 74,678 48,867 351,510 400,377 508,470 12,960,100 0 2007 78,793 63,913 408,751 472,664 650,608 12,371,900 0 2008 128,400 123,625 914,392 1,038,017 1,270,567 12,593,300 0 2009 66,716 67,943 634,033 701,976 724,509 13,646,600 0 Yearly average 89,042 76,659 440,073 516,732 786,027 11,234,086 98,676

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Table 4: SPARROW total phosphorus and nitrogen export by HUC.

Total Phosphorus Export Total Nitrogen Export HUC Watershed Area(km2) (Kg/year) (kg/year) 5120201 Upper White 852,713 1,300,546 10,999,782 5120202 Lower White 513,648 785,995 8,096,025 5120203 Eel 242,980 266,538 2,700,179 5120204 Driftwood 318,669 549,548 6,092,624 5120205 Flatrock-Haw 120,762 401,095 3,841,537 Upper East Fork 5120206 White 356,744 460,028 4,444,444 5120207 Muscatatuck 325,146 478,208 4,171,939 5120208 Lower East Fork White 463,784 514,460 5,966,038 5120209 Patoka 273,273 397,412 4,117,977 5120101 Upper Wabash 4,606 977,985 9,534,804 5120102 Salamonie 1,173 147,650 1,549,173 5120103 Mississinewa 2,154 410,983 4,024,902 5120104 Eel 1,927 274,648 2,405,189 5120105 Middle Wabash-Deer 1,828 578,952 5,103,974 5120106 Tippecanoe 5,191 574,192 7,754,604 5120107 Wildcat 1,898 594,435 4,815,876 Middle Wabash-Little 5120108 Vermilion 5,588 1,040,670 12,097,107 5120109 Vermilion 3,718 500,711 6,140,661 5120110 Sugar 2,047 388,375 3,473,322 Middle Wabash- 5120111 Busseron 5,383 824,688 10,216,784 5120112 Embarras 6,297 1,110,480 13,737,797 5120113 Lower Wabash 3,303 1,011,079 10,793,546 5120114 Little Wabash 6,008 757,200 7,993,325 5120115 Skillet 2,207 214,343 2,849,409

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Table 5: Comparison of sediment and nutrient simulated results against available literature.

Simulated Results Bracmort et al. (2006) EPA (2004) Illinois EPA (1999) Driftwood Tippecanoe Dreisbach Smith Fry Dreisbach Smith Fry Watershed Watershed Subbasin Subbasin Subbasin Subbasin Variable (Site 6) (Site 2) Average Monthly Yield Suspended Solids 0.04 a 0.02 a 0.027 c 0.151 c See Bracmort et See Bracmort et - (ton/ha) 0.07b 0.01b 0.032 d 0.052 d al. (2006) al. (2006) TP (kg/ha) 0.16 a 0.07a 0.077 c 0.075 c See Bracmort et See Bracmort et - 0.22 b 0.10 b 0.074 d 0.241 d al. (2006) al. (2006) TN (kg/ha) 1.62 a 1.82 a - - 1.35 c 8.81 c - 2.83 b 3.71 b 1.227 d 2.59 d Average Annual Yield TSS (kg/ha/yr) 340 to 1,540 90 to 280 - - - - 91.59 to 1,606.15 e Inorganic N 9.37 to 50.87 16.01 to 26.87 - - - - 1.20 to 49.58 e (kg/ha/yr) TN (kg/ha/yr) 11.63 to 56.64 16.76 to 28.94 - - - - 1.92 to 35.49 e TP (kg/ha/yr) 1.61 to 3.85 0.50 to 2.04 - - - - 0.11 to 4.37 e a Calibration period 2000-2004 c Calibration period (1974-1975) e Range of yield values for monitoring stations within the Wabash watershed in Illinois b Validation period 2005-2009 d Calibration period (1976-1977) (1980 to 1996)

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Table 5 - continued

Simulated Results Smith et al (2008) – Wabash River TMDL Variable Driftwood Watershed Tippecanoe Watershed St Joseph Watershed (Tetratech, 2006) Seasonal average yield (April-November) TP (kg/ha/yr) 0.37 to 1.83 0.02 to 0.12 0.052 to 3.08f - f NO3 (kg/ha/yr) 5.29 to 44.03 0.74 to 2.59 0.05 to 19.2 - TKN (ORGN) 0.31 to 2.01 0.09 to 0.58 0.12 to 8.77 f - (kg/ha/yr) Mean Concentration TP Conc. (mg/L) 0.38a/0.33b 0.20a/0.22b Seasonal: 0.131-1.68 g Upper Wabash: 0.21 to 0.44h (Seasonal: 0.29 to 0.51) (Seasonal: 0.18 to 0.37) Middle Wabash: 0.13 to 0.40 h Lower Wabash: 0.16 to 0.49 h a b a g h NO3 (mg/L) 5.07 /6.07 6.38 /6.33b Seasonal : 0.08 to 6.55 Upper Wabash: 0.67 to 11.00 (Seasonal: 2.11 to 11.30) (Seasonal: 6.44 to 15.46) Middle Wabash: 0.01 to 4.90 h Lower Wabash: 0.06 to 3.56 h TKN (ORGN) (mg/L) 0.55a/0.51b 0.27a/0.26b 0.70 to 3.88 g - (Seasonal: 0.25 to 0.65) (Seasonal: 0.85 to 1.76) f- Range of seasonal (April-November) yield values at monitoring stations (Years 2002 to 2006) g- Range of seasonal (April-November) flow-weighted mean concentrations at monitoring stations (Years 2002 to 2006) h- Range of average concentration values at sampling stations (number of years varied by stations)

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C- FLOW SEPARATION ANALYSIS

A hydrograph separation analysis was also included within the calibration process for both watersheds. Selected storm events were analyzed using the HYSEP4 program to determine whether the separation between surface runoff and groundwater base flow in streamflow was simulated accurately. The value in applying the HYSEP assessment is that this check confirms that flow pathways are representative of dissolved and sediment attached nutrient processes. For instance, a change on the land by installing a BMP to reduce surface erosion (e.g., high residue tillage regimes) does not necessarily result in a reduction of nitrate loading that is delivered via a shallow aquifer pathway; this pathway may experience an increase in water yield from this type of BMP. Therefore, the HYSEP analysis provides a level of assurance that the flow calibration reflects current real world dynamics.

For both watersheds, the proportion of surface runoff in simulated streamflow was relatively similar to the proportion of runoff in observed data (Figure 8Figure 9). In some cases, SWAT’s over-predictions of surface flows may be related to limitations in the climate data used.

100% Observed 90% Simulated

80%

70%

60%

50%

40%

30%

20% % of flow ofcomingflow %from surface runoff

10%

0% 06/03/01 06/08/01 06/13/01 06/18/01 06/23/01 06/28/01 07/03/01 07/08/01 07/13/01

Figure 8: Proportion of surface runoff in streamflow at gage Sugar Creek near Edinburgh for one selected event (Driftwood).

4 USGS Hydrograph Separation Program-as available within Indiana’s Flow and Load Duration Curves Excel Tool.

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100% Observed 90% Simulated

80%

70%

60%

50%

40%

30%

20% % of flow ofcomingflow %from surface runoff

10%

0% 5/17/04 5/22/04 5/27/04 6/1/04 6/6/04 6/11/04 6/16/04 6/21/04 6/26/04 7/1/04

Figure 9: Proportion of surface runoff in streamflow at gage Tippecanoe River at Delphi for one selected event.

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REFERENCES

Bracmort K.S., M. Arabi, J.R. Frankenberger, B.A. Engel, J.G. Arnold. 2006. Modeling Long- term water quality impact of structural BMPs. Transactions of the ASABE Vol 49 (2): 367-374.

Galloway, J.M., Dennis A. Evans, and W. Reed Green. 2005. Comparability of suspended- sediment concentration and total suspended-solids data for two sites on the L’Anguille River, Arkansas, 2001-2003. USGS Scientific Investigations Report 2005-5193.

Illinois Environmental Protection Agency, 1999. Baseline Loadings of Nitrogen, Phosphorus, and Sediments from Illinois Watersheds. Prepared by Matthew B. Short.

Santhi, S., J.G. Arnold, J.R. Williams, W.A. Dugas, R. Srinivasan, and L.M. Hauck. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. Journal of the American Water Resources Association 37:1169:1188.

Sloto, Ronald A., and Michele Y. Crouse. 1996. HYSEP: A computer program for streamflow hydrograph separation and analysis. U.S. Geological Survey, Water Resources Investigations Report 96-4040.

Smith, D.R., S.J. Livingston, B.W. Zuercher, M. Larose, G.C. Heathman, and C. Huang. Nutrient losses from row crop agriculture in Indiana. Journal of Soil and Water Conservation 63(6): 396- 409.

Tetratech, Inc. 2006. Wabash River Nutrient and Pathogen TMDL Development Final Report. Prepared for Illinois Environmental Protection Agency and Indiana Department of Environmental Management.

U.S. Environmental Protection Agency. 2004. Impact of Best Management Practices on Water Quality of Two Small Watersheds in Indiana: Role of Spatial Scale. Prepared by M. Arabi and R. S. Govindaraju. EPA/600/R-05/080

U.S. Environmental Protection Agency. 2007. An Approach for Using Load Duration Curves in the Development of TMDLs. Watershed Branch, Office of Wetlands, Oceans and Watersheds, EPA 841-B-07-066.

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APPENDIX A CALIBRATION PARAMETERS

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Table A- 1: Calibration parameters used for Driftwood Watershed.

FLOW PARAMETERS Default SWAT value Range New Value

SCS runoff curve number for -3 for Corn/Soybean CN2 varies by soil moisture condition II -1 for forest/pasture ALPHA_BF Baseflow alpha factor. 0.048 0-1 0.043 Threshold depth of water in the GWQMN shallow aquifer required for 0 0-5000 35 return flow to occur. 0.1 corn/soybean GW_REVAP Groundwater "revap" coefficient. 0.02 0.02-0.2 0.15 forest/pasture 30/38 (depending on GWDELAY Groundwater delay. 31 0-500 subwatershed location) SURLAG Surface runoff lag time. 4 1-24 1 Soil evaporation compensation ESCO 0.95 0-1 0.86 factor. SEDIMENT PARAMETERS Peak rate adjustment factor for PRF sediment routing in the main 1 0-2 0.55 channel.

Linear parameter for calculating the maximum amount of SPCON 0.0001 0.0001-0.01 0.0002 sediment that can be reentrained during channel sediment routing.

NUTRIENT PARAMETERS PSP Phosphorus sorption coefficient. 0.4 0.1-0.7 0.69

Phosphorus uptake distribution P_UPDIS 20 0-100 50 parameter. Organic phosphorus settling rate RS5 0.05 0.001-0.1 0.1 in the reach at 20 °C. Nitrogen uptake distribution N-UPDIS 20 0-100 50 parameter. Rate coefficient for organic N RS4 0.05 0.001-0.1 0.1 settling in the reach at 20 °C.

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Table A- 2: Calibration parameters used for Tippecanoe Watershed.

Default SWAT FLOW PARAMETERS value Range New Value

SCS runoff curve number for CN2 moisture condition II varies by soil -3 for Corn/Soybean -1 for forest/pasture ALPHA_BF Baseflow alpha factor. 0.048 0-1 0.08 GWDELAY Groundwater delay. 31 0-500 55 SURLAG Surface runoff lag time. 4 1-24 1

ESCO Soil evaporation compensation 0.95 0-1 0.7 factor. SEDIMENT PARAMETERS [OPTIONAL] Peak rate adjustment PRF factor for sediment routing in the 1 0-2 0.6 main channel. [OPTIONAL] Linear parameter for calculating the maximum amount 0.0001- SPCON of sediment that can be 0.0001 0.0002 0.01 reentrained during channel sediment routing. NUTRIENT PARAMETERS Phosphorus uptake distribution P_UPDIS 20 0-100 50 parameter. Organic phosphorus settling rate in RS5 0.05 0.001-0.1 0.1 the reach at 20 °C. Nitrogen uptake distribution N-UPDIS 20 0-100 50 parameter. Rate coefficient for organic N RS4 0.05 0.001-0.1 0.1 settling in the reach at 20 °C.

NPERCO Nitrogen percolation coefficient. 0.2 0 - 1 0.1

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APPENDIX B FLOW HYDROGRAPHS FOR CALIBRATION AND VALIDATION PERIODS

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DRIFTWOOD WATERSHED

Figure B- 1: Comparison of monthly simulated and observed flows at Sugar Creek gage (mouth of subbasin 23).

120 Simulated monthly flow Observed monthly flow at Sugar Creek 100

80

60 m3/sec

40

20

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure B- 2: Comparison of monthly simulated and observed flows at Big Blue River gage (mouth of subbasin 14).

100 Simulated monthly flow Observed monthly flow 80

60 m3/sec 40

20

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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TIPPECANOE WATERSHED

Figure B- 3: Comparison of monthly simulated and observed flows at gage Tippecanoe River near Delphi (mouth of subbasin 33).

250 Simulated monthly flow Observed monthly flow 200

150 m3/sec 100

50

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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APPENDIX C MONITORING DATA ANALYSIS

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Sediment and nutrient sampling data were available for both watersheds from a limited number of STORET stations. Records were limited to the period 2000-2004. Monitoring data were also available from one USGS station in Driftwood watershed (located at the mouth of subbasin 7) for the period 2000-2008.

For Driftwood watershed, out of four STORET stations, only two stations were associated with a USGS gage and only one station (SGR-1) had a sample dataset extensive enough for load calculation. Six STORET stations were located within the Tippecanoe watershed but the only station (Wed060-0032) located near a USGS gage had only one sample value available. Flow was calculated for station TR-56 using area-weighted ratios so that samples could be plotted against flow. However, analysis of STORET and USGS sample dates against flows (Figure C- 1, C-2 and C-3) revealed that most samples were taken either during low flows or on the fall of the hydrograph. Figure C- 4 represents one USGS sampled storm event pollutograph plotted chronologically with the event hydrograph. This USGS study, taken across multiple hydrographs within this watershed, indicates how sampling after the peak of the hydrograph skews the concentration value estimate for the event. The samples repeatedly illustrated the highest concentrations occur on the rise of the hydrograph and concentrations quickly drop off after the peak of the hydrograph for phosphorus. Therefore, when storm flows start to subside, pollutant concentrations can be considerably lower than at their peak and sometimes close to their pre-storm values. Based on the review of these hydrograph plots of STORET data sampling times the available data is considered highly likely to introduce a skewed loading representation for the watershed. As a result, the available STORET and USGS sample values were considered and not used for sediment and nutrient calibration.

160 Flow (m3/sec) STORET TP Sampling Dates 140

120

100

80

Flow (m3/sec) Flow 60

40

20

0 1/1/03 2/1/03 3/1/03 4/1/03 5/1/03 6/1/03 7/1/03 8/1/03 9/1/03 10/1/03 11/1/03 12/1/03 Figure C- 1: Flow hydrograph with STORET sampling dates at USGS gage Sugar Creek near Edinburgh (STORET Station SGR-1) – Driftwood Watershed.

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160 STORET sampling dates Flow (m3/sec) 140

120

100

80

Flow (m3/sec) Flow 60

40

20

0 1/1/01 2/1/01 3/1/01 4/1/01 5/1/01 6/1/01 7/1/01 8/1/01 9/1/01 10/1/01 11/1/01 12/1/01

Figure C- 2: Flow hydrograph with STORET sampling dates downstream of USGS gage Tippecanoe River at Winamac (STORET station TR-56) – Tippecanoe Watershed.

50

45 Flow (m3/sec) USGS Sampling dates 40

35

30

25

Flow (m3/sec) Flow 20

15

10

5

0 1/1/02 2/1/02 3/1/02 4/1/02 5/1/02 6/1/02 7/1/02 8/1/02 9/1/02 10/1/02 11/1/02 12/1/02

Figure C- 3: Flow hydrograph showing USGS sampling dates at USGS gage Sugar Creek at New Palestine.

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250 0.45

Flow (cfs) 0.4 TP conc. (mg/L) 200 0.35

0.3 150

/sec 0.25 3

0.2

Flow (m Flow 100 TP Conc.(mg/L) TP 0.15

0.1 50

0.05

0 0 1 2 3 4 5 6 Days Figure C- 4: Pollutograph for storm event (started 05/14/2004) sampled by USGS at Sugar Creek at New Palestine.

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Kieser & Associates , L L C MEMORANDUM Environmental Science and Engineering LLCLLCLLC

To: Christa Jones, CTIC Date: December 15, 2010 Kellie Dubay, Tetra Tech Kevin Kratt, Tetra Tech Elizabeth Hansen, Tetra Tech

From: Jim Klang, K&A cc: project files Laurence Picq, K&A

Re: Agricultural scenarios in the Driftwood and Tippecanoe Watersheds.

Four scenarios were created to model commonly used agricultural best management practices (BMP). These four scenarios were applied to selected subbasins within the Driftwood and Tippecanoe watersheds. The modeling results will be used to provide an estimate of the phosphorus and nitrogen loading reductions from BMP implementation in pounds per acre. Each of the four scenarios’ assumptions, modeling procedures and results are described below.

1. Description of agricultural BMP scenarios

1.1. Scenario 1: Residue Management

Residue management was modeled in SWAT by: - removing all tillage practices before/after crop is planted, - increasing the biological mixing efficiency parameter (BIOMIX) from 0.2 to 0.5, - reducing the curve number by 2 units from its calibrated value.

 Driftwood Watershed In the Driftwood watershed, available data indicates that no-till practices on average are applied to 37 percent of corn and 84 percent of soybean. However, the application of no-till practices can vary considerably between counties. For instance, Johnson County has the lowest rate of no- till application for corn 10 percent and soybean 56 percent; Hancock County has the highest rate 53 percent of reduced-till practices for corn; Henry County has the highest application rate for no-till corn 59 percent (ISDA 20071).

To evaluate the potential for producers using residue management for Water Quality Trading credit generation, no-till will be applied to all corn/soybean HRUs currently modeled as conventional tillage in the following subbasins (Figure 1): - Subbasins 8 and 23: These subbasins are located within counties with a high rate of conventional tillage practices.

1 Indiana State Department of Agriculture (ISDA) - 2007 Conservation Tillage Data by county- Available at: http://www.in.gov/isda/2354.htm

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- Subbasin 3: This subbasin is located upstream in Henry County (44% of corn HRUs are in continuous no-till and 100% of soybean HRUs are modeled as conventional corn/no-till drill soybeans).

Figure 1: SWAT subbasins in the Driftwood watershed.

 Tippecanoe watershed In the Tippecanoe watershed, no-till practices are not applied in as large of percentages as in the Driftwood watershed. The highest rates of no-till application are 26 percent for corn in Kosciuko, and 68 percent for soybean in Pulaski County (ISDA 20072).

2 Indiana State Department of Agriculture (ISDA) - 2007 Conservation Tillage Data by county- Available at: http://www.in.gov/isda/2354.htm

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Residue management will be applied to all corn/soybean HRUs currently modeled as conventional tillage in the following subbasins (Figure 2):  Subbasins 30 and 34: These subbasins are located within White County, the county with the highest rate of conventional tillage.  Subbasin 19: This subbasin is located in Pulaski County where mulch till and no-till practices are applied to 69 percent of corn and 93 percent of soybeans.

Figure 2: SWAT subbasins in the Tippecanoe watershed.

1.2. Scenario 2: Filterstrips

In Swat2009, filterstrips can be applied to any HRU to reduce sediment and nutrients. Filter strips, as modeled in Swat2009, do not affect surface runoff (Neitsch et al. 2010). Although the starting date of the filter strip implementation can now be set up (i.e., the starting date of the simulation run does not necessarily correspond to the implementation date of the BMP), the filterstrip will be modeled from the start of the simulation run as the purpose of this scenario is to provide an estimate of the long-term load reduction average value, and not to model real-life time based buffer installations regarding when change occurs within a watershed.

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The following parameters are required to model filterstrips in an HRU in SWAT3:

FILTERW: Width of edge-of-field filter strip (in meters). The filter strip width will be set to 10 meters (30 ft); this value corresponds to the Federal program’s minimum width acceptable for edge-of-field buffers. YEAR/MONTH/DAY: Implementation of the filter strip is assumed to start with the start of the simulation. Modeling results will provide load reduction estimates corresponding to a fully established filter strip. FILTER_RATIO: Ratio of field area to filter strip area (ha2/ha2) – Ranges from 0 to 300 with values at 30-60 being most common. Default value is 40. Default value will be used. FILTER_CON: Fraction of the HRU which drains to the most concentrated 10% of the filter strip area (ha2/ha2). 10% of the filter strip can receive between 0.25 and 0.75 of the runoff from the entire field. Default value is 0.5. Default value will be used. FILTER_CH: Fraction of the flow within the most concentrated 10% of the filter strip which is fully channelized. Default value is 0.0. Default value will be used.

A 30-ft edge-of-field buffer strip was applied to all row crop HRUs within subbasins with the highest, middle of the range and lowest loadings for TP and TN (see Table 1), i.e. subbasins 1, 4, and 9 for Driftwood and subbasins 15, 28 and 34 for Tippecanoe.

Table 1: Average simulated TP and TN loading per SWAT subbasin.

Driftwood Watershed Tippecanoe Watershed

Average Monthly Average Monthly TN Average Monthly Average Monthly Subbasin TP Loading Loading TP Loading TN Loading (lbs/acre) (lbs/acre) (lbs/acre) (lbs/acre) 1 0.49 4.30 0.09 1.04 2 0.14 1.77 0.14 1.37 3 0.25 3.26 0.09 1.09 4 0.28 2.44 0.12 2.21 5 0.18 3.33 0.08 1.19 6 0.25 2.36 0.09 1.16 7 0.17 3.05 0.10 1.19 8 0.23 3.93 0.11 1.38 9 0.15 1.74 0.09 1.20 10 0.18 2.98 0.09 1.84 11 0.16 2.28 0.11 1.74 12 0.22 2.96 0.10 1.66 13 0.24 2.74 0.10 1.52 14 0.21 2.42 0.08 1.68

3 Parameter definitions were taken from the SWAT2009 Input/Output File Documentation (Neitsch et al., 2010).

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Driftwood Watershed Tippecanoe Watershed

Average Monthly Average Monthly TN Average Monthly Average Monthly Subbasin TP Loading Loading TP Loading TN Loading (lbs/acre) (lbs/acre) (lbs/acre) (lbs/acre) 15 0.17 2.15 0.08 0.96 16 0.18 3.45 0.13 2.86 17 0.21 2.32 0.08 1.54 18 0.21 2.91 0.14 2.00 19 0.17 2.37 0.09 1.61 20 0.18 2.37 0.13 1.75 21 0.18 2.35 0.14 1.97 22 0.20 2.48 0.15 1.71 23 0.17 2.18 0.04 2.33 24 0.19 2.23 0.09 1.59 25 0.01 0.30 0.08 2.78 26 0.17 1.98 0.09 2.49 0.08 1.65 27 0.11 7.21 28 0.08 1.71 29 0.10 2.45 30 0.07 1.82 31 0.15 2.08 32 0.07 1.73 33 0.19 2.11 34 0.07 1.82 35 0.08 1.80 36

1.3. Scenario 3: Nutrient Management

Simulated conventional tillage corn/soybean and conventional tillage corn/no-till drill soybean rotations receive 93 lbs/ac of commercial fertilizer 10-34-0 in the spring before corn and 180 lbs/ac of anhydrous ammonia in the fall after soybean. In total, 189.3 lbs/ac of nitrogen and 32 lbs/ac of phosphorus are applied to corn/soybean rotations. In the nutrient management scenario, nitrogen fertilizer application rate was reduced to be close to the agronomic rate.

According to the USDA 2007 Census of Agriculture, the average production rate for corn in Driftwood watershed is 137 bushels/acre and 42 bushels/acre for soybean (Table 2). Therefore, a corn/soybean rotation would require about 130 lbs/ac of nitrogen, and 50 lbs/ac of phosphorus if fertilizer was applied at agronomic rates based on current yields and median soil test values (Table 3 and Table 4). In the Tippecanoe watershed, the average production rate for corn is 159 bu/ac and 47 bu/ac for soybeans (Table 2). A corn/soybean rotation would require about 160 lbs/ac of nitrogen and 60 lbs/ac of phosphorus based on agronomic rates (Table 3 and Table 4).

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In this scenario, anhydrous ammonia application rate was reduced from 180 lbs/ac to 155 lbs/ac in conventional tillage HRUs in subbasins 1, 4 and 9 for Driftwood and subbasins 15, 28 and 34 for Tippecanoe.

No reduction was applied to phosphorus fertilizer applications as the model is currently below agronomic rates.

Table 2: Average production of corn and soybean per county (in bushel per acre).

Watershed County Corn for grain – Soybean for beans - Average bushel per acre Average bushel per acre (2007) (2007) Shelby 125 35 Hancock 145 46 Driftwood Henry 144 47 Rush 133 40 Johnson 139 41 Kosciuko 153 50 Pulaski 158 44 Tippecanoe Fulton 153 45 White 171 49 Source: USDA 2007 Census of Agriculture by county. http://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_2_County_Level/Indiana/st18_2 _001_001.pdf

Table 3: Nitrogen recommendations for corn based on yield potential and previous crop (1995).

Source: http://www.agry.purdue.edu/ext/soilfertility/historical-recommendations.html

4-YEAR SUMMARY OF CORN RESPONSE TO NITROGEN FERTILIZER The average Agronomic Optimum N Rate (AONR) for all of our corn/soy sites since 2006 was 186 lbs/ac total applied N (with an average trial yield of 196 bu/ac). At the five Purdue locations where we conducted paired trials of corn/soy and corn/corn in 2007-2009, the average AONR for

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corn/corn was 47 lbs greater than for corn/soy while average corn/corn yields were 20 bu/ac less than the corn/soy yields. Source: Nitrogen Management Update for Indiana (Jan. 2010) - http://www.agry.purdue.edu/Ext/corn/news/timeless/nitrogenmgmt.pdf

Table 4: Phosphate recommendations for corn.

Yield potential - bu/acre

Soil test 100 120 140 160 180

ppm (lb/acre) lb P2O5 per acre 5 (10)1 85 95 100 110 115 10 (20) 60 70 75 85 90 15-30 (30-60)2 35 45 50 60 65 35 (70) 20 20 25 30 35 40 (80) 0 0 0 0 0 1 Values in parentheses are lb/acre. 2 Maintenance recommendations are given for this soil test range. Source: “Tri-State Fertilizer Recommendations for Corn, Soybeans, Wheat and Alfalfa” http://ohioline.osu.edu/e2567/

Table 5: Phosphate recommendations for soybeans.

Yield potential - bu/acre

Soil test 30 40 50 60 70

ppm (lb/acre) lb P2O5 per acre 5 (10)1 75 80 90 100 105 10 (20) 50 55 65 75 80 15-30 (30-60)2 25 30 40 50 55 35 (70) 10 15 25 25 30 40 (80) 0 0 0 0 0 1 Values in parentheses are lb/acre. 2 Maintenance recommendations are given for this soil test range. Source: “Tri-State Fertilizer Recommendations for Corn, Soybeans, Wheat and Alfalfa” http://ohioline.osu.edu/e2567/

1.4. Scenario 4: Cover Crops

The cover crop was modeled by planting annual ryegrass just after the last fall practice (either tillage or harvest), and by killing the crop just before the first spring practice (either tillage or planting). A cover crop was planted after soybean, winter wheat or after conventional tillage corn. A cover crop was not planted after no-till corn.

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A winter cover crop of annual ryegrass was simulated in all row crop HRUs in subbasins 3, 8 and 23 for Driftwood and in subbasins 19, 30 and 34 for Tippecanoe.

2. Scenarios’ results

2.1. Driftwood watershed

 Residue management scenario

Table 6: Modeling results for residue management scenario – Driftwood watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN Subbasin (in metric (in metric tons) (in kg) (in kg) (in kg/ha) (in kg/ha) tons/ha) Baseline 43,112 21,055 275,089 0.61 0.34 4.22 3 Residue Mngt 39,951 19,029 295,329 0.57 0.30 4.45 % change -7.3 -9.6 7.4 -7.3 -9.6 5.3 Baseline 48,817 25,923 445,634 0.59 0.32 4.99 8 Residue Mngt 43,260 23,253 492,639 0.52 0.29 5.39 % change -11.4 -10.3 10.5 -11.5 -11.6 7.9 Baseline 154,929 282,838 3,543,633 0.30 0.14 2.36 23 Residue Mngt 152,716 279,948 3,610,022 0.26 0.12 2.44 % change -1.4 -1.0 1.9 -13.1 -11.9 3.1 Note: Increase in TN in notill scenario includes a decrease in ORGN compared to baseline and an increase in NO3.

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ac) (in lb/ac) (in lb/ac)

Baseline 47,523 21,055 275,089 0.27 0.30 3.77 3 Residue Mngt 44,039 19,029 295,329 0.25 0.27 3.97 % change -7.3 -9.6 7.4 -7.3 -9.6 5.3 Baseline 53,811 57,150 982,454 0.26 0.29 4.45 8 Residue Mngt 47,687 51,265 1,086,082 0.23 0.25 4.81 % change -11.4 -10.3 10.5 -11.5 -11.6 7.9 Baseline 170,780 623,551 7,812,374 0.13 0.13 2.11 23 Residue Mngt 168,340 617,181 7,958,735 0.11 0.11 2.17 % change -1.4 -1.0 1.9 -13.1 -11.9 3.1

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 Filterstrip scenario

Table 7: Modeling results for filterstrip scenario - Driftwood watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN Subbasin (in metric (in metric (in kg) (in kg) (in kg/ha) (in kg/ha) tons) tons/ha) Baseline 57,710 44,767 394,980 0.79 0.59 5.48 1 Filterstrip 41,222 33,956 318,837 0.57 0.45 4.40 % change -28.6 -24.1 -19.3 -28.0 -24.3 -19.7 Baseline 113,815 126,399 1,119,151 0.53 0.51 4.09 4 Filterstrip 92,384 105,036 983,433 0.39 0.40 3.31 % change -18.8 -16.9 -12.1 -26.3 -22.4 -18.9 Baseline 45,105 50,347 601,097 0.34 0.18 2.28 9 Filterstrip 35,587 41,218 497,229 0.26 0.15 1.88 % change -21.1 -18.1 -17.3 -22.8 -18.8 -17.6

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ac) (in lb/ac) (in lb/ac)

Baseline 63,614 44,767 394,980 0.35 0.53 4.89 1 Filterstrip 45,439 33,956 318,837 0.25 0.40 3.93 % change -28.6 -24.1 -19.3 -28.0 -24.3 -19.7 Baseline 125,460 278,662 2,467,305 0.24 0.46 3.65 4 Filterstrip 101,836 231,565 2,168,100 0.18 0.35 2.96 % change -18.8 -16.9 -12.1 -26.3 -22.4 -18.9 Baseline 49,720 110,997 1,325,193 0.15 0.16 2.03 9 Filterstrip 39,227 90,870 1,096,202 0.12 0.13 1.67 % change -21.1 -18.1 -17.3 -22.8 -18.8 -17.6

 Nutrient management scenario

Table 8: Modeling results for nutrient management scenario - Driftwood watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN Subbasin (in metric (in metric (in kg) (in kg) (in kg/ha) (in kg/ha) tons) tons/ha) Baseline 57,710 44,767 394,980 0.79 0.59 5.48 1 Nutrient Mngt 57,798 44,799 387,476 0.79 0.59 5.40 % change 0.2 0.1 -1.9 0.2 0.1 -1.5

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Baseline 113,815 126,399 1,119,151 0.53 0.51 4.09 4 Nutrient Mngt 113,877 126,432 1,102,233 0.54 0.51 4.00 % change 0.1 0.0 -1.5 0.0 0.0 -2.1 Baseline 45,105 50,347 601,097 0.34 0.18 2.28 9 Nutrient Mngt 45,171 50,283 574,579 0.34 0.18 2.19 % change 0.1 -0.1 -4.4 0.2 -0.1 -3.8

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 63,614 44,767 394,980 0.35 0.53 4.89 1 Nutrient Mngt 63,711 44,799 387,476 0.35 0.53 4.81 % change 0.2 0.1 -1.9 0.2 0.1 -1.5 Baseline 125,460 278,662 2,467,305 0.24 0.46 3.65 4 Nutrient Mngt 125,528 278,735 2,430,007 0.24 0.46 3.57 % change 0.1 0.0 -1.5 0.0 0.0 -2.1 Baseline 49,720 110,997 1,325,193 0.15 0.16 2.03 9 Nutrient Mngt 49,792 110,856 1,266,729 0.15 0.16 1.96 % change 0.1 -0.1 -4.4 0.2 -0.1 -3.8

 Cover crop scenario

Table 9: Modeling results for cover crop scenario - Driftwood watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN Subbasin (in metric (in metric (in kg) (in kg) (in kg/ha) (in kg/ha) tons) tons/ha) Baseline 43,112 21,055 275,089 0.61 0.34 4.22 3 Cover crop 32,628 16,752 207,022 0.47 0.28 3.27 % change -24.3 -20.4 -24.7 -24.2 -16.5 -22.5 Baseline 48,817 25,923 445,634 0.59 0.32 4.99 8 Cover crop 37,937 21,469 440,387 0.46 0.26 4.83 % change -22.3 -17.2 -1.2 -22.3 -19.4 -3.2 Baseline 154,929 282,838 3,543,633 0.30 0.14 2.36 23 Cover crop 149,933 277,957 3,532,684 0.20 0.11 1.67 % change -3.2 -1.7 -0.3 -32.9 -23.0 -29.2

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Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 47,523 21,055 275,089 0.27 0.30 3.77 3 Cover crop 35,966 16,752 207,022 0.21 0.25 2.92 % change -24.3 -20.4 -24.7 -24.2 -16.5 -22.5 Baseline 53,811 57,150 982,454 0.26 0.29 4.45 8 Cover crop 41,818 47,332 970,887 0.20 0.23 4.31 % change -22.3 -17.2 -1.2 -22.3 -19.4 -3.2 Baseline 170,780 623,551 7,812,374 0.13 0.13 2.11 23 Cover crop 165,272 612,790 7,788,234 0.09 0.10 1.49 % change -3.2 -1.7 -0.3 -32.9 -23.0 -29.2

2.2. Tippecanoe watershed

 Residue management scenario

Table 10: Modeling results for residue management scenario - Tippecanoe watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN Subbasin (in metric (in metric tons) (in kg) (in kg) (in kg/ha) (in kg/ha) tons/ha) Baseline 112,565 393,477 6,396,567 0.14 0.11 3.04 19 Residue Mngt 112,411 394,058 6,397,470 0.14 0.12 3.08 % change -0.1 0.1 0.0 0.0 5.8 1.2 Baseline 12,069 49,768 1,079,106 0.12 0.16 3.22 30 Residue Mngt 12,016 51,496 1,081,430 0.12 0.16 3.24 % change -0.4 3.5 0.2 0.0 0.0 0.5 Baseline 42,371 37,811 396,166 0.30 0.28 2.97 34 Residue Mngt 41,223 39,757 393,466 0.29 0.29 2.95 % change -2.7 5.1 -0.7 -2.8 2.7 -0.9

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 124,082 393,477 6,396,567 0.06 0.10 2.72 19 Residue Mngt 123,912 394,058 6,397,470 0.06 0.11 2.75 % change -0.1 0.1 0.0 -0.9 5.8 1.2 30 Baseline 13,303 109,721 2,379,021 0.05 0.14 2.87

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Residue Mngt 13,245 113,530 2,384,145 0.05 0.14 2.89 % change -0.4 3.5 0.2 0.0 0.0 0.5 Baseline 46,706 83,358 873,397 0.13 0.25 2.65 34 Residue Mngt 45,441 87,649 867,444 0.13 0.26 2.63 % change -2.7 5.1 -0.7 0.0 2.7 -0.9

 Filter Strip scenario

Table 11: Modeling results for filterstrip scenario - Tippecanoe watershed.

Average annual load Average monthly yield Sediment TP TN Sediment TP TN Subbasin (in metric (in kg) (in kg) (in tons/ha) (in kg/ha) (in kg/ha) tons) Baseline 7,632 13,863 165,244 0.06 0.10 1.30 15 Filterstrip 5,423 10,374 131,327 0.04 0.07 1.03 % change -28.9 -25.2 -20.5 -31.7 -26.3 -20.9 Baseline 7,741 17,774 1,007,296 0.07 0.15 8.27 28 Filterstrip 6,057 14,025 838,990 0.05 0.12 6.91 % change -21.8 -21.1 -16.7 -22.3 -21.5 -16.5 Baseline 42,371 37,811 396,166 0.30 0.28 2.97 34 Filterstrip 31,796 29,267 318,478 0.23 0.22 2.38 % change -25.0 -22.6 -19.6 -25.5 -22.8 -19.9

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 8,413 13,863 165,244 0.03 0.09 1.16 15 Filterstrip 5,978 10,374 131,327 0.02 0.07 0.92 % change -28.9 -25.2 -20.5 -31.7 -26.3 -20.9 Baseline 8,533 39,186 2,220,708 0.03 0.13 7.38 28 Filterstrip 6,677 30,921 1,849,657 0.02 0.11 6.16 % change -21.8 -21.1 -16.7 -22.3 -21.5 -16.5 Baseline 46,706 83,358 873,397 0.13 0.25 2.65 34 Filterstrip 35,049 64,522 702,124 0.10 0.20 2.12 % change -25.0 -22.6 -19.6 -25.5 -22.8 -19.9

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 Nutrient management scenario

Table 12: Modeling results for nutrient management scenario - Tippecanoe watershed.

Average annual load Average monthly yield Sediment Sediment TP TN TP TN (in metric (in metric (in kg) (in kg) (in kg/ha) (in kg/ha) Subbasin tons) tons/ha) Baseline 7,632 13,863 165,244 0.06 0.10 1.30 15 Nutrient Mngt 7,630 13,861 157,430 0.06 0.10 1.25 % change 0.0 0.0 -4.7 0.0 0.0 -4.0 Baseline 7,741 17,774 1,007,296 0.07 0.15 8.27 28 Nutrient Mngt 7,743 17,773 998,047 0.07 0.15 8.22 % change 0.0 0.0 -0.9 0.0 0.0 -0.6 Baseline 42,371 37,811 396,166 0.30 0.28 2.97 34 Nutrient Mngt 42,351 37,774 372,229 0.30 0.28 2.81 % change 0.0 -0.1 -6.0 0.0 -0.1 -5.4

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 8,413 13,863 165,244 0.03 0.09 1.16 15 Nutrient Mngt 8,410 13,861 157,430 0.03 0.09 1.11 % change 0.0 0.0 -4.7 0.0 0.0 -4.0 Baseline 8,533 39,186 2,220,708 0.03 0.13 7.38 28 Nutrient Mngt 8,536 39,183 2,200,317 0.03 0.13 7.33 % change 0.0 0.0 -0.9 0.0 0.0 -0.6 Baseline 46,706 83,358 873,397 0.13 0.25 2.65 34 Nutrient Mngt 46,684 83,277 820,624 0.13 0.25 2.51 % change 0.0 -0.1 -6.0 0.0 -0.1 -5.4

 Cover crop scenario

Table 13: Modeling results for cover crop scenario - Tippecanoe watershed.

Average annual load Average monthly yield Sediment TP TN Sediment TP TN Subbasin (in metric (in kg) (in kg) (in tons/ha) (in kg/ha) (in kg/ha) tons) Baseline 112,565 393,477 6,396,567 0.14 0.11 3.04 19 Cover Crop 106,991 385,883 6,101,486 0.10 0.09 2.02 % change -5.0 -1.9 -4.6 -27.6 -18.1 -33.6

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Baseline 12,069 49,768 1,079,106 0.12 0.16 3.22 30 Cover Crop 9,656 37,935 635,856 0.09 0.12 1.95 % change -20.0 -23.8 -41.1 -28.7 -23.9 -39.3 Baseline 42,371 37,811 396,166 0.30 0.28 2.97 34 Cover Crop 27,300 25,811 246,225 0.19 0.20 1.89 % change -35.6 -31.7 -37.8 -35.5 -28.5 -36.5

Average annual load Average monthly yield

Sediment TP TN Sediment TP TN Subbasin (in tons) (in lbs) (in lbs) (in tons/ha) (in lb/ac) (in lb/ac)

Baseline 124,082 393,477 6,396,567 0.06 0.10 2.72 19 Cover Crop 117,937 385,883 6,101,486 0.05 0.08 1.80 % change -5.0 -1.9 -4.6 -27.6 -18.1 -33.6 Baseline 13,303 109,721 2,379,021 0.05 0.14 2.87 30 Cover Crop 10,644 83,631 1,401,823 0.04 0.11 1.74 % change -20.0 -23.8 -41.1 -28.7 -23.9 -39.3 Baseline 46,706 83,358 873,397 0.13 0.25 2.65 34 Cover Crop 30,093 56,904 542,834 0.09 0.18 1.68 % change -35.6 -31.7 -37.8 -35.5 -28.5 -36.5

2.3. Scenario results comparison and analysis

 Driftwood watershed

TP load decreases significantly under all BMP scenarios. The 30-ft filterstrip consistently produces the largest reductions in TP load for all subbasins modeled. The cover crop also gives important reductions in TP for 2 out of 3 subbasins modeled (Figure 3).

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0 1 4 9 3 8 23 1 4 9 3 8 23

-5 Filterstrip Residue Management Nutrient Cover crop Management

-10

-15 % change%

-20

-25 TP

% change in TP annual load compared to baseline -30 Figure 3: Percentage change in TP annual load under BMP scenarios for subbasins modeled - Driftwood watershed.

TN is reduced under all BMP scenarios except residue management. Modeling results show that the TN increase is due to an increase in nitrate (NO3). Studies have shown that residue management can either increase or decrease soluble nitrogen leaching losses depending on the combination of soils, crop, and tillage management factors4. However, no-till does increase soil porosity and water infiltration. The increased soil water may have contributed to the modeled increase in nitrate leaching in the Driftwood watershed.

4 The article “Considering no-till as a nitrogen-reducing best management practice” by D. Osmond et al provides an extensive review of no-till studies and literature –Available at: http://www.neuse.ncsu.edu/Final_release_no-till.htm

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15

10

5

0 1 4 9 3 8 23 1 4 9 3 8 23 -5 Filterstrip Residue Management Nutrient Management Cover crop

-10 % change%

-15

-20 TN -25 % change in TN annual load compared to baseline -30 Figure 4: Percentage change in TN annual load under BMP scenarios for subbasins modeled - Drifwood watershed.

 Tippecanoe watershed

In the Tippecanoe watershed, cover crops and filterstrips provide large reductions in TP that are more significant than in the Driftwood watershed (Figure 5). However, under residue management, annual TP load increases slightly (5%) although TP yield at the HRU level remain mostly unchanged (Table 10). The modeling increase corresponds to both an increase in organic/sediment attached P and an increase in soluble P, the latter being the most important. Therefore, the small increase in TP load under residue management is likely coming from an increased release in TP through channel processes as modeled by SWAT, and possible a small increase in sediment-bound P as phosphorus tends to accumulate near the soil surface in no-till systems5.

5 “Best Management Practices: Managing fertility in no-till” (Ohio State University Extension, AGF-209-95) http://ohioline.osu.edu/agf-fact/0209.html

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10

5

0 15 28 34 19 30 34 15 28 34 19 30 34 -5 Filterstrip Residue Management Nutrient Management Cover crop -10

-15 % change%

-20

-25 TP -30 % change in TP annual load compared to baseline -35

Figure 5: Percentage change in TP annual load under BMP scenarios for subbasins modeled - Tippecanoe watershed.

5

0 15 28 34 19 30 34 15 28 34 19 30 34 -5 Filterstrip Residue Management Nutrient Cover crop -10 Management

-15

-20 % change% -25

-30

-35 TN -40 % change in TN annual load compared to baseline -45

Figure 6: Percentage change in TN annual load under BMP scenarios for subbasins modeled - Tippecanoe watershed.

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REFERENCES

Osmond, Deanna, Noah Ranells, George Naderman, Michael Wagger, Greg Hoyt, John Havlin, and Steve Hodges. Date unknow. Considering no-till as a nitrogen-reducing best management practice. North Caroline State University, Departments of Soil Science and Crop Science. Available at: http://www.neuse.ncsu.edu/Final_release_no-till.htm

Best Management Practices: Managing Fertility in No-till. Ohio State University Extension, Departement of Horticulture and Crop Science. Factsheet AGF-209-95. Available at: http://ohioline.osu.edu/agf-fact/0209.html

No-till Corn Production: Achieving Maximum Nutrient Efficiency. Maryland Cooperative Extension. Facsheet 514. Available at: http://extension.umd.edu/publications/pdfs/fs514.pdf

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Kieser & Associates , L L C MEMORANDUM Environmental Science and Engineering LLCLLCLLC

To: Christa Jones, CTIC Date: February 17, 2011 Kellie Dubay, Tetra Tech Kevin Kratt, Tetra Tech Elizabeth Hansen, Tetra Tech

From: Jim Klang, K&A cc: project files Laurence Picq, K&A

Re: Potential boundary conditions when trading in the Wabash River watershed

Watershed physical features for consideration as boundary conditions

a. Reservoirs

Several major reservoirs and lakes are located throughout the watershed.

Table 1: Major lakes and reservoirs in Wabash River watershed.

Area (in Name acres) Monroe Lake 10,659.8 Patoka Lake 8,731.7 Mississinewa Lake 3,344.5 Broad Pond 2,957.8 Salamonie Lake 2,712.4 Cecil M Harden Lake 2,081.9 Lake Maxinkuckee 1,896.6 Geist Reservoir 1,799.4 Newton Lake 1,761.5 Lake Freeman 1,542.9 Lake Lemon 1,480.7 Cagles Mill Lake 1,454.0 Dogwood Lake 1,245.7 Eagle Creek Reservoir 1,214.2 Prairie Creek Reservoir 1,190.4 Lake Mattoon 916.2 Tippecanoe Lake 834.6 Huntington Lake 831.2 Greenwood Lake 812.3

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Figure 1: Lakes/reservoirs in the Wabash River watershed.

Large lake and reservoir systems will exhibit different nutrient attenuation capability than stream networks. Unless sufficiently justified by a vetted mass budget demonstrating a known level of nutrient persistence through the impoundment a discount factor for lake or reservoir attenuation cannot be created. In settings where an acceptable mass budget analysis is available for the impoundment in question, trading can occur across the feature by using an additional discount factor or adjusting the source location factor accordingly.

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b. Karst features

Karst features cover an approximate area of 263,500 acres within the Wabash River watershed (Figure 2).

Figure 2: Karst features in the Wabash River watershed.

Source: Indiana Geological Survey (KARST_MM65_IN: Sinkhole Areas and Sinking-Stream Basins in Southern Indiana)

Karst features create uncertainty within water quality trading programs by affecting the time of travel of the runoff and altering the chemical and biological dynamics the nutrient runoff is exposed to. However, water quality trading can occur within a broad karst region if the specific field generating credits does not reside within an individual karst feature’s contributing area. Therefore, this potential boundary condition does not apply to the whole region. A smaller scale restriction on eligibility can be provided by producing a protocol for the karst region that

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provides guidance on how to identify individual karst features and then by providing the policy restrictions for eligibility within the site’s contributing area.

c. Coal mines

Figure 3: Surface coal mines in the Wabash River watershed.

Source: Indiana Geological Survey (COAL_MINE_SURFACE_IN: Surface Coal Mines in Indiana)

Coal mines and associated landuse practices can create scenarios that influence water quality trading credit estimation. These sites can change hydrology, chemistry and biology of within a subwatershed. Chemical and physical interactions can change the nutrient spiraling dynamics. For instance, subwatersheds with lower pH from mine runoff can experience different

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bioavailability of nutrients from all sources. Care should be used when trading is attempted in close proximity to coal mine operations. The site specific conditions may not be predictable using the program’s broader credit estimation method projections.

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Appendix E: Point Source Survey Results

Water Quality Trading 2011

1. How familiar are you with the concept of water quality trading? Response Response Percent Count

10.0% 1

I am not familiar with the concept. 50.0% 5

I am somewhat familiar with the

concept. 40.0% 4

I am familiar with the concept. answered question 10

skipped question 0

2. Please rate your level of confidence in the water quality trading concept.

Response Response

Percent Count

I am very skeptical of the concept. 22.2% 2

I am skeptical of the concept. 33.3% 3

I am neutral. 22.2% 2

I have confidence in this concept. 22.2% 2

I have a great deal of confidence in 0.0% 0 this concept.

I need more information before I 0.0% 0 can answer this.

Why did you answer this way? 6

answered question 9

skipped question 1

3. If your facility was facing costly upgrades to comply with more stringent nutrient permit limits, would you consider participating in a water quality trade to meet limits and avoid a costly upgrade?

Response Response

Percent Count

Yes, I would consider it. 44.4% 4

I might consider it given more 44.4% 4 information.

No, I would not consider it. (Skip to 11.1% 1 Question 6)

Comments 1

answered question 9

skipped question 1

4. Who would you feel most comfortable trading with?

Response Response

Percent Count

Another waste water treatment 37.5% 3 facility that has excess credits

An agricultural producer and/or 0.0% 0 landowner generating credits

Both 50.0% 4

Neither 12.5% 1

Other (please specify) 0

answered question 8

skipped question 2

5. Some WWTP representatives raise concern about how to identify opportunities for buying credits, and how those conservation practices will be verified. Who would you trust to facilitate credit buying and ensure conservation practices are installed and working? You can choose more than one.

Response Response

Percent Count

USDA Natural Resources 42.9% 3 Conservation Service

Private consultants 71.4% 5

Soil and Water Conservation 57.1% 4 Districts

State officials 42.9% 3

Other (please specify) 2

answered question 7

skipped question 3

6. When WWTPs are in the process of upgrading, water quality trading can be a flexible tool. One example is to extend compliance schedules. Would a five or ten year delay in construction provide your facility with any advantages?

Response Response

Percent Count

Yes, to extend useful life of 25.0% 2 existing facility

Yes, to allow the nutrient upgrade to occur after another pending but preliminary activity is 50.0% 4 completed (e.g., TMDL, other non- nutrient effluent limit, new pre-treatment requirement)

Yes, that would allow me to sell 0.0% 0 credits to neighboring facilities

No 25.0% 2

Other (please specify) 2

answered question 8

skipped question 2

7. Would you like to learn more about water quality trading?

Response Response

Percent Count

Yes 87.5% 7

No 12.5% 1

Comments 0

answered question 8

skipped question 2

Page 4, Q6. When WWTPs are in the process of upgrading, water quality trading can be a flexible tool. One example is to extend compliance schedules. Would a five or ten year delay in construction provide your facility with any advantages?

2 It just depends on what the upgrade is. If its something that could wait that long Jun 15, 2011 1:29 PM then "Yes"

Page 4, Q8. Do you have additional questions or comments about water quality trading? If so, please enter here.

1 This is a commodity created by regulations. If the regulations did not allow for it Jun 22, 2011 11:04 AM there would be nothing to buy or sell. If nutrient loading on a body of water is too high then the source needs to be addressed. If the source is agricultural practices then address the problem. This method will put ultral low limits on permitted dischargers to almost force them to pay landowners to reduce runoff of nutrients. That being said, if I was looking at limits that would be very costly to achieve I would look at all options that were available to me, even trading credits.

2 None at this time.... Thank You! Jun 15, 2011 1:29 PM