Citation
Evaluation of Retention Basins and Soil Amendments to Improve Stormwater Management in Florida

Material Information

Title:
Evaluation of Retention Basins and Soil Amendments to Improve Stormwater Management in Florida
Creator:
Bean, Eban
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (302 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural and Biological Engineering
Committee Chair:
Dukes, Michael D.
Committee Co-Chair:
Sansalone, John
Committee Members:
Clark, Mark W.
Jones, Pierce H.
Heaney, James
Graduation Date:
8/7/2010

Subjects

Subjects / Keywords:
Basins ( jstor )
Fly ash ( jstor )
Lysimeters ( jstor )
Rain ( jstor )
Soil infiltration ( jstor )
Soil science ( jstor )
Soil strength ( jstor )
Soil texture ( jstor )
Soils ( jstor )
Stormwater ( jstor )
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
amendments, ash, compost, fly, leachate, retention, runoff, soil, stormwater
Alachua County ( local )
Genre:
Electronic Thesis or Dissertation
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
Agricultural and Biological Engineering thesis, Ph.D.

Notes

Abstract:
The research presented here addressed two aspects of stormwater prevention. The first aspect was the elimination of stormwater through retention basins. Infiltration rates were measured by Double Ring Infiltrometer (DRI) within 40 basins in Alachua, Leon, and Marion counties. Measured rates were compared to designed rates to determine whether basins were operating as designed. The 40 basins were equally divided between residential and Florida Department of Transportation land uses. Texture analysis was also performed on soil samples taken from each infiltration location; soil types ranged from sand to sandy clays. Eleven of the 40 basins were also instrumented with monitoring equipment to measure drawdown rates. Three basins did not adequately store water to determine drawdown rates. However, 6 of the remaining 8 basins had drawdown rates less than DRI rates while the other 2 basins were not statistically different from DRI rates. This indicated that subsurface conditions were controlling basin drawdown rates. DRI rates frequently varied by at least an order of magnitude within basins. Based on DRI rates, 16 (40%) basins had rates less than their designed rates, 10 (25%) had rates equal to their designed rates, and 14 (35%) basins had rates greater than their designed rates. Additionally, FDOT basins had a higher proportion of basins with greater DRI rates than residential basins and coarser basins were also more likely to have DRI rates greater than designs. Greater size and diversity of vegetation resulting from less frequent maintenance in FDOT basins may have resulted in a higher proportion of sites with rates equal to or greater than designs. The second aspect is stormwater generation. Traffic during construction has been shown to compact soils, resulting in reduced porosity and infiltration rates and increased runoff. In agricultural settings soil amendments have been found to counteract compaction effects. Two soil amendments (compost and fly ash) were evaluated for mitigating compacted soils. Forty-two lysimeters were filled with two soils (Orangeburg Sandy Loam and Arredondo Fine Sand) overlaying a drainage layer of quartz stone. Runoff was directed into collection tanks and volumes were recorded. The soils were compacted to levels representative of observed levels found in North Central Florida based on bulk densities and infiltration rates. Runoff and leachate samples were analyzed for nitrogen species and orthophosphorus. Incorporating fly ash did not significantly reduce runoff. Tillage to at least 10 cm decreased runoff compared to compacted soils. However, adding compost treatments did not significantly reduced runoff compared to just tillage. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2010.
Local:
Adviser: Dukes, Michael D.
Local:
Co-adviser: Sansalone, John.
Statement of Responsibility:
by Eban Bean.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Embargo Date:
10/8/2010
Resource Identifier:
004979789 ( ALEPH )
706489315 ( OCLC )
Classification:
LD1780 2010 ( lcc )

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1 EVALUATION OF RETEN T ION BASINS AND SOIL AMENDMENTS TO IMPROVE STORMWATER MANAGEMENT IN FLORIDA By EBAN ZACHARY BEAN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Eban Zachary Bean

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3 I dedicate this work t o my wife who has provided unwavering support

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4 ACKNOWLEDGMENTS For challenging me intellectually and supporting me, I thank my advisor, Dr. Michael Dukes. Dr. Dukes has encouraged me in my research and reiterated its importance. His professionalism and example has been an inspiration to me. For providing guidance and support as members of my Ph.D. committee, I also thank Drs. John Sansalone, James Heaney, Mark Clark, and Pierce Jones. I sincerely appreciate the time and energy they have invested in guiding my research and assisting in my professional development I am extremely thankful for the assistance and friendship of Christian Guzman, who worked tirelessly under the most trying conditions. I also would like to thank the staff within the Agricultural and Biological Engineering department, specifically: Jimmy Rummel, Billy Duckworth, Dan Burch, Orlando Lanni, Hannah OMalley and Steve Feagle. This research would not have been completed without each one of them. In particular, Paul Lane went above and beyond to assist me with completing my project. For sa mple analysis and advising on sample submission I thank Nancy Wilkinson, Bill DAngelo and Lamar Moon at the Analytical Research Laboratory. I would also like to thank Eric Livingston and the Florida Department of Environmental Protection for funding this research. Numerous officials from Suwannee and Northwest Florida Water Management Districts, Leon, Alachua, and Marion Counties, the City of Tallahassee, and the Florida Department of Transportation assisted specifically with supplying access and documentation for basins studied. Finally for encouragement, support, friendship, and great discussion, I thank Hal Knowles and Brent Philpot.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 15 ABSTRACT ................................................................................................................... 19 CHAPTER 1 INTRODUCTION AND RESEARCH OBJECTIVES ................................................ 21 Introduction ............................................................................................................. 21 Federal Regulations ......................................................................................... 21 Florida Regulations .......................................................................................... 23 Reducing stormwater production ............................................................... 25 Low Impact development ........................................................................... 26 Soil Compaction ...................................................................................................... 27 Compost ........................................................................................................... 29 Fly Ash ............................................................................................................. 31 Objectives ............................................................................................................... 36 2 EVALUATION OF RETENTION BASIN PERFORMANCE IN FLORIDA ................ 39 Introduction ............................................................................................................. 39 Stormwater Control .......................................................................................... 39 Retention Basi ns .............................................................................................. 39 Design and Permitting ...................................................................................... 41 Materials & Methods ............................................................................................... 45 Infiltration Basin Selection ................................................................................ 45 Basi n documentation ................................................................................. 45 Basin inspection ......................................................................................... 47 Permission ................................................................................................. 47 Selected basins .......................................................................................... 47 Infiltration Rate Measurements ......................................................................... 47 So il Sample Collection ..................................................................................... 50 Bulk density and volumetric water content ................................................. 51 Soil organic matter by loss on ignition ........................................................ 51 Soil texture by hydrometer ......................................................................... 52 Monitoring ......................................................................................................... 53 Data Analysis ................................................................................................... 54 Modeling ........................................................................................................... 55 Results .................................................................................................................... 58 Soil Texture ...................................................................................................... 58

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6 Infiltration Rates ............................................................................................... 59 Soil Organic Matter ........................................................................................... 59 Bulk Density ..................................................................................................... 60 Modeling ........................................................................................................... 60 Analysis .................................................................................................................. 61 Monitored vs. DRI ............................................................................................. 61 DRI and Monitored vs. Design .......................................................................... 62 DRI Infiltration Rates ........................................................................................ 62 Effects of Age ................................................................................................... 63 Vegetation ........................................................................................................ 65 Hydraulic Conductivity Models ......................................................................... 68 Summary and Conclusions ..................................................................................... 71 3 SOIL AMEMDMENTS FOR COMPACTED SOIL MITIGATION I: HYDROLOGY ... 92 Introduction ............................................................................................................. 92 Materials and Methods ............................................................................................ 93 Non compacted Phase ..................................................................................... 95 Compaction Phase ........................................................................................... 98 Amendment Phas e ......................................................................................... 101 Results and Discussion ......................................................................................... 104 Non compacted Phase ................................................................................... 105 Compacted Phase .......................................................................................... 107 Bulk densities ........................................................................................... 107 Infiltration rat es ........................................................................................ 108 Rainfall and runoff data ............................................................................ 109 Runoff coefficients and curve numbers .................................................... 110 Cone index profiles .................................................................................. 111 Amendment Ph ase ......................................................................................... 112 Bulk densities ........................................................................................... 112 Cone index profiles .................................................................................. 113 Infiltration rates ........................................................................................ 114 Runoff coefficients ................................................................................... 116 Curve number s ........................................................................................ 117 Conclusions .......................................................................................................... 119 4 SOIL AMEMDMENTS FOR COMPACTED SOIL MITIGATION II: WATER QUALITY .............................................................................................................. 147 Introduction ........................................................................................................... 147 Methods and Materials .......................................................................................... 148 Soils and Amendments ................................................................................... 148 Column Study ................................................................................................. 149 Lysimeter Study .............................................................................................. 150 Sampling and Analysis Methodology .............................................................. 151 Data Analysis ................................................................................................. 152 Results and Discussion ......................................................................................... 153

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7 Column Study ................................................................................................. 153 Nitrogen ................................................................................................... 153 Ortho phosphorus .................................................................................... 155 Metals ...................................................................................................... 155 Lysimeter Results ........................................................................................... 156 Rainfall Water Quality .............................................................................. 156 NO2+3N .................................................................................................... 157 NH4N ....................................................................................................... 158 TKN .......................................................................................................... 159 OP ............................................................................................................ 161 pH ............................................................................................................ 162 Lysimeter Runoff Loadings ............................................................................. 163 Nitrogen ................................................................................................... 163 Ortho phosphorus .................................................................................... 164 Lysimeter Leachate Loadings ......................................................................... 165 Nitrogen ................................................................................................... 165 Ortho phosphorus .................................................................................... 166 Conclusions .......................................................................................................... 167 5 CONCLUSIONS ................................................................................................... 179 Retention Basins ................................................................................................... 179 Performance Conclusions .............................................................................. 179 Recommendations and Future Research ....................................................... 181 Soil Amendments .................................................................................................. 182 Hydrologic Conclusions .................................................................................. 182 Amendment phase ................................................................................... 182 Applications .............................................................................................. 183 Water Quality Conclusions ............................................................................. 184 Recommendations and Future Research ....................................................... 185 APPENDIX A RETENTION BASIN DATA ................................................................................... 188 B SOIL MOISTURE AND CONE PENETROMETER DATA ..................................... 195 Soil Moisture ......................................................................................................... 195 Time Domain Reflectometer ........................................................................... 195 Volumetric Water Content .............................................................................. 195 Soil S trength ......................................................................................................... 196 C MONITORING DATA ............................................................................................ 203 D ADDITIONAL HYDROLOGIC AND SOILS DATA ................................................. 210 E LYSIMETER WATER QUALITY RESULTS .......................................................... 245

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8 F ADDITIONAL COLUMN STUDY WATER QUALITY DATA .................................. 277 Leachate Column Results ..................................................................................... 277 Total Phosphorus (TP) ................................................................................... 277 Potassium (K) ................................................................................................. 277 Sodium (Na) ................................................................................................... 277 Magnesium (Mg) ............................................................................................ 278 Calcium (Ca) .................................................................................................. 278 Aluminum (Al) ................................................................................................. 278 Iron (Fe) .......................................................................................................... 278 Manganese (Mn) ............................................................................................ 279 Zinc (Zn) ......................................................................................................... 279 Copper (Cu) .................................................................................................... 279 Boron (B) ........................................................................................................ 280 Nickel (Ni), Cadmium (Cd), and Lead (Pb) ..................................................... 280 Summary ........................................................................................................ 280 Fly Ash TCLP ........................................................................................................ 281 LIST OF REFERENCES ............................................................................................. 290 BIOGRAPHICAL SKETCH .......................................................................................... 302

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9 LIST OF TABLES Table page 2 1 Pedotransfer function models ............................................................................. 75 2 2 Pedotransfer model definitions for models in Table 21. ..................................... 77 2 3 Number of soil sample textures between land uses. .......................................... 77 2 4 Distribution of total and monitored number of basins .......................................... 78 2 5 Design, double ring infiltrometer an d monitored infiltration rates ....................... 78 2 6 S oil organic matter percentages for all sites by soil texture classification. .......... 79 2 7 Summary of model variable values from fitting double ring infiltrometer infiltration rate data. ............................................................................................ 79 2 8 Student t statistic and p values for infiltration rate comparisons for monitored basins. ................................................................................................................ 80 2 9 Summary of measured infiltration rate analysis for all basins. ............................ 80 2 10 R egression values for log r atio against log of basin age by Department of Transportation basins. ........................................................................................ 81 2 11 R egression values for log ratio against log of basin age r esidential basins. ....... 81 3 1 Summary of properties for soils and amendments i ncluded in this study. ........ 122 3 2 Non compacted bulk densities. ......................................................................... 122 3 3 Non compacted infiltration rates ....................................................................... 122 3 4 Pearson correlation coefficients and for noncompacted bulk density and infiltration rate. .................................................................................................. 122 3 5 Pearson correlation co efficients and for cone indices with bulk densities and infiltration rates. ................................................................................................ 123 3 6 Arredondo bulk densities and mean bulk density increase for each compaction iteration. ........................................................................................ 123 3 7 Orangeburg bulk densities and mean bulk density increase from each compaction iterat ion. ........................................................................................ 124 3 8 C ompacted bulk densities and percent of g rowth l imiting b ulk d ensities .......... 124

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10 3 9 Summary of compacted infiltration rates. ......................................................... 124 3 10 Scaling Factors for compacted phase .............................................................. 125 3 11 Rainfall event dates, depth, and effective rainfall depths. ................................. 125 3 12 Summary of compaction phase runoff coefficients for each soil. ...................... 126 3 13 Summary of compacted regressed curve numbers. ......................................... 126 3 14 Mean bulk densities (g/cm3) for each soil for each treatment. .......................... 126 3 15 ANOVA for amended phase bulk density results. ............................................. 127 3 16 Multiple linear regression results of amended bulk densities. ........................... 127 3 17 ANOVA for amended phase log of infiltration rates results. .............................. 127 3 18 Summary of log of infiltration rate mean. .......................................................... 128 3 19 R egression results of log transformed amended infiltration rates. .................... 128 3 20 Geometric m eans and standard deviat ions of amended infiltration rates ......... 129 3 21 Summary of depths from the surface of significant difference in cone index .... 129 3 22 Amended phase rainfall events and depths which runoff was measured. ........ 130 3 23 Summary of amendment phase Arredondo runoff coefficients. ........................ 130 3 24 Summary of amendment phase Orangeburg treatment runoff coefficients. ..... 131 3 25 Arredondo curve number regression against inverse of rainfall depth. ............. 131 3 26 Orangeburg curve number regression against inverse of rainfall depth. .......... 131 3 27 Summarized mean curve numbers for amended Arredondo treatments. ......... 132 3 28 Summarized mean curve numbers for amended Orangeburg treatments. ....... 132 3 29 Hypothetical runoff for Gainesville, FL for open areas treated with tillage ........ 132 4 1 Summary of properties for soils and amendments included in this study. ........ 169 4 2 Practical q uantitation limits, method detection limit, and column water matrix concentrations for analytes. .............................................................................. 169 4 3 p values for comparing soil and amendment mixture to soil column leached concentrations. ................................................................................................. 170

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11 4 4 Summary of rain event types, depths, and water quality characteristics .......... 171 4 5 Arredondo median runoff pHs and concentrations. .......................................... 171 4 6 Orangeburg runoff median pHs and concentrations. ........................................ 172 4 7 Arredondo leachate median pHs and concentrations. ...................................... 172 4 8 Orangeburg leachate median pHs and concentrations. .................................... 172 4 9 Mean Arredondo total runoff loadings ............................................................... 173 4 10 Mean Orangeburg total runoff loadings ............................................................ 173 4 12 Mean Orangeburg total leachate loadings ........................................................ 174 A 1 Comparison of stormwater retention design criteria for Water Management Districts in Florida. ............................................................................................ 188 A 2 Basin number, county location, land use, age, and design infiltration rates. .... 190 A 3 Soil textures for each basin test location and corresponding median basin texture. ............................................................................................................. 191 A 4 Soil organic matter percentages by percent weight from loss on ignition. ........ 192 A 5 Bulk density measurements from each basin location. ..................................... 193 A 6 Measured double ring infiltrometer infiltration rates for each basin test location. ............................................................................................................ 194 B 1 Volumetric w ater content by TDR attempts and successes for each basin. ..... 198 B 2 Gravimetric volumetric water content measurements from each basin location. ............................................................................................................ 199 B 3 Summary table of attempts, complete, truncated profile measurements, and ave rage depth of maximum reading for each basin. ......................................... 200 B 4 Correlation and probability values between cone penetrometer measurements at 2.5 cm increments and measured infiltration rates in basins for full profiles. .................................................................................................. 201 C 1 Summary of drawdown events for monitoring in basin 4. ................................. 203 C 2 Summary of drawdown events for monitoring in basin 5 .................................. 205 C 3 Summary of drawdown events for monitoring in basin 6 .................................. 206

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12 C 4 Summary of drawdown ev ents for monitoring in basin 13 ................................ 206 C 5 Summary of drawdown events for monitoring in basin 18. ............................... 207 C 6 Summary of drawdown ev ents for monitoring in basin 21 ................................ 208 C 7 Summary of drawdown events for monitoring in basin 25 ................................ 208 C 8 Summary of drawdown ev ents for monitoring in basin 30 ............................... 209 D 1 D istribution uniformities and unifor mity coefficients for natural and simulated events ............................................................................................................... 210 D 2 Non compacted bulk densities. ......................................................................... 211 D 3 Non compacted infiltration rates ....................................................................... 212 D 4 Non compacted summary of cone indices profiles. .......................................... 213 D 5 Student t test results for cone index values ...................................................... 214 D 6 Compacted bulk densities. ............................................................................... 215 D 7 Compacted infiltration rates. ............................................................................. 216 D 8 Runoff coefficients for each Arredondo lysimeter ............................................. 217 D 9 Runoff coefficients for each Orangeburg lysimeter ........................................... 218 D 10 Calculated and regressed curve numbers from compacted Arredondo lysimeters. ........................................................................................................ 219 D 11 Calculated and regressed curve numbers from compacted Orangeburg lysimeters. ........................................................................................................ 220 D 12 Amendment phase Arredondo bulk densities. .................................................. 221 D 13 Amendment phase Orangeburg bulk densities. ................................................ 222 D 14 Amendment phase Arredondo infiltration rates. ............................................... 223 D 15 Amendment phase Orangeburg infiltration rates. ............................................. 224 D 16 Summary of Arredondo cone index profiles indi cating significant differ ence between control treatments .............................................................................. 225 D 17 Summary of Orangeburg cone index profiles indicating significant differ ence between control treatments .............................................................................. 226

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13 D 18 Arredondo amended runoff coefficients. ........................................................... 227 D 18 Arredondo amended runoff coefficients. ........................................................... 228 D 19 Orangeburg amended runoff coefficients. ........................................................ 229 D 20 Calculated curve numbers for amended Arredondo lysimeters. ....................... 231 D 21 Calculated curve numbers from amended Orangeburg soils. ........................... 233 D 22 Summary of Arredondo calculated curve numbers regressed against inverse rainfall depths. .................................................................................................. 235 D 23 Summary of Orangeburg calculated curve numbers regressed against inverse rainfall depths. ...................................................................................... 236 E 1 Type, date, depth and water quality results for 16 rainfall events on lysimeters. ........................................................................................................ 245 E 2 Concentrations from homogenous column samples. ........................................ 246 E 3 Column leachate concentrations from Arredondo and compost mixtures. ........ 247 E 4 Column leachate concentrations from Arredondo and fly ash mixtures. ........... 247 E 5 Column leachate concentrations from Orangeburg and compost mixtures. ..... 248 E 6 Column leachate concentrations from Orangeburg and fly ash mixtures. ........ 248 E 7 Arredondo NH4N concentrations (mg/l) ........................................................... 249 E 8 Orangeburg NH4N concentrations (mg/l). ........................................................ 251 E 9 Arredondo NO2+3N concentrations (mg/l) ........................................................ 253 E 10 Orangeburg NO2+3N concentrations (mg/l) ...................................................... 255 E 11 Arredondo TKN concentrations (mg/l) .............................................................. 257 E 12 Orangeburg TKN concentrations (mg/l) ............................................................ 259 E 13 Arredondo OP concentrations (ug/l). ................................................................ 261 E 14 Orangeburg OP sample concentrations (ug/l). ................................................. 262 E 15 Arredondo pH ................................................................................................... 263 E 16 Orangeburg ph. ................................................................................................ 265

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14 F 1 Practical quantitation limits, minimum detection l imits and applied water matrix concentrations. ...................................................................................... 281 F 2 Toxicity characteristic l eaching p rotocol results for fly ash sample, with corresponding lab p ractical q uantitation l evel, m inimum d etection l evel, and toxicity limits. .................................................................................................... 282

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15 LIST OF FIGURES Figure page 2 1 Various testing location orientations based on basi n geometry. ......................... 8 2 2 2 Infiltration rate measurement using doublering infiltrometer with Mariotte siphon. ................................................................................................................ 82 2 3 I nfiltration rate measurement data from six sites at one infiltration basin ........... 83 2 4 Monitoring installation wi th water level recorder housing and rain gauges ......... 83 2 5 Frequency and cumulative distribution of double ring infiltrometer infiltration rates ................................................................................................................... 84 2 6 Frequency and cumulative distribution of log transf ormed double ring infiltrometer infiltration rates ................................................................................ 84 2 7 Modeled Ks versus measured Ks for models. .................................................... 85 2 8 Regression of double ring infiltrometer (DRI) and monitored infiltration rates .... 88 2 9 Distribution of t statistics log of infiltration rate ratios .......................................... 88 2 10 Example of size and diversity of vegetation in basin 9. ...................................... 89 2 11 Limited vegetation size and diversity in basin 39. ............................................... 90 2 12 Photo of basin 8 during double ring inf iltrometer testing. .................................... 91 3 1 Centerline, cross sectional diagram of a lysimeter from the left side. ............... 133 3 2 A) Well screen installed in the bottom of a lysimeter prior to filling. B) Measurement of drainage layer depth after filling. ............................................ 133 3 3 A) Filter fabric installed over drainage layer. B) Screening of Orangeburg soil during lysimeter filling. ...................................................................................... 133 3 4 A) Moving filled lysimeter via forklift. B) Lysimeters placed in their respective locations. .......................................................................................................... 134 3 5 Soil moisture sensor diagram. .......................................................................... 134 3 6 Soil compaction using tamper and slide weight. ............................................... 135 3 7 Compaction during final iteration using modified tamper. ................................. 135

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16 3 8 Schematic of lysimeter layout and rainfall simulator. Soil types are identified for each lysimeter as A (Arredondo) or O (Orangeburg). .................................. 136 3 9 Non compacted bulk density values. ................................................................ 136 3 10 Non compacted infiltration rates ....................................................................... 137 3 11 Maximum, mean, median, and minimum value cone penetrometer profiles ..... 137 3 12 Bulk densities following compaction iterations. ................................................. 138 3 13 Infiltration rates versus bulk densities for noncompacted and compacted lysimeters. ........................................................................................................ 139 3 14 Comparison of median noncompacted and control cone index profiles. ......... 140 3 15 Median Arredondo null amended cone index profiles. ...................................... 141 3 16 Median Arredondo compost amended cone index profiles. .............................. 142 3 17 Median Arredondo fly ash amended cone index profiles. ................................. 143 3 18 Median Orangeburg null amended cone index profiles. ................................... 144 3 19 Median Orangeburg compost amended cone index profiles. ........................... 145 3 20 Median Orangeburg Fly Ash amended cone index profiles. ........................... 146 4 1 NH4N water matrix and column leachate concentrations ................................. 174 4 2 NO2+3N water matrix and column leachate concentrations .............................. 175 4 3 TKN water matrix and column leachate concentrations .................................... 175 4 4 ON water matrix and column leachate conc entrations ..................................... 176 4 5 TN water matrix and column leachate concentrations ...................................... 176 4 6 OP water matrix and column leachate concentrations ...................................... 177 4 7 pH of water matrix and column leachate .......................................................... 177 4 8 Total nitrogen median results from each of the four soil amendment combinations .................................................................................................... 178 B 1 Average TDR VWC readings vs. Gravimetric VWC for each location tested. ... 202 D 1 Results from standard proctor density method soil samples. ........................... 237

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17 D 2 Curve number regression for Arredondo Null incorporation at 0 cm. ................ 237 D 3 Curve number regression for Arredondo Null incorporation at 10 cm. .............. 238 D 4 Curve number regression for Arredondo Null incorporation at 20 cm. .............. 238 D 5 Curve number regression for Arredondo Fly Ash incorporation at 10 cm. ........ 239 D 6 Curve number regression for Arredondo Fly Ash incorporation at 20 cm. ........ 239 D 7 Curve number regression for Arredondo Compost incorporation at 10 cm. ...... 240 D 8 Curve number regression for Arredondo Compost incorporation at 20 cm. ...... 240 D 9 Curve number regression for Orangeburg Null incorporation at 0 cm. ............. 241 D 10 Curve number regression for Orangeburg Null incorporation at 10 cm. ........... 241 D 11 Curve number regression for Orangeburg Null incorporation at 20 cm. ........... 242 D 12 Curve number regression for Orangeburg Fly Ash incorporation at 10 cm. ...... 242 D 13 Curve number regr ession for Orangeburg Fly Ash incorporation at 20 cm. ...... 243 D 14 Curve number regression for Orangeburg Compost incorporation at 10 cm ... 243 D 15 Curve number regression for Orangeburg Compost incorporation at 20 cm. ... 244 E 1 Arredondo mean runoff NH4N concentrations ................................................. 267 E 2 Orangeburg mean runoff NH4N concentrations. .............................................. 267 E 3 Arredondo mean leachate NH4N concentrations ............................................. 268 E 4 Orangeburg mean leachate NH4N concentrations .......................................... 268 E 5 Arredondo mean runoff NO2+3N Concentrations ............................................. 269 E 6 Orangeburg mean runoff NO2+3N concentrations ............................................ 269 E 7 Arredondo mean leachate NO2+3N concentrations .......................................... 270 E 8 Orangeburg mean leachate NO2+3N concentrations ........................................ 270 E 9 Arredondo mean runoff TKN concentrations .................................................... 271 E 10 Orangeburg mean runoff TKN concentrations .................................................. 271 E 11 Arredondo mean leachate TKN concentrations ................................................ 272

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18 E 12 Orangeburg mean leachate TKN concentration ............................................... 272 E 13 Arredondo mean runoff OP concentrations ...................................................... 273 E 14 Orangeburg mean runoff OP concentrations .................................................... 273 E 15 Arredondo mean leachate OP concentrations .................................................. 274 E 16 Orangeburg mean leachate OP concentrations ................................................ 274 E 17 Arredondo mean runoff pH ............................................................................... 275 E 18 Arredondo mean leachate pH ............................................................................. 275 E 19 Orangeburg mean runoff pH ............................................................................. 276 E 20 Orangeburg mean leachate pH ........................................................................ 276 F 1 TP column leachate concentrations for soil and amendment mixtures. ............ 283 F 2 K column leachate concentrations for soil and amendment mixtures. .............. 283 F 3 Na column leachate concentrations for soil and amendment mixtures. ............ 284 F 4 Mg column leachate concentrations for soil and amendment mixtures. ........... 284 F 5 Ca column le achate concentrations for soil and amendment mixtures. ............ 285 F 6 Al column leachate concentrations for soil and amendment mixtures. ............. 285 F 7 Fe column leachate concentrations for soil and amendment mixtures. ............ 286 F 8 Mn column leachate concentrations for soil and amendment mixtures. ........... 286 F 9 Zn column leachate concentrations for soil and amendment mixtures. ............ 287 F 10 Cu column l eachate concentrations for soil and amendment mixtures. ............ 287 F 11 B column leachate concentrations for soil and amendment mixtures. .............. 288 F 12 Ni column leachate concentrations for soil and amendment mixtures. ............. 288 F 13 Cd column leachate concentrations for soil and amendment mixtures. ............ 289 F 14 Pb column leachate concentrations for soil and amendment mixtures. ............ 289

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19 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EVALUATION OF RETENTION BASINS AND SOIL AMENDMENTS TO IMPROVE STORMWATER MANAGEMENT IN FLORIDA By Eban Zachary Bean August 2010 Chair: Michael Dukes Cochair: John Sansalone Major: Agricultural and Biological Engineering The research presented here addressed two aspects of stormwater prevention. The first aspect was the elimination of stormwater through retention basins. Infiltration rates were measured by Double Ring Infiltrometer (DRI) within 40 basins in Alachua, Leon, and Marion counties. Measured rates were compared to designed rates to determine whether basins were operating as designed. The 40 basins were equally divided between residential and Florida Department of Transportation land uses. Texture analysis was also performed on soil samples taken from each infiltration location; soil types rang ed from sand to sandy clays. Eleven of the 40 basins were also instrumented with monitoring equipment to measure drawdown rates Three basins did not adequately store water to determine drawdown rates. However, 6 of the remaining 8 basins had drawdown rates less than DRI rates while the other 2 basins were not statistically different from DRI rates. This indicated that subsurface conditions were controlling basin drawdown rates. DRI rates frequently varied by at least an order of magnitude within basins. Base d on DRI rates, 16 (40%) basins had rates less than their designed rates, 10 (25%) had rates equal to their designed rates, and 14 (35%) basins

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20 had rates greater than their designed rates Additionally, FDOT basins had a higher proportion of basins with gr eater DRI rates than residential basins and coarser basins were also more likely to have DRI rates greater than designs. Greater size and diversity of vegetation resulting from less frequent maintenance in FDOT basins may have resulted in a higher proporti on of sites with rates equal to or greater than designs. The second aspect is stormwater generation. Traffic during construction has been shown to compact soils resulting in reduced porosity and infiltration rates and increased runoff. In agricultural settings soil amendments have been found to counteract compaction effects. T wo soil amendments (compost and fly ash) were evaluated for mitigat ing com pacted soils. Forty two lysimeters were filled with two soils (Orangeburg Sandy Loam and Arredondo Fine Sand) overlaying a drainage layer of quartz stone. Runoff was directed into collection tanks and volumes were recorded. The soils were compacted to levels representative of observed levels found in North Central Florida based on bulk densities and infiltration rates. Runoff and leachate samples were analyzed for nitrogen species and orthophosphorus. Incorporating fly ash did not significantly reduce runoff Tillage to at least 10 cm decreased runoff compared to compacted soils. However, adding compost treatments did not significantly reduce d runoff compared to just tillage

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21 CHAPTER 1 INTRODUCTION AND RESEARCH OBJECTIVES Introduction Urban stormwater runoff is the sixth greatest source of impairment in assessed lakes, ponds, and reservoirs (EPA 2009a ). Stormwater was the eighth greatest impairment source for estuaries and tenth greatest for streams (EPA 2009a ). Runoff from paved surfac es ha s increased peak flow, time to peak, and runoff volumes through stream channels, causing overland erosion and stream bank instability (NRCS 1 986). Urban runoff also carries pollutants, such as sediments, nutrients, and heavy metal s, into surface water s (Barrett et al. 1 998; Davis et al. 2 00 1 ; Lee and Bang 2 000; He et al. 2 001). When urbanization occurs, impervious surfaces are typically added to the landscape, including streets, sidewalks, parking lots, driveways and buildings. Urbanization and development adversely affect surface waters physically, biologically, and chemically (Paul and Meyer 2 001). I ncreased runoff volumes, rates, peaks, and pollutant loadings as well as decreased time to peak and baseflow are dependent upon impervious area, specific ally directly connected impervious areas (Booth et al. 2 002; Lee and Heaney 2 003; Hatt et al. 2 004; Livingston et al. 2 006). Impervious surfaces decrease infiltration while increasing runoff. Decreased infiltration also decreases groundwater recharge, whic h is detrimental in the state of Florida (Delfino and Heaney 2 004). Federal Regulations In 1972, Congress created the Federal Water Pollution Control Act, commonly known as the Clean Water Act (CWA), to protect surface waters of the United States

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22 Section 303 of the CWA delegated responsibility of enforcing water quality to the individual states and established Total Maximum Daily Loads (TMDLs) as the pollutant measurement standard. These standards initially focused on point sources of pollution such as dis charges of industrial process wastewater and municipal sewage treatment plants. However, nonpoint sources such as stormwater runoff and discharge still accounted for a substantial amount of pollution for impaired waters (EPA 1 996). Approximately 46% of identified estuarine water quality impairment cases surveyed across the United States were attributable to stormwater runoff. As a result, Congress amended the CWA in 1987 establishing requirements for storm water quality (EPA 1 996). The National Pollutant Discharge Elimination System (NPDES) storm water program was developed to regulate stormwater discharges in large (Phase I) and medium (Phase II) communities. Under the NPDES program, all point source discharges must be permitted. Phase I communities, or groups of municipalities, had populations exceeding 100,000 (1996), while Phase II (1999) applied to municipalities of less than 100,000 people each. Under NPDES discharges of runoff from municipal separate storm sewer systems (MS4s) are point sources that must be permitted (EPA 2 000b). Additionally, small MS4s are responsible for developing, implementing, and enforcing a program to address discharges of post construction stormwater runoff from new development and redevelopment areas (EPA 2 000b). Total Ma ximum Daily Loads (TMDLs) are developed for impaired waters that do not meet water quality criteria and standards (EPA 2 00 9 a ). A TMDL for a specific water body is developed by determining the maximum pollutant loading the water body can

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23 assimilate and stil l meet water quality standards/criteria (EPA 2 006). The loading is then divided among existing permitted point discharges, safety factors, and a small portion to future pollutant sources (EPA 2 006). A substantial problem arises when municipalities experience urbanization in watersheds of impaired waters and must conform to TMDLs and are not allowed to exceed allotted loadings. A common solution for addressing urban runoff pollution is implementing Best Management Practices (BMPs). BMPs can be separated int o two categories: nonstructural and structural (EPA 2 000c). Nonstructural BMPs refer to certain styles of planning considerations that limit imperviousness and disturbance which reduces runoff production (EPA 2 000c). Structural refers to retention or det ention, infiltration, and vegetative BMPs (EPA 2 000c). Retention/detention BMPs function by collecting stormwater runoff and then permanently storing or slowly releasing it over time. These are typically wet/dry basins or ponds that remove pollutants by f iltration and/or settling (EPA 2 000c). Infiltration BMPs are designed to allow runoff to infiltrate through the soil, which filters pollutants out, into the groundwater. Examples of infiltration BMPs include permeable pavements, dry wells, and infiltration basins (EPA 2 000c). Florida Regulations In 1982, Florida was the first state to adopt a stormwater management rule which required a stormwater permit for all new or modified stormwater discharges that increased flows or discharges (Livingston 2 001) The law initially set two effluent limits: technology based and water quality based. However, due to rapid growth, increased stormwater discharges, and lack of understanding of stormwater impacts on receiving waters, water quality limits were not implemented (Livingston 2 001) The technology -

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24 based rule was implemented within the framework of the CWA and the role of water quality criteria. The rule relies on BMPs to achieve treatment standards of 80% removal of suspended solids; 95% if directly discharging to high quality, pristine water bodies In addition, Water Management Districts (WMD s ) have established water quantity criteria, such as peak runoff and volume limits (Livingston 2 001) In 1990, the stormwater management program was revised by the State Water Resource Implementation rule, which established that one of the primary goals of the program was to maintain the predeveloped stormwater characteristic during and after development. The rule also required 80% removal of post development stormwater pollut ant loadings which caused or contributed to impaired water quality. However, DEP and WMD rules were never updated to achieve this treatment (Livingston 2 001) In 1993, stormwater management and wetlands permitting were combined under Environmental Resour ce Permitting. Most development projects were then required to receive an Environmental Resource Permit (ERP) that would minimize the stormwater quantity and quality impacts. Some of the most widely used structural BMPs in developing areas are retention or infiltration areas Projects also must also comply with comprehensive plans and land development regulations. Managing growth is a nonstructural BMP that local governments utilize. In addition, Floridas Growth Management Program requires the use of these nonstructural BMPs, such as land use management, preservation of wetlands and floodplains, and minimizing impervious cover. In general, these BMPs promote Low Impact Development (LID) or conservation design (Livingston 2 001).

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25 In 1999, the Florida Water shed Restoration Act was enacted leading to the implementation of Floridas water body restoration program and the establishment of Total Maximum Daily Loads (TMDLs) (Livingston 2 001) Since the program began over 2000 impairments have been verified in Floridas surface waters with nutrients identified as the major cause of impairments (FDEP 2 008) As a result, the state is currently developing a Statewide Stormwater Treatment Rule. The Statewide Stormwater Treatment Rule will increase the level of nutrient removal required of stormwater treatment systems serving new development to address the nutrient enrichment of Floridas surface and ground waters This rule will be based upon a performance standard that the post development nutrient load will not exceed the nutrient load from natural, undeveloped, areas (FDEP 2 008). Harper and Baker (2007) demonstrated that wet detention would not be able to achieve 80% reduction of nitrogen while retention basins could by infiltration of stormwater Therefore, retention systems will likely be critical to achieving the nutrient goals for protecting Floridas water bodies. Reducing s tormwater p roduction In 2008, the National Research Council (NRC) issued a report commissioned by the EPA titled Urban Stormwater in the United States. The report summarized the current state of knowledge with regards to stormwater and its effects on water quality. The report concluded that drastic restructuring of the EPAs regulatory program was needed to effectively meet the requirements of the CWA (NRC 2 008) Land cover was found to be directly tied to biological conditions of downstream receiving waters (NRC 2 008). Roads and parking lots, which constitute up to 80% of directly connected impervious cover, capture and transport stormwater pollutants more

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26 quickly and directly than other land uses, especially for small stormwater events (NRC 2 008). Limiting directly connected areas can reduce the stormwater impacts of impervious cover. Although during large events, pervious areas become more significant contributors of stormwater and pollutants. In addition, individual stormwater controls were inadequate as individual solutions. The report concludes that stormwater control measures that harvest, infiltrate, and evaporate stormwater are criti cal to reducing stormwater volumes and pollutant loadings, especially from small events (NRC 2 008). The report cites better site design, downspout disconnects, conservation of natural areas and better landuse planning as practices which can dramatically r educe stormwater runoff volumes and pollutant loadings from new developments (NRC 2 008). Low Impact d evelopment Low Impact Development (LID) is an increasingly attractive approach to limit the impacts of development on the hydrologic balance (Dietz 2 007) LID incorporates decentralized stormwater management to limit the hydrologic and water quality impacts on downstream water bodies. (Dechesne et al. 2 004; EPA 1 999a). Initiated in Prince Georges County, Maryland, one of the main components of LID is minim izing and mitigating hydrologic impacts of land use activities closer to the source of generation (EPA 1 999 a ). LID also emphasizes open space and limiting the production of stormwater by reducing impervious cover, especially directly connected and increas ing open vegetated space that can infiltration rainfall and runoff from adjacent areas on site. The benefit of LID over conventional development is the ability to abate runoff from smaller more frequent rainfall events; however, these benefits diminish as event sizes increase (Alexander and Heaney 2 002; Hood et al. 2 007). In addition, LID is often the

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27 most simple and economic path for developers, reducing the cost of design, installation, operation, and maintenance of stormwater treatment and control system s (EPA 2 007). Soil Compaction Open vegetated spaces are typically assumed to produce much less runoff than impervious areas (NRCS 1 986). However, conventional development practices compact soils (Gregory et al. 2 006; Alberty et al. 1984; Pitt et al. 1 999). Compaction increases soil strength at the expense of large voids (Greacen and Sands 1 980). As a result the field capacity is increased while infiltration rates, porosity, and saturated hydraulic conductivities are decreased (Greacen and Sands 1 980). Compaction also shifts activity from aerobic to anaerobic (Whalley et al. 1 995). Anaerobic conditions from increased soil moisture resulting from compaction can promote denitrification of the soil (Ruser et al. 2 006; Hansen et al. 1 993; Breland and Hansen 1 996). Compaction also reduces biotic activity of roots and earthworms (Breland and Hansen 1 996; Whalley et al. 1 995), Residential soil compaction can be a greater influence on infiltration than soil series variability (Woltemade 2 010). Similarly, the e ffect on infiltration rate was not found to be dependent on the level of compaction; soils compacted to different levels did not have significantly different infiltration rates among the treatments (Gregory et al. 2006). Gregory et al. (2006) found that co mpaction from construction equipment on sandy soils in Northern Florida reduced infiltration rates between 80 and 99 percent. Additionally, in a study for the US EPA, noncompacted and compacted sandy soils had infiltration rates of 414 mm/h and 64 mm/h, r espectively (Pitt et al. 1 999 b ). Assuming undisturbed soil conditions when predicting runoff from open areas may lead to substantial underestimation of runoff volumes (Woltemade 2 010). The greatest

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28 difference in runoff for compacted and noncompacted soils is for small, frequent storm events that LID should provide the most runoff reduction (Woltemade 2 010). T he level of compaction depends greatly on the soil type, pH, moisture level, organic matter, iron oxides in addition to others (Kozlowski 1 999) Recovery from compaction is very soil specific as well While s urface layers of sandy soils can take b etween 4 and 9 years to recover, some clayey (~40%) soils take longer than 4 0 years to recover (Kozlowski 1 999; Woltemade 2 010; Radford et al. 2 007) Ve hicular traffic compacts soils in three ways, the normal force of the vehicle weight, shear from wheel slippage, and vibrations from the engine (Kozlowski 1 999; Gill and Vanden Berg 1 968) Traffic can compact soils up to 1 m deep, but usually most compacti on occurs in the root zone or top 30 cm (Kozlowski 1 999) Additionally, the first few vehicle passes have been shown to result in the most compaction (Gregory et al. 2 005; Ampoorter et al. 2 007). Physical processes, such as freezing and thawing, wetting and drying, nonuniform water absorption, and soil dehydration by root system uptake, cannot eliminate the effects of compaction all together (Kozlowski 1 999) However, root and earthworms can r egenerate the soil structure after compaction through physical and biological processes (Langmaack et al. 1 999) Bartens et al. (2008) found that tree roots could improve infiltration through compacted subsoils, even when bulk densities were greater than growth limiting values. Infiltration rate increases were evident after only 12 weeks (Bartens et al. 2 008). Methods for mitigating compaction are typically specific to the land use, but preventing compaction is typically much less expensive (Kozlowski 1 999). Several methods have been used to mitigate compaction in agr icultural settings,

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29 including allowing natural processes to occur and tillage to depths greater than 35 cm, also known as subsoiling (Raper and Kirby 2 006; Naseri et al. 2 007). Hamza and Anderson (2005) reviewed soil compaction mitigation practices and sug gested, among others, maintaining vegetative soil cover, deep ripping and increasing soil organic matter. Compost Composting is defined by NRCS as the process of providing optimal conditions for bacteria to decompose organic material at an increased rate; compost is the resulting product (NRCS 1 998). The Florida Department of Environmental Protection classifies compost based on type of waste processed, product maturity, amount of foreign matter in the product, particle size and organic matter content, and concentrations of heavy metals (FDEP 1 989). Additionally the National Organics Standard Board recommended to the National Organic Program that quality compost can be produced from raw materials ranging in carbon to nitrogen (C:N) ratio from 15:1 to 60:1, r ather than previously thought 25:1 to 40:1 range (National Organic Standards Board 2 002). The pH of compost ranges from 6.0 to 8.0; outside of this range can be detrimental to vegetation, causing metal toxicity or reducing availability of nutrients (Landsc hoot 2002) Amending soils with compost decreases bulk densities (Landschoot and McNitt 1994; Cogger 2005), increases infiltration rates (Landschoot and McNitt, 1994; Aggelides and Londra 2000; Curtis et al. 2007) and increases water holding capacity (Pandey 2005; Loper 2009; Weindorf et al. 2004). Decreased bulk density has been attributed to two processes; 1) dilution of highdensity material and 2) increasing porosity (Cogger 2005).

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30 Compost has been used as a replacement for inorganic fertilizers since it contains many nutrients (Filcheva and Tsadilas 2002). To supply enough nutrients for turf for at least a year, 2.5 to 10 cm of compost can be tilled into a depth of 10 to 15 cm (Landschoot 2002). C ompost typically contains low concentrations of nutrients relative to inorganic fertilizers n utrient content depend upon the source of organic material (Landschoot 2002) O nly about 10% of the nitrogen in composted biosolids is available to plants in the first growing season (Landschoot 2002) Eghball (2002) found similar nitrogen and phosphorus runoff concentrations between compost and inorganic fertilizers. While compost can be a source of i ncreased nutrient concentrations, increased infiltration typically decrease overall runoff loadings compared to non amended soils (Glanville et al. 2004; Landschoot 2002). Nutrient leaching from compost applications, especially in sandy soils, could pose a threat to ground water quality. Loper (2005) reported that while compost did increase nitrogen losses on fine sandy soils, most NO2+3N losses occurred immediately after application. Similarly Gaskin et al. (2005) reported that runoff nutrient loadings w ere initially higher from compost applications on clay loam soil, but a year later runoff loadings were between 10 and 75 % of bare soil loadings. Compared to inorganic fertilizers compost applications do not increase nitrogen or phosphorus in groundwater and produced similar or higher crop yields (Jaber et al. 2005; Jaber et al. 2006; Pandey 2005). Nitrate leaching is primarily dependent upon C:N ratios. Studies using compost with C:N ratios of less than 20:1 detected nitrate leaching, however, composts with C:N ratios greater than 30:1 allow microorganisms to immobilize nitrogen making it

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31 unavailable to plants (Landshoot 2002) Thus, compost with C:N ratios between 20 and 30 are optimal for crops, but higher C:N ratios would further reduce available NO2+ 3N (Landschoot 2002). While compost has been demonstrated to generally improve soil quality without impacting water quality, most research has occurred on agricultural soils. Cogger (2005) noted that little research has been conducted on amended soils di sturbed by urban development; specifically lawn recommendations need to be developed and research compost amendments on water relations to the urban landscape. However in one such study near Seattle, Washington, Pitt et al. (1999b ) attempted to improve soi l characteri stics by incorporating compost into compacted sandy soils. Total porosity increased from 41 to 48%, bulk density decreased from 1.7 g/cm3 to 1.1 g/cm3, and particle density decreased from 2.5 to 2.1 g/cm3. Infiltration rates for composted plots were 1.5 to 10 times greater than nonamended soils. A mended soils also had higher nitrogen and phosphorus concentrations, but with increased infiltration the runoff loadings w ere significantly reduced. Fly Ash Another amendment that has been investigat ed is fly ash, a byproduct of coal burning energy plants. As of 2006, 48% (2 trillion kilowatt hours) of the United States (US) power was produced from coal, followed by natural gas (20%), nuclear (19.4%), and hydroelectric (7.0%). By 2030 coal generated electricity is expected to reach approximately 3 trillion kilowatt hours (EIA 2008). Coal resources in the US are concentrated in the Rocky and Appalachian Mountains, Illinois, and a region stretching from Texas to Alabama (USGS 2009).

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32 Energy is released from coal by combustion which produces the byproducts bottom ash and fly ash. This residual ash is the noncombustible inorganic material incorporated into coal, which ranges from 3 30 % (Torrey 1978). Once collected, fly ash is typically either landfilled (75 80%) or used in concrete mixtures (2025%) (Reddy 1997). Fly ash makes up between 10 and 85% of the ash from coal burning power plants and ranges in color from tan to black, depending on the remaining carbon content (Torrey 1978). Bulk density ranges from 0.79 to 1.16 g/cm3, particle densities from 2.14 to 2.48 g/cm3 (Torrey 1978; Pathan et al. 2003). Due to the particle size of fly ash (0.5 to 100 m) it typically must be removed from the exhaust fumes, also referred to as flue gas, by scrubbers. Particles are highly insoluble aluminosilicates known as cenospheres (Khandekar et al. 1997). Noncombusted carbon tends to be of larger particle sizes, exceeding 300 m. Fly ash specific gravity varies greatly from 1.2 to 3.0 g/cm3, but are commonly clos e to 2.0 (Sarkar et al. 2005; Pathan et al. 2003; Bayat 1997; Khandekar et al. 1997). Two classes of fly ash are common in the United States: Class C and F. Class F fly ash is commonly produced in the eastern US, while Class C is predominantly produced in the mid west and western regions. These materials are distinguished by their combined content of silicon dioxide, aluminum oxide, and iron oxides. Class F which is noncementitious has at least 70% oxide content (ASTM 2008). Class C has is cementitious an d has less than 70% oxide content (ASTM 2008). Fly ash provides several potential physical benefits to incorporation into crop fields: increased the water holding capacity, increased plant available water, and decreased bulk densities (Khandekar et al. 1997; Gangloff et al. 2000; Chang et al.

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33 1977), which can aid reduce the susceptibility of grass to drought (Adriano and Weber 2001).Increased soil moisture may result from a shift from primarily large macropores to more micropores (Pathan et al. 2003) Multi ple researchers have also noted that infiltration rates were significantly reduced when fly ash was mixed with soils ranging from sand to sandy clay loam (Kalra et al. 1998; Gangloff et al. 2000; Pathan et al. 2003). However, fly ash additions to silty loa m soils either did not significantly decrease or improved infiltration rates (Cox et al. 2001; Adriano and Weber 2001). Relative texture between fly ash and soil may not solely determine fly ash effects on infiltration. Chang et al. (1977) reported that for three soils, a silty clay and two sandy loams, that the hydraulic conductivity decreased when fly ash fractions were above 10% by volume. However, for two other soils, a sandy loam and a loam, hydraulic conductivities increased with fly ash fractions unt il between 20 and 25% by volume. The first three soils were acidic while the final two had neutral pH values. Researchers posited that different hydraulic conductivity responses to increasing fly ash may have resulted from soil pH effects on pozzolanic reactions, which cement soil particles, between the soil and fly ash. Pathan et al. (2002) analyzed the effect of incorporating fly ash into soil to reduce leaching of nutrients due to the chemical properties and high surface area. Soil was composed of 92% c oarse sand. Two types of fly ash were used: unweathered (fresh) or approximately 3year old stockpile (weathered). Column experiments were done in uniformly packed columns containing 0, 5, 10, or 20% fly ash/soil (wt/wt) mixtures;. Batch studies showed that sorption of NO2+3N NH4N and P was higher in the fly ash than the sand. Pathan et al. (2002) speculated that since Al2O3 and Fe2O3 were higher

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34 in t he fly ash more positive binding sites may have been available for NO2+3N Fly ash provided a higher s ource of extractable P and cationic exchange capacity. In another study by Pathan et al. (2003) the extractable P was 20 to 88 times higher on fly ash amended soils than sandy soils. R elatively high levels of extractable P in some ash samples may indicate that fly ash provides plant available P. Therefore, sandy soils amended with weathered fly ash that has a moderate capacity to adsorb P may show a decrease in P leaching without comprom ising P availability to plants. The pH of fly ash can range from 4.5 t o 12.8 (Reddy 1997; BinShafique et al. 2006), but tends to be more alkaline, typically between 8 and 10. In addition, the CEC of fly ash ranges from 2.3 to 15.4 cmol/kg (Pathan et al. 2003). Compared to soils, fly ash tends to have higher concentrations o f heavy metals. While metal concentrations tend to be below US EPA (2004) standards for hazardous waste (Pathan et al. 2003; Hower et al. 1995; Nathan et al. 1999), not all are nontoxic (Baba and Kaya 2004) and should be analyzed before amending soils. In a column study BinShafique et al. (2006) found that while leaching of Cd, Cr, Se, and Ag did increase initially, concentrations were one to two orders below Wisconsin standards. Field measurements were similar or slightly lower than concentrations measur ed in the column leaching test (Bin Shafique et al. 2006) Metals tend to have limited mobility in fly ash due to alkaline pHs, however mixing with soils can reduce the pH and cause leaching of metals from fly ash surfaces, specifically As, Fe, Ni, Cu, Mn, Pb, Cd, Cr, and Zn (Ram et al. 2007; BinShafique et al. 2006). Ram et al. (2007) noted that the decrease in pH coupled with the increase in release of Ca+ suggests that Ca+ is a principal component in controlling the pH among cations, by releasing OHio ns on hydrolysis. At higher pHs Pb, Cd, Zn, and Cr are not

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35 very soluble and were the only trace metals above the detection limit from the initial leaching. While fly ash can lead to phytotoxicity in plants due to increased B availability it can also supply essential plant nutrients such as Ca, Mg, K, S, Mn, and Zn (Adriano et al. 2002). Although corn production was not negatively affected, fly ash did not increase soil pH from a range of 4.6 to 5.2 to the target of 6.5 due to low application rates (Tarkal son et al. 2005) Gupta et al. (2007) noted that low amendment rates (10%) were optimal for palak, a leafy vegetable, growth over lower and higher fly ash rates, suggesting the benefits of the incorporation diminish at rates over 10%. Singh et al. (2008) applied varying rates of fly ash (0 20%) to fields growing palak. Increased fly ash rates resulted in increased damage and reduction in productivity to the vegetation. Concentrations of heavy metals, specifically Ni, Cd, and Pb, all increased significantly with increased fly ash rates. Singh et al. (2008) recommend that fly ash not be applied to areas where leafy vegetables are to be grown due to the potential for toxicity. Tripathi et al. (2004) compared plant growth on fly ash and blends with garden soil, press mud, and cow manure. Plant material was analyzed every 20 days for 60 days and Cu, Zn, Ni, and Fe were found to be significantly accumulated in the plant material. Fly ash was limiting to vegetation development, possibly due to low nutrients (N and P) in the fly ash (Tripathi et al. 2004). However, lack of plant development may have been more accurately attributed to the high levels of aluminum and pH (8.8) in the fly ash. Soluble salts from the fly ash, while in the root zone of the soil have a det rimental effect on the plants, since they can be taken up by the plant, producing phytotoxicity

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36 (Adriano et al. 2002; Stevens and Dunn 2004). However, once the salts are removed, the remaining metals and nutrients do not seem to inhibit the typical crop pr oduction (Adriano et al. 2002), and based on Stevens and Dunn (2004), may improve the production over areas not receiving fly ash. It should be noted that plants develop optimally under various conditions, so the limited number of crops observed in these studies may not be typical of all vegetation. Previous research into amending soils with fly ash have mostly been limited to agricultural settings and no previous research has been discovered which examined how fly ash may affect compacted urban soils. While fly ash may reduce the benefits of tillage alone with respect infiltration rates on coarse soils, fly ash may provide additional water quality or horticultural benefits in return. Objectives Nutrients in stormwater runoff remain a significant source of pollution to surface waters in Florida. Infiltration most effectively eliminates stormwater nutrient loadings from directly entering surface waters. With increasing concern over nutrient impacts on surface waters (i.e. possible numeric water quality criteria (Obreza et al. 2010)) stormwater retention and infiltration practices may be the predominant means for stormwater treatment. However, very little is known about the hydraulic performance and factors affecting the performance of Florida retention basins. To better understand factors affecting retention basin performance in Florida, the following objecti ves were included in this study: Determine whether retention basin infiltration rates were different than their design rates.

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37 Evaluate whether using doublering infiltrometers accurately estimated basin performance. Identify whether basin attributes or soil properties were correlated to basin performance. The detrimental effects of stormwater may also be diminished by reducing its production. T he FDEP plans to release an updated stormwater rule which will include elements of low impact development es pecially those which achieve onsite infiltration of stormwater (FDEP 2008). However, compaction has been found to significantly reduce infiltration rates in developed areas (Gregory et al. 2006; Pitt et al. 1999b). Compost has been shown to improve infil tration rates on agricultural soils but tend to increase soil nitrogen and phosphorus concentrations (Cogger 2005). Research focusing on amending compost into compacted urban soils found similar changes to infiltration and nutrients but was limited to a si ngle study (Pitt et al. 1999b). Pitt et al. (1999b) found that while concentrations of nitrogen and phosphorus increased, increased infiltration significantly reduced runoff loadings. Fly ash has been shown to decrease infiltration rates on sandy agricult ural soils ( Gangloff et al. 2 000 ; Kalra et al. 1998; Pathan et al. 2003) but increase or not significantly affect infiltration rates on agricultural soils with higher silt and clay contents (Chang 1977; Adriano et al. 2002). In addition fly ash has been sh own to increase water holding capacity and plant available water which could aid establishing of vegetation. While fly ash heavy metal content does not usually exceed levels for hazardous waste, metals may be released which can produced phytotoxicity. H owever, research has not investigated how fly ash amending may affect infiltration rates and water quality on previously compacted soils. To evaluate compost

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38 and fly ash as potential soil amendments for urban soil compaction mitigation, the following objec tives were included in this study: Determine whether incorporating compost or fly ash could improve infiltration through compacted urban soil. Evaluate whether soil amendments contribute pollutants to runoff or leachate. Materials and methods used to complete these objectives along with results and conclusions are described separately in the following chapters. A final conclusion chapter summarizes the findings and recommendations from this study.

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39 C HAPTER 2 EVALUATION OF RETENT ION BASIN PERFOR MANCE IN FLORIDA Introduction Stormwater Control Runoff is produced when rainfall intensities exceed the infiltration rate and storage has been satisfied. Development practices decrease the infiltration potential of lands and increase runoff predominantly by increasing imperviousness compared to the previous land use (NRCS 1 986). Increased runoff rates and volumes from developed areas can erode established drainage pathways in a watershed and often carry pollutant loadings (McCuen and Moglen 1 988; Lee and B ang 2 000). Two strategies have been used in stormwater control to address increased runoff rates and volumes: detention and retention (Dykehouse 2 001). Detention refers to the detaining of runoff, typically in wet or dry ponds. Runoff is collected in an i mpoundment and the outlet flow rate is controlled to mitigate the increased runoff rate (Dykehouse 2 001). Thus the outlet hydrograph is longer in duration and flatter with respect to the flow rates than the inflow hydrograph, however with the same volume. There are often water quality benefits to detention, especially with sedimentation and particle bound pollutants. Retention Basins Retention refers to the retaining of runoff, typically by infiltration, but also by evapotranspiration to a lesser extent. T he initial runoff carries proportionally more of the pollutants from a catchment than later runoff; this is often referred to as the first flush (Sansalone and Cristina 2004). With retention, either all or a portion of runoff is infiltrated. While there are concerns about potential soil and groundwater contamination

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40 (Barraud et al. 1999; Pitt et al. 1999a), research has found that pollutants are confined to top 1 to 3 m soil layer of infiltration basins across textures from silty loam to coarse sand (Dechesn e et al. 2004; Bardin et al. 2001; Le Coustumer et al. 2007; Winiarski et al. 2006). Often retention structures are designed to capture and retain a design volume and allow excess flows to pass by or through the structure (Dykehouse 2 001). In Florida, ret ention basins receive stormwater runoff to typically be infiltrated which must be achieved within 72 hours (Harper and Baker 2 007). The drawdown is set at 3 days to allow for runoff storage from subsequent storms, maintain aesthetic value and inhibit mosq uito larvae production ( Harper and Baker 2007). However, basins can fail due to various changes in the soil, watershed, or surrounding hydrology (Livingston 2 000; Tan et al. 2 003; Sumner et al. 1 999) Retention structures with smaller footprints must maint ain higher infiltration rates to completely recover captured volumes to achieve water quantity or quality goals (Lee et al. 2010). In addition, retention basins that do not drawdown quickly may provide optimum habitat for mosquitoes to breed (Kaufman et al. 2005; Hunt et al. 2006). Retention systems have two driving design criteria: storage volume and recovery. The recovery time is dependent on the infiltration rate of soils and available soil porosity above the water table. Improper infiltration rate estimations can result in stormwater remaining in basins beyond design holding times (Livingston 2000) The risk of retention basin overflow depends on the probability of a single large event or multiple events occurring during the drawdown phase (Guo and Hughes 1999). The latter of which can become more common if infiltration rates are significantly below rates assumed for the design (Livingston 2000). A lterations to the soil, such as compaction or sealing of the

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41 surface (Hillel 1 998; Gregory et al. 2 006 ; Tan et al. 2003), can affect infiltration rates to the point where basins fail to recover their entire volume in the required time, resulting in failure. The International Stormwater Best Management Practices Database shows that retention ponds (infiltration basins) are highly effective at removing Total Suspended Solids (TSS) (GC & WWE 2 00 8 ). Due to the ability to trap sediments, clogging is the predominant cause of retention basin failure (Siriwardene et al. 2007; Tan et al. 2003; Bouwer et al. 2001). Quickly settling large particles accumulate at the surface of the soil matrix (Teng and Sansalone 2 004). This forms a schmutzdecke, which traps smaller and smaller particles This phenomenon occurs when the ratio of soil media particle diameter ( dm) to infiltrating particle diameter (dp) is less than 10. When dm/dp > 10, particles are trapped within the soil media or they can pass through the media, if dm/dp is large enough. However, particles trapped in the schmutzdecke or within the soil media fill flow pathways and reduce fillable porosity, which reduces hydraulic conductivity ( Sansalone et al. 2 00 8 ). Clogging can occur from excessive sedimentation without maintenance, especially during construction (Livingston 1995). Compaction during construc tion can also significantly decrease soil infiltration rates (Gregory et al. 2006). Both compaction and clogging could invalidate infiltration rate estimations (Livingston 1995). Therefore, maintenance is necessary for infiltration structures to consistent ly achieve the drawdown criteria (Livingston 2 000). Design and Permitting Harper and Baker (2007) summarized the stormwater regulation and authority structure within Florida. Floridas stormwater regulatory program was implemented cooperatively by the Flor ida Department of Environmental Protection (FDEP) and the

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42 five Water Management Districts (WMDs): North West Florida (NWFWMD), Suwannee River (SRWMD), St. Johns River (SJRWMD), South Florida (SFWMD), and Southwest Florida (SWFWMD). Each WMD has its own set of rules and regulations established in FAC Chapter 40, A E, for administering the stormwater management program. A summary of rules specific to retention designs and volume recovery for each WMD is in Appendix A. Volume recovery is stated as less than 72 h ours for all WMDS, except SWFWMD, which requires no more than half the volume recovered in 24 h ( SWFWMD 2 009 ). Retention basin volume recovery occurs via vertical and horizontal or l ateral flow. Initially, vertical flow dominates lateral flow. However, i f the volume is great enough to saturate the underlying soil, vertical infiltration can be limited. This may occur once infiltration reaches a limiting soil layer which intersects the water table or has a significantly lower hydraulic conductivity. At thi s point, lateral flow begins and may eventually dominate (SJRWMD 2 006). As a result, both vertical and horizontal hydraulic conductivities are essential for accurate recovery time calculations In general, WMDs favor a geotechnical analysis of the soils by an appropriate certified professional for justification of design values. Various field and laboratory methodologies for determining these values are accepted (FDEP 1 988). One of the most common methods for determining the vertical hydraulic conductivity is the DoubleRing Infiltrometer (DRI) (SJRWMD 2 006); a constant head infiltration rate measurement (ASTM 2 003). Use of DoubleRing Infiltrometers (DRIs) is an approved method for determining vertical infiltrate rates as long as a safety factor of 0.5 is applied to the infiltration rate in design calculations (FDEP 1 988). These measurements and

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43 parameters are typically input into one of two models, PONDS or MODRET, which are based on MODFLOW, to verify that drawdown will occur in 3 days for a specific return period storm (SJRWMD 1 99 3 ). Retention basin designs must be reviewed and approved by WMD officials to be permitted. Basin design calculations, along with design infiltration rates, are typically included within permit applications. In 1986, 30 of 65 (4 8%) surveyed Maryland retention basins were working properly; four years later 1990, only 18 of 48 (38%) were working properly (Livingston 2000). Harper and Baker (2007) summarized the only two available references of infiltration basins in Florida. The basin in the first study was located in a commercial watershed in Orlando, FL. Harper and Baker (2007) noted in the first that concentrationbased removals for total phosphorus and total nitrogen were 61% and 90%, respect ively. The second study was a simulation study which estimated removal efficiencies based on yearly rainfall and runoff events (Harper and Baker 2007). Basins were assumed to completely recover their storage volume between events. R emoval efficiencies of 85%, 90%, and 95% were associated w ith capture depths of 0.6, 1.3 and 1.9 cm respectfully. Harper and Baker (2007) note that retention system performances has been estimated throughout Florida based on these values. Measuring infiltration rates within a basin can be used to determine whether vertical infiltration rates are equal to their permitted rates. Differences between design and DRI infiltration rates may suggest the surface soil is limiting the basin infiltration performance. However, point measurements do not characterize the enti re basin and

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44 infiltration rates have been shown to be log normally distributed and vary greatly within close proximity ( Logsdon and Jaynes 1 996). Basin performance can also be affected by the presence of a hydraulically limiting soil layer or changes in t he water table which likely would not be observed by DRI measurements at the surface. However, the effects of infiltration limiting layers or water table interactions would affect basin drawdown rates. Monitoring changes in basin water levels determine whether sub surface processes are limiting volume recovery. These sub surface processes would likely be controlling recovery rates as opposed to DRI rates if monitored rates were lower than DRI rates. Therefore, to assess whether point measurements are adequate measures of basin performance, actual drawdown rates need to be measured. Monitoring water level changes and measuring drawdown rates within basins could evaluate the value of the DRI measurements to evaluate normal basin performance. Measuring DRI rates can be a time intensive procedure, especially at multiple locations within a basin. Thus, predicting the infiltration rates from soil information that can quickly be collected and used may improve and hasten the process of basin evaluat ion. Therefore two pedotransfer functions and to quasi physically based models were selected to evaluate their potential for fitting the collected data to the hydraulic conductivities. Wagner et al. (2001) reviewed several pedo transfer functions. Of those the Wosten (et al. 1 999) and Brakensiek (et al. 1 984 ) models had data inputs matching those available from this study. In addition, one of the most common physically based models for predicting hydraulic conductivity is the Kozeny Carman (Chapuis and Auber tin 2 003). The model assumes flow through media is analogous to flow through a

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45 number of pipes of a certain diameter. Finally, Ahuja et al. (1984) modified the Kozeny Carman model to fit conductivity to effective porosity and empirical constants. The obje ctives of this study were to 1) determine whether measured infiltration rates within basins were different from design rates 2) determine whether measured infiltration rates could be used to evaluate basin performance, and 3) evaluate different models to p redict measured infiltration rates based on soil characteristics within selected Florida basins. Materials & Methods Infiltration Basin Selection Basins included in this study were selected from Leon, Alachua, and Marion counties, around the cities of Tallahassee, Gainesville, and Ocala, respectively. Two soil types (fine sand and loamy fine sand) were targeted as representative of textures in Florida. Basins were selected from Alachua and Marion Counties to represent fine sands and from Leon County for loamy fine sand due to the prevalence of these soil textures in their respective areas. Basins selected were also divided between two land uses: residential and Florida Department of Transportation ( DOT). Approximately 250 retention basins were considered for this study. Basins were inspected once they were identified. Basins included in this study met three criteria: design infiltration rates available, regular storage volume recovery, and permission granted by land owner Basin d ocumentation Next, permi ts and design calculations were located and pertinent information acquired. Officials from the Florida Department of Environmental Protection (FDEP), Suwannee River Water Management District (SRWMD), Southwest Florida Water

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46 Management District (SWFWMD), Al achua, Marion, and Leon County DOT and permitting offices assisted with these documents. At a minimum, information collected from these documents included the design infiltration rate of basins, estimated year of construction, and location. Design infiltr ation rates for basins included in this study were based on a variety of methods to measure the hydraulic conductivity or soil permeability. Common methods included double ring infiltrometer, field and laboratory permeability measurements, and estimations from Natural Resources Conservation Service (NRCS) soils information. Information (county, land use, age, and design infiltration rate) for each basin included in this study is listed in Appendix A Design information for older basins was not always avail able which eliminated those basins from consideration. Alachua County basin information came from a combination of Environmental Resource Permits ( ERPs ) on file with both SRWMD and SJRWMD, and design calculations from F lorida DOT officials. Marion County DOT information was collected from ERPs on file with the SWFWMD. Leon County residential basin information was obtained through ERPs from the FDEP and permits with Leon County. Leon DOT basin information was obtained from FDEP via the DOT. Typically, only o ne basin was selected from a per mitted project, residential or DOT, to increase the diversity of the sample population. However, only three independent projects with design information were obtained for Leon County. No additional projects with design infor mation were found within the surrounding counties, which limited the diversity of the sample subpopulation. Thus, the 11 DOT retention basins in Leon County were from only three projects. All other basins were from independent permits.

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47 Basin i nspection Te sting could not be completed if a basin held water due to measurements and collection of basin bottom soils. In this study, out of approximately 250 basins inspected, 48 basins (19%) of those considered were eliminated based on ponded conditions upon inspection. Basins also had to be accessible by vehicle for transporting testing equipment. Permission Most residential basins in Alachua County were within developments where a Home Owners Association (HOA) was the governing body and property manager. This b ecame a significant issue, since nearly all HOAs denied requests to include one of their basins in this study. Lack of permission was the leading cause of residential basins being excluded from this study rather than uncertainties about performance. However, with the assistance of SRWMD officials, this issue was resolved and 11 residential basins were included in this study from Alachua County. Most residential basins in Leon County were managed by Leon County, which allowed all requested basins to be inc luded in this study. Florida DOT also allowed including basins within this study. Selected b asins Based on these criteria, 40 basins were selected from both residential and DOT land uses. Basins were located in Alachua (16), Leon (20) and Marion (4) counti es in Florida and were equally distributed between residential (20) and DOT (20) land uses. Infiltration R ate M easurements In each basin, six locations were selected for infiltration rate measurements, except in Basins 1, 2, and 29, which had 9, 9, and 3 locations, respectively, totaling 243 locations within the 40 basins. These locations were selected based on basin geometry

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48 to evenly distribute measurements throughout the basin. See Figure 2 1 for typical basin shapes and corresponding typical locations of testing. Infilt ration tests, soil samples, soil moisture readings, and cone penetrometer tests were conducted at each of the within basin locations. The Double Ring Infiltrometer (DRI) is comprised of two concentric rings to measure infiltration rates ( Figure 22 ) Most research evaluating DRIs have focused on comparisons to other methods and have found DRIs to be suitable based on limited variability of results (AnguloJaramillo et al. 2 000; Ahuja et al. 1 993; Touma and Albergel 1 992). Comparison to rainfall simulators may provide the best comparison to actual infiltration rates however findings vary on this evaluation. Sidiras and Roth (1987) found that doublering infiltrometers ha d rates 2 to 5 times greater than the rainfall simulator, while Touma and Albergel (1992) found rates to be indistinguishable from simulator rates. A DRI based on the ASTM (2003) Standard Test Method for Infiltration Rate of Soils in Field Using Double Ring Infiltrometer was used to measure infiltration rates. This method is a constant head measurement of infiltration rates. The ASTM (2003) standard ring diameters are 30 and 60 cm, while the ring diameters used in this study were 15 and 30 cm. Smaller r ing sets have been criticized for having higher infiltration rates than larger ring sets due to limited area representation (Lai and Ren 2 007). However, Gregory et al. (2005) found more consistent results using 15 and 30 cm rings rather than ASTM standard sized rings. In addition, smaller rings were easier for transporting between sites and required a smaller water volume due to the reduced crosssectional area.

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49 Concentric rings were driven into the soil and then filled with water to equal depths. Equal water levels are maintained to prevent horizontal flow of water from the inner ring. To maintain a constant head in the inner ring, a M ariotte siphon constantly resupplied the inner ring, while a manually controlled water supply tank replenished the outer ri ng ( Figure 22 ). By preventing horizontal flow, the rate of inner ring water replenishment was equivalent to the vertical infiltration rate. Water levels from both ri ngs, the m arriotte siphon and the outer supply tank were collected approximately every five minutes for at least one hour or until the Mariotte siphon was completely depleted (between 50 and 55 cm of water). Prior to data collection, all equipment were cal ibrated. Figure 2 3 shows an example of basin infiltration rate data as a constant infiltration rate was approached. The infiltration rate decay was often not observed for infiltration rate measurements in this study (see lowest two curves in Figure 2 3 ). It is thought that the infiltration rates became constant very rapidly and constant rates were reached be fore data collection could capture this process (Gregory 2 004). The final infiltration rate was determined to be the infiltration rate of the testing location after the infiltration rate stabilized. However, if the rates did not stabilize, or the final ra te deviated from the trend of preceding values, then professional judgment was used to determine the infiltration rate. For example, in Figure 2 3 if the data point designated with (~ 8 cm/h) had occurred at the end of that test rather than before, professional judgment would have been used to select or estimate a more representative value since it would have deviated from the trend of values approaching 6 cm/h. I nfiltration rates for 8 of the 243 tests were less than 0.1 cm/h. However, since

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50 the minimum measurable infiltration rate for the equipment was determined to be 0.1 cm/h, a value of 0.05 cm/h was assumed for analysis purposes. As previously mentioned, the DRI is a common and accepted method used in designing retention basins. Darcys law is: = (2 1) where q is the flow rate (L3T1), i is the gradient (unitless) and k is the conductivity for the media and fluid (LT1). With the double ring infiltrometer, as the infiltrated depth increases the gradient approaches 1 a s time tends to infinity. Therefore, assuming a constant conductivity, the infiltration rate approaches the hydraulic conductivity of the soil. As a result, the measured infiltration rates are assumed to be equal to the hydraulic conductivity of the soil, which is the com mon method for determining the hydraulic conductivity for retention basin design. Soil Sample Collection An intact core soil sampler was used to collect soil samples for analysis (Blake and Hartge 1 986). The soil sampler drove a 60 mm long by 54 mm diamet er metal cylinder into the surface soil. Once the sampler was removed from the soil, the soil sample was extracted from the cylinder and excess soil extruding from the cylinder was trimmed away flush with the end of the cylinder. Soil samples were collected at each location within a basin, except at site 3, where only one sample was collected from the first location. Although the property manager had agreed for the site to be included in the study, the site developer insisted that data collection end and th e researchers leave the premises after the first sample had been collected. A cross all basins, a total of 238 samples were collected. Soil samples were analyzed for soil bulk density, volumetric

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51 water content, soil organic matter and sand, silt, and clay particle size mineral composition. Bulk d ensity and v olumetric w ater c ontent Prior to sample collection the soil cylinder and sample bag masses were recorded. After collection, the mass of the cylinder, bag, and moist soil was measured. Samples were then h eated to 105 C for at least 24 hours to remove soil water. Samples were B, g/cm3) : = / (2 2) where Ms was the dried soil mass (g) and VT was the soil core volume (137 cm3). Bulk densities were compared to growth limiting bulk densities (GLBD) (Daddow and Warrington 1 983) to determine whether the soil structure may have inhibited vegetation establishment. Soil o rganic m atter by l oss on i gnition Organic Matter percentage (OM% ) was quantified by Loss On Ignition (LOI). Approximately 10 g soil samples were massed and dried at 105C for 72 hrs. Samples were weighed and then heated at 550 C for three hours (Heiri et al. 2 001). Samples were weighed again to determine the mass after organic matter was lost. The organic matter percentage was calculated by: % = / ( + ) (2 3) where MM was the mass of the mineral material remaining after firing and MOM was the mass of the organic matter in the sample determined as the dif ference in sample weights before and after firings.

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52 Soil t exture by h ydrometer Soil texture was determined by particlesize analysis (ASTM 2 007) for each soil sample. Samples were passed through a No. 10 sieve (ASTM 2 003) to remove any aggregates greater than 2 mm. Due to the low clay content expected for most of these soils, between 50 and 100 g samples were used. Higher sample masses allow for a more accurate measurement of the clay in suspension. Although, Gee and Bauder recommend that samples with OM g reater than 5% be treated to remove OM (Gee and Bauder 1986), a slightly lower threshold of 3.5% was used in this study. Samples which were determined to have OM% greater than 3.5% were treated with hydrogen peroxide, H2O2 (Baquacil (27%), Arch Chemicals, Inc., Norwalk, Connecticut ) and heated at 90C to oxidize and remove the organic matter Sample masses were measured and soaked in 350 ml of 15 mg/l Sodium Hexametaphosphate (NaHMP) solution for 24 hrs. Samples were then dispersed for 5 minutes and transferred to sedimentation cylinders. Deionized water was added to the cylinder until the volume was 1 L. Cylinders were shaken for 60 seconds to suspend the sediments. Hydrometer measurements were recorded at 30 s, 60 s, 90 m, and 24 h. The sample solution was then passed through a No. 200 sieve and the mass remaining on the sieve, the sand fraction, was collected and dried. Sand and clay fractions were calculated from hydrometer readings; sand fractions were verified by sieved mass. Silt was determined to be t he remaining fraction of the sample. Textures were determined for each location and basin based on the respective sand, silt, and clay content (NRCS 1 993). Basin textures were determined to be the median sand and clay contents of samples from each basin; s ilt was assumed to be the remaining fraction. Particle size percentages were further used to calculate the

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53 average pore radius (Gupta and Larson 1 979) to predict the growth limiting bulk density for samples with less than 3% OM (Daddow and Warrington 1 983) Additional measurements were also performed at each location, including soil moisture measurement by Time Domain Reflectometer (TDR) and soil strength by a Field Scout (Spectrum Technologies, Inc., Plainfield, Illinois) SC 900 Cone Penetrometer. Both pen etrometer and TDR data were not found to be significant and were not incorporated into the analysis However, these data are summarized in Appendix A. Monitoring Of the 40 basins included in this study, 11 were selected for monitoring; six DOT and five r esidential. Basins were instrumented with equipment to measure and record water levels within the basin. Monitored basins were instrumented with an Infinities (Infinities USA, Inc., Port Orange, Florida) pressure water level data logger, a HOBO (Onset C omputer Corporation, Bourne, MA) data logging rain gauge, and a manual rain gauge ( Figure 2 4 ). Logge rs were enclosed in a PVC pipe and well screen housing The well screen was covered by a filter sock and installed in wells approximately 100 cm below basin soil surface. The void space surrounding the PVC housing was then filled in with coarse sand. Bentonite chips covered the top of the well. The housing extended approximately another 250 cm above the basin surface. Both rain gauges and PVC housing were mounted to a post. Water levels were recorded hourly and hourly rainfall totals were determined from rain gauge data. Individual rainfall events for each site were isolated based on a 6 hour period without rainfall. Drawdown rates were determined to be the average decrease of water depths while still above the basin surface. Individual continuous drawdown events

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54 were isolated from each site and paired with corresponding rainfall events. Data collection began between March and May 2009 for each of the eleven basins. Data was collected approximately every three months. Data Analysis For clarity in this and following sections, the term log refers to log10 while ln refers to loge. Three different tests were performed on the infiltration rate data to determine whether populations were signi ficantly (p < 0.05) different. The three tests compared: 1) DRI vs. design, 2) monitored vs. design and 3) DRI vs. monitored. Previous research has shown that infiltration rates tend to be log normally distributed (Minasny and McBratney 2 007; Logsdon and Jaynes 1 996; Zhai and Benson 2 006; Kosugi 1 996). Analysis of histograms also determined that the entire population of infiltration rates measured in this study were log normally distributed. However, design rates were also log normally distributed, suggest ing that the log normality may have resulted from the random basin selection. Since the total number of measurements within each basin ranged from 3 to 9, assessing the distribution is difficult. However, rates from 30 of 40 sites were found to either be l og normally distributed, or be closer to log normally distributed using the Shaprio Wilks test. Therefore, infiltration rates were assumed to be log normally distributed. The log ratio represents the log scale difference between these rates. The t statist ic was calculated by = ( 0) / 2/ (2 4) where is the mean log ratio, 0 is 0 s is the standard deviation of the log ratios and n is the number of measurements in the sample. Since DRI rates were assumed equal to

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55 design, which results in a ratio of 1 and log ratio of 0, the log ratios were evaluated with comparison to 0. Basins with infiltration rates greater than design had log ratios significantly greater than 0, while significantly lower rates resulted in log ra tios significantly less than 0. Basins with rates not significantly different had log ratios not significantly different than 0. Populations were determined to be significantly different if the calculated t statistic was greater than the t statistic corre sponding to a 95% confidence interval for the respective degrees of freedom. Geometric mean infiltration rates were determined as 10 raised to the power equal to the mean log ratio and then multiplied by the design rate. Modeling As an alternative to measuring, many different models have been developed to define the relationship between various soil characteristics and infiltration rates. Models vary from physically to empirically based. Parameters can range from the basic like bulk density to the complex such as pore size distribution index. Several models were evaluated to determine if they may be useful for estimating DRI rates in the future. For modeling analysis DRI rates were assumed equal to the saturated hydraulic conductivity Ks. Samples with b < 1.0 g/cm3 (9) or > 1.8 g/cm3 (2); b < 1.0 g/cm3), and OM > 8% (5)) were omitted for the modeling analysis as suggested by Saxton et al. (2006). Nine models were evaluated for predicting the D RI infiltration rates using available soil data collected. The units for Ks for all models are cm/d. The models are listed in Table 21 with parameter definitions in Table 22 While Ahuja et al. (1984) noted that similar models had found the value of n to vary narrowly and was approximately equal to 4 and Sulieman and Ritchie (19 99) found

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56 n equal to 4.09, however, Franzmeier (1991) and Messing (1989) found n values between 1.50 and 3.25. Additional data, not collected for this study was required for the AhujaKozeny Carman and Kozeny Carman models. Both total and effective porosity were synthesized for each soil sample. Total porosity was calculated by accounting for the mineral and organic fractions. Four models required porosity inputs, which were calculated for each soil sample by the following: = 1 ( ( 1 0 01 ) / + 0 01 / ) (2 5 ) s, particle density, was assumed to be 2.65 g/cm3OM was assumed to be 1.25 g/cm3 (Boyd 1 995), and OM was the organic matter percent. The Saxton model however calculated porosity by the following: = 0 332 7 251 10 4 + 0 1276 ( ) (2 6 ) Effective porosity was calculated using the following equations (Saxton and Rawls 2 006) : = + ( 0 6360 0 107 ) (2 7 ) = 0 278S + 0 034 C + 0 022 OM 0 018S OM 0 027C OM + 0 452S C + 0 299 (2 8 ) For the Kozeny Carman model, the soil specific surface areas were calculated using two methods. The following equation was used to determine SSA: SSA = / (2 9 ) where dn was either d50, the soil median particle diameter, or dH the harmonic mean diameter and for d50 and is 1 for dH. Both d50 and dH were calculated from mineral fraction results. The

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57 d50 was interpolated on l n scale within the texture class limits of the gradation where the median particle size fell. The harmonic mean was determined from a Particle Size Distribution (PSD). The PSD was determined from sand, silt, and clay fraction values (Sk aggs et al. 2 001): = 1 /( 1 + ( 1 / 1 ) [ ( 1 ) ^ ] ) (2 10) = 0 609 ( ) (2 11) = 2 941 94 (2 12) = ( ( ( + ) 1 1 ) ( 1 1 ) ) (2 13) = ( ( ( + + ) 1 1 ) ( 1 1 ) ) (2 14) where P is the fraction of mass between the ri and ri 1, and FVFS is very fine sand fractions where C is the clay fraction, r is the particle radius and 1m < r < 1000 m Values for FVFS were interpolated within the sand fraction based a particle diameter of 250 m. The resulting conversion was 0.436 times the sand fraction. Models were evaluated using both effective and total porosities and specific surface areas based on median and harm onic mean particle diameter to compare model accuracies. Two multiple linear regression (MLR) models were developed to predict DRI rates from soils data collected from each basin. Stepwise multiple linear regression was used for model building (SAS Instit ute Inc. 2 00 1 ). The first model (MLR 1) began with all collected inputs from the previous models in the regression. The second model (MLR 2) limited the parameters to the direct physical measurements of: B, OM, S, Si, and C This model was intended to ev aluate the potential of estimating DRI rates by a simpl er model. Model results were evaluated by the Geometric Mean Error Ratio (GMER) and Geometric Standard Deviation Error Ratio (GSDER), where:

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58 = (2 15) = exp ( 1 [ ( ) ] ) = 1 (2 16) = exp 1 1 [ ( ) ln ( ) ]2 = 11 2 (2 17) where pi is the predicted value, mi is the measured value, and n is the total number of paired measured and predicted values. A GMER value of 1 indicates a balance of positive and negative error, which indicated over and under prediction by the model, respectively. The minimum GSDER value is 1, signifying perfect agreement between measured and predicted data. This method account s for the log normal distribution of hydraulic conductivities (Tietje et al. 1 996). T he l n of DRI infiltration rates were regressed against the l n of the effective and total porosities to determine the coefficients B and n for the AhujaKozeny Carman mode l. This procedure forces the GMER to 1 due to minimizing the sum of squared errors In addition, s are at least partially empirical, these values were adjusted for the Kozeny Carman model to optimize the GMER Results Soil Texture Texture analysis by hydrometer method of basin soil samples was completed on 238 samples from the 40 basins Individual sample textures are listed in Appendix A Textures ranged from Sand (S) to Heavy Clay (HC) ( Table 2 3 ). However, 91% of samples were distributed between the following four textures Sand (S), Loamy Sand (LS), Sandy Loam (SL), and Sandy Clay Loam (SCL). Median sand and clay fractions

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59 from each basin were used t o determine the basin texture. The 40 basin textures were also well distributed, mostly between S, LS, LS, and SCL textures ( Table 24 ). Between three and seven basins were included in each of the eight main land use and texture subgroups ( Table 24 ). In addition, at least one monitored basin was from each of the eight primary texture and land use subgroups ( Table 24 ). Infiltration Rates Basin designed infiltration rates ranged from 0.3 cm/h to 43.7 cm/h and are listed in App endix A Measured DRI infiltration rates were log normally distributed ( Figure 2 5 versus Figure 2 6 ). Geometric mean DRI rates ranged from 0.2 cm/h to 56.7 cm/h. Rainfall characteristics (depth, max intensity, duration, avg., average intensity) and drawdown event parameters (maximum water level, average drawdown rate, and ponded duration) are listed in A ppendix A for monitored basins Basin 4, 5, and 6 are missing rainfall data due to a clogged rain gauge. These clogged events were identified in the data by having durations of several days with low and gradually decreasing intensities. Three monitored basins, 12, 32 and 38, had no water level data collected from them. G eom etric mean m onitored and DRI rates are listed with their respective design rates in Table 2 5 Measured DRI rates from 126 test locations (52%) were less than respective design rates. Soil Organic Matter Organic matter percentages ranged from 0.09% to 48.5% with a median of 3.06% and had correlation values of 0.52, 0.51, and 0.43 for sand, silt, and clay percentages from texture analysis (Appendix A). By comparison, SOM% was less correlated with age (0.31) and correlated with infiltration rate (0.52). Soil organic matter percentages for each site are listed in Appendix A and are summarized by soil texture in Table 26

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60 Bulk Density Bulk density values were determined from 238 samples and ranged from 0.23 g/cm3 to 1.82 g/cm3 (Appendix A) Most (91%) of bulk density values ranged between 1.30 g/cm3 and 1.80 g/cm3. The median bulk density for each of the eight primary texture and land use subgroups ranged from 1.49 g/cm3 to 1.62 g/cm3 with no clear pattern based on either factor. Organic matter was less than 3% for 117 samples. Only 11 of the 117 (9.4%) had bulk densities greater than the growth limiting bulk density (Daddow and Warrington 1 983) Therefore bulk densities were not limit ing to vegetation growth. Modeling Nine models were evaluated for their potential of fitting retention basin soil data to DRI infiltration rates. For comparison, simple and complex multiple linear regressions were developed. Results of GMER and GSDER are listed in Table 2 7 Modeled rates were plotted against measured rates for each model as well ( Figure 27 ). The resulting complex (MLR1) and simple (MLR2) regressions for collected data are listed in Table 21 All parameters were significant (p < 0.05). Only optimized models (both MLR models, both AhujaKozeny Carman models and the Kozeny Carman ( dH) ) had GMER values of 1, which ranged from 0.01 to 1.72. The complex MLR (1) had the lowest GSDER at 3.28 and was the best model overall. The simple MLR (2) was only slightly more variable (GSDER = 3.60) although it had half the inputs; six compared to three. The optimized adjustable parameters in the Kozeny Carman models were the

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61 been shown to be approximately 0.2 for uniform spheres (Xu and Yu 2 008). Compared to the MLRs, the Kozeny Carman ( dH) model had a comparable GSDER of 3.92 with adjustable coefficients representative of spherical particles. The exponential coefficient from the AhujaKozeny Carman ( ) model (3.9) was close to 4, which Ahuja (et al. 1 984) said the coefficient varied narrowly around. Both exponential coefficients were comparabl e to 3.29, which Ahuja et al. (1989) found for US soils and was comparable to 3.25 reported by Franzmeier (1991). The GMER values ranged from 0.07 to 1.72 for the six pedotransfer functions, while GSDER ranged from 4.19 to 8.50. Wosten I (1999) had the cl osest GMER to 1 (1.19) and second lowest GSDER (4.28), behind Saxton (4.19). A nalysis Monitored vs. DRI If surface conditions were not limiting basin drawdown rates, then monitored rates should be independent of DRI rates. The relatively brief duration of DRI measurements (approximately 1 hour) likely prevented infiltration rates from being affected by limiting sub surface processes in most basins. Equal DRI and monitored rates would indicate that the basin was surface limited. Monitored rates were signifi cantly (p < 0.05) less than DRI rates for six of the eight basins with drawdown data indicating that surface soil conditions were not limiting to these basins overall performance. Rates from basin 5 (p = 0.63) and basin 21 (p = 0.93) were not significant ly different ( Table 2 8 ). This suggests that surface soil conditions may have been limiting basin performance. No basins were found to have DRI rates significantly less than monitored rates. While a significant (p = 0.004) relationship was found between DR I and monitored geometric mean infiltration rates

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62 ( Figure 2 8 ), the relationship seems to be due mostly to three basins with DRI rates above 10 cm/h. A conversion coefficient for DRI rates equal to 0.024 resulted from forcing the regression intercept through the origin. DRI and Monitored vs. Design Monitored rates were significantly less than design rates for seven of the eight basins, indicating either surface or subsurface factors were controlling basin drawdown. Monitored rates from the remaining si te ( basin 38) were not significantly different (p = 0. 057 ) from the design rate ( Table 28 ) However, only two drawdown events from basin 38 were analyzed with respect to the design (Appendix A). Log ratios of DRI data were analyzed to determine whether DRI rates were significantly different from respective design rates for each basin. Of the 11 basins, DRI rates from six (basins 5, 6, 13, 21, 25, and 30) were significantly (p < 0.05) greater than design, one (basin 18) was equal to design (p = 0.17) and the remaining four (basins 4, 12, 32, and 38) were significantly (p < 0.05) less than design rates. The three basins without monitoring data (basins 12, 32, and 38) all had DRI rates significantly greater than design rates. The lack of monitoring data may have resulted from rapid infiltration within the basin. Basin 32 may have been sized to capture runoff from a roadway expansion not yet constructed during monitoring. Basin 12 was located in western Alachua County (Florida) where karst formations are comm on under sandy soils. Sink holes develop in this area and may have influenced the basin infiltration. It is unknown why water levels in basin 38 did not drawdown more slowly. DRI Infiltration Rates Student t statistics tested the hypothesis that log ratios were equal to zero. The critical t statistic value for 95% confidence was 2.56. Log ratios significantly (p < 0.05)

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63 greater or less than zero indicated significantly greater or slower DRI rates than design. Figu re 2 9 shows the distribution of t statistics calculated from log ratios of DRI and design infiltration rates for all 40 sites with data points indicating the basin soil texture and land use. Figure 2 9 shows that 40% (16 basins) had measured rates significantly less than design (p < 0.05), 35% (14 basins) had measured rates significantly greater (p < 0.05) than design rates, and 25% (10 basins) had measured rates not significantly different from design rates. Table 2 9 summarizes Figure 2 9 based on soil type and land use. Coarser textured basins had a higher proportion with significantly greater DRI rates than design rates. Likewise, the proportion of basins with significantly lower infiltration rates than design increased as soil texture became finer. This relationship was evident over the entire population of basins and within each land use. M ore DOT basins (11) than residential basins (3) had DRI rates signific antly greater than design rates In addition, more residential basins (10) than DOT basins (6) had DRI rates significantly lower than design rates. Each residential texture group had at least one basin with significantly lower DRI infiltra tion rates than designs. Only three of the twenty residential sites had significantly greater measured infiltration rates than design; two with sand texture and one with loamy sand texture. Effects of Age It was previously shown that DOT basins tended to have greater DRI rates than their design rates while residential basins predominantly had DRI rates less than design. To determine whether these relationships changed with time, age was analyzed with respect to the log ratio of D RI to design inf iltration rates. Log ratios for each

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64 infiltration location (typically six per basin) were grouped by soil texture and land use. Ratios were then regressed against the log of the basin age Regression results are listed in Table 210 f or DOT basins and Table 2 11 for residential basins. The slopes indicate whether basin DRI rates increased (positive) or decreased (negative) compared to design with age. T he intercept estimates basin performance when newly constructed. Equilibrium age was calculated as the age when the DRI and design infiltration rates were equal based on the regression model Slopes for each land use were found to be significant (p < 0.05) However, DOT basin ratios decreased with time while residential basin ratios increased with time Based on the regression models DOT basin performance was initially greater (p < 0.05) than design but declined with age and reached equilibrium after appr oximately 13 years. By comparison, residential DRI rates were initially significantly (p < 0.05) below design, but improved with age and reach ed equilibrium after 18 years. Therefore, while DOT basins were greater than design and residential basins were less than design, regressions showed that this discrepancy between the land uses diminishes with time. The effect of age was also analyzed across soil textures. T he number of data points for the four main texture classes and land use subgroups ranged from 18 to 41 and R2 values ranged from 0.00 to 0.49. Outside of the four main textures, R2 values were as high as 0.99, however these regressions resulted from five data points or less for each land use and soil texture subgroup. Therefore, analysis focused on d ata from the four main soil textures. S lopes were significantly different from zero and negative for loamy sand and sandy loam DOT basins while intercepts were significantly different from zero and

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65 positive for three of the four texture classes T he DOT equilibrium age s decreased from over 1, 000 to 0 years a s textures became finer While only the slope was significant, the slope and intercept for sandy clay loam DOT basins were both negative. These basins would only be expected to possibly perform as designed soon after their construction. For r esidential basins, the slopes were positive for the four main soil textures, significantly (p < 0.05) for three, and the intercepts were negative with three being significant (p < 0.05). The equilibrium age inc rease d with finer soil texture for three of the four residential soil texture groups; the sandy loam regression did not reach equilibrium. Vegetation Although basin bottom vegetation was not directly accounted for in this study, previous research has show n that biota help to maintain or decrease soil bulk densities and increase infiltration rates with respect to compaction (Katsvairo et al. 2 007; Kozlowski 1 999; Meek et al. 1 992; Jastrow and Miller 1 991). Additionally, bulk density was found to be signific ant for predicting DRI rates for both multiple linear regression models. However, vegetation prevalence and size varied widely for the basins included in this study. Though not quantified, higher prevalence and larger vegetation sizes seemed to correspond to increased infiltration rates and reduced bulk densities. For example, sites 4 and 9, which had vegetation as large as small trees and moderately dense coverage ( Figure 210) had some of the highest infiltration rates, (medians: 47 and 108 cm/hr, respectively) and lowest bulk densities (medians: 1.11 and 0.69 g/cm3, respectively). Additionally, site 9 had the finest texture of all basins with some of the lowest bulk density values. By comparison, sites 1 and 39, which had the same soil texture (Loamy Sand) and land use (DOT), were either predominantly bare soil or

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66 limited to grass cover ( Figure 211) and had much lower infiltration rates (0.92 and 1.24 cm/hr, respectively) and higher bulk densities (1.56 and 1.58 g/cm3, respectively). In a study by Bartens et al. (2008), soils were compacted to simulate urban infiltration BMP soils to determine whether trees could improve infiltration. Clay loam soil was used and compacted to bulk densities above (1.6 g/cm3) and below (1.3 g/ cm3) the gr owth limiting bulk density (1.45 g/cm3) for clay loam (Daddow and Warrington 1 983). Bartens et al. (2008) found that roots from red maple and black oak trees were able to penetrate both compacted soils and began to improve infiltration rates after only 12 weeks, even before the occurrence of root turn over, or the cyclical process of root growth and decay. Root turnover is attributed as the typical mechanism for improved infiltration since decayed roots leave behind drainage path ways. Infiltration rates w ere 63% higher for soils with trees and 153% higher when only considering the more densely compacted soil after only seven months. Dierks (2007) discussed the effect of different types of vegetation in open spaces and how they affect hydrologic response. S pecifically, Dierks highlight ed the difference in rooting depth of common blue grass versus other types of vegetation. The prevalence and size of vegetation within these basins may be an effect of maintenance type and frequency. Residential basins are likely maintained more regularly than DOT basins. Residential basins can be valuable amenities, offering open spaces for recreation when dry ( Figure 212) Therefore, there is incentive for vegetation maintenance and possibly prescription. By comparison, older DOT basins had limited surface area and were commonly surrounded by chain link fences due to steep side slopes. These basins are not accessible by the public and may be less frequently maintained as a result. Longer

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67 periods between mowing would allow larger and more diverse vegetation to establish. Residential basins likely receive lawn runoff, thus basins may receive products, including herbicides, which are designed to promote a monoculture lawn. The combination of more frequent maintenance and possibly receiving chemicals which suppress vegetation diversity may limit root mass development and depth. Thus, vegetation is not allowed to increase soil porosity and infil tration. Furthermore, biodiversity within these basins may be negatively affected by herbicides and pesticides commonly applied to residential lawns, which can be transported by stormwater to these basins. These chemicals may limit the bioturbation in the soil by limiting the biodiversity. As a result, these hypotheses offer an explanation why residential basins tended to have lower infiltration rates than DOT basins. Infiltration through surface soils may be maintained or improved by allowing or promoting the growth of larger vegetation within retention basins. Vegetation and root mass may also explain why residential basins improved with time but DOT basins did not. While vegetation establishment can be limited by maintenance, root depth may gradually inc rease with time, slowly improving basin hydraulics. However, vegetation in DOT basins may establish most of the rooting depth soon after construction. Continuous sedimentation from roadway runoff or other unobserved factors may slowly counteract and overcome the benefits of vegetation. Vegetation and sedimentation may be the cause of the difference between DOT and residential log ratio trends with age. For DOT basins, the establishment of and unabated growth of vegetation may provide initial benefits that enhance infiltration initially. However, the benefits of vegetation establishment may be reached fairly soon

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68 without maintenance and with gradual sedimentation, the benefit of larger vegetation may be slowly eroded. In addition, with rapid infiltration, l ess of the sediment load is likely to exit the basin through an overflow or by pass structure. Conversely, with residential basins, vegetation growth is limited throughout operation due to more frequent maintenance. While this practice likely retards rooti ng depth and bioturbation, with time rooting depth would gradually increase, improving infiltration. Sedimentation may initially impair infiltration, but as vegetation establishment increases, vegetation may have slowly diminished the effects of sedimentat ion. Future research should investigate these trends and determine the potential counteractive effects of vegetation establishment and sedimentation. Hydraulic C onductivity M odels Nine models were optimized to fit DRI infiltration rates to soils data collected in this study. Models included six pedotransfer functions, and two physico empirical models. Two multiple linear regression models were developed for comparison; one complex (MLR 1) and one requiring minimal inputs (MLR 2). Both Ahuja Kozeny Carman models, both MLR models and the Kozeny Carman ( dH) had GMER values equal to one. The least acceptable models were the Kozeny Carman ( d50) with a GMER of 0.01 and the Kozeny Carman ( dH) with a GSDER of 10.98. Both models utilize the median particle diameter, which Hansen (2004) suggests may be utilized by uninformed users. Both models were improved when using dH rather than d50. In addition, total porosity produced much better results than effective porosity. However, in the AhujaKozeny Carman model, effective porosity produced a smaller GSDER compared to total porosity.

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69 The Kozeny Carman model incorporates two parameters from the soil: porosity and specific surface area. Fine soils m ay have very high porosity along with slow hydraulic conductivities due to the pore size distribution. The Kozeny Carman model accounts for finer soils by including the specific surface area to account for very small porosity which contributes minimal flow However, coarse soils with low specific surface area and high porosity tend to have larger hydraulic conductivities. This may be an effect of adsorbed water bound to clay particles which occupies pore space, but is not free to flow (Singh and Wallender 2 008). Using the effective porosity instead of the total porosity reduces the hydraulic conductivity, which may be more representative of finer textured soils with adsorbed water. The texture of most soils in this study was dominated by sand, which may expl ain why total porosity fit the data much better than using effective porosity. The model with the best results (GMER: 1, GSDER: 3.28) was the more complex multiple linear regression. The simpl e multiple linear regression model, MLR 2, was only slightly mo re variable (GMER: 1; GSDER: 3.60). Even though MLR 1 had four additional terms and incorporated more interactions between parameters, the GSDER for MLR 2 was only slightly higher (< 10%). In addition, MLR 2 only required three inputs, only two from soil s amples: land use, bulk density, and sand fraction, compared to six for MLR 1: sand, silt, clay, effective porosity, total porosity, and bulk density. The Wosten I (1999) and Cosby et al. (1984) models were the only ones to over predict infiltration rates (GMER: 1.19 and 1.72, respectively). In total fifteen different model configurations were evaluated. Five configurations achieved GMER values of 1, with GSDER ranging from 3.20 to 5.94, none of whic h were pedotransfer functions. Li et

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70 al. (2008) reviewed s everal pedotransfer models, including Brakensiek, Cosby, Vereecken, and Saxton, which all had GSDER values below 3.41, lower than most of the models in this study. However, soils data used by Li et al. (2008) was very uniform, with maximum clay and silt contents of 3% and 4%. However, Wagner et al. (2001) reviewed Wosten (1999 and 1997), Vereecken, Cosby, Saxton, and Brakensiek models which had GSDER from 9.45 to 19.89 for German soils. Tietje et al. (1996) reported GSDER values from 7.72 to 12.91 for Brakensiek, Cosby, Vereecken, and Saxton models on German soils as well. Model accuracy is dependent on the input data set. Therefore, the magnitude and range of the GSDER may be more indicative of the data variability. The best results came from the MLR 1 mo del, which had the lowest GSDER. By comparison, the simpl er MLR 2 model had a slightly higher variability but only needed three inputs. Tietje et al. (1999) also found that simple models performed as adequate as more complex models such as Brakensiek et al (1984), Saxton et al. (1986), Vereecken et al. (1990), and Cosby et al. (1984). By contrast, the researchers concluded that including additional parameters such as bulk density and organic matter did improve the GMER, but not the GSDER. Multiple models presented here estimated the DRI infiltration rates well with GMER values of 1, although with GSDER values of at least 3. 28. The GMER and GSDER for DRI values compared to design were 0.81 and 7.64, respectively and 0.13 and 3.93, respectively, for monitor ed rates compared to DRI rates. Thus since models has GMERs closer equal to 1 and 3 of 5 had lower GSDER values, the models estimated the DRI rate more accurately than the monitored or design rates.

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71 The MLR 2 had slightly greater variability compared to the MLR 1, which could indicate that hydraulic conductivities could be estimated with relatively few and easily measured inputs costing only slightly more error. Though the Kozeny Carman ( dH) mode l was optimized, with empirical coefficients approximated for spherical particles the model fit the data with slightly greater variability (GSDER = 3.92) than the other regression models. In addition, the Kozeny Carman ( dH) model had better results tha n the other seven pedotransfer functions. Measuring infiltration rates by DRI requires substantially more time and resource investment than soil sample collection, from which model inputs can be determined through traditional soil analysis methods (soil t exture, bulk density, and organic matter). Therefore, modeling of surface soil infiltration rates may be more efficient than direct measurement. Future research analyzing sediment loadings and the effects on soil texture may be able to incorporate the Kozeny Carman( dH) equation or MLRs determined here to predict basin drawdown rate changes. Summary and Conclusions Retention or infiltration basins are one of the most common stormwater best management practices used in Florida. Their performance can be affected by surface and subsurface factors. Water levels in 11 basins were monitored with water level recorders. Infiltration rates were measured using a DRI at 40 sites, including the 11 monitored sites. Of the 11 monitored sites, 8 had sufficient data t o determine drawdown rates; the remaining sites never accumulated water. Monitored rates for 7 of 8 were significantly less than their design rate. Monitored rates were significantly less than DRI rates for 6

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72 basins and not significantly different for the remaining basin. Subsurface conditions controlled the drawdown of the 6 basins with significantly lower monitored rates. However, for the site with equal rates, the surface was likely limiting infiltration. Of the 40 basins included in this study, DRI rat es from 14 (35%) were significantly greater than design rates, 10 (25%) were not different than the design rate and 16 (40%) were significantly less than design rate. Based on these results, drawdown rates for 40% of the basins were at least limited by sur face soil conditions. This may have resulted from clogging and/or compaction. However, subsurface conditions may have reduced drawdown rates further. In addition, two of the seven monitored basins which had monitored rates significantly less than design had DRI rates equal to or significantly greater than design. Surface measurements indicated DRI rates were equal or better than design, however subsurface conditions were controlling the basin drawdown rates. The remaining five basins had both monitored and DRI rates significantly less than design. As for the 24 basins which did not have DRI rates less than design or did not have monitored rates less than design, it is unknown whether these basins were actually performing as designed. It was only determined that the surface conditions were not limiting to the extent that DRI rates were less than design. However, subsurface conditions could adversely affect the performance of any of these basins, resulting in drawdown rates significantly less than design. Thus, the DRI infiltration rate measurements can only determine whether the surface soil is limiting rates sufficiently below design. However, subsurface conditions may be much more limiting, regardless of surface conditions. I f surface soil conditions are fo und to be limiting drawdown, and

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73 then corrected, subsurface conditions may be controlling drawdown rates further, and the corrective action may be insignificant. Surface soil conditions varied across the basins. A higher proportion of DOT basins than resi dential basins had significantly greater DRI rates than design. However, the DOT basin advantage decreased with time. Newer DOT basins were more likely to have significantly greater infiltration rates than design as compared to older basins. While o lder DOT basins still had greater DRI rates, the difference between DRI and design rates was diminished compared to newer DOT basins Comparably, new residential basin DRI rates were significantly less than design. However, DRI rates were closer to designs for older basins. Soil texture also affected DRI rates. Coarse textured basins had a higher proportion of basins with DRI rates significantly greater than designs. Similarly, finer textured basins had a higher proportion of basins with rates significantly less than design. This may indicate that design rates for coarse textured basins are lower than necessary. However, allowing for greater infiltration rates for coarse textured basins would decrease basin surface areas. Decreasing the sedimentation area could a ccelerate clogging. Since infiltration rate measurement can be time and resource intensive, nine models were evaluated to estimate DRI rates from basin soil data. The Kozeny Carman model incorporating porosity and harmonic mean particle diameter accurately estimated DRI rates. U sing half the inputs t he simplistic MLR (2) had only slightly greater variability than the more complex MLR (1) This result indicates that simpler models may be as effective as more complex models for estimating DRI rates in basi ns This

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74 study showed that multiple models accurately estimated DRI infiltration rates. While making evaluations based solely on model results may not be expected, modeling could be used as an initial evaluation of whether future monitoring should occur F urthermore, sediment and hydraulic loadings may be modeled to determine changes in soil surface characteristics over time. Soil characteristics could then be used to estimate how basin drawdown rates change with time to determine when surface soils became limiting. This could be beneficial to scheduling sediment removal maintenance. While vegetation was not directly measured in this study, DOT basins are typically maintained less frequently. The increased vegetation size and variety may enhance infiltratio n through soils at the basin surface. Future research of stormwater infiltration structures should include analysis of vegetation in addition to soil characteristics. Furthermore, the presence of soil biota may also enhance soil infiltration and should als o be considered. Finally, the hydraulics of retention basins are not only vertical, but horizontal as well, by lateral seepage flow. Lateral flow can be a significant flow path for storage volume recovery, but was not considered in this study. A supplemental study focusing on lateral flow monitoring within basins would contribute to understanding retention basins performance.

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75 Table 2 1 Pedotransfer function models Model Reference Equation(s) Number Wosten I Wosten et al. 1 999 = 1 15741 10 7 exp ( ) = 7 755 + 0 0352 + 0 93 0 967 2 0 000484 2 0 000322 2+ 0 001 1 0 0748 1 0 643 ln ( ) 0 01398 0 1673 + 0 02986 0 03305 2 18 Wosten II Wosten et al. 1 997 = 1 15741 10 7 exp ( ) for sands: = 9 5 1 471 2 0 688 + 0 0369 2 0 332 ( ) for loamy and clayey soils: = 43. 1 + 64 8 22 21 2+ 7 02 0 156 2+ 0 985 ( ) 0 01332 4 71 2 19 Cosby Cosby et al. 1 984 = 60 69 10 ^ ( 0 6 + 0 0126 0 0064 ) 2 20 Jabro Jabro 1 992 = 24 10 ^ ( 9 56 0 81 ( ) 1 09 ( ) 4 64 ) 2 21 Vereecken Vereecken et al. 1 990 = ( 20 62 0 96 ( ) 0 66 ( ) 0 46 ( ) 8 43 ) 2 22 Saxton Saxton et al. 1 986 = 24 ( 12 012 7 55 10 2 + ( 3 895 + 6 671 10 2 0 1103 + 8 756 10 4 2 ) / ) 2 23

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76 Table 2 1 Continued Model Reference Equation(s) Number Brakensiek Brakensiek et al. 1 984 = 2 78 10 6 exp ( ) = 19. 52348 8 96847 0 28212 + 1 8107 10 4 2 9 4125 10 3 2 8 395215 2+ 0 077718 0 00298 2 2 0 01942 2 2+ 1 73 10 5 2 + 0 02733 2 3 5 10 6 2 2 24 Ahuja Kozeny Carman Ahuja et al. 1 984 = 2 25 Kozeny Carman Singh and Wallender 2 008 = / ( ) 3 / ( ( 1 ) 2 2 ) 2 26 Multiple Linear Regression 1 (MLR1) N/A = exp ( 54 01 + 0 01622 2 + 1 565 0 0104 2 213 82 e+ 1 114 9 31 10 5 2 0 01077 2 + 0 2818 2 27 Multiple Linear Regression 2 (MLR2) N/A = exp ( 0 3974 0 1063 + 8 445 + 0 00289 2 0 156 ) 2 28

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77 Table 2 2 Pedotransfer model definitions for models in Table 21 Symbol Definition K s Saturated hydraulic conductivity, m/s Si Silt, %w Ts 1 for topsoil, 0 for other soil layers b Soil bulk density, g/cm 3 C Clay, %w OM Organic Matter, %w S Sand, %w Total porosity B, n Empirical constants for Ahuja Kozeny Carman model Effective porosity Kozeny Carman Shape and tortuosity parameter g Gravitational acceleration 9.81 m/s 2 w Dynamic viscosity of water 1.003*10 3 kg/m s w Density of water, 0.9982 kg/m 3 SSA Volum et ric Specific Surface Area (m 2 /m 3 ) LU Land use: DOT = 1; Res = 0 Table 2 3 Number of soil sample textures between land uses. Texture DOT # Residential Total Sand (S) 41 28 69 Loamy Sand (LS) 23 18 41 Sandy Loam (SL) 23 32 55 Sandy Clay Loam(SCL) 25 27 52 Sandy Clay (SC) 4 5 9 Loam (L) 3 1 4 Clay (C) 1 4 5 Heavy Clay (HC) 3 0 3 All Textures 123 115 238 # Florida Department of Transportation

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78 Table 24 Distribution of total and monitored number of basins between texture and land use. Texture DOT # Residential Total Sand(S) 7 (1 ) 5 (1) 12 (2) Loamy Sand (LS) 3 (1) 4 (2) 7 (3) Sandy Loam (SL) 4 (3) 6 (1) 10 (4) Sandy Clay Loam (SCL) 5 (1) 5 (1) 10 (2) Sandy Clay (SC) 1 (0) 0 (0) 1 (0) Total 20 (6) 20 (5) 40 (11) #Florida Department of Transportation. *Number of respective monitored basins in parenthesis. Table 2 5 Design and geometric mean double ring infiltrometer (DRI) and monitored infiltration rate s for all basins. Infiltration Rates (cm/h) Infiltration Rates (cm/h) Site Design DRI Monitored Site Design DRI Monitored 1 3.6 0.8 21 12.3 0.6 0.6 2 7.9 0.5 22 4.1 0.9 3 8.6 13.1 23 0.5 0.5 4 21.6 47.5 1.1 24 0.8 0.4 5 4.1 0.3 0.4 25 43.7 27.2 0.9 6 2.5 0.7 0.2 26 5.1 1.2 7 12.7 18.5 27 2.3 1.6 8 5.1 2.0 28 1.1 0.6 9 1.5 56.7 29 5.1 0.2 10 7.6 33.1 30 12.7 0.8 0.2 11 2.8 1.1 31 12.7 1.2 12 5.8 14.2 a 32 3.2 18.4 a 13 12.8 3.1 0.5 33 12.7 0.9 14 12.7 14.9 34 12.7 3.0 15 6.4 0.3 35 2.0 8.1 16 5.7 15.7 36 0.3 5.3 17 1.5 19.9 37 0.3 2.0 18 11.0 15.4 0.2 38 0.3 2.6 a 19 5.9 22.0 39 0.3 1.2 20 4.4 13.0 40 3.2 28.4 aSite monitored, but insufficient water level data w as collected.

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79 Table 26 Maximum, median, minimum, average, standard deviation and number of soil organic matter percentages for all sites by soil texture classification. Texture Max imum Median Min imum Average St. Dev. Count S 3.3 0.8 0.1 1.1 0.7 69 LS 9.3 2.1 1.1 2.5 1.4 41 SL 17.5 3.7 1.9 4.0 2.4 55 SCL 7.5 4.3 2.4 4.4 1.0 52 SC 20.7 5.6 4.2 8.0 5.5 5 C 11.9 9.3 6.0 9.2 2.6 9 L 22.0 14.2 5.0 13.9 9.3 4 HC 48.5 27.3 8.3 28.0 20.1 3 All 48.5 3.1 0.1 3.7 4.5 238 Table 2 7 Summary of model variable values and resulting Geometric Mean Error Ratio (GMER) and Geometric Standard Deviation of the Error Ratio (GSDER) from fitting double ring infiltrometer infiltration rate data. Model Porosity Particle Diameter n B GMER GSDER Ahuja e 2.2 1328 1.00 4.64 Kozeny Carman 3.9 2705 1.00 5.94 Kozeny e d 50 0.50 6.0 0.01 6.96 Carman* e d H 0.50 6.0 0.46 5.66 d 50 0.50 6.0 0.02 10.98 d H 0.20 6.8 1.00 3.92 Wosten 1999 1.19 4.28 Breskein 0.76 7.10 Saxton 0.70 4.19 Cosby 1.72 4.84 Jabro et al. 0.18 5.77 Vereecken 0.11 4.42 Wosten 1997 0.07 8.50 MLR 1 Both 1.00 3.28 MLR 2 Total 1.00 3.60 Carman models using d50.

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80 Table 28 Student t statistic and p values for monitored, Double Ring Infiltrometer (DRI), and design infiltration rate comparisons for monitored basins. Basin Monitored vs. DRI DRI vs. Design Monitored vs. Design T statistic p value T statistic p value T statistic p value 4 14.71 < 0.0001 4.30 0.0077 36.44 < 0.0001 5 0.49 0.6267 7.02 0.0009 23.94 < 0.0001 6 5.94 < 0.0001 3.25 0.0226 42.76 < 0.0001 12 4.04 0.0099 13 4.75 0.0032 6.92 0.0010 42.76 < 0.0001 18 13.52 < 0.0001 1.59 0.1721 29.34 < 0.0001 21 0.09 0.9279 4.17 0.0087 11.09 0.0572 25 14.11 < 0.0001 2.72 0.0420 28.02 < 0.0001 30 4.90 0.0001 5.59 0.0025 54.16 < 0.0001 32 6.75 0.0011 38 9.33 0.0002 *positive or negative T statistic indicates rates were greater or less than the compared population, respectively. Table 2 9 Summary of measured infiltration rate analysis for all basins based on land use and soil texture. Land Use Texture Significantly Greater Not Different Significantly Less ( Pass ) (Pass) ( Fail ) Florida Department of Transportation Sand 5 2 0 Loamy Sand 2 1 0 Sandy Loam 2 0 2 Sandy Clay Loam 1 0 4 Sandy Clay 1 0 0 Total 11 3 6 Residential Sand 2 2 1 Loamy Sand 1 1 2 Sandy Loam 0 3 3 Sandy Clay Loam 0 1 4 Total 3 7 10 Total 14 10 16

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81 Table 2 10. Summary of regression values for log of double ring infiltrometer infiltration rates to design rates ratio against log of basin age by soil texture for Department of Transportation basins. Texture Slope p value Intercept p value R 2 Equilibrium Age Data Points S 0.31 0.263 0.94 0.001 0.03 1019.3 41 LS 0.79 0.003 1.18 0.000 0.36 31.7 23 SL 2.01 0.002 1.23 0.020 0.39 4.1 23 SCL 0.03 0.070 0.18 0.213 0.02 0.0 25 SC 9.83 0.023 10.87 0.025 0.95 12.8 4 L 2.44 0.005 3.55 0.003 0.99 28.3 3 C ------1 HC 12.13 0.065 13.19 0.071 0.99 12.2 3 All 0.90 0.003 0.99 0.001 0.07 12.8 123 Table 2 11. Summary of regression values for log of double ring infiltrometer infiltration rates to design rates ratio against log of basin age by soil texture for Residential basins. Texture Slope p value Intercept p value R 2 Equilibrium Age Data Points S 0.93 0.006 0.66 0.005 0.22 5.1 33 LS 1.60 0.001 1.43 0.000 0.49 7.8 18 SL 0.00 1.000 0.38 0.147 0.00 -32 SCL 0.82 0.016 1.15 0.000 0.21 25.5 27 SC 0.23 0.628 0.87 0.059 0.09 5530.1 5 L ------1 C 6.88 0.084 6.10 0.112 0.84 7.7 4 HC ------0 All 0.69 0.000 0.86 0.000 0.11 17.7 120

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82 Figure 2 1 Various testing location orientations based on basin geometry. Figure 2 2 Infiltration rate measurement using doublering infiltrometer with Mariotte siphon (left) and water supply tank (right) to maintain constant equal heads in the inner and outer rings, respectively. 1 2 1 2 2 3 4 5 6 3 6 5 4 3 4 5 6 1 1 2 1 2 2 3 4 5 6 3 6 5 4 3 4 5 6 1

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83 Figure 2 3 Example of infiltration rate measurement data from six sites at one infiltration basin with individual infiltration measurements shown. Figure 2 4 Monitoring installation with water level recorder housing, manual rain gauge, and tipping bucket rain gauge. 0 4 8 12 16 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8DRI Infiltration Rate (cm/h)Time (hrs) 1 2 3 4 5 6 Model Infiltration Rates

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84 Figure 2 5 Frequency and cumulative di stribution of double ring infiltrometer (DRI) infiltration rates from all basins. Figure 2 6 Frequency and cumulative distribution of log transformed double ring infilt ro meter (DRI) infiltration rates from all basins. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 20 40 60 80 100 120 140 0 5 10 15 20 25 30 35 40 45 50 55 60 Cumulative Percentage FrequencyDRI Infiltration Rate (cm/h) Frequency Cumulative % 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 Cumulative Percentage FrequencyLog DRI Infiltration Rate (10xcm/h) Frequency Cumulative %

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85 Figure 2 7 Modeled Ks versus measured Ks for models. AhujaKozenCarman with A) Effective Porosity and B) Total Porosity. Kozeny Carman with C) Effective Porosity and d50, D) Effective Porosity and dH, E) Total Porosity and d50, F) Total Porosity and dH, G) Wosten 1999, H) Brakensiek, I) Saxton, J) Wosten 1997, K) Cosby, L) Jabro. M) Vereecken, N) Multiple Linear Regression 1, O) Multiple Linear Regression 2. 0.01 1 100 10000 0.01 10 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 10 10000Modeled Ks, cm/d 0.01 1 100 10000 0.01 10 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 A B C D Measured Ks, cm/d E F

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86 Figure 27 Continued 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0 10 10000 0.01 1 100 10000 0.01 1 100 10000 Measured Ks, cm/d Modeled Ks, cm/d G H I J K L

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87 Figure 27 Continued 0.01 1 100 10000 0.01 1 100 10000 0.01 1 100 10000 0 1 100 10000 0.01 1 100 10000 0 10 10000 Measured Ks, cm/d Modeled Ks, cm/d M N O

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88 Figure 2 8 Regression of double ring infiltrometer ( DRI ) and monitored geometric mean infiltration rates for monitored basins with 95% confidence band. Figure 2 9 Distribution of t statistics calculated from logs of Double Ring Infiltrometer (DRI) infiltration rates and design rates for all 40 basins based on land use and soil texture. DOT: Florida Dept. of Transportation; Res: Residential; S: Sand; LS: Loamy Sand; SL: Sandy Loam; SCL: Sandy Clay Loam; SC: Sandy Clay. y = 0.024x R2 = 0.72 0.0 0.5 1.0 1.5 2.0 0 10 20 30 40 50Geometric Mean Monitored Infiltration Rate (cm/h)Geometric Mean Double Ring Infiltrometer Infiltration Rate (cm/h) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 20 10 0 10 20Percent of sites with smaller t statistict statistic DOT S DOT LS DOT SL DOT SCL DOT SC Res S Res LS Res SL Res SCL 25% DRI not different from Design Rates 40% DRI Less than Design Rates p < 0.05 35% DRI Greater than Design Rates p < 0.05

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89 Figure 2 10. Example of size and diversity of vegetation in basin 9. Soil textue: s andy clay. L and use: Department of Transportation

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90 Figure 2 11. Limited vegetation size and diversity in basin 39. Soil texture: loamy sand. Land use: Department of Transportation.

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91 Figure 2 12. Photo of basin 8 during double ring infiltrometer testi ng. Soil texture: sandy loam. Land use: residential.

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92 CHAPTER 3 SOIL AMEMDMENT S FOR COMPACTED SOIL M ITIGATION I: HYDROLOGY Introduction Gregory et al. (2006) showed that soil compaction coinciding with typical development activities and vehicle traffic reduced infiltration rates from between 23 and 65 cm/h to between 1 and 19 cm/h and increased bulk densities from between 1.20 and 1.42 g/cm3 to between 1.48 to 1.52 g/cm3 on fine sand soils in North Central Florida. Pitt et al. (1999b ) reported a similar decrease of infiltration rates from 41.4 cm/h to 6.4 cm/h on sandy soils in Alabama. Current development practices leave soils compacted from heavy equipment traffic, with reduced porosity, infiltration rates, and increased runoff volumes and rat es. Agricultural lands are commonly selected for these new development sites, increasing the potential for accumulated pollutants, especially nutrients, in the soil to be transported by runoff into surface waters. Surface water impairment in Florida is lar gely a result of excess nutrients, which can lead to low dissolved oxygen. Low dissolved oxygen has been attributed to hydrologic modifications and pollution discharges. In addition, fertilizers applied to agriculture and residential lawns are large nitrat e contributors to groundwater. (2006 Florida 305b Report) Soil amendments have previously been studied to evaluate their potential for improving soil properties, mostly in agricultural settings. Two soil amendments, compost and fly ash, have potential to i mprove soil properties. Both soil amendments have been found to decrease bulk density and improve soil moisture holding capacity ( Pitt et al. 1 999 b ; Cogger 2 005; Khandekar et al. 1997; Gangloff et al. 2 000; Adriano and Weber 2 001). Fly ash, a bi product of coal burning, is mostly comprised of silt sized particles

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93 (Torrey 1 978). Bulk densities range from 0.79 to 1.16 g/cm3 and particle densities from 2.14 to 2.48 g/cm3 (Torrey 1 978; Pathan et al. 2 003). Most research on the effect of fly ash incorporation o n soil infiltration has found that infiltration rates significantly decrease (Campbell et al. 1 983; Gangloff et al. 2 000 ; Kalra et al. 1998 ; Pathan et al. 2 003). This has been attributed to the cementing properties of fly ash, which have been utilized to stabilize soils from deformation ( Consoli et al. 2 001). However, a few studies have found that fly ash either was no influence (Adriano et al. 2 002) or increased infiltration rates (Chang et al. 1 977). Though compost is produced from a wide range of parent materials, studies have shown that compost incorporation typically increases infiltration rates, decreases bulk density, and increases porosity ( Pitt et al. 1 999 b ; Cogger 2 005). However the potential exists for compost to become a source for nutrients in r unoff or leachate, depending on the parent material of the compost and potential for plant uptake (Cogger 2 005; Jaber et al. 2 005; Gilley and Eghball 2 002). This study sought to evaluate the hydrologic effects of incorporating two soil amendments into com pacted soils by measuring differences in runoff volumes, infiltration rates, and soil structure. Materials and Methods The study site was located on the University of Florida campus in Gainesville, FL. Forty two fiberglass lysimeters, measuring 0.76 m x 0.76 m x 0.76 m, were manufactured for this study. Lysimeters had two 2.5 cm diameter horizontal outlets installed; one each for runoff and leachate collection ( Figure 3 1 ). The outlets were centered on the same side and the leachate outlets were typically within 3.8 cm of the lysimeter bottom, while the runoff outlets were typically 5.0 cm below the top lip. Well

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94 screen (1.3 cm diameter, 0.025 cm slot ) was installed fr om the inside of the bottom outlet and spanned slightly less than the depth of the tank ( Figure 3 2 ). The exterior of the leachate outlet was fitted with a 2 cm diamet er ball valve. A wooden footer spanning the width of the lysimeter was attached to bottom of the lysimeter at both the front and rear. These footers raised the lysimeters above the ground which allowed a forklift to transport the lysimeters into their final positions, after being filled. The soils used in this study were Arredondo fine sand (A) and Orangeburg loamy fine sand (O). The Arredondo soil was collected from a site on the University of Florida campus while the Orangeburg soil was collected from a stockpile at the North Florida Research and Education Center near Quincy, FL. The Orangeburg soil was screened to remove aggregates prior to filling lysimeters. Two soil amendments used in this study were Black Kow composted dairy cow manure (0.50.5 0.5 ; N P K respectively) (C) and Class F fly ash (F) from the Gainesville Regional Utilities (GRU) Deerhaven power plant. Black Kow is a composted cattle manure product produced by the Black Gold Composting Co. from Oxford, FL, that is commonly available to consumers at home and garden retailers. Soil and amendment characteristics are summarized in Table 3 1 Both soils and amendments were analyzed for texture by particl e size analysis (ASTM 2 007a) Standard maximum proctor densities were also determined from samples of each soil (ASTM 2 007b). Particle density was measured for each soil and amendment as well (Blake and Hartge 1 978). I n addition, organic matter was quantified for five samples of each soil and amendment by Loss On Ignition (LOI) (Heiri et al. 2 001) ( Table 3 1 )

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95 The study timeline w as split into three phases: noncompacted, compacted, and amended. The noncompacted phase began with construction and lasted until soil compaction began, the compacted phase ran from compaction through amending, and the amended phase ran from amending unt il study completion. The leachate valve remained open during the first two phases H owever the valve was closed for amended phase events to allow water quality sample collection. N oncompacted P hase This study initiated with filling 42 lysimeters in Septe mber 2008. Between 20 and 25 cm of No. 57 (ASTM 2003a) quartz stone was laid in the bottom of each lysimeter. Quartz was selected due to its relatively inert chemical properties. A layer of geotextile was installed over the drainage layer to prevent soil f rom settling into the drainage layer pore space or moving around the edges of the filter fabric. Approximately 50 to 55 cm of soil was transferred into each lysimeter. A front end loader was used to transport soils from stockpiles on site to the lysimeters where they were completely filled with soil ( Figure 3 3 ). The Orangeburg soil was also screened to remove large stones and aggregates during lysimeter filling; screen openings measured 7.6 cm by 3.8 cm. Lysimeters were then moved into place by forklift ( Figure 3 4 ). The locations of the lysimeters were randomized based on treatments to be applied to the soils. Soils were allowed to settle for approximately eight months until data was collected from the noncompacted soil at the end of April and beginning of May 2009. To control vegetation establishment, lysimeters were sprayed for the first 3 months and then covered with sunlight blocking fabric for the remaining 5 months. After filling the lysimeters, soil surfaces were above the runoff outlets resulting in no runoff collection during this phase.

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96 In February 2009 each lysimeter had one horizontal Acclima Digital TDT soil moisture sensors installed approximately 15 cm below the soil surface. In addition, one of the three treatment replicate lysimeters also had a vertical profile of sensors installed. Profile sensors were installed to monitor different soil layers within the lysimeter. Profile sensors were installed to maximize the cumulative sensing depth between the three sensors ( Figure 3 5 ). Data from the 84 sensors was collected hourly by two CS 3500 controllers. However, during May and June 2009 nearly all sensors failed due to a manufacturing defect. All sensors were replaced in coordination with amendment incorporation in September 2009. It would have been necessary to remove the sensors from respective lysimeters prior to incorporation regardless of failures to prevent damage to sensors and cables. However, control lysimeters would not have needed to be disturbed. In addit ion, some profile sensors may not have required removal either. Disturbed areas within control lysimeters were limited during sensor replacement and were subsequently recompacted under the final compaction iteration conditions. Bulk density, infiltratio n rate, and cone penetrometer measurements were collected from each lysimeter the week prior to compaction. Bulk density measurements were performed using the intact core method (Blake and Hartge 1 986). The infiltration rate measurement procedure was based on ASTM (2003b) D 3385 using a double ring infiltrometer. To maintain a constant head in the inner ring, a Mariotte siphon was used, while a manually controlled water supply tank was used to maintain an equivalent head in the outer ring. As a result, the rate of water replenishment to maintain the constant head within the inner ring was equivalent to the vertical infiltration rate. Due to the high infiltration rates of these soils, measurements were continued until either the outer tank

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97 or Mariotte siphon water was exhausted. Infiltration equipment was calibrated prior to testing. Infiltration rates were estimated by regression of the infiltration rates and cumulative infiltration depth using a simplification of the infiltration GreenAmpt model: = + (3 1) where f is the infiltration rate (cm/h), F is the cumulative infiltration (cm), A is the product of multiple soil parameters, and B is the infiltration rate at 1/F = 0 (Green and Ampt 1 911). A Field Scout SC 900 Cone Penetrometer (Spectrum Technologies, Inc., Plainfield, Illinois) was used to collect cone index profiles. The cone penetrometer had a maximum depth of 45 cm and a resolution of 2.5 cm. The penetrometer measured the force applied to drive the cone tip deeper into the soil profile. Three profiles were collected from each lysimeter. A rainfall simulator (RFS) was constructed in the event sufficient natural rainfall did not occur and to add control over rainfall rates and depth applications. The RFS was supplied by a groundwater well at 2 40 kPa located approximately 40 m from the simulator. From the connection, a 2.5 cm PVC water line split into two 2.5 cm water lines. Four spray nozzles were each connected to the supply lines through a 138 kPa pressure regulator, for a total of 8 spray heads. The RFS was divided into two rows of four bays; each bay with one spray nozzle. Plastic curtains were added to the rainfall simulator frame to block over spray between bays and reduce wind effects. The RFS was operated for 32 minutes with curtains on April 16, 2009, and without curtains on April 18, 2009 to evaluate the application uniformity. After each simulation,

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98 catch can volumes were recorded and divided by the opening cross sections (57.8 cm2) to determine rainfall depths. Rainfall depths were analyzed to evaluate the uniformity of the RFS. The low quarter distribution uniformity, DUlq,was calculated as: = (3 2) where d is the average depth for all catch cans and dlq is the average depth for the lowest quarter of observations. The UC was calculated by: = 1 ( ) (3 3) where y is the average of the absolute values of the deviations in collected depth from the average depth. Values of one for both measurements resul t from perfect uniformity. For comparison, catch cans were also distributed prior to a 1.26 cm rainfall event on April 19, 2009 to determine DUlq and CU for a natural event. A manual rain gauge was installed under the simulator to capture natural and simul ated total rainfall depths. In addition a HOBO (Onset Computing Corporation, Bourne, Massachusetts) data logging tipping bucket rain gauge with a tip resolution of 0.2 mm (RG3M) was also installed to measure rainfall rates. Although natural and simulated rainfall events had variability, distributions were assumed to be uniform and equal to data collected from the rain gauges. Compaction Phase Soil compaction began at the end of April 2009. Gregory et al. (2006) reported that compacted soils had bulk dens ities between 1.47 g/cm3 and 1.52 g/cm3, while undisturbed, or noncompacted, soils had bulk densities ranging from 1.20 g/cm3 to 1.42 g/cm3. These were collected from fine sand texture soils (Apopka and Bonneau). Based

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99 on these findings, the threshold com pact bulk density of the Arredondo soil was set at 1.45 g/cm3 midway between the maximum noncompacted density and the minimum compacted density. A comparable threshold applicable for Orangeburg soils was not found in literature. However, the Growth Limiting Bulk Density (GLBD) for Arredondo soil was estimated to be 1.80 g/cm3, compared to 1.64 g/cm3 for Orangeburg based on soil texture (Daddow and Warrington 1 983). Thus the Arredondo threshold of 1.45 g/cm3 was 81% of the GLBD. Assuming a compaction threshold of an equal percent GLBD for the Orangeburg produces a threshold of 1.32 g/cm3. Both soils received the same compaction procedure. The compaction procedure was iterative; bulk densities were measured from three of the 21 lysimeters for each soil and used as a representative sample to determine whether the bulk density compaction criteria had been met and determine the effects of the previous iteration. Four iterations were required to surpass the threshold bulk densit ies Soils were initially com pacted using a 25 cm by 25 cm tamper with a single 5.8 kg sliding weight drop. The sliding weight was raised until flush with the top of the tamper handle and then released ( Figure 3 6 ). The second iteration was two weight drops. The third iteration was two weight drops after the surface soil had been wetted. The fourth iteration was two weight drops on wetted soil, but for this iteration the tamper dim ensions had been modified to 12.7 cm x 12.7 cm to enable exertion of more force on the tamper ( Figure 3 7 ). Greater bulk densities may have been achieved by using larger compaction equipment, such as a plate compactor or a jumping jack type compactor. However,

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100 such equipment was avoided due to the spatial constraints and effort required to move this size equipment in and out of the lysimeters. In addi tion, it was unknown if equipment operation may have applied forces great enough to compromise the structural integrity of the lysimeters. Once soils were compacted, bulk density and infiltration rates were measured again. Measuring cone penetrometer indi ces leaves voids extending up to 45 cm into the soil profile. These voids likely would have created preferential flow and hydrologic response would have been greatly affected. Therefore, cone penetrometer measurements were not repeated during the compaction phase After compaction, the soil level in most lysimeters was below the outlet invert. For these lysimeters, new outlets were installed to make the outlet invert even with the soil surface. Runoff was collected in 38 L cylindrical polyethylene tanks. Runoff data collection began following soil compaction. Measurement tapes were attached to the side of runoff collection tanks. The tanks had previously been calibrated for total volume based on water depth. After each event, depths were recorde d and converted to volumes. Volumes were divided by the soil surface area to determine runoff depths. Analysis of compacted runoff volumes indicated more runoff was produced than rainfall fell on some lysimeters. It was later determined that rainfall hitt ing the lysimeter flanges was at least partially flowing or splashing into the lysimeters, contributing an unaccounted for volume. To account for the additional volume, the flange areas were measured and rainfall depths were scaled up to account for the ad ditional inflow contributed by the flange areas. Effective rainfall depths were then calculated by

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101 multiplying the rainfall by the respective scaling factor for each lysimeter. Prior to the amendment phase a berm was formed around the inner edge of the flanges to prevent sheet flow and screening was attached to the flanges to absorb raindrop energy. These additions were successful in preventing splash and sheet flow from flanges into the lysimeters. Amendment Phase Compacted soils were amended in September 2009. The lysimeters were evenly divided between soils; 21 each. There were seven treatments ( Figure 3 8 ) combining amendments (Null (N), C, and F) and incorporation depths (0 cm, 10 cm, and 20 cm). The seven treatments included all combinations of amendments and depths except for C and F at 0 cm. Each treatment was replicated three times ( Figure 3 8 ) Lysimeter bulk densities were ranked for each soil and treatments were applied to a lysimeter from each of the highest, middle, and lowest seven bulk densities to minimize the effect compaction variability on results from the subsequent amendment phase. The three nonamended lysimeters for each soil were controls. Amendments were applied at 5 cm depth over the area of the lysimeters. A Craftsman (Sears Brands, LLC, Hoffman Estates, Illinois) cultivator attached to a Craftsman trimme r 4 cycle engine incorporated amendments into the top 10 to 20 cm of compacted soil. A depth gauge was attached to the cultivator during incorporation to ensure the accurate depth of incorporation. For 20 cm depths, the incorporation was done in multiple s teps. First, the top 10 cm of compacted soil was removed from the lysimeter and 2.5 cm of amendment was applied over the exposed soil. The amendment was then incorporated to down to the 20 cm incorporation depth. The

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102 removed soil and remaining amendment were then returned to the lysimeter where they were incorporated together. Amending soils raised the soil surface above the outlet inverts. Pipes were cut through their cross section to form semi cylinder risers. These risers were installed flush with the soil surface along the lysimeter side around the runoff outlet. This allow ed runoff to flow from the surface directly into the runoff outlet, which prevented surface ponding. After each event, the rainfall depth and runoff volumes were recorded. In addition, outlets were checked for sediment and cleared if necessary. Soil surfaces were leveled if necessary following events to limit channeling or depressional storage. The depth between the top of each lysimeter and the soil surface was measured after most events to monitor subsidence. As amended soils settled and subsided, risers were adjusted to remain flush with the soil surface. Bulk densities, infiltration rates, and cone penetrometer profiles were repeated to complete the amended phase. Runoff volumes were then used to calculate runoff depths from each lysimeter. Rainfall an d runoff depths were used to calculate runoff coefficients C = Q/P, where C is the runoff coefficient (unitless), Q is the runoff depth (cm), and P is the rainfall depth (cm). Runoff depths were calculated by dividing the runoff volume by the respective ly simeter area. Effective curve numbers (CN) were also calculated using rainfall and runoff depths, by: CN = 25400/(254+S) (3 4) where S is effective maximum storage depth (cm) (NRCS 1 986). Rearranging the NRCS curve number equation,

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103 = ( 0 2 )2( + 0 8 ) (3 5) to solve for S gives: = 5 [ + 2 ( 4 2+ 5 ) ] (Hawkins 1 993). (3 6) Curve numbers were also estimated for each lysimeter during the compacted and amended phases following the method described by Hawkins (1993). Rainfall and runoff depth pairs were independently ranked and paired. Storage depths and curve numbers were then calculated for each pair. Since the Curve Number method assumes a maximum storage depth, as rainfall depths approach infinity so S and CNs should approach a constant. This rel ationship resembles the decaying infiltration rate as it approaches the saturated hydraulic conductivity. To determine the effective curve number, the calculated curve numbers are plotted against the inverse of rainfall depths. The effective curve numbers is assumed to be the intercept or value at 1/P = 0 for the linear regression of this relationship (Hawkins 1 993). Rainfall events which produced no runoff from a lysimeter were ex cluded in this analysis. Infiltration rate distributions were evaluated by ShapiroWilk test for normality. While a few populations passed the test without transformation, after log transforming the data virtually all populations were normally distributed. The outstanding populations were from amended treatments where the sample size was only three, which is difficult to definitively evaluate the distribution. In addition, infiltration rates have commonly been found to be log normally distributed (Logsdon and Jaynes 1 996; Haws et al. 2 004; Kosugi 1 996), even within relatively clos e proximity (Sisson and Wierenga 1 981). Therefore, all infiltration rate analyses were performed on log transformed data. As a result, the expected value is the Geometric Mean (GM):

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104 = 10 ^ ( log ( ) / ) (3 7) where n is the number of measurements. In addition the geometric standard deviation is = 10 ^ log ( ) log ( ) 2 / 1 (3 8) To determine the range of one GSD the GM is multiplied and divided by the GSD. Runoff coefficients were analyzed for signifi cant differences by Wilcoxon Sign Rank Test for nonparamet ric comparisons. Curve numbers were analyzed using Tukeys multiple pairwise comparison. Results and Discussion Based on the LOI testing, the Arredondo soil had a sand texture with 1% OM while the Orangeburg had a sandy clay loam texture with 5% OM ( Table 3 1 ). Higher organic matter contents are commonly associated with higher clay content due to reduced microb ial turnover rates (Parton et al. 1 987). Orangeburg texture had been listed as sandy loam in NRCS soil surveys (1993). The maximum proctor density values were found to be 1.77 g/cm3 for both soils ( Table 3 1 ). The DUlq and UC were calculated for the rain simulator on various scales: from the entire simulator down to individual lysimeters ( Appendix D ). The DUlq and UC for this event were 0.93 and 0.95, respectively, over the entire RFS area. With curtains, the rain simulator had an overall DUlq of 0.88 and a UC of 0.92; without curtains only 0.71 and 0.80, respectively. Smaller scale values had higher uniformity for DUlq and UC. In addition, ther e was a substantial drop in the uniformity values when the scale increased from individual lysimeters to bays. However, there was much less decrease for larger scales. The average rainfall rate was 10. 3 cm/h for the two tests By comparison the

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105 half hour i ntensity for a 10year return period event for Alachua County, FL is 10.4 cm/h (Eaglin et al. 2 009). Non compacted Phase Non compacted bulk density ranges from Arredondo and Orangeburg lysimeters only slightly overlapped ( Figure 39 ). Arredondo bulk densities were significantly (p < 0.05) greater than Orangeburg soils The Arredondo mean bulk density was 14% greater than the Orangeburg mean; 1.24 g/cm3 compared to 1.07 g/cm3 ( Table 3 2 ) Variability was essentially equal for the two soils. Non compacted Arredondo rates ranged from 111.4 to 196.8 cm/h while Orangeburg rates ranged from 109.8 to 318.1 cm/h ( Figure 310, Table 3 3 ). Orangeburg rates were significantly (p < 0.05) greater than Arredondo rat es, although only seven Orangeburg rates were greater than the maximum Arredondo infiltration rate. The Orangeburg GM was 27% greater than Arredondo; 1 44.5 compared to 178.0 cm/h. Maximum, minimum, median, and mean cone indices at each depth were determined for all noncompacted profiles for each soil ( Figure 3 11). Both means and medians overlapped over most of the profiles and decreased with depth. While the maximum Arredondo profile also decreased with depth, the maximum Orangeburg profile diverged from this pattern below 2 5 cm where indices changed erratically with greater depths. However, this resulted from maximum values coming from only four profiles within two lysimeters for depths greater than 25 cm. The m aximum s of the mean cone indices profiles were 239 and 235 kPa for Arredondo and Orangeburg soils respectively Maximum cone indices were at the surface and generally decreased as

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106 depth increased. Orangeburg cone indices were significantly (p < 0.05) greater than Arredondo at depths between 22.5 cm and 30 cm and at 42.5 cm (Appendix D) By comparison, the native profiles reported by Gregory et al. (2006) for noncompacted areas had maximum values at depths of 32, 25, and 32 cm, rather than at the surface. Profiles from Gregory et al. (2006) showed that the near surface values started at effectively zero and increased with depth to the maximums at the peak in a parabolic curve, after which they decreased along t he parabolic path until the 45 cm depth limit. While the maximum value of the Arredondo profiles was profiles was 239 kPa and decreased to a minimum value of 86 kPa at the depth limit of 45 cm, maximum values from Gregory et al. (2006) were approximately 1,000, 2,600, and 1,800 kPa, for the natural wood lot and the two planted forest lots, respectively. Thus the natural soil profiles had much greater cone indices, by almost an order of magnitude, than the Arredondo profiles. This likely due to the lysimeter construction method which did not compact soil layers. Filling lysimeters in lifts or layers and ensuring these layers were compacted to representative bulk densities may have resulted in more representative cone index profiles. Infiltration rates and bu lk densities were not found to be significantly (p < 0.05) correlated for either soil ( Table 3 4 ). Cone indices and bulk densities were only significantly (p < 0.05) correlated for Arredondo soils at 42.5 cm ( Table 35 ). The lack of evidence for a relation between bulk density and infiltration rate was unexpected since the two parameters are typically linked through porosity and pore size distribution. Howe ver, the low correlation may have resulted from the unconsolidated state of the soils due to reconstruction.

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107 C ompacted P hase Bulk densities The compaction procedure increased the bulk densities with each iteration ( Figure 3 12). Soil samples were collected from three random lysimeters for each soil following each compaction iteration. However, the initial and final data points in Figure 3 12 show the minimums, medians, and maximums for all 21 lysimeters for each soil. Therefore, the bulk density ranges for the first and last iterations were greater t han the three iterations in between, when only three samples were taken. Bulk densities taken after compaction iterations are listed in Table 3 6 for Arredondo lysimeters and in Table 37 for Orangeburg lysimeters. The first iteration, single drop, increased bulk densities the most of any iteration on both soils. The second iteration, double drop had the least increase for both soils ( Arredondo: 0. 03 g/cm3 and Orangeburg: 0.04 g/cm3). The bulk density increase by the third and forth iterations were approximately opposite for the two soils. Arredondo bulk densities increased 0.06 g/cm3 on the third iteration and 0.11 g/cm3 on the fourth iteration, while the Orangeburg increased 0.10 g/cm3 on the third iteration compared to 0.05 g/cm3 on the final iteration. A ll Arredondo and Orangeburg lysimeter bulk densities were greater than the respective thresholds. Compacted bulk densities for Arredondo lysimet ers ranged from 1.50 to 1.59 g/cm3 and from 1.36 to 1.55 g/cm3 for Orangeburg lysimeters ( Table 3 8 ). Arredondo compacted bulk densities were significantly greater than Orangeburg (p < 0.05). Compacted bulk densities for both soils were significantly (p < 0.05) greater than noncompacted. Mean Arredondo bulk densities increased 0.32 g/cm3 (26%) while Orangeburg bulk densities increased 0.37 g/cm3 (35%). Arredondo mean bulk density was 88% of proctor density while Orangeburg mean bulk density was 82% of proctor

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108 density. Compacted bulk densities were not significantly (p < 0.05) correlated to noncompacted bulk densities for either soil. The compa cted percentages of GLBD ranged from 83 to 88% for Arredondo and from 83 to 95% for Orangeburg. By comparison, Bartens et al. (2008) compacted clay soils which had GLBDs between 1.45 and 1.55 g/cm3 to two bulk densities, 1.31 and 1.59 g/cm3. The lesser of the two compacted soils were between 84% and 90% of the GLBD. Thus, the Orangeburg lysimeters were compacted to similar percentages of GLBD as Bartens et al.(2008) and slightly higher percentages than the Arredondo soil. Therefore, both Arredondo and Orang eburg soils met the compaction criteria. Infiltration rates While Arredondo surface soil bulk densities were comparable to those found by Gregory et al. (200 6 ), infiltration rates were about 25 cm/h greater than those reported by Gregory et al. (2006) and Pitt et al. (1999) for compacted urban sandy soils. Compacted Arredondo infiltration rates ranged from 29 cm/h to 44 cm/h while Orangeburg rates ranged from 0.3 cm/h to 14.9 cm/h ( Table 3 9 ). Arredondo rates were significantly (p < 0.05) greater than mean infiltration rates reported by Gregory et al. (2006) which ranged from 6.4 to 9.1 cm/h. A lack of subsoil compaction may indicate why infiltration rate s were greater than those reported by Gregory et al. (2006) and Pitt et al. (1999) even though surface bulk densities were comparable. Compacted infiltration rates for both soils were significantly (p < 0.05) less than noncompacted rates. The Arredondo G M infiltration rate decreased by a factor of four after compaction, while the Orangeburg GM infiltration rate decreased by a factor of 43. The compacted Orangeburg infiltration rate GSD (2.9 cm/h ) was much greater than both noncompacted GSD (1.2 cm/h and 1.3 cm/h ) and the compacted Arredondo GSDs (1.1

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109 cm/h ), indicating a greater variability between these measurements. Bulk densities and infiltration rates were not significantly (p < 0.05) correlated for either soil. Compacted infiltration rates were not si gnificantly correlated to noncompacted infiltration rates for either soil indicating there was no bias of non compacted infiltration rates on compacted rates. Figure 3 13 shows the relationship between bulk densities and infiltration rate with respect to compaction for each soil. As bulk densities increased, infiltration rates decreased logarithmically. While infiltration rates decreased much more for Orangeburg than Arredondo, bulk density increases were comparable. I nfiltration rates on the order of 100 cm/h, which would not be expected below compacted in situ soil profiles were attributed to limited subsoi l compaction in lysimeters. Had the subsoils been representative of compacted soil profiles, infiltration rates of amended soils likely would have been much lower. Rainfall and runoff data Scaling factors were calculated as the ratio of the combined flang e and soil surface area to soil surface are alone. Factors ranged from 1.41 to 1.59, with a median of 1.44 ( Table 310) and when applied to rainfall depths assumed all flange rainfall contributed to the soil surface. Since the volume of additional rainfall contributed to the lysimeters by the flanges was unknown, it was estimated that the entire flange area was contributing inflow to the lysimeter. However, flange rainfall may have flowed over the edges as well. Runoff data were collected from seven natur al events and one RFS (simulated) event run during the compacted phase. Natural event depths ranged from 1.28 cm to 3.70 cm with an average of 2.39 cm. The simulated event depth was 4.43 cm. However,

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110 to account for all rainfall contributing to the lysimeters rainfall was multiplied by the scaling factor to determine the effective rainfall depth. Incorporating scaling factors, the effective natural depths ranged from 1.84 cm to 5.32 cm, while the effective simulated depth was 6.37 cm ( Table 3 11 ). Runoff coefficients and curve numbers Runoff coefficients and effective CNs were calculated from rainfall and runoff from each lysimeter for each event (Appendi x D) Curve numbers were not calculated for lysimeters from events which produced no runoff (Hawkins 1 993). In general the Orangeburg lysimeters produced more runoff and thus higher runoff coefficients and effective curve numbers than the Arredondo soil. W hile the ranges overlapped for all events, the maximum Orangeburg CNs were comparable to the median Arredondo CNs and average CNs from each storm were greater for Orangeburg than for Arredondo. At least one Arredondo and Orangeburg lysimeter produced no runoff from at least six events. Event median runoff coefficients ranged from 0.02 to 0.42 for Arredondo lysimeters and 0.06 to 0.70 for Orangeburg lysimeters ( Table 312) Event median effective CNs ranged from 64 to 83 for Arredondo lysimeters and 71 to 92 for the Orangeburg lysimeters. Event CNs for average rainfall and runoff depths ranged from 60 to 82 for Arredondo and from 68 to 91 for Orangeburg lysimeters. Rainfall and runoff depths were also used to calculate effective CNs for each lysimeter. Averag e effective CNs for soils were 88 and 94 for Arredondo and Orangeburg, respectively ( Table 3 13). By comparison, the CN for commercial land use on A and B soils are 89 and 92, respectively (NRCS 1 986). Event runoff coefficients and effective curve numbers for each soil were not highly correlated to infiltration rate or bulk density measurements; all correlation coefficients had absolute values were les s than 0.55.

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111 Cone index profiles Cone index profiles were not taken during the compacted phase since measurements would create voids extending the entire soil profile likely altering hydrologic response during compacted and amended phases. However, profiles were collected just before the amendment phase on the control lysimeters which remained compacted. Median cone index profiles of noncompacted and compacted states are shown in Figure 3 14 for both soils. These profiles show that the soil surface decreased by 5 and 10 cm for Arredondo and Orangeburg soils, respectively. In addition, while the cone index profiles were essentially the same during the no n compacted phase, the Arredondo had much higher cone indices along the entire profile than the Orangeburg soils. Both compacted profiles increased from the soil surface until reaching a maximum where profiles decreased and approached a constant rate of decrease in index with depth. The maximum cone index for Arredondo soils was 10 cm below the compacted soil surface and 5 cm below the Orangeburg compacted soil surface. The increase in cone index indicates the limiting infiltration layer. Compaction effects seemed to be limited to the top 30 cm of the noncompacted profile, or 20 and 25 cm for compacted Orangeburg and Arredondo soils. Below 30 cm, the compacted profiles are similar in changes with depth to the noncompacted profiles, except offset by 200 and 300 kPa for Orangeburg and Arredondo, respectively. The change in cone indices below 30 cm may have resulted from reduced soil moisture resulting from decreased infiltration after compaction. The maximum cone indices for compacted Arredondo and Orangebur g lysimeters were 600 and 770 kPa and occurred at depths of 5 cm and 10 cm below the soil surface, respectively. By comparison, post development cone index profiles from

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112 Gregory et al. (2007) had maximum values between 4000 and 4500 kPa at depths between 2 7.5 cm and 37.5 cm below the surface. Although Arredondo infiltration rates were well above those reported by Gregory et al. (2006), effective curve numbers and runoff coefficients typically exceeded values expected from compacted for A and B soils (Dir t Roads, NRCS 1986). Compacted phase runoff coefficients and curve numbers were conservative due to flange rainfall contributions. Theref ore, the compaction procedure produced hydrologic relationships representative of compacted soils. However, based on i nfiltration rates, runoff production should have been much lower. This may indicate that infiltration rates measured by doublering infiltrometer did not accurately measure the hydrologic performance. A mendment P hase Bulk densities Bulk density values wer e measured from samples collected from each lysimeter after amendments were incorporated and are summarized in Table 3 14. An analysis of variance was used to quantify the effects of the amendments and incorporation depths on bulk densities for both soils (Table 315) All coefficient estimates were significant (p < 0.05), indicating that, at least when combining the soils data, altering incorporation depth or amendment type for a soil would significantly change the bulk density. However, increasing incorporation depth from 10 to 20 cm had the smallest and least significant effect on the bulk density. Multiple linear regression was also performed on the bulk density data ( Table 316). The resulting model, using coefficient estimates resulted in a RMSE of 0.07 g/cm3. Soils had significantly (p < 0.05) different bulk densities

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113 Significant differences between treatments were determined using Tukey Kramer multiple pairwise comparisons (SAS Institute 2001). Bulk densities were significantly lower than the control for all fly ash and compost amended soils and null incorporation at 10 cm for Orangeburg soils. Compost decreased bulk densities the most for both soils, followed by fly ash and then null. Except for the Ar redondo null incorporations, the deeper incorporation had insignificantly higher bulk densities than the shallow incorporation depths. Incorporating the same amendment volume into a larger volume of soil reduced the amendment fraction of the amended soil. Therefore, the amendments effect of reducing bulk density was diminished by diluting the amendment. Cone index profiles Three cone index profiles were measured on each lysimeter at the end of the amendment phase to prevent affecting other soils measurements. Median treatment cone index profiles are shown in Figure 3 15 through Figure 3 20 with respective soil control profiles. Paired t test analyses were performed between cone indices at common soil depths to the respective controls to determine treatment effects on soil strength (Appendix D) Cone index values were significantly (p < 0.05) less than control (compacted) values for all treatments to depths of the respective incorporation Profiles show that both 10 and 20 cm incorporation depths were below the depth of maximum cone index for the compacted soils and 20 cm incorporations may have eliminated compacted soil layers completely for Orangeburg soils. Thus the limiting layer, or layer of maximum compaction, was eliminated allowing for increased infiltration. For Arredondo soils, the 20 cm treatment cone index values approached the same values for 10 cm treatment and control profiles below 20 cm depth This indicates the bottom of the compacted Arredondo soil layer may not have been affected by the 20

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114 cm incorporation depth. Gregory et al. (2006) showed that the limiting layer within compacted cone index profiles was approximately 25 cm below the surface, and Randrup and Lichter (2001) reported that compaction effects can exceed 40 cm below the soil surface. Thus, while incorporation depths may have partially or completely eliminated the limiting soil layer in this study the same treatments would not be expec ted to exceed the compacted soil layer depth on c ompacted in situ soil profiles. S ignificant differences were also found to extend below incorporation depths for multiple treatment profiles. These phenomena may have occurred due to increased soil moisture at these depths which may have resulted from increased infiltration for null and compost amended treatments and increased soil moisture holding capacity from fly ash amended treatments. Fly ash tends to have a texture dominated by silt sized particles, shi fting the particle size distribution for respective treatments to smaller sizes. As a result, the pore size distribution may have shifted to increase the water content at field capacity. Soil moisture variability can change soil strength and change the soi l response to applied forces. In addition, more Orangeburg profiles had significant differences below the designated incorporation depth than Arredondo ( Table 321 ) T he Orangeburg likely had higher soil moisture content at field capacity, based on texture and resultant pore size distribution. Compost incorporated to a depth of 20 cm produced significantly lower cone index profile s below the incorporation depth. This m ay have also been due to increased infiltration and soil moisture which can affect soil strength and cone index readings. Infiltration r ates An analysis of variance was used to quantify the effects of the amendments and incorporation depths on infiltratio n rates for both soils ( Table 3 17). All coefficient

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115 estimates were significant (p < 0.05). Therefore, all depths and amendments had significant effec ts. Comparing geometric means of all treatments found that each was significantly different when compared to the control and each amendment and depth was significant ( Table 318). Multiple linear regression was also performed on the log transformed infiltration data ( Table 319). The resulting model, using coefficient estimates resulted in a RMSE of 0. 32 7 Converting from log transformed data, this is a multiplying factor of 2.12. Geometric mean and standard deviations of i nfiltration rates from amended Arredondo and Orangeburg lysimeters are summarized in Table 320 The mean control infiltration rates w ere 24.7 cm/h and 1.6 cm/h for Arredondo and Orangeburg lysimeters, respectively. For Arredondo soils, the fly ash incorporation significantly decreased the infiltration rates (10 cm: 4.4 cm/h; 20 cm: 12.7 cm/h) compared to 24.7 cm/h for the control; the difference was significant at 10 cm incorporation. Null incorporation at 20 cm significantly increased the infiltration rates to 84 cm/h and while rates increased at 10 cm to 39.6 cm/h the difference was not significant. Null and compost incorporations to 10 cm (39.6 cm/h and 75.7 cm/h, respectively) and 20 cm (84 cm/h and 92.7 cm/h, respectively ) on Arredondo soils were not significantly different. For Orangeburg soils, the minimum infiltration rate, 1.6 cm/h, was the control. Both fly ash amended treatm ents 5.0 cm/h at 10 cm and 6.5 cm/h at 20 cm/h, were not significantly (p < 0.05) different from the control or between the two incorporation depths Tillage (null incorporation) significantly (p < 0.05) increased infiltration rates with deeper incorporat ion depths from 1.6 cm/h to 9.3 cm/h to 94 cm/h for 0, 10, and 20 cm incorporation depths The highest infiltration rates were from compost treatments at 10

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116 cm (105.7 cm/h) and 20 cm/h (112.1 cm/h) Infiltration rates for tillage at 20 cm and 10 and 20 cm compost incorporations were not significantly different. Infiltration rates for 10 cm tillage were significantly greater on Arredondo than Orangeburg soil. However, Orangeburg infiltration rates were greater than Arredondo (not significantly) for three other treatments: null incorporation at 20 cm and both compost depths. The limiting layer was only 5 cm below the compacted soil surface for the Orangeburg soil but was 10 cm below Arredondo compacted surface based on control treatment cone index profiles Comparing 20 cm compost incorporation and tillage only cone penetrometer profiles, residual effects of compaction remained below the incorporated depth on the Arredondo soil while the Orangeburg profiles have no remaining compaction. As previously noted the non compacted infiltration rates for Orangeburg soils were greater than the Arredondo soils. Thus the combination of insufficient subsoil compaction and a shallower compaction layer which was more fully eliminated produced greater infiltration rates for Orangeburg treatments and would not be expected on real world soil profiles. Runoff coefficients During the amendment phase 9 natural and 10 simulated events fell on the lysimeters ( Table 322 ) with depths ranging from 0.4 to 11.4 cm. Runoff coefficients were calculated as the ratio of rainfall depth to runoff depth. Values for amended Arredondo and Orangeburg treatments are listed in Appendix D with means in Table 3 23 and Table 324, respectively. Significant differences were determined by analyzing values with Wilcoxon sign rank test due to the nonparametric distribution of values. The control treatments (Null, 0 cm) had mean runoff coefficients for the amended phase of 0.21 and 0.41 for the Arredondo and Orangeburg soils. By comparison, runoff

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117 coefficients for acre residential areas, assuming 38% impervious, on A and B soils are 0.22 and 0.50, respectively (NRCS 1 986). The treatment with the largest runoff production for both soils was fly ash incorporated at 10 cm. Fly ash amendments incorporated at both depths significantly increased runoff production from both compacted soils. The treatments with the least runoff production were from compost treatments; 20 cm incorporation for Arredondo and 10 cm for Orangeburg. Treatments of null or compost incorporated to 10 or 20 cm significantly decreased runoff coefficients compared to the compacted control. All Orangeburg treatments were significantly (p < 0.05) different from each other. Three Arredondo treatments were not significantly different from each other: Null at 10 and 20 cm incorporation and Compost at 10 cm. Curve numbers Curve numbers were calculated for runoff producing event s from each lysimeter (Appendix D) These curve numbers w ere then regressed against the inverse rainfall depths for each lysimeter. Summarized linear regressions are listed in Table 325 and Table 326 for Arredondo and Orangeburg lysimeters, respectively. Mean effective curve numbers for each treatment are listed in Table 3 27 and Table 3 28 for Arredondo and Orangeburg soils, respectively. Fly ash incorporated at 10 cm produced t he greatest mean curve number of all treatments for both soils, followed closely by the 20 cm fly ash incorporations, which w ere not significantly different. The mean control curve number for both soils was lower than both fly ash treatments although not significantly Tillage alone significantly decreased runoff production compared to the control for both 10 and 20 cm depths. While mean curve numbers were lower on both soils for 20

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118 cm tillage compared to 10 cm, the differences were not significant. Compost incorporations at both depths also significantly decreased mean curve numbers compared to the controls. However, curve numbers were not significantly different between compost treat ment depths. The lowest mean curve numbers were from tillage with compost at 10 cm. Incorporating compost with tillage did not significantly decrease effective curve numbers compared to tillage alone. Although, mean curve numbers for curve numbers were lo wer for 10 cm incorporations. Including an organic amendment would also likely add horticultural benefits over the absence of amendments, especially in situations where topsoil has been removed. The increased available organic matter may increase soil fert ility and provide habitat for soil organisms more rapidly than natural accumulation of organic matter. The minimum treatment of compacted soils, null amendment at 10 cm for both soils significantly reduced runoff production compared to the compacted soil and was not significantly different from deeper tillage. Significant reductions in runoff volumes can be achieved for small rainfall events with minimal tillage and without including amendments for these two soil types. This seems to be true especially if the limiting layer to infiltration is near the surface. To quantify the potential runoff reduction of 10 cm tillage without amendments runoff depths were calculated for a hypothetical residential watershed where 0, 50, or 100% of the open area had been tr eated receiving a 2yr 24 hour rainfall event for Gainesville, FL (9.2 cm; Eaglin 1996). All open space was assumed to be compacted and have curve numbers equal to those found in this study. The watershed assumed an

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119 imperviousness of 25%. These values were compared to undisturbed pasture and wooded areas in fair condition. Results are shown in Table 3 29 Assuming Arredondo as an A soil and Orangeburg as a B soil, runoff depths were approximately 3/4 and 2/3 of the runoff depth of the nontreated watershed. Stormwater regulations frequently require mitigating the increased runoff after development. Since the Arredondo soils had much lower runoff depths under predeveloped conditions, tillage was not as effective at reducing the additional runoff as on Orangeburg soils. Thus, the runoff depth differences between preand post development with tillage were less on Orangeburg soils than on Arredondo soils. As a result, soil amending may prove to be more effective at mitigating compaction on predeveloped soils with lower infiltration rates, with respect to runoff generation. Furthermore, soil amending could offset the costs of conventional stormwater struc tures by reducing their size. Without considering land purchase, the costs of traditional retention basins are a power function of the basin volume (SEWRPC 1991). Exponents range from 0.51 to 0.75. Thus reducing the runoff volume by half would reduce costs between 30 and 40%. In addition, the reduced size of a retention basin would increase the available are for land development, which could be the more valuable benefit. Conclusions Although the compaction procedure produced surface soil bulk densities comparable, infiltration rates were greater and cone indices were lower than those reported by Gregory et al. (2006). Thus, insufficient subsoil compaction did not replicate

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120 in situ soil conditions. Future studies should ensure representative subsoil characteristics to more accurately represent compacted soil profiles. Fly ash treatments resulted in infiltration rates less than or not significantly different from compacted soils except for 20 cm incorporation on Orangeburg soils. Similarly, runoff coefficients and effective curve numbers were also found to be greater than or not significantly different from compacted soils. While fly ash amended bulk densities were significantly lower than the compacted soils, the increase in silt sized particles likely reduced the pore sizes Increasing incorporation depth from 10 to 20 cm increased geometric mean infiltration rates for all amendments, however it was only significant for tillage on Orangeburg soils Increased incorporation depths also decr eased curve numbers for null treatments on both soils although not significantly. C ompost incorporated to 20 cm rather than 10 cm did not further reduce runoff production. Mean curve numbers were actually slightly greater for deeper compost incorporations but not si gnificantly. Increasing incorporation depths did not significantly reduce runoff most likely due to the shallow compaction layer depths. The maximum cone index value was approximately 25 cm below the soil surface for compacted sites reported by Gregory et al. (2006). Limiting layer depths can exceed 40 cm on construction sites (Randrup and Lichter 2001). Therefore, although not significantly demonstrated in this study, 20 cm incorporation depths would be expected to increase infiltration rates and further r educe runoff compared to 10 cm depths. Future research should further investigate the effect of deeper incorporation on in situ compacted soils.

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121 Additional research should also investigate whether the benefits of treatments extend to a larger scale at eit her the watershed or plot level. Findings from such study would hopefully quantify the effects of treatments more accurately, especially with native soils below the amendment profile. R esults presented here have shown there is a potential to reduce runoff by tilling soil, with or without compost, to depths as shallow as 10 cm For compacted profiles with limiting layers below the incorporation depth, deeper incorporations would be expected to improve hydrologic response, especially for larger rainfall event s. Cost and energy increase as mitigation increases in depth. As depth increases, the benefits become negligible, no matter the infiltration rate due to the increased storage capacity of the soil. Even i f incorporated depths do not exceed the most limiting depth of compaction, the available water storage above that layer is increased. In this way, the amended soil functions similarly to permeable pavement systems, where the surface layers are not limiting to infiltration, rainfall and runoff are captured and stored and then infiltrated at a slower rate. Thus, runoff may be significantly reduced, especially from smaller rainfall events. As a result, soil amending could be incorporated into low impact development, which seeks to mimic predevelopment hydrology

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122 Table 3 1 Summary of properties for soils and amendments included in this study. Arredondo Orangeburg Compost Fly Ash Sand 94 61 81 23 Silt 3 13 11 71 Clay 3 26 8 6 Texture Sand Sandy Clay Loam Loamy Sand Silty Loam Particle Density (g/cm 3 ) 2.41 2.56 2.26 2.10 Organic Matter by LOI% 1 5 79 51 Maximum Proctor Density (g/cm3) 1.77 1.77 Table 3 2 Non compacted bulk densities. Arredondo Orangeburg b (g/cm 3 ) b (g/cm 3 ) Maximum 1.38 1.17 Median 1.23 1.08 Minimum 1.16 0.95 Mean 1.24 1.07 St. Dev. 0.05 0.05 *Student t test used to determine that values were significantly (p < 0.05) different. Table 3 3 Non compacted infiltration rates Arredondo Orangeburg ( cm/h ) ( cm/h ) Maximum 196.8 318.1 Median 140.6 163.4 Minimum 111.4 109.8 Geometric Mean 144.5 178.0 Geometric Standard Deviation 1.2 1.3 *Student t test used to determine that values were significantly (p < 0.05) different Table 3 4 Pearson correlation coefficients and pvalues for noncompacted bulk densit y and infiltration rate. Soil r p value Arredondo 0.23 0.32 Orangeburg 0.07 0.76

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123 Table 35 Pearson correlation coefficients and pvalues for cone indices with bulk densities and infiltration rates. Cone Indices vs. Bulk Densities Cone Indices vs. Infiltration Rates Soil Depth Arredondo Orangeburg Arredondo Orangeburg (cm) r p value r p value r p value r p value 0.0 0.06 0.78 0.18 0.43 0.03 0.91 0.29 0.21 2.5 0.26 0.26 0.04 0.88 0.21 0.37 0.06 0.78 5.0 0.06 0.79 0.03 0.89 0.53 0.01 0.23 0.32 7.5 0.01 0.98 0.08 0.73 0.32 0.16 0.28 0.22 10.0 0.11 0.65 0.07 0.76 0.18 0.42 0.26 0.26 12.5 0.08 0.72 0.04 0.85 0.04 0.87 0.31 0.17 15.0 0.05 0.83 0.08 0.74 0.00 0.98 0.16 0.50 17.5 0.03 0.91 0.01 0.98 0.10 0.67 0.07 0.76 20.0 0.06 0.81 0.05 0.82 0.03 0.89 0.05 0.84 22.5 0.03 0.91 0.04 0.87 0.03 0.88 0.10 0.68 25.0 0.13 0.58 0.05 0.82 0.07 0.78 0.11 0.64 27.5 0.35 0.12 0.04 0.88 0.07 0.77 0.04 0.87 30.0 0.22 0.35 0.01 0.98 0.08 0.72 0.07 0.75 32.5 0.13 0.56 0.05 0.84 0.20 0.38 0.09 0.71 35.0 0.04 0.85 0.16 0.49 0.20 0.38 0.08 0.73 37.5 0.10 0.68 0.02 0.92 0.18 0.44 0.19 0.41 40.0 0.31 0.17 0.22 0.34 0.09 0.70 0.25 0.27 42.5 0.45 0.04 0.24 0.29 0.36 0.11 0.09 0.69 45.0 0.09 0.70 0.06 0.78 0.24 0.30 0.20 0.38 Table 3 6 Arredondo bulk densities and mean bulk density increase for each compaction iteration. Bulk Densities (g/cm 3 ) Non compact Single Drop Double Drop Wet Double Drop Quartered Wet Double Drop Maximum 1.38 1.39 1.41 1.47 1.59 Median 1.23 1.36 1.40 1.46 1.56 Minimum 1.16 1.33 1.36 1.42 1.50 Mean 1.24 1.36 1.39 1.45 1.56 b increase 0.12 0.03 0.06 0.11

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124 Table 3 7 Orangeburg bulk densities and mean bulk density increase from each compaction iteration. Bulk Densities (g/cm 3 ) Non compact Single Drop Double Drop Wet Double Drop Quartered Wet Double Drop Maximum 1.17 1.27 1.26 1.35 1.55 Median 1.08 1.17 1.22 1.31 1.44 Minimum 0.95 1.08 1.15 1.26 1.36 Mean 1.07 1.17 1.21 1.31 1.36 b increase 0.10 0.04 0.10 0.05 Table 3 8 Summary of c ompacted bulk densities and percent of Growth Limiting Bulk Densities (GLBD). Arredondo Orangeburg g/cm 3 %GLBD* g/cm 3 %GLBD* Maximum 1.59 88 1.55 95 Median 1.56 87 1.44 88 Minimum 1.50 83 1.36 83 Mean 1.56 87 1.44 88 St andard Dev iation 0.02 1 0.05 3 *GLBD for Arredondo: 1.80 g/cm3; Orangeburg: 1.64 g/cm3 (Daddow and Warrington 1 983) Table 3 9 Summary of c ompacted infiltration rates. Arredondo Orangeburg cm/h cm/h Maximum 44.2 14.9 Median 36.4 4.8 Minimum 28.5 0.3 Geometric Mean 36.1 4.1 Geometric Standard Deviation 1.1 2.9

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125 Table 3 10. Scaling Factors (SF) calculated for compacted phase as the ratio of soil area and flange area to soil area for each lysimeter. SFs for each lysimeter are applied to the rainfall depth to account for additional rainfall from lysimeter flanges Lysimeter SF Lysimeter SF Lysimeter SF 1 1.54 15 1.45 29 1.44 2 1.48 16 1.50 30 1.42 3 1.44 17 1.43 31 1.43 4 1.59 18 1.42 32 1.42 5 1.42 19 1.45 33 1.42 6 1.46 20 1.42 34 1.43 7 1.43 21 1.47 35 1.44 8 1.47 22 1.43 36 1.41 9 1.56 23 1.44 37 1.45 10 1.44 24 1.46 38 1.44 11 1.51 25 1.44 39 1.44 12 1.44 26 1.45 40 1.41 13 1.42 27 1.47 41 1.42 14 1.43 28 1.43 42 1.44 Table 3 11. Rainfall event dates depth, and effective rainfall depths calculated from the median scaling factor. Date Type Depth ( m m) Effective Depth (mm) 8/4/2009 Natural 3 1 21 8/6/2009 Natural 4 3 30 8/7/2009 Simulated 6 4 44 8/12/2009 Natural 1 8 13 8/21/2009 Natural 19 13 8/28/2009 Natural 51 36 9/2/2009 Natural 24 17 9/3/2009 Natural 53 37

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126 Table 3 12. Summary of compaction phase runoff coefficients for each soil. Rainfall (mm) 44 37 36 30 21 17 13 13 Arredondo Maximum 0.59 0.54 0.15 0.30 0.33 0.07 0.16 0.12 Median 0.42 0.37 0.00 0.09 0.03 0.00 0.00 0.00 Minimum 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mean 0.41 0.31 0.04 0.11 0.07 0.01 0.02 0.02 Standard Deviation 0.12 0.17 0.05 0.10 0.08 0.02 0.04 0.03 Orangeburg Maximum 0.91 0.71 0.31 0.64 0.80 0.97 0.37 0.34 Median 0.62 0.53 0.07 0.24 0.46 0.02 0.00 0.00 Minimum 0.38 0.31 0.00 0.00 0.00 0.00 0.00 0.00 Mean 0.65 0.52 0.10 0.27 0.42 0.17 0.09 0.06 Standard Deviation 0.14 0.12 0.11 0.19 0.18 0.29 0.14 0.11 Table 3 13. Summary of compacted regressed curve numbers. Arredondo Orangeburg Maximum 100 100 Median 87 94 Minimum 70 85 Mean 88 94 St. Dev. 9 3 Table 3 14. Mean bulk densities (g/cm3) for each soil for each treatment. Amendment Depth (cm) Arredondo Orangeburg Mean St. Dev. Mean St. Dev Null 0 1.51 0.07 a* 1.42 0.07 a b 10 1.3 5 0.07 ab c 1.19 0.03 cdef 20 1.31 0.05 abc d 1.27 0.10 bcde Fly Ash 10 1.13 0.13 def 1.07 0.05 efg 20 1.23 0.02 bcd e 1.18 0.01 cdef Compost 10 1.01 0.05 fg 0.91 0.04 g 20 1.07 0.07 efg 0.99 0.11 fg *Values with the same letter are not significantly different. (p < 0.05) Means were analyzed using Tukey multiple pairwise comparison.

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127 Table 3 15. ANOVA for amended phase bulk density results. Source Degrees of Freedom Sum of Squares Mean Square Error F ratio p value Model 5 1.1 56 0.2 31 49. 27 < 0.0001 Soil 1 0.07 1 0.07 1 15. 18 0.000 4 Amendment 3 1.045 0. 348 74.16 < 0.0001 Depth 1 0. 041 0. 041 8.66 0.00 57 Error 36 0.16 9 Total 41 1.3 26 All interactions were not significant (p<0.05). Table 3 16. Multiple linear regression results of amended bulk densities. Parameter Estimate S tandard Error F ratio p value Intercept 1. 43 0.0 30 47.67 < 0.0001 Soil Arredondo 0.08 0.021 3. 9 0 0. 000 4 Orangeburg ----Amendment Compost 0. 44 0. 0 36 12 20 < 0.0001 Fly Ash 0. 28 0. 0 36 7.82 < 0.0001 Null 0.15 0.036 4.22 0.0002 Incorporation Depth 10 cm ----20 cm 0.07 0.023 2.94 0.0057 Model R2 = 0.872; RMSE = 0.068 Table 3 17. ANOVA for amended phase log of infiltration rates results. Source Degrees of Freedom Sum of Squares Mean Square F ratio P value Model 5 15.33 1.18 31.41 <.0001 Soil (S) 1 0.67 0.67 17.94 0.0 002 Amendment (A) 3 10 85 3.62 96.31 <.0001 Depth (D) 1 1.02 1.02 27.09 <.0001 S*A # 3 1.78 0.59 15.81 <.0001 A*D 2 0.57 0.28 7.57 0.0024 S*D*A 3 0.44 0.15 3.94 0.0183 Error 36 1.05 Total 41 16.39 #S*D was not significant (p < 0.05)

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128 Table 3 18. Summary of log of infiltration rate mean. Contrast Sum of Squares F ratio p value Control vs. Treatments 2.319 61.75 <.0001 Depth (cm) 0 vs. 10 1. 140 30.37 <.0001 0 vs. 20 3.172 84.47 <.0001 10 vs. 20 1.017 27.09 <.0001 Amendment N ull vs. C ompost 0.796 21.18 < .0 001 N ull vs. F ly Ash 3.853 102.59 <.0001 C ompost vs. F ly Ash 8.150 217.01 <.0001 Table 3 19. Multiple linear regression results of log transformed amended infiltration rates. Parameter Estimate S tandard Error F ratio p value Intercept 0.67 0. 14 0 4.71 <.0001 Soil Arredondo 0.25 0.101 2.51 0.0167 Orangeburg ----Amendment Compost 1.35 0.172 7.83 < .00 01 Fly Ash 0.18 0. 172 1.07 0.2925 Null 0.99 0.172 5.72 <.0001 Incorporation Depth 10 cm ----20 cm 0.34 0.011 3.09 0.0039 Model R2 = 0.765; RMSE = 0.327

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129 Table 3 20. Geometric Means (GM) and Standard Deviations (GSD) of amended i nfiltration rates (cm/h) for both soils Treatment Arredondo Orangeburg Amendment Depth (cm) GM GSD GM GSD Null 0 24.7 1.2 bc d 1.6 2.1 f 10 39.6 1.4 ab c 9.3 1.4 de 20 84.0 1.4 a b 94.0 1.3 a Fly Ash 10 4.4 1.8 ef 5.0 2.7 ef 20 12.7 1.5 c de 6.5 1.7 e Compost 10 75.7 1.2 a b 105.7 1.0 a 20 92.7 1.1 a b 112.1 1.1 a *Values with the same letter are not significantly (p < 0.05) different. Geometric means were analyzed using Tukey comparison of means Table 3 21. Summary of depths from the surface of significant (p < 0.01) difference in cone index between treatments and controls. Treatment Arredondo Orangeburg Amendment Depth (cm) Depth (cm) Depth (cm)* Null 10 15.0 a 20.0 Null 20 27.5 12.5 c Fly Ash 10 15.0 b 12.5 Fly Ash 20 25.0 22.5 a Compost 10 15.0 25.0 Compost 20 15.0 37.5 *Depths are from the surface. Differences were significant (p > 0.05) between surface and 5 cm(a), 10 cm (b), or 15 cm (c).

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130 Table 3 22. Amended phase rainfall events and depths which runoff was measured. Date Type Depth (mm) 09/23/09 Simulated 114.4 09/30/09 Simulated 77.2 10/07/09 Simulated 61.6 10/14/09 Simulated 67.3 10/21/09 Simulated 54.7 10/28/09 Natural 4.4 11/04/09 Simulated 75.4 11/10/09 Natural 12.3 11/12/09 Simulated 50.4 11/18/09 Simulated 71.6 11/23/09 Simulated 69.8 11/25/09 Natural 58.8 12/02/09 Natural 14.5 12/05/09 Natural 34.8 12/18/09 Natural 5.4 12/25/09* Natural 6.9 01/01/10 Natural 18.5 (25.4) 01/13/10 Simulated 71.6 01/17/10 Natural 29.5 01/22/10 Natural 19.2 *Event depths from 12/25/09 and 01/01/10 were combined together since collection tanks were not emptied between events. Table 3 23. Summary of amendment phase Arredondo runoff coefficients. Amendment Depth (cm) Mean Coefficient Fly Ash 10 0.49 a* 20 0.36 b Null 0 0.21 c 10 0.01 d 20 0.01 d Compost 10 0.01 d 20 <0.005 e *Runoff Coefficients with the same letter are not significantly (p < 0.05) different via Wilcoxon paired tests.

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131 Table 3 24. Summary of amendment phase Orangeburg treatment runoff coefficients. Amendment Depth (cm) Mean Coefficient Fly Ash 10 0.45 a* 20 0.45 b Null 0 0.41 c 10 0.17 d 20 0.11 e Compost 10 0.08 f 20 0.12 g *Runoff Coefficients with the same letter are not significantly (p < 0.05) different via Wilcoxon paired tests. Table 3 25. Median slope, intercept, and r2 values for Arredondo curve number regression against inverse of rainfall depth. Amendment Incorporation Depth (cm) Slope Intercept r 2 Null 0 4.4 72 0.66 Null 10 10.4 54 0.71 Null 20 22. 8 44 0.92 Fly Ash 10 1. 4 90 0.50 Fly Ash 20 1.1 86 0.27 Compost 10 27. 1 40 0.95 Compost 20 20. 1 48 0.99 Table 3 26. Median slope, intercept, and r2 values for Orangeburg curve number regression against inverse of rainfall depth. Amendment Incorporation Depth (cm) Slope Intercept r 2 Null 0 1.7 86 0.35 Null 10 5.5 71 0.56 Null 20 9.2 62 0.60 Fly Ash 10 1.2 89 0.34 Fly Ash 20 1.9 87 0.52 Compost 10 6.7 61 0.73 Compost 20 11.4 64 0.62

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132 Table 3 27. Summarized mean c urve n umbers for amended Arredondo treatments. Amendment Depth (cm) Mean Curve Number St. Dev. Fly Ash 10 91 a* 3 Fly Ash 20 86 ab 4 Null 0 7 5 b 6 Null 10 49 c 8 Compost 20 44 c 9 Null 20 44 c 1 Compost 10 40 c 2 *Curve numbers with the same letter are not significantly (p < 0.05) different via Tukey Kramer multiple pairwise comparison. Table 3 28. Summarized mean c urve n umbers for amended Orangeburg treatments. Amendment Depth (cm) Mean Curve Number St. Dev. Fly Ash 10 89 a* 1 Fly Ash 20 88 a 3 Null 0 87 a 2 Null 10 71 b 3 Null 20 64 b 5 Compost 20 6 2 b 3 Compost 10 6 2 b 5 *Curve numbers with the same letter are not significantly (p < 0.05) different via Tukey Kramer multiple pairwise comparison. Table 3 29. Hypothetical runoff depths from a 2 yr 24 hr rainfall event for Gainesville, FL (9.2 cm) for various percentages of open area treated with tillage at 10 cm compared with undisturbed conditions. Percent of Open Area Treated* Runoff Depths (cm) Arredondo Orangeburg 0 % 4.32 6.35 50 % 3.28 4.80 100 % 2.21 3.25 Undisturbed Pasture (Fair) 0.50 2.61 Woods (Fair) 0.00 1.48 *Assumed from 25% impervious area with all open area compacted.

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133 Figure 3 1 Centerline, cross sectional diagram of a lysimeter from the left side. A B Figure 3 2 A) Well screen installed in the bottom of a lysimeter prior to filling. B) Measurement of drainage layer depth after filling. A B Figure 3 3 A) Filter fabric installed over drainage layer. B) Screening of Orangeburg soil during lysimeter filling.

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134 A B Figure 3 4 A) Moving filled lysimeter via forklift. B) Lysimeters placed in their respective locations. Figure 3 5 Soil moisture sensor diagram.

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135 Figure 3 6 Soil compaction using tamper and slide weight. Figure 3 7 Compaction during final iteration using modified tamper

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136 Figure 3 8 Schematic of lysimeter layout and rainfall simulator. Soil types are identified for each lysimeter as A (Arredondo) or O (Orangeburg). Figure 3 9 Non compacted bulk density values. 0.9 1 1.1 1.2 1.3 1.4Bulk Density (g/cm3)Arredondo Orangeburg

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137 Figure 3 10. Non compacted infiltration rates Figure 3 11. Maximum, mean, median, and minimum value cone penetrometer profiles for Arredondo and Orangeburg soils 100 150 200 250 300 350Infiltration Rate (cm/h)Arredondo Orangeburg 0 5 10 15 20 25 30 35 40 45 0 500Depth (cm)Arredondo Cone Index (kPa) 0 500 Orangeburg Cone Index (kPa) Maximum Mean Median Minimum

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138 Figure 3 12. Bulk densities following compaction iterations. Pre = Noncompacted; SD = Single Drop; DD = Double Drop; WDD = Wetted Double Drop; QWDD = Quartered Wetted Double Drop 0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.60 Pre SD DD WDD QWDD Bulk Density (g/cm3)Compaction Iteration Orangeburg Max. Arredondo Max. Orangeburg Med. Arredondo Med. Orangeburg Min. Arredondo Min. 1.45 g/cm 3 1.32 g/cm3

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139 Figure 3 13. Infiltration rates versus bulk densities for noncompacted and compacted lysimeters for both soil types. 0.1 1 10 100 1000 0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.60Infiltration Rate (cm/h)Bulk Density (g/cm3) Arredondo Uncompacted Arredondo Compacted Orangeburg Uncompacted Orangeburg Compacted

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140 Figure 3 14. Comparison of median noncompacted and control cone index profiles. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Depth below original soil surface (cm)Cone Index (kPa) Arredondo Non Compacted Orangeburg Non Compacted Arredondo Compacted Orangeburg Compacted

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141 Figure 3 15. Median Arredondo null amended cone index profiles. Offset depths are referenced to compacted soil surface. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Soil Depth (cm)Arredondo Cone Index (kPa) Null 0 cm (control) Null 10 cm Null 20 cm 10 cm 20 cm 0 cm

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142 Figure 3 16. Median Arredondo compost amended cone index profiles. Profiles are referenced to the top of the drainage layer. Offset depths are referenced to compacted soil surface. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Soil Depth (cm)Arredondo Cone Index (kPa) Null 0 cm (control) Compost 10 cm Compost 20 cm 10 cm 20 cm 0 cm

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143 Figure 3 17. Median Arredondo fly ash amended cone index profiles. Profiles are referenced to the top of the drainage layer. Offset depths are referenced to compacted soil surface. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Soil Depth (cm)Arredondo Cone Index (kPa) Null 0 cm (control) Fly ash 10 cm Fly Ash 20 cm 10 cm 20 cm 0 cm

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144 Figure 3 18. Median Orangeburg null amended cone index profiles. Profiles are referenced to the top of the drainage layer. Offset depths are referenced to compacted soil surface 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Depth (cm)Orangeburg Cone Index (kPa) Null 0 cm (control) Null 10 cm Null 20 cm 10 cm 20 cm 0 cm

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145 Figure 3 19. Median Orangeburg compost amended cone index profiles. Profiles are referenced to the top of the drainage layer. Of fset depths are referenced to compacted soil surface. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Depth (cm)Orangeburg Cone Index (kPa) Null 0 cm (control) Compost 10 cm Compost 20 cm 10 cm 20 cm 0 cm

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146 Figure 3 20. Median Orangeburg Fly Ash amended cone index profiles. Profiles are referenced to the top of the drainage layer. Offset depths are referenced to the compacted soil surface. 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000Depth (cm)Orangeburg Cone Index (kPa) Null 0 cm (control) Fly Ash 10 cm Fly Ash 20 cm 10 cm 20 cm 0 cm

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147 CHAPTER 4 SOIL AMEMDMENT S FOR COMPACTED SOIL M ITIGATION I I: WATER QUA L ITY Introduction Gregory et al. (2006) showed that soil compaction coinciding with typical development activities and vehicle traffic reduced infiltration rates and increased bulk densities on fine sand soils in North Central Florida. Current development practices leave soils compacted from heavy equipment traffic, with reduced porosity, infiltration rates, and increased runoff volumes and rates. Soil amendments have previously been studied to evaluate their potential for improving soil properties, m ostly in agricultural settings. While soil amendments have been shown to improve hydrologic characteristics of soils, the potential exists for water quality impacts. T wo amendments which have frequently been researched are fly ash and compost. T he potential exists for compost to become a source for nutrients into runoff or leachate, depending on the parent material of the compost and potential for plant uptake (Cogger 2 005; Jaber et al. 2 005; Gilley and Eghball 2 002). Nutrients can cause water quality impairment by contributing to eutrophication. The EPA (2009) maximum contaminant level (MCL) is 10 mg NO2+3N/l for drinking water. However, Jaber et al.(2005) reported t hat groundwater under fields fertilized with various composts on calcareous soils did had concentrations less than the MCL for nearly all samples. Composts with C:N ratios greater than 30:1 are recommended to minimize leaching of NO2+3N (Landshoot 2006). The higher ratio allows m icroorganisms to immobilize nitrogen (Landshoot 2006) While incorporating compost into soils does increase nitrogen and phosphorus (Eghball 2003; Filcheva and Tsadilas 2002), most

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148 NO2+3N losses occur immediately after application (Loper 2009) and increased infiltration significantly reduces runoff loadings ( Pitt et al. 1999). Jaber et al. (2006) found that ground water under composted sandy soils had orthophosphorus (OP) concentrations less than 1.2 mg/l. Fly ash has also been used as a soil amendment. Fly ash typically contains trace concentrations of toxic metals (Torrey 1 978; Khandekar et al. 1 997) however, leaching of metals are typically well below water quality standards (Pathan et al. 2 003). Pathan et al. ( 2002) also found that fly ash increased sorption of NO2+3N, NH4N, and OP, possibly due to Al2O3 and Fe2O3 in the fly ash, when incorporated with sand in column tests. However in a field study, Pathan et al. (2003) found that fly ash could significantly increase OP when mixed with sandy soils. Since both amendments have been shown to potentially affect nutrients, it was determined that the water quality effects of these amendments needed to be investigated. Therefore, the objective of this study was to determine whether incorporating fly ash or compost at 10 and 20 cm into compacted soils significantly affected water quality with respect to runoff and leachate. M ethods and Materials Soils and Amendments The soils used in this study were Arredondo (A) and Orangeburg (O). The Arredondo soil was collected from a site on the University of Florida campus in Gainesville, FL. which was historically used for irrigation and crop research studies. The Orangeburg soil was collected from a stockpile at the North Flor ida Research and Education Center near Quincy, FL. Both soils were analyzed for texture (ASTM 2 007) and organic matter (OM) by loss on ignition (Heiri et al. 2 001). Analyses determined the

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149 Arredondo had a sand texture with 1% OM while the Orangeburg had a sandy clay loam texture with 5% OM (See Chapter 3) Two soil amendments used in this study were Black Kow ( Black Gold Composting Co. Oxford, FL ) composted dairy cow manure (0.5% 0.5 % 0.5 % ; N P K respectively) (C) and Class F fly ash (F) from the Gainesv ille Regional Utilities (GRU) Deerhaven power plant stockpiles. Black Kow is a composted cattle manure product that is commonly available to consumers at home and garden retailers. All samples were collected in 20 ml scintillation vials (Thermo Fisher Sci entific, Waltham, MA; 0333723C ). Column Study A column study was conducted to evaluate potential water quality impacts from each soil, amendment, and various incorporation ratios. Columns were constructed of 15 cm diameter PVC pipe, 30 cm in length. Testing caps were fixed to the base of the pipes and a hose barb was inserted through the cap. Vinyl tubing was attached to the hose barb to allow for drainage. Approximately 2.5 cm of washed No. 57 quartz drain stone was laid in the bottom of the columns. F ilter fabric was then placed over the drain stone. Finally, the columns were filled with respective media to a depth of 30 cm. Three amendment fractions by volume were mixed for each soil and amendment combination along with columns of only soil or amendment. The treatments included the two soils (0.0 amendment fraction), the two amendments (1.00 amendment fraction) and three amendment fraction mixtures of soil and amendment (0.05, 0.10 and 0.30) for the four soil amendment combinations Each of the 16 trea tments had four replicates resulting in 64 columns. Pore volumes (PV) for each mixture were estimated by:

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150 = + + (4 1) where Fs and Fa were the volumetric fractions of soil and amendment, respectively, ns and na are porosities of the soil and amendment, respectively, and Vm is the volume of the media mixture. Porosities were calculated by = 1 / (4 2) where b was the material bulk density (Blake and Hartge 1986) and s was the material par ticle density (Blake and Hartge, 1978) which are listed in Table 4 Two pore volumes were applied to each column. The first pore volume was applied to saturate the co lumn and was drained after 24 h. The second pore volume was applied a week later and was column drainage was collected for water quality analysis. Rainwater was captured to apply to the columns. However, the total rainfall volume was about half of the nec essary volume. Thus, the rainwater was supplemented by tap water to meet the necessary volume. Lysimeter Study Lysimeter description, preparation, and location were detailed in Chapter 3 Methods section. Half of the 42 lysimeters were filled with each soi l and compacted. Treatments consisted of compost, fly ash, or no amendment (Null (N)) incorporated at 10 or 20 cm, and a control where no amendment or incorporation was applied to the lysimeter. Compost and fly ash were applied 5 cm deep over compacted lys imeters, which was then incorporated into 10 or 20 cm of soil. The resulting amendment fractions by volume were 33% and 20% for 10 and 20 cm incorporations. Treatments are referenced by the amendment and incorporation depth. For example C10 refers to compo st incorporated 10 cm deep. Each treatment had three replicates.

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151 Runoff samples were taken from runoff collection tanks. Leachate samples were collected by opening the leachate valve and collecting a sample after 510 seconds of flow. This allowed for the sample to be collected from drainage layer storage rather than the drainage pipe. Both runoff and leachate samples were collected if available following 16 events, 9 simulated and 7 natural. Rainfall samples were also collected and analyzed. Sampling and Analysis Methodology Three samples were collected from each column; one each for nitrogen species (Nitrate Nitrite Nitrogen (NO2+ 3N), EPA Method 353.2; Ammonia Nitrogen (NH4N), EPA Method 350.1 modified; Total Kjeldahl Nitrogen (TKN), EPA Method 351.2, ortho phosphorus (OP, ppb, EPA Method 365.1) and metals ( Inductively Coupled PlasmaAtomic Emission Spectroscopy EPA Method 200.7). At least one replicate for every 20 samples was also collected for all events except the initial two events (EPA, 2001). Or ganic Nitrogen (O rg. N) was calculated as the difference between TKN and NH4N, and Total Nitrogen was the sum of NO2+3N and TKN Measurements for pH were performed using a Fisher Scientific Accumet AP85 pH Meter. Column leachate concentrations were used to determine at what amendment fractions nutrient concentrations were significantly changed with respect to leachate from soil only columns. The Practical Quantitation Limit (PQL) and Method Detection Limit (MDL) co ncentrations for each analyte and corresponding applied water matrix concentrations are listed in Table 4 2 Nitrogen samples were acidified by H2SO4 a nd metals by HNO3 to a pH < 2. OP samples were immediately transported to Analytical Research Laboratory (ARL) for analysis (1.4 km away). Nitrogen and metals samples were refrigerated to < 6C and

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152 then delivered to ARL for analysis per preservation guidel ines included in EPA analysis methods listed above. Due to the close proximity to the lab, OP samples were not refrigerated until arriving at the lab since the cooling time would further reduce the available time for analysis. Metals results are not analyz ed in this chapter since lysimeter results were not available. Column leachate metal results are included in Appendix F. Analysis of OP is required within 48 h of collection. Due to the operating schedule of ARL and timing of rainfall events, OP analysis w ould not have occurred within the 48 h window for four of the six natural events: Nov. 25, Dec. 5, Jan 17 and 22. Data Analysis Results which were flagged for improper preservation or unacceptable spike recovery by the lab were removed for analysis. Resul ts under the minimum detection limit (MDL) were assumed to be half of the MDL for data analysis and are listed as less than the respective MDL (i.e. < 0.06 for NH4N). The minimum detection level is the minimum concentration which is statistically differen t from zero and is determined by each lab. The practical Quantitation limit (PQL) is the minimum concentration that can be determined that has statistically supported level of accuracy. Concentrations between the MDL and PQL are identified with E only to indicate a reduced level of confidence values below this level (i.e. E 0.22 for NO2+3N). The MDLs and PQLs from the lab used for the analytes in this study are included in ( Table 4 2 ). Values between PQL and MDL were used as reported for data analysis. All statistical analysis was performed using SAS (SAS, 2009). Concentrations from mixed soil and amendment columns and amendment columns were compared to concentrati ons from soil columns using Wilcoxon rank sum test. Median concentrations replaced

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153 concentrations excluded due to QA/QC or lack of leachate sample collection for loading calculations. Runoff loadings were determined from runoff volumes reported in Chapter 3 and concentrations reported in this study. Leachate volumes were not directly measured, but estimated for loading calculations as the difference between rainfall and runoff volumes, which inherently over estimates leachate volumes by assuming no losses t o soil storage. Loadings and concentrations were analyzed using SAS. Data sets were treated as nonparametric due to the large number nondetect concentrations. Concentrations and loadings were rank transformed and then analyzed using Tukeys HSD to determ ine significant difference. R esults and Discussion Column Study Resulting concentrations from the column study are listed in Appendix E. Analyte concentrations are plotted against amendment percentages in Figure 41 through Figure 46 Resulting p values from Wilcoxon rank sum tests comparing analyte concentrations of varying amendment fractions to soil only columns are listed in Table 4 3 Nitro gen Acid added to multiple nitrogen samples from Arredondo and compost mixed columns and all four compost only columns was insufficient to reduce the pH to less than 2. Thus a pH greater than 2 may have allowed microbial transformations to transform nitrog en species between collection and analysis, per EPA Method 353.2. Samples buffered the acid addition and resulted in pH values of between 2 and 3.5. These data were not included in statistical analysis ( Table 43 ) or Figure 41 through Figure 45

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154 The NO2+3N and TKN concentrations of the water matrix applied to the columns were both below their respective MDLs ( Table 4 2 ). Since all but four column sample concentrations of TKN or NO2+3N, were greater than respective MDLs, both soils and both amendments were sources of NO2+3N and TKN ( Figure 42 and Figure 43 respectively). Table 43 contains statistical analysis results of column water quality between amended mixtures and respective soil only columns. Due to improper preservation, no nitrogen data was available for Arredondo with 10% compost or for compost only columns ( Table 43 ). Compost did not significantly affect nitrogen species concentrations at 5% and 30% incorporations. However, OP and pH were both significantly increased at all percentages of compost additions to Arredondo ( Table 43 ). Fly ash incorporated into Arredondo only produced significant differences in Org. N ( Figure 44 ) and subsequently TKN at 30% content ( Table 43 ). However, no other nitrogen species were significantly affected at any other incorporation percentage ( Table 43 ). Fly ash additions at 10% and 30% did produce significantly higher OP concentrations, and pH was significantly higher at 30% as well. Compost fractions of 5% and greater significantly increased OP concentrations from Orangeburg soils. At 10% fractions, NH4N was significantly lower while Org. N was significantly greater than leachate from Orangeburg columns ( Table 43 ). No other nitrogen species were significantly different for compost additions to Orangeburg. Compost additions to Orangeburg did not significantly increase pHs. However, compost only column pHs were significantly greater than those from Orangeburg columns ( Table 4 3 ).

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155 Ortho phosphorus Fly ash additions to Orangeburg soils did not significantly affect nitrogen species concentrations ( Table 43 Figure 46 ). However, 5% and 30% fly ash fractions did produce significantly greater OP concentrations over Orangeburg only columns. Mixtures of fly ash and Orangebur g soil did not produce significantly different pH values than from Orangeburg alone, however pHs from Orangeburg only columns were significantly lower than those from fly ash only columns ( Table 43 ). As little as 5% compost and between 5% and 10% fly ash significantly increased OP concentrations ( Table 43 Figure 46 ). Arredondo leachate pH was significantly affected by both amendments; 5% for compost and 30% for fly ash, while neither amendment significantly changed Orangeburg leachate pH ( Figure 47 ). Due to limited data, results are inconclusive as to what compost fraction, if any, would significantly affect leachate concentrations on Arredondo soils ( Table 43 ). It is expected, due to coarse texture of the Arredondo soil and results from Orangeburg soils, that nitrogen would be significantly affected eventually w ith increasing compost fractions. Although this is not supported by results from 5% and 30% compost fractions in Arredondo. Fly ash did not significantly affect nitrogen concentrations for Orangeburg soils and only significantly increased Org. N concentrat ions at 30% content on Arredondo soils. Metals Metals concentration results are listed in Appendix F. Concentrations of TP, K, Na, Ca, Mg, Cu, B, and Fe increased from both soils as both amendment fractions increased. In addition, soil and amendment inter actions produced nonlinear transitions between soil and amendment concentrations for Zn, Mn, and Al. Therefore, it was determined that these concentrations may be affected by incorporating fly ash or

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156 compost into lysimeter soils. All Pb and Cd concentrati ons were below their respective MDLs. Concentrations of Ni were also mostly below MDLs. Thus, these metals would not be expected to significantly affect water quality. Lysimeter Results The hydrology of lysimeters determined whether runoff or leachate samples were collected. Low infiltration rates limited leachate collection from Orangeburg and Arredondo soils amended with fly ash and the Orangeburg control lysimeters. By comparison high infiltration rates limited runoff production and sample collection fro m Arredondo lysimeters with null and compost incorporations at 10 and 20 cm. In addition, rainfall and infiltration was not great enough for any lysimeter on Oct. 28 to produce leachate. Rainfall Water Quality Rainfall water quality and event depths are li sted in Appendix E. Rainfall event characteristics are summarized in Table 44 Although the 16 events were divided between natural (6) and simulated (10), concentrat ions were not significantly (p < 0.05) different for nitrogen species or OP concentrations. However, natural rainfall pHs (median: 5.2) were significantly (p < 0.05) less than simulated rainfall (median: 7.3) and simulated depths (median: 70.7 mm) were si gnificantly (p < 0.05) greater than natural depths (median: 24.4 mm). Simulated and natural rain NH4N and NO2+3N median concentrations were approximately equal to concentrations in the water matrix applied to the columns. All simulated and natural rain sample NH4N and NO2+3N concentrations were less than their respective Practical Quantitation Limits (PQLs). Median TKN and OP concentrations were greater for the rainfall events than the column matrix while rain fall

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157 pHs were all lower than the column matrix. Rainfall NO2+3N concentrations were less than the 0.13 mg/l for all but three events, with a maximum of 0.22 mg NO2+3N/l. NO2+3N All runoff concentrations were less than 1.09 mg NO2+3N/l. Thus, no runoff sample NO2+3N concentrations exceeded the EPAs Maximum Contaminant Level of 10 mg NO2+3N/l (EPA, 2009b). Only 7% of runoff samples had NO2+3N concentrations greater than the MDL. Arredondo and Orangeburg runoff concentrations were not significantly (p < 0.05) different between treatments or wi th respect to rainfall concentrations for either soil ( Table 45 and Table 46 ). Only one leachate sample was below the NO2+3N MDL. The remaining concentrations for Orangeburg lysimeters ranged from 1.5 to 15.3 mg NO2+3N/l and to 61.9 mg NO2+3N/l for Arredondo lysimeters. All treatment median NO2+3N leachate concentrations were greater for Arredondo than corresponding Orangeburg lysimeters. All 21 Arredondo lysimeters had at least one leachate sample concentration greater than the NO2+3N MCL. In addition, each Arredondo leachate treatment median NO2+3N concentration across a ll events was greater than the NO2+3N MCL of 10 mg/l. However initial NO2+3N leachate concentrations were much greater than later concentrations. Leachate mean NO2+3N concentrations for all Arredondo treatments decreased from between 17 and 31 mg NO2 +3N/l on Sept. 23 to less than 8 mg NO2+3N/l by Dec. 2. This suggests that NO2+3N losses diminish with time and long term water quality is less likely to exceed the MCL and impair water quality. By comparison, only 12 of 21 Orangeburg lysimeters had at least one leachate sample over the NO2+3N MCL. One Orangeburg treatment, C10 had no samples over the MCL. Null Arredondo leachate

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158 concentrations were above 10 mg/l until then as well. By comparison, column concentrations from all Arredondo and amendment m ixtures were less than 10 mg/l. All mean Arredondo leachate concentrations were tightly grouped (within a range of 15 mg/l) for each event in this study, except for the two fly ash treatments beginning on Dec. 2. The two fly ash treatments had event conce ntrations ranging from 17 to 62 mg NO2+3N/l through the remainder of the study, while all other treatment concentrations were less than 15 mg/l. As a result, fly ash treatments had higher NO2+3N leachate concentrations significantly (p < 0.05) for the 20 cm depth, than null treatments ( Table 47 ). Both median fly ash treatment NO2+3N leachate concentrations were also equal or greater than control. This suggests that fly ash was a source of NO2+3N rather than a sink. Orangeburg treatment leachate NO2+3N concentrations also generally declined over time, though less noticeably. However, unlike Arredondo soils, all median NO2+3N concentrations were les s than 10 mg/l, except from F20 lysimeters. Incorporation depths of 20 cm produced significantly greater NO2+3N leachate concentrations over 10 cm incorporations for compost and fly ash. This may have resulted from the reduced soil depth between the amend ed layer and the leachate collection layer. Fly ash had the highest leachate NO2+ 3N concentrations, followed by compost, and tillage alone. The only treatment which produced significantly lower NO2+3N concentrations than the control (N0) was N20. NH4N All but one of the NH4N concentrations were less than 0.5 mg/l, and just under half were less than the MDL of 0.06 mg NH4N/l. No treatments produced significantly different NH4N concentrations for leachate or runoff from either soil. However, the

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159 leacha te from three Orangeburg treatments (N10, C10, and C20) was significantly less than rain concentrations ( Table 48 ). Runoff and leachate concentrations generally decreased from initial concentrations through the first five events, except for on Dec. 5 which resulted from increased natural rainfall concentrations. Runoff concentrations followed rainfall concentrations cl osely, with virtually all event treatment means being within 0.1 mg/l of other treatments. In general, leachate concentrations were less than or equal to rainfall concentrations for both soils. Lysimeter leachate NH4N concentrations were similar to soil a nd soil amendment column results. However, compost treatment leachate concentrations from Arredondo soils were not significantly different from null or fly ash amended Arredondo lysimeters as column study results suggested ( Figure 4 8 ). Increased release of NH4N from compost amendments may have been converted to NO2+3N by nitrification before leaching out of the lysimeters. TKN NH4N did not greatly contribute to TK N concentrations. Only 6% of runoff and 11% of leachate TKN samples were primarily NH4N rather than Org. N. Sample TKN concentrations ranged from less than the MDL (0.125 mg/l) to 28.2 mg/l for runoff and 7.6 mg/l for leachate. Mean TKN runoff concentrations from Arredondo fly ash treatments were approximately 4 mg/l through Nov 4 as opposed to other treatments which were approximately 1 mg/l. Both Arredondo fly ash treatments had significantly greater runoff concentrations than C10, and F10 was signific antly greater than N0 and N20 ( Table 45 ). All Arredondo treatments had significantly greater TKN concentrations than rainfall as well ( Table 47 ).

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160 Leachate TKN concentrations from both soils were within their respective ranges of column concentrations for amendments fractions between 0 and 10%. How ever median leachate TKN concentrations from Arredondo treatments were greater than corresponding Orangeburg treatments. Mean TKN leachate concentrations from Arredondo c ompost treatments increased from between 1 and 2 mg/l for the first event (Sept. 23) t o between 3.6 and 4.2 mg/l on Nov 12. The increase for these treatments coincided with NO2+3N concentration decreases on the same treatments. Although the TKN concentration increase (3 4 mg/l) did not account for the total NO2+3N decrease (10 20 mg/l), it does suggest that during this period, an increasing fraction of NO2+3N was assimilated into TKN, thus partially reducing NO2+3N losses to leachate. On Orangeburg soils C10 and C20 produced significantly greater TKN concentrations than all other treatments as well, except for N0, which was only significantly different from C20 ( Table 48 ). The differences were greatest early in this study and diminished as compost TKN concentrations d ecreased C20 was also the only treatment with significantly greater TKN leachate concentrations than rainfall for Orangeburg soils ( Table 48 ). Lower rainfall pH produced higher TKN runoff concentrations for nearly all lysimeters except N10, C10 and C20 for Arredondo soils. The first leachate collected from a natural event (Nov. 23) had much higher TKN concentrations fr om Orangeburg lysimeters for all treatments and from compost amended Arredondo lysimeters. However the rainfall TKN concentration for this event was nearly double the next highest event concentration. Elevated rainfall TKN concentrations were likely the ca use of elevated TKN rather than response to lowered rainfall pH.

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161 OP Event mean runoff OP concentrations for treatments on both soils were all less than 400 g/l, except for Arredondo C10. Runoff OP concentrations from Arredondo C10 were greater than 400 g/l for only four of the twelve events which were between Oct 28 and Dec 2. Increased concentrations were due to runoff produced from one lysimeter which only produced runoff for those four events. Arredondo fly ash treatments produced significantly greater runoff OP concentrations than other treatments ( Table 45 ). Arredondo mean runoff OP concentrations from fly ash treatments were initially twice as high as other t reatments. However, by the fourth event on Oct 14 concentrations were indistinguishable from other treatment concentrations over the remaining duration of the study. This likely resulted from surface soil instability which contributed noticeable sediment t o runoff, especially from F10 treatments. However, sediment was not quantified in this study. Orangeburg compost treatments produced significantly greater OP runoff concentrations than null and fly ash treatments ( Table 46 ). Except for Orangeburg C20, all treatment runoff concentrations were not significantly different than rainfall concentrations of OP ( Table 45 and Table 46 ). While the maximum leachate from Orangeburg lysimeters was 307 g/l, 88% of samples were less than the maximum OP concentration from Orangeburg columns with up to 10% compost or 30% fly ash, 59 ug/l. Mean leachate OP concentrations from Arredondo lysimeters mostly ranged between 100 g/l and 300 g/l and were comparable to concentrations from columns with Arredondo only and Arredondo mixed with fly ash (110 to 520 g/l), but below 5 10% compost concentrations (1090 1700 g/l). The one exception was from Arredondo C20 treatment, which had concentrations

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162 greater than 300 g/l for the final three events. This resulted from one lysimeter which had concentrations of 1400, 430, and 350 ug/l for those events, which were greater than all other Arredondo leachate for this event. The Arredondo F20 treatment was the only treatment that did not significantly increase OP in le achate over rainfall concentrations ( Table 47 ). By comparison, no Orangeburg treatments had significantly greater OP concentrations than rainfall ( Table 4 8 ). Thus, Orangeburg soils overall functioned as an OP sink, while Arredondo soils were a source. Leachate OP concentrations from Arredondo C20 were significantly greater than from F20, C10, and N10. N20 leachate OP concentrations were significantly greater than N10 OP concentrations for both soils. Orangeburg N20 leachate OP concentrations were also significantly greater than C10 and C20. Additionally N0 leachate OP concentrati ons were significantly greater than N10 and C10. Compost treatments may not have significantly. pH Runoff pHs ranged from 8.7 to 5.2 and while leachate pHs ranged from 6.1 to 8.1. Column leachate for both soils with up to 30% of either amendment ranged f rom 5.8 to 7.4. In general pHs for runoff and leachate fluctuated with rainfall pH variation. No treatments significantly affected runoff or leachate pH for either soil. However, median runoff pHs from both soils were greater than rainfall pH, except for C20 on Arredondo. Three treatments had significantly greater pHs than rainfall (Arredondo: N10 and F20; Orangeburg: N20), however they were not significantly different from the other treatments on each soil ( Table 45 and Table 46 ). Leachate pHs decreased when rainfall pHs decreased, but the change was buffered by both soils.

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163 Lys imeter Runoff Loadings Nitrogen Under suitable conditions (adequate carbon source, presence of microbes, aerobic or anaerobic conditions depending on the microbe, and temperature) Org. N can be converted to NH4N by ammonification and then nitrified produce NO2+3. Since elevated NO2+3 is a common cause of eutrophication of surface waters, all nitrogen species can contribute to diminished water quality. As a result, runoff loadings for all three nitrogen species and their sum, TN, were analyzed between tr eatments for Arredondo ( Table 49 ) and Orangeburg soils ( Table 410 ). However, runoff concentrations of NH4N and NO2+3N tended to be close to, if not below, their respective MDLs. TKN concentrations in runoff were much higher by comparison. Since NH4N c oncentrations were essentially negligible, Org. N was the only relevant species of nitrogen in runoff. As a result, differences among treatments were similar between TKN and TN loadings. Since Orangeburg runoff TKN concentrations were not significantly different, except between N0 and C20, Orangeburg loading differences were mostly attributed to differences in runoff volumes. As a result of variations in runoff, although C20 had significantly greater TKN concentrations than N0, due to reduced runoff from C20 compared to N0, TKN and TN runoff loadings were not significantly (p < 0.05) less. In addition, the only treatment to have significantly lower runoff loadings than the control (N0) was N20 due to increased infiltration since runoff concentrations not significantly different While runoff concentrations of TKN were significantly different among multiple Arredondo treatments, runoff production also mostly governed TN loadings. Although

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164 C20 had runoff concentrations not significantly different from other treatments, the mean runoff TN and TKN loading were the lowest for Arredondo treatments and were significantly less than both fly ash treatments and the control. Fly ash treatments produced the highest runoff volumes for Arredondo lysimeters and highest TKN concentrations of all Arredondo treatments. The F20 treatment was the only treatment to produce higher TKN and TN loadings than was supplied by rainfall for both soils. Arredondo F10 treatments produced much higher total runoff loadings than rainfall supplied, while the mean Orangeburg F10 was essentially equal to that supplied by rainfall. Fly ash additions were due to increased Org. N contributions likely incorporated in runoff sediment loads. Ortho phosphorus OP can also contribute to eutrophicatio n of surface water, especially in phosphate limited systems. Three treatments, N20, C20 and C10, significantly reduced runoff loadings from Arredondo lysimeters compared to the control (N0) due to increased infiltration since concentrations. The only signi ficant differences among Orangeburg runoff OP concentrations were between both compost treatments and the remaining treatments. However, due to differences in runoff volumes, OP runoff loading differences were not dependent on compost. Although the fly ash and control treatments had significantly lower OP runoff concentrations from Orangeburg, since these treatments produced the highest runoff loadings, these treatments had higher OP runoff loadings than the other four treatments. Similarly, although compos t treatments increased runoff OP concentrations, increased infiltration rates prevented runoff loadings from also increasing.

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165 Arredondo N10, N20, and C20 had significantly lower runoff OP loadings than the control N0. Although OP runoff concentrations wer e not significantly different except between fly ash treatments and N10, differences were more evident between Arredondo runoff loadings. Though Orangeburg fly ash runoff concentrations were not significantly different from N0, N20, and C10, runoff loading s were significantly greater. Lysimeter Leachate Loadings Leachate loadings were estimated to determine whether treatments had significant effects on leachate nutrient loadings ( Table 411 and Table 412). NO2+3N is of particular concern in Florida groundwater due to numerous spring fed water bodies and which tend to be nitrogen limited for eutrophication. Nitrogen Mean leachate loadings of NH4N and TKN were generally comparable in scale to runoff loadings. However, mean NO2+3N, and as a result mean TN, leachate loadings were between one and three orders of magnitude greater than runoff loadings from corresponding soils and treatments. All leachate loadings were also significantly greater than rainfall loadings, indicating that soils supplied nitrogen to leachate, primarily in the form of NO2+3N. NH4N and TKN leachate loadings from Arredondo lysimeters were significantly different between amendment types. However, since leachate concentrations of NO2+3N were much higher than NH4N and TKN in leachate and no significant differences existed between treatments for NO2+3N loadings, TN leachate loadings were not significantly different between treatments. Therefore, although increased infiltration significantly reduced TN runoff loadings from Arredondo lysimeters, NO2+3N and TN leachate loadings were not significantly affected by treatments.

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166 Orangeburg leachate TN loadings were also primarily contri buted by NO2+3N and significant differences were mostly the same for NO2+3N and TN across treatments. Compost treatments had significantly greater leachate TN loadings than control treatments and F10. This is attributed to increased infiltration since leachate NO2+3N concentrations, which were the primary contributors to TN, were not significantly different. No other treatments had significantly different leachate TN loadings. Ortho phosphorus Arredondo mean total leachate OP loadings were higher than m ean runoff loadings for each treatment. All Arredondo treatment loadings were greater than rainfall loadings, significantly for all but fly ash treatments. This indicates that the soils were a source of OP to leachate. In addition, leachate OP loadings fro m fly ash treatments were significantly less than all other treatments. In addition C20 and N20 had significantly higher leachate loadings than the control. These differences are attributed to lowered infiltration fly ash and increased infiltration C20 and N20. Although Orangeburg F10 leachate OP concentrations were not significantly different than other treatments, leachate OP loadings were significantly less than all other treatments and rainfall. This difference is attributed to reduced infiltration vol umes on F10 lysimeters. Leachate from N20 treatment s was significantly greater than all other treatments. Since concentrations were not significantly different from control and fly ash treatment leachate concentrations, the increase is attributed to increased infiltration. No other treatments were significantly different among Orangeburg treatment leachate OP loadings.

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167 Conclusions In Chapter 3, fly ash treatments were not found to reduce runoff and increased it in some cases. Fly ash was found to increase TKN runoff concentrations from Arredondo soils, and coupled with increased runoff produced equal or greater TN and OP loadings from both Arredondo and Orangeburg soils. Increased nutrient loadings may have partially resulted due to sediment loadings in ru noff. Therefore, fly ash should be avoided as a soil amendment for mitigating similar compacted soils. Though compost increased TKN and OP runoff concentrations over null incorporations for Orangeburg and Arredondo soils respectively, reduced runoff generally eliminated differences between runoff loadings. Compost treatments increased leachate TKN concentrations on both soils, however NO2+3N concentrations were generally not affected. Increasing incorporation depth from 10 to 20 cm generally did not affe ct water quality for any of the amendments. Lysimeter leachate concentrations were nearly all within ranges of concentrations from columns with representative soil and amendment ratios. Arredondo lysimeters had greater TKN, and NO2+3N leachate concentrat ions than Orangeburg lysimeters for all treatments. All Arredondo treatments had median leachate NO2+3N concentrations greater than the MCL of 10 mg NO2+3N/l. However, this was due to elevated initial concentrations which diminished below the MCL after t hree months. Orangeburg lysimeters tended to have leachate OP concentrations less than rainfall while Arredondo leachate OP concentrations were greater than rainfall. Thus, Orangeburg soils functioned as a sink for OP, while Arredondo soils functioned as a source. In addition, leachate NO2+3N loadings were not significantly affected by treatments while significant differences between Orangeburg treatment leachate NO2+3-

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168 N loadings were attributed to leachate volume differences. Thus, treatments applied to Arredondo soils may be less likely to adversely affect groundwater compared to preamended conditions. Annual recommended nitrogen fertilizer applications for Florida range between 100 and 150 kg N/ha (Sartain, 2007). By comparison, NO2+3N concentrations ranged from 93 to 148 kg/ha over only four months. Though treatments did not significantly increase Arredondo leachate TN loadings, leachate NO2+3N loadings from both soils were orders of magnitude greater than runoff loadings. Thus leachate loadings should not be ignored when considering water quality impacts of treatments considered in this study. While differences in runoff volumes controlled runoff loadings, similarly, infiltration differences generally determined leachate nutrient loadings. Impacts to groundwater quality are often not considered when accounting for nutrient reductions to surface waters. However, especially with NO2+3N, redirecting nutrients to groundwater instead of surface waters may eventually impact surface waters. This study did not allow for vegetation establishment. Therefore future research should investigate the potential water quality impacts with vegetation, specifically with respect to leaching of nutrients which could be reduced by plant uptake. Studies would ideally ex tend through multiple seasons as well. Applicability of compost and null (or tillage) treatments should be investigated at the plot or watershed scale to more accurately determine the real world effects of implementing these treatments. Future studies should attempt to more directly measure leaching volumes and loadings. Ideally, a nutrient balance would be determined to account for nutrient transport and transformations.

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169 Table 41 Summary of properties for soils and amendment s included in this study. Arredondo Orangeburg Compost Fly Ash % Sand 94 61 81 23 % Silt 3 13 11 71 % Clay 3 26 8 6 Particle Density (g/cm 3 ) 2.41 2.56 2.26 2.10 Bulk Density (g/cm 3 )* 1.28 1.02 0.49 0.68 Texture Sand Sandy Clay Loam Loamy Sand Silty Loam Organic Matter by LOI% 1 5 79 51 Carbon (mg/kg) 5.11 6.82 17.13 111.46 Nitrogen (mg/kg) 0.36 0.47 0.30 6.47 C:N Ratio 14 14 57 18 *From column study for determining pore volume. Table 4 2 P ractical q uantitation limits (PQL), method detection limit (MDL), and column water matrix concentrations f or analytes Analyte Units Practical Quantitation Limit Method Detection Limit Column Matrix Concentration NH 4 N (mg/l) 0.500 0.063 0. 124 NO 2+3 N (mg/l) 0.500 0.148 < 0.148 # TKN (mg/l) 0.500 0.125 < 0.125 OP (g/l) 10.000 2.500 9.250 pH -0.1 00 8. 3 00 # Values reported as less than MDL are indicated as less than respective MDL.

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170 Table 43 p values for Wilcoxon rank sum test results for comparing soil and amendment mixture to soil column leached concentrations. Analyte* Soil # Amend. # Amend. % NH 4 N NO 2+3 N TKN ON TN OP pH A C 5 0.100 0.247 0.105 0.105 0.817 0.030 0.029 A C 10 -a ----0.030 0.029 A C 30 0.277 0.289 0.289 0.289 0.289 0.030 0.030 C 100 -a ----0.030 0.030 A F 5 0.072 0.596 0.052 0.052 0.216 0.052 0.061 A F 10 0.381 1.000 0.112 0.112 0.665 0.030 0.312 A F 30 0.172 0.194 0.030 0.030 0.061 0.029 0.030 F 100 1.000 1.000 0.488 0.488 0.817 0.067 0.067 O C 5 0.100 0.817 0.247 0.219 0.488 0.028 0.885 O C 10 0.029 0.665 0.061 0.027 0.665 0.028 0.885 O C 30 0.050 0.052 0.052 0.044 0.052 0.026 0.066 C 100 -a ----0.029 0.030 O F 5 0.058 0.377 0.194 0.183 0.216 0.028 0.471 O F 10 0.309 0.885 1.000 0.435 1.000 0.189 0.561 O F 30 0.100 0.488 0.105 0.085 0.518 0.029 0.147 F 100 0.100 0.247 0.247 0.085 0.299 0.066 0.030 #A: Arredondo; O: Orangeburg; C: Compost; F: Fly ash. NH4N: Ammonia; NO2+3N: Nitrate and Nitrite; TKN (Total Kjeldahl Nitrogen); ON: Organic Nitrogen; TN: Total Nitrogen; OP: Ortho Phosphorus. aAll nitrogen samples were eliminated from analysis due to improper sample preservation.

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171 Table 44 Summary of rain event types, depths, and water quality characteristics Type Depth NH 4 N NO 2+3 N TKN OP pH (mm) (mg/l) (mg/l) (mg/l) (ug/l) Natural Min 4.4 < 0.0 6* < 0. 15 < 0. 13 2 7 5.0 Median 24. 4 E 0.11 < 0. 15 E 0.24 50 5.2 Max 58.8 E 0.23 E 0.22 E 0.31 73 7. 3 Mean 26. 9 E 0.12 < 0.1 5 E 0.21 50 5. 6 SD 19.0 E 0.07 0.06 0.09 33 0. 9 Count 6 6 6 5 2 6 Simulated Min 50.4 < 0.0 6 < 0. 15 E 0.19 53 7.0 Median 70.7 E 0.11 < 0. 15 E 0.31 99 7. 3 Max 114.4 E 0.17 E 0.18 0.87 11 2 7.8 Mean 71.4 E 0.11 < 0. 15 E 0.35 95 7. 3 SD 17.4 E 0.04 0.03 0.20 17 0.2 Count 10 10 10 10 10 10 p value # 0.003 0.956 0.301 0.123 0.067 0.007 E: Reported value was between practical quantitation limit and minimum detectable level. *Concentrations less than minimum detectable limit were reported are indicated as less than the respective MDL. #P value resulting from Wilcoxon rank sum test. Table 45 Arredondo median runoff pHs and concentrations. Treatment pH NH4N (mg/l) NO3N (mg/l) TKN (mg/l) OP (ug/l) N0 7.46 a 0.08 a 0.07 a 0.75 bc 109.9 ab N10 7.53 a 0.08 a 0.07 a 1.03 abc 63.3 b N20 7.39 a 0.07 a 0.07 a 0.82 bc 74.2 ab F10 7.33 a 0.08 a 0.07 a 2.44 a 117.2 a F20 7.49 a 0.07 a 0.07 a 1.81 ab 110.9 a C10 7.57 a 0.07 a 0.07 a 0.54 c 103.5 ab C20 6.90 a 0.03 a 0.07 a 1.20 abc -# Rainfall 7.12 a 0.11 a 0.07 a 0.26 c 84.1 ab Concentrations with the same letter are not significantly (p < 0.05) different based on Tukey Kramer comparison of ranks. #No runoff was produced for OP sampled events.

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172 Table 46 Orangeburg runoff median pHs and concentrations. Treatment pH NH 4 N NO 2+3 N TKN OP (mg/l) (mg/l) (mg/l) (ug/l) N0 7.40 ab 0.08 a 0.07 a 0.60 b 60.9 c N10 7.45 ab 0.08 a 0.07 a 0.74 ab 66.3 c N20 7.48 a 0.07 a 0.07 a 0.91 ab 56.6 c F10 7.42 ab 0.08 a 0.07 a 0.71 ab 58.3 c F20 7.37 ab 0.08 a 0.07 a 0.77 ab 66.6 c C10 7.34 ab 0.08 a 0.07 a 0.93 ab 107.3 ab C20 7.40 ab 0.08 a 0.07 a 0.99 a 123.6 a Rainfall 7.12 b 0.11 a 0.07 a 0.26 c 84.1 bc Concentrations with the same letter are not significantly (p < 0.05) different based on Tukey Kramer comparison of ranks. Table 47 Arredondo leachate median pHs and concentrations. Treatment pH NH 4 N NO 2+3 N TKN OP (mg/l) (mg/l) (mg/l) (ug/l) N0 6.85 a 0.07 a 14.95 bc 0.81 b 167.1 a b N10 6.98 a 0.03 a 11.60 c 0.75 b 148.1 b N20 6.80 a 0.06 a 12.41 c 0.78 b 177.7 a F10 6.47 a 0.10 a 26.65 ab 0.71 b 290.4 a F20 6.78 a 0.07 a 26.14 a 0.67 b 140.8 ab C10 6.72 a 0.08 a 12.84 bc 3.42 a 152.6 ab C20 6.64 a 0.08 a 13.54 bc 3.83 a 252.9 a Rainfall 7.12 a 0.11 a 0.07 d 0.26 c 84.1 c Concentrations with the same letter are not significantly (p < 0.05) different based on Tukey Kramer comparison of ranks. Table 48 Orangeburg leachate median pHs and concentrations. Treatment pH NH 4 N NO 2+3 N TKN OP (mg/l) (mg/l) (mg/l) (ug/l) N0 6.77 a 0.07 ab 7.97 a b c 0.25 bc 213.1 a N10 7.10 a 0.03 b 6.04 cd 0.16 c 12.7 ab N20 7.10 a 0.07 ab 5.84 d 0.22 c 24.2 a F10 6.79 a 0.08 ab 7.84 bcd 0.22 c 18.7 ab F20 6.89 a 0.05 ab 10.90 a 0.20 c 26.9 a C10 6.87 a 0.03 b 6.46 cd 0.36 ab 8.9 b C20 6.86 a 0.03 b 8.18 ab 0.50 a 14.8 ab Rainfall 7.12 a 0.11 a 0.07 e 0.26 bc 84.1 a Concentrations with the same letter are not significantly (p < 0.05) different based on Tukey Kramer comparison of ranks.

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173 Table 49 Mean Arredondo total runoff loadings Treatment NH 4 N NO 2+3 N TKN TN OP (kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha) N 0 0. 2 5 c 0. 51 ab 2. 63 c 3.14 c 0.27 bc N 10 0.00 d 0.47 ab 0. 12 c 0. 59 c 0.00 d N 20 0. 0 1 d 0.30 b 0. 1 6 c 0. 45 c 0.01 d F 10 0. 5 8 b 1.12 a 14.81 a 15.92 a 0.77 a F 20 0. 3 1 c 0.83 ab 6. 94 b 7.78 b 0. 4 8 b C 10 0. 0 2 d 0. 45 ab 0. 1 2 c 0. 56 c 0. 0 4 cd C 20 0.00 d 0.49 ab 0.00 c 0. 49 c 0.00 d Rainfall 1.00 a 0.79 a b 2.84 bc 3.63 bc 0.19 bcd Concentrations with the same letter are not significantly (p < 0.05) different Tukey comparison of means *OP runoff loading C20 set equal to 0 since no runoff was produced for OP sampling events. Table 410. Mean Orangeburg total runoff loadings Treatment NH 4 N NO 2+3 N TKN TN OP (kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha) N 0 0. 4 2 b 1.03 a 2.86 ab 3.88 ab 0. 2 7 a N 10 0. 1 4 c 0. 44 ab 1.27 a b 1.71 ab 0. 14 b N 20 0. 0 7 c 0. 54 ab 0.84 b 1.38 b 0. 0 5 b F 10 0. 4 4 b 0. 92 a 3.67 a 4.60 a 0. 32 a F 20 0. 4 2 b 0. 77 ab 3.74 a 4. 52 a 0. 34 a C 10 0. 0 6 c 0. 22 b 1.40 a b 1. 63 ab 0. 12 b C 20 0. 09 c 0. 1 9 b 2.62 ab 2.81 ab 0. 15 b Rainfall 1.00 a 0.79 ab 2.84 ab 3.63 ab 0.19 ab Concentrations with the same letter are not significantly (p < 0.05) different based on Wilcoxon ranks analysis. Table 411. Mean Arredondo total leachate loadings Treatment NH 4 N NO 2+3 N TKN TN OP (kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha) N 0 0.64 ab 93.2 1 a b 5.26 c 98. 48 a b 1. 1 7 b N 10 0.62 ab 120. 66 a b 7.01 c 127. 67 a b 1. 2 8 b N 20 0. 6 6 ab 124.5 3 a b 6.89 c 131.4 3 a 1.58 ab F 10 0.39 b 103.9 2 a b 2.67 c 106. 59 a b 0. 9 5 b F 20 0.41 b 148.3 4 a 3.25 c 151. 58 a 0. 8 4 b C 10 0.82 a 133.3 1 a 25.94 b 159. 25 a 1. 4 1 ab C 20 0.78 a 137. 58 a 33.80 a 171. 38 a 2.43 a* Rainfall 1.00 a 0.79 b 2.84 c 3.63 b 0.19 b Concentrations with the same letter are not significantly (p < 0.05) different based on Wilcoxon ranks analysis. *OP runoff loading C20 set equal to 0 since no runoff was produced for OP sampling events.

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174 Table 412. Mean Orangeburg total leachate loadings Treatment NH 4 N NO 2+3 N TKN TN OP (kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha) N 0 0.31 cd 34.30 a bc 0.93 c 35.22 b c 0. 3 7 a N 10 0.48 bcd 48.14 abc 1.24 c 49.37 abc 0.09 a N 20 0.62 ab 50.39 ab 2.06 b c 52.45 ab 0. 7 8 a F 10 0.26 d 31.91 b c 0.87 c 32.78 b c 0. 0 3 a F 20 0.29 d 43.53 a bc 1.00 c 44.54 a b c 0.12 a C 10 0.51 bcd 50.46 ab 4.26 a b 54. 7 2 ab 0.12 a C 20 0.59 bc 68.42 a 5.32 a 73.74 a 0. 1 3 a Rainfall 1.00 a 0.79 c 2.84 ab c 3.63 c 0.19 a Concentrations with the same letter are not significantly (p < 0.05) different based on Wilcoxon ranks analysis. Figure 41 NH4N water matrix and column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.1 1.0Concentration NH4N mg/lAmendment Fraction Matrix AC AF OC OF

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175 Figure 42 NO2+3N water matrix and column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure 43 TKN water matrix and column leachate concentrations for soi l and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0 5 10 15 20 25 30 0.0 0.1 1.0Concentration NO3N mg/lAmendment Fraction Matrix AC AF OC OF 0 5 10 15 20 25 30 0.0 0.1 1.0Concentration TKN mg/lAmendment Fraction Matrix AC AF OC OF

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176 Figure 44 ON water matrix and column leachate concentrations for soil and amendment mixtur es. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure 45 TN water matrix and column leachate concentrations for soil and amendment mixtures. AC: Arredondo and c ompost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 5.0 10.0 15.0 20.0 25.0 30.0 0.0 0.1 1.0Concentration ON mg/lAmendment Fraction Matrix AC AF OC OF 0 5 10 15 20 25 30 35 0.0 0.1 1.0Concentration TN mg/lAmendment Fraction Matrix AC AF OC OF

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177 Figure 46 OP water matrix and column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure 47 pH of water matrix and column leachate for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0 1 2 3 4 5 6 7 8 9 10 0.0 0.1 1.0Concentration OP mg/lAmendment Fraction Matrix AC AF OC OF 5.5 6.0 6.5 7.0 7.5 8.0 8.5 0.0 0.1 1.0pHAmendment Fraction Matrix AC AF OC OF

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178 Figure 4 8 Total n itrogen median results as the sum of o rganic n itrogen (O rg. N), n itrate and n itrite (NO2+3N), and a mmonia (NH4N ) median concentrations from each of the four soil amendment combinations for varying amendment fractions. A) Arredondo and compost; B) Orangeburg and compost; C) Arredondo and fly ash; D) Orangeburg and fly ash. 0 20 40 60 80 100 0 5 10 30 100 Org. N NH3 N NO2+3 N 0 20 40 60 80 100 0 5 10 30 100 0 5 10 15 20 25 0 5 10 30 100 0 5 10 15 20 25 0 5 10 30 100 Amendment % Concentration, mg/l A B C D

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179 CHAPTER 5 CONCLUSIONS Retention Basin s Performance Conclusions Retention (or infiltration) basins are commonly incorporated into the landscape since retention basins primarily infiltrate stormwater on site. Captured stormwater is eliminated primarily by infiltration which prevents contamination of downstream water resources. However, reduced infiltration of the capture volume can reduce basin capture volumes for subsequent runoff events, potentially allowing stormwater to bypass treatment or elimination. To evaluate hydrologic performance of retention basins, this study investigated infiltration rates and soil properties within 40 retention basins in Florida, ranging in age from 0 to 20 years. Basins were equally divided between Florida Department of Transportation (DOT) and resident ial land uses, while basin soil textures ranged from sand to sandy clay. These data were complemented by hourly water level monitoring at 11 of the basins. The analysis in Chapter 2 showed that infiltration rates measured by Double Ring Infiltrometer (DR I) were lower in 16 basins and higher in 14 basins, compared to design values, while rates from the remaining basins were not different. Based on these results, drawdown rates for 40% of the basins were at least limited by surface soil conditions, which may have resulted from clogging, compaction, or a combination of these two. A higher proportion of DOT basins than residential basins had DRI rates greater than their design rates. Newer DOT basins were more likely to have significantly greater infiltration rates than design as compared to older basins. While o lder DOT basins still had greater DRI rates, the difference between DRI and design rates was

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180 diminished compared to newer DOT basins. Comparably, new residential basin DRI rates were significantly less than design. However, residential DRI rates were closer to designs for older basins. Increased size and diversity of vegetation as a result of less frequent maintenance was also theorized as a potential cause of improved infiltration within DOT basins com pared to residential basins. I t would be expected that basins would have improve d their performance with time as vegetation increased, however DOT basin performance decreased with time. This indicates that other factors (i.e. changes in construction methods with time, variable sediment loading rates), in addition to vegetation, may have equal or greater influence on how basins perform as they age. Future research should seek to identify these factors and determine their effects on basin performance. Basin soil texture also affected DRI rates. Coarse textured basins had a higher proportion of basins with DRI rates greater than their designs. Similarly, finer textured basins had a higher proportion of basins with DRI rates below their design. This may indicate that current basin design methods are inadequate for determining long term performance. Furthermore, allowing for greater infiltration rates for coarse textured basins would decrease basin surface areas could accelerate clogging and/ or create groundwater mounding that inhibits drawdown. Therefore, it is recommended that retention basin designs account for groundwater influence on basin performance by incorporating water table fluctuations into continuous simulation models. Basin monitoring data showed that basin infiltration rates could also be controlled by subsurface hydrology. Of the 8 monitored basins with sufficient data, six had monitored rates less than DRI rates. Monitored rates from the remaining two basins were not different. As a result, reduced

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181 storage volume recovery in retention basins may result from reduced infiltration either through surface soils or due to subsurface controlling conditions. Therefore, maintenance measures aimed at improving infiltration through surface soils may not improve storage volume recovery if subsurface hydrology is controlling infiltration. Since infiltration rate measurement can be time and resource intensive, nine models were evaluated to estimate DRI rates from basin soil data. The Kozeny Ca rman model incorporating porosity and harmonic mean particle diameter accurately estimated DRI rates. U sing half the inputs t he simpl er MLR (2) had only slightly greater variability than the more complex MLR (1) This result indicates that simpler models may be as effective as more complex models for estimating DRI rates in basins While making evaluations based solely on model results may not be expected, modeling could be used as an initial evaluation of whether future monitoring should occur Recommend ations and Future Research While vegetation was not directly measured in this study, DOT basins are typically maintained less frequently. The increased vegetation size and variety may enhance infiltration through soils at the basin surface. Future research of stormwater infiltration structures should include analysis of vegetation diversity and abundance in addition to soil characteristics. Furthermore, the presence of soil biota may also enhance soil infiltration and should also be considered. The hydraul ics of retention basins are not only vertical, but horizontal due to lateral seepage flow. Lateral flow can be a significant flow path for storage volume recovery, but was not considered in this study. A supplemental study focusing on groundwater fluctuati ons and lateral flow from basins would contribute to understanding retention basins performance more thoroughly

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182 Soil Amendments Previous research by Gregory et al. (2006) and Pitt et al. (1999) demonstrated the impacts of urban soil compaction on decreased infiltration rates. A lysimeter research study presented in Chapters 3 and 4 investigated the respective hydrologic and water quality effects of amending compacted sandy (Arredondo) and sandy clay loam (Orangeburg) soils found in Florida. Six amendment t reatments were applied to each soil, combining an amendment (compost, fly ash, or no amendment (null)) and incorporation depths of 10 or 20 cm. Amendment treatments were compared to soils which remained compacted. Hydrologic Conclusions Lysimeter soil co mpaction produced runoff volumes equal to or greater than expected from dirt roads according to the curve number methodology. Arredondo soils were compacted at least to bulk densities reported for compacted Florida sands. Orangeburg soils were compact ed by the same procedure. However, insufficient compaction of subsurface soils produced greater infiltration rates and lower measures of soil strength than those reported for compacted sandy soils in Florida. Amendment phase Fly ash treatments decreased bulk densities for both soils compared to the respective controls. However, fly ash treatments also either produced equal or greater runoff coefficients and curve numbers due to equal or decreased infiltration rates. These effects were attributed to the cem entitious properties of fly ash which functioned to seal soil surfaces and allowed comparatively little infiltration. Therefore it is recommended to avoid fly ash as a soil amendment for soil compaction mitigation.

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183 Null and compost treatments had greater infiltration rates and reduced runoff production compared to the compacted lysimeters for both soils. Compost treatments had significantly lower bulk densities than the control lysimeters. While null incorporation mean bulk densities were lower than the co ntrol, differences were not significant except for the 10 cm null incorporations on Orangeburg soils. Curve numbers were not significantly different between null and compost treatments for each soil Though infiltration rates were greater for deeper incorp orations, depth did not generally affect Curve Numbers for treatments. Thus, shallower incorporations produced the same benefit as deeper incorporations. While this may have resulted from insufficient subsoil compaction, it may also suggest that the increasing incorporation depths may not further reduce runoff production. Applications To quantify the potential runoff reduction of 10 cm tillage without amendments runoff depths were calculated using the Curve Number method (NRCS 1986) and values determined i n this stu d y for a hypothetical residential watershed. For a 2yr 24 hour rainfall event for Gainesville, FL (9.2 cm; Eaglin 1996) runoff depths were approximately 3/4 and 2/3 of the runoff depth of the nontreated watershed for Arredondo and Orangeburg soils. In ad dition, runoff increases over undisturbed areas were much lower for the amended simulated watersheds compared to nonamended simulated watersheds. Due to greater predeveloped runoff depths, tillage reduced the expected runoff increase more on Orangeburg s oils than Arredondo soils. Therefore, soil amending may provide greater benefits on native soils with lower infiltration rates. Furthermore, soil amending could also offset the costs of conventional stormwater structures by reducing their size.

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184 In addition the reduced size of a retention basin would increase the available are for development, which could be the more valuable benefit. Water Quality Conclusions Total nitrogen in runoff was dominated by TKN, while NO2+3N dominated leachate TN. Arredondo lys imeters had greater TKN, and NO2+3N leachate concentrations than Orangeburg lysimeters for all treatments. In addition, all Arredondo treatments had median leachate NO2+3N concentrations greater than the MCL of 10 mg NO2+3N/l. However, this was due to elevated initial concentrations which diminished below the MCL after three months. In addition, leachate NO2+3N loadings were not significantly affected by treatments while significant differences between Orangeburg treatment leachate NO2+3N loadings were attributed to leachate volume differences. Thus, treatments applied to Arredondo soils may be less likely to adversely affect groundwater compared to preamended conditions. Orangeburg lysimeters tended to have leachate OP concentrations less than rainfal l while Arredondo leachate OP concentrations were greater than rainfall. Thus, Orangeburg soils functioned as a sink for OP, while Arredondo soils functioned as a source. Therefore, amending Orangeburg soils would be less likely to increase OP loadings than Arredondo soils. Differences between treatment runoff and leachate nutrient loadings were primarily determined by differences of runoff and infiltration volumes rather than concentrations. Though compost increased TKN and OP runoff concentrations over null incorporations for Orangeburg and Arredondo lysimeters, respectively, runoff loadings were not different. Compost treatments increased leachate TKN concentrations on both soils, however NO2+3N concentrations were generally not affected. Increasing inc orporation depth from 10 to 20 cm also generally did not affect runoff water quality for any of the

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185 amendments. Fly ash produced equal or greater TN and OP loadings from both Arredondo and Orangeburg soils as a result of increased runoff TKN concentrations from Arredondo soils, coupled with increased runoff by fly ash treatments on both soils. Annual recommended nitrogen fertilizer applications for Florida range between 100 and 150 kg N/ha (Sartain 2007). By comparison, NO2+3N concentrations ranged from 9 3 to 148 kg/ha over only four months. Though treatments did not significantly increase Arredondo leachate TN loadings, leachate NO2+3N loadings from both soils were one to three orders of magnitude greater than runoff loadings. In addition, compost treatm ents increased NO2+3N loadings in leachate compared to controls due to increased infiltration volumes and leachate concentrations. Thus leachate loadings should not be ignored when considering water quality impacts of treatments considered in this study. While differences in runoff volumes controlled runoff loadings, similarly, infiltration differences generally determined leachate nutrient loadings. Impacts to groundwater quality are often not considered when accounting for nutrient reductions to surface waters. Recommendations and Future Research Fly ash incorporation was not determined to decrease runoff volumes compared to the compacted state for either soil H owever 20 cm incorporations on Orangeburg soils significantly increased infiltration rates due in combination to the low infiltration rate on the compacted soils and the reduced fly ash fraction within the amended layer compared to the 10 cm depth. In addition, due to greater nutrient concentrations and equal or greater runoff production, fly as h produced equal or greater runoff nutrient loadings. Therefore, fly ash should be avoided as an amendment for mitigating soil compaction.

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186 Soil compaction can occur at depths too deep to address with conventional methods. In compacted native profiles, 10 or 20 cm amendment depths would not likely meet or surpass the most limiting layer depths, which were found by Gregory et al. (2006) to be deeper than 25 cm. Limiting layer depths can exceed 40 cm on construction sites (Randrup and Lichter 2001). Therefore deeper incorporation depths (20 cm) would be expected to further improve infiltration and reduce runoff compared to 10 cm depths However, due to nonrepresentative subsoil compaction in this study, runoff reduction may not be as great on compacted in sit u soils. N ull and compost treatments also decreased the bulk densities which increased the porosity of the null and compost treatments and increased infiltration rates. As incorporation depth increases, the additional benefits eventually diminish to neglig ible, regardless of the infiltration rate due to the storage capacity of the soil. If incorporated depths do not exceed the most limiting depth of compaction, the available water storage above the limiting layer is increased. In this way, the amended soil functions similarly to permeable pavement systems, where the surface layers are not limiting to infiltration, rainfall and runoff are captured and stored and then infiltrated much more slowly. Thus, runoff may be significantly reduced, especially from smal ler rainfall events. Null and compost incorporations were found to reduce runoff volumes and nutrient runoff loadings from compacted soils. However, compost treatments increased NO2+3N leachate loading rates, which could adversely affect groundwater qual ity. Increasing incorporation depths from 10 to 20 cm generally did not affect hydrologic and water quality outcomes. Applicability of compost and null (or tillage) treatments should be

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187 investigated at the plot or watershed scale to more accurately determi ne the real world effects of implementing these treatments. Future studies should directly measure leaching volumes and loadings. Ideally, a nutrient balance would be determined to account for nutrient transport and transformations. Additional research should investigate whether the benefits of treatments extend to a larger scale at either the watershed or plot level. Findings from such study would hopefully quantify the hydrologic effects of treatments more accurately, especially with native soils below the amendment layer. While similar results would be expected at the watershed scale, treatment effects would not expect to be as large due to the absence of representative subsoil conditions in this study. However, results here have shown there is a potential to reduce runoff by tilling soil, with or without compost, down to at least 10 cm. For compacted profiles with limiting layers below the incorporation depth, deeper incorporations would be expected to improve hydrologic response, especially for larger events. In addition, this study did not allow for vegetation establishment. Therefore future research should investigate the potential water quality impacts with vegetation, specifically with respect to leaching of nutrients which could be reduced by plan t uptake. Studies would ideally extend through multiple seasons to identify fluctuations in hydrologic and water quality processes.

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188 APPENDIX A RETENTION BASIN DATA Table A 1 Comparison of stormwater retention design criteria for Water Management Districts (WMD) in Florida. Design Parameter St. Johns River WMD Suwannee River WMD Southwest Florida WMD South Florida WMD Northwest Florida WMD* Treatment Volume Off line retention of the first 0.5" of runoff or 1.25" of runoff from the impervious area; whichever is greater Retention of the runoff from the first 1" of rainfall On line retention of the runoff from 1" of rainfall Retention of the first 0.5" of runoff of 1.25" times the percentage of imperviousn ess; whichever is greater Off line retention of the first 0.5" of runoff On line retention of the first 1" of runoff, or 1.25" of runoff from the impervious area plus 0.5" of runoff from entire basin; whichever is greater. If project discharges to sink, then off line or online retention of the runoff from the first 2" of rainfall. If project <100 ac, online retention of 0.5" of runoff On line retention of the runoff from 1" of rainfall. Minimum volume of 0.5" of runoff i s required. On line retention that percolates the runoff from the 3year/1hour storm Off line retention of runoff from 1" of rainfall Reproduced from Harper and Baker ( 2 007 ) and updated from NWFWMD ERP Handbook II for NWFWMD criteria.

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189 Table A 1 Continued. Design Parameter St. Johns River WMD Suwannee River WMD Southwest Florida WMD South Florida WMD Northwest Florida WMD Treatment Volume For projects with <40% imperv i ous and only HSG A soils, online retention from 1" of rainfall or 1.25" of runoff from impervious area If pr oject <100 ac, off line retention of 0.5" of runoff Volume Recovery Provide design capacity in 72 hours using percolation, evaporation, or evapotranspiration. Provide design capacity in 72 hours using percolation, evaporation, or evapotranspiration Treatment volume recovered in < 72 hours No more than half o f treatment volume in 24 hours. < 72 hours following storm using percolation, evaporation, or evapotranspiration

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190 Table A 2 Basin number, county location, land use, age, and design infiltration rates. Basin County (FL) Land Age (years) # Design Infiltration Rate (cm/h) 1 Alachua DOT 16 3.56 2 Alachua Res 1 7.87 3 Alachua Res 4 8.57 4 Alachua DOT 19 21.59 5 Alachua DOT 6 4.06 6 Alachua Res 9 2.54 7 Alachua DOT 13 12.70 8 Alachua Res 11 5.08 9 Alachua DOT 18 1.52 10 Alachua Res 18 7.62 11 Alachua Res 4 2.79 12 Alachua Res 11 5.79 13 Alachua Res 13 12.80 14 Alachua Res 3 12.70 15 Alachua Res 1 6.35 16 Alachua Res 4 5.72 17 Marion DOT 13 1.52 18 Marion DOT 16 11.01 19 Marion DOT 3 5.93 20 Marion DOT 14 4.45 21 Leon Res 5 12.34 22 Leon Res 1 4.06 23 Leon Res 2 0.51 24 Leon Res 2 0.76 25 Leon Res 2 43.69 26 Leon Res 2 5.08 27 Leon Res 2 2.29 28 Leon Res 9 1.14 29 Leon Res 19 5.08 30 Leon DOT 10 12.70 31 Leon DOT 10 12.70 32 Leon DOT 6 3.18 33 Leon DOT 10 12.70 34 Leon DOT 10 12.70 35 Leon DOT 6 2.03 36 Leon DOT 3 0.33 37 Leon DOT 3 0.33 38 Leon DOT 3 0.33 39 Leon DOT 3 0.33 40 Leon DOT 6 3.18 *DOT: Department of Transportation; Res: Residential. #Age in 2008.

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191 Table A 3 Soil textures for each basin test location and corresponding median basin texture. Site Basin Median 1 2 3 4 5 6 7 8 9 1 SL SL C SL SL SCL SCL LS S SL 2 LS LS LS LS LS SCL S LS S S 3 # S S 4 SL L SL S LS L S 5 SL SL SL SL SL SL SL 6 SCL C SL SCL C SCL SC 7 S LS SL S S S S 8 SL SL SL SL L SL SL 9 SC HC HC SC SCL SCL SC 10 S S SCL S S S S 11 SL SC SL SL SL SL SL 12 LS S S LS SCL LS LS 13 LS SL LS SL LS LS SCL 14 S S S S S S LS 15 SCL SCL SC SCL SCL SCL SL 16 S S S S S S S 17 S S S LS S S LS 18 LS S S LS LS LS LS 19 S S S S S S S 20 S S S S LS S S 21 SL SCL SL SL SL SL SL 22 SL SCL SL SCL SL SCL SL 23 SL SL SL SL SL SL SL 24 SCL SCL SCL SCL SCL SCL SC 25 S S S S S S S 26 LS LS LS LS SL LS SCL 27 SL SCL LS LS SL SL SCL 28 SCL SCL SCL SL SC C SCL 29 SCL SCL SCL C 30 SCL SL SCL SCL SCL SCL SCL 31 SCL SCL HC SC SCL SCL SCL 32 S S S S S S S 33 SCL SCL SL SL SCL SCL SCL 34 SCL SCL SCL SL SCL SCL SC 35 S S S S LS S S 36 SCL L SCL SCL SCL SCL LS 37 LS LS LS LS LS SL SL 38 SL LS SL SL LS SL SL 39 LS LS LS LS SL LS LS 40 S S S S S S S *S: Sand; LS: Loamy Sand; SL: Sandy Loam; SCL: Sandy Clay Loam; SC: Sandy Clay; L: Loam; C: Clay; HC: Heavy Clay. #Limited site access prevented additional soil sample collection.

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192 Table A 4 Soil organic matter percentages by percent weight from loss on ignition. Basin Site 1 2 3 4 5 6 7 8 9 1 4.39 7.38 2.70 3.81 4.09 3.66 1.45 2.44 2.59 2 1.20 1.39 1.40 2.00 4.55 1.23 1.79 0.51 0.78 3* 3.32 4 21.85 9.20 0.68 9.28 22.02 1.95 5 3.65 3.83 3.62 4.52 4.15 3.20 6 11.88 3.60 7.52 11.50 7.19 8.69 7 1.51 8.37 0.31 0.41 0.49 0.54 8 4.30 17.46 6.10 5.04 3.60 4.03 9 48.53 27.26 12.69 3.81 5.26 20.66 10 1.14 4.46 1.57 0.74 1.02 0.50 11 4.16 1.88 2.34 5.08 2.17 1.92 12 2.34 2.70 3.52 5.14 1.07 1.43 13 3.90 3.64 3.83 3.03 1.85 5.86 14 2.65 1.99 2.27 2.76 2.43 2.76 15 4.11 5.19 4.90 3.80 3.95 2.58 16 0.92 0.73 0.93 1.32 1.01 1.11 17 1.24 1.05 1.45 1.01 1.45 1.78 18 2.17 1.89 2.55 3.14 1.62 2.48 19 0.57 0.76 1.19 0.59 0.54 1.05 20 1.46 1.38 1.67 1.97 0.84 0.63 21 3.47 4.13 3.92 4.07 2.48 4.85 22 3.13 2.64 2.70 2.07 2.44 1.96 23 5.43 7.46 5.57 3.61 4.09 3.86 24 4.75 3.88 3.69 4.08 3.70 4.23 25 0.25 0.49 0.91 0.72 0.62 0.69 26 2.17 2.15 2.14 2.42 1.92 2.74 27 3.38 2.08 1.44 3.16 3.18 4.58 28 4.54 4.96 5.24 5.57 5.96 4.30 29 5.80 4.83 9.28 30 3.63 3.47 3.73 3.74 3.63 3.76 31 5.18 8.34 5.90 4.74 4.36 3.93 32 0.62 0.64 1.14 0.34 0.82 1.55 33 4.42 3.72 3.65 4.21 4.39 3.94 34 6.28 6.03 4.61 5.58 5.38 5.16 35 0.39 0.55 0.38 1.31 0.32 1.22 36 6.58 5.31 4.38 4.07 5.55 2.98 37 1.26 2.10 1.87 2.87 2.05 2.36 38 3.49 2.33 2.58 3.17 3.01 3.10 39 3.70 2.27 3.54 3.85 4.07 3.70 40 0.09 0.33 0.51 0.59 0.30 0.70 Limited site access prevented additional soil sample collection.

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193 Table A 5 Bulk density (g/cm3) measurements from each basin location. Site Basin 1 2 3 4 5 6 7 8 9 1 1.57 1.18 1.60 1.60 1.41 1.56 1.65 1.61 1.49 2 1.65 1.79 1.67 1.74 1.80 1.72 1.77 1.58 1.68 3 1.37 4 0.66 0.97 1.67 1.24 0.62 1.40 5 1.44 1.50 1.58 1.43 1.38 1.64 6 0.99 1.43 1.10 1.01 1.33 1.26 7 1.67 1.21 1.55 1.62 1.58 1.63 8 1.38 0.91 1.36 1.58 1.47 1.53 9 0.23 0.46 0.84 1.35 1.21 0.54 10 1.53 1.55 1.35 1.64 1.58 1.61 11 1.48 1.67 1.58 1.45 1.65 1.63 12 1.55 1.54 1.51 1.40 1.27 1.64 13 1.44 1.54 1.52 1.57 1.62 1.13 14 1.22 1.44 1.49 1.45 1.57 1.46 15 1.57 1.57 1.42 1.41 1.61 1.72 16 1.54 1.59 1.50 1.53 1.61 1.56 17 1.64 1.58 1.76 1.66 1.65 1.72 18 1.49 1.51 1.31 1.41 1.52 1.53 19 1.62 1.58 1.55 1.61 1.64 1.65 20 1.65 1.64 1.66 1.69 1.73 1.63 21 1.70 1.47 1.63 1.59 1.62 1.43 22 1.78 1.71 1.78 1.67 1.73 1.73 23 1.51 1.54 1.37 1.41 1.75 1.57 24 1.63 1.78 1.71 1.61 1.82 1.64 25 1.54 1.61 1.61 1.58 1.63 1.56 26 1.61 1.50 1.44 1.45 1.54 1.41 27 1.78 1.54 1.81 1.69 1.74 1.77 28 1.61 1.56 1.46 1.34 1.50 1.37 29 1.41 1.65 1.34 30 1.39 1.66 1.67 1.60 1.67 1.61 31 1.49 1.36 1.51 1.64 1.61 1.42 32 1.68 1.63 1.66 1.67 1.52 1.55 33 1.51 1.46 1.65 1.58 1.59 1.59 34 1.36 1.34 1.44 1.42 1.43 1.59 35 1.63 1.70 1.61 1.69 1.68 1.70 36 1.40 1.30 1.42 1.47 1.42 1.56 37 1.67 1.55 1.61 1.61 1.71 1.63 38 1.54 1.63 1.50 1.64 1.71 1.52 39 1.36 1.62 1.58 1.59 1.63 1.54 40 1.58 1.42 1.60 1.59 1.56 1.60 Limited site access prevented additional soil sample collection.

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194 Table A 6 Measured double ring infiltrometer infiltration rates (cm/h) for each basin test location. Site Basin 1 2 3 4 5 6 7 8 9 1 0.01 0.96 0.24 0.82 1.50 0.92 1.94 5.96 0.58 2 1.20 0.87 0.20 0.01 0.01 3.38 0.01 1.33 7.37 3 6.93 13.82 9.66 15.59 23.56 14.65 4 58.20 36.40 37.34 27.03 56.15 96.00 5 0.39 0.23 0.37 0.39 0.09 1.33 6 1.04 3.55 0.67 0.20 0.48 0.55 7 8.06 4.98 56.89 22.65 43.91 17.56 8 5.08 2.22 3.66 0.40 6.24 0.55 9 252.00 108.00 18.00 5.53 112.89 109.08 10 14.39 122.84 18.09 38.87 25.41 41.39 11 0.46 1.11 1.54 1.04 1.39 1.37 12 9.60 9.95 10.37 33.81 23.56 10.43 13 4.12 4.98 2.30 1.85 5.52 1.85 14 17.51 5.77 15.45 35.61 15.67 12.47 15 0.28 0.40 0.13 0.69 0.58 0.25 16 28.55 18.08 13.86 12.38 20.05 8.31 17 35.05 27.12 4.48 29.11 28.34 17.55 18 10.83 7.48 12.47 27.05 18.52 26.05 19 19.41 16.63 34.31 22.08 20.74 22.18 20 3.88 13.56 22.01 18.66 12.42 18.01 21 0.26 4.73 0.39 0.69 4.16 0.01 22 0.13 2.77 0.46 4.15 0.43 2.42 23 1.19 0.92 0.92 3.04 0.01 0.14 24 0.39 0.39 0.31 0.31 0.46 0.39 25 19.40 41.66 23.19 24.40 17.51 50.62 26 1.11 2.22 2.76 1.11 4.74 0.08 27 1.11 1.58 0.55 4.69 1.98 1.57 28 0.22 0.83 1.04 0.20 1.38 0.92 29 0.12 0.7 4 0.01 30 1.66 1.39 0.13 0.28 3.31 0.92 31 1.66 1.10 1.18 0.91 1.66 0.83 32 27.64 15.94 5.91 33.24 15.71 28.11 33 4.16 5.52 0.34 0.01 1.66 1.10 34 4.16 2.40 4.34 6.47 2.22 1.11 35 24.48 45.63 28.89 2.30 0.55 6.90 36 79.47 9.24 2.07 1.94 2.32 3.23 37 8.32 8.31 2.77 0.54 1.11 0.55 38 1.66 2.77 5.99 3.86 1.66 1.66 39 0.81 1.38 1.11 0.55 2.22 2.22 40 55.19 19.36 19.39 24.69 52.69 19.32

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195 APPENDIX B SOIL MOISTURE AND CONE PENETROMETER DATA Soil Moisture Time Domain Reflectometer A Field Scout TDR 300 Soil Moisture Probe is a Time Domain Reflectometer (TDR) and was used to measure volumetric water content at five locations surrounding each infiltration measurement location. A probe with two 20 cm long rods was inserted into the soil for this measurement. The TDR was successfully used to record 809 individual measurements at 180 locations within 36 basins (Table B1). In four basins, soil conditions were too dry to insert the TDR probe to obtain moisture readings. Since soil strength is dependent on moisture content, it was determined that attempting to replace failed measurements with additional successful readings, would bias the sample population to higher moisture contents. Volumetric Water Content Volumetric water contents were measured from soil samples collected from each site. Values were determined as the difference in sample masses before and after drying divided by the sample volume. Figure B1 shows corresponding volumetric water content measurements from soil samples and average TDR readings from all locations where both were collected. The regression shows that the average TDR values were about 10% higher than the v values. One explanation for this is that the soil below the soil samples had a slightly higher VWC than the samples. Since the TDR rods extended 20 cm into the soil profile, compared to the 10 cm of the soil sample, TDR readings would be slightly higher. This

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196 difference would be expected given that evaporation dries soils from the s urface down. However, previous research in an uncompacted sandy Florida soil by Dukes et al. (2006) showed that gravimetric samples had a slightly higher VWC when compared to TDR readings. Soil Strength A Field Scout SC 900 Soil Compaction Meter is a cone penetrometer (CP) used to measure the pressure required at various soil depths to continuously force a cone through a soil profile. Penetrometer profile measurements were conducted at five locations surrounding each infiltration rate measurement. The SC 900 recorded pressure (kPa) measurements at 2.5 cm increments (up to 45 cm) with a maximum value of 7,000 kPa. If the CP did not reach a depth of 10 cm, data was not recorded. As with the TDR measurements, soil moisture often seemed to determine whether a CP soil profile data was able to be collected. Therefore, several profiles were neither complete (45 cm) nor truncated (10 42.5 cm) and data was not collected. Attempting to replace failed measurements by additional measurements could lead to bias in the data. The cone penetrometer (CP) was used to collect 776 soil profiles from 185 basins in 36 basins. In an expected profile, the entire 45 cm soil profile would be measured, where the maximum value and corresponding depths are clearly identifiable. Thus the maximum CP reading for a profile would likely be correlated to the infiltration rate (Gregory et al. 2 006). As mentioned previously, complete profiles were difficult to achieve, only 81 of 776 were complete, as seen in Table B3. All other CP attempts were truncated or not recorded.

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197 Correlations between reading and depth values from the 81 complete profiles and soil sample volumetric water content and measured infiltration rates yielded no significant relationships across basins. The only significant (p < 0.05) depth for correlation was at 10 cm; all other depths were not significant (p > 0.05). By comparison, Gregory et al. (2006) reported all depths greater than 0.0 had pvalues less than 0.13, however, only three of the 18 values in this study had c omparable correlation. In addition, all Pearsons r values for Gregory et al. (2006) were negative, suggesting that a greater force corresponded to a decreased infiltration rate, however for depths greater than 20 cm in the present study, Pearsons r value s are positive.

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198 Table B 1 Volumetric water content by TDR attempts and successes for each basin. Basin Attempted Success Success Rate Basin Attempted Success Success Rate 1 45 44 0.98 21 30 29 0.97 2 55 55 1.22 22 30 24 0.80 3 0 0 0.00 23 30 30 1.00 4 30 30 1.00 24 30 25 0.83 5 36 36 1.20 25 30 29 0.97 6 30 30 1.00 26 30 0 0.00 7 30 25 0.83 27 30 0 0.00 8 30 8 0.27 28 30 9 0.30 9 31 31 1.03 29 15 15 1.00 10 31 31 1.03 30 30 30 1.00 11 30 10 0.33 31 30 0 0.00 12 30 7 0.23 32 30 22 0.73 13 30 8 0.27 33 30 4 0.13 14 30 29 0.97 34 30 1 0.03 15 30 21 0.70 35 30 30 1.00 16 30 14 0.47 36 30 2 0.07 17 30 30 1.00 37 30 1 0.03 18 30 30 1.00 38 30 0 0.00 19 30 30 1.00 39 30 30 1.00 20 30 29 0.97 40 30 30 1.00 Total 1203 809 0.67

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199 Table B 2 Gravimetric volumetric water (m3/m3) content measurements from each basin location. Site Basin 1 2 3 4 5 6 7 8 9 1 0.34 0.47 0.28 0.31 0.39 0.29 0.28 0.24 0.33 2 0.07 0.10 0.16 0.12 0.29 0.09 0.13 0.09 0.07 3 0.25 4 0.35 0.29 0.10 0.22 0.35 0.19 5 0.11 0.22 0.25 0.26 0.16 0.20 6 0.45 0.30 0.40 0.44 0.31 0.32 7 0.08 0.24 0.01 0.02 0.02 0.10 8 0.05 0.14 0.05 0.11 0.17 0.13 9 0.43 0.46 0.47 0.27 0.20 0.24 10 0.03 0.20 0.03 0.05 0.03 0.01 11 0.26 0.11 0.13 0.09 0.12 0.08 12 0.02 0.03 0.03 0.12 0.09 0.03 13 0.12 0.11 0.09 0.15 0.07 0.18 14 0.02 0.02 0.02 0.02 0.03 0.02 15 0.26 0.33 0.20 0.19 0.28 0.17 16 0.04 0.03 0.02 0.02 0.03 0.02 17 0.08 0.07 0.10 0.07 0.09 0.09 18 0.08 0.10 0.10 0.13 0.09 0.18 19 0.04 0.06 0.06 0.06 0.05 0.06 20 0.14 0.08 0.08 0.11 0.06 0.06 21 0.19 0.17 0.18 0.16 0.14 0.17 22 0.24 0.16 0.22 0.15 0.18 0.15 23 0.30 0.30 0.28 0.22 0.23 0.31 24 0.25 0.22 0.19 0.19 0.24 0.27 25 0.05 0.06 0.07 0.06 0.06 0.04 26 0.09 0.12 0.09 0.12 0.11 0.19 27 0.19 0.12 0.12 0.18 0.21 0.25 28 0.24 0.25 0.28 0.25 0.28 0.22 29 0.38 0.31 0.42 30 0.18 0.18 0.23 0.26 0.25 0.22 31 0.14 0.25 0.21 0.14 0.13 0.10 32 0.03 0.05 0.05 0.04 0.03 0.03 33 0.12 0.09 0.13 0.12 0.14 0.11 34 0.13 0.13 0.12 0.16 0.14 0.16 35 0.22 0.16 0.13 0.13 0.10 0.13 36 0.18 0.13 0.11 0.07 0.13 0.07 37 0.06 0.07 0.06 0.07 0.11 0.10 38 0.06 0.08 0.07 0.09 0.10 0.10 39 0.26 0.26 0.28 0.29 0.27 0.23 40 0.01 0.01 0.01 0.01 0.01 0.02

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200 Table B 3 Summary table of attempts, complete, truncated profile measurements, and average depth of maximum reading for each basin. Basin Potential Tests Full Profile Success Rate Truncated Profile Success Rate Depth of Max (cm) 1 45 8 0.18 37 0.82 22.7 2 45 0 0.00 0 0.00 N/A 3 0 0 0.00 0 0.00 N/A 4 30 0 0.00 30 1.00 24.9 5 30 5 0.17 18 0.60 16.1 6 30 2 0.07 18 0.60 23.1 7 30 0 0.00 3 0.10 11.7 8 30 0 0.00 0 0.00 N/A 9 30 6 0.20 24 0.80 23.3 10 30 5 0.17 26 0.87 21.8 11 30 2 0.07 3 0.10 8.5 12 30 0 0.00 9 0.30 11.1 13 30 1 0.03 10 0.33 14.5 14 30 0 0.00 16 0.53 12.2 15 30 5 0.17 19 0.63 14.0 16 30 0 0.00 28 0.93 10.5 17 30 0 0.00 30 1.00 20.0 18 30 0 0.00 31 1.03 21.8 19 30 27 0.90 3 0.10 29.2 20 30 0 0.00 30 1.00 19.2 21 30 0 0.00 28 0.93 12.7 22 30 1 0.03 19 0.63 8.1 23 30 1 0.03 29 0.97 18.8 24 30 1 0.03 21 0.70 12.8 25 30 0 0.00 30 1.00 21.8 26 30 3 0.10 27 0.90 15.3 27 30 0 0.00 20 0.67 10.1 28 30 9 0.30 15 0.50 17.2 29 15 1 0.07 14 0.93 26.5 30 30 4 0.13 25 0.83 23.7 31 30 0 0.00 1 0.03 35.0 32 30 0 0.00 26 0.87 18.9 33 30 0 0.00 2 0.07 8.8 34 30 0 0.00 2 0.07 8.8 35 30 0 0.00 28 0.93 23.2 36 30 0 0.00 5 0.17 11.0 37 30 0 0.00 0 0.00 N/A 38 30 0 0.00 3 0.10 7.5 39 30 0 0.00 30 1.00 29.7 40 30 0 0.00 30 1.00 25.8 Total 1185 81 0.07 690 0.58 No profiles, complete or truncated, were successfully collected at these basins.

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201 Table B 4 Correlation and probability values (p) between cone penetrometer measurements at 2.5 cm increments and measured infiltration rates in basins for full profiles. Depth (cm) Pearson's Coefficient (r) p value 0.0 0.0805 0.173 2.5 0.0174 0.746 5.0 0.1261 0.158 7.5 0.2105 0.209 10.0 0.3057 0.043 12.5 0.1757 0.193 15.0 0.1086 0.189 17.5 0.0925 0.112 20.0 0.1040 0.144 22.5 0.1206 0.885 25.0 0.1855 0.992 27.5 0.1871 0.547 30.0 0.2157 0.827 32.5 0.4065 0.331 35.0 0.2467 0.422 37.5 0.3026 0.224 40.0 0.2720 0.225 42.5 0.1648 0.462 45.0 0.1966 0.101

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202 Figure B 1 Average TDR VWC readings vs. Gravimetric VWC for each location tested. y = 1.10x R2 = 0.70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 Gravimetric VWC (m3/m3) Average Location TDR VWC (m3/m3)

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203 APPENDIX C MONITORING DATA Table C 1 Summary of drawdown events for monitoring in basin 4. Soil texture: Sandy Loam. Land use: Department of Transportation. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 03/29/09 60.8 31.6 7 0.9 270 2.28 49 04/01/09 26.6 9.6 21 0.1 147 0.84 20 04/14/09 23.0 4.8 13 0.2 145 0.94 16 04/20/09 19.8 9.0 9 0.2 157 1.24 22 05/14/09 278 3.17 05/16/09 144 1.12 16 05/17/09 128 1.05 05/18/09 146 0.95 05/18/09 155 1.38 05/20/09 224 2.14 05/21/09 194 2.34 05/22/09 222 2.88 05/23/09 167 1.30 25 05/26/09 142 0.69 22 05/28/09 146 0.66 26 06/05/09 138 0.86 20 06/06/09 118 0.20 5 06/13/09 228 1.70 50 06/16/09 158 1.22 06/17/09 178 0.91 50 06/23/09 126 0.37 12 06/30/09 213 1.68 47 07/03/09 12.4 11.6 5 0.2 133 0.60 17 07/07/09 11.8 6.4 5 0.2 129 0.95 15 07/08/09 19.2 6.0 12 0.2 162 1.29 07/09/09 2.6 2.4 2 0.1 134 0.62 16 07/10/09 30.0 20.6 4 0.8 200 1.62 07/16/09 4.4 2.8 5 0.1 125 0.41 11 07/18/09 14.8 12.0 8 0.2 143 0.72 26 07/24/09 17.0 10.4 2 0.9 148 1.34 07/25/09 7.2 5.4 2 0.4 134 0.67 15 07/30/09 41.4 39.0 5 0.8 253 2.63 07/31/09 22.0 12.4 11 0.2 234 1.88 08/02/09 8.6 8.4 3 0.3 150 1.38 22 08/03/09 13.4 13.2 2 0.7 155 1.31 08/04/09 23.0 22.4 7 0.3 228 2.20

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204 Table C 1 Continued. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 08/06/09 12.8 9.0 13 0.1 184 1.81 08/06/09 2.2 1.2 2 0.1 165 0.98 31 08/13/09 9.4 9.0 7 0.1 175 1.30 08/15/09 2.8 1.8 5 0.1 121 0.21 6 08/18/09 9.0 8.2 2 0.5 120 0.25 8 08/21/09 28.4 26.8 2 1.4 171 1.35 08/27/09 51.8 12.4 9 0.6 245 1.61 65 09/02/09 27.4 24.8 5 0.5 183 1.04 51 09/12/09 5.0 4.2 3 0.2 118 0.19 4 09/18/09 6.0 3.6 10 0.1 142 0.72 22 10/05/09 15.8 9.2 7 0.2 150 0.94 23 11/11/09 10.8 5.2 27 0.0 115 0.04 2 11/22/09 13.0 4.2 9 0.1 134 0.63 15 11/24/09 8.0 5.4 3 0.3 124 0.67 11/25/09 45.4 22.0 13 0.3 259 1.72 65 12/02/09 10.2 6.0 6 0.2 133 0.50 20 12/05/09 34.2 4.4 22 0.2 190 0.87 62 12/25/09 7.4 7.0 4 0.2 121 0.20 9 01/01/10 18.8 7.6 8 0.2 153 0.62 37 Max 60.8 39.0 27 1.4 278 3.17 65 Median 13.4 8.4 6 0.2 150 0.98 21 Min 2.2 1.2 2 0.0 115 0.04 2 Mean 18.3 10.8 8 0.3 166 1.15 26 SD 14.0 8.8 6 0.3 44 0.71 18

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205 Table C 2 Summary of drawdown events for monitoring in basin 5. Soil texture: Sandy Loam. Land use: Department of Transportation. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 03/28/09 8.78 2.94 9 0.98 36.35 0.29 70 04/03/09 1.48 1.28 5 0.30 25.41 0.23 89 04/06/09 0.10 0.04 10 0.01 6.61 0.42 14 05/20/09 0.56 0.22 5 0.11 3.18 0.44 7 05/23/09 20.86 0.37 55 05/26/09 12.22 0.44 27 05/28/09 37.62 0.54 69 06/30/09 8.29 0.26 31 07/02/09 3.26 1.24 2 07/03/09 11.03 0.37 29 07/08/09 17.89 0.41 44 07/10/09 24.90 0.49 46 07/15/09 0.66 0.04 73 0.01 16.16 0.42 35 07/25/09 18.27 0.37 47 08/25/09 2.36 2.18 7 0.34 3.54 0.22 15 08/27/09 2.46 1.16 8 0.31 18.65 0.29 17 08/28/09 1.82 0.98 4 0.46 22.28 0.38 54 12/05/09 2.76 0.44 22 0.13 3.18 0.19 16 01/01/10 2.78 1.92 10 0.28 4.43 0.18 23 Max 8.78 2.94 73 0.98 37.62 1.24 89 Median 2.09 1.07 9 0.29 16.16 0.37 31 Min 0.10 0.04 4 0.01 3.18 0.18 2 Mean 2.38 1.12 15 0.29 15.48 0.40 36 St Dev 2.45 0.98 21 0.28 10.78 0.23 24

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206 Table C 3 Summary of drawdown events for monitoring in basin 6. Soil texture: Sandy Clay Loam. Land use: Residential. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 5/24/09 2.90 0.28 102 0.03 51.53 0.24 108 5/28/09 42.46 0.23 165 6/5/09 0.86 0.18 8 0.11 9.11 0.16 59 6/30/09 0.16 0.02 29 0.01 17.29 0.19 65 7/4/09 2.06 2.02 6 0.34 16.81 0.16 82 7/8/09 1.96 0.66 22 0.09 23.13 0.16 44 7/12/09 1.76 0.44 16 0.11 35.15 0.23 53 7/14/09 25.16 0.18 51 7/16/09 18.48 0.18 105 8/7/09 1.78 0.30 22 0.08 6.65 0.15 47 8/27/09 0.28 0.14 9 0.03 2.91 0.11 10 8/29/09 0.30 0.08 24 0.01 32.68 0.18 182 9/12/09 0.96 0.14 21 0.05 1.49 0.10 17 9/19/09 4.40 2.50 10 0.44 26.56 0.18 111 9/24/09 1.58 0.70 5 0.32 11.45 0.17 70 10/16/09 2.22 1.20 11 0.20 4.87 0.11 47 12/6/09 2.84 0.44 22 0.13 7.10 0.12 63 Max 4.40 2.50 102 0.44 51.53 0.24 182 Median 1.77 0.37 19 0.10 17.29 0.17 63 Min 0.16 0.02 5 0.01 1.49 0.10 10 Average 1.72 0.65 22 0.14 19.58 0.17 75 St Dev 1.18 0.76 24 0.14 14.55 0.04 46 Table C 4 Summary of drawdown events for monitoring in basin 13. Soil texture: Loamy Sand. Land use: Residential. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 08/06/09 5.54 2.90 2 2.77 12.81 0.54 23 09/17/09 8.38 4.72 8 1.05 10.61 0.50 21 Average 6.96 3.81 5 1.91 11.71 0.52 22 St Dev 2.01 1.29 4 1.22 1.56 0.03 1

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207 Table C 5 Summary of drawdown events for monitoring in basin 18. Soil texture: Loamy Sand. Land use: Department of Transportation. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (hr) Avg. Intensity (cm/h) Avg. Drawdown Rate (cm/h) Max Water Level (cm) Ponded Duration (h) 5/18/09 16.18 0.18 69 0.18 0.15 16.7 5/22/09 0.62 0.44 5 0.12 0.08 22.3 5/22/09 0.14 0.06 3 0.05 0.08 21.9 5/23/09 3.26 2.06 6 0.54 0.13 21.2 5/24/09 0.84 0.36 5 0.17 0.10 22.4 5/25/09 0.36 0.34 2 0.18 0.09 22.0 5/26/09 0.90 0.86 2 0.45 0.08 21.5 5/28/09 0.58 0.58 1 0.58 0.11 20.5 136 6/5/09 1.44 2.14 5 0.58 0.07 17.2 6/5/09 0.38 1.50 2 0.66 0.09 17.0 6/6/09 1.38 0.78 11 0.13 0.07 17.6 6/7/09 0.30 1.04 1 0.19 0.26 17.2 25 7/6/09 1.86 0.42 8 0.13 0.43 17.2 15 7/7/09 0.82 1.06 2 0.30 0.47 17.2 14 7/8/09 1.78 0.28 8 0.23 0.26 17.1 7/9/09 1.28 0.30 7 0.41 0.43 16.4 20 7/10/09 2.88 2.10 6 0.48 0.19 18.3 47 7/13/09 0.70 0.84 1 0.18 0.51 17.0 12 7/14/09 0.44 0.80 1 0.48 0.60 16.9 9 7/18/09 2.78 0.70 3 0.70 0.31 17.3 21 7/20/09 4.54 2.82 2 2.27 0.13 19.0 85 8/4/09 2.78 0.44 9 0.93 0.52 17.6 14 8/15/09 1.28 2.38 4 2.27 0.50 16.6 10 8/16/09 0.82 1.92 2 0.31 0.51 16.5 9 9/2/09 3.10 1.26 6 0.32 0.39 17.1 15 9/18/09 9.42 3.46 6 1.57 0.11 21.2 156 11/22/09 4.02 0.78 7 0.52 0.39 16.5 12 11/25/09 2.10 1.88 15 1.57 0.20 16.5 23 12/4/09 3.84 1.70 21 0.57 0.11 16.1 56 1/1/10 2.62 1.08 12 0.22 0.38 15.8 8 Max 16.18 3.46 69 2.27 0.60 22.4 156 Median 1.41 0.85 5 0.43 0.20 17.2 15 Min 0.14 0.06 1 0.05 0.07 15.8 8 Mean 2.45 1.15 8 0.58 0.26 18.3 36 St. Dev. 3.20 0.86 12 0.59 0.17 2.2 43

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208 Table C 6 Summary of drawdown events for monitoring in basin 21. Soil texture: Sandy Loam. Land use: Residential. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 04/02/09 2.92 0.17 92 0.12 7.21 0.43 15 04/13/09 3.84 0.29 20 0.24 2.68 0.75 2 Average 3.38 0.23 56 0.18 4.94 0.59 9 St Dev 0.65 0.09 51 0.08 3.20 0.23 9 Table C 7 Summary of drawdown events for monitoring in basin 25. Soil texture: Sand. Land use: Residential. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 12/02/09 12.98 3.14 30 0.43 66.65 1.11 24 06/29/09 3.08 0.78 74 0.04 11.03 1.09 4 04/02/09 9.30 0.42 278 0.03 10.22 0.68 5 04/14/09 7.16 1.42 13 0.55 3.13 0.67 2 Max 12.98 3.14 278 0.55 66.65 1.11 24 Median 8.23 1.10 52 0.24 10.62 0.89 5 Min 3.08 0.42 13 0.03 3.13 0.67 2 Average 8.13 1.44 99 0.26 22.76 0.89 9 St Dev 4.14 1.21 122 0.27 29.48 0.24 10

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209 Table C 8 Summary of drawdown events for monitoring in basin 30. Soil texture: Sandy Clay Loam. Land use: Department of Transportation. Event Date Rainfall Depth (cm) Max Hourly Intensity (cm/h) Duration (h) Avg. Intensity (cm/h) Max Water Level (cm) Avg. Drawdown Rate (cm/h) Ponded Duration (h) 03/27/09 4.76 1.82 8 0.60 5.65 0.27 20 03/28/09 2.04 1.32 6 0.34 9.43 0.30 30 04/02/09 18.12 3.2 0 31 0.58 55.56 0.34 12 04/02/09 18.12 3.20 31 0.58 60.64 0.19 259 04/14/09 6.38 1.88 12 0.53 37.65 0.13 278 05/29/09 9.26 5.16 5 1.85 62.34 0.20 166 06/05/09 5.5 0 2.24 11 0.50 60.36 0.14 428 06/28/09 4.44 3.26 3 1.48 10.22 0.11 16 06/29/09 3.14 3.12 2 1.57 30.54 0.13 240 07/18/09 3.04 1.62 5 0.61 7.05 0.13 56 07/27/09 3.36 1.76 2 1.68 9.69 0.16 36 07/28/09 1.24 1.04 2 0.62 7.43 0.14 41 10/15/09 4.98 1.46 7 0.71 24.60 0.22 110 12/02/09 10.62 2.88 23 0.46 57.19 0.24 166 12/09/09 5.62 5.06 2 2.81 57.29 0.26 63 12/12/09 6.94 1.24 28 0.25 58.71 0.22 15 12/13/09 56.22 0.20 116 12/18/09 3.52 0.64 18 0.20 45.48 0.15 153 12/25/09 2.06 0.58 14 0.15 29.42 0.11 264 Max 18.12 5.16 31 2.81 62.34 0.34 428 Median 4.98 1.82 8 0.58 37.65 0.19 110 Min 1.24 0.58 2 0.15 5.65 0.11 12 Average 6.32 2.25 13 0.83 36.08 0.19 130 St. Dev. 4.80 1.33 11 0.71 22.41 0.07 118

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210 APPENDIX D ADDITIONAL HYDROLOGIC AND SOILS DATA Table D 1 Low quarter distribution uniformities and uniformity coefficients for a natural event, the rainfall simulator (RFS) with curtains and the rainfall simulator without curtains at different scales within the rainfall simulator area. Natural Rainfall RFS wi th Curtains RFS w ithout Curtains Scale DU lq UC DU lq UC DU lq UC Full Simulator 0.93 0.95 0.88 0.92 0.71 0.80 Rows 0.93 0.95 0.89 0.93 0.72 0.80 Bays 0.93 0.95 0.90 0.93 0.77 0.81 Lysimeter 0.97 0.97 0.95 0.97 0.90 0.92

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211 Table D 2 Non compacted bulk densities. Lysimeter Arredondo Lysimeter Orangeburg b (g/cm 3 ) b (g/cm 3 ) 1 1.16 2 1.11 9 1.24 3 1.09 10 1.27 4 1.03 12 1.24 5 1.08 13 1.26 6 1.03 15 1.27 7 1.07 16 1.22 8 1.12 18 1.20 11 0.95 21 1.18 14 1.11 22 1.23 17 1.11 23 1.25 19 1.17 25 1.38 20 1.12 26 1.23 24 1.03 28 1.23 27 0.99 29 1.22 31 1.08 30 1.24 33 1.06 32 1.23 34 1.08 35 1.31 36 1.02 38 1.27 37 1.03 40 1.21 39 1.07 42 1.21 41 1.09 Maximum 1.38 1.17 Median 1.23 1.08 Minimum 1.16 0.95 Mean 1.24 1.07 St. Dev. 0.05 0.05

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212 Table D 3 Non compacted infiltration rates Lysimeter Arredondo Lysimeter Orangeburg cm/h cm/h 1 135.4 2 137.5 9 139.4 3 140.7 10 128.2 4 163.4 12 112.7 5 109.8 13 111.4 6 242.1 15 156.2 7 197.0 16 137.5 8 195.4 18 121.8 11 163.1 21 158.0 14 297.3 22 172.4 17 136.8 23 196.8 19 243.2 25 149.4 20 237.3 26 137.5 24 318.1 28 144.2 27 178.5 29 140.6 31 159.1 30 147.5 33 165.9 32 136.6 34 141.2 35 170.2 36 150.8 38 185.6 37 211.2 40 124.2 39 136.0 42 160.9 41 159.3 Maximum 196.8 318.1 Median 140.6 163.4 Minimum 111.4 109.8 Geometric Mean 144.5 178.0 Geometric Standard Deviation 1.2 1.3

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213 Table D 4 Non compacted summary of cone indices profiles. Soil Arredondo Orangeburg Depth ( kPa ) ( kPa ) (cm) Min. Median Max. Mean St. Dev. Min. Median Max. Mean St. Dev. 0.0 0 211 667 239 123 105 211 526 235 85 2.5 0 211 456 221 91 105 175 386 209 60 5.0 105 211 421 214 75 105 211 316 197 38 7.5 105 211 386 209 70 105 175 351 196 41 10.0 105 175 386 200 64 140 211 316 196 35 12.5 105 175 386 194 60 105 175 316 193 34 15.0 105 175 386 190 64 70 211 316 195 44 17.5 70 175 386 179 54 70 211 351 194 49 20.0 70 175 351 183 66 70 175 351 197 48 22.5 70 175 351 172 59 70 211 351 197 56 25.0 70 140 246 159 39 70 211 386 196 59 27.5 70 175 246 156 38 70 175 351 184 54 30.0 70 140 246 143 37 70 175 456 171 70 32.5 70 140 246 134 36 70 140 316 146 54 35.0 70 105 246 126 37 70 140 246 135 43 37.5 70 105 211 113 33 70 105 351 126 59 40.0 70 105 211 105 31 70 105 421 120 72 42.5 70 70 211 91 30 70 105 386 114 64 45.0 70 70 211 86 32 70 70 246 92 44

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214 Table D 5 Student T test results for determining whether cone index values were significantly different at each depth. Soil Depth (cm) p value 0.0 0.79 2.5 0.42 5.0 0.10 7.5 0.21 10.0 0.59 12.5 0.90 15.0 0.64 17.5 0.10 20.0 0.17 22.5 0.02 25.0 0.00 27.5 0.00 30.0 0.01 32.5 0.17 35.0 0.23 37.5 0.14 40.0 0.14 42.5 0.02 45.0 0.47

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215 Table D 6 Compacted bulk densities. Lysimeter Arredondo Lysimeter Orangeburg b (g/cm 3 ) b (g/cm 3 ) 1 1.59 2 1.47 9 1.57 3 1.43 10 1.57 4 1.39 12 1.54 5 1.47 13 1.55 6 1.55 15 1.57 7 1.43 16 1.55 8 1.36 18 1.55 11 1.42 21 1.50 14 1.41 22 1.58 17 1.44 23 1.57 19 1.48 25 1.56 20 1.47 26 1.56 24 1.39 28 1.56 27 1.36 29 1.54 31 1.45 30 1.56 33 1.38 32 1.54 34 1.49 35 1.54 36 1.48 38 1.56 37 1.48 40 1.54 39 1.38 42 1.55 41 1.46 Maximum 1.59 1.55 Median 1.56 1.44 Minimum 1.50 1.36 Mean 1.56 1.44 Standard Deviation 0.02 0.05

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216 Table D 7 Compacted infiltration rates. Lysimeter Arredondo Lysimeter Orangeburg cm/h cm/h 1 28.5 2 5.0 9 32.6 3 14.0 10 39.9 4 4.6 12 34.1 5 14.6 13 36.4 6 14.9 15 34.1 7 7.5 16 36.3 8 8.9 18 39.9 11 6.3 21 36.8 14 10.2 22 41.2 17 6.2 23 35.5 19 4.8 25 37.3 20 4.8 26 34.5 24 13.6 28 36.7 27 2.8 29 36.5 31 0.6 30 35.1 33 0.3 32 33.2 34 2.1 35 34.2 36 0.9 38 44.2 37 1.2 40 37.8 39 3.6 42 36.6 41 2.5 Maximum 44.2 14.9 Median 36.4 4.8 Minimum 28.5 0.3 Geometric Mean 36.1 4.1 Geometric Standard Deviation 1.1 2.9

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217 Table D 8 Runoff coefficients for each Arredondo lysimeter from rainfall events during compaction phase. #Rainfall depth accounting for flange contributions. Effective Rainfall Depth (cm) # Lysimeter 6.37 5.32 5.12 4.31 3.07 2.41 1.93 1.84 1 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9 0.35 0.54 0.13 0.14 0.07 0.06 0.08 0.05 10 0.56 0.45 0.10 0.29 0.00 0.00 0.06 0.06 12 0.59 0.46 0.04 0.14 0.01 0.01 0.00 0.02 13 0.30 0.19 0.00 0.09 0.22 0.00 0.00 0.00 15 0.38 0.33 0.06 0.23 0.02 0.00 0.00 0.00 16 0.52 0.38 0.02 0.20 0.00 0.00 0.00 0.00 18 0.54 0.17 0.03 0.24 0.00 0.00 0.05 0.05 21 0.39 0.51 0.15 0.30 0.00 0.07 0.16 0.12 22 0.44 0.48 0.00 0.00 0.01 0.00 0.00 0.00 23 0.43 0.37 0.00 0.09 0.02 0.00 0.00 0.00 25 0.31 0.31 0.00 0.08 0.09 0.00 0.00 0.01 26 0.36 0.01 0.00 0.00 0.03 0.00 0.00 0.00 28 0.25 0.04 0.00 0.00 0.08 0.00 0.00 0.00 29 0.40 0.19 0.00 0.01 0.07 0.00 0.00 0.00 30 0.29 0.10 0.00 0.00 0.05 0.00 0.00 0.00 32 0.49 0.43 0.03 0.13 0.33 0.00 0.00 0.00 35 0.47 0.38 0.00 0.03 0.16 0.00 0.00 0.00 38 0.52 0.45 0.06 0.10 0.06 0.00 0.00 0.00 40 0.42 0.25 0.00 0.09 0.13 0.00 0.00 0.00 42 0.55 0.40 0.12 0.18 0.03 0.01 0.00 0.00

PAGE 218

218 Table D 9 Runoff coefficients for each Orangeburg lysimeter from rainfall events during compaction phase. #Rainfall depth accounting for flange contributions. Effective Rainfall Depth (cm) # Lysimeter 6.37 5.32 5.12 4.31 3.07 2.41 1.93 1.84 2 0.59 0.44 0.00 0.06 0.25 0.97 0.00 0.00 3 0.60 0.55 0.11 0.24 0.51 0.09 0.00 0.00 4 0.63 0.43 0.00 0.17 0.37 0.95 0.00 0.00 5 0.77 0.68 0.20 0.47 0.63 0.25 0.00 0.15 6 0.62 0.59 0.13 0.34 0.52 0.08 0.28 0.00 7 0.90 0.35 0.25 0.50 0.45 0.29 0.31 0.27 8 0.83 0.67 0.18 0.39 0.64 0.27 0.21 0.11 11 0.60 0.49 0.02 0.09 0.21 0.02 0.04 0.02 14 0.66 0.53 0.07 0.31 0.47 0.00 0.00 0.00 17 0.72 0.51 0.03 0.24 0.46 0.00 0.00 0.00 19 0.78 0.68 0.31 0.58 0.51 0.00 0.37 0.34 20 0.91 0.71 0.28 0.64 0.80 0.39 0.33 0.28 24 0.73 0.59 0.14 0.39 0.55 0.06 0.05 0.02 27 0.58 0.53 0.07 0.13 0.31 0.00 0.00 0.00 31 0.47 0.46 0.00 0.12 0.36 0.00 0.00 0.00 33 0.47 0.37 0.00 0.06 0.16 0.00 0.00 0.00 34 0.62 0.54 0.06 0.26 0.49 0.02 0.00 0.00 36 0.38 0.31 0.00 0.00 0.00 0.00 0.00 0.00 37 0.51 0.45 0.00 0.11 0.27 0.00 0.00 0.00 39 0.61 0.45 0.00 0.14 0.32 0.00 0.00 0.00 41 0.72 0.69 0.24 0.47 0.52 0.28 0.30 0.12

PAGE 219

219 Table D 10. Calculated and regressed curve numbers from compacted Arredondo lysimeters. Lysimeter Rainfall (mm) Regressed 44 37 36 30 21 17 13 13 1 70 70 9 85 92 77 81 81 85 88 87 82 10 92 90 75 87 73 87 88 87 12 92 90 69 81 74 79 84 85 13 83 80 78 89 78 15 86 87 71 85 77 95 16 90 88 67 84 73 100 18 91 79 68 86 72 87 87 80 21 86 92 79 88 72 85 91 90 87 22 88 91 76 100 23 88 88 78 77 99 25 84 86 77 83 83 82 26 85 64 78 78 28 81 69 82 70 29 87 80 68 82 93 30 83 75 65 80 86 32 90 90 69 80 92 96 35 89 88 72 86 98 38 91 90 72 78 81 93 40 87 83 78 85 86 42 91 89 77 83 78 80 92 Absent values indicate runoff was not produced from rainfall event by the corresponding lysimeter.

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220 Table D 11. Calculated and regressed curve numbers from compacted Orangeburg lysimeters. Lysimeter Rainfall (mm) Regressed 44 37 36 30 21 17 13 13 2 92 90 76 90 100 95 3 93 92 76 85 95 86 93 4 93 90 83 92 100 91 5 96 95 82 92 97 92 91 94 6 93 93 77 89 95 86 94 91 7 99 87 84 93 94 93 94 94 90 8 97 95 80 90 97 92 92 90 94 11 93 91 67 77 88 80 86 84 85 14 94 92 73 88 94 99 17 95 92 68 86 94 100 19 96 95 86 94 95 95 95 93 20 99 96 85 95 98 94 95 94 96 24 95 93 78 90 96 84 87 84 96 27 92 92 73 81 91 95 31 89 90 80 92 92 33 89 88 76 86 93 34 93 92 72 86 95 81 98 36 86 86 88 37 90 90 79 90 94 39 93 90 81 91 95 41 95 95 83 92 95 92 94 90 93 Absent values indicate runoff was not produced from rainfall event by the corresponding lysimeter.

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221 Table D 12. Amendment phase Arredondo bulk densities. Lysimeter Amendment # Incorporation Depth ( cm) Replicate Bulk density (g/cm 3 ) 1 F 10 3 1.28 9 C 10 1 1.06 10 N 20 1 1.37 12 N 0 1 1.48 13 N 10 3 1.34 15 C 20 3 0.98 16 N 20 3 1.28 18 N 0 3 1.46 21 F 10 2 1.07 22 F 20 3 1.25 23 N 0 2 1.60 25 N 20 2 1.28 26 F 20 1 1.23 28 N 10 1 1.42 29 C 20 2 1.13 30 C 10 2 0.97 32 F 20 2 1.22 35 C 10 3 0.99 38 F 10 1 1.03 40 N 10 2 1.29 42 C 20 1 1.08 # Amendment N : Null; F: Fly Ash; C: Compost

PAGE 222

222 Table D 13. Amendment phase Orangeburg bulk densities. Lysimeter Amendment # Incorporation Depth (cm) Replicate Bulk density (g/cm 3 ) 2 N 10 1 1.15 3 C 20 2 1.11 4 N 20 2 1.22 5 C 10 2 0.95 6 C 20 1 0.90 7 F 20 2 1.17 8 C 20 3 0.97 11 C 10 1 0.88 14 C 10 3 0.89 17 F 10 3 1.13 19 N 10 3 1.20 20 F 20 1 1.18 24 F 10 1 1.03 27 N 0 1 1.47 31 N 0 3 1.34 33 N 10 2 1.22 34 F 10 2 1.04 36 N 0 2 1.45 37 N 20 1 1.21 39 F 20 3 1.18 41 N 20 3 1.39 # Amendment N : Null; F: Fly Ash; C: Compost

PAGE 223

223 Table D 14. Amendment phase Arredondo infiltration rates. Lysimeter Amendment # Incorporation Depth (cm) Replicate Infiltration Rate (cm/h) 1 F 10 3 4.0 9 C 10 1 75.4 10 N 20 1 121.9 12 N 0 1 20.0 13 N 10 3 27.9 15 C 20 3 91.7 16 N 20 3 80.5 18 N 0 3 27.8 21 F 10 2 8.2 22 F 20 3 8.6 23 N 0 2 27.1 25 N 20 2 60.4 26 F 20 1 18.6 28 N 10 1 42.2 29 C 20 2 87.8 30 C 10 2 61.3 32 F 20 2 12.6 35 C 10 3 93.7 38 F 10 1 2.5 40 N 10 2 53.0 42 C 20 1 98.8 # Amendment N : Null; F: Fly Ash; C: Compost

PAGE 224

224 Table D 15. Amendment phase Orangeburg infiltration rates. Lysimeter Amendment # Incorporation Depth (cm) Replicate Infiltration Rate (cm/h) 2 N 10 1 10.8 3 C 20 2 111.2 4 N 20 2 119.8 5 C 10 2 108.2 6 C 20 1 125.4 7 F 20 2 11.8 8 C 20 3 101.1 11 C 10 1 108.1 14 C 10 3 100.8 17 F 10 3 7.9 19 N 10 3 11.3 20 F 20 1 5.2 24 F 10 1 10.1 27 N 0 1 0.8 31 N 0 3 1.5 33 N 10 2 6.5 34 F 10 2 1.6 36 N 0 2 3.5 37 N 20 1 93.2 39 F 20 3 4.5 41 N 20 3 74.4 # Amendment N : Null; F: Fly Ash; C: Compost

PAGE 225

225 Table D 16. Summary of Arredondo cone index profiles indicating significant difference between control treatments. Soil depths are from control treatments referenced with maximum depth at drainage layer. Soil Depth Treatment # (cm) N10 N20 F10 F20 C10 C20 0.0 X* XX X XX XX XX 2.5 XX XX X XX XX XX 5.0 XX XX X XX XX XX 7.5 XX XX XX XX XX XX 10.0 XX XX XX XX XX XX 12.5 XX XX XX XX XX XX 15.0 XX XX XX XX XX XX 17.5 X XX XX X XX 20.0 XX XX XX 22.5 XX XX XX 25.0 XX XX XX 27.5 XX X 30.0 X X 32.5 X X 35.0 X X 37.5 X XX X 40.0 XX X X 42.5 XX X X 45.0 X XX X X *X and XX indicate significant differences at p < 0.05 and p < 0.01, respectively, from t test analysis. #Treatment: [Amendment N: Null; F: Fly Ash; C: Compost ][Depth 10 cm, 20 cm]

PAGE 226

226 Table D 17. Summary of Orangeburg cone index profiles indicating significant difference between control treatments. Soil depths are from control treatments referenced with maximum depth at drainage layer. Soil Depth Treatment # (cm) N10 N20 F10 F20 C10 C20 0.0 XX* X XX X XX XX 2.5 XX X XX XX XX XX 5.0 XX XX XX XX XX XX 7.5 XX XX XX XX XX XX 10.0 XX XX XX XX XX XX 12.5 XX XX XX XX XX XX 15.0 XX X X XX XX XX 17.5 XX X XX XX XX XX 20.0 XX X XX XX XX XX 22.5 X XX XX XX 25.0 XX XX 27.5 X XX 30.0 X XX 32.5 X XX 35.0 X X XX 37.5 X XX *X and XX indicate significant differences at p < 0.05 and p < 0.01, respectively, from t test analysis. #Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 10 cm, 20 cm]

PAGE 227

227 Table D 18. Arredondo amended runoff coefficients. Rainfall Depth (mm) 114.4 77.2 75.4 71.6 71.6 69.8 67.3 61.6 58.8 54.7 Treatment* Sim. # Sim. Sim. Sim. Sim. Sim. Sim. Sim. Nat. Sim. N01 0.13 0.28 0.25 0.38 0.17 0.40 0.06 0.34 0.01 0.24 N02 0.19 0.26 0.19 0.35 0.05 0.37 0.32 0.15 0.04 0.34 N03 0.42 0.42 0.43 0.57 0.16 0.55 0.41 0.35 0.20 0.55 N101 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 N102 0.00 0.00 0.00 0.00 0.03 0.01 0.00 0.00 0.00 0.00 N103 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 N201 0.00 0.00 0.01 0.01 0.07 0.01 0.00 0.02 0.01 0.01 N202 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 N203 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 F101 0.20 0.63 0.64 0.61 0.82 0.28 0.62 0.66 0.49 0.71 F102 0.15 0.46 0.73 0.67 0.69 0.70 0.56 0.69 0.50 0.66 F103 0.32 0.74 0.77 0.81 0.82 0.77 0.84 0.90 0.61 1.00 F201 0.04 0.33 0.50 0.63 0.62 0.68 0.55 0.44 0.42 0.65 F202 0.00 0.00 0.52 0.53 0.41 0.68 0.48 0.29 0.39 0.57 F203 0.04 0.53 0.64 0.73 0.72 0.76 0.59 0.55 0.45 0.71 C101 0.34 0.00 0.00 0.01 0.00 0.00 0.01 0.01 0.01 0.01 C102 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.05 C103 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 C201 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C202 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C203 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][ Depth 0 cm, 10 cm, 20 cm][Replicate] #Sim.: Simulated; Nat.: Natural.

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228 Table D 18. Arredondo amended runoff coefficients. Rainfall Depths (mm) 50.4 34.8 29.5 25.3 19.2 14.5 12.3 5.4 4.4 Treatment* Sim. # Nat. Nat. Nat. Nat. Nat. Nat. Nat. Nat. N01 0.41 0.04 0.04 0.00 0.01 0.02 0.00 0.06 0.03 N02 0.38 0.00 0.14 0.01 0.16 0.13 0.01 0.02 0.00 N03 0.65 0.08 0.24 0.12 0.24 0.36 0.04 0.11 0.03 N101 0.05 0.01 0.00 0.01 0.01 0.01 0.03 0.03 0.00 N102 0.00 0.00 0.00 0.02 0.09 0.00 0.00 0.00 0.00 N103 0.01 0.02 0.04 0.01 0.02 0.04 0.00 0.08 0.00 N201 0.01 0.10 0.01 0.01 0.01 0.01 0.01 0.00 0.00 N202 0.02 0.00 0.06 0.05 0.00 0.12 0.00 0.00 0.00 N203 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.00 0.00 F101 0.80 0.12 0.34 0.44 0.45 0.54 0.00 0.00 0.16 F102 0.77 0.13 0.39 0.44 0.45 0.59 0.00 0.00 0.17 F103 0.76 0.24 0.34 0.43 0.38 0.60 0.00 0.00 0.16 F201 0.79 0.00 0.27 0.13 0.20 0.37 0.00 0.00 0.02 F202 0.68 0.00 0.23 0.16 0.14 0.38 0.00 0.00 0.09 F203 0.93 0.12 0.29 0.34 0.39 0.50 0.00 0.00 0.08 C101 0.00 0.01 0.01 0.00 0.01 0.01 0.03 0.00 0.00 C102 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C103 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C201 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C202 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C203 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate] #Sim.: Simulated; Nat.: Natural.

PAGE 229

229 Table D 19. Orangeburg amended runoff coefficients. Rainfall Depth (mm) 114.4 77.2 75.4 71.6 71.6 69.8 67.3 61.6 58.8 54.7 Treatment* Sim. # Sim. Sim. Sim. Sim. Sim. Sim. Sim. Nat. Sim. N01 0.43 0.39 0.42 0.61 0.52 0.65 0.43 0.57 0.55 0.54 N02 0.45 0.47 0.43 0.58 0.66 0.63 0.47 0.69 0.54 0.58 N03 0.30 0.41 0.53 0.62 0.58 0.72 0.52 0.68 0.63 0.66 N101 0.01 0.03 0.11 0.32 0.12 0.40 0.02 0.06 0.37 0.11 N102 0.00 0.00 0.09 0.21 0.16 0.68 0.03 0.03 0.51 0.10 N103 0.02 0.03 0.09 0.25 0.26 0.52 0.03 0.15 0.65 0.10 N201 0.02 0.00 0.04 0.08 0.00 0.01 0.01 0.00 0.39 0.03 N202 0.00 0.00 0.02 0.02 0.12 0.19 0.01 0.01 0.30 0.02 N203 0.00 0.00 0.02 0.04 0.22 0.33 0.01 0.00 0.26 0.03 F101 0.20 0.56 0.43 0.69 0.64 0.77 0.60 0.78 0.62 0.68 F102 0.22 0.42 0.57 0.69 0.56 0.71 0.53 0.65 0.54 0.66 F103 0.21 0.47 0.45 0.72 0.46 0.71 0.64 0.50 0.59 0.67 F201 0.17 0.51 0.60 0.83 0.41 0.86 0.74 0.73 0.62 0.72 F202 0.17 0.32 0.50 0.69 0.46 0.70 0.53 0.57 0.55 0.66 F203 0.17 0.47 0.55 0.74 0.54 0.13 0.50 0.77 0.68 0.67 C101 0.01 0.02 0.04 0.05 0.22 0.25 0.02 0.03 0.48 0.03 C102 0.00 0.00 0.00 0.01 0.06 0.06 0.00 0.00 0.29 0.00 C103 0.00 0.00 0.00 0.01 0.09 0.16 0.00 0.00 0.09 0.00 C201 0.00 0.00 0.02 0.11 0.20 0.30 0.01 0.04 0.41 0.02 C202 0.00 0.00 0.01 0.05 0.13 0.28 0.01 0.01 0.18 0.02 C203 0.01 0.02 0.15 0.18 0.26 0.38 0.01 0.03 0.25 0.02 Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate] #Sim.: Simulated; Nat.: Natural.

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230 Table D 19 Cont inued. Rainfall Depths (mm) 50.4 34.8 29.5 25.3 19.2 14.5 12.3 5.4 4.4 Treatment Sim. # Nat. Nat. Nat. Nat. Nat. Nat. Nat. Nat. N01 0.73 0.24 0.26 0.21 0.21 0.41 0.00 0.00 0.03 N02 0.69 0.15 0.26 0.17 0.20 0.38 0.00 0.00 0.00 N03 0.79 0.36 0.38 0.47 0.44 0.63 0.00 0.00 0.05 N101 0.13 0.02 0.13 0.06 0.21 0.39 0.01 0.00 0.02 N102 0.35 0.09 0.17 0.07 0.09 0.42 0.00 0.03 0.00 N103 0.47 0.35 0.23 0.24 0.23 0.57 0.01 0.00 0.02 N201 0.47 0.05 0.21 0.15 0.05 0.32 0.00 0.00 0.00 N202 0.10 0.00 0.16 0.05 0.15 0.22 0.00 0.00 0.02 N203 0.37 1.00 0.31 0.08 0.38 0.25 0.00 0.00 0.00 F101 0.87 0.35 0.43 0.52 0.61 0.70 0.00 0.00 0.09 F102 0.85 0.09 0.26 0.33 0.22 0.47 0.00 0.00 0.03 F103 0.79 0.30 0.36 0.52 0.47 0.59 0.00 0.00 0.12 F201 0.86 0.33 0.27 0.58 0.40 0.59 0.00 0.00 0.12 F202 0.65 0.16 0.26 0.39 0.29 0.55 0.00 0.00 0.12 F203 0.88 0.44 0.36 0.58 0.51 0.66 0.00 0.00 0.00 C101 0.42 0.14 0.16 0.08 0.25 0.31 0.13 0.08 0.06 C102 0.02 0.01 0.11 0.00 0.18 0.16 0.00 0.00 0.02 C103 0.08 0.00 0.16 0.01 0.15 0.09 0.00 0.00 0.00 C201 0.28 0.00 0.41 0.06 0.42 0.26 0.00 0.00 0.00 C202 0.28 0.00 0.27 0.06 0.25 0.25 0.00 0.00 0.00 C203 0.31 0.00 0.28 0.02 0.28 0.23 0.00 0.00 0.00 Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate] #Sim.: Simulated; Nat.: Natural.

PAGE 231

231 Table D 20. Calculated curve numbers for amended Arredondo lysimeters. Rainfall Depth (mm) Treatment 114 77 75 72 72 70 62 67 59 55 N01 52 72 71 79 66 81 80 57 51 77 N02 57 71 67 78 54 79 68 77 58 81 N03 73 80 81 87 66 87 80 81 73 89 N101 a 44 47 44 N102 44 52 46 49 N103 44 44 47 N201 46 46 57 49 53 47 52 55 N202 45 44 43 50 N203 48 F101 58 89 89 89 95 74 92 90 87 94 F102 54 82 93 91 92 92 93 87 87 93 F103 67 93 94 95 95 94 98 96 91 10 0 F201 41 75 84 90 89 91 84 87 84 92 F202 42 85 86 81 92 77 84 83 90 F203 42 85 89 93 92 94 88 89 85 94 C101 68 44 47 43 45 50 49 52 54 C102 44 48 43 46 61 C103 49 C201 C202 43 C203 Absent values indicate runoff was not produced for the event by the corresponding lysimeter a Curve numbers were not calculated when less than 100 ml of runoff was produced. Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate]

PAGE 232

232 Table D 20 Cont inued Rainfall Depth (mm) Treatment 50 35 30 25 19 15 12 5 4 Regressed N001 85 69 74 70 76 83 a 94 95 70 N002 84 81 72 88 90 84 93 72 N003 93 74 86 82 90 95 87 96 94 82 N101 63 63 66 72 78 81 86 94 54 N102 53 66 73 84 39 N103 54 68 73 72 79 85 95 54 N201 56 76 68 72 77 81 84 45 N202 59 75 78 75 89 44 N203 65 78 82 44 F101 96 77 89 93 95 97 97 89 F102 96 78 90 93 95 97 97 90 F103 95 84 89 93 93 97 97 94 F201 96 62 87 83 89 95 94 86 F202 94 85 84 87 95 96 82 F203 99 77 88 91 94 96 96 90 C101 54 66 68 70 76 81 86 41 C102 66 74 38 C103 65 75 40 C201 66 75 48 C202 65 75 34 C203 66 74 50 Absent values indicate runoff was not produced for the event by the corresponding lysimeter a Curve numbers were not calculated when less than 100 ml of runoff was produced. Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate]

PAGE 233

233 Table D 21. Calculated curve numbers from amended Orangeburg soils. Rainfall Depth (mm) Treatment 114 77 75 72 72 70 62 67 59 55 N001 74 79 80 89 86 90 89 82 89 89 N002 75 82 81 88 91 90 93 84 88 90 N003 65 80 85 89 88 93 92 86 91 93 N101 35 49 61 76 62 81 60 52 82 67 N102 a 58 70 66 91 55 52 87 67 N103 37 49 59 72 72 86 68 53 92 67 N201 39 52 58 49 49 83 59 N202 47 50 63 68 49 48 78 57 N203 48 54 70 77 49 77 58 F101 58 86 81 92 90 94 95 89 91 93 F102 60 80 87 91 87 92 91 86 88 93 F103 58 82 82 92 83 92 87 90 90 93 F201 55 84 88 96 81 96 94 94 91 94 F202 56 75 84 92 83 92 89 86 89 93 F203 55 82 86 93 86 64 95 85 93 93 C101 35 48 51 55 70 73 56 51 86 59 C102 43 46 56 57 78 C103 46 60 66 64 C201 48 61 68 76 57 47 84 55 C202 46 55 63 75 51 47 71 55 C203 34 47 64 68 73 80 55 48 76 56 Absent values indicate runoff was not produced for the event by the corresponding lysimeter. a Curve numbers were not calculated when less than 100 ml of runoff was produced. Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate]

PAGE 234

234 Table D 21 Cont inued. Rainfall Depth (mm) Treatment 50 35 30 25 19 15 12 5 4 Regressed N001 b 95 84 87 87 89 95 a 95 86 N002 94 79 86 85 89 95 86 N003 96 88 90 93 94 98 95 90 N101 71 67 81 78 89 95 83 94 68 N102 83 75 83 79 85 95 93 71 N103 88 88 86 88 90 97 84 94 74 N201 88 71 85 84 81 94 60 N202 68 82 77 87 92 94 62 N203 84 101 88 80 93 93 69 F101 98 88 91 94 97 98 96 91 F102 97 75 87 90 90 96 94 88 F103 96 86 90 94 95 97 96 89 F201 98 87 87 95 94 97 96 91 F202 93 80 86 92 91 97 96 87 F203 98 90 90 95 96 98 86 C101 86 79 82 80 91 94 91 95 95 67 C102 58 64 79 69 89 91 94 61 C103 66 82 73 87 88 57 C201 80 91 78 94 93 64 C202 80 63 87 78 91 93 59 C203 81 62 87 74 91 92 64 Absent values indicate runoff was not produced for the event by the corresponding lysimeter a Curve numbers were not calculated when less than 100 ml of runoff was produced. Treatment: [Amendment N: Null; F: Fly Ash; C: Compost][Depth 0 cm, 10 cm, 20 cm][Replicate]

PAGE 235

2 35 Table D 22. Summary of Arredondo calculated curve numbers regressed against inverse rainfall depths. Regression Amendment Incorporation Depth (cm) Replicate Slope Intercept r 2 N 0 1 4.4 70 0.66 N 0 2 5.1 72 0.63 N 0 3 2.7 82 0.67 N 10 1 10.4 54 0.71 N 10 2 29.2 39 0.96 N 10 3 10.1 54 0.69 N 20 1 21.5 45 0.92 N 20 2 22.9 44 0.94 N 20 3 22.8 44 0.90 F 10 1 1.5 89 0.50 F 10 2 1.4 90 0.57 F 10 3 0.4 94 0.02 F 20 1 1.1 86 0.23 F 20 2 2.1 82 0.36 F 20 3 1.0 90 0.27 C 10 1 23.4 41 0.92 C 10 2 28.2 38 0.95 C 10 3 27.1 40 0.98 C 20 1 20.1 48 0.99 C 20 2 32.4 34 0.96 C 20 3 18.1 50 0.99 N: Null; F: Fly Ash; C: Compost

PAGE 236

236 Table D 23. Summary of Orangeburg calculated curve numbers regressed against inverse rainfall depths. Regression Amendment Incorporation Depth (cm) Replicate Slope Intercept r 2 N 0 1 1.7 86 0.50 N 0 2 2.1 86 0.10 N 0 3 1.3 90 0.35 N 10 1 5.9 68 0.62 N 10 2 5.5 71 0.44 N 10 3 4.6 74 0.56 N 20 1 14.7 60 0.60 N 20 2 6.5 62 0.66 N 20 3 9.2 69 0.42 F 10 1 1.2 91 0.34 F 10 2 1.0 88 0.32 F 10 3 1.7 89 0.46 F 20 1 1.1 91 0.43 F 20 2 1.9 87 0.56 F 20 3 6.3 86 0.52 C 10 1 6.7 67 0.67 C 10 2 6.4 61 0.73 C 10 3 17.5 57 0.85 C 20 1 11.4 64 0.62 C 20 2 13.3 59 0.73 C 20 3 9.7 64 0.50 N: Null; F: Fly Ash; C: Compost

PAGE 237

237 Figure D 1 Results from standard proctor density method for Arredondo and Orangeburg soil samples. Figure D 2 Curve number regression for Arredondo Null incorporation at 0 cm. 1.25 1.50 1.75 2.00 0 10 20 30 40Bulk Density (g/cm3)Volumetric Water Content (%) Arredondo Orangeburg 1.77 g/cm3 y = 4.43x + 70.10 y = 5.14x + 71.73 y = 2.66x + 81.52 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AN01 AN02 AN03

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238 Figure D 3 .Curve number regression for Arredondo Null incorporation at 10 cm. Figure D 4 Curve number regression for Arredondo Null incorporation at 20 cm. y = 10.40x + 53.74 y = 29.15x + 39.33 y = 10.08x + 53.88 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AN101 AN102 AN103 y = 21.51x + 44.75 y = 22.95x + 43.68 y = 22.78x + 43.99 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AN201 AN202 AN203

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239 Figure D 5 Curve number regression for Arredondo Fly Ash incorporation at 10 cm. Figure D 6 Curve number regression for Arredondo Fly Ash incorporation at 20 cm. y = 1.43x + 89.14 y = 1.33x + 89.90 y = 0.39x + 93.82 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AF101 AF102 AF103 y = 1.05x + 85.82 y = 2.08x + 82.23 y = 1.01x + 89.93 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm 1) AF201 AF202 AF203

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240 Figure D 7 Curve number regression for Arredondo Compost incorporation at 10 cm. Figure D 8 Curve number regression for Arredondo Compost incorporation at 20 cm. y = 23.42x + 41.19 y = 28.24x + 37.77 y = 27.07x + 39.95 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AC101 AC102 AC103 y = 20.05x + 48.48 y = 32.44x + 33.69 y = 18.15x + 50.42 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) AC201 AC202 AC203

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241 Figure D 9 Curve number regression for Orangeburg Null incorporation at 0 cm. Figure D 10. Curve number regression for Orangeburg Null incorporation at 10 cm. y = 1.64x + 86.30 y = 2.09x + 86.49 y = 1.35x + 89.55 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) ON01 ON02 ON03 y = 5.55x + 67.83 y = 5.55x + 70.98 y = 4.16x + 74.40 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) ON101 ON102 ON103

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242 Figure D 11. Curve number regression for Orangeburg Null incorporation at 20 cm. Figure D 12. Curve number regression for Orangeburg Fly Ash incorporation at 10 cm. y = 14.69x + 60.47 y = 6.51x + 61.86 y = 9.20x + 69.13 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) ON201 ON202 ON203 y = 1.18x + 91.07 y = 1.01x + 88.45 y = 1.68x + 88.90 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) OF101 OF102 OF103

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243 Figure D 13. Curve number regression for Orangeburg Fly Ash incorporation at 20 cm. Figure D 14. Curve number regression for Orangeburg Compost incorporation at 10 cm. y = 1.02x + 91.22 y = 1.85x + 87.07 y = 6.26x + 85.96 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) OF201 OF202 OF203 y = 6.23x + 66.83 y = 6.43x + 60.89 y = 17.47x + 56.71 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) OC101 OC102 OC103

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244 Figure D 15. Curve number regression for Orangeburg Compost incorporation at 20 cm. y = 11.37x + 64.39 y = 13.33x + 59.21 y = 9.74x + 63.97 0 20 40 60 80 100 0.00 1.00 2.00 3.00 4.00 5.00 6.00Curve NumberInverse Rainfall Depth (cm1) OC201 OC202 OC203

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245 APPENDIX E LYSIMETER WATER QUALITY RESULTS Table E 1 Type, date, depth and water quality results for 16 rainfall events on lysimeters. Type Date Depth NH 4 N N O 2+3 N TKN OP pH mm mg/l mg/l mg/l ug/l Natural 10/28/09 4.4 < 0.0 6 < 0. 15 E 0.31 27 7.26 11/25/09 58.8 E 0.14 < 0. 15 E 0.18 -5.00 12/02/09 14.5 E 0.07 < 0. 15 -73 5.09 12/05/09 34.8 E 0.23 < 0. 15 < 0. 13* -5.71 01/17/10 29.5 E 0.11 E 0.17 E 0.26 -5.30 01/22/10 19.2 E 0.11 E 0.22 E 0.24 -4.98 Simulated 09/23/09 114.4 E 0.14 < 0. 15 E 0.39 99 7.80 09/30/09 77.2 E 0.17 E 0.18 E 0.31 107 7.08 10/07/09 61.6 E 0.07 < 0. 15 E 0.19 95 7.00 10/14/09 67.3 E 0.08 < 0. 15 E 0.31 111 7.23 10/21/09 54.7 E 0.12 < 0. 15 E 0.24 103 7.28 11/04/09 75.4 < 0.03 < 0. 15 E 0.43 107 7.08 11/12/09 50.4 E 0.10 < 0. 15 E 0.32 89 7.30 11/18/09 71.6 E 0.14 < 0. 15 E 0.20 53 7.32 11/23/09 69.8 E 0.11 < 0. 15 0.87 89 7.56 01/13/10 71.6 E 0.09 < 0. 15 E 0.24 98 7.26 E: Reported value was between practical quantitation limit and minimum detectable level. *Concentrations less than minimum detectable limit were reported are indicated as less than the respective MDL.

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246 Table E 2 Concentrations from homogenous column samples. NH 4 N NO 2+3 N TKN O rg. N TN OP pH mg/l mg/l mg/l mg/l mg/l ug/l Arredondo E 0.15 5.47 2.31 2.16 7.78 378 6.3 0 E 0.17 7.57 3.48 3.31 11.05 409 6.23 E 0.16 6.46 3.35 3.18 9.81 435 6.1 0 E 0.16 3.64 2.57 2.41 6.21 522 5.83 Compost # -----6642 7.51 -----8663 7.46 -----6613 7.4 0 -----7627 7.33 Orangeburg E 0.36 14.92 0.85 E 0.49 15.77 E 9 6.64 E 0.33 19.45 E 0.31 < 0.13 19.84 15 6.7 0 E 0.40 17.13 E 0.40 < 0.13 17.59 E 8 6.52 E 0.33 8.12 E 0.32 < 0.13 8.51 E 8 6.15 Fly Ash E 0.27 9.18 0.82 0.54 10.00 59 7.9 0 -----88 7.87 E 0.15 0.19 2.77 2.62 2.96 76 8.05 -----81 7.84 Matrix E 0.12 < 0.15 < 0.13 < 0.13 < 0.27 E 9 8.25 E: Reported value was between practical quantitation limit and minimum detectable level. *Concentrations less than minimum detectable limit were reported are indicated as less than the respective MDL. #Values eliminated due to violations of QA/QC.

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247 Table E 3 Column leachate concentrations from Arredondo and compost mixtures. Compost NH 4 N NO 2+3 N TKN ON TN OP pH % mg/l mg/l mg/l mg/l mg/l ug/l 5 0.71 1.68 5.63 4.91 7.3 0 1189 6.63 0.67 4.57 6.74 6.08 11.31 1087 6.65 # -----1088 6.63 -----1071 6.64 10 -----1577 6.92 -----1699 6.88 -----1318 6.88 -----1373 6.69 30 1.14 1.45 26.65 25.51 28.1 0 4848 7.39 -----4553 7.36 -----4450 7.26 -----4908 7.29 #Values eliminated due to violations of QA/QC. Table E 4 Column leachate concentrations from Arredondo and fly ash mixtures. Fly Ash NH 4 N NO 2+3 N TKN ON TN OP pH % mg/l mg/l mg/l mg/l mg/l ug/l 5 E 0.12 5.20 0.61 E 0.49 5.81 204 6.36 E 0.14 3.60 1.12 0.98 4.71 191 6.25 E 0.15 7.34 0.65 0.51 7.99 196 6.43 # ------6.40 10 E 0.18 4.70 2.85 2.66 7.55 181 6.09 E 0.17 4.09 0.60 E 0.43 4.69 168 6.03 E 0.14 6.30 E 0.40 E 0.26 6.70 169 6.05 E 0.19 18.51 1.07 0.87 19.57 208 5.98 30 E 0.16 4.86 0.79 0.63 5.65 160 6.59 E 0.17 1.93 0.87 0.69 2.79 171 6.66 E 0.17 1.70 0.78 0.62 2.48 160 6.88 E 0.18 6.06 0.67 E 0.49 6.73 115 6.70 E: Reported value was between practical quantitation limit and minimum detection level. #Values eliminated due to violations of QA/QC.

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248 Table E 5 Column leachate concentrations from Orangeburg and compost mixtures. Compost NH 4 N NO 2+3 N TKN ON IN TN OP pH % mg/l mg/l mg/l mg/l mg/l mg/l ug/l 5 E 0.19 19.3 1.14 0.95 19.49 20.44 46 6.89 # ------46 6.63 ------29 6.61 E 0.22 15.18 0.66 E 0.44 15.4 15.84 39 6.35 10 E 0.18 2.97 1.67 1.49 3.15 4.64 50 6.63 E 0.14 6.62 0.76 0.62 6.76 7.38 56 6.61 E 0.16 17.97 0.98 0.83 18.13 18.96 47 6.59 E 0.12 17.4 0 0.94 0.81 17.53 18.34 47 6.68 30 E 0.16 23.45 3.57 3.41 23.61 27.02 429 7.44 E 0.19 27.34 5.19 4.99 27.54 32.53 477 7.44 ------477 7.25 E 0.21 27.45 5.05 4.84 27.65 32.49 477 7.22 E: Reported value was between practical quantitation limit and minimum detectable level. #Values eliminated due to violations of QA/QC. Table E 6 Column leachate concentrations from Orangeburg and fly ash mixtures. Compost NH 4 N NO 2+3 N TKN ON IN TN OP pH % mg/l mg/l mg/l mg/l mg/l mg/l ug/l 5 E 0.45 17.75 0.72 E 0.27 18.20 18.47 7 6.83 E 0.46 # -0.70 E 0.23 --7 6.56 E 0.45 17.63 0.97 0.52 18.08 18.60 5 6.78 E 0.39 19.39 0.79 E 0.40 19.78 20.18 3 6.23 10 E 0.45 20.36 < 0.13 <0.13 20.81 20.87 21 6.70 E 0.14 18.35 --18.48 -14 6.38 E 0.17 13.57 0.68 0.52 13.74 14.25 12 6.04 E 0.15 10.23 0.53 E 0.38 10.39 10.76 14 6.16 30 E 0.30 10.42 0.89 0.59 10.72 11.31 22 6.93 ------21 6.70 E 0.28 11.48 0.97 0.68 11.76 12.44 20 6.68 ------16 6.67 E: Reported value was between practical quantitation limit and minimum detectable level. #Values eliminated due to violations of QA/QC.

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249 Table E 7 Arredondo NH4N concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 E 0.21 E 0.14 E 0.14 <0.06 E 0.08 <0.06 E 0.08 E 0.09 N02 E 0.17 E 0.17 E 0.16 <0.06 E 0.07 E 0.07 <0.06 E 0.08 N03 E 0.29 E 0.21 <0.06 E 0.07 E 0.18 <0.06 E 0.2 0 E 0.1 0 N101 E 0.19 E 0.15 E 0.07 <0.06 E 0.07 <0.06 E 0.09 N102 <0.06 <0.06 <0.06 <0.06 E 0.09 N103 E 0.18 E 0.14 E 0.19 <0.06 E 0.11 <0.06 <0.06 E 0.09 N201 <0.06 <0.06 <0.06 <0.06 <0.06 N202 <0.06 E 0.1 0 N203 <0.06 <0.06 E 0.08 F101 E 0.18 E 0.15 <0.06 <0.06 E 0.11 <0.06 <0.06 E 0.09 F102 <0.06 <0.06 E 0.14 <0.06 E 0.2 0 F103 E 0.15 E 0.13 <0.06 <0.06 <0.06 <0.06 <0.06 E 0.09 F201 E 0.18 E 0.14 E 0.34 <0.06 E 0.07 <0.06 <0.06 E 0.08 F202 E 0.1 0 F203 E 0.2 0 E 0.14 E 0.16 <0.06 E 0.14 <0.06 E 0.1 0 C101 C102 E 0.13 E 0.08 <0.06 E 0.13 <0.06 E 0.07 C103 E 0.17 <0.06 C201 E 0.07 <0.06 E 0.09 <0.06 E 0.09 C202 E 0.15 E 0.14 E 0.07 E 0.06 E 0.12 <0.06 <0.06 E 0.09 C203 E 0.12 E 0.27 E 0.07 E 0.1 0 <0.06 <0.06 E 0.1 0 Leachate Samples N01 E 0.14 N02 E 0.12 N03 E 0.10 E 0.08 <0.06 <0.06 <0.06 <0.06 <0.06 N101 E 0.11 E 0.09 <0.06 <0.06 <0.06 <0.06 <0.06 N102 E 0.1 0 E 0.1 0 <0.06 <0.06 <0.06 <0.06 <0.06 N103 E 0.09 N201 E 0.1 0 E 0.08 <0.06 <0.06 <0.06 <0.06 <0.06 N202 E 0.10 E 0.10 <0.06 < 0.06 E 0.11 <0.06 <0.06 N203 E 0.11 E 0.08 <0.06 E 0.12 <0.06 <0.06 E 0.08 F101 E 0.12 E 0.1 0 <0.06 E 0.12 F102 E 0.11 E 0.09 E 0.19 <0.06 E 0.11 <0.06 E 0.11 F103 E 0.08 <0.06 F201 E 0.10 E 0.08 <0.06 F202 E 0.1 0 E 0.09 E 0.1 0 E 0.18 <0.06 <0.06 E 0.06 F203 E 0.12 E 0.07 E 0.15 <0.06 E 0.12 <0.06 E 0.07 C101 E 0.11 <0.06 E 0.06 <0.06 E 0.07 C102 E 0.15 E 0.1 0 E 0.19 <0.06 E 0.09 <0.06 E 0.07 C103 E 0.11 E 0.12 <0.06 E 0.11 E 0.12 <0.06 E 0.08 C201 E 0.13 <0.06 E 0.08 < 0.06 E 0.1 0 <0.06 <0.06 C202 E 0.08 C203 E 0.14 Rainfall E 0.14 E 0.17 E 0.07 E 0.08 E 0.12 <0.06 <0.06 E 0.1 0

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250 Table E 7 Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 E 0.1 0 <0.06 <0.06 E 0.07 E 0.23 E 0.28 E 0.21 <0.06 N02 E 0.09 <0.06 < 0.06 E 0.07 E 0.21 E 0.09 <0.06 <0.06 N03 E 0.1 0 E 0.06 <0.06 E 0.06 E 0.26 E 0.08 <0.06 <0.06 N101 E 0.12 E 0.08 E 0.12 E 0.08 E 0.12 <0.06 <0.06 N102 E 0.10 E 0.07 <0.06 E 0.08 E 0.24 E 0.13 <0.06 <0.06 N103 E 0.1 0 E 0.07 <0.06 E 0.07 E 0.21 E 0.09 <0.06 <0.06 N201 E 0.1 0 E 0.08 <0.06 E 0.07 E 0.14 <0.06 <0.06 N202 E 0.09 <0.06 E 0.08 <0.06 N203 E 0.07 E 0.07 <0.06 E 0.06 E 0.24 E 0.15 <0.06 <0.06 F101 E 0.11 E 0.07 <0.06 E 0.08 E 0.19 E 0.15 <0.06 <0.06 F102 E 0.1 0 E 0.08 <0.06 E 0.08 E 0.11 E 0.21 <0.06 F103 E 0.11 E 0.08 <0.06 <0.06 E 0.16 E 0.11 <0.06 <0.06 F201 E 0.09 E 0.07 <0.06 E 0.07 E 0.21 E 0.08 <0.06 E 0.19 F202 E 0.08 <0.06 E 0.07 E 0.23 E 0.15 <0.06 E 0.21 F203 E 0.11 E 0.08 <0.06 E 0.08 E 0.23 E 0.13 <0.06 <0.06 C101 C102 E 0.08 <0.06 <0.06 E 0.07 E 0.36 <0.06 <0.06 C103 E 0.11 E 0.08 C201 E 0.08 E 0.07 <0.06 E 0.06 E 0.32 E 0.14 <0.06 <0.06 C202 E 0.09 E 0.08 <0.06 E 0.06 E 0.10 E 0.08 <0.06 <0.06 C203 E 0.09 E 0.09 E 0.06 E 0.07 E 0.29 E 0.13 E 0.28 <0.06 Leachate Samples N01 E 0.07 E 0.25 <0.06 N02 E 0.07 E 0.21 <0.06 N03 E 0.07 <0.06 <0.06 E 0.20 <0.06 <0.06 <0.06 N101 E 0.08 E 0.07 <0.06 E 0.07 0.56 <0.06 <0.06 N102 E 0.06 <0.06 <0.06 <0.06 E 0.19 <0.06 <0.06 N103 E 0.08 E 0.21 N201 <0.06 <0.06 <0.06 <0.06 E 0.23 <0.06 <0.06 <0.06 N202 E 0.08 <0.06 E 0.06 E 0.07 E 0.20 <0.06 <0.06 <0.06 N203 <0.06 <0.06 <0.06 <0.06 E 0.31 <0.06 <0.06 <0.06 F101 <0.06 <0.06 E 0.17 <0.06 F102 E 0.06 <0.06 <0.06 <0.06 E 0.23 E 0.25 <0.06 <0.06 F103 E 0.07 E 0.2 0 E 0.18 E 0.21 E 0.07 <0.06 F201 <0.06 <0.06 E 0.23 <0.06 F202 E 0.09 <0.06 <0.06 E 0.08 E 0.25 <0.06 <0.06 <0.06 F203 <0.06 <0.06 <0.06 <0.06 E 0.26 <0.06 <0.06 C101 E 0.12 E 0.08 E 0.08 E 0.15 E 0.23 E 0.08 <0.06 <0.06 C102 E 0.07 E 0.08 E 0.08 E 0.09 E 0.31 E 0.09 <0.06 <0.06 C103 E 0.10 E 0.08 E 0.07 E 0.08 E 0.18 E 0.08 <0.06 <0.06 C201 E 0.06 <0.06 <0.06 E 0.07 E 0.3 0 <0.06 <0.06 <0.06 C202 <0.06 E 0.13 C203 E 0.07 E 0.25 <0.06 Rainfall E 0.14 E 0.11 E 0.14 E 0.07 E 0.23 E 0.09 E 0.11 E 0.11

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251 Table E 8 Orangeburg NH4N concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 E 0.34 E 0.13 E 0.07 E 0.08 E 0.09 <0.06 <0.06 E 0.1 0 N02 E 0.12 E 0.14 E 0.12 <0.06 E 0.09 N03 E 0.08 N101 E 0.15 E 0.14 E 0.14 E 0.1 0 E 0.14 <0.06 <0.06 E 0.09 N102 E 0.10 N103 N201 N202 E 0.12 E 0.13 E 0.28 E 0.1 0 E 0.15 <0.06 E 0.09 N203 F101 E 0.14 E 0.1 0 E 0.13 <0.06 E 0.09 F102 E 0.16 E 0.14 E 0.13 <0.06 <0.06 <0.06 E 0.08 F103 E 0.17 E 0.2 0 E 0.2 0 E 0.22 E 0.13 <0.06 <0.06 E 0.09 F201 E 0.12 E 0.13 E 0.13 E 0.08 E 0.13 <0.06 <0.06 E 0.09 F202 E 0.12 E 0.13 E 0.12 E 0.08 E 0.14 E 0.06 E 0.09 F203 E 0.12 E 0.14 E 0.14 E 0.08 E 0.12 <0.06 <0.06 E 0.09 C101 E 0.07 C102 E 0.17 E 0.15 E 0.12 <0.06 E 0.06 <0.06 <0.06 E 0.09 C103 E 0.09 E 0.19 <0.06 E 0.1 0 C201 E 0.2 0 E 0.14 E 0.3 0 <0.06 E 0.06 E 0.08 E 0.09 E 0.1 0 C202 C203 E 0.34 E 0.13 E 0.07 E 0.08 E 0.09 <0.06 <0.06 E 0.1 0 Leachate Samples N01 E 0.11 E 0.08 <0.06 <0.06 E 0.09 <0.06 <0.06 N02 E 0.1 0 E 0.12 <0.06 N03 E 0.10 E 0.36 E 0.40 <0.06 <0.06 <0.06 < 0.06 N101 E 0.1 0 E 0.11 <0.06 E 0.12 E 0.12 <0.06 <0.06 N102 E 0.11 <0.06 E 0.13 <0.06 <0.06 <0.06 <0.06 N103 E 0.09 N201 E 0.1 0 <0.06 E 0.12 <0.06 <0.06 <0.06 <0.06 N202 E 0.10 E 0.12 <0.06 E 0.10 E 0.14 <0.06 E 0.07 N203 E 0.11 <0.06 E 0.13 <0.06 <0.06 <0.06 E 0.07 F101 F102 E 0.12 E 0.1 0 E 0.13 E 0.24 <0.06 <0.06 F103 E 0.08 E 0.14 E 0.13 E 0.12 E 0.11 <0.06 <0.06 F201 E 0.09 <0.06 F202 E 0.1 0 F203 E 0.12 E 0.11 E 0.14 <0.06 E 0.12 <0.06 C101 E 0.08 C102 E 0.1 0 E 0.08 E 0.14 E 0.09 <0.06 <0.06 C103 E 0.09 E 0.14 E 0.09 E 0.08 E 0.13 <0.06 <0.06 C201 E 0.1 0 E 0.08 C202 E 0.09 E 0.11 E 0.08 E 0.07 E 0.17 <0.06 E 0.07 C203 E 0.11

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252 Table E 8 Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 E 0.12 E 0.09 <0.06 E 0.06 E 0.25 E 0.1 0 <0.06 <0.06 N02 E 0.11 E 0.1 0 < 0.06 E 0.07 E 0.14 E 0.1 0 <0.06 <0.06 N03 E 0.1 0 E 0.07 E 0.08 E 0.22 E 0.17 <0.06 <0.06 N101 E 0.1 0 E 0.08 <0.06 <0.06 E 0.11 E 0.08 E 0.3 0 <0.06 N102 E 0.13 E 0.09 <0.06 E 0.07 E 0.12 E 0.13 <0.06 N103 N201 <0.06 N202 E 0.11 E 0.08 E 0.08 E 0.16 E 0.11 E 0.07 <0.06 <0.06 N203 E 0.08 E 0.14 <0.06 F101 E 0.11 E 0.06 <0.06 E 0.07 E 0.18 <0.06 <0.06 F102 E 0.1 0 E 0.07 <0.06 E 0.07 E 0.21 E 0.08 <0.06 <0.06 F103 E 0.1 0 E 0.07 E 0.06 E 0.07 E 0.18 E 0.09 <0.06 <0.06 F201 E 0.11 E 0.08 <0.06 E 0.07 E 0.23 E 0.09 <0.06 <0.06 F202 E 0.12 E 0.07 <0.06 E 0.07 E 0.16 E 0.08 <0.06 <0.06 F203 E 0.1 0 E 0.07 <0.06 E 0.06 E 0.23 E 0.07 <0.06 <0.06 C101 E 0.07 <0.06 E 0.12 E 0.32 <0.06 C102 E 0.12 E 0.07 <0.06 E 0.07 E 0.21 E 0.07 < 0.06 E 0.16 C103 E 0.11 E 0.15 E 0.08 E 0.07 E 0.1 0 E 0.2 0 <0.06 <0.06 C201 E 0.08 <0.06 <0.06 E 0.07 E 0.24 E 0.12 <0.06 <0.06 C202 C203 E 0.12 E 0.09 <0.06 E 0.06 E 0.25 E 0.1 0 <0.06 <0.06 Leachate Samples N01 <0.06 <0.06 <0.06 <0.06 E 0.28 <0.06 <0.06 <0.06 N02 <0.06 E 0.07 E 0.14 <0.06 <0.06 N03 E 0.08 <0.06 <0.06 E 0.07 E 0.28 E 0.23 <0.06 N101 E 0.07 <0.06 <0.06 E 0.11 <0.06 <0.06 <0.06 N102 E 0.08 <0.06 <0.06 E 0.06 E 0.2 0 <0.06 <0.06 E 0.16 N103 <0.06 E 0.12 <0.06 <0.06 N201 E 0.07 <0.06 <0.06 <0.06 E 0.23 <0.06 <0.06 <0.06 N202 E 0.10 E 0.09 E 0.07 E 0.09 E 0.24 E 0.28 <0.06 <0.06 N203 E 0.09 E 0.08 E 0.09 E 0.08 E 0.26 <0.06 <0.06 <0.06 F101 <0.06 <0.06 E 0.2 0 <0.06 <0.06 F102 E 0.09 <0.06 <0.06 E 0.06 E 0.22 E 0.07 E 0.23 F103 E 0.09 E 0.07 E 0.07 E 0.2 0 <0.06 <0.06 <0.06 F201 <0.06 E 0.20 <0.06 F202 <0.06 E 0.17 F203 <0.06 E 0.07 E 0.15 E 0.18 E 0.21 <0.06 <0.06 C101 E 0.06 <0.06 E 0.1 0 <0.06 <0.06 C102 E 0.07 <0.06 <0.06 E 0.22 <0.06 <0.06 C103 E 0.08 E 0.07 <0.06 E 0.07 E 0.09 E 0.07 <0.06 <0.06 C201 <0.06 E 0.24 <0.06 C202 E 0.09 E 0.07 <0.06 E 0.07 E 0.1 0 E 0.28 <0.06 <0.06 C203 <0.06 E 0.23

PAGE 253

253 Table E 9 Arredondo NO2+3N concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N02 E 0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N03 <0.15 <0.15 E 0.18 <0.15 < 0.15 <0.15 <0.15 <0.15 N101 <0.15 N102 <0.15 N103 <0.15 N201 <0.15 <0.15 <0.15 <0.15 <0.15 N202 <0.15 <0.15 N203 F101 <0.15 E 0.46 <0.15 E 0.35 E 0.38 E 0.31 E 0.45 <0.15 F102 <0.15 E 0.26 <0.15 <0.15 <0.15 < 0.15 E 0.27 <0.15 F103 <0.15 E 0.16 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F201 <0.15 E 0.17 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F202 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F203 <0.15 E 0.15 <0.15 <0.15 <0.15 <0.15 E 0.15 <0.15 C101 <0.15 <0.15 < 0.15 <0.15 <0.15 <0.15 C102 1.09 E 0.48 C103 C201 C202 C203 Leachate Samples N01 13.76 14.62 15.39 15.24 17.60 13.81 15.42 N02 15.90 19.39 J 22.32 17.63 18.42 13.28 16.63 N03 22.40 22.30 25.80 24.98 26.29 27.54 N101 32.03 24.71 17.74 14.14 14.46 13.04 14.97 N102 25.11 26.69 18.60 13.55 12.22 10.26 14.32 N103 23.52 23.06 15.49 13.81 14.00 11.44 12.98 N201 20.62 23.32 15.99 13.20 12.74 13.65 13.74 N202 38.72 27.48 17.90 13.54 14.16 12.41 14.41 N203 30.38 29.63 18.35 14.03 15.45 13.75 F101 24.72 F102 28.59 F103 10.05 F201 28.57 23.18 20.98 17.88 F202 31.73 32.95 27.71 F203 25.61 C101 15.42 13.84 J 11.07 9.38 9.60 8.32 12.92 C102 35.94 23.46 19.93 14.51 14.35 12.97 14.65 C103 28.41 32.93 27.37 20.77 21.88 19.76 24.48 C201 33.39 25.58 17.52 12.34 13.41 11.91 15.01 C202 31.35 19.65 18.05 16.13 20.24 C203 29.71 21.29 15.31 11.33 13.25 11.30 17.73 Rainfall <0.15 E 0.18 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15

PAGE 254

254 Table E 9 Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N02 <0.15 <0.15 E 0.18 < 0.15 <0.15 E 0.16 <0.15 N03 <0.15 0.04 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N101 <0.15 <0.15 <0.15 <0.15 E 0.26 <0.15 <0.15 N102 <0.15 <0.15 E 0.24 <0.15 <0.15 N103 <0.15 <0.15 <0.15 <0.15 E 0.27 <0.15 <0.15 N201 <0.15 <0.15 <0.15 <0.15 <0.15 < 0.15 <0.15 <0.15 N202 <0.15 <0.15 <0.15 <0.15 N203 <0.15 E 0.16 <0.15 F101 <0.15 <0.15 <0.15 <0.15 <0.15 Y E 0.21 <0.15 <0.15 F102 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F103 <0.15 <0.15 E 0.21 <0.15 <0.15 E 0.36 <0.15 <0.15 F201 < 0.15 <0.15 <0.15 <0.15 <0.15 E 0.19 <0.15 <0.15 F202 <0.15 <0.15 <0.15 <0.15 E 0.16 <0.15 <0.15 F203 <0.15 <0.15 E 0.18 <0.15 E 0.21 E 0.16 <0.15 <0.15 C101 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 C102 E 0.32 E 0.29 C103 C201 C202 C203 <0.15 Leachate Samples N01 15.17 14.85 8.30 4.76 4.03 1.48 5.03 N02 16.78 15.05 11.20 4.44 3.73 6.58 6.82 N03 25.64 20.36 12.89 10.58 6.93 7.00 7.33 N101 12.94 8.02 4.29 2.71 2.57 4.23 7.68 7.52 N102 11.76 8.91 6.60 2.61 2.65 Y 2.80 7.99 8.57 N103 12.27 9.61 5.63 3.54 2.90 3.83 7.18 7.31 N201 12.77 8.63 3.67 3.13 2.78 4.33 5.19 5.69 N202 10.62 5.88 5.21 3.38 3.19 5.62 7.68 7.73 N203 13.43 8.47 4.23 2.52 2.60 3.99 4.74 F101 54.88 J 67.66 Y F102 46.26 J 61.92 F103 11.85 J 35.60 17.87 F201 20.40 29.69 39.53 20.76 F202 8.26 20.58 38.85 26.14 23.21 F203 44.49 J 48.36 46.47 C101 12.54 9.89 6.28 3.62 3.55 3.61 9.72 11.83 C102 13.58 9.95 5.97 4.84 4.61 6.80 13.23 13.15 C103 22.61 17.63 11.16 6.88 6.74 6.17 12.84 14.02 J C201 13.67 11.00 2.83 4.68 3.88 6.30 13.63 13.88 C202 20.31 14.62 7.53 6.11 5.30 6.36 13.54 13.91 C203 15.80 13.26 8.27 5.15 5.30 6.79 13.35 14.41 Rainfall <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 E 0.17 E 0.22

PAGE 255

255 Table E 10 Orangeburg NO2+3N concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 E 0.15 E 0.18 <0.15 E 0.2 <0.15 <0.15 <0.15 <0.15 N02 <0.15 E 0.24 <0.15 E 0.37 E 0.29 <0.15 <0.15 N03 <0.15 <0.15 <0.15 E 0.3 E 0.2 0 <0.15 <0.15 <0.15 N101 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N102 E 0.18 <0.15 <0.15 <0.15 <0.15 N103 <0.15 E 0.17 E 0.15 <0.15 E 0.16 0.66 <0.15 <0.15 N201 <0.15 <0.15 <0.15 <0.15 <0.15 N202 <0.15 <0.15 <0.15 <0.15 <0.15 N203 <0.15 <0.15 <0.15 <0.15 F101 <0.15 E 0.19 E 0.22 E 0.17 E 0.25 <0.15 <0.15 <0.15 F102 <0.15 E 0.15 <0.15 <0.15 E 0.15 <0.15 Y <0.15 <0.15 F103 <0.15 E 0.25 <0.15 <0.15 <0.15 < 0.15 <0.15 F201 <0.15 E 0.23 <0.15 <0.15 E 0.17 <0.15 <0.15 <0.15 F202 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F203 <0.15 <0.15 <0.15 <0.15 0.05 <0.15 <0.15 C101 E 0.18 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 C102 <0.15 <0.15 <0.15 C103 <0.15 C201 <0.15 0.56 E 0.17 <0.15 <0.15 C202 E 0.21 0.50 <0.15 <0.15 <0.15 C203 <0.15 E 0.2 E 0.18 0.61 <0.15 <0.15 <0.15 Leachate Samples N01 12.11 13.51 N02 N03 7.22 N101 3.60 4.44 4.74 4.49 5.43 3.91 5.10 N102 8.46 7.17 7.09 6.37 6.65 6.42 6.94 N103 9.24 11.51 11.63 10.31 10.02 8.25 N201 5.83 5.85 5.06 5.79 6.91 4.98 7.15 N202 6.95 5.93 6.45 6.21 6.92 5.30 6.78 N203 13.44 9.37 10.46 8.01 8.05 6.79 7.66 F101 7.84 F102 4.15 F103 7.16 <0.15 F201 9.28 11.22 F202 7.16 J 7.82 8.14 F203 8.22 C101 5.08 4.57 4.36 5.58 6.72 5.76 8.19 C102 6.89 7.18 7.74 6.51 6.62 4.43 5.65 C103 7.48 6.72 7.56 7.85 8.13 6.87 8.42 C201 5.70 5.44 5.97 5.10 5.89 6.17 7.87 C202 15.25 12.25 14.19 10.63 10.01 10.14 12.67 C203 9.50 7.63 8.21 7.43 8.96 8.01 10.28

PAGE 256

256 Table E 10. Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N02 <0.15 <0.15 <0.15 < 0.15 <0.15 <0.15 <0.15 <0.15 N03 <0.15 <0.15 <0.15 <0.15 <0.15 Y <0.15 <0.15 <0.15 N101 <0.15 <0.15 <0.15 <0.15 E 0.24 <0.15 <0.15 <0.15 N102 <0.15 <0.15 <0.15 <0.15 E 0.15 <0.15 <0.15 <0.15 N103 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 N201 < 0.15 <0.15 <0.15 <0.15 <0.15 Y <0.15 <0.15 N202 <0.15 <0.15 <0.15 <0.15 E 0.21 <0.15 <0.15 N203 <0.15 <0.15 <0.15 <0.15 <0.15 Y <0.15 <0.15 <0.15 F101 <0.15 <0.15 <0.15 <0.15 <0.15 E 0.25 <0.15 <0.15 F102 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 < 0.15 F103 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F201 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 F202 <0.15 <0.15 <0.15 <0.15 <0.15 E 0.17 <0.15 <0.15 F203 <0.15 <0.15 E 0.15 <0.15 E 0.26 Y <0.15 <0.15 <0.15 C101 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 C102 E 0.17 <0.15 <0.15 <0.15 <0.15 E 0.23 <0.15 <0.15 C103 E 0.34 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 C201 <0.15 <0.15 <0.15 <0.15 E 0.17 <0.15 <0.15 C202 <0.15 <0.15 <0.15 <0.15 <0.15 E 0.30 <0.15 <0.15 C203 <0.15 <0.15 < 0.15 <0.15 <0.15 <0.15 <0.15 <0.15 Leachate Samples N01 14.63 12.86 11.35 8.90 7.31 7.02 N02 2.22 2.54 8.63 5.76 5.04 N03 4.66 11.53 N101 5.66 6.17 5.83 5.39 3.17 3.66 3.83 N102 6.80 6.93 6.05 6.02 4.99 4.07 3.96 N103 9.22 8.79 2.95 7.91 5.11 4.63 N201 6.79 6.31 5.98 4.57 Y 2.74 2.99 3.30 N202 6.37 5.73 4.71 4.61 4.88 3.04 2.92 2.76 N203 7.11 5.80 4.65 4.36 4.33 Y 3.14 3.27 2.86 F101 2.48 14.02 F102 5.47 10.08 10.83 10.16 F103 6.37 10.83 12.64 F201 9.18 14.88 12.91 F202 7.40 10.58 11.61 10.17 F203 11.87 12.50 14.73 Y 14.13 12.12 C101 6.46 7.93 8.35 8.64 8.28 4.47 7.56 7.00 C102 5.88 5.51 4.05 3.22 3.10 2.22 4.28 4.12 C103 8.78 7.39 6.13 5.49 4.91 3.93 5.52 4.94 C201 8.27 9.12 9.81 7.24 8.18 6.21 7.88 7.45 C202 12.57 11.47 9.01 7.55 7.85 6.01 11.09 C203 10.33 10.15 8.81 7.88 7.58 5.04 7.74

PAGE 257

257 Table E 11 Arredondo TKN concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 1.86 0.51 1.19 3.35 J 0.64 0.9 0 E 0.48 0.52 N02 0.81 E 0.43 0.73 1.02 0.75 E 0.49 0.57 N03 0.69 E 0.44 J 1.15 0.87 0.78 J 0.94 0.77 0.71 N101 1.06 J N102 0.78 N103 E 0.42 N201 E 0.47 0.76 0.65 E 0.31 E 0.28 N202 2.86 1.97 N203 F101 1.86 J 1.64 8.85 8.8 0 3.24 3.66 2.66 1.34 F102 2.75 1.12 4.51 J 4.41 4.02 4.01 4.32 1.01 F103 2.27 J 0.82 J 3.33 3 .00 2.4 0 4.26 4.87 1.22 J F201 1.88 0.63 1.34 2.39 1.89 2.51 1.31 0.59 F202 12.06 5.26 4.1 0 2.39 3.53 J 1.73 1.31 F203 2.33 1.45 6.01 3.71 2.38 6.39 2.07 0.66 C101 0.52 1.22 0.52 0.61 E 0.35 E 0.39 C102 3.08 1.18 J C103 C201 C202 C203 Leachate Samples N01 0.81 0.61 0.53 J 0.68 J 0.87 0.95 0.55 N02 1.04 0.81 0.62 0.81 0.62 E 0.47 0.8 0 N03 0.98 0.91 0.86 J 0.89 0.89 0.60 N101 1.21 0.9 0 0.92 0.79 1.12 0.65 0.75 N102 0.86 0.71 0.75 0.76 0.88 0.58 0.71 N103 0.86 0.71 J 0.64 0.92 0.66 0.8 0 0.59 N201 0.91 0.79 0.66 0.64 0.55 J 0.55 0.85 N202 0.82 0.79 0.6 0.78 0.62 J 0.83 0.76 N203 0.83 0.92 0.63 0.82 0.66 0.9 0 F101 0.86 F102 1.03 J F103 1.07 F201 1.09 J 0.75 0.64 1.09 F202 0.82 J 0.92 J 0.67 F203 0.94 C101 1.29 2.08 2.35 2.95 2.82 3.19 4.11 C102 1.13 1.95 2.15 2.62 3.65 3.37 3.56 C103 1.01 1.92 J 1.53 2.71 0.82 2.79 3.25 C201 1.66 2.36 2.75 3.64 3.73 6.06 3.75 C202 1.17 J 3.12 4.32 J 4.21 4.84 C203 1.22 J 0.94 2.51 3.26 3.82 3.67 3.92 Rainfall E 0.39 E 0.31 E 0.19 E 0.31 E 0.24 E 0.31 E 0.43 E 0.32

PAGE 258

258 Table E 11. Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 E 0.23 0.59 0.51 0.72 <0.13 Q 1.95 1.66 J 1.74 N02 E 0.42 E 0.41 2.86 7.54 1.98 11.25 15.04 N03 E 0.31 0.62 J E 0.45 3.68 E 0.26 Q 3.85 6.58 9.46 N101 1.77 1.4 0 0.96 J E 0.43 Q 1.52 0.58 0.62 N102 1.21 J 2.75 2.47 0.59 11.74 N103 0.59 J 1.02 1.58 <0.13 Q 1.04 0.56 1.45 N201 E 0.35 0.87 <0.13 0.55 E 0.14 Q 4.01 E 0.48 0.77 N202 1.03 3.02 1.79 6.21 N203 4.78 1.26 6.8 F101 1.38 1.82 0.96 1.78 1.43 Q,Y,J 1.7 6.14 9.11 F102 1.3 0 0.88 E 0.41 1.38 0.63 Q 2.08 3.19 7.23 F103 1.2 J 0.74 J 1.4 0 J 1.84 <0.13 Q 1.06 2.44 5.64 F201 0.59 J E 0.43 <0.13 5.15 E 0.32 Q 1.59 7.16 6.15 F202 1.09 0.85 E 0.34 4.01 1.27 5.43 7.58 F203 0.84 0.66 E 0.26 1.52 0.54 Q 1.03 4.47 3.01 C101 E 0.26 0.83 E 0.14 0.55 E 0.22 Q E 0.4 0.64 C102 3.43 1.69 C103 C201 C202 C203 1.2 Leachate Samples N01 0.65 1.02 0.63 0.8 0.71 Q 0.75 J 0.84 N02 0.73 1.33 0.66 0.81 1.17 Q 0.86 1.05 J N03 1.15 0.69 1.1 J 0.97 Q 1.1 1.02 1.28 N101 0.66 1.5 0.64 0.99 0.6 Q 0.66 0.74 J 0.8 N102 0.7 0.95 0.8 0.72 0.64 Q,Y 0.75 0.71 0.86 N103 0.72 1.05 0.56 0.88 0.61 Q 0.69 0.74 1.17 J N201 0.74 1.33 E 0.49 0.76 0.78 Q 0.72 0.81 0.92 N202 0.72 0.9 0.63 0.66 0.78 Q 0.72 0.85 1.01 J N203 0.92 1.12 0.68 0.83 0.66 Q 0.85 0.73 F101 E 0.5 E 0.47 Q,Y F102 0.88 0.77 Q F103 0.61 Q 0.64 F201 1.21 E 0.48 0.59 Q 0.71 F202 <0.13 J E 0.48 E 0.47 Q 0.52 0.74 F203 0.66 0.82 Q 0.55 C101 3.73 5.25 5.44 4.23 4.23 Q 3.53 3.66 4.33 C102 4.32 4.92 4.34 3.53 3.11 Q 3.43 3.04 3.74 C103 2.93 3.82 3.57 3.44 3.56 Q 3.41 1.54 3.49 C201 4.27 5.11 2.4 3.32 3.18 Q 3.86 3.31 2.02 C202 3.96 5.69 4.62 4.09 3.75 Q 3.85 2.55 3.28 C203 4.26 6.68 6.77 4.77 5.51 Q 4.2 3.97 4.39 Rainfall E 0.2 0.87 E 0.18 E 0.36 J <0.13 Q E 0.24 E 0.26 E 0.24

PAGE 259

259 Table E 12 Orangeburg TKN concentrations (mg/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 E 0.42 E 0.4 0.81 0.95 0.65 1.6 0.76 E 0.42 N02 <0.13 0.52 0.6 0.79 <0.13 0.58 J E 0.42 N03 0.6 E 0.46 1.03 1.06 1.39 1.77 1.15 0.66 N101 0.87 0.96 J E 0.24 0.58 1.22 1.25 2.16 0.69 N102 0.59 E 0.16 1 0.53 E 0.4 N103 E 0.48 0.6 0.79 1.26 1.28 1.25 2.25 E 0.25 N201 E 0.27 1.1 0.73 J 0.69 E 0.47 N202 0.7 1.59 0.75 1.12 0.73 N203 1.35 1.1 1.31 E 0.37 F101 1.36 E 0.41 2.27 1.22 0.92 1.18 1.15 0.5 F102 1.2 E 0.47 0.9 1.16 E 0.16 J 1.1 Y <0.13 E 0.47 F103 0.85 E 0.47 1.48 1.16 0.87 0.71 E 0.31 F201 0.52 0.55 1.17 1.5 0.84 1.13 0.78 E 0.44 F202 0.75 E 0.4 J 0.85 J 1.09 0.77 0.74 J 1 E 0.34 F203 1.4 E 0.48 0.9 1.22 1.37 1.19 0.61 C101 E 0.41 E 0.31 E 0.34 E 0.48 0.55 0.75 0.5 C102 1.18 0.83 1.02 C103 E 0.42 C201 1.32 0.78 1.95 0.9 6.6 C202 0.59 J 1.02 2.29 0.73 0.59 C203 0.99 0.78 1.1 1.23 0.87 0.83 J E 0.43 Leachate Samples N01 0.51 0.53 N02 N03 E 0.14 N101 E 0.25 E 0.16 J <0.13 E 0.21 <0.13 E 0.18 <0.13 N102 E 0.28 E 0.17 E 0.14 E 0.19 E 0.2 <0.13 0.57 N103 E 0.26 E 0.14 E 0.15 E 0.18 E 0.13 <0.13 N201 E 0.45 E 0.27 <0.13 E 0.22 E 0.15 <0.13 J E 0.34 N202 E 0.33 E 0.27 J <0.13 E 0.17 <0.13 E 0.18 <0.13 N203 0.63 E 0.35 E 0.49 E 0.31 E 0.16 E 0.28 E 0.41 F101 E 0.17 F102 E 0.22 F103 E 0.23 1.15 F201 E 0.31 <0.13 F202 E 0.2 E 0.19 J E 0.16 F203 E 0.46 C101 0.99 0.93 1.43 1.21 0.83 J 0.5 E 0.36 C102 0.69 0.73 J E 0.27 E 0.36 <0.13 E 0.38 0.8 C103 0.78 0.56 E 0.35 0.52 E 0.33 E 0.39 E 0.26 C201 0.87 0.61 J 0.51 0.7 E 0.48 0.54 J 7.56 C202 0.78 0.75 J <0.13 E 0.44 E 0.3 0.65 0.75 C203 1.07 0.78 0.65 0.65 J 0.57 0.53 1.28

PAGE 260

260 Table E 12. Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 E 0.43 E 0.33 <0.13 2.34 E 0.23 Q E 0.47 0.81 1.85 N02 E 0.29 E 0.44 E 0.28 0.76 E 0.29 Q 0.51 2.95 2.67 N03 0.58 0.66 E 0.38 1.33 <0.13 Q,Y E 0.28 0.66 7.53 N101 E 0.5 0.76 <0.13 0.86 E 0.35 Q 0.52 2 0.93 N102 E 0.31 0.7 J <0.13 1.92 0.58 Q 0.71 3.45 1.79 N103 0.75 J 0.72 0.73 1.58 0.6 Q 0.77 2.33 4.63 N201 0.91 0.98 E 0.32 1.38 E 0.36 Q,Y 4.69 0.92 N202 0.9 0.83 E 0.41 1.67 0.57 1.07 E 0.43 N203 2.25 0.52 0.62 2.15 E 0.46 Q,Y 1.45 0.82 7.96 F101 0.53 0.68 E 0.25 0.56 0.58 Q E 0.41 J 1.52 2.53 F102 0.54 0.8 J E 0.35 1.84 E 0.36 Q E 0.48 1.77 3.18 F103 0.52 0.54 E 0.22 2.15 E 0.27 Q <0.13 1.13 2.33 F201 E 0.37 1.37 <0.13 1.67 E 0.36 Q E 0.31 2.63 2.86 F202 0.5 0.72 E 0.36 0.73 E 0.33 Q 0.57 0.84 2.92 F203 E 0.46 1.41 <0.13 0.96 E 0.42 Q,Y E 0.45 E 0.4 4.25 C101 0.86 1.37 E 0.19 4.5 <0.13 Q 1.21 5.59 13.67 C102 0.62 0.86 1.1 2.1 E 0.45 Q 1 4.67 3.26 C103 1.31 0.7 2.99 2.04 E 0.49 20.57 16.61 C201 2.08 0.81 0.51 2.96 0.73 0.78 3.97 C202 0.81 1.11 J E 0.49 6.44 E 0.31 Q 3.81 4.52 2.71 C203 0.61 0.64 E 0.29 7.58 0.64 Q 1.47 28.19 14.95 Leachate Samples N01 0.8 E 0.33 E 0.25 Q E 0.26 E 0.24 0.56 N02 0.51 <0.13 <0.13 Q E 0.18 E 0.14 J N03 <0.13 <0.13 Q N101 1.17 J 0.62 <0.13 <0.13 Q <0.13 E 0.13 <0.13 N102 <0.13 E 0.41 <0.13 E 0.23 Q E 0.19 <0.13 E 0.25 N103 <0.13 0.52 <0.13 E 0.25 Q E 0.23 E 0.3 N201 E 0.16 0.94 J <0.13 E 0.2 Q,Y <0.13 E 0.23 <0.13 N202 E 0.2 0.54 E 0.2 E 0.22 E 0.19 Q E 0.18 <0.13 <0.13 N203 E 0.41 E 0.32 <0.13 E 0.29 E 0.35 Q,Y 0.52 E 0.36 0.56 F101 E 0.14 E 0.31 Q F102 <0.13 E 0.15 Q E 0.27 E 0.28 F103 <0.13 E 0.15 Q E 0.23 F201 E 0.17 E 0.2 Q E 0.19 F202 E 0.44 <0.13 E 0.16 Q E 0.19 F203 0.55 E 0.25 E 0.13 Q,Y E 0.25 E 0.23 C101 1.02 0.82 J E 0.28 E 0.42 E 0.21 Q E 0.27 E 0.3 E 0.39 C102 E 0.38 0.63 E 0.26 E 0.33 E 0.14 Q E 0.24 E 0.26 E 0.38 J C103 E 0.37 0.81 E 0.25 E 0.48 E 0.31 Q E 0.29 E 0.29 0.56 J C201 E 0.45 0.76 J 0.53 E 0.42 E 0.34 Q E 0.34 E 0.33 E 0.2 C202 0.58 1.02 E 0.47 J E 0.48 E 0.42 Q E 0.5 E 0.39 C203 0.52 0.84 E 0.39 E 0.5 E 0.32 Q E 0.3 E 0.43

PAGE 261

261 Table E 13 Arredondo OP concentrations (ug/l) Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 11/18/09 11/23/09 12/2/09 1/13/10 Runoff Sa mples N01 140.92 130.72 109.87 152.99 118.44 31.57 112.91 J 99.2 85.23 89.05 36.76 116.43 N02 114.16 131 103.78 128.97 111.66 92.8 67.81 86.57 121.52 98.1 N03 88 128.62 112.41 126.9 114.72 19.77 127.34 89.94 55.72 101.13 250.31 102.73 N101 84.31 E 5.98 31.54 6.53 89.06 N102 50.38 63.05 122.33 N103 71.85 22.62 63.47 96.2 J 84.05 N201 79.01 74.25 66.22 63.27 50.18 64.73 63.79 4.04 271.21 N202 135.43 134.3 53.11 195.42 N203 160.35 144.06 F101 405.36 352.53 339.34 905.9 J 55.6 E 8.46 136.38 76.67 24.22 45.03 155.02 234.63 F102 445.17 151.34 J 210.9 528.04 J 54.44 E 7.1 199.57 94.27 10.9 58.42 117.18 198.09 F103 284.44 149.56 J 171.28 172.66 84.39 12.61 J 92.3 J 71.89 88.69 J 83.88 J 115.3 102.29 J F201 410.8 88.02 J 107.41 223.35 93.84 14.53 129.57 75.58 26.84 J 81.45 124.23 207.08 F202 315.11 J 207.59 192.01 78.48 15.26 110.93 68.26 35.06 86.51 148.23 172.99 F203 339.7 394.82 J 228.26 377.03 114 E 6.51 122.67 72.82 16.13 67.29 99.22 117.76 C101 36.76 60.27 136.58 103.54 58.2 38.99 60.86 219.77 3.43 C102 2467.77 977.74 1614.76 1391.16 C103 C201 C202 C203 Leachate Samples N01 189.46 167.5 161.92 165.98 160.53 158.51 142.86 127.37 127.34 157.02 149.68 N02 214.58 171.83 173.64 J 182.64 165.76 158.1 145.18 140.79 144.23 167.11 N03 290.4 265.27 247.84 243.17 246.94 218.04 209.2 238.67 258.02 N101 140.66 134.65 148.08 157.44 162.92 138.24 56.96 137.77 157.59 166.38 157.26 N102 125.75 105.39 117.43 143.78 130.17 122.66 120.46 122.46 120.59 142.84 127.8 N103 171.6 152.71 177.81 186.44 185.49 186.7 164.73 168.06 177.56 198.45 199.09 N201 187.65 158.05 167.77 178.53 179.46 164.25 159.12 156.08 176 215.36 192.4 N202 130.47 142.29 163.83 177.43 173.23 155.49 156.56 175.9 232.12 175.19 164.11 N203 194.2 178.01 204.79 219.65 224.24 199.8 183.34 217.99 243.7 267.15 F101 128.43 F102 400.78 F103 290.38 F201 173.51 160.81 153.08 160.14 128.56 F202 118.52 98.05 99.14 115.64 F203 289.3 C101 173.13 111 101.89 79.89 119.34 125.06 150.72 192.45 318.81 310.31 211.24 C102 170.99 152.63 149.18 171.18 215.95 173.51 17.58 260.86 345.96 281.59 229.83 C103 130.31 86.56 97.69 114.84 137.56 115.78 137.19 134.29 169.21 231.73 206.77 C201 138.65 116.05 140.54 184.53 180.69 273.48 262.93 267.25 350.32 360.49 331.89 J C202 142.26 123.09 195.31 225.29 J 51.58 237.52 428.2 366.05 343.77 C203 173.20 130.94 148.39 183.33 287.67 252.89 296.48 288.3 1408.31 807.56 418.24 Rainfall 83.93 84.19 82.25 86.71 90.11 27.39 86.13 83.33 35.67 86.67 9.37 90.55

PAGE 262

262 Table E 14 Orangeburg OP sample concentrations (ug/l). Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 11/18/09 11/23/09 12/2/09 1/13/10 Runoff Samples N01 40.58 91.73 51.27 60.86 57.3 125.04 86.19 58 29.91 65.25 43.34 J 76.36 N02 49.64 80.6 52.51 77.29 50.05 71.86 56.23 17.12 61.54 55.05 J 71.02 N03 88.02 68.13 53.29 47.16 46.83 59.47 73.13 73.58 13.89 55.5 94.77 82.98 N101 90.13 145.96 96.89 87.49 98.98 14.49 131.34 62.1 341.15 69.41 22.03 J 79.15 N102 43.51 59.93 54.77 65.37 53.8 15.4 67.23 151.09 73.38 N103 23.98 69.11 62.34 61.39 49.31 E 5.84 77.42 53.01 12.29 J 61.73 159.96 J 75.65 N201 174.49 50.37 53 61.5 53.73 E 4.44 38.39 70.78 N202 73.86 75.46 52.22 24.62 47.52 61.85 75.17 75.68 73.15 N203 47.15 56.58 45.36 53.48 E 4.45 63.15 74.24 68.02 F101 256.73 131.9 60.99 43.74 46.01 E 2.97 72.56 49.13 E 8.4 55.11 46.68 77.25 F102 84.22 69.5 77.14 172.79 41.03 E 6.86 75.94 51.99 16.63 58.3 20.45 91.16 F103 160.53 174.28 59.78 43.08 E 3.12 81.72 55.06 13.58 61.5 39.49 79.48 F201 107.41 159.32 61.34 239.74 J 48.46 E 5.11 79.69 J 57.73 21.56 66.57 33.25 81.68 F202 167.40 108.64 82.31 91.8 J 49.64 E 3.33 84.6 51.36 68.73 60.81 130.72 83.13 F203 193.94 74.65 J 70.29 58.09 40.27 71.04 46.49 E 4.54 39.07 218.83 78.14 C101 32.34 J 70.67 70.49 65.85 E 3.49 93.98 98.66 98.4 109.87 307.97 140.13 C102 20.91 55.43 97.07 183.79 114.6 370.76 175.8 C103 104.65 622.86 117.92 312.21 146.47 C201 145.78 124.73 192.01 106.58 93.91 107.45 100.54 344.85 136.06 C202 132.95 134.14 188.44 122.26 92.9 126.42 104.58 286.7 162.75 C203 55.5 122.37 141.37 15 133.08 153.26 92.52 112.87 101.17 287.19 J 113.26 Leachate Samples N01 235.23 239.79 241.66 190.92 N02 11.93 N03 E 9.98 N101 14.79 12.72 11.52 14.08 15.4 13.35 11.37 E 2.77 13.31 11.22 N102 12.95 J <2.5 E 8.32 E 6.33 E 7.33 E 7.2 E 3.48 E 4.52 E 6.18 E 4.35 N103 23.5 17.23 24.4 23.77 23.9 21.7 13.65 17.8 N201 41.43 20.01 26.61 24.71 24.65 26.89 17.86 20.51 22.66 24.19 N202 32.56 J E 9.9 14.79 11.9 14.87 15.42 10.4 10.58 11.89 9.47 11.48 N203 344.87 J 169 306.95 279.33 263.56 262.22 266.63 256.12 J 213.92 209.81 232.39 F101 16.10 J F102 16.07 J F103 E 3.64 33.8 F201 41.40 45.25 F202 E 9.93 E 6.53 E 9.97 E 8.45 F203 46.24 38.37 26.9 C101 <2.50 11.02 61.77 51.34 48.01 11.61 E 5.25 94.2 24.07 8.06 E 7.62 C102 E 4.14 E 8.86 E 8.19 E 8 E 9.07 E 7.34 E 6.89 E 4.54 E 7.45 8.02 E 7.28 C103 <2.50 E 9.73 14.27 10.06 14.65 10.25 E 10 E 2.75 E 9.17 11.89 E 8.86 C201 <2.50 E 6.96 E 9.75 12.25 13.68 E 9.96 E 7.95 E 9.31 E 8.02 8.17 10.02 C202 11.21 15.05 18.84 18.71 22.59 18.55 16.1 16.28 15.54 16.38 31.81 C203 11.71 21.08 15.99 129.44 20.05 17.13 21.41 13.3 14.37 14.8 13.64

PAGE 263

263 Table E 15 Arredondo pH Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 7.88 6.94 7.17 7.48 7.69 7.46 7.44 7.27 N02 7.98 7.30 7.00 7.58 7.66 7.60 N03 8.10 7.03 7.39 7.43 7.59 7.39 7.57 7.52 N101 7.19 7.42 N102 7.21 7.84 N103 6.45 7.53 N201 7.52 7.71 7.64 7.50 7.39 N202 7.87 7.76 N203 6.30 F101 7.62 7.16 7.21 7.46 7.62 7.21 7.27 7.42 F102 8.20 7.32 7.38 7.55 7.62 7.28 7.57 7.47 F103 8.03 6.69 6.77 7.40 7.33 7.18 7.37 7.34 F201 7.82 7.30 7.32 7.50 7.64 7.38 7.68 7.48 F202 8.00 7.57 7.34 7.53 7.77 7.25 7.66 7.56 F203 7.40 7.45 7.56 7.58 7.65 7.65 7.55 C101 8.16 7.65 7.75 7.77 7.50 7.69 C102 6.94 7.49 7.81 C103 7.19 C201 6.90 C202 C203 6.30 Leachate Samples N01 7.71 6.70 6.79 6.35 7.09 6.97 6.79 N02 7.72 6.80 6.61 6.85 7.31 7.07 6.79 N03 7.53 6.89 6.85 6.95 7.15 6.86 N101 7.53 6.62 6.90 7.07 6.97 6.80 N102 7.83 6.91 7.03 7.33 6.88 7.03 N103 7.84 6.64 6.61 6.98 6.66 6.91 N201 7.58 6.52 7.17 7.04 7.29 6.81 6.84 N202 7.52 6.78 6.92 7.22 7.41 7.02 6.86 N203 7.42 6.28 6.39 6.68 6.60 F101 7.68 F102 7.70 F103 7.78 F201 7.56 6.78 6.85 7.19 F202 7.72 7.01 6.78 F203 7.71 C101 7.52 6.44 6.98 6.55 6.75 6.64 6.40 C102 7.55 6.41 6.47 6.96 6.97 6.48 C103 7.70 6.72 6.88 7.19 7.11 6.86 C201 7.62 6.51 6.52 6.95 6.53 6.48 C202 7.70 6.52 6.95 6.70 6.40 C203 7.41 6.25 6.22 6.76 6.59 6.43 Rainfall 7.80 7.08 7.00 7.23 7.28 7.16 7.08 7.30

PAGE 264

264 Table E 15. Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 7.44 7.77 6.42 7.80 7.27 7.45 6.89 6.86 N02 7.59 7.93 6.72 7.07 7.49 7.51 6.14 N03 7.49 7.84 6.85 6.66 7.67 7.50 7.52 6.13 N101 7.87 8.28 8.07 8.53 7.61 7.53 6.69 N102 8.24 7.08 7.57 7.19 6.17 N103 7.82 8.10 7.84 7.33 7.68 6.99 6.77 N201 7.64 8.03 6.46 6.68 6.81 7.29 6.69 6.46 N202 7.95 6.85 8.68 7.22 N203 6.99 7.51 6.66 F101 7.53 7.92 6.87 6.76 6.85 7.43 7.31 6.38 F102 7.60 7.86 6.77 6.96 6.58 7.48 7.41 6.45 F103 7.21 7.30 6.44 6.36 6.31 7.34 7.62 6.82 F201 7.52 7.84 6.70 7.95 7.11 7.42 7.58 6.36 F202 7.61 7.83 6.79 6.81 7.47 7.18 6.39 F203 7.57 7.90 6.88 6.93 6.84 7.27 7.13 6.34 C101 7.57 7.98 6.56 6.91 6.66 6.29 6.78 C102 7.62 8.18 C103 C201 C202 C203 7.18 Leachate Samples N01 7.04 7.04 6.17 7.29 6.89 7.53 6.58 N02 6.75 6.90 6.21 6.64 6.36 6.77 6.24 N03 7.03 6.34 6.63 7.01 7.44 6.71 6.42 N101 7.03 7.15 6.50 7.40 7.69 7.64 7.19 6.62 N102 6.99 7.16 6.67 6.62 6.62 7.60 6.85 6.46 N103 7.02 6.95 6.21 7.29 6.98 7.60 6.67 6.32 N201 6.88 6.92 6.06 6.48 6.43 7.43 6.28 6.04 N202 7.13 6.91 6.35 7.63 6.55 7.63 6.73 6.40 N203 6.44 6.53 6.17 6.52 6.34 7.67 6.59 F101 5.89 5.96 F102 6.28 6.05 F103 6.42 6.52 6.71 F201 7.19 6.25 6.48 6.77 F202 7.39 6.35 6.42 6.78 6.47 F203 6.05 6.08 6.55 C101 6.34 6.21 6.00 6.41 5.98 7.22 6.14 5.89 C102 6.70 6.77 6.45 6.75 6.79 7.13 6.73 6.55 C103 6.74 6.95 6.58 6.55 6.63 7.11 6.85 6.62 C201 6.90 6.72 6.73 6.50 7.29 6.60 6.48 C202 6.64 6.73 6.40 7.12 7.26 7.34 6.85 6.35 C203 6.64 6.51 6.22 6.88 6.60 7.31 6.85 6.19 Rainfall 7.32 7.56 5.00 5.09 5.71 7.26 5.30 4.98

PAGE 265

265 Table E 16 Orangeburg ph. Treatment 9/23/09 9/30/09 10/7/09 10/14/09 10/21/09 10/28/09 11/4/09 11/12/09 Runoff Samples N01 7.98 7.18 7.31 7.56 7.69 7.03 7.59 7.57 N02 7.90 7.28 7.35 7.58 7.64 7.63 7.28 N03 8.05 7.29 7.21 7.51 7.56 7.07 7.55 7.40 N101 8.03 7.20 7.15 7.63 7.51 7.25 7.33 7.47 N102 7.44 7.47 7.71 7.74 7.70 7.58 N103 7.59 7.36 7.54 7.60 7.33 7.62 7.49 N201 7.76 7.76 7.66 7.55 N202 7.98 7.48 7.68 7.66 7.21 7.46 N203 7.72 7.78 7.52 7.55 F101 7.93 7.35 7.16 7.44 7.61 7.37 7.57 7.50 F102 8.17 7.39 7.41 7.59 7.66 7.56 7.69 7.57 F103 7.93 7.29 7.33 7.35 7.60 7.13 7.48 7.55 F201 8.20 7.46 7.37 7.44 7.56 7.31 7.58 7.50 F202 7.88 7.02 7.33 7.47 7.50 7.14 7.56 7.41 F203 8.03 7.04 7.35 7.55 7.64 7.32 7.44 C101 7.47 7.23 7.69 7.05 7.28 7.45 7.33 C102 6.93 7.04 7.51 7.64 C103 6.41 7.48 C201 7.27 7.57 7.62 7.48 7.54 C202 7.48 7.71 7.63 7.44 7.56 C203 8.18 7.42 7.58 7.77 7.67 7.48 7.38 Leachate Samples N01 6.89 7.31 N02 7.91 N03 N101 7.96 6.84 6.91 7.34 7.27 7.08 6.94 N102 7.98 6.95 7.22 7.37 7.39 7.30 7.08 N103 7.84 7.21 7.15 7.28 7.36 7.19 N201 7.91 7.09 7.10 7.43 7.52 7.34 7.18 N202 7.81 6.43 7.33 7.36 7.35 7.15 6.93 N203 7.43 6.87 6.77 7.33 7.42 6.97 6.90 F101 7.93 F102 8.08 F103 8.36 F201 7.91 7.17 F202 8.05 6.68 7.20 F203 7.91 C101 7.58 6.86 6.73 7.08 7.76 7.11 6.71 C102 7.21 6.29 6.80 7.06 6.92 7.17 C103 7.43 6.60 6.61 6.99 6.60 6.73 C201 7.58 6.21 6.94 7.13 7.19 7.00 6.86 C202 7.50 6.43 6.88 7.17 7.17 6.84 6.79 C203 7.80 6.94 6.99 7.15 7.26 7.00 6.90

PAGE 266

266 Table E 16 Continued. Treatment 11/18/09 11/23/09 11/25/09 12/2/09 12/5/09 1/13/10 1/17/10 1/22/10 Runoff Samples N01 7.56 7.87 6.79 7.86 7.03 7.48 7.55 6.03 N02 7.56 7.87 7.06 7.18 7.16 7.40 7.14 6.37 N03 7.54 7.79 6.94 7.20 7.21 7.34 7.02 6.28 N101 7.30 7.47 6.49 6.68 6.52 7.34 7.10 5.18 N102 7.69 7.78 6.88 6.75 6.90 7.54 7.60 6.41 N103 7.63 7.88 6.80 7.18 6.99 7.50 7.45 6.22 N201 7.69 8.00 7.00 6.91 6.93 7.41 6.53 N202 7.41 7.64 6.10 7.72 7.33 6.88 5.39 N203 7.70 7.89 7.03 7.24 6.88 7.47 7.23 6.28 F101 7.55 7.81 6.59 6.98 6.48 7.43 7.40 6.25 F102 7.55 7.91 6.83 6.54 6.71 7.43 7.37 6.39 F103 7.49 7.86 6.24 6.71 6.34 7.42 7.07 6.15 F201 7.56 7.87 6.43 7.02 6.58 7.53 7.27 6.19 F202 7.37 7.67 6.36 6.92 6.61 7.28 6.26 5.90 F203 7.67 7.93 6.57 6.59 6.50 7.51 7.00 6.28 C101 7.49 7.81 6.42 8.05 7.73 7.37 7.00 6.09 C102 7.64 7.67 6.08 7.33 6.20 7.35 6.64 5.84 C103 7.62 7.88 6.29 7.42 7.63 7.19 6.46 C201 7.42 7.64 6.38 7.05 7.23 6.29 5.85 C202 7.30 7.53 6.23 6.53 6.84 7.34 6.54 5.60 C203 7.41 7.69 6.59 6.88 6.51 7.38 6.98 6.05 Leachate Samples N01 7.30 6.52 6.50 7.47 6.74 6.38 N02 7.45 6.51 6.60 7.07 6.75 N03 6.65 6.78 N101 6.56 7.06 6.29 6.33 7.29 7.02 6.44 N102 7.11 7.59 6.44 6.51 7.48 7.17 6.68 N103 7.07 7.28 6.17 6.31 6.89 6.42 N201 7.40 7.64 6.55 6.60 7.51 7.20 6.57 N202 6.76 7.04 6.09 6.99 6.32 7.24 6.48 6.13 N203 7.28 7.31 6.53 6.85 6.43 7.52 6.75 6.58 F101 6.45 6.52 F102 6.87 6.54 7.05 6.70 F103 6.62 6.31 7.02 F201 6.65 6.09 6.89 6.45 F202 7.16 6.33 6.18 6.92 F203 7.42 6.58 6.54 7.45 6.78 C101 6.95 7.04 6.11 7.19 7.09 7.35 6.69 6.46 C102 6.84 6.95 6.12 6.78 6.28 7.27 6.26 6.27 C103 7.02 7.26 6.39 6.90 6.71 7.49 6.87 6.66 C201 6.79 6.90 6.06 6.61 6.27 7.23 6.37 6.39 C202 6.46 6.69 6.05 6.42 6.42 7.29 6.29 C203 6.80 6.93 6.14 6.65 6.22 7.33 6.41

PAGE 267

267 Figure E 1 Arredondo mean runoff NH4N concentrations Figure E 2 Orangeburg mean runoff NH4N concentrations. 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10NH4N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 0.05 0.10 0.15 0.20 0.25 0.30 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NH4N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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268 Figure E 3 Arredondo mean leachate NH4N concentrations Figure E 4 Orangeburg mean leachate NH4N concentrations 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NH4N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 0.05 0.10 0.15 0.20 0.25 0.30 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NH4N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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269 Figure E 5 Arredondo mean runoff NO2+3N Concentrations Figure E 6 Orangeburg mean runoff NO2+3N concentrations 0.00 0.10 0.20 0.30 0.40 0.50 0.60 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NO3N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 0.10 0.20 0.30 0.40 0.50 0.60 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NO3N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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270 Figure E 7 Arredondo mean leachate NO2+3N concentrations Figure E 8 Orangeburg mean leachate NO2+3N concentrations 0.00 10.00 20.00 30.00 40.00 50.00 60.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NO3N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 NO3N Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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271 Figure E 9 Arredondo mean runoff TKN concentrations Figure E 10. Orangeburg mean runo ff TKN concentrations 0.00 2.00 4.00 6.00 8.00 10.00 12.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 TKN Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 2.00 4.00 6.00 8.00 10.00 12.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 TKN Concentrat (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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272 Figure E 11. Arredondo mean leachate TKN concentrations Figure E 12. Orangeburg mean leachate TKN concentration 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 TKN Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 TKN Concentration (mg/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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273 Figure E 13. Arredondo mean runoff OP concentrations Figure E 14. Orangeburg mean runoff OP concentrations 0 200 400 600 800 1000 1200 1400 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 OP Concentration (ug/l)Date 20 N 0 N 10 N 20 F 10 F 20 C 10 Rainfall 0 50 100 150 200 250 300 350 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 OP Concentration (ug/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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274 Figure E 15. Arredondo mean leachate OP concentrations Figure E 16. Orangeburg mean leachate OP concentrat ions 0 100 200 300 400 500 600 700 800 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 OP Concentration (ug/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 0 50 100 150 200 250 300 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 OP Concentration (ug/l)Date N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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275 Figure E 1 7 Arredondo mean runoff pH Figure E 18 Arredondo mean leachate pH 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 pHDate N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 pHDate N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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276 Figure E 19. Orangeburg mean runoff pH Figure E 20. Orangeburg mea n leachate pH 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 pHDate N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9/23/09 10/21/09 11/18/09 12/16/09 1/13/10 pHDate N 0 N 10 N 20 F 10 F 20 C 10 C 20 Rainfall

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277 APPENDIX F ADDITIONAL COLUMN STUDY WATER QUALITY DATA Leachate Column Results Leachate samples collected from the column study were analyzed for analytes listed in Table F 1 along with respective Practical Quantitation Limits (PQLs) and Minimum Detection Limits (MDLs). Analyte concentrations of water matrix applied to columns are also listed in Table F 1 Concentrations were determined by Inductively Coupled PlasmaAtomic Emission Spectrometry (ICP AES). Total Phosphorus (TP) Leachate concentrations of TP were highest for compost, followed in order by Arredondo, fly ash, and Orangeburg columns. Concentrations of TP increased with compost additions, more so on Arreodondo than Orangeburg though ( Figure F 1 ). However, fly ash incorporations did not produce noticeable increases in TP concentrations. Potassium (K) Leachate concentrations of K were highest for compost, followed in order by fly ash, Arredonodo, and Orangeburg columns. Incorporating higher fractions of both amendments increased leachate K concentrations ( Figure F 2 ). Sodium (Na) Leachate concentrations of Na were highest for compost, foll owed in order by fly ash, Arredonodo, and Orangeburg columns. Incorporating higher fractions of both amendments increased leachate Na concentrations ( Figure F 3 ).

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278 Ma gnesium (Mg) Leachate concentrations of Mg were highest for fly ash, followed in order by compost, Arredonodo, and Orangeburg columns. Incorporating higher fractions of both amendments increased leachate Mg concentrations ( Figure F 4 ). Calcium (Ca) Leachate concentrations of Ca were highest for fly ash, followed by compost and both soils. Arredondo and Orangeburg Ca concentrations were essentially equal. Incorporating higher fractions of both amendments increased leachate Ca concentrations ( Figure F 5 ). Aluminum (Al) Leachate concentrations of Al were highest for fly ash, followed in order by Arredondo, compost and Orangeburg soils. Al concentrations fluctuated between increases and decreases compared to the Arredondo column as both amendments fractions increased. However, increased fractions of both amendments in Orangeburg soils increased Al concentrations ( Figure F 6 ). Iron (Fe) Leachate concentrations of Fe were highest for fly ash, followed in order by compost, Arredondo, and Orangeburg soils. Increasing amendment fractions of compost and fly ash increased Fe concentrations from Orangeburg soils ( Figure F 7 ). However, increasing compost fractions on Arredondo soils caused concentrations to dip and then rise again, while increasing fly ash fractions did not increase Fe concentrations except when comparing fly ash only to Arredondo only leachate concentrations. Interactions between Arredondo and compost are attributed to the behavior of concentrations from these mixtures.

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279 Manganese (Mn) Concentration fluctuations of Mn from increasing amendment fractions were similar to Fe. Leachate concentrations were comparable for Arredondo and fly ash while compost concentrations were lower and followed by Orangeburg. Increased fly ash fractions in Arredondo caused concentrations to dip before increasing towards fly ash only concentrations ( Figure F 8 ). Similarly, Mn leachate concentrations from fly ash and compost mixes with Orangeburg soil were lower than the Orangeburg only and respective amendment only columns. This phenomenon was attributed to chemical interactions between the fly ash and the soil. No trend was identified for increased compost additions to Arredondo. Zinc (Zn) Leachate concentrations of Zn were highest for fly ash, followed by Orangeburg and compost, which were essentially equal, and finally by Arredondo with the lowest concentrations. Increasing compost fractions mixed with Arredondo increased Zn concentrations, while increasing compost fractions mixed with Orangeburg caused Zn concentrations to decrease for soil and amendment mixtures ( Figure F 9 ). Increasing fly ash additions to Arredondo decreased Zn concentrations until fly ash only concentrations which were all greater than Arredondo only concentrations. Copper (Cu) Concentrations of Cu were greatest for fly ash, followed in order by compost, Arredondo and Orangeburg. Increasing fractions of both amendments in both soils eventually increased Cu concentrations ( Figure F 10). However, no concentrations were above the Cu MDL when increasing fly ash fractions into Orangeburg soils, except for fly ash only.

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280 Boron (B) Concentrations of B were greatest for fly ash, followed in order by compost, and both soils, which had concentrations below the B MDL. Increasing fractions of both amendments in both soils eventually increased B concentrations ( Figure F 11). Concentrations increased more on Arredondo soils for each amendment than for similar incorporations on Orangeburg soils. However, no concentrations were above the B MDL when increasing compost fractions into Orangeburg soils, except for those from the compost only column. Nickel (Ni), Cadmium (Cd), and Lead (Pb) All Ni concentrations were below the Ni MDL except those from fly ash only columns and a single sample from a 0.1 fraction fly ash and Arredondo column ( Figure F 12). All sample concentrations of Cd and Pb were less than their respective MDLs. Summary Nearly all sample concentrations were greater than or equal to applied water matrix concentrations. Concentrations of TP, K, Na, Ca, Mg, Cu, B and Fe increased for both soils as both amendment fractions increased. Both amendments increased Al concentrations while soil and amendment interactions resulted in nonlinear concentrations transitions between Ar redondo and both amendment concentrations. Interactions between Arredondo and both amendments also produced nonlinear Fe concentration changes as amendment fractions increased, while both soils increased Fe concentrations from Orangeburg soils. Nonlinear changes in Zn and Mn concentrations were attributed to soil and amendment chemical interactions.

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281 Fly Ash TCLP A Toxicity Characteristic Leaching Procedure (TCLP) was conducted concentrations of several metals on a sample of fly ash used in this study. Additional metals were also included in the analysis. Results are presented in Table F 2 None of the eight TCLP metals exceeded their respective toxicity limits (EPA, 2004). Thus, the fly ash was determined not to be toxic by TCLP analysis. Table F 1 Practical Quantitation Limits (PQL), Minimum Detection Limits (MDL) and applied water matrix concentrations. Analyte Units PQL MDL Water Matrix Concentration Al (mg/l) 0.5000 0.1250 < 0.1250 # B (mg/l) 0.6000 0.1500 < 0.1500 Ca (mg/l) 0.2500 0.0525 13.0336 Cd (mg/l) 0.5000 0.1250 < 0.1250 Cu (mg/l) 0.0500 0.0125 < 0.0125 Fe (mg/l) 0.0500 0.0125 E0.0184 K (mg/l) 5.0000 1.2500 < 1.2500 Mg (mg/l) 0.1000 0.0250 9.1407 Mn (mg/l) 0.0500 0.0125 < 0.0125 Na (mg/l) 5.0000 1.2500 5.5720 Ni (mg/l) 0.0500 0.0125 < 0.0125 P (mg/l) 0.1000 0.0250 < 0.0250 Pb (mg/l) 0.1000 0.0250 < 0.0250 Zn (mg/l) 0.0500 0.0125 E 0.0259 E: Reported values were greater than the MDL but less than the PQL. # Values were reported as less than MDL

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282 Table F 2 Toxicity Characteristic Leaching Protocol results for fly ash sample, with corresponding lab Practical Quantitation Level (PQL) M inimum D etection L evel (MDL) and t oxicity limits. Parameter Fly a sh result (mg/kg) PQL (mg/kg) MDL (mg/kg) Toxicity l imit (EPA, 2004) (mg/kg) Antimony ( Sb ) 3.1 0.98 0.16 Arsenic ( As ) 31 0.33 0.3 100 Barium ( Ba ) 160 0.065 0.037 2000 Be ryllium (Be) 4.1 0.0098 0.0028 Cadmium ( Cd ) 0.15 0.02 0.0041 20 Chromium ( Cr ) 19 0.13 0.077 100 Cobalt ( Co ) 11 0.13 0.023 Copper ( Cu ) 49 0.13 0.07 Lead ( Pb ) 15 0.23 0.066 100 Mercury ( Hg ) 0.0012 0.0065 0.00091 4 Nickel ( Ni ) 21 0.21 0.063 Selenium ( Se ) 2.5 0.65 0.24 20 Silver ( Ag ) 0.039 0.13 0.039 100 Thallium ( Tl ) 0.17 0.81 0.17 Tin ( Sn ) 2.7 0.65 0.14 V anadium (V) 63 0.049 0.011 Zinc ( Zn ) 28 0.33 0.24

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283 Figure F 1 TP column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 2 K column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 2.0 4.0 6.0 8.0 10.0 12.0 0.0 0.1 1.0Concentration TP mg/lAmendment Fraction Matrix AC AF OC OF 0 100 200 300 400 500 600 700 800 900 1000 0.0 0.1 1.0Concentration K mg/lAmendment Fraction Matrix AC AF OC OF

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284 Figure F 3 Na column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 4 Mg column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0 50 100 150 200 250 0.0 0.1 1.0Concentration Na mg/lAmendment Fraction Matrix AC AF OC OF 0 20 40 60 80 100 120 0.0 0.1 1.0Concentration Mg mg/lAmendment Fraction Matrix AC AF OC OF

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285 Figure F 5 Ca column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 6 Al column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0 100 200 300 400 500 600 700 800 0.0 0.1 1.0Concentration Ca mg/lAmendment Fraction Matrix AC AF OC OF 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 0.0 0.1 1.0Concentration Al mg/lAmendment Fraction Matrix AC AF OC OF

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286 Figure F 7 Fe column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 8 Mn column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 0.0 0.1 1.0Concentration Fe mg/lAmendment Fraction Matrix AC AF OC OF 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 1.0Concentration Mn mg/lAmendment Fraction Matrix AC AF OC OF

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287 Figure F 9 Zn column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangebu rg and fly ash. Figure F 10. Cu column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.0 0.1 1.0Concentration Zn mg/lAmendment Fraction Matrix AC AF OC OF 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.0 0.1 1.0Concentration Cu mg/lAmendment Fraction Matrix AC AF OC OF

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288 Figure F 11. B column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 12. Ni column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.1 1.0Concentratino B mg/lAmendment Fraction Matrix AC AF OC OF 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.0 0.1 1.0Concentration Ni mg/lAmendment Fraction Matrix AC AF OC OF

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289 Figure F 13. Cd column leachate concentrations for soil and amendment mixt ures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. Figure F 14. Pb column leachate concentrations for soil and amendment mixtures. AC: Arredondo and compost; AF: Arredondo and fly ash; OC: Orangeburg and compost; OF: Orangeburg and fly ash. 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.0 0.1 1.0Concentration Cd mg/lAmendment Fraction Matrix AC AF OC OF 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.0 0.1 1.0Concentration Pb mg/lAmendment Fraction Matrix AC AF OC OF

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302 BIOGRAPHICAL SKETCH Eban Bean was born and raised in Mount Olive, NC. He received a B.S. in biological and agricultural engineering from North Carolina State University in 2003. He continued his education at North C arolina State University where he received his M.S. in 2005 in biological and agricultural engineering after researching permeable pavements. Eban plans to continue research in stormwater management after graduation.