OPTIMAL ALLOCATION OF STORMWATER POLLUTION CONTROL TECHNOLOGIES IN A WATERSHED

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

by

We-Bin Chen, M.A., B.S.

* * * * *

The Ohio State University

2006

Dissertation Committee: Approved by:

Prof. Steven I. Gordon, Co-Adviser Co-Adviser Prof. Jean-Michel Guldmann, Co-Adviser

Prof. Maria Manta Conroy Co-Adviser

Graduate Program in

City and Regional Planning

ABSTRACT

In recent decades, more than 90 percent of urban growth in the United States has taken place in the suburbs. The phenomenon, referred to as urban sprawl, has led to long-term degradation of environmental quality.

Best Management Practices (BMPs) serve as novel effective technologies to reduce the movement of pollutants from land into surface or ground waters, in order to achieve water quality protection within natural and economic limitations.

Four types of BMPs are discussed in this study—Pond, Wetland, Infiltration, and

Filtering Systems. Each has different installation requirements, costs, and pollutant removal efficiency. The purpose of this research is to find out the minimum-cost combinations of these four technologies, with a focus on total suspended sediments

(TSS), in order to achieve TMDL (Total Maximum Daily Loads) and EQS

(Environmental Quality) standards.

The methodology uses three major models: Spatial Model, Watershed Model, and

Economic Model. These models provide suitability analyses for potential residential developments and BMP technology installations, stormwater and pollutant simulations, and minimum cost optimization procedure.

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The results of this research will provide a practical reference for decision making about the balance between the urban development and environment protection. It can further provide EPA with economic assessment information regarding existing TMDL and EQS standards.

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Dedicated to my parents

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ACKNOWLEDGEMENTS

Most of all, I would like to express my deepest gratitude to Drs. Steven I. Gordon and Jean-Michel Guldmann for their intellectual support, encouragement, and enthusiasm wich made this dissertation possible, and for their patience in correcting and editing. Their broad knowledge and keen intuition have wisely guided my work. I would like to express my sincere thanks to Dr. Maria Manta Conroy, committee member, for guidance and thoughtful suggestions on this dissertation. I also thank the faculty members of City and Regional Planning at OSU, the administrative and technical staffs, and fellow students for support and encouragement. They made my life joyful and meaningful at OSU. I am also grateful for the prayers and love from my friends at Tzu Chi Foundation, Columbus Service Center.

I dedicate this research to my parents and brother for their love, care, and always having faith in me. I am in debt to my family and deeply thank them for love, encouragement, and sacrifice. Special thanks to Dr. Yu-Ting Huang, I-Chuan Wu,

Pei-Fen Jung, Chiungtzu Hou, Dr. Bornain Chiu, Shu-Yun Lin, Fang-Wen Huang,

Jin-Hui Kuo, Shen-Wu Jiang, Dr. Yi-Fei Chu, Carolyn Kan, Dr. Yi-Wen Huang, Dr.

Li-Shu Wang, Chieh-Ti Kuo, the LP family and all my wonderful friends for their precious friendship, concern, and encouragement throughtout my doctoral study.

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VITA

March 21, 1967 Born—Taoyuan, Taiwan

1989 Intern, Ruiming Engineering Consultant Co., Taichung, Taiwan

1990 B.S., Urban Planning, Fengchia University

1990 Planner, Urban and Regional Development Center, Tunghai University, Taichung, Taiwan

1992 M.A., Graduate Institute of Urban Planning, National Chunghsin University, Taichung, Taiwan

1992-1994 Second Lieutenant (Planner), Office of the Deputy Chief Staff for Logistics, Army

1994-1996 Associate Researcher, Graduate Institute of Land Economics, National Chengchi University, Taipei, Taiwan

1997-2004 Graduate Research/Teaching Associate, The Ohio State University, Columbus, OH

PUBLICATIONS

Liu, SL and WB Chen, 1999. The Emergy Analysis on Taiwan Agricultural Land Use. City and Planning. 26 (1): 41-54. (Chinese)

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Liu, SL and WB Chen, 1996. A Study of Urban Development: A Case Study of Taichung City. City and Planning. 23 (1): 55-74. (Chinese)

Chen, WB and SL Liu, 1995. A Study of Urban Configuration under Speculation. First Sino-Japanese Symposium on Applications of Management Sciences.

Huang, SL, SC Wu, and WB Chen, 1995. Ecosystem, Environmental Quality and Ecotechnology in the Taipei Metropolitan Region, Journal of Ecological Engineering 4: 233-248.

Huang, SL, SC Wu and WB Chen, 1994. Applied Ecological Engineering Approach for Assessing Environmental Quality of the Urban Ecological-Economic System. City and Planning. 21 (2): 215-232. (Chinese)

Huang, SL., SC Wu, and WB Chen, 1993, Ecological Economic System and the Environmental Quality of the Taipei Metropolitan Region. Conference on Environmental Quality Evaluation Systems for Metropolitan Areas, pp. 1-1--1-10. National Science Council, Taipei, Taiwan. (Chinese)

FIELD OF STUDY

Major Field: City and Regional Planning

Environmental Planning, Computer Simulation, Geographic Information Systems, Remote Sensing Optimization and Location Analysis, Quantitative Methods Environmental Economics, Ecological Economics

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TABLE OF CONTENTS

Page ABSTRACT ...... ii

DEDICATION ...... iv

ACKNOWLEDGEMENTS...... v

VITA...... vi

LIST OF TABLES ...... xv

LIST OF FIGURES ...... xx

CHAPTERS:

1. INTRODUCTION ...... 1

2. LITERATURE REVIEW...... 5

2.1 THE NON-POINT SOURCE POLLUTION PROBLEM...... 5

2.2 HYDROLOGICAL PROCESSES...... 9

2.3 WATERSHED MODELING ...... 13

2.4 BEST MANAGEMENT PRACTICES ...... 19

2.4.1 Pond Systems...... 21

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2.4.2 Wetland Systems...... 21

2.4.3 Infiltration Systems...... 23

2.4.4 Filtering Systems ...... 25

2.5 WATER QUALITY STANDARDS...... 27

2.5.1 Environmental Quality Standard ...... 27

2.5.2 TMDL Standard...... 28

2.6 INTEGRATED SIMULATION AND OPTIMIZATION APPROACHES...... 31

3. MODELING METHODOLOGY...... 34

3.1 GENERAL MODELING APPROACH ...... 34

3.1.1 Overview of the Spatial Model...... 35

3.1.2 Overview of the Watershed Model ...... 36

3.1.3Overview of the Economic Model37

3.2 SPATIAL MODEL...... 37

3.2.1 Overview Of Suitability Analysis...... 38

3.2.2 Residential Suitability Analysis Model ...... 41

3.2.3 BMP Suitability Analysis Model...... 46

3.3 WATERSHED MODEL ...... 48

3.4 ECONOMIC MODEL...... 52

3.4.1 Model Objective ...... 54

3.4.2 Model Constraints...... 55

3.4.2.1 BMP C ...... 55

3.4.2.2 BMP Pollutant Removal Efficiency...... 55 ix

3.4.2.3 Net Pollutant Loading after BMP Treatment...... 56

3.4.2.4 Pollutant Transportation Rate ...... 57

3.4.2.5 The Installation Area of a BMP ...... 57

3.4.2.6 BMP Selection Constraints...... 59

3.4.2.7 Water Quality Standard Constraints...... 61

3.5 SUMMARY...... 62

4. DATA SOURCES AND PROCESSING ...... 64

4.1 OVERVIEW ...... 64

4.2 DESCRIPTION OF THE STUDY AREA...... 65

4.2.1 The Big Darby Watershed...... 65

4.2.2 The Study Area ...... 69

4.2.3 Catchment Delineation ...... 70

4.3 ANALYSIS OF LANDFORM AND SOIL ...... 74

4.3.1 Landform ...... 74

4.3.2 Soil...... 78

4.4 LAND-USE AND TRANSPORTATION NETWORK ...... 80

4.5 STREAM DATA...... 83

4.5.1 Types of Streams...... 83

4.6 INPUTS TO THE SWMM MODEL ...... 87

4.6.1 Precipitation...... 87

4.6.1.1 Storm Types ...... 88

4.6.1.2 Rainfall Characteristics...... 89 x

4.6.1.3 Estimation of precipitation ...... 90

4.6.1.4 The SCS Storm Distribution...... 93

4.6.2 Infiltration...... 97

4.6.3 Routing ...... 99

4.6.3.1 Overland Flow ...... 99

4.6.3.2 Watershed Manning’s Roughness Coefficient...... 103

4.6.3.3 Channel/ Pipe Data ...... 104

4.6.4 Water Quality...... 107

4.6.4.1 Buildup ...... 107

4.6.4.2 Washoff...... 108

4.6.4.3 Erosion...... 109

4.7 INPUT TO THE ECONOMIC MODEL ...... 110

4.7.1 Pollutant Loads and Stream Flow...... 111

4.7.2 Land Purchasing Cost...... 111

4.7.3 Installation and Maintenance Cost...... 116

4.7.3.1 Pond Systems...... 116

4.7.3.2 Wetland Systems...... 116

4.7.3.3 Infiltration Systems...... 117

4.7.3.4 Filtering Systems ...... 118

4.7.4 Final BMP Unit Cost ...... 119

4.7.5 BMP Sediment Removal Rate ...... 120

4.7.6 Suspended Sediment Transport Rate ...... 120

4.7.7 BMP Installation Area Constraint...... 124 xi

4.7.8 TMDL Standards ...... 124

4.7.8.1 Annual TMDL Standards...... 124

4.7.8.2 Single Storm TMDL Standards...... 127

4.7.9 Environmental Quality Standards...... 128

5. MODEL CALIBRATION TO THE BIG DARBY WATERSHED...... 129

5.1 SPATIAL MODEL...... 129

5.1.1 Residential Suitability Model ...... 130

5.1.1.1 Scenario A...... 135

5.1.1.2 Scenario B...... 140

5.1.1.3 Scenario C...... 142

5.1.2 BMP Suitability Model...... 144

5.1.2.1 Comparative Feasibility...... 144

5.1.2.2 Environmental Restrictions and Benefits ...... 145

5.1.2.3 BMP Suitability Analysis Criteria ...... 146

5.2 WATERSHED MODEL ...... 154

5.2.1 Scenario A Output...... 154

5.2.2 Scenario B Output...... 164

5.2.3 Scenario C Output...... 169

5.3 ECONOMIC MODEL...... 175

5.3.1 BMP Cost...... 175

5.3.2 BMP Pollutant Removal Efficiency...... 177

5.3.3 Gross Sediment Loads ...... 177 xii

5.3.4 Pollutant Transportation Rates...... 177

5.3.5 The Installation Areas for BMPs ...... 180

5.3.6 BMP Selection Constraints...... 181

5.3.7 Water Quality Standard Constraints...... 187

6. RESULTS AND DISCUSSION...... 189

6.1 OPTIMIZATION MODEL...... 189

6.2 SINGLE STORM EVENT ...... 190

6.3 ANNUAL STORM EVENT...... 194

6.4 SENSITIVITY ANALYSES...... 201

6.4.1 Single Storm ...... 201

6.4.1.1 Scenario A...... 201

6.4.1.2 Scenario B...... 209

6.4.1.3 Scenario C...... 216

6.4.2 Annual Storm ...... 224

6.4.2.1 TMDL ...... 224

6.4.2.2 EQS...... 229

6.5 EQS versus TMDL ...... 233

6.6 MARGINAL COST ANALYSIS...... 236

6.7 SUMMARY...... 240

7. CONCLUSIONS...... 241

7.1 CONCLUSIONS ...... 241 xiii

7.2 LIMITATIONS ...... 246

7.3 FURTHER RESEARCH RECOMMENDATIONS...... 247

BIBLIOGRAPHY...... 250

APPENDICES...... 275

A. SPATIAL REFERENCE INFORMATION ...... 275

B. WATERSHED DELINEATION ARCVIEW SCRIPT...... 277

C. THE DETAILED DESCRIPTION OF IDF CURVE...... 289

D. EQUATIONS FOR IDF CURVE ESTIMATION...... 292

E. THE SCS STROM DISTRIBUTION ...... 294

F. RUNOFF CURVE NUMBER FOR HYDROLOGIC SOIL-COVER COMPLEX....299

G. STREAM DIMENSION ESTIMATION ...... 303

H. SEDIMENT TRANSPORTATION...... 309

I. SAMPLE OF GAMS PROGRAM FOR THE ANNUAL TMDL STANDARD ...322

J. SMAPLE OF GAMS PRGRAM FOR THE TMDL STANDARD SENSITIVITY ANALYSIS...... 327

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LIST OF TABLES

Table Page

2.1 Total suspended sediment (TSS) targets for the Big Darby Creek watershed .....28

3.1 Overlay Value and Weight ...... 44

3.2 An Example of Score Calculation ...... 45

4.1 Models and Data Sources ...... 66

4.2 Population Change Between 1990 and 2000 in the Darby Watersheds...... 69

4.3 Land Uses in Study Watershed in 1994 ...... 81

4.4 Types of Channels in the Study Area...... 88

4.5 Number of Days with Precipitations Greater Than 0.5-in in Columbus ...... 91

4.6 Average Number of Days with Precipitation Over 10 and 30 Years in Columbus ...... 91

4.7 Frequencies of Storm Types...... 92

4.8 Rainfall Intensity of a Two-Hour Normal Storm in the Study Area ...... 95

4.9 Infiltration Capacity Values by Hydrologic Soil Group ...... 98

4.10 Representative Values for f0...... 100

4.11 Relationship Between Land Uses and Imperviousness ...... 102

4.12 Surface Losses...... 104

4.13 Manning’s n Roughness coefficients for sheet flow—TR-55 ...... 105

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4.14 Measured Dust and Dirt (DD) Accumulation in Chicago ...... 108

4.15 Nationwide Data on Linear Dust and Dirt Buildup Rates ...... 108

4.16 Unit Land Purchasing Cost...... 115

4.17 Estimated Annual Cost of BMPs ...... 118

4.18 Final BMP Unit Cost (per Acre)...... 119

4.19 Transport Rates for Medium Sand: Atlanta, Georgia ...... 121

4.20 Example of stream flow data and sediment transport rate...... 122

4.21 Unit Drainage Area and Installation Area of BMPs ...... 124

4.22 Description of Hydrologic Units in the Big Darby Creek Watershed ...... 125

4.23 Allocations for Big Darby Creek Between Flat Branch and Milford Center (190-030) ...... 126

4.24 Allocations to Robinson Run2 (190-060)...... 126

4.25 Allocations for Sugar Run2 (190-070) ...... 126

4.26 Different TMDL Standards Based on Precipitation Frequencies ...... 127

4.27 Total Suspended Sediment (TSS) Targets for the Big Darby Creek watershed ...... 128

5.1 Soil Maps, Scales, and Weights ...... 131

5.2 Potential for Development—Natural Factors ...... 132

5.3 Potential Development Areas—All Factors...... 135

5.4 The Land Use of the Study Area in 1994...... 137

5.5 Original vs. Reclassified Land Uses...... 138

5.6 Land-Use in Scenario A...... 138

5.7 Land-Use Scenario B...... 140

5.8 Land-Use of Scenario C...... 142

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5.9 Feasibility Criteria for Different Stormwater BMP Options ...... 145

5.10 Environmental Benefits and Drawbacks of BMP Options ...... 146

5.11 Criteria for BMPs Suitability Analysis ...... 148

5.12 Area of Potential BMPs Technologies...... 149

5.13 Share of Impervious Cover in Each Catchment ...... 156

5.14 Runoff Depth, Peak Rate, and Peak Unit Runoff in Each Catchment...... 157

5.15 Summary Statistics for Streamflow ...... 159

5.16 Sediment and Erosion Loads from Different Catchments (1994) ...... 162

5.17 Runoff of Low Intensity Residential Development (LIRD)...... 165

5.18 Sediment and Erosion Loads from Different Catchements (LIRD) ...... 168

5.19 Runoff of High Intensity Residential Development (HIRD)...... 170

5.20 Sediment and Erosion Loads from Different Catchments (HIRD)...... 173

5.21 BMP Unit Control Costs for Each Catchment ($/acre) ...... 176

5.22 Gross Sediment Loads (lbs) Under Storm Type 2 (0.05-in.)...... 178

5.23 Pollutant Transportation Rate Under Type 2 Storm...... 179

5.24 Total Drainage Area in Each Catchment (TDAi )...... 180

5.25 Minimum Drainage and Unit Installation Area of BMP (acre) ...... 181

5.26 Maximum Areas for BMPs (acre)...... 183

5.27 Number of Days With Storm Type ...... 187

5.28 Streamflow under Scenario A Development (liter)...... 188

6.1 Water Quality Standards ...... 190

6.2 BMP Installation Area (acre) and Total Annual Control Cost ($1000) ...... 191

6.3 Sediment Reduction Rate in Each Catchment (%)...... 193 xvii

6.4 Type of BMP Installation Areas for Scenario A under the TMDL Standard ...... 195

6.5 BMP Installation Areas and Net Sediment Loads under the EQS Standard...... 197

6.6 Water Quality at Control Points after BMP Treatment...... 198

6.7 Sensitivity Analysis of the TMDL Standard—Scenario A ...... 203

6.8 Sediment Reduction Rates under TMDL Standards—Scenario A ...... 205

6.9 Sensitivity Analysis of the EQS Standard—Scenario A...... 207

6.10 Sediment Reduction Rates under the EQS Standard—Scenario A ...... 208

6.11 Sensitivity Analysis of the TMDL Standard—Scenario B ...... 210

6.12 Sediment Reduction Rates under the TMDL Standard—Scenario B...... 212

6.13 Sensitivity Analysis of the EQS Standard—Scenario B...... 214

6.14 Sediment Reduction Rates under the EQS Standard—Scenario B...... 215

6.15 Sensitivity Analysis of the TMDL Standard—Scenario C ...... 218

6.16 Sediment Reduction Rates under the TMDL Standard—Scenario C...... 220

6.17 Sensitivity Analysis of the EQS Standard—Scenario C...... 222

6.18 Sediment Reduction Rate under the EQS Standard—Scenario C ...... 223

6.19 Sensitivity Analysis of the TMDL Standard for an Annual Storm ...... 226

6.20 Sediment Reduction Rates of the TMDL Standard for an Annual Storm...... 228

6.21 Sensitivity Analysis of the EQS Standard for Annual Storm...... 231

6.22 Sediment Reduction Rates under the EQS Standard for an Annual Storm ...... 232

6.23 Incremental Control Cost vs. EQS Standard ...... 236

6.24 Shadow Prices for Different TMDL Standards...... 238

6.25 Shadow Price of Maximum Available BMP Installation Area ...... 238

6.26 Shadow Prices for the TMDL and EQS Standard Constraints ...... 239 xviii

A.1 Spatial Reference Information...... 276

E.1 SCS Type II Storm Distribution Data...... 297

E.2 Rainfall Intensity of a Two-Hour Normal Storm in the Study Area...... 298

F.1 Runoff Curve Number for Hydrologic Soil-Cover Complex ...... 300

G.1 Comparison of Empirical Equation Estimation, Field Survey, and Aerial Photography Measurement...... 307

G.2 Comparison of Empirical Equation Estimation, Field Survey, and FEMA Measurement...... 308

H.1 Transport Rate of Medium Sand: Atlanta, Georgia ...... 320

H.2 Transport Rate of Medium Gravel: Atlanta, Georgia ...... 321

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LIST OF FIGURES

Figure Page

2.1 Watershed Hydrologic Cycle...... 11

2.2 Process-based classification of watershed models, after Singh (1995) ...... 15

3.1 General Modeling Approach...... 35

3.2 Diagram of McHarg’s Suitability Analysis Method ...... 38

3.3 Suitability Analysis Procedure (Steiner, 1991)...... 39

3.4 Conceptual Landuse Suitability Model ...... 42

3.5 Conceptual Map Overlay...... 45

3.6 Conceptual BMP Suitability Model...... 47

3.7 Watershed Model...... 49

3.8 Overview of the SWMM model structure, with linkages among the computational blocks...... 51

3.9 Economic Model...... 53

3.10 Conceptual Diagram of Subcatchment and BMP Installation...... 59

3.11 Example of BMP Combinations ...... 60

3.12 Integrated Model Flowchart...... 63

4.1 Land Uses in the Big Darby Creek Watershed ...... 68

4.2 Delineation of watershed and subwatershed boundaries...... 71

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4.3 The Diagram of DEM Filling ...... 72

4.4 Flow Direction and Output Cell’s Value...... 73

4.5 Catchments ...... 75

4.6 Elevations ...... 76

4.7 Slopes...... 77

4.8 Land Uses and Transportation Networks in 1994...... 82

4.9 Detail Streams...... 84

4.10 Approximate Geographic Area for SCS Rainfall Distributions. (SCS, 1986)....94

4.11 Rainfall Intensity Distribution of a Two-Hour Normal Storm...... 96

4.12 Idealized Subcatchment Overland Flow and Outflow Computation Without Snow Melt...... 101

4.13 Hypothetical Parcel Map...... 114

4.14 Example of stream structure diagram...... 122

5.1 Potential Development Based on Natural Factors...... 134

5.2 Potential Development Areas...... 136

5.3 Scenario A...... 139

5.4 Scenario B...... 141

5.5 Scenario C...... 143

5.6 Potential Pond Systems Candidates...... 150

5.7 Potential Wetland Systems Candidates...... 151

5.8 Potential Infiltration Systems Candidates...... 152

5.9 Potential Filtering Systems Candidates ...... 153

5.10 Effects of Urbanization on Volume and Rates of Surface Runoff ...... 155

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5.11 The Stream Hydrograph at the Outlet of the Watershed...... 160

5.12 The Total Suspended Sediment and Erosion at the Outlet of the Watershed....161

5.13 Diagram of Catchment, Stream ID, Streamflow Direction, and Water Quality Control Point...... 163

5.14 The Stream Hydrograph at the Outlet of the Watershed (LIRD)...... 166

5.15 TSS and Erosion at the Outlet of the Watershed (LIRD) ...... 167

5.16 The Stream Hydrograph at the Outlet of the Watershed (HIRD) ...... 171

5.17 TSS and Erosion at the Outlet of the Watershed (HIRD) ...... 171

5.18 Plot of Simulated Erosion vs. Agriculture Land...... 174

5.19 The Conceptual Combination of Type A...... 182

5.20 The Conceptual Combination of Type B ...... 184

5.21 The Conceptual Combination of Type C ...... 185

5.22 The Conceptual Combination of Type D...... 186

6.1 TSS TMDL Standard vs. Control Cost —Scenario A ...... 202

6.2 TSS EQS Standard vs. Control Cost—Scenario A...... 206

6.3 TSS TMDL Standard vs. Control Cost—Scenario B ...... 209

6.4 TSS EQS Standard vs. Control Cost of Scenario B...... 213

6.5 TSS TMDL Standard vs. Control Cost of Scenario C ...... 217

6.6 TSS EQS Standard vs. Control Cost of Scenario C...... 221

6.7 Control Cost vs. TMDL Standards ...... 225

6.8 Control Cost vs. EQS Standards...... 230

6.9 Control Cost vs. TMDL and EQS...... 233

6.10 Relationships between Control Cost, TMDL, and EQS ...... 234

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6.11 Detail of the Relationships between Control Cost, TMDL, and EQS ...... 235

C.1 Rainfall Intensity-Duration-Frequency (IDF) Curves for Columbus ...... 291

E.1 SCS 24-hour Rainfall Distribution...... 296

E.2 Soil Conservation Service Type II Storm Distribution...... 296

G.1 Field Survey Sample Points ...... 305

H.1 Free-body Diagram...... 311

H.2 Lift Force and Rotation Motion Due to Velocity Profile ...... 313

H.3 Grain Size and Transport Mechanism...... 318

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CHAPTER 1

INTRODUCTION

Urban development has undergone extensive geographic changes in the past several decades. The World Commission on Environment and Development (WCED) reported that nearly half the world’s population would live in urban areas in 2000

(WCED, 1987). From 1970 to 1990, urban density in the United States decreased by 23 percent. Moreover, during the same period, more than 30,000 square miles (19 million acres) of once-rural lands have become urban, as classified by the U.S. Bureau of the

Census (Associated Press, 1991). Almost every urban area in the United States has significantly expanded its developed land surface in recent decades (USEPA, 2001).

These land-use changes generate many social and economic benefits. However, they also come at a cost to the natural environment with changes in air and water quality, plant and animal population dynamics, biodiversity, movements of materials

(e.g. soil, and nutrients) and water in upland catchments, evapotranspiration rates, and primary productivity. One of the major direct environmental impacts of development is the degradation of water resources and water quality (USEPA, 2001). Conversion of agriculture, forest, grass, and wetlands to urban land usually implies a major increase in impervious surfaces, which can alter natural hydrologic conditions within a 1

watershed (Tang et al., 2005). The outcome of this alteration is typically increases in the volume and rate of surface runoff. The conversion from pervious to impervious surfaces can also degrade the quality of the storm runoff. Impervious surfaces collect pollutants, either dissolved in runoff or associated with sediment, such as nutrients, heavy metals, sediment, oil and grease, pesticides, and fecal coliform bacteria. These pollutants are washed off and delivered to aquatic systems by storms (Schueler, 1995;

Gove et al., 2001). The impact of pollution from diffuse sources on surface runoff has been of increasing concern during the past several decades, and nonpoint source (NPS) water pollution has become the leading cause of water quality impairment (USEPA,

2000b). These negative impacts threaten the ability of the landscape to provide natural resources on a sustained basis, and can result in a long-term degradation of environmental quality (Pimentel and Krummel, 1987).

Increased urbanization implies more impervious areas and higher storm runoff than in rural areas, which are more pervious. Urban stormwater runoff can be controlled by the use of Best Management Practices (BMPs), which can be nonstructural, such as reduction of road width and elimination of sidewalks, or structural, varying from small site-specific practices to large-scale regional practices.

An urban stormwater BMP is believed to be a “best” way to treat or limit pollutants in stormwater runoff (Villarreal and Bengtsson, 2004).

In this research, four BMPs technologies—Pond, Wetland, Infiltration, Filtering

Systems—are used to simulate reduction in the total suspended sediment load resulting from suburbanization. Each BMP has different installation site requirements (e.g. slope, soil characteristics, and groundwater depth), cost, and pollutant removal efficiency. The 2

objective of this research is to find out the minimum cost combination of BMP technologies to reduce the total suspended sediment load to an appropriate load. TMDL

(Total Maximum Daily Loads) and EQS (Environmental Quality Standards) are the two commonly used water quality standards and are adopted here. TMDL focuses on the total maximum pollutant load, while EQS focuses on pollution concentrations along the river.

BMPs are ecological engineering technologies. Unlike traditional environmental engineering approaches, BMPs incorporate the unique features and abilities of nature to purify the water. There is little research on how to use these novel technologies to solve problems related to urban development and land-use changes. This is the major objective of this research. Further, as discussed above, TMDL and EQS are the two water quality standards commonly used by the USEPA. Some watersheds apply TMDL, while others apply EQS, but there has never been a comparison of the differential effects of these two standards. Their trade-offs are also a focus of this research.

This research will (1) Investigate the relationship between urban development and water quality; (2) Provide a comprehensive understanding of the costs and pollutant removal efficiencies of BMPs; (3) Provide a basis for decision making regarding the balance between urban development and environment protection; and (4) Provide an economic basis for the evaluation of the TMDL and EQS standards.

Three different land-use developmental scenarios are simulated—existing land-use, low-intensity residential development, and high-intensity residential development. Each scenario generates different storm runoff and total suspended sediments (TSS) loads after a storm. These flow into stream channels and affect stream 3

water quality. In order to estimate storm runoff and TSS generation, several models are used: Spatial Model, Watershed Model, and Economic Model. The Spatial Model focuses on (1) data preparation for the Watershed Model (SWMM model); (2) future residential development suitability analysis; and (3) BMPs site suitability analysis. The

Watershed Model focuses on (1) stormwater runoff estimations; (2) total suspended sediment estimations; and (3) soil erosion estimations. Finally, the Economic Model uses optimization techniques to derive minimum total-cost combinations of BMPs subject to the environmental standards, and provides extensive sensitivity analyses of the TMDL and EQS standards. These results can serve as the basis for decision making and environmental and land-use policies.

The remainder of the dissertation is organized as follows. Chapter 2 consists of a literature review. The modeling methodology is presented in Chapter 3. Data sources and processing are described in Chapter 4. The model calibration to the Big Darby

Watershed is discussed in Chapter 5, and the economic optimization and sensitivity analysis results are discussed in Chapter 6. Chapter 7 concludes this dissertation and outlines areas for future research.

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CHAPTER 2

LITERATURE REVIEW

Five streams of literature are reviewed in this chapter: non-point source pollution problems, hydrological processes, watershed modeling, best management practices for removal of non-point source pollutants, water quality standards, and integrated simulation-optimization approaches.

2.1 THE NON-POINT SOURCE POLLUTION PROBLEM

Stream water pollutants are the product of numerous natural and anthropogenic factors, which can be either spatially diffused and based on land-use covers, generating non-point source (NPS) pollution, or concentrated, generating point source pollution

(Ahearn et al., 2005). Calculating point source pollution is relatively simple, as direct measurements can be made at the sources, such as industrial and sewage treatment plants, but measuring NPS pollution is much more difficult (Baker, 2003; Ahearn et al.,

2005).

NPS pollution is caused by rainfall or snowmelt moving over and through the ground (Lane 1983, Browne 1990, Huber 1993). Water runoff picks up and carries away natural and human-made pollutants, then deposits them into lakes, rivers, 5

wetlands, coastal waters, and even underground sources of drinking water. NPS pollution is widely recognized as a significant cause of surface water impairment, but had not received much attention in the U.S. until about 10 years ago (Schreiber et al.,

2000; Meals, 1996). These pollutants are difficult to locate and control, and have become one of the main reasons that urban rivers fail to reach water quality objectives

(Mitchell, 2004).

The United States Environmental Protection Agency (USEPA) (1994) classifies

NPS pollutants into six categories: (1) excess fertilizers, herbicides, and insecticides from agricultural lands and residential areas; (2) oil, grease, and toxic chemicals from urban runoff and energy production; (3) sediments from improperly managed construction sites, crop and forest lands, eroding streambanks, and urban areas; (4) salt from irrigation practices and acid drainage from abandoned mines; (5) bacteria and nutrients from livestock, pet wastes, and faulty septic systems; (6) atmospheric deposition and hydromodification. Ryding and Thornton (1999) discuss the main factors that influence the magnitude of NPS loads after storm events: (1) drainage basin physiography (Hunsacker and Levin, 1995); (2) type and chemistry of soil (Brusseau et al., 1994); (3) type and extent of vegetative cover (Gordon and Majumder, 2000); (4) density of drainage channels; (5) type and quantities of materials applied to the land surface; (6) duration of the dry period preceding a rainfall event; and (7) volume/intensity/quality of the rainfall.

Despite the difficulties in locating NPS pollution, researchers have concluded that its generation, transport, and transformation are highly related to both human activities and natural factors (Karr and Schlosser, 1978; Karr and Dudley, 1981; Wang 1995; 6

Gordon and Majumder, 2000; Yoder and Rankin 1995, Richards and Host, 1994;

Hunsacker and Levin, 1995; Osborne and Wiley, 1988; Schueler and Holland, 2000).

Urban and agricultural areas have been recognized by the US EPA as major sources of

NPS pollution, because of their highly polluted runoff (Browne 1990).

Urban runoff contains suspended solids, bacteria, heavy metals, oxygen- consuming substances, nutrients, oil and grease, and toxic chemicals, derived from construction sites, developed urban lands, streets and parking lots (Olivera, 1996;

Schueler and Holland, 2000; USEPA, 1994). Pollution loadings from residential areas are determined by the extent of the imperviousness, street sweeping practices, curb heights, types of storm-water drainage systems, soil and slope of pervious surfaces, traffic, and atmospheric pollution (Schueler and Holland, 2000). In urban areas with wastewater treatment plants (WWTPs), drainage is frequently routed to

WWTPs (which may or may not be in the same basin), and then discharged to local rivers as point sources (Hill, 1981; Ahern, 2005).

The runoff from agricultural areas carries sediments, nutrients, organic materials and pathogens, and excess fertilizer, herbicides, pesticides, and insecticides (Olivera,

1996; Johnson et al., 2001; David et al, 1997; Robinson, 1971; Novotny, 1999; Jordan et al., 1997). Some studies have used GIS data and regression analysis to examine the relationship between land-use cover and suspended sediments (Allan et al., 1997;

Bolstad and Swank, 1997; Hill, 1981; Johnson et al., 1997; Ahearn et al., 2005) and nutrients (Allan et al., 1997; Arheimer et al., 1996; Basnyat et al., 1999; Hill, 1981;

Omernik et al., 1981; Osborne and Wiley, 1998; Sliva and Williams, 2001; Ahearn et al., 2005). Most of these studies conclude that agricultural land-use strongly influences 7

stream water nitrogen (Arheimer and Liden, 2000; Johnson et al., 1997; Smart et al.,

1998; Ahern et al., 2005), phosphorous (Arheimer and Liden, 2000; Hill, 1981), and sediments (Allan et al., 1997; Johnson et al., 1997; Ahern et al., 2005).

Sediments appear to be the leading cause of impaired U.S. rivers in a 1998 analysis, with 40% of the assessed river-miles appearing stressed because of alteration in natural sediment processes (USEPA, 2000). Behind nutrients and metals, sediments are also the third leading cause of stress for lakes, reservoirs, and ponds. NPS agricultural and urban runoff and hydromodification are the leading sources of sediment stress. Aside from return irrigation water in agricultural areas, rainfall runoff is the main mechanism for sediment transfer to surface waters (Nietch et al., 2005). In addition, suspended sediments also carry other pollutants in the streamflow (Svensson,

1987; Schreiber et al., 2001). Therefore, there is a strong justification for using suspended sediment load and concentration as an index of water quality.

Generally, the cost of NPS pollution control is lower than that for point source control, and pollution trading would allow point sources with higher abatement cost, to sponsor implementation of non-point source abatement at relatively lower costs, and therefore the total cost for achieving water quality objectives could be reduced (Zhang and Wang, 2002; USEPA, 1996; Jarvie and Solomon, 1998; Crutchfield et al., 1994;

Malik et al., 1994). However, unlike point sources, where water quality problems are easy to identify, non-point sources are influenced by stochastic factors, such as temperature and precipitation, and localized features, such as land uses, climate and geology. In particular, land use changes have had a significant influence on the amount of runoff (volume of water per unit time) and pollutant loads (mass of pollutant per unit 8

of time), causing both to increase (Huber 1993; Tang et al., 2005). NPS pollutant loads cannot be measured with certainty, and it is difficult to attribute stream water pollutants to specific non-point sources, with at best approximate calculations of discharge loads

(Zhang and Wang, 2002; Stephenson et al., 1998; Baker, 2003; Ahearn et al., 2005). In watersheds where non-point sources account for the major share of the total effluent load, failure to control non-point discharges can lead to failure to achieve water quality objective (Letson, 1992; Stephenson et al., 1998).

Management practices for NPS pollution are different from those PS for pollution, as NPS requires area-wide control practices to reduce pollutants. Olivera (1996) proposes the following means for controlling urban runoff quality: (1) preventing or reducing pollutant deposition in urban areas; (2) preventing pollutant contact with runoff; (3) minimizing directly connected impervious areas; (4) designing controls for small storms (usually less than 1-in/hr rainfall); (5) using the treatment train concept, which assumes source controls, individual building lot controls, group of lots controls, and regional controls, in sequence. Treatment practices are grouped into two broad categories: infiltration and detention. Infiltration practices include swales (wetlands) and filter strips, porous pavement, percolation trenches, and infiltration basins; detention practices include extended detention basins, retention ponds, and wetlands

(Urbonas and Roesnar, 1993).

2.2 HYDROLOGICAL PROCESSES

Hydrological processes start with the evaporation of water from oceans. Under proper conditions, the vapor, transported by moving air masses, is condensed to form 9

clouds, which in turn result in precipitation. Rain falling on earth either enters a water body directly, travels over land surfaces from the point of impact to a water source, or infiltrates into the ground. Some precipitation is intercepted by vegetation, and some is stored in surface depressions, but, ultimately, evaporates back to the atmosphere or infiltrates into the ground. Under the influence of gravity, both surface stream flows and groundwater move toward lower elevations, and eventually discharge into the oceans, as illustrated in Figure 2.1 (Donmenico and Schwartz, 1990; Wang, 1995;

Linsley, Kohler, and Paulhus, 1982; McCuen, 1998). Evaporation is the process by which the phase of water is changed from liquid to vapor. All surfaces exposed to precipitation, such as vegetation, buildings, and paved streets, are potentially evaporation surfaces. Since the rate of evaporation during rainy periods is low, the quantity of storm rainfall disposed of in this manner is essentially limited to that required to saturate the surface, and the evaporation is only appreciable on an annual basis (Linsley et al., 1982; McCuen, 1998).

Surface runoff occurs when rainfall intensity exceeds soil infiltration capacity

(Donmenico and Schwartz, 1990; Wang, 1995), and moves across land surfaces during or after a storm, transporting dissolved and suspended materials picked up along the flow path. Pollutants carried to streams and lakes by surface runoff are major contributions to water pollution (Linsley et al., 1982). Overland flow is also a source of subsurface water, but the latter is relatively free of pollution because of multiple processes of infiltration and percolation (Linsley et al., 1982; McCuen, 1998).

As discussed earlier, NPS pollution is derived from diffuse sources located in different land uses, and is transported by runoff and subsurface water; therefore, the 10

interactions among the three environmental media—water, land, and atmosphere—have significant impacts on NPS generation, transport, and fate.

Hydrological processes link these three media together and are the most important factors determining pollution loads (Yeo, 2005). The relationships between water, land, and atmosphere have been described by many models and equations.

Source: USDA (1998).

Figure 2.1 Watershed Hydrologic Cycle

11

The USDA-SCS curve number (CN) procedure and the Universal Soil Loss

Equation (USLE) are empirical models used to describe runoff and erosion. They are used in many watershed simulation models, such as AGNPS, SWAT, BASINS, and

SWMM. SCS curve numbers are used to estimate the share of precipitation becoming runoff and that infiltrating into the soil. The amount of infiltration is determined by hydrological conditions, soil characteristics, and land use (USDA, 1986; McCuen,

1982; Chow et al., 1989; Dilshad and Peel, 1994). The results from this model are very sensitive to soil moisture (USDA, 1986; Hawkins and VerWeire, 2005). The USLE was derived from statistical analyses of soil loss and associated data, obtained in 40 years of research by the Agricultural Research Service (ARS) and assembled at the ARS runoff and soil loss data center at Purdue University. These data include more that 250,000 runoff events at 48 research stations in 26 states, representing about 10,000 plot-years of erosion studies under natural rain. The USLE was developed by Wischmeier and

Smith (1958) as an estimate of the average annual soil erosion for a given upland as a function of rainfall, soil erodability factors (K factors), slopes, land cover, and management factors (Renard et al., 1997; Wischmeir and Smith 1978).

Besides runoff, infiltration is another important hydrological process. A dry soil has a certain capacity for water infiltration, which can be expressed as a depth of water per unit time (e.g., inches per hour). The Horton and Green-Ampt infiltration models have been developed to describe the physical processes of infiltration (McCuen, 1998;

Wang, 1995; Parsons and Abrahams, 1993; Moore et al., 1991). Horton's equation

12

assumes that the soil infiltration rate decreases exponentially as a function of time since the beginning of the storm, while the Green-Ampt infiltration equation accounts for soil-moisture’s storage (Zarriello, 1998).

2.3 WATERSHED MODELING

Modeling is a key approach to watershed management. The spatial and temporal detail of watershed modeling is a most compelling concern (Kelly and Wool, 1995).

Scientific knowledge and observations are used to model watershed hydrology and water quality. These models can be grouped into (1) process-based models, and (2) empirical models. Process-based models use the most detailed scientific knowledge, often considering properties and processes at small spatial and temporal scales, and therefore requiring extensive data. The Stanford Watershed Model is an example of process-based models (Crawford and Linsey, 1966). They require a minimal effort of calibration, but a large number of input parameters. In contrast, empirical models require far less input parameters and are easier to apply (Renschler and Flanagan,

2002). They use statistical methods to establish relationships between existing variables, but do not provide explanations for the underlying mechanisms of these relationships. Empirical models have limited applicability outside the conditions used in their development. However, most watershed models combine process-based and empirical approaches, with process-based models utilizing empirical equations.

Watershed modeling emphasizes representation of watershed hydrology and water quality, including runoff, erosion, and washoff of sediment and pollutants. Since the establishment of the Stanford Watershed Model (Crawford and Linsley, 1966), 13

numerous hydrological models have been developed, using watersheds as the fundamental spatial unit to describe the various components of the hydrological cycle.

Watershed models have five basic components: watershed (hydrological) processes and characteristics, input data, governing equations, initial and boundary conditions, and output (Singh, 1995). Different treatments of the five model components have resulted in a significant range of watershed models, which can be grouped into two categories, according to how they treat the spatiality of watershed hydrology—lumped or distributed (Léon et al., 2001; Hornberger and Boyer, 1999).

Lumped models treat an entire watershed as one unit and take no account of the spatial variability in processes, inputs, boundary conditions, or hydrological properties.

Distributed models ideally account explicity for all spatial variations in the watershed, solving basic equations for each pixel in the grid. In reality, neither of these extremes alone is suitable for watershed modeling: a lumped framework is a gross oversimplification, while a distributed framework requires enormous amounts of data that are not readily obtainable. As a result, most models display aspects of both approaches, subdividing the watershed into smaller elements with similar hydrologic properties that can be described by lumped parameters. This modeling approach is commonly described as partially distributed, or quasi-distributed, as illustrated in

Figure 2.2 (Kite and Kouwen, 1992; Burns et al., 2004).

The description of hydrological processes within a watershed model can be deterministic, stochastic, or represents some combination. Deterministic models do not use random variables, i.e., for each unique set of input data, the models compute fixed, repeatable results (Law and Kelton, 1982), following a fully predictable behavior. The 14

governing equations describing the hydrological and soil erosion processes in a deterministic model are a major factor for model selection. Models with equations based on fundamental principles of physics or robust empirical methods, are most widely used for computing surface runoff and sediment yield (Burns et al., 2004).

Stochastic models, in contrast, use variable distributions to generate random values for model inputs (Zielinski and Ponnambalam, 1994; Clarke, 1998). The output from a stochastic model is random, with its own distribution, and can thus be presented as a range of values with confidence levels.

Process

Lumped Quasi-Distributed Distributed

Deterministic Mixed Stochastic

Figure 2.2 Process-based classification of watershed models, after Singh (1995)

Concern about NPS pollution is more recent than that about runoff (Olivera,

1996). In response to this concern, several watershed models, such as the Agricultural

Non-point Source Pollution (AGNPS), the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), the Hydrologic Simulation Program—FORTRAN

(HSPF), the Soil and Water Assessment Tool (SWAT), and the Storm Water

15

Management Model (SWMM), have been developed by different research institutions.

The following is a brief description of these models.

The AGNPS is an event-based distributed parameter model developed by the

USDA Agricultural Research Service to simulate pollution loads from agricultural watersheds and to assess the effects of different management programs. The model uses geographic data cells of 0.4 to 16 hectares (1-40 acres) to represent land surface conditions. Each cell has twenty-two parameters, including: SCS curve number, terrain description, channel parameters, soil-loss equation data, fertilization level, soil texture, channel and point source indicators, and an oxygen demand factor. Sediment runoff is estimated via the modified version of USLE (Universal Soil Loss Equation) and its routing is performed for five particle size classes. Runoff characteristics and transport processes for sediment, nutrients, and chemical oxygen demand are simulated at the cell level. AGNPS is limited to watersheds no larger than 200 km2 (Young et al., 1989;

DeVries and Hromadka, 1993; Engel et al., 1993), and can only be used to simulate a single event.

BASINS is a lumped watershed-scale model developed in 1996 by the EPA’s

Office of Water, to support environmental and ecological studies in a watershed context.

It is a multipurpose environmental analysis system, designed for use by regional, state, and local agencies in performing watershed and water quality-based studies. BASINS works within a geographic information system (GIS) framework, and needs large GIS data sets to setup the model. The system provides a user-friendly interface to conduct simple watershed-level screening analysis or detailed water quality modeling studies

(Shoemaker et al., 2005). BASINS also provides several hydrological modeling options, 16

such as Hydrologic Simulation Program—FORTRAN (HSPF), Soil and Water

Assessment Tool (SWAT), and Kinematic Runoff and Erosion Model (KINEROS), but requires training to use these advanced modeling options (Singh, 2004; Shoemaker et al., 2005).

HSPF simulates, over an extended period of time, the hydrological and associated water quality processes on pervious and impervious land surfaces and in streams and well-mixed impoundments (DeVries and Hromadka, 1993; Al-Abed and Whiteley,

1995). It is a lumped parameter model. With predecessors dating back to the 1960s,

HSPF is the culmination of the Stanford Watershed Model (SWM), the watershed-scale Agricultural Runoff Model (ARM), and the Nonpoint Source Loading

Model (NPS) into an integrated basin-scale model that combines watershed processes with in-stream fate and transport in one-dimensional stream channels (Donigian and

Davis, 1995; Shoemaker et al., 2005). HSPF also simulates transport of sand, clay and silt sediments, and a single organic chemical. It requires extensive model calibration, a high level of expertise for application, and is limited to well-mixed rivers and reservoirs and one-directional flow (Olivera, 1996; Bicknell et al., 2001; Shoemaker et al., 2005).

SWAT (Arnold et al., 1998; Arnold and Fohrer, 2005) is a river basin, or watershed-scale model developed for the U.S. Department Agriculture (USDA)

Agricultural Research Service (ARS), and it has proven to be an effective tool for assessing water resource and diffuse pollution problems for a wide range of scales and environmental conditions across the globe (Gassman et al., 2005). SWAT is most typically used in situations where one is modeling a mostly agricultural/rural watershed, 17

and was developed to predict the impact of land management practices on water, sediments, and agricultural chemical yields in large complex watersheds with varying soils, land uses, and management conditions over long periods of time (Srinivasan et al., 1998b; Arnold et al., 1999; Santhi et al., 2001; Saleh et al., 2000). The model is physics-based and computationally efficient, using readily available inputs, and allowing users to study long-term impacts. SWAT is a continuous time model (i.e., a long-term yield model). The model is not designed to simulate detailed, single-event flood routing, and is limited to one-dimensional well mixed streams and reservoirs

(Shoemaker et al., 2005).

SWMM is a dynamic rainfall-runoff simulation model developed by the USEPA.

It is applied primarily to urban areas and for single-event or long-term (continuous) simulation of water quantity and quality, using various time steps (Huber and

Dickinson, 1988). SWMM is most useful for simulating urban areas or areas likely to become urban. It is a non-linear, lumped and deterministic model that allows the user to simulate most of the flow and transport processes that occur in a watershed during and after a storm (Olivera, 1996). SWMM uses the USLE to estimate soil loss, Horton or Green-Ampt to estimate runoff from impervious surfaces. The SWMM model is based on four simulation blocks—Runoff, Transport, Extran, and Storage/Treatment, each of which is used for simulating a specific part of the flow or transport process.

The Runoff Block is used for simulating surface and subsurface flows, and generates hydrographs based on rainfall, soil moisture condition, soil type, land use, drainage area and topography. It accounts for constituent buildup and washoff processes, and generates pollutographs. The Transport Block is used for routing water and pollutants 18

through the drainage system. The Extran Block is a very sophisticated hydraulic routing block, used for backwater and tidal simulation. The Storage/Treatment Block characterizes the effects of control devices upon flow and quality, such as BMP (Best

Management Practices) treatment, and makes elementary cost computations (Huber and Dickinson, 1998).

SWMM is integrated with GIS technology, and continues to be widely used throughout the world for planning, analysis and design related to storm water runoff,

NPS pollution, combined sewers, sanitary sewers, and other drainage systems in urban areas, with many applications in non-urban areas as well (Tsihrintzis and Hamid, 1998;

Choi and Ball, 2002; Smith et al., 2004; Rossman, 2005). However, it has weak groundwater simulation capability (Shoemaker et al., 2005).

Each watershed model has its own strengths and is limitations, and is limited to certain spatial and temporal scales. Therefore these models cannot guarantee optimality, nor can they provide a precise link between locational land-use changes and pollution yields at the watershed outlet (Yeo, 2005). To solve the NPS pollution problem, abatement treatments and economic optimization must be considered in the modeling process.

2.4 BEST MANAGEMENT PRACTICES

Unlike point source pollution, which can be reduced through wastewater treatment plants (WWTPs), NPS pollution requires area-wide abatement practices. Urban and rural stormwater runoff can be controlled by using various best management practices

(BMPs). BMPs are novel technologies, based on the concept of ecological engineering, 19

which was first defined by Howard T. Odum (1962) as “those cases where the energy supplied by man is small relative to the natural sources but sufficient to produce large effects in the resulting patterns and processes.” Uhlmann (1983), Straškraba (1984,

1985), and Straškraba and Gnauck (1985) have defined ecological engineering as ecosystem management based on deep ecological understanding to minimize the costs of measures and their harm to the environment. Mitsch & Jørgensen (1989) define it as the design of human society with its natural environment for the benefit of both.

BMPs are either nonstructural, such as reduced road widths and elimination of sidewalks, or structural, varying from small, site-specific practices to large-scale regional practices. An urban stormwater BMP is believed to be the “best” way of treating or limiting pollutants in stormwater runoff. Stormwater treatment practices

(STPs) are the structural stormwater BMPs, including wetland, wet pond, infiltration, and filtering systems. Structural best management practices (BMPs) are now commonplace for stormwater management in new suburban developments (Villarreal and Bengtsson, 2004).

Before 1991, only a few states and municipalities had formal programs in place requiring that STPs be constructed to mitigate runoff pollution. With the advent of

Phase I of the federal National Pollutant Discharge Elimination System (NPDES) stormwater program in the early 1990's, many additional municipalities began stormwater pollution control programs, typically including STPs. As a result, numerous

STPs have been constructed throughout the U.S., but STPs are still a new tool for most engineers, and require more practice and information (CWP, 2004).

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2.4.1 Pond Systems

Pond systems are inexpensive to construct, but require a significant surface area per treated volume (Culp and Doering, 1995). Reduction of suspended solids (SS) and particulate pollutants is significant in pond systems (Martin, 1988; Pettersson, 1996).

Leersnyder (1993) found that 78% of suspended solids, 79% of total Phosphours, 84% of total Copper, 88% of total Zinc, and 93% of total Lead were removed in pond systems.

Most pond systems have very good performance for rainfall stormwater runoff in both urban and suburban areas (Villarreal and Bengtsson, 2004), but not for snowmelt.

A seasonal comparison of removal efficiencies shows that removal of Cd (75%) and Cu

(49%) is similar in summer and winter–spring, but removal of Pb, Zn and total suspended sediment (TSS) drops from 79%, 81% and 80% to 42%, 48% and 49%, respectively (Semadeni-Davies, 2006). There are several reasons for the poor performance of stormwater ponds in winter. The primary reason is the thick ice layer, sometimes reaching three feet in depth, which can effectively eliminate as much as half of the permanent storage volume needed for effective treatment of the incoming runoff

(Oberts et. al., 1989; 1994; 2003; Marsalek et al., 2003).

2.4.2 Wetland Systems

The use of natural or constructed wetlands for runoff treatment has shown promise for nonpoint source pollution control (Baker, 1992; Hammer, 1992; Knight, 1992). It has been well established that wetlands can improve water quality under certain circumstances (Kadlec and Kadlec, 1979; Nichols, 1983; Horner, 1986; Martin, 1988). 21

In particular, there is much research on the use of wetlands for wastewater treatment

(Chan et al., 1981; Heliotis, 1982; USEPA, 1985; Hammer, 1989; Geary and Moore,

1999; Pinney et al., 2000; Ko et al., 2004). More recently, research has been focused on the use of natural or constructed wetlands for treatment of non-point source pollution

(Martin, 1988; Stockdale, 1991; Baker, 1992; Carleton et al., 2001). Wetlands are significantly efficient for the treatment of urban runoff (Strecker et al., 1992; Schueler,

1993; Carapeto and Purchase, 2000; Carleton et al., 2001) and mine drainage (Mays and Edwards, 2001; Sheoran and Sheoran, 2006; Batty et al., 2005). Interest in using wetlands for the treatment of agricultural runoff is increasing (Hammer, 1992; Rodgers and Dunn, 1992; Poe et al., 2003). Sedimentation is one of the principal mechanisms of pollutant removal in wetlands. The retention of suspended solids in wetlands is controlled by particle size, hydrological regime, flow velocity, wetland morphometry, residence time, and storm surges (Boto and Patrick, 1979; Kranck, 1984; Schubel and

Carter, 1984; Walker, 2001). The Center for Watershed Protection has monitored many wetland systems and has found that they are capable of meeting an 80% TSS removal requirement (Brown and Schueler, 1997).

However, wetland systems have the same problem as pond systems in winter, when most of the plants in the wetlands die or hibernate, and the water is frozen.

Stormwater ponds and wetlands are the most popular STPs for several reasons: stormwater flooding control, aesthetics, pollutant removal capability, habitat value, and relatively low maintenance burden. Stormwater ponds can be pleasing to look at. There have been studies linking increases in property value associated with proximity to wet ponds/wetlands (Brown and Schueler, 1997; Cappiella and Brown, 2001; CWP, 2004). 22

Stormwater wetlands can provide diverse habitats for aquatic and terrestrial species. The large permanent volume of ponds and wetlands enhances pollutant removal, because of relatively long residence times (the length of time for water to pass through the pond or wetland), reduced flow velocities, and the ability to retain settled sediments and pollutants (Winer, 2000). Stormwater wetlands also provide biological uptake of pollutants through contact between wetland plants and stormwater runoff.

2.4.3 Infiltration Systems

Infiltration systems are frequently used for stormwater drainage management

(Dechesne et al., 2004), and they have valuable technical and environmental advantages (Ferguson, 1994): decrease of stormwater flows in sewer systems, retention of stormwater pollution, and recharge of groundwater (Dechesne et al., 2004).

Infiltration systems are innovative technologies designed to promote stormwater infiltration into subsoils. They help control floods, reduce contamination of stormwater runoff, and groundwater recharge and channel protection (USEPA, 1999a). Infiltration systems rely on maintaining the mechanism of soil infiltration. The retention of pollutants by porous media is the result of complex processes. There are two types of filtration, depending on the size of the stormwater particles (Herzig et al., 1970;

McDowell-Boyer et al., 1986): mechanical filtration affects large stormwater particles

(diameter > 30 μm), while small particles (diameter about 1μm) undergo physico-chemical filtration. Mid-range particles (3 μm < diameter < 30 μm) can be treated by both types of filtration (Dechesne, 2004).

23

Infiltration systems recharge the groundwater because runoff is treated for water quality by filtering through the soil and discharging to the groundwater. Few data are available regarding pollutant removal associated with infiltration systems. It is generally assumed that they have a very high pollutant removal rate, because none of the stormwater entering the filtration system remains on the surface. Schueler (1987) estimates the following pollutant removal rates: 75% of total suspended sediment

(TSS), 60-70% of phosphorous, 55-60% of nitrogen, 85-90% of metals, and 90% of bacteria. These removal efficiencies assume that the system is well designed and maintained.

The main environmental problem encountered with such systems is the possible contamination of the underlying soil and groundwater (Barraud et al., 1999; Pitt et al.,

1999). Research has shown that the topsoil layer acts as an effective pollutant barrier

(Nightingale, 1987; Mikkelsen et al., 1994; Hutter et al., 1998), but pollutant migration remains an issue. In addition, infiltration systems have the shortest lifetime and the highest cost among all BMPs. Failure of these systems has been attributed to poor design, inadequate construction techniques, low-permeability soils, and lack of pretreatment. Some design factors can significantly increase the longevity of infiltration systems (USEPA, 1999a), including: (1) better geotechnical and groundwater investigation, (2) standardization of observation well caps, (3) better specification of clean stone material for the reservoir, and (4) regular cleanout of sump pits (Cailli, 1993). These means not only extend the system lifespan, but also increase pollutant removal efficiency.

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2.4.4 Filtering Systems

Ever since 1885, when Percy F. Frankland discovered that London’s slow sand filters removed bacteria (Baker, 1981), many particle removal mechanisms have been developed, and different filter media have been tested for water pollutant removal.

Stormwater filtering systems represent a diverse group of techniques using such filtering media as peat, soil, sand, gravel, vegetation, or compost (Deletic, 1999;

Weber-Shirk, 2002; DeBusk et al., 1997; Abu-Zreig, 2001; Borin et al., 2005; Delgado et al., 1995). Flows greater than treatment capacity are bypassed around the filter to downstream stormwater management facilities to ensure the filter lifespan.

The four basic design components of a filtering system are: (1) inflow regulation, to divert a defined flow volume into the system; (2) pretreatment, to capture coarse sediments; (3) filter bed surface and unique filter media; and (4) outflow mechanism, to return treated flows back to the conveyance system and/or to safely handle storm events that exceed filter capacity (Claytor and Schueler, 1996). There are five groups of filtering systems that can be used for stormwater treatment: sand filters, open vegetated channels, bioretention areas, filter strips, and submerged gravel filters (Claytor and

Schueler, 1996). Surface sand and grass filters are the most commonly used filtering systems.

Surface sand filter systems have stormwater runoff first flow through a pretreatment chamber, where large particles settle down. The runoff is then treated as it flows through the filtering system (sand bed), collected in the underdrain and returned to the stream channels. Materials such as peat or compost can be used in place of sand.

A sand filter system was the first system to treat urban storm runoff in the early 1980’s 25

in the city of Austin, Texas (City of Austin, 1988). It is the most popular filtering system because of easy setup and maintenance. Sand filters have the following removal rates: 45 to 65% for various forms of organic carbon (BOD, COD, and TOC), 35% for total nitrogen, 35-90% for trace metals such as lead and zinc, and 40 to 80% for bacteria (Claytor and Schueler, 1996).

An important advantage of vegetation (grass) filter strips is that they are relatively cheap to construct and maintain (Dillaha et al., 1986). Grass filter strips have been recognized as good management practices for localized containment of urban pollutants, in particular heavy metals and organics associated with suspended solids

(Deletic, 1999). In one of the earliest reports, Mather (1969) finds that 94 to 99% of the

Biochemical Oxygen Demand (BOD) of a cannery effluent was removed in the course of an overland flow process. Bendixen et al. (1969) observe a 66% reduction in BOD.

Nitrogen (N) removal in these two studies varies between 61 and 94%, and phosphorus

(P) reduction between 39 and 81%. Grass filters can remove 65 to 70% of TSS,

20-50% of trace metals, and 65% of hydrocarbons. However, they have no ability to remove bacteria (Claytor and Schueler, 1996).

Claytor and Schueler (1996) also review nearly forty performance monitoring studies of stormwater filtering systems, in order to derive general design principles with regard to pollutant removal. These cases encompass different geographic and climatic conditions, different basin designs, different methods to compute pollutant removal, different storm events, and different inflows and outflows. They were conducted in Texas, Washington, Florida, California, and Virginia. Despite these differences, several important generalization principles with respect to the pollutant 26

removal performance of filtering systems have been formulated, pointing to sand filters’ excellent ability to remove suspended sediments, and to a mean total suspended sediment (TSS) removal rate of 75 to 90%.

2.5 WATER QUALITY STANDARDS

Water Quality Standards are the foundation of the water quality-based pollution control programs mandated by the Clean Water Act. They define the goals for a water body, by designating its uses, setting criteria to protect those uses, and establishing provisions to protect water bodies from pollutants. Two water quality standards are applied in this study: Environmental Quality Standard (EQS), and Total Maximum

Daily Load (TMDL). EQS is an ambient standard, using pollutant concentration as an index of water quality, while TMDL is an emission standard, using pollutant loading.

2.5.1 Environmental Quality Standard

An environmental quality standard (EQS) or event mean concentration (EMC) represents the mass concentration of a substance that should not be exceeded in an environmental system, often expressed as a time-weighted average measurement over a defined period. The USEPA set up this standard by sampling water quality three times between April 15th and October 15th. The ecosystem itself has abilities to purify the water, and the water body will dilute the pollutants after some amount of time. The

USEPA has different standards based on different time periods.

27

Watershed TSS mg/l Size Use Designation: WWH EWH

Headwaters (drainage area < 20 mi2) 10 10

Wadeable (20 mi2 < drainage area < 200 mi2) 31 26

Small Rivers (200 mi2 < drainage area < 1000 mi2) 44 41 WWH: Warm water habitat EWH: Exceptional warm water habitat Source: Based on the Eastern Corn Belt Plains Ecoregion, Ohio EPA (2006)

Table 2.1 Total suspended sediment (TSS) targets for the Big Darby Creek watershed

2.5.2 TMDL Standard

Before 1900, most legislation on surface water protection primarily dealt with point source pollution, such as industrial and municipal discharges (Schreiber et al.,

2000). The federal government did not do much about protecting surface water quality until the 1960s. The U.S. Congress, in the early 1960s, was not satisfied with the

States’ progress in pollution control and passed the Water Quality Act of 1965, requiring each state to adopt water quality standards better than or equal to those of the federal government. In the early 1970s, Congress felt that the states had failed to enact comprehensive water quality control legislation (Beck, 1991). This led to the passage of the Federal Water Quality Control Act Amendments of 1972, commonly knows as

PL 92-500 and the 1972 Clean Water Act (CWA). This became the framework for water pollution control policy during the past 20 years (Bayley, 1970). In the 1990s, ecosystem health and integrated management of water quality on a watershed basis became the major issues (Beck, 1991). The CWA establishes a national goal of

28

“fishable and swimmable” water bodies. Still many water bodies in the U.S. do not meet this goal, with diffuse pollution now being blamed for a large share of the problem. CWA section 303 (d) addresses these problematic water bodies by requiring states to develop and implement Total Maximum Daily Loads (TMDLs) standards to make water bodies fully functional ecologically (Brezonik and Cooper, 1994).

TMDL is the maximum level of a water quality parameter that a water body can assimilate without violating the standard for specific uses, such as drinking or recreation. Another goal is to set up plans for the allocation of maximum allowable pollution and strategies to meet these limits. If a stream or lake has been identified as not meeting water quality criteria and is “listed” by a state, the CWA requires that a

TMDL be completed. Once a TMDL is established, the responsibility for reducing pollution among both point sources (pipes) and diffuse sources is assigned. Diffuse

“sources” are included, but not limited to stormwater runoff from urban areas and agricultural fields, septic systems, eroding stream banks, and other sources.

Total maximum daily loads are watershed-based analyses of the quantities and sources of pollutants that prevent water from achieving its beneficial uses. The aim is to restore those uses through reductions in the pollutants discharged into the water. A watershed-based approach recognizes the effects of both point and non-point sources of pollution in degrading water quality. The analysis must identify the causes of beneficial use impairment and estimate pollutant loads that will meet water quality criteria and restore impaired uses within a specified time.

Water quality standards are set by states, territories, and tribes. They identify the uses for each water body, such as drinking water supply, contact recreation (swimming), 29

and aquatic life support (fishing), as well as the scientific criteria to support those uses.

The TMDL is a pollutant budget. This budget is most simply expressed in terms of loads, the quantity or mass of pollutants added to a waterbody (Idaho Division of

Environmental Quality, 1999). Pollutant loads can be calculated as the product of concentration and flow. According to USEPA regulations and guidance, this budget takes into account loads from point and non-point sources, and human-caused as well as natural-background loads. The budget is balanced at the point where water quality standards are just being met and is allocated among all the various sources. The pollutant budget must take into account the seasonality or cyclic nature of pollutant loads and water capacity, so that a temporary shortfall does not occur.

Under Section 303(d) of the Clean Water Act, each State must prepare a list of water bodies that are not meeting their water quality standards. The list needs to be submitted to the USEPA for review and approval every April of even years (e.g. 2000,

2002). Total Maximum Daily Loads (TMDLs) are then established based on the most recently approved list.

In January 1985, the first Water Quality Planning and Management rules implementing 303 (d) were adopted in 40 CFR, part 130 (Idaho Division of

Environmental Quality, 1999). At that time the USEPA still saw a limited role for

TMDLs, stating in the Federal Register that “EPA believes it best serves the purposes of the Clean Water Act to require States to establish TMDLs and submit them to EPA for approval only where such TMDLs are needed to ‘bridge the gap’ between existing effluent limitations, other pollution controls, and WQS (Water Quality Standards).”

According to these rules, the EPA provide the definitions of load, loading capacity, load 30

allocations, wasteload allocations, and the requirements for a 303(d) list.

In April 1991, the USEPA published the first guidance document on TMDLs:

Guidance for Water Quality-based Decisions: The TMDL Process. This document is still current and describes both the listing process and TMDL development. In it, the

USEPA first formalized the notions of phased TMDLs, pollution source trade-offs, reasonable assurance, negotiating a schedule, listing of threatened good quality waters, and biennial submission of lists, starting in 1992. This submission was subsequently codified in July 1992 amendments to 40 CFR Part 130, as a step to merge reporting requirements under 305(b) and 303(d). It was specified that the 1992 lists expired on

22 October 1992. These amendments also require specific identification of TMDLs to be completed during the two years preceding the next list. The USEPA regulations, guidance, and policy memos were assembled and published in February 1997 as Total

Maximum Daily Load (TMDL) Program: Policy and Guidance Volume 1. The Ohio

EPA has established its own TMDL program since 1996. There are 881 listed water bodies, and now the Ohio EPA is moving forward on several TMDL projects.

2.6 INTEGRATED SIMULATION AND OPTIMIZATION APPROACHES

In order to account for the spatial variability of land-use changes and to select different BMP technologies for reducing NPS pollution, optimization methods have been recently integrated into process-based models (Kalin et al., 2004; Baresel et al.,

2006; Veith et al., 2004; Cho et al., 2004). These models are used to select types and location of BMPs within a watershed. The integrated simulation-optimization approach is also used to assign sediment yield to sources (inverse problem), by analyzing the 31

sedimentographs generated at the catchment level within a watershed (Kalin et al.,

2004).

Baresel et al. (2006) apply a cost-effectiveness analysis to solve a mine water pollution problem at a watershed scale. A linear programming model is used to find the optimal solution. Two wetland systems are installed in order to reduce the wastewater coming from active and abandoned mines. However, the authors assume a constant pollutant transport rate regardless of stream flow, which is an oversimplification of the pollutant transport process.

Veith et al. (2004) apply a Genetic Algorithmic (GA) to improve current BMP management strategies, using a scenario-based approach (i.e. targeting) and a sediment simulation model (Universal Soil Loss Equation-USLE). The goal is to find alternative management plans that provide NPS reduction as compared to the current plan, but at low costs. The USLE model is a simple soil erosion estimation model, and cannot be used for urban areas, because it would underestimate pollutants from urban land uses.

Cho et al. (2004) integrate a GA and a water quality model (Qual2e) to achieve water quality goals and wastewater treatment cost minimization in a river basin. They only focus on wastewater treatment plants (WWTP), used for point source pollution.

However, most of the land uses in the study basin are forests and arable lands. NPS pollution is excluded from this study.

Morari et al. (2004) integrate and NPS model and a GIS system to evaluate the production and environmental effects of alternative BMPs in the Mincio River Basin

(Italy). The water transport model is not considered in this integrated model. In addition, the channel networks are also neglected. 32

These recent integrated approaches seem to be very promising and efficient, providing better alternatives for scenario-based simulation, especially regarding the relationships between land uses and NPS pollution. Some approaches focus on economic solutions, while others focus on pollutant estimations. However, few can really integrate all system components and provide more comprehensive information to decision makers. The purpose of this research is to propose such a comprehensive, integrated modeling approach.

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CHAPTER 3

MODELING METHODOLOGY

The review of the literature suggests a need for (1) spatial models (Spatial Model) to analyze the geographical data input to water quality analysis, (2) water quality simulation models (Watershed Model) to estimate pollutants and runoff, and (3) optimization models (Economic Model) to select least-cost BMP strategies that achieve water quality standards. This chapter presents these modeling approaches.

3.1 GENERAL MODELING APPROACH

The general model includes three major components: a Spatial Model, a Watershed

Model, and an Economic Model. Figure 3.1 depicts the basic relationships among these three major models. First, the Spatial Model is used to delineate different residential development scenarios and the BMP technologies appropriate for the study watershed.

Next, the Watershed Model is used to estimate the stormwater runoff and pollutants, from both urban and agricultural sources, for each of the residential scenarios. Finally, the Economic Model is used to find the optimal (minimum cost) combination of BMP

34

technologies that achieve USEPA water quality standards under each development scenario.

Spatial Model

Watershed Model

Economic Model

Figure 3.1 General Modeling Approach

3.1.1 Overview of the Spatial Model

This model is a set of distinct and independent computerized procedures that use

GIS (Geographic Information Systems) tools to delineate and better understand the study watershed, develop different land-use scenarios (Residential Suitability Analysis), delineate BMP technologies installation possibilities (BMP Technology Suitability

35

Analysis), and prepare data required by the watershed model (Watershed Model Data

Preparation). Both ArcView® 3.3 and ArcGIS® 9.1 are used.

Residential Suitability Analysis Model: This model is used to delineate potential residential development areas. Natural and human factors are used to search for areas suitable for residential development, such as slope, soil characteristics, existing land uses, and transportation network. The suitability analysis technique developed by

McHarg (1969) is applied. The output of this model is used as input to the Watershed

Model, to generate stormwater runoff and pollutants.

BMP Technology Suitability Analysis Model: This model uses suitability analysis to delineate areas suitable for BMP technologies setup. Each BMP technology has its own mechanism to reduce pollutant flows, but also different requirements for system installation. For example, Infiltration Systems and Filtering Systems cannot be installed in areas with a very high groundwater table, because of the risk of groundwater pollution, and Wetland Systems must be located where the soil has high organic matter. The output of this model will be used to specify constraints in the

Economic Model.

Watershed Model Data Preparation: The Watershed Model requires several data inputs to be generated by the GIS, such as watershed and stream channels morphology.

Surveys and aerial photography may help adjust the output of this analysis.

3.1.2 Overview of the Watershed Model

The Stormwater Management Model (SWMM) is used to simulate pollutant generation and runoff in each subcatchment and stream under each land-use scenario, 36

The SWMM is a sophisticated stormwater runoff and pollution simulation model developed by the USEPA. Its outputs are used as inputs to the Economic Model. The

PCSWMM® software is used.

3.1.3 Overview of the Economic Model

Linear programming is used to represent the optimization problem of seeking the minimum-cost combination of BMPs that achieve USEPA standards. Both Total

Maximum Daily Loads (TMDL) and Environmental Quality Standards (EQS) are considered. Sensitivity analyses are used to assess changes in the solution as a result of variations in the standards. The General Algebraic Modeling System (GAMS®) is used to solve this linear program. GAMS is a high-level modeling system for mathematical programming and optimization.

The following sections provide more details and discussions of the building blocks of the modeling methodology.

3.2 SPATIAL MODEL

Suitability analysis is used, as developed by McHarg (1969), who used a transparent map overlay technique to find the most appropriate locations for human developments. This technique is the basis for much work in environmental planning, and has been a major factor in the development of GIS software tools. Two applications are implemented: residential suitability analysis and BMP technology suitability analysis.

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3.2.1 Overview Of Suitability Analysis

McHarg’s suitability analysis method involved superimposing layers of geographical data (Figure 3.2), so that their spatial intersections (relationships) can be used in making land-use decisions. The output of a suitability analysis is a set of maps showing the level of suitability of each parcel of land.

Source: Landscape Architecture & Environmental Planning, University of California, Berkeley

Figure 3.2 Diagram of McHarg’s Suitability Analysis Method

38

A simplified illustration of how the suitability procedure works is provided in

Figure 3.3 (Steiner, 1991).

STEP 1

Select Map Data Factors by Type

Example 1 Example 2

A: 0-10% C A: Slightly Eroded A B: 10-20% B B: Slight to Moderate C B C: 20-40% C: Moderate A

Slope Map Erosion Map

STEP 2

Rate Each Factor for its Suitability for Land Uses

Factor Types Agriculture Housing Example 1 (Slope Map) A 1 1 B 2 3 C 3 3 Example 2 (Erosion Map) A 1 1 B 2 2 C 3 2

Continued

Figure 3.3 Suitability Analysis Procedure (Steiner, 1991)

39

Figure 3.3 continued

STEP 3

Map Out the Ratings for Each Land Use

Soil Map Erosion Map Soil Map Erosion Map

3 2 1 1 2 2 3 2 3 3 1 1

Agriculture Housing

STEP 4

Overlay Single Factor Suitability Maps to Obtain Composite Maps for Each Land Use

6 5 4 5 5 3 The Lowest numbers are best suited for the land 5 4 3 5 5 3 use, and the highest 4 3 2 4 4 2 numbers least suited.

Agriculture Housing

Since the various factors do not have the same importance, more complicated map calculation methods have been implemented using computer technology, in particular the linear weighted model, which better describes the capability of a land unit (Gordon,

1985), with:

40

n CWXjk= ∑ ik ijk (3.1) i=1

where:

i = environmental variables (factors) index;

j = spatial unit index;

k = utility (e.g. residential development) index;

Cjkjk = the final weighted index or score for spatial unit and utility ;

Wik = weight for variable ik and utility ;

Xijkijk = numerical value of variable in unit for utility ; n = number of variables used in the rating.

Use of equation (3.1) allows varying the weights associated with different variables, based on their relative importance. The linear weighted model is used to delineate potential residential development areas.

3.2.2 Residential Suitability Analysis Model

Several maps, representing both natural and social phenomena, contain data that can be classified or factored into groups related to some proposed land uses, such as low- and high-intensity residential development.

These maps become layers in the suitability analysis and can be classified into three categories: (1) factors that constrain (Constraints) a proposed land use; (2) factors that act as catalysts (Opportunities) for a proposed land use; and (3) factors acting as

“Knock-Out Constraints”, identifying areas where a proposed land use is strictly prohibited. (e.g., an existing urban area). These factor maps are then overlaid to produce composite maps representing the combination of all the factor maps. 41

Natural Human Natural Human EnvironmentNatural Factors EnvironmentHuman Factors Environment Factors Environment Factors Environment Factors Environment Factors

Weighting

Opportunity Constraint Knock-Out Map Map Constraint Map

Final Scenario Maps

BMPs Suitability Analysis Model

Figure 3.4 Conceptual Landuse Suitability Model

Figure 3.4 depicts the major components of the land-use suitability analysis. The natural environment factors play the role of a resource supply sector, pointing to favorable locations for future residential developments. The human environment

42

factors include transportation networks and existing land-uses, which restrict future residential developments.

The natural environment factors considered in this study include soil characteristics and slope, which are first rated according to their relative fit for residential development. The lowest numbers represent areas best suited and the highest numbers areas least-suited for residential development. Next, a linear weighting system is used to account for the relative importance of each factor. The final output overlay map represents the opportunity map for residential development.

Table 3.1 presents a hypothetical overlay value and weighting system. Input Maps

A-F represent natural environment factors. The input value is the original code number in the source map For example, Factor D is “Flood Frequency”, and the input value “1” represents “None”, “2” “Occasionally”, and “3” “Frequently”. The Scale Value is a rating given to environment factors, ranging from 1 to 5. The lower number represents a factor better suited to residential development, and the higher number the opposite.

The weight percentages represent the relative importance of the factors. The final score for each parcel is: ∑ Score Value× Weight (%) , and it ranges from 1 to 5. The final score map is the opportunity map, and the scores can be reclassified into such groups as Favorable, Neutral, and Unfavorable.

Table 3.2 presents an example of score calculations for a single parcel area. The final score is 2.45, which represents moderate suitability for residential development.

The actual data collection and score calculations of the study watershed are discussed in Chapter 4.

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Input Map Input Value Input Label Scale Value Weight (%) A 1 Low 1 10 2 Moderate 3 3 High 5 B 1 Low 1 10 2 Moderate 3 3 High 5 C 3 Very Long 5 20 D 1 None 1 15 2 Occasionally 3 3 Frequently 5 E 1 Good 1 15 2 Good 2 4 Moderate 4 5 Slight Sever 5 F 1 Low 1 30 3 Moderate 3 5 High 5

Table 3.1 Overlay Value and Weight

The human environment factors considered in this study are existing land uses.

The constraint and knockout constraint maps are derived from these factors. For example, buffers can be delineated along stream channels, and development may be forbidden in these areas to protect water quality. Transportation networks may also be considered as an accessibility factor, and only parcels along these networks may be developed.

44

Input Map Input Value Input Label Scale Value Weight (%) After Weighting A 1 Low 1 10 0.10 B 5 High 3 10 0.30 C 3 Very Long 5 20 1.00 D 2 Occasionally 3 15 0.45 E 2 Good 2 15 0.30 F 1 Low 1 30 0.30 Final 2.45 Score

Table 3.2 An Example of Score Calculation

Opportunity Map

Constraint Map

Knock-Out Constraint Map

Final Suitability Map

Figure 3.5 Conceptual Map Overlay

45

Urban areas can be used as “Knock-Out” areas, whatever the opportunity map overlay scores. Figure 3.5 depicts the overlay concept. Different development scenarios are defined, based on the final output scores, and will be used as the input of watershed model.

3.2.3 BMP Suitability Analysis Model

In addition to delineating potential residential development areas, it is also necessary to delineate areas that can be used for each best management practice (BMP).

Since a BMP technology is nature-based, it is necessary to understand the features of each BMP technology and its requirements, and then to use GIS tools to find locations where specific BMP technologies can be applied.

Each BMP has specific site installation requirements and varying efficiencies in dealing with the pollutants in stormwater runoff. There is no “best” technology, and technology choice depends on the location of the installation and the type of pollutants involved.

In the BMP suitability analysis, given the feasibility, pollutant removal capability, and environmental restrictions and benefits of each BMP, the overlay technique is used to delineate suitable areas for each BMP installation.

Figure 3.6 illustrates the BMPs suitability analysis model. First, identify the installation requirements of each BMP technology and select the factors that are considered in the suitability analysis such as slope, groundwater depth, and other natural environment factors. Next, create natural environment factor maps and overlay them to derive the BMP installation opportunity maps. Each BMP technology has its 46

own opportunity map. Two constraints are next used as knockout constraints: existing urban development and potential residential development.

Review the installation requirements of BMPs and select suitability factors

Existing Urban Development Natural Constraints

Environment Factors

Potential Residential Development BMPs Installation Scenario Constraints BMPs Installation BMPsOpportunity Installation Opportunity Opportunity

BMPs Suitability

Maps

Economic Model

Figure 3.6 Conceptual BMP Suitability Model 47

Finally, the opportunity and constraint maps are overlaid, leading to the final suitability maps for each BMP technology, which are then to be used as inputs to the economic model.

3.3 WATERSHED MODEL

The watershed model generates data input into the final economic model, and focuses on non-point source (NPS) pollution. The four major data inputs to the watershed model are: (1) residential development scenarios, (2) watershed characteristics, including boundaries and stream channel structure, (3) empirical equations used to estimate several variables, and (4) precipitation data, (either real or design storm data) These various components are illustrated in Figure 3.7.

NPS pollution is considered from urban and non-urban areas. In impervious urban areas, it is assumed that a supply of pollutants is built up on the land surface during the dry weather preceding a storm. This buildup may or may not be a function of time and factors such as traffic flow, dry fallout, and street sweeping (James and Boregowda,

1985). These pollutants are then washed off into the drainage system by the storm.

Non-urban areas include forests, wetlands, and agricultural land. Forests and wetlands have very low runoff, as compared to urban areas, and have low pollutant concentrations. However, agriculture lands have erosion potential. Erosion and sedimentation are often cited as major problems related to agriculture land runoff, contributing to the degradation of land surfaces, soil loss, and sedimentation in channels. This research assumes that soil erosion in agriculture lands is the major contributor to non-point source pollution in non-urban areas. 48

Residential Development Watershed Empirical Equation Scenarios Characteristics Estimations

Precipitation Watershed Model Data (SWMM)

Urban Areas Non-Urban Areas

Pollutant Loads Stream Runoff Pollutant Concentrations

Economic Model

Figure 3.7 Watershed Model

The outputs of the Watershed Model include pollutant loads, stream runoff, and pollutant concentrations, and will be used as inputs to the Economic Model.

The EPA Storm Water Management Model (SWMM) is used to simulate the watershed hydrological behavior, including stormwater runoff, pollutant concentration and pollutant loads. The SWMM was developed in 1969-71 and programmed in

49

FORTRAN. It is one of the first such models, and it has been continually maintained and updated. It is perhaps the best known and most widely used of the available urban runoff quantity/quality models (Huber and Dickinson, 1988).

SWMM is a dynamic rainfall-runoff simulation model that is used for single-event or long-term (continuous) simulation of runoff quantity and quality. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. SWMM continues to be widely used throughout the world for planning, analysis and design related to storm water runoff, combined sewers, sanitary sewers, and other drainage systems in urban areas, with many applications in non-urban areas as well. It is also used to evaluate the effectiveness of BMPs for reducing wet weather pollutant loadings

(Rossman, 2005).

Four major system simulation blocks make up the SWMM model: Runoff Block,

Transport Block, Extran Block, and Storage/Treatment Block. An overview of the model structure is presented in Figure 3.8. Based on their characteristics, blocks are categorized into three groups: input sources, central cores, and correctional devices

(Huber and Dickinson, 1988).

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OFF-LINE RUNOFF

LINE INPUT: refers to data input from terminal.

TRANSPORT

STORAGE/ EXTRAN TREATMENT

Source: Modified from Huber and Dickinson, 1988

Figure 3.8 Overview of the SWMM model structure, with linkages among the computational blocks.

Input Sources: The Runoff Block is the most important block when using the

SWMM model. It generates surface and subsurface runoff, based on arbitrary rainfall

(and/or snowmelt) hyetographs, antecedent conditions, land use, and topography.

Dry-weather flow and infiltration into the sewer system may be optionally generated using the Transport Block.

The Central Cores: The Runoff, Transport and Extended Transport (Extran)

Blocks route flows and pollutants through the sewer or drainage system. (Pollutant routing is not available in the Extran Block.) The Extran Block is a very sophisticated hydraulic routing block. It is used for backwater and tidal simulation purposes.

51

The Correctional Devices: The Storage/Treatment Block characterizes the effects of control devices on flow and quality, such as BMP treatment. Elementary cost computations are also made.

In Figure 3.8, the one-direction arrows represent data input from one block to another, and correspond to internal computer computations. Bi-directional arrows represent off-line data inputs, or external data inputs. For example, the output of the

Transport Block can be used as an input to the Extran Block, but this must be done outside the SWMM model software. The output of the Extran Block can be also used as an input to the Transport Block, but the SWMM model cannot do that by itself, and it must be done off-line.

This research does not consider backwater and tidal simulations (Extran Block), but uses the Runoff Block to estimate stormwater quality and quantity. Two important simulations, transportation (Transport Block) and treatment (Storage/Treatment Block), will be used in the Economic Model.

3.4 ECONOMIC MODEL

The Economic Model is used to seek optimal solutions that achieve water quality standards. Different land-use activities generate different impacts on the environment.

How best to reduce these environmental impacts to ensure environmental quality, as measured by certain standards, has become an issue for planners, governments, and developers. The cost-effectiveness method is used to select the environmental policies or technologies that have the lowest cost, while keeping the environmental impacts within the given standards. 52

BMP Suitability BMP Watershed Model Analysis Characteristics (SWMM)

Economic Model (Linear Programming)

Optimization Sensitivity Analysis Analysis

Decision Making

Figure 3.9 Economic Model

Figure 3.9 depicts the Economic Model. It is first necessary to know which BMP technologies can be applied, and their pollutant removal abilities. Next, the Watershed

Model generates the pollution input to the Economic Model under the different development scenarios. Then, optimization and sensitivity analyses are conducted for each development scenario, providing information to the decision makers to help them specify their environmental policies and regulations.

53

As measures of water quality, TMDL (Total Maximum Daily Load) and EQS

(Environmental Quality Standard) are the two standards considered. The problem is to minimize control costs while using different BMP technology combinations, subject to

TMDL and EQS standards.

Two major issues need to be considered: sediment transportation and BMP combinations.

1. Pollutant Transportation: Pollutant loads are transported downstream by stream

flow. Therefore, when calculating downstream water quality, it is necessary to

consider how much pollutant has been carried from upstream. The stream

tributary structure provides the relationship between stream segments. It is

necessary to calculate the transport rate of each stream segment.

2. BMP Combinations: Either one BMP or a combination of BMPs can be used to

reduce pollutant loads. However, locations vary in their suitability to setup of any

system. Each BMP has different physical requirements, such as slope, soil

characteristics, minimum drainage area, minimum setup area, and groundwater

depth. Sometimes, a given location may be suitable for more than one BMP

installation.

3.4.1 Model Objective

The objective is to determine the minimum-cost combination of BMP technologies for each development scenario, subject to water quality standards. The total cost is a function of the areas of BMP installations, and the unit cost of BMPs, with: 54

nm Total Cost = ∑∑CAij ij (3.2) ij==11 where:

C = unit cost for BMP installation and maintenance, per acre, A = area of BMP installation in acre,

in=→ 1 :subcatchment index, jm=→ 1 : BMP (including no BMP) index.

Equation (3.2) represents the total cost of BMP installation. The unit costs are given, and the BMP areas are the decision variables.

3.4.2 Model Constraints

3.4.2.1 BMP Cost

The costs for structural stormwater quality (BMP) include land buying costs, design and construction costs, and maintenance costs. Since each BMP has a different lifetime, the design and construction cost must be annualized. To simplify the problem, inflation is not considered.

3.4.2.2 BMP Pollutant Removal Efficiency

Each BMP has a different removal efficiency(β ) . For example, CWP (1996) reports that pond systems can reduce the total sediment load by 80%, wetland systems by 75%, infiltration systems by 90%, and filtering systems by 85%.

55

3.4.2.3 Net Pollutant Loading after BMP Treatment

Pollutants coming from buildup in urban areas and soil erosion in agriculture areas are carried by stormwater runoff into streams during storm events. Varying storm intensities have varying storm runoff, and generate varying amounts of sediments. The pollutant loading that remains after BMP treatment is given by the equation:

m NSis=××−∑γ ij GS is(1β j ) (3.3) j=1 where:

in=→ 1 : subcatchment, jm=→ 1 : BMP and (including no BMP), sh=→ Storm index (1 ),

GSis = Gross pollutant loading under storm s in subcatchment i ,

NSis = Net pollutant loading under storm s after BMP treatment in subcatchment i ,

γ ij = Share of the total area of subcatchment ij treated by BMP ,

β j = Pollutant removal rate of BMP j .

m ∑γ ij =1 (3.4) j=1

γ ij ≥ 0 (3.5)

The gross pollutant loading is an output of the SWMM model, and is related to stormwater runoff, land-use types, soil characteristics, and surface topographical and stream morphological information. The gross pollutant loading is an exogenous input to the management/planning model, but the area shares are unknown variables. Note that one of the BMP is “no treatment at all”. The whole area of subcatchment i must be

treated, and therefore the sum of theγ ij is equal to 1 (Equations 3.4).

56

3.4.2.4 Pollutant Transportation Rate

Different storms generate different streamflows. The final pollutant loading at each water quality control point along the stream is computed as:

n FSks=×∑α iks NS is (3.6) i=1 where:

kl=→Water quality control points (1 ),

FSks = Final pollutant load at water quality control point k under storm s ,

αiks = Transport rate from subcatchment iks to control point under storm .

The transport rate depends upon the flow in each stream segment. The higher the stream flow the higher the transport rate.

3.4.2.5 The Installation Area of a BMP

CWP (1996) reports that different BMPs have different installation area requirements. Integer programming must be used to force the use of the minimum area

requirement for a technology, if this technology is selected. Ifγ ij > 0 , then BMP technology j is selected in subcatchment i. Then, define:

⎧1 if BMP j is selected. X ij = ⎨ ⎩0 if BMP j is not selected.

It follows that:

X ij≥ γ ij (3.7)

TDAi γ ij×≥−−×1(1X ij )M (3.8) UDAj

57

where:

TDAi = total drainage area in subcatchment i ,

UDAj = minimum drainage area requirement for BMP j , M = large number.

The area Aij used by BMP j in subcatchment i is:

TDAi AUAij=×γ ij × j (3.9) UDAj

where UAj = unit installation area for setup of BMP j .

The area used (γ ij×TDA i ) is at least equal to the minimum drainage area

requirement UDAj , if technology j is used (X ij = 1) . If X ij = 0, then Equation (3.7)

guarantees that γ ij = 0 . Once γ ij is known, the installation area of BMP j can be calculated with Equation (3.9). Figure 3.10 illustrates the relationship between TDAi,

UDAj, and Aij. The blue arrow line represents the stream, and the outer solid line the subcatchment boundary. The dotted lines divide the subcatchment into three subareas associated with three different BMPs. The three small polygons present the BMP

TDAi practice areas. For example, γ i4 × represents the number of BMP 4 technology UDA4

TDAi units needed for installation, and γ i44××UA represents the needed area for UDA4

BMP 4 installation ( Ai4 ).

58

TDAi TDAi γ i4 × UDA4

Ai4 Ai1

Ai2

TDA γ × i i1 UDA 1

TDA γ × i i2 UDA2

Figure 3.10 Conceptual Diagram of Subcatchment and BMP Installation.

3.4.2.6 BMP Selection Constraints

Several distinct BMPs can be applied to reduce pollutant loading. It is also possible that none is needed because the water quality is good enough under specific land-use conditions.

Different BMPs have different setup limitations, such as slope, soil characteristics, groundwater depth, and drainage area size. Equations (3.7) - (3.9) guarantee the respect of the drainage area size constraint. A suitability analysis must be conducted to find suitable areas for BMPs in each subcatchment, leading to the possible combinations in the subcatchments. Each combination has different BMP selection constraints. The 59

max suitability analysis first provides the maximum area Aij that can be used by each BMP j standing alone in subcatchment i, with:

max Aij≤ A ij (3.10)

The following is a diagram (Figure 3.11) representing a combinations of BMPs in a subcatchment. This subcatchment can receive BMP 1, BMP 2, BMP 3 and BMP 4 technology. The possible BMPs selection combinations are:

1 No Selection 2 BMP 1 alone. 3 BMP 2 alone. 4 BMP 3 alone. 5 BMP 4 alone. 6 BMP 1 and BMP 2. 7 BMP 1 and BMP 3. 8 BMP 1 and BMP 4. 9 BMP 2 and BMP 3. 10 BMP 2 and BMP 4. 11 BMP 3 and BMP 4. 12 BMP 1, BMP 2, and BMP 3. 13 BMP 1, BMP 2, and BMP 4. 14 BMP 1, BMP 3, and BMP 4. 15 BMP 2, BMP 3, and BMP 4. 16 BMP 1, BMP 2, BMP 3, and BMP 4.

Figure 3.11 Example of BMP Combinations 60

The following joint constraints then apply:

max AAAii34+≤ i 4 (3.11)

max Aii12341+++≤AAAA ii i (3.12)

3.4.2.7 Water Quality Standard Constraints

Two water quality standards are considered: the Environmental Quality Standard

(EQS) and the Total Maximum Daily Load (TMDL). The EQS focuses on pollutant concentration, while the TMDL focuses on the total pollutant load.

1. EQS

The environmental quality standard (EQS), or Ambient Standard, represents the mass concentration of a substance that should not be exceeded and is expressed as a time-weighted average measure over a defined period. The final pollutant loading divided by the streamflow at control point k must be less than EQS, with

hh ⎡⎤FSks ⎢⎥∑∑ddEQSss()/()≤ (3.13) ⎣⎦ss==11ROks where:

ROks= stream flow at control point under storm , ks dss = number of days with storm type .

2. TMDL

The Total Maximum Daily Load (TMDL) represents a pollutant generation standard. Since most pollutants are washed off by storm runoff, it is necessary to estimate the total pollutant load for each storm event. It follows that

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nh ∑∑dNSTMDLsis×≤ (3.14) is==11

3.5 SUMMARY

The details of the Spatial Analysis, Watershed, and Economic Models have been discussed in the previous sections, and their interactions are summarized in Figure 3.12.

Natural and human environment factors are data sources for both the Spatial Model and the Watershed Model. The Spatial Model includes the residential suitability model and the BMPs suitability model, which interact because they share the same area. An area used for residential development cannot be used for BMPs installation. The Watershed

Model is used to generate pollutant loads and runoff under specific residential scenarios. Hence, the residential suitability analysis is an input to this model. The

Economic Model includes optimization and sensitivity analyses, with inputs from the watershed and BMP suitability models. The Watershed Model provides the gross pollutant generations, and the BMP suitability model provides the pollutant removal ability of the watershed. The Economic Model searches for the least-cost technological solution, which should provide information to decision makers for developing environmental policies and for better understanding the relationships between urban development and water quality.

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Natural Environment Human Environment Factors Factors

Spatial Analysis Model

BMPs Suitability Residential Analysis Suitability Analysis

Watershed Model (SWMM)

Economic Model

Optimization Sensitivity Analysis Analysis

Decision Making

Figure 3.12 Integrated Model Flowchart

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CHAPTER 4

DATA SOURCES AND PROCESSING

The spatial, watershed, and economic models require a lot of data, parameters, and input coefficients. This chapter discusses the assumptions, data requirements, and estimations in these three models. The first section is watershed characteristics which including catchment delineation, land use form, soil characteristics, and stream data.

The second section is for the inputs of the SWMM model, including precipitation, runoff routing, channel/pipe data, and water quality. The last section is for the economic model, including installation and maintenance cost, land purchasing cost, pollutant removal and transport rate, and water quality standards. Some are from empirical equation estimation, some from survey, and some from literature reviews.

4.1 OVERVIEW

Several spatial data sets are used to implement the modeling approach: (1) Digital

Elevation Model (DEM) from the U.S. Geological Survey (USGS); (2) stream channel dimensions from field survey; (3) soil data from the Soil Survey Geographic Database; and (4) land-use information from the Ohio Department of Natural Resources (ODNR).

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The land-use/cover information is interpreted from satellite images (Landsat Thematic

Mapper), taken in September and October, 1994. All data are provided at the 1:25,000 scale, with a 30-m resolution. Historical land-use and population trends in the study area and the state of Ohio are derived from the U.S. Census and a report downloaded from Steve Gordon’s Big Darby Watershed Project website: http://facweb.knowlton.ohio-state.edu/sgordon/research/darby/start.html. All GIS data are projected into the UTM zone 17 with the North American Datum 1983. See

Appendix A for detailed projection information.

In addition to spatial data sets, many parameter inputs to the watershed and economic models must be estimated, using empirical equations or secondary data collection, such as stream channel dimension and precipitation. These will also be discussed in this chapter. Table 4.1 lists the data and data sources.

4.2 DESCRIPTION OF THE STUDY AREA

4.2.1 The Big Darby Watershed

The Darby Creek watershed, including the Big and Little Darby Creeks, is an important water resource in Central Ohio. Many studies describe these streams as the most biologically diverse streams of their size in the Midwest (Ohio EPA, 2006). The

Big and Little Darby Creeks have been designated as State and National Scenic Rivers, and the watershed is known to provide habitats for several state and federally listed endangered species.

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Model Data Data Source Spatial Analysis Model Landform (Slope) USGS Soil USGS Land-use MRLC Satellite Image Transportation Network Big Darby Website Watershed Model (SWMM) Precipitation Empirical Equation

Stream Dimension Empirical Equation Field Survey Aerial Photo Manning’s Coefficient (n). USGS

Subcatchment Data Dimension GIS Measurement Pervious/Impervious Cover Hill, 1998; GIS Manning’s Coefficient (n) EPA (TR-55) Depression Storage Empirical Equation Infiltration USEPA Soil Conservation Service Horton’s Equation Water Quality Land-use Data GIS Buildup Literature Washoff Literature Erosion USLE Equation

Economic Model Land Purchasing Cost County Auditors BMPs Installation Cost CWP, EPA Literature BMPs Maintenance Cost CWP, EPA Literature TSS Transport Rate Literature Water Quality Standards EPA

Table 4.1 Models and Data Sources 66

The Big Darby Creek is an exceptionally diverse, warm water aquatic ecosystem near Columbus, Ohio. The river meanders over approximately 86 miles, with 245 miles of tributaries that flow into it, from its headwater near Marysville to its confluence with the Scioto River near Circleville. The Big and Little Darby Creeks are home to 86 species of fish and 41 species of mollusks, with 7 fish species and 6 mollusk species on the Ohio endangered species list (Gordon and Simpson, 1994; USGS, 2000).

The Big Darby Creek watershed drains 557 square miles of agricultural areas and suburbs, located to the northwest and west of Columbus. The basin is primarily in

Logan, Union, Champaign, Madison, Franklin, and Pickaway counties, and the predominant land use is agriculture (Figure 4.1). Recent studies document declines in water quality and stream habitat. Point source pollution (from pipes), runoff from urban areas and agricultural land, and poor stream bank land management are degrading some stream segments today. Among the most visible and widely publicized future threats to the Darby is the conversion of farm land into suburban and commercial land uses (Ohio EPA, 2006).

Some of the Darby Creeks tributaries, such as Sugar Run, and Robison Run, are near Plain City and Marysville. The rapid development of the watershed has a significant impact on water quality and the ecosystem. Several key Darby tributaries suffer from too much sediment, sewage, and farm and lawn chemicals. They include

Hellbranch Run in western Franklin County and Sugar Run, Robinson Run and Treacle

Creek in Union County (Hunt, 2005). Total phosphorus concentration in sediment was evaluated at all sites (except two sites on Little Darby Creek), which all exceeded LEL

(Lowest Effect Level) concentrations. These concentrations were the highest in Little 67

Darby Creek, Hellbranch Run at its mouth, Treacle Creek, Robinson Run and Sugar

Run (Ohio EPA, 2004).

Source: Ohio EPA, 2006

Figure 4.1 Land Uses in the Big Darby Creek Watershed

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4.2.2 The Study Area

Urbanization and agricultural activities, which are major NPS pollution sources, characterize the study area. Two of the Creeks tributaries, Sugar Run and Robinson

Run, are only miles from downtown Marysville and Plain City. While the currently predominant land use in these subwatersheds is agriculture, urban areas have increased by 3,634-acre, or eleven times, between 1992 and 1999. In addition, according to 1990 and 2000 Census data, the populations of these two subwatersheds increased by 3.23% and 49.57%, while the statewide population grew by only 0.46 %. Those urban land-use and population changes point to rapid urbanization (Table 4.2).

Block Population in Population in Absolute Relative County Tract Group 2000 1990 Change Change (%) Madison 401 1 1637 1505 132 8.77 Madison 401 2 1720 1150 570 49.57 Madison 401 3 1488 1378 110 7.98 Madison 401 4 1054 1007 47 4.67 Union 505 2/3 2652 1992 660 33.13 Union 506 2 1644 1164 480 41.24 Union 506 3 1090 1042 48 4.61 Union 506 4 1124 1059 65 6.14 Union 507 1 1888 1787 101 5.65 Union 507 2 1323 1111 212 19.08 Union 507 3 1151 1115 36 3.23 Union 507 4 1323 987 336 34.04

Table 4.2 Population Change Between 1990 and 2000 in the Darby Watersheds

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4.2.3 Catchment Delineation

The study subwatersheds must be divided into catchments for simulation and

BMPs planning purposes. The watershed and economic models require smaller areal units, in order to distinguish and estimate pollutant contribution from different areas.

Also, the costs of BMPs implementations vary geographically, requiring a spatially disaggregated approach. Based on the Qualitative Habitat Evaluation Index (QHEI) sample locations, the subwatersheds are divided into 23 catchments, each characterized by their land uses and stream features.

There are several ways that watersheds can be delineated with GIS software. One way is to pre-define the minimum area of a watershed. In this case, the size of the watershed is controlled by the number of cells that need to flow into a cell to classify it as a stream. The size of a cell is pre-defined, and usually depends on the map source resolution. In this case, a 30-m × 30-m cell is used. One of the software using this concept is ESRI’s “Hydrologic Modeling” sample extension, included in Spatial

Analyst. Another way is to specify a point with a cursor in the view for which a watershed should be created. Fridjof Schmidt (2001) uses the method to write an

Avenue Script running under ArcView. It takes a point theme as the catchment outlet input (Points A and B in Figure 4.2). It simultaneously creates watersheds for multiple points that a user defines as watershed outlets. Despite the differences between approaches using GIS software, the fundamental concepts of watershed delineation are the same. Under the assumption that all the water that falls onto the watershed can potentially reach a channel, Jenson and Domingue (1988) describe the watershed delineation procedure as: (1) fill, (2) flow direction/accumulation, (3) stream network 70

delineation, and (4) watershed delineation. An algorithm corrects for the problem of sparse data in the DEM data matrix and the interpolation of values that produce "sinks"

- places where the water arriving at that cell would disappear from the system and never reach a channel. The goal of this adjustment is to force all water to flow across the watershed until it reaches a channel and for all water in the watershed to get to a pour point, the cell at the watershed outlet to which everything drains.

B

A

Source: McCuen, 1998

Figure 4.2 Delineation of watershed and subwatershed boundaries

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The first step in any of the hydrological modeling tools in ArcView is to fill single- cell depressions (sinks) by raising this cell’s elevation to that of its lowest neighbor, if that neighbor’s elevation is higher. Filling these cells reduces the number of depressions to be dealt with. These sinks are usually generated by an error of the

DEM. Sinks need to be filled because a drainage network is built, that finds the flow path of every cell, eventually draining water off the edge of the grid. If cells do not drain off the edge of the grid, they may attempt to drain into each other, leading to an endless processing loop (Jenson and Domingue, 1988). Figure 4.3 illustates how the

FILLing functions operate:

Source: http://gis.washington.edu/cfr250/lessons/hydrology/

Figure 4.3 The Diagram of DEM Filling

The second surface process, flow direction-accumulation, is to identify the flow direction, based on cell elevation and neighboring areas, and to count the number of accumulated cells (or accumulated weights) from upstream areas. The flow direction

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for a cell is the direction in which water will flow out of the cell. This flow is oriented towards one of the eight cells surrounding the central cell, as illustrated in Figure 4.4.

For example, if a cell’s flow direction is due north, the cell's value is 64 in the output grid. These numbers do not have any absolute, relative, or ratio meaning, they are numeric place holders for nominal direction data values (since grid values are always numeric). This D-8 method was introduced by O’Callaghan and Mark (1984) to identify flow direction, and has been widely used (Jenson and Domingue, 1988; Mark,

1988; Mark et al., 1984; Band, 1986).

The third step is to delineate the stream network from the flow accumulation with a threshold value (Mark 1988). The last step in watershed delineation is to perform the function itself. The grid processor needs three grid themes: pour points, flow

Input flow Direction Output Value Source: http://gis.washington.edu/cfr250/lessons/hydrology/

Figure 4.4 Flow Direction and Output Cell’s Value

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accumulation, and flow direction. The actual task of delineating watersheds is performed with an Avenue script (Appendix B). Following those procedures and using the Wshed_point.ave written by Schmidt (2001), this study delineates 23 catchments

(Figure 4.5) based on the original seven QHEI subcatchments.

4.3 ANALYSIS OF LANDFORM AND SOIL

4.3.1 Landform

Landform is an important factor affecting stormwater runoff and pollutant generation. It is also a key factor for the suitability of residential development and

BMPs installation. A suitable landform saves construction and maintenance costs, but also reduces such environmental hazards as erosion and flooding. Steeper slopes usually involve higher costs, but too small slopes may create drainage problems. A slope map is derived from the DEM available on Gordon’s Big Darby Website. The

DEM is derived with ArcGIS, using USGS hydrography (DLG) files for hydrological correction. Elevation is measured in meters (Figure 4.6), and slope is reclassified into five groups: 0-0.5%, 0.5-3%, 3-5%, 5-10%, and above 10% (Lynch and Hack, 1984).

Slopes of less than 0.5% have drainage problems, and slopes over 10% have higher cost for construction (Figure 4.7). In general, the study watershed is very flat, except for some locations near the streams.

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Figure 4.5 Catchments

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Figure 4.6 Elevations

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Figure 4.7 Slopes

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4.3.2 Soil

Soil data (GIS layer in vector format) have been obtained from the Madison and

Union County Engineering Offices. Additional detailed soil information has been obtained from the Soil Survey Geographic Database. There are fifty-five types of soils in the study area, each with has its own characteristics and suitability. For example,

Muck is bad for construction because of the drainage problem, but good for wetland systems because of its high organic matter content. Specific soil characteristics are discussed below that are important for the residential and BMPs suitability analyses.

Surface Soil Texture: The effective depth of residential construction (shallow foundation construction) varies between 3 and 16 feet. The soil texture of the deepest layer is considered here. Surface soil texture and soil drainage class are used to identify the soil liquefaction vulnerability, a lower classification number indicating higher liquefaction vulnerability, or unstable foundations. Muck is a highly organic soil, usually found in the bottom of rivers and lakes, and is the worst surface soil texture for building construction. Silty clay loam is the best, and silty loam is the second best.

Shrink-Swell Ability: Some types of soil, following changes in moisture level, have the ability to shrink or swell, and thus release an extreme amount of stress upon the surrounding environment, including pipes and the foundations of residential or commercial buildings. The lower the shrink ability, the higher the suitability for construction.

Ponding Duration: It is important to avoid seasonal ponding areas, which imply higher construction and maintenance costs. The focus is on long-period ponding areas.

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Flood: Areas with high flooding potential should be restricted from residential development. Based on soil information, three categories of flood are derived: frequent, occasional, and none.

Drainage: Sites with good drainage ability have lesser construction cost. Based on soil data, four groups of drainage ability can be delineated: good, moderate, slightly severe, and severe.

Concrete Corrosion: Concrete corrosion affects building foundations. The higher the concrete corrosion, the lesser the potential for residential development. Based on soil data, three categories of concrete corrosion are defined: high, moderate, and low.

Groundwater Depth: Groundwater is water located beneath the ground surface in soil pore spaces and in the fractures of geologic formations. A formation of rock/soil is called an aquifer when it yields a useable quantity of water. It is naturally recharged from, and eventually flows to, the surface. Natural discharge often occurs at springs and seeps, and can form wetlands. Some BMP technologies, such as infiltration and filtering systems, are not suitable with a high groundwater level.

Organic Soil: Soil organic matter is any material in the soil that was originally produced by living organisms. It consists of a range of materials, from the intact original tissues of plants (mainly) and animals to the substantially decomposed mixture of materials known as humus (Dunn, 2003). Every soil has a certain degree of organic matter in it. The OML and OMH represent the range of organic matter (“Low” and

“High” in soil). Areas with organic soil have a higher probability of success with constructed wetlands. The OMH is used in this case.

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Infiltration Rate: Infiltration refers to the movement of water into the soil layer.

The rate of this movement is called the infiltration rate. If rainfall intensity is greater than the infiltration rate, water will accumulate on the surface and runoff will occur.

Infiltration systems depend on soil infiltration ability.

4.4 LAND-USE AND TRANSPORTATION NETWORK

The Multi-Resolution Land Characteristics (MRLC) map is used for this land-use analysis. The MRLC consortium of federal agencies was originally created in 1992

(MRLC 1992) to purchase Landsat imagery for the nation and to develop a land cover dataset. Beginning in 1999, a second-generation consortium has been created to generate a new Landsat image and land cover database, called MRLC 2001. The

MRLC consortium now includes the USGS (United States Geological Survey), EPA,

BLM (Bureau of Land Management), USFS (United States Forest Service), NOAA

(National Oceanic & Atmospheric Administration), NASA (National Aeronautic and

Space Administration), NPS (National Park Service), USDA (United States Department of Agriculture), and USFWS (US Fish and Wildlife Service).

MRLC 2001 is designed to meet the current needs of Federal agencies for nationally consistent satellite remote sensing and land-cover data. The data used in the present research are from the 1994 MRLC map. Table 4.3 indicates that Row Crop is the major land use (72%) in the study watershed. Pasture and Hay are the second dominant land use (17%). Urban land uses, including commercial, industrial, transportation, and residential activities, represent less than 1.6% of the total study

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Land Use Area (100 m2) Area (acres) Percentage (%) Commercial/ Industrial/ Transportation 10,618 262 0.56 Deciduous Forest 130,057 3,213 6.92 Emergent Herbaceous Wetlands 1,251 31 0.07 Evergreen Forest 378 9 0.02 High Intensity Residential 2,647 65 0.14 Low Intensity Residential 15,948 394 0.85 Mixed Forest 81 2 0.00 Open Water 4,658 115 0.25 Pasture/ Hay 321,993 7,957 17.12 Row Crops 1,374,052 33,954 73.06 Urban/ Recreational Grasses 15,571 385 0.83 Woody Wetlands 3,461 86 0.18 Total 1,880,714 46,473 100.00

Table 4.3 Land Uses in Study Watershed in 1994

areas. Figure 4.8 presents the distribution of land uses in 1994 in the Big Darby Creek watershed.

The transportation network is another factor in the search for potential development areas. The higher the accessibility, the higher this potential. The Darby watershed is close to Marysville and Plain City, and is crossed by major state routes and interstate highways (Figure 4.8).

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Figure 4.8 Land Uses and Transportation Networks in 1994

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4.5 STREAM DATA

According to USGS and USEPA’s RF3 data base (Reach File Version 3.0), and

Ohio Environmental Protection Agency’s (Ohio EPA) Planning and Engineering Data

Management System for Ohio (PEMSO) GIS database, there are three major streams in the study area, Big Darby Creek, Robinson Run, and Sugar Run.

RF3 was created in 1989 and released in draft form in early 1993. It contains over

3,100,000 reaches, representing streams, wide rivers, reservoirs, lakes, a variety of hydrographic features, and U.S. the coastal shorelines. The PEMSO database is a collection of streams and other waterbodies in Ohio, assessed and recorded as part of the U.S. EPA 305(b) Waterbody System.

The Big Darby Creek is the most important stream running through the study area, from the northwest to the south. Robinson Run and Sugar Run are the other two minor streams. The rest of the streams are ditches, which make it very difficult to get all the information needed to run the SWMM model. In order to obtain this information, a field survey was conducted to measure the dimensions of all the streams.

4.5.1 Types of Streams

Besides the three major streams, the other streams are natural or manmade ditches for agriculture irrigation. One small stream, Sweeny Run, runs through the southern part of Union County and the northern part of Madison County, and merges with the

Big Darby Creek. Two big ditches in Madison County are the Washington Ditch and the Jones Ditch (Figure 4.9).

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Figure 4.9 Detail Streams

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1. Big Darby Creek

The Big Darby Creek is the major stream in the study area. Its width ranges from

52 to 104 feet, as measured from aerial photographs taken in 2002. Based on aerial photographs the stream width can only be assessed by the different colors for the water body and land. This is not bankfull width, and should be smaller than bankfull width.

The Big Darby Creek is a natural creek, with overland slope less than 0.39%, and an average overland slope equal to 0.1125%. The predominant land use along the creek is agriculture. The Big Darby Creek also runs through Plain City, which had 2,832 residents in 2003. There is a 5-ft to 10-ft green wooded corridor along the creek, which prevents sediments from reaching the water, and where shade reduces temperature during summertime, thus helping improve water quality.

2. Sugar Run

Sugar Run is one of the Big Darby Creek’s tributaries. Unlike the Big Darby

Creek, it has steeper channels. The overland slope varies from 0.06% to 0.72%, with an average of 0.23%. The headwater channels of Sugar Run have been maintained by the

S.C.S. (Soil Conservation Service) program, and were expanded to uniform dimension of 7-ft depth and 17-ft width. While the headwater section of Sugar Run does not have tree corridor protection, it is still protected by a green belt, even in wintertime. The dominant land use along the stream is agriculture. The downstream part of Sugar Run, south of Taylor Road, becomes a natural stream with tree corridor protections. It merges with the Big Darby Creek at Plain City.

3. Robinson Run

Robinson Run stretches from the northern to the southern parts of the study area, 85

and merges with the Big Darby Creek near Plain City. It is a natural stream, so its channel dimension does vary. The overland slope of Robinson Run varies from 0.09% to 0.69%, with an average slope of 0.26%. The upstream Robinson Run is steeper than downstream. Unlike Sugar Run, Robinson Run has not been modified by human intervention, and has a naturally irregular channel. The width of the upstream part is narrower than that of the downstream part, due to natural hydraulic factors. The dominant land use along Robinson Run is agriculture.

4. Sweeny Run

Sweeny Run is a small steam running from West to East. It has only one tributary.

Based on its shape, it is clear that some sections of Sweeny Run have been modified by human intervention. The average stream width is around 20-ft, and its depth is around

4-ft.

5. Washington Ditch and Jones Ditch

Washington Ditch and Jones Ditch are located in the northern part of Madison

County. Parts of these ditches are manmade, for agricultural irrigation and drainage.

The widths of the channels, from upstream to downstream, are maintained in the range of 7-ft to 10-ft, and channel depths are kept within 5-ft.

6. Other Streams

The other streams in the study area are small streams or ditches. Most of them do not even have a name. Some are manmade or have been modified by human intervention, and some have been formed naturally. Natural streams usually do not have a regular shape, in contrast to manmade streams.

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The streams in the northern area are usually smaller than in other areas, since most of them are the headwaters of Robinson Run. However, the central area streams are larger than those in the northern area, because some of them connect to the Big Darby

Creek directly, and some of them are close to urban areas. The streams in the western and southern areas have a more uniform shape, and have been well dredged.

Table 4.4 presents a summary of the above descriptions.

4.6 INPUTS TO THE SWMM MODEL

The data inputs required by the SWMM Runoff Block model can be grouped into four categories: Precipitation, Infiltration, Routing, and Water Quality.

4.6.1 Precipitation

The total precipitation for a one-year normal storm with a 2-hr duration is derived from the IDF Columbus curve (Rainfall Intensity-Duration-Frequency curve). The SCS storm distribution curve is then used to distribute the rainfall intensity over the 2-hr period.

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Name Type Size Picture Natural Stream The channel Major Stream width is around Big Darby 100-ft; the Creek Agricultural channel depth is irrigation/Drainage around 7-ft. Stormwater Natural Stream The channel Major Stream width is around Robinson 15-20-ft. The Run Agricultural depth is around irrigation/Drainage 3-ft. Stormwater Natural / Manmade The size depends Stream on the location of the stream. Major Stream Upstream Sugar Run channels are Agricultural manmade, of irrigation/Drainage uniform size. Downstream Stormwater channels are not.

Natural / Manmade The size depends Minor Streams on where the Ditches channels are Agricultural located. irrigation/Drainage

Table 4.4 Types of Channels in the Study Area

4.6.1.1 Storm Types

Precipitation can take many forms, such as rain, snow, sleet, hail, and mist. With respect to hydrological design, McCuen (1998) points out that only rain and snow are 88

important. Rainfall directly affects the amount of suspended sediments going to stream channels, while the impact of snow is related to its melting, which depends on temperature. The focus in this research is on rainfall only.

The time distribution of rainfall is presented by a hyetograph, which is the graph of rainfall intensity or volume as a function of time (duration). Precipitation intensity and duration are two of the most important factors that affect stormwater runoff and sediment generation.

There are two types of storm events: actual storms and design storms. Rainfall analysis is based on actual storms. Either actual or design storms can be used in hydrological design. Here, actual storm data are used to generate the probabilities of different rainfalls, and design storms are used to simulate storm runoff and sediment generation.

4.6.1.2 Rainfall Characteristics

Duration, volume, intensity, and frequency are the four important rainfall characteristics for hydrological analysis and simulation. See Appendix C for a detailed discussion of the Intensity-Duration-Frequency (IDF) curve.

Figure C.1 presents design storms for Columbus. The maximum duration is 150 minutes (2.5 hours). Figure C.1 presents curves for one-year to 100-year storms. I1 represents a normal storm, happening once a year, and I10 represents a 10-year storm,

(once in 10 years).

For a one-year normal storm, rainfall duration ranges from 5 to 150 minutes. The shorter the rainfall duration, the higher the rainfall intensity. For example, a 5-min 89

one-year normal storm has a 7.5 in/hr intensity and yields 0.625-in rainfall, while a

150-min one-year normal storm has a 0.45 in/hr intensity, and yields 1.125-in rainfall.

4.6.1.3 Estimation of precipitation

The IDF curve can be presented mathematically by a set of equations described in detail in Appendix D. However, these equations can be used only when the IDF curve is available. Here, there is no IDF curve for a 1-yr storm, and therefore these equations are not suitable for precipitation estimation.

The method used here to estimate precipitation is based on the historical precipitation data from the National Climatic Data Center of NOAA (National Oceanic and Atmospheric Administration). NOAA has classified precipitation data for

Columbus into three groups, based on 30 years of data: (1) number of days with precipitation greater than 0.01-inch, (2) greater than 0.1-inch and (3) greater than

1-inch. Since a higher rainfall volume has a higher impact on stormwater runoff and sediment loads, the 0.5-inch category is used. In addition to the 30 years of data, this study also uses 10 years of daily precipitation data, counting the number of days with precipitation greater than 0.5-inch (middle point between 0.1 inch and 1 inch). This amount of rainfall also generates enough surface runoff to wash off the pollutants.

Table 4.5 presents detailed data for each month over 10 years. The average number of days with precipitation greater than 0.5-inch is 26.2. Table 4.6 presents the

30 and 10 year averages of the number of days with precipitation greater than 0.01-inch,

0.1-inch, 0.5-inch, and 1.0-inch for every month. May, June, July, and August are the months with the most rain in Columbus. 90

Month Year Jan Feb Mar AprMay Jun Jul Aug Sep Oct Nov Dec Total 2004 2 1 1 3 4 243314 3 31 2003 1 2 1 4 5 219512 2 35 2002 0 1 2 2 4 121312 1 20 2001 0 1 0 3 6 232032 1 23 2000 1 2 3 2 3 242221 1 25 1999 2 2 2 2 1 031101 2 17 1998 1 1 0 5 2 622122 1 25 1997 2 1 2 1 3 534111 1 25 1996 2 1 2 3 4 341313 3 30 1995 3 2 1 3 4 444122 1 31 Average 1.4 1.4 1.4 2.8 3.6 2.7 3 2.9 2 1.4 2 1.6 26.2

Table 4.5 Number of Days with Precipitations Greater Than 0.5-in in Columbus

Normal No. Days with: Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year Precipitation ≧0.00 in. 30 31 28 31 30 31 30 31 31 30 31 30 31 365 Precipitation ≧0.01 in. 30 13.8 11.4 13.1 13.2 12.6 10.9 10.7 10.5 8.5 9.4 11.6 13.2 138.9 Precipitation ≧0.1 in. 30 6.22 5.22 7 7.56 10.4 7.11 6.56 6.11 4.78 4.56 7 5.67 70.4 Precipitation ≧0.50 in. 10 1.4 1.4 1.4 2.8 3.6 2.7 3 2.9 2 1.4 2 1.6 26.2 Precipitation ≧1.00 in. 30 0.3 0.3 0.2 0.5 0.6 1.1 1.1 1 0.7 0.4 0.6 0.4 7.2

Table 4.6 Average Number of Days with Precipitation Over 10 and 30 Years in Columbus

Table 4.7 presents the shares of different storm types in a year. There are 226.10 days in a year with precipitation less than 0.01 inch. The National Climatic Data Center denotes precipitation of less than 0.001-inch as “trace,” which is similar to the “no precipitation” category. There are no precipitations in 61.95 percent of the days in a

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year. The frequency of precipitations between 0.01 and 0.1 inch a day is 18.77% (68.5 days). Only 1.97% of the days (7.2 days) in a year have more than 1.0-inch precipitation. There are the four types of storm events considered in the research.

The ranges of precipitation (Table 4.7) must be converted into storm events for use in the SWMM model. The mid-point of a range represents a storm event: 0.05-inch,

0.25-inch, and 0.75-inch.

NOAA precipitation data show that the average precipitation duration in

Columbus is two hours. A two-hour normal storm can generate enough surface runoff to wash off the buildup of pollutants (James and James, 2001). This duration is used for

SWMM simulation. The IDF curve of Columbus shows that a one-year normal storm event with two-hour rainfall duration has a 0.55 rainfall intensity, generating a 1.1-inch rainfall. However, the precipitation data input into the SWMM model requires time-intensity data (i.e., rainfall intensity during each time step). Hence, the total rainfall needs to be distributed over a 2-hr duration.

Precipitation (inch) Number of Days Percentage (%) p ≤ 0.01 (~0) 226.10 61.95 0.01 < p ≤ 0.1 68.50 18.77 0.1 < p ≤ 0.5 44.20 12.11 0.5 < p ≤ 1.0 19.00 5.20 1.0 ≤ p 7.20 1.97

Table 4.7 Frequencies of Storm Types

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4.6.1.4 The SCS Storm Distribution

The SCS (Soil Conservation Service) has developed four dimensionless rainfall distributions, using the Weather Bureau’s Rainfall Frequency Atlases (NWS, 1961).

SCS data analyses indicate four major regions, with rainfall distributions labeled I, IA,

II, and III. Figure 4.10 presents the regions where these design storms are applicable.

Ohio is located in the Type II design storm region. See Appendix E for details on storm distribution.

Based on the Columbus IDF curve, a 2-hour normal storm event accumulates

1.1-in rainfall. Table 4.8 presents rainfall intensity for each time step. Column (1) is the time step from 0-min to 120-min. Column (2) is rainfall accumulation in inch. The total accumulation at the end of the storm is 1.1-in. Column (3) is the volume of rainfall at each time step, and Column (4) is the rainfall intensity at each time step, converted from Column (3).

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Figure 4.10 Approximate Geographic Area for SCS Rainfall Distributions. (SCS, 1986)

Figure 4.11 and Table 4.8 show that the rainfall intensity of a two-hour normal storm event peaks 59 minute after the storm begins. This rainfall intensity is used as input to the SWMM model.

The same procedure is repeated to obtain intensity-duration data for 0.05-in,

0.25-in, and 0.75-in storms. These data are also used as input to the SWMM model. For a total 0.05-in rainfall over 2 hours, the peak rainfall intensity occurs at the 59th minute, with an intensity of 1.08 in/hr. A 0.25-in rainfall has a peak intensity of 5.42 in/hr, and a 0.75-in rainfall has a peak intensity of 16.25 in/hr. The rainfall distribution shapes are similar, because they are all derived from the same SCS type II storm distribution.

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Time (min) Rainfall Accumulation (in.) Rainfall (in.) Rainfall Intensity (in/hr) (1) (2) (3) (4) 0 0.00 0.00 0.00 5 0.01 0.01 0.14 12 0.03 0.02 0.14 18 0.04 0.02 0.17 24 0.07 0.02 0.22 30 0.09 0.02 0.22 36 0.11 0.02 0.22 40 0.13 0.02 0.37 42 0.14 0.01 0.28 46 0.17 0.02 0.37 48 0.18 0.02 0.41 50 0.21 0.03 0.69 52 0.22 0.01 0.55 53 0.23 0.01 0.55 54 0.24 0.01 0.55 55 0.25 0.01 0.55 56 0.29 0.03 1.65 58 0.33 0.04 2.20 58 0.37 0.04 4.40 58 0.41 0.03 8.25 59 0.55 0.14 23.83 60 0.70 0.15 7.70 62 0.80 0.10 2.47 64 0.83 0.02 1.10 65 0.85 0.02 1.10 66 0.86 0.01 0.55 67 0.88 0.02 1.10 68 0.89 0.01 0.55 70 0.90 0.01 0.55 72 0.92 0.02 0.41 76 0.95 0.03 0.46 78 0.96 0.01 0.28 80 0.97 0.01 0.28 84 0.98 0.02 0.28 86 1.00 0.02 0.41 90 1.01 0.01 0.18

Continued

Table 4.8 Rainfall Intensity of a Two-Hour Normal Storm in the Study Area

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Table 4.8 continued

92 1.02 0.01 0.27 96 1.03 0.01 0.18 100 1.05 0.01 0.18 102 1.06 0.01 0.28 104 1.07 0.01 0.27 108 1.08 0.01 0.18 114 1.09 0.01 0.11 120 1.10 0.01 0.11

30.00

25.00

20.00

15.00

Rainfall(in/hr) Intensity 10.00

5.00

0.00

0 2 4 6 2 8 2 4 6 8 9 5 7 0 6 0 6 0 1 2 3 4 4 5 5 5 5 5 62 6 6 7 7 8 8 92 10 104 114 Time (Minute)

Figure 4.11 Rainfall Intensity Distribution of a Two-Hour Normal Storm

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4.6.2 Infiltration

Horton’s equation is used for infiltration estimation in the SWMM model. The higher the infiltration capacity, the lower the surface runoff. The infiltration capacity depends on soil characteristics and land-use cover. The equation is:

-kt fp = fc + (f0 – fc) e (4.1)

where:

f p = infiltration capacity into the soil, ft/sec,

ffcp = minimum or ultimate value of (variable WLMIN in the SWMM model), ft/sec,

ff0 = maximum or initial value of p (variable WLMAX in the SWMM model), ft/sec, t = time from the beginning of the storm, sec, k = decay coefficient (DECAY in the SWMM model, 1/sec)

(Source: USEPA, 1988, p. 111).

The U.S. Soil Conservation Service (SCS) has classified most soils into

Hydrologic Soil Groups (A, B, C, and D), depending on their infiltration capacities, fc.

(Well drained, sandy soils are “A”; poorly drained, clayey soils are “D.”) A listing of the groupings for more than 4000 soil types can be found in the SCS Hydrology

Handbook (1972, pp. 7.6-7.26). Alternatively, Musgrave (1955) provided values for fc in Table 4.9. These values will be used in the SWMM model.

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Hydrologic Soils Group Minimum Infiltration Capacity fc (in/hr.) A 0.45-0.30 B 0.30-0.15 C 0.15-0.05 D 0.05-0.00 Source: Musgrave, 1955

Table 4.9 Infiltration Capacity Values by Hydrologic Soil Group

To estimate the maximum initial value of the infiltration capacity (f0), the CN

(Curve Number) is used. CN is derived from SCS tables, based on a combination of land cover and hydrological soil group (USDA, 1985). A higher CN means more runoff or less infiltration. This number is then used to calculate a multiplier, by comparing the

CN for a cell to the minimum CN for the watershed. A higher multiplier or a higher infiltration rate is assigned to the cells with lower CN values. For a given hydrological soil group, the highest multipliers occur in the higher end of the range, as presented in

Table F.1 in Appendix F, while lower multipliers occur in the lower end of the range.

CN is used to estimate f0. This infiltration parameter is analogous to the S parameter used in the SCS hydrologic model. S is related to CN by the equation:

1000 S = −10 (4.2) CN

S = saturation condition.

In addition to using CN to estimate the infiltration parameter f0, the USEPA also publishes values of f0 that vary, depending on soil, moisture, and vegetation conditions.

The f0 values listed in Table 4.10 can be used as a rough guide.

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Values of k found in the literature (Viessman et al., 1977; Linsley et al., 1975;

Overton and Meadows, 1976; Wanielista, 1978) range from 0.67 to 49 hr-1. Yet, most

-1 -1 of the values appear to be in the range 3-6 hr (0.00083-0.00167 sec ). The evidence is not clear as to whether there is any relationship between soil texture and the k value, although several published curves seem to indicate a lower value for sandy soils

(USEPA, 1988). USEPA (1988) suggests an estimate of 0.00115 sec-1 (4.14 hr-1) could be used if no field data are available, which implies that, under ponded conditions, the infiltration capacity will fall 98 percent of the way towards its minimum value in the first hour, a not uncommon observation.

4.6.3 Routing

4.6.3.1 Overland Flow

Catchments are subdivided into three subareas: one simulating a pervious area and the other two simulating impervious areas, with and without depression storage. Such subareas are presented in Figure 4.12 as A1, A2, and A3, respectively, illustrating the catchment profile. They can be delineated based on land-use cover. For example, commercial, industrial, and transportation land uses are classified as impervious cover; high-density and low-density residential land uses are classified as partial impervious cover; and other land uses, such as agriculture, forest, and wetland, are classified as pervious cover. Table 4.11 presents the relationship between land uses and imperviousness. The more urbanization, the more imperviousness.

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A. DRY soils (with little or no vegetation):

i. Sandy soils: 5 in. /hr

ii. Loam soils: 3 in. /hr

iii. Clay soils: 1 in. /hr

B. DRY soils (with dense vegetation):

i. Multiply values given in A by 2 (after Jens and McPherson, 1964)

C. MOIST soils (change from dry f0 value required for single event simulation

only):

i. Soils which have drained but no dried out (i.e. field capacity): divide

values from A and B by 3.

ii. Soils close to saturation: Choose value close to fc value.

iii. Soils which have partially dried out: divide values from A and B by

1.5-2.5.

Source: SWMM 4 Manual, page 110

Table 4.10 Representative Values for f0

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Source: Modified from Huber and Dickinson, 1988

Figure 4.12 Idealized Subcatchment Overland Flow and Outflow Computation Without Snow Melt.

Depression storage may be derived by plotting rainfall runoff volume (depth) for impervious areas against rainfall volume for several storms. The rainfall intercept at zero runoff is the depression storage. A regression of depression storage versus slope

(Kidd, 1978) has produced the following:

-0.49 dp = 0.0303 × S , (r = -0.85) (4.3)

where:

dp = depression storage, in., (variable WSTORE in the SWMM model) and

S = catchment slope, percent (variable WSLOPE in the SWMM model).

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Land Use Criteria Land Use Imperviousness Land Use Types County Elements** Element (%) Low Residential, 1.0 DU/A* 2 10 Low Residential, 2.0 DU/A* 3 20 Low Residential, 2.9 DU/A* 4 25 Medium Density Residential, 4.3 DU/A* 5 30 Medium Density Residential, 7.3 DU/A* 6 40 Medium Density Residential, 10.9 DU/A* 7 45 Medium Density Residential, 14.5 DU/A* 8 50 High Density Residential, 43.0 DU/A* 9 80 High Density Residential, 24.0 DU/A* 10 65 Commercial/Industrial Office Professional/Commercial 11 90 Commercial/Industrial Neighborhood Commercial 12 80 Commercial/Industrial General Commercial 13 85 Commercial/Industrial Service Commercial 14 90 Commercial/Industrial Limited Industrial 15 90 Commercial/Industrial General Industrial 16 95 Source: Hill, 1998 * Dwelling Units/Acre ** Land Use Elements 2 through 6 typically represent single-family housing and Land Use Elements 7 through 10 typically represent townhouses, condominiums, and apartments. Land Use Element 8 represents typical Mobil Home Parks.

Table 4.11 Relationship Between Land Uses and Imperviousness

Separate values of depression storage for pervious and impervious areas are required inputs to the SWMM model. Representative values for impervious areas can be obtained from the Table 4.11. The percentage of imperviousness for low-intensity residential development is 20%, 73% for high-intensity residential, and 85% for commercial and industrial development. Pervious area measurements are not available.

Pervious area values are expected to exceed those for impervious areas. Also,

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infiltration loss, often included as an initial water abstraction, and caused by such phenomena as surface ponding, surface wetting, interception and evaporation, is computed explicitly in SWMM.

Hence, pervious area depression storage might best be represented as an interception loss, based on the type of surface vegetation. Many interception estimates are available for natural and agricultural areas (Viessman et al., 1977, Linsley et al.,

1949). For grassed urban surfaces, a value of 0.10 in. (2.5 mm) may be appropriate.

In SWMM, depression storage may be treated as a calibration parameter, particularly to adjust runoff volumes. If so, extensive empirical work to obtain an accurate a priori value may be pointless, since this value would be changed during calibration. Table 4.12 presents pervious area depression storage estimates for different pervious land uses.

4.6.3.2 Watershed Manning’s Roughness Coefficient

The roughness of a watershed’s surface affects stormwater runoff. Urban areas have smooth surfaces and generate a higher runoff after a storm. Forests have the largest roughness coefficient, with the least stormwater runoff. USDA Technical

Release 55 (1986) (TR-55) provides Manning’s roughness coefficient (n) for sheet flow, which is a portion of the precipitation that moves initially as overland flow in very shallow depths before eventually reaching a stream channel. This coefficient is used to represent the roughness of a watershed’s surface (Table 4.13).

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Interception Losses Agricultural Areas Crop Height (ft.) Interception (in.) Corn 6 0.03 Cotton 4 0.33 Tobacco 4 0.07 Small grains 3 0.16 Meadow grass 1 0.08 Alfalfa 1 0.11 (from Linsley, Kohler, and Paulhus 1975) Forest Area (from Viessman et al. 1977) 10-20% total rainfall, maximum 0.5 in. Detention Storage (from Horton 1935) Agricultural Areas 0.5-1.5 in. (Depending on time sense tillage) Forests/Grasslands 0.5-1.5 in. Total Surface Loss Urban Areas Open Areas 0.1-0.5 in. Impervious Areas 0.1-0.2 in. Source: US Army Corps of Engineers, 1994, p. 4

Table 4.12 Surface Losses

4.6.3.3 Channel/ Pipe Data

Three major streams are simulated in this study: Big Darby Creek, Sugar Run, and

Robinson Run. With regard to the flow routing method, no backwater effects can be calculated (i.e., in an upstream direction) in the RUNOFF block, because each conduit element simply provides an inflow to a downstream element, with no effect of the latter on the former. Since most stream channels are trapezoidal, and in order to simplify model simulations, it is assumed that all channels are trapezoidal.

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Surface Manning’s Coefficient (n) Smooth surfaces (concrete. asphalt, 0.011 gravel, or bare soil) Fallow (no residue) 0.05 Cultivated soils: Residue cover <~ 20% 0.06 Residue cover >20% 0.17 Grass: Short grass prairie 0.15 Dense grass 0.24 Bermuda grass 0.41 Range (natural) 0.13 Woods Light underbrush 0.40 Dense underbrush 0.80

Table 4.13 Manning’s n Roughness coefficients for sheet flow—TR-55

1. Manning’s Roughness Coefficient

Most hydraulic computations related to indirect estimates of discharges require an evaluation of the roughness of the channel. In the absence of a satisfactory quantitative procedure, this evaluation remains primarily an educated guess, based on experience.

One way of gaining such experience is by examining the appearances of some typical channels, whose roughness coefficients are known (Barnes, 1967). USGS provides photographs and related data for a wide range of channel conditions. Familiarity with the appearance, geometry, and roughness features of these channels improves the ability to select proper roughness coefficients for other channels. According to Barnes

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(1967), channels’ Manning’s Roughness Coefficient n in the study area should vary between 0.036 and 0.045, depending on appearance and geometry.

2. Channel Dimension

Channel morphology plays a critical role in the understanding and interpretation of the hydrological and geomorphic characteristics of an area. The accurate estimation of channel dimensions is a critical element in many hydrological and geomorphic investigations. Process-based hydrological models that simulate the various processes controlling runoff, such as transmission losses, may require that channel width and depth be known in order to accurately simulate runoff (Smith et al. 1995). Channel bankfull depth and width are important elements when running stormwater simulation models, such as SWMM. However, it is very difficult to measure every channel’s depth and width in the study area, because some of these channels are too deep and/or too wide. In general, there are four ways to derive channels’ dimensions—direct measurement, measurement from aerial photography, adaptation from national surveys

(Gilliom, 1995; Hirsch et al, 1988; Leah et al., 1990), and empirical equations (Bjerklie et al., 2000; Dingman and Sharma, 1997; Williams, 1978; Dunne and Leopold, 1978).

Many researchers have noted strong relationships between bankfull (channel forming) discharge, bankfull width, bankfull depth, and drainage area, and have used scientific methods to represent these relationships via graphs and equations (Dune and Leopold,

1978). The Big Darby Creek Watershed does not have any channel dimension survey so far. See Appendix G for the detailed channel estimation methodology in this study.

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4.6.4 Water Quality

In most SWMM applications, the RUNOFF block computes the concentration of runoff water quality constituents. Methods for predicting such concentrations are reviewed extensively by Huber (1985, 1986). Several mechanisms determine stormwater quality, most notably buildup and washoff.

In an impervious urban area, it is usually assumed that a supply of constituents is built up on the land surface during the dry weather that precedes a storm. This buildup may or may not be related to time and such factors as traffic flow, dry fallout and street sweeping (James and Boregowda, 1985). When the storm occurs, this material is then washed off into the drainage system. In rural areas, soil erosion is the major contribution to stormwater pollution.

4.6.4.1 Buildup

One of the most influential early studies of stormwater pollution was conducted in

Chicago by the American Public Works Association (APWA, 1969). As part of this project, street surface accumulation of “dust and dirt” (DD) (anything passing through a quarter inch mesh screen) was measured by sweeping streets with brooms and vacuum cleaners. The accumulations were then measured for different land uses and curb length, and the data were normalized in terms of pounds of dust and dirt per 100 ft of curb or gutter (Table 4.14). Manning et al. (1977) surveyed more than 100 cities in the U.S., and provided a summary of linear buildup rates. This research uses the results of Manning’s study for buildup coefficients inputs (Table 4.15). These coefficients are equal to 1.17 and 2.2 per 100-ft-curb for low-intensity and high-intensity residential 107

Pounds of Dust & Dirt Type Land-Use Per 100 ft-curb 1 Single Family Residential 0.7 2 Multi-Family Residential 2.3 3 Commercial 3.3 4 Industrial 4.6 5 Undeveloped or Park 1.5 Source: APWA, 1969

Table 4.14 Measured Dust and Dirt (DD) Accumulation in Chicago

Dust and Dirt Accumulation Pounds of Dust & Dirt No. of Land Use (lb/curb-mi/day) Per 100 ft-curb Observation Mean Single Family 74 62 1.17 Residential Multi-Family 101 113 2.2 Residential Commercial 158 116 2.19 Industrial 67 319 6.04 Source: Manning et al., 1977

Table 4.15 Nationwide Data on Linear Dust and Dirt Buildup Rates

development, respectively. However, since Manning et al. did not provide coefficients for undeveloped and park areas, the data from APWA are used instead (Table 4.14).

4.6.4.2 Washoff

Washoff is the process of erosion or solution of pollutant constituents from a subcatchment surface during a period of runoff in urban areas. Burdoin (Huber and 108

Disckson, 1988) assumed that one-half inch of total runoff in one hour washes off 90 percent of the initial surface load, leading to the now familiar value of washoff coefficient (RCOEF) 4.6 in.-1 (James and James, 2001). Sonnen (1980) has estimated values for RCOEF from sediment transport theory, ranging from 0.052 to 6.6 in.-1, and increasing as particle diameter, rainfall intensity, and catchment area decrease. Sonnen has also pointed out that 4.6 in.-1 is relatively large, compared to most of his calculated values. This study uses RCOEFF= 4.6 and 3.3 (mean of Sonnen’s research) to test and see if the outputs have significant differences.

4.6.4.3 Erosion

Erosion and sedimentation are often citied as major problems related to urban/suburban runoff. They not only contribute to the degradation of land surfaces and to soil loss, but are also harmful to water quality in channels. In keeping with the simplified procedure in the RUNOFF Module, the Universal Soil Loss Equation

(USLE) has been adapted for use in SWMM. Full details on the USLE are provided by

Heaney et al. (1975). If erosion is to be simulated, several additional parameters are needed: erosion area, soil factor, slope length gradient ratio, cropping management factor, and control practice factor. This study assumes that agriculture areas are the potential erosion areas, and they can be measured with GIS. The soil factor, K, is a measure of potential erodibility. The slope length gradient ratio is an empirical function of runoff length and slope; it can also be measured and estimated with GIS for each subcatchment.

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Factor C is the cropping management factor. It is used to determine the relative effectiveness of soil and crop management systems in terms of preventing soil loss.

The C factor is a ratio comparing the soil loss from land under a specific cropping management system to the corresponding loss from continuously fallow and tilled land.

Most local farmers are using no-till farming, which is a way of growing crops from year to year without disturbing the soil through tillage. The cropping management factor C is equal to 0.25 (Maryland Water Resources Administration, 1973).

Factor P is the support practice factor. It reflects the effects of practices and slope that will reduce the amount and rate of the water runoff and thus reduce the amount of erosion. The P factor represents the ratio of soil loss by a support practice to that of straight-row farming up and down the slope. The most commonly used supporting cropland practices are cross slope cultivation, contour farming and strip cropping. As for the control practice factor (P), the slope in the study area is very small and the contour irrigation practice is suggested, therefore a P factor value of 0.25 is used, as provided by Wischmeier and Smith (1958).

4.7 INPUT TO THE ECONOMIC MODEL

The economic model is used to seek optimal solutions that achieve water quality standards. Different land-use activities generate different impacts on the stream water quality. The cost-effectiveness method is used to select the BMP technology combinations that have the lowest cost while keeping the environmental impacts within the given standards. Several variables are considered in this model: pollutant loads and

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streamflow, costs, pollutant removal rate, pollutant transport rate, BMP treatments installation limitation, and water quality standards.

4.7.1 Pollutant Loads and Stream Flow

The pollutant loads and streamflow are two basic variables for water quality calculation. Land-use activities, rainfall, and many watershed characteristics have strong influences on these two variables. The pollutant loads and streamflow are outputs of the SWMM model. Different land-use development scenarios have different pollutant loads and streamflow outputs.

4.7.2 Land Purchasing Cost

The BMP treatments are area-wide technologies, requiring land for installation.

The installation cost should include land purchasing, as actual purchase costs or opportunity costs, even if the land is not actually purchased. Since land market value is uncertain in the study area, the appraisal value from Madison County and Union

County Auditors is used as a proxy for land purchasing costs. The appraisal value is different from parcel to parcel. An average value is estimated for each subcatchment.

Figure 4.13 illustrates the layout of a parcel map, with four types of parcels, based on their boundaries: (1) the parcel is entirely within the subcatchment and watershed

(i.e., parcels 11, 12, and 5); (2) the parcel boundary crosses the watershed area (i.e., parcels 2, 3, and 4); (3) the parcel crosses the watershed area and subcatchment boundaries (i.e., parcels 1, and 6); and (4) the parcel crosses subcatchment boundaries

(i.e., parcels 7, 8, 9, 10, and 13). 111

The appraisal data represent the total value of the parcel. Figure 4.13 shows that many of the parcels are only partially included in the study area or catchment.

Therefore, the unit value ($/acre) is first calculated for each parcel, and so is the “actual area” inside the study area and catchment. Once these two values are computed, the total parcel appraisal value is obtained by multiplying these two values. After proper summation over a catchment, the unit land purchasing cost for each catchment can be computed (see Table 4.16). The following represents the detailed step-by-step procedure for estimating unit land purchasing costs.

1. Calculate the unit appraisal value for each parcel. The unit appraisal value is equal

to the total appraisal value divided by the parcel area (in acre). For example, if a

parcel’s appraisal value is $7,250, and its area 1.25 acre, the unit appraisal value is

$5,800 per acre (7250/1.25=5800).

2. Extract the potential BMPs installation areas from the parcel layer. Based on the

BMPs suitability analysis, it is possible to locate the suitable sites for BMPs setup.

3. Overlay catchment boundary and parcels.

4. Recalculate parcel areas. After extraction and overlay, some parcels are not fully

located in the potential BMPs installation area, and it is necessary to recalculate

the parcel area.

5. Calculate the catchment average appraisal value. First, calculate the total appraisal

value in a catchment, ∑()PAii× UPV . Then, divide it by the total area of the

catchment:

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n ∑()PAii× UPV i n (4.4) ∑ PAi i

where:

PAi = New Parcel Area,

UPAi = Unit Parcel Value, in=→Parcel index (1 ).

n = Number of parcels in the catchment.

Table 4.16 presents the unit land purchasing cost for each catchment.

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Figure 4.13 Hypothetical Parcel Map

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Subcatchment Total Appraisal Price Total Catchment Unit Price ID ($) Area (Acre) ($USD/Acre) 1 $5,744,378.56 1137.73 $5,049 2 $2,634,644.87 400.21 $6,583 3 $5,426,950.13 1050.43 $5,166 4 $7,913,860.81 2352.92 $3,363 5 $12,507,677.65 3272.85 $3,822 6 $3,370,324.72 653.78 $5,155 7 $8,074,646.41 3743.31 $2,157 8 $2,431,668.68 805.93 $3,017 9 $6,313,959.99 1011.54 $6,242 10 $3,460,551.47 1074.62 $3,220 11 $8,358,952.20 1868.98 $4,472 12 $10,125,275.43 1579.94 $6,409 13 $4,786,785.96 1137.99 $4,206 14 $8,681,389.37 1117.50 $7,769 15 $4,076,650.30 699.27 $5,830 16 $2,927,629.42 642.03 $4,560 17 $917,340.10 137.74 $6,660 18 $655,981.59 64.25 $10,210 19 $152,538.62 33.57 $4,544 20 $119,127.83 62.07 $1,919 21 $4,880,106.27 2148.19 $2,272 22 $1,433,630.41 185.81 $7,716 23 $3,092,690.56 607.94 $5,087

Table 4.16 Unit Land Purchasing Cost

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4.7.3 Installation and Maintenance Cost

Four different BMPs are considered, with their own installation (including design and construction) and maintenance costs. Each BMP has a different lifetime, which must be considered in computing average annualized costs.

4.7.3.1 Pond Systems

CWP (1998) and U.S. EPA (2001) report an annual maintenance cost ≈ 3-6% of the construction cost. Walsh (2001) reports typical construction costs around

$60,000/acre. The longevity of Pond Systems is around 20-50 years (CWP, 1996). The average longevity used here is 35 years. After annualizing these costs over 35 years, the annual design and construction cost is relatively low. Including maintenance, the annual cost is around $5,300/acre.

4.7.3.2 Wetland Systems

Wetlands are the most sophisticated systems among BMPs, entailing very high initial construction or restoration costs. However, once a wetland system is established and operates in a stable fashion, its benefits include not only clean water, but also ecosystem and habitat conservation. CWP (1988), Weber (2001) and the U.S EPA

(2001) report annual maintenance costs ≈ 2% of construction costs. The wetland systems in Penrith/Blacktown, Austrialia (based on 10 years experience) have the following costs: $500,000 per hectare (ha) of surface for design and construction;

$10,000 per ha for maintenance during the first two years (i.e. ~2% of design and construction cost, or ~1.96% of total acquisition cost); $5,000 per ha for maintenance 116

(i.e. 1% of design and construction cost, or 0.98% of total acquisition cost); and major maintenance every 10 years (~5% of construction cost), (Primary source: Geoff Hunter,

2003). The Ohio EPA Surface Water Division (2003) estimates the cost of wetland restoration by tree planting at $400,000/ acre. In addition, wetland restoration also requires tile search (trenching), tile blocking, excavation, and wetland wildlife management. These tasks add an extra $200,000/ acre.

The longevity of Wetland Systems is around 20-50 years (CWP, 1996). The annualized cost, including annual maintenance is estimated at $29,100 over an average lifetime of 35 years.

4.7.3.3 Infiltration Systems

Earthtech Engineering P/L (2003) in Melbourne (Australia) uses an estimate of

$46-48 / linear meter for construction cost. CWP (1998) and the US EPA (2001) report annual maintenance costs ≈ 5-20% of construction costs. Fletcher et al. (2003) suggest that the construction cost of an infiltration trench is about $60-80/m3 of trench

(assuming the trench is 1-m wide and 1-m deep). USR (2003) estimates the unit cost for the construction of a 1-m wide, 1-m deep infiltration trench in Sydney as $138/m.

This estimate includes excavation, installation of geofabric liner, perforated pipe, gravel layer, filter layer, application of top-soil, grass seed, fertilizer, and watering.

Taylor (2000) estimates the total installation cost of an infiltration system at around

$20,396 per acre.

In addition to installation and annual maintenance costs, an infiltration system has additional decommissioning costs, around 34.80% of the total cost, because of its short 117

lifespan (one to five years). The total annualized cost, including installation and annual maintenance, is around $99,000/acre.

4.7.3.4 Filtering Systems

The installation cost of filtering systems includes planting, soil excavation, soil swale cross-overs, initial maintenance, and irrigation. Bryant (2003) reports a cost around $500,000 per acre. Lloyd et al. (2002) suggest grassed swales cost around

$101,117/acre/yr to maintain (but if residents do regular mowing, there is less or no cost to local authorities). The total annualized cost is $51,765 per acre, which is higher than that of pond systems and wetland systems, but lower than that of infiltration systems. Table 4.17 presents the estimated annual cost of the four different BMP technologies.

BMP Pond Infiltration Cost Wetland System Filtering System System System Design and Construction Cost $60,000 $600,000 $186,200 $499,800 (per Acre) Annual Maintenance Cost $3,600 $12,000 $37,200 $10,100 (per Acre) Longevity (Year) 35 35 3 12 Annual Cost $5,300 $29,100 $99,300 $51,800 (per Acre)

Table 4.17 Estimated Annual Cost of BMPs 118

4.7.4 Final BMP Unit Cost

The final BMP unit cost is the sum of land, installation, and maintenance costs, as presented in Table 4.18.

Subcatchment Pond Wetlands Infiltrations Filter ID 1 $7,851 $31,680 $101,819 $54,303 2 $7,817 $31,646 $101,786 $54,269 3 $7,497 $31,326 $101,466 $53,949 4 $7,673 $31,502 $101,642 $54,125 5 $7,658 $31,486 $101,626 $54,109 6 $7,574 $31,403 $101,543 $54,026 7 $7,644 $31,472 $101,612 $54,095 8 $7,764 $31,592 $101,732 $54,215 9 $7,739 $31,568 $101,708 $54,191 10 $7,696 $31,524 $101,664 $54,147 11 $7,549 $31,377 $101,517 $54,000 12 $7,802 $31,630 $101,770 $54,253 13 $7,548 $31,377 $101,517 $54,000 14 $7,851 $31,680 $101,819 $54,303 15 $7,631 $31,460 $101,600 $54,083 16 $7,617 $31,446 $101,586 $54,069 17 $7,861 $31,690 $101,829 $54,312 18 $8,314 $32,143 $102,283 $54,766 19 $8,297 $32,125 $102,265 $54,748 20 $7,800 $31,628 $101,768 $54,251 21 $7,671 $31,499 $101,639 $54,122 22 $8,128 $31,957 $102,097 $54,580 23 $8,002 $31,830 $101,970 $54,453

Table 4.18 Final BMP Unit Cost (per Acre) 119

4.7.5 BMP Sediment Removal Rate

BMP technologies can remove pollutants and help to achieve the water quality standard. CWP (1996) reports that pond systems can reduce 80% of total the sediment load, wetland systems 75%, infiltration systems 90%, and filtering systems 85%.

4.7.6 Suspended Sediment Transport Rate

Water pollutants are carried by the streamflow. Therefore, upstream water pollution affects downstream water quality. Upstream water pollutant abatement helps improve downstream water quality. The suspended sediment transport rate is affected by two major factors: the streamflow and the size of the particles. See Appendix H for further discussion on sediment transport processes and rates.

Because sediment sample data are not available in the study area, results in Rohrer et al. (2004) are used to estimate sediment transport in streams, with a focus on total suspended sediments. The SWMM model output includes the peak flow discharge to each stream segment. This discharge is divided by the stream average cross-section, yielding the discharge bin flow. A discharge bin is a 1-meter by 1-meter unit area used to measure the unit streamflow. Comparing the discharge bin flow with the peak discharge (Table 4.19) yields the suspended sediment transport rate for each stream segment.

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Peak Discharge Discharge Bin Transport Total Accumulated Return (cms) Rate Transport Rate Interval (Years) 0.07≧Q＞0.0029 ＞ baseflow 43% 43% 0.11≧Q＞0.07 ＞0.1 12% 55% 0.17≧Q＞0.11 ＞0.25 6% 61% 0.30≧Q＞0.17 ＞0.5 10% 71% 0.42≧Q＞0.30 ＞1 6% 77% 0.49≧Q＞0.42 ＞1.5 4% 81% 1.01≧ Q＞0.49 ＞2 14% 95% 1.31≧ Q＞1.01 ＞10 3% 98% 1.61≧ Q＞1.31 ＞25 1% 99% 1.92≧ Q＞1.61 ＞50 1% 100% Q＞1.92 ＞83.6 0% 100% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 216

Table 4.19 Transport Rates for Medium Sand: Atlanta, Georgia

Figure 4.14 presents an example of four stream segments. Each has its own peak discharge flow, leading to different sediment transport rates.Table 4.20 presents an example of stream flow and channel data. The peak discharge is estimated by the

SWMM model. The peak Discharge Bin (column (3)) is equal to the Peak Discharge

(column (1)) divided by the Stream Cross-Section Area (column (2)). Since the unit in

Table 4.19 is cubic meter per second, the unit of column (3) is converted to cubic meter per second (column (4)). Comparing column (4) data with the data in Table 4.19, the sediment transport rate can be derived for each stream channel (column (5)).

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Figure 4.14 Example of stream structure diagram

Stream Peak Peak Peak Cross-Section Discharge Discharge Sediment Stream Discharge Area Bin Bin Transport Rate ID (cfs) (sq. ft) (cfs) (cms) (5) (1) (2) (3) (4) 1 246.30 139.15 1.77 0.05 43% 2 96.85 95.89 1.01 0.03 43% 3 471.37 148.23 3.18 0.09 55% 4 705.01 133.02 5.30 0.15 61% 5 1590.49 145.25 10.95 0.31 77%

Table 4.20 Example of stream flow data and sediment transport rate

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In order to calculate the sediment load at point A, the sediments from streams 2 and 3 must be taken into consideration. The sediment transport from stream 2 to point

A is 43% of the total suspended sediment in stream 2. Stream 3 has 55% of its total suspended sediment load transported to point A.

The total suspended sediment load at point A is:

TSSA =×0.43 TSS23 +× 0.55 TSS

At point B, streams 1 and 4 must also be taken into consideration, in addition to the sediment load at A. The transport rate of stream 1 is 43%, and that of stream 4 is

61%. Therefore, the total suspended sediment load at point B is:

TSSBA=×+×0.43 TSS14 0.61 ( TSS + TSS ), or

TSSB =×+×0.43 TSS1234 0.61 (0.43 × TSS +× 0.55 TSS + TSS ), or

TSSB =×+×0.43 TSS1234 0.26 TSS +× 0.34 TSS +× 0.61 TSS .

At point C, the sediment load in stream 5 must be considered with the sediment load at point B. The transport rate of the stream 5 is 77%, based on its peak flow discharge. Therefore, the total suspended sediment load at point C is:

TSSCB=×+×0.77 TSS5 0.77 TSS , or

TSSC =×+×0.77 TSS51234 0.77 (0.43 ×+× TSS 0.26 TSS +×+× 0.34 TSS 0.61 TSS ), or

TSSC =×+×+×0.77 TSS512 0.33 TSS 0.20 TSS +×+× 0.26 TSS 3 0.47 TSS 4

On the basis of the above calculation principles, the transport rate at any water quality control point can be estimated.

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4.7.7 BMP Installation Area Constraint

Based on a literature review, CWP (1996) reports that different BMPs have different installation area requirements (Table 4.21).

4.7.8 TMDL Standards

There are 47 Ohio watersheds where a TMDL standard is applied. Some have been approved and implemented, and some are still under development. The Big Darby

Creek TMDL has been approved by the EPA in 2006. The watershed is located at the

Upper Big Darby Creek, which includes Sugar Run, Robison Run, most of BDC 3, and very small part of BDC 4. Therefore, the TMDL standard used in this study should take into account those sub-basins (Table 4.22).

4.7.8.1 Annual TMDL Standards

The TMDL report on Big Darby Creek assigns a total suspended sediment (TSS) allowance to each subwatershed, including point and nonpoint sources. The Big Darby

Creek must reduce 74% of its TSS between Flat Branch and Milford Center. Most of

Pond Wetland Infiltration Filter BMP Systems Systems Systems Systems Unit Drainage Area 10 Acre 10 Acre 3.5 Acre 3.5 Acre ()UDAj Installation Area Required 0.25 Acre 0.4 Acre 0.105 Acre 0.15 Acre

()UAj

Table 4.21 Unit Drainage Area and Installation Area of BMPs 124

these sediments are from NPS (overland runoff) and septic sources (Table 4.23).

Robinson Run must reduce 60% of its TSS in order to match the TMDL standard.

Sugar Run needs to reduce 65% of its TSS. Overland runoff is also the major sediment source for these two subwatersheds (Table 4.24 and Table 4.25).

Major sub-watershed Description Minor sub-watershed and streams in Reference Number HUC 11 the sub-watershed (HUC 14) Upper Big Darby BDC1: Big Darby Creek, Headwaters to 190-010 Creek Flat Branch Flat Branch 190-020

BDC2: Big Darby Creek, from Flat 190-030 Branch to Milford Center ; includes From the Little Darby Creek (Logan Co.), and headwaters to Spain Creek Sugar Run BDC3: Big Darby Creek, Milford Center 190-040 to Sugar Run Buck Run 190-050 05060001-190 Robinson Run 190-060 Sugar Run 190-070 Middle Big Darby 200-010 Creek BDC4: Big Darby Creek, below Sugar Run to High Free Pike , includes Sugar Run to Little Worthington, Ballenger-Jones, Powell, Darby Creek Yutzy and Fitzgerald Ditches. Source: Ohio EPA, 2006

Table 4.22 Description of Hydrologic Units in the Big Darby Creek Watershed

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Suspended Sediment (kg/y) Septic Point Margin Nonpoint Total (Direct) Source of Safety Overland Runoff Natural Allowable 1439288 138 1250 71964 1365936 Existing 5535063 713 1250 0 5533100 % 74% 81% 0% -- 75% Reduction Source: Ohio EPA, 2006

Table 4.23 Allocations for Big Darby Creek Between Flat Branch and Milford Center (190-030)

Suspended Sediment (kg/y) Septic Point Margin Nonpoint Sources Total (Direct) Source of Safety Overland Runoff Natural Allowable 245590 114 153 12280 233043 Existing 611582 429 153 0 611000 % 60% 73% 0% -- 62% Reduction Source: Ohio EPA, 2006

Table 4.24 Allocations to Robinson Run2 (190-060)

Suspended Sediment (kg/y) Septic Point Margin Nonpoint Sources Total (Direct) Source of Safety Overland Runoff Natural Allowable 447107 121 0 22355 424631 Existing 1264947 547 0 0 1264400 % Reduction 65% 78% 0% -- 66% Source: Ohio EPA, 2006

Table 4.25 Allocations for Sugar Run2 (190-070)

According to the TMDL standard, the total allowable load of suspended sediments in the study area is 2,023,610 kilogram per year, or 4,461,296 pound per year. This is 126

the annual sediment loading. However, most sediments from NPS can only be “washed off” by stormwater runoff. During dry days, they are in a “build-up” process. Therefore, it is not appropriate to just divide the total yearly loading by 365 days to get the daily maximum loads, because precipitation does not occur every day. Moreover, not every rainfall provides enough water to carry the sediments to the stream. Therefore, annual precipitation data are used to estimate the annual sediment yield in this study.

4.7.8.2 Single Storm TMDL Standards

Since most sediments are “washed off” by storm runoff, therefore, besides the annual standard, we can also estimate different storm event TMDL standards. Table

4.26 presents three TMDL standards, based on different precipitation loads and numbers of days. For example, Case 1 corresponding to 138.9 days with precipitation greater than 0.01 inch. Therefore, the yearly load must be divided by 138.9, yielding

32,051 lbs. This means that only 32,051 lbs of sediments are allowed into the river during each rainfall event. However, when 0.01 inch precipitation cannot generate enough surface runoff to carry the sediments into the rivers, Cases 1 and 2 are used for simulation purposes (Table 4.26).

TMDL Year Load (lb) Precipitation (in) Days Event Load (lb) Case 1 4,451,942 ≥ 0.01 138.9 32,051 2 4,451,942 ≥ 0.50 26.2 169,921 3 4,451,942 ≥ 1.00 7.2 618,325

Table 4.26 Different TMDL Standards Based on Precipitation Frequencies

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4.7.9 Environmental Quality Standards

The US EPA has different standards based on different time periods, while the

Ohio EPA does not currently have statewide criteria for total suspended sediments

(TSS). Potential targets have been identified in the technical report Association between Nutrients, Habitat, and the Aquatic Biota in Ohio Rivers and Streams (Ohio

EPA, 1999), which provides the results of a study analyzing the effects of nutrients and other parameters on the biological communities of Ohio streams. It recommends TSS target concentrations, based on observed concentrations associated with acceptable ranges of biological community performance within each ecoregion. The TSS standard for the study area is 10 mg/l. For a warm water habitat (WWH), the USEPA (2002) suggests that TSS be less than 90 mg/l on a 30-day average, and 158 m/l as daily maximum. In addition, the background sediment concentration, 3 mg/l (USGS, 1996), must be considered when the EQS standard is applied.

Watershed TSS mg/l Size Use Designation: WWH EWH Headwaters (drainage area < 20 mi2) 10 10

Wadeable (20 mi2 < drainage area < 200 mi2) 31 26

Small Rivers (200 mi2 < drainage area < 1000 mi2) 44 41 WWH: Warm water habitat EWH: Exceptional warm water habitat Source: Based on the Eastern Corn Belt Plains Ecoregion (EPA, 1999; EPA, 2002)

Table 4.27 Total Suspended Sediment (TSS) Targets for the Big Darby Creek watershed

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CHAPTER 5

MODEL CALIBRATION TO THE BIG DARBY WATERSHED

The data and parameters discussed in the previous chapter are processed to become inputs to the spatial and watershed models presented in chapter 3. The outputs of the spatial and watershed models serve as inputs to the economic model. The relationship between urbanization, runoff, and sediment generation is also discussed in this chapter.

5.1 SPATIAL MODEL

The spatial model is a set of distinct and independent computerized procedures that use GIS (Geographic Information Systems) tools to (1) delineate and better understand the study watershed, (2) develop different land-use scenarios (Residential

Suitability Analysis), (3) delineate BMP technologies installation possibilities (BMP

Technology Suitability Analysis), and (4) prepare the data required by the watershed model (Watershed Model Data Preparation).

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5.1.1 Residential Suitability Model

This model is used to delineate potential residential development areas, using such natural and human factors as slope, soil characteristics, existing land uses, and the transportation network. The Big Darby watershed is spread over two counties—Madison and Union. Each county has its own soil map. Therefore, two different soil analyses are conducted, and then combined to generate the final soil analysis maps.

Slope, soil texture, shrink-swell potential, ponding duration, flood, drainage, concrete corrosion, transportation network, and existing land uses are considered in searching for future residential development areas. The first seven factors are considered as opportunity factors, and the last two factors (transportation network and existing land uses) as constraint factors. As the opportunity factors do not have each the same importance, the weighted overlay technique is applied. Among these factors, ponding is considered the most important one, with a 20% weight. Flood, drainage, concrete corrosion, and slope are given a 15% weight, and soil texture and shrink-swell potential a 10% weight.

Table 5.1 presents (1) the initial input values and input labels for each input map,

(2) the weights, and (3) a scale value ranging from 1 to 5, with higher values representing worse suitability for residential development. The scale numbers represent a mapping of the input number into the standardized interval (1-5). The final weighted number measures the overall suitability for residential development, and is in the range from 1 to 5. The lower numbers represent areas with better development potential, because of lower construction and maintenance costs. The higher numbers correspond 130

to areas with higher costs. The interval 1-5 is subdivided into equal-size intervals

(1-2.33, 2.33-3.66, and 3.66-5), which correspond to “Good”, “Moderate”, and “Poor” development potential.

Input Map Input Value Input Label Scale Value Weight (%) Surface Texture 1 Silty Clay Loam 1 10 2 Silty Loam 3 3 Muck 5 Shrink-Swell 1 Low 1 10 Potential 2 Moderate 2 5 High 5 Ponding 3 Very Long 5 20 Duration Flood 1 None 1 15 2 Occasionally 3 3 Frequently 5 Drainage 1 Good 1 15 2 Slight Moderate 2 3 Moderate 3 4 Slight Severe 4 5 Severe 5 Concrete 1 Low 1 15 Corrosion 2 Medium Low 2 3 Medium 3 4 Medium High 4 5 High 5 Slope 1 0.5 % - 3% 1 15 2 0% - 0.5% 2 3 3% - 5% 3 4 5% - 10% 4 5 10% + 5

Table 5.1 Soil Maps, Scales, and Weights

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The ModelBuilder function in ArcGIS is used to process the overlay, and a final suitability map is then obtained, based exclusively on natural factors (Figure 5.1). This map includes three levels of development potential: Good, Moderate, and Poor. Areas in the “Good” category have the lowest construction and maintenance costs, while those in the “Poor” category have the highest ones. Only “Good” areas, with a weighted number less than 2.33, are considered as potential development areas in this study. None of the areas in the “Good” category are located in environmentally sensitive areas.

Table 5.2 represents the distribution of potential development areas. There are

16,130 acres in the “Good” category, or 35% of the whole watershed. Only 10% of the total area is in the “Poor” category. However, these assessments are based on natural factors only. Existing land uses and the transportation network must also be considered to derive a comprehensive assessment of development potential.

Area Category 1000 Square Meters Acres % Good 65,277 16,130 35 Moderate 102,810 25,405 55 Poor 19,183 4,740 10

Table 5.2 Potential for Development—Natural Factors

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Another very important decision factor is the transportation network, which determines development accessibility. The major road network is used to measure site accessibility. A 500-meter (1,640-ft) buffer is created along each link of this network, indicating accessible areas, and only such areas may be selected for future development. However, areas within this buffer may already be developed. It is assumed that only agricultural and forested land can be converted to residential land.

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Figure 5.1 Potential Development Based on Natural Factors

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The final potential residential development areas result from a combination of the above considerations. “Good” areas have low development costs based on natural conditions, are located within the transportation buffers, and are agricultural or forested.

“Poor” areas are located on steep slopes, have bad erosion, or are otherwise environmentally sensitive, and have the highest development costs.

Figure 5.2 presents these potential development areas. “Moderate” areas make up the bulk of future development—12,895 acres, or 55%. “Good” areas make up 20,085 acres, or 35%. “Poor” land makes up only 3,781 acres, or 10% (Table 5.3).

5.1.1.1 Scenario A

Three development scenarios—A, B, and C—are considered in simulating pollutant generation and concentration. Only residential area and intensity vary across these scenarios. Scenario A is based on 1994 existing land uses, and is used as a baseline.

Area Category 1000 Square Meters Acres % Good 52,187 12,895 35 Moderate 81,282 20,085 55 Poor 15,301 3,781 10

Table 5.3 Potential Development Areas—All Factors

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Figure 5.2 Potential Development Areas

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There are twelve land uses in the watershed. Over 90% of the area is used for agriculture, including 72.6% for row crop and 17.1% for pasture or hay. Less than 2% of the area is urban (Table 5.4). Because the simulation parameter data are not available for these twelve land uses, they are reclassified into seven categories: commercial/industrial/transportation, agriculture, forest, high intensity residential, low intensity residential, park, and open water, for which the necessary data are available

(Table 5.5). Under the new categories, agriculture occupies 90% of the watershed, and urban areas around 1.6% (Table 5.6). The major urban land-use center is Plain City

(Figure 5.3).

Area Land Use Sq. meter Acres % Commercial/ Industrial/ Transportation 1,061,779 262 0.56 Deciduous Forest 13,005,670 3,213 6.92 Emergent Herbaceous Wetlands 125,100 31 0.07 Evergreen Forest 37,800 9 0.02 High Intensity Residential 264,689 65 0.14 Low Intensity Residential 1,594,848 394 0.85 Mixed Forest 8,100 2 0.00 Open Water 465,753 115 0.25 Pasture/ Hay 32,199,300 7,957 17.12 Row Crops 137,405,201 33,954 73.06 Urban/ Recreational Grasses 1,557,053 385 0.83 Woody Wetlands 346,110 86 0.18 Total 188,071,403 46,473 100.00

Table 5.4 The Land Use of the Study Area in 1994.

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Original Land Use Reclassified Land Use Commercial/ Industrial/ Transportation Commercial/Industrial/Transportation Deciduous Forest Forest Emergent Herbaceous Wetlands Park Evergreen Forest Forest High Intensity Residential High Intensity Residential Low Intensity Residential Low Intensity Residential Mixed Forest Forest Open Water Open Water Pasture/ Hay Agriculture Row Crops Agriculture Urban/ Recreational Grasses Park Woody Wetlands Forest

Table 5.5 Original vs. Reclassified Land Uses

Area Land-Use Sq. meter Acres % Agriculture 169,604,501 41,910 90.18 Commercial/Industrial/Transportation 1,061,779 262 0.56 Forest 13,397,680 3,311 7.12 High Intensity Residential 264,689 65 0.14 Low Intensity Residential 1,594,848 394 0.85 Open Water 465,753 115 0.25 Park 1,682,153 416 0.89 Total 188,071,405 46,473 100.00

Table 5.6 Land-Use in Scenario A

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Figure 5.3 Scenario A

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5.1.1.2 Scenario B

Scenario B assumes that all the “Good” agriculture and forest areas are converted to Low Intensity Residential Areas, with a density of 2.0 dwelling units/acre (Figure

5.4). This land use (light yellow) is scattered along the transportation networks.

Sixty-five percent of the total watershed still remains in agriculture. Low Intensity

Residential land use becomes the second largest land-use (Table 5.7).

Area Land-Use Sq. meter Acres % Agriculture 121,920,998 30,127 64.83 Commercial/Industrial/Transportation 1,061,779 262 0.56 Forest 9,306,421 2,300 4.95 High Intensity Residential 264,689 65 0.14 Low Intensity Residential 53,369,611 13,188 28.38 Open Water 465,753 115 0.25 Park 1,682,153 416 0.89 Total 188,071,405 46,473 100.00

Table 5.7 Land-Use Scenario B

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Figure 5.4 Scenario B

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5.1.1.3 Scenario C

Scenario C uses Scenario B as a basis, but all the “Good” areas are converted into

High Intensity Residential Areas, with a density of 33 dwelling units/ acre (Figure 5.5), thus almost 14 times more intense than Scenario B. This scenario is extreme, and is designed to test the impact of intense urbanization on environmental quality. The High

Intensity Residential land use becomes the second largest land use in the whole watershed (Table 5.8).

Area Land-Use Sq. meter Acres % Agriculture 121,920,998 30,127 64.83 Commercial/Industrial/Transportation 1,061,779 262 0.56 Forest 9,306,421 2,300 4.95 High Intensity Residential 52,039,452 12,859 27.67 Low Intensity Residential 1,594,848 394 0.85 Open Water 465,753 115 0.25 Park 1,682,153 416 0.89 Total 188,071,405 46,473 100.00

Table 5.8 Land-Use of Scenario C

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Figure 5.5 Scenario C

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5.1.2 BMP Suitability Model

In addition to delineating potential residential development and conservation areas, it is also necessary to delineate areas that can be used for each of the four best management practices (BMPs) considered: Pond Systems, Wetland Systems,

Infiltration Systems, and Filtering Systems.

It is necessary to understand the features of each BMP technology and its requirements, and then to use GIS tools to find locations where certain BMP technologies can be applied. The four different technologies have their own site installation requirements and different efficiencies in dealing with various pollutants of stormwater runoff. The following is a comparison of the four technologies in terms of feasibility, pollutant removal capability, and environmental restrictions and benefits.

5.1.2.1 Comparative Feasibility

Ponds and wetlands are similar systems in terms of soil, drainage area, and longevity. However, wetlands require more controls at the setup stage, so their initial cost is higher than for ponds. For infiltration systems, the soil infiltration rate must be greater than 0.5” per hour, and the groundwater table must be 4-ft below ground level in order to protect groundwater from pollution. Infiltration systems have the highest construction costs. Filtering systems are subject to fewer physical restrictions than the other three systems (Table 5.9). For example, they require small drainage areas, and have no soil restrictions. However, their cost is moderately high. In addition, one must consider pollutant removal capability in the selection process.

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Feasibility Pond Wetland Infiltration Filtering Criteria Systems Systems Systems Systems Soils Most Soils Most Soils Need infiltration All Soils Rate .5”/hr Drainage 10-Acre Min. 10-Acre Min. 2-5-Acre Max. 2-5-Acre Area Recommended Head Water 3-6 Feet 1-6 Feet 2-4 Feet 1-8 Feet Distance Space 2-3% Site 3-5% Site 2-3% Site 2-7% Site Requirement Cost/Acre Low Moderate High Moderate-High Water Table No No 4 Feet below 2 Feet below Restriction Restriction Filter Bottom Cleanout 2-10 Years 2-5 Years 1-2 Years 1-3 Years Storm Water Yes Yes No No Managment Longevity 20-50 Years 20-50 Years 1-5 Years 5-20 Years depends on the maintenance Source: Center for Watershed Protection, 1996

Table 5.9 Feasibility Criteria for Different Stormwater BMP Options

5.1.2.2 Environmental Restrictions and Benefits

When ecological engineering is used to deal with pollution problems, it is necessary to consider the impact technology has on the environment. Table 5.10 presents the environmental benefits and drawbacks of the four technologies.

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Selection Factor Pond Systems Wetland Infiltration Filtering Systems Systems Systems Groundwater Low Risk Low Risk Moderate Risk No Risk Quality Groundwater Moderate Low Benefit High Benefit No Benefit (a) Recharge Benefit Temperature High Risk High Risk No Risk Low Risk Wetlands High Risk Moderate Risk No Risk Low Risk Safety High Risk Low Risk No Risk No Risk Habitat Moderate High Benefit No Benefit No Benefit Benefit Flood Control High Benefit High Benefit No Benefit No Benefit Streambank Moderate Moderate Low Benefit Low Benefit Protection Benefit Benefit Property Value High Premium Moderate No Premium Unknown Premium Landscaping High Benefit High Benefit No Benefit Low Benefit Notes: (a) Assumes that a filtering system has an underdrain system. Source: Center for Watershed Protection, 1996

Table 5.10 Environmental Benefits and Drawbacks of BMP Options

5.1.2.3 BMP Suitability Analysis Criteria

Based on the previous discussion, the following factors must be considered in the process of technology selection: slope, groundwater depth, organic soil, infiltration rate, drainage area size, and existing land use.

Slope: Ponds and Wetlands serve as stormwater runoff detention or retention systems. Smaller slopes incur less construction costs. The best slopes for these systems are less than 0.5%. Infiltration Systems and Filtering Systems need to have better drainage ability, so the best slopes for them are less than 3% and more than 0.5%. 146

Groundwater Depth: Infiltration Systems and Filtering Systems have negative impacts on groundwater. For Infiltration Systems, the groundwater depth should be below 4 feet, and for Filtering Systems below 2 feet.

Organic Soil: Organic soil is one of the most important elements for a constructed wetland system. Areas with organic soil have a higher probability of success. The standard used for organic soil is OMH greater than 12% (i.e., hundred mg of soil have twelve mg of organic matter).

Infiltration Rate: Infiltration Systems are dependent on the soil infiltration ability, requiring infiltration rates greater than 0.5-inch per hour. The other BMP technologies do not have this requirement.

Drainage Area: Every BMP technology has a minimum drainage unit requirement.

For example, Ponds and Wetlands need at least a 10-acre drainage area to achieve an economic scale. The size of the drainage area for Infiltration and Filtering Systems varies from 2 to 5 acres.

Existing Land Use: All BMP technologies must be installed in agricultural or forested areas. It is impossible to turn urban areas into BMP areas. Therefore, only natural areas can be selected for future BMPs.

Table 5.11 lists the criteria for BMPs suitability analysis. By using GIS tools, the suitable areas for each BMP are delineated.

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Wetland Infiltration Filtering Pond Systems Systems Systems Systems Slope < 0.5% < 0.5% 0.5% - 3% 0.5% - 3% Groundwater/Water - - 4 feet below 2 feet below Table Organic Soil - OMH* ≥ 12% - - Soil Infiltration - - .5” per hour - Rate Drainage Area Min. 10-Acre Min. 10-Acre Max. 2-5-Acre 2-5-Acre Existing Land-Use Natural Area Natural Area Natural Area Natural Area OMH* = High percentage limit of organic matter in soil (mg/mg)

Table 5.11 Criteria for BMPs Suitability Analysis

Figure 5.6 to 5.9 display the feasible areas for BMP installation. Most Pond areas are located in the southwestern part of the watershed. Pond Systems make up the largest potential aggregate area (14,428-acre), because they incur the fewest restrictions.

Filtering Systems are possible over 14,331 acres. Wetland Systems have the strictest setup restriction, because of the organic soil component requirement: only 414 acres in the whole watershed can be used to setup wetland systems. Infiltration Systems must have soil infiltration rates greater than 0.5” per hour, and groundwater depth below 4 feet. Only 1,167 acres match these criteria (Table 5.12).

The potential areas for the different BMP technologies can be overlaid, pointing to areas that can be suitable for more than one BMP technology. The joint or exclusive use of BMP technology is discussed in the description of the economic model.

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Existing Land-Use Square Meter Acre Agriculture 56,439,794 13,946 Pond Systems Forest 1,947,925 481 Subtotal 58,387,719 14,428 Agriculture 1,599,291 395 Wetland Systems Forest 74,576 18 Subtotal 1,673,867 414 Agriculture 3,547,195 877 Infiltration Systems Forest 535,479 132 Park 638,191 158 Subtotal 4,720,866 1,167 Agriculture 50,350,833 12,442 Filtering Systems Forest 6,174,133 1,526 Park 1,470,137 363 Subtotal 57,995,103 14,331

Table 5.12 Area of Potential BMPs Technologies

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Figure 5.6 Potential Pond Systems Candidates

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Figure 5.7 Potential Wetland Systems Candidates

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Figure 5.8 Potential Infiltration Systems Candidates

152

Figure 5.9 Potential Filtering Systems Candidates

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5.2 WATERSHED MODEL

The Stormwater Management Model (SWMM) is used to simulate pollutant generation and runoff in each catchment and stream under each land-use development scenario. Under a two-hour-per-year normal storm (total rainfall 1.1-inch), the different scenarios generate different sediment outputs from urban and rural areas.

5.2.1 Scenario A Output

This scenario is based on the actual 1994 land uses. The Big Darby Creek runs through the watershed from the Northwest to the South. Robinson Run flows from the

North and merges into the Big Darby Creek at the center of the watershed. Sugar Run flows from the Northeast and merges with the Big Darby Creek on the South side. Two major urbanization areas (Plain City and Unionville Center) are located in the center and northwestern part of the watershed. Most of the watershed is agricultural. Forest areas are scattered in the North and along the rivers. The high- and low-intensity residential areas are mixed with commercial areas. There are several ponds located on the southern side of the Big Darby Creek.

Twenty-three catchments and stream segments are delineated in the watershed.

Impervious land-use covers, such as urban areas, generate large runoff in a shorter time than pervious land-use covers. In addition, given the same amount of rainfall, impervious areas have higher peak runoff than pervious ones (Figure 5.10). Thus, urbanization increases peak flow and runoff volume.

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Figure 5.10 Effects of Urbanization on Volume and Rates of Surface Runoff Source: Modified from Gordon, 1985

Table 5.13 presents the share of impervious cover in each catchment. Catchment

#18 has the highest share (13.9%), because of Plain City urbanization. This catchment has also the highest peak runoff, despite the small extent of its area (Table 5.14).

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Catchment ID Stream ID Area (Acre) Impervious Area (%) 1 230 2650.14 3.00 2 120 833.25 2.60 3 30 1473.79 0.60 4 220 4002.30 0.40 5 200 4821.10 0.70 6 90 1068.82 0.00 7 150 6895.08 0.10 8 160 1494.00 0.20 9 80 1841.49 0.80 10 130 2121.33 0.40 11 10 2909.20 0.20 12 210 2938.93 0.40 13 40 1916.41 0.60 14 70 2248.09 1.30 15 20 1065.07 0.80 16 50 1167.35 1.30 17 60 302.46 0.10 18 100 289.51 13.90 19 110 85.77 1.20 20 140 211.99 1.50 21 170 4005.65 0.10 22 180 699.84 1.20 23 190 1567.65 1.40

Table 5.13 Share of Impervious Cover in Each Catchment

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Catchment ID Runoff Depth (in) Peak Runoff Rate (cfs) Peak Unit Runoff (in/hr) 1 0.383 1765.95 0.666 2 0.563 649.57 0.780 3 0.261 320.42 0.217 4 0.096 445.85 0.111 5 0.175 962.38 0.200 6 0.393 148.82 0.139 7 0.082 314.26 0.046 8 0.754 569.76 0.381 9 0.199 430.98 0.234 10 0.313 405.43 0.191 11 0.213 323.68 0.111 12 0.203 417.92 0.142 13 0.300 399.48 0.208 14 0.183 739.13 0.329 15 0.677 471.82 0.443 16 0.832 750.06 0.643 17 1.144 264.15 0.873 18 0.629 527.19 1.821 19 0.449 41.52 0.484 20 1.192 311.11 1.468 21 0.141 207.63 0.052 22 0.295 263.64 0.377 23 0.298 677.75 0.432

Table 5.14 Runoff Depth, Peak Rate, and Peak Unit Runoff in Each Catchment

Streamflow: The streamflow is affected by the base flow, the catchment runoff, and the upstream flow. Moreover, the stream morphology (i.e. shape, slope, and width) also affects the streamflow. The three major streams (Big Darby Creek, Robison Run, and Sugar Run) are made up of twenty-three stream segments.

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The total SWMM simulation period represents six hours, from 12:00 a.m. to 6:00 a.m., March 21st, 1994. However, only the first two hours have precipitation. The peak rainfall intensity occurs at the 59th minute. Table 5.15 presents the time of the peak flow occurrence, ranging from minute 5 to hour 6. Catchment #18 is the most urbanized catchment, and has the shortest peak flow onset time (8th minute). The peak flow also affects downstream stream segments. The segments below Catchment #18 have all their peak flow taking place in the 8th minute. The other stream segments have their peak flow at hour 3, following the rainfall. Catchments that have their peak flow in the first 8 minutes also have their 2nd peak flow around hour 2:30.

Figure 5.11 presents the inflow hydrograph at the outlet of the watershed. It has two peaks. The first one occurs in the first 8 minutes, because of Catchment #18 influence. The second peak occurs at the hour 2:30 after the storm starts. From the 8th minute to the first hour, the flow decreases because of soil infiltration. The flow increases after the first hour, because, at that time, the rainfall intensity is the highest, the soil is saturated with water, and no more water can infiltrate into the ground.

Therefore, the stream flow runs high again. The rainfall stops at the 2nd hour, and there is no more rainfall input. However, the upstream streamflow will move on downstream.

The streamflow starts decreasing from the hour 2:30 (2:30 a.m.) at the watershed outlet.

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Maximum Flow Time of Occurrence Catchment ID Stream ID (cfs) (hr.) 1 230 1759.03 1:33 2 120 1266.68 0:17 3 30 317.45 0:08 4 220 443.27 4:58 5 200 1009.21 6:00 6 90 148.33 2:33 7 150 1988.56 0:08 8 160 2862.33 0:17 9 80 832.79 3:08 10 130 809.01 0:08 11 10 318.14 0:08 12 210 555.67 0:08 13 40 393.08 3:08 14 70 1116.48 3:33 15 20 563.12 1:08 16 50 786.78 0:08 17 60 699.28 0:33 18 100 934.11 0:08 19 110 742.16 0:08 20 140 805.71 3:08 21 170 1867.24 2:58 22 180 514.95 0:08 23 190 758.13 6:00

Table 5.15 Summary Statistics for Streamflow

159

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1750

1500

1250

1000 Flow (cfs)

750

500

250

0 21 Mon 3AM 6AM Mar 94 Date/Time

Figure 5.11 The Stream Hydrograph at the Outlet of the Watershed

Total Suspended Sediment and Soil Erosion: In the SWMM model, the simulations of total suspended sediment and soil erosion are separate processes. Total suspended sediment is related to the buildup-washoff process both in urban and non-urban areas, while soil erosion is related to agriculture. At the beginning of the simulation, rainfall intensity is relatively low, does not wash off much of the buildup, and triggers only little soil erosion. Along with increasing rainfall intensity, suspended sediment and soil erosion increase.

Figure 5.12 presents the relationship between the amounts of total suspended sediment, soil erosion and simulation time.

160

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TS (mg/l x cfs) TS (mg/l 25000

0 75000

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25000 Erosion (mg/l x cfs) 0 21 Mon 3AM 6AM Mar 94 Date/Time

Figure 5.12 The Total Suspended Sediment and Erosion at the Outlet of the Watershed

Soil erosion increases rapidly after the first hour of rainfall, when the peak rainfall intensity occurs. At the 1:30 hour, erosion starts decreasing, because the rainfall intensity decreases. Total suspended sediments (TSS) are detached (wash off) and carried by the runoff and streamflow. TSS needs the “initial force” to wash off at the beginning, therefore the amount of TSS increases very rapidly at the peak rainfall intensity. However, unlike soil erosion particles, suspended sediments do not need much flow to carry them. Hence, even as the streamflow decreases after the 3rd hour, the TSS load still increases for a while, and then decreases 1.5 hours after the rain stops. 161

The buildup-washoff process in urban and non-urban areas generates the sediments, and agriculture is the largest contributor of soil erosion (Table 5.16). Figure

5.13 depicts the relationships between catchments and stream segments.

Stream Catchment Agriculture CIT Forest HIR LIR Park TSS Sediment Erosion ID ID (acre) (acre) (acre) (acre) (acre) (acre) (mg/l) (lb) (100 lb) 10 11 2632.3 1.7 241.9 1.5 22.3 0.9 114.67 1,100 5,081 20 15 979.4 10.5 73.5 0.0 0.0 1.5 80.18 1,005 3,302 30 3 1247.0 10.9 213.8 0.0 0.0 0.0 100.41 668 2,070 40 13 1837.7 12.4 56.9 0.0 0.6 1.7 99.21 997 2,942 50 16 1101.6 16.9 43.3 0.0 4.8 0.0 71.48 1,154 3,872 60 17 278.4 0.0 22.4 0.0 1.1 0.0 64.98 340 1,416 70 14 1740.8 24.6 441.0 3.4 26.6 0.9 65.70 572 2,283 80 9 1636.0 17.3 185.4 0.0 0.0 0.4 72.24 708 2,091 90 6 933.9 0.0 100.4 0.0 0.1 33.9 112.02 772 2,931 100 18 75.9 8.3 46.4 22.4 87.6 46.0 42.74 77 26 110 19 52.6 0.2 11.1 0.3 3.0 10.3 42.15 33 31 120 2 694.6 6.4 30.2 10.6 42.9 30.3 48.09 628 1,188 130 10 2029.0 10.4 78.6 0.0 0.6 0.2 68.66 964 2,870 140 20 201.5 3.3 5.1 0.0 1.7 0.2 61.19 241 654 150 7 6573.1 8.2 251.7 0.0 0.7 6.2 58.48 1,297 12,020 160 8 1352.3 0.9 61.5 0.0 8.9 29.3 53.41 1,348 6,073 170 21 3898.6 0.4 73.1 1.6 6.0 13.2 52.94 1,148 6,701 180 22 564.5 9.0 122.0 0.0 4.2 0.4 79.76 215 354 190 23 1212.8 16.5 237.2 4.2 22.4 52.2 15.03 100 361 200 5 4328.1 27.4 270.0 0.5 39.9 138.4 36.62 451 3,154 210 12 2499.8 10.3 414.3 0.6 9.7 1.2 102.85 1,058 4,937 220 4 3644.0 17.5 280.6 0.0 0.4 26.6 29.12 139 1,135 230 1 2396.6 49.5 50.9 20.4 110.5 21.5 87.35 1,527 4,851 *CTI: Commercial/Transportation/Industrial; HIR: High Intensity Residential; LIR: Low Intensity Residential; TSS: Total Suspended Sediment, which including sediment and erosion.

Table 5.16 Sediment and Erosion Loads from Different Catchments (1994) 162

Figure 5.13 Diagram of Catchment, Stream ID, Streamflow Direction, and Water Quality Control Point 163

The catchments with more urbanization generate more sediments, but also larger surface runoffs, and therefore, do not necessarily generate higher TSS concentrations.

In addition, sediment and erosion generation is also related to catchment length, width, and slope.

5.2.2 Scenario B Output

Scenario B is the Low Intensity Residential Development (LIRD) scenario. Based on the output of SWMM simulations, Catchments #17~20 have the highest peak unit runoff (Table 5.17). These catchments not only have higher percentages of impervious covers, but also are located at the junction of two streamflows.

Streamflow: Figure 5.14 presents the inflow hydrograph at the outlet of the watershed. This hydrograph has two peaks. The first one occurs in the first 10 minutes, because of Catchment #18 influence. The second peak occurs at the hour 1:30 after the storm starts, which is earlier than in Scenario A, because LIRD has more urban areas, which generates higher and earlier surface runoff. The peak flow remains at this level for 15 minutes (see the flat line in Figure 5.14), which means that surcharging is taking place and the model is artificially holding back waters that would normally cause a flood. From the 5th minute to the first hour, the flow decreases because of soil infiltration. The flow increases after the first hour, because, at that time, the rainfall intensity is highest, and the soil is saturated with water. No more water can infiltrate into the ground, and the streamflow runs high again. The streamflow decreases after hour 2:00 because the rain stops.

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Pervious Impervious Total Catchment Area % of Peak Catchment Stream Area Peak Imper. Runoff ID ID (Acre) Runoff Covers Rate Runoff Peak Peak Unit (cfs) (cfs) Depth Runoff Rate Runoff (in) (cfs) (in/hr) 1 230 2650.14 9.2 198.03 2723.66 0.507 2921.68 1.102 2 120 833.25 8.4 129.5 1270.42 0.704 1399.91 1.680 3 30 1473.79 3.1 75.33 890.61 0.282 965.94 0.655 4 220 4002.3 5.7 101.57 1802.57 0.239 1904.14 0.476 5 200 4821.1 5.5 241.09 3330.46 0.329 3571.55 0.741 6 90 1068.82 4.0 107.27 910.48 0.44 1017.75 0.952 7 150 6895.08 5.1 149.13 2629.04 0.212 2778.17 0.403 8 160 1494 6.9 413.37 2288.54 0.891 2701.91 1.809 9 80 1841.49 6.9 75.06 1379.54 0.335 1454.6 0.790 10 130 2121.33 6.1 153.19 1927.68 0.421 2080.86 0.981 11 10 2909.2 3.7 149.66 1792.01 0.299 1941.67 0.667 12 210 2938.93 7.6 173.77 2925.55 0.425 3099.32 1.055 13 40 1916.41 3.8 110.27 1199 0.328 1309.27 0.683 14 70 2248.09 8.7 91.31 2067.42 0.378 2158.72 0.960 15 20 1065.07 5.2 192.35 1229.59 0.71 1421.94 1.335 16 50 1167.35 5.2 277.36 1377.63 0.834 1654.99 1.418 17 60 302.46 8.4 183.09 603.2 1.272 786.28 2.600 18 100 289.51 19.4 29.37 552.94 0.78 582.32 2.011 19 110 85.77 14.0 13.64 177.62 0.768 191.26 2.230 20 140 211.99 8.0 162.87 405.81 1.29 568.68 2.683 21 170 4005.65 5.5 136.18 2027.1 0.271 2163.27 0.540 22 180 699.84 13.0 50.47 988.85 0.575 1039.32 1.485 23 190 1567.65 10.9 157.37 2556.25 0.576 2713.63 1.731

Table 5.17 Runoff of Low Intensity Residential Development (LIRD)

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Flow (cfs) 1500

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0 21 Mon 3AM 6AM Mar 94 Date/Time

Figure 5.14 The Stream Hydrograph at the Outlet of the Watershed (LIRD)

Total Suspended Sediment and Soil Erosion: Figure 5.15 presents the relationship between TSS, soil erosion and simulation time. Unlike Scenario A, the peak TSS concentration takes place at hour 1:45, and is higher than in Scenario A. However, it also decreases very quickly after the second hour, when the rain stops, because this scenario generates large streamflow, and sediments are carried downstream in a shorter time.

Sediment loads increase in Scenario B, because agriculture and forest lands are converted to LIRD areas. For example, in upstream Catchment #11, 496 acres of agriculture and forest lands are converted to LIRD area, the sediment load increases by

166

168 lbs, and soil erosion decreases by 86,300 lbs. Urbanization thus generates an increase of around 15% in sediment loads, but a decrease of 17% in soil erosion. In downstream Catchment #7, 1,716 acres of agriculture and forest land are converted to

LIRD, the sediment load increases by 1,815 lbs, and erosion also increases by 497,000 lbs, despite the decrease in agriculture area, because urbanization increases surface runoff and intensity. This generates much more erosion in non-urban areas. In addition, changes from forest cover to urban cover also produce heavier sediment loads.

240

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TS (mg/l x cfs) TS (mg/l 50000

0 100000

75000

50000

25000 Erosion (mg/l x cfs) (mg/l Erosion 0 21 Mon 3AM 6AM Mar 94 Date/Time

Figure 5.15 TSS and Erosion at the Outlet of the Watershed (LIRD)

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Stream Catchment Agriculture CIT Forest HIR LIR Park TSS Sediment Erosion ID ID (acre) (acre) (acre) (acre) (acre) (acre) mg/l (lb) (100 lb) 10 11 2177.50 1.74 200.99 1.45 517.69 0.87 85.85 1,268 4,218 20 15 763.07 10.54 55.59 0.00 234.27 1.49 55.13 1,165 3,377 30 3 1072.58 10.89 207.80 0.00 180.43 0.00 74.15 561 1,384 40 13 1524.44 12.41 56.51 0.00 313.86 1.72 99.38 1,103 3,135 50 16 884.93 16.91 34.41 0.00 230.27 0.00 67.19 997 2,774 60 17 161.46 0.00 13.07 0.00 127.27 0.00 50.29 337 1,016 70 14 1041.02 24.61 307.41 3.36 859.80 0.89 51.50 1,154 3,166 80 9 1110.75 17.29 150.80 0.00 559.79 0.37 58.71 1,095 2,688 90 6 727.51 0.00 92.96 0.00 214.44 33.55 99.96 839 2,270 100 18 30.23 8.28 13.76 22.38 166.78 45.13 46.89 111 34 110 19 5.66 0.23 3.79 0.33 57.74 9.84 49.42 50 35 120 2 463.94 6.36 19.72 10.59 284.49 29.83 50.76 730 1,244 130 10 1444.81 10.38 65.47 0.00 597.92 0.21 58.37 1,101 2,852 140 20 132.60 3.28 4.68 0.00 71.12 0.21 53.48 236 559 150 7 4894.60 8.21 213.40 0.00 1717.49 6.16 61.00 3,112 16,990 160 8 879.52 0.87 42.28 0.00 501.36 28.91 57.03 1,454 4,501 170 21 2822.65 0.40 59.89 1.60 1094.72 13.18 55.14 1,996 7,906 180 22 219.63 9.03 53.91 0.00 417.14 0.35 61.57 369 479 190 23 577.98 16.54 123.63 4.17 771.77 51.31 23.32 316 740 200 5 3307.59 27.39 130.69 0.48 1202.63 135.49 51.65 1,308 5,308 210 12 1592.55 10.28 254.27 0.59 1076.97 0.88 73.57 1,891 5,739 220 4 2714.03 17.46 156.38 0.00 1055.76 25.40 46.54 721 2,895 230 1 1578.03 49.54 37.88 20.40 942.26 21.19 86.65 1,950 6,785 *CTI: Commercial/Transportation/Industrial HIR: High Intensity Residential LIR: Low Intensity Residential TSS: Total Suspended Sediment, which including sediment and erosion.

Table 5.18 Sediment and Erosion Loads from Different Catchements (LIRD)

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5.2.3 Scenario C Output

Scenario C is the scenario of high intensity residential development (HIRD). In this simulation, those catchments with higher peak unit runoff usually have relatively high percentage of impervious cover, such as Catchments #8, 17, 19, and 20 (Table

5.19).

Streamflow: Figure 5.16 presents the inflow hydrograph at the outlet of the watershed. This hydrograph also has two peaks. The first one occurs in the first 10 minutes, and the second at hour 1:10 after storm start, which is earlier than in Scenarios

A and B. Unlike the previous scenarios, the HIRD hydrograph does not drop down after reaching the second peak. In SWMM, this normally means that surcharging is taking place that would normally cause a flood. Therefore, the simulation period is extended to 24 hours, and the peak streamflow starts decreasing at hour 6:50. Scenario

C not only generates a great amount of sediment and erosion, but also cause a flood.

Urbanization has a great impact on streamflow.

Total Suspended Sediment and Soil Erosion: Figure 5.17 presents the relationships between TSS, soil erosion and simulation time. Unlike the previous two scenarios, the peak TSS concentration occurs at hour 1:35, and is higher. In the HIRD simulation, urbanization generates a large amount of surface runoff, which continues to washoff sediment and causes soil erosion. When water is held back (flat area in Figure 5.16), water quality remains steady until the streamflow drops off, and then sediment concentration starts decreasing (Figures 5.16 and 5.17) around hour 6:50.

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Pervious Total Catchment Area Impervious % of Peak Catchment Stream Area Peak Imper. Runoff Peak Peak ID ID (Acre) Runoff Runoff Covers Rate Runoff Unit (cfs) Depth (cfs) Rate Runoff (in) (cfs) (in/hr) 1 230 2650.14 25 193.52 3464.39 0.864 3657.91 1.380 2 120 833.25 23.1 134.49 2034.82 1.067 2169.32 2.603 3 30 1473.79 9.2 73.5 1575.52 0.431 1649.02 1.119 4 220 4002.3 18.8 100.81 2155.01 0.524 2255.82 0.564 5 200 4821.1 17.6 239.32 4535.24 0.61 4774.56 0.990 6 90 1068.82 14 104.93 1911 0.688 2015.93 1.886 7 150 6895.08 17.7 152.88 3118.49 0.483 3271.37 0.474 8 160 1494 24.1 428.45 5316.42 1.314 5744.88 3.845 9 80 1841.49 22.1 74.85 1789.72 0.686 1864.57 1.013 10 130 2121.33 20.2 158.14 2880.04 0.765 3038.18 1.432 11 10 2909.2 12.1 146.73 2876.81 0.501 3023.53 1.039 12 210 2938.93 25.8 168.95 4088.2 0.846 4257.14 1.449 13 40 1916.41 12 111.71 1858.77 0.53 1970.48 1.028 14 70 2248.09 27.3 90.42 2634.12 0.798 2724.54 1.212 15 20 1065.07 16.3 188.15 2617.62 0.975 2805.77 2.634 16 50 1167.35 14.8 280.57 3017.09 1.075 3297.66 2.825 17 60 302.46 29.3 179.97 1740.81 1.702 1920.78 6.350 18 100 289.51 33.2 35 630.95 1.153 665.95 2.300 19 110 85.77 45.5 14.48 260.56 1.504 275.04 3.207 20 140 211.99 24.4 157.27 1113.14 1.618 1270.41 5.993 21 170 4005.65 19.1 133.11 2533.96 0.574 2667.07 0.666 22 180 699.84 42.5 47.57 1282.23 1.235 1329.79 1.900 23 190 1567.65 35.1 164.41 3789.93 1.171 3954.34 2.522

Table 5.19 Runoff of High Intensity Residential Development (HIRD)

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Flow (cfs) Flow 1500

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0 21 Mon 3AM 6AM 9AM 12PM 3PM 6PM 9PM 22 Tue Mar 94 Date/Time

Figure 5.16 The Stream Hydrograph at the Outlet of the Watershed (HIRD)

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TS (mg/l x cfs) 50000

0 50000

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10000 Erosion (mg/l x cfs) 0 21 Mon 3AM 6AM 9AM 12PM 3PM 6PM 9PM 22 Tue Mar 94 Date/Time

Figure 5.17 TSS and Erosion at the Outlet of the Watershed (HIRD) 171

In the HIRD scenario, some agriculture and forest lands are converted to HIRD areas, leading to increase in sediment loads. In Catchment #11, 496 acres of agriculture and forest lands are converted to HIRD, the TSS load increase by 889 lbs (81% gain), and soil erosion decreases by 48,300 lb, as compared with Scenario A. In downstream

Catchment #7, 1,716 acres of agriculture and forest land are converted to HIRD, the sediment load increases by 43,29 lbs, and erosion also increases by 332,000 lbs, as compared with Scenario A. The TSS loads are twice as large as those under Scenario A in Catchment #7.

It is very clear that total sediment generation is highly related to urban land uses.

The higher-intensity urban land uses generate more sediments. On the other hand, agriculture land uses are highly related to soil erosion. Based on the simulation results, a quantitative relationship between agriculture land use and soil erosion has been estimated (Figure 5.18), with:

ERO=1.774 AG1.015 (5.1)

where:

ERO = amount of soil erosion (100 lb), AG = agriculture land use (acre). R2 = 0.806

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Stream Catchment Agriculture CIT Forest HIR LIR Park TSS Sediment Erosion ID ID (acre) (acre) (acre) (acre) (acre) (acre) mg/l (lb) (100 lb) 10 11 2177.50 1.74 200.99 496.81 22.33 0.87 74.14 1,989 4,598 20 15 763.07 10.54 55.59 234.27 0.00 1.49 44.27 1,340 2,439 30 3 1072.58 10.89 207.80 180.43 0.00 0.00 67.44 821 1,499 40 13 1524.44 12.41 56.51 313.29 0.57 1.72 86.46 1,612 3,771 50 16 884.93 16.91 34.41 225.49 4.78 0.00 52.64 1,149 2,383 60 17 161.46 0.00 13.07 126.21 1.06 0.00 37.14 372 563 70 14 1041.02 24.61 307.41 836.53 26.62 0.89 36.94 2,178 2,829 80 9 1110.75 17.29 150.80 559.79 0.00 0.37 44.61 1,789 2,335 90 6 727.51 0.00 92.96 214.33 0.11 33.55 85.84 1,213 2,168 100 18 30.23 8.28 13.76 101.56 87.60 45.13 35.93 160 18 110 19 5.66 0.23 3.79 55.11 2.96 9.84 37.79 81 5 120 2 463.94 6.36 19.72 252.22 42.87 29.83 37.62 926 962 130 10 1444.81 10.38 65.47 597.29 0.64 0.21 44.60 1,764 280 140 20 132.60 3.28 4.68 69.38 1.74 0.21 40.57 251 345 150 7 4894.60 8.21 213.40 1716.80 0.68 6.16 47.83 5,626 15,340 160 8 879.52 0.87 42.28 492.50 8.86 28.91 45.54 1,769 3,074 170 21 2822.65 0.40 59.89 1090.33 5.99 13.18 41.75 3,449 7,350 180 22 219.63 9.03 53.91 412.94 4.20 0.35 45.03 670 282 190 23 577.98 16.54 123.63 753.54 22.41 51.31 19.86 705 621 200 5 3307.59 27.39 130.69 1163.23 39.88 135.49 48.68 2,594 7,377 210 12 1592.55 10.28 254.27 1067.87 9.69 0.88 59.62 3,283 5,721 220 4 2714.03 17.46 156.38 1055.37 0.40 25.40 48.32 1,889 4,081 230 1 1578.03 49.54 37.88 852.19 110.46 21.19 70.44 2,980 6,752 *CTI: Commercial/Transportation/Industrial HIR: High Intensity Residential LIR: Low Intensity Residential TSS: Total Suspended Sediment, which including sediment and erosion.

Table 5.20 Sediment and Erosion Loads from Different Catchments (HIRD)

173

Figure 5.18 Plot of Simulated Erosion vs. Agriculture Land

Compared with the scale of soil erosion in the simulations, the simulated sediment loads seem relatively larger than expected. TSS is generated by two mechanisms: buildup-washoff and erosion. Dust and dirt buildup takes place in dry days in all land uses, including urban and non-urban areas, but urban areas have higher buildup rates

(6.04 lb/100 ft-curb), due to commercial and industrial areas, and non-urban areas have relatively low buildup rates (0.7 lb/100 ft-curb). This dust and dirt is washed off by the storm. Therefore, the sediment loads in the SWMM model output represent all the dust and dirt that goes into the stream from all land uses. While non-urban areas have lower buildup rates, they still generate large sediment loads because of their large surfaces. 174

Also, agriculture contributes pollutants through soil erosion, which is distinct from dust and dirt buildup.

This study includes both washoff and erosion for agriculture, because their sources are different. The washoff process focuses on dust and dirt buildup (street pavements, vehicles, atmospheric fallout, vegetation, land surfaces, litter, spills, anti-skid compounds and chemicals, construction, and drainage networks). Erosion takes place when rainfall energy detaches soil particles from the ground. However, since both processes are estimated using empirical equations, it is possible that there might be some double counting of sediment loads in agriculture areas. However, ignoring either process would lead to an underestimation of the total sediment load.

Therefore, the possible overestimation should be viewed as a conservative approach.

5.3 ECONOMIC MODEL

The mathematical structure of the economic model has been presented in Section

3.4 of Chapter 3. This section focuses on the values of the parameters of the model.

5.3.1 BMP Cost

There are five different BMP technologies (Ponds, Wetlands, Infiltrations,

Filterings, and no BMP). The objective is to determine the minimum control cost combination of these technologies for each development scenario, subject to water quality standards.

175

BMP control costs include land buying costs, design and construction costs, and maintenance costs. Since each BMP has a different lifetime, costs must be annualized.

Inflation is not considered. The unit costs are presented in Table 5.21.

Pond Wetland Infiltration Filtering Catchment ID No BMP System System System System 1 7,851 31,680 101,819 54,303 0 2 7,817 31,646 101,786 54,269 0 3 7,497 31,326 101,466 53,949 0 4 7,673 31,502 101,642 54,125 0 5 7,658 31,486 101,626 54,109 0 6 7,574 31,403 101,543 54,026 0 7 7,644 31,472 101,612 54,095 0 8 7,764 31,592 101,732 54,215 0 9 7,739 31,568 101,708 54,191 0 10 7,696 31,524 101,664 54,147 0 11 7,549 31,377 101,517 54,000 0 12 7,802 31,630 101,770 54,253 0 13 7,548 31,377 101,517 54,000 0 14 7,851 31,680 101,819 54,303 0 15 7,632 31,460 101,600 54,083 0 16 7,617 31,446 101,586 54,069 0 17 7,861 31,690 101,829 54,312 0 18 8,314 32,143 102,283 54,766 0 19 8,297 32,125 102,265 54,748 0 20 7,800 31,628 101,768 54,251 0 21 7,671 31,499 101,639 54,122 0 22 8,128 31,957 102,097 54,580 0 23 8,002 31,830 101,970 54,453 0

Table 5.21 BMP Unit Control Costs for Each Catchment ($/acre) 176

5.3.2 BMP Pollutant Removal Efficiency

Each BMP has a different removal efficiency(β j ) . Pond systems reduce the sediment load by 80%, wetland systems by 75%, infiltration systems by 90%, and filtering systems by 85%.

5.3.3 Gross Sediment Loads

The gross sediment load (GSis ) from the SWMM model output is one of the exogenous inputs to the economic model, and includes the sediment and soil erosion output from the watershed model. This load depends on storm type and development scenario. Table 5.22 presents gross sediment loads under storm type 2 (0.05-in) in each catchment under the three development scenarios (A, B, C).

5.3.4 Pollutant Transportation Rates

Different storms generate different stream flows. There are 18 water quality control points in the study watershed (Figure 5.13). The final pollutant loading at each water quality control point along the stream is computed, using Equation (3.6),

requiring the knowledge of the transportation rates αiks . Table 5.23 presents the pollutant transport rates under the streamflow generated by a type 2 storm.

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Scenario Catchment ID A B C 1 5 19 98 2 418 823 1,602 3 0 3 8 4 0 1 5 5 53 95 149 6 0 15 91 7 960 2,301 4,439 8 984 2,404 4,782 9 272 620 1,362 10 238 580 1,349 11 0 9 31 12 86 252 600 13 1 6 42 14 158 319 663 15 82 175 421 16 71 137 214 17 203 439 965 18 172 427 838 19 228 523 1,100 20 298 723 1,676 21 473 1,139 3,790 22 102 304 706 23 31 85 160

Table 5.22 Gross Sediment Loads (lbs) Under Storm Type 2 (0.05-in.)

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Catchment Water Quality Control Point ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.55 0.3 0.18 0.13 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.55 0.34 0.24 3 1 0 0.43 0.24 0.1 0.04 0.03 0 0 0 0 0 0 0 0.01 0.01 0 0 4 0 0 0 0 00 0 0 0 0.18 0.08 0.03 1 0.43 0.02 0.01 0.01 0 5 0 0 0 0 0 0 0 0 0 0.43 0.18 0.08 0 1 0.04 0.02 0.01 0.01 6 0 0 0 0 0 0 0 0 0 0 1 0.55 0 0 0.3 0.17 0.1 0.07 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.71 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 9 0 0 0 0 0 1 0.61 0 0 0 0 0 0 0 0.34 0.18 0.11 0.08 10 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0.55 0.3 0.18 0.13 11 0 0 0 0 0 0 0 1 0.55 0.3 0.17 0.07 0 0 0.04 0.02 0.01 0.01 12 0 0 0 0 0 0 0 0 1 0.55 0.3 0.13 0 0 0.07 0.04 0.02 0.02 13 0 1 0.55 0.3 0.13 0.06 0.03 0 0 0 0 0 0 0 0.02 0.01 0.01 0 14 0 0 0 0 1 0.43 0.26 0 0 0 0 0 0 0 0.14 0.08 0.05 0.03 15 0 0 1 0.55 0.24 0.1 0.06 0 0 0 0 0 0 0 0.03 0.02 0.01 0.01 16 0 0 1 0.55 0.24 0.1 0.06 0 0 0 0 0 0 0 0.03 0.02 0.01 0.01 17 0 0 0 1 0.43 0.18 0.11 0 0 0 0 0 0 0 0.06 0.03 0.02 0.01 18 0 0 0 0 0 0 0 0 0 0 1 0.43 0 0 0.24 0.13 0.08 0.06 19 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.55 0.3 0.18 0.13 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.55 0.34 0.24 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.61 0.43 22 0 0 0 0 0 0 0 0 0 1 0.55 0.24 0 0 0.13 0.07 0.04 0.03 23 0 0 0 0 0 0 0 0 0 1 0.43 0.18 0 0 0.1 0.06 0.03 0.02

Table 5.23 Pollutant Transportation Rate Under Type 2 Storm

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5.3.5 The Installation Areas for BMPs

CWP (1996) reports that different BMPs have different installation area requirements. The total drainage area in each catchment i (TDAi), the minimum drainage area requirement (UDAj), and the unit installation area (UAj) for each BMP j are presented in Tables 5.24 and 5.25.

Total Drainage Area Total Drainage Area Catchment ID Catchment ID (acre) (acre) 1 2,650 12 2,939 2 833 13 1,916 3 1,474 14 2,248 4 4,002 15 1,065 5 4,821 16 1,167 6 1,069 17 302 7 6,895 18 290 8 1,494 19 86 9 1,841 20 212 10 2,121 21 4,006 11 2,909 22 700 23 1,568

Table 5.24 Total Drainage Area in Each Catchment (TDAi )

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Minimum Drainage Area Unit Installation Area BMP ()UDAj ()UAj

Pond System 10 0.25 Wetland System 10 0.4 Infiltration System 3.5 0.105 Filtering System 3.5 0.15 No BMP 0 0

Table 5.25 Minimum Drainage and Unit Installation Area of BMP (acre)

5.3.6 BMP Selection Constraints

This section presents the adaptation of the area constraints discussed in Section

3.4.2.6. Several distinct BMPs can be applied to reduce pollutant loading. It is also possible that none is needed because the water quality is good enough under specific land-use conditions. The BMP installation area must be less than the maximum available area, with:

max AAij≤ ij (5.2)

max ∑ AAij≤ i (5.3) j

The maximum areas are presented in Table 5.26. The four BMPs are coded as follows: 1=Pond Systems; 2=Wetland Systems; 3=Infiltration Systems; 4=Filtering

Systems. The following area variables are defined:

AiiP = Area used for Pond Systems in catchment .

AiiW = Area used for Wetland Systems in catchment .

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AiiI = Area used for Infiltration Systems in catchment .

AiiF = Area used for Filtering Systems in catchment .

max Aii = Maximum Area used in catchment .

The following diagrams represent different combinations of BMPs in the four types of catchments.

1. Type A

Type A catchment can accommodate Pond, Wetland, and Filtering systems anywhere within the catchment and to the extent of the maximum catchment area, as illustrated in Figure 5.19. This is the case of catchments #3, #11, #12, #13, #15, and

#16.

Figure 5.19 The Conceptual Combination of Type A

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max BMP ()Aij Maximum Total Area Catchment ID max ()Ai Pond Wetland Infiltration Filtering No BMP 1 1,062 0 7 184 0 1,062 2 358 0 3 61 0 358 3 148 34 0 63 0 148 4 1,646 4 1 157 0 1,646 5 2,673 1 51 336 0 2,673 6 389 0 5 90 0 389 7 3,392 0 21 557 0 3,392 8 651 0 0 150 0 651 9 175 26 1 42 0 175 10 761 0 2 148 0 761 11 165 10 0 66 0 165 12 143 53 0 65 0 143 13 386 20 0 157 0 386 14 61 13 1 20 0 61 15 43 3 0 29 0 43 16 105 24 0 40 0 105 17 4 0 0 3 0 4 18 5 0 0 3 0 5 19 1 0 0 0 0 1 20 64 0 0 15 0 64 21 2,006 0 126 305 0 2,006 22 8 0 1 4 0 8 23 185 0 73 91 0 185

Table 5.26 Maximum Areas for BMPs (acre)

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The following constraints apply:

max AAiP++≤ iW A iF A i (5.4)

max AiP≤ A iP (5.5)

max AiW≤ A iW (5.6)

max AAiF≤ iF (5.7)

2. Type B

Type B catchment can accommodate Pond, Wetland, Infiltration, and Filtering

Systems. This is the case of catchments #4, #5, #9, and #14, as illustrated in Figure

5.20.

Figure 5.20 The Conceptual Combination of Type B

Filtering and Infiltration systems can be installed in a specific subarea of the

max catchment, up to a total area, AiF . There are no setup restrictions for Pond and Wetland systems.

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The following constraints apply:

max AAAiF+≤ iI iF (5.8)

max AiP+++≤AAAA iW iI iF i (5.9)

max AiP≤ A iP (5.10)

max AAiW≤ iW (5.11)

max AiI≤ A iI (5.12)

3. Type C

Type C catchment can receive only Pond and Filtering systems. This is the case of catchments #17, and #20, as illustrated in Figure 5.21.

Figure 5.21 The Conceptual Combination of Type C

The following constraints apply:

max AiP+≤AA iF i (5.13)

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max AAiP≤ iP (5.14)

max AiF≤ A iF (5.15)

4. Type D

Type D catchment can only have Pond Systems, Infiltration Systems, and Filtering systems. This is the case of catchments #1, #2, #6, #7, #8, #10, #18, #19, #21, #22, and

#23, as illustrated in Figure 5.22.

Figure 5.22 The Conceptual Combination of Type D

Filtering and Infiltration systems can be installed in a specific subarea of the

max catchment, up to a total area, AiF .

The following constraints applied:

max AAAiF+≤ iI iF (5.16)

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max AAAAiP++ iI iF ≤ i (5.17)

max AiP≤ A iP (5.18)

max AiI≤ A iI (5.19)

5.3.7 Water Quality Standard Constraints

Two water quality standards are considered: the Environmental Quality Standard

(EQS) and the Total Maximum Daily Load (TMDL). The EQS focuses on pollutant concentration, while the TMDL focuses on the total pollutant load. The TMDL standard for total suspended sediment (TSS) for the study watershed is 4,461,296 pounds per year, while the EQS standard is 158 mg/l for a daily standard and 10 mg/l for an annual standard.

The TMDL Equations (3.14) requires the knowledge of the number of days in a year for each storm type (ds). These are presented in Table 5.27. The ambient constraint

(3.13) requires the knowledge of the streamflows (ROks) at each control point k under each storm type. These are presented in Table 5.28.

Storm Type Days 1 226.1 2 68.5 3 44.2 4 19.0 5 7.2

Table 5.27 Number of Days With Storm Type

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Water Quality Storm Type Control Point 1 2 3 4 5 1 20,206 67,354 561,189 13,801,910 39,535,100 2 24,765 82,551 671,559 22,198,520 59,005,500 3 23,333,350 23,670,120 25,936,950 119,341,100 273,519,500 4 39,761,500 40,072,800 42,223,600 141,839,600 303,376,000 5 45,053,600 45,449,800 49,015,600 156,980,100 191,591,000 6 65,769,200 66,250,300 70,127,400 166,602,100 216,212,000 7 83,230,300 83,739,700 87,928,100 196,600,100 287,245,000 8 15,902 53,006 425,915 21,929,670 63,561,800 9 18,598,760 18,737,430 19,620,390 54,675,600 126,982,100 10 39,676,600 39,874,700 41,091,600 74,485,600 148,263,700 11 135,104,200 135,472,372 138,942,116 189,564,720 299,215,900 12 189,723,200 190,091,100 193,345,600 237,720,000 333,940,000 13 25,207 84,023 882,394 13,306,660 39,393,600 14 40,101,100 40,355,800 42,308,500 73,268,700 135,330,600 15 354,117,900 355,448,000 366,909,500 553,434,800 817,021,000 16 265,425,700 266,274,700 273,887,400 376,956,000 576,754,000 17 449,687,000 451,102,000 462,422,000 668,729,000 955,125,000 18 524,116,000 525,248,000 536,568,000 772,307,000 1,116,152,000

Table 5.28 Streamflow under Scenario A Development (liter)

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CHAPTER 6

RESULTS AND DISCUSSION

The outputs of the watershed model—streamflow and total sediment loads—are used as inputs to the economic model to generate the minimum cost pollution control solutions. These costs are compared under two standards—TMDL and EQS—and different precipitation patterns (number of days and intensity).

6.1 OPTIMIZATION MODEL

Mixed-integer linear programming is used to represent the optimization problem of seeking the minimum-cost combination of BMPs that achieve USEPA standards.

Both Total Maximum Daily Loads (TMDL) and Environmental Quality Standards

(EQS) are considered. Sensitivity analyses are conducted to assess changes in the solution as a result of variations in the standards. Different land-use development scenarios generate different impacts on the environment.

Three different TMDL standards are considered: TMDL1, based on the number of days with precipitation greater than 0.01 inch; TMDL2, based on the number of days with precipitation greater than 0.5 inch; and TMDL3 based on the number of days with

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Single Storm Water Quality Annual Precipitation Precipitation Precipitation Daily Standard ≥ 0.01-in ≥ 0.5-in ≥ 1.0-in Maximum Storm TMDL1 TMDL2 TMDL3 TMDL (lb) 32,051 169,921 618,325 -- 4,461,296 EQS (mg/l) ------158 10

Table 6.1 Water Quality Standards

precipitation greater than 1.0 inch. Two different EQS standards are also considered, based on period length (Table 6.1). These standards have been discussed in Section

4.7.8.2, and used in the water quality constraints (Equations 5.13 and 5.14 in Chapter

5).

6.2 SINGLE STORM EVENT

There are three different EQS standards, depending on duration. The EPA (2002) suggests a TSS of less than 90 mg/l for a 30-day average, and 158 mg/l as a daily maximum. The TSS annual standard of the study area is 10 mg/l based on the warm water habitat standard. The daily maximum standard (158 mg/l) is applied since a one-year normal storm with a 2-hr duration is considered here.

Scenario A is based on 1994 land uses. TMDL1 is the strictest standard. There is no BMP technologies combinations that can meet the TMDL1 standard. Under the

TMDL2 standard, 12 catchments require pond systems installations, over 361.51 acres and at an annual cost of $2,787,010 (Table 6.2). Most upstream catchments do not need

BMP technology. Under the TMDL3 standard, only 50.07 acres of pond systems are

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TMDL2 TMDL3 EQS Catchment Scenario Scenario Scenario A B C A B C A B C 1 0 0 66.25 0 0 0 0 0 0 2 20.8 20.8 20.83 0 20.8 20.8 0 0 0 3 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 5 0 0 120.5 0 0 0 0 0 0 6 0 0 26.7 0 0 0 0 0 0 7 125.5 172.4 172.4 0 76.5 172.4 0 0 0 8 37.3 37.4 37.3 37.3 37.4 37.3 0 0 0 9 46.0 46.0 46.0 0 0 31.6 0 0 0 10 53.0 53.0 53.03 0 53.0 53.0 0 0 0 11 0 0 8.7 0 0 0 0 0 0 12 0 0 73.5 0 0 0 0 0 0 13 0 0 47.9 0 0 0 0 0 0 14 0 26.3 56.2 0 0 0 0 0 0 15 26.6 26.6 26.6 0 0 0 0 0 0 16 29.2 29.2 29.2 0 0 0 0 0 0 17 3.7 3.7 3.7 3.7 3.7 3.7 0 0 0 18 5.5 5.5 5.4 2.9 5.5 5.5 0 0 0 19 0.8 0.8 0.83 0.8 0.8 0.8 0 0 0 20 5.3 5.3 5.3 5.3 5.3 5.30 0 0 0 21 0 100.1 100.1 0 0 100.1 0 0 0 22 7.6 7.6 7.6 0 7.6 7.6 4.21 0 0 23 0 0 39.2 0 0 0 0 0 0 Total Area 361.5 534.8 947.5 50.1 210.6 438.2 4.21 0 0 Total Annual 2,787 4,119 7,314 391 1,630 3,376 34 0 0 Control Cost *Only pond systems are needed in these simulations.

Table 6.2 BMP Installation Area (acre) and Total Annual Control Cost ($1000)

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needed over 5 catchments, at an annual cost of $391,299. Under the EQS standard, only one pond system is needed in Catchment #22, at an annual cost of $34,220. This catchment is located in the downstream area. The sediment reduction rates are presented in Table 6.3.

Scenario B is the low-intensity residential development (LIRD) scenario. It generates larger TSS loads and surface runoff than Scenario A. There is no solution under the TMDL1 standard. Under the TMDL2 standard, 14 catchments need pond systems over 534.79 acres (173.28 acres more than for Scenario A), at an annual cost of $4,119,635 ($1,332,625 more than for Scenario A). Most upstream catchments do not require treatment. Under TMDL3, only 210.62 acres are needed, at an annual cost of $1,630,342. While this scenario generates more TSS than Scenario A, it also generates more surface runoff, which increases the streamflow, and the TSS concentration is diluted by the large streamflow. No BMP treatment is needed under the

EQS standard alone.

Scenario C is the high-intensity residential development (HIRD) scenario. It generates the largest amount of total sediment and surface runoff, and requires more

BMP treatments. There is no solution under the TMDL1 standard. Under TMDL2, there is a need for 947.50 acres of pond systems, at an annual cost of $7,314,000. Only

Catchments # 3 and 4 do not need BMP treatment. The TMDL3 standard provides savings of 509.26 acres and $3,938,405, as compared to TMDL2. Under the EQS standard, no BMP treatment is needed because of the “dilution phenomena.” Scenario

C generates larger sediment loads than the previous scenarios, but it also generates a larger surface runoff, which decreases the TSS concentration. 192

TMDL2 TMDL3 EQS Catchment Scenario Scenario Scenario A B C A B C A B C 1 0 0 80 0 0 0 0 0 0 2 80 80 80 0 80 80 0 0 0 3 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 5 0 0 80 0 0 0 0 0 0 6 0 0 80 0 0 0 0 0 0 7 58 80 80 0 35 80 0 0 0 8 80 80 80 80 80 80 0 0 0 9 80 80 80 0 0 55 0 0 0 10 80 80 80 0 80 80 0 0 0 11 0 0 10 0 0 0 0 0 0 12 0 0 80 0 0 0 0 0 0 13 0 0 80 0 0 0 0 0 0 14 0 37 80 0 0 0 0 0 0 15 80 80 80 0 0 0 0 0 0 16 80 80 80 0 0 0 0 0 0 17 39 39 39 39 39 39 0 0 0 18 60 60 60 32 60 60 0 0 0 19 31 31 31 31 31 31 0 0 0 20 80 80 80 80 80 80 0 0 0 21 0 80 80 0 0 80 0 0 0 22 35 35 35 0 35 35 17 0 0 23 0 0 80 0 0 0 0 0 0

Table 6.3 Sediment Reduction Rate in Each Catchment (%)

Only pond systems are used to reduce TSS. No wetland, infiltration, and filtering systems are required in any scenario, because pond systems have the lowest costs and can be applied in most areas. The highest sediment reduction rate in any scenario is 193

80%, because only pond systems are used to reduce the total sediment load, and their sediment removal rate is 80% (Table 6.3). Sensitivity analyses will point out the conditions under which the other BMP technologies are required.

6.3 ANNUAL STORM EVENT

In addition to the single storm event analyzed in the previous section, this research also considers the annual storm event, under the USEPA corresponding standards. The

TMDL standard is 2,023,610 kilograms, or 4,461,296 pounds per year, while the TSS concentration standard, EQS, is 10 mg/l.

Under the 1994 land-use scenario A, the annual TSS load (both sediment and erosion) is equal to 7,823,248 kilograms or 17,247,310 pounds. The total target of TSS removal is at least 5,799,638 kilograms, or 12,786,014 pounds. The BMP cost to achieve the TMDL standard is $5,908,738, and it is $1,015,187 to achieve the EQS standard.

Table 6.4 presents the required BMP installation in each subcatchment under the

TMDL standard. Catchments #3, 4, 5, 11, and 23 do not need any BMP technology.

These catchments are located in the upstream areas. The other catchments need pond systems. Catchments #13, 17, 18, 19, and 22 need partial pond systems; they also have lower sediment reduction rates. The largest pond system is located in Catchment #7, with 172 acres, because of its relatively small cost. Catchment #21 has the second largest pond system, at also a relatively small cost. The highest sediment reduction rate is 80%, because only pond systems are used in this scenario. The TMDL standard cannot be achieved under Scenarios B and C with the available BMP technologies, and 194

Catchment ID Area (acre) Net Sediment (kg) Sediment Load Reduction (%) 1 66 27,785 80 2 21 93,236 80 3 0 53,202 0 4 0 12,195 0 5 0 73,842 0 6 27 14,333 80 7 172 221,537 80 8 37 236,961 80 9 46 76,013 80 10 53 81,415 80 11 0 96,395 0 12 73 38,720 80 13 34 36,171 57 14 56 64,435 80 15 27 33,270 80 16 29 40,473 80 17 4 231,607 39 18 5 75,913 60 19 1 173,917 31 20 5 82,390 80 21 100 112,569 80 22 8 114,952 35 23 0 32,316 0 * Only pond systems are used.

Table 6.4 Type of BMP Installation Areas for Scenario A under the TMDL Standard

the only way to meet this standard would be to reduce development areas or modify land-use intensities.

Table 6.5 presents the required BMP installations for the different scenarios under the EQS standard. Catchments #1, 2, 3, 4, 5, 6, 7, 13, 15, 18, and 21 do not need any 195

BMP technology. Pond systems are the only technology installed under Scenario A. In contrast to the TMDL standard, which considers the TSS load for the whole watershed, the EQS standard must be achieved at each water quality control point. Therefore, the catchments with BMP technologies do not always have the lowest control costs. If the upstream catchments have a BMP installed, the downstream catchments usually do not need it, and still can achieve the EQS standard at control points #10, 15, 16, 17, and 18, because the stream does not carry much sediment from upstream catchments. Table 6.6 presents the TSS concentrations at the 18 water quality control points after BMP installation. There are 8 control points where the concentration reaches the EQS standard (10 mg/l).

Scenario B is the low-intensity residential development scenario (LIRD), with more residential areas than Scenario A. In general, this will generate more surface runoff and a larger sediment load (26,916,346 pounds per year). In order to meet the

TMDL standard, the total reduction target is 10,185,440 kilograms (22,455,050 pounds). This reduction cannot be achieved under any BMP technology combination.

The only way to meet the TMDL standard is to reduce development areas or modify land-use intensities.

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Scenario A Scenario B Scenario C Net Sediment Net Sediment Net Sediment Catchment Area Area Area Sediment Reduction Sediment Reduction Sediment Reduction (acre) (acre) (acre) (kg) (%) (kg) (%) (kg) (%) 1 0 138,927 0 0 211,166 0 19.5 329,379 24 2 0 552,884 0 20.8 176,482 80 0 1,389,488 0 3 0 53,202 0 2.2 54,877 5 0 84,499 0 4 0 12,195 0 0 60,320 0 0 206,317 0 5 0 88,480 0 0 190,076 0 46.5 286,957 31 6 0 71,666 0 0 88,810 0 0 163,704 0 7 0 1,271,764 0 43.9 1,818,524 20 109.5 1,869,684 51 8 3.9 1,376,086 8 20.9 1,322,242 45 26.6 1,705,112 57 9 16.9 404,068 29 24.9 367,592 43 27.7 522,710 48 10 16.8 437,450 25 28.9 397,870 44 32.9 585,140 50 11 1.6 96,395 2 16.0 113,840 18 18.9 187,977 21 12 12.6 200,390 14 73.5 77,418 80 73.5 144,046 80 13 0 84,412 0 10.5 86,145 18 27.1 112,332 45 14 15.0 338,619 21 22.0 344,824 31 13.4 603,289 19 15 0 169,585 0 25.8 45,175 78 16.5 139,429 49 16 6.3 207,650 17 0 233,641 0 0 320,405 0 17 3.7 397,064 39 3.7 276,804 39 3.7 367,461 39 18 0 240,241 0 0 482,476 0 5.5 333,463 60 19 0.4 321,182 14 0.8 411,972 31 0.8 700,139 31 20 5.3 454,494 80 5.3 148,821 80 5.3 242,900 80 21 0 659,723 0 4.4 1,336,925 3 43.2 1,965,086 35 22 7.6 191,028 35 7.6 264,104 35 7.6 489,323 35 23 39.2 55,382 80 36.1 25,695 74 39.2 36,644 80 Total Area 129.2 347.3 517.6

Table 6.5 BMP Installation Areas and Net Sediment Loads under the EQS Standard

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The total control cost to achieve the EQS standard is $2,696,533, more than twice the cost of Scenario A. Catchments #1, 4, 5, 6, 16, and 18 (Table 6.5) do not need any

BMP technology. BMP installation areas and net sediment loads are presented in Table

6.5. Table 6.6 presents TSS concentrations at the control points after BMP installation.

There are 11 control points reaching the EQS standard.

Water Quality (mg/l) Control Point Scenario A Scenario B Scenario C 1 9.19 10 9.44 2 9.74 10 10 3 10 8.49 10 4 9.45 10 9.79 5 10 10 10 6 10 10 10 7 10 10 10 8 10 10 10 9 9.82 3.79 3.68 10 10 10 10 11 6.39 8.79 7.12 12 6.81 8.68 10 13 1.73 3.18 5.86 14 3.5 5.07 6.08 15 6.82 5.4 9.91 16 10 10 10 17 8.88 10 10 18 10 10 10

Table 6.6 Water Quality at Control Points after BMP Treatment

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Scenario C is the high-intensity residential development scenario (HIRD), and generates the larger surface runoff and total sediment load. The TSS load is 21,702,077 kilograms (47,844,887 pounds) per year. In order to meet the TMDL standard, the total target of TSS removal is at least 43,383,591 pounds. There is no solution that can meet the TMDL standard.

Under the EQS standard, Catchments #2, 3, 4, 6, and 16 (Table 6.5) do not need any BMP technology. The total cost is $4,002,850, or four times the control cost of

Scenario A. Table 6.6 presents the TSS concentrations at the control points. Eleven points (out of 18) reach the EQS standard. Of the remaining seven control points, three have concentrations very close to the standard. (9.91-9.44 mg/l).

In these three scenarios, only pond systems are used; therefore, the highest sediment reduction rate is 80% in the watershed. Many catchments have lower sediment reduction rates, because only partial pond systems are required.

In summary, The TMDL standard can be achieved under Scenario A, but not under Scenarios B and C. However, the EQS standard can be met under any scenario, because the EQS standard is a concentration standard, while the TMDL is a pollutant load standard. Concentration is related to the amount of pollutants and to the streamflow. Urbanization increases both pollutant loads and surface runoff, and therefore pollutants are diluted in more water. The above analyses point to very different control outcomes under the TMDL and EQS standards, which represent two different environmental considerations.

Although the watershed model shows that the larger amount of runoff dilutes the pollutants during the storm, allowing the standard to be achieved, the sediments do not 199

disappear from the environment. The simulations show that there are thousands of kilograms of sediments left in the streams. As the storm subsides, these sediments will be deposited on the stream bottom. These sediments will actually accumulate in the system and be stirred up in subsequent storms, being added to the newly deposited load.

Thus, the standard will not be met.

The effects of sedimentation on a water body include reducing the amount of sunlight to aquatic plants, covering spawning areas and food sources for water habitats, reducing the filtering capacity of filter feeders, and harming the gills of fish (NCSU

Water Quality Group, 2000). Fine sediment accumulation also reduces the availability of oxygen (O2) to water species (Greig et al., 2005). Sediments can carry soluble pollutants, such as organics, chemicals, and metals, which will be released into the water and degrade water quality. These factors lead to a reduction of water species, such as fish and plants, and to a less productive body of water.

Although sediments can be carried out of the watershed by a large streamflow, they will eventually deposit downstream, affect downstream aquatic environment, stream morphology, and water level fluctuations (Temmerman, 2005). The present approach does not model this full cycle and the larger watershed system. Nevertheless, a focus on only the EQS standard may generate the false impression that “dilution is solution,” while pollutants are simply transported to other locations (downstream).

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6.4 SENSITIVITY ANALYSES

The previous section suggests that, when a high TMDL standard is imposed on sediment control, no solution may be achieved with the selected BMP technologies.

However, too loose a standard is not good for the environment, and the issue is to find a balance between environmental conservation and economic development. A sensitivity analysis is carried out to assess the relationship between control costs and the TMDL standard, which should help formulate a TMDL policy.

6.4.1 Single Storm

In Section 6.2, only three TMDL and one EQS standards are considered, and the corresponding optimal control costs are derived. In this section, sensitivity analyses of these standards are conducted for each land-use scenario.

6.4.1.1 Scenario A

This analysis is based on 1994 land uses and the 2-hr one-year normal storm.

When the TMDL is considered, there is no solution if TMDL is less than 175,000 lb.

When TMDL= 176,000 lb, the control cost is $84,205,584.

The higher TMDL, the larger the allowance for suspended sediments, and the lesser the control costs. The TMDL critical point is 211,000 lb, with costs increasing very strongly for TMDL below 211,000 lb, as illustrated in Figure 6.1.

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90000000

80000000

70000000

60000000

50000000

Cost

40000000 Control Cost ($) Control

30000000

20000000

10000000

0 0 100000 200000 300000 400000 500000 600000 700000 800000 TMDL Standard (lb)

Figure 6.1 TSS TMDL Standard vs. Control Cost —Scenario A

Tables 6.7 and 6.8 present, in the case of four different TMDL standards, the areas of BMP installation and the rates of sediment reduction in each catchment. When

TMDL=300,000 lb, only pond systems are required, and many catchments do not need any BMP treatment. When TMDL=211,000 lb, only pond systems are installed, except in Catchment #8, where both pond and infiltration systems are required. When TMDL=

198,000 lb, infiltration systems are needed in many catchments, and this is the most expensive BMP technology. This explains why total control costs increase strongly.

When TMDL=176,000 lb, most catchments use infiltration and filtering systems. These two systems have the highest TSS removal rate and control costs. Many catchments have sediment removal rates higher than 80%, because of infiltration and filtering systems installation (Table 6.8). 202

TMDL (lb) Catchment BMP 176,000 198,000 211,000 300,000 BMP Installation Area (acre) 1 P 0 66.3 66.3 0 1 I 6.8 0 0 0 1 F 103.8 0 0 0 2 P 0 0 20.8 20.8 2 I 3.3 3.3 0 0 2 F 31.1 31.1 0 0 3 P 0 36.8 36.9 0 3 F 63.2 0 0 0 4 P 100.2 100.1 0.9 0 5 P 63.5 120.5 120.5 0 5 I 51.1 0 0 0 5 F 24.6 0 0 0 6 P 0 26.7 26.7 10.6 6 I 5.2 0 0 0 6 F 38.4 0 0 0 7 P 0 155.4 172.4 172.4 7 I 20.5 20.4 0 0 7 F 266.2 0 0 0 8 P 0 0 37.2 37.4 8 I 0.2 0.2 0.2 0 8 F 63.8 63.8 0 0 9 P 21.3 45.2 46.0 46.0

Continued

Table 6.7 Sensitivity Analysis of the TMDL Standard—Scenario A

203

Table 6.7 continued TMDL (lb) Catchment BMP 176,000 198,000 211,000 300,000 BMP Installation Area (acre) 9 I 1.0 1.0 0 0 9 F 41.0 0 0 0 10 P 0 51.2 53.0 53.0 10 I 2.2 2.2 0 0 10 F 87.7 0 0 0 11 P 34.4 72.7 72.7 0 11 F 65.8 0 0 0 12 P 35.3 73.8 73.5 0 12 F 65.4 0 0 0 13 P 0 47.9 47.9 0 13 F 82.1 0 0 0 14 P 49.2 56.2 56.2 56.2 14 I 0.6 0 0 0 14 F 11.1 0 0 0 15 P 9.7 26.6 26.6 26.6 15 F 29.0 0 0 0 16 P 5.6 29.2 29.2 29.2 16 F 40.4 0 0 0 17 P 3.7 3.7 3.7 3.7 18 P 5.5 5.5 5.5 5.5 19 P 0.8 0.8 0.8 0.8 20 F 9.1 9.1 9.1 5.3 21 P 0 100.1 100.1 100.1 21 I 120.2 0 0 0 22 P 7.6 7.6 7.6 7.6 23 P 0 39.2 39.2 . 23 I 47.0 0 0 0 Annual Control Cost ($) 84,205,584 16,581,835 8,550,708 4,435,110 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

204

TMDL (lb) Catchment 176,000 198,000 211,000 300,000 Sediment Load Reduction (%) 1 85 80 80 0 2 86 86 80 80 3 85 80 80 0 4 80 80 1 0 5 84 80 80 0 6 86 80 80 32 7 85 81 80 80 8 85 85 80 80 9 83 80 80 80 10 85 80 80 80 11 83 80 80 0 12 83 80 80 0 13 85 80 80 0 14 81 80 80 80 15 83 80 80 80 16 84 80 80 80 17 39 39 39 39 18 60 60 60 60 19 31 31 31 31 20 85 85 85 80 21 90 80 80 80 22 35 35 35 35 23 90 80 80 0

Table 6.8 Sediment Reduction Rates under TMDL Standards—Scenario A

205

EPA sets 158 mg/l as the daily standard for TSS concentration. Under this standard, the control cost is $34,220. There is no solution for TSS-EQS under 66 mg/l

(Table 6.9). When EQS=67 mg/l (control cost= $25,267,252), Catchments #5 and #23 require infiltration systems, and their sediment removal rates are 85% and 90%, respectively (Table 6.10). For TSS-EQS above 174 mg/l, no BMP technology is needed;

73 mg/l is a critical standard, with costs increasing very strongly for standards below this threshold, as illustrated in Figure 6.2.

30000000

25000000

20000000

15000000

Cost

10000000 Control Cost ($) Cost Control

5000000

0 0 50 100 150 200 250

-5000000 TSS EQS Standard (mg/l)

Figure 6.2 TSS EQS Standard vs. Control Cost—Scenario A

When EQS= 79 mg/l, only pond systems are required. When EQS= 72 mg/l, pond systems can still handle the sediment loads, but they do need more treatment areas.

However, when EQS= 67 mg/l, infiltration systems and filtering systems are needed, in

206

EQS (mg/l) Catchment BMP 67 72 79 100 BMP Installation Area (acre) 1 P 31.8 18 0 0 2 P 20.8 20.8 20.8 14.9 3 P 15.3 13.1 9.8 0.3 4 P 100.1 0 0 0 5 P 38.6 119.8 47.0 0 5 I 51.1 0 0 0 5 F 67.4 0 0 0 6 N 0 0 0 0 7 P 60.3 48.8 32.5 0 8 P 24.0 22.3 19.9 10.5 9 P 30.4 28.4 25.5 16.9 10 P 31.5 28.9 25.4 14.6 11 P 34.4 72.7 72.7 11.7 11 F 65.8 0 0 0 12 P 35.3 73.5 73.5 73.5 12 F 65.4 0 0 0 13 P 19.5 16.5 12.2 0 14 P 47.2 45.5 43.0 29.1 15 P 3.5 0 0 0 16 P 29.2 25.1 15.2 0 17 P 3.7 3.7 3.7 3.7 18 P 5.5 5.5 5.2 0 19 P 0.8 0.8 0.8 0.8 20 P 5.3 5.3 5.3 5.3 21 P 42.4 34.9 24.4 0 22 P 7.6 7.6 7.6 7.6 23 P 0 39.2 39.2 16.5 23 I 47.0 0 0 0 Annual Control Cost ($) 25,267,252 4,868,312 3,744,007 1,604,081 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

Table 6.9 Sensitivity Analysis of the EQS Standard—Scenario A

addition to pond systems (Table 6.9). Only Catchment #6 does not need any BMP technology, because it has a relatively small area and generates a small amount of sediments.

207

EQS (mg/l) Catchment 67 72 79 100 Sediment Load Reduction (%) 1 38 22 0 0 2 80 80 80 57 3 33 28 21 1 4 80 0 0 0 5 85 79 31 0 6 0 0 0 0 7 27 22 14 0 8 51 47 42 22 9 53 49 44 29 10 47 43 38 22 11 83 80 80 13 12 83 80 80 80 13 33 27 20 0 14 67 65 61 41 15 10 0 0 0 16 80 69 42 0 17 39 39 39 39 18 60 60 56 0 19 30 30 30 30 20 80 80 80 80 21 33 27 19 0 22 35 35 35 35 23 90 79 79 32

Table 6.10 Sediment Reduction Rates under the EQS Standard—Scenario A

208

6.4.1.2 Scenario B

Scenario B corresponds to low intensity residential development and a 2-hr one-year normal storm. When the TMDL is less than 251,000 lb, there is no solution.

The critical point is TMDL= 317,000 lb, with a control cost of $8,010,244. Costs increase strongly for TMDL below 317,000 lb (Figure 6.3).

100000000

90000000

80000000

70000000

60000000

50000000 Cost

Control Cost ($) 40000000

30000000

20000000

10000000

0 0 100000 200000 300000 400000 500000 600000 700000 800000 TMDL Standard (lb)

Figure 6.3 TSS TMDL Standard vs. Control Cost—Scenario B

Tables 6.11 and 6.12 present the optimal BMP required areas and sediment reduction rates for various TMDL standards. When TMDL≧317,000lb, only pond systems are required. However, if TMDL ≦ 317,000 lb, infiltration and filtering systems are needed. These two BMPs have better pollutant removal rates, but higher costs, than pond systems. The sediment removal rate for each catchment is presented in

Table 6.12. 209

TMDL (lb) Catchment BMP 251,000 293,000 317,000 340,000 BMP Installation Area (acre) 1 P 0 66.3 66.3 66.3 1 I 6.8 0 0 0 1 F 103.8 0 0 0 2 P 0 14.7 20.8 20.8 2 I 3.2 3.3 0 0 2 F 31.0 5.8 0 0 3 P 30.0 36.9 28.4 0 3 F 11.6 0 0 0 4 P 100.1 100.1 0 0 5 P 0 120.5 120.5 6.5 5 I 51.1 0 0 0 5 F 133.6 0 0 0 6 P 0 26.7 26.7 26.7 6 I 5.2 0 0 0 6 F 38.5 0 0 0 7 P 0 172.4 172.4 172.4 7 I 20.5 0 0 0 7 F 266.2 0 0 0 8 P 0 0 37.4 37.4 8 I 0.2 0.2 0 0 8 F 63.8 63.8 0 0 9 P 21.3 46.0 46.0 46.0 9 I 1.0 0 0 0 9 F 41.0 0 0 0

Continued

Table 6.11 Sensitivity Analysis of the TMDL Standard—Scenario B

210

Table 6.11 continued

10 P 0 53.0 53.0 53.0 10 I 2.2 0 0 0 10 F 87.7 0 0 0 11 P 34.4 72.7 72.7 72.7 11 F 65.8 0 0 0 12 P 35.3 73.5 73.5 73.5 12 F 65.4 0 0 0 13 P 0 47.9 47.9 47.9 13 F 82.1 0 0 0 14 P 49.2 56.2 56.2 56.2 14 I 0.6 0 0 0 14 F 11.1 0 0 0 15 P 9.7 26.6 26.6 26.6 15 F 29.0 0 0 0 16 P 5.6 29.2 29.2 29.2 16 F 40.4 0 0 0 17 P 3.7 3.7 3.7 3.7 18 P 5.5 5.5 5.5 5.5 19 P 0.8 0.8 0.8 0.8 20 P 0 0 5.3 5.3 20 F 9.1 9.1 0 0 21 P 0 100.1 100.1 100.1 21 I 120.2 0 0 0 22 P 7.6 7.6 7.6 7.6 23 P 0 39.2 39.2 39.2 23 I 47.0 0 0 0 Total Control Cost ($) 87,058,504 13,077,616 8,010,244 6,924,665 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

211

TMDL (lb) Catchment 251,000 293,000 317,000 340,000 Sediment Load Reduction (%) 1 85 80 80 80 2 86 82 80 80 3 81 80 62 0 4 80 80 0 0 5 87 80 80 4 6 86 80 80 80 7 85 80 80 80 8 85 85 80 80 9 83 80 80 80 10 85 80 80 80 11 83 80 80 80 12 83 80 80 80 13 85 80 80 80 14 81 80 80 80 15 83 80 80 80 16 84 80 80 80 17 39 39 39 39 18 60 60 60 60 19 31 31 31 31 20 85 85 80 80 21 90 80 80 80 22 35 35 35 35 23 90 80 80 80

Table 6.12 Sediment Reduction Rates under the TMDL Standard—Scenario B

212

For the EQS standard, no BMP technology is needed under the EPA standard.

When the standard is below 53 mg/l, there is no solution, and when it is above 143 mg/l, there is no need to install any BMP. There are two critical points, at 60 mg/l, and

123 mg/l, with control costs of $5,488,426, and $56,760, respectively. When compared with Scenario A under the same standard, this scenario incurs lower control costs, because more pollutants have been diluted (Figure 6.4). The optimal BMP installation areas and sediment reduction rates are presented in Table 6.13 and 6.14.

35000000

30000000

25000000

20000000

15000000 Cost Control Cost ($) Cost Control 10000000

5000000

0 0 50 100 150 200 250

-5000000 TSS EQS Standard (mg/l)

Figure 6.4 TSS EQS Standard vs. Control Cost of Scenario B

When EQS ≧ 123 mg/l, most of the water quality control points meet the EQS standard. Only Catchment # 22 must install a pond system. However, if EQS= 60 mg/l, several catchments must setup pond systems in order to achieve the standard. 213

EQS (mg/l) Catchment BMP 54 60 123 142 BMP Installation Area (acre) 1 P 0 66.3 0 0 1 I 6.8 0 0 0 1 F 103.8 0 0 0 2 P 2.3 0 0 0 3 P 12.5 8.8 0 0 4 P 100.1 0 0 0 5 P 77.9 120.5 0 0 5 I 51.1 0 0 0 6 P 0 5.9 0 0 6 I 5.2 0 0 0 6 F 38.4 0 0 0 7 P 128.6 118.9 0 0 8 P 26.8 24.6 0 0 9 P 31.9 29.0 0 0 10 P 36.2 32.9 0 0 11 P 54.6 59.5 0 0 11 F 31.2 0 0 0 12 P 35.3 73.5 0 0 12 F 65.4 0 0 0 13 P 27.4 23.7 0 0 14 P 23.9 18.7 0 0 15 N 0 0 0 0 16 P 28.7 19.4 0 0 17 P 3.7 3.7 0 0 18 P 5.5 5.5 0 0 19 P 0.8 0.8 0 0 20 P 5.3 5.3 0 0 21 P 55.7 47.2 0 0 22 P 7.6 7.6 7.0 0.3 23 P 0 39.2 . . 23 I 47.0 0 . . Total Control Cost ($) 29,262,692 5,488,426 56,760 2,136 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

Table 6.13 Sensitivity Analysis of the EQS Standard—Scenario B

214

EQS (mg/l) Catchment 54 60 123 142 Sediment Load Reduction (%) 1 85 80 0 0 2 8 0 0 0 3 27 19 0 0 4 80 0 0 0 5 83 80 0 0 6 0 0 0 0 7 59 55 0 0 8 57 52 0 0 9 55 50 0 0 10 54 49 0 0 11 81 65 0 0 12 83 80 0 0 13 46 40 0 0 14 34 26 0 0 15 0 0 0 0 16 79 53 0 0 17 39 39 0 0 18 60 60 0 0 19 30 30 0 0 20 80 80 0 0 21 44 37 0 0 22 35 35 32 1 23 90 80 0 0

Table 6.14 Sediment Reduction Rates under the EQS Standard—Scenario B

215

When EQS standard < 54 mg/l, there is no solution. At EQS=54 mg/l, it is necessary to install more expensive but more efficient BMP technologies, such as infiltration and filtering systems, in more catchments. In contrast to Scenario A,

Scenario B can achieve better water quality standards because it also generates a greater amount of surface runoff, which dilutes pollutants and reduces pollutant concentrations.

6.4.1.3 Scenario C

Scenario C corresponds to high-intensity residential development. In general, the more intense the urban development, the greater the impact on water pollution. Figure

6.5 displays the relationship between the TMDL standard and control costs. The critical

TMDL standard is 340,000 lb (control cost= $8,771,118), with cost increasing very strongly for standards below this point. The optimal BMP installation areas and sediment reduction rates are presented in Tables 6.15 and 6.16.

When TMDL ＞340,000 lb, only pond systems are needed in selected catchments.

The total control costs are related to the areas of the installed pond systems. When

TMDL ＜ 340,000 lb), infiltration and filtering systems are needed. These two systems have higher pollutant removal rates, but are more expensive.

216

120000000

100000000

80000000

60000000 Cost Control Cost ($) Control

40000000

20000000

0 0 100000 200000 300000 400000 500000 600000 700000 800000 TMDL Standard (lb)

Figure 6.5 TSS TMDL Standard vs. Control Cost of Scenario C

217

TMDL (lb) Catchment BMP 276,000 335,000 340,000 358,000 BMP Installation Area (acre) 1 P 0 66.3 66.3 66.3 1 I 6.8 0 0 0 1 F 103.8 0 0 0 2 P 0 20.8 20.8 20.8 2 I 3.3 0 0 0 2 F 31.07 0 0 0 3 P 0 36.9 36.9 30.6 3 F 63.2 0 0 0 4 P 34.2 100.1 90.9 0 4 I 1.3 0 0 0 4 F 111.2 0 0 0 5 P 0 120.5 120.5 120.5 5 I 51.1 0 0 0 5 F 133.6 0 0 0 6 P 0 26.7 26.7 26.7 6 I 5.2 0 0 0 6 F 38.4 0 0 0 7 P 0 172.4 172.4 172.4 7 I 20.5 0 0 0 7 F 266.2 0 0 0 8 P 0 36.5 37.4 37.4 8 I 0.2 0.2 0 0 8 F 63.8 1.2 0 0 9 P 21.3 46.0 46.0 46.0 9 I 1.0 0 0 0 9 F 41.0 0 0 0

Continued

Table 6.15 Sensitivity Analysis of the TMDL Standard—Scenario C 218

Table 6.15 continued

TMDL (lb) Catchment BMP 276,000 335,000 340,000 358,000 BMP Installation Area (acre) 10 P 0 53.0 53.0 53.0 10 I 2.2 0 0 0 10 F 87.7 0 0 0 11 P 34.4 72.7 72.7 72.7 11 F 65.8 0 0 0 12 P 35.3 73.5 73.5 73.5 12 F 65.4 0 0 0 13 P 0 47.9 47.9 47.9 13 F 82.1 0 0 0 14 P 49.2 56.2 56.2 56.2 14 I 0.6 0 0 0 14 F 11.1 0 0 0 15 P 9.7 26.6 26.6 26.6 15 F 29.0 0 0 0 16 P 5.6 29.2 29.2 29.2 16 F 40.4 0 0 0 17 P 3.7 3.7 3.7 3.7 18 P 5.5 5.5 5.5 5.5 19 P 0.8 0.8 0.8 0.8 20 P 0 0 5.3 5.3 20 F 9.1 9.1 0 0 21 P 0 100.1 100.14 100.1 21 I 120.2 0 0 0 22 P 7.6 7.6 7.63 7.6 23 P 0 39.2 39.19 39.2 23 I 47.0 0 0 0 Total Control Cost ($) 95,253,073 9,372,391 8,771,118 8,027,229 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

219

TMDL (lb) Catchment 276,000 335,000 340,000 358,000 Sediment Load Reduction (%) 1 85 80 80 80 2 86 80 80 80 3 85 80 80 67 4 83 80 73 0 5 87 80 80 80 6 86 80 80 80 7 85 80 80 80 8 85 80 80 80 9 83 80 80 80 10 85 80 80 80 11 83 80 80 80 12 83 80 80 80 13 85 80 80 80 14 81 80 80 80 15 83 80 80 80 16 84 80 80 80 17 39 39 39 39 18 60 60 60 60 19 31 31 31 31 20 85 85 80 80 21 90 80 80 80 22 35 35 35 35 23 90 80 80 80

Table 6.16 Sediment Reduction Rates under the TMDL Standard—Scenario C

220

A 2-hr one-year normal storm under Scenario C not only generates more pollutants, but also more stormwater runoff. The relationship between total cost and the

EQS standard is illustrated in Figure 6.6. Two critical points appear: EQS= 47 mg/l and

EQS=103 mg/l. Costs increase very strongly below 47 mg/l, but less so between 103 mg/l and 47 mg/l. The optimal BMP areas and sediment reduction rates are presented in Tables 6.17 and 6.18.

16000000

14000000

12000000

10000000

8000000

Cost

6000000 Control Cost ($) Cost Control

4000000

2000000

0 0 50 100 150 200 250

-2000000 TSS EQS Standard (mg/l)

Figure 6.6 TSS EQS Standard vs. Control Cost of Scenario C

221

EQS (mg/l) Catchment BMP 44 47 103 116 BMP Installation Area (acre) 1 P 0 66.3 0 0 1 I 6.8 0 0 0 1 F 103.8 0 0 0 2 P 20.8 20.8 0 0 3 P 16.0 14.0 0 0 4 P 100.1 3.5 0 0 5 P 120.5 120.5 0 0 6 P 19.9 19.3 0 0 6 I 5.2 0 0 0 6 F 4.2 0 0 0 7 P 118.5 111.9 0 0 8 P 30.6 29.4 1.4 0 9 P 32.4 30.6 0 0 10 P 30.7 28.3 0 0 11 P 72.7 67.6 0 0 12 P 73.5 73.5 0 0.3 13 P 29.4 27.3 0 0 14 P 16.0 12.3 0 0 15 P 1.7 . 0 0 16 P 29.2 25.3 0 0 17 P 3.7 3.7 0 0 18 P 5.5 5.5 0 0 19 P 0.8 0.8 0 0 20 P 5.3 5.3 0 0 21 P 39.8 33.2 0 0 22 P 7.6 7.6 6.3 0 23 P 39.2 39.2 0 0 Annual Control Cost ($) 13,351,545 5,753,793 62,395 2,028 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

Table 6.17 Sensitivity Analysis of the EQS Standard—Scenario C 222

EQS (mg/l) Catchment 44 47 103 116 Sediment Load Reduction (%) 1 85 80 0 0 2 80 80 0 0 3 35 30 0 0 4 80 3 0 0 5 80 80 0 0 6 82 58 0 0 7 55 52 0 0 8 65 63 2 0 9 56 53 0 0 10 46 42 0 0 11 80 74 0 0 12 80 80 0 0 13 49 46 0 0 14 22 17 0 0 15 5 0 0 0 16 80 69 0 0 17 39 39 0 0 18 60 60 0 0 19 30 30 0 0 20 80 80 0 0 21 31 26 0 0 22 35 35 29 0 23 80 80 0 0

Table 6.18 Sediment Reduction Rate under the EQS Standard—Scenario C

When EQS standard ≧ 103 mg/l, there are only a few catchments with pond systems. When EQS∈[47-103 mg/l], control costs increase gradually, because only

223

pond systems are required. When EQS ＜ 47 mg/l, infiltration and filtering systems are needed in order to achieve the standards, which rapidly increases the total control costs. Again, under Scenario C (HIRD), it is possible to achieve stricter EQS standards than under the previous two scenarios, because of the dilution phenomena.

6.4.2 Annual Storm

The previous single-storm analyses reflect a “one-shot” situation. However, besides short-term standards, the EPA has also setup annual standards for both TMDL and EQS. An annual averaging may reduce the significance of pollutant dilution for the

EQS standard. The sensitivity analysis below is carried out under the framework of an annual average storm event

6.4.2.1 TMDL

Figure 6.7 presents the relationships between TMDL standards and control costs for the different scenarios. These three curves are clearly distinct from each other, particularly the curve for Scenario C. Only under Scenario A can the EPA’s TMDL standard (4,461,296 lb) be achieved. There is no solution for Scenario B, if TMDL ≦

5,670,000 lb, and no solution for Scenario C, if TMDL ≦ 9,880,000 lb. The optimal

BMP areas and sediment reduction rates are presented in Tables 6.19 and 6.20.

224

120000000

100000000

80000000 Control Cost ($)

Scenario A Scenario B 60000000 Scenario C

40000000

20000000

0 0 2000000 4000000 6000000 8000000 10000000 12000000 14000000 16000000 TMDL Standard (lb)

Figure 6.7 Control Cost vs. TMDL Standards

Since the TMDL standard is a maximum total load standard, the search for the optimal solution is related to the search for the catchments with lower costs to setup

BMP technologies. The critical points in Figure 6.7 are 4,110,000 lb for the Scenario A,

6,950,000 lb for Scenario B (LIRD) and 11,460,000 lb for Scenario C (HIRD). For standards below these critical points, total control costs increase strongly, because expensive BMPs are involved, such as infiltration and filtering systems.

Pond systems are the cheapest. Hence, the system starts selecting pond systems.

With stricter standards, more land is needed for pond systems. If pond systems cannot sufficiently reduce the sediment loads to meet the standard, filtering systems are next to be applied, followed by infiltration systems. When infiltration and filtering systems are 225

Scenario A Scenario B Scenario C TMDL (1,000 lb) TMDL ($1,000 lb) TMDL (1,000 lb) Catchment BMP 3,280 4,110 4,820 6,480 6,950 7,320 10,830 11,460 12,050 BMP Installation Area BMP Installation Area BMP Installation Area (acre) (acre) (acre) 1 P 0 66 0 66 66 66 66 66 66 1 I 7 0 0 0 0 0 0 0 0 1 F 104 0 0 0 0 0 0 0 0 2 P 0 21 21 0 21 21 0 0 21 2 I 3 0 0 3 0 0 3 3 0 2 F 31 0 0 31 0 0 31 31 0 3 P 0 37 0 37 37 37 37 37 37 3 F 63 0 0 0 0 0 0 0 0 4 P 100 0 0 100 0 0 100 100 100 5 P 88 0 0 121 121 1 121 121 121 5 I 39 0 0 0 0 0 0 0 0 6 P 0 27 27 27 27 27 27 27 27 6 I 5 0 0 0 0 0 0 0 0 6 F 38 0 0 0 0 0 0 0 0 7 P 0 172 172 172 172 172 171 172 172 7 I 21 0 0 0 0 0 1 0 0 7 F 266 0 0 0 0 0 0 0 0 8 P 0 37 37 0 37 37 0 0 37 8 I 0 0 0 0 0 0 0 0 0 8 F 64 0 0 64 0 0 64 64 0 9 P 21 46 46 46 46 46 46 46 46 9 I 1 0 0 0 0 0 0 0 0

Continued

Table 6.19 Sensitivity Analysis of the TMDL Standard for an Annual Storm

226

Table 6.19 continued

9 F 41 0 0 0 0 0 0 0 0 10 P 0 53 53 53 53 53 53 53 53 10 I 2 0 0 0 0 0 0 0 0 10 F 88 0 0 0 0 0 0 0 0 11 P 34 73 0 73 73 73 73 73 73 11 F 66 0 0 0 0 0 0 0 0 12 P 35 73 72 73 73 73 73 73 73 12 F 65 0 0 0 0 0 0 0 0 13 P 0 48 0 48 48 48 48 48 48 13 F 82 0 0 0 0 0 0 0 0 14 P 49 56 56 56 56 56 56 56 56 14 I 1 0 0 0 0 0 0 0 0 14 F 11 0 0 0 0 0 0 0 0 15 P 10 27 27 27 27 27 27 27 27 15 F 29 0 0 0 0 0 0 0 0 16 P 6 29 29 29 29 29 29 29 29 16 F 40 0 0 0 0 0 0 0 0 17 P 4 4 4 4 4 4 4 4 4 18 P 5 5 5 5 5 5 5 5 5 19 P 1 1 1 1 1 1 1 1 1 20 P 0 5 5 0 1 5 0 0 0 20 F 9 0 0 9 7 0 9 9 8 21 P 0 100 100 100 100 100 0 100 100 21 I 120 0 0 0 0 0 120 0 0 22 P 8 8 8 8 8 8 8 8 8 23 P 0 31 0 39 39 39 39 39 39 23 I 47 0 0 0 0 0 0 0 0 Control Cost 81,789 7,085 5,118 14,373 8,401 7,157 25,903 14,363 9,262 ($1,000)

*P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

227

Scenario A Scenario B Scenario C TMDL (1,000 lb) TMDL (1,000 lb) TMDL (1,000 lb) Catchment 3,280 4,110 4,820 6,480 6,950 7,320 10,830 11,460 12,050 Sediment Reduction (%) Sediment Reduction (%) Sediment Reduction (%) 1 85 80 0 80 80 80 80 80 80 2 86 80 80 86 80 80 86 86 80 3 85 80 0 80 80 80 80 80 80 4 80 0 0 80 0 0 80 0 0 5 83 0 0 80 80 1 80 80 80 6 86 80 80 80 80 80 80 80 80 7 85 80 80 80 80 80 80 80 80 8 85 80 80 85 80 80 85 85 80 9 83 80 80 80 80 80 80 80 80 10 85 80 80 80 80 80 80 80 80 11 83 80 0 80 80 80 80 80 80 12 83 80 78 80 80 80 80 80 80 13 85 80 0 80 80 80 80 80 80 14 81 80 80 80 80 80 80 80 80 15 83 80 80 80 80 80 80 80 80 16 84 80 80 80 80 80 80 80 80 17 39 39 39 39 39 39 39 39 39 18 60 60 60 60 60 60 60 60 60 19 31 31 31 31 31 31 31 31 31 20 85 80 80 85 84 80 85 85 85 21 90 80 80 80 80 80 90 80 80 22 35 35 35 35 35 35 35 35 35 23 90 63 0 80 80 80 80 80 80

Table 6.20 Sediment Reduction Rates of the TMDL Standard for an Annual Storm

228

installed, there is no or a reduced need for pond systems, because these two BMPs have better pollutant removal rates (90% and 85%, respectively). Catchments with infiltration or filtering systems have higher sediment reduction rates (Table 6.20).

6.4.2.2 EQS

Under an annual storm, the “dilution phenomena” does no longer exist, because extreme storm runoffs have been averaged out. Figure 6.8 points to similar cost-standard patterns for the three scenarios. Scenario A has the lowest control costs, followed by Scenario B, and Scenario C has the highest control costs under any given

EQS standard. The three curves have all threshold points below which costs increase steeply, particularly for Scenario C, because the urbanization scenario generates the highest TSS loads during the storm.

Unlike the TMDL standards, the EQS standards must be achieved at all 18 water quality control points. Therefore, the optimal solution is not just simply derived by moving from the lowest-cost areas to the highest-cost areas. The EQS standard is a concentration standard. Concentration depends on pollutant loads and streamflow.

Different development scenarios generate different surface runoffs and sediment loads.

The three scenarios have different standard limitations. The strictest EQS standard that Scenario A can achieve is 6 mg/l, with a cost of $7,069,420. For Scenario B, it is (8 mg/l, $5,776,713), and for Scenario C it is (8 mg/l, $14,819,582).

In order to achieve the standard EQS= 8 mg/l, Scenario A incurs a cost of

$2,369,000, and Scenario C a cost of 14,820,000 (6 times more). With EQS= 10 mg/l,

229

16000000

14000000

12000000 Control Cost ($)

10000000

Scenario A Scenario B 8000000 Scenario C

6000000

4000000

2000000

0 051015 20 25 TSS EQS Standard (mg/l)

Figure 6.8 Control Cost vs. EQS Standards these costs are $1,015,000 for Scenario A, and $4,001,000 for Scenario C (about 4 times more).

Table 6.21 and 6.22 presents the optimal solutions under different EQS standards for the three development scenarios. In the case of TMDL, control costs increased strongly because more expensive and higher pollutant removal efficiency technologies, such as filtering and infiltration systems, had to be used. For EQS, increasing costs are related to large areas of pond systems, while few infiltration and filtering systems are involved. For example, in the case of Scenario A with EQS= 10 mg/l, only 12 catchments (total 129.23 acres) must have pond systems, but when EQS= 8 mg/l, 16 catchments (304.66 acres) are involved, and more than twice the pond systems area is

230

Scenario A Scenario B Scenario C

EQS (mg/l) EQS (mg/l) EQS (mg/l) Catchment ID BMP 6 8 10 9 11 13 8 10 12 BMP Installation Area (acre) BMP Installation Area (acre) BMP Installation Area (acre) 1 P 0 0 0 0 0 0 66.25 19.55 0

2 P 20.83 20.83 0 20.83 14.16 0 5.62 0 0

3 P 36.85 5.97 0 6.61 0 0 7.04 0 0

4 P 0 0 0 0 0 0 100.06 0 0

5 P 120.53 0 0 72.78 0 0 120.53 46.55 0

6 P 0 0 0 0 0 0 17.73 0 0

7 P 58.94 3.91 0 60.92 26.59 0 130.76 109.53 88.26

8 P 24.88 17.603.93 23.44 18.27 11.97 30.62 26.61 22.61

9 P 33.07 24.98 16.85 28.10 21.58 15.02 33.70 27.75 21.78

10 P 36.49 26.6116.75 32.62 25.17 17.72 39.57 32.93 26.27

11 P 60.93 19.43 1.56 23.52 8.54 0 72.73 18.88 4.48

12 P 73.47 61.6312.56 73.47 73.47 52.07 35.41 73.47 73.47

12 F 0 0 0 0 0 0 65.25 0 0

13 P 47.91 10.70 0 15.44 5.56 0 33.67 27.12 20.56

14 P 38.08 27.7015.03 26.81 17.19 4.50 25.24 13.36 0

15 P 26.63 0 0 26.63 13.36 2.36 26.63 16.46 6.46

16 P 9.79 28.616.33 12.89 0 0 14.92 0 0

16 F 33.25 0 0 0 0 0 0 0 0

17 P 3.73 3.73 3.73 3.73 3.73 3.73 3.73 3.73 3.22

18 P 0 0 0 0 0 0 5.45 5.45 1.49

19 P 0.83 0.83 0.38 0.83 0.83 0.83 0.83 0.83 0.83

20 P 5.30 5.30 5.30 5.30 5.30 3.81 5.30 5.30 5.30

21 P 39.73 0 0 20.35 0 0 60.02 43.23 20.79

22 P 7.63 7.63 7.63 7.63 7.63 7.63 7.63 7.63 7.63

23 P 39.19 39.19 39.19 39.19 17.05 0 0 39.19 13.09

23 I 0 0 0 0 0 0 47.03 0 0

Control Costs ($1,000) 7,069 2,369 1,015 3,874 2,009 933 14,820 4,001 2,445 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems

Table 6.21 Sensitivity Analysis of the EQS Standard for Annual Storm

231

Scenario A Scenario B Scenario C EQS (mg/l) EQS (mg/l) EQS (mg/l) Catchment 6 8 10 9 11 13 8 10 12 Sediment Reduction (%) Sediment Reduction (%) Sediment Reduction (%) 1 0 0 0 0 0 0 80 24 0 2 80 80 0 80 54 0 22 0 0 3 80 80 0 14 0 0 15 0 0 4 0 0 0 0 0 0 80 0 0 5 80 80 0 48 0 0 80 31 0 6 0 0 0 0 0 0 53 0 0 7 27 27 0 28 12 0 61 51 41 8 53 53 8 50 39 26 66 57 48 9 57 57 29 49 37 26 59 48 38 10 55 55 25 49 38 27 60 50 40 11 67 67 2 26 9 0 80 21 5 12 80 80 14 80 80 57 83 80 80 13 80 80 0 26 9 0 56 45 34 14 54 54 21 38 24 6 36 19 0 15 80 80 0 80 40 7 80 49 19 16 83 83 17 35 0 0 41 0 0 17 39 39 39 39 39 39 39 39 34 18 0 0 0 0 0 0 60 60 16 19 31 31 14 31 31 31 31 31 31 20 80 80 80 80 80 58 80 80 80 21 32 32 0 16 0 0 48 35 17 22 35 35 35 35 35 35 35 35 35 23 80 80 80 80 35 0 90 80 27

Table 6.22 Sediment Reduction Rates under the EQS Standard for an Annual Storm

needed. Similar patterns take place under Scenarios B (258.43 vs. 119.65 acres) and C

(517.56 vs. 316.25 acres). 232

6.5 EQS versus TMDL

Another way to analyze the relationship between control cost, TMDL, and EQS, is to generate a three-dimension cost surface as illustrated in Figure 6.9. The Z-axis represents control costs; the X-axis the TMDL standard, and the Y-axis the EQS standard. This surface allows for an analysis of the trade-offs between costs and standards. For example, for a given control cost, the trade-offs between the TMDL and the EQS standard can be assessed. However, it may be difficult to build the whole three-dimensional surface, and an alternative method is used.

First, after fixing EQS, a sensitivity analysis is conducted to relate control cost to the TMDL standard, providing a cross-section of the surface, such as the curve A-A’ in

Figure 6.9. This procedure may be repeated for additional cross-sections, such as B-B’.

Figure 6.9 Control Cost vs. TMDL and EQS

233

Scenario A is used for this trade-off analysis, because it is the only scenario for which there are solutions under the TMDL and EQS standards. EQS ranges from 6 mg/l to 15 mg/l (if EQS < 6 mg/l, there is no solution, and if EQS > 15 mg/l no BMP technology is needed).

Figure 6.10 displays the results of these analyses. Control costs vary little when

TMDL ≤ 4,200,000 lb, because the TMDL standard is stricter than any EQS standard, and control costs are dominated by the TMDL standard. When TMDL ≥ 4,400,000 lb,

TMDL is no longer dominating and, therefore, the ten different EQS standard curves start to spread out with increasing TMDL. The ranking of curves is apparent on Figure

6.11, with higher curves corresponding to tighter EQS standards. These results are also illustrated in Table 6.23.

90000000

80000000

70000000

EQS<=6 mg/l 60000000 EQS<=7 mg/l EQS<=8 mg/l 50000000 EQS<=9 mg/l EQS<=10 mg/l EQS<=11 mg/l 40000000 EQS<=12 mg/l

Control Cost ($) Control Cost EQS<=13 mg/l EQS<=14 mg/l 30000000 EQS<=15 mg/l

20000000

10000000

0 4E+06 4E+06 4E+06 4E+06 4E+06 4E+06 4E+06 5E+06 5E+06 5E+06 5E+06 5E+06 5E+06 5E+06 6E+06 6E+06 6E+06 6E+06 6E+06 6E+06 6E+06 6E+06 7E+06 7E+06 7E+06 7E+06 TMDL Standard (lb)

Figure 6.10 Relationships between Control Cost, TMDL, and EQS 234

12000000

10000000

8000000 EQS <= 6 mg/l EQS <= 7 mg/l EQS <= 8 mg/l EQS <= 9 mg/l EQS <= 10 mg/l 6000000 EQS <= 11 mg/l EQS <= 12 mg/l

Control Cost ($) Control EQS < = 13 mg/l EQS <= 14 mg/l 4000000 EQS <= 15 mg/l

2000000

0 0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 TMDL Standard (lb)

Figure 6.11 Detail of the Relationships between Control Cost, TMDL, and EQS

The annual TMDL standard for TSS in the Big Darby watershed is 4,460,000 lb per year, and the EQS standard is 10 mg/l. The control cost for both these standards is

$8,141,488. However, if the EQS standard becomes tighter, the control cost does not change until EQS reaches 6 mg/l (Table 6.23). If TMDL=5,100,000 lb per year, and

EQS= 10 mg/l, the control cost is $5,732,874. If EQS shifts to 9 mg/l, the control cost increases by $56,409, and if EQS shifts to 11 mg/l, the control cost decreases by

$2,909.

The above information can be provided to decision makers to help them make decisions regarding the appropriate TMDL and EQS standards, in particular how strict the standards need to be, and how large are the control costs they are willing to incur.

235

EQS TMDL= 4,460,000 (lb) TMDL= 5,100,000 (lb) TMDL= 7,000,000 (lb) (mg/l) Cost Increment Cost Increment Cost Increment 6 $9,678,112 -1,536,624$8,732,691 -2,585,995 $7,558,761 -2,715,984 7 $8,141,488 0 $6,146,696 -267,866 $4,842,777 -526,822 8 $8,141,488 0 $5,878,830 -86,638 $4,315,955 -237,731 9 $8,141,488 0 $5,792,192 -56,409 $4,078,224 -205,501 10 $8,141,488 0 $5,735,783 -2,909 $3,872,723 -136,521 11 $8,141,488 0 $5,732,874 0 $3,736,202 -119,972 12 $8,141,488 0 $5,732,874 0 $3,616,230 -67,706 13 $8,141,488 0 $5,732,874 0 $3,548,524 0 14 $8,141,488 0 $5,732,874 0 $3,548,524 0 15 $8,141,488 0 $5,732,874 0 $3,548,524 0

Table 6.23 Incremental Control Cost vs. EQS Standard

The fundamental trade-offs between control cost and environmental quality are embodied in Table 6.23.

6.6 MARGINAL COST ANALYSIS

The dual solution can be used to analyses the optimization solutions, and to provide decision makers further insight. A dual variable is associated to each constraint, and measure the change in the optimal value of the objective function resulting from a unite change in the right side of the constraint. Dual variables are also referred to as shadow prices.

There are several constraints in the economic model that may be of interest in a dual solution analysis, such as the total final sediment load (TMDL), the sediment concentrations at the water quality control points (EQS), and the maximum available

236

areas for BMP installations.

As an illustration, consider the case of Scenario A, with EQS=10 mg/l. Three

TMDL standards are considered (TMDL=3,690,000, 3,700,000, and 3,710,000). An examination of the outputs of the economic model shows that the TMDL constraint is binding under these three standards, and that the available areas of several catchments are fully used.

Table 6.24 presents the values of the shadow price of the TMDL standards. For instance, when TMDL=3,690,000 lb is relaxed by one unit (increasing it to 3,690,001), the total control cost is reduce by $706. The total cost is similarly reduced by $582 when TMDL=3,700,000 lb, and by $438 when TMDL=3,710,000 lb. These shadow prices measure the slope of the curve of the total cost vs. TMDL.

Table 6.25 presents the shadow prices of the maximum available BMP area constraints in all the catchments where this constraint is binding. For example, in the case of TMDL= 3,690,000 lb and Catchment #1, if one more acre were available for infiltration system installation, the total cost would be reduced by $112,000. This marginal savings decreases when the TMDL standard increases, and is only $60,000 when TMDL=3,710,000 lb. In land economics terms, these shadow prices may also be interpreted as land rents.

237

Marginal Cost TMDL (lb) (Shadow Price) ($) 3,690,000 -706 3,700,000 -582 3,710,000 -438

Table 6.24 Shadow Prices for Different TMDL Standards

TMDL Standard (lb) Catchment ID BMP 3,690,000 3,700,000 3,710,000 MARGINAL (Shadow Price) (1,000$) 1 I -112 -88 -60 2 I -1,700 -1,400 -1,040 6 I -150 -119 -84 7 I -445 -370 -272 8 I -2,370 -1,950 -1,460 9 I -695 -564 -413 10 I -511 -417 -307 11 F -11 0 0 12 F -74 -52 -27 14 I -597 -479 -341 15 F -240 -189 -130 16 F -274 -217 -151 17 P -65,400 -53,900 -40,500 18 P -41,300 -34,100 -25,600 19 P -187,000 -154,000 -116,000 22 P -13,600 -11,200 -8,420

Table 6.25 Shadow Price of Maximum Available BMP Installation Area

238

Table 6.26 presents the trade-offs between the shadow prices of the TMDL and

EQS standard constraints. The average shadow price is used as an indicator to represent the whole watershed marginal cost. When EQS=6 mg/l and TMDL=4,790,000 lb, increasing the TMDL standard by one pound would reduce the total cost by $1.60. This marginal savings decreases when the TMDL standard increases, and is only $0.66 when TMDL=5,500,000 lb. However, relaxing the EQS standard by 1 mg/l would reduce the total cost by $1,250,038. The marginal savings decrease when the EQS standard increases, and is only $974 when EQS=8 mg/l.

EQS (mg/l) TMDL 6 7 8 (lb) Shadow Price ($) Shadow Price ($) Shadow Price ($) TMDL EQS TMDL EQS TMDL EQS 3,930,000 -93.63 -169,278 -104.63 0 -104.63 0 3,970,000 -88.02 -233,222 -93.63 0 -93.63 0 4,000,000 -78.69 -339,556 -88.02 0 -88.02 0 4,140,000 -70.27 -435,667 -78.70 0 -78.70 0 4,170,000 -35.70 -830,000 -70.27 0 -70.27 0 4,200,000 -29.13 -905,000 -35.70 0 -35.70 0 4,250,000 -20.97 -997,778 -29.13 0 -29.13 0 4,400,000 -19.54 -1,014,444 -20.97 0 -20.97 0 4,420,000 -9.01 -1,134,444 -19.54 0 -19.54 0 4,630,000 -2.12 -1,240,200 -2.94 -21,483 -5.91 0 4,690,000 -2.12 -1,240,200 -2.43 -25,571 -3.23 0 4,790,000 -1.60 -1,250,083 -2.12 -28,330 -2.99 -974 5,030,000 -0.74 -1,266,678 -1.60 -33,026 -2.12 -4,813 5,160,000 -0.66 -1,268,865 -0.78 -40,383 -2.12 -4,813 5,500,000 -0.66 -1,268,865 -0.66 -41,966 -0.74 -12,204

Table 6.26 Shadow Prices for the TMDL and EQS Standard Constraints

239

6.7 SUMMARY

The results in this chapter show that the EQS standards are more achievable than the TMDL standards under any development scenario. In particular under the single storm simulations, the high-intensity development scenario generates higher runoff, thus dilute pollutants. Under the annual storm simulation, the big storm dilution impact is not so significant, because such storms only happen a couple of times a year.

However, after the storm subsides, sediments settle down on the bottom of stream channels (sediment bed loads), and will cause further environmental problems. The

TMDL standards are load standards, thus do not involve dilution issues, but they are more difficult to meet. Using BMP treatments to achieve the EPA standard is only feasible under Scenario A. Finding a balance between pollutant concentration (EQS) and total pollutant load (TMDL) standards is an important issue.

As for sediment reduction rates, if pond systems are the only BMP technology installed in a catchment, the maximum sediment reduction rate is 80%. If other BMP technologies are installed, such as infiltration and filtering systems, the maximum sediment reduction rate may increase up to 90%, but total costs increase also.

Under the TMDL standard, the optimization model selects first the lowest-cost technology for the whole watershed to reduce sediments as much as needed, because

TMDL focuses only on total load reduction in the whole watershed, regardless of where the treatments are located. However, under the EQS standard, there are 18 water quality control points where the standard must be met. Thus, the search for the optimum is more complicated because upstream reductions directly affect downstream reductions. 240

CHAPTER 7

CONCLUSIONS

The objective of this research was to develop a methodology for finding optimal solutions to control the NPS pollution caused by urbanization, while accounting for spatial decision-making processes in land-use planning and best management practices, and for water quality standards. The general modeling approach involves a suitability analysis model (Spatial Model), a stormwater runoff and pollution simulation model

(Watershed Model), and an optimization model for cost control and sensitivity analyses

(Economic Model). This multi-step approach has been adopted to model the spatial relationship between land-use scenarios and water quality and to search for optimal

BMPs installations.

7.1 CONCLUSIONS

The results indicate that urbanization has great impact on water quality and that

BMPs can help reduce sediment loads, but with some limitations.

Urban sprawl or suburbanization impacts water quality. Three different scenarios have been simulated in the Watershed Model. High-intensity urban developments not

241

only generate more buildup pollutants, but also larger surface runoff, which will wash off or cause more soil erosion. Agriculture is highly related to total soil erosion

(R2=0.8). Preventing soil erosion in agriculture areas is another important issue.

This study arbitrarily chose a few scenarios related to land use changes based on residential suitability analyses, and then used these outputs as inputs to the Economic

Model to achieve the optimal allocation of BMPs. Many other possibilities exist: including combinations of conservation areas and higher intensity development areas that employ multiple BMPs in a conservation area to reduce the total pollution loads and potentially meet the standards. Thus there may be other combinations of land use and treatment policies that could meet the standards. The framework demonstrated here could be used to test that wider array of policies and treatment options.

Agriculture is the biggest contributor to the sediment load, both in buildup sediments and soil erosion. Based on the Scenario A watershed model simulations, over

99% of the total sediment load comes from agriculture erosion (Scenario B: 96%,

Scenario C: 94%). Additional agricultural NPS control practices are needed to achieve water quality standards. Several practices are used to control agricultural erosion, including structural and non-structural practices. Combinations of these practices can be used to reduce soil erosion from agriculture (USDA-SCS, 1988; U.S. EPA, 1993).

Non-structural practices include conservation covers, conservation cropping sequences, conservation tillage, contour farming, contour strip-cropping, field strip-cropping, cover and green manure crop, critical area planting, and crop residue use. Structural practices include field border, sediment basins, grassed waterway, and terraces (U.S.

EPA, 2003). The Conservation Reserve Program (CRP) is another non-structural 242

practice that can reduce soil erosion. Under this voluntary program, the U.S.

Department of Agriculture (USDA) establishes contracts with agricultural producers and landowners to retire highly erodible and environmentally sensitive cropland and pasture from production for a period of 10-15 years. Enrolled land is planted with grasses, trees, and other covers, thereby reducing erosion and water pollution, providing other environmental benefits, and reducing the supply of agricultural commodities (USDA, 2004). With these agriculture NPS control practices, the total control cost might be reduced and water quality might be more achievable.

Three land-use scenarios have been simulated in this research. Scenario A is based on the 1994 land uses, Scenario B adds low-intensity residential developments, and

Scenario C adds high-intensity residential developments. Only Scenario A can achieve the TMDL standard by using existing BMP technologies. Over 97% of the watershed is non-urban in Scenario A (agriculture: 90.18%, forest: 7.12%). Under Scenario B, 69% of entire watershed remain non-urban (agriculture: 64.83%, forest: 4.95%), but the

TMDL cannot be achieved with any existing BMP technologies. Therefore, non-urban areas should make up between 69% to 97% of the entire watershed, as a rough benchmark. Additional urban development requires additional agriculture NPS control practices. Yeo (2005) also suggests, as policy guidance, that urban land-use should be kept at less than 12% of the total watershed, and at least 30% of the watershed should be maintained in a conservation state.

Besides the structural BMP technologies proposed in this research, there are other non-structural BMPs options that can help reduce water pollution, such as changes in policies to encourage street sweeping and waste recycling, to reducing impervious 243

surfaces in new developments through permeable pavement or reduction of street surface. These alternative policies could have a great impact on reducing pollutants at little cost.

About 1.55% of the watershed are urban areas in Scenario A (commercial/ industrial/transportation: 0.56%, low-intensity residential: 0.85%, and high-intensity residential: 0.14%). The total control cost to achieve the TMDL standard is $5,910,000, and 764 acres of pond systems are required. The average pond system control cost is

$7,734. In sensitivity analyses, both Scenarios A and B have feasible solutions for

TMDL=5,670,000 lbs, with costs of $4,052,193 and $94,416,850, respectively. This research assumes that the cost increase is caused by land-use conversion from agriculture to low-intensity residential. The total conversion area is 12,794 acres.

Therefore, the average control cost for one additional acre of low-intensity residential development is $7,000. This could be a reference for land-use policy decision makers.

However, this is only based on Scenarios A and B, and under TMDL=5,670,000 lbs.

The real control cost is likely to be higher, because the TMDL standard is stricter.

When the TMDL is tighter, expensive and high-removal efficiency BMP technologies are required in order to meet this standard. To further assess urban development areas, more urban land-use scenarios must be considered.

Pollution dilution is not a solution. In the simulations, the higher intensity development has a lower sediment concentration, especially during bigger storms, because the larger amount of runoff dilutes pollutants during the storm. However, sediments do not disappear from the environment after the storm, and there are thousands of kilograms of sediment left in the streams. As the storm subsides, these 244

sediments will be deposited and cause major environmental problems. They will accumulate in the system and be stirred up in subsequent storms, being added to newly deposited loads. Dilution does not contribute to pollutant elimination, but simply covers it up. A high peak runoff load (over flood) can also put the stream out of balance and cause bank erosion (Scenario C). In order to simulate a reduction of the peak runoff and prevent bank erosion, virtual detention ponds and storage/treatment blocks could be introduced into this modeling framework. Virtual detention ponds can slow down peak runoff loads and prevent over flood and bank erosion. The storage/treatment block in the SWMM model has been developed to simulate the routing of flows and pollutants through a dry- or wet-weather storage/treatment plant containing up to five units or processes. Each unit may be modeled as having detention or non-detention characteristics. Additionally, capital costs and operation and maintenance costs may be estimated for each unit (Huber and Dickinson, 1988). These costs represent a trade-off between bank erosion and control costs. Bank erosion control costs could become inputs to the economic model.

The EQS standard for pollutant concentration is used as an index measuring water quality. It involves the issue of pollution dilution. This standard can be easily achieved under the three scenarios. However, this does not guarantee environmental quality.

While sediments may be carried out of the study watershed by a large streamflow, they will eventually settle down in downstream watersheds, causing environmental problems in these watersheds. Therefore, a focus on only the EQS standard will lead to the false impression that “dilution is a solution.”

245

Sensitivity analyses under any given scenario show that the EQS standard is much easier to meet than the TMDL standard. The TMDL is only achievable under Scenario

A. In addition to the level of the TMDL standards, the sensitivity analyses also suggest that the maximum available BMPs installation areas are major factors that influence the optimal solutions. The larger the available areas, the tighter the TMDL standard that can be achieved. These results provide references to decision makers setting up TMDL standards, or controlling land-use intensity.

Pond Systems are the best to reduce sediment loads because of their low control costs and fair sediment removal rates. Infiltration and Filtering Systems are applied only when stricter water quality standards are required. Wetland Systems have never been selected because of the small amounts of available areas, expensive costs, and limited sediment removal rates.

7.2 LIMITATIONS

This research was conducted under several limitations:

Data Collection: The models require many data inputs, especially the Watershed

Model. Without field surveys, many data and parameter values has been derived from literature reviews and empirical equation estimations, possible affecting the results.

Moreover, BMPs costs and efficiency are derived from the literature. They might achieve different performances and costs in different areas or with different combinations.

Modeling Scale: The modeling scale of this study is based on the watershed.

Therefore, the results of the study can only be applied to this decision-making level. 246

The final control costs are not real market prices, but can be used for comparing different development scenarios and as references for decision making.

Model Calibration: Model coefficients and parameters calibration is an important step. Due to the lack of real data, it was not possible to calibrate the Watershed Model.

Therefore, the results of these analyses are only valid for comparison purpose.

Long-term Simulation: The precipitation data used in this study are statistical data

(design storms). Snowmelt was not simulated in this study. Therefore, the output of the

Watershed Model has some degree of bias. Real long-term precipitation data could help eliminate these biases.

BMPs Location: The BMPs installation areas in this research are at the catchment scale. This research can only estimate possible locations for BMPs based on suitability analyses, not real installation locations. This is another reason for which the results of this research are suitable only at the policy level, but not at the engineering scale.

7.3 FURTHER RESEARCH RECOMMENDATIONS

Based on the findings and limitations of this study, the following are recommendations for further research are given:

z Use long-term real precipitation data to compare design storms and real

storms.

z This research shows that the watershed can accommodate a modest amount of

urban growth and still meet the TMDL sediment standard as long as sufficient

investment is made in BMPs. However, agriculture generates a large part of

the total sediment load, and better agriculture NPS control practices could 247

reduce total control costs and probably accommodate more areas for urban

development. To accomplish this objective, an agriculture NPS control

simulation model could be introduced, that could have different levels of

agricultural sediment load reduction. Under each sediment load reduction

scenario, different levels of urban growth could be assessed in terms of

TMDL and BMP implementation costs. These results would provide decision

makers a clearer assessment of land-use planning options. z Other urban scenarios could be considered in order to determine limits for

urban development, such as higher development intensity with non-structural

BMPs policy implementations. z Treatment train approaches could be introduced to deal with pollutants. This

concept has been introduced in 1994 (Horner and Skupien, 1994). The

concept of treatment train involves a series of separate treatment devices or

“boxes.” For example, a wet pond followed by a shallow marsh wetland.

Nieth et al. (2005) suggest a treatment train to deal with stormwater runoff,

with a wet pond, grass filter strip, wetland, and riparian buffer. This system

has a much better performance than any single unit. Moreover, the treatment

train usually uses at least two treatments that serve different functions, such

as sedimentation, adsorption, and filtration. The U.S. EPA suggests the

combination of constructed wetland and grass filter strips (U.S. EPA, 2002).

Combination treatments can remove sediment up to 96% (GDNR, 2001).

There are two ways to introduce treatment trains into the economic model. A

first approach is to list all possible combinations and treat them as new 248

technologies with new costs, area requirements, and pollutant removal rates.

The other method is to modify the model to include variables that would

describe such technology sequences, probably integer variables, as in

complex task scheduling problems. z Introduce more pollutants. BMPs have usually more than one pollutant

removal ability. For example, wetland systems not only help removing

sediments, but also phosphorus and nitrogen. z Integrate sediment bed-load movement equations into the Economic Model to

calculate the actual sediment burden in the stream. This would modify the

Economic Model by including variables describing time, sediment particle

sizes, pore, and sediment distribution.

249

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274

APPENDIX A

SPATIAL REFERENCE INFORMATION

275

Projected Coordinate System Universal Transverse Mercator

Zone Name and Number Zone 17N

Horizontal Datum Name North American Datum of 1983 (NAD83)

Spheroid GRS 1980

Projection Distance Units Meters

False Easting 500000.0000

False Northing 0.0000

Central Meridian -81.0000

Scale Factor 0.9996

Latitude of Projection Origin 0.0000

Table A.1 Spatial Reference Information

276

APPENDIX B

WATERSHED DELINEATION ARCVIEW SCRIPT

277

Date: 13/Aug/2001

Author: Fridjof Schmidt

[email protected]

The script delineates watersheds using a point theme as the pour point input.

1. Purpose of the script

2. How to use the script

3. Known issues

4. Further references

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1. Purpose of the script

In ArcView Spatial Analyst, the following hydrologic data requests are available:

FlowAccumulation(weightGrid)

FlowDirection(ForceEdgeBoolean)

FlowLength(weightGrid,upStreamBoolean)

FocalFlow(aThresholdNumber)

Sink

SnapPourPoint(aGrid,aNumber)

StreamLink(flowDirectionGrid)

StreamOrder(FlowDirectionGrid,useShreveMethodBoolean)

Watershed(sourceGrid)

ZonalFill(weightGrid)

The purpose of this script is to delineate the watersheds (contributing area, basin, catchment) at a set of points that can be specified by the user. The script makes use of 278

the FlowAccumulation, the FlowDirection, the Sink, the SnapPourPoint, the ZonalFill, and the Watershed requests.

There are several ways in which watersheds can be delineated in ArcView. One way is to pre-define the minimum area of a watershed. In this case, the size of the watersheds returned is controlled by the number of cells that need to flow into a cell to classify it as a stream. ESRI's "Hydrologic Modeling" sample extension that ships with Spatial Analyst allows for a delination of watersheds in this way.

Another way is to specify a point with a cursor in the view for which a watershed should be created. See ESRI's program help topic "Watershed (Request)" for an example. The example must be executed from an Apply event (i. e., with a Tool) and returns a watershed for a single point only.

This script is a modification of the second method. It takes a point theme as the pour point input, that is, it creates watersheds for multiple points simultaneously that the user defines as watershed outlets.

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2. How to use the script

Run the script from a view (e.g., make a new script, load wshed_point.ave as a text file, compile the script, go to the view window, go back to the script window, run the script). The view should contain (at least) a grid theme representing elevation, and a point theme representing the watershed outlets.

First, the script checks if at least two themes are present in the view. Then you are required to select the point theme that represents the watershed outlets, or pour points. If any points in the theme are selected, only those points will be used for watershed delination. Please make sure that the pour point theme has at least one string or number attribute field which can be used as an ID for labeling the watersheds.

Next, you will be asked to specify an elevation grid theme. In order to delineate watersheds, a flow direction grid and a flow accumulation grid are also necessary. You may specify whether to use existing flow direction and flow accumulation grids, or whether to create them from your elevation grid by choosing "--compute--".

The script will now convert the pour points to a grid which is necessary for watershed delineation, using a string or number field specified by the user for obtaining the cell values. These values will be used later to identify the watersheds. The pour point grid will have the same extent and cell size as the elevation grid. In order to ensure points of high accumulated flow when delineating watersheds, it is recommended to allow for a "snap distance", a spatial tolerance when converting the points. The script will search within the snap distance around each pour point for the cell of highest accumulated 279

flow, and move the pour point to that location using the SnapPourPoint request. By default, the snap distance is set to 3 times the cell size of the elevation grid.

If you chose to compute the flow direction grid, the script will do so now, using the FlowDirection request. In order to create an accurate representation of flow direction and therefore accumulated flow, it is generally recommended to "fill" the elevation grid in case any sinks are detected with the Sink request. Sinks, or areas of internal drainage, in elevation data are most commonly due to errors in the data. These errors are often due to sampling effects and the rounding of elevations to integer numbers. Naturally-occurring sinks in elevation data with a cell size of 10 meters or larger are rare except for glacial or karst areas, and generally can be considered errors. As the cell size increases, the number of sinks in a data set often also increases.

The identification and removal of sinks, when trying to create a depressionless DEM (digital elevation model) is an iterative process. When a sink is filled, the boundaries of the filled area may create new sinks which then need to be filled.

In case you chose to use an existing flow direction grid, the script will not check for sinks, or cells with undefined flow direction. Therefore, it makes sense to use this option only if you created the flow direction grid from a depressionless, or filled, DEM. If you don't specify an existing flow direction grid, the script will also create a new flow accumulation grid.

Eventually, the watersheds will be created from the flow accumulation grid and the pour point grid. They will be labeled with the attributes from the field that you chose for obtaining the cell values of the pour points. The watersheds will be added to the view as a temporary grid theme. You will be able to join the pour point attributes to the grid, to save the grid, and to convert the grid to a shape file.

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3. Known issues

Sometimes the shape and size of the watersheds may not match the appearance that you expected. This may be due to several causes. First of all, the quality of the results greatly depends on the quality of the DEM. A hydrologically correct DEM should not have any sinks.

In some cases when your pour points are too far away from a cell of high accumulated flow, the watershed will be very small. On the other hand, if there is a major stream link downstream of the pour point, the neighbouring subcatchment might be included, resulting in a much bigger watershed than desired. Playing around with the snap distance might help.

When creating the flow accumulation grid, you might observe a very angular 280

appearance of the flow network. This is due to the rather simplistic D8 algorithm ArcView uses for routing flow accumulation in which water from one cell flows only into one of its eight neighbours. There are other programs which use more sophisticated algorithms, such as the TAPESG and the TARDEM program (see below).

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4. Further references

For a brief discussion of ArcView's hydrologic data requests, please refer to the corresponding discussion section in the program help files.

For some more detailed information about hydrological analysis of DEMs and its pitfalls, you might want to read Ivan Petras' tips: http://support.esri.com/search/ViewListPost.asp?dbid=25841

At the Center for Resource and Environmental Studies, Australian National Unversity, you can get information about the TAPES programs (you can download TAPESG for free): http://cres.anu.edu.au/outputs/tapes.html

You might also want to check out David Tarboton's pages and his TARDEM programs at the Utah Water Research Laboratory, Utah State University: http://www.engineering.usu.edu/dtarb/

At the Center for Research in Water Resources, University of Texas, you will find information about the new ArcGIS Hydro Data Model: http://www.crwr.utexas.edu/giswr/

Wshed_point.ave

' Name: wshed_point.ave ' ' Date: 25/Feb/2000 ' Updated: 20/Apr/2000 ' 14/Aug/2001 (minor changes) ' Author: Fridjof Schmidt ' [email protected] ' bug reports and suggestions welcome ' ' Title: Delineates watersheds using a point theme as the pour point input ' ' Topics: Spatial Analyst, Hydrologic Modeling 281

' ' Description: Run this script from a view. ' You will be asked to select a point theme representing the ' pour points (outlets), and a grid theme representing elevation. ' The point theme will be converted to a temporary grid, the extent ' and cell size of which will be set to the elevation grid's extent and ' cell size. ' Sinks will be identified and filled upon request. ' Flow direction and flow accumulation will be created as temporary grids. ' Alternatively, they can be selected if they have been computed already. ' The pour points will be snapped to the maximum flow accumulation based ' on a snap distance you specify (default is 3 times the cell size). ' Please note that watersheds may overlay in reality while they don't ' in the results of this script. Overlaying watersheds can be created ' manually by copying, pasting + joining adjacent watershed polygons. ' The script borrows code from scripts included in the Hydrologic ' Modeling extension that comes with the Spatial Analyst, and from other ' ArcView system scripts. ' ' Requires: Spatial Analyst extension must be loaded. ' A view with at least one point and one grid theme must be active. ' ' Self: ' ' Returns:

' Delineate Watersheds from a Point Theme theScript = "Watersheds from Point Theme" theView = av.GetActiveDoc computeStr = "-- compute --" themeList = theView.getthemes if (nil = themeList) then return nil end if (themeList.count < 2) then Msgbox.Warning("At least 2 themes must be present in the view",theScript) return nil end

'Choose the point theme pointlist = list.make for each atheme in themelist if (atheme.canselect=true) then if (atheme.getftab.findfield("Shape").gettype = #FIELD_SHAPEPOINT) then pointlist.add(atheme) end else end 282

end thePointTheme = MsgBox.ChoiceAsString(pointlist,"Please select a point theme representing"++ "the POUR POINTS.",theScript) if (thePointTheme=Nil) then return nil end

'Choose the elevation grid theme gridlist = list.make for each atheme in themelist if (atheme.GetClass.GetClassName = "GTheme") then gridlist.add(atheme) end end theElevTheme = MsgBox.ChoiceAsString(gridlist,"Please select a grid theme representing"++ "ELEVATION.",theScript) if (theElevTheme=Nil) then return nil end theElevGrid = theElevTheme.GetGrid theFlowDirTheme = computeStr theFlowAccTheme = computeStr if (gridlist.count > 1) then 'Choose a flow direction grid theme if applicable gridlist = {computeStr} + gridlist theFlowDirTheme = MsgBox.ChoiceAsString(gridlist,"FLOW DIRECTION: please specify whether to compute"++ "a new grid OR select an existing one",theScript) if (theFlowDirTheme=Nil) then exit end if (gridlist.count > 3) then 'Choose a flow accumulation grid theme if applicable theFlowAccTheme = MsgBox.ChoiceAsString(gridlist,"FLOW ACCUMULATION: please specify whether to compute"++ "a new grid OR select an existing one",theScript) if (theFlowAccTheme=Nil) then return nil end end end

' convert selected features of Point Theme to Grid thePointFTab = thePointTheme.GetFTab

' make a list of fields fl = {} for each f in thePointFTab.GetFields if (f.IsVisible and (f.IsTypeNumber or f.IsTypeString)) then fl.Add(f) end end

283

' check if valid conversion field exists if (fl.Count = 0) then MsgBox.Warning("No valid conversion field exists.",theScript) return NIL end

'set extent and cell size for conversion cellSize = theElevGrid.GetCellSize box = theElevGrid.GetExtent

' obtain field to convert with theValueField = MsgBox.List(fl,"Please select a field for obtaining the cell values:", "Conversion Field:" ++ thePointTheme.GetName) if (theValueField = NIL) then return NIL elseif (theValueField.IsTypeString) then ' make list stringlist = list.make theBitmap = thePointFTab.GetSelection if (theBitmap.Count = 0) then totalNumRecords = theBitmap.GetSize theBitmap = thePointFTab else totalNumRecords = theBitmap.Count end for each r in theBitmap stringlist.add(thePointFTab.ReturnValueString(theValueField,r)) end stringlist.RemoveDuplicates doString = TRUE else doString = FALSE end

' actually do conversion aPrj = theView.GetProjection theSrcGrid = Grid.MakeFromFTab(thePointFTab,aPrj,theValueField,{cellSize, box}) if (theSrcGrid.HasError) then MsgBox.Warning("Error:" ++ thePointTheme.GetName ++ "could not be converted to a grid",theScript) return NIL end

' Get snap distance SnapDist = 3 * cellSize status = TRUE while (status) SnapDist = MsgBox.Input("Enter the maximum distance in map units to snap pour points"++ "to the flow accumulation grid.",theScript, SnapDist.AsString) if (SnapDist = NIL) then return NIL elseif (SnapDist.IsNumber) Then status = FALSE 284

else status = TRUE MsgBox.Warning("The snap distance must be a number.",theScript) end end

'Create a flow direction grid if(theFlowDirTheme = computeStr) then theFlowDir = theElevGrid.FlowDirection(FALSE) ' Check for Sinks on The currently selected elevation grid's direction grid theSinkGrid = theFlowDir.Sink if ((theSinkGrid.getVTab = NIL).Not) Then 'YesNo to fill sinks fillStat = MsgBox.YesNo(TheSinkGrid.GetVTab.GetSelection.GetSize.AsString++ "sinks were identified!"+nl+""+NL+ "Do you want to fill the sinks? (recommended)", theScript,true) 'Fill sinks if(fillStat) then ' fill sinks in Grid until they are gone sinkCount = 0 numSinks = 0 while (TRUE) if (theSinkGrid.GetVTab = NIL) Then ' check for errors if (theSinkGrid.HasError) Then MsgBox.Warning("Error in sink grid",theScript) return NIL end theSinkGrid.BuildVAT end ' check for errors if (theSinkGrid.HasError) Then MsgBox.Warning("Error in sink grid",theScript) return NIL end if (theSinkGrid.GetVTab <> NIL) Then theVTab = theSinkGrid.GetVTab numClass = theVTab.GetNumRecords newSinkCount = theVTab.ReturnValue(theVTab.FindField("Count"),0) else numClass = 0 newSinkCount = 0 end if (numClass < 1) Then break elseif ((numSinks = numClass) and (sinkCount = newSinkCount)) Then break end theWater = theFlowDir.Watershed(theSinkGrid) zonalFillGrid = theWater.ZonalFill(theElevGrid) fillGrid = (theElevGrid < (zonalFillGrid.IsNull.Con(0.AsGrid,zonalFillGrid))).Con(zonalFillGrid,theElevGrid) theElevGrid = fillGrid 285

numSinks = numClass sinkCount = newSinkCount end ' Create new flow direction grid theFlowDir = theElevGrid.FlowDirection(FALSE) end 'fillStat end 'Check for no sinks else theFlowDir = theFlowDirTheme.GetGrid end

' Create a flow accumulation grid if (theFlowAccTheme = computeStr) then theFlowAcc = theFlowDir.FlowAccumulation(NIL) else theFlowAcc = theFlowAccTheme.GetGrid end theWater = theFlowDir.Watershed(theSrcGrid.SnapPourPoint(theFlowAcc,SnapDist.AsNumber)) if (theWater.HasError) then MsgBox.Warning("Error in watershed grid",theScript) return NIL end theVTab = theWater.GetVTab theValue = theVTab.FindField("Value") if (doString.Not) then toField = theValue theValue.SetAlias(theValueField.GetAlias) else theSValue = Field.Make(theValueField.GetName,#FIELD_CHAR,theValueField.GetWidth,0) theSValue.SetEditable(TRUE) if (theVTab.StartEditingWithRecovery) then theVTab.BeginTransaction theVTab.AddFields({theSValue}) for each r in (1..theVTab.GetNumRecords) ' theString = thePointFTab.ReturnValue(theValueField,theVTab.ReturnValue(theValue,r-1)-1) theString = stringList.Get(theVTab.ReturnValue(theValue,r-1)-1).AsString theVTab.SetValue(theSValue,r-1,theString) end theVTab.EndTransaction end theVTab.StopEditingWithRecovery(TRUE) toField = theSValue theValue.SetVisible(False) end

' create a theme and add it to the view theGTheme = GTheme.Make(theWater) ' check if output is ok if (theWater.HasError) then MsgBox.Warning("Error in watershed grid",theScript) return NIL 286

end theGTheme.SetName("Watersheds of"++ThePointTheme.Getname) theView.AddTheme(theGTheme) theLegend = theGTheme.GetLegend theLegend.Unique(theGTheme,theValueField.GetName) if (thePointFTab.IsBase and thePointFTab.IsBeingEditedWithRecovery.Not) then if (MsgBox.YesNo("Join attributes of" ++ thePointTheme.GetName ++ "to the grid?",theScript,FALSE)) then theVTab.Join(toField,thePointFTab,theValueField) theGTheme.UpdateLegend end end

' save watershed grid if (Msgbox.YesNo("Do you want to save" ++ theGTheme.GetName ++ "as a grid?",theScript,false)) then def = av.GetProject.MakeFileName("wshed", "") aFN = SourceManager.PutDataSet(GRID,"Save Watershed Grid",def,TRUE) if ((aFN = NIL).Not) then status = Grid.GetVerify Grid.SetVerify(#GRID_VERIFY_OFF) if (theWater.SaveDataSet(aFN).Not) then MsgBox.Warning("Unable to save the watershed grid.",theScript) Grid.SetVerify(status) else Grid.SetVerify(status) end end end

'export to shapefile if (Msgbox.YesNo("Do you want to export" ++ theGTheme.GetName ++ "to a shape file?",theScript,true)) then if (theGTheme.CanExportToFtab.Not) then MsgBox.Warning("Error occurred while exporting" ++ theGTheme.GetName) return nil end def = av.GetProject.MakeFileName("wshed", "shp") def = FileDialog.Put(def, "*.shp", "Convert " + theGTheme.getName) if ((def = NIL).not) then theFTab = theGTheme.ExportToFtab(def) ' For Database themes, which can return a nil FTab sometimes if (theFTab=nil) then MsgBox.Warning("Error occurred while converting to shapefile."+NL+ "Shapefile was not created.",theScript) return nil end theValue = theFTab.FindField("Gridcode") theId = theFTab.FindField("Id") if (doString.Not) then theValue.SetName(theValueField.GetName) else theSValue = Field.Make(theValueField.GetName,#FIELD_CHAR,theValueField.GetWidth,0) theSValue.SetEditable(TRUE) 287

theSValue.SetVisible(TRUE) if (theFTab.StartEditingWithRecovery) then theFTab.BeginTransaction theFTab.AddFields({theSValue}) for each r in (1..theFTab.GetNumRecords) ' theString = thePointFTab.ReturnValue(theValueField,theFTab.ReturnValueNumber(theValue,r-1)-1) theString = stringList.Get(theFTab.ReturnValue(theValue,r-1)-1).AsString theFTab.SetValue(theSValue,r-1,theString) end theFTab.RemoveFields({theValue,theId}) theFTab.EndTransaction end saveEdits = TRUE theFTab.StopEditingWithRecovery(saveEdits) end

shpfld = theFTab.FindField("Shape")

' build the spatial index theFTab.CreateIndex(shpfld)

' create a theme and add it to the View fthm = FTheme.Make(theFTab)

if (MsgBox.YesNo("Do you want to add" ++ fthm.getName ++"to the view?",theScript,true)) then theView.AddTheme(fthm) end end end theView.GetWin.Activate

288

APPENDIX C

DETAILED DESCRIPTION OF THE IDF CURVE

289

The IDF curve is a rainfall Intensity-Duration-Frequency curve (Figure C.1), which represents the relationships between the following rainfall characteristics.

1. Duration: the length of time over which a precipitation event occurs (see Figure

C.1, x-axis)

2. Vo l u me : the amount of precipitation occurring over the storm event duration, it

often is reported as a depth, with such units of length as inches or centimeters.

3. Intensity: the rainfall intensity equals the volume divided by duration. For

example, if a storm has a duration of 3 hours and a volume of 12 acre-in., its

intensity is 4 acre-in./hour (see Figure C.1, y-axis).

4. Frequency: the frequency of occurrence of events having the same volume and

durations can be measured in terms of exceedance probability or return period (see

Figure C.1: I is the return period in years and the number indicates which period).

For example, if a storm of a specified depth and duration has a 1% chance of

occurring in any one year, it has an exceedence probability of 0.01 and a return

period of 100 years. This is called a 100-year storm. Storm events occur randomly,

so there is a finite probability that a 100-year event could occur in two

consecutive years, a few times in a year, or not at all in a period of 500 years.

IDF (intensity-duration-frequency) curves have been compiled for most

locations. The IDF curves for Columbus Ohio are presented in Figure C.1. The

IDF curve is most often used to find the rainfall intensity for given duration and

frequency. For example, the 10-yr, 2-hr rainfall intensity for Columbus is found in

Figure C.1 by entering a duration of 2 hour (120 minutes), moving vertically to

the 10-yr frequency curve, and then moving horizontally to the intensity ordinate, 290

yielding 1.1 in/hour. Therefore, the total rainfall depth for this storm event is

2.2-in (intensity x time, 1.1 x 2 =2.2). There is a 10 % of probability of having a

2.2-in rainfall accumulation in 2 hours.

Figure C.1 Rainfall Intensity-Duration-Frequency (IDF) Curves for Columbus

291

APPENDIX D

EQUATIONS FOR IDF CURVE ESTIMATION

292

The basic IDF equation is:

⎧ a ⎪ for D≤ 2 hr (D.1) i = ⎨ Db+ d ⎩⎪ cD for D> 2 hr (D.2)

where:

i = rainfall intensity (in./hr), D = duration (hr), and abc, , , and d= fitting coefficients that vary with storm frequency.

Equation (D.1) can be transformed into a linear form as follows:

a i = (D.3) Db+ 1 bD+ = (D.4) ia yfgD=+ (D.5)

where:

11b yf==,, g = iaa

Equation (D.2) can be transformed into a linear form by using the logarithmic transformation:

icD= d (D.6) logicdD=+ log log (D.7) yhdx=+ (D.8)

Equation (D.5) and (D.8) are linear equations, which can be solved as two simultaneous equations, using any two points from an IDF curve. The accuracy depends on the reading accuracy of the two selected points and on the ability of Equation (D.1) to represent the IDF curve (McCuen, 1998).

293

APPENDIX E

THE SCS STORM DISTRIBUTIONS

294

The distributions are based on the generalized rainfall volume-duration- frequency relationships presented in technical publications of the Weather Bureau. Rainfall depths, for durations ranging 6 min to 24-hr, were obtained from the volume-duration- frequency information in these publications, and used to derive storm distribution.

Using increments of 6 min, incremental rainfall depths are determined. For example, the maximum 6-min depth is subtracted from the maximum 12-min depth and this

12-min depth is subtracted from the maximum 18-min depth, and so on up to 24 hours.

Figure E.1 depicts the resulting distributions and the differences in the peak of each of the four rainfall distribution types, representing a 24-hour storm event. The x-axis represents the rainfall period from 0-hour to 24-hour. The y-axis represents the fraction of 24-hour rainfall (rainfall/total rainfall). The SCS rainfall distribution curve is a general historical statistical output. Although they are not perfectly applicable to

IDF values in every single case for all locations in the region for which they are intended, the matched results are statistically acceptable when compared with the

Weather Bureau atlases (McCuen, 1998). For example, in the regions with type II, the peak is found to occur at the center of the storm.

The Division of Water of Indiana State (DWIS) extracted and modified the type II storm distribution from SCS 24-hour distribution graphs. The x-axis is modified to become the fraction of raining time (time/total raining time), so that it can be applied to any rainfall duration (Figure E.2). DWIS also converts type II storm distribution graph into a spreadsheet, which can be used to calculate the rainfall intensity for every time step, from the beginning of the rainfall to the end of the rainfall (Table E.1).

295

Figure E.1 SCS 24-hour Rainfall Distributions. (SCS, 1984)

Figure E.2 Soil Conservation Service Type II Storm Distribution 296

Based on the Columbus IDF curve, a 2-hour normal storm event accumulates

1.1-in rainfall. Table E.1 presents rainfall intensity for each time step. Column (1) is the time step from 0-min to 120-min. The size of each time step is calculated based on the time fraction in Table E.1 (Columns (1) and (3)). Column (2) is rainfall accumulation in inch, which is calculated based on the rainfall fraction in Table E.1 (Columns (2) and

(4)). The total accumulation at the end of the storm is 1.1-in. Column (3) is the volume of rainfall at each time step, and Column (4) is the rainfall intensity at each time step, converted from Column (3).

Time/Total Time Rainfall/Total Rainfall Time/Total Time Rainfall/Total Rainfall (1) (2) (3) (4) 0.000 0.000 0.520 0.730 0.040 0.010 0.530 0.750 0.100 0.025 0.540 0.770 0.150 0.040 0.550 0.780 0.200 0.060 0.560 0.800 0.250 0.080 0.570 0.810 0.300 0.100 0.580 0.820 0.330 0.120 0.600 0.835 0.350 0.130 0.630 0.860 0.380 0.150 0.650 0.870 0.400 0.165 0.670 0.880 0.420 0.190 0.700 0.895 0.430 0.200 0.720 0.910 0.440 0.210 0.750 0.920 0.450 0.220 0.770 0.930 0.460 0.230 0.800 0.940 0.470 0.260 0.830 0.950 0.480 0.300 0.850 0.960 0.485 0.340 0.870 0.970 0.487 0.370 0.900 0.980 0.490 0.500 0.950 0.990 0.500 0.640 1.000 1.000

Table E.1 SCS Type II Storm Distribution Data

297

Time (min) Rainfall Accumulation (in.) Rainfall (in.) Rainfall Intensity (in/hr) (1) (2) (3) (4) 0 0.00 0.00 0.00 5 0.01 0.01 0.14 12 0.03 0.02 0.14 18 0.04 0.02 0.17 24 0.07 0.02 0.22 30 0.09 0.02 0.22 36 0.11 0.02 0.22 40 0.13 0.02 0.37 42 0.14 0.01 0.28 46 0.17 0.02 0.37 48 0.18 0.02 0.41 50 0.21 0.03 0.69 52 0.22 0.01 0.55 53 0.23 0.01 0.55 54 0.24 0.01 0.55 55 0.25 0.01 0.55 56 0.29 0.03 1.65 58 0.33 0.04 2.20 58 0.37 0.04 4.40 58 0.41 0.03 8.25 59 0.55 0.14 23.83 60 0.70 0.15 7.70 62 0.80 0.10 2.47 64 0.83 0.02 1.10 65 0.85 0.02 1.10 66 0.86 0.01 0.55 67 0.88 0.02 1.10 68 0.89 0.01 0.55 70 0.90 0.01 0.55 72 0.92 0.02 0.41 76 0.95 0.03 0.46 78 0.96 0.01 0.28 80 0.97 0.01 0.28 84 0.98 0.02 0.28 86 1.00 0.02 0.41 90 1.01 0.01 0.18 92 1.02 0.01 0.27 96 1.03 0.01 0.18 100 1.05 0.01 0.18 102 1.06 0.01 0.28 104 1.07 0.01 0.27 108 1.08 0.01 0.18 114 1.09 0.01 0.11 120 1.10 0.01 0.11 Table E.2 Rainfall Intensity of a Two-Hour Normal Storm in the Study Area 298

APPENDIX F

RUNOFF CURVE NUMBER FOR

HYDROLOGIC SOIL-COVER COMPLEX

299

Hydrologic Soil Cover Group Treatment or Hydrologic Land Use A B C D Practice Condition Open Space Poor 68. 79. 86. 89. Fair 49. 69. 79. 84. Good 39. 61. 74. 80. Impervious 98. 98. 98. 98. Roads Paved 98. 98. 98. 98. Paved w/ditch 83. 89. 92. 93. Gravel 76. 85. 89. 91. Dirt 72. 82. 87. 89. Urban Desert Natural 63. 77. 85. 88. Artificial 96. 96. 96. 96. Urban 85% imp 89. 92. 94. 95. 72% imp 81. 88. 91. 93. Residential 65% imp 77. 85. 90. 92. 38% imp 61. 75. 83. 87. 30% imp 57. 72. 81. 86. 25% imp 54. 70. 80. 85. 20% imp 51. 68. 79. 84. 12% imp 46. 65. 77. 82. Urban Newly graded 77. 86. 91. 94. Fallow Bare 77. 86. 91. 94. CR Poor 76. 85. 90. 93. CR Good 74. 83. 88. 90. (Antecedent moisture condition II, and Ia = 0.25) CR: Contoured Row, SR: Straight Row; C: Contoured; T: Terraced Source: USDA, 2004

Continued

Table F.1 Runoff Curve Number for Hydrologic Soil-Cover Complex 300

Table F.1 continued

Row Crop SR Poor 72. 81. 88. 91. SR Good 67. 78. 85. 89. SR + CR Poor 71. 80. 87. 90. SR + CR Good 64. 75. 82. 85. C Poor 70. 79. 84. 88. C Good 65. 75. 82. 86. C + CR Poor 69. 78. 83. 87. C + CR Good 64. 74. 81. 85. C & T Poor 66. 74. 80. 82. C & T Good 62. 71. 78. 81. C & T + CR Poor 65. 73. 79. 81. C & T + CR Good 61. 70. 77. 80. Small Grain SR Poor 65. 76. 84. 88. SR Good 63. 75. 83. 87. SR + CR Poor 64. 75. 83. 86. SR + CR Good 60. 72. 80. 84. C Poor 63. 74. 82. 85. C Good 61. 73. 81. 84. C + CR Poor 62. 73. 81. 84. C + CR Good 60. 72. 80. 83. C & T Poor 61. 72. 79. 82. C & T Good 59. 70. 78. 81. C & T + CR Poor 60. 71. 78. 81. C & T + CR Good 58. 69. 77. 80. Close Seeded SR Poor 66. 77. 85. 89. SR Good 58. 72. 81. 85. C Poor 64. 75. 83. 85. C Good 55. 69. 78. 83. C & T Poor 63. 73. 80. 83. C & T Good 51. 67. 76. 80.

301

Table F.1 continued

Pasture Poor 68. 79. 86. 89. Fair 49. 69. 79. 84. Good 39. 61. 74. 80. Meadow 30. 58. 71. 78. Brush Poor 48. 67. 77. 83. Fair 35. 56. 70. 77. Good 30. 48. 65. 73. Woods – Poor 57. 73. 82. 86. Grass Fair 43. 65. 76. 82. Good 32. 58. 72. 79. Woods Poor 45. 66. 77. 83. Fair 36. 60. 73. 79. Good 30. 55. 70. 77. Farmstead 59. 74. 82. 86. Rangeland Herbaceous Poor 30. 80. 87. 93. Herbaceous Fair 30. 71. 81. 89. Herbaceous Good 30. 62. 74. 85. Oak-Aspen Poor 30. 66. 74. 79. Oak-Aspen Fair 30. 48. 57. 63. Oak-Aspen Good 30. 30. 41. 48. Pinyon-Juniper Poor 30. 75. 85. 89. Pinyon-Juniper Fair 30. 58. 73. 80. Pinyon-Juniper Good 30. 41. 61. 71. Sagebrush Poor 30. 67. 80. 86. Sagebrush Fair 30. 51. 63. 70. Sagebrush Good 30. 35. 47. 55. Desert Shrub Poor 63. 77. 85. 88. Desert Shrub Fair 55. 72. 81. 86. Desert Shrub Good 49. 68. 79. 84.

302

APPENDIX G

STREAM DIMENSION ESTIMATION

303

Three methods are used to estimate channel dimensions: direct measurement, aerial photography measurement, and empirical equations. z Direct measurement: If the channel is accessible (has a bridge or road crossing)

and is small enough, direct measurement are taken. There are 23 points in the

study area where it is possible to directly measure the dimension of the channels

(Figure G.1). z Measurement from aerial photographies: If the channel is inaccessible, but big

enough, its dimensions can be measured from aerial photographies, using GIS

software. z Empirical Equations: Bankfull width and depth are estimated by using empirical

equations developed by the Ohio Department of Natural Resources (ODNR) and

the Ohio EPA.

G.1 Bankfull Width

Research at the ODNR indicates that the wider the stream corridor, the better the stream environment. The bankfull width for streams in the Big Darby watershed can be estimated from an equation developed by Dan Mecklenburg of ODNR. Bankfull data points, drainage areas, and other stream channel parameters were collected for

West-Central Ohio streams. A regression line relates bankfull width (W, ft) to drainage area (DA, Mi2) (Ohio EPA, 2006), with:

WDA=×13.3 0.43 (G.1)

304

Figure G.1 Field Survey Sample Points

305

G.2 Bankfull Depth

Dunne and Leopold (1978) show that the maximum bankfull depth (Dmax, ft) is a strong function of the drainage area (DA, mi2). A drainage area-depth relationship is derived empirically for streams in West-Central Ohio (including several observations from the Big Darby Creek watershed), with (Ohio EPA, 2006):

0.26 DDAmax =×1.9 (G.2)

For bankfull depth, this research uses the elevation map of FEMA flood plain and direct measurements as checkpoints, to double check the output accuracy from the above equations. Bankfull width can be measured from County aerial photographies, and sample points direct measurement, and then compared with the output of the above equations to check their accuracy. Tables G.1 and G.2 present the difference between different measurements and estimations. The error and its percentage represent the difference between the empirical equation estimation and other measurements. The bankfull widths have an error ranging from 15% to 0%, while bankfull depths error varies between 15% to -1%. Most errors remain within 10%. Therefore, the empirical equation estimations are used in the SWMM model.

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Bankfull Width STREAM Empirical Field Survey (ft) Aerial-Photo (ft) ID Equation (ft) Measurement Error % Measurement Error % 10 25.50 23.50 2.00 8% - 20 24.05 24.00 0.05 0% - 30 19.04 17.30 1.74 9% - 40 21.31 - - 50 26.15 24.30 1.85 7% - 60 34.63 29.30 5.33 15% - 70 39.76 - - 80 43.39 39.50 3.89 9% - 90 16.58 15.00 1.58 10% - 100 111.52 - 107.50 4.02 4% 110 112.13 - 109.50 2.63 2% 120 113.96 - 110.25 3.71 3% 130 47.13 45.00 2.13 5% 43.00 4.13 9% 140 47.48 - - 150 125.25 - 115.45 9.80 8% 160 125.93 - 119.28 6.65 5% 170 116.01 - 100.09 15.92 14% 180 36.15 32.40 3.75 10% - 190 107.79 - 103.52 4.27 4% 200 106.91 - 102.31 4.60 4% 210 34.43 33.50 0.93 3% - 220 104.15 - 98.42 5.73 6% 230 24.50 - -

Table G.1 Comparison of Empirical Equation Estimation, Field Survey and Aerial Photography Measurements.

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Bankfull Depth STREAM Empirical Field Survey (ft) FEMA (ft) ID Equation (ft) Measurement Error % Measurement Error % 10 2.82 3.00 -0.18 -7% - 20 2.72 3.00 -0.28 -10% - 30 2.36 2.50 -0.14 -6% - 40 2.53 - - 50 2.86 2.50 0.36 13% - 60 3.39 3.90 -0.51 -15% - 70 3.68 - - 80 3.88 3.50 0.38 10% - 90 2.17 2.00 0.17 8% - 100 6.87 - - 110 6.90 - - 120 6.96 - - 130 4.08 4.00 0.08 2% - 140 4.10 - - 150 7.37 - - 160 7.40 - - 170 7.04 - - 180 3.48 3.50 -0.02 -1% - 190 6.73 - - 200 6.70 - - 210 3.38 3.40 -0.02 -1% - 220 6.59 - 7.00 -0.41 -6% 230 2.75 - -

Table G.2 Comparison of Empirical Equation Estimation, Field Survey and FEMA Measurements.

308

APPENDIX H

SEDIMENT TRANSPORTATION

309

H.1 Physical Transport Processes in Sedimentation

When minerals and soil particles are detached from the ground and transported, two major processes take place, erosion and sedimentation. These natural processes have been active throughout geological times and have shaped the present landscape of our world. Sedimentation is the deposition of particles when gravitational forces overcome the forces that cause movement. The principal external dynamic agents of erosion and sedimentation are water, wind, gravity, and ice. In this research, hydrodynamic forces are the primary focus. Suspended and bed-load sediments are two types of sediments in streams.

Transport functions, as typified by H.A. Einstein (1950), deal only with the

“transportation” process. Sediment transportation is often separated into two classes, based on the mechanism by which grains move. There are the suspended load, wherein grains are picked up off the bed and move through the water column in generally wavy paths defined by turbulent eddies in the flow, and the bed-load, wherein grains move along or near the bed by sliding, rolling, or hopping. In many streams, grains smaller than about 1/8 mm tend to always travel in suspension, grains coarser than about 8 mm tend to always travel as bed load, and grains in between these sizes travel as either bed load or suspended load, depending on the strength of the flow. Methods for predicting suspended sediment transport have a more theoretical basis. Hence, the best way to discuss the behavior of suspended sediments is to understand the forces acting on soil particles. Figure H.1 presents a free-body diagram of the forces acting on a suspended particle. There are four forces acting on the particle. The momentum of the water in motion acts to move the sediment particle in the direction of flow (Fm). The particle 310

Figure H.1 Free-body Diagram (McCuen, 1998)

itself is subject to friction and pressure drag (Fd), that will slow down the movement of the sediment. Of course, the gravity (Fg) plays an important role in dragging down the particle, and the lift force (Fl) pushes the particle up. The total force

(0Fl+++≠ Fd Fm Fg ) causes a rotational motion (M) (McCuen, 1998).

H.2 Suspended Sediment Transport

Suspended sediments are part of the total sediment loads, and are carried by the streamflow. They contain a portion named “wash load”, or that part of the suspended load not represented in the bed material. Most of the suspended sediment transport and

311

wash load relations are derived from measured sediment rating curves and flow-duration curves. However, these curves are not available for the Big Darby Creek.

H.2.1 The Von Kármán Equation

The Von Kármán Equation is used to estimate the total sediment transport rate.

Based on the concept of continuity of mass, the total transport rate can be computed for a unit width of a channel by integrating the velocity and concentration over a vertical section:

qCVdz= (H.1) so∫

where

qs = sediment transport rate, C = concentration, o V = velocity, z = cross section.

Using the concepts of shear stress in turbulent flow and the transfer of momentum due to turbulent mixing, the following relationship has been derived for estimating the transport rate.

−1 qVCIIfhdseh=+11.6*1 [ 2 1 ln (30.2 65 )] (H.2) where:

qs = sediment transport rate,

V* = shear velocity,

C1 = the concentration at level 1, h = the total depth of flow,

fh = a factor reflecting hydraulic roughness,

d65 = the mean diameter of soil particles for which 65% of the particles by weight are smaller,

II12,integrals.= 312

To determine the total sediment transport rate, the variables of Equation (H.2) must be determined. However, it is possible to establish a relationship between the concentration at any level and the concentration at some reference level; the relationship would be a function of the settling velocity of the particles, the flow level, and the sediment transfer coefficient (McCuen, 1998).

H.3 Bed-Load Transportation

Bed-load is the portion of the total sediment that is carried by occasional contact with the streambed, with rolling, sliding, and bouncing.

Yang (1996) summarizes nine specific bed-load formula approaches:

1. Shear stress, 2. Energy slope, 3. Discharge, 4. Velocity, 5. Bed form, 6. Probabilistic, 7. Stochastic, 8. Regression, and 9. Equal mobility.

Figure H.2 Lift force and rotation motion due to velocity profile (McCuen, 1998) 313

There are as many approaches to bed-load transport in the literature as there are varied stream conditions. An earlier summary of the status of sediment transport formulas by Vanoni (1975) was:

“Unfortunately, available methods or relations for computing sediment discharge are far from satisfactory, with the result that plans for works involving sediment movement by water cannot be based strongly on such relations. At best these relations serve as guides to planning, and usually the engineer is forced to rely on experience and judgment in such work.”

Reid and Dunne (1996) summarize the sediment transport equations, including bed-load relations. Below are some basic concepts, which relate to the prediction methods.

Once the flow condition exceeds the criteria for the first motion, sediment particles on the streambed start to move. The transport of streambed particles is a function of the fluid forces per unit. It is called tractive force or shear stress (τ). Under steady, uniform flow conditions, the shear stress is:

τ = γ DS (H.3)

where:

γ = the specific weight of the fluid, D = the mean depth, and S = the water surface slope.

The gravitational force resisting particle entrainment, Fg (see Figure H.2) is proportional to:

γ −γ Fd∝ ()s (H.4) g γ

314

where:

γ = the specific weight of sediment, and s d = the particle diameter.

Graft (1971) modified the Shields relation (Shields, 1939) into relations associated with initiation of particle movement, using the ratio of the fluid forces to the gravitational force. The higher gravitational force will have higher initiation shear

* stress to move the particle. The critical dimensionless shear stress (τ c ) is:

γ −γ d τ * ∝ ()()s (H.5) c γ DS

The dimensionless bed-material transport rate per unit width of streambed is

(Carmenen and Larson, 2005):

q Φ= sb (H.6) 3 (1)sgd− 50

where:

qsb = the volumetric bed load sediment transport rate per unit time and width from bedload samples, γ −γ s = the ratio between densities of sediment and water, (s ) γ g = acceleration due to gravity,

d50 = the mean diameter of soil particles for which 50% of the particles by weight are smaller.

The empirical function developed by Parker (1979) is

**4.5 11.2()ττ− c Φ= (H.7) * 3 ()τ c

315

where:

** ττc is the threshold value of required to initiate particle motion.

McCuen (1998) summarizes most empirical approaches to estimating bed-loads, based on the following general relations:

Φ=f ()τ −τ c Φ=f ()qq − s (H.8) Φ=f ()VV − c

Φ=f ()ω −ωc

where:

τ = shear stress, q = water discharge per unit width, V = mean flow velocity, and ω = stream power per unit bed area. (The subscript c signifies critical values for incipient motion.)

From the above reviews, sediment transport in stream, both suspended sediment and bed-load sediment, is related to sediment concentration, particle diameter, flow velocity, water discharge, stream depth, streambed roughness, and the cross-section area of the stream. However, none of the models can consider all of these factors and all rely on empirical data for model adjustment.

H.5 The Size of Sediment

The size of sediment is one of the most important factors that affects the sediment transport rate. Sediment transport in stream can be divided into two classes, based on the source of the grains. These are the bed material load, which is composed of grains found in the streambed, and wash load, which is composed of grains found in only

316

small (less than a percent or two) amount in the bed. The sources of wash load grains are either the channel banks or the hill-slope area contributing runoff to the stream.

Wash load grains tend to be very small (clays and silts and sometimes fine sands) and, hence, have a very small settling velocity. Once introduced into the channel, wash-load grains are kept in suspension by the flow turbulence and essentially pass straight through the stream with negligible deposition or interaction with the bed.

The boundary between bed load and suspended load is not sharp and depends on the flow strength. Consider a stream with a mixed bed material of sand and gravel. At moderate flows, the sand in the bed may travel as bed load; as flow increases, the sand may begin moving partly or entirely in suspension. Even when traveling in suspension, much of this sediment (particularly the coarse sand) may travel very close to the bed, down among the coarser gravel grains in the bed. That makes it very difficult to sample the suspended load in these streams or, for that matter, to even distinguish between bed load and suspended load. This difficulty is one reason why this study does not discuss the separate sediment movements, but focuses on overall sediment transportation.

317

Figure H.3 Grain size and transport mechanism

Rohrer, Roesner, and Bledsoe (2004) have evaluated the potential impact of watershed development on sediment transport in a prototype headwater stream subjected to typical residential development. Event-based and continuous simulations, using 50 years of hourly rainfall records, were performed for two climatically different locales. The first is in the semiarid climate of Fort Collins, Colorado, and the other is in a typical southeastern climate, Atlanta, Georgia. Since the annual precipitation data of the study area is similar to that of Atlanta, the results for Atlanta are used as reference.

Traditional geomorphic estimates (Leopold et al., 1964) indicate that storms at

1.5- to 2-year recurrence intervals are responsible for the form of active channel. In

Atlanta, at least 88% of all sediment loads in each scenario is transported by storms with a peak discharge return interval of 2 years. That is to say, without any treatment,

318

88% of all sediment loads will be transported to downstream (Rohrer, Roesner, and

Bledsoe, 2004).

For the medium sand transport rate, during the baseflow period (unit discharge flow between 0.07 and 0.0029 cms), only 43% of medium sand will be transported to downstream. When the peak flow discharge increases, the total medium sand transport rate increases. The peak flow discharge equals 1.01 cms. Under a two year storm, 95% of medium sand will be carried downstream (Tabel H.3).

Table H.4 shows that a 0.1-year peak flow discharge return interval is required to initiate sediment transport in a gravel bed channel in Atlanta. The largest percentage of sediment is transported in the 2-year peak discharge return interval bin across all scenarios examined. Seventy-eight to 86% of all sediment is transported in the 0.5- to

2-year peak discharge return intervals (Rohrer, Roesner, and Bledsoe, 2004).

319

Peak Discharge Discharge Bin Transport Total Accumulated Return (cms) Rate Transport Rate Interval (Years) 0.07≧Q＞0.0029 ＞ baseflow 43% 43% 0.11≧Q＞0.07 ＞0.1 12% 55% 0.17≧Q＞0.11 ＞0.25 6% 61% 0.30≧Q＞0.17 ＞0.5 10% 71% 0.42≧Q＞0.30 ＞1 6% 77% 0.49≧Q＞0.42 ＞1.5 4% 81% 1.01≧ Q＞0.49 ＞2 14% 95% 1.31≧ Q＞1.01 ＞10 3% 98% 1.61≧ Q＞1.31 ＞25 1% 99% 1.92≧ Q＞1.61 ＞50 1% 100% Q＞1.92 ＞83.6 0% 100% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 216

Table H.3 Transport Rate of Medium Sand: Atlanta, Georgia

320

Peak Discharge Discharge Bin Transport Total Accumulated Return (cms) Rate Transport Rate Interval (Years) 0.07≧Q＞0.0029 ＞ baseflow 0% 0% 0.11≧Q＞0.07 ＞0.1 0% 0% 0.17≧Q＞0.11 ＞0.25 4% 4% 0.30≧Q＞0.17 ＞0.5 19% 23% 0.42≧Q＞0.30 ＞1 15% 38% 0.49≧Q＞0.42 ＞1.5 11% 49% 1.01≧ Q＞0.49 ＞2 37% 86% 1.31≧ Q＞1.01 ＞10 8% 94% 1.61≧ Q＞1.31 ＞25 3% 97% 1.92≧ Q＞1.61 ＞50 3% 100% Q＞1.92 ＞83.6 0% 100% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 217

Table H.4 Transport Rate of Medium Gravel: Atlanta, Georgia

321

APPENDIX I

GAMS PROGRAM FOR THE ANNUAL TMDL STANDARD

322

$Title BMPs Minimun Cost with the annual TMDL stadnard Sets i subcatchments /1*23/ suba (i) /3, 11, 12, 13, 15, 16/ subb (i) /4, 5, 9, 14/ subc (i) /17, 20/ subd (i) /1, 2, 6, 7, 8 10, 18, 19, 21, 22, 23/ * Four types of subcatchments base on their BMPS suitability. j BMPs technologies /P, W, I, F, N/ * P: Pond, W: Wetland, I: Infiltration, F: Filtering, N: Nothing k control points /1*18/ s storm types /1*5/; * 1: No Precipitation, 2: 0.05-in, 3: 0.25-in, 4: 0.75-in, 5: 2.20-in.

Table GS(i,s) gross sediment load (mg) in subcatchment i for storm type s. 1 2 3 4 5 1 0 2092420 116527785 3574758552 9126724632 2 0 189782893 1473720408 11240916944 24230884640 3 0 185274 8748429 1274185287 3971197960 4 0 34453 2076816 201848440 1148041352 5 0 23868011 172868447 1519533200 4957760560 6 0 3240 668912 1939559392 4831208392 7 0 435266883 3224585528 28045593360 55891606240 8 0 446289169 3290356368 30490454240 59647348000 9 0 123308985 943108486 11441858200 15629873136 10 0 107891393 816057367 11656860808 19740323840 11 0 149087 6822477 2292907560 7294212952 12 0 39208492 267347125 4474685080 13066624744 13 0 254148 11405117 2195385280 5858140680 14 0 71604033 575698966 10586837280 12593528288 15 0 37253511 264035903 4593072592 9007883528 16 0 32241319 232511259 5623180024 11533937376 17 0 91865988 647094347 10825426672 19718551424 18 0 78135758 625412650 4162160192 10951525248 19 0 103242075 741078610 5825482056 14086299560 20 0 135247527 1004252688 11583832496 19194652664 21 0 214458298 1641549448 13451270760 30558493040 22 0 46470500 321324573 3893180136 11831947320 23 0 14006921 94338064 659613486 2035267304 ;

Parameters b (j) sediment removal efficiency for each bmps j / P 0.8 W 0.75 I 0.90 F 0.85 N 0 /

TDA (i) total drainage area (acre) in subcatchment i / 1 2650.1444 2 833.2475 3 1473.7901 4 4002.3006 5 4821.1036 6 1068.8153 7 6895.0845 8 1494.0038 323

9 1841.4918 10 2121.3342 11 2909.2020 12 2938.9291 13 1916.4102 14 2248.0886 15 1065.0726 16 1167.3542 17 302.4646 18 289.5074 19 85.7685 20 211.9907 21 4005.6504 22 699.8365 23 1567.6489 /

UDA (j) unit drainage area (acre) for bmps j installation / P 10 W 10 I 3.5 F 3.5 N 0.000000001 /

UA (j) bmps j unit installation area (acre) / P 0.25 W 0.4 I 0.105 F 0.15 N 0.0/ d (s) the number of days of storm s /1 226.10 2 68.50 3 44.20 4 19 5 7.2/;

Table c (i, j) cost (USD) of bmps j in subcatchment i P W I F N 1 7850.97 31679.54 101819.35 54302.51 0 2 7817.45 31646.02 101785.83 54269.00 0 3 7497.36 31325.93 101465.74 53948.91 0 4 7673.39 31501.96 101641.77 54124.94 0 5 7657.55 31486.12 101625.93 54109.10 0 6 7574.43 31403.00 101542.81 54025.97 0 7 7643.73 31472.30 101612.11 54095.27 0 8 7763.51 31592.08 101731.89 54215.06 0 9 7739.34 31567.92 101707.73 54190.89 0 10 7695.90 31524.47 101664.28 54147.45 0 11 7548.55 31377.12 101516.93 54000.10 0 12 7801.62 31630.19 101770.00 54253.16 0 13 7548.47 31377.05 101516.86 54000.02 0 14 7850.99 31679.56 101819.37 54302.53 0 15 7631.50 31460.07 101599.88 54083.04 0 16 7617.30 31445.87 101585.68 54068.85 0 17 7860.93 31689.50 101829.31 54312.48 0 18 8314.29 32142.86 102282.67 54765.83 0 19 8296.81 32125.39 102265.20 54748.36 0 20 7799.62 31628.19 101768.00 54251.16 0 324

21 7670.65 31499.22 101639.03 54122.20 0 22 8128.48 31957.05 102096.86 54580.02 0 23 8001.72 31830.29 101970.10 54453.27 0 ;

Table amax (i,j) maximum area (acre) for each bmps in subcatchment i P W I F N 1 1061.62 0.00 6.83 183.53 0.00 2 358.15 0.00 3.25 60.96 0.00 3 147.72 33.64 0.00 63.25 0.00 4 1646.24 3.53 1.28 157.39 0.00 5 2672.80 1.49 51.14 335.59 0.00 6 388.98 0.00 5.22 89.97 0.00 7 3392.45 0.00 20.54 557.39 0.00 8 651.15 0.00 0.19 150.22 0.00 9 175.24 25.92 0.99 41.98 0.00 10 760.62 0.00 2.23 148.49 0.00 11 165.35 9.92 0.00 65.76 0.00 12 142.50 53.16 0.00 65.37 0.00 13 385.55 20.23 0.00 156.86 0.00 14 60.94 13.03 0.63 19.68 0.00 15 43.47 2.77 0.00 29.02 0.00 16 104.61 24.03 0.00 40.36 0.00 17 3.73 0.00 0.00 2.66 0.00 18 5.45 0.00 0.20 2.62 0.00 19 0.83 0.00 0.10 0.29 0.00 20 63.99 0.00 0.00 15.37 0.00 21 2005.63 0.00 125.65 304.53 0.00 22 7.63 0.00 0.73 3.53 0.00 23 185.34 0.00 72.95 91.22 0.00 ;

Scalar TMDL total maximum daily load (annual load (mg)) /2023610000000/ M a very large number /10E10/;

Variables

z total cost of setup bmps technologies ;

Positive Variables r (i, j) ratio of different bmps j in each subcatchment i ns (i,s) net sediment concentration after bmps in subcatchment i a (i, j) area required for bmps j setup in subcatchment i ;

Binary Variables x (i, j) if selected x=1 not x=0;

Equations cost define objective function cns (i,s) net sediment loading after bmps in subcatchment i ca (i, j) area required for bmps j setup in subcatchment i ctda (i, j) minimum drainage area of BMPs sr (i) sum of share in subcatchment i cTMDL constraint of TMDL cr (i, j) conarmax (i,j) all BMPs should less than their area limited cona1 (i) 325

cona2 (i) cona3 (i) cona4 (i) cona5 (i) cona6 (i) ; ctda (i, j).. ((r (i, j) * TDA (i)) / UDA (j)) =g= 1 - (1-x (i,j))*M; cr (i,j).. r (i, j) =l= x (i, j); cost .. z =e= sum ((i, j), c (i, j) * a (i, j)); cns (i,s) .. ns (i,s) =e= sum (j, r (i,j) * GS (i,s) * (1-b(j))); sr (i) .. sum (j, r (i, j)) =e= 1; ca (i, j) .. a (i, j) =e= r (i, j) * (TDA (i) / UDA (j)) * UA (j); cTMDL.. sum (i, sum (s, d(s)* ns (i, s)))=l= TMDL; ;

*All Types conarmax(i,j).. a(i,j) =l= amax(i,j);

*Type A cona1 (i) $ suba (i).. a(i,'P') + a(i,'W') + a(i,'F') =l= amax (i,'P');

*Type B cona2 (i) $ subb (i).. a(i,'F') + a(i,'I') =l= amax (i,'F'); cona3 (i) $ subb (i).. a(i,'P') + a(i,'W') + a(i,'I') + a(i,'F') =l= amax (i,'P');

*Type c cona4 (i) $ subc (i).. a(i,'P') + a(i,'F') =l= amax (i,'P');

*Type D cona5 (i) $ subd (i).. a(i,'F') + a(i,'I') =l= amax (i,'F'); cona6 (i) $ subd (i).. a(i,'P') + a(i,'I') + a(i,'F') =l= amax (i,'P');

Option limrow = 0, limcol = 0;

Model mincost /all/;

Solve mincost using mip minimizing z;

File results/BTMDLresult1.txt/; put results; put "Model Status",mincost.modelstat/; put "Solve Status",mincost.solvestat/; put "Objective",z.l/; put "BMPS Area"/; loop((i,j), put i.tl,j.tl,a.l(i,j)/ ); putclose;

326

APPENDIX J

GAMS PROGRAM FOR THE ANNUAL TMDL STANDARD

SENSITIVITY ANALYSIS

327

$Title BMPs Minimun Cost with the TMDL standard sensitivity analysis Sets i subcatchments /1*23/ suba (i) /3, 11, 12, 13, 15, 16/ subb (i) /4, 5, 9, 14/ subc (i) /17, 20/ subd (i) /1, 2, 6, 7, 8 10, 18, 19, 21, 22, 23/ j BMPs technologies /P, W, I, F, N/ k control points /1*18/; * P: Ponds, W: Wetlands, I: Infiltration, F: Filters, N: Nothing

Parameters GS (i) gross sediment concentration in subcatchment i /1 20121 2 53420 3 8755 4 2531 5 10930 6 10651 7 123220 8 131500 9 34458 10 43520 11 16081 12 28807 13 12915 14 27764 15 19859 16 25428 17 43472 18 24144 19 31055 20 42317 21 67370 22 26085 23 4487 /

b (j) sediment removal efficiency for each bmps j / P 0.8 W 0.75 I 0.90 F 0.85 N 0 /

TDA (i) total drainage area in subcatchment i / 1 2650.1444 2 833.2475 3 1473.7901 4 4002.3006 5 4821.1036 6 1068.8153 7 6895.0845 8 1494.0038 9 1841.4918 10 2121.3342 11 2909.2020 12 2938.9291 13 1916.4102 14 2248.0886 328

15 1065.0726 16 1167.3542 17 302.4646 18 289.5074 19 85.7685 20 211.9907 21 4005.6504 22 699.8365 23 1567.6489 /

UDA (j) unit drainage area for bmps j installation / P 10 W 10 I 3.5 F 3.5 N 0.000000001 /

UA (j) bmps j unit installation area / P 0.25 W 0.4 I 0.105 F 0.15 N 0.0/ ;

Table c (i, j) cost of bmps j in subcatchment i P W I F N 1 7850.97 31679.54 101819.35 54302.51 0 2 7817.45 31646.02 101785.83 54269.00 0 3 7497.36 31325.93 101465.74 53948.91 0 4 7673.39 31501.96 101641.77 54124.94 0 5 7657.55 31486.12 101625.93 54109.10 0 6 7574.43 31403.00 101542.81 54025.97 0 7 7643.73 31472.30 101612.11 54095.27 0 8 7763.51 31592.08 101731.89 54215.06 0 9 7739.34 31567.92 101707.73 54190.89 0 10 7695.90 31524.47 101664.28 54147.45 0 11 7548.55 31377.12 101516.93 54000.10 0 12 7801.62 31630.19 101770.00 54253.16 0 13 7548.47 31377.05 101516.86 54000.02 0 14 7850.99 31679.56 101819.37 54302.53 0 15 7631.50 31460.07 101599.88 54083.04 0 16 7617.30 31445.87 101585.68 54068.85 0 17 7860.93 31689.50 101829.31 54312.48 0 18 8314.29 32142.86 102282.67 54765.83 0 19 8296.81 32125.39 102265.20 54748.36 0 20 7799.62 31628.19 101768.00 54251.16 0 21 7670.65 31499.22 101639.03 54122.20 0 22 8128.48 31957.05 102096.86 54580.02 0 23 8001.72 31830.29 101970.10 54453.27 0 ;

Table amax (i,j) maximum area for each bmps in subcatchment i P W I F N 1 1061.62 0.00 6.83 183.53 0.00 2 358.15 0.00 3.25 60.96 0.00 3 147.72 33.64 0.00 63.25 0.00 4 1646.24 3.53 1.28 157.39 0.00 5 2672.80 1.49 51.14 335.59 0.00 6 388.98 0.00 5.22 89.97 0.00 329

7 3392.45 0.00 20.54 557.39 0.00 8 651.15 0.00 0.19 150.22 0.00 9 175.24 25.92 0.99 41.98 0.00 10 760.62 0.00 2.23 148.49 0.00 11 165.35 9.92 0.00 65.76 0.00 12 142.50 53.16 0.00 65.37 0.00 13 385.55 20.23 0.00 156.86 0.00 14 60.94 13.03 0.63 19.68 0.00 15 43.47 2.77 0.00 29.02 0.00 16 104.61 24.03 0.00 40.36 0.00 17 3.73 0.00 0.00 2.66 0.00 18 5.45 0.00 0.20 2.62 0.00 19 0.83 0.00 0.10 0.29 0.00 20 63.99 0.00 0.00 15.37 0.00 21 2005.63 0.00 125.65 304.53 0.00 22 7.63 0.00 0.73 3.53 0.00 23 185.34 0.00 72.95 91.22 0.00 ;

Scalar TMDL total maximum daily load /0/ M a very large number /10E10/ M1 Multiplier of TMDL;

Variables

z total cost of setup bmps technologies ;

Positive Variables r (i, j) ratio of different bmps j in each subcatchment i ns (i) net sediment concentration after bmps in subcatchment i a (i, j) area required for bmps j setup in subcatchment i;

Binary Variables x (i, j) if selected x=1 not x=0;

Equations cost define objective function cns (i) net sediment concentration after bmps in subcatchment i ca (i, j) area required for bmps j setup in subcatchment i * cpq (k) sediment concentration at control point k cTMDL constraint of TMDL ctda (i, j) minimum drainage area of BMPs sr (i) sum of share in subcatchment i cr (i, j) conarmax (i,j) all BMPs should less than their area limited cona1 (i) cona2 (i) cona3 (i) cona4 (i) cona5 (i) cona6 (i) ; ctda (i, j).. ((r (i, j) * TDA (i)) / UDA (j)) =g= 1 - (1-x (i,j))*M; cr (i,j).. r (i, j) =l= x (i, j); cost .. z =e= sum ((i, j), c (i, j) * a (i, j)); cns (i) .. ns (i) =e= sum (j, r (i, j) * GS (i) * (1-b (j))); sr (i) .. sum (j, r (i, j)) =e= 1; 330

ca (i, j) .. a (i, j) =e= r (i, j) * (TDA (i) / UDA (j)) * UA (j); * cpq (k) .. sum (i, t (i, k) * ns (i))* 453592/ RO(k) =l= EQS; cTMDL.. sum (i, ns (i)) =l= TMDL + M1;

*All Types conarmax(i,j).. a(i,j) =l= amax(i,j);

*Type A cona1 (i) $ suba (i).. a(i,'P') + a(i,'W') + a(i,'F') =l= amax (i,'P');

*Type B cona2 (i) $ subb (i).. a(i,'F') + a(i,'I') =l= amax (i,'F'); cona3 (i) $ subb (i).. a(i,'P') + a(i,'W') + a(i,'I') + a(i,'F') =l= amax (i,'P');

*Type c cona4 (i) $ subc (i).. a(i,'P') + a(i,'F') =l= amax (i,'P');

*Type D cona5 (i) $ subd (i).. a(i,'F') + a(i,'I') =l= amax (i,'F'); cona6 (i) $ subd (i).. a(i,'P') + a(i,'I') + a(i,'F') =l= amax (i,'P');

Model mincost /all/;

File result/BASETMDLSEN.txt/;

Put result;

For (M1=160000 to 720000 by 1000,

Solve mincost using mip minimizing z;

Put M1:7:0,@9, z.l: 10:0;

*Loop ((i,j), put a.l (i,j):6:2); put/;

);

331