In recent decades, more than 90 percent of urban growth in the United States has ...... removal of non-point source pollutants, water quality standards, and .... precipitation, such as vegetation, buildings, and paved streets, are potentially ...... Muck is bad for construction because of the drainage problem, but good for wetland.
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.
ii
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.
iii
Dedicated to my parents
iv
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.
v
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)
vi
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
vii
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 viii
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
xiv
LIST OF TABLES
Page
Table 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 xv
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 xvi
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
xix
LIST OF FIGURES
Figure 2.1
Page
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 xx
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 xxi
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 xxii
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
xxiii
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.
4
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, oxygenconsuming 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
Deterministic
Quasi-Distributed
Mixed
Distributed
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).
20
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.
24
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 Size
TSS mg/l Use Designation:
WWH
EWH
Headwaters (drainage area < 20 mi2)
10
10
Wadeable (20 mi2 < drainage area < 200 mi2)
31
26
44 Small Rivers (200 mi2 < drainage area < 1000 mi2) WWH: Warm water habitat EWH: Exceptional warm water habitat Source: Based on the Eastern Corn Belt Plains Ecoregion, Ohio EPA (2006)
41
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.
33
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.
37
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 A C
B
Example 2 A: 0-10% B: 10-20% C: 20-40%
Slope Map
C B A
A: Slightly Eroded B: Slight to Moderate C: Moderate
Erosion Map
STEP 2 Rate Each Factor for its Suitability for Land Uses Factor Types Example 1 (Slope Map) A B C Example 2 (Erosion Map) A B C
Agriculture
Housing
1 2 3
1 3 3
1 2 3
1 2 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 3
1 3
Soil Map 1
2
2
Erosion Map
3
1 Agriculture
3
2 2 1
Housing
STEP 4 Overlay Single Factor Suitability Maps to Obtain Composite Maps for Each Land Use
6
5 5
4
4 4 3
3 2
Agriculture
5
5 5
3 5
4
4
3 2
The Lowest numbers are best suited for the land use, and the highest numbers least suited.
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
C jk = ∑ Wik X ijk
(3.1)
i =1
where: i = environmental variables (factors) index; j = spatial unit index; k = utility (e.g. residential development) index; C jk = the final weighted index or score for spatial unit j and utility k ; Wik = weight for variable i and utility k ; X ijk = numerical value of variable i in unit j for utility k ; 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 Natural Environment Factors Natural Environment Factors Environment Factors
Human Human Environment Factors Human Environment Factors Environment Factors
Weighting
Opportunity Map
Constraint Map
Knock-Out 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.
43
Input Map
Input Value
A
1 2 3
B
Input Label
Scale Value
Weight (%)
Low Moderate High
1 3 5
10
1 2 3
Low Moderate High
1 3 5
10
C
3
Very Long
5
20
D
1 2 3
None Occasionally Frequently
1 3 5
15
E
1 2 4 5
Good Good Moderate Slight Sever
1 2 4 5
15
F
1 3 5
Low Moderate High
1 3 5
30
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 Score
2.45
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
Natural Environment Factors
Existing Urban Development Constraints
Potential Residential Development Scenario Constraints
BMPs Installation BMPs Installation Opportunity BMPs 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 Scenarios
Watershed Characteristics
Precipitation Data
Watershed Model (SWMM)
Urban Areas
Pollutant Loads
Empirical Equation Estimations
Non-Urban Areas
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).
50
OFF-LINE
RUNOFF
LINE INPUT: refers
to data input from terminal. TRANSPORT
STORAGE/ TREATMENT
EXTRAN
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 Analysis
BMP Characteristics
Watershed Model (SWMM)
Economic Model (Linear Programming)
Optimization Analysis
Sensitivity 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
n
Total Cost =
m
∑∑ C i =1 j =1
ij
(3.2)
Aij
where:
C = unit cost for BMP installation and maintenance, per acre, A = area of BMP installation in acre, i = 1 → n : subcatchment index, j = 1 → m : 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 × GSis × (1 − β j )
(3.3)
j =1
where: i = 1 → n : subcatchment, j = 1 → m : BMP and (including no BMP), s = Storm index (1 → h), 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 i treated by BMP j , β j = Pollutant removal rate of BMP j.
m
∑γ j =1
ij
=1
(3.4)
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 the γ 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
FS ks = ∑ α iks × NSis
(3.6)
i =1
where: k = Water quality control points (1 → l ), FS ks = Final pollutant load at water quality control point k under storm s,
α iks = Transport rate from subcatchment i to control point k under storm s. 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. If γ 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
γ ij ×
(3.7)
TDAi ≥ 1 − (1 − X ij ) × M UDAj
(3.8)
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: Aij = γ ij ×
TDAi × UAj UDAj
(3.9)
where UAj = unit installation area for setup of BMP j.
The area used (γ ij × TDAi ) 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 practice areas. For example, γ i 4 ×
TDAi represents the number of BMP 4 technology UDA4
units needed for installation, and γ i 4 ×
TDAi × UA4 represents the needed area for UDA4
BMP 4 installation ( Ai 4 ).
58
TDAi
γ i4 ×
TDAi UDA4
Ai4
Ai1
Ai2
γ i1 ×
TDAi UDA1
γ i2 ×
TDAi 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
suitability analysis first provides the maximum area Aijmax that can be used by each BMP j standing alone in subcatchment i, with: Aij ≤ Aijmax
(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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
No Selection BMP 1 alone. BMP 2 alone. BMP 3 alone. BMP 4 alone. BMP 1 and BMP 2. BMP 1 and BMP 3. BMP 1 and BMP 4. BMP 2 and BMP 3. BMP 2 and BMP 4. BMP 3 and BMP 4. BMP 1, BMP 2, and BMP 3. BMP 1, BMP 2, and BMP 4. BMP 1, BMP 3, and BMP 4. BMP 2, BMP 3, and BMP 4. BMP 1, BMP 2, BMP 3, and BMP 4.
Figure 3.11 Example of BMP Combinations 60
The following joint constraints then apply: Ai 3 + Ai 4 ≤ Aimax 4
(3.11)
Ai1 + Ai 2 + Ai 3 + Ai 4 ≤ Aimax 1
(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 ⎡ h FS ks ⎤ h d ( ) ⎥ /(∑ d s ) ≤ EQS ⎢∑ s ROks ⎦ s =1 ⎣ s =1
(3.13)
where: ROks = stream flow at control point k under storm s, d s = number of days with storm type s.
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 61
n
h
∑∑ d i =1 s =1
3.5
s
× NSis ≤ TMDL
(3.14)
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.
62
Natural Environment Factors
Human Environment Factors
Spatial Analysis Model
BMPs Suitability Analysis
Residential Suitability Analysis
Watershed Model (SWMM)
Economic Model
Optimization Analysis
Sensitivity Analysis Decision Making
Figure 3.12 Integrated Model Flowchart
63
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). 64
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.
65
Model Spatial Analysis Model
Watershed Model (SWMM)
Data
Data Source
Landform (Slope)
USGS
Soil
USGS
Land-use
MRLC Satellite Image
Transportation Network
Big Darby Website
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
Economic Model
Land-use Data
GIS
Buildup
Literature
Washoff
Literature
Erosion
USLE Equation
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 68
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).
County
Tract
Block Group
Population in 2000
Population in 1990
Absolute Change
Relative 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
69
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
71
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
72
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
73
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.
74
Figure 4.5 Catchments
75
Figure 4.6 Elevations
76
Figure 4.7 Slopes
77
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.
78
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.
79
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
80
Land Use
Area (100 m2) Area (acres) Percentage (%)
Commercial/ Industrial/ Transportation
10,618
262
0.56
130,057
3,213
6.92
1,251
31
0.07
378
9
0.02
High Intensity Residential
2,647
65
0.14
Low Intensity Residential
15,948
394
0.85
81
2
0.00
4,658
115
0.25
321,993
7,957
17.12
1,374,052
33,954
73.06
15,571
385
0.83
3,461
86
0.18
1,880,714
46,473
100.00
Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest
Mixed Forest Open Water Pasture/ Hay Row Crops Urban/ Recreational Grasses Woody Wetlands Total
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).
81
Figure 4.8 Land Uses and Transportation Networks in 1994
82
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).
83
Figure 4.9 Detail Streams
84
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.
86
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.
87
Name
Type Natural Stream Major Stream
Big Darby Creek
Agricultural irrigation/Drainage
Size
Picture
The channel width is around 100-ft; the channel depth is around 7-ft.
Stormwater Natural Stream Major Stream Robinson Run
Agricultural irrigation/Drainage Stormwater Natural / Manmade Stream Major Stream
Sugar Run Agricultural irrigation/Drainage Stormwater
The channel width is around 15-20-ft. The depth is around 3-ft. The size depends on the location of the stream. Upstream channels are manmade, of uniform size. Downstream channels are not.
Natural / Manmade Ditches
Minor Streams Agricultural irrigation/Drainage
The size depends on where the channels are located.
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 Apr May Jun Jul Aug Sep Oct Nov Dec
Total
2004
2
1
1
3
4
2
4
3
3
1
4
3
31
2003
1
2
1
4
5
2
1
9
5
1
2
2
35
2002
0
1
2
2
4
1
2
1
3
1
2
1
20
2001
0
1
0
3
6
2
3
2
0
3
2
1
23
2000
1
2
3
2
3
2
4
2
2
2
1
1
25
1999
2
2
2
2
1
0
3
1
1
0
1
2
17
1998
1
1
0
5
2
6
2
2
1
2
2
1
25
1997
2
1
2
1
3
5
3
4
1
1
1
1
25
1996
2
1
2
3
4
3
4
1
3
1
3
3
30
1995
3
2
1
3
4
4
4
4
1
2
2
1
31
2 1.6
26.2
Average 1.4 1.4 1.4 2.8 3.6 2.7
3 2.9
2 1.4
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
Precipitation ≧0.01 in.
30 13.8 11.4 13.1 13.2 12.6 10.9 10.7 10.5
Precipitation ≧0.1 in.
30 6.22 5.22
Precipitation ≧0.50 in.
10
1.4
1.4
1.4
2.8
3.6
2.7
3
2.9
2
Precipitation ≧1.00 in.
30
0.3
0.3
0.2
0.5
0.6
1.1
1.1
1
0.7
Table 4.6
28
31
31
30
30
31
31
30
31
8.5
9.4 11.6 13.2 138.9
7 7.56 10.4 7.11 6.56 6.11 4.78 4.56
30
31
365
7 5.67
70.4
1.4
2
1.6
26.2
0.4
0.6
0.4
7.2
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 91
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
7.20
1.97
1.0 ≤ p
Table 4.7 Frequencies of Storm Types
92
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).
93
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. 94
Time (min) (1) 0 5 12 18 24 30 36 40 42 46 48 50 52 53 54 55 56 58 58 58 59 60 62 64 65 66 67 68 70 72 76 78 80 84 86 90
Rainfall Accumulation (in.) (2) 0.00 0.01 0.03 0.04 0.07 0.09 0.11 0.13 0.14 0.17 0.18 0.21 0.22 0.23 0.24 0.25 0.29 0.33 0.37 0.41 0.55 0.70 0.80 0.83 0.85 0.86 0.88 0.89 0.90 0.92 0.95 0.96 0.97 0.98 1.00 1.01
Rainfall (in.) (3) 0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.03 0.01 0.01 0.01 0.01 0.03 0.04 0.04 0.03 0.14 0.15 0.10 0.02 0.02 0.01 0.02 0.01 0.01 0.02 0.03 0.01 0.01 0.02 0.02 0.01
Rainfall Intensity (in/hr) (4) 0.00 0.14 0.14 0.17 0.22 0.22 0.22 0.37 0.28 0.37 0.41 0.69 0.55 0.55 0.55 0.55 1.65 2.20 4.40 8.25 23.83 7.70 2.47 1.10 1.10 0.55 1.10 0.55 0.55 0.41 0.46 0.28 0.28 0.28 0.41 0.18 Continued
Table 4.8 Rainfall Intensity of a Two-Hour Normal Storm in the Study Area 95
Table 4.8 continued 92 96 100 102 104 108 114 120
1.02 1.03 1.05 1.06 1.07 1.08 1.09 1.10
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.27 0.18 0.18 0.28 0.27 0.18 0.11 0.11
30.00
20.00
15.00
10.00
5.00
4
0
4
11
10
10
92
86
80
76
70
67
65
62
59
58
56
54
52
48
42
36
24
12
0.00
0
Rainfall Intensity (in/hr)
25.00
Time (Minute)
Figure 4.11 Rainfall Intensity Distribution of a Two-Hour Normal Storm
96
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: fp = fc + (f0 – fc) e-kt
(4.1)
where:
f p = infiltration capacity into the soil, ft/sec, f c = minimum or ultimate value of f p (variable WLMIN in the SWMM model), ft/sec, f 0 = maximum or initial value of f 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.
97
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 Source: Musgrave, 1955 Table 4.9
0.05-0.00
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: S=
1000 − 10 CN
(4.2)
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.
98
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 of the values appear to be in the range 3-6 hr-1 (0.00083-0.00167 sec-1). 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.
99
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
100
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: dp = 0.0303 × S-0.49 , (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).
101
Land Use Criteria Land Use Types County Elements**
Land Use Imperviousness 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
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
*
8
50
*
9
80
*
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
Medium Density
High Density High Density
Residential, 14.5 DU/A
Residential, 43.0 DU/A Residential, 24.0 DU/A
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, 102
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).
103
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.
104
Surface
Manning’s Coefficient (n)
Smooth surfaces (concrete. asphalt, gravel, or bare soil)
0.011
Fallow (no residue)
0.05
Cultivated soils: Residue cover 20%
0.06 0.17
Grass: Short grass prairie Dense grass Bermuda grass
0.15 0.24 0.41
Range (natural)
0.13
Woods Light underbrush Dense underbrush
0.40 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
105
(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.
106
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
Type
Land-Use
Pounds of Dust & Dirt Per 100 ft-curb
1
Single Family Residential
0.7
2
Multi-Family Residential
2.3
3
Commercial
3.3
4
Industrial
4.6
Undeveloped or Park
1.5
5 Source: APWA, 1969
Table 4.14 Measured Dust and Dirt (DD) Accumulation in Chicago
No. of Observation
Dust and Dirt Accumulation (lb/curb-mi/day) Mean
Pounds of Dust & Dirt
Single Family Residential
74
62
1.17
Multi-Family Residential
101
113
2.2
Commercial
158
116
2.19
319
6.04
Land Use
Industrial 67 Source: Manning et al., 1977
Per 100 ft-curb
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.
109
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
110
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 $5,800 per acre (7250/1.25=5800).
2.
Extract the potential BMPs installation areas from the parcel layer. Based on the BMPs suitability analysis, it is possible to locate the suitable sites for BMPs setup.
3.
Overlay catchment boundary and parcels.
4.
Recalculate parcel areas. After extraction and overlay, some parcels are not fully located in the potential BMPs installation area, and it is necessary to recalculate the parcel area.
5.
Calculate the catchment average appraisal value. First, calculate the total appraisal value in a catchment, ∑ ( PAi × UPVi ) . Then, divide it by the total area of the catchment:
112
n
∑ ( PA × UPV ) i
i
i
(4.4)
n
∑ PA
i
i
where: PAi = New Parcel Area, UPAi = Unit Parcel Value, i = Parcel index (1 → n). n = Number of parcels in the catchment. Table 4.16 presents the unit land purchasing cost for each catchment.
113
Figure 4.13 Hypothetical Parcel Map
114
Subcatchment Total Appraisal Price ID ($)
Total Catchment Area (Acre)
Unit Price ($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
115
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 Cost
Pond System
Wetland System
Infiltration System
Filtering System
Design and Construction Cost (per Acre)
$60,000
$600,000
$186,200
$499,800
Annual Maintenance Cost (per Acre)
$3,600
$12,000
$37,200
$10,100
35
35
3
12
$5,300
$29,100
$99,300
$51,800
Longevity (Year) Annual Cost (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 ID
Pond
Wetlands
Infiltrations
Filter
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.
120
Discharge Bin (cms)
Peak Discharge Return Interval (Years)
Transport Rate
Total Accumulated Transport Rate
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% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 216
100%
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)).
121
Figure 4.14 Example of stream structure diagram
Stream Peak Cross-Section Stream Discharge Area (cfs) ID (sq. ft) (1) (2) 1 246.30 139.15
Peak Discharge Bin (cfs) (3) 1.77
Peak Discharge Bin (cms) (4) 0.05
Sediment Transport Rate (5) 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
122
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: TSS A = 0.43 × TSS2 + 0.55 × TSS3 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: TSS B = 0.43 × TSS1 + 0.61× (TSS A + TSS 4 ), or TSS B = 0.43 × TSS1 + 0.61× (0.43 × TSS2 + 0.55 × TSS3 + TSS 4 ), or TSS B = 0.43 × TSS1 + 0.26 × TSS 2 + 0.34 × TSS3 + 0.61× TSS 4 . 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:
TSSC = 0.77 × TSS5 + 0.77 × TSS B , or TSSC = 0.77 × TSS5 + 0.77 × (0.43 × TSS1 + 0.26 × TSS 2 + 0.34 × TSS3 + 0.61× TSS 4 ), or TSSC = 0.77 × TSS5 + 0.33 × TSS1 + 0.20 × TSS 2 + 0.26 × TSS3 + 0.47 × TSS 4 On the basis of the above calculation principles, the transport rate at any water quality control point can be estimated.
123
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
BMP
Pond Systems
Wetland Systems
Infiltration Systems
Filter Systems
Unit Drainage Area (UDAj )
10 Acre
10 Acre
3.5 Acre
3.5 Acre
Installation Area Required (UAj )
0.25 Acre
0.4 Acre
0.105 Acre
0.15 Acre
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 HUC 11 Upper Big Darby Creek
From the headwaters to Sugar Run
05060001-190
Minor sub-watershed and streams in the sub-watershed BDC1: Big Darby Creek, Headwaters to Flat Branch
Reference Number (HUC 14) 190-010
Flat Branch
190-020
BDC2: Big Darby Creek, from Flat Branch to Milford Center ; includes Little Darby Creek (Logan Co.), and Spain Creek
190-030
BDC3: Big Darby Creek, Milford Center to Sugar Run
190-040
Buck Run
190-050
Robinson Run
190-060
Sugar Run
190-070
Middle Big Darby Creek
200-010
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
125
Suspended Sediment (kg/y)
Total
Septic (Direct)
Point Source
Margin of Safety
Nonpoint Overland Runoff Natural
Allowable
1439288 138
1250
71964
1365936
Existing
5535063 713
1250
0
5533100
0%
--
75%
% 74% 81% 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)
Total
Septic (Direct)
Point Source
Margin of Safety
Overland Runoff
Allowable
245590
114
153
12280
233043
Existing
611582
429
153
0
611000
0%
--
62%
% 60% 73% Reduction Source: Ohio EPA, 2006
Nonpoint Sources Natural
Table 4.24 Allocations to Robinson Run2 (190-060)
Suspended Sediment (kg/y)
Total
Septic (Direct)
Point Source
Margin of Safety
Overland Runoff
Allowable
447107
121
0
22355
424631
Existing
1264947
547
0
0
1264400
0%
--
66%
% Reduction 65% 78% Source: Ohio EPA, 2006
Nonpoint Sources Natural
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) Case 1 4,451,942 2 4,451,942 3 4,451,942
Precipitation (in)
Days
Event Load (lb)
≥ 0.01 ≥ 0.50 ≥ 1.00
138.9 26.2 7.2
32,051 169,921 618,325
Table 4.26 Different TMDL Standards Based on Precipitation Frequencies 127
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 Size
TSS mg/l Use Designation:
WWH
EWH
Headwaters (drainage area < 20 mi2)
10
10
Wadeable (20 mi2 < drainage area < 200 mi2)
31
26
44 41 Small Rivers (200 mi2 < drainage area < 1000 mi2) 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
128
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).
129
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
Surface Texture
1 2 3
Shrink-Swell Potential
Scale Value
Weight (%)
Silty Clay Loam Silty Loam Muck
1 3 5
10
1 2 5
Low Moderate High
1 2 5
10
3
Very Long
5
20
Flood
1 2 3
None Occasionally Frequently
1 3 5
15
Drainage
1 2 3 4 5
Good Slight Moderate Moderate Slight Severe Severe
1 2 3 4 5
15
Concrete Corrosion
1 2 3 4 5
Low Medium Low Medium Medium High High
1 2 3 4 5
15
Slope
1 2 3 4 5
0.5 % - 3% 0% - 0.5% 3% - 5% 5% - 10% 10% +
1 2 3 4 5
15
Ponding Duration
Input Label
Table 5.1 Soil Maps, Scales, and Weights 131
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 Good Moderate Poor
1000 Square Meters
Acres
%
65,277
16,130
35
102,810
25,405
55
19,183
4,740
10
Table 5.2 Potential for Development—Natural Factors
132
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.
133
Figure 5.1 Potential Development Based on Natural Factors
134
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
135
Figure 5.2 Potential Development Areas
136
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).
Land Use
Area Sq. meter
Commercial/ Industrial/ Transportation
Acres
%
1,061,779
262
0.56
13,005,670
3,213
6.92
125,100
31
0.07
37,800
9
0.02
High Intensity Residential
264,689
65
0.14
Low Intensity Residential
1,594,848
394
0.85
8,100
2
0.00
465,753
115
0.25
32,199,300
7,957
17.12
137,405,201
33,954
73.06
1,557,053
385
0.83
346,110
86
0.18
188,071,403
46,473
100.00
Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest
Mixed Forest Open Water Pasture/ Hay Row Crops Urban/ Recreational Grasses Woody Wetlands Total
Table 5.4 The Land Use of the Study Area in 1994. 137
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 Agriculture
Acres
%
169,604,501
41,910
90.18
1,061,779
262
0.56
13,397,680
3,311
7.12
High Intensity Residential
264,689
65
0.14
Low Intensity Residential
1,594,848
394
0.85
465,753
115
0.25
Park
1,682,153
416
0.89
Total
188,071,405
46,473
100.00
Commercial/Industrial/Transportation Forest
Open Water
Table 5.6 Land-Use in Scenario A
138
Figure 5.3 Scenario A
139
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 Agriculture
Acres
%
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
465,753
115
0.25
Park
1,682,153
416
0.89
Total
188,071,405
46,473
100.00
Open Water
Table 5.7 Land-Use Scenario B
140
Figure 5.4 Scenario B
141
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 Agriculture
Acres
%
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
465,753
115
0.25
Park
1,682,153
416
0.89
Total
188,071,405
46,473
100.00
Open Water
Table 5.8 Land-Use of Scenario C
142
Figure 5.5 Scenario C
143
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.
144
Feasibility Criteria
Pond Systems
Wetland Systems
Infiltration Systems
Filtering Systems
Soils
Most Soils
Most Soils
Need infiltration All Soils Rate .5”/hr
Drainage Area
10-Acre Min.
10-Acre Min.
2-5-Acre Max.
2-5-Acre Recommended
Head Water Distance
3-6 Feet
1-6 Feet
2-4 Feet
1-8 Feet
Space Requirement
2-3% Site
3-5% Site
2-3% Site
2-7% Site
Cost/Acre
Low
Moderate
High
Moderate-High
Water Table
No Restriction
No Restriction
4 Feet below
2 Feet below Filter Bottom
Cleanout
2-10 Years
2-5 Years
1-2 Years
1-3 Years
Storm Water Managment
Yes
Yes
No
No
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.
145
Selection Factor
Pond Systems Wetland Systems
Infiltration Systems
Filtering Systems
Groundwater Quality
Low Risk
Low Risk
Moderate Risk
No Risk
Groundwater Recharge
Moderate Benefit
Low Benefit
High Benefit
No Benefit (a)
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 Benefit
High Benefit
No Benefit
No Benefit
Flood Control
High Benefit
High Benefit
No Benefit
No Benefit
Streambank Protection
Moderate Benefit
Moderate Benefit
Low Benefit
Low Benefit
Property Value
High Premium Moderate Premium
No Premium
Unknown
Landscaping
High Benefit
No Benefit
Low Benefit
High 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.
147
Pond Systems
Wetland Systems
Infiltration Systems
Filtering Systems
< 0.5%
< 0.5%
0.5% - 3%
0.5% - 3%
Groundwater/Water Table
-
-
4 feet below
2 feet below
Organic Soil
-
OMH* ≥ 12%
-
-
Soil Infiltration Rate
-
-
.5” per hour
-
Drainage Area
Min. 10-Acre
Min. 10-Acre
Max. 2-5-Acre
2-5-Acre
Slope
Existing Land-Use Natural Area Natural Area Natural Area * OMH = High percentage limit of organic matter in soil (mg/mg)
Natural Area
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.
148
Existing Land-Use Pond Systems
Agriculture
13,946
1,947,925
481
58,387,719
14,428
1,599,291
395
74,576
18
1,673,867
414
3,547,195
877
Forest
535,479
132
Park
638,191
158
4,720,866
1,167
50,350,833
12,442
Forest
6,174,133
1,526
Park
1,470,137
363
57,995,103
14,331
Forest Agriculture Forest
Subtotal Agriculture Infiltration Systems Subtotal Agriculture Filtering Systems
Acre
56,439,794
Subtotal Wetland Systems
Square Meter
Subtotal
Table 5.12 Area of Potential BMPs Technologies
149
Figure 5.6 Potential Pond Systems Candidates
150
Figure 5.7 Potential Wetland Systems Candidates
151
Figure 5.8 Potential Infiltration Systems Candidates
152
Figure 5.9 Potential Filtering Systems Candidates
153
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.
154
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).
155
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
156
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. 157
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.
158
Maximum Flow Time of Occurrence (cfs) (hr.)
Catchment ID
Stream ID
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
240 2250 2000 1750
Flow (cfs)
1500 1250 1000 750 500 250 0 21 Mon Mar 94
3AM Date/Time
6AM
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
240
TS (mg/l x cfs)
100000 75000 50000 25000 0
Erosion (mg/l x cfs)
75000
50000
25000
0 21 Mon Mar 94
Figure 5.12
3AM Date/Time
6AM
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 (acre)
Forest
HIR
LIR
(acre) (acre) (acre) (acre)
Park
TSS
Sediment
Erosion
(acre)
(mg/l)
(lb)
(100 lb)
ID
ID
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.
164
Catchment ID
Pervious Total Catchment Area Impervious % of Peak Stream Area Peak Imper. Runoff ID (Acre) Runoff Runoff Peak Peak Unit Covers Rate (cfs) Depth Runoff Rate Runoff (cfs) (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)
165
240
3500 3000
Flow (cfs)
2500 2000 1500 1000 500 0 21 Mon Mar 94
3AM Date/Time
6AM
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
TS (mg/l x cfs)
200000 150000 100000 50000
Erosion (mg/l x cfs)
0 100000 75000 50000 25000 0 21 Mon Mar 94
3AM Date/Time
Figure 5.15 TSS and Erosion at the Outlet of the Watershed (LIRD)
167
6AM
Stream Catchment Agriculture CIT Forest HIR ID ID (acre) (acre) (acre) (acre) 10
11
20
15
30
2177.50
1.74 200.99
LIR (acre)
Park (acre)
TSS Sediment Erosion mg/l (lb) (100 lb)
1.45
517.69
0.87 85.85
1,268
4,218
55.59
0.00
234.27
1.49 55.13
1,165
3,377
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
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
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
65.47
0.00
597.92
0.21 58.37
1,101
2,852
140
20
132.60
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
21.19 86.65
1,950
6,785
763.07 10.54
0.00
1444.81 10.38 3.28
3.79
37.88 20.40
942.26
*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)
168
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.
169
Catchment ID
% of Stream Area Imper. ID (Acre) Covers
Pervious Total Catchment Area Impervious Peak Peak Runoff Peak Peak Runoff Runoff 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)
170
240
3500 3000
Flow (cfs)
2500 2000 1500 1000 500 0 21 Mon
3AM
6AM
9AM
Mar 94
12PM Date/Time
3PM
6PM
9PM
22 Tue
Figure 5.16 The Stream Hydrograph at the Outlet of the Watershed (HIRD)
240
TS (mg/l x cfs)
200000 150000 100000 50000 0
Erosion (mg/l x cfs)
50000 40000 30000 20000 10000 0 21 Mon Mar 94
3AM
6AM
9AM
12PM
3PM
6PM
9PM
22 Tue
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). R 2 = 0.806
172
Stream ID
Catchment ID
Agriculture CIT Forest HIR LIR Park (acre) (acre) (acre) (acre) (acre) (acre)
10
11
2177.50 1.74 200.99 496.81 22.33
20
15
763.07 10.54 55.59 234.27
30
3
40
TSS mg/l
Sediment Erosion (lb) (100 lb)
0.87
74.14
1,989
4,598
0.00
1.49
44.27
1,340
2,439
1072.58 10.89 207.80 180.43
0.00
0.00
67.44
821
1,499
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
0.37
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
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
140
20
150
5.66 0.23
3.79
55.11
1444.81 10.38 65.47 597.29
0.00
2.96
0.64
0.21
44.60
1,764
280
69.38
1.74
0.21
40.57
251
345
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
132.60 3.28
4.68
*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.
Catchment ID
Pond System
Wetland System
Infiltration System
Filtering System
No BMP
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 α iks . Table 5.23 presents the pollutant transport rates under the streamflow generated by a type 2 storm.
177
Catchment ID
Scenario 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.)
178
Water Quality Control Point
Catchment ID
1
2
3
4
5
6
7
8
9
10
11
12
13
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
3
1
0 0.43 0.24 0.1 0.04 0.03
0
0
0
0
0
0
0 0.01 0.01
4
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
6
0
0
0
0
0
0
7
0
0
0
0
0
8
0
0
0
0
9
0
0
0
10
0
0
11
0
12
14
15
16
17
18
1 0.55 0.34 0.24 0
0
0 0.18 0.08 0.03
1 0.43 0.02 0.01 0.01
0
0
0 0.43 0.18 0.08
0
1 0.04 0.02 0.01 0.01
0
0
0
0
1 0.55
0
0
0
0
0
0
0
0
0
0
0
0
0
1 0.71
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1 0.61
0
0
0
0
0
0
0 0.34 0.18 0.11 0.08
0
0
0
0
1
0
0
0
0
0
0
0 0.55 0.3 0.18 0.13
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
0
0
0
0
0
0
0
0
1 0.55
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
14
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
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
0.3 0.13
Table 5.23 Pollutant Transportation Rate Under Type 2 Storm
179
0.3 0.17 0.1 0.07
1
0
1 0.61 0.43
0.1 0.06 0.03 0.02
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.
Catchment ID
Total Drainage Area Total Drainage Area 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 )
180
Minimum Drainage Area
Unit Installation Area
(UDAj )
(UAj )
Pond System
10
0.25
Wetland System
10
0.4
Infiltration System
3.5
0.105
Filtering System
3.5
0.15
0
0
BMP
No BMP
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: Aij ≤ Aijmax
(5.2)
∑A
(5.3)
ij
≤ Aimax
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: AiP = Area used for Pond Systems in catchment i. AiW = Area used for Wetland Systems in catchment i.
181
AiI = Area used for Infiltration Systems in catchment i. AiF = Area used for Filtering Systems in catchment i.
Aimax = Maximum Area used in catchment i. 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
182
BMP ( Aijmax )
Catchment ID Pond
Maximum Total Area ( Aimax )
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)
183
The following constraints apply:
2.
AiP + AiW + AiF ≤ Aimax
(5.4)
AiP ≤ AiPmax
(5.5)
max AiW ≤ AiW
(5.6)
AiF ≤ AiFmax
(5.7)
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 catchment, up to a total area, AiFmax . There are no setup restrictions for Pond and Wetland systems. 184
The following constraints apply:
3.
AiF + AiI ≤ AiFmax
(5.8)
AiP + AiW + AiI + AiF ≤ Aimax
(5.9)
AiP ≤ AiPmax
(5.10)
max AiW ≤ AiW
(5.11)
AiI ≤ AiImax
(5.12)
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: AiP + AiF ≤ Aimax
(5.13) 185
4.
AiP ≤ AiPmax
(5.14)
AiF ≤ AiFmax
(5.15)
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 catchment, up to a total area, AiFmax . The following constraints applied: AiF + AiI ≤ AiFmax
(5.16) 186
AiP + AiI + AiF ≤ Aimax
(5.17)
AiP ≤ AiPmax
(5.18)
AiI ≤ AiImax
(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 187
Water Quality Control Point
Storm Type 1
2
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
5
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
25,207
53,006
4
425,915 21,929,670
13
15,902
3
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)
188
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 189
Single Storm Water Quality Standard TMDL (lb) EQS (mg/l)
Precipitation Precipitation Precipitation ≥ 0.01-in ≥ 0.5-in ≥ 1.0-in TMDL1 TMDL2 TMDL3 32,051 169,921 618,325 --
--
--
Daily Maximum
Annual Storm
--
4,461,296
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 190
Catchment 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Total Area
TMDL2 Scenario A B 0 0 20.8 20.8 0 0 0 0 0 0 0 0 125.5 172.4 37.3 37.4 46.0 46.0 53.0 53.0 0 0 0 0 0 0 0 26.3 26.6 26.6 29.2 29.2 3.7 3.7 5.5 5.5 0.8 0.8 5.3 5.3 0 100.1 7.6 7.6 0 0 361.5 534.8
C 66.25 20.83 0 0 120.5 26.7 172.4 37.3 46.0 53.03 8.7 73.5 47.9 56.2 26.6 29.2 3.7 5.4 0.83 5.3 100.1 7.6 39.2 947.5
TMDL3 Scenario A B 0 0 0 20.8 0 0 0 0 0 0 0 0 0 76.5 37.3 37.4 0 0 0 53.0 0 0 0 0 0 0 0 0 0 0 0 0 3.7 3.7 2.9 5.5 0.8 0.8 5.3 5.3 0 0 0 7.6 0 0 50.1 210.6
Total Annual 2,787 4,119 7,314 391 1,630 Control Cost *Only pond systems are needed in these simulations.
C 0 20.8 0 0 0 0 172.4 37.3 31.6 53.0 0 0 0 0 0 0 3.7 5.5 0.8 5.30 100.1 7.6 0 438.2 3,376
EQS Scenario A B C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.21 0 0 0 0 0 4.21 0 0 34
0
Table 6.2 BMP Installation Area (acre) and Total Annual Control Cost ($1000)
191
0
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
1
A 0
TMDL2 Scenario B 0
A 0
TMDL3 Scenario B 0
C 0
EQS Scenario A B C 0 0 0
C 80
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
Catchment
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)
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
32,316
0
23 0 * Only pond systems are used.
Net Sediment (kg)
Sediment Load Reduction (%)
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.
196
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
197
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.
Control Point
Water Quality (mg/l) 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
198
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).
200
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 $84,205,584. The higher TMDL, the larger the allowance for suspended sediments, and the lesser the control costs. The TMDL critical point is 211,000 lb, with costs increasing very strongly for TMDL below 211,000 lb, as illustrated in Figure 6.1.
201
90000000
80000000
70000000
Control Cost ($)
60000000
50000000 Cost 40000000
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
*P: Pond Systems; I: Infiltration Systems; F: Filtering Systems 204
8,550,708
4,435,110
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= $25,267,252), Catchments #5 and #23 require infiltration systems, and their sediment removal rates are 85% and 90%, respectively (Table 6.10). For TSS-EQS above 174 mg/l, no BMP technology is needed; 73 mg/l is a critical standard, with costs increasing very strongly for standards below this threshold, as illustrated in Figure 6.2. 30000000
25000000
Control Cost ($)
20000000
15000000 Cost 10000000
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) 72 79 BMP Installation Area (acre) 1 P 31.8 18 0 2 P 20.8 20.8 20.8 3 P 15.3 13.1 9.8 4 P 100.1 0 0 5 P 38.6 119.8 47.0 5 I 51.1 0 0 5 F 67.4 0 0 6 N 0 0 0 7 P 60.3 48.8 32.5 8 P 24.0 22.3 19.9 9 P 30.4 28.4 25.5 10 P 31.5 28.9 25.4 11 P 34.4 72.7 72.7 11 F 65.8 0 0 12 P 35.3 73.5 73.5 12 F 65.4 0 0 13 P 19.5 16.5 12.2 14 P 47.2 45.5 43.0 15 P 3.5 0 0 16 P 29.2 25.1 15.2 17 P 3.7 3.7 3.7 18 P 5.5 5.5 5.2 19 P 0.8 0.8 0.8 20 P 5.3 5.3 5.3 21 P 42.4 34.9 24.4 22 P 7.6 7.6 7.6 23 P 0 39.2 39.2 23 I 47.0 0 0 Annual Control Cost ($) 25,267,252 4,868,312 3,744,007 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems Catchment
BMP
67
100 0 14.9 0.3 0 0 0 0 0 0 10.5 16.9 14.6 11.7 0 73.5 0 0 29.1 0 0 3.7 0 0.8 5.3 0 7.6 16.5 0 1,604,081
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 $8,010,244. Costs increase strongly for TMDL below 317,000 lb (Figure 6.3). 100000000
90000000
80000000
Control Cost ($)
70000000
60000000
50000000
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 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems
211
6,924,665
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 $56,760, respectively. When compared with Scenario A under the same standard, this scenario incurs lower control costs, because more pollutants have been diluted (Figure 6.4). The optimal BMP installation areas and sediment reduction rates are presented in Table 6.13 and 6.14. 35000000
30000000
25000000
Control Cost ($)
20000000
15000000
Cost
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) 60 123 BMP Installation Area (acre) 66.3 0 0 0 0 0 0 0 8.8 0 0 0 120.5 0 0 0 5.9 0 0 0 0 0 118.9 0 24.6 0 29.0 0 32.9 0 59.5 0 0 0 73.5 0 0 0 23.7 0 18.7 0 0 0 19.4 0 3.7 0 5.5 0 0.8 0 5.3 0
Catchment
BMP
1 1 1 2 3 4 5 5 6 6 6 7 8 9 10 11 11 12 12 13 14 15 16 17 18 19 20
P I F P P P P I P I F P P P P P F P F P P N P P P P P
0 6.8 103.8 2.3 12.5 100.1 77.9 51.1 0 5.2 38.4 128.6 26.8 31.9 36.2 54.6 31.2 35.3 65.4 27.4 23.9 0 28.7 3.7 5.5 0.8 5.3
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
.
.
54
Total Control Cost ($) 29,262,692 5,488,426 56,760 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems Table 6.13 Sensitivity Analysis of the EQS Standard—Scenario B
214
142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2,136
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= $8,771,118), with cost increasing very strongly for standards below this point. The optimal BMP installation areas and sediment reduction rates are presented in Tables 6.15 and 6.16. When TMDL ＞340,000 lb, only pond systems are needed in selected catchments. The total control costs are related to the areas of the installed pond systems. When TMDL ＜ 340,000 lb), infiltration and filtering systems are needed. These two systems have higher pollutant removal rates, but are more expensive.
216
120000000
100000000
Control Cost ($)
80000000
60000000
Cost
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 ($) 95,253,073 9,372,391 8,771,118 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems
8,027,229
219
TMDL (lb) Catchment
276,000
335,000
340,000
358,000
Sediment Load Reduction (%) 1
85
80
80
80
2
86
80
80
80
3
85
80
80
67
4
83
80
73
0
5
87
80
80
80
6
86
80
80
80
7
85
80
80
80
8
85
80
80
80
9
83
80
80
80
10
85
80
80
80
11
83
80
80
80
12
83
80
80
80
13
85
80
80
80
14
81
80
80
80
15
83
80
80
80
16
84
80
80
80
17
39
39
39
39
18
60
60
60
60
19
31
31
31
31
20
85
85
80
80
21
90
80
80
80
22
35
35
35
35
23
90
80
80
80
Table 6.16 Sediment Reduction Rates under the TMDL Standard—Scenario C
220
A 2-hr one-year normal storm under Scenario C not only generates more pollutants, but also more stormwater runoff. The relationship between total cost and the EQS standard is illustrated in Figure 6.6. Two critical points appear: EQS= 47 mg/l and EQS=103 mg/l. Costs increase very strongly below 47 mg/l, but less so between 103 mg/l and 47 mg/l. The optimal BMP areas and sediment reduction rates are presented in Tables 6.17 and 6.18.
16000000
14000000
12000000
Control Cost ($)
10000000
8000000 Cost 6000000
4000000
2000000
0 0
50
100
150
200
-2000000 TSS EQS Standard (mg/l)
Figure 6.6 TSS EQS Standard vs. Control Cost of Scenario C
221
250
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 *P: Pond Systems; I: Infiltration Systems; F: Filtering Systems Table 6.17 Sensitivity Analysis of the EQS Standard—Scenario C 222
2,028
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 Scenario C
60000000
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
Catchment BMP
Scenario A
Scenario B
Scenario C
TMDL (1,000 lb)
TMDL ($1,000 lb)
TMDL (1,000 lb)
3,280
4,110
4,820
6,480
BMP Installation Area (acre)
6,950
7,320
10,830
BMP Installation Area (acre)
11,460
12,050
BMP Installation Area (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
81,789
7,085
5,118
14,373
8,401
7,157
25,903
14,363
9,262
Control Cost ($1,000)
*P: Pond Systems; I: Infiltration Systems; F: Filtering Systems 227
Catchment
Scenario A
Scenario B
Scenario C
TMDL (1,000 lb)
TMDL (1,000 lb)
TMDL (1,000 lb)
3,280
4,110
4,820
Sediment Reduction (%)
6,480
6,950
7,320 10,830 11,460 12,050
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 Scenario C
8000000
6000000
4000000
2000000
0 0
5
10
15
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 Catchment ID
BMP 6
A
Scenario B
Scenario C
EQS (mg/l)
EQS (mg/l)
EQS (mg/l)
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
17.60
3.93
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
26.61
16.75
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
61.63
12.56
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
27.70
15.03
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
28.61
6.33
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
7,069
2,369
1,015
3,874
2,009
933
14,820
4,001
2,445
Control Costs ($1,000)
*P: Pond Systems; I: Infiltration Systems; F: Filtering Systems
Table 6.21 Sensitivity Analysis of the EQS Standard for Annual Storm 231
Catchment
6
Scenario A
Scenario B
Scenario C
EQS (mg/l)
EQS (mg/l)
EQS (mg/l)
8
10
Sediment Reduction (%)
9
11
13
Sediment Reduction (%)
8
10
12
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 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.
Volume: 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 ⎪ i = ⎨D+b ⎪⎩ cD d
for D ≤ 2 hr
(D.1)
for D > 2 hr
(D.2)
where:
i = rainfall intensity (in./hr), D = duration (hr), and a, b, c, and d = fitting coefficients that vary with storm frequency. Equation (D.1) can be transformed into a linear form as follows: a D+b 1 b+D = i a y = f + gD i=
(D.3) (D.4) (D.5)
where: 1 b 1 y = , f = ,g = i a a Equation (D.2) can be transformed into a linear form by using the logarithmic transformation: i = cD d log i = log c + d log D y = h + dx
(D.6) (D.7) (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-durationfrequency 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) (1) 0 5 12 18 24 30 36 40 42 46 48 50 52 53 54 55 56 58 58 58 59 60 62 64 65 66 67 68 70 72 76 78
Rainfall Accumulation (in.) (2) 0.00 0.01 0.03 0.04 0.07 0.09 0.11 0.13 0.14 0.17 0.18 0.21 0.22 0.23 0.24 0.25 0.29 0.33 0.37 0.41 0.55 0.70 0.80 0.83 0.85 0.86 0.88 0.89 0.90 0.92 0.95 0.96
Rainfall (in.) (3) 0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.03 0.01 0.01 0.01 0.01 0.03 0.04 0.04 0.03 0.14 0.15 0.10 0.02 0.02 0.01 0.02 0.01 0.01 0.02 0.03 0.01
Rainfall Intensity (in/hr) (4) 0.00 0.14 0.14 0.17 0.22 0.22 0.22 0.37 0.28 0.37 0.41 0.69 0.55 0.55 0.55 0.55 1.65 2.20 4.40 8.25 23.83 7.70 2.47 1.10 1.10 0.55 1.10 0.55 0.55 0.41 0.46 0.28
80 84 86 90 92 96 100 102 104 108 114 120
0.97 0.98 1.00 1.01 1.02 1.03 1.05 1.06 1.07 1.08 1.09 1.10
0.01 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.28 0.28 0.41 0.18 0.27 0.18 0.18 0.28 0.27 0.18 0.11 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 Group
Cover Land Use
Treatment or Practice
Open Space
Hydrologic Condition
A
B
C
D
Poor
68.
79.
86.
89.
Fair
49.
69.
79.
84.
Good
39.
61.
74.
80.
98.
98.
98.
98.
Paved
98.
98.
98.
98.
Paved w/ditch
83.
89.
92.
93.
Gravel
76.
85.
89.
91.
Dirt
72.
82.
87.
89.
Natural
63.
77.
85.
88.
Artificial
96.
96.
96.
96.
85% imp
89.
92.
94.
95.
72% imp
81.
88.
91.
93.
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.
76.
85.
90.
93.
CR Good 74. 83. (Antecedent moisture condition II, and Ia = 0.25) CR: Contoured Row, SR: Straight Row; C: Contoured; T: Terraced Source: USDA, 2004
88.
90.
Impervious Roads
Urban Desert Urban Residential
CR
Poor
Continued
Table F.1 Runoff Curve Number for Hydrologic Soil-Cover Complex 300
Table F.1 continued
Row Crop
Small Grain
Close Seeded
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.
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.
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.
30.
58.
71.
78.
Poor
48.
67.
77.
83.
Fair
35.
56.
70.
77.
Good
30.
48.
65.
73.
Poor
57.
73.
82.
86.
Fair
43.
65.
76.
82.
Good
32.
58.
72.
79.
Poor
45.
66.
77.
83.
Fair
36.
60.
73.
79.
Good
30.
55.
70.
77.
59.
74.
82.
86.
Meadow Brush
Woods – Grass Woods
Farmstead 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:
W = 13.3 × DA0.43
(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): Dmax = 1.9 × DA0.26
(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.
306
Bankfull Width STREAM Empirical Field Survey (ft) ID Equation (ft) Measurement Error %
Aerial-Photo (ft) 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
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
190
107.79
-
103.52
4.27
4%
200
106.91
-
102.31
4.60
4%
210
34.43
33.50
220
104.15
-
98.42
5.73
6%
230
24.50
-
-
-
-
2.13
3.75
0.93
5%
10%
3%
-
-
Table G.1 Comparison of Empirical Equation Estimation, Field Survey and Aerial Photography Measurements.
307
Bankfull Depth STREAM Empirical Field Survey (ft) ID Equation (ft) Measurement Error %
FEMA (ft) Measurement
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
140
4.10
-
-
150
7.37
-
-
160
7.40
-
-
170
7.04
-
-
180
3.48
3.50
190
6.73
-
-
200
6.70
-
-
210
3.38
3.40
220
6.59
-
7.00
230
2.75
-
-
Error
%
-0.41
-6%
-
-
0.08
-0.02
-0.02
2%
-1%
-1%
-
-
-
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 ( Fl + Fd + Fm + Fg ≠ 0 ) 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: qs = ∫ CoVdz
(H.1)
where qs = sediment transport rate, Co = concentration, 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.
qs = 11.6V*C1[ I 2 + I1 ln e (30.2 f h hd 65−1 )] where: qs = sediment transport rate, V* = shear velocity, C1 = the concentration at level 1, h = the total depth of flow, f h = a factor reflecting hydraulic roughness, d 65 = the mean diameter of soil particles for which 65% of the particles by weight are smaller, I1 , I 2 = integrals. 312
(H.2)
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. 2. 3. 4. 5. 6. 7. 8. 9.
Shear stress, Energy slope, Discharge, Velocity, Bed form, Probabilistic, Stochastic, Regression, and 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: Fg ∝ (
γs −γ )d γ
(H.4) 314
where:
γ s = the specific weight of sediment, and 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:
τ c* ∝ (
γs −γ d )( ) γ DS
(H.5)
The dimensionless bed-material transport rate per unit width of streambed is (Carmenen and Larson, 2005): Φ=
qsb
(H.6)
( s − 1) gd503
where: qsb = the volumetric bed load sediment transport rate per unit time and width from bedload samples, s = the ratio between densities of sediment and water, (
γs −γ ) 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 Φ=
11.2 (τ * − τ c* )
4.5
(H.7)
(τ )
* 3 c
315
where:
τ c* is the threshold value of τ *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 ( q − qs )
(H.8)
Φ = f (V − Vc ) 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
Discharge Bin (cms)
Peak Discharge Return Interval (Years)
Transport Rate
Total Accumulated Transport Rate
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% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 216
100%
Table H.3 Transport Rate of Medium Sand: Atlanta, Georgia
320
Discharge Bin (cms)
Peak Discharge Return Interval (Years)
Transport Rate
Total Accumulated Transport Rate
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% Source: Rohrer, Roesner, and Bledsoe, 2004, P. 217
100%
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 1 0 2092420 116527785 3574758552 2 0 189782893 1473720408 11240916944 3 0 185274 8748429 1274185287 4 0 34453 2076816 201848440 5 0 23868011 172868447 1519533200 6 0 3240 668912 1939559392 7 0 435266883 3224585528 28045593360 8 0 446289169 3290356368 30490454240 9 0 123308985 943108486 11441858200 10 0 107891393 816057367 11656860808 11 0 149087 6822477 2292907560 12 0 39208492 267347125 4474685080 13 0 254148 11405117 2195385280 14 0 71604033 575698966 10586837280 15 0 37253511 264035903 4593072592 16 0 32241319 232511259 5623180024 17 0 91865988 647094347 10825426672 18 0 78135758 625412650 4162160192 19 0 103242075 741078610 5825482056 20 0 135247527 1004252688 11583832496 21 0 214458298 1641549448 13451270760 22 0 46470500 321324573 3893180136 23 0 14006921 94338064 659613486 ; Parameters b (j) /
TDA (i) / 2 3 4 5 6 7 8
sediment removal efficiency for each bmps j P 0.8 W 0.75 I 0.90 F 0.85 N 0 /
total drainage area (acre) in subcatchment i 1 2650.1444 833.2475 1473.7901 4002.3006 4821.1036 1068.8153 6895.0845 1494.0038
323
5 9126724632 24230884640 3971197960 1148041352 4957760560 4831208392 55891606240 59647348000 15629873136 19740323840 7294212952 13066624744 5858140680 12593528288 9007883528 11533937376 19718551424 10951525248 14086299560 19194652664 30558493040 11831947320 2035267304
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1841.4918 2121.3342 2909.2020 2938.9291 1916.4102 2248.0886 1065.0726 1167.3542 302.4646 289.5074 85.7685 211.9907 4005.6504 699.8365 1567.6489
UDA (j) / P W I F N
/
unit drainage area (acre) for bmps j installation 10 10 3.5 3.5 0.000000001 /
UA (j) / W I F N
bmps j unit installation area (acre) P 0.25 0.4 0.105 0.15 0.0/
d (s) /1 2 3 4 5
the number of days of storm s 226.10 68.50 44.20 19 7.2/;
Table c (i, j) cost (USD) of bmps j in subcatchment i P W I 1 7850.97 31679.54 101819.35 2 7817.45 31646.02 101785.83 3 7497.36 31325.93 101465.74 4 7673.39 31501.96 101641.77 5 7657.55 31486.12 101625.93 6 7574.43 31403.00 101542.81 7 7643.73 31472.30 101612.11 8 7763.51 31592.08 101731.89 9 7739.34 31567.92 101707.73 10 7695.90 31524.47 101664.28 11 7548.55 31377.12 101516.93 12 7801.62 31630.19 101770.00 13 7548.47 31377.05 101516.86 14 7850.99 31679.56 101819.37 15 7631.50 31460.07 101599.88 16 7617.30 31445.87 101585.68 17 7860.93 31689.50 101829.31 18 8314.29 32142.86 102282.67 19 8296.81 32125.39 102265.20 20 7799.62 31628.19 101768.00
324
F 54302.51 54269.00 53948.91 54124.94 54109.10 54025.97 54095.27 54215.06 54190.89 54147.45 54000.10 54253.16 54000.02 54302.53 54083.04 54068.85 54312.48 54765.83 54748.36 54251.16
N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21 22 23 ;
7670.65 8128.48 8001.72
31499.22 31957.05 31830.29
101639.03 102096.86 101970.10
54122.20 54580.02 54453.27
Table amax (i,j) maximum area (acre) for each bmps in subcatchment i P W I F 1 1061.62 0.00 6.83 183.53 2 358.15 0.00 3.25 60.96 3 147.72 33.64 0.00 63.25 4 1646.24 3.53 1.28 157.39 5 2672.80 1.49 51.14 335.59 6 388.98 0.00 5.22 89.97 7 3392.45 0.00 20.54 557.39 8 651.15 0.00 0.19 150.22 9 175.24 25.92 0.99 41.98 10 760.62 0.00 2.23 148.49 11 165.35 9.92 0.00 65.76 12 142.50 53.16 0.00 65.37 13 385.55 20.23 0.00 156.86 14 60.94 13.03 0.63 19.68 15 43.47 2.77 0.00 29.02 16 104.61 24.03 0.00 40.36 17 3.73 0.00 0.00 2.66 18 5.45 0.00 0.20 2.62 19 0.83 0.00 0.10 0.29 20 63.99 0.00 0.00 15.37 21 2005.63 0.00 125.65 304.53 22 7.63 0.00 0.73 3.53 23 185.34 0.00 72.95 91.22 ;
0 0 0
N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Scalar TMDL total maximum daily load (annual load (mg)) M a very large number
/2023610000000/ /10E10/;
Variables z
total cost of setup bmps technologies
; Positive Variables r (i, j) ns (i,s) a (i, j)
ratio of different bmps j in each subcatchment i net sediment concentration after bmps in subcatchment i area required for bmps j setup in subcatchment i
x (i, j)
if selected x=1 not x=0;
; Binary Variables Equations cost cns (i,s) ca (i, j) ctda (i, j) sr (i) cTMDL cr (i, j) conarmax (i,j) cona1 (i)
define objective function net sediment loading after bmps in subcatchment i area required for bmps j setup in subcatchment i minimum drainage area of BMPs sum of share in subcatchment i constraint of TMDL all BMPs should less than their area limited
325
cona2 (i) cona3 (i) cona4 (i) cona5 (i) cona6 (i) ; ctda (i, j).. cr (i,j).. cost .. cns (i,s) .. sr (i) .. ca (i, j) .. cTMDL.. ; *All Types conarmax(i,j)..
((r (i, j) * TDA (i)) / UDA (j)) =g= 1 - (1-x (i,j))*M; r (i, j) =l= x (i, j); z =e= sum ((i, j), c (i, j) * a (i, j)); ns (i,s) =e= sum (j, r (i,j) * GS (i,s) * (1-b(j))); sum (j, r (i, j)) =e= 1; a (i, j) =e= r (i, j) * (TDA (i) / UDA (j)) * UA (j); sum (i, sum (s, d(s)* ns (i, s)))=l= TMDL;
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).. cona3 (i) $ subb (i)..
a(i,'F') + a(i,'I') =l= amax (i,'F'); 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).. cona6 (i) $ subd (i)..
a(i,'F') + a(i,'I') =l= amax (i,'F'); 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) /1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 /
gross sediment concentration in subcatchment i
20121 53420 8755 2531 10930 10651 123220 131500 34458 43520 16081 28807 12915 27764 19859 25428 43472 24144 31055 42317 67370 26085 4487 b (j) /
TDA (i) / 2 3 4 5 6 7 8 9 10 11 12 13 14
sediment removal efficiency for each bmps j P 0.8 W 0.75 I 0.90 F 0.85 N 0 /
total drainage area in subcatchment i 1 2650.1444 833.2475 1473.7901 4002.3006 4821.1036 1068.8153 6895.0845 1494.0038 1841.4918 2121.3342 2909.2020 2938.9291 1916.4102 2248.0886
328
15 16 17 18 19 20 21 22 23
1065.0726 1167.3542 302.4646 289.5074 85.7685 211.9907 4005.6504 699.8365 1567.6489
UDA (j) / P W I F N UA (j) / W I F N
/
unit drainage area for bmps j installation 10 10 3.5 3.5 0.000000001 /
bmps j unit installation area P 0.25 0.4 0.105 0.15 0.0/
;
Table c (i, j) cost of bmps j in subcatchment i P W 1 7850.97 31679.54 2 7817.45 31646.02 3 7497.36 31325.93 4 7673.39 31501.96 5 7657.55 31486.12 6 7574.43 31403.00 7 7643.73 31472.30 8 7763.51 31592.08 9 7739.34 31567.92 10 7695.90 31524.47 11 7548.55 31377.12 12 7801.62 31630.19 13 7548.47 31377.05 14 7850.99 31679.56 15 7631.50 31460.07 16 7617.30 31445.87 17 7860.93 31689.50 18 8314.29 32142.86 19 8296.81 32125.39 20 7799.62 31628.19 21 7670.65 31499.22 22 8128.48 31957.05 23 8001.72 31830.29 ;
I 101819.35 101785.83 101465.74 101641.77 101625.93 101542.81 101612.11 101731.89 101707.73 101664.28 101516.93 101770.00 101516.86 101819.37 101599.88 101585.68 101829.31 102282.67 102265.20 101768.00 101639.03 102096.86 101970.10
F 54302.51 54269.00 53948.91 54124.94 54109.10 54025.97 54095.27 54215.06 54190.89 54147.45 54000.10 54253.16 54000.02 54302.53 54083.04 54068.85 54312.48 54765.83 54748.36 54251.16 54122.20 54580.02 54453.27
Table amax (i,j) maximum area for each bmps in subcatchment i P W I F 1 1061.62 0.00 6.83 183.53 2 358.15 0.00 3.25 60.96 3 147.72 33.64 0.00 63.25 4 1646.24 3.53 1.28 157.39 5 2672.80 1.49 51.14 335.59 6 388.98 0.00 5.22 89.97
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N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N 0.00 0.00 0.00 0.00 0.00 0.00
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ;
3392.45 651.15 175.24 760.62 165.35 142.50 385.55 60.94 43.47 104.61 3.73 5.45 0.83 63.99 2005.63 7.63 185.34
0.00 0.00 25.92 0.00 9.92 53.16 20.23 13.03 2.77 24.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00
20.54 0.19 0.99 2.23 0.00 0.00 0.00 0.63 0.00 0.00 0.00 0.20 0.10 0.00 125.65 0.73 72.95
557.39 150.22 41.98 148.49 65.76 65.37 156.86 19.68 29.02 40.36 2.66 2.62 0.29 15.37 304.53 3.53 91.22
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Scalar TMDL total maximum daily load M a very large number M1 Multiplier of TMDL;
/0/ /10E10/
Variables z
total cost of setup bmps technologies
; Positive Variables r (i, j) ns (i) a (i, j)
ratio of different bmps j in each subcatchment i net sediment concentration after bmps in subcatchment i area required for bmps j setup in subcatchment i;
x (i, j)
if selected x=1 not x=0;
Binary Variables 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).. cr (i,j).. cost .. cns (i) .. sr (i) ..
((r (i, j) * TDA (i)) / UDA (j)) =g= 1 - (1-x (i,j))*M; r (i, j) =l= x (i, j); z =e= sum ((i, j), c (i, j) * a (i, j)); ns (i) =e= sum (j, r (i, j) * GS (i) * (1-b (j))); sum (j, r (i, j)) =e= 1;
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ca (i, j) .. * cpq (k) .. cTMDL.. *All Types conarmax(i,j)..
a (i, j) =e= r (i, j) * (TDA (i) / UDA (j)) * UA (j); sum (i, t (i, k) * ns (i))* 453592/ RO(k) =l= EQS; sum (i, ns (i)) =l= TMDL + M1; 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).. cona3 (i) $ subb (i)..
a(i,'F') + a(i,'I') =l= amax (i,'F'); 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).. cona6 (i) $ subd (i)..
a(i,'F') + a(i,'I') =l= amax (i,'F'); 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/; );
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