Neshanic River Watershed Restoration Plan

SWAT Modeling Analysis for the Neshanic River Watershed

Submitted by

Department of Chemistry and Environmental Science New Jersey Institute of Technology

Newark, New Jersey

September 2010

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Table of Contents

1. Background ................................................................................................................. 1

2. The Neshanic River Watershed ................................................................................. 1

3. Water Quality Criteria ............................................................................................... 3

4. Pollutant Source Characterization ............................................................................ 5 4.1. Point Sources ........................................................................................................ 6 4.2. Nonpoint Sources ................................................................................................. 6

4.2.1. Row Crop and Other Agricultural Lands ................................................. 6 4.2.2. Livestock .................................................................................................. 7 4.2.3. Wildlife ..................................................................................................... 8 4.2.4. Urban / Industrial Lands ........................................................................... 9 4.2.5. Household Sewage Treatment Systems (HSTS) .................................... 10

5. Modeling Process ...................................................................................................... 14 5.1. Introduction to SWAT ........................................................................................ 14 5.2. Input Data ........................................................................................................... 18

5.2.1. Digital Elevation Model Data ................................................................. 18 5.2.2. Soil Data ................................................................................................. 19 5.2.3. Land Use Data ........................................................................................ 21 5.2.4. Weather Data .......................................................................................... 23 5.2.5. Streamflow and Water Quality Data ...................................................... 23 5.2.6. Manure Content Data ............................................................................. 24

5.3. Model Setup ........................................................................................................ 24 5.4. Baseline Scenario of Pollutant Sources and Management Practices .................. 26 5.5. Model Calibration and Validation ...................................................................... 29

5.5.1. Flow ........................................................................................................ 30 5.5.2. Water Quality Calibration ...................................................................... 32 5.5.3. Crop Yield Calibration ........................................................................... 35

6. Calibration Results and Discussion ......................................................................... 36 6.1. Flow Calibration ................................................................................................. 36 6.2. Sediment Calibration .......................................................................................... 41 6.3. Nutrient Calibration ............................................................................................ 47 6.4. Bacteria calibration ............................................................................................. 63 6.5. Crop Yield Calibration ....................................................................................... 70

7. Baseline Results ......................................................................................................... 72 7.1. Stream flow and Water Balance ......................................................................... 72

7.1.1. Watershed Streamflow Discharges ......................................................... 72 7.1.2. Water Yields, Balance and Source Assessment ..................................... 74

7.2. Sediment Loading and Yields ............................................................................ 79 7.2.1. Watershed Sediment Loading ................................................................. 79

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7.2.2. Sediment Yields and Source Assessment ............................................... 80 7.3. Nitrogen Loading and Yields ............................................................................. 86

7.3.1. Watershed Nitrogen Loading ................................................................. 86 7.3.2. Nitrogen Yields and Source Assessment ................................................ 87

7.4. Phosphorus Loading and Yields ......................................................................... 92 7.4.1. Watershed Phosphorus Loading ............................................................. 92 7.4.2. Phosphorus Yields and Source Assessment ........................................... 93

7.5. Fecal Coliform Loading and Yields ................................................................... 98 7.5.1. Watershed Fecal Coliform Loading ....................................................... 98 7.5.2. Fecal Coliform Yields and Source Assessment ...................................... 99

7.6. E. coli Loading and Yields ............................................................................... 103 7.6.1. Watershed E. Coli Loading .................................................................. 103 7.6.2. E. Coli Yields and Source Assessment ................................................. 104

7.7. TMDL Targets .................................................................................................. 108 7.7.1. TMDL and Load Duration Curve ......................................................... 108 7.7.2. TMDL Targets of the Neshanic River watershed ................................. 110

7.8. Critical Areas of Pollutant Loads Reduction .................................................... 120

8. BMP Scenarios ........................................................................................................ 121 8.1. Definition of BMP Scenarios ........................................................................... 121

8.1.1. Single-focus Management Scenarios ................................................... 121 8.1.2. Combinational Management Scenarios ................................................ 123

8.2. Load Reductions of BMP Scenarios ................................................................. 127 8.2.1. Single-focus BMP Scenario Results ..................................................... 127 8.2.2. Combinational BMP Scenario Results ................................................. 128

9. Conclusions .............................................................................................................. 133

10. References ................................................................................................................ 135

Appendix A. Management Schedules for Crops and Lawns .......................... 139

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List of Tables

Table 4.1. Estimated manure production, fecal coliform and E. coli concentrations for livestock .............................................................................................................................. 7

Table 4.2. Manure production, nutrient and pathogen loads from livestock in the Neshanic River Watershed .................................................................................................. 8

Table 4.3. Estimated manure production, fecal coliform and E. coli concentrations for wildlife ................................................................................................................................ 9

Table 4.4. Manure production, nutrient and pathogen loads from wildlife in the Neshanic River Watershed.................................................................................................................. 9

Table 4.5. Estimated septic tanks and failures in Hunterdon County ............................... 10

Table 4.6. Pollutant loads from failing septic systems into streams ................................. 13

Table 5.1. Soils and area distributions .............................................................................. 20

Table 5.2. Land uses and are distributions* ...................................................................... 23

Table 5.3. Designed HRUs with cow and horse grazing .................................................. 27

Table 5.4. Designed HRUs with manure application ....................................................... 28

Table 5.5. Hydrology and sediment calibration parameters and their final calibrated values ................................................................................................................................ 31

Table 5.6. Nutrient calibration parameters and their final calibrated values .................... 34

Table 5.7. Bacterial calibration parameters and their final calibrated values ................... 35

Table 6.1. Calibration results for flows at Reaville from 1997 to 2002 ........................... 38

Table 6.2. Daily flow calibration and validation at Reaville for each year ...................... 39

Table 6.3. Validation results for flows at Reaville from 2003 to 2008 ............................ 40

Table 6.4. Statistics of TSS loading calibration and validation at Reaville ...................... 44

Table 7.1. Base flow fractions from observed stream flow at the Reaville station .......... 73

Table 7.2. Water balance components on an annual average basis for the Neshanic river watershed .......................................................................................................................... 75

Table 7.3. Average annual water yields of subbasins during 1997- 2008 ........................ 76

Table 7.4. Average annual yields of land uses in the Neshanic River Watershed ............ 77

Table 7.5. Average annual yields per unit area of land uses in the Neshanic River Watershed ......................................................................................................................... 78

Table 7.6. Average annual water yields of land uses in subbasins during 1997- 2008 .... 79

Table 7.7. Average annual sediment yields of subbasins during 1997- 2008................... 81

Table 7.8. Average annual sediment yields of land uses in subbasins during 1997- 200885

Table 7.9. Average annual TN yields of subbasins during 1997- 2008 ............................ 88

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Table 7.10. Average annual nitrogen yields of land uses in subbasins during 1997- 2008........................................................................................................................................... 91

Table 7.11. Average annual TP yields of subbasins during 1997- 2008 .......................... 94

Table 7.12. Average annual phosphorus yields of land uses in subbasins during 1997- 2008................................................................................................................................... 97

Table 7.13. Average annual fecal coliform yields of subbasins during 1997- 2008 ...... 100

Table 7.14. Average annual E. coli yields of subbasins during 1997- 2008 ................... 105

Table 7.15. Frequencies of TMDL exceedances and target reduction percentages ........ 111

Table 7.16. Land classification for load reduction ......................................................... 120

Table 8.1. Definition of BMP scenarios ......................................................................... 126

Table 8.2. Reductions of sediment yields and loads under BMP scenarios ................... 129

Table 8.3. Reductions of TN yields and loads under BMP scenarios ............................. 130

Table 8.4. Reductions of TP yields and loads under BMP scenarios ............................. 131

Table 8.5. Reductions of fecal coliform loads under BMP scenarios ............................. 132

Table 8.6. Reductions of E. coli loads under BMP scenarios ......................................... 133

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List of Figures

Figure 2.1. The Neshanic River watershed and study area ................................................. 2

Figure 4.1. Septic system areas in the Neshanic River Watershed ................................... 12

Figure 5.1. Nitrogen Processes Modeled in SWAT (USDA-ARS, 1999). ....................... 16

Figure 5.2. Phosphorus Processes Modeled in SWAT (USDA-ARS, 1999). ................... 17

Figure 5.3. Elevation map of the Neshanic River Watershed ........................................... 18

Figure 5.4. Soils of the Neshanic River Watershed .......................................................... 19

Figure 5.5. Land use of the Neshanic River Watershed ................................................... 22

Figure 5.6. Delineation of the Neshanic River Watershed ............................................... 25

Figure 6.1. Annual observed and simulated stream flows at Reaville .............................. 37

Figure 6.2. Monthly observed and simulated stream flows at Reaville ............................ 37

Figure 6.3. Daily observed and simulated stream flows at Reaville during calibration period ................................................................................................................................ 38

Figure 6.4. Filtered monthly base flows of observed and simulated stream flows at Reaville ............................................................................................................................. 38

Figure 6.5. Observed and simulated and daily stream flows and precipitation during 1999........................................................................................................................................... 39

Figure 6.6. Daily observed and simulated stream flows at Reaville during validation period ................................................................................................................................ 40

Figure 6.7. Observed versus simulated stream flow duration curves at Reaville, 1997 to 2008................................................................................................................................... 41

Figure 6.8. Observed instantaneous stream flows, TSS concentrations and loads based on water quality sampling at Reaville .................................................................................... 42

Figure 6.9. Observed instantaneous and daily flows loads and SWAT simulated daily flows at Reaville on water quality sampling days ............................................................ 43

Figure 6.10. Simulated and USGS measured TSS concentrations at Reaville during the calibration period .............................................................................................................. 43

Figure 6.11. Simulated and USGS measured TSS loads at Reaville during the calibration period ................................................................................................................................ 43

Figure 6.12. Simulated and USGS measured TSS concentrations at Reaville during the validation period ............................................................................................................... 44

Figure 6.13. Simulated and USGS measured TSS loads at Reaville during the validation period ................................................................................................................................ 45

Figure 6.14. Simulated and project measured TSS concentrations at Reaville (N1) ........ 45

Figure 6.15. Simulated and project measured TSS concentrations at FN1 ...................... 45

Figure 6.16. Simulated and project measured TSS concentrations at SN1 ...................... 46

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Figure 6.17. Simulated and project measured TSS concentrations at TN3 ...................... 46

Figure 6.18. Simulated and project measured TSS concentrations at TN3a .................... 46

Figure 6.19. Simulated and project measured TSS concentrations at UNT1 ................... 47

Figure 6.20. Simulated and project measured TSS concentrations at UNT2 ................... 47

Figure 6.21. Simulated and USGS measured ammonia nitrogen concentrations at Reaville during the calibration period ............................................................................................. 48

Figure 6.22. Simulated and USGS measured ammonia nitrogen concentrations at Reaville during the validation period .............................................................................................. 48

Figure 6.23. Simulated and USGS measured nitrite + nitrate concentrations at Reaville during the calibration period ............................................................................................. 49

Figure 6.24. Simulated and USGS measured nitrite + nitrate concentrations at Reaville during the validation period .............................................................................................. 49

Figure 6.25. Simulated and USGS measured TN concentrations at Reaville during the calibration period .............................................................................................................. 49

Figure 6.26. Simulated and USGS measured TN concentrations at Reaville during the validation period ............................................................................................................... 50

Figure 6.27. Simulated and USGS measured MinP concentrations at Reaville during the calibration period .............................................................................................................. 50

Figure 6.28. Simulated and USGS measured MinP concentrations at Reaville during the validation period ............................................................................................................... 50

Figure 6.29. Simulated and USGS measured TP concentrations at Reaville during the calibration period .............................................................................................................. 51

Figure 6.30. Simulated and USGS measured TP concentrations at Reaville during the validation period ............................................................................................................... 51

Figure 6.31. Simulated and project measured ammonia nitrogen concentrations at Reaville (N1) ..................................................................................................................... 52

Figure 6.32. Simulated and project measured ammonia nitrogen concentrations at FN1 52

Figure 6.33. Simulated and project measured ammonia nitrogen concentrations at SN1 52

Figure 6.34. Simulated and project measured ammonia nitrogen concentrations at TN3 53

Figure 6.35. Simulated and project measured ammonia nitrogen concentrations at TN3a........................................................................................................................................... 53

Figure 6.36. Simulated and project measured ammonia nitrogen concentrations at UNT1........................................................................................................................................... 53

Figure 6.37. Simulated and project measured ammonia nitrogen concentrations at UNT2........................................................................................................................................... 54

Figure 6.38. Simulated and project measured nitrite +nitrate concentrations at Reaville (N1) ................................................................................................................................... 54

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Figure 6.39. Simulated and project measured nitrite +nitrate concentrations at FN1 ...... 54

Figure 6.40. Simulated and project measured nitrite +nitrate concentrations at SN1 ...... 55

Figure 6.41. Simulated and project measured nitrite +nitrate concentrations at TN3 ...... 55

Figure 6.42. Simulated and project measured nitrite +nitrate concentrations at TN3a .... 55

Figure 6.43. Simulated and project measured nitrite +nitrate concentrations at UNT1 ... 56

Figure 6.44. Simulated and project measured nitrite +nitrate concentrations at UNT2 ... 56

Figure 6.45. Simulated and project measured TN concentrations at Reaville (N1) ......... 56

Figure 6.46. Simulated and project measured TN concentrations at FN1 ........................ 57

Figure 6.47. Simulated and project measured TN concentrations at SN1 ........................ 57

Figure 6.48. Simulated and project measured TN concentrations at TN3 ........................ 57

Figure 6.49. Simulated and project measured TN concentrations at TN3a ...................... 58

Figure 6.50. Simulated and project measured TN concentrations at UNT1 ..................... 58

Figure 6.51. Simulated and project measured TN concentrations at UNT2 ..................... 58

Figure 6.52. Simulated and project measured MinP concentrations at Reaville (N1) ...... 59

Figure 6.53. Simulated and project measured MinP concentrations at FN1 .................... 59

Figure 6.54. Simulated and project measured MinP concentrations at SN1 .................... 59

Figure 6.55. Simulated and project measured MinP concentrations at TN3 .................... 60

Figure 6.56. Simulated and project measured MinP concentrations at TN3a .................. 60

Figure 6.57. Simulated and project measured MinP concentrations at UNT1 ................. 60

Figure 6.58. Simulated and project measured MinP concentrations at UNT2 ................. 61

Figure 6.59. Simulated and project measured TP concentrations at Reaville (N1) .......... 61

Figure 6.60. Simulated and project measured TP concentrations at FN1 ......................... 61

Figure 6.61. Simulated and project measured TP concentrations at SN1 ......................... 62

Figure 6.62. Simulated and project measured TP concentrations at TN3 ........................ 62

Figure 6.63. Simulated and project measured TP concentrations at TN3a ....................... 62

Figure 6.64. Simulated and project measured TP concentrations at UNT1 ...................... 63

Figure 6.65. Simulated and project measured TP concentrations at UNT2 ...................... 63

Figure 6.66. Simulated and USGS measured fecal coliform concentrations at Reaville during the calibration period ............................................................................................. 64

Figure 6.67. Simulated and USGS measured fecal coliform concentrations at Reaville during the validation period .............................................................................................. 64

Figure 6.68. Simulated and USGS measured E. coli concentrations at Reaville during the calibration period .............................................................................................................. 65

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Figure 6.69. Simulated and USGS measured E. coli concentrations at Reaville during the validation period ............................................................................................................... 65

Figure 6.70. Simulated and project measured fecal coliform concentrations at Reaville (N1) ................................................................................................................................... 66

Figure 6.71. Simulated and project measured fecal coliform concentrations at FN1 ....... 66

Figure 6.72. Simulated and project measured fecal coliform concentrations at SN1 ....... 66

Figure 6.73. Simulated and project measured fecal coliform concentrations at TN3 ....... 67

Figure 6.74. Simulated and project measured fecal coliform concentrations at TN3a ..... 67

Figure 6.75. Simulated and project measured fecal coliform concentrations at UNT1 .... 67

Figure 6.76. Simulated and project measured fecal coliform concentrations at UNT2 .... 68

Figure 6.77. Simulated and project measured E. coli concentrations at Reaville (N1) .... 68

Figure 6.78. Simulated and project measured E. coli concentrations at FN1 ................... 68

Figure 6.79. Simulated and project measured E. coli concentrations at SN1 ................... 69

Figure 6.80. Simulated and project measured E. coli concentrations at TN3 ................... 69

Figure 6.81. Simulated and project measured E. coli concentrations at TN3a ................. 69

Figure 6.82. Simulated and project measured E. coli concentrations at UNT1 ................ 70

Figure 6.83. Simulated and project measured E. coli concentrations at UNT2 ................ 70

Figure 6.84. Simulated and observed annual crop yields of corn and soybean. ............... 71

Figure 6.85. Simulated and observed annual crop yields of hay. ..................................... 71

Figure 7.1. Monthly stream flow and variation at the watershed outlet during 1997 – 2008........................................................................................................................................... 73

Figure 7.2. Monthly TSS loads and variation at the watershed outlet during 1997-2008 80

Figure 7.3. Annual sediment yields from lands in each subbasin ..................................... 82

Figure 7.4. Annual sediment yields from reaches in each subbasin ................................. 83

Figure 7.5. Source contributions for sediment average annual load ................................. 84

Figure 7.6. Contributions of sediment average annual yield from different land uses ..... 86

Figure 7.7. Monthly TN loads and variation at the watershed outlet during 1997-2008 .. 87

Figure 7.8. Annual nitrogen yields from lands in each subbasin ...................................... 89

Figure 7.9. Source contributions for TN average annual load .......................................... 90

Figure 7.10. Contributions of TN average annual yield from different land uses ............ 92

Figure 7.11. Monthly TP loads and variation at the watershed outlet during 1997-2008 93

Figure 7.12. Annual phosphorus yields from lands in each subbasin ............................... 95

Figure 7.13. Source contributions for TP average annual load ......................................... 96

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Figure 7.14. Contributions of TP average annual yield from different land uses ............. 98

Figure 7.15. Monthly fecal coliform loads and variation at the watershed outlet during 1997-2008 ......................................................................................................................... 99

Figure 7.16. Annual fecal coliform yields from cattle direct deposits in subbasins ....... 101

Figure 7.17. Annual fecal coliform yields from failing septic systems in subbasins ..... 102

Figure 7.18. Source contributions of average annual load for fecal coliform ................ 103

Figure 7.19. Monthly E. coli loads and variation at the watershed outlet during 1997-2008......................................................................................................................................... 104

Figure 7.20. Annual E. coli yields from cattle direct deposits in subbasins ................... 106

Figure 7.21. Annual E. coli yields from failing septic systems in subbasins ................. 107

Figure 7.22. Source contributions of average annual load for E. coli ............................ 108

Figure 7.23. TSS load duration curve at Reaville (N1) based on measured data ........... 112

Figure 7.24. TN load duration curve at Reaville (N1) based on measured data ............. 112

Figure 7.25. TP load duration curve at Reaville (N1) based on measured data ............. 113

Figure 7.26. Fecal coliform load duration curve at Reaville (N1) based on measured data......................................................................................................................................... 113

Figure 7.27. E. coli load duration curve at Reaville (N1) based on measured data ........ 114

Figure 7.28. TSS load duration curve at Reaville (N1) based on simulation ................. 114

Figure 7.29. TN load duration curve at Reaville (N1) based on simulation ................... 115

Figure 7.30. TP load duration curve at Reaville (N1) based on simulation .................... 115

Figure 7.31. Fecal coliform load duration curve at Reaville (N1) based on simulation . 116

Figure 7.32. E. coli load duration curve at Reaville (N1) based on simulation .............. 116

Figure 7.33. TSS load duration curve at watershed outlet based on simulation ............. 117

Figure 7.34. TN load duration curve at watershed outlet based on simulation .............. 117

Figure 7.35. TP load duration curve at watershed outlet based on simulation ............... 118

Figure 7.36. Fecal coliform load duration curve at watershed outlet based on simulation......................................................................................................................................... 118

Figure 7.37. E. coli load duration curve at watershed outlet based on simulation ......... 119

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1. Background

Federal guidance for Clean Water Act 319(h) grant funding calls for projects developing watershed management plans to quantify pollutant sources and locate critical areas in the watershed for prioritizing goals for restoration activity. In addition, projects should esti-mate load reductions for the proposed restoration activities. The Neshanic River Wa-tershed is located in a suburban area of Central New Jersey. Land and water resources in the watershed are adversely affected by rapid population growth, rampant urban devel-opment, and intense agricultural operations. Based upon numerous monitoring sources, including the New Jersey Department of Environmental Protection (NJDEP) Ambient Biomonitoring Network, the NJDEP/USGS water quality monitoring network, and the Metal Recon Program, the Neshanic River and its branches are impaired for dissolved oxygen, phosphorus, total suspended solids (TSS) and are on Sublists 5 of the New Jersey Integrated Water Quality Monitoring and Assessment Reports (NJDEP, 2004; NJDEP, 2006; NJDEP, 2008a) for aquatic life, drinking water, and industrial water and Sublists 4 for recreation impairments. The watershed is also experiencing higher occurrences of no/low stream flow in the Neshanic River in the late summer (Reiser, 2004). There is a need for hydrological and water quality research of the Neshanic River Watershed to make a comprehensive restoration plan that can better safeguard water resources, control soil loss, and reduce phosphorus and pathogen loadings. Although there are a number of studies of landscape, water budget, groundwater and surface water quality at the basin scale for the Raritan River Basin that contains the Neshanic River Watershed, there is none specifically investigating the hydrology and water quality restoration in the Neshan-ic River Watershed. A 319(h) Grant project funded by the New Jersey Department of En-vironmental Protection was initiated in 2006 for such purposes.

The objectives of this analysis are to evaluate current conditions for loading of se-diment, nutrients (nitrogen and phosphorous), fecal coliform and E. coli, and then to eva-luate the reduction of loads as a result of management alternatives and best management practices to be implemented. The Soil and Water Assessment Tool (SWAT) was used to support load assessment and reduction analysis with various management practices.

2. The Neshanic River Watershed

The Neshanic River Watershed (Figure 2.1) is located in Hunterdon County, Central New Jersey, encompassing Raritan, Delaware, East Amwell and Flemington townships and is a part of the Raritan River Basin. The Neshanic River is a tributary to the South Branch of the Raritan River which drains to the Atlantic Ocean. The watershed restoration plan-ning area is about 31 mi2 of the Neshanic River watershed, covering most of its drainage area, including Walnut Brook, First, Second and Third Neshanic River and the Neshanic River main branch immediately above the Back Brook entrance into the Neshanic River. The study area consists of five Hydrological Unit Code (HUC) 14 areas, namely, 02030105030010, 02030105030020, 02030105030030, 02030105030040 on the west and a potion of HUC 02030105030060 on the east.

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Figure 2.1. The Neshanic River watershed and study area

The climate of the region is humid subtropical, with typically hot and humid sum-mers and usually cold winters. According to weather data for the period 1955 – 2008, the air temperatures during summers (June to August) show average high of 81 – 86 °F (27 – 30 °C) and lows of 55 – 61 °F (13 – 16 °C) with temperatures exceed 90 °F (32 °C) on average 19 days each summer, though rarely exceed 100 °F (38 °C). The average high temperatures during winters (December to February) are 37 – 41 °F (3 – 5 °C) and aver-age lows are 19 – 29 °F (-7 – -5 °C), but temperatures could, for brief interludes, be as low as 10 – 20 °F(-12 – -7 °C) and sometimes rise to 50 – 60 °F (10 – 16 °C). Spring and autumn may feature wide temperature variations, ranging from chilly to warm, although they are usually mild with lower humidity than summer.

The mean annual precipitation of the catchment area is about 1218 mm (1955-2008), falling on an average of 104 days a year, uniformly spread through the year. Snowfall per winter season is about 5 – 30 inches (12 – 77 cm), but this varies from year to year. During winter and early spring in some years the watershed can experience nor'easters, which are capable of causing blizzards or flooding. There may also experience drought and rain-free period for weeks. Hurricanes and tropical storms (such as Hurricane Floyd in 1999) are rare. The annual mean evapotranspiration, groundwater

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recharge, and runoff of the Neshanic River watershed are estimated to be 609 mm (23.96 inches), 133 mm (5.25 inches) and 401 mm (15.78 inches), respectively, according to the a long-term water budget analysis for Raritan River Basin, which assumes that long-term stream base flow is equivalent to long-term groundwater recharge (below the plant root zone) except for the impacts of depletive and consumptive uses within the watershed (NJWSA, 2000).

Like many other parts in New Jersey, this watershed had been experiencing rapid suburbanization during the last two decades. Based on the land use/cover database com-piled by the New Jersey Department of Environmental Protection (NJDEP), the percen-tage of the urban land in the watershed had increased from 16.6 percent in 1986 to 30.7 percent in 2002. The increases in urban land primarily came from agricultural land in the watershed. While other land uses were relatively steady (forest: 20 percent; wetlands 10 percent; water 0.2 percent and barren 1.6 percent), the agricultural lands in the watershed had decreased from 51.4 percent in 1986 to 36.4 percent in 2002.

The Neshanic River is classified as FW2-NT, or freshwater (FW) non-trout (NT). “FW2” refers to water bodies that are used for primary and secondary contact recreation; industrial and agricultural water supply; maintenance, migration, and propagation of nat-ural and established biota; public potable water supply after conventional filtration treat-ment and disinfection; and any other reasonable uses. “NT” means those freshwaters that have not been designated as trout production or trout maintenance. NT waters are not suitable for trout due to physical, chemical, or biological characteristics, but can support other fish species (NJDEP, 2008a). The Neshanic River was considered to be one of the worst water bodies in terms of overall water quality in the Raritan River Basin, as it had either the highest concentrations of constituents or the highest frequency of not meeting water quality standards for 13 of the 17 constituents (Reiser, 2004).

3. Water Quality Criteria

The results of this analysis are for planning use. As such, water quality goals were identi-fied from available New Jersey surface water quality standards or from guidelines or rec-ommended target levels. The designed use for the water bodies in the Neshanic River Watershed is for total body contact recreational use during the recreation season. Indica-tors of water quality are constituents that are measured through analysis and used to esti-mate the status of a water body. The State of New Jersey has established surface water quality standards and drinking water quality standards for some of these constituents and the others assist with the assessment of the overall quality of the water (NJDEP, 2010b).

Total Suspended Solids

Total suspended solids (TSS) in surface waters occur primarily from storm water runoff, stream bank and channel erosion, dead plant matter, plankton, and re-suspension of sedi-ment into the water column. A high concentration of TSS negatively affects the surface water’s ecosystem and aesthetics. Fish and shellfish can be injured or killed from the TSS by abrasive injuries, clogging gills and respiratory passages, and by blanketing the bot-

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tom, killing eggs, young, and destroying spawning beds. The waters become cloudy and the system can develop noxious conditions, reducing the aesthetic value of the waters. Other pollutants, such as phosphorus and petroleum hydrocarbons, adsorb or bond to the particles therefore magnifying the impact the solids have on the surface water quality. TSS also interferes with the treatment processes for water purveyors. The surface water criterion is 25 mg/L in trout waters and 40 mg/L in non-trout waters. The Neshanic River is a non-trout river.

Total Phosphorus

Total phosphorus in surface water is a key nutrient for stimulating excessive growth of aquatic plants and algae, resulting in the eutrophication of water bodies. Phosphorus is a common element in igneous minerals and sediments and is found both in solution and adsorbed to particulates. Orthophosphate is the soluble form of phosphorous and is readi-ly available for uptake by aquatic plants and algae. Although phosphorus occurs in sur-face waters naturally through weathering of minerals and sediment, human influence has increased its presence. The use of orthophosphorus on lawns, gardens, and agricultural lands leads to its presence in runoff. Phosphorus also enters streams from wastewater treatment plant effluent. The surface water quality criterion for phosphorus is 0.1 mg/L, and this criterion shall not be exceeded in any stream unless it can be demonstrated that total phosphorus is not a limiting nutrient and will not render the waters unsuitable for the designated uses. A second surface water quality criterion of 0.05 mg/L exists for lakes, reservoirs, and streams at the point of entry to these water bodies. The NJDEP recently adopted an amendment to the NJ surface water criterion for total phosphorus to allow wa-tershed-based criteria in addition to site-specific criteria.

Ammonia plus Organic Nitrogen

Ammonia plus organic nitrogen, also called Total Kjeldahl Nitrogen or TKN, represents the reduced portion of total nitrogen in a stream. When a compound is reduced it gains an electron by bonding with hydrogen. Ammonia in natural waters results from either direct discharge, such as wastewater discharges and animal wastes, or the decomposition of ni-trogenous organic matter, such as detritus (dead plant matter). Ammonia is highly soluble in water and high concentrations are a concern for water purveyors because of increased treatment costs. Ammonia and organic nitrogen are both oxygen consumers and indica-tors of ecosystem health. No water quality criterion exists for ammonia plus organic nitrogen.

Nitrate plus Nitrite

Nitrate plus nitrite represents the oxidized form of nitrogen in the stream. When a com-pound is oxidized it loses an electron (by adding oxygen in this case). They are found in surface waters as a result from wastewater discharge, runoff from land application of fer-tilizers, and ground water polluted by fertilizers. Nitrate concentrations in surface waters tend to be higher than nitrite because nitrite rapidly oxidizes to nitrate. They are a prima-ry nutrient for rooted aquatic plants and algae and are a concern for water users. The drinking water standard for nitrate is 10 mg/L and nitrite is 1 mg/L. The surface water

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criterion for nitrate is the same as the drinking water standard and there is no criterion for nitrite. Nitrate is less toxic than either ammonia or nitrite, but can cause methemog-lobinemia (“blue baby” syndrome) in small children and fish.

Total Nitrogen

Total nitrogen is the sum of organic nitrogen, ammonia, and nitrate plus nitrite. The tar-get criterion for total nitrogen was set to 10 mg/L for this planning. Note that this crite-rion was selected for panning purpose only and is not meant to be used to set any dis-charge limitation on any point source or identify any water quality violation, but is used simply as a benchmark for achieving improved water quality conditions from the current observed level.

Fecal Coliform

An indication of the sanitary quality of a water body is determined from fecal coliform bacteria counts. Fecal coliform bacteria are used as an indicator of fecal contamination and of the possible existence of waterborne enteric pathogens. This is because coliform bacteria are derived from the digestive tract of mammals. Sources for fecal coliform in surface water are untreated wastewater, failing septic systems, and animal waste. High fecal coliform counts can render the effected water body unsuitable for swimming. In New Jersey’s previous surface water quality standards two surface water quality criteria were adopted that concentrations should not exceed: 1) a geometric mean of 200 colo-nies per 100 milliliters, and 2) 400 colonies per 100 milliliter in more than 10 percent of total samples collected in a 30 day period. This standard was still applied in this plan-ning.

E. coli

Recently, non-point sources have surpassed point sources as the major source of fecal contamination to surface waters. This creates a need to identify the source of fecal con-tamination from non-point pollution. Fecal coliform measurements do not provide infor-mation on the specific source of pollution, such as animal versus human. To address this need the USGS and NJDEP have begun monitoring for other indicator organisms such as Enterococcus bacteria, Escherichia coli (E. coli), and bacteriophages (viruses that infect bacteria) specific to humans or using DNA testing to determine species. In 2004, the fecal coliform standard in New Jersey for FW2 waters was replaced by a water quality stan-dard for E. coli. E. Coli levels shall not exceed a geometric mean of 126/100 ml or a single sample maximum of 235/100 ml. The Neshanic River violated both the previous coliform standard and the new E. coli standard. Because the goal of this planning is to meet current water quality standards, best management practice (BMP) selection and placement will be based on the E. coli standard.

4. Pollutant Source Characterization

In order to assess the loading conditions in the watershed for sediment, nutrients, fecal coliform and E. coli, an inventory of contributing sources was necessary.

6

4.1. Point Sources

According to the regulation in the United States, generally point sources include munici-pal wastewater (sewage), industrial wastewater discharges, municipal separate storm sewer systems (MS4) and industrial stormwater discharges (Public Law 100-4, 1987). These facilities are required to obtain National Pollution Discharge Elimination System (NPDES) permits or state/local permits. According to NJDEP’s point source surface dis-charges GIS data layer, there are only two permitted industrial point sources (Suburban Sunoco Inc, and Hess Station 30333) discharging treated petroleum products cleanup wastewater to the Neshanic River via a unnamed tributary and storm sewer, located near the watershed boundary with East Amwell Township, both at latitude 40° 26' 29.7" and longitude 74° 51' 26.5". Since their discharge levels were small, no point source was con-sidered in the plan.

4.2. Nonpoint Sources

Nonpoint source (NPS) pollution comes from diffuse sources that cannot be identified as entering the water body at a single location. These sources generally involve land activi-ties that contribute pollution to streams during wet weather events. Rain or snow-melt moves over and through the ground where pollutants have accumulated, transports the contaminants, and deposits them into nearby water bodies. Bacterial NPS pollution is generated by both human and non-human (animal) sources via land use activities. Non-point sources are predominately agriculture and non-regulated residential area that are outside storm sewer systems service.

Typical non-point sources of nutrients and pathogens in the Neshanic River Wa-tershed include, but are not limited to:

1 Fertilizer and manure application to croplands 1 Livestock grazing on pastureland 1 Livestock with direct access to streams 1 Wildlife 1 Urban land activities 1 Leaking/failing septic systems

4.2.1. Row Crop and Other Agricultural Lands

While many of the water quality problems were attributed to the rapid urban develop-ment, agriculture was still an important source of water pollution in the watershed. The Neshanic River watershed had the highest percentage of agricultural lands among all wa-tersheds in the Raritan River Basin. Therefore, controlling agricultural nonpoint source runoff is important for achieving the overall water quality goals in this watershed. The Neshanic River watershed was recognized as one of the priority watersheds to implement agricultural BMPs to improve water quality because of its relatively poor water quality and high percentage of agricultural lands (NJWSA, 2002).

Area-specific information of cropping activities was gathered that included crop frown, types of tillage, fertilizers and pesticides used. Corn and soybeans are the predo-

7

minant crops in the watershed and a small amount of farmland grows wheat/rye, hay and timothy. Nitrogen fertilizer to corn lands is primarily applied as anhydrous ammonia liq-uid fertilizer. Nitrogen fertilizer is also applied to rye, hay, timothy, pasture and orchard lands. Phosphorus is usually applied corn, soybean and other crop lands in granular form blended in various combinations with other nutrients. There is no spatial location data for tillage. However, reduced tillage is common for all crop lands in the watershed. For spe-cific operation schedules refer to tables in Appendix A.

4.2.2. Livestock

Livestock in the watershed include mainly beef cattle and horse. Manure from livestock was considered as a potential source of nutrients, fecal coliform and E. coli. Despite the lack of distribution information of livestock operations in the watershed, animal live tock numbers by county were obtained from the National Agricultural Statistics Service (NASS)’s 2007 agricultural census, and were proportioned to the Neshanic watershed based on total agricultural areas. Cow (beef and dairy cattle) and horse are selected as representative livestock since they generate much more manure amounts than other ani-mals. It was estimated that there were 560 cows and 408 horses in the Neshanic River watershed in average during the simulation period.

The number of animals, the amount of manure produced by each animal, and the concentrations of nutrients and bacteria in the manure were used to calculate the impact of livestock (Table 4.1). The manure productions, fecal coliform contents and loading rates of cows and horses were calculated from reported daily manure production and fec-al coliform amounts from standards for typical livestock (ASAE, 2003). E. coli contents of all animals were calculated as 62.5% of the contents of fecal coliform of correspond-ing animals (IDNR, 2006).

Table 4.1. Estimated manure production, fecal coliform and E. coli concentrations for lives-tock

Animal Manure

production (kg/d/animal)

Fecal coliform in fecal matter (cfu/kg feces)

E. coli in fecal matter (cfu/kg feces)

Fecal coliform loading rate

(cfu/d/animal)

E. coli loading rate

(cfu/d/animal)

Cow 20.880 4.83E+09 3.02E+09 1.01E+11 6.30E+10 Horse 22.950 1.80E+07 1.13E+07 4.14E+08 2.59E+08

The manure production, nutrient and pathogen loads from livestock in the Neshanic River Watershed are listed in Table 4.2, in which dry manure productions were calculated based on the values for typical mature beef cow and horse in ASAE (2003). The nutrient loads were calculated based on the content percentages in dry manure given in SWAT fertilizer database.

8

Table 4.2. Manure production, nutrient and pathogen loads from livestock in the Neshanic River Watershed

Animal Number Manure

production (kg/d)

Dry manure

production (kg/d)

Fecal coliform

load (cfu/d)

E. coli load

(cfu/d)

Min-N (kg/d)

Min-P (kg/d)

Org-N (kg/d)

Org-P (kg/d)

Cow 560 11692.800 1713.600 5.64E+13 3.53E+13 17.136 6.854 51.408 11.995 Horse 408 9363.600 2754.000 1.69E+11 1.06E+11 16.524 2.754 38.556 8.262

4.2.3. Wildlife

Wildlife in the Neshanic River Watershed include many animals, including but not li-mited to deer, raccoons, rodents, geese, and ducks, most of them difficult to inventory. Although New Jersey’s Landscape Project maps rare and imperiled wildlife habitats, there is no inventory for density distribution of common wildlife at the county level in New Jersey. Hunting reports in previous years were utilized to quantify wildlife popula-tion and densities. Nutrient and fecal contributions by wildlife in the Neshanic River Wa-tershed were estimated. Deer and goose were considered as the representatives. New Jer-sey’s white-tailed herd is a major component of the landscape throughout all but the most urbanized areas of the state, and the estimated annual populations during 1984 and 2006 range from 120,000 to 200,000, or 13.7 to 22.9 per square mile (NJDEP, 2008b). Ap-proximately, 64,000 deer are harvested annually from about 5,000 square miles of deer range in the Garden State. Each square mile yields an average of 4 antlered bucks and 8 antlerless deer (NJDEP, 2010a). The average density of deer in the Neshanic River Wa-tershed was assumed to be 20 deer per square mile across the whole watershed.

Canada geese are grazers that have a clear preference for tender, mowed and ferti-lized turf grass, although they also feed heavily on small grains such as corn and soy-beans during the fall and winter. They prefer to feed in large open areas with few obstruc-tions that give the birds a 360-degree view of potential predators. Giant Canada geese differ from seasonally migrating interior Canada geese. The NJDEP’s Division of Fish and Wildlife conducts a breeding population survey each spring, when only resident spe-cies are present in New Jersey because the migrating geese already have traveled to northern breeding grounds. The population of “resident” Canada geese in New Jersey was estimated at approximately 98,000, or 11.2 per square mile (NJDEP, 2010c). Subur-ban development often leads to an increase in lawns, recreational fields and other grassy areas, all viewed as appealing habitat by Canada geese. Therefore, as development con-tinues in New Jersey, it is likely that the population of resident geese will continue to in-crease. To reflect the seasonal immigration of interior Canada geese during winter and the hatching and growing of young residential Canada geese during spring and summer, the average goose density in the watershed was assumed to be double of resident geese, about 22 geese per square mile throughout the year.

The numbers of these animals and amount of manure produced by each type, along with nitrogen, phosphorus, fecal coliform, and E. coli concentrations were used to calcu-late loads from wildlife (Table 4.3, Table 4.4). Manure productions of deer and goose

9

were taken from the Salt Creek Watershed TMDL study (WHPA, 2004). Daily fecal coli-form loading rates of deer and goose were from TMDL for Pathogens in Beeds Lake Franklin County, Iowa (IDNR, 2006). The nutrient loads were calculated based on the content percentages in dry manure given in SWAT fertilizer database. This was achieved by assuming deer and goose have the same dry manure percentages and nutrient concen-trations as goat and duck, respectively.

Table 4.3. Estimated manure production, fecal coliform and E. coli concentrations for wild-life

Animal Density (#/mi2)

Manure pro-duction

(kg/d/animal)

Fecal coliform in fecal matter (cfu/kg feces)

E. coli in fecal matter (cfu/kg feces)

Fecal coliform loading rate

(cfu/d/animal)

E. coli in fecal matter loading rate

(cfu/d/animal) Deer 20 0.772 6.48E+08 4.05E+08 5.00E+08 3.13E+08

Goose 22 0.163 3.01E+11 1.88E+11 4.90E+10 3.06E+10

Table 4.4. Manure production, nutrient and pathogen loads from wildlife in the Neshanic River Watershed

Animal Number Manure

production (kg/d)

Dry manure

production (kg/d)

Fecal coliform

load (cfu/d)

E. coli load (cfu/d)

Min-N (kg/d)

Min-P (kg/d)

Org-N (kg/d)

Org-P (kg/d)

Deer 608 469.376 148.827 3.04E+11 1.90E+11 1.935 0.446 3.274 0.744 Goose 668 108.884 30.685 3.27E+13 2.05E+13 0.706 0.245 0.767 0.276

4.2.4. Urban / Industrial Lands

Runoff from urban and industrial areas can potentially contribute nutrients and bacteria to streams and rivers. The nutrients and bacteria can come from such sources as pet feces, urban wildlife, sanitary sewer cross-connections, and deficient solid waste collection. To assess the impact of the urban runoff, the built-up areas are classified into four sub-categories and the loading rates for each of these divisions can be calculated based on published accumulation rates (USEPA, 2000). Unfortunately, similar accumulation rates are not available for E. coli.

E. coli is a subset of fecal coliform, meaning measurement of fecal coliform in-cludes all measurement of E. coli, along with other pathogens. The amount of E. coli will be lower than the amount of fecal coliform in manure. Therefore, the low-end of the range for the fecal coliform accumulation rates can be used as estimation for E. coli. The accumulation rates for fecal coliform range from 1.8x108– 2.1x1010

cfu/ac/day (USEPA, 2000). If the accumulation rate for E. coli in urban areas is designated as 1.8x108

cfu/ac/day, assuming E. coli is 62.5% of fecal coliform, accumulation rate for fecal coli-form is about 2.88 x 108 cfu/ac/day.

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4.2.5. Household Sewage Treatment Systems (HSTS)

Household sewage treatment systems (e.g., septic systems) provide the potential to deliv-er nutrient and bacteria loads to surface waters due to system failures caused by improper maintenance, malfunctions, and/or close proximity to a stream.

The 2000 census data and GIS layers were utilized to estimate failing septic sys-tems in the watershed. From GIS analysis, there are 1508 and 1188 households located in sewer service areas and non-sewer service areas, respectively (Figure 4.1). Note that even within sewer service areas, there are households utilizing septic systems. The 2000 cen-sus indicates that the household sizes of Raritan, Delaware and east Amwell were 2.81, 2.72 and 2.80, respectively.

No study clearly estimates how many septic systems fail or do not properly func-tion in the watershed. Generally, the failures of septic systems are attributed to older homes. However, maintenance also affects septic failure rates. The failure rate of these units can be estimated from their construction dates, also determined from the 2000 cen-sus data. Three categories of units were considered: before 1970, 1970-1989, and after 1989. The rates of failure were assumed to be 40%, 20%, and 5%, respectively. These rates have been used in Virginia for the development of TMDLs and were backed up by studies done in that area that found 30 % of all septic tanks were either failing or not functioning at all (VDEQ, 2002). Using these rates and the number of septic systems in Hunterdon County, we estimated the number of failing systems in Table 1. Considering 10 years have passed the 2000 census, the failure rate of septic systems is estimate as 30% to account for more failures of houses built after year 2000 in the Neshanic river watershed.

Table 4.5. Estimated septic tanks and failures in Hunterdon County

Structure age Number of units Failure rate (%) Number failed Pre 1970 20,410 40 8164

1970-1989 16,902 20 3380 Post 1989 7,720 5 386

Total 45,032 26.5 11930

The nutrient and pathogen loads from failed septic systems were defined as the amount per day reaching streams, and calculated obtained based on the following assump-tions:

One septic system per household for those located in non-sewer service areas and those located in sewer service areas but do not discharge wastewater to sewer collection sys-tems;

Assume 20 percent of households located in sewer service areas utilize septic systems; Average number of persons served by each system: 2.8; Failure percentage: 30%; Septic system effluent discharge rate of 70 gallons/person/day Average pollutant concentrations of septic sewage at the point when it reaches the stream

was not available, so the concentrations in septic tank effluents were used, 40 mg/l TN,

11

12 mg/l TP, 1 x 106 cfu/100mL fecal coliform, and 6.3 x 105 cfu/100mL E. coli, re-spectively.

Daily loads discharged by a failing septic system: 29.674 g/d TN, 8.902 g/d TP, 7.42 x 109 cfu/d fecal coliform, and 4.67 x 109 cfu/100mL E. coli, respectively.

A further assumption was made after calibration testing: only septic systems within 200 meters distance of streams can have failing tank effluents reach streams, and only 20% effluent loads enter streams due to the removal on pathways.

Based on the above assumptions, as shown in Table 4.6, the total loads of effluents

from failing septic systems and entering streams in the watershed were estimated as 0.973 kg/d TN, 0.292 kg/d TP, 2.433E+11cfu/d fecal coliform, and 1.533E+11 cfu/d E. coli.

Note that, the nonpoint source loads presented in the Section Pollutant Source Cha-racterization are estimates at overland deposit sites. They are most likely higher than the loads received by the streams, because nutrient and pathogen loads would probably be reduced from detrimental environmental conditions as it moved from the septic tank to the stream. However, there is evidence that E. coli can survive and even reproduce in the natural environment given the right environmental conditions (WHPA, 2004).

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Figure 4.1. Septic system areas in the Neshanic River Watershed

13

Table 4.6. Pollutant loads from failing septic systems into streams

Subbasin Area (ha)

Number of septic systems

Failing systems close to streams

TN load

(kg/d)

TP load

(kg/d)

Fecal coliform

load (cfu/d)

E. coli load

(cfu/d)

1 599.026 190 18 0.107 0.032 2.671E+10 1.683E+10 2 278.54 305 5 0.030 0.009 7.419E+09 4.674E+09 3 437.737 111 7 0.042 0.012 1.039E+10 6.543E+09 4 293.92 77 2 0.012 0.004 2.967E+09 1.869E+09 5 134.592 61 0 0.000 0.000 0.000E+00 0.000E+00 6 448.664 152 16 0.095 0.028 2.374E+10 1.496E+10 7 387.066 418 14 0.083 0.025 2.077E+10 1.309E+10 8 298.953 157 22 0.131 0.039 3.264E+10 2.056E+10 9 175.867 2 0 0.000 0.000 0.000E+00 0.000E+00

10 234.944 72 0 0.000 0.000 0.000E+00 0.000E+00 11 355.807 17 1 0.006 0.002 1.484E+09 9.347E+08 12 387.757 216 6 0.036 0.011 8.902E+09 5.608E+09 13 225.388 121 10 0.059 0.018 1.484E+10 9.347E+09 14 252.104 71 6 0.036 0.011 8.902E+09 5.608E+09 15 268.884 33 5 0.030 0.009 7.419E+09 4.674E+09 16 355.547 30 1 0.006 0.002 1.484E+09 9.347E+08 17 263.871 56 5 0.030 0.009 7.419E+09 4.674E+09 18 287.215 53 4 0.024 0.007 5.935E+09 3.739E+09 19 253.395 44 1 0.006 0.002 1.484E+09 9.347E+08 20 206.806 9 1 0.006 0.002 1.484E+09 9.347E+08 21 403.386 299 13 0.077 0.023 1.929E+10 1.215E+10 22 264.972 19 1 0.006 0.002 1.484E+09 9.347E+08 23 515.315 98 14 0.083 0.025 2.077E+10 1.309E+10 24 313.962 45 8 0.047 0.014 1.187E+10 7.478E+09 25 251.694 40 4 0.024 0.007 5.935E+09 3.739E+09

Total 7895.412 2696 18 0.973 0.292 2.433E+11 1.533E+11

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5. Modeling Process

The watershed model named Soil and Water Assessment Tool (SWAT) version 2005 was applied to the Neshanic River Watershed to evaluate hydrology and sediment, nutrients and bacteria loadings by simulating the influence of topographic, soil, land use, and cli-matic condition on stream flow and sedimentation. The application involved sensitivity, calibration, validation, and water, sediment, nutrients, and bacteria balance analyses. The calibrated SWAT was then applied to evaluate the effects of various best management practices for reducing nonpoint source pollutant loadings.

5.1. Introduction to SWAT

SWAT is a continuous, daily time-step spatially distributed hydrological river basin scale model that simulates the water, sediment, nutrient, chemical and bacteria transport in a watershed resulting from the interaction of weather, soil properties, stream channel cha-racteristics, vegetation and crop growth, and land management practices, and calculates various pollutant loads from landscape and point sources (Arnold et al., 1994; Neitsch et al., 2005). It delineates a watershed into hydrologic response units (HRUs) that consist of specific land use, soil and slope characteristics to represent spatial heterogeneity in terms of land cover, soil type and slope class within a watershed. The model estimates relevant hydrologic responses such as evapotranspiration, surface runoff and peak rate of runoff, groundwater flow, sediment and pollutant yields for each HRU to the changing climate and land use conditions. ArcSWAT is an ArcGIS extension that provides a graphic user interface for the SWAT2005 model. SWAT integrates field-scale BMPs being imple-mented within a watershed and evaluates their water quality benefits at sub-watershed- and watershed-scale over a long period of time. This model has be widely applied to es-timate water quality impacts of BMPs (Fohrer et al., 2002; Gitau et al., 2006; Santhi et al., 2001b; Tripathi et al., 2003) and effectiveness of alternative regulatory instruments (Qiu and Prato, 1999; Whittaker et al., 2003).

The hydrologic cycle in SWAT is based on the following water balance equation:

t

iiqwseepasurfdayt QWEQRSWSW

10 (1)

where, t is the index of time step (days), tSW is the final soil water content (mm water)

by the end time step t, 0SW is the initial soil water content (mm water), dayR is the

amount of precipitation in day i (mm water), surfQ is the amount of surface runoff in day

i (mm water), aE is the amount of evapotranspiration in day i (mm water), seepW is the

amount of water entering the vadose zone from the soil profile in day i (mm water), and

qwQ is the amount of return flow in day i (mm water). SWAT can estimate surface runoff

using two methods: the SCS curve number procedure (USDA-SCS, 1972) and the Green & Ampt infiltration method (Green and Ampt, 1911). In this study, the SCS curve num-

15

ber method was chosen to estimate surface runoff. The SCS curve number (CN) is de-fined by:

SR

SRQ

day

daysurf 8.0

)2.0( 2

(2)

where, S is the retention parameter (mm), )10/1000(4.25 CNS . The SCS curve number (CN) for the day is a function of the soil’s permeability, land use and antecedent soil water conditions. SCS defines three antecedent moisture conditions: 1-dry (wilting point), 2-average moisture, and 3-wet (filed capacity). The moisture condition 1 curve number is the lowest value that the daily curve number can be assumed in dry conditions. The curve numbers under moisture conditions 1 (CN1) and 3 (CN3) are calculated from the moisture condition 2 (CN2) by:

)))100(0636.0533.2exp(100(

)100(20

22

221 CNCN

CNCNCN

(3)

))100(00673.0exp( 223 CNCNCN (4)

Peak runoff rate is computed using a modification to the Rational formula (Kuich-ling, 1889) or using the SCS TR-55 method (USDA-SCS, 1986). Lateral subsurface flow is computed using the Sloan et al. (1983) kinematic storage model and ground-water flow using empirical relations. Routing of in-channel runoff is based on the variable storage coefficient method (Williams, 1969) and flow is computed using Manning’s equation (Chow, 1959) with adjustments for transmission losses, evaporation, diversions, and re-turn flow as described in Arnold et al. (1995). Flow routing in reservoirs is based on wa-ter balance and user provided measured or targeted outflow.

Sediment yield is computed using the Modified Universal Soil Loss Equation (MUSLE) (Williams and Berndt, 1977) expressed in terms of runoff volume, peak flow, and Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) factors. Sedi-ment routing in the channel is based on Bagnold’s (1977) stream power concept, as mod-ified by Williams (1980), for bed degradation and sediment transport. Bed degradation is adjusted with USLE soil erodibility and cover factors, and deposition is based on particle fall velocity. Reservoir sediment routing is based on a simple continuity equation on vo-lumes and concentrations of inflow, outflow, and reservoir storage. The modified univer-sal soil loss equation (Williams, 1995) adopted in SWAT is given out in the following mass balance equation:

CFRGLSPCKareaqQsed USLEUSLEUSLEUSLEhrupeaksurf 56.0)(8.11 (5)

where, sed is the sediment yield on a given day (metric tons), Qsurf is the surface runoff volume (mm H2O/ha), qpeak is the peak runoff rate (m3/s), areahru is the area of the HRU (ha), KUSLE is the USLE soil erodibility factor (0.013 metric ton m2 hr/(m3-metric ton cm)), CUSLE is the USLE cover and management factor, PUSLE is the USLE support prac-

16

tice factor, LSUSLE is the USLE topographic factor and CFRG is the coarse fragment fac-tor.

The nitrogen processes modeled by SWAT and the various pools of nitrogen in the soil are shown in Figure 5.1 (Arnold et al., 1998; Neitsch et al., 2005). Plant use of nitro-gen is estimated using the supply and demand approach (Williams et al., 1984). Daily plant demand is a function of plant biomass and biomass N concentration. Available ni-trogen in the soil (root depth) is supplied to the plant. When demand exceeds supply, there is a nutrient stress. Masses of NO3-N contained in runoff, lateral flow and percola-tion are estimated as products of the volume of water and the average concentration of nitrate (NO3-N) in the soil layer. Organic N transport with sediment is calculated with a loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978) for application to individual runoff events. The loading function estimates daily organic N runoff loss based on the concentration of organic N in the top soil layer, the sediment yield, and an enrichment ratio (that is, the ratio of organic N in sediment to or-ganic N in soil and typically ranging from two to four). The phosphorus processes mod-eled by SWAT and the various pools of phosphorus in the soil are depicted in Figure 5.2 (Neitsch et al., 2005). Plant use of phosphorus is estimated using the supply and demand approach similar to nitrogen. The loss of dissolved phosphorus in surface runoff is based on the concept of partitioning pesticides into solution and sediment phases as described by Leonard and Wauchope (1980). The amount of soluble P removed in runoff is pre-dicted using labile P concentration in the top 10 mm of the soil, the runoff volume and a phosphorus soil partitioning factor (that is, the ratio of P attached to sediment to P dis-solved in soil water and typical values range from 100 to 175 depending on the soil). Se-diment transport of P is simulated with a loading function as described in organic N transport (Santhi et al., 2001a).

Instream nutrient dynamics have been incorporated into SWAT (Ramanarayanan et al., 1996) using the kinetic routines from an instream steady-flow water quality model, QUAL2E (Brown and Barnwell, 1987).

Figure 5.1. Nitrogen Processes Modeled in SWAT (USDA-ARS, 1999).

17

Figure 5.2. Phosphorus Processes Modeled in SWAT (USDA-ARS, 1999).

The SWAT microbial sub-model for predicting pathogen loadings in surface and

groundwater at watershed and basin scale was developed by Sadeghi and Arnold (2002). Its approach involves developing a comprehensive and flexible bacteria submodel that can allow simultaneous risk evaluation of nutrients, pathogens and sediment loadings as-sociated with various land management practices in water catchments. The model has been tested in a watershed in Missouri (USA) for E. coli and fecal coliform (Baffaut and Benson, 2003). Parajuli (2007) has also evaluated and applied the SWAT microbial sub-model to watersheds in Kansas (USA). This sub component of SWAT simulates the sur-vival of enteric organisms as two different populations: (i) nonpersistent microorganisms and (ii) persistent microorganisms, such as Cryptosporidium species and E. coli (Jamie-son et al., 2004). Persistent bacteria are characterized by slower die-off rates in the natu-ral environment (Coffey et al., 2007; Foppen and Schijven, 2006). The rationale for this approach is that the population of persistent bacteria like E. coli and Cryptosporidium may initially be low in comparison to less persistent bacteria (i.e. fecal coliform). How-ever, because of more rapid die-off rates for less persistent bacterial species, the popula-tion of more persistent bacteria would likely comprise a greater proportion of the total remaining pathogens (Sadeghi and Arnold, 2002). The impact of persistent pathogens is thus evaluated based on population densities. Recent enhancements in relation to micro-bial risk assessment include bacteria transport routines in sediment and in-stream (Sadeg-hi and Arnold, 2002). In reality, bacterial transport is associated with both dissolution in overland flow and sorption onto sediments. It is unknown if this sorption occurs on the land, during runoff events, or during in-stream processes. However, significant uncertain-ty in modeling both bacterial partitioning and sediment transport has caused most models to treat bacteria transport as being entirely associated with dissolution in surface runoff (Chin et al., 2009; Coffey et al., 2010).

18

5.2. Input Data

In order to run the SWAT model, various input data are needed. Many of the data are readily available in the form of geographic data sets (elevation, soil and land use) from public sources; and others such as cropping practices, tillage, fertilizer application, waste and stormwater management are locally dependent and are not readily available. ArcSWAT, the ArcGIS interface of SWAT2005, was used to delineate the watershed and process SWAT input files (Winchell et al., 2009).

5.2.1. Digital Elevation Model Data

Topography was defined by a digital elevation model (DEM) that describes the elevation of any point in a given area at a specific spatial resolution. A 10 m DEM was obtained for the watershed from NJDEP. The geography and topological relief of the watershed are shown in Figure 5.3. This grid file was used by ArcSWAT to delineate subwatersheds and to analyze the drainage patterns of the land surface terrain. A 1:24K stream network obtained from NJDEP was used to set the stream network for the delineation. Subbasin parameters such as slope gradient, slope length of the terrain, and stream characteristics such as channel length, width and slope were calculated from the DEM. There are 5593, 2020 and 282 ha of watershed areas falling into the slope ranges of 0-2%, 2-5% and above 5%, which are 70.84%, 25.59%, and 3.57% of the total area, respectively.

Figure 5.3. Elevation map of the Neshanic River Watershed

19

5.2.2. Soil Data

SWAT model requires different soil textural and physicochemical properties such as soil texture, available water content, hydraulic conductivity, bulk density and organic carbon content for different layers of each soil type. A digital soil survey layer (Figure 5.4) was created for the watershed from the Soil Survey Geographic (SSURGO) Database for Hunterdon County, New Jersey, which is obtained from Natural Resources Conservation Service (NRCS), United States Department of Agriculture. Excluding water and ROPF (rough broken land, shale), fifty two soil types were classified in the watershed according to soil composition and slopes. Table 5.1 lists the types of soils and their area distribu-tions. Major soils types in the watershed are: Penn channery silt loam with 6 to 12 per-cent slopes, eroded (16.63%), Penn channery silt loam with 2 to 6 percent slopes (16.44%), Bucks silt loam, 2 to 6 percent slopes (8.07%), Reaville silt loam, 2 to 6 per-cent slopes (6.81%), Chalfont silt loam, 2 to 6 percent slopes(4.70%), Rowland silt loam, 0 to 2 percent slopes, frequently flooded (4.52%), Hazleton channery loam, 6 to 12 per-cent slopes, eroded (3.81%), and Abbottstown silt loam, 2 to 6 percent slopes (3.58%).

Figure 5.4. Soils of the Neshanic River Watershed

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Table 5.1. Soils and area distributions

musym Soil Name Acres Percent AbrA1 Abbottstown silt loam, 0 to 2 percent slopes 94.20 0.47 AbrB1 Abbottstown silt loam, 2 to 6 percent slopes 709.69 3.58 BhnA2 Birdsboro silt loam, 0 to 2 percent slopes 7.95 0.04 BhnB2 Birdsboro silt loam, 2 to 6 percent slopes 48.16 0.24

BoyAt1 Bowmansville silt loam, 0 to 2 percent slopes, frequently flooded

153.06 0.77

BucB2 Bucks silt loam, 2 to 6 percent slopes 1601.24 8.07 BucC21 Bucks silt loam, 6 to 12 percent slopes, eroded 200.30 1.01 ChcA1 Chalfont silt loam, 0 to 2 percent slopes 143.03 0.72 ChcB1 Chalfont silt loam, 2 to 6 percent slopes 932.21 4.70 ChcC21 Chalfont silt loam, 6 to 12 percent slopes, eroded 577.50 2.91 ChfB1 Chalfont-Quakertown silt loams, 0 to 6 percent slopes 417.51 2.10 CoxA1 Croton silt loam, 0 to 2 percent slopes 24.84 0.13 CoxBb Croton silt loam, 0 to 6 percent slopes, very stony 35.11 0.18 HdyB1 Hazleton channery loam, 2 to 6 percent slopes 308.73 1.56 HdyC21 Hazleton channery loam, 6 to 12 percent slopes, eroded 755.20 3.81 HdyD Hazleton channery loam, 12 to 18 percent slopes 174.98 0.88

HdyDb Hazleton channery loam, 6 to 18 percent slopes, very sto-ny

228.71 1.15

HdyEb Hazleton channery loam, 18 to 40 percent slopes, very stony

113.93 0.57

KkoC Klinesville channery loam, 6 to 12 percent slopes 264.83 1.33 KkoD Klinesville channery loam, 12 to 18 percent slopes 214.15 1.08 LbmB2 Lansdale loam, 2 to 6 percent slopes 63.37 0.32 LbmC21 Lansdale loam, 6 to 12 percent slopes, eroded 66.12 0.33 LbtB1 Lansdowne silt loam, 2 to 6 percent slopes 11.81 0.06 LdmB2 Lawrenceville silt loam, 2 to 6 percent slopes 5.71 0.03 LegB2 Legore gravelly loam, 2 to 6 percent slopes 92.41 0.47 LegC1 Legore gravelly loam, 6 to 12 percent slopes 92.39 0.47 LegD Legore gravelly loam, 12 to 18 percent slopes 54.17 0.27 LemB1 Lehigh silt loam, 2 to 6 percent slopes 1.99 0.01 MonB2 Mount Lucas silt loam, 2 to 6 percent slopes 15.48 0.08 NeeB2 Neshaminy gravelly loam, 2 to 6 percent slopes 7.89 0.04 NehB2 Neshaminy silt loam, 2 to 6 percent slopes 24.74 0.12 NehCb Neshaminy silt loam, 6 to 12 percent slopes, very stony 6.31 0.03 NehDb Neshaminy silt loam, 12 to 18 percent slopes, very stony 7.96 0.04 NehEb Neshaminy silt loam, 18 to 35 percent slopes, very stony 35.39 0.18 PeoB2 Penn channery silt loam, 2 to 6 percent slopes 3262.31 16.44 PeoC21 Penn channery silt loam, 6 to 12 percent slopes, eroded 3299.31 16.63 PeoD Penn channery silt loam, 12 to 18 percent slopes 373.27 1.88 PepB2 Penn-Bucks complex, 2 to 6 percent slopes 537.57 2.71 PepC21 Penn-Bucks complex, 6 to 12 percent slopes, eroded 118.70 0.60 QukB2 Quakertown silt loam, 2 to 6 percent slopes 442.38 2.23 QukC21 Quakertown silt loam, 6 to 12 percent slopes, eroded 106.70 0.54

QupC21 Quakertown-Chalfont silt loams, 6 to 12 percent slopes, eroded

214.42 1.08

ROPF Rough broken land, shale 355.24 1.34 RarAr2 Raritan silt loam, 0 to 3 percent slopes, rarely flooded 265.12 0.34 RarB2 Raritan silt loam, 3 to 8 percent slopes 68.37 0.84 RedB2 Readington silt loam, 2 to 6 percent slopes 165.74 0.30

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RedC21 Readington silt loam, 6 to 12 percent slopes, eroded 59.90 0.11 RehA1 Reaville silt loam, 0 to 2 percent slopes 21.70 6.81 RehB1 Reaville silt loam, 2 to 6 percent slopes 1350.82 0.83 RehC21 Reaville silt loam, 6 to 12 percent slopes, eroded 164.68 1.53 RepwA Reaville wet variant silt loam, 0 to 2 percent slopes 304.02 1.67 RepwB Reaville wet variant silt loam, 2 to 6 percent slopes 330.66 1.79

RorAt Rowland silt loam, 0 to 2 percent slopes, frequently flooded

897.05 4.52

Water Water 12.26 0.06

5.2.3. Land Use Data

Land use significantly affects soil erosion, runoff, and evapotranspiration processes in a watershed. The 2002 land use/cover data obtained from NJDEP was used for SWAT modeling. NJDEP land use/cover data used a modified Anderson Land Classification sys-tem. The land uses in this watershed were classified into six broad land use categories including agriculture, barren, forest, urban, water and wetlands, and 50 subcategories us-ing a 4-digital land use classification codes. Since the NJDEP land use classification did not distinguish the specific uses of agricultural lands, two rounds of agricultural land use inventories throughout the watershed were conducted during the period 2007-2008 to identify the crops and agricultural activities in agricultural lands. There are 21 types of land uses/covers identified including residential areas (high, medium, medium-low and low densities), other urban type areas (commercial, institutional and transportation), fo-rests (deciduous, evergreen and mixed), wetlands (forested, non-forested and mixed), and agricultural lands (corn, soybean, rye, timothy, regular hay, pasture, orchard and other agriculture) (Figure 5.5). Table 5.2 lists the types of land uses and their area distributions.

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Figure 5.5. Land use of the Neshanic River Watershed

23

Table 5.2. Land uses and are distributions*

Land use Code Acres Percent Residential-High Density URHD 96.18 0.48 Residential-Medium Density URMD 216.70 1.09 Residential-Med/Low Density URML 382.03 1.92 Residential-Low Density URLD 4894.54 24.66 Commercial UCOM 290.34 1.46 Institutional UINS 456.68 2.30 Transportation UTRN 163.54 0.82 Agricultural Land-Generic AGRL 351.82 1.77 Corn CORN 1846.58 9.30 Soybean SOYB 1846.64 9.30 Rye RYE 346.39 1.75 Hay HAY 757.32 3.82 Timothy TIMO 1681.05 8.47 Pasture PAST 888.51 4.48 Orchard ORCD 112.65 0.57 Forest-Deciduous FRSD 3038.28 15.31 Forest-Evergreen FRSE 208.22 1.05 Forest-Mixed FRST 914.05 4.61 Wetlands-Forested WETF 1089.34 5.49 Wetlands-Non-Forested WETN 6.56 0.03 Wetlands-Mixed WETL 204.67 1.03 Water WATR 53.99 0.27 * 2002 land use data with 2007-2008 updates

5.2.4. Weather Data

SWAT requires daily meteorological data that can either be read from a measured data set or be generated by a weather generator model. The weather data at the Flemington weather station that is just outside of the watershed boundary from NOAA's National Climate Data Center were utilized to set up the SWAT model. The historical weather data include the precipitation, maximum and minimum temperature during 1960-2008, solar radiation and relative humidity during 1960-2004 in the Flemington weather station. The weather generator in SWAT can automatically utilize historical data to interpolate and fill the gaps due to missing data.

5.2.5. Streamflow and Water Quality Data

Daily stream flow in the Neshanic River has been monitored at the intersection between Reaville Road and the Neshanic River since 1930 by the U.S. Geological Survey (USGS). These daily streamflow data were obtained from USGS and used for flow cali-bration and validation. Water quality data including instantaneous discharge, TSS, nu-trients, E coli and Fecal coliform, are available at the same site with two to five sam-

24

plings a year in some years since 1979. Additional water quality monitoring was con-ducted at seven locations in the watershed including the USGS station and six other sites in the tributaries of Neshanic River (Figure 2.1) during 2007 to 2008 through the restora-tion plan project: biweekly surface water sampling from June -November 2007 (12 events), additional bacteriology sampling three times in June, July and August 2007 (9 events), biological sampling once in early summer of 2007 (1 event), wet weather surface water sampling three times between June -November 2008 (3 events). The instantaneous stream flows and TSS concentrations obtained through these samplings were utilized to validate the model.

5.2.6. Manure Content Data

The fertilizer database in SWAT contains nutrient content data of many commercial ferti-lizers and manures of several types of animals. Each manure type in the fertilizer data-base used by SWAT contains an associated bacteria count for persistent (e.g. E. coli) and less persistent bacteria (e.g. Fecal coliforms). Values required by the database are the bacteria content per gram of dry manure. However, these required data are not recorded in the database and need user’s definition. These inputs were estimated based on the val-ues reported in literature as described in the previous section on pollutant source charac-terization.

5.3. Model Setup

The model setup involved five steps: data preparation, subbasin delineation, HRU defini-tion, parameter sensitivity analysis, and calibration and validation. The required spatial datasets were projected to the same projected coordinate system defined by NJDEP: Sta-tePlane_New_Jersey_FIPS_2900 _Feet, which is a transverse mercator projection based on NAD 1983 for New Jersey. The DEM was used to delineate the watershed and to ana-lyze its drainage patterns. NJDEP stream network for the watershed was imported and superimposed onto the DEM to accurately delineate the location of streams. The land use/cover data were reclassified into SWAT land cover/plant types. A look-up table was created to identify the SWAT code for the different categories of land use/cover on the map as per the required format. The required soil attributes were extracted from the SSURGO database to create a user soil table used in SWAT. A look-up table was created to link the locations of soil types in the soil map to the soil attributes in user soil table in SWAT.

Based on DEM and the stream network the watershed and subwatersheds were de-lineated by further outlet and inlet definition, watershed outlets selection, and definition and calculation of subbasin parameters. For the stream definition the threshold based stream definition option was used to define the minimum size of the subbasin. The ArcSWAT interface allows the user to fix the number of subbasins by deciding the initial threshold area. The threshold area defines the minimum drainage area required to form the origin of a stream. 25 subbasins were delineated in the watershed (Figure 5.6), where the sampling sites for water quality monitoring were selected as subbasin outlets, as SWAT outputs simulated flow and loading values at subbasin watersheds.

25

During set up of the SWAT model the distribution of hydrological response units (HRUs) within the watershed are determined based on user criteria. HRUs are unique combinations of land use, soil and slope which allow the model to reflect differences in evapotranspiration and other hydrologic conditions across the watershed. Three slope classes were considered for Neshanic River watershed, 0-2%, 2-5% and >5%. The large number of HRUs needs to be reduced to simplify the input to SWAT model while still the major differences among subwatersheds are accounted for. To achieve this, area percen-tage thresholds for HRU definition were set at 1%, 20% and 25% for land use, soil and slope, respectively. This forced the model to create HRUs comprised of combinations of land use, soils and slope meeting the minimum threshold areas for each subbasin. As a result, a land use in a subbasin covering less than 1% of the total area for that subbasin, or a soil type comprising less than 20% of the land use area in that subbasin, or a slope clas-sification comprising less than 25% of the soil area in that subbasin, would be ignored and reclassified as a more dominant combination. Using these thresholds 625 individual HRUs were defined for the 25 subbasins delineated in the watershed.

Figure 5.6. Delineation of the Neshanic River Watershed

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5.4. Baseline Scenario of Pollutant Sources and Management Practices

A scenario details the management options in the manageable land uses including lawn managements in urban lands and crop management in agricultural lands, such as tillage, fertilization applications and harvest. The baseline scenario was developed through a se-ries of interviews and meetings with farmers and natural resource professionals in the wa-tershed. The baseline scenario is characterized with modest N and P commercial fertilizer applications and reduced tillage for agricultural lands. Cattle and horse manures were as-sumed to be applied to corn lands at standard rates. The pesticide application was ig-nored. Tillage operations include: minimum (chisel/disk) plows for corn, soybean and rye, and 6-year rotation moldboard/disk/hallow plows for timothy, hay and pasture lands. Agricultural general lands (AGRL) were modeled as 2-year rotation of corn and soybean. Orchards, forests, and wetlands were modeled using their default SWAT schedules.

5.4.1.1. Agricultural Management Practices

The schedules of management practices for various land uses are given in Appendix A. Operations for agricultural lands normally involve tillage, fertilization, planting, and harvest. A specific date was assigned to each operation. Although the dates of actual ac-tivities may span over a large range, specific dates are required for a schedule in SWAT. Note that estimate of operation dates may lead to big difference between the simulated and observed daily times series of stream flows and pollutant loads. However, good com-parable results can be obtained at the monthly level.

It was assumed that cattle and horses grazed on pasture lands from May to October after which they were hay supplemented in the winter pastures until the end of March. Distributions of livestock among HRUs in a subbasin were assigned to reflect the relative areas of cows and horses in that subbasin as close as possible (Table 5.3). Either cows or horses were allowed on a HRU but not both. A GIS layer of livestock distribution parcels were created based on consultation of farmers. Cow and horse densities were assumed to be the same across the whole watershed, being 4.5 cows/ha and 1.7 horses/ha, respective-ly. Daily dry matter intakes were taken as 2.5% of mature body weights (Rinehart, 2006).

Commercial fertilizer application amounts for crops were set according to common practice recommendations and farmer consultation. Land application of manure helps to reduce or eliminate the need for commercial fertilizers. It can be applied in four different ways: 1) surface broadcast followed by disking 2) broadcast without incorporation 3) in-jection under the surface, or 4) irrigation. It was difficult to get data about how much and which areas were applied to manure in the watershed. Cow and horse manure application rates were set to 45 Mg/ha (based on N requirement) (Santhi et al., 2006) and applied to corn lands only with incorporation in Spring. With these manure application rates, the maximum corn land areas receiving cow and horse manures were 47 and 38 ha, respec-tively. In total, only 11.5% percentage of corn lands might be applied with the available manure stored during the livestock housing period. Cow and horse manures were as-signed to corn land HRUs to match the spatial distribution pattern of livestock (

27

Table 5.4). The small amount of remaining manure storage was assumed to be well ma-naged and not modeled with SWAT.

Table 5.3. Designed HRUs with cow and horse grazing

Land use Subbasin HRU Acres Soil Slope

Cow grazing

Horse grazing

PAST 3 60 9.98 ChfB 2.57 yes PAST 3 61 23.08 ChfB 1.21 yes PAST 5 104 3.48 ChcB 0.92 yes PAST 5 105 9.07 RarAr 0.57 yes PAST 6 128 16.31 PeoB 1.00 yes PAST 6 129 30.39 PeoC2 0.96 yes PAST 6 130 25.70 RehB 0.74 yes PAST 7 154 10.01 PeoB 0.77 yes PAST 7 155 4.27 PepB 0.39 yes PAST 10 227 19.40 PeoB 0.76 yes PAST 10 228 12.01 PeoC2 1.16 yes PAST 10 229 13.20 RepwB 0.80 yes PAST 11 262 13.22 PeoB 0.45 yes PAST 12 298 7.34 PeoC2 1.50 yes PAST 12 299 8.97 PeoC2 3.23 yes PAST 12 300 4.25 PeoD 3.32 yes PAST 12 301 6.50 PeoD 1.31 yes PAST 13 318 1.48 LbmC2 1.72 yes PAST 13 319 2.36 LbmC2 2.44 yes PAST 13 320 1.99 PeoC2 3.13 yes PAST 13 321 2.94 RehB 2.85 yes PAST 14 351 4.62 PeoC2 2.63 yes PAST 14 352 8.70 PeoC2 1.24 Yes PAST 15 368 8.92 QukB 0.62 Yes PAST 16 392 75.37 PeoB 0.87 Yes PAST 16 393 44.97 PeoC2 2.93 Yes PAST 16 394 48.93 PeoC2 1.24 yes PAST 17 423 34.84 PeoB 0.44 yes PAST 17 424 32.87 PeoC2 0.54 yes PAST 18 442 49.17 RedB 0.95 yes PAST 20 492 38.30 PeoB 0.61 yes PAST 20 493 33.85 RehB 0.58 yes PAST 22 539 59.55 RehB 1.21 yes PAST 22 540 21.00 RehB 2.65 yes PAST 23 567 43.24 AbrB 1.09 yes PAST 23 568 54.36 PeoC2 1.19 yes PAST 23 569 19.00 PeoC2 2.76 yes PAST 24 590 37.07 PeoB 0.68 yes PAST 24 591 17.79 RehB 0.92 yes PAST 25 624 21.42 ChcA 0.71 yes PAST 25 625 12.50 HdyC2 1.02 yes

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Table 5.4. Designed HRUs with manure application

Land use Subbasin HRU Acres Soil Slope

Cow ma-nure

Horse manure

CORN 8 171 9.41 RehB 0.36 yes CORN 9 197 18.58 RepwA 0.50 yes CORN 12 285 8.01 PeoB 2.71 yes CORN 16 380 27.92 PeoC2 2.95 yes CORN 17 416 9.41 PeoC2 0.65 yes CORN 18 433 46.95 BucB 0.66 yes CORN 20 483 12.95 PeoC2 3.01 yes CORN 24 582 39.78 QukB 0.73 yes CORN 25 612 27.92 QukB 0.87 yes

5.4.1.2. Cattle Direct Deposit

Access to streams allows livestock to input manure directly into the streams. It was as-sumed that most horse owners did not allow their horses access for fear of disease, so no access was input for horses. Since no accurate information about fence placement was available, it was estimated that on 20% of pasture lands, cattle would be able to access stream directly and would only spend 2% of grazing time in the stream. This estimation is based on rough judgment and bacteria calibration testing.

5.4.1.3. Wildlife Deposit

The nutrient and bacteria loads associated with deer and geese were taken onto accounted though continuous grazing operations. Deer were assumed to living on various forest lands, while geese on low-density residential, commercial, institutional and transportation urban area lawns. The deer and goose manure deposit rates on these lands were estimated by dividing densities in the whole watershed by the fractions of their total habitat areas relative to watershed area.

Fecal coliform and nutrient loads that are deposited by wildlife directly into the streams can be treated as direct point source loadings in SWAT model. However, it is very difficult to estimate the amount of manure directly deposited by these animals into the water. Furthermore, direct deposition into stream from wildlife is a natural activity hardly to be dealt with any BMPs. The direct deposits in the stream from wildlife were not specified in this study.

5.4.1.4. Urban Runoff

Sediment and nutrients on both pervious and impervious areas from urban lands are si-mulated with default loading rates in SWAT. However, the SWAT model currently does not simulate the accumulation of bacteria on impervious areas and their subsequent wash-off by storm runoff. Two approaches may be adopted to remedy this situation. One is to estimate an average bacteria concentration for all urban storm runoff using wet weather

29

data published in the NPDES permit annual reports and wet weather urban runoff data, and code it into the SWAT model to calculate the approximated average bacteria loads carried by urban runoff. Baffaut (2006) utilized this method and calculated the average fecal coliform concentration of urban runoff in Little Sac River Watershed was to be 549 ± 238 colonies/100 ml. A second approach is to assume all bacteria are deposited through grazing on pervious lands. The second method was adopted in this study such that only bacteria deposits on lawns by the representative wildlife, geese, were simulated. This ap-proximation is reasonable considering low density residential areas are the dominant type of urban lands in the Neshanic River Watershed.

5.4.1.5. Failing Septic Systems

Failing septic systems are normally be regarded as nonpoint sources, which can be simu-lated in SWAT through continuous fertilization management operation (Coffey et al., 2010) or as aggregate point sources (Parajuli, 2007). In this report, the effluents from failing septic systems in a subbasin were aggregated to obtain point source loading input to the main river network. Once total nitrogen and phosphorus loads were aggregated for each subbasin, they were partitioned into organic and mineral forms using the following relationships from Northern Virginia Planning District Commission (1979) and adopted within SWAT. Total nitrogen loads consist of 70 percent organic nitrogen and 30 percent mineral (nitrate). Total phosphorus loads were divided into 75 percent organic phospho-rus and 25 percent orthophosphate. Failing septic systems and cattle direct manure depo-sits to streams within each subbasin were aggregated into a point source with monthly flows and pollutant loadings for SWAT modeling.

5.4.1.6. Groundwater Contamination

According to the 2007 groundwater quality report by New Jersey Geology Survey (NJGS, 2007) , the concentration ranges of TN, Dissolved P (Orthophosphate) in the Ne-shanic River Watershed were 0.3-4.38 mg/L and 0.02-0.06 mg/L, respectively. SWAT can simulate the leaching process of nitrogen and its concentrations in aquifers, but it as-sumes that phosphorus and bacteria cannot be leached beyond the top soil layer. Dis-solved phosphorus concentrations in groundwater need to be input manually, which were set to be 0.02 mg/L evenly across the Neshanic River Watershed.

5.5. Model Calibration and Validation

Calibration is the process of obtaining an optimal agreement between the model predic-tion and an observed data set. Parameters of the model are adjusted iteratively to improve predicted values against the actual observed values for stream flow, sediment, nutrients and bacteria. Once calibration is complete, validation is performed to evaluate the accu-racy of the model to predict values from an observational data set different from the cali-bration observational data set used. Poor model validation results may imply possible model calibration inaccuracies. The SWAT model for Neshanic River Watershed was calibrated with the crop management schedules under the baseline scenario, which reflect the existing crop management conditions. The scenario was developed through a series of interviews and meetings with farmers and natural resource professionals in the watershed.

30

The baseline scenario is characterized by minimum tillage (chisel plow and disk plow) and typical fertilizer applications in the area. To accommodate the 6-year rotation for hay and pasture standings, the calibration and validation periods were both set to 6 years, as 1998-2002 and 2003-2008, respectively. The simulation started at 1997, and hence a 6-year warm-up period was used to establish proper initial parameter values for the SWAT modeling before the calibration period.

It is important to note that the SWAT model is built with state-of-the-art compo-nents with an attempt to simulate the processes physically and realistically as possible. Most of the model inputs are physically based (that is, based on readily available infor-mation). SWAT is not a “parametric model” with a formal optimization procedure (as part of the calibration process) to fit any data. Instead, a few important variables that are not well defined physically such as runoff curve number and Universal Soil Loss Equa-tion’s (USLE) cover and management factor (C factor) may be adjusted to provide a bet-ter fit. SWAT has been widely used in the United States and other countries (Borah and Bera, 2004). Borah and Bera (2004) have extensively reviewed the various nonpoint source pollution models and their applications and indicated that SWAT is found to be sound and suitable for long-term continuous simulations especially in agricultural water-sheds.

5.5.1. Flow

Calibration was performed for annual, monthly and daily simulated flows using observed flows from the Reaville USGS streamflow gage station at the intersection of Reaville Road and the Neshanic River which is marked as N1 (USGS station 01398000 near the outlet of subbasin 12) in Figure 2.1. There is an area of about 6,493 ha that drains to the gage station.

The parameter sensitivity analysis was done using the Latin-Hypercube One-Factor-At-a-Time (LH-OAT) approach embedded in ArcSWAT for the whole catchment area. Twenty six hydrological parameters that affect stream flows were tested for sensi-tivity analysis for the simulation of stream flows in the study area. Default lower and up-per bound parameter values were used. For details of all hydrological parameters refer to the ArcSWAT Interface for SWAT user’s manual (Winchell et al., 2009).

The calibration process was done manually to ensure that not only simulated stream flows matched the observed stream flows but also their properly split (filtered) surface runoffs and base flows were matched. VIZSWAT version 1.1 (Baird, 2007) was used to separate base flow and runoff for the observed and simulated daily stream flows at Rea-ville which utilizes an automated digital filter technique comparable with Nathan and McMahon (1990), and Arnold and Allen (1999).

Calibration parameters adjusted for surface runoff were mainly curve number, soil evaporation compensation factor, maximum canopy storage, available water capacity of the soil layer, hydraulic conductivity in the main channel alluvium, and surface runoff lag. The parameters adjusted for base flow proportioning were base flow alpha factor, groundwater revap coefficient, groundwater delay, and deep aquifer percolation coeffi-

31

cient. These parameters were adjusted within the reported ranges (Table 5.5). Surface ru-noff was calibrated until average observed and simulated surface runoffs were within 15% and coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) greater than 0.5, as far as possible. Similarly, base flow was calibrated until the simulated base flow is within 15% of the observed base flow, and surface runoff was continually verified as the base flow calibration variables also affect surface runoff. Detailed calibration pro-cedures for SWAT model and the definitions of various calibration parameters are de-scribed by Neitsch et al. (2005) and Santhi et al. (2001a).

Table 5.5. Hydrology and sediment calibration parameters and their final calibrated values

Model process

Parameter Description Model range

Value used

Flow Cn2 Curve number ±25% +1.5% Esco Soil evaporation compensation factor 0 – 1 0.40 Canmx Maximum canopy storage (mm H2O) 0 –10 5

Sol_Awc Available water capacity of the soil layer (mm H2O/mm soil)

±25% +25%

Ch_K2 Hydraulic conductivity in the main channel alluvium (mm/hr)

0 – 150 4

Surlag Surface runoff lag coefficient 0 – 100 1 Alpha_Bf Base flow alpha factor (days) 0 – 1 0.11 GW_Revap Groundwater revap coefficient 0.02 –0.2 0.2 GW_Delay Groundwater delay time (days) 0 – 100 20 Rchrg_DP Deep aquifer percolation fraction 0 – 1 0. 4

Sediment USLE_C Minimum value for the cover and management factor for the land cover

0.003 – 0.45

Pasture, hay, timothy: 0.003, rye: 0.03, other croplands: 0.20

USLE_P Universal Soil Loss Equation support practice factor

0.1 – 1 1

Ch_cov Channel cover factor 0 – 1 0.5 Ch_erod Channel erodibility factor 0 to 1 0.01

SPCON Linear factor for channel sediment routing

0.0001– 0.01

0.001

SPEXP Exponential factor for channel sedi-ment routing

1.0 – 1.5 1.5

The calibration and validation were carried out by graphical comparison and statis-tics. Several statistics including the mean, mean relative error (D), standard deviation, coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) coefficient (Nash and Sutcliffe, 1970) were used to evaluate the model predictions against the observed values. The R2 value is an indicator of strength of relationship between the observed and simulated values. The Nash-Sutcliffe simulation efficiency indicates how well the plot of observed versus simulated value fits the 1:1 line. The simulation efficiency indicates the model’s ability to describe the probability distribution of the observed results. If the R2 and NSE values are less than or very close to 0.0, the model prediction is considered “un-acceptable or poor”. If the values are 1.0, then the model prediction is “perfect”. Ramana-rayanan et al. (1997) suggests that model prediction is acceptable if R2 > 0.5 and NSE > 0.4. Mean relative error (D), coefficients of determination (R2) and Nash-Sutcliffe effi-ciency (NSE) are calculated by following equations.

32

OOSD )( (6)

n

ii

n

ii

n

iii

SSOO

SSOO

R

1

2

1

2

2

12

)()(

))((

(7)

n

ii

n

iii

OO

OSNSE

1

2

1

2

)(

)(1 (8)

where, iO is the observed value, O is the mean observer value, iS is the simulated value,

S is the mean simulated value.

5.5.2. Water Quality Calibration

After hydrology was sufficiently calibrated, water quality calibration was performed for total suspended solids, nitrogen components, mineral and total phosphorus, fecal coliform and E. coli bacteria. Continuous records of water quality monitoring data were not avail-able for calibration in the Neshanic River Watershed. However, grab sample data were available for some years since 1979 for the USGS monitoring station at Reaville. Usually 4 samples per year (1 per quarter) were taken for pollutants, and bacteria were sampled 5 times during the 30-day summer period from July to August. Due to limited available ob-served water quality data, a robust calibration and validation procedure is not possible for pollutants. Limited model calibrations were carried out by comparing simulated in-stream concentration time series with the available observed concentrations with graphic me-thod. The additional data obtained at the seven sites during 2007-2008 through the water quality monitoring program of the Neshanic restoration plan project were used for valida-tion purpose. The objective was to best simulate the observed data for individual samples, as well as to obtain modeling output within the same ranges as the observed data. Be-cause of the simplified implementation of instream kinetics in SAWT, coupled with un-certainty in the specification of boundary conditions, particularly for point source loads, it cannot be expected that all observations will be reproduced in the modeling. However, the general trend should be replicated.

Note that daily concentrations were computed from daily average loads and flows output from SWAT, while observed concentrations were actually instantaneous values mostly only one during a sampling day. For a few days when two samples were collected within a day, observed pollutant concentration in a day was simply approximated as the flow weighted average of samples at a site during that day.

Careful considerations were given to verify the processes related to sediment and nutrients. Instream suspend solids result from the interaction of upland loading and

33

scour/deposition processes in the stream channels. The model parameters related to sedi-ment (Table 5.5) were set based on expertise and experience from previous studies (Neitsch et al., 2005; Santhi et al., 2001a). Model parameters verified for sediment (for upland processes) calibration were the Universal Soil Loss Equation’s land cover and management factor (C factor) and support practice factor (P factor). Parameters verified for channel sediment routing processes were channel cover factor, channel erodibility factor, linear factor and exponential factors for channel sediment routing.

For nitrogen components, the primary calibration parameters considered for upland processes were fraction of fertilizer application to top soil layer (FRT_SURFACE) and nitrogen percolation factor (NPERCO). Initial concentrations of nitrate in soil layers and shallow aquifers and soil organic nitrogen were found to have no affection on simulation since a long warming up period was used in the modeling. For instream nitrogen trans-form processes, the settling rates for organic fraction (RS4), transform rates of minerali-zation (organic nitrogen to ammonia, BC3), ammonia to nitrite (BC1), and nitrite to ni-trate (BC2) were verified. Algae play an important role in instream cycles of nutrients. The die-off and growth of algae contribute organic nutrients to and consume mineral nu-trients in stream water, respectively. The algal related parameters including chlorophyll-algal ratio (AI0), mass fraction of nitrogen in algae (AI1), algal settling (RS1), death (RHOQ) and growth (MUMAX) rates were calibrated.

For phosphorus, the primary calibration parameters considered for upland processes were fraction of fertilizer application to top soil layer (FRT_SURFACE), phos-phorus percolation factor (PPERCO), soil and runoff partition factor (PGOSKD), and concentration in groundwater (GWSOLP). Similar to nitrogen, initial concentrations were set as defaults. For instream phosphorus transform processes, the settling rates for organic fraction (RS5), mineralization rate (BC4), mass fraction of phosphorus in algae (AI2), and other algal related parameters were calibrated. Since nitrogen and phosphorus cycles are coupled by algal processes, the changes for parameters of one of them may affect si-mulation results of another.

For bacteria, only bacteria wash off and die off coefficients (Table 5.7) were cali-brated. Other parameters, such as the fraction of manure applied to land areas that has active colony forming units, bacteria partition coefficient between solution and soil parti-culates, bacteria soil partitioning coefficient between solution in top 10 mm soil and sur-face runoff, bacteria percolation coefficient, and temperature adjustment factor for bacte-ria die-off/growth were set to their default values 0.15, 0.9, 175 m3/Mg, 10 (10 m3/Mg), and 1.07, respectively. The wash-off fraction, die-off factor for bacteria in soil solution, die-off factor for bacteria adsorbed to soil particles, die-off factor for bacteria on foliage, and die-off factor for bacteria in were calibrated for both E. coli and fecal coliform. Die-off coefficients for E. coli and fecal coliform vary in the literature. Fecal coliform is con-sidered less persistent compared to E. coli, though its die-off coefficients were found to be much low in some studies. A conservative estimation was made for fecal coliform for planning purpose in which the die-off coefficients of fecal coliform were assumed to be the same as those of E. coli.

34

Table 5.6. Nutrient calibration parameters and their final calibrated values

Model process

Parameter Description Model range

Value used

Both

FRT_SURFACE Fraction of fertilizer applied to top 10mm of soil

0 – 1 0.2

AI0 Ratio of chlorophyll-a to algal biomass (μg-chla/mg algae)

10 - 100 100

RS1 Local algal settling rate in the reach at 20º C (m/day)

0.15-1.82 1

MUMAX Maximum specific algal growth rate at 20º C (1/day)

1.0 – 3.0 0.006

RHOQ Algal respiration rate at 20º C (1/day) 0.05 - 0.5 0.0031

Nitrogen

NPERCO Nitrate percolation coefficient (concentra-tion of nitrate in the runoff to the concentra-tion of nitrate in percolate)

0 – 1 0.2

AI1 Fraction of algal biomass that is nitrogen (mg N/mg alg)

0.02 – 0.09 0.08

RS4 Rate coefficient for organic N settling in the reach at 20º C (1/day)

0.001 – 0.1 0.001

BC1 Rate constant for biological oxidation of NH4 to NO2 in the reach at 20º C in well-aerated conditions (1/day)

0.1 – 1 1

BC2 Rate constant for biological oxidation of NO2 to NO3 in the reach at 20º C in well-aerated conditions (1/day)

0.2– 2 2

BC3 Rate constant for hydrolysis of organic N to NH4 in the reach at 20º C (1/day).

0.2-0.4 0.21

Phosphorus

PPERCO Phosphorus percolation coefficient (10 m3/Mg). (ratio of the solution phosphorus concentration in the surface 10 mm of soil to the concentration of phosphorus in perco-late)

10 – 17.5 10

PHOSKD Phosphorus soil partitioning coefficient (m3/Mg) (ratio of the soluble phosphorus concentration in the surface 10 mm of soil to the concentration of soluble phosphorus in surface runoff)

100 – 200 100

GWSOLP Concentration of soluble phosphorus in groundwater contribution to stream flow from subbasin (mg P/L or ppm)

0.02 - 0.06 0.02

AI2 Fraction of algal biomass that is phosphorus (mg P/mg alg)

0.01 – 0.02 0.015

RS5 Organic phosphorus settling rate in the reach at 20º C (1/day)

0.001 – 0.1 0.001

BC4 Rate constant for mineralization of organic P to dissolved P in the reach at 20º C (1/day)

0.01 – 0.7 0.01

35

Table 5.7. Bacterial calibration parameters and their final calibrated values

Model process

Parameter Description Value used

E. Coli

WOF_P Wash-off fraction for persistent bacteria 0.25 WDPQ Die-off factor for persistent bacteria in soil solu-

tion at 20°C (1/day) 0.1

WDPS Die-off factor for persistent bacteria adsorbed to soil particles at 20°C. (1/day)

0.01

WDPF Die-off factor for persistent bacteria on foliage at 20°C (1/day)

0.2

WDPRCH Die-off factor for persistent bacteria in streams (moving water) at 20°C. (1/day)

0.4

Fecal coliform

WOF_LP Wash-off fraction for persistent bacteria 0.25 WDLPQ Die-off factor for persistent bacteria in soil solu-

tion at 20°C (1/day) 0.1

WDLPS Die-off factor for persistent bacteria adsorbed to soil particles at 20°C. (1/day)

0.01

WDLPF Die-off factor for persistent bacteria on foliage at 20°C (1/day)

0.2

WDLPRCH Die-off factor for persistent bacteria in streams (moving water) at 20°C. (1/day)

0.4

5.5.3. Crop Yield Calibration

Crop yields were calibrated for corn, soybean and hay by comparing annual average yields cross the whole watershed to Hunterdon County average values. For other crops, yields were not calibrated since county statistic data were not available. Radiation use efficiency coefficients or bio-energy ratios, harvest indexes under optimum growing and highly stressed conditions were selected as the parameters for calibration. For soybean, radiation use efficiency was increased 20% from the default value to 30 (kg/ha)/(MJ/m2), and the harvest index under stressed growing conditions was set to be 0.31, the same as under optimum conditions. Other parameters were set at their default values.

36

6. Calibration Results and Discussion

6.1. Flow Calibration

Relative sensitivity values were found in the parameter sensitivity analysis. Of the twenty six parameters top 10 ranked sensitive parameters affecting stream flow at Reaville were identified with the relative sensitivity values ranges from 0.008 to 0.092, which were, in the order from high to low: channel effective hydraulic conductivity, initial SCS Curve Number II value, base flow alpha factor, Manning’s “n” value for main channel, surface runoff lag, snow pack temperature lag factor, shallow aquifer for return flow to occur, soil evaporation compensation factor, maximum plant leaf area index, and maximum ca-nopy storage. In addition to the top 10 sensitive parameters, available water capacity of the soil layer, groundwater revap coefficient, groundwater delay, and deep aquifer perco-lation coefficient were considered for model calibration according to literature. Note that, only those parameters whose default values were adjusted during the calibration are listed in Table 5.5.

Measured and simulated annual flows at Reaville match well (Figure 6.1 and Table 6.1). The simulated annual flows are slightly lower for the years 1997 to 1999 and slightly higher for the years 2001 and 2002. Monthly simulated and observed stream flows match well except for a few months, where the model underpredicts the flow Sep-tember 1999 and overpredicts for June 2001 (Figure 6.2). The time series of measured and simulated daily flows during the calibration period (Figure 6.3) show that simulated flows are usually underpredicted on days with daily rainfall more than 15 m3/s. Over es-timation occurs on days with lower daily rainfall during the summers. As aforemen-tioned, the streamflow calibration was carried out after acceptable calibration of filtered surface runoff and base flow. Figure 6.4 shows the graphic comparison between the fil-tered monthly base flows of observed and simulated stream flows at Reaville.

Means, standard deviations, R2, and NSE values indicate the good agreement be-tween simulated and observed values for the monthly calibration at Reaville, with coeffi-cients of determination 0.73, 0.66 and 0.72, and Nash-Sutcliffe efficiency 0.67, 0.65 and 0.69 for monthly surface runoff, base flow and total stream flow, respectively (Table 6.1). Annual simulated and observed flows show lower fitness than at the monthly levels, due to the limited number of years for statistics. Notable improvement in the correlation and simulation efficiency of daily calibration of surface runoff and stream flow was found by just switching observed streamflow values on two days, September 16 and 17 in 1999. After the switch, coefficients of determination for daily calibration of surface runoff, base flow and total stream flow reaches 0.66, 0.41 and 0.63, while Nash-Sutcliffe efficiency coefficients reach 0.58, 0.37 and 0.57, respectively. The rationality of this switch is con-firmed by checking the daily time series of precipitation, and simulated and observed stream flows. As shown in Figure 6.5, there is a large rainstorm event, actually the Hurri-cane Floyd, arrived this watershed on September 17 in 1999 according to records at the nearest meteorological station in Flemington. The “observed” high flow was recorded on September 16, contradicting to the order of rainfall events. Actually in the original data file, it is commented that the stream values during the Hurricane were estimated rather

37

than truly measured. The example indicates that the one-to-one match of time series of simulated and observed values determines the coefficients of determination and Nash-Sutcliffe efficiency, and large storm events have greater affection. It is acceptable that calibration at the daily level has lower performance than at monthly level due to mis-match in some days. Overall, the model underpredicts flows during large storm events, resulting about 4% to 7% lower for the mean runoff, base flow and stream flow (Table 6.1).

Table 6.2 lists various performance statistics of simulated and observed daily sur-face runoffs, base flows and stream flows at Reaville for each year. The NSE and R2 values are good for most of the years. However, the evaluation statistics for surface ru-noff and stream flow show lower NSE values for the years 2001, 2002, 2004 and 2008. This might be caused by input (precipitation, temperature and other climate data) or measured streamflow data uncertainty during those years. Moreover, NSE is more sensi-tive to extreme values hence lower NSE values can be the result of higher variability be-tween the simulated and observed peak flows. As shown in the table, a switch of the ob-served stream flow on September 16 and 17 in 1999 will greatly improve NSE values from -0.09 and -0.04 to 0.62 and 0.61 for runoff and stream flow, respectively. The switch also improves R2 between the simulated and observed base flows but decreases the NSE.

0.0

0.5

1.0

1.5

2.0

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Ann

ual F

low

(m

3/s)

.

Observed Simulated

Figure 6.1. Annual observed and simulated stream flows at Reaville

0

1

2

3

4

5

6

7

8

9

Jan-

97A

pr-9

7Ju

l-97

Oct

-97

Jan-

98A

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l-98

Oct

-98

Jan-

99A

pr-9

9Ju

l-99

Oct

-99

Jan-

00A

pr-0

0Ju

l-00

Oct

-00

Jan-

01A

pr-0

1Ju

l-01

Oct

-01

Jan-

02A

pr-0

2

Jul-0

2O

ct-0

2Ja

n-03

Apr

-03

Jul-0

3O

ct-0

3Ja

n-04

Apr

-04

Jul-0

4

Oct

-04

Jan-

05A

pr-0

5Ju

l-05

Oct

-05

Jan-

06A

pr-0

6Ju

l-06

Oct

-06

Jan-

07A

pr-0

7Ju

l-07

Oct

-07

Jan-

08A

pr-0

8Ju

l-08

Oct

-08

Month

Mon

thly

Flo

w (

m3/

s)

.

Observed Simulated

Figure 6.2. Monthly observed and simulated stream flows at Reaville

38

0

5

10

15

20

25

30

35

40

45

50

01/0

1/97

03/0

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01/1

5/01

03/1

5/01

05/1

3/01

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1/01

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8/01

11/0

6/01

01/0

4/02

03/0

4/02

05/0

2/02

06/3

0/02

08/2

8/02

10/2

6/02

12/2

4/02

Date

Dai

ly F

low

(m

3/s)

.

Observed Simulated

Figure 6.3. Daily observed and simulated stream flows at Reaville during calibration period

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Jan-

97A

pr-9

7Ju

l-97

Oct

-97

Jan-

98A

pr-9

8Ju

l-98

Oct

-98

Jan-

99A

pr-9

9Ju

l-99

Oct

-99

Jan-

00A

pr-0

0Ju

l-00

Oct

-00

Jan-

01A

pr-0

1Ju

l-01

Oct

-01

Jan-

02A

pr-0

2Ju

l-02

Oct

-02

Jan-

03A

pr-0

3Ju

l-03

Oct

-03

Jan-

04A

pr-0

4Ju

l-04

Oct

-04

Jan-

05A

pr-0

5Ju

l-05

Oct

-05

Jan-

06A

pr-0

6Ju

l-06

Oct

-06

Jan-

07A

pr-0

7Ju

l-07

Oct

-07

Jan-

08A

pr-0

8Ju

l-08

Oct

-08

Month

Bas

eflo

w (

m3/

s)

.

Observed Simulated

Figure 6.4. Filtered monthly base flows of observed and simulated stream flows at Reaville

Table 6.1. Calibration results for flows at Reaville from 1997 to 2002

Flow Time period Mean (m3/s) Mean rel-

ative error STDEV (m3/s)

R2 NSE Obs Sim Obs Sim

Surface runoff

Annual 0.532 0.496 -6.77% 0.218 0.120 0.55 0.48

Monthly 0.532 0.495 -6.95% 0.889 0.542 0.73 0.67

Daily 0.530 0.495 -6.60% 4.660 2.341 0.05

(0.66*) -0.04

(0.58*)

Base flow

Annual 0.445 0.424 -4.72% 0.045 0.066 0.10 -1.46

Monthly 0.445 0.424 -4.72% 0.406 0.297 0.66 0.65

Daily 0.442 0.423 -4.30% 0.676 0.460 0.35

(0.41*) 0.32

(0.37*)

Stream flow

Annual 0.977 0.920 -5.83% 0.235 0.175 0.46 0.38

Monthly 0.977 0.920 -5.83% 1.172 0.780 0.72 0.69

Daily 0.972 0.918 -5.56% 4.684 2.535 0.08

(0.63*) 0.01

(0.57*)

* Switch the observed stream flows on September 16 and 17, 1999.

39

09/16/99

09/17/99

09/17/99

0

50

100

150

200

250

01/0

1/99

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Date

Dai

ly F

low

(m

3/s)

.

0

50

100

150

200

250

300

350

400

Pre

cipi

tatio

n (m

m)

.

Observed Simulated Precipitation

Figure 6.5. Observed and simulated and daily stream flows and precipitation during 1999

Table 6.2. Daily flow calibration and validation at Reaville for each year

Year Surface runoff Base flow Stream flow

R2 NSE R2 NSE R2 NSE 1997 0.40 0.33 0.41 0.31 0.41 0.34 1998 0.45 0.44 0.80 0.71 0.53 0.52

1999 0.01

(0.84*) -0.09

(0.62*)0.14

(0.24*)0.00

(-0.21*)0.03

(0.80*)-0.04

(0.61*) 2000 0.31 0.29 0.33 0.31 0.33 0.31 2001 0.30 -0.01 0.48 0.47 0.38 0.07 2002 0.40 0.07 0.64 0.62 0.44 0.19 2003 0.48 0.48 0.52 0.50 0.49 0.48 2004 0.28 -0.03 0.40 0.16 0.29 0.01 2005 0.39 0.30 0.50 0.30 0.44 0.35 2006 0.43 0.43 0.67 0.66 0.47 0.47 2007 0.40 0.39 0.57 0.54 0.44 0.43 2008 0.19 0.10 0.66 0.66 0.25 0.18

* Switch the observed stream flows on September 16 and 17, 1999.

The validation results are good for stream flows at Reaville (Figure 6.6 and Table 6.3). The simulated annual flow is slightly lower for the year 2003 (Figure 6.1). Monthly simulated and observed stream flows match well except for a few months, where the model underpredicts the flow in March, June and December in 2003, and April in 2007, and overpredicts for July 2004 and November 2007 (Figure 6.2). The time series of measured and simulated daily flows during the validation period (Figure 6.6) show that simulated flows are usually underpredicted on days with daily rainfall more than 15 m3/s.

Means, standard deviations, R2, and NSE values indicate good agreement between simulated and observed values for the monthly calibration at Reaville, with coefficients of determination 0.65, 0.72 and 0.69, and Nash-Sutcliffe efficiency 0.64, 0.71 and 0.68

40

for monthly surface runoff, base flow and total stream flow, respectively (Table 6.3). An-nual simulated and observed flows show higher coefficients of determination but lower Nash-Sutcliffe efficiency than at the monthly level. Coefficients of determination for dai-ly calibration of surface runoff, base flow and stream flow are 0.36, 0.53 and 0.39, while Nash-Sutcliffe efficiency coefficients are 0.33, 0.44 and 0.37, respectively. The runoff shows lower coefficients of determination and Nash-Sutcliffe efficiency compared to base flow, however, it is acceptable for calibration at the daily level. Overall, the model underpredicts surface runoff about 10% and overpredicts base flow about 6% during the validation time period 2003 to 2008, and thus leads to a 3% underprediction for stream flow.

Figure 6.7 displays the observed versus simulated stream flow duration curves at Reaville during 1997 – 2008, which confirms a good agreement between observed and simulated daily flows, however, the model generally overpredicts during days with a low flow less than 0.1 m3/s.

0

5

10

15

20

25

30

35

40

45

50

01/0

1/03

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07

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08

3/3/

08

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08

6/29

/08

8/27

/08

10/2

5/08

12/2

3/08

Date

Dai

ly F

low

(m

3/s)

.

Observed Simulated

Figure 6.6. Daily observed and simulated stream flows at Reaville during validation period

Table 6.3. Validation results for flows at Reaville from 2003 to 2008

Flow Time period Mean (m3/s) Mean rel-

ative error STDEV (m3/s)

R2 NSE Obs Sim Obs Sim

Surface runoff Annual 0.766 0.688 -10.18% 0.126 0.048 0.86 -0.18

Monthly 0.766 0.688 -10.18% 0.828 0.633 0.65 0.64 Daily 0.764 0.686 -10.21% 3.303 2.536 0.36 0.33

Base flow Annual 0.619 0.654 5.65% 0.120 0.062 0.86 0.59

Monthly 0.619 0.654 5.65% 0.471 0.375 0.72 0.71 Daily 0.618 0.653 5.66% 0.662 0.590 0.53 0.44

Stream flow Annual 1.385 1.341 -3.18% 0.216 0.096 0.84 0.51

Monthly 1.385 1.341 -3.18% 1.230 0.914 0.69 0.68 Daily 1.382 1.339 -3.11% 3.589 2.788 0.39 0.37

* Switch the observed stream flows on September 16 and 17, 1999.

41

0.000

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of time that flow is equaled or exceeded

Dai

ly f

low

(m

3/s)

Observed Simulated

Figure 6.7. Observed versus simulated stream flow duration curves at Reaville, 1997 to 2008

6.2. Sediment Calibration

Observations show that high TSS concentrations and loads at Reaville are usually hap-pened with occurrence of high stream flows when high precipitation generates more ru-noff and soil erosion (Figure 6.8). The simulated daily flows with calibrated SWAT mod-el generally have good consistence with observed daily flows, except for some days with high rainfall events. The USGS observed instantaneous flows from water quality sam-pling during 1991 to 2008 are close to corresponding daily flows in most times, but are more than double of daily flows on 7/13/1996 and 3/19/1998 (Figure 6.9). The sediment calibration was carried out such that simulated TSS concentrations matched observed in-stantaneous concentrations as far as possible.

There are two days having extremely high observed instantaneous TSS concentra-tions, which are 205 and 302 mg/L on 07/24/1997 and 03/19/98, respectively. The simu-lated daily TSS concentrations are much lower, at 39.728 and 26.305 mg/L on those two days, respectively. Also on 07/24/1997 and 03/19/98, the observed TSS loads are 75 and 759 Mg/d, which are significantly larger than simulated loads of 41.190 and 8.500 Mg/d, respectively. In other sampling days, observed and simulated loads are comparable. A visual comparison with the concentration (Figure 6.10) and loading (Figure 6.11) graphs, in which the observed TSS values on those two days are excluded, indicates a good ap-proximate sediment calibration.

Table 6.4 gives out the statistic results of TSS loading calibration and validation at Reaville. Average simulated loads and standard deviation for sediment are much lower than observed values if the two extreme observed loads are included. Once the two ex-treme values are excluded, the mean observed and simulated loads during calibration pe-riod are 2.388 Mg/d and 2.236 Mg/d, and standard deviations are 5.287 Mg/d and 5.372 Mg/d, respectively. The slight underprediction of sediment loads by the model is mainly due to the underprediction of stream flows during high rain fall events. Given the facts

42

that there were only a few sampling days per year to calibrate the model, and matching the daily simulated values to those days alone is tedious, the results obtained seem to be reasonable. Note that the observed TSS loads are computed from the product of instanta-neous stream flows and concentrations of samples, while the simulated loads are daily outputs, hence they are not the same items and the comparison is just used for approx-imate calibration.

Observed instantaneous flow (m3/s)

05

1015202530354045

08/1

3/91

10/2

9/91

02/0

6/92

03/1

8/92

05/2

7/92

07/2

3/92

11/0

5/92

02/1

7/93

04/0

6/93

06/0

9/93

07/2

6/93

11/2

3/93

02/0

1/94

03/2

3/94

05/2

5/94

08/3

1/94

05/0

2/96

05/2

1/96

06/0

4/96

06/1

8/96

07/1

0/96

07/1

3/96

08/0

7/96

09/0

3/96

10/0

8/96

11/1

3/96

12/1

0/96

01/0

6/97

02/2

5/97

04/0

1/97

04/2

4/97

05/1

9/97

06/1

2/97

07/2

4/97

08/2

1/97

10/0

8/97

11/1

8/97

12/2

3/97

01/1

6/98

02/2

3/98

03/1

9/98

04/0

9/98

05/0

7/98

06/1

0/98

06/0

1/00

08/3

0/00

02/1

0/05

06/0

1/05

06/0

6/06

11/1

4/07

04/2

9/08

Observed instantaneous TSS concentration (mg/L)

0

100

200

300

400

500

600

08/1

3/91

10/2

9/91

02/0

6/92

03/1

8/92

05/2

7/92

07/2

3/92

11/0

5/92

02/1

7/93

04/0

6/93

06/0

9/93

07/2

6/93

11/2

3/93

02/0

1/94

03/2

3/94

05/2

5/94

08/3

1/94

05/0

2/96

05/2

1/96

06/0

4/96

06/1

8/96

07/1

0/96

07/1

3/96

08/0

7/96

09/0

3/96

10/0

8/96

11/1

3/96

12/1

0/96

01/0

6/97

02/2

5/97

04/0

1/97

04/2

4/97

05/1

9/97

06/1

2/97

07/2

4/97

08/2

1/97

10/0

8/97

11/1

8/97

12/2

3/97

01/1

6/98

02/2

3/98

03/1

9/98

04/0

9/98

05/0

7/98

06/1

0/98

06/0

1/00

08/3

0/00

02/1

0/05

06/0

1/05

06/0

6/06

11/1

4/07

04/2

9/08

Observed instantaneous load (Mg/d)

0

40

80

120

160

200

8/13

/199

110

/29/

1991

2/6/

1992

3/18

/199

25/

27/1

992

7/23

/199

211

/5/1

992

2/17

/199

34/

6/19

936/

9/19

937/

26/1

993

11/2

3/19

932/

1/19

943/

23/1

994

5/25

/199

48/

31/1

994

5/2/

1996

5/21

/199

66/

4/19

966/

18/1

996

7/10

/199

67/

13/1

996

8/7/

1996

9/3/

1996

10/8

/199

611

/13/

1996

12/1

0/19

961/

6/19

972/

25/1

997

4/1/

1997

4/24

/199

75/

19/1

997

6/12

/199

77/

24/1

997

8/21

/199

710

/8/1

997

11/1

8/19

9712

/23/

1997

1/16

/199

82/

23/1

998

3/19

/199

84/

9/19

985/

7/19

986/

10/1

998

6/1/

2000

8/30

/200

02/

10/2

005

6/1/

2005

6/6/

2006

11/1

4/20

074/

29/2

008

2020 759

Figure 6.8. Observed instantaneous stream flows, TSS concentrations and loads based on water quality sampling at Reaville

43

0

5

10

15

20

25

30

35

40

45

02/1

3/91

02/0

6/92

02/1

7/93

02/0

1/94

02/0

2/95

01/2

3/96

05/2

3/96

07/1

6/96

11/1

3/96

02/2

5/97

05/2

2/97

09/2

2/97

11/1

8/97

02/2

3/98

06/1

0/98

08/1

9/99

11/2

1/00

02/2

8/02

05/1

3/03

07/0

7/04

08/0

9/04

07/1

3/05

11/1

6/05

07/1

9/06

11/2

8/06

11/1

4/20

0

8/28

/200

8

Flo

w (

m3/

s)

.

Obserbed instanteneous flow

Observed daily flow

Simulated daily flow

Figure 6.9. Observed instantaneous and daily flows loads and SWAT simulated daily flows at Reaville on water quality sampling days

0

10

20

30

40

50

60

70

80

1/1

/1997

6/3

0/1

997

12/2

7/1

997

6/2

5/1

998

12/2

2/1

998

6/2

0/1

999

12/1

7/1

999

6/1

4/2

000

12/1

1/2

000

6/9

/2001

12/6

/2001

6/4

/2002

TS

S (m

g/L

)

Observed Simulated

Figure 6.10. Simulated and USGS measured TSS concentrations at Reaville during the cali-bration period

0

10

20

30

40

50

60

70

80

90

100

1/1

/19

97

6/3

0/1

99

7

12

/27

/19

97

6/2

5/1

99

8

12

/22

/19

98

6/2

0/1

99

9

12

/17

/19

99

6/1

4/2

00

0

12

/11

/20

00

6/9

/20

01

12

/6/2

00

1

6/4

/20

02

TS

S (M

g/d

)

.

Observed Simulated

Figure 6.11. Simulated and USGS measured TSS loads at Reaville during the calibration period

44

Table 6.4. Statistics of TSS loading calibration and validation at Reaville

Period Obs (Mg/d) Sim (Mg/d)

1997-2002 mean (19 days) 46.031 (2.388*) 5.011 (2.236*) 1997-2002 STDEV 173.524(5.287*) 11.631 (5.372*)

2003-2008 mean (5 days) 3.474 3.505

2003-2008 STDEV 6.484 5.388 * After the removal the two days with daily load ≥ 75 Mg/d.

Figure 6.12 and Figure 6.13 present the discrete USGS observed TSS concentra-tions and loads along with the simulated time series of daily average values, respectively. An initial visual comparison constitutes a good sediment validation. The mean USGS ob-served and simulated loads are 3.474 Mg/d and 3.505 Mg/d, and their standard deviations are 6.484 Mg/d and 5.388 Mg/d, respectively. The statistics show good agreement be-tween the simulated and observed loads although this is based on limited number of sam-pling days. Good agreements were also obtained with the addition water quality monitor-ing data at the seven designed sites through the Neshanic River watershed restoration plan project. Plots comparing simulated time series of TSS concentration with observed data obtained during the project at seven sites are presented in Figure 6.14 to Figure 6.20. Note that many of the sampling days were low flow days which cause corresponding low observed and simulated loads.

0

10

20

30

40

50

60

70

80

11/6

/2002

5/5

/2003

11/1

/2003

4/2

9/2

004

10/2

6/2

004

4/2

4/2

005

10/2

1/2

005

4/1

9/2

006

10/1

6/2

006

4/1

4/2

007

10/1

1/2

007

4/8

/2008

10/5

/2008

TS

S (m

g/L

)

Observed Simulated

Figure 6.12. Simulated and USGS measured TSS concentrations at Reaville during the vali-

dation period

45

0

10

20

30

40

50

60

70

80

90

100

11

/6/2

00

2

5/5

/20

03

11

/1/2

00

3

4/2

9/2

00

4

10

/26

/20

04

4/2

4/2

00

5

10

/21

/20

05

4/1

9/2

00

6

10

/16

/20

06

4/1

4/2

00

7

10

/11

/20

07

4/8

/20

08

10

/5/2

00

8

TS

S (

Mg

/d)

.

Observed Simulated

Figure 6.13. Simulated and USGS measured TSS loads at Reaville during the validation pe-riod

0

10

20

30

40

50

60

70

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.14. Simulated and project measured TSS concentrations at Reaville (N1)

0

20

40

60

80

100

120

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.15. Simulated and project measured TSS concentrations at FN1

46

0

5

10

15

20

25

30

35

40

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.16. Simulated and project measured TSS concentrations at SN1

0

10

20

30

40

50

60

70

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.17. Simulated and project measured TSS concentrations at TN3

0

10

20

30

40

50

60

70

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.18. Simulated and project measured TSS concentrations at TN3a

47

0

10

20

30

40

50

60

70

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.19. Simulated and project measured TSS concentrations at UNT1

0

20

40

60

80

100

120

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TS

S (m

g/L

)

Observed

Simulated

Figure 6.20. Simulated and project measured TSS concentrations at UNT2

6.3. Nutrient Calibration

To obtain a reasonable calibration, the algal growth and death rates at 20º C were set out-side the recommended ranges in SWAT. The best fit for instream concentrations of vari-ous forms of nitrogen and phosphorus was obtained with low algal growth and death rates. The concentrations of organic N and P, ammonia nitrogen, and mineral P are sensi-tive to algal growth and death rates. A high death rate would cause unreasonable high peaks of mineral and organic N/P concentrations, because SWAT simulates chlorophyll in surface runoff and tributaries without accounting the transform into nutrients but calcu-late the transform through algal die-off process once water enters into the main reaches of subbasins. On the contrary, a high algal growth rate would cause ammonia nitrogen and TP concentrations lower than observed data when algal death rate was set to be low.

SWAT modeling results are presented graphically for the USGS station and the seven project calibration sites in Figure 6.21 to Figure 6.65. The graphs compare ob-

48

served versus simulated daily ammonia, nitrite + nitrate (NO2+NO3) and total nitrogen (TN), mineral (MinP) and total phosphorus (TP) concentrations. These daily time series graphs allow visual assessment of the model’s ability to reproduce observed trends, al-though it should be noted that the model predicts daily averages, whereas the observa-tions are point-in-time grabs. Because the observations are not daily averages, they are likely to exhibit greater variability than model predictions.

A second type of graphs, simulated and observed load duration curves were also plotted and compared for water quality calibration purpose. The load duration curves plot loads and matching flow occurrence frequencies and are often applied to find TMDL goals and target reductions. The load duration curves are presented in Section 7.1.7. TMDL Targets.

0

0.1

0.2

0.3

0.4

0.5

0.6

1/1

/1997

6/3

0/1

997

12/2

7/1

997

6/2

5/1

998

12/2

2/1

998

6/2

0/1

999

12/1

7/1

999

6/1

4/2

000

12/1

1/2

000

6/9

/2001

12/6

/2001

6/4

/2002

12/1

/2002

NH

4-N

(m

g/L

)

Observed Simulated

Figure 6.21. Simulated and USGS measured ammonia nitrogen concentrations at Reaville

during the calibration period

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1/1

/2003

6/3

0/2

003

12/2

7/2

003

6/2

4/2

004

12/2

1/2

004

6/1

9/2

005

12/1

6/2

005

6/1

4/2

006

12/1

1/2

006

6/9

/2007

12/6

/2007

6/3

/2008

11/3

0/2

008

NH

4-N

(m

g/L

)

Observed Simulated

Figure 6.22. Simulated and USGS measured ammonia nitrogen concentrations at Reaville

during the validation period

49

0

5

10

15

20

25

30

1/1

/19

97

6/3

0/1

99

7

12

/27

/19

97

6/2

5/1

99

8

12

/22

/19

98

6/2

0/1

99

9

12

/17

/19

99

6/1

4/2

00

0

12

/11

/20

00

6/9

/20

01

12

/6/2

00

1

6/4

/20

02

12

/1/2

00

2

NO

2N

O3

-N (

mg

/L)

Observed Simulated

Figure 6.23. Simulated and USGS measured nitrite + nitrate concentrations at Reaville dur-ing the calibration period

0

2

4

6

8

10

12

14

16

18

20

1/1

/20

03

6/3

0/2

00

3

12

/27

/20

03

6/2

4/2

00

4

12

/21

/20

04

6/1

9/2

00

5

12

/16

/20

05

6/1

4/2

00

6

12

/11

/20

06

6/9

/20

07

12

/6/2

00

7

6/3

/20

08

11

/30

/20

08

NO

2N

O3

-N (

mg

/L)

Observed Simulated

Figure 6.24. Simulated and USGS measured nitrite + nitrate concentrations at Reaville dur-

ing the validation period

0

5

10

15

20

25

30

1/1

/1997

6/3

0/1

997

12/2

7/1

997

6/2

5/1

998

12/2

2/1

998

6/2

0/1

999

12/1

7/1

999

6/1

4/2

000

12/1

1/2

000

6/9

/2001

12/6

/2001

6/4

/2002

12/1

/2002

TN

(m

g/L

)

Observed Simulated

Figure 6.25. Simulated and USGS measured TN concentrations at Reaville during the cali-

bration period

50

0

2

4

6

8

10

12

14

16

18

20

1/1

/2003

6/3

0/2

003

12/2

7/2

003

6/2

4/2

004

12/2

1/2

004

6/1

9/2

005

12/1

6/2

005

6/1

4/2

006

12/1

1/2

006

6/9

/2007

12/6

/2007

6/3

/2008

11/3

0/2

008

TN

(m

g/L

)

Observed Simulated

Figure 6.26. Simulated and USGS measured TN concentrations at Reaville during the vali-

dation period

0

0.05

0.1

0.15

0.2

0.25

1/1

/1997

6/3

0/1

997

12/2

7/1

997

6/2

5/1

998

12/2

2/1

998

6/2

0/1

999

12/1

7/1

999

6/1

4/2

000

12/1

1/2

000

6/9

/2001

12/6

/2001

6/4

/2002

12/1

/2002

Min

P (m

g/L

)

Observed Simulated

Figure 6.27. Simulated and USGS measured MinP concentrations at Reaville during the

calibration period

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

1/1

/2003

6/3

0/2

003

12/2

7/2

003

6/2

4/2

004

12/2

1/2

004

6/1

9/2

005

12/1

6/2

005

6/1

4/2

006

12/1

1/2

006

6/9

/2007

12/6

/2007

6/3

/2008

11/3

0/2

008

Min

P (m

g/L

)

Observed Simulated

Figure 6.28. Simulated and USGS measured MinP concentrations at Reaville during the

validation period

51

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1/1

/1997

6/3

0/1

997

12/2

7/1

997

6/2

5/1

998

12/2

2/1

998

6/2

0/1

999

12/1

7/1

999

6/1

4/2

000

12/1

1/2

000

6/9

/2001

12/6

/2001

6/4

/2002

12/1

/2002

TP

(m

g/L

)

Observed Simulated

Figure 6.29. Simulated and USGS measured TP concentrations at Reaville during the cali-

bration period

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1/1

/2003

6/3

0/2

003

12/2

7/2

003

6/2

4/2

004

12/2

1/2

004

6/1

9/2

005

12/1

6/2

005

6/1

4/2

006

12/1

1/2

006

6/9

/2007

12/6

/2007

6/3

/2008

11/3

0/2

008

TP

(m

g/L

)

Observed Simulated

Figure 6.30. Simulated and USGS measured TP concentrations at Reaville during the vali-

dation period

52

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.31. Simulated and project measured ammonia nitrogen concentrations at Reaville

(N1)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.32. Simulated and project measured ammonia nitrogen concentrations at FN1

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.33. Simulated and project measured ammonia nitrogen concentrations at SN1

53

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.34. Simulated and project measured ammonia nitrogen concentrations at TN3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.35. Simulated and project measured ammonia nitrogen concentrations at TN3a

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.36. Simulated and project measured ammonia nitrogen concentrations at UNT1

54

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NH

4-N

(m

g/L

)

Observed

Simulated

Figure 6.37. Simulated and project measured ammonia nitrogen concentrations at UNT2

0

2

4

6

8

10

12

14

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.38. Simulated and project measured nitrite +nitrate concentrations at Reaville

(N1)

0

1

2

3

4

5

6

7

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.39. Simulated and project measured nitrite +nitrate concentrations at FN1

55

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.40. Simulated and project measured nitrite +nitrate concentrations at SN1

0

5

10

15

20

25

30

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.41. Simulated and project measured nitrite +nitrate concentrations at TN3

0

5

10

15

20

25

30

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.42. Simulated and project measured nitrite +nitrate concentrations at TN3a

56

0

1

2

3

4

5

6

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.43. Simulated and project measured nitrite +nitrate concentrations at UNT1

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

NO

2N

O3-N

(m

g/L

)

Observed

Simulated

Figure 6.44. Simulated and project measured nitrite +nitrate concentrations at UNT2

0

2

4

6

8

10

12

14

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.45. Simulated and project measured TN concentrations at Reaville (N1)

57

0

1

2

3

4

5

6

7

8

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.46. Simulated and project measured TN concentrations at FN1

0

1

2

3

4

5

6

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.47. Simulated and project measured TN concentrations at SN1

0

5

10

15

20

25

30

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.48. Simulated and project measured TN concentrations at TN3

58

0

5

10

15

20

25

30

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.49. Simulated and project measured TN concentrations at TN3a

0

2

4

6

8

10

12

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.50. Simulated and project measured TN concentrations at UNT1

0

1

2

3

4

5

6

7

8

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TN

(m

g/L

)

Observed

Simulated

Figure 6.51. Simulated and project measured TN concentrations at UNT2

59

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.52. Simulated and project measured MinP concentrations at Reaville (N1)

0

0.05

0.1

0.15

0.2

0.25

0.3

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.53. Simulated and project measured MinP concentrations at FN1

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.54. Simulated and project measured MinP concentrations at SN1

60

0

0.05

0.1

0.15

0.2

0.25

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.55. Simulated and project measured MinP concentrations at TN3

0

0.05

0.1

0.15

0.2

0.25

0.3

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.56. Simulated and project measured MinP concentrations at TN3a

0

0.05

0.1

0.15

0.2

0.25

0.3

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.57. Simulated and project measured MinP concentrations at UNT1

61

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

Min

P (m

g/L

)

Observed

Simulated

Figure 6.58. Simulated and project measured MinP concentrations at UNT2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.59. Simulated and project measured TP concentrations at Reaville (N1)

0

0.2

0.4

0.6

0.8

1

1.2

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.60. Simulated and project measured TP concentrations at FN1

62

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.61. Simulated and project measured TP concentrations at SN1

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.62. Simulated and project measured TP concentrations at TN3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.63. Simulated and project measured TP concentrations at TN3a

63

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.64. Simulated and project measured TP concentrations at UNT1

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Jan-0

7

Feb-0

7

Mar-07

Apr-07

May-

07

Jun-0

7

Jul-07

Aug-0

7

Sep-0

7

Oct

-07

Nov-

07

Dec-

07

Jan-0

8

Feb-0

8

Mar-08

Apr-08

May-

08

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct

-08

Nov-

08

Dec-

08

TP

(m

g/L

)

Observed

Simulated

Figure 6.65. Simulated and project measured TP concentrations at UNT2

6.4. Bacteria calibration

The calibration for bacteria is challenging since no enough statistic and watershed specif-ic data and great uncertainties for sources, such as wild life distribution and densities, bacteria contents in manures, and septic systems failing rates and amounts to reach streams. With literature die-off rates for fecal coliform and E. coli, the calibration mainly focused on source loads estimation. The SWAT modeling results are presented graphical-ly for the USGS station and the seven project calibration sites in Figure 6.66 to Figure 6.83. The graphs compare observed versus simulated daily fecal coliform and E. coli concentrations. These daily time series graphs allow visual assessment of the model’s ability to reproduce observed trends, although it should be noted that the model predicts daily averages, whereas the observations are point-in-time grabs. Because the observa-tions are not daily averages, they are likely to exhibit greater variability than model pre-dictions.

64

The simulated fecal coliform and E. coli daily concentrations at Reaville match USGS measurements well. The observed and simulated concentrations generally fall in the same ranges during both calibration and validation time periods. There are some dis-crepancies evident in the simulation with higher fecal coliform concentrations at Stations N1, FN1, UTN1 and UTN2 and higher E. coli concentrations at Stations N1, FN1, SN1 UTN1 and UTN2 during 2007. Examination of the data shows that all of these high con-centration anomalies are associated with very low to moderately low flow conditions. This situation is consistent with the fact that the monitoring task during 2007 of this project was designed to grab samples reflecting dry weather conditions.

The absence of water quality observation data hinders the ability to understand the necessary adjustments needed to conduct a rigorous calibration of the model. Further col-lections of monitoring data are necessary for adequate validation of the model. Despite these limitations, model performance in general is good across the whole suit of moni-tored sites. The calibrated model generally provides results approximate to values and trends seen in the monitoring data that were available and appears acceptable for use in sediment, nutrients, fecal coliform and E. coli load reduction planning.

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Figure 6.66. Simulated and USGS measured fecal coliform concentrations at Reaville dur-

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Figure 6.67. Simulated and USGS measured fecal coliform concentrations at Reaville dur-

ing the validation period

65

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Figure 6.68. Simulated and USGS measured E. coli concentrations at Reaville during the

calibration period

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Figure 6.69. Simulated and USGS measured E. coli concentrations at Reaville during the

validation period

66

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Figure 6.70. Simulated and project measured fecal coliform concentrations at Reaville (N1)

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Figure 6.71. Simulated and project measured fecal coliform concentrations at FN1

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Figure 6.72. Simulated and project measured fecal coliform concentrations at SN1

67

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Figure 6.73. Simulated and project measured fecal coliform concentrations at TN3

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Figure 6.74. Simulated and project measured fecal coliform concentrations at TN3a

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Figure 6.75. Simulated and project measured fecal coliform concentrations at UNT1

68

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Figure 6.76. Simulated and project measured fecal coliform concentrations at UNT2

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Figure 6.77. Simulated and project measured E. coli concentrations at Reaville (N1)

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Figure 6.78. Simulated and project measured E. coli concentrations at FN1

69

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Figure 6.79. Simulated and project measured E. coli concentrations at SN1

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Figure 6.80. Simulated and project measured E. coli concentrations at TN3

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Figure 6.81. Simulated and project measured E. coli concentrations at TN3a

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Figure 6.82. Simulated and project measured E. coli concentrations at UNT1

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Figure 6.83. Simulated and project measured E. coli concentrations at UNT2

6.5. Crop Yield Calibration

The simulated annual crop yields of corn and soybean generally match well with ob-served average yields in Hunterdon County. The simulated corn yield is relative high in 1999 when there was an extreme flooding event happened in spring. Simulated corn yields are relative lower than observation after year 2004, which may be because more relative lower yield lands were converted into urban lands. Note that, the land use data in the modeling is based on 2002 GIS layer, and may not reflect the land use changes in most recent years.

71

0

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Figure 6.84. Simulated and observed annual crop yields of corn and soybean.

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Figure 6.85. Simulated and observed annual crop yields of hay.

72

7. Baseline Results

The calibrated and validated model was utilized for hydrological and pollutant loading analysis under the baseline scenario during the calibration and validation time period from 1997 to 2008.

7.1. Stream flow and Water Balance

7.1.1. Watershed Streamflow Discharges

The time series of stream flows at a location are the result of a range of climate and hy-drological processes, including precipitation, surface runoff, lateral flow, and groundwa-ter recharge and discharge. Uncertainties associated with a lot of process parameters de-termine the variation of time series of stream flows.

Average annual streamflow discharge at the Neshanic River Watershed outlet (sub-basin 16) is 1.51E+09 ft3 per year, or equivalently 21.318 inches of precipitation. Monthly stream flows and variations at the outlet of are depicted in Figure 7.1. The monthly precipitation varies from 3.111 to 4.883 inches, with the highest in June and lowest in February. For comparison purpose, monthly stream flows were represented with the same unit as precipitation, which was obtained by dividing the outflow volume at this location with watershed area. Monthly stream flows vary from 0.876 (August) to 2.172 inches (January), occupying 24% (August) to 66% (February) of monthly precipi-tation. There are six months (January to April, October and December) having average stream flows more than 2 inches, five months (May to July, September and November) between 1 and 2 inches, and one month (August) below 1 inch. Medians of monthly stream flows are close to averages during January to March, May, August, November and December, and are lower 0.3 to 0.8 inches for other months. Variations measured by the spans between the 25th and 75th percentiles are larger during April, June, July, September, October, November and December ranging from 1.507 (April) to 2.215 (October) mm, compared to other months from 0.394 (May) to 0.981 (March) inches. The seasonal dis-tribution of average monthly stream flows generally follows the pattern of precipitation, with larger variations during some monthly due to weather changes. The changes of land covers also affect the seasonal distribution of stream flows. Increasing vegetation covers intercept more water and decrease stream flow. For example, even the highest precipita-tion happens in June, better vegetation covers during June lead to stream flow lower than those during fall, winter and spring months (September to April).

73

0

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Figure 7.1. Monthly stream flow and variation at the watershed outlet during 1997 – 2008

Base flow contribution to stream flow was evaluated by applying a digital filter program (Arnold and Allen, 1999) which separates base flow portions through three passes. Each pass produces a pair of time series of daily base flow and runoff. The actual average base flow contribution is generally between the first and second pass averages. Table 7.1 lists the mean fractions of base flows filtered from the observed flow at Rea-ville during each decades from 1930 to 2008. The variations between decades are not dis-tinct, however there are small percentage decreases since 1970s. The actual base flow contribution was 31%~47% in 1930s, and gradually increased to 34% ~51% in 1960s, but fell to 30%~46% since 1970s and reached a lowest percentage of 28%~44% in 1990s, and then back to 30%~46% in 2000s. The variations among decades may reflect the land use change trend of converting agricultural lands into urban lands. The estimated propor-tion of base flow of the observed flow at Reaville is between 30%~45% during the SWAT model calibration and validation period and it is 33%~48% for the same location of the simulated flow. The proportion for surface runoff and base flow reveal that hydro-logic processes and flow regimes in SWAT are modeled reasonably well.

Table 7.1. Base flow fractions from observed stream flow at the Reaville station

Time period

Base flow Fraction 1

Base flow Fraction 2

Base flow Fraction 3

1930-1939 0.47 0.31 0.25 1940-1949 0.48 0.32 0.25 1950-1959 0.49 0.33 0.26 1960-1969 0.51 0.34 0.27 1970-1979 0.46 0.30 0.24 1980-1989 0.46 0.30 0.24 1990-1999 0.44 0.28 0.22 2000-2008 0.46 0.30 0.24 1930-2008 0.47 0.31 0.24 1997-2008 0.45 0.30 0.24

1997-2008 (Sim) 0.48 0.33 0.28

74

7.1.2. Water Yields, Balance and Source Assessment

The main components of water balance for soil moisture include: the total amount of precipitation falling on the lands, actual evapotranspiration from the basin, surface ru-noff, lateral flow contribution to stream flow (water flowing laterally within the soil pro-file that enters the main channel), soil water content and percolation. Water yield is the net amount of water that leaves the basin and contributes to stream flow in the reach. The water yield includes surface runoff contribution to stream flow, lateral flow contribution to stream flow, groundwater contribution to stream flow (water from the shallow aquifer that returns to the reach) minus the transmission losses (water lost from tributary chan-nels in the HRU via transmission through the bed and becomes recharge for the shallow aquifer).

To understand the prediction performance of SWAT2005 model for different rain-fall conditions the simulated annual average rainfall and other hydrological components for the Neshanic River Watershed over the years from 1997 to 2008 are compared and listed in Table 7.2. The results indicate that more than 50% of the annual precipitation is lost by evapotranspiration in the basin during dry years as compared to less than 50% during wet years. Lateral flow contribution to stream flow and tributary loss in the wa-tershed are not significant. Water yields mainly come from surface runoff and groundwa-ter discharge. According to the annual precipitations, 1997 and 1998 are two dry years, and 2003 and 2006 are two wet years during the simulation period. The use of the term “dry” is relative as the rainfall is greater than 40 inches. The wet years produce larger wa-ter yields than the dry years. In wet years 2003 and 2006, surface runoff annual contribu-tions to stream flow are 66.3% and 65.1% and groundwater contributions are 33.0% and 34.2%, while mean soil water contents are 5.5 and 5.6 inches, respectively,. In dry years 1997 and 1998, surface runoff annual contributions are 70.4% and 62.6% and groundwa-ter contributions are 28.9% and 37.4%, while mean soil water contents are 5.6 and 4.7 inches, respectively. Therefore, surface runoff dominates water yield no matter in a wet or dry year. Compared to wet years, the annual base flow contribution to stream flow dur-ing a dry year may be increased or reduced depending on the initial soil water content and temporal distribution of precipitation over the year.

Average annual water yields from subwatershed lands during the simulation period are given in Table 7.3. Annual water yields of subbasins range from 19.908 inch/yr to 24.010 inch/yr. Surface runoff is the main component of water yields, contributing 57.62% to 76.02%, while groundwater is the second main component contributing to 23.37% to 40.85%. The contributions from lateral flow are small, less than 3%. The Av-erage annual water yield exported from watershed outlet is 21.689 inch/yr, of which 68.54%, 30.44% and 1.02% are contributed from surface runoff, groundwater and lateral flow, respectively.

The average annual water yields of land uses and per unit area of land uses in the whole watershed are given in Table 7.4 and Table 7.5, respectively. Residential-low den-sity, corn, soybean, timothy, forest-deciduous, and wetlands-forested lands generate wa-ter yields more than 108 ft3/yr, as their areas are larger than 1100 acres. Other land uses have less water yields, in the range of 7.119E+06 to 7.535E+07 ft3/yr. The water yields from failing septic systems and cattle direct deposit are negligible compared with those

75

from uplands. Water yields per unit area of land uses are more than 30 inches per year for residential-high density, commercial, institutional, and transportation lands. Rye, hay, timothy, orchard, and forest-mixed lands generate water yields less than 20 inches per year.

Average annual water yields of land uses in each subbasin during 1997- 2008 as shown in Table 7.6 are grouped into five classifications, urban, row crop, other agricul-ture, forest and wetland. The urban lands include residential-high density, residential-medium density, residential-med/low density, residential-low density, commercial, insti-tutional and transportation uses. Row crop lands are agricultural land-generic, corn, soy-bean and rye. Other agriculture includes hay, timothy, pasture and orchard lands. The grouped urban lands have larger variations ranging from 17.213 in/yr (subbasin 18) to 26.624 in/yr (subbasin 9) among subbasins due to the difference in compositions of land uses. Lower urban water yield result from the contribution of dominant percentages of residential-medium density, residential-med/low density and residential-low density, since they generate less water yield per unit land area.

Table 7.2. Water balance components on an annual average basis for the Neshanic river watershed

Period Rainfall (in)

ET (in)

SurQ (in)

LatQ (in)

GWQ (in)

WYLD (in)

SW (in)

PERC (in)

TLOSS (in)

1997 (Dry)

41.4 23.5 10.0 0.1 4.1 14.2 5.6 6.4 0.076

1998 (Dry)

41.6 21.6 10.2 0.2 6.1 16.3 4.7 11.9 0.081

1999 50.3 21.3 17.4 0.2 5.2 22.7 5.5 10.7 0.089

2000 45.2 25.5 10.2 0.1 4.1 14.4 5.5 7.7 0.080

2001 46.6 24.7 14.1 0.2 5.4 19.6 5.1 9.6 0.089

2002 49.6 23.3 13.1 0.2 5.5 18.7 5.7 12.3 0.089

2003 (Wet)

64.3 25.0 18.9 0.3 9.4 28.5 5.5 20.2 0.119

2004 56.1 25.3 17.0 0.2 7.7 24.9 5.7 13.4 0.102

2005 55.2 22.0 18.1 0.2 7.5 25.7 5.7 14.8 0.103

2006 (Wet)

59.9 24.7 17.5 0.3 9.2 26.9 5.6 17.6 0.107

2007 57.0 22.6 17.5 0.2 6.8 24.4 6.0 15.8 0.109

2008 51.4 21.9 15.4 0.3 8.2 23.8 5.6 14.5 0.103

ET = Actual evapotranspiration from HRU, SW = Soil water content, PERC = Water that percolates past the root zone during the time step, SURQ = Surface runoff contribution to stream flow during time step, TLOSS = Transmission losses, water lost from tributary channels in the HRU via transmission through the bed, GWQ = Ground water contribution to stream flow, LATQ = Lateral flow contribution to stream flow, WYLD = water yield (WYLD = SURQ + LATQ + GWQ – TLOSS – pond abstractions), ∆SW = Change of soil water content (∆SW = Rainfall – ET – SURQ – LATQ – PERC).

76

Table 7.3. Average annual water yields of subbasins during 1997- 2008

Subbasin Area (ac) Precipitation

(inch/yr)

Water yielda

(inch/yr)

Surface runoff

(%)

Lateral flow (%)

Groundwater discharge

(%)

1 1480.161 51.545 21.147 67.01% 1.89% 31.09%

2 689.424 51.545 21.923 70.44% 2.65% 26.92%

3 1082.322 51.545 20.812 73.31% 0.64% 26.05%

4 726.490 51.545 24.010 76.02% 0.61% 23.37%

5 333.592 51.545 22.534 69.46% 0.34% 30.20%

6 1109.503 51.545 21.742 66.49% 0.31% 33.20%

7 956.298 51.545 19.908 62.01% 1.15% 36.83%

8 738.845 51.545 20.704 61.16% 1.28% 37.56%

9 434.905 51.545 23.984 75.48% 0.16% 24.36%

10 580.698 51.545 21.031 65.31% 0.65% 34.04%

11 879.695 51.545 23.581 75.30% 0.60% 24.11%

12 958.769 51.545 22.284 71.58% 0.86% 27.56%

13 555.987 51.545 20.706 59.09% 2.86% 38.05%

14 622.706 51.545 21.187 66.90% 1.43% 31.68%

15 664.713 51.545 20.916 66.60% 1.37% 32.03%

16 879.695 51.545 21.693 69.23% 0.80% 29.97%

17 652.358 51.545 22.927 73.84% 0.42% 25.73%

18 709.192 51.545 21.107 65.84% 0.62% 33.54%

19 625.177 51.545 22.554 73.56% 0.65% 25.78%

20 511.508 51.545 22.672 72.88% 0.35% 26.77%

21 995.835 51.545 19.958 57.62% 1.53% 40.85%

22 654.829 51.545 22.231 65.37% 1.58% 33.05%

23 1272.593 51.545 22.094 71.19% 0.71% 28.11%

24 775.911 51.545 21.668 70.24% 0.29% 29.47%

25 622.706 51.545 21.602 66.16% 1.68% 32.15%

Watershed 19513.912 51.545 21.689 68.54% 1.02% 30.44%

a. Water yield is defined as the net amount of water that leaves the subbasin and contributes to streamflow in the main channel (reach). The surface runoff given here is the net surface contribution to the main channel streamflow calculated by subtracting tributary channel loss from surface runoff generated in lands. Pond abstractions are assumed to be nil.

77

Table 7.4. Average annual yields of land uses in the Neshanic River Watershed

Land use name Land use

Area (ac) Water yield

(ft3/yr) Sediment

yield (ton/yr) TN (lb/yr)

TP (lb/yr)

Residential-High Density

URHD 92.269 1.027E+07 11.576 1021.197 107.593

Residential-Medium Density

URMD 190.970 1.793E+07 6.261 1115.771 137.945

Residential-Med/Low Density

URML 336.454 2.919E+07 2.310 1193.714 245.228

Residential-Low Density

URLD 4899.210 3.152E+08 11.445 9421.990 1833.523

Commercial UCOM 256.733 3.050E+07 43.282 3571.587 336.459

Institutional UINS 451.266 4.919E+07 44.507 4362.169 466.571

Transportation UTRN 149.051 1.976E+07 71.041 3774.507 376.716

Agricultural Land-Generic

AGRL 328.947 2.834E+07 35.593 6322.259 374.734

Corn CORN 1834.412 1.602E+08 142.733 111135.074 2907.535

Soybean SOYB 1847.508 1.654E+08 217.696 28408.390 1940.959

Rye RYE 321.963 2.095E+07 1.907 1110.364 127.873

Hay HAY 748.803 5.179E+07 10.757 5175.374 144.800

Timothy TIMO 1671.273 1.189E+08 22.316 16710.316 382.384

Pasture PAST 892.456 7.535E+07 69.892 5074.226 1521.275

Orchard ORCD 99.929 7.119E+06 0.141 1002.356 19.720

Forest-Deciduous FRSD 3047.600 2.482E+08 1.650 8420.022 222.372

Forest-Evergreen FRSE 179.554 1.329E+07 0.023 839.891 15.186

Forest-Mixed FRST 902.409 5.762E+07 0.094 2032.460 63.204

Wetlands-Forested WETF 1101.952 1.015E+08 0.257 12698.042 89.178

Wetlands-Mixed WETL 139.718 1.391E+07 0.045 769.674 75.470

Cattle direct deposit

3.039E+02 111.220 30.585

Septic 3.146E+05 785.667 235.700

78

Table 7.5. Average annual yields per unit area of land uses in the Neshanic River Wa-tershed

Land use name Land use

Area (ac) Water yield

(inch) Sediment yield

(ton/ac) TN

(lb/ac) TP

(lb/ac) Residential-High

Density URHD 92.269 30.676 0.125 11.068 1.166

Residential-Medium Density

URMD 190.970 25.866 0.033 5.843 0.722

Residential-Med/Low Density

URML 336.454 23.903 0.007 3.548 0.729

Residential-Low Density

URLD 4899.210 17.724 0.002 1.923 0.374

Commercial UCOM 256.733 32.730 0.169 13.912 1.311

Institutional UINS 451.266 30.027 0.099 9.667 1.034

Transportation UTRN 149.051 36.516 0.477 25.324 2.527

Agricultural Land-Generic

AGRL 328.947 23.734 0.108 19.220 1.139

Corn CORN 1834.412 24.055 0.078 60.584 1.585

Soybean SOYB 1847.508 24.665 0.118 15.377 1.051

Rye RYE 321.963 17.928 0.006 3.449 0.397

Hay HAY 748.803 19.052 0.014 6.912 0.193

Timothy TIMO 1671.273 19.591 0.013 9.999 0.229

Pasture PAST 892.456 23.259 0.078 5.686 1.705

Orchard ORCD 99.929 19.626 0.001 10.031 0.197

Forest-Deciduous FRSD 3047.600 22.439 0.0005 2.763 0.073

Forest-Evergreen FRSE 179.554 20.391 0.0001 4.678 0.085

Forest-Mixed FRST 902.409 17.590 0.0001 2.252 0.070

Wetlands-Forested WETF 1101.952 25.370 0.0002 11.523 0.081

Wetlands-Mixed WETL 139.718 27.419 0.0003 5.509 0.540

* The average water yield of urban lands is low due to the dominance of low density residential urban land use and its relative low capacity of surface runoff generation.

79

Table 7.6. Average annual water yields of land uses in subbasins during 1997- 2008

Subbasin

Water yield (inch/yr)

Urban Row crop Other agriculture Forest Wetland

1 18.744 23.940 18.025 21.468 24.445

2 21.226 23.894 0.000 20.939 25.430

3 18.438 23.478 20.228 21.181 24.477

4 24.505 24.317 20.050 22.546 25.561

5 21.702 22.773 20.935 21.653 26.074

6 18.278 24.343 21.228 20.045 26.006

7 18.692 24.341 20.998 20.621 0.000

8 18.103 24.334 18.617 20.819 26.428

9 26.624 22.372 19.458 20.118 26.147

10 20.634 23.913 20.888 20.119 26.791

11 26.346 23.586 19.888 21.870 24.362

12 21.763 24.231 20.411 20.857 25.590

13 18.547 24.533 19.430 21.890 26.574

14 17.835 23.572 19.824 22.065 25.571

15 17.213 23.383 18.872 21.545 25.883

16 17.495 23.377 21.309 20.218 25.556

17 25.110 23.474 20.966 20.332 25.565

18 17.814 23.902 19.576 21.698 25.583

19 22.570 23.347 19.947 22.389 25.581

20 17.829 23.412 22.011 21.844 26.071

21 18.853 25.131 19.190 19.935 27.688

22 19.540 24.039 20.741 23.246 25.679

23 17.819 24.144 20.998 22.387 25.600

24 18.040 23.929 20.460 20.573 26.003

25 18.868 23.996 20.404 22.154 25.833

Watershed 20.396 23.835 20.433 21.291 25.600

7.2. Sediment Loading and Yields

7.2.1. Watershed Sediment Loading

The average annual sediment/TSS load discharged at the Neshanic River Watershed out-let (subbasin 16) is 1715.105 ton/year. Figure 7.2 shows the monthly TSS loads and vari-ations obtained from the simulated daily loads during the period from 1997 to 2008. Av-erage monthly loads vary from 6.014 (August) to 19.564 lb/ac (June), with two months

80

below 10 lb/ac (6.014 lb/ac in August and 9.246 lb/ac in November) and other ten months having higher loads between 12.411 (May) and 19.564 lb/ac. Medians of monthly sedi-ment loads are close to averages during January to March and December, and are 2.357 to 10.827 lb/ac lower for other months. Variations measured by the spans between the 25th and 75th percentiles are larger during January, April, June, July, September, Novem-ber and December ranging from 12.143 (November) to 24.951 (October) lb/ac, compared to other months from 6.623 (August) to 10.252 (February) lb/ac. The seasonal distribu-tion of average monthly sediment loads generally follows the pattern of precipitation, with larger variations during some monthly due to weather changes. Of course, land cov-ers and streamflow also affect the seasonal distribution of stream flows.

0

5

10

15

20

25

30

35

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

Month

TS

S (

lb/a

c)

.

Average Median 25th Percentile 75th Percentile

Figure 7.2. Monthly TSS loads and variation at the watershed outlet during 1997-2008

7.2.2. Sediment Yields and Source Assessment

Annual sediment yields from subwatershed lands (Table 7.7) range from 0.008 (subbasin 8) to 0.079 (subbasin 16) ton/ac/yr, due to many combinations of types of land use/cover and soil in this suburban watershed. Total sediment yields of subbasin lands range from 6.125 (subbasin 8) to 69.884 (subbasin 16) ton/yr, with only three subbasins having se-diment yields more than 50 (subbasins 12, 16, 23) ton/yr. The total sediment yield of a subbasin is the product of yield per unit area and land area in that subbasin. Sediment contributions from reaches range from 0.367 (subbasin 22) to 208.337 (subbasin 16) ton/yr, six of which have sediment yield more than 50 ton/yr. Note that reach sediment contribution in each subbasin is the net sediment amount added when stream flow rou-tines through the main channel with erosion and settling processes, and is calculated by subtracting inflow load from outflow load. The spatial distributions of annual sediment yields from the lands and reaches in subbasins are mapped in Figure 7.3 and Figure 7.4, respectively. The Average annual sediment yield exported from the watershed outlet is 1715.105 ton/yr, 40.47% of which is from land yields, and 59.53% from reach contribu-tion. This indicates that stream channel erosion is the major contributor to sediment load-ing in the watershed (Figure 7.5).

81

Table 7.7. Average annual sediment yields of subbasins during 1997- 2008

Sub-basin

Area (ac) Reach

drainage areaa (ac)

Stream discharge

(ft3/s)

Land yield

(ton/ac/yr)

Land yield

(ton/yr)

Reach contribu-

tionb (ton/yr)

Totalc (ton/yr)

1 1480.161 1480.161 3.593 0.012 17.828 39.068 56.895

2 689.424 689.424 1.733 0.036 24.501 6.981 31.482

3 1082.322 1082.322 2.584 0.015 15.973 27.475 43.448

4 726.490 2891.133 7.290 0.048 34.758 16.498 51.255

5 333.592 1413.443 3.443 0.021 6.907 27.760 34.667

6 1109.503 3830.133 9.249 0.019 21.076 29.147 50.223

7 956.298 956.298 2.188 0.043 40.989 1.157 42.146

8 738.845 738.845 1.759 0.008 6.125 10.765 16.890

9 434.905 7141.346 17.687 0.051 22.375 56.402 78.777

10 580.698 580.698 1.401 0.026 14.809 2.169 16.977

11 879.695 14529.796 36.150 0.053 46.960 51.349 98.309

12 958.769 16061.850 39.938 0.058 55.494 161.121 216.615

13 555.987 555.987 1.325 0.027 14.861 4.224 19.084

14 622.706 17668.035 43.617 0.051 31.783 155.977 187.760

15 664.713 664.713 1.596 0.025 16.630 14.302 30.932

16 879.695 19521.325 47.887 0.079 69.884 208.337 278.221

17 652.358 6498.872 16.239 0.039 25.197 81.847 107.043

18 709.192 1373.906 3.308 0.014 9.992 17.288 27.280

19 625.177 5831.687 14.535 0.057 35.651 24.435 60.085

20 511.508 1885.414 4.632 0.057 29.340 22.662 52.002

21 995.835 995.835 2.280 0.023 22.471 8.304 30.775

22 654.829 654.829 1.673 0.032 20.691 0.367 21.059

23 1272.593 3335.923 8.346 0.045 56.769 33.226 89.995

24 775.911 2053.446 5.120 0.031 24.431 10.803 35.233

25 622.706 622.706 1.544 0.046 28.542 7.652 36.194

Wa-tershed

19513.912 19521.325 47.887 0.036 694.034 1021.070 1715.105

a. A reach drainage area includes the containing subbasin area and all upstream subbasin areas. b. Reach contribution is the net sediment amount added within a main channel through the erosion and settling processes. c. Total yield of a subbasin is the sum of yields from both land and main channel sources.

82

Figure 7.3. Annual sediment yields from lands in each subbasin

83

Figure 7.4. Annual sediment yields from reaches in each subbasin

84

Source contributions of average annual load for sediment

40.47%

59.53%

Lands

Stream reaches

Figure 7.5. Source contributions for sediment average annual load

The average annual sediment yields of land uses and per unit area of land uses in the whole watershed are given in Table 7.4 and Table 7.5, respectively. Commercial, in-stitutional, transportation, corn, soybean, pasture, and agricultural land-generic lands yield 35.593 to 217.696 ton/yr, while other land uses have less sediment yields, in the range of 0.023 to 22.316 ton/yr. The sediment yields from failing septic systems and cat-tle direct deposit are nil. Urban areas generally have high sediment yields per unit area, especially the transportation zone with 0.477 ton/ac much higher than all of others. Commercial, residential-high density, soybean, agricultural land-generic (rotation of soy-bean and corn), institutional, corn and pasture land uses have the next high sediment loading rates ranging from 0.169 and 0.078 ton/ac in the descending order. Middle and low density residential and other agricultural land uses have relative lower sediment load-ing rates between 0.002 and 0.033 ton/ac. Forests and wetlands have the lowest loading rates below 0.0005 ton/ac.

Average annual sediment yields of grouped land uses in each subbasin and the wa-tershed during 1997- 2008 are shown in Table 7.8. At the whole watershed level, annual sediment yields of grouped urban, row crop, other agriculture, forest lands and wetlands are 0.0299, 0.0918, 0.0302, 0.0004 and 0.0002 ton/ac/yr, respectively. Row crop lands have higher yields varying from 0.0274 ton/ac/yr (subbasin 9) to 0.3305 ton/ac/yr (subba-sin 13). Other agriculture and urban lands have medium yields varying from 0.0042 ton/ac/yr (subbasin 9) to 0.0656 ton/ac/yr (subbasin 16), and 0.0013 ton/ac/yr (subbasin 20) to 0.0962 ton/ac/yr (subbasin 9), respectively. Variations in sediment yields of these three groups of land uses among subbasins are caused by the difference in compositions of land uses and types of soils. Sediment yield variations of forests and wetlands are small. Figure 7.6 presents the contributions of sediment average annual yield from differ-ent land uses.

85

Table 7.8. Average annual sediment yields of land uses in subbasins during 1997- 2008

Subbasin Sediment yield (ton/ac/yr)

Urban Row crop Other

agriculture Forest Wetland

1 0.0047 0.2140 0.0076 0.0004 0.0003

2 0.0113 0.1368 0.0000 0.0000 0.0001

3 0.0309 0.0525 0.0409 0.0002 0.0004

4 0.0685 0.0326 0.0072 0.0013 0.0000

5 0.0256 0.0781 0.0139 0.0001 0.0000

6 0.0070 0.0484 0.0262 0.0000 0.0001

7 0.0236 0.1494 0.0140 0.0003 0.0000

8 0.0028 0.0421 0.0143 0.0007 0.0002

9 0.0962 0.0274 0.0042 0.0000 0.0000

10 0.0215 0.0821 0.0287 0.0001 0.0000

11 0.0868 0.0712 0.0167 0.0002 0.0001

12 0.0542 0.0945 0.0470 0.0004 0.0001

13 0.0034 0.3305 0.0326 0.0016 0.0011

14 0.0018 0.1140 0.0279 0.0015 0.0001

15 0.0015 0.1168 0.0110 0.0006 0.0012

16 0.0015 0.1330 0.0656 0.0002 0.0000

17 0.0636 0.0667 0.0188 0.0000 0.0000

18 0.0015 0.0332 0.0249 0.0001 0.0000

19 0.0752 0.0768 0.0114 0.0004 0.0000

20 0.0013 0.1091 0.0281 0.0000 0.0001

21 0.0291 0.0684 0.0189 0.0003 0.0004

22 0.0091 0.0751 0.0408 0.0012 0.0005

23 0.0018 0.0763 0.0381 0.0005 0.0001

24 0.0106 0.0688 0.0197 0.0000 0.0000

25 0.0030 0.1505 0.0281 0.0007 0.0001

Watershed 0.0299 0.0918 0.0302 0.0004 0.0002

86

Contributions of sediment average annual yield from different land uses

27.46%

57.38%

14.87%0.04%

0.25%

Urban

Row crop

Other agriculture

Forest

Wetland

Figure 7.6. Contributions of sediment average annual yield from different land uses

7.3. Nitrogen Loading and Yields

7.3.1. Watershed Nitrogen Loading

The average annual total nitrogen (TN) load discharged at the Neshanic River Watershed outlet (subbasin 16) is 229134.145 lb/year. Figure 7.7 shows the monthly TN loads and variations obtained from the simulated daily loads during the period from 1997 to 2008. Average monthly loads vary from 0.244 (August) to 1.597 lb/ac (January), with five months (June to October) below 1 lb/ac and other seven months having higher loads be-tween 1.048 (May) and 1.597 lb/ac (January). Medians of monthly TN loads are close to averages during March, August to October, and December; 0.131 lb/ac higher for January and 0.156 lb/ac higher for February; and are 0.124 to 0.310 lb/ac lower for other months. Variations measured by the spans between the 25th and 75th percentiles are larger during January to April and October to December ranging from 0.847 (February) to 1.332 (De-cember) lb/ac, compared to other months from 0.266 (August) to 0.771 (June) lb/ac. The seasonal distribution of average monthly TN loads does not follows the pattern of preci-pitation, which shows a “V” shape with lower monthly loads during the growing months. This indicates that increasing land vegetations greatly decrease nitrogen loads and TN losses are larger during growing-off season due to nitrogen’s easy mobility in soils and solubility in water.

87

0

0.5

1

1.5

2

2.5

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

Month

TN

(lb

/ac)

Average Median 25th Percentile 75th Percentile

Figure 7.7. Monthly TN loads and variation at the watershed outlet during 1997-2008

7.3.2. Nitrogen Yields and Source Assessment

Annual TN yields from subwatershed lands (Table 7.9) range from 4.340 (subbasin 21) to 39.795 (subbasin 18) lb/ac/yr, due to many combinations of types of land uses/covers and soils in this suburban watershed. Total TN yields of subbasins range from 28222.3 (sub-basin 18) to 2594.786 (subbasin 5) lb/yr, with 8 subbasins (subbasins 18, 25, 24, 16, 23, 12, 9 and 6) having TN yields more than 10000 lb/yr. The total TN yield of lands in a subbasin is the product of TN yield per unit area and land area in that subbasin. In addi-tion to uplands, TN sources in a subbasin also include cattle direct deposit and failing septic system effluents into its main reach and reach contribution due to nitrogen cycle processes. Cattle direct deposits of subbasins vary from 0 to 24.289 lb/yr, while failing septic effluents carry 0 to 105.138 lb/yr. TN contributions from reaches range from -164.061 (subbasin 18) to 1321.488 (subbasin 11) lb/yr, seven of which (subbasins 8, 13, 14, 16, 18 and 25) have negative TN contributions and other have position contributions. Note that reach TN contribution in each subbasin is the net TN load added when stream flow routines through the main channel with alga death and growth, nitrogen transform and settling processes, and is calculated by subtracting inflow load from outflow load. Many reaches have position TN contributions because of there are large amount of algae flowing into stream together with surface runoff and their die-off provides the sources of addition nitrogen input to the reach. The spatial distribution of annual TN yields from the lands in subbasins is mapped in Figure 7.8. 97.84% of the average annual TN load in the watershed is from land yields. Cattle direct deposit, septic systems, and reach contribu-tions are only 0.05%, 0.34% and 1.77%. This indicates that fertilizer applications and other overland practices are the dominant contributors to TN loading in the watershed (Figure 7.9).

88

Table 7.9. Average annual TN yields of subbasins during 1997- 2008

Subbasin Area (ac) Reach drai-nage areaa

(ac)

Stream discharge

(ft3/s)

Land yield (lb/ac/yr)

Land yield (lb/yr)

Cattle direct

deposit (lb/yr)

Septic (lb/yr)

Reach contributio-

na(lb/yr) Totalb (lb/yr)

1 1480.161 1480.161 3.593 4.826 7143.728 0.000 86.022 62.483 7292.232

2 689.424 689.424 1.733 7.226 4981.458 0.000 23.895 4.777 5010.129

3 1082.322 1082.322 2.584 5.149 5572.540 0.000 33.453 50.357 5656.350

4 726.490 2891.133 7.290 6.522 4737.935 0.000 9.558 441.494 5188.987

5 333.592 1413.443 3.443 7.778 2594.786 0.000 0.000 73.543 2668.328

6 1109.503 3830.133 9.249 9.890 10972.606 0.000 76.464 212.066 11261.136

7 956.298 956.298 2.188 5.783 5529.944 0.000 66.906 138.120 5734.970

8 738.845 738.845 1.759 8.369 6183.403 0.000 105.138 -39.334 6249.207

9 434.905 7141.346 17.687 26.911 11703.751 0.000 0.000 177.527 11881.278

10 580.698 580.698 1.401 6.063 3520.569 16.001 0.000 111.844 3648.414

11 879.695 14529.796 36.150 10.546 9277.072 4.743 4.779 1321.488 10608.081

12 958.769 16061.850 39.938 13.024 12487.463 9.707 28.674 283.110 12808.954

13 555.987 555.987 1.325 4.849 2695.854 3.151 47.790 -27.817 2718.977

14 622.706 17668.035 43.617 9.703 6041.861 0.000 28.674 -42.255 6028.280

15 664.713 664.713 1.596 6.236 4144.929 0.000 23.895 3.417 4172.241

16 879.695 19521.325 47.887 17.244 15169.301 17.552 4.779 -68.711 15122.921

17 652.358 6498.872 16.239 14.083 9187.337 24.289 23.895 153.956 9389.477

18 709.192 1373.906 3.308 39.795 28222.300 0.000 19.116 -164.061 28077.355

19 625.177 5831.687 14.535 14.089 8808.110 0.000 4.779 743.546 9556.435

20 511.508 1885.414 4.632 15.831 8097.482 13.740 4.779 63.181 8179.182

21 995.835 995.835 2.280 4.340 4321.706 0.000 62.127 97.003 4480.836

22 654.829 654.829 1.673 8.948 5859.582 7.535 4.779 58.068 5929.964

23 1272.593 3335.923 8.346 11.480 14608.848 6.817 66.906 159.064 14841.635

24 775.911 2053.446 5.120 20.259 15719.070 0.000 38.232 316.625 16073.927

25 622.706 622.706 1.544 26.667 16605.650 7.686 19.116 -49.604 16582.847

Watershed 19513.912 19521.325 47.887 11.489 224187.283 111.220 783.755 4051.887 229134.145

a. Reach contribution is the net amount added within a main channel through the algae growth, decay, settling and other instream processes.

b. Total yield of a subbasin is the sum of yields from both land and main channel contributions.

89

Figure 7.8. Annual nitrogen yields from lands in each subbasin

90

Source contributions of average annual load for TN

97.84%

0.34%

0.05%

1.77%

Lands

Livestock access tostreamsFailing septic tanks

Stream reaches

Figure 7.9. Source contributions for TN average annual load

The average annual TN yields of land uses and per unit area of land uses in the whole watershed are given in Table 7.4 and Table 7.5, respectively. Corn, soybean, timo-thy, wetlands-forested, residential-low density, forest-deciduous, and agricultural land-generic lands yield 111135.074 to 6322.259 lb/yr in the descending order, while other land uses have less TN yields, in the range of 769.674 to 5175.374 lb/yr. The TN yields from failing septic systems and cattle direct deposit are 783.755 and 111.22 lb/yr, respec-tively. Corn lands have high TN yields per unit area, with 60.584 lb/ac much higher than all of others. Transportation, agricultural land-generic, soybean, commercial, wetlands-forested, residential-high density, orchard, timothy and institutional land uses have the next high sediment loading rates ranging from 25.324 to 9.667 lb/ac in the descending order. Other land uses have relative lower sediment loading rates between 1.923 and 6.912 lb/ac.

Average annual TN yields of grouped land uses in each subbasin and the watershed during 1997- 2008 are shown in Table 7.10. At the whole watershed level, annual TN yields of grouped urban, row crop, other agriculture, forest lands and wetlands are 3.836, 33.922, 8.194, 2.735 and 10.846 lb/ac/yr, respectively. Row crop lands have higher yields varying from 13.444 lb/ac/yr (subbasin 3) to 165.859 lb/ac/yr (subbasin 18). Other agri-culture lands and wetlands have medium yields varying from 6.937 lb/ac/yr (subbasin 18) to 14.958 lb/ac/yr (subbasin 4), and 8.885 lb/ac/yr (subbasin 15) to 13.345ton/ac/yr (sub-basin 5), respectively. TN yield variations of urban and forest lands are relative smaller. Variations in TN yields of the groups of land uses among subbasins are caused by the dif-ference in compositions of land uses and types of soils. Figure 7.10 presents the contribu-tions of TN average annual yield from different land uses.

91

Table 7.10. Average annual nitrogen yields of land uses in subbasins during 1997- 2008

Subbasin TN yield (lb/ac/yr)

Urban Row crop Other agriculture Forest Wetland

1 1.711 15.351 8.142 3.041 10.937

2 3.120 20.806 0.000 3.060 9.768

3 4.375 13.444 7.197 3.192 10.930

4 6.104 18.379 14.958 2.407 12.346

5 5.286 13.639 8.280 3.628 13.345

6 2.423 22.676 8.302 2.043 10.855

7 2.305 20.393 9.401 1.851 0.000

8 1.816 37.148 7.386 2.057 10.109

9 8.049 134.553 11.555 2.334 9.982

10 3.519 20.341 7.919 2.160 10.153

11 7.467 18.334 9.182 2.090 8.950

12 5.348 30.471 9.201 1.790 12.328

13 1.808 18.452 9.121 3.289 11.510

14 2.316 16.911 8.721 2.387 12.344

15 1.659 17.421 6.139 2.091 8.885

16 1.504 35.121 7.355 1.626 12.346

17 6.394 67.653 7.877 2.422 12.345

18 5.632 165.859 6.937 5.389 12.754

19 10.147 18.354 10.259 2.019 12.339

20 3.540 27.095 7.291 3.108 10.130

21 3.033 15.343 7.772 2.510 10.981

22 2.913 21.463 8.081 2.017 8.926

23 2.254 18.909 8.705 2.019 10.505

24 3.009 46.084 8.297 3.702 11.016

25 1.926 97.186 7.707 2.109 9.862

Watershed 3.836 33.922 8.194 2.735 10.846

92

Contributions of TN average annual yield from different land uses

10.91%

65.57%

12.47%

5.04%

6.01%

Urban

Row crop

Other agriculture

Forest

Wetland

Figure 7.10. Contributions of TN average annual yield from different land uses

7.4. Phosphorus Loading and Yields

7.4.1. Watershed Phosphorus Loading

The average annual total phosphorus (TP) load discharged at the Neshanic River Wa-tershed outlet (subbasin 16) is 12287.089 lb/year. Figure 7.11 shows the monthly TP loads and variations obtained from the simulated daily loads during the period from 1997 to 2008. Average monthly loads vary from 0.025 (August) to 0.073 lb/ac (June), with three months (February, August and November) below 0.05 lb/ac and other months hav-ing higher loads between 0.05 (January and May) and 0.073 lb/ac (June). Medians of monthly TP loads are close to averages during January to March, July to September, No-vember and December; lower 0.013 to 0.039 lb/ac for other months. Variations measured by the spans between the 25th and 75th percentiles are larger during January, April, June, September and October and December ranging from 0.054 (January) to 0.074 (June) lb/ac, compared to other months from 0.027 (May) to 0.042 (July) lb/ac. The seasonal distribution of average monthly TP loads generally follows the pattern of precipitation. This trend is different from that of TN, which should be due to phosphorus’s immobility in soils, relative smaller solubility in water and its strong adsorbability to sediment. Of course, land covers and streamflow also affect the seasonal distribution of TP loads.

93

0

0.02

0.04

0.06

0.08

0.1

0.12

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

Month

TP

(lb

/ac)

Average Median 25th Percentile 75th Percentile

Figure 7.11. Monthly TP loads and variation at the watershed outlet during 1997-2008

7.4.2. Phosphorus Yields and Source Assessment

Annual TP yields from subwatershed lands (Table 7.11) range from 0.233 (subbasin 1) to 0.960 (subbasin 16) lb/ac/yr, due to many combinations of types of land uses/covers and soils in this suburban watershed. Total TP yields of subbasins range from 153.371 (sub-basin 5) to 862.847 (subbasin 23) lb/yr, with 10 subbasins (subbasins 23,16, 12, 11, 25, 24, 6, 7, 17 and 18) having TN yields more than 500 lb/yr. The total TP yield of lands in a subbasin is the product of TP yield per unit area and land area in that subbasin. In addi-tion to uplands, TP sources in a subbasin also include cattle direct deposit and failing sep-tic system effluents into its main reach and reach contribution due to phosphorus cycle processes. Note that reach TP contribution in each subbasin is the net TP load added when stream flow routines through the main channel with alga death and growth, nitro-gen transform and settling processes, and is calculated by subtracting inflow load from outflow load. 24 out of 25 reaches have position TP contributions because of there are large amount of algae flowing into stream together with surface runoff and their die-off provides the sources of addition phosphorus input to the reach. The spatial distribution of annual TP yields from the lands in subbasins is mapped in Figure 7.12. 92.84% of the average annual TP load in the watershed is from land yields. Cattle direct deposit, septic systems, and reach contributions are only 0.25%, 1.91%, and 5.00%. This indicates that fertilizer applications and other overland practices are the dominant contributors to TP loading in the watershed (Figure 7.13).

94

Table 7.11. Average annual TP yields of subbasins during 1997- 2008

Subbasin Area (ac) Reach drai-nage areaa

(ac)

Stream dis-charge

(ft3/s)

Land yield (lb/ac/yr)

Land yield (lb/yr)

Cattle direct

deposit (lb/yr)

Septic (lb/yr)

Reach con-tributiona

(lb/yr) Totalb (lb/yr)

1 1480.161 1480.161 3.593 0.233 344.538 0.000 25.807 27.098 397.444

2 689.424 689.424 1.733 0.562 387.491 0.000 7.168 17.802 412.462

3 1082.322 1082.322 2.584 0.319 345.333 0.000 10.036 35.347 390.717

4 726.490 2891.133 7.290 0.551 400.455 0.000 2.867 65.220 468.542

5 333.592 1413.443 3.443 0.460 153.371 0.000 0.000 27.135 180.506

6 1109.503 3830.133 9.249 0.499 553.258 0.000 22.939 40.418 616.615

7 956.298 956.298 2.188 0.558 533.669 0.000 20.072 26.088 579.828

8 738.845 738.845 1.759 0.393 290.538 0.000 31.541 15.083 337.163

9 434.905 7141.346 17.687 0.838 364.253 0.000 0.000 44.827 409.080

10 580.698 580.698 1.401 0.623 361.989 4.400 0.000 31.269 397.658

11 879.695 14529.796 36.150 0.705 620.260 1.304 1.434 6.981 629.979

12 958.769 16061.850 39.938 0.786 753.414 2.669 8.602 46.848 811.533

13 555.987 555.987 1.325 0.412 229.098 0.866 14.337 6.375 250.676

14 622.706 17668.035 43.617 0.581 361.586 0.000 8.602 -8.451 361.738

15 664.713 664.713 1.596 0.400 266.099 0.000 7.168 11.519 284.787

16 879.695 19521.325 47.887 0.960 844.253 4.827 1.434 29.395 879.908

17 652.358 6498.872 16.239 0.815 531.562 6.680 7.168 44.460 589.869

18 709.192 1373.906 3.308 0.719 509.795 0.000 5.735 15.157 530.686

19 625.177 5831.687 14.535 0.734 458.741 0.000 1.434 0.000 460.174

20 511.508 1885.414 4.632 0.862 440.689 3.779 1.434 21.936 467.837

21 995.835 995.835 2.280 0.376 374.190 0.000 18.638 24.049 416.877

22 654.829 654.829 1.673 0.479 313.337 2.072 1.434 26.805 343.647

23 1272.593 3335.923 8.346 0.678 862.847 1.875 20.072 39.316 924.109

24 775.911 2053.446 5.120 0.711 551.743 0.000 11.470 9.811 573.023

25 622.706 622.706 1.544 0.891 554.519 2.114 5.735 19.327 581.695

Watershed 19513.912 19521.325 47.887 0.585 11407.028 30.585 235.126 614.350 12287.089

a. Reach contribution is the net amount added within a main channel through the algae growth, decay, settling and other instream processes.

b. Total yield of a subbasin is the sum of yields from both land and main channel contributions.

95

Figure 7.12. Annual phosphorus yields from lands in each subbasin

96

Source contributions of average annual load for TP

92.84%

5.00%

0.25%

1.91%

Lands

Livestock access tostreams

Failing septic tanks

Stream reaches

Figure 7.13. Source contributions for TP average annual load

The average annual TP yields of land uses and per unit area of land uses in the

whole watershed are given in Table 7.4 and Table 7.5, respectively. Corn, soybean, resi-dential-low density and pasture lands yield 2907.535 to 1521.275 lb/yr in the descending order, while other land uses have less TP yields, in the range of 15.186 to 466.571 lb/yr. The TP yields from failing septic systems and cattle direct deposit are 235.126 and 30.585 lb/yr, respectively. Transportation lands have high TP yields per unit area, with 2.527 lb/ac much higher than all of others. Pasture, corn, commercial, residential-high density, agricultural land-generic, soybean, institutional land uses have the next high se-diment loading rates ranging from 1.705 to 1.034 lb/ac in the descending order. Other land uses have relative lower sediment loading rates between 0.070 and 0.729 lb/ac.

Average annual TP yields of grouped land uses in each subbasin and the watershed during 1997- 2008 are shown in Table 7.12. At the whole watershed level, annual TP yields of grouped urban, row crop, other agriculture, forest lands and wetlands are 0.550, 1.235, 0.606, 0.073 and 0.133 lb/ac/yr, respectively. Row crop lands have higher yields varying from 0.800 lb/ac/yr (subbasin 4) to 2.546 lb/ac/yr (subbasin 18). Other agricul-ture and urban ands have medium yields varying from 0.197 lb/ac/yr (subbasin 9) to 1.192 lb/ac/yr (subbasin 20), and 0.201 lb/ac/yr (subbasin 20) to 0.935 ton/ac/yr (subba-sin 9), respectively. TP yield variations of forests and wetlands are relative small. Varia-tions in TP yields of the groups of land uses among subbasins are caused by the differ-ence in compositions of land uses and types of soils. Figure 7.14 presents the contribu-tions of TP average annual yield from different land uses.

97

Table 7.12. Average annual phosphorus yields of land uses in subbasins during 1997- 2008

Subbasin TP yield (lb/ac/yr)

Urban Row crop Other agriculture Forest Wetland

1 0.460 1.404 0.206 0.064 0.071

2 0.574 1.113 0.000 0.060 0.190

3 0.589 1.005 0.815 0.062 0.070

4 0.713 0.800 0.207 0.105 0.085

5 0.576 1.136 0.513 0.082 0.086

6 0.399 0.972 0.608 0.072 0.185

7 0.443 1.251 0.436 0.071 0.000

8 0.379 1.107 0.226 0.091 0.233

9 0.935 2.152 0.197 0.066 0.278

10 0.535 1.135 1.022 0.067 0.092

11 0.875 1.051 0.403 0.067 0.196

12 0.714 1.208 0.807 0.067 0.085

13 0.452 1.561 0.682 0.099 0.098

14 0.352 1.083 0.321 0.085 0.085

15 0.458 1.237 0.294 0.067 0.084

16 0.378 1.329 0.947 0.076 0.085

17 0.775 1.557 1.041 0.080 0.085

18 0.201 2.546 0.460 0.069 0.075

19 0.856 0.988 0.214 0.067 0.085

20 0.201 1.159 1.192 0.063 0.255

21 0.477 0.926 0.203 0.067 0.157

22 0.530 0.951 0.516 0.111 0.085

23 0.348 0.993 0.631 0.067 0.085

24 0.535 1.321 0.447 0.062 0.191

25 0.481 2.449 0.578 0.105 0.245

Watershed 0.550 1.235 0.606 0.073 0.133

98

Contributions of TP average annual yield from different land uses

30.77%

46.99%

18.16%

2.64%

1.45%

Urban

Row crop

Other agriculture

Forest

Wetland

Figure 7.14. Contributions of TP average annual yield from different land uses

7.5. Fecal Coliform Loading and Yields

The current bacteria model of SWAT2005 does not provide accurate monthly and annual outputs for bacteria loads or concentrations at hru, subbasin and reach levels. Though dai-ly bacteria loads for reaches are correct as confirmed with communication with the model developers. Therefore average annual fecal coliform and E. coli yields of land uses and subbasins are not given in this report. The daily bacteria loads at the watershed outlet were utilized to compute monthly and annual bacteria loads for the whole watershed. Nevertheless, some general observations were obtained during the modeling.

7.5.1. Watershed Fecal Coliform Loading

The average annual fecal coliform (FC) loading discharged at the Neshanic River Wa-tershed outlet (subbasin 16) is 1.535E+14 cfu/year, or equivalently 7.865E+09 cfu/ac. Figure 7.15 shows the monthly fecal coliform loads and variations obtained from the si-mulated daily loads during the period from 1997 to 2008. Average monthly loads vary from 3.224E+08 (February) to 2.667E+09 cfu/ac (April), with eleven months (except for April) below 109 cfu/ac between 3.224E+08 (February) and 6.592E+08 cfu/ac (October). Medians of monthly stream flow are close to averages except for April when median is 2.031E+09 cfu/ac lower than average. Variations measured by the spans between the 25th and 75th percentiles are 4.244E+08 cfu/ac during April which is larger than other months ranging from 9.648E+06 (January) to 1.745E+08 (July) cfu/ac. The seasonal distribution of average monthly fecal coliform loads does not follows the pattern of precipitation, which indicates the surface runoff sources are not dominate in the watershed. On the con-tract, cattle direct deposit and failing septic system effluents are the major contributors. The exceptional high peak of fecal coliform load during April is consistent with the as-

99

sumption that all manure applications occur in that month. Larger variations for April among years are due to the variable precipitations in different years.

0.0E+00

5.0E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

3.0E+09

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

Month

FC

(cf

u/ac

)

Average Median 25th Percentile 75th Percentile

Figure 7.15. Monthly fecal coliform loads and variation at the watershed outlet during 1997-2008

7.5.2. Fecal Coliform Yields and Source Assessment The total fecal coliform yield of lands in a subbasin is the product of fecal coliform

yield per unit area and land area in that subbasin. In addition to uplands, fecal coliform sources in a subbasin also include cattle direct deposit and failing septic system effluents into its main reach. Cattle direct deposits of subbasins vary from 0 to 9.073E+12 cfu/yr, with 15 subbasins (subbasins 1 to 9, 14, 15, 18,19, 21 and 24) having no cattle direct de-posit and other subbasins are in the range of 1.177E+12 (subbasin 13) to 9.073E+12 (subbasin 17) cfu/yr. Fecal coliform loads from failing septic effluents vary from 0 to 1.192E+13 cfu/yr, with 3 subbasins (subbasins 5, 9 and 10) having no septic input and other subbasins are in the range of 5.419E+11 (subbasin 22) to 1.192E+13 (subbasin 8) cfu/yr. Fecal coliform contributions from reaches are not calculated because the current version of SWAM only output outflow bacteria loads of reaches. The spatial distributions of annual fecal coliform loads from cattle direct deposits and failing septic systems are mapped in Figure 7.16 and Figure 7.17, respectively. As shown in Figure 7.18 the major source contributions of average annual load for fecal coliform are from manure applica-tion, cattle direct deposits and failing septic effluents, occupying 31.34%, 18.90% and 45.94%, respectively. Since there are some issues with the latest version of SWAT2005, the monthly and annual output results of bacteria yields from HRUs and loads at subbasin outlets are not reliable. The bacteria contributions were estimated as the differences of watershed loads of the baseline scenario and corresponding operation removal scenarios. Sensitivity analyses indicate that even the original estimates of the deer and goose densi-ties are increased to ten times, the load from animal grazing is still negligible compared to septic and cattle direct deposits. Therefore, to reduce fecal coliform loads in the wa-tershed, measures should focus on the control of manure application, cattle direct deposits and failing septic effluents into streams.

100

Table 7.13. Average annual fecal coliform yields of subbasins during 1997- 2008

Subbasin Area (ac) Reach drainage

areaa (ac) Stream dis-

charge (ft3/s) Cattle direct

deposit (cfu/yr) Septic

(cfu/yr)

1 1480.161 1480.161 3.593 0.000E+00 9.755E+12

2 689.424 689.424 1.733 0.000E+00 2.710E+12

3 1082.322 1082.322 2.584 0.000E+00 3.794E+12

4 726.490 2891.133 7.290 0.000E+00 1.084E+12

5 333.592 1413.443 3.443 0.000E+00 0.000E+00

6 1109.503 3830.133 9.249 0.000E+00 8.671E+12

7 956.298 956.298 2.188 0.000E+00 7.587E+12

8 738.845 738.845 1.759 0.000E+00 1.192E+13

9 434.905 7141.346 17.687 0.000E+00 0.000E+00

10 580.698 580.698 1.401 5.977E+12 0.000E+00

11 879.695 14529.796 36.150 1.772E+12 5.419E+11

12 958.769 16061.850 39.938 3.626E+12 3.252E+12

13 555.987 555.987 1.325 1.177E+12 5.419E+12

14 622.706 17668.035 43.617 0.000E+00 3.252E+12

15 664.713 664.713 1.596 0.000E+00 2.710E+12

16 879.695 19521.325 47.887 6.557E+12 5.419E+11

17 652.358 6498.872 16.239 9.073E+12 2.710E+12

18 709.192 1373.906 3.308 0.000E+00 2.168E+12

19 625.177 5831.687 14.535 0.000E+00 5.419E+11

20 511.508 1885.414 4.632 5.133E+12 5.419E+11

21 995.835 995.835 2.280 0.000E+00 7.045E+12

22 654.829 654.829 1.673 2.815E+12 5.419E+11

23 1272.593 3335.923 8.346 2.546E+12 7.587E+12

24 775.911 2053.446 5.120 0.000E+00 4.335E+12

25 622.706 622.706 1.544 2.871E+12 2.168E+12

Watershed 19513.912 19521.325 47.887 4.155E+13 8.888E+13

101

Figure 7.16. Annual fecal coliform yields from cattle direct deposits in subbasins

102

Figure 7.17. Annual fecal coliform yields from failing septic systems in subbasins

103

Source contributions of average annual load for fecal coliform

45.94%

31.34%

2.46%

1.37%

18.90%

Failing septic tanks

Livestock access tostreams

Manure application

Livestock grazing

Wildlife

Figure 7.18. Source contributions of average annual load for fecal coliform

7.6. E. coli Loading and Yields

7.6.1. Watershed E. Coli Loading

The average annual E. coli (EC) loading discharged at the Neshanic River Watershed out-let (subbasin 16) is 9.632E+13 cfu/year, or equivalently 4.937E+09 cfu/ac. Figure 7.19 shows the monthly E. coli loads and variations obtained from the simulated daily loads during the period from 1997 to 2008. Average monthly loads vary from 2.031E+08 (Feb-ruary) to 1.671E+09 cfu/ac (April), with eleven months (except for April) below 109 cfu/ac between 2.009E+08 (February) and 4.115E+08 cfu/ac (October). Medians of monthly stream flow are close to averages except for April when median 1.272E+09 cfu/ac lower than average. Variations measured by the spans between the 25th and 75th percentiles are 2.658E+08 cfu/ac during April which is larger than other months ranging from 5.979E+06 (January) to 1.095E+08 (July) cfu/ac. The seasonal distribution of aver-age monthly E. coli loads does not follows the pattern of precipitation, which indicates the surface runoff sources are not dominate in the watershed. On the contract, cattle direct deposit and failing septic system effluents are the major contributors for E. coli loading. The exceptional high peak of E. coli load during April is consistent with the assumption that all manure applications occur in that month. Larger variations for April among years are due to the variable precipitations in different years.

104

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08

1.0E+09

1.2E+09

1.4E+09

1.6E+09

1.8E+09

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

Month

EC

(cf

u/ac

)

Average Median 25th Percentile 75th Percentile

Figure 7.19. Monthly E. coli loads and variation at the watershed outlet during 1997-2008

7.6.2. E. Coli Yields and Source Assessment

The total E. coli yield of lands in a subbasin is the product of E. coli yield per unit area and land area in that subbasin. In addition to uplands, E. coli sources in a subbasin also include cattle direct deposit and failing septic system effluents into its main reach. Cattle direct deposits of subbasins vary from 0 to 5.671E+12 cfu/yr, with 15 subbasins (subba-sins 1 to 9, 14, 15, 18,19, 21 and 24) having no cattle direct deposit and other subbasins are in the range of 7.355E+11 (subbasin 13) to 5.671E+12 (subbasin 17) cfu/yr. E. coli loads from failing septic effluents vary from 0 to 7.511E+12 cfu/yr, with 3 subbasins (subbasins 5, 9 and 10) having no septic input and other subbasins are in the range of 3.414E+11 (subbasin 22) to 7.511E+12 (subbasin 8) cfu/yr. E. coli contributions from reaches are not calculated because the current version of SWAM only output outflow bacteria loads of reaches. The spatial distributions of annual E. coli loads from cattle di-rect deposit and failing septic system are mapped in Figure 7.20 and Figure 7.21, respec-tively. As shown in Figure 7.22 the major source contributions of average annual load for E. coli are from manure application, cattle direct deposits and failing septic effluents, oc-cupying 31.25%, 18.81% and 46.09%, respectively. Sensitivity analyses indicate that even the original estimates of the deer and goose densities are increased to ten times, the load from animal grazing is still negligible compared to septic and cattle direct deposits. Therefore, to reduce E. coli loads in the watershed, measures should focus on the control of manure application, cattle direct deposits and failing septic effluents into streams.

It should be reminded that the modeling results of bacteria loads rely on estimates of related sources, which is a very difficult task due to no enough statistic data about wild life distribution densities and bacteria contents in manures nevertheless to say data in a specific watershed. The uses of bacteria modeling results are most sound in comparing relative reduction effects of BMPs.

105

Table 7.14. Average annual E. coli yields of subbasins during 1997- 2008

Subbasin Area (ac) Reach drainage

areaa (ac) Stream dis-

charge (ft3/s) Cattle direct

deposit (cfu/yr) Septic

(septic/yr)

1 1480.161 1480.161 3.593 0.000E+00 6.145E+12

2 689.424 689.424 1.733 0.000E+00 1.707E+12

3 1082.322 1082.322 2.584 0.000E+00 2.390E+12

4 726.490 2891.133 7.290 0.000E+00 6.828E+11

5 333.592 1413.443 3.443 0.000E+00 0.000E+00

6 1109.503 3830.133 9.249 0.000E+00 5.463E+12

7 956.298 956.298 2.188 0.000E+00 4.780E+12

8 738.845 738.845 1.759 0.000E+00 7.511E+12

9 434.905 7141.346 17.687 0.000E+00 0.000E+00

10 580.698 580.698 1.401 3.736E+12 0.000E+00

11 879.695 14529.796 36.150 1.107E+12 3.414E+11

12 958.769 16061.850 39.938 2.266E+12 2.048E+12

13 555.987 555.987 1.325 7.355E+11 3.414E+12

14 622.706 17668.035 43.617 0.000E+00 2.048E+12

15 664.713 664.713 1.596 0.000E+00 1.707E+12

16 879.695 19521.325 47.887 4.098E+12 3.414E+11

17 652.358 6498.872 16.239 5.671E+12 1.707E+12

18 709.192 1373.906 3.308 0.000E+00 1.366E+12

19 625.177 5831.687 14.535 0.000E+00 3.414E+11

20 511.508 1885.414 4.632 3.208E+12 3.414E+11

21 995.835 995.835 2.280 0.000E+00 4.438E+12

22 654.829 654.829 1.673 1.759E+12 3.414E+11

23 1272.593 3335.923 8.346 1.592E+12 4.780E+12

24 775.911 2053.446 5.120 0.000E+00 2.731E+12

25 622.706 622.706 1.544 1.794E+12 1.366E+12

Watershed 19513.912 19521.325 47.887 2.597E+13 5.599E+13

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Figure 7.20. Annual E. coli yields from cattle direct deposits in subbasins

107

Figure 7.21. Annual E. coli yields from failing septic systems in subbasins

108

Source contributions of average annual load for E. coli

46.09%

31.25%

2.45%

1.40%

18.81%

Failing septic tanks

Livestock access tostreams

Manure application

Livestock grazing

Wildlife

Figure 7.22. Source contributions of average annual load for E. coli

7.7. TMDL Targets

7.7.1. TMDL and Load Duration Curve

A Total Maximum Daily Load (TMDL) is a calculation of the maximum amount of a pollutant that a water body can receive and still meet water quality standards, and an allo-cation of that amount to the pollutant's sources. A TMDL is the sum of the allowable loads of a single pollutant from all contributing point and nonpoint sources. The calcula-tion must include a margin of safety to ensure that the water body can be used for the purposes the State has designated. The calculation must also account for seasonable vari-ation in water quality. A TMDL is defined by the simple equation:

TMDL = LC =ΣWLA+ΣLA +MoS + RC

where: TMDL = total maximum daily load; LC = loading capacity; WLA = wasteload allocation for point sources; LA = load allocation for nonpoint sources; MoS = margin of safety; and RC = reserve capacity.

A modified loading capacity (LC') can be defined as:

LC'= LC −MoS − RC =ΣWLA+ΣLA

Since it incorporates both RC and MoS implicitly or explicitly through reduced wa-ter quality targets, LC' is equal to the total maximum daily load allocated among all point and nonpoint sources.

It is important to recognize that LC' is sometimes expressed as an average daily load based upon average long term flow conditions. These long term average TMDLs

109

have been dubbed as “bare bones” TMDLs due to the simplicity of the calculation and their lack of usefulness. While these TMDLs seem to satisfy the requirements of the Clean Water Act, they have contributed little to any watershed/water body assessment and restoration plans. These types of TMDLs do little to characterize the problems the TMDLs are intended to address. Without adequate characterizations, appropriate solu-tions cannot be identified and implemented.

For TMDLs to be more beneficial in the assessment and implementation process, TMDLs should reflect adequate water quality across flow conditions rather than at a sin-gle flow event such as average daily flow. Many states have begun to use load duration curves as a more robust method for setting TMDL targets. It is also a useful tool for bet-ter characterizing the pollutant problems over the entire flow regime. A duration curve is a graph representing the percentage of time during which the value of a given parameter (e.g. flow, load) is equaled or exceeded. Such a graph can be easily generated using a spreadsheet computer program. The following presents the steps involved in developing a load duration curve.

Step 1. Develop Flow Duration Curve:

Using available daily streamflow data, a flow duration curve is developed for the site in question. Data for the curve is generated by: 1) ranking the daily flow data from highest to lowest; 2) calculating percent of days these flows were exceeded (= rank ÷ number of data points).

Step 2. Develop Load Duration Curve:

The load duration curve is developed by multiplying the ranked stream flows by the water quality standard for the parameters under examination and by a conversion factor. To apply a 10% margin of safety (MOS), the results are divided by 1.1. In this case, a 10% MOS was selected to account for uncertainties in the gauged flow data.

Step 3. Plot Water Quality Sample Data on Load Duration Curve:

In order to compare water quality sample data to the load duration curve, the first task is to calculate daily loads for each sample using the pollutant concentration and stream flow for the particular day. Next, the flow values for each day are compared to the flow duration curve data in order to determine the value for “Percent of Days Flow Exceeded” which is equiva-lent to “Percent of Days Load Exceeded”. These load and percent data points are then plotted on the load duration curve. Points above the curve represent exceedances of the water stan-dards and the associated allowable loadings.

Use of Load Duration Curves in Assessments and TMDLs

A load duration curve has a number of uses and benefits:

The load duration curve approach is useful for characterizing the problem and providing a visual display for people to better understand the problem and the

110

TMDL targets. With the load duration curves, the frequency and magnitude of the water quality standards and allowable loads are easily presented. The magni-tude of loading reduction can be better understood.

Load duration curves can be used to characterize flow conditions under which standard exceedances are occurring. In general, exceedances that occur in the 0 to 10% area of the curve may be considered to represent unique high flow prob-lems that may exceed feasible management remedies. Exceedances in the 99 to 100% reflect extreme drought conditions.

Different loading mechanisms can dominate at different flow regimes. The load duration curve can be used to begin differentiating between nonpoint source and point source problems. In general, exceedances of the load duration curve during the higher flows can be indicative of nonpoint source problems. Exceedances during the lower flows can be indicative of point source problems. However, each water body must be considered on a case by case basis.

Load duration curves can show seasonal water quality effects. Data points that cluster within a narrow range of the percent of load exceeded can be associated with the season when that range of flows typically occur.

Water quality conditions between multiple reaches and watersheds can be ex-amined through the comparison of load duration curves at different sites.

Using the load duration as a TMDL target. TMDLs can be developed which set load limitations over the entire flow range, not just for an annual average. With this type of target, the goal of the TMDL may be to reduce the number of sam-ples exceeding the load duration curve (or TMDL target) to less than 10% for the period of concern.

It should be recognized that not only there are uncertainties with the water quality standards and the associated load duration curves, but one must critically consider the use of the “less than 10%” exceedance threshold as the TMDL goal. While these value is commonly used by many states in their assessments, actual allowable exceedance fre-quency could be more or need to be less than 10% for the beneficial uses, depending upon the particular water body and pollutant.

7.7.2. TMDL Targets of the Neshanic River watershed

The TMDL targets for the Neshanic River Watershed in this plan is defined as the total pollutant loadings that satisfy the water quality at the watershed outlet. A 10% safety margin and less than 10% exceedance threshold were adopted to determine the targets. The time period of load duration curves shown in Figure 7.23 to Figure 7.37 are 1997- 2008. The load duration curves of TSS, TN and TP based on measured data at the Rea-ville station indicate that nonpoint sources are dominate in the watershed, since the trend that loads increase with stream flows are very apparent. This kind of increasing trend of fecal coliform and E. coli in the load duration curves based on measured data at the Rea-ville station is not distinct, and many observed loads during medium and low flows are as high as those during high flow conditions. This phenomenon indicates that there are point sources are the major contributors for bacteria, which should be the direct deposit of ma-

111

nures from cattle and failing septic effluents. The same trends are followed for the load duration curves obtained with simulated data at the watershed outlet. The almost flat bands of simulated loads in the duration curves of fecal coliform and E. coli are the re-sults of the simplified assumption that input from direct deposit of failing septic effluent into streams remain constant through the year and cattle direct deposits remain constant during grazing period.

The frequencies of TMDL exceedances and target reduction percentage at the Rea-ville station and watershed outlet are given in Table 7.15. The measured flow and con-centration data at Reaville indicate that frequencies of TMDL exceedances for TSS, TN, TP, FC and EC are 7.32%, 0, 30.49%, 37.50% and 59.02%, respectively. TSS and TN satisfy the TMDL goal of “less than 10%” exceedance threshold, while the loads of TP, fecal coliform and E. coli need to be reduced by 48%, 90% and 91% according the targets at Reaville. The simulated results at the watershed outlet are comparable to those at Rea-ville due to the short distance from it. The frequencies of TMDL exceedances for TSS, TN, TP, FC and EC at the outlet are 12.25%, 1.76%, 37.96%, 60.96% and 63.38%, re-spectively. The target reductions at the watershed outlet are 9% for TSS, 49% for TP, 89 for Fecal coliform and E. coli.

Table 7.15. Frequencies of TMDL exceedances and target reduction percentages

Frequencies of exceedances Target reduction

Pollutant Measured at Reaville (N1)

Simulated at Reaville (N1)

Simulated at watershed

outlet

Measured at Reaville (N1)

Simulated at Reaville (N1)

Simulated at watershed

outlet TSS 7.32% 8.30% 12.25% 0 0 9% TN 0 2.03% 1.76% 0 0 0 TP 30.49% 38.49% 37.96% 48% 48% 49% FC 37.50 61.15% 60.96% 90% 90% 89% EC 59.02% 63.91% 63.38 % 91% 91% 89%

112

TSS load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TS

S lo

ad (to

n/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% margin of safety)Actual loads as sampled

Figure 7.23. TSS load duration curve at Reaville (N1) based on measured data

TN load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

100000.000

1000000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TN

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% margin of safety)Actual loads as sampled

Figure 7.24. TN load duration curve at Reaville (N1) based on measured data

113

TP load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TP

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% margin of safety)Actual loads as sampled

Figure 7.25. TP load duration curve at Reaville (N1) based on measured data

FC load duration curve 1997-2008, Station N1

1.E+07

1.E+08

1.E+09

1.E+10

1.E+11

1.E+12

1.E+13

1.E+14

1.E+15

1.E+16

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

FC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% margin of safety)Actual loads as sampled

Figure 7.26. Fecal coliform load duration curve at Reaville (N1) based on measured data

114

EC load duration curve 1997-2008, Station N1

1.0E+07

1.0E+08

1.0E+09

1.0E+10

1.0E+11

1.0E+12

1.0E+13

1.0E+14

1.0E+15

1.0E+16

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

EC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% margin of safety)Actual loads as sampled

Figure 7.27. E. coli load duration curve at Reaville (N1) based on measured data

TSS load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TS

S lo

ad (to

n/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.28. TSS load duration curve at Reaville (N1) based on simulation

115

TN load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

100000.000

1000000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TN

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.29. TN load duration curve at Reaville (N1) based on simulation

TP load duration curve 1997-2008, Station N1

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TP

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.30. TP load duration curve at Reaville (N1) based on simulation

116

FC load duration curve 1997-2008, Station N1

1.E+10

1.E+11

1.E+12

1.E+13

1.E+14

1.E+15

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

FC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.31. Fecal coliform load duration curve at Reaville (N1) based on simulation

EC load duration curve 1997-2008, Station N1

1.0E+07

1.0E+08

1.0E+09

1.0E+10

1.0E+11

1.0E+12

1.0E+13

1.0E+14

1.0E+15

1.0E+16

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

EC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.32. E. coli load duration curve at Reaville (N1) based on simulation

117

TSS load duration curve 1997-2008, watershed outlet

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TS

S lo

ad (to

n/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.33. TSS load duration curve at watershed outlet based on simulation

TN load duration curve 1997-2008, watershed outlet

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

100000.000

1000000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TN

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.34. TN load duration curve at watershed outlet based on simulation

118

TP load duration curve 1997-2008, watershed outlet

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

TP

load

(lb

/day

)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.35. TP load duration curve at watershed outlet based on simulation

FC load duration curve 1997-2008, watershed outlet

1.E+10

1.E+11

1.E+12

1.E+13

1.E+14

1.E+15

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

FC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.36. Fecal coliform load duration curve at watershed outlet based on simulation

119

EC load duration curve 1997-2008, watershed outlet

1.0E+07

1.0E+08

1.0E+09

1.0E+10

1.0E+11

1.0E+12

1.0E+13

1.0E+14

1.0E+15

1.0E+16

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of days load exceeded

EC

load

(cf

u/da

y)

Load duration curve (at water quality standard)Load duration curve (with 10% marginl of safety)Simulated loads with SWAT

Figure 7.37. E. coli load duration curve at watershed outlet based on simulation

120

7.8. Critical Areas of Pollutant Loads Reduction

Based on analysis about simulated pollutant yields from land uses in subbasins and reach contributions, and estimates of cattle direct deposits and failing septic system effluents, grouped land use areas and reaches are classified into five categories for each kind of loads according their yields per unit area and loads directly added to reaches. The results are summarized in Table 7.16, in which the lower the rank the higher the priority is. Cat-egory I represent the highest priority areas for load reduction; II the next priority; and so on. Both classes I and II may be considered as critical areas for pollutant loads reduction. Since bacteria yields from wetlands were not simulated in the model, they are excluded from classification for bacteria loading in the table.

The ranks were not classified for specific subbasins but for land uses, because the same type of land use areas generally have similar yields per unit area regardless the dif-ference in soil types. Furthermore, this assignment should eliminate the inaccurate or in-appropriate simulation results for specific locations caused by uncertain factors or imper-fect model calibration.

Table 7.16. Land classification for load reduction

Load Urban Row crop Other

agricultureForest Wetland Reach

TSS III I II IV V I (all reaches) TN IV I III V II V TP III I II V IV V

FC IV II (manure application

areas)

III (grazing pasture lands)

V

I (reaches with cattle deposit

and septic efflu-ent)

EC IV II (manure application

areas)

III (grazing pasture lands)

V

I (reaches with cattle deposit

and septic efflu-ent)

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8. BMP Scenarios

8.1. Definition of BMP Scenarios

The calibrated SWAT model was applied to analyze twenty scenarios of potential best management practice options in order to reduce pollutant loads by putting them at the lands use sources, overland pathways and stream channels. These BMP scenarios are de-fined in Table 8.1, in which a BMP was applied to all applicable lands or channels across the whole watershed. Each management option was considered alone in one of the eleven single-focus management scenarios, and then combinations of effective individual man-agement options were investigated.

8.1.1. Single-focus Management Scenarios

The baseline scenario (S0) represents the existing land use management conditions in the watershed, which is characterized by modest fertilizer applications and minimum tillage (chisel plow and disk plow). In addition to the baseline, the following eleven single-focus scenarios with individual BMPs were evaluated:

Scenario S1 – Reduce manure application

Manure application is one of the major bacteria sources in the Neshanic River watershed. The application rates of cattle and horse manures to corn lands were reduced from 45 Mg/ha in baseline scenario to 11.6 Mg/ha.

Scenario S2 – Grazing management

Over grazing can lead to poor land covers and cause severe soil loss. To preserve certain amount of grass on the land, the minimal dry grass biomass to be left on pasture lands was increased from 200 kg/ha in baseline scenario to 700 kg/ha. It was assumed that grazing stopped once the minimal grass biomass limit was reached, and animals would be fed with hays harvested from other lands.

Scenario S3 – Nitrogen commercial fertilizer management

This scenario involves reducing nitrogen commercial fertilizer application rates by 25% from existing levels, for all agricultural lands and urban lawns.

Scenario S4 – Phosphorus commercial fertilizer management

This scenario involves reducing phosphorus commercial fertilizer application rates by 25% from existing levels, for all agricultural lands and urban lawns.

Scenario S5 – Phosphorus commercial fertilizer management

Phosphorus commercial fertilizer application rates were reduced by 25% for all agricul-tural lands from existing levels, and by 100% for urban lawns.

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Scenario S6 – No Tillage

Minimum tillage (chisel plow and disk plow) was already applied as a conservation til-lage approach to row crop land uses (AGRL, corn, soybean, and rye) in the watershed. This scenario investigated going to a very low tillage approach on row crops, with only one no-tillage operation prior to planting each year. No-till agriculture involves the amount, orientation and distribution of crop and other plant residue on the soil surface year round while limiting soil-disturbing activities to only those necessary to place nu-trients, condition residue and plant crops. No-till reduces sheet and rill and wind erosion, slows down surface runoff and peak runoff, increases infiltration and reduces surface ru-noff by increasing land cover and surface roughness, and works to improve soil organic matter content. To represent this scenario, the no-till operation in SWAT model was ap-plied and SCS curve numbers (CN) was reduced by three percent. Overland Manning’s roughness coefficients (OV_N) and biomass mixing factor (BIOMIX) were increased to 0.3 and 0.4, respectively. USLE cover factors (USLE_C) were decreased to be 0.03 for corn, soybean and corn-soybean rotation fields, and 0.01 for rye lands.

Scenario S7 – Cover crop

Cover crops were represented with SWAT by scheduling a crop rotation within a single year. Notice that SWAT does not allow growing of two crops in a single HRU simulta-neously. The cover crop BMP was simulated by planting winter rye following crop harv-est and killing winter rye by crop planting for corn-soybean rotation (AGRL), corn and soybean lands.

Scenario S8 – Filter strips

Field borders are installed along the perimeter of a field to reduce sediment, nutrients, pesticides, and bacteria in surface runoff as it passes through the edge-of-the-field vegeta-tive strip. Pollutant loads in surface runoff are trapped in the strip of vegetation. The function of filter strips is similar to the field borders except filter strips are installed along the edge of a channel segment. Properly designed filter strips or riparian buffers provide effective strategy for removing pollutants in surface runoff from agricultural lands. In this scenario, 5-m (15-ft) filter strips were assumed to be applied to all agricultural lands.

The previous versions of SWAT provided a specific method to incorporate filter strips through the FILTERW parameter defined in an HRU that reflects the width of the strip (Neitsch et al., 2005). The trapping efficiency for sediment, nutrients and pesticides is calculated:

29670)(367.0 .filtstripef widthtrap

where trapef is the fraction of the constituent loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m).

The filter strip trapping efficiency for bacteria (Moore et al., 1988) is calculated:

123

100

)5.412(,

filtstripbactef

widthtrap

where trapef,bact is the fraction of the bacteria loading trapped by the filter strip, and widthfiltstrip is the width of the filter strip (m).

However, filter strips utilizing these experience functions were disabled in recent versions of SWAT2005 due to the prospect to develop a process-based filter strips func-tion. In order to simulate filter strips, the pollutant yields from lands and loads at the wa-tershed outlet output generated the model under various scenarios were simply deducted from baseline scenario with removal rates estimated utilizing the removal efficiencies obtained using the prescribed experience functions, 59.16% for TSS, TN and TP, and 34.50%for bacteria.

Scenario S9 – Fencing

The model includes loads from direct deposit of cattle manure into streams, input as point sources. This scenario simulated the elimination of direct deposit by constructing fences for all pasture lands close to streams.

Scenario S10 – Eliminate failing septic tanks

The model includes loads from failing septic tanks, input as point sources. This scenario simulated the elimination of failing septic tanks by re-estimating the failure rate changed to zero. This was assumed to be accomplished by improving the maintenance of septic systems and increase the reliability.

Scenario S11 – Channel protection

Many segments in the watershed are subject to channel erosion. The channel protection practice is to cover channel segment with erosion resistant material to reduce gully ero-sion. This scenario evaluated the benefits of reducing channel erosion by increased ve-getative cover of channel banks or the stabilization using riprap. In the model, the chan-nel erodibility (CH_EROD) was reduced from 0.01 to 0.001 in all subbasins. Here a small number (i.e. 0.001) was used instead of zero to avoid the default values set by SWAT. The channel cover factor (CH_COV) was adjusted to 0.5, and channel Man-ning’s roughness coefficients were set to 0.02 according to Arabi et al. (2008).

8.1.2. Combinational Management Scenarios Normally full compliance with TMDL target annual load reductions and daily ex-

ceedance frequencies is hard to be achieved by applying just one kind of BMPs. None of the single-focus scenarios produces satisfied load reduction. Thus, a combination of sev-eral of the more promising management options needs to be pursued.

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Scenario S12 – Combo 1

This scenario was constructed to reduce sediment, TN, TP and bacteria loads by combin-ing the BMPs tested in S1 (reduce manure), S2 (grazing management), S3 (nitrogen ferti-lizer management), S4 (phosphorus fertilizer management), S7 (cover crop), S9 (fenc-ing), and S10 (eliminating septic failure).

Scenario S13 – Combo 2

This scenario was constructed to reduce sediment, TN, TP and bacteria loads by combin-ing the BMPs tested in S1 (reduce manure), S2 (grazing management), S3 (nitrogen ferti-lizer management), S4 (phosphorus fertilizer management), S8 (filter strips), S9 (fenc-ing), and S10 (eliminating septic failure).

Scenario S14 – Combo 3

This scenario was constructed to reduce sediment, TN, TP and bacteria loads by combin-ing the BMPs tested in S1 (reduce manure), S2 (grazing management), S3 (nitrogen ferti-lizer management), S4 (phosphorus fertilizer management), S7 (cover crop), S8 (filter strips), S9 (fencing), and S10 (eliminating septic failure).

Scenario S15 – Combo 4

This scenario was constructed to reduce sediment, TN, TP and bacteria loads by combin-ing the BMPs tested in S1 (reduce manure), S2 (grazing management), S3 (nitrogen ferti-lizer management), S4 (phosphorus fertilizer management), S7 (cover crop), S8 (filter strips), S9 (fencing), S10 (eliminating septic failure), and S11 (channel protection).

Scenario S16 – Combo 5

This scenario was designed to evaluate the effectiveness of major promising BMPs for TP load reduction by combining the BMPs tested in S7 (cover crop) and S8 (filter strips).

Scenario S17 – Combo 6

This scenario was designed to evaluate the effectiveness of major promising BMPs for TP load reduction by combining the BMPs tested in S4 (phosphorus fertilizer manage-ment), S7 (cover crop) and S8 (filter strips).

Scenario S18 – Combo 7

This scenario was designed to evaluate the effectiveness of major promising BMPs for bacteria load reductions by combining the BMPs tested in S1 (reduce manure), and S9 (fencing).

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Scenario S19 – Combo 8

This scenario was designed to evaluate the effectiveness of major promising BMPs for bacteria load reductions by combining the BMPs tested in S1 (reduce manure), S8 (filter strips), and S9 (fencing).

Scenario S20 – Combo 9

This scenario was designed to evaluate the effectiveness of major promising BMPs for bacteria load reductions by combining the BMPs tested in S1 (reduce manure), S8 (filter strips), S9 (fencing), and S10 (eliminating septic failure).

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Table 8.1. Definition of BMP scenarios Scenario BMPs Description

S0 Baseline

The baseline scenario is characterized with modest N and P commercial fertilizer applications and reduced tillage for agri-cultural lands. Cattle and horse manures are applied to 11% of corn lands at standard rates. Tillage operations include: mini-mum (chisel/disk) plows for corn, soybean and rye; 6-year rota-tion moldboard/disk/hallow plows for timothy, hay and pasture. AGRL is modeled as 2-year rotation of corn and soybean. Orc-hards, forests, and wetlands are modeled using their default SWAT schedules.

S1 Reduce manure applica-tion

Reduce application rates of cattle and horse manures to corn lands from 45 Mg/ha to 11.6 Mg/ha.

S2 Grazing management Increase the minimal grass biomass of pasture lands from 200 kg/ha to 700 kg/ha to reduce soil erosion cause by over grazing.

S3 Nitrogen commercial fertilizer management

Reduce N commercial fertilizer application rates by 25% for all agricultural lands and urban lawns.

S4 Phosphorus commercial fertilizer management

Reduce P commercial fertilizer application rates by 25% for all agricultural lands and urban lawns.

S5 Phosphorus commercial fertilizer management

Reduce P commercial fertilizer application rates by 25% for all agricultural lands and by 100% for urban lawns.

S6 No tillage Change to no-till operations for all row crop lands (AGRL, corn, soybean, and rye).

S7 Cover crop Plant winter rye following crop harvest and kill winter rye by crop planting for AGRL, corn and soybean lands.

S8 Filter strips Apply 5-m (15-ft) filter strips to all agricultural lands.

S9 Fencing Construct fences for all pasture lands within 100 meters of a stream.

S10 Eliminate failing septic tanks

Improve the maintenance of septic systems and increase the re-liability. Assume 0% failing rate after improvement.

S11 Channel protection Increased vegetative cover of channel banks or the stabilization using riprap.

S12 Combo 1 Combination of BMPs in S1, S2, S3, S4, S7, S9, S10 S13 Combo 2 Combination of BMPs in S1, S2, S3, S4, S8, S9, S10 S14 Combo 3 Combination of BMPs in S1, S2, S3, S4, S7, S8, S9, S10 S15 Combo 4 Combination of BMPs in S1, S2, S3, S4, S7, S8, S9, S10, S11 S16 Combo 5 Combination of BMPs in S7, S8 S17 Combo 6 Combination of BMPs in S4, S7, S8 S18 Combo 7 Combination of BMPs in S1, S9 S19 Combo 8 Combination of BMPs in S1, S8, S9 S20 Combo 9 Combination of BMPs in S1, S8, S9, S10

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8.2. Load Reductions of BMP Scenarios

8.2.1. Single-focus BMP Scenario Results

Average annual pollutant yields from grouped land uses, direct loads to streams, wa-tershed average annual loads and reduction percentages, and daily load exceedance fre-quencies at the watershed outlet under BMP scenarios are summarized are in Table 8.2 to Table 8.6.

In general, no tillage, cover crop, filter strips and channel protection are effective BMPs for sediment removal, which lead to watershed average annual load reduction of 9.80, 15.21, 17.28 and 59.60 percent compared to baseline scenario. Reducing channel erosion has a large impact on TSS loads. With each individual of these BMPs applied to appropriate lands across the watershed, the target daily load exceedance frequency less than 10 percent can be satisfied.

TN is not an issue as to satisfy its TMDL goal in the watershed even under the baseline scenario. The simulations indicate that reducing manure application, nitrogen fertilizer management, cover crop and filter strips are effective measures for TN reduc-tion all with removal percentages more than 10 percent. No tillage operations reduce the TN average annual load only 2.46 percent. The relative small reduction is primarily be-cause the existing tillage practices already high conservation levels.

For TP load reduction, reduce manure application, phosphorus fertilizer manage-ment, cover crop and filter strips are more effective than other BMPs, with average an-nual load reduction percentages of 4.44, 15.36, 15.77 and 35.72 percent, respectively. Edge-of-field filter strips provide the greatest reduction since they can be applied to all agricultural lands and function throughout the year. However, the daily load exceedance frequencies with these individual BMP applications are still more than the goal 10%. Note that, the exceedance frequency for filter strop scenario is marked as less than 37.96%, due to the prescribed reason that the filter function is not simulated in the current version of SWAT2005. Although the yields and loads can be reduced by applying esti-mated removal rates of filter strips, the reductions of daily exceedance frequencies cannot be computed.

Bacteria pollution in the watershed is relative severe and need large reductions of source loads. The scenario modeling shows that reducing manure application, filter strips, fencing, eliminate failing septic tanks, and channel protection are effective measure for both fecal coliform and E. coli load reductions, with reduction percentages of 23.23, 11.91, 19.30, 46.92 and 4.05 percent for fecal coliform, and 23.17, 11.88, 19.22, 47.09 and 4.05 percent for E. coli, respectively. However, daily exceedance frequencies are high with each individual of these BMPs applied to the watershed, which are more than 30% even when it is assumed that no failing septic tanks exist.

One may observe that there are some negative load reduction percentages given in the tables. These negative reductions mean that the corresponding loads actually in-creased. This is because the coupled overland and instream process models in SWAT in-

128

deed have affections to each other. For example, the decrease application of phosphorus fertilizer while maintaining existing nitrogen fertilizer application will increase the ratio of N:P components that received by lands, and result in more loss of nitrogen since it cannot be taken by vegetation.

8.2.2. Combinational BMP Scenario Results

Since application of individual BMPs such as no tillage, cover crop, filter strips and channel protection can reduce sediment load to satisfy daily exceedance frequency target, the combinational scenarios of effective BMPS were designed to evaluate the possibility to meet the goals for TP and bacteria. For TP, a combination of major effective BMPs, reducing phosphorus fertilizer application rates, cover crop, and filter strips will lead to a 51.07 percent reduction of average annual load, which is more than the target 49%. Com-bination of only cover crop and filter strips will not achieve the target. With addition BMPs including grazing management, reducing manure and nitrogen fertilizer applica-tions, fencing, eliminating septic failure, and channel protection will have a larger reduc-tion of load amounting to 51.86 percent. However, this small more reduction is cost inef-ficient compared to the combination of reducing phosphorus fertilizer application rates, cover crop, and filter strips.

To satisfy the target reduction of bacteria loads, a combination of effective BMPS including at least reducing manure application, filter strips, fencing, and eliminating sep-tic failure is needed. This lest combination reduce average annual load by 92.49 percent for fecal coliform and 92.51 percent for E. coli. With addition BMPs including grazing management, nitrogen and phosphorus fertilizer applications, cover crop, and channel protection will increase small reductions of load amounting to 94.45 percent for fecal co-liform and 94.47 percent for E. coli. The daily exceedance frequencies of both fecal and E. coli meet the target less than 10 percent for any BMP combination consisting reducing manure application, filter strips, fencing, and eliminating septic failure.

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Table 8.2. Reductions of sediment yields and loads under BMP scenarios

Scenario

Yield from land (ton/ac/yr) Direct load to

stream (ton/yr) Watershed outlet load

Urban Row crop

Other agricul-

ture Forest Wetland

Cattle direct

deposit

Septic effluent

Average annual

load (ton/yr)

Load reduction

percentage (%)

Daily load exceedance frequency

(%)

S0 0.0299 0.0918 0.0302 0.0004 0.0002 0.000 0.000 1715.105 12.25

S1 0.0299 0.0917 0.0302 0.0004 0.0002 0.000 0.000 1714.186 0.05 12.21

S2 0.0299 0.0918 0.0164 0.0004 0.0002 0.000 0.000 1667.981 2.75 11.36 S3 0.0305 0.0918 0.0318 0.0004 0.0002 0.000 0.000 1727.414 -0.72 12.25 S4 0.0299 0.0918 0.0302 0.0004 0.0002 0.000 0.000 1715.105 0.00 12.25

S5 0.0337 0.0918 0.0302 0.0004 0.0002 0.000 0.000 1742.019 -1.57 12.55

S6 0.0299 0.0205 0.0302 0.0004 0.0002 0.000 0.000 1547.002 9.80 9.51

S7 0.0299 0.0383 0.0302 0.0004 0.0002 0.000 0.000 1454.316 15.21 7.76

S8* 0.0299 0.0375 0.0123 0.0004 0.0002 0.000 0.000 1418.678 17.28 <12.25

S9 0.0299 0.0918 0.0302 0.0004 0.0002 0.000 0.000 1715.105 0.00 12.25

S10 0.0299 0.0918 0.0302 0.0004 0.0002 0.000 0.000 1715.105 0.00 12.25

S11 0.0299 0.0918 0.0302 0.0004 0.0002 0.000 0.000 692.986 59.60 2.30

S12 0.0305 0.0391 0.0175 0.0004 0.0002 0.000 0.000 1303.575 23.99 5.86

S13* 0.0305 0.0374 0.0072 0.0004 0.0002 0.000 0.000 1285.820 25.03 <10.75

S14* 0.0305 0.0160 0.0072 0.0004 0.0002 0.000 0.000 1167.944 31.90 <5.86

S15* 0.0305 0.0160 0.0072 0.0004 0.0002 0.000 0.000 290.872 83.04 <1.12

S16* 0.0299 0.0156 0.0123 0.0004 0.0002 0.000 0.000 1295.241 24.48 <7.76

S17* 0.0299 0.0156 0.0123 0.0004 0.0002 0.000 0.000 1295.160 24.49 <7.76

S18 0.0299 0.0917 0.0302 0.0004 0.0002 0.000 0.000 1714.186 0.05 12.21

S19* 0.0299 0.0375 0.0123 0.0004 0.0002 0.000 0.000 1418.099 17.32 <12.21

S20* 0.0299 0.0375 0.0123 0.0004 0.0002 0.000 0.000 1298.866 24.27 <11.43

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Table 8.3. Reductions of TN yields and loads under BMP scenarios

Scenario

Yield from land (lb/ac/yr) Direct load to stream (lb/yr)

Watershed outlet load

Urban Row crop

Other agricul-

ture Forest Wetland

Cattle direct

deposit

Septic effluent

Average annual

load (lb/yr)

Load re-duction

percentage (%)

Daily load exceedance frequency

(%) S0 3.836 33.922 8.194 2.735 10.846 111.220 783.755 229134.145 1.76

S1 3.836 20.354 8.194 2.735 10.846 111.220 783.755 170345.495 25.66 0.34

S2 3.836 33.922 7.592 2.735 10.846 111.220 783.755 227224.391 0.83 1.78

S3 3.418 30.073 6.239 2.735 10.846 111.220 783.755 203711.356 11.10 1.00

S4 3.836 33.916 8.194 2.735 10.846 111.220 783.755 229534.284 -0.17 1.76

S5 19.900 33.916 8.194 2.735 10.846 111.220 783.755 331470.155 -44.66 13.00

S6 3.836 31.965 8.194 2.735 10.846 111.220 783.755 223491.597 2.46 1.21

S7 3.836 21.605 8.194 2.735 10.846 111.220 783.755 176363.012 23.03 0.62

S8* 3.836 13.325 3.333 2.735 10.846 111.220 783.755 123260.342 46.21 <1.62

S9 3.836 32.630 8.161 2.735 10.846 0.000 783.755 229049.635 0.04 1.76

S10 3.836 33.922 8.194 2.735 10.846 111.220 0.000 228468.165 0.29 1.76

S11 3.836 33.922 8.194 2.735 10.846 111.220 783.755 232924.810 -1.65 1.78

S12 3.418 7.158 5.728 2.735 10.846 0.000 0.000 102193.444 55.40 0.00

S13* 3.418 6.548 2.239 2.735 10.846 0.000 0.000 87237.938 61.93 <0.07

S14* 3.418 2.849 2.239 2.735 10.846 0.000 0.000 71584.473 68.76 0.00

S15* 3.418 2.849 2.239 2.735 10.846 0.000 0.000 72425.960 68.39 0.00

S16* 3.836 8.468 3.201 2.735 10.846 111.220 783.755 102257.689 55.37 <0.55

S17* 3.836 8.465 3.201 2.735 10.846 785.667 111.220 102312.065 55.35 <0.55

S18 3.836 20.354 8.194 2.735 10.846 111.220 783.755 170249.593 25.70 0.34

S19* 3.836 8.312 3.346 2.735 10.846 785.667 0.000 101530.117 55.69 <0.34

S20* 3.836 8.312 3.346 2.735 10.846 0.000 783.755 100733.697 56.04 <0.34

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Table 8.4. Reductions of TP yields and loads under BMP scenarios

Scenario

Yield from land (lb/ac/yr) Direct load to stream (lb/yr)

Watershed outlet load

Urban Row crop

Other agricul-

ture Forest Wetland

Cattle direct

deposit

Septic effluent

Average annual

load (lb/yr)

Load reduction percen-tage (%)

Daily load exceedance frequency

(%) S0 0.550 1.235 0.606 0.073 0.133 30.585 235.126 12287.089 37.96

S1 0.550 1.100 0.606 0.073 0.133 30.585 235.126 11741.445 4.44 36.46

S2 0.550 1.235 0.549 0.073 0.133 30.585 235.126 12099.696 1.53 37.58

S3 0.613 1.231 0.633 0.073 0.133 30.585 235.126 12764.765 -3.89 38.60

S4 0.412 1.036 0.529 0.073 0.133 30.585 235.126 10400.307 15.36 33.45

S5 0.221 1.036 0.529 0.073 0.133 30.585 235.126 9096.273 25.97 28.04

S6 0.550 1.274 0.606 0.073 0.133 30.585 235.126 13010.955 -5.89 43.44

S7 0.550 0.773 0.606 0.073 0.133 30.585 235.126 10349.601 15.77 30.80

S8* 0.550 0.504 0.247 0.073 0.133 30.585 235.126 7897.635 35.72 <37.96

S9 0.550 1.235 0.606 0.073 0.133 0.000 235.126 12261.383 0.21 37.37

S10 0.550 1.235 0.606 0.073 0.133 30.585 0.000 12085.014 1.64 35.75

S11 0.550 1.235 0.606 0.073 0.133 30.585 235.126 12182.370 0.85 37.76

S12 0.472 0.511 0.492 0.073 0.133 0.000 0.000 8266.409 32.72 22.56

S13* 0.472 0.366 0.201 0.073 0.133 0.000 0.000 6599.333 46.29 <29.93

S14* 0.472 0.209 0.201 0.073 0.133 0.000 0.000 5964.142 51.46 <22.56

S15* 0.472 0.209 0.201 0.073 0.133 0.000 0.000 5915.103 51.86 <22.36

S16* 0.550 0.316 0.247 0.073 0.133 30.585 235.126 7143.257 41.86 <30.80

S17* 0.412 0.257 0.216 0.073 0.133 235.700 30.585 6012.446 51.07 <26.17

S18 0.550 1.100 0.606 0.073 0.133 30.585 235.126 11719.399 4.62 36.09

S19* 0.550 0.449 0.247 0.073 0.133 235.700 0.000 7676.055 37.53 <36.09

S20* 0.550 0.449 0.247 0.073 0.133 0.000 235.126 7475.809 39.16 <34.43

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Table 8.5. Reductions of fecal coliform loads under BMP scenarios

Scena-rio

Direct load to stream (cfu/yr) Watershed outlet load

Cattle direct deposit

Septic effluent

Average annual load

(cfu/yr)

Load reduction

percentage (%)

Daily load exceedance frequency

(%) S0 4.155E+13 8.888E+13 1.535E+14 60.96 S1 4.155E+13 8.888E+13 1.178E+14 23.23 60.57 S2 4.155E+13 8.888E+13 1.533E+14 0.12 60.94 S3 4.155E+13 8.888E+13 1.528E+14 0.39 60.99 S4 4.155E+13 8.888E+13 1.534E+14 0.00 60.96 S5 4.155E+13 8.888E+13 1.530E+14 0.31 60.89 S6 4.155E+13 8.888E+13 1.656E+14 -7.90 59.84 S7 4.155E+13 8.888E+13 1.518E+14 1.04 62.79 S8* 4.155E+13 8.888E+13 1.352E+14 11.91 <60.96 S9 0.000E+00 8.888E+13 1.238E+14 19.30 51.22 S10 4.155E+13 0.000E+00 8.145E+13 46.92 32.56 S11 4.155E+13 8.888E+13 1.472E+14 4.05 59.73 S12 0.000E+00 0.000E+00 1.504E+13 90.20 0.18

S13* 0.000E+00 0.000E+00 9.485E+12 93.82 <0.25 S14* 0.000E+00 0.000E+00 9.123E+12 94.05 <0.18 S15* 0.000E+00 0.000E+00 8.522E+12 94.45 <0.16 S16* 4.155E+13 8.888E+13 1.336E+14 12.95 <62.79 S17* 8.909E+13 4.155E+13 1.336E+14 12.95 <62.79 S18 4.155E+13 8.888E+13 8.818E+13 42.53 50.67

S19* 8.909E+13 0.000E+00 8.354E+13 45.56 <50.67 S20* 0.000E+00 8.888E+13 1.153E+13 92.49 <0.25

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Table 8.6. Reductions of E. coli loads under BMP scenarios

Scenario

Direct load to stream (cfu/yr) Watershed outlet load (cfu/yr)

Cattle direct deposit

Septic effluent

Average annual load

(cfu/yr)

Load reduction

percentage (%)

Daily load exceedance frequency

(%) S0 2.597E+13 5.599E+13 9.632E+13 63.38 S1 2.597E+13 5.599E+13 7.400E+13 23.17 63.08 S2 2.597E+13 5.599E+13 9.621E+13 0.12 63.40 S3 2.597E+13 5.599E+13 9.595E+13 0.39 63.56 S4 2.597E+13 5.599E+13 9.632E+13 0.00 63.38 S5 2.597E+13 5.599E+13 9.603E+13 0.31 63.40 S6 2.597E+13 5.599E+13 1.039E+14 -7.88 62.15 S7 2.597E+13 5.599E+13 9.532E+13 1.04 65.16 S8* 2.597E+13 5.599E+13 8.488E+13 11.88 <63.38 S9 0.000E+00 5.599E+13 7.781E+13 19.22 54.96 S10 2.597E+13 0.000E+00 5.097E+13 47.09 34.20 S11 2.597E+13 5.599E+13 9.242E+13 4.05 61.76 S12 0.000E+00 0.000E+00 9.412E+12 90.23 0.23

S13* 0.000E+00 0.000E+00 5.933E+12 93.84 <0.27 S14* 0.000E+00 0.000E+00 5.707E+12 94.07 <0.23 S15* 0.000E+00 0.000E+00 5.329E+12 94.47 <0.21 S16* 2.597E+13 5.599E+13 8.388E+13 12.92 <65.16 S17* 5.613E+13 2.597E+13 8.388E+13 12.92 <65.16 S18 2.597E+13 5.599E+13 5.549E+13 42.39 54.48

S19* 5.613E+13 0.000E+00 5.258E+13 45.41 <54.48 S20* 0.000E+00 5.599E+13 7.213E+12 92.51 <0.27

9. Conclusions

The SWAT2005 model was reasonably calibrated and validated in the Neshanic River Watershed, a suburban watershed in Central New Jersey, for the baseline land use/management scenario. DEM, land use, soil, long term monitored weather data and estimated manure application and literature bacteria content data were utilized to build the model, while long term monitored daily stream flows at the Reaville USGS station and water quality data from grabbed samples at the USGS station and the seven project sites were used for calibration and validation purpose.

During the modeling period 1997-2008, simulated annual total stream flow at the watershed outlet is 1.51E+9 ft3 or equivalent 21.318 inches of precipitation, with monthly stream flows varying from 0.876 to 2.172 inches, about 24% to 66% of observed monthly precipitation. The hydrological water balance analysis shows that more than 50% of losses in the watershed are through evapotranspiration during dry years while less than 50% during wet years. Surface runoff dominates water yield no matter in a wet or dry year. Base flow (30%~45%) mainly contributed from groundwater discharges and is an important component of the total discharge within the study area that compliment to the surface runoff.

134

The baseline simulation indicates that the main pollutant sources for sediment are stream channels, row crops and other agricultural lands. Row crops, other agricultural lands, and urban areas are main sources for TN and TP, while failing septic tanks, cattle direct deposit into steams, manure applications are the major sources for fecal coliform and E. coli loads. It was believed that the SWAT model, by considering land and soil characteristics and pollutant movement on the lands and in the water, gave more realistic load estimates than empirical models for nonpoint sources loading estimation. Load dura-tion curves were developed for pollutants at the watershed outlet based on baseline scena-rio simulation. The target reductions for TSS, TP, fecal coliform and E. coli were esti-mated to be 9, 48, 89 and 89 percent, respectively, to meet the water quality goal with less than 10% daily exceedance frequencies during 1997- 2008. TN is not an issue ac-cording to the water quality standard for nitrate.

Twenty BMP scenarios were simulated for the watershed to derive the effects of BMP implementation would have on water quality at the outlet of watershed. Among the eleven individual BMPs considered, filter strips are overall the most effective in reducing loadings for all pollutants from overland sources. Eliminating septic failure and fencing are the two key BMPs to reduce direct deposits of bacteria loads into streams. To meet the water quality goal of less than 10% daily exceedance frequencies, any individual BMPs of no tillage, cover crop, filter strips or channel protection would work for sedi-ment reduction. For TP, a combination consisting effective BMPs, reducing phosphorus fertilizer application rates, cover crop, and filter strips provided satisfied reduction, while a combination of effective BMPS including at least reducing manure application, filter strips, fencing, and eliminating septic failure is needed to meet the target reductions of fecal coliform and E. coli loads.

In summary, in spite of the coarse nature of model setup for bacteria simulation and the limited water quality monitoring data available for model calibration and validation, this SWAT modeling study produced valuable quantitative information on the effective-ness of BMPs in reducing pollutant loads and improving water quality.

135

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Appendix A. Management Schedules for Crops and Lawns

Table A.1. Schedule of management operations for corn under the baseline scenario

Date Operation Type Rate 4/10 Tillage Chisel plow 4/15 Fertilizer Anhydrous ammonia* 130 lbs/ac N 4/15 Pesticide Atrazine 4/15 Pesticide Prowl 4/17 Tillage Disk 5/10 Planting Regular Corn 5/10 Fertilizer N granule 10.5 lbs/ac N 5/10 Fertilizer P2O5 31.5 lbs/ac P 5/10 Fertilizer K2O 9 lbs/ac K 6/1 Pesticide Bicep or lumax

6/15 Pesticide Distinct or Banvel or Clari-

ty or Celebrity Plus

7/15 Pesticide Headline or Warrior fungi-

cide

10/15 Harvest and kill * Cow and horse manures are applied to 9 corn HRUs, both at the rates of 45,000 kg/ha.

Table A.2. Schedule of management operations for soybean under the baseline scenario Date Operation Type Rate 5/1 Tillage Chisel

5/20 Tillage Disk 5/27 Planting Regular Soybean 5/27 Fertilizer P2O5 25 lbs/ac P 5/27 Fertilizer K2O 70 lbs/ac K 6/15 Pesticide Classic or First rate

10/15 Harvest and kill

Table A.3. Schedule of management operations for rye under the baseline scenario Date Operation Type Rate 3/15 Fertilizer N granule 40 lbs/ac N 4/20 Pesticide harmony 0.5 oz/ac 7/1 Harvest and kill

10/8 Tillage Chisel 10/8 Tillage Disk

10/10 Planting Rye/wheat 10/15 Fertilizer N granule 20 lbs/ac N 10/15 Fertilizer P2O5 50 lbs/ac P

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Table A.4. Schedule of management operations for timothy under the baseline scenario Date Operation Type Rate 4/15 Fertilizer N granule 60 lbs/ac N 4/15 Fertilizer P2O5 13 lbs/ac P 6/15 Harvest 6/20 Fertilizer N granule 50 lbs/ac N

8/15 (1st-5th years)

Harvest

Renew stand in 6th year 8/15 Harvest and kill 10/1 Tillage Moldboard 10/1 Tillage Disk 10/1 Tillage Hallow

10/10 Fertilizer N granule 60 lbs/ac N 10/10 Fertilizer P2O5 45 lbs/ac P 10/10 Planting Timothy

Table A.5. Schedule of management operations for hay under the baseline scenario Date Operation Type Rate 4/10 Fertilizer N granule 40 lbs/ac N 4/10 Fertilizer P2O5 13 lbs/ac P 4/10 Fertilizer K2O 25 lbs/ac K 4/20 Pesticide 2,4-D 1 qt/ac 4/20 Pesticide Banvel 1pt/ac 4/20 Pesticide Sevin 5/15 Harvest 5/20 Fertilizer N granule 50 lbs/ac N 7/15 Harvest 7/20 Fertilizer N granule 50 lbs/ac N

9/15 (1st-5th years)

Harvest

Renew stand in 6th year 9/15 (6th

year) Harvest and kill

10/1 Tillage Moldboard 10/1 Tillage Disk 10/1 Tillage Hallow 10/1 Fertilizer N granule 50 lbs/ac N 10/1 Fertilizer P2O5 45 lbs/ac P

10/10 Planting Orchard grass mix with wheat or rye nurse crop

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Table A.6. Schedule of management operations for pasture under the baseline scenario Date Operation Type Rate 4/15 Fertilizer N granule 25 lbs/ac N 4/15 Fertilizer P2O5 20 lbs/ac P 4/15 Fertilizer K2O 25 lbs/ac K 4/20 Pesticide 2,4-D 1 qt/ac 4/20 Pesticide Banvel 1 pt/ac 4/20 Pesticide Sevin

5/1 Grazing (May 1-Oct 31) Manure 92.824 kg/ha/d cow

manure, 39.727 kg/ha/d horse manure

7/10 Fertilizer N granule 25 lbs/ac N Renew stand in 6th year

11/1 Harvest and kill 11/5 Tillage Moldboard 11/5 Tillage Disk 11/5 Tillage Harrow

11/10 Planting Orchard grass mix with wheat or rye

nurse crop

Table A.7. Schedule of management operations for lawns in urban lands (except transporta-tion) under the baseline scenario

Date Operation Type Rate 3/20 Fertilizer N granule 25 lbs/ac N 3/20 Fertilizer P2O5 5 lb/ac P 4/15 Harvest 5/1 Fertilizer N granule 25 lbs/ac N 5/1 Fertilizer P2O5 5 lb/ac P 5/1 Harvest

5/15 Harvest 6/1 Harvest

6/12 Fertilizer N granule 25 lbs/ac N 6/12 Fertilizer P2O5 5 lb/ac P 6/15 Harvest 7/1 Harvest

7/15 Harvest 7/24 Fertilizer N granule 25 lbs/ac N 7/24 Fertilizer P2O5 5 lb/ac P 8/1 Harvest

8/15 Harvest 9/1 Harvest

9/14 Fertilizer N granule 25 lbs/ac N 9/14 Fertilizer P2O5 5 lb/ac P 9/15 Harvest 10/1 Harvest

10/16 Fertilizer N granule 25 lbs/ac N 10/16 Fertilizer P2O5 5 lb/ac P 10/20 Harvest

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Table A.8. Schedule of management operations for lawns in urban transportation lands un-der the baseline scenario

Date Operation Type Rate 5/1 Harvest 6/1 Harvest 7/1 Harvest 8/1 Harvest 9/1 Harvest

10/1 Harvest