DEVELOPMENT OF NUMERIC NUTRIENT CRITERIA 

FOR THE ESTUARINE WATERS OF THE CHARLOTTE 

HARBOR NATIONAL ESTUARY PROGRAM 

Task 7: Numeric Nutrient Criteria Development 

Interim Report 6

Prepared for: 

Charlotte Harbor National Estuary Program 

1926 Victoria Avenue

Fort Myers, FL 33901-3414

Prepared By:

Janicki Environmental, Inc.

1155 Eden Isle Drive NE.

St. Petersburg, Florida 33704

Final Draft February 15, 2011
i 

Table of Contents

LIST OF FIGURES ...................................................................................................................................... III

LIST OF TABLES.......................................................................................................................................VII

FOREWORD..............................................................................................................................................VIII

ACKNOWLEDGMENTS..............................................................................................................................IX

EXECUTIVE SUMMARY .............................................................................................................................. X

1.0 INTRODUCTION........................................................................................................................... 1-1

2.0 OBJECTIVES ............................................................................................................................... 2-1

3.0 CHNEP GOALS AND TARGETS................................................................................................. 3-1

3.1 SEAGRASS TARGETS................................................................................................................... 3-2

3.2 WATER CLARITY TARGETS .......................................................................................................... 3-2

3.3 CHLOROPHYLL A TARGETS AND THRESHOLDS .............................................................................. 3-5

3.3.1 Regulatory Method ............................................................................................................... 3-5

3.3.2 Optical Model Method .......................................................................................................... 3-5

3.3.3 Reference Period Method .................................................................................................... 3-7

4.0 APPROACH AND RATIONALE................................................................................................... 4-1

4.1 DATA SOURCES .......................................................................................................................... 4-1

4.1.1 Water Quality........................................................................................................................ 4-1

4.1.2 Loadings ............................................................................................................................... 4-1

4.1.3 Bathymetry and Residence Time ......................................................................................... 4-2

4.2 LIMITING NUTRIENTS AND DATA ANALYSIS APPROACHES .............................................................. 4-2

4.2.1 Limiting Nutrients.................................................................................................................. 4-2

4.2.2 Data Analysis Approaches ................................................................................................... 4-3

4.2.2.1 Linear Regression........................................................................................................ 4-4

4.2.2.2 Change Point Analysis................................................................................................. 4-4

5.0 DEVELOPMENT OF NUTRIENT CRITERIA ............................................................................... 5-1

5.1 DONA AND ROBERTS BAYS.......................................................................................................... 5-1

5.1.1 Dona and Roberts Bays Water Quality and Nutrient Loadings............................................ 5-3

5.1.2 Water Quality and Seagrass Targets and Thresholds ......................................................... 5-4

5.1.3 Relationships Between Chlorophyll a and Concentrations and Loads ................................ 5-7

5.1.4 Candidate Criteria - Dona and Roberts Bays..................................................................... 5-11

5.2 LEMON BAY .............................................................................................................................. 5-12

5.2.1 Lemon Bay Water Quality and Nutrient Loadings .............................................................. 5-15

5.2.2 Water Quality and Seagrass Targets and Thresholds ....................................................... 5-16

5.2.3 Relationship Between Chlorophyll a and Concentrations and Loads - Lemon Bay........... 5-17

5.2.4 Candidate Criteria - Lemon Bay......................................................................................... 5-24

5.3 CHARLOTTE HARBOR PROPER................................................................................................... 5-29

5.3.1 Charlotte Harbor Proper Water Quality and Nutrient Loadings.......................................... 5-35

5.3.2 Water Quality and Seagrass Targets and Thresholds ....................................................... 5-37

5.3.3 Relationship Between Chlorophyll a and Concentrations and Loads - Charlotte Harbor 

Proper................................................................................................................................. 5-39

5.3.4 Candidate Criteria - Charlotte Harbor Proper..................................................................... 5-44

5.4 TIDAL MYAKKA.......................................................................................................................... 5-47

5.4.1 Tidal Myakka Water Quality and Nutrient Loadings ........................................................... 5-49

5.4.2 Water Quality and Seagrass Targets and Thresholds ....................................................... 5-52

5.4.3 Relationship Between Chlorophyll a and Concentrations and Loads - Tidal Myakka........ 5-53

5.4.4 Candidate Criteria - Tidal Myakka...................................................................................... 5-57

5.5 TIDAL PEACE ............................................................................................................................ 5-60

5.5.1 Tidal Peace Water Quality and Nutrient Loadings ............................................................. 5-61
ii

5.5.2 Water Quality and Seagrass Targets and Thresholds ....................................................... 5-62

5.5.3 Relationship Between Chlorophyll a and Concentrations and Loads - Tidal Peace.......... 5-62

5.5.4 Candidate Criteria - Tidal Peace ........................................................................................ 5-69

5.6 PINE ISLAND SOUND AND MATLACHA PASS ................................................................................ 5-72

5.6.1 Pine Island Sound and Matlacha Pass Water Quality and Nutrient Loadings ................... 5-75

5.6.2 Pine Island Sound and Matlacha Pass Targets ................................................................. 5-79

5.6.3 Relationship Between Chlorophyll a and Concentrations and Loads - Pine Island Sound and 

Matlacha Pass .................................................................................................................... 5-82

5.6.4 Candidate Criteria - Pine Island Sound and Matlacha Pass .............................................. 5-90

5.7 SAN CARLOS BAY AND TIDAL CALOOSAHATCHEE........................................................................ 5-93

5.7.1 San Carlos Bay and Tidal Caloosahatchee Water Quality and Nutrient Loadings ............ 5-95

5.7.2 San Carlos Bay and Tidal Caloosahatchee Targets ........................................................ 5-101

5.7.3 Relationship Between Chlorophyll a and Concentrations and Loads - San Carlos Bay and 

Tidal Caloosahatchee....................................................................................................... 5-102

5.7.4 Candidate Criteria - San Carlos Bay and Tidal Caloosahatchee ..................................... 5-115

5.8 ESTERO BAY........................................................................................................................... 5-117

5.8.1 Estero Bay Water Quality and Nutrient Loadings............................................................. 5-118

5.8.2 Water Quality and Seagrass Targets and Thresholds ..................................................... 5-122

5.8.3 Relationship Between Chlorophyll a and Concentrations and Loads - Estero Bay ......... 5-122

5.8.4 Candidate Criteria - Estero Bay........................................................................................ 5-128

6.0 CONCLUSIONS AND RECOMMENDATIONS............................................................................ 6-1

7.0 LITERATURE CITED.................................................................................................................... 7-1

APPENDIX 1 - WATER QUALITY SAMPLING STATIONS........................................................................ 1

APPENDIX 2 – PULSE RESIDENCE TIME................................................................................................. 2

APPENDIX 3 – TREND TESTS RESULTS.................................................................................................. 3

APPENDIX 4 – CHANGEPOINT METHODS............................................................................................... 4
iii

List of Figures

Figure 1-1. CHNEP Bay Segments and Water Management District Boundaries............................. 1-2

Figure 5-1. Dona and Roberts Bays water segment. ......................................................................... 5-2

Figure 5-2. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Dona 

and Roberts Bays............................................................................................................. 5-5

Figure 5-3. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Dona and Roberts Bays...................................................................................... 5-6

Figure 5-4. Seagrass acreage in Dona and Roberts Bays................................................................. 5-7

Figure 5-5. Relationship between chlorophyll a and TN concentrations for Dona and Roberts Bays. ...

......................................................................................................................................... 5-8

Figure 5-6. Relationship between chlorophyll a and TP concentrations for Dona and Roberts Bays. ...

......................................................................................................................................... 5-8

Figure 5-7. Relationship between chlorophyll a and TN loads for Dona and Roberts Bays. ............. 5-9

Figure 5-8. Relationship between chlorophyll a and TP loads for Dona and Roberts Bays. ............. 5-9

Figure 5-9. Relationship between chlorophyll a and hydrologic loads for Dona and Roberts Bays. 5-10

Figure 5-10. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Dona and Roberts Bays.......................................................................... 5-11

Figure 5-11. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Dona and Roberts Bays. .............................................................. 5-12

Figure 5-12. Upper and Lower Lemon Bay water segments.............................................................. 5-14

Figure 5-13. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Lemon 

Bay. ................................................................................................................................ 5-18

Figure 5-14. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Lemon Bay........................................................................................................ 5-19

Figure 5-15. Seagrass acreage in Upper and Lower Lemon Bay...................................................... 5-20

Figure 5-16. Relationship between chlorophyll a and TN concentrations for Upper Lemon Bay. ..... 5-20

Figure 5-17. Relationship between chlorophyll a and TP concentrations for Upper Lemon Bay....... 5-21

Figure 5-18. Relationship between chlorophyll a and TN loads for Upper Lemon Bay. .................... 5-21

Figure 5-19. Relationship between chlorophyll a and TP loads for Upper Lemon Bay...................... 5-22

Figure 5-20. Relationship between chlorophyll a and hydrologic loads for Upper Lemon Bay.......... 5-22

Figure 5-21. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Upper Lemon Bay. .................................................................................. 5-23

Figure 5-22. Relationship between chlorophyll a and TN concentrations for Lower Lemon Bay. ..... 5-24

Figure 5-23. Relationship between chlorophyll a and TP concentrations for Lower Lemon Bay....... 5-25

Figure 5-24. Relationship between chlorophyll a and TN loads for Lower Lemon Bay. .................... 5-25

Figure 5-25. Relationship between chlorophyll a and TP loads for Lower Lemon Bay...................... 5-26

Figure 5-26. Relationship between chlorophyll a and hydrologic loads for Lower Lemon Bay.......... 5-26

Figure 5-27. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Upper Lemon Bay. ....................................................................... 5-27

Figure 5-28. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Lower Lemon Bay. ....................................................................... 5-29

Figure 5-29. Charlotte Harbor Proper water segments. ..................................................................... 5-30

Figure 5-30. Cape Haze water segment. ........................................................................................... 5-31

Figure 5-31. Bokeelia water segment................................................................................................. 5-32

Figure 5-32. West Wall water segment. ............................................................................................. 5-33

Figure 5-33. East Wall water segment. .............................................................................................. 5-34

Figure 5-34. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Charlotte 

Harbor Proper. ............................................................................................................... 5-36

Figure 5-35. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Charlotte Harbor Proper. .................................................................................. 5-38

Figure 5-36. Seagrass acreage in Charlotte Harbor Proper. ............................................................. 5-39

Figure 5-37. Relationship between chlorophyll a and TN concentrations for Charlotte Harbor Proper. ...

....................................................................................................................................... 5-40
iv

Figure 5-38. Relationship between chlorophyll a and TP concentrations for Charlotte Harbor Proper. ...

....................................................................................................................................... 5-40

Figure 5-39. Relationship between chlorophyll a and TN loads for Charlotte Harbor Proper............ 5-41

Figure 5-40. Relationship between chlorophyll a and TP loads for Charlotte Harbor Proper. ........... 5-41

Figure 5-41. Relationship between chlorophyll a and hydrologic loads for Charlotte Harbor Proper.5-42

Figure 5-42. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Charlotte Harbor Proper.......................................................................... 5-43

Figure 5-43. Results of changepoint analysis using TN and chlorophyll concentrations averaged 

monthly across Charlotte Harbor Proper. ...................................................................... 5-44

Figure 5-44. Histogram of monthly average TN concentrations in Charlotte Harbor corresponding to 

the water quality data with changepoint identified as solid vertical line......................... 5-45

Figure 5-45. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Charlotte Harbor Proper............................................................... 5-46

Figure 5-46. Tidal Myakka water segment. ........................................................................................ 5-48

Figure 5-47. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Tidal 

Myakka........................................................................................................................... 5-50

Figure 5-48. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Tidal Myakka..................................................................................................... 5-51

Figure 5-49. Seagrass acreage in Tidal Myakka................................................................................ 5-52

Figure 5-50. Relationship between chlorophyll a and TN concentrations for Tidal Myakka. ............. 5-53

Figure 5-51. Relationship between chlorophyll a and TP concentrations for Tidal Myakka. ............. 5-54

Figure 5-52. Relationship between chlorophyll a and TN loads for Tidal Myakka. ............................ 5-54

Figure 5-53. Relationship between chlorophyll a and TP loads for Tidal Myakka. ............................ 5-55

Figure 5-54. Relationship between chlorophyll a and hydrologic loads for Tidal Myakka. ................ 5-55

Figure 5-55. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Tidal Myakka. .......................................................................................... 5-56

Figure 5-56. Results of changepoint analysis for Charlotte Harbor chlorophyll and Myakka River Loads

....................................................................................................................................... 5-57

Figure 5-57. Histogram of TN loadings from the Myakka River corresponding to the water quality data 

with changepoint identified as solid vertical line. ........................................................... 5-58

Figure 5-58. Comparison of the potential TN criteria to observed TN loads in Tidal Myakka............ 5-59

Figure 5-59. Tidal Peace water segment. .......................................................................................... 5-60

Figure 5-60. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Tidal 

Peace. ............................................................................................................................ 5-63

Figure 5-61. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Tidal Peace....................................................................................................... 5-64

Figure 5-62. Seagrass acreage in Tidal Peace. ................................................................................. 5-65

Figure 5-63. Relationship between chlorophyll a and TN concentrations for Tidal Peace. ............... 5-65

Figure 5-64. Relationship between chlorophyll a and TP concentrations for Tidal Peace................. 5-66

Figure 5-65. Relationship between chlorophyll a and TN loads for Tidal Peace. .............................. 5-66

Figure 5-66. Relationship between chlorophyll a and TP loads for Tidal Peace................................ 5-67

Figure 5-67. Relationship between chlorophyll a and hydrologic loads for Tidal Peace.................... 5-67

Figure 5-68. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Charlotte Harbor Proper.......................................................................... 5-68

Figure 5-69. Results of changepoint analysis for Charlotte Harbor chlorophyll and Peace River Loads.

....................................................................................................................................... 5-69

Figure 5-70. Histogram of TN loadings from the Peace River corresponding to the water quality data 

with changepoint identified as solid vertical line. ........................................................... 5-70

Figure 5-71. Comparison of the potential TN criteria to observed TN loads in Tidal Peace.............. 5-72

Figure 5-72. Pine Island Sound water segment. ................................................................................ 5-74

Figure 5-73. Matlacha Pass water segment....................................................................................... 5-75

Figure 5-74. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Pine 

Island Sound. ................................................................................................................. 5-77

Figure 5-75. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Pine Island Sound............................................................................................. 5-78
v 

Figure 5-76. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Matlacha 

Pass. .............................................................................................................................. 5-80

Figure 5-77. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Matlacha Pass. ................................................................................................. 5-81

Figure 5-78. Seagrass acreage in Pine Island Sound and Matlacha Pass........................................ 5-82

Figure 5-79. Relationship between chlorophyll a and TN concentrations for Pine Island Sound. ..... 5-83

Figure 5-80. Relationship between chlorophyll a and TP concentrations for Pine Island Sound. ..... 5-83

Figure 5-81. Relationship between chlorophyll a and TN loads for Pine Island Sound. .................... 5-84

Figure 5-82. Relationship between chlorophyll a and TP loads for Pine Island Sound. .................... 5-84

Figure 5-83. Relationship between chlorophyll a and hydrologic loads for Pine Island Sound. ........ 5-85

Figure 5-84. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Pine Island Sound................................................................................... 5-86

Figure 5-85. Relationship between chlorophyll a and TN concentrations for Matlacha Pass............ 5-87

Figure 5-86. Relationship between chlorophyll a and TP concentrations for Matlacha Pass. ........... 5-87

Figure 5-87. Relationship between chlorophyll a and TN loads for Matlacha Pass........................... 5-88

Figure 5-88. Relationship between chlorophyll a and TP loads for Matlacha Pass........................... 5-88

Figure 5-89. Relationship between chlorophyll a and hydrologic loads for Matlacha Pass. .............. 5-89

Figure 5-90. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for Matlacha Pass......................................................................................... 5-90

Figure 5-91. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Pine Island Sound. ....................................................................... 5-91

Figure 5-92. Comparison of the potential TN criteria to observed TN concentrations (expressed as 

geometric means) in Matlacha Pass.............................................................................. 5-92

Figure 5-93. San Carlos Bay water segment. .................................................................................... 5-94

Figure 5-94. Tidal Caloosahatchee water segment............................................................................ 5-95

Figure 5-95. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for San 

Carlos Bay...................................................................................................................... 5-97

Figure 5-96. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for San Carlos Bay................................................................................................. 5-98

Figure 5-97. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Tidal 

Caloosahatchee. ............................................................................................................ 5-99

Figure 5-98. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Tidal Caloosahatchee. .................................................................................... 5-100

Figure 5-99. Seagrass acreage in San Carlos Bay.......................................................................... 5-101

Figure 5-100. Relationship between chlorophyll a and TN concentrations for San Carlos Bay. ....... 5-102

Figure 5-101. Relationship between chlorophyll a and TP concentrations for San Carlos Bay......... 5-103

Figure 5-102. Relationship between chlorophyll a in San Carlos Bay and TN loads from San Carlos 

Bay. .......................................................................................................................................

..................................................................................................................................... 5-103

Figure 5-103. Relationship between chlorophyll a in San Carlos Bay and TP loads from San Carlos 

Bay. .............................................................................................................................. 5-104

Figure 5-104. Relationship between chlorophyll a in San Carlos Bay and hydrologic loads from San 

Carlos Bay.................................................................................................................... 5-104

Figure 5-105. Relationship between chlorophyll a and TN concentrations for Tidal Caloosahatchee........

..................................................................................................................................... 5-106

Figure 5-106. Relationship between chlorophyll a and TP concentrations for Tidal Caloosahatchee........

..................................................................................................................................... 5-106

Figure 5-107. Relationship between chlorophyll a and TN loads for Tidal Caloosahatchee. ............ 5-107

Figure 5-108. Relationship between chlorophyll a and TP loads for Tidal Caloosahatchee.............. 5-107

Figure 5-109. Relationship between chlorophyll a and hydrologic loads for Tidal Caloosahatchee. . 5-108

Figure 5-110. Tidal Caloosahatchee water segment, including regional divisions from Doering et al. 

(2006)........................................................................................................................... 5-109

Figure 5-111. Relationship between chlorophyll a and TN concentrations for the lower portion of the 

Tidal Caloosahatchee. ................................................................................................. 5-110

Figure 5-112. Relationship between chlorophyll a and TP concentrations for the lower portion of the 

Tidal Caloosahatchee. ................................................................................................. 5-110
vi

Figure 5-113. Relationship between chlorophyll a and TN loads for the lower portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-111

Figure 5-114. Relationship between chlorophyll a and TP loads for the lower portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-111

Figure 5-115. Relationship between chlorophyll a and hydrologic loads for the lower portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-112

Figure 5-116. Relationship between chlorophyll a and TN loads for the upper portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-112

Figure 5-117. Relationship between chlorophyll a and TN loads for the middle portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-113

Figure 5-118. Relationship between chlorophyll a and TN loads for the lower portion of the Tidal 

Caloosahatchee. .......................................................................................................... 5-113

Figure 5-119. Predicted versus observed chlorophyll a concentrations based on regression equation 

developed for the lower portion of the Tidal Caloosahatchee. .................................... 5-114

Figure 5-120. Comparison of the potential TN criterion to observed TN concentrations in San Carlos 

Bay. .............................................................................................................................. 5-115

Figure 5-121. Comparison of the potential TN criterion (grey bars) to observed TN loads (blue bars) in 

Tidal Caloosahatchee. ................................................................................................. 5-116

Figure 5-122. Map of Estero Bay water segment............................................................................... 5-118

Figure 5-123. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for Estero 

Bay. .............................................................................................................................. 5-120

Figure 5-124. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads (bottom 

plot) for Estero Bay. ..................................................................................................... 5-121

Figure 5-125. Seagrass acreage in Estero Bay. ................................................................................ 5-122

Figure 5-126. Relationship between chlorophyll a and TN concentrations for Estero Bay................ 5-123

Figure 5-127. Relationship between chlorophyll a and TP concentrations for Estero Bay. ............... 5-124

Figure 5-128. Relationship between chlorophyll a and TN loads for Estero Bay............................... 5-124

Figure 5-129. Relationship between chlorophyll a and TP loads for Estero Bay. .............................. 5-125

Figure 5-130. Relationship between chlorophyll a and hydrologic loads for Estero Bay. .................. 5-126

Figure 5-131. Map of Estero Bay water quality sampling stations used in changepoint analysis...... 5-127

Figure 5-132. Results of changepoint analysis for Estero Bay chlorophyll and Loads. ..................... 5-128

Figure 5-133. Histogram of TN loadings from Estero Bay corresponding to the water quality data with 

identified changepoint. ................................................................................................. 5-129

Figure 5-134. Comparison of the potential TN criteria to observed TN loads in Estero Bay. ............ 5-130
vii

List of Tables

Table 3-1. CHNEP Seagrass targets. ............................................................................................... 3-3

Table 3-2. Segment-specific empirically-derived light attenuation Kd (m-1) values for the 30th

percentile and 70th percentile based on data collected from 2003-2007. ........................ 3-4

Table 3-3. Estimated maximum concentration of each partial attenuation property assuming the 

other two properties are zero. (from CHNEP, 2006). Data not available for Dona and 

Roberts Bays.................................................................................................................... 3-6

Table 3-4. Recommended chlorophyll a targets and thresholds (μg/L) developed based on reference 

period (2003-2007)........................................................................................................... 3-8

Table 4-1. Average TN and TP concentrations and TN:TP by CHNEP segments (1996-2009). ..... 4-3

Table 6-1. Candidate numeric nutrient criteria based on the Reference Period, Optical Model, and 

Regulatory methods for the regions of CHNEP. .............................................................. 6-1
viii

FOREWORD

This report was prepared by Janicki Environmental, Inc. for the Charlotte Harbor 

National Estuary Program in fulfillment of Task 7 of the Water Quality Target 

Refinement Project. 
ix

ACKNOWLEDGMENTS

We wish to thank the many participants of the Technical Advisory Committee and Water 

Quality Quantifiable Objectives sub-committee meetings that led to the development of 

this process. We are grateful for the data collection efforts of the Coastal Charlotte 

Harbor Monitoring Network, the Charlotte Harbor Volunteer Monitoring Network, Lee 

County, Charlotte Harbor Aquatic Preserves, Cape Coral, Florida International 

University, The Peace River Manasota Water Supply Authority, Sarasota County, and 

Mote Marine Laboratory. We sincerely appreciate the contributions of Keith Kibbey, 

Peter Doering, Craig Hankins, Rob Johnson, Renee Duffy, Mindy Brown, Ray Leary, 

Ralph Montgomery, Sam Stone, and Kellie Dixon for allowing access to their data. 

Finally, we thank Lizanne Garcia and Kris Kaufman of the Southwest Florida Water 

Management District, Kevin Carter and Peter Doering of the South Florida Water 

Management District, and Lisa Beever, Liz Donley, and Judy Ott of the Charlotte Harbor 

National Estuary Program for supporting this work. 
x 

EXECUTIVE SUMMARY

Over-enrichment of water bodies by nitrogen and phosphorus typically stimulates plant 

and microbial growth, and can result in biological and physical responses that adversely 

affect water quality and aquatic life. The U.S. Environmental Protection Agency 

(USEPA) is developing numeric nutrient water quality standards for Florida waters, 

including lakes and flowing waters, and estuarine and coastal waters. The goal of 

defining numeric nutrient water quality criteria levels for nutrients (i.e., nitrogen and 

phosphorus) is to protect the designated uses of water bodies as prescribed by the 

Clean Water Act. The USEPA nutrient criteria guidance recommends development of 

criteria for both total nitrogen (TN) and total phosphorus (TP) while not precluding the 

use of alternative causal or response constituents (USEPA, 2009).

The Charlotte Harbor estuarine system is located in southwest Florida. The system 

includes 224,000 acres of estuarine waters downstream from a 3,008,000 acre 

watershed. The Charlotte Harbor National Estuary Program (CHNEP) is a partnership 

of citizens, elected officials, resource managers and commercial and recreational 

resource users working to improve and maintain the water quality and ecological 

integrity of the greater Charlotte Harbor Watershed. 

Seagrass targets have been established as a CHNEP management tool to track 

changes in an important ecological indicator over time. These seagrass targets were 

designed to maintain and/or restore seagrass acreage to its historical extent. While the 

extent of seagrass in the CHNEP area may be governed by a variety of processes 

including erosion, salinity changes, biological perturbations, prop scarring, and 

sedimentation, water clarity is thought to be a principal controlling factor in the long-term 

health of seagrasses in the study area. 

Management-level water clarity targets that are related to the light requirements of 

seagrass have also been developed (Janicki Environmental, 2009). Based on that 

evaluation, interim water clarity targets were proposed for a period when seagrasses in 

Charlotte Harbor were stable (2003-2007) and the distribution of water clarity is being 

used as a benchmark from which to compare future years (Janicki Environmental, 

2009). Thus, segment-specific seagrass and water clarity targets have been 

established for the segments of the CHNEP.

In order to develop numeric nutrient criteria, three components are necessary:

• a defensible threshold, i.e., a chlorophyll a concentration above which water 

quality is likely to degrade,

• knowledge of the most likely limiting nutrient (nitrogen or phosphorus), and

• a defensible quantitative relationship between chlorophyll a concentrations and 

either nutrient loads or concentrations. 

First, segment-specific chlorophyll a thresholds were estimated. Segment-specific 

target chlorophyll a concentrations, i.e., a desired chlorophyll a concentration that 
xi

results in water clarity conditions that are protective of seagrasses have been 

estimated. In turn, segment-specific chlorophyll a thresholds, i.e., the chlorophyll a

concentrations above which water quality is likely to degrade were estimated. In 

estimating these thresholds it was noted that natural variability can result in years in 

which these thresholds are exceeded without causing significant reductions in seagrass 

cover. Therefore, there is some allowable amount of variation that should not elicit a 

significant degradation in water quality and therefore seagrass coverage. As a result, 

the chlorophyll a threshold for each segment is the sum of the target and the standard 

deviation around the mean annual chlorophyll a concentrations in each segment.

Three potential candidate methods for defining chlorophyll a thresholds were examined. 

These methods are the Regulatory, Optical Model, and Reference Period methods.

• Regulatory Method

The Florida Department of Environmental Protection (FDEP) developed the 

Impaired Waters Rule (IWR) in 2002 and is applied to identify those waterbodies 

that are impaired, i.e., those that do not meet water quality standards. Given the 

existing nutrient criteria were narrative, an acceptable numeric criterion for those 

nutrients or an appropriate surrogate to allow identification of nutrient impaired 

waterbodies was needed. In the case of marine waters, a mean annual 

chlorophyll a threshold of 11.0 μg/l was established as the numeric surrogate. 

• Optical Model Method

Chlorophyll a targets were developed based on a regression model (McPherson 

and Miller, 1987) relating Kd to color, turbidity, and chlorophyll a concentrations. 

Corbett and Hale (2006) and CHNEP (2006) used segment-specific critical Kd

values to estimate maximum chlorophyll a thresholds for the estuarine waters of 

Charlotte Harbor assuming color and turbidity were zero.

• Reference Period Method

Given that the recent extents of seagrasses are meeting the established targets 

for many of the segments, it logically follows that recent water clarity and 

chlorophyll a concentrations are protective of the seagrasses in those segments 

that are meeting established targets. The reference period used to establish the 

water clarity targets was 2003-2007. This also corresponds to the time period 

used in establishing the water clarity targets for CHNEP. The mean chlorophyll 

a concentrations from this period plus one standard deviation are proposed 

thresholds for each segment. 

Secondly, setting numeric nutrient criteria depends upon knowledge of the nutrient most 

likely limiting in the waterbodies of concern. In most estuarine ecosystems, nitrogen is 

the most limiting nutrient (the nutrient whose concentration in the environment of an 

organism determines the growth and productivity of that organism). Most of the northern 
xii

segments of the CHNEP have molar N:P ratios near or less than 10:1 and would 

therefore be considered nitrogen limited. Ratios are higher in the southern segments, 

indicating potential co-limitation. The exception to this is the Tidal Caloosahatchee 

which has a higher TP concentration and a lower TN:TP ratio than do the adjacent 

segments.

Thirdly, after the chlorophyll a thresholds were estimated, the relationships between 

chlorophyll a concentrations and nutrient loads or concentrations were investigated. 

The data analysis methods employed in these investigations were previously identified 

in the data analysis plan developed by Janicki Environmental (2010). These methods 

were developed based on peer-reviewed literature, input from the many local scientists 

and natural resource managers studying southwest Florida estuaries, previous USEPA 

documents (USEPA, 2009), and reviews by its Science Advisory Board (SAB, 2010) on 

methods for establishing numeric nutrient criteria. The methods included a series of 

techniques that can be used to estimate statistically defensible relationships between 

chlorophyll a concentrations and nutrient concentrations and/or loadings. These 

techniques included several regression models and changepoint analysis. 

A number of data sources were used to develop the data base used to determine 

candidate numeric nutrient criteria for the estuarine waters of the CHNEP. These 

included ambient water quality data from numerous ambient water quality sampling 

programs, hydrologic and nutrient loading estimates, seagrass coverage, and 

bathymetry. 

The candidate numeric nutrient criteria for the CHNEP jurisdiction are presented in the 

table below. It is anticipated that the water quality subcommittee of the CHNEP will 

determine which of the candidate criteria should be the proposed criteria.

Candidate numeric nutrient criteria based on the Reference Period, Optical Model, and 

Regulatory methods.

Bay Segment Reference Period

Method

Optical Model

Method

Regulatory

Method

Dona and Roberts Bays 0.47 mg/l n/a 1.05 mg/l

Upper Lemon Bay 0.69 mg/l 0.63 mg/l 0.85 mg/l

Lower Lemon Bay 0.48 mg/l 0.63 mg/l 0.85 mg/l

Tidal Myakka 1,330 tons/year 2,279 tons/year 2,759 tons/year

Tidal Peace 3,018 tons/year 9,619 tons/year 10,423 tons/year

Charlotte Harbor Proper 0.62 mg/l 0.81 mg/l 1.00 mg/l

Matlacha Pass 228 tons/year 437 tons/year 798 tons/year

Pine Island Sound 0.99 mg/l 1.14 mg/l 2.14 mg/l

Tidal Caloosahatchee 1,834-4,056 tons/year n/a n/a

San Carlos Bay 0.62 mg/l n/a n/a

Estero Bay 660 tons/year n/a n/a
1-1 

1.0 INTRODUCTION 

The U.S. Environmental Protection Agency (USEPA) is developing numeric nutrient 

water quality standards for Florida waters, including lakes and flowing waters, and 

estuarine and coastal waters. The schedule for estuarine and coastal water criteria has 

been recently modified and requires USEPA to propose estuarine and coastal waters 

nutrient criteria and downstream protective values in Florida by November 14, 2011. 

This will allow adequate time for public comment and peer review by the Science 

Advisory Board (SAB), followed by USEPA revision of the proposed numeric nutrient 

criteria.

In this document, candidate numeric nutrient criteria for the estuarine waters of the 

Charlotte Harbor National Estuary Program (CHNEP) are presented, including the 

estuarine bay segments in the Charlotte Harbor National Estuary Program’s jurisdiction 

(Figure 1-1). The methods employed in deriving these candidate criteria have been 

previously identified in a data analysis plan (Janicki Environmental, 2010). These 

methods have been developed based on peer reviewed literature, the many local 

scientists and natural resource managers studying southwest Florida estuaries, 

previous USEPA documents (USEPA, 2009) and reviews by its Science Advisory Board 

(SAB, 2010) on methods for establishing numeric nutrient criteria. 

Numeric nutrient water quality criteria define levels of nutrients (i.e., nitrogen and 

phosphorus) that are protective of the designated uses of water bodies as prescribed by 

the Clean Water Act. Over-enrichment of water bodies by nitrogen and phosphorus 

typically stimulates plant and microbial growth, and can result in biological and physical 

responses that adversely affect water quality and aquatic life. The USEPA nutrient 

criteria guidance recommends development of criteria for both total nitrogen (TN) and 

total phosphorus (TP), the primary causal constituents, through a stressor response 

relationship involving the response variables, chlorophyll a, water clarity, and dissolved 

oxygen, while not precluding the use of alternative causal or response constituents 

(USEPA, 2009).
1-2 

Figure 1-1. CHNEP Bay Segments and Water Management District Boundaries.
2-1 

2.0 OBJECTIVES 

The primary objectives of this project are:

• To develop a data base of nutrient loads for each of the major bay segments for 

which seagrass targets exist for the period 1989-2008;

• To define the chlorophyll thresholds to meet the light attenuation and seagrass 

targets in each bay segment;

• To define the quantitative relationships between nutrient concentrations or 

loading and chlorophyll concentrations in each bay segment; and

• To estimate the numeric nutrient criteria, i.e., the nutrient concentration or 

loading consistent with the chlorophyll thresholds, for each bay segment.
3-1 

3.0 CHNEP GOALS AND TARGETS

The Charlotte Harbor estuarine system is located in southwest Florida (Figure 1-1) and 

includes 224,000 acres (350 square miles) of estuarine waters downstream from a 

3,008,000 acre (4,700 square mile) watershed. The CHNEP is a partnership of citizens, 

elected officials, resource managers and commercial and recreational resource users 

working to improve the water quality and ecological integrity of the greater Charlotte 

Harbor Watershed. A cooperative decision-making process is used within the program 

to address diverse resource management concerns in the jurisdiction of the CHNEP.

This study addresses some of the priority problems that have been identified by the 

CHNEP’s Comprehensive Conservation and Management Plan (CCMP) (CHNEP, 

2008) and that could impede the health of the watersheds and estuaries including water 

quality degradation and hydrologic alterations. The area economy is driven by tourism 

and recreational and commercial fishing. Good water quality is a principal feature that 

attracts users to the Charlotte Harbor region. Coastal areas have been urbanized in the 

last 50 years while much of the upland watersheds remain agricultural with some mining 

activities, predominantly in the Peace River watershed. 

This study focuses on the relationships between nutrient loads from the watershed, 

nutrient concentrations in the estuarine waters, and water quality responses such as 

phytoplankton biomass (as measured by chlorophyll a concentrations) and water clarity. 

These responses are critical determinants of management programs that seek to 

protect and restore seagrasses. The goal is to identify predictive relationships among 

these “stressors” and “responses” that can be used to set numeric nutrient criteria for 

total nitrogen (TN) and total phosphorus (TP) in the CHNEP bay segments. 

The CHNEP area is comprised of eight regions that are divided into 14 segments 

(Figure 1-1). These segments, along with their watersheds, are delineated based on 

hydrologic, ecologic, and management characteristics. The 14 bay segments are:

• Dona and Roberts Bays,

• Lemon Bay

o Upper Lemon Bay,

o Lower Lemon Bay,

• Charlotte Harbor Proper

o Cape Haze,

o Bokeelia,

o West Wall of Charlotte Harbor,

o East Wall of Charlotte Harbor,

• Tidal Myakka River,

• Tidal Peace River,

• Pine Island Sound/Matlacha Pass,

• Tidal Caloosahatchee River/San Carlos Bay, and

• Estero Bay.
3-2 

The bay segmentation scheme in the CHNEP was designed to take into account the 

various ecosystem factors that are important to the CHNEP region and its inhabitants. 

This segmentation scheme is the result of an effort to subdivide the CHNEP area into 

separate reporting units which represent relatively homogeneous conditions with 

respect to variations in water quality and to resultant seagrass protection and 

restoration within the estuary (CHNEP, 2009; Janicki Environmental, 2009). 

3.1 Seagrass Targets

Seagrass targets were established as a CHNEP management tool to track changes in 

an important ecological indicator over time. Baseline seagrass coverage was 

determined through photo-interpretation of aerial photos of the study area from circa 

1950 obtained from the National Archives in Washington, DC. The CHNEP contracted 

Photo Science, experts in photo-interpretation services, which provided electronic data 

coverage of the area of interest for the CHNEP using ArcGIS9. This coverage served 

as a historic baseline from which to compare recent surveys conducted by the South 

and Southwest Florida Water Management Districts (Janicki Environmental, 2009). 

Historic and recent surveyed seagrass acreages were compared to establish seagrass 

targets. These seagrass targets were designed to maintain and/or restore seagrass 

acreage to its historical extent. While the extent of seagrass in the CHNEP area may 

be governed by a variety of processes including erosion, salinity changes, biological 

perturbations, prop scarring and sedimentation, water clarity is thought to be the 

principal controlling factor in the long-term health of seagrasses in the study area. 

Therefore, management-level water clarity targets that are related to the light 

requirements of seagrass were developed to allow managers to correlate changes in 

water clarity conditions and seagrass conditions over time.

The CHNEP seagrass target for each harbor segment is the greater of either the 

adjusted baseline acreage or the mean of all recent seagrass surveys (Janicki 

Environmental, 2009). Application of this definition provided the targets identified for 

each harbor segment in Table 3-1.

In addition to defining these targets, an appropriate definition of a target range, i.e., the 

range of acceptable seagrass area, was also desired. It is recommended that this 

target range be defined by the range between the minimum and maximum areas from 

the recent seagrass surveys.

3.2 Water Clarity Targets

The CHNEP identified the need to develop water quality targets that preserve and 

restore seagrass health throughout the estuarine system. The resource-based water 

quality targets address the Priority Problems (Hydrologic Alterations and Water Quality 

Degradation) identified in the CHNEP CCMP. 
3-3 

Table 3-1. CHNEP Seagrass targets. 

Harbor Segment Adjusted 

Baseline

Mean Annual 

Extent 

(all years)

Protection 

Target

Restoration 

Target

Total 

Target Target Range

Dona and Roberts 

Bay* 112 91 91 21 112 70-124

Upper Lemon Bay 880 1,009 1,009 1,009 949-1,175

Lower Lemon Bay 2,882 2,502 2,502 380 2,882 2,396-2,597

Tidal Myakka River* 344 456 456 456 331-539

Tidal Peace River* 975 384 384 591 975 295-573

West Wall 2,106 1,907 1,907 199 2,106 1,676-2,121

East Wall 3,898 3,465 3,465 433 3,898 3,275-3,591

Cape Haze 5,670 6,998 6,998 6,998 6,709-7,464

Bokeelia 2,964 3,342 3,342 3,342 3,101-3,520

Pine Island Sound 23,757 26,837 26,837 26,837 25,941-29,204

Matlacha Pass 9,315 7,582 7,582 1,733 9,315 6,055-7,619

Tidal Caloosahatchee

River* 93 87 87 6 93 2-103

San Carlos Bay 3,118 4,372 4,372 4,372 3,709-5,376

Estero Bay 3,662 3,071 3,071 591 3,662 2,393-3,409

* These riverine segments may have underreported seagrass acreages, due to difficulty in 

delineating seagrass in highly colored waters. These numbers are presented for completeness 

only and should not be used for reporting of seagrass loss or gain over time.

Optical models have recently been applied to establish water quality targets to protect 

estuarine seagrass coverage (Gallegos and Kenworthy, 1996; Gallegos, 2001). In 

Charlotte Harbor, initial resource-based water quality targets were developed based on 

measured deep edge of seagrasses and the light requirements necessary to maintain 

seagrass at that deep edge (CHNEP, 2006). An optical model developed by 

McPherson and Miller (1994) served as the basis for partitioning the light attenuating 

properties of three water quality variables; color (PtCo units), turbidity (NTU), and 

chlorophyll a concentrations (μg/l). A comparison of field collected light attenuation data 

and modeled estimates indicated that a large degree of variability was observed 

between model predictions and field collected data (Wessel and Corbett, 2009). 

Based on this evaluation, interim water clarity targets were proposed for Charlotte 

Harbor based on conditions when seagrasses in Charlotte Harbor were stable (2003-

2007) and the distribution of water clarity is being used as a benchmark from which to 

compare future years (Janicki Environmental, 2009). To develop these targets, the 

empirical distribution of Kd in each segment for a reference period when seagrasses 

were stable or improving was defined as an internal reference. This reference period is 

2003-2007. 
3-4 

Two points along each segment-specific Kd distribution (i.e., the 30th and 70th

percentiles) were used to establish benchmark points for comparison to empirical data 

collected in future years (Table 3-2). These benchmark points were chosen based on 

previous work to estimate the light attenuation requirements of seagrass in Charlotte 

Harbor (CHNEP, 2006). Based on these benchmark points, a management tool was 

developed to determine if each year’s Kd distribution was improved, similar, or degraded 

relative to the reference period. The binomial test was established as the evaluation 

tool such that statistical uncertainty could be incorporated into the evaluation metric, 

providing confidence that observed differences were not due to chance alone. For 

example, if more than 30% of the Kd measurements in a given future year’s worth of 

data are below the benchmark value with statistical significance (α=0.05), then the 

water clarity was considered to be improving and assigned a scoring value of +1. If less 

than 30% of the values are below the benchmark value with statistical significance 

(α=0.05), then the water clarity is considered to be degrading and is assigned a value of 

negative 1. Otherwise, the value is 0. The evaluation is performed on the 30th and 70th

percentile values and the scores are summed to provide an evaluation measure for 

each year of data collection. 

Table 3-2. Segment-specific empirically-derived light attenuation 

Kd (m-1) values for the 30th percentile and 70th

Harbor Segment

percentile based on data collected from 2003-2007.

30th Percentile 70th

Dona and Roberts Bay 

Percentile

0.64 1.04

Lemon Bay

Upper Lemon Bay 0.73 1.18

Lower Lemon Bay 0.73 1.12

Charlotte Harbor Proper 

West Wall 0.73 1.36

East Wall 0.64 1.16

Bokeelia 0.58 1.16

Cape Haze 0.63 1.15

Tidal Myakka River 1.3 2.27

Tidal Peace River 1.06 2.4

Pine Island Sound 0.64 1.1

Matlacha Pass 0.73 1.63

Tidal Caloosahatchee River 1.58 2.93

San Carlos Bay 0.73 1.16

Estero Bay 0.91 1.58

Recent spectral light attenuation modeling efforts in Charlotte Harbor have suggested 

that non-linearity in attenuation as a function of sample depth may result in biased 

estimates of the specific light requirements of seagrasses and that previously specified 
3-5 

seagrass light requirements may need to be revisited in future years (Dixon et al., 

2010). Based on these efforts, the CHNEP is using the interim targets developed by 

Janicki Environmental (2009) in an adaptive management strategy until 2012 when 

water clarity targets will be revisited.

3.3 Chlorophyll a Targets and Thresholds

Establishment of defensible, protective environmental endpoints largely depends upon 

the definition of a defensible baseline. Three potential candidate methods for defining 

chlorophyll a targets and thresholds are discussed below. These potential methods are:

• Regulatory-based Method,

• Optical Model Method, and

• Reference Period Method.

3.3.1 Regulatory Method

The Florida Department of Environmental Protection developed the Impaired Waters 

Rule (IWR) in 2002. The IWR was to be used to identify those waterbodies that are 

impaired, i.e., those that do not meet water quality standards. Given the existing criteria 

for nutrients (i.e., N and P) were narrative, there was a need to identify an acceptable 

numeric criterion for those nutrients or an appropriate surrogate to allow identification of 

waterbodies that are nutrient impaired. In the case of marine waters, a chlorophyll a

threshold was established as the numeric criterion surrogate and this value was a mean 

annual chlorophyll a concentration of 11.0 μg/l. 

3.3.2 Optical Model Method

The results from Corbett and Hale (2006) and CHNEP (2006) discussed above can be 

used to estimate chlorophyll a targets for the estuarine waters of Charlotte Harbor. The 

general model is:

 Kd = α + β1C1 + β2C2 + β3C3

where: C1

C

= coefficient related to water color

2

C

= coefficient related to turbidity

3 = coefficient related to chlorophyll a concentrations.

The partial attenuation coefficients were derived by stepwise multiple regression 

analysis. Using the equation, a value for any of the three light attenuation properties 

can be calculated given values for the other two properties. Therefore, given a critical 

Kd and some assumed values for color and turbidity, the chlorophyll a concentration 

needed to meet the critical Kd can be calculated.
3-6 

Given a critical Kd, the simplest application of the optical model would assume that two 

of the three light attenuation properties are fixed values and estimate the third. The 

maximum concentration of each partial light attenuation component that meets the 

critical Kd was calculated by assuming two properties are zero and solving for the third 

(CHNEP, 2006). The results from these calculations are presented in Table 3-3.

DEP developed a TMDL for the tidal Caloosahatchee River and used the Corbett and 

Hale method to define the chlorophyll a target (FDEP, 2009). 

For this study, two methods were attempted to apply the thresholds from CHNEP 

(2006). In the first method, it was assumed that color and turbidity were equal to zero 

and the resulting chlorophyll a concentrations were estimated. However, as noted by 

CHNEP (2006):

“These intercepts are the maximum potential concentration of the analytes 

and are acceptable for meeting the percent-light-at-depth goal only when 

the concentrations for both the other 2 analytes are zero, an unlikely 

situation except when color is sufficiently high to limit phytoplankton 

production.” 

Examining the database of water quality used for the analyses, there were no samples 

that had zeros for two of the three constituents.

Table 3-3. Estimated maximum concentration of each partial attenuation

property assuming the other two properties are zero. (from 

CHNEP, 2006). Data not available for Dona and Roberts Bays.

Bay Segment Critical K Color

d (Pt-Co units)

Turbidity 

(NTU)

Chlorophyll a

(μg/L)

Dona and Roberts Bays n/a n/a n/a n/a

Lemon Bay

Upper Lemon Bay 0.7 28.6 6.5 8.2

Lower Lemon Bay 0.7 28.6 6.5 8.2

Charlotte Harbor Proper

West Wall 1.0 50.0 11.3 14.3

East Wall 1.0 50.0 11.3 14.3

Bokeelia 0.6 20.2 4.6 5.8

Cape Haze 0.7 31.2 7.0 8.9

Tidal Myakka River 1.6 89.7 20.3 25.6

Tidal Peace River 1.4 78.6 17.7 22.4

Matlacha Pass 0.7 28.6 6.5 8.2

Pine Island Sound 0.6 24.0 5.4 6.9

Tidal Caloosahatchee River 1.2 61.9 14.0 17.7

San Carlos Bay 0.6 24.0 5.4 6.9

Estero Bay 0.9 41.1 9.3 11.7
3-7 

Secondly, the long-term mean values of color and turbidity for each calendar month 

were used to calculate chlorophyll a concentrations based on the critical Kd values. The 

results from this application of the CHNEP optical model included negative chlorophyll a

concentrations in each segment. 

Therefore, the CHNEP optical model application where both color and turbidity are 

assumed to be zero was used to estimate the chlorophyll a targets for the Optical Model 

Method.

3.3.3 Reference Period Method

As was discussed in the previous sub-sections, seagrass and water clarity targets have 

been established for the segments of the CHNEP area. Given that the recent extents of 

seagrasses in some segments are meeting their established targets, it logically follows 

that recent water clarity and chlorophyll a concentrations are protective of the 

seagrasses in those segments that are meeting established targets. However, for the 

segments that are not meeting their targets, the same assumption is not valid. 

The Tampa Bay Estuary Program (TBEP) (Janicki and Wade, 1996) and Sarasota Bay 

Estuary Program (SBEP) (Janicki Environmental, 2010) used a similar reference period 

approach to define chlorophyll a targets. Both programs recognized that there may be 

years in which chlorophyll a targets may be exceeded without causing significant 

reductions in seagrass cover. Therefore, there is some allowable amount of variation 

that should not elicit a significant degradation in water quality and therefore seagrass 

coverage. The SBEP defined this level of variation as the standard deviation around 

the mean annual chlorophyll a concentrations in each segment for the entire period of 

record. The target was defined as the annual mean of the reference period, i.e., the 

period that was deemed to be protective on seagrass and water clarity. Therefore, a 

distinction is made between a target, i.e., a desired chlorophyll a concentration and 

a threshold, i.e., a chlorophyll a concentration above which undesirable 

chlorophyll a concentrations exist. 

Given the experience of the neighboring estuary programs, the CHNEP Technical 

Advisory Committee (TAC) decided to use a similar approach, but with different 

thresholds depending on whether or not a segment was classified as “restoration” or 

“protection” for seagrass. If a segment is classified as “protection”, it logically follows 

that water clarity and chlorophyll concentrations are protective of seagrasses. 

Therefore, the chlorophyll a threshold for segments that are classified as “protection” is 

calculated by summing the mean plus one standard deviation. This is the same 

strategy that was employed by the SBEP in criteria development for the SBEP 

segments. However, if the segment is under “restoration”, the chlorophyll target is 

calculated by summing the mean plus one half standard deviation. This provides a 

threshold that is more stringent than for segments that are classified as “protection” 

because the “restoration” segments have not achieved the desired levels of seagrass 

coverage. The reference period used to establish the chlorophyll a targets was 2003-

2007. This corresponds to the time period used in establishing the water clarity targets 
3-8 

for CHNEP. The mean chlorophyll a concentrations for the reference period (targets), 

along with the standard deviation and the thresholds are presented for each segment in 

Table 3-4. 

Table 3-4. Recommended chlorophyll a targets and thresholds (μg/L) developed 

based on reference period (2003-2007).

Bay Segment

Restoration/

Protection

Target

Chlorophyll a 

(μg/L)

Standard Deviation 

of Annual Means

(μg/L)

Threshold 

Chlorophyll a

(μg/L)

Dona and Roberts Bays Restoration 4.3 1.2 4.9 

Upper Lemon Bay Protection 6.7 2.2 8.9

Lower Lemon Bay Restoration 5.1 2.0 6.1

Tidal Myakka Protection 8.9 2.8 11.7

Tidal Peace Restoration 10.6 4.0 12.6

Charlotte Harbor Proper 

(EW+WW+BK+CH)

Restoration/ 

Protection 4.9 2.4 6.1

Matlacha Pass Restoration 4.0 4.1 6.1

Pine Island Sound Protection 5.1 1.4 6.5 

San Carlos Bay Protection 2.8 0.7 3.5

Tidal Caloosahatchee Restoration 9.0 n/a 6.9 (TMDL)

Estero Bay Restoration 4.9 2.0 5.9

Since establishing seagrass targets for tidal rivers is questionable due to issues with 

color and visibility (Janicki Environmental, 2009), the chlorophyll thresholds for the Tidal 

Myakka, Tidal Peace, and Tidal Caloosahatchee rivers were not used in criteria 

development. Please note that a proposed Total Maximum Daily Load (TMDL) has 

been developed for the Tidal Caloosahatchee River as a result of the system being 

identified as impaired. In the proposed TMDL, a target chlorophyll a concentration of 

6.9 μg/L was identified for the Tidal Caloosahatchee.
4-1 

4.0 APPROACH AND RATIONALE

This chapter includes a discussion of the following:

• Data sources

- Water quality data

- Loadings

- Bathymetry and residence times

• Data analyses

- Linear regression

- Changepoint analysis

4.1 Data Sources

A number of data sources were used to develop the data base used to determine 

numeric nutrient criteria for the estuarine waters of the CHNEP. These included 

ambient water quality data, hydrologic and nutrient loading estimates, seagrass 

coverage, and bathymetry. These data sources are described in the following 

subsections.

4.1.1 Water Quality

Ambient water quality data were obtained from the following agencies and programs:

• Southwest Florida Water Management District,

• South Florida Water Management District,

• Sarasota County,

• Lee County,

• Peace River Manasota Regional Water Supply Authority,

• Coastal Charlotte Harbor Monitoring Network,

• Charlotte Harbor Volunteer Monitoring Network,

• Charlotte Harbor Aquatic Preserves,

• City of Cape Coral, and

• Florida International University.

4.1.2 Loadings

Estimates of hydrologic, TN, and TP loadings have recently been developed for 

Charlotte Harbor (Janicki Environmental, 2010). The loading sources include:

• atmospheric deposition directly to the surface of the estuary,

• nonpoint sources,
4-2 

• septic tanks (on-site sewage treatment and disposal systems), and

• domestic and industrial point sources (wastewater treatment facilities and 

industrial facility discharges).

Monthly loading estimates for each CHNEP segment are reported.

4.1.3 Bathymetry and Residence Time

The bathymetry data used in this study were obtained from NOAA (2005) and National 

Geophysical Data Center (2009). A box model was developed to estimate the hydraulic 

residence times within the CHNEP segments. The box model was based on observed 

salinity distributions within the system and estimated freshwater inflows to the system. 

Methods were similar to those described by Hagy et al. (2000), with the exception that 

the CHNEP system was assumed to be well mixed vertically, so that all transport was 

horizontal. Hydraulic residence time is also called pulse residence time (PRT), as 

residence times are dependent upon introducing a pulse of tracer into a selected 

segment at the beginning of the box model simulation and tracking the time necessary 

for the concentration of this tracer to decrease to a certain level. The specific method 

used to estimate the pulse residence times for each segment is given in Appendix 2. 

The box model iterations resulted in segment- and monthly-specific estimates of PRT. 

For each segment, the median PRT for each year was calculated, and then the median 

PRT of the annual values for each segment were calculated. These segment-specific 

PRTs represent the median hydraulic residence time within each segment given the 

observed conditions of 1995-2007. 

4.2 Limiting Nutrients and Data Analysis Approaches

This section presents a discussion of limiting nutrients and the data analysis 

approaches used in estimating the numeric nutrient criteria for the CHNEP estuarine 

waters.

4.2.1 Limiting Nutrients

Setting numeric nutrient criteria depends upon knowledge of the nutrient most likely 

limiting in the waterbodies of concern. In many estuarine ecosystems, nitrogen is the 

most limiting nutrient (the nutrient whose concentration in the environment of an 

organism determines the growth and productivity of that organism) (Boynton et al., 

1982; Howarth et al., 1988a, 1988b; Chapra, 1997; National Research Council, 2000; 

Smith, 1984). As such, nitrogen has been identified as the primary nutrient of concern

in estuarine ecosystems nationwide (see review in National Research Council, 1993). 

Aquatic ecosystems are commonly characterized by their N:P ratios. Receiving waters 

with ratios less than 10:1 (molar) are considered nitrogen limited, ratios of greater than 

30:1 (molar) indicate phosphorus limitation, and ratios of 10-30:1 (molar) indicate colimitation (FDEP, 2002). The average N:P ratios for the segments of the CHNEP, both 
4-3 

by weight and molar, are presented in Table 4-1 for the period 1996 to 2009. Most of 

the northern segments have molar N:P ratios near or less than 10:1 and would therefore 

be considered nitrogen limited. Ratios are higher in the southern segments, indicating 

potential co-limitation. The exception to this is the Tidal Caloosahatchee, which 

receives discharge from Lake Okeechobee and thus has a higher TP concentration, and 

lower TN:TP ratio, than do the other adjacent segments. Several segments have molar 

N:P ratios of nine or less, including Dona and Roberts Bays, Upper Lemon Bay, Tidal 

Peace, Tidal Myakka, East Wall, and West Wall, indicating nitrogen limitation of 

phytoplankton growth in these segments. While nitrogen limitation seems to be 

indicated in several of the segments of CHNEP, the nutrient that is most limiting can 

vary seasonally (Malone et al., 1996; Conley et al., 2009). So areas that are generally 

nitrogen limited may by phosphorus limited at times. In addition to nutrient limitation, 

phytoplankton growth may also be light-limited during certain parts of the year (Pennock 

and Sharp, 1994). 

Table 4-1. Average TN and TP concentrations and TN:TP by CHNEP segments 

(1996-2009).

Bay Segment TN (mg/l) TP (mg/l) TN : TP (Weight) TN : TP (Molar)

Dona and Roberts Bays 0.39 0.15 2.59 5.75

Lemon Bay 

Upper Lemon Bay 0.62 0.22 2.87 6.23

Lower Lemon Bay 0.69 0.11 6.45 13.87

Charlotte Harbor Proper 

West Wall 0.76 0.21 3.67 8.00

East Wall 0.67 0.17 4.06 8.72

Bokeelia 0.50 0.09 5.73 12.29

Cape Haze 0.68 0.12 5.47 12.53

Tidal Myakka 0.97 0.27 3.62 7.94

Tidal Peace 1.07 0.42 2.58 5.63

Pine Island Sound 0.50 0.05 9.61 22.11

Matlacha Pass 0.61 0.08 7.83 16.86

Tidal Caloosahatchee 0.91 0.11 8.23 8.27

San Carlos Bay 0.52 0.06 8.38 19.17

Estero Bay 0.53 0.06 8.25 19.53

4.2.2 Data Analysis Approaches

A data analysis plan was developed previously by Janicki Environmental (2010). 

This plan identifies a series of techniques that could be used to estimate statistically 

defensible relationships between chlorophyll a concentrations and nutrient 

concentrations and/or loadings. These techniques included linear regression, logistic 
4-4 

regression, and change point analysis (CPA). These techniques are described in detail 

in the plan and are summarized below.

4.2.2.1 Linear Regression

Linear regression is a parametric statistical technique that is used to explore the 

relationship between two or more variables. In ordinary least-squares regression, the 

relationship between the dependent variable (y-axis) and independent variable (x-axis) 

is developed. This is done by fitting a straight line through the set of points such that 

the sum of squared residuals of the model is as small as possible. That is to say, the 

vertical distances between the individual points and the fitted line are minimized. 

In linear regression, it is assumed that the data are independent samples from the 

population that is being sampled. Thus, the data should come from samples that are 

representative of the spatial and temporal variability of the system. Another important 

assumption of linear regression is that the error term of the model is normally 

distributed, with constant variance. Often times, one or more of the variables exhibits a 

non-linear relationship with the other variables. While there are non-linear regression 

techniques that can be employed, one should attempt to transform the data before 

resorting to nonlinear methods. Often, linear relationships can be developed using 

transformed data and these models will satisfy the assumptions of linear regression.

The independent variables used in the model building process included nutrient 

loadings, nutrient concentrations, and estimates of residence time. The loadings data 

included monthly hydrologic, TN and TP loads as well as cumulative total loads from the 

previous two to six months (e.g., 2-month cumulative TN load = TN load current month 

+ TN load one- month prior). The water quality constituents included TN and TP 

concentrations along with numerous other water quality constituents. 

4.2.2.2 Change Point Analysis

In those segments where no distinct linear relationship between chlorophyll a and 

nutrient concentrations or loadings could be identified, a changepoint analysis method 

was applied. Changepoint analyses were also used to provide an alternative approach 

to linear regression to identify potential weight-of-evidence conclusions.

The changepoint method employed uses a decision tree approach which has been 

employed successfully in a previous effort to develop numeric nutrient criteria (Soranno 

et al., 2008). Decision tree analysis is founded on well-established techniques designed 

to identify a threshold value corresponding to a non-linear change in a response 

variable as a function of some independent (predictor) variable. The Conditional 

Inference tree was implemented using the statistical software package R (R Core 

Development Team, 2009) to determine threshold values in the chlorophyll a – TN 

relationship. The analysis was performed to identify a potential threshold TN value that 

was associated with a non-linear increase in chlorophyll a concentrations. 
4-5 

Decision tree analysis performs an iterative search through the data sorted in increasing 

order of TN values to identify a “changepoint” in the relationship that maximizes the 

difference in chlorophyll a concentrations between two groups of data. In this way a 

threshold value for TN is identified. No a priori threshold is specified. The decision tree 

approach defines the changepoint as that which maximizes the difference by minimizing 

the p value (Hothorn et al., 2006). The point in the covariate (TN concentration) at 

which the p value is minimized, after adjustment for multiple comparisons using the 

Bonferroni correction (Dunn, 1961), is assigned as the changepoint defining the split of 

the chlorophyll data into 2 groups. These statistical stopping rules eliminate over-fitting 

that is common in some other types of recursive partitioning algorithms. The approach 

can be implemented using a continuous response variable (Regression Tree) or 

nominal variable (Classification Tree). The classification tree approach is similar in 

construct to the Logistic regression approach though the p values are based on 

permutation tests and are non-parametric. Once the first split is made the process 

continues to test for subsequent splits that are conditional on the first split. This is 

called “conditional inference” or “conditional probability analysis” which has been 

popularized recently by the EPA as a potential approach for establishing numeric 

nutrient criteria. Bootstrap methods have not been employed for this analysis to date. If 

it is decided that results of changepoint analysis are to be used as the final numeric 

nutrient criteria, it is recommended that bootstrap methods be investigated to insure that 

the changepoint is robust to the influence of outliers.
5-1 

5.0 DEVELOPMENT OF NUTRIENT CRITERIA 

In each subsection of section 5.0, the development of nutrient criteria is described for 

the individual regions of CHNEP. These “regions” consist of one or more of the bay 

segments that have previously been defined for CHNEP. Each subsection contains the 

following for each region:

• a description of the hydrology,

• a summary of the water quality and loadings,

• a synthesis of available seagrass data,

• a summary of the relationships between chlorophyll a and other water quality 

constituents and/or loads that were identified, and

• the candidate numeric nutrient criteria. 

5.1 Dona and Roberts Bays

The Dona and Roberts Bays segment is comprised of the open water estuary, primarily 

Dona and Roberts Bay proper (Figure 5-1). The surface area of this segment is 

approximately 617 acres. The watershed is approximately 61,673 acres, or 96.4 square 

miles (mi2

). The watershed to segment water surface ratio is 100 which is high relative 

to other CHNEP segments. 

Dona Bay has three main tributaries, Fox Creek, the Cow Pen Slough Canal, and Salt 

Creek, which converge in Shakett Creek at the upstream end of Dona Bay. Roberts 

Bay has two main tributaries, Blackburn Canal/Curry Creek and Hatchett Creek. The 

Blackburn Canal drains to Curry Creek, which is connected to the eastern end of 

Roberts Bay. The Blackburn Canal was constructed in the 1950s to provide relief from 

periodic flooding of the Blackburn property east of the Myakka River. The Blackburn 

Canal connects Roberts Bay to the Myakka River. Hatchett Creek flows into the Gulf 

Intracoastal Waterway at the southern end of Roberts Bay (SWFWMD, 2009).

The main tributary to Dona and Roberts Bay is Cow Pen Slough, which drains into Dona 

Bay. Cow Pen Slough did not always flow into Dona Bay. Analysis of historical surveys 

shows that Cow Pen Slough once flowed east to the Myakka River (SWFMWD, 2009). 

However, efforts to curb the flooding that impacted local pastures and rangelands were 

pursued through a series of hydrologic modifications, which included rerouting Cow Pen 

Slough from the Myakka River system south and west into Dona Bay (SWFWMD, 

2009).
5-2 

Figure 5-1. Dona and Roberts Bays water segment.
5-3 

The alterations to the hydrologic connections have resulted in Dona Bay’s watershed 

area increasing nearly five-fold. The corresponding increase in the amount of 

freshwater entering the Dona and Roberts Bays system has had significant effects on 

conditions in the estuary, including increased flushing and circulation above historical 

conditions (SWFWMD, 2009). Construction of the Cow Pen Slough and Blackburn 

Canals has led to an increase in the volume of freshwater entering Dona and Roberts 

Bays and has also altered the timing of freshwater inflows. Construction of the Gulf 

Intracoastal Waterway and the Venice Inlet has influenced horizontal (longitudinal) and 

vertical (water column) salinity gradients in the system and exchange with the Gulf of 

Mexico. The construction of bridges over Dona Bay and Roberts Bay has also altered 

salinity patterns by restricting longitudinal exchange of water within the estuary 

(SWFWMD, 2009).

About one-third of the Dona and Roberts Bays watershed is developed and one-fifth of 

the watershed used for agricultural activities. Approximately 40% of the watershed 

remains relatively undeveloped, with wetlands and uplands each comprising about onefifth of the watershed (SWFWMD, 2009). 

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Dona and Roberts Bays was 17 

days, indicating relatively rapid flushing of the system. 

5.1.1 Dona and Roberts Bays Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations are 

presented in Figure 5-2. As can be seen in the plots, there is marked seasonality in 

nutrient concentrations with highest concentrations observed during the wet season. 

The monthly average chlorophyll a concentrations varied from 0.5 to 33.1 μg/l, and the 

interquartile range (the difference between the 25th and 75th percentiles) was 1.8 to 5.8 

μg/l. The mean and median chlorophyll a concentrations were 4.5 and 3.4 μg/l, 

respectively. This difference between the mean and median indicates that the mean is 

influenced by a few high concentration values. Monthly average TN concentrations in 

Dona and Roberts Bays varied from 0.17 to 1.24 mg/l. The interquartile range was 0.27 

to 0.44 mg/l. The mean TN concentration was 0.39 mg/l, while the median was 0.35 

mg/l. The monthly average TP concentrations varied from 0.06 to 0.42 mg/l, and the 

interquartile range was 0.12 to 0.18 mg/l. The mean and median TP concentrations 

were 0.15 and 0.14 mg/l, respectively. Unlike chlorophyll a, the differences between the 

mean and median for TN and TP were much less as TN and TP do not exhibit the 

peaks that are sometimes observed in chlorophyll a concentrations. 

There were no statistically significant trends in chlorophyll a or TN concentrations in 

Dona and Roberts Bays during this period (complete trend test results are presented in 
5-4 

Appendix 3). A significant decreasing trend (p<0.01) was detected in TP during the 

period analyzed. 

Figure 5-3 presents the time series of monthly TN, TP, and hydrologic loads to Dona 

and Roberts Bays. The monthly TN loads varied from 0.6 to 104.6 tons/month, and the 

interquartile range was 2.9 to 20.8 tons/month. The mean and median TN loads were 

15.9 and 6.2 tons/month, respectively. Monthly TP loads varied from 0.1 to 20.5 

tons/month. The interquartile range was 0.5 to 4.0 tons/month. The mean TP load was 

3.0 tons/month, while the median was 1.1 tons/month. The monthly hydrologic loads 

varied from 0.1 to 64.7 million m3/month, and the interquartile range was 11.2 million 

m3/month (1.6 to 12.8 million m3/month). The mean and median hydrologic loads were 

9.7 and 3.6 million m3

/month, respectively. This difference between the mean and 

median indicates that the mean is highly influenced by a series of high loadings values 

that are typically seen in the wet season when both hydrologic loads and nutrient loads 

are elevated. Though increased runoff associated with increased rainfall in the wet 

season is expected, it is at times magnified in the Dona and Roberts Bays system 

because there is a structure which is used to control flows into the system from the Cow 

Pen Slough watershed. Also, as noted in SWFWMD (2009), the historical Dona and 

Roberts Bays watershed has been significantly altered resulting in more freshwater 

entering the system currently than had entered the system historically.

5.1.2 Water Quality and Seagrass Targets and Thresholds

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined;

• Regulatory Method = 11 μg/l,

• Optical Model Method = NA, and

• Reference Period Method = 4.9 μg/l.

Seagrass acreages in Dona and Roberts Bays from 1988-2008 are provided in Figure 

5-4. The seagrass target for this segment is 112 acres (Janicki Environmental, 2009). 

Acreages have typically ranged from 75 to124 acres, with the greatest extent identified 

in 2006.
5-5 

Figure 5-2. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Dona and Roberts Bays.
5-6 

Figure 5-3. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Dona and Roberts Bays.
5-7 

Figure 5-4. Seagrass acreage in Dona and Roberts Bays.

5.1.3 Relationships Between Chlorophyll a and Concentrations and Loads 

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and nutrient loads and concentrations. 

These plots included chlorophyll a versus TN concentration, TP concentration, TN 

loads, TP loads, and hydrologic loads (Figures 5-5 through 5-9, respectively). 

Additional bivariate plots between chlorophyll a and pulse residence time, 2-month, 3-

month, 4-month, 5-month, and 6-month average loads were examined. TN 

concentrations appeared to best explain the variability in chlorophyll a concentrations in 

Dona and Roberts Bays. As can be seen in Figure 5-5, with the exception of a few 

outliers, there is a distinct pattern of increasing chlorophyll a concentrations with 

increasing TN concentrations. 

0

20

40

60

80

100

120

140

1988 1994 1999 2001 2004 2006 2008

Seagrass 
5-8 

Figure 5-5. Relationship between chlorophyll a and TN concentrations for Dona and Roberts 

Bays.

Figure 5-6. Relationship between chlorophyll a and TP concentrations for Dona and Roberts 

Bays.
5-9 

Figure 5-7. Relationship between chlorophyll a and TN loads for Dona and Roberts Bays.

Figure 5-8. Relationship between chlorophyll a and TP loads for Dona and Roberts Bays.
5-10

Figure 5-9. Relationship between chlorophyll a and hydrologic loads for Dona and Roberts 

Bays.

A regression model between the Box-Cox transformed chlorophyll a and Log 

transformed TN concentrations was developed and the residuals from this model were 

examined to identify any other explanatory variables that might contribute to the overall 

variance accounted for by the model. The residual analysis revealed a seasonal 

difference in residuals. Specifically, given the same TN concentrations, higher 

chlorophyll a concentrations can be expected during the wetter, warmer summer 

months (July-October) than during the remainder of the year. Therefore, a seasonal 

term was added to the regression equation. The season term is a dummy variable 

which equals one during July-October and zero other months of the year. The final 

regression equation is:

Transformed [Chlorophyll a] = 2.81 + (1.50 * Log [TN]) + (0.57 * season)

The model was fit with 76 observations and resulted in an R2 value of 0.57. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope and parameter coefficients were also highly significant. A plot of predicted 

versus observed chlorophyll a concentrations is presented in Figure 5-10.
5-11

Figure 5-10. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Dona and Roberts Bays. 

5.1.4 Candidate Criteria - Dona and Roberts Bays

The above regression equation was used to estimate the TN concentrations that 

correspond to the chlorophyll a thresholds identified for Dona and Roberts Bays. 

Assuming the more stringent threshold (4.9 μg/l), the TN concentrations corresponding 

to this threshold can be calculated for each season. The annual geometric mean of the 

seasonally-specific TN concentrations can also be calculated. This annual geometric 

mean TN concentration, 0.47 mg/l, is the TN concentration based on the Reference 

Period Method. Including estimates of uncertainty, the 95% confidence intervals are 

0.34 -0.63 mg/l. Assuming the Regulatory Method threshold (11 μg/l ), the same 

procedure was used to calculate the TN concentration that corresponds to the 11 μg/l 

chlorophyll a threshold. The annual geometric mean TN concentration was 1.05 mg/l. 

Including estimates of uncertainty, the 95% confidence intervals are 0.79 – 1.39 mg/l. 

There is no Optical Model Method threshold for Dona and Roberts Bays.

A comparison of the candidate numeric TN concentration criteria based on the 

Regulatory and Reference Period methods is presented in Figure 5-11.
5-12

Figure 5-11. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Dona and Roberts Bays. 

5.2 Lemon Bay

Lemon Bay has been divided into the Upper and Lower Lemon Bay segments for 

previous work performed by the CHNEP (Janicki Environmental, 2009; Janicki 

Environmental, 2010). However, CHNEP (2006) and Corbett and Hale (2006) treated 

Lemon Bay as a single entity for development of light and chlorophyll a targets. 

Because the reference period divided Lemon Bay into Upper and Lower Lemon Bay 

segments and light and seagrass targets were developed individually for Upper and 

Lower Lemon Bay, an attempt was made to develop numeric nutrient criteria that are 

specific to the Upper and Lower Lemon Bay segments. 

The Upper Lemon Bay segment is the northernmost portion of Lemon Bay and consists 

of approximately 2,357 acres of open water (Figure 5-12). The Upper Lemon Bay 

watershed has approximately 17,676 acres or 27.6 mi2

. This results in a watershed to 

water ratio of 7.5, which is intermediate with respect to other bay segments. The Upper 

Lemon Bay segment is hydraulically connected to the north, via the Intracoastal 

Waterway, to Dona and Roberts Bays (Janicki Environmental, 2010; Jones Edmunds, 

2010). The watershed is primarily urban, with urbanization along the coast and open 

land and wetlands inland (Janicki Environmental, 2010). 
5-13

A few small tidal creeks drain into Upper Lemon Bay, including Alligator, Woodmere, 

and Forked Creeks; however, these creeks are not major freshwater inputs. Coastal 

wetlands supplement the freshwater inflows to Lemon Bay after they have been 

inundated and yield freshwater flows, as sheetflow, into the estuary. 

Lemon Bay is considered a coastal lagoon ecosystem, running parallel to the coastal 

barrier islands on the west and the Florida mainland shoreline on the east. Lemon Bay 

has limited connectivity with the Gulf of Mexico to the west, only through a series of 

passes in between the coastal barrier islands. 

Because of the small watershed area with only a few freshwater inflows it can be 

anticipated that Upper Lemon Bay has a relatively small freshwater inflow. Also, one 

would expect very limited estuarine circulation to dilute and process pollutant loads 

because of the limited tidal connectivity. 

Lower Lemon Bay is the southernmost portion of Lemon Bay and consists of 

approximately 4,713 acres of open water (Figure 5-12). The Lower Lemon Bay 

segment is connected to the south, via the Intracoastal Waterway, with Gasparilla 

Sound, a primary feature in the Cape Haze bay segment. Several creeks drain into 

Lower Lemon Bay, including Ainger, Oyster, and Buck Creeks.

The Lower Lemon Bay watershed’s surface area is approximately 34,941 acres, or 54.6 

mi2

. This results in a watershed to water ratio of 7.4, about the same as Upper Lemon 

Bay. The northern and eastern portions of the watershed are primary open lands and 

wetlands, with the urbanized areas along the coast. Although large areas of the upper 

watershed are in a relatively natural state, significant dredging and channelization has 

occurred in coastal areas, especially to the south. This would be expected to contribute 

to higher peak wet season flows, as discussed above. 

A significant hydrologic feature in the Lower Lemon Bay watershed is the Rotonda, a 

closed series of dredged canals within the community of Rotonda West. The canal 

network of the Rotonda resembles an incomplete circle. The Rotonda is bounded by 

wetlands to its south. Buck Creek historically drained a portion of the Rotonda but a 

control structure at the west boundary of the Rotonda now restricts freshwater flow to 

the west. The capture of freshwater in the hydraulically isolated Rotunda canals has 

reduced the volume of freshwater entering the estuary from pre-development 

conditions, which reduces flushing and circulation.

Circulation in Lower Lemon Bay does benefit from Stump Pass, which facilitates tidal 

flows between the bay and the Gulf of Mexico. Stump Pass is very dynamic inlet. 

Several flood-tidal deltas in Lemon Bay near the inlet have been stabilized over the 

years and are covered with extensive seagrasses. Some .of the deltaic deposits have 

become intertidal and are vegetated with mangroves. There is significant southerly 

longshore drift at Stump Pass that was closing off the inlet and prompted recent 

dredging. 
5-14

Figure 5-12. Upper and Lower Lemon Bay water segments.
5-15

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Lemon Bay was 248 days, 

indicating a very low flushing rate for the system. 

5.2.1 Lemon Bay Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in Upper 

Lemon Bay are presented in Figure 5-13. The monthly average concentration data for 

Upper Lemon Bay were analyzed for the period 1998 through 2009. The chlorophyll a

concentrations varied from 0.9 μg/l to 35.3 μg/l, and the interquartile range (the 

difference between the 25th and 75th percentiles) was 3.7 to 10.3 μg/l. The mean and 

median chlorophyll a concentrations were 8.5 μg/l and 6.4 μg/l, respectively. This 

difference between the mean and median indicates that the mean is influenced by a few 

high concentration values. Monthly average TN concentrations in Upper Lemon Bay 

varied from 0.11 to 1.23 mg/l. The interquartile range was 0.42 to 0.60 mg/l. The mean 

TN concentration was 0.53 mg/l, while the median was 0.51 mg/l. The monthly average 

TP concentrations in Upper Lemon Bay varied from 0.11 to 0.57 mg/l, and the 

interquartile range was 0.19 to 0.26 mg/l. The mean and median TP concentrations 

were 0.23 and 0.21 mg/l, respectively. Unlike chlorophyll a, the differences between the 

mean and median for TN and TP were much less as TN and TP do not exhibit the 

peaks that are sometimes observed in chlorophyll a concentrations. 

There were no statistically significant trends in TN in Upper Lemon Bay during this 

period (p>0.05) (Appendix 3 presents the complete trend test results). A significant 

decreasing trend was detected in TP for the period analyzed (p<0.0001, slope=-0.005), 

as well as for chlorophyll a (p<0.0001, slope=-0.39).

Time series plots of monthly average chlorophyll a, TN, and TP concentrations are 

presented for Lower Lemon Bay in Figure 5-13 as well. The monthly average 

chlorophyll a concentrations varied from 1.3 to 20.0 μg/l, and the interquartile range was 

3.5 to 7.7 μg/l. The mean and median chlorophyll a concentrations were 6.0 and 5.3 

μg/l, respectively. This difference between the mean and median indicates that the 

mean is influenced by several high concentration values. Monthly average TN 

concentrations in Lower Lemon Bay varied from 0.03 to 1.59 mg/l. The interquartile 

range was 0.36 to 0.78 mg/l. The mean TN concentration was 0.58 mg/l, while the 

median was 0.55 mg/l. The monthly average TP concentrations varied from 0.01 to 

1.03 mg/l, and the interquartile range was 0.05 to 0.12 mg/l. The mean and median TP 

concentrations were 0.11 and 0.07 mg/l, respectively. Unlike chlorophyll a, the 

differences between the mean and median for TN were much less as TN does not 

exhibit the bloom characteristics that are not unusual with chlorophyll a concentrations. 
5-16

However, in this instance, TP exhibited a large difference between the mean and 

median concentrations due to several unusually high TP concentrations.

There were no statistically significant trends in chlorophyll a, TN or TP in Lower Lemon 

Bay during this period (p>0.05) (Appendix 3 presents the complete trend test results).

Previous efforts have linked Lemon Bay water quality to loadings to the system and 

estimated the effects on seagrass extent (Tomasko et al., 2001), Loadings to Upper 

Lemon Bay were analyzed for the period 1995 through 2007. Figure 5-14 presents the 

time series of monthly TN, TP, and hydrologic loads to Upper Lemon Bay. The monthly 

TN loads varied from 0.2 to 36.4 tons/month, and the interquartile range was 1.3 to 7.1 

tons/month. The mean and median TN loads to Upper Lemon Bay were 5.6 and 2.8 

tons/month, respectively. Monthly TP loads varied from 0.02 to 7.0 tons/month. The 

interquartile range was 0.2 to 1.3 tons/month. The mean TP load was 1.0 ton/month, 

while the median was 0.4 tons/month. The monthly hydrologic loads varied from 0.003 

to 22.0 million m3

/month, and the interquartile range was 4.0 million m3/month (0.9 to 

4.9 million m3/month). The mean and median hydrologic loads were 3.8 and 2.2 million 

m3

/month, respectively. This difference between the mean and median indicates that 

the mean is highly influenced by a series of high loadings values that are typically seen 

in the wet season when both hydrologic loads and nutrient loads are elevated.

Figure 5-14 also presents the time series of monthly TN, TP, and hydrologic loads to 

Lower Lemon Bay. The monthly TN loads varied from 0.3 to 48.0 tons/month, and the 

interquartile range was 1.6 to 10.5 tons/month. The mean and median TN loads to 

Lower Lemon Bay were 7.8 and 4.2 tons/month, respectively. Monthly TP loads to 

Lower Lemon Bay varied from 0.04 to 7.75 tons/month. The interquartile range was 0.2 

to 1.6 tons/month. The mean TP load was 1.2 tons/month, while the median was 0.5 

tons/month. The monthly hydrologic loads varied from 0.03 to 39.2 million m3/month, 

and the interquartile range was 7.3 million m3/month (1.2 to 8.5 million m3/month). The 

mean and median hydrologic loads were 6.1 and 3.4 million m3/month, respectively. 

This difference between the mean and median indicates that the mean is highly 

influenced by a series of high loadings values that are typically seen in the wet season 

when rainfall driven hydrologic loads and corresponding nutrient loads are substantially 

higher than during the dry season.

5.2.2 Water Quality and Seagrass Targets and Thresholds

As described above in Section 3.3.3, interim water clarity targets were developed for the 

segments of the Charlotte Harbor system based on conditions during the 2003-2007 

period, when seagrass extents in the system were stable. As described in Section 3.3 

above, three candidate methods of deriving chlorophyll a thresholds were utilized; the 

FDEP IWR value of 11 μg/l, the thresholds from the CHNEP (2006) work, and the 

reference period (2003-2007) approach developed for this current effort (see Table 3-4). 
5-17

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined for Upper Lemon Bay;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 8.2 μg/l, and

• Reference Period Method = 8.9 μg/l.

For Lower Lemon Bay, these candidate thresholds were defined as ;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 8.2 μg/l, and

• Reference Period Method = 6.1 μg/l.

Seagrass acreages in Upper and Lower Lemon Bay over the 1988-2008 period are 

provided in Figure 5-15. The seagrass targets are 1,009 acres for Upper Lemon Bay 

and 2,882 acres for Lower Lemon Bay (Janicki Environmental, 2009). Acreages have 

been relatively constant, with the greatest extent identified in 2008.

5.2.3 Relationship Between Chlorophyll a and Concentrations and Loads - 

Lemon Bay

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and Hydrologic loads (Figures 5-16 through 5-20, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). TN concentration was identified as the variable that contributes to 

explaining the variability in chlorophyll a concentrations in Upper Lemon Bay. As can 

be seen in Figure 5-16, there is a pattern of increasing chlorophyll a concentrations with 

increasing TN concentrations.
5-18

Figure 5-13. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Lemon Bay.
5-19

Figure 5-14. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Lemon Bay.
5-20

Figure 5-15. Seagrass acreage in Upper and Lower Lemon Bay.

 

Figure 5-16. Relationship between chlorophyll a and TN concentrations for Upper Lemon Bay.
5-21

Figure 5-17. Relationship between chlorophyll a and TP concentrations for Upper Lemon Bay.

Figure 5-18. Relationship between chlorophyll a and TN loads for Upper Lemon Bay.
5-22

Figure 5-19. Relationship between chlorophyll a and TP loads for Upper Lemon Bay.

Figure 5-20. Relationship between chlorophyll a and hydrologic loads for Upper Lemon Bay.
5-23

A regression model between the Box-Cox transformed chlorophyll a and Log 

transformed TN and color concentrations was developed and the residuals from this 

model were examined to identify any other explanatory variables that might contribute to 

the overall variance accounted for by the model. The residual analysis revealed a 

seasonal difference in residuals. Therefore, a seasonal term was added to the 

regression equation. The season term is a dummy variable which equals one during 

July-October and zero other months of the year. The final regression equation is:

Transformed[Chlorophyll a] = 2.15 + (1.05 * Log[TN]) + (0.02 * color) + (0.57 

* season)

The model was fit with 136 observations and resulted in an R2 value of 0.59. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope and parameter coefficients were also highly significant. A plot of predicted 

versus observed chlorophyll a concentrations is presented in Figure 5-21.

Figure 5-21. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Upper Lemon Bay. 

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and Hydrologic loads (Figures 5-22 through 5-26, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 
5-24

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). In addition to the time series plots, a series of bivariate plots were 

examined to investigate relationships between chlorophyll a and other potential 

explanatory variables. Unlike Upper Lemon Bay, where a defensible linear relationship 

between chlorophyll a and TN exists, no defensible linear relationship between 

chlorophyll a and concentrations or loads was identified in Lower Lemon Bay. Given 

the proximity to Upper Lemon Bay, the relationship developed for Upper Lemon Bay 

was applied to Lower Lemon Bay (Section 5.3).

5.2.4 Candidate Criteria - Lemon Bay

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Upper Lemon Bay (8.9 μg/l). This 

resulted in an estimated TN concentration for each season. The annual geometric 

mean of the seasonally-specific TN concentrations was then calculated. This annual 

geometric mean TN concentration, 0.69 mg/l, is the TN concentration that corresponds 

to the Reference Period Method for Upper Lemon Bay. Including estimates of 

uncertainty, the 95% confidence intervals are 0.54 – 0.89 mg/l. Assuming the 

Regulatory Method threshold (11 μg/l ), the same procedure was used to calculate the 

TN concentration that corresponds to the 11 μg/l Regulatory threshold. The annual 

geometric mean TN concentration was 0.85 mg/l. Including estimates of uncertainty, the 

95% confidence intervals are 0.69 – 1.02 mg/l. 

Figure 5-22. Relationship between chlorophyll a and TN concentrations for Lower Lemon Bay.
5-25

Figure 5-23. Relationship between chlorophyll a and TP concentrations for Lower Lemon Bay.

Figure 5-24. Relationship between chlorophyll a and TN loads for Lower Lemon Bay.
5-26

Figure 5-25. Relationship between chlorophyll a and TP loads for Lower Lemon Bay.

Figure 5-26. Relationship between chlorophyll a and hydrologic loads for Lower Lemon Bay.
5-27

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 

a target of 8.2 μg/l for Lemon Bay, the annual geometric mean TN concentration was 

0.63 mg/l. Including estimates of uncertainty, the 95% confidence intervals are 0.54 – 

0.76 mg/l. Lastly, an attempt was made to use the mean monthly values of color and 

turbidity to calculate the monthly target TN values. However, this resulted in negative 

chlorophyll targets and corresponding negative TN targets in eleven of the twelve 

months.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-27 for Upper 

Lemon Bay.

Figure 5-27. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Upper Lemon Bay. 

The regression equation developed for Upper Lemon Bay was used to estimate the TN 

concentrations that correspond to the Reference Period Method for Lower Lemon Bay 

(6.1 μg/l). This resulted in an estimated TN concentration for each season. The annual 

geometric mean of the seasonally-specific TN concentrations was then calculated. This 

annual geometric mean TN concentration, 0.48 mg/l, is the TN concentration that 
5-28

corresponds to the Reference Period Method for Lower Lemon Bay. Including 

estimates of uncertainty, the 95% confidence intervals are 0.41 – 0.56 mg/l. Assuming 

the Regulatory Method threshold (11 μg/l ), the same procedure was used to calculate 

the TN concentration that corresponds to the 11 μg/l Regulatory threshold. The annual 

geometric mean TN concentration was 0.85 mg/l. Including estimates of uncertainty, the 

95% confidence intervals are 0.69 – 1.02 mg/l. 

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 

a target of 8.2 μg/l for Lemon Bay, the annual geometric mean TN concentration was 

0.63 mg/l. Including estimates of uncertainty, the 95% confidence intervals are 0.54 – 

0.76 mg/l. Lastly, an attempt was made to use the mean monthly values of color and 

turbidity to calculate the monthly target TN values. However, this resulted in negative 

chlorophyll targets and corresponding negative TN targets in eight of the twelve months.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-28 for Lower 

Lemon Bay.
5-29

Figure 5-28. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Lower Lemon Bay. 

5.3 Charlotte Harbor Proper

Just as Upper and Lower Lemon Bay were grouped together for purposes of numeric 

nutrient criteria development, the segments of Charlotte Harbor proper (Figure 5-29; 

West Wall, East Wall, Bokeelia, and Cape Haze) have been grouped, producing an 

average water quality condition for Charlotte Harbor Proper. With regard to loadings to 

Charlotte Harbor Proper, this area of the harbor is influenced by the loads from the Tidal 

Peace and Tidal Myakka rivers. Therefore, loadings for Charlotte Harbor Proper were 

calculated by adding together the estimated loadings to the Tidal Peace, Tidal Myakka, 

West Wall, East Wall, Bokeelia, and Cape Haze.

 

Charlotte Harbor proper consists of the four segments: Cape Haze, Bokeelia, West 

Wall, and East Wall. The Cape Haze bay segment includes the northwest open water 

portion of Charlotte Harbor proper and Gasparilla Sound, and is approximately 13,106 

acres (Figure 5-30). It is bounded on the west by coastal barrier islands and Lower 

Lemon Bay. The surface area of the Cape Haze watershed is approximately 19,402 

acres, or 30.3 mi2

. This results in a watershed to water ratio of 1.5, which is fairly low 

compared to other bay segments.
5-30

Figure 5-29. Charlotte Harbor Proper water segments. 

The Cape Haze watershed does not have any major freshwater inputs to Charlotte 

Harbor or Gasparilla Sound. Coral Creek, in the northwest portion of the Cape Haze 

watershed, historically drained a portion of the Rotonda. However, a dam was 

constructed across the creek just south of the southern boundary of the development, 

greatly restricting freshwater flow into the estuary. The majority of the land cover within 

the watershed is classified as wetlands. The primary source for freshwater inputs to the 

Gasparilla Sound estuary is direct precipitation and sheet flow from the coastal 

wetlands. The watershed of Cape Haze is predominantly wetlands, with some open 

lands in the northern portions (Janicki Environmental, 2010).

Water quality in Gasparilla Sound is profoundly influenced by tidal interaction with the 

Gulf of Mexico. The sound opens into Charlotte Harbor near its mouth at Boca Grande 

Pass, and strong currents flush the sound daily with water from the gulf and Charlotte 

Harbor. Therefore, controlling pollutant loadings to areas with larger contributing 

watersheds will have a great influence on water quality in the sound. 
5-31

Figure 5-30. Cape Haze water segment. 

The Bokeelia bay segment contains the lower portion of the Charlotte Harbor estuary 

(Figure 5-31). The open water portion of the segment (33,331 acres) is much larger 

than its direct watershed, which is approximately 16,172 acres, or 25.3 mi2. This results 

in a watershed to water ratio of 0.5, making inflows from the watershed largely 

insignificant on a regional level. 

Bokeelia is bounded on the west by coastal barrier islands between the estuarine 

portion of Charlotte Harbor and the Gulf of Mexico and to the south by a narrow strip of 

northern Pine Island. The Bokeelia watershed lacks major hydrologic features, and 

receives freshwater from a series of small tidal creeks and man-made canals along its 

eastern shore, as well as direct precipitation. Land use in the watershed is 

predominantly open lands and wetlands (Janicki Environmental, 2010).

This portion of Charlotte Harbor is a major mixing zone, where freshwater from the 

upper bay watershed mixes with salt water from the Gulf of Mexico. Major sources of 

freshwater include the Peace River, and water from the Caloosahatchee River that has 

mixed with estuarine waters in Matlacha Pass. 
5-32

Figure 5-31. Bokeelia water segment. 

The West Wall bay segment includes a significant portion of northern Charlotte Harbor 

(Figure 5-32). The Tidal Myakka River and Tidal Peace River bay segments flow into 

the West Wall segment from the north. The open water area of the segment is 

approximately 16,701 acres, while the watershed is approximately 4,579 acres, or 7.2 

mi2. This results in a watershed to water ratio of 0.3, making inflows from the watershed 

relatively insignificant on a regional basis.

The West Wall watershed is characterized by small tidal creeks on its western shore 

and the man-made canals of the Punta Gorda Isles community on its eastern shore. 

The western portion of the watershed is largely covered in coastal wetlands, resulting in 

freshwater inflow patterns that mirror those of its westerly neighbor, Cape Haze. A 

small amount of urbanized lands is on the eastern shore (Janicki Environmental, 2010). 

This portion of Charlotte Harbor is also a major mixing zone, where freshwater from the 

upper bay segment watershed mixes with salt water from the Gulf of Mexico.

Direct freshwater inflows to this segment from the tributary area are very small 

compared to inflows to the estuary from adjoining segments, especially the Tidal Peace 

and Myakka Rivers. Circulation and flushing are good in the estuary due to the high 

river inflows and connection to the open water harbor.
5-33

Figure 5-32. West Wall water segment. 

The East Wall bay segment contains the eastern half of northern Charlotte Harbor 

(Figure 5-33). It is bordered on its west by the West Wall and to its south by Bokeelia. 

The East Wall bay segment proper is approximately 21,910 acres. Its watershed is 

approximately 61,349 acres (95.8 mi2). This results in a watershed to open water area 

ratio of 2.8, relatively low compared to other bay segments in the CHNEP area.
5-34

Figure 5-33. East Wall water segment. 
5-35

In its northern portions, the East Wall watershed contains the City of Punta Gorda, 

which has an extensive network of canals serving its residential developments. 

Similarly, man-made canals of the Burnt Store community exist along the southern 

shore of East Wall, albeit on a smaller scale. In between these two communities are 

coastal wetlands, where water levels are augmented by natural tidal streams as well as 

some additional man-made canals flowing from the east.

Circulation in Charlotte Harbor Proper is driven by tidal exchange through Boca Grande 

Pass, at the western end of Bokeelia, and freshwater inflows from the Peace and 

Myakka rivers. Net transport in the northern portion of the Harbor is southward fresher 

water transport along the West Wall, and northward saltier water transport along the 

East Wall (Zheng and Weisberg, 2004). This can result in relatively large horizontal 

salinity gradients across the Harbor, with high variability.

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Upper Harbor (West Wall and 

East Wall) was 299 days, indicating a relatively low flushing rate for the upper portion of 

Charlotte Harbor Proper, but was 104 days for the Lower Harbor (Cape Haze and 

Bokeelia), indicative of the more rapid flushing given the proximity to the Gulf. 

5.3.1 Charlotte Harbor Proper Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations are 

presented in Figure 5-34. Monthly average concentration data for Charlotte Harbor 

Proper were analyzed for the period 1998 through 2008. The chlorophyll a

concentrations varied from a low of 1.6 μg/l to a high of 30.4 μg/l. The 25th and 75th

percentile chlorophyll a concentrations were 3.3 and 7.7 μg/l, resulting in an interquartile 

range of 4.4 μg/l. The mean chlorophyll a concentration was 6.4 μg/l and the median 

was 4.9 μg/l. The mean is influenced by several high concentration values. The 

monthly average TN concentrations varied from 0.15 to 1.58 mg/l. The interquartile 

range was 0.30 mg/l (0.45 to 0.75 mg/l). The mean TN concentration was 0.63 mg/l, 

while the median was 0.61 mg/l. The monthly average TP concentrations in Charlotte 

Harbor Proper varied from 0.05 to 0.47 mg/l, and the interquartile range was 0.13 mg/l 

(0.08 to 0.21 mg/l). The mean and median TP concentrations were 0.15 and 0.12 μg/l, 

respectively. As expected, the differences between the mean and median for TN and 

TP were much less as TN and TP do not show the inter-annual variability exhibited by 

chlorophyll a concentrations. 

There were no statistically significant trends in chlorophyll a, TN or TP in Charlotte 

Harbor Proper during this period (p>0.05) (Appendix 3 presents the complete trend test 

results).
5-36

Figure 5-34. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Charlotte Harbor Proper. 
5-37

Figures 5-35 presents the times series of monthly TN, TP, and hydrologic loads to 

Charlotte Harbor Proper. The monthly TN loads varied from 14.2 to 2212.4 tons/month, 

with an interquartile range of 374.2 tons/month (84.6 to 458.8 tons/month). The mean 

and median TN loads to Charlotte Harbor Proper were 375.9 and 183.1 tons/month, 

respectively. Monthly TP loads varied from 2.2 to 757.1 tons/month. The interquartile 

range for TP loads was 123.2 tons/month (23.3 to 146.5 tons/month). The mean TP 

load was 127.7 tons/month, while the median was 61.8 tons/month. The monthly 

hydrologic loads varied from a low of 6.7 million m3/month to a high of 1441.5 million 

m3/month. The interquartile range was 274.2 million m3/month (58.5 to 332.7 million 

m3/month). The mean and median hydrologic loads were 266.6 and 138.2 million 

m3/month, respectively. These differences between the means and medians are a sign 

that the means are highly influenced by a series of high loadings values that are 

typically seen in the wet season when rainfall driven hydrologic loads and 

corresponding nutrient loads are substantially higher than during the dry season.

5.3.2 Water Quality and Seagrass Targets and Thresholds

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined for the Charlotte Harbor Proper area (West Wall, East Wall, Bokeelia, Cape 

Haze). the candidate chlorophyll a thresholds were:

• Regulatory Method = 11 μg/l,

• Optical Model Method = 10.1 μg/l, and

• Reference Period Method = 6.1 μg/l.

For the Optical Model Method, the Charlotte Harbor Proper chlorophyll threshold was 

calculated by taking the area-weighted average of the chlorophyll thresholds from the 

individual segments. The Reference Period Method was complicated by the fact that 

two segments are classified as restoration (West Wall and East Wall) and two segments 

are classified as protection (Bokeelia and Cape Haze). In order to be conservative, 

Charlotte Harbor Proper was treated as if the entire area is under restoration. 

Therefore, the chlorophyll a threshold for Charlotte Harbor proper was calculated by 

summing the average chlorophyll during the reference period plus one half the standard 

deviation (Table 3-4).

Seagrass acreages in Charlotte Harbor Proper over the 1988-2008 period are provided 

in Figure 5-36, for comparison to the seagrass target for the combined segments of 

16,344 acres (Janicki Environmental, 2009). 
5-38

Figure 5-35. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Charlotte Harbor Proper. 
5-39

Figure 5-36. Seagrass acreage in Charlotte Harbor Proper. 

5.3.3 Relationship Between Chlorophyll a and Concentrations and Loads - 

Charlotte Harbor Proper

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and Hydrologic loads (Figures 5-37 through 5-41, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). TN concentration was identified as the main variable that 

contributes to explaining the variability in chlorophyll a concentrations in Charlotte 

Harbor Proper, though this relationship was not as strong as seen in other areas. 

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1988 1994 1999 2001 2004 2006 2008

West Wall East Wall Cape Haze Bokeelia

Seagrass Coverage ( )
5-40

Figure 5-37. Relationship between chlorophyll a and TN concentrations for Charlotte Harbor 

Proper.

Figure 5-38. Relationship between chlorophyll a and TP concentrations for Charlotte Harbor 

Proper.
5-41

Figure 5-39. Relationship between chlorophyll a and TN loads for Charlotte Harbor Proper.

Figure 5-40. Relationship between chlorophyll a and TP loads for Charlotte Harbor Proper.
5-42

Figure 5-41. Relationship between chlorophyll a and hydrologic loads for Charlotte Harbor 

Proper. 

A regression model between the chlorophyll a and TN concentrations was developed 

and the residuals from this model were examined to identify any other explanatory 

variables that might contribute to the overall variance accounted for by the model. The

final regression equation is:

[Chlorophyll a] = 1.20 + (8.20 * [TN])

The model was fit with 88 observations and resulted in an R2 value of 0.18. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope coefficient was not significant, but the parameter coefficient (TN 

concentration) was highly significant. A plot of predicted versus observed chlorophyll a

concentrations is presented in Figure 5-42.
5-43

Figure 5-42. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Charlotte Harbor Proper. 

Although the regression analysis produced a significant relationship, the amount of 

variation explained by the regression was quite low. There, changepoint analysis was 

also investigated. As discussed above, the intent of this analysis was to develop 

relationships between the grand average monthly chlorophyll a concentration in 

Charlotte Harbor Proper and the grand average monthly concentration of TN in 

Charlotte Harbor Proper. A TN concentration changepoint of 0.82 mg/l was identified 

using the changepoint analysis on these data (Figure 5-43). The overall average of the 

mean chlorophyll concentrations in Charlotte Harbor was 6.1 μg/l. The chlorophyll a 

average when TN was below 0.82 mg/l was 5.27 μg/l and the chlorophyll average when 

TN was above 0.82 mg/l was 11.08 μg/l. The TN changepoint of 0.82 mg/l was 

exceeded in 19 percent of the empirical observations. 
5-44

Figure 5-43. Results of changepoint analysis using TN and chlorophyll concentrations 

averaged monthly across Charlotte Harbor Proper. 

5.3.4 Candidate Criteria - Charlotte Harbor Proper

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Charlotte Harbor Proper (6.1 μg/l). This 

resulted in an estimated TN concentration. The annual geometric mean TN 

concentration, 0.60 mg/l, is the TN concentration that corresponds to the Reference 

Period Method for Upper Lemon Bay. Including estimates of uncertainty, the 95% 

confidence intervals are 0.48 - 0.73 mg/l. Assuming the Regulatory Method threshold 

(11 μg/l), the same procedure was used to calculate the TN concentration that 

corresponds to the 11 μg/l Regulatory threshold. The annual geometric mean TN 

concentration was 1.19 mg/l. Including estimates of uncertainty, the 95% confidence 

intervals are 0.92 – 1.49 mg/l. 

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 

a target of 10.15 μg/l for Charlotte Harbor Proper, the annual geometric mean TN 
5-45

concentration was 1.09 mg/l. Including estimates of uncertainty, the 95% confidence 

intervals are 0.85 -1.33 mg/l. Lastly, an attempt was made to use the mean monthly 

values of color and turbidity to calculate the monthly target TN values. However, this 

resulted in negative chlorophyll targets and corresponding negative TN targets in six of 

the twelve months.

Because of the weakness of the regression approach for Charlotte Harbor Proper, 

potential numeric nutrient criteria were developed based on the changepoint analysis 

described above that correspond to the three methods for Charlotte Harbor Proper. 

Monte Carlo simulation was used to identify the TN concentration that was expected to 

result in an average chlorophyll concentration in Charlotte Harbor equivalent to each of 

the three threshold values. A detailed description of how Monte Carlo methods were 

used to derive potential numeric nutrient criteria from the changepoint analysis is 

described in Appendix 4. The distribution of TN concentrations above and below the 

changepoint is presented in Figure 5-45. The average monthly TN concentration below 

the changepoint was 0.54 mg/l and the average above the changepoint was 1.01 mg/l. 

The TN concentration that resulted in an expected annual average chlorophyll 

equivalent to the Reference Period threshold of 6.1 μg/l based on monthly sampling was 

0.62 mg/l. Including estimates of uncertainty, the 95% confidence intervals are 0.53 mg/l 

– 0.73 mg/l. The TN concentration expected to result in annual average chlorophylls for 

the Optical Model Method threshold of 10.1 μg/l was 0.81 mg/l with 95% confidence 

intervals of 0.72-0.91 mg/l. The TN concentration expected to result in annual average 

chlorophylls for the Regulatory threshold of 11 μg/l was 1.0 mg/l with 95% confidence 

intervals of 0.90-1.12 mg/l. 

Figure 5-44. Histogram of monthly average TN concentrations in Charlotte Harbor 

corresponding to the water quality data with changepoint identified as solid 

vertical line. 
5-46

Implementation of this proposed TN criterion will require assessing the influence of 

inter-annual hydrologic variation and residence times on the target exceedance 

frequencies and the annual average chlorophyll and TN concentrations. Using the 

confidence interval provides one mechanism by which this might be accomplished. 

A comparison of the candidate TN concentrations arrived at using the regression 

approach and the changepoint analysis, reveals a weight of evidence as both methods 

result in similar values, 0.60 mg/l for the regression approach versus 0.62 mg/l for the 

changepoint approach. Since both values are well within the uncertainty estimates, the 

value generated using the changepoint approach is considered as the better candidate 

for the TN criterion for Charlotte Harbor Proper.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-44 for Charlotte 

Harbor Proper.

Figure 5-45. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Charlotte Harbor Proper. 
5-47

5.4 Tidal Myakka

The Tidal Myakka River bay segment includes the mouth of the Myakka River (Figure 5-

46). The Myakka River proper drains into Charlotte Harbor at its northern end, with an 

approximate bay segment open water surface area of 7,055 acres. In relation to the 

bay segment proper, the Tidal Myakka River watershed is large, at approximately 

385,866 acres, or 603 mi2. This results in a watershed to open water area ratio of 55, 

high compared to most others in the CHNEP area and generating relatively high runoff 

per rainfall unit area.

The dendritic Myakka River is a regionally large river that is a significant source of 

freshwater inflow to the Charlotte Harbor estuary. It flows southwest nearly 66 miles 

from its source at Myakka Head to Charlotte Harbor (SWFWMD, 2005). Major 

tributaries to the Myakka River include Big Slough and Deer Prairie Creek. The 

hydrology of Myakka River is influenced by the Flatford Swamp, which is located 

immediately upstream from the USGS flow gage on the Myakka River at Myakka City, 

as well as the extensive freshwater wetlands in the basin. A number of small creeks 

have their confluence with the Myakka River at Flatford Swamp, which functions to slow 

the movement of the water in the system, leading to higher evapotranspiration and 

groundwater influx rates. Although the Myakka River watershed is relatively large with 

respect to others in the CHNEP and has experienced some stream channelization, the 

numerous wetlands help to moderate peak flows to the receiving waters. Agricultural 

irrigation has been identified as contributing to river flows (SWFWMD, 2005). The vast 

majority of the Tidal Myakka River watershed is natural land, as open lands and 

wetlands can be observed throughout the drainage basin, particularly in its central 

reaches. Urbanization is most predominant in the southern portion of the watershed, 

where the City of North Port is located. Agriculture is a major land use in the northern 

portion of the watershed, with some mining also observed (Janicki Environmental, 

2010). 

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Tidal Myakka was 232 days, 

indicating a relatively slow flushing rate for the system. 
5-48

Figure 5-46. Tidal Myakka water segment. 
5-49

5.4.1 Tidal Myakka Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations are 

presented in Figure 5-47. The chlorophyll a concentrations varied from a low of 1.1 μg/l

to a high of 27.9 μg/l. The 25th and 75th percentile chlorophyll a concentrations were 5.4 

and 9.7 μg/l, resulting in an interquartile range of 4.3 μg/l. The mean chlorophyll a

concentration was 8.3 μg/l and the median was 7.2 μg/l. The mean is influenced by 

several high concentration values. The monthly average TN concentrations varied 

from 0.37 to 2.29 mg/l. The interquartile range was 0.40 mg/l (0.72 to 1.12 mg/l). The 

mean TN concentration was 0.95 mg/l, while the median was 0.90 mg/l. The monthly 

average TP concentrations in Tidal Myakka varied from 0.01 to 0.85 mg/l, and the 

interquartile range was 0.11 mg/l (0.20 to 0.21 mg/l). The mean and median TP 

concentrations were 0.27 and 0.25 mg/l, respectively. As expected, the differences 

between the mean and median for TN and TP were much less as TN and TP do not 

show the inter-annual variability exhibited by chlorophyll a concentrations. 

There were no statistically significant trends in chlorophyll a, TN or TP in Tidal Myakka 

during this period (p>0.05) (Appendix 3 presents the complete trend test results).

Figure 5-48 presents the time series of monthly TN, TP, and hydrologic loads to Tidal 

Myakka. The monthly TN loads varied from 2.4 to 520.6 tons/month, with an 

interquartile range of 83.3 tons/month (12.5 to 95.8 tons/month). The mean and median 

TN loads to Tidal Myakka were 73.2 and 31.2 tons/month, respectively. Monthly TP 

loads varied from 0.2 to 146.5 tons/month. The interquartile range for TP loads was 

20.3 tons/month (2.4 to 22.7 tons/month). The mean TP load was 18.5 tons/month, 

while the median was 7.1 tons/month. The monthly hydrologic loads varied from a low 

of 0.8 million m3

/month to a high of 384.8 million m3/month. The interquartile range was 

62.7 million m3/month (9.5 to 72.2 million m3/month). The mean and median hydrologic 

loads were 55.3 and 24.6 million m3/month, respectively. These differences between 

the means and medians are a sign that the means are highly influenced by a series of 

high loadings values that are typically seen in the wet season when rainfall driven 

hydrologic loads and corresponding nutrient loads are substantially higher than during 

the dry season.
5-50

Figure 5-47. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Tidal Myakka. 
5-51

Figure 5-48. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Tidal Myakka. 
5-52

5.4.2 Water Quality and Seagrass Targets and Thresholds

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 25.6 μg/l, and

• Reference Period Method = NA.

Seagrass acreages in the Tidal Myakka over the 1988-2008 period are provided in 

Figure 5-49, for comparison to the seagrass target for the segment of 456 acres (Janicki 

Environmental, 2009). As noted in Janicki Environmental (2009), the riverine segments 

of the system, including the Tidal Myakka, may have underreported seagrass acreages, 

due to water color impacts on aerial photography capabilities.

Figure 5-49. Seagrass acreage in Tidal Myakka. 

0

100

200

300

400

500

600

1988 1994 1999 2001 2004 2006 2008

Seagrass Coverage 
5-53

5.4.3 Relationship Between Chlorophyll a and Concentrations and Loads - Tidal 

Myakka

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and Hydrologic loads (Figures 5-50 through 5-54, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). No statistically defensible relationships were found for Tidal 

Myakka chlorophyll a concentrations and other water quality parameters or loadings in 

Tidal Myakka.

Therefore, a downstream compliance area was investigated. This included using the 

average chlorophyll a concentration for Charlotte Harbor Proper (as described in 

Section 5.3) and loading and concentrations from Tidal Myakka.

Figure 5-50. Relationship between chlorophyll a and TN concentrations for Tidal Myakka.
5-54

Figure 5-51. Relationship between chlorophyll a and TP concentrations for Tidal Myakka.

Figure 5-52. Relationship between chlorophyll a and TN loads for Tidal Myakka.
5-55

Figure 5-53. Relationship between chlorophyll a and TP loads for Tidal Myakka.

Figure 5-54. Relationship between chlorophyll a and hydrologic loads for Tidal Myakka.
5-56

A regression model between the chlorophyll a in Charlotte Harbor Proper and TN loads 

from the Tidal Myakka was developed and the residuals from this model were examined 

to identify any other explanatory variables that might contribute to the overall variance 

accounted for by the model. The residual analysis revealed a seasonal difference in 

residuals. Therefore, a seasonal term was added to the regression equation. The 

season term is a dummy variable which equals one during January-June and zero other 

months of the year. The final regression equation is:

[Chlorophyll a] = 6.83 + (0.0072 * Myakka TN load) + (-3.23 * season)

The model was fit with 78 observations and resulted in an R2 value of 0.46. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope and parameter coefficients were all significant. A plot of predicted versus 

observed chlorophyll a concentrations is presented in Figure 5-55.

Figure 5-55. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Tidal Myakka. 

As discussed above, the intent of this analysis was to develop relationships between the 

grand average monthly chlorophyll a concentration in Charlotte Harbor Proper and TN 

loads from the Myakka River. A TN changepoint of 120 tons per month was identified 

using the changepoint analysis on these data (Figure 5-56). The overall average of the 

mean chlorophyll concentrations in Charlotte Harbor was 6.39 μg/l . The chlorophyll a 

average when TN was below 120 tons per month was 4.93 μg/l and the chlorophyll 

average when TN was above 120 tons per month was 11.72 μg/l. The TN changepoint 

of 120 tons per month was exceeded in 21 percent of the empirical observations.
5-57

Figure 5-56. Results of changepoint analysis for Charlotte Harbor chlorophyll and Myakka River 

Loads. 

5.4.4 Candidate Criteria - Tidal Myakka

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Charlotte Harbor Proper (7.3 μg/l). 

Note that this is the “protection” level for chlorophyll in Charlotte Harbor proper because 

the Tidal Myakka River is classified as “protection” for seagrass. This resulted in an 

annual TN load. This annual TN load, 2,570 tons/year, is the TN load that corresponds 

to the Reference Period Method for Tidal Myakka. Including estimates of uncertainty, 

the 95% confidence intervals are 1,350 – 3,820 tons/year. Assuming the Regulatory 

Method threshold (11 μg/l ), the same procedure was used to calculate the TN 

concentration that corresponds to the 11 μg/l Regulatory threshold. The annual TN load 

was 8,750 tons/year. Including estimates of uncertainty, the 95% confidence intervals 

are 5,420 – 12,100 tons/year. 

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 
5-58

a target of 10.15 μg/l for Charlotte Harbor Proper, the TN load was 7,300 tons/year. 

Including estimates of uncertainty, the 95% confidence intervals are 4,300 – 10,300 

tons/year. Lastly, an attempt was made to use the mean monthly values of color and 

turbidity to calculate the monthly target TN values. However, this resulted in negative 

chlorophyll targets and corresponding negative TN targets in six of the twelve months.

Potential numeric nutrient criteria were developed based on the changepoint analysis 

described above that corresponded to the different methods for Charlotte Harbor 

Proper. Monte Carlo simulation was used to identify the annual Myakka River load that 

was expected to result in an average chlorophyll concentration in Charlotte Harbor 

equivalent to each of the three threshold values. A detailed description of how Monte 

Carlo methods were used to derive potential numeric nutrient criteria from the 

changepoint analysis is described in Appendix 4. The TN loading distribution above 

and below the changepoint is presented in Figure 5-57. The average monthly TN load 

below the changepoint was 33 tons and the average above the changepoint was 267 

tons. The annual TN loading that resulted in an expected annual average chlorophyll 

equivalent to the Reference Period threshold of 7.3 μg/l based on monthly sampling 

was1,330 tons per year. Including estimates of uncertainty, the 95% confidence 

intervals are 930 tons – 1,844 tons. The annual TN loading that resulted in an expected 

annual average chlorophyll equivalent to the Optical Model threshold of 10.2 μg/l based 

on monthly sampling was 2,279 tons per year. Including estimates of uncertainty, the 

95% confidence intervals are 1,707 tons – 3,004 tons. The annual TN loading that 

resulted in an expected annual average chlorophyll equivalent to the Regulatory 

threshold of 11 μg/l based on monthly sampling was 2,759 tons per year. Including 

estimates of uncertainty, the 95% confidence intervals are 2,065 tons – 3,570 tons.

Figure 5-57. Histogram of TN loadings from the Myakka River corresponding to the water 

quality data with changepoint identified as solid vertical line. 
5-59

Implementation of this proposed TN criterion will require assessing the influence of 

inter-annual hydrologic variation and residence times on the target exceedance 

frequencies. Using the confidence interval provides one mechanism by which this might 

be accomplished. The effect of seasonality on these relationships is also not accounted 

for explicitly via the simulation; however, the resulting estimate is likely conservative 

given that higher loads during colder months would likely not elicit the same water 

quality response given the colder water temperatures and reduced photoperiod. 

Chlorophyll biomass may also be mitigated by other factors associated with higher 

loads such as the light limiting effects of colored dissolved organic matter which 

deserves further consideration. The combination of Peace and Myakka loads was not 

evaluated and should be explored in the implementation phase. 

A comparison of the target TN loads arrived at using the regression approach and the 

changepoint analysis, reveals that the changepoint approach results in a load that is 

more protective of Charlotte Harbor Proper, 1,330 tons/year for the changepoint 

approach versus 2,570 tons/year for the regression approach. Therefore, 1,330 

tons/year was selected as the TN load criterion for Tidal Myakka.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-58 for Tidal 

Myakka.

Figure 5-58. Comparison of the potential TN criteria to observed TN loads in Tidal Myakka. 
5-60

5.5 Tidal Peace

The Tidal Peace River segment defines the mouth of the Peace River, the largest 

tributary of the CHNEP watershed (Figure 5-59). The bay segment proper is comprised 

of the estuarine portion of the Peace River and Shell Creek. It has an open water 

surface area of approximately 12,894 acres and is connected to Charlotte Harbor at its 

southern boundary. Its watershed has a surface area of approximately 1,477,486 acres 

or 2,308 mi2

, which is the largest of all of the 14 bay segment watersheds in the study 

area. This results in a watershed to open water area ratio of 114, the highest ratio 

compared to others in the CHNEP area and generating high runoff per rainfall unit.

The Peace River meanders 120 miles from its source in the Green Swamp to its mouth 

at Port Charlotte. There are several large tributaries in the river’s dendritic network, 

including Joshua Creek, Payne Creek, Charlie Creek, Horse Creek, and Shell Creek. It 

is the major source of freshwater inflow to Charlotte Harbor. The Peace River 

headwaters are in the Green Swamp, a vast series of inland wetlands comprising 

approximately 560,000 acres in parts of Polk, Lake, Sumter, Hernando and Pasco 

counties. Periods of extreme drought can occasionally cause the Green Swamp to

become dry and reduce Peace River flows in its northern reaches to zero (SWFWMD, 

2007). A multitude of lakes, where flow is dictated by control structures, are located 

adjacent to the Green Swamp in the northern extent of the watershed in central Polk 

County.

Figure 5-59. Tidal Peace water segment. 
5-61

The Peace River meanders 120 miles from its source in the Green Swamp to its mouth 

at Port Charlotte. There are several large tributaries in the river’s dendritic network, 

including Joshua Creek, Payne Creek, Charlie Creek, Horse Creek, and Shell Creek. It 

is the major source of freshwater inflow to Charlotte Harbor. The Peace River 

headwaters are in the Green Swamp, a vast series of inland wetlands comprising 

approximately 560,000 acres in parts of Polk, Lake, Sumter, Hernando and Pasco 

counties. Periods of extreme drought can occasionally cause the Green Swamp to 

become dry and reduce Peace River flows in its northern reaches to zero (SWFWMD, 

2007). A multitude of lakes, where flow is dictated by control structures, are located 

adjacent to the Green Swamp in the northern extent of the watershed in central Polk 

County.

While open lands and wetlands have the highest proportions of land use in the Tidal 

Peace River watershed, significant amounts of developed land exist throughout. The 

northwestern portion of the watershed is dominated by active or closed phosphate 

mines and represents the vast majority of the mining land uses in the entire CHNEP 

watershed. There are urbanized areas of central Polk County to the north and Port 

Charlotte to the, and large-scale agricultural operations, particularly in the south (Janicki 

Environmental, 2010).

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Tidal Peace was 152 days, 

indicating a mid-range flushing rate for the system. 

5.5.1 Tidal Peace Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations are 

presented in Figure 5-60. The monthly average chlorophyll a concentrations varied 

from 1.4 to 65.8 μg/l, and the interquartile range was 8.0 μg/l (7.9 to 15.9 μg/l). The 

mean and median chlorophyll a concentrations were 13.8 and 11.2 μg/l, respectively. 

Monthly average TN concentrations in Tidal Peace varied from 0.36 to 2.88 mg/l. The 

interquartile range was 0.52 mg/l (0.76 to 1.28 mg/l). The mean TN concentration was 

1.06 mg/l, while the median was 0.96 mg/l. The monthly average TP concentrations 

varied from 0.12 to 1.20 mg/l, and the interquartile range was 0.21 mg/l (0.31 to 0.52 

mg/l). The mean and median TP concentrations were 0.43 and 0.40 mg/l, respectively. 

There were no statistically significant trends in chlorophyll a, TN or TP in Tidal Peace 

during this period (p>0.05) (Appendix 3 presents the complete trend test results).

Figure 5-61 presents the time series of monthly TN, TP, and hydrologic loads. The 

monthly TN loads varied from 7 to 1675 tons/month, and the interquartile range was 290 
5-62

tons/month (50 to 340 tons/month). The mean and median TN loads were 269 and 133 

tons/month, respectively. Monthly TP loads in Tidal Peace varied from 2 to 672 

tons/month. The interquartile range was 113 tons/month (19 to 132 tons/month). The 

mean TP load was 107 tons/month, while the median was 49 tons/month. The monthly 

hydrologic loads varied from 3 to 976 million m3/month, and the interquartile range was 

171 million m3/month (31 to 202 million m3/month). The mean and median hydrologic 

loads were 164 and 77 million m3

/month, respectively. These differences between the 

means and medians are a sign that the means are highly influenced by a series of high 

loadings values that are typically seen in the wet season when rainfall driven hydrologic 

loads and corresponding nutrient loads are substantially higher than during the dry 

season.

5.5.2 Water Quality and Seagrass Targets and Thresholds

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 22.4 μg/l, and

• Reference Period Method = NA.

Seagrass acreages in the Tidal Peace over the 1988-2008 period are provided in Figure 

5-62, for comparison to the seagrass target for the segment of 975 acres (Janicki 

Environmental, 2009). As noted in Janicki Environmental (2009), the riverine segments 

of the system, including the Tidal Peace, may have underreported seagrass acreages, 

due to water color impacts on aerial photography capabilities.

5.5.3 Relationship Between Chlorophyll a and Concentrations and Loads - Tidal 

Peace

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and Hydrologic loads (Figures 5-63 through 5-67, 

respectively). As mentioned in subsection 5.3, the water quality data used for these 

analyses were the average of the four segments that comprise Charlotte Harbor Proper 

(Cape Haze, Bokeelia, West Wall, and East Wall). For the loadings, the Tidal Peace 

loads alone were used. Additional bivariate plots were run between chlorophyll a and 

pulse residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads 

(TN, TP, and hydrologic). The scatterplots and linear regression analysis used to 

assess the bivariate relationship between TN concentrations or loads and chlorophyll a 

concentrations in Tidal Peace suggested a lack of a linear relationship.
5-63

Figure 5-60. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Tidal Peace. 
5-64

Figure 5-61. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Tidal Peace. 
5-65

Figure 5-62. Seagrass acreage in Tidal Peace. 

Figure 5-63. Relationship between chlorophyll a and TN concentrations for Tidal Peace.
5-66

Figure 5-64. Relationship between chlorophyll a and TP concentrations for Tidal Peace.

Figure 5-65. Relationship between chlorophyll a and TN loads for Tidal Peace.
5-67

Figure 5-66. Relationship between chlorophyll a and TP loads for Tidal Peace.

Figure 5-67. Relationship between chlorophyll a and hydrologic loads for Tidal Peace.
5-68

Therefore, a downstream compliance area was investigated. This included using the 

average chlorophyll a concentration for Charlotte Harbor Proper (as described in 

Section 5.3) and loading and concentrations from Tidal Peace.

A regression model between the chlorophyll a in Charlotte Harbor Proper and TN loads 

from the Tidal Peace was developed and the residuals from this model were examined 

to identify any other explanatory variables that might contribute to the overall variance 

accounted for by the model. The final regression equation is:

[Chlorophyll a] = 4.77 + (0.00596 * Peace TN load)

The model was fit with 78 observations and resulted in an R2 value of 0.23. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope and parameter coefficients were all significant. A plot of predicted versus 

observed chlorophyll a concentrations is presented in Figure 5-68.

Figure 5-68. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Charlotte Harbor Proper. 

In addition to regression analysis, changepoint analysis was also investigated. As 

discussed above, the intent of this analysis was to develop relationships between the 

grand average monthly chlorophyll a concentration in Charlotte Harbor Proper and TN 

loads from the Peace River. A TN changepoint of 424 tons per month was identified 

using the changepoint analysis on these data (Figure 5-69). The overall average of the 

mean chlorophyll concentrations in Charlotte Harbor was 6.39 μg/l. The chlorophyll a 

average when TN was below 424 tons per month was 4.93 μg/l and the chlorophyll 

average when TN was above 424 tons per month was 11.72 μg/l. The TN changepoint 

of 424 tons per month was exceeded in 21 percent of the empirical observations.
5-69

Figure 5-69. Results of changepoint analysis for Charlotte Harbor chlorophyll and Peace River 

Loads. 

5.5.4 Candidate Criteria - Tidal Peace

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Charlotte Harbor Proper (6.1 μg/l). 

Unlike Tidal Myakka where the protection level was used for Charlotte Harbor 

chlorophyll, the restoration value of 6.1 μg/l was used for Tidal Peace River as the 

system is classified as restoration. This annual TN load, 2,688 tons/year, is the TN load 

that corresponds to the Reference Period Method for Tidal Peace. Including estimates 

of uncertainty, the 95% confidence intervals are 714 – 4,700 tons/year. Assuming the 

Regulatory Method threshold (11 μg/l ), the same procedure was used to calculate the 

TN concentration that corresponds to the 11 μg/l Regulatory threshold. The annual TN 

load was 12,550 tons/year. Including estimates of uncertainty, the 95% confidence 

intervals are 8,425 – 16,620 tons/year. 

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 
5-70

a target of 10.15 μg/l for Charlotte Harbor Proper, the TN load was 10,840 tons/year. 

Including estimates of uncertainty, the 95% confidence intervals are 7,320 – 14,325 

tons/year. Lastly, an attempt was made to use the mean monthly values of color and 

turbidity to calculate the monthly target TN values. However, this resulted in negative 

chlorophyll targets and corresponding negative TN targets in six of the twelve months.

Potential numeric nutrient criteria were developed based on the changepoint analysis 

described above that correspond to the methods described for the regression above for 

Charlotte Harbor Proper. Monte Carlo simulation was used to identify the annual Peace 

River load that was expected to result in an average chlorophyll concentration in 

Charlotte Harbor equivalent to each of the three threshold values. A detailed description 

of how Monte Carlo methods were used to derive potential numeric nutrient criteria from 

the changepoint analysis is described in Appendix 4. The TN loading distribution 

above and below the changepoint is presented in Figure 5-70. The average monthly TN 

load below the changepoint was 119 tons and the average above the changepoint was 

931 tons. The annual TN load that resulted in an expected annual average chlorophyll 

concentration equivalent to the Reference Period threshold of 6.1 μg/l based on monthly 

sampling was 3,018 tons per year. Including estimates of uncertainty, the 95% 

confidence intervals are 2,005 tons – 4,227 tons. The annual TN load that resulted in an 

expected annual average chlorophyll concentration equivalent to the Optical Model 

threshold of 10.2 μg/l based on monthly sampling was 9,619 tons per year. Including 

estimates of uncertainty, the 95% confidence intervals are 7,596 tons – 12,085 tons. 

The annual TN load that resulted in an expected annual average chlorophyll 

concentration equivalent to the Regulatory threshold of 11.0 μg/l based on monthly 

sampling was 10,423 tons per year. Including estimates of uncertainty, the 95% 

confidence intervals are 8,348 tons – 12,909 tons.

Figure 5-70. Histogram of TN loadings from the Peace River corresponding to the water quality 

data with changepoint identified as solid vertical line. 
5-71

Implementation of this proposed TN criterion will require assessing the influence of 

inter-annual hydrologic variation and residence times on the target exceedance 

frequencies. Using the confidence interval provides one mechanism by which this might 

be accomplished. The effect of seasonality on these relationships is also not accounted 

for explicitly via the simulation; however, the resulting estimate is likely conservative 

given that higher loads during colder months would likely not elicit the same water 

quality response given the colder water temperatures and reduced photoperiod. 

Chlorophyll biomass may also be mitigated by other factors associated with higher 

loads such as the light limiting effects of colored dissolved organic matter which 

deserves further consideration. The combination of Peace and Myakka loads was not 

evaluated and should be explored in the implementation phase. 

A comparison of the target TN loads arrived at using the regression approach and the 

changepoint analysis, reveals that the changepoint approach results in an annual load 

that is more protective of Charlotte Harbor Proper, 3,018 tons/year than the regression 

approach (5,085 tons/year). Therefore, the more protective load, 3,018 tons TN/year is 

proposed as the TN criterion for Tidal Peace.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-71 for Tidal 

Peace.
5-72

Figure 5-71. Comparison of the potential TN criteria to observed TN loads in Tidal Peace. 

5.6 Pine Island Sound and Matlacha Pass

For purposes of numeric nutrient criteria development, the Pine Island Sound and 

Matlacha Pass segments were combined, as each are midway between the northern 

freshwater inflows of the Peace and Myakka rivers, and the southern inflow from the 

Caloosahatchee River (Figures 5-72 and 5-73). 

The Pine Island Sound segment consists of the Pine Island Sound estuary and a 

fringing watershed. It is bounded on the north by the Bokeelia bay segment in Charlotte 

Harbor, to the east by Pine Island, to the west by various barrier islands and the Gulf of 

Mexico, and to the south by Sanibel island and San Carlos Bay (Figure 5-72). The 

surface area of the open water bay segment is approximately 50,483 acres. The 

watershed contains wetlands and some urban lands along the coast, and open lands 

and agricultural lands in the central area of Pine Island (Janicki Environmental, 2010). 

Its comparatively small watershed is approximately 19,259 acres, or 30.1 mi2. This

results in a watershed to open water area ratio of 0.4, one of the lowest ratios compared 

to others in the CHNEP area and generating low runoff per rainfall unit.
5-73

With the islands of Pine Island, Captiva, Sanibel, and numerous other smaller barrier 

islands comprising its surrounding watershed, there are minimal amounts of freshwater 

entering the bay segment from streams or overland flow. However, with coastal 

wetlands on either side of Pine Island Sound, freshwater inputs can be received from 

the watershed when the wetlands become inundated and flow into the open water as 

sheetflow.

Matlacha Pass connects Charlotte Harbor to San Carlos Bay at the mouth of the 

Caloosahatchee River (Figure 5-73). It is bounded on the west by Pine Island and on 

the east by the City of Cape Coral. Its watershed includes an extensive network of 

man-made waterways in Cape Coral, which drain into Matlacha Pass. The open water 

surface area of Matlacha Pass is approximately 13,190 acres, and its watershed is 

approximately 63,095 acres, or 98.6 mi2. This results in a watershed to open water area 

ratio of 4.8, intermediate compared to others in the CHNEP area. The watershed is 

heavily urbanized, with the City of Cape Coral located on the eastern shore. The 

eastern watershed of Matlacha Pass has been heavily altered by human activities. The 

canal network in Cape Coral includes over 400 miles of navigable waterways, all of 

which drain either to Matlacha Pass or the Caloosahatchee River to the south (City of 

Cape Coral, 2009). Many of these canals, such as Gator Slough and Horseshoe, 

Hermosa, and Shadroe canals, are controlled by weirs that regulate the passage of 

water into the coastal wetlands buffering the canals from Matlacha Pass. These 

structures are particularly useful in times of high flows, where diversions are used to 

sustain agricultural operations and for wetlands maintenance. 

The relatively large watershed and highly channelized conveyance system promote high 

peak runoff rates during rain events. A “spreader canal” was constructed along the 

shoreline to intercept runoff from the residential canal system, with the intent of treating 

and distributing the water to coastal mangroves in the pass via sheet flow over the west 

side of the canal. The west canal bank has developed a series of breaches that allow 

water to flow into the mangroves at a few concentrated locations, rather than be evenly 

distributed along the entire 7-mile canal length (Janicki Environmental and Florida 

Conflict Resolution Consortium Consensus Center, 2010). 

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Pine Island Sound was 692 

days, indicating a very slow flushing rate, and was 534 days for Matlacha Pass, 

indicative that both these segments typically experience very long residence times. 
5-74

Figure 5-72. Pine Island Sound water segment.
5-75

Figure 5-73. Matlacha Pass water segment.

5.6.1 Pine Island Sound and Matlacha Pass Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in Pine 

Island Sound are presented in Figure 5-74. The monthly average chlorophyll a

concentrations varied from 1.1 to 19.2 μg/l, and the interquartile range was 3.7 μg/l (2.3 

to 6.0 μg/l). The mean and median chlorophyll a concentrations were 4.7 and 3.3 μg/l, 

respectively. This difference between the mean and median is a sign that the mean is 
5-76

influenced by several high concentration values. Monthly average TN concentrations in 

Pine Island Sound varied from 0.05 to 2.53 mg/l. The interquartile range was 0.21 mg/l 

(0.26 to 0.47 mg/l). The mean TN concentration was 0.40 mg/l, while the median was 

0.34 mg/l. The monthly average TP concentrations varied from 0.02 to 0.35 mg/l, and 

the interquartile range was 0.02 mg/l (0.03 to 0.05 mg/l). The mean and median TP 

concentrations were 0.05 and 0.04 mg/l, respectively. Unlike chlorophyll a, the 

differences between the mean and median for TN and TP were much less as TN and 

TP do not exhibit the sharp increases that are sometimes associated with chlorophyll a

blooms. 

There were no statistically significant trend in chlorophyll a in Pine Island Sound during 

this period (p>0.05). However, significant decreasing trends were detected in TN 

(p<0.0001, slope=-0.08) and TP (p<0.01, slope=-0.005) (Appendix 3 presents the 

complete trend test results).

Figure 5-75 presents the time series of TN, TP, and hydrologic loads to Pine Island 

Sound. The monthly TN loads varied from 0.1 to 62.6 tons/month, and the interquartile 

range was 18.4 tons/month (3.1 to 21.5 tons/month). The mean and median TN loads 

were 13.5 and 9.9 tons/month, respectively. Monthly TP loads in Pine Island Sound 

varied from 0.01 to 2.5 tons/month. The interquartile range was 0.6 tons/month (0.1 to 

0.7 tons/month). The mean TP load was 0.5 tons/month, while the median was 0.3 

tons/month. The monthly hydrologic loads varied from 0.1 to 83.2 million m3

/month, and 

the interquartile range was 30.8 million m3

/month (6.4 to 37.2 million m3/month). The 

mean and median hydrologic loads were 23.6 and 16.9 million m3/month, respectively. 

This difference between the mean and median is indicative of the fact that the mean is 

influenced by a series of high loadings values that are typically seen in the wet season 

when rainfall driven hydrologic loads and corresponding nutrient loads are higher.

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in 

Matlacha Pass are presented in Figure 5-76. The chlorophyll a concentrations varied 

from a low of 0.8 μg/l to a high of 52.3 μg/l. The 25th and 75th percentile chlorophyll a

concentrations were 2.7 and 6.6 μg/l, resulting in an interquartile range of 3.9 μg/l. The 

mean chlorophyll a concentration was 6.0 μg/l and the median was 3.7 μg/l. The mean 

is influenced by several high concentration values. The monthly average TN 

concentrations varied from 0.01 to 1.43 mg/l. The interquartile range was 0.25 mg/l 

(0.35 to 0.60 mg/l). The mean TN concentration was 0.51 mg/l, while the median was 

0.46 mg/l. The monthly average TP concentrations in Matlacha Pass varied from 0.01 

to 0.26 mg/l, and the interquartile range was 0.03 mg/l (0.05 to 0.08 mg/l). The mean 

and median TP concentrations were 0.07 and 0.06 mg/l, respectively. As expected, the 

differences between the mean and median for TN and TP were much less as TN and 

TP do not show the inter-annual variability exhibit by chlorophyll a concentrations. 

There were no statistically significant trends in TN or chlorophyll a in Matlacha Pass 

during this period (p>0.05) (Appendix 3 presents the complete trend test results). A 

significant decreasing trend was detected in TP (p<0.05, slope=-0.003).
5-77

Figure 5-74. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Pine Island Sound.
5-78

Figure 5-75. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Pine Island Sound.
5-79

Figure 5-77 presents the time series of monthly TN, TP, and hydrologic loads to 

Matlacha pass. The monthly TN loads varied from 0.5 to 64.9 tons/month, with an 

interquartile range of 17.4 tons/month (2.6 to 20.0 tons/month). The mean and median 

TN loads to Matlacha Pass were 13.0 and 8.6 tons/month, respectively. Monthly TP 

loads varied from 0.05 to 10.7 tons/month. The interquartile range for TP loads was 1.4 

tons/month (0.2 to 1.6 tons/month). The mean TP load was 1.5 tons/month, while the 

median was 0.6 tons/month. The monthly hydrologic loads varied from a low of 0.2 

million m3/month to a high of 80.8 million m3/month. The interquartile range was 24.8 

million m3/month (3.4 to 28.2 million m3/month). The mean and median hydrologic 

loads were 17.9 and 9.2 million m3/month, respectively. These differences between the 

means and medians are a sign that the means are highly influenced by a series of high 

loadings values that are typically seen in the wet season when rainfall driven hydrologic 

loads and corresponding nutrient loads are substantially higher than during the dry 

season.

5.6.2 Pine Island Sound and Matlacha Pass Targets

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined. For Pine Island Sound, these were;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 6.9 μg/l, and

• Reference Period Method = 6.5 μg/l.

For Matlacha Pass, these candidate chlorophyll a thresholds were;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 8.2 μg/l, and

• Reference Period Method = 6.1 μg/l.

Seagrass acreages in Pine Island Sound and Matlacha Pass over the 1988-2008 period 

are provided in Figure 5-78, for comparison to the seagrass target for the combined 

segments of 36,152 acres (Janicki Environmental, 2009). 
5-80

Figure 5-76. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Matlacha Pass.
5-81

Figure 5-77. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Matlacha Pass.
5-82

Figure 5-78. Seagrass acreage in Pine Island Sound and Matlacha Pass.

5.6.3 Relationship Between Chlorophyll a and Concentrations and Loads - Pine 

Island Sound and Matlacha Pass

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and hydrologic loads (Figures 5-79 through 5-83, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). In addition to the time series plots, a series of bivariate plots were 

examined to investigate relationships between chlorophyll a and other potential 

explanatory variables. As can be seen in these plots, no strong linear relationships 

were identified. However, TN concentration explained a reasonable amount of the 

variation in chlorophyll a. 

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

1999 2003 2004 2006 2008

Pine Island Sound Matlacha Pass

Seagrass 
5-83

Figure 5-79. Relationship between chlorophyll a and TN concentrations for Pine Island Sound.

Figure 5-80. Relationship between chlorophyll a and TP concentrations for Pine Island Sound.
5-84

Figure 5-81. Relationship between chlorophyll a and TN loads for Pine Island Sound.

Figure 5-82. Relationship between chlorophyll a and TP loads for Pine Island Sound.
5-85

Figure 5-83. Relationship between chlorophyll a and hydrologic loads for Pine Island Sound.

A regression model between the Log transformed chlorophyll a and TN concentrations

was developed and the residuals from this model were examined to identify any other 

explanatory variables that might contribute to the overall variance accounted for by the 

model. Analysis of the residuals revealed a seasonal difference in residuals. 

Specifically, given the same TN concentrations, higher chlorophyll a concentrations can 

be expected during the wetter, warmer summer months (July-October) than during the 

remainder of the year. Therefore, a seasonal term was added to the regression 

equation. The season term is a dummy variable which equals one during July-October 

and zero other months of the year. The final regression equation is:

Log [Chlorophyll a] = 0.25 + (0.52*TN concentration) + (0.34*season)

The model was fit with 118 observations and resulted in an R2 value of 0.49. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The parameter coefficients were also highly significant. A plot of predicted versus 

observed chlorophyll a concentrations is presented in Figure 5-84.
5-86

Figure 5-84. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Pine Island Sound. 

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory variables 

in Matlacha Pass. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and hydrologic loads (Figures 5-85 through 5-89, 

respectively). Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic). In addition to the time series plots, a series of bivariate plots were 

examined to investigate relationships between chlorophyll a and other potential 

explanatory variables. As can be seen in these plots, no strong linear relationships 

were identified. However, the 2-month average log transformed TN load explained a 

reasonable amount of the variation in chlorophyll a. 
5-87

Figure 5-85. Relationship between chlorophyll a and TN concentrations for Matlacha Pass.

Figure 5-86. Relationship between chlorophyll a and TP concentrations for Matlacha Pass.
5-88

Figure 5-87. Relationship between chlorophyll a and TN loads for Matlacha Pass.

Figure 5-88. Relationship between chlorophyll a and TP loads for Matlacha Pass.
5-89

Figure 5-89. Relationship between chlorophyll a and hydrologic loads for Matlacha Pass.

A regression model between the Box-Cox transformed chlorophyll a and Log 

transformed 2-month cumulative TN load was developed and the residuals from this 

model were examined to identify any other explanatory variables that might contribute to 

the overall variance accounted for by the model. The final regression equation is:

Transformed[Chlorophyll a] = 0.43 + (0.28 * Log 2-month TN load)

The model was fit with 139 observations and resulted in an R2 value of 0.54. The 

regression was highly significant with a probability of a greater |F| value of < 0.0001. 

The slope and parameter coefficients were also highly significant. A plot of predicted 

versus observed chlorophyll a concentrations is presented in Figure 8-90.
5-90

Figure 5-90. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for Matlacha Pass. 

5.6.4 Candidate Criteria - Pine Island Sound and Matlacha Pass

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Pine Island Sound (6.5 μg/l). The 

annual geometric mean TN concentration, 0.99 mg/l, is the TN concentration that 

corresponds to the Reference Period Method for Pine Island Sound. Including 

estimates of uncertainty, the 95% confidence intervals are 0.50 – 1.36 mg/l. Assuming 

the Regulatory Method threshold (11 μg/l ), the same procedure was used to calculate 

the TN concentration that corresponds to the 11 μg/l Regulatory threshold. The annual 

geometric mean TN concentration was 2.14 mg/l. Including estimates of uncertainty, the 

95% confidence intervals are 1.36 – 2.68 mg/l.

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 

a target of 6.9 μg/l for Pine Island Sound, the annual geometric mean TN concentration 

was 1.14 mg/l. Including estimates of uncertainty, the 95% confidence intervals are 

0.81 – 1.44 mg/l. Lastly, an attempt was made to use the mean monthly values of color 

and turbidity to calculate the monthly target TN values. However, this resulted in 

negative chlorophyll targets and corresponding negative TN targets in seven of the 

twelve months.
5-91

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-91 for Pine 

Island Sound.

Figure 5-91. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Pine Island Sound. 

The above regression equation was used to estimate the TN concentrations that 

correspond to the Reference Period Method for Matlacha Pass (6.1 μg/l). This resulted 

in an estimated annual TN load to Matlacha Pass. The annual TN load, 228 tons/year, 

is the TN load that corresponds to the Reference Period Method for Matlacha Pass. 

Including estimates of uncertainty, the 95% confidence intervals are 182 – 282 tons TN 

per year. Assuming the Regulatory Method threshold (11 μg/l ), the same procedure 

was used to calculate the TN concentration that corresponds to the 11 μg/l Regulatory 

threshold. The annual TN load was 798 tons TN/year. Including estimates of 

uncertainty, the 95% confidence intervals are 581 – 1,087 tons TN per year. 

In addition to the values based on the Reference Period and Regulatory thresholds, two 

methods were attempted using the Optical Model Method (Table 3-3). In the first 
5-92

method, it was assumed that color and turbidity were equal to zero and the chlorophyll 

intercepts were used (Table 3-3). The TN target was calculated using the same 

procedure as was used for the two previously mentioned methods. Given a chlorophyll 

a target of 8.2 μg/l for Matlacha Pass, the annual TN load was 437 tons/year. Including 

estimates of uncertainty, the 95% confidence intervals are 330 – 589 tons TN per year. 

Lastly, an attempt was made to use the mean monthly values of color and turbidity to 

calculate the monthly target TN values. However, this resulted in negative chlorophyll 

targets and corresponding negative TN targets in six of the twelve months.

A comparison of the candidate numeric TN concentration criteria based on Regulatory, 

Optical Model, and Reference Period methods is presented in Figure 5-92 for Matlacha 

Pass. 

Figure 5-92. Comparison of the potential TN criteria to observed TN concentrations (expressed 

as geometric means) in Matlacha Pass.
5-93

5.7 San Carlos Bay and Tidal Caloosahatchee

For purposes of numeric nutrient criteria development, the San Carlos Bay and Tidal 

Caloosahatchee segments were combined, as the Tidal Caloosahatchee River 

discharges directly to San Carlos Bay, which serves not only as the receiving water for 

freshwater from the river, but also as a conduit for more saline Gulf water to reach the 

tidal river. 

The San Carlos Bay segment is an embayment located between Pine Island Sound to 

the west, Matlacha Pass to the north, the Caloosahatchee River to the east, and the 

Gulf of Mexico and Estero Bay to the south (Figure 5-93). Its watershed is comprised of 

areas of the mainland, Pine Island, and coastal barrier islands, with wetlands the 

primary land use other than open water, and some urban land uses at the southern tip 

of Pine Island, Sanibel Island, and the western tip of the mainland (Fort Myers and Fort 

Myers Beach). The bay segment has a surface area of approximately 19,921 acres, 

while the watershed is approximately 11,599 acres, or 18.1 mi2. This results in a 

watershed to open water area ratio of 0.6, one of the lower ratios compared to all others 

in the CHNEP area and generating low runoff per rainfall unit.

The southern boundary of San Carlos Bay, on the Gulf of Mexico, is the largest pass 

along the outer CHNEP boundary between Estero Bay and Charlotte Harbor proper. 

This interface factors into the mixing and flushing of the southern open water portion of 

the CHNEP area. With the freshwater of the Caloosahatchee River and the advective 

forces coming in from the Gulf of Mexico converging with the waters of the neighboring 

passes (Pine Island Sound and Matlacha Pass), San Carlos Bay functions as a 

hydrological crossroads in this region of the CHNEP. Thus inflows from the watershed 

proper are not significant compared to the other segments’ inputs.

The Tidal Caloosahatchee River bay segment’s dominant feature is the estuarine 

portion of the Caloosahatchee River, which drains to the west into San Carlos Bay 

(Figure 5-94). The watershed contains primarily open lands, with urban lands making 

up about one-quarter of the watershed, and wetlands about one-sixth (Janicki 

Environmental, 2010). The bay segment proper is approximately 16,760 acres, while 

the watershed is approximately 356,477 acres, or 557 mi2. This results in a watershed 

to open water area ratio of 20, one of the higher ratios compared to others in the 

CHNEP area. However, this ratio is not valid for comparison with other segments 

because flows include discharges from Lake Okeechobee which is outside the historical 

watershed.

The Caloosahatchee River is the major freshwater input into the estuarine waters of 

San Carlos Bay. The flows on the Caloosahatchee River are controlled by a structure 

known as S-79 at the far eastern boundary of the Tidal Caloosahatchee River 

watershed. A series of canals link Lake Okeechobee to the Caloosahatchee River. 

Flows at S-79 are regulated in order to help manage water levels on Lake Okeechobee, 

thus river flows are not directly proportional to local rainfall. The Orange River is a 

major tributary to the Caloosahatchee River, which also has several smaller streams 
5-94

and man-made canals draining into it on its northern bank. Telegraph Swamp is a large 

wetland located in the upstream portion of the watershed.

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for the Tidal Caloosahatchee River 

was 340 days, indicating a very slow flushing rate, and was 78 days for San Carlos Bay, 

indicative of the strong influence of the Gulf on more rapid tidal mixing and flushing in 

San Carlos Bay. 

Figure 5-93. San Carlos Bay water segment.
5-95

Figure 5-94. Tidal Caloosahatchee water segment.

5.7.1 San Carlos Bay and Tidal Caloosahatchee Water Quality and Nutrient 

Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in 

San Carlos Bay are presented in Figure 5-95. The chlorophyll a concentrations varied 

from a low of 0.9 μg/l to a high of 16.0 μg/l. The 25th and 75th percentile chlorophyll a

concentrations were 2.3 and 4.8 μg/l, resulting in an interquartile range of 2.5 μg/l. The 

mean chlorophyll a concentration was 4.1 μg/l and the median was 3.4 μg/l. The mean 

is influenced by several high concentration values. The monthly average TN 

concentrations varied from 0.10 to 2.30 mg/l. The interquartile range was 0.24 mg/l 

(0.30 to 0.54 mg/l). The mean TN concentration was 0.46 mg/l, while the median was 

0.39 mg/l. The monthly average TP concentrations in San Carlos Bay varied from 0.01 

to 0.35 mg/l, and the interquartile range was 0.03 mg/l (0.04 to 0.07 mg/l). The mean 

and median TP concentrations were 0.06 and 0.05 mg/l, respectively. As expected, the 

differences between the mean and median for TN and TP were much less as TN and 

TP do not show the inter-annual variability exhibited by chlorophyll a concentrations. 
5-96

There was no statistically significant trend in chlorophyll a in San Carlos Bay during this 

period (p>0.05). Significant decreasing trends were detected in TN (p<0.0001, slope=-

0,02) and TP (p<0.0001, slope=-0.003) (Appendix 3).

Figure 5-96 presents the time series of monthly TN, TP, and hydrologic loads to San 

Carlos Bay. The monthly TN loads varied from 0.3 to 33.2 tons/month, with an 

interquartile range of 8.9 tons/month (1.5 to 10.4 tons/month). The mean and median 

TN loads to San Carlos Bay were 6.5 and 4.6 tons/month, respectively. Monthly TP 

loads varied from 0.02 to 1.92 tons/month. The interquartile range for TP loads was 

0.39 tons/month (0.08 to 0.47 tons/month). The mean TP load was 0.4 tons/month, 

while the median was 0.2 tons/month. The monthly hydrologic loads varied from a low 

of 0.2 million m3

/month to a high of 40.1 million m3/month. The interquartile range was 

14.4 million m3/month (2.6 to 17.0 million m3/month). The mean and median hydrologic 

loads were 10.6 and 6.3 million m3/month, respectively. The difference between means 

and medians is a sign that the means are highly influenced by a series of high loadings 

values that are typically seen in the wet season when rainfall driven hydrologic loads 

and corresponding nutrient loads are substantially higher than during the dry season.

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in the 

Tidal Caloosahatchee are presented in Figure 5-97. The monthly average chlorophyll a

concentrations varied from 1.6 to 67.0 μg/l, and the interquartile range was 5.7 μg/l (4.2 

to 9.9 μg/l). The mean and median chlorophyll a concentrations were 8.7 and 7.1 μg/l,

respectively. Monthly average TN concentrations in Tidal Caloosahatchee varied from 

0.16 to 1.94 mg/l. The interquartile range was 0.38 mg/l (0.68 to 1.06 mg/l). The mean 

TN concentration was 0.89 mg/l, while the median was 0.88 mg/l. The monthly average 

TP concentrations varied from 0.03 to 0.51 mg/l, and the interquartile range was 0.06 

mg/l (0.07 to 0.13 μg/l). The mean and median TP concentrations were 0.11 and 0.10 

μg/l, respectively. 

There was a statistically significant increase in TP in Tidal Caloosahatchee during this 

period (p<0.01, slope=0.006) (Appendix 3 presents the complete trend test results). 

Lastly, a significant decreasing trend was detected in TN (p<0.0001, slope=-0.08).

Figure 5-98 presents the times series of monthly TN, TP, and hydrologic loads to Tidal 

Caloosahatchee. The monthly TN loads varied from 5 to 1875 tons/month, and the 

interquartile range was 456 tons/month (67 to 523 tons/month). The mean and median 

TN loads were 341 and 168 tons/month, respectively. Monthly TP loads in Tidal Peace 

varied from 1 to 223 tons/month. The interquartile range was 41 tons/month (7 to 48 

tons/month). The mean TP load was 33 tons/month, while the median was 17 

tons/month. The monthly hydrologic loads varied from 3 to 1150 million m3

/month, and 

the interquartile range was 296 million m3/month (46 to 342 million m3/month). The 

mean and median hydrologic loads were 225 and 46 million m3

/month, respectively. 

These differences between the means and medians are a sign that the means are 

highly influenced by a series of high loadings values that are typically seen in the wet 

season when rainfall driven hydrologic loads and corresponding nutrient loads are 

substantially higher than during the dry season.
5-97

Figure 5-95. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

San Carlos Bay.
5-98

Figure 5-96. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for San Carlos Bay.
5-99

Figure 5-97. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Tidal Caloosahatchee.
5-100

Figure 5-98. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Tidal Caloosahatchee.
5-101

5.7.2 San Carlos Bay and Tidal Caloosahatchee Targets

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined. For San Carlos Bay, these were;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 6.9 μg/l, and

• Reference Period Method = 5.2 μg/l.

For Tidal Caloosahatchee, these candidate chlorophyll a thresholds were;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 17.7 μg/l, and

• Reference Period Method = n/a. 

Seagrass acreages in San Carlos Bay over the 1988-2008 period are provided in Figure 

5-99, for comparison to the seagrass target for the combined segments of 4,372 acres 

(Janicki Environmental, 2009). The highly variable seagrass acreage estimates for the 

Tidal Caloosahatchee (2-300 acres) are not included in Figure 5-99. As noted in Janicki 

Environmental (2009), the riverine segments of the system, including the Tidal 

Caloosahatchee, may have underreported seagrass acreages, due to water color 

impacts on the interpretation of aerial photography. 

Figure 5-99. Seagrass acreage in San Carlos Bay.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1999 2003 2004 2006 2008
5-102

5.7.3 Relationship Between Chlorophyll a and Concentrations and Loads - San 

Carlos Bay and Tidal Caloosahatchee

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory variables

in San Carlos Bay. These plots included chlorophyll a versus TN concentration, TP 

concentration, TN loads, TP loads, and hydrologic loads (Figures 5-100 through 5-104, 

respectively). Additional bivariate plots of chlorophyll a and pulse residence time, 2-

month, 3-month, 4-month, 5-month, and 6-month average loads (TN, TP, and 

hydrologic) were produced and analyzed. As can be seen in these plots, no strong 

linear relationships were identified for San Carlos Bay. 

Figure 5-100. Relationship between chlorophyll a and TN concentrations for San Carlos Bay.
5-103

Figure 5-101. Relationship between chlorophyll a and TP concentrations for San Carlos Bay.

Figure 5-102. Relationship between chlorophyll a in San Carlos Bay and TN loads from San 

Carlos Bay.
5-104

Figure 5-103. Relationship between chlorophyll a in San Carlos Bay and TP loads from San 

Carlos Bay. 

Figure 5-104. Relationship between chlorophyll a in San Carlos Bay and hydrologic loads from 

San Carlos Bay. 
5-105

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory variables 

in the Tidal Caloosahatchee. These plots included chlorophyll a versus TN 

concentration, TP concentration, TN loads, TP loads, and hydrologic loads (Figures 5-

105 through 5-109, respectively). Additional bivariate plots were run between 

chlorophyll a and pulse residence time, 2-month, 3-month, 4-month, 5-month, and 6-

month average loads (TN, TP, and hydrologic). As can be seen in these plots, no 

strong linear relationships were identified. Various transformations were applied to the 

data to determine if a statistically significant relationship could be identified. But, this 

did not yield positive results.

Members of the Technical Advisory Committee recommended investigating subsets of 

the data as numerous stations are close to the shoreline where they may be influenced 

by canals and other tributaries. A buffer was applied to the shoreline in order to 

eliminate stations that were close to the shoreline. Additionally, separate attempts were 

made to use only samples from random sampling programs or only samples from fixed 

station sampling programs. None of these efforts yielded positive results as there are 

confounding factors which affect the relationship between loads and chlorophyll 

spatially. 

As was documented by Doering et al. (2006), significant relationships were identified 

between Tidal Caloosahatchee loads and chlorophyll a concentrations when the data 

were divided into regions (Figure 5-10). Doering et al. (2006) identified positive 

correlations between Tidal Caloosahatchee TN loads and chlorophyll concentrations in 

the lower portion of the Tidal Caloosahatchee and San Carlos Bay. They also found no 

significant relationship between TN loads and chlorophyll in the middle portion of the 

Tidal Caloosahatchee and a significant negative correlation between TN load and 

chlorophyll concentrations in the upper portion of the Tidal Caloosahatchee. Because 

significant positive correlations have been identified by Doering et al. (2006) for the 

lower portion of the Tidal Caloosahatchee, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a in the lower Tidal Caloosahatchee and 

other potential explanatory variables. Plots included chlorophyll a versus TN 

concentration, TP concentration, TN loads, TP loads, and hydrologic loads (Figures 5-

111 through 5-115, respectively). 

 

The Tidal Caloosahatchee is a highly managed system, with Lake Okeechobee flows 

being diverted through the C-43 canal to the Tidal Caloosahatchee. Further 

investigation revealed an inflection point in the loadings above which chlorophyll 

concentrations began to decline (Figures 5-116 to 5-118). Doering et al. (2006) 

documented declines in chlorophyll concentrations in the Tidal Caloosahatchee above 

certain flows at S-79. Due to geomorphic differences in the system, the inflection point 

varies spatially. As expected, chlorophyll concentrations begin to decline as a result of 

smaller loads (200 tons/month) in the upper region as this portion of the system is 

narrower and has a smaller volume relative to the downstream region. Given that the 

volume and tidal mixing are greater downstream, the inflection point occurs at higher 

loads (400 tons/month for the middle region and 600 tons/month for lower region).
5-106

Figure 5-105. Relationship between chlorophyll a and TN concentrations for Tidal 

Caloosahatchee.

Figure 5-106. Relationship between chlorophyll a and TP concentrations for Tidal 

Caloosahatchee.
5-107

Figure 5-107. Relationship between chlorophyll a and TN loads for Tidal Caloosahatchee.

Figure 5-108. Relationship between chlorophyll a and TP loads for Tidal Caloosahatchee.
5-108

Figure 5-109. Relationship between chlorophyll a and hydrologic loads for Tidal Caloosahatchee.
5-109

Figure 5-110. Tidal Caloosahatchee water segment, including regional divisions from Doering et 

al. (2006).
5-110

Figure 5-111. Relationship between chlorophyll a and TN concentrations for the lower portion of 

the Tidal Caloosahatchee.

Figure 5-112. Relationship between chlorophyll a and TP concentrations for the lower portion of 

the Tidal Caloosahatchee.
5-111

Figure 5-113. Relationship between chlorophyll a and TN loads for the lower portion of the Tidal 

Caloosahatchee.

Figure 5-114. Relationship between chlorophyll a and TP loads for the lower portion of the Tidal 

Caloosahatchee.
5-112

Figure 5-115. Relationship between chlorophyll a and hydrologic loads for the lower portion of 

the Tidal Caloosahatchee.

Figure 5-116. Relationship between chlorophyll a and TN loads for the upper portion of the Tidal 

Caloosahatchee.
5-113

Figure 5-117. Relationship between chlorophyll a and TN loads for the middle portion of the Tidal 

Caloosahatchee.

Figure 5-118. Relationship between chlorophyll a and TN loads for the lower portion of the Tidal 

Caloosahatchee.
5-114

A regression model between chlorophyll a in the lower portion of Tidal Caloosahatchee 

and TN load in the Tidal Caloosahatchee was developed. Because of the inflection 

point in the data, this relationship was developed for TN loads between 0 and 600 tons. 

The residuals from this model were examined to identify any other explanatory variables 

that might contribute to the overall variance accounted for by the model. As a result of 

this analysis, color was added to the regression equation. The final regression equation 

is:

ln [Chlorophyll a] = -0.305 + (0.294 * Log (TN load)) + (0.00889 * color) 

The model was fit with 57 observations and resulted in an R2 value of 0.57. The 

regression was significant with a probability of a greater |F| value of < 0.0001. The 

slope and parameter coefficients were also significant. A plot of predicted versus 

observed chlorophyll a concentrations is presented in Figure 5-119. 

Figure 5-119. Predicted versus observed chlorophyll a concentrations based on regression 

equation developed for the lower portion of the Tidal Caloosahatchee. 
5-115

5.7.4 Candidate Criteria - San Carlos Bay and Tidal Caloosahatchee

As mentioned above, no defensible relationships were identified between chlorophyll 

concentrations in San Carlos Bay and load and/or concentrations in San Carlos Bay 

and Tidal Caloosahatchee. However, as documented in Figure 5-99 and Table 3-1, 

San Carlos Bay has seen a strong trend in seagrass growth over the last decade. In 

fact, the most recent seagrass estimate of 6,469 acres is well above the seagrass target 

range for San Carlos Bay (3,709–5,376 acres). Since the seagrass population in San 

Carlos Bay has been steadily increasing over the past decade and is currently 

exceeding the target range, the water quality in San Carlos Bay has obviously been 

supportive of seagrasses in San Carlos Bay. Therefore, it is recommended to use the 

reference period approach (Subsection 3.3.3) to identify the TN threshold concentration 

which has been supportive of seagrass growth. This is calculated by taking the annual 

average TN concentration in San Carlos Bay during the reference period (2003-2007) 

and adding an estimate of inter-annual variability (1 standard deviation of the annual 

mean TN concentrations for the period of record). The TN threshold (0.62 mg/l) for San 

Carlos Bay is the target (0.49 mg/l) plus one standard deviation (0.13 mg/l).

A comparison of the candidate numeric TN criterion based on the Reference Period 

method is presented in Figure 5-120 for San Carlos Bay.

Figure 5-120. Comparison of the potential TN criterion to observed TN concentrations in San 

Carlos Bay.
5-116

In order to estimate the TN load criteria for the Tidal Caloosahatchee, an acceptable 

chlorophyll target is necessary. Because the system has been identified as impaired, it 

is inappropriate to use the Reference Period approach based on Tidal Caloosahatchee 

chlorophyll concentrations. As mentioned above, a relationship was developed 

between chlorophyll concentrations in the lower portion of the Tidal Caloosahatchee 

and the TN loads and color in the lower Tidal Caloosahatchee. A significant positive 

correlation between the variables was identified (r2=0.57). Since the chlorophyll 

concentrations in the lower Tidal Caloosahatchee are greater than the chlorophyll 

concentrations in San Carlos Bay on average, the San Carlos Bay chlorophyll threshold 

(5.17 μg/l) was used. This is conservative as the chlorophyll concentrations in San 

Carlos Bay are less than those in Tidal Caloosahatchee. Because color is included in 

the regression, the annual TN load criteria were calculated for each year based on the 

observed color data in each year. The annual TN criteria loads for the period 1999 to 

2007 ranged between 1,834 and 4,056 tons/year. These are the TN load criteria from 

Tidal Caloosahatchee that corresponds to the Reference Period chlorophyll in San 

Carlos Bay. 

A comparison of the candidate numeric TN criteria (grey bars) and the annual loads 

(blue bars) is presented in Figure 5-121 for Tidal Caloosahatchee. 

Figure 5-121. Comparison of the potential TN criterion (grey bars) to observed TN loads (blue 

bars) in Tidal Caloosahatchee. 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1999 2000 2001 2002 2003 2004 2005 2006 2007

TN (tons)

TN Criterion TN Load
5-117

5.8 Estero Bay

The Estero Bay segment is bounded on the north by San Carlos Bay and is the 

southernmost of the bay segments in the CHNEP watershed (Figure 5-122). The 

Estero Bay segment surface area is approximately 10,813 acres, with its watershed 

covering approximately 229,671 acres, or 359 mi2. This results in a watershed to open 

water area ratio of 21, relatively high compared to others in the CHNEP area and 

generating moderate runoff per rainfall unit.

Estero Bay, like Lemon Bay, is a coastal lagoon that has limited connectivity with the 

Gulf of Mexico on the west, and is relatively shallow. Four main rivers/streams provide 

freshwater inputs to Estero Bay: Estero River, Imperial River, Six Mile Cypress, and Ten 

Mile Canal. Coastal wetlands along the shores of Estero Bay augment hydrologic 

inputs to the system. The watershed is also characterized by a large network of manmade canals serving the communities throughout the Estero Bay drainage basin. Land 

used in the watershed is predominantly wetlands (40%), with about one-quarter of the 

watershed classified as open lands and one-fifth as urban (Janicki Environmental, 

2010). The urban lands in the western portion of the watershed are parts of Fort Myers 

and Estero.

A measure of residence time in the system is provided by the pulse residence time 

(PRT), which is estimated as a function of tidal mixing and freshwater inflows, as 

described previously (see Appendix 2). The median PRT for each year was calculated 

based on monthly data, and then the median PRT of the annual values was calculated, 

as representative of the median hydraulic residence time given the observed conditions 

of 1995-2007. The median hydraulic residence time for Estero Bay was 150 days, 

indicating a relatively slow flushing rate, indicative of the relatively small freshwater 

influx to the system and the limited tidal exchange with the Gulf.
5-118

Figure 5-122. Map of Estero Bay water segment.

5.8.1 Estero Bay Water Quality and Nutrient Loadings

Time series plots of monthly average chlorophyll a, TN, and TP concentrations in 

Estero Bay are presented in Figure 5-123. The chlorophyll a concentrations varied from 

a low of 1.6 μg/l to a high of 17.7 μg/l. The 25th and 75th percentile chlorophyll a

concentrations were 3.3 and 6.4 μg/l, resulting in an interquartile range of 3.1 μg/l. The 

mean chlorophyll a concentration was 5.1 μg/l and the median was 4.4 μg/l. The mean 

is influenced by several high concentration values. The monthly average TN 

concentrations varied from 0.01 to 1.67 mg/l. The interquartile range was 0.26 mg/l 

(0.30 to 0.56 mg/l). The mean TN concentration was 0.48 mg/l, while the median was 

0.43 mg/l. The monthly average TP concentrations in Estero Bay varied from 0.01 to 

0.75 mg/l, and the interquartile range was 0.03 mg/l (0.04 to 0.07 mg/l). The mean and 

median TP concentrations were 0.05 and 0.04 mg/l, respectively. As expected, the 
5-119

differences between the mean and median for TN and TP were much less as TN and 

TP do not show the inter-annual variability exhibited by chlorophyll a concentrations. 

There was no statistically significant trend in TP in Estero Bay during this period 

(p>0.05) (Appendix 3 presents the complete trend test results). A significant decreasing 

trend was detected in TN (p<0.01, slope= -0.07) and a significant increasing trend was 

detected in chlorophyll a (p<0.01, slope=0.58).

Figure 5-124 presents the times series of monthly TN, TP, and hydrologic loads to 

Estero Bay. The monthly TN loads varied from 2 to 229 tons/month, with an 

interquartile range of 37 tons/month (7 to 44 tons/month). The mean and median TN 

loads to Estero Bay were 36 and 16 tons/month, respectively. Monthly TP loads varied 

from 0.2 to 39.1 tons/month. The interquartile range for TP loads was 4.9 tons/month 

(0.7 to 5.6 tons/month). The mean TP load was 4.5 tons/month, while the median was 

1.7 tons/month. The monthly hydrologic loads varied from a low of 2 million m3/month 

to a high of 241 million m3

/month. The interquartile range was 37 million m3/month (6 to 

43 million m3/month). The mean and median hydrologic loads were 34 and 17 million 

m3

/month, respectively. These differences between the means and medians are a sign 

that the means are highly influenced by a series of high loadings values that are 

typically seen in the wet season when rainfall driven hydrologic loads and 

corresponding nutrient loads are substantially higher than during the dry season. 
5-120

Figure 5-123. Time series of chlorophyll a (top plot), TN (middle plot), and TP (bottom plot) for 

Estero Bay.
5-121

Figure 5-124. Time series of TN loads (top plot), TP loads (middle plot), and hydrologic loads 

(bottom plot) for Estero Bay.
5-122

5.8.2 Water Quality and Seagrass Targets and Thresholds

As described in Section 3.3 above, three candidate chlorophyll a thresholds were 

defined;

• Regulatory Method = 11 μg/l,

• Optical Model Method = 11.7 μg/l, and

• Reference Period Method = 6.9 μg/l.

Seagrass acreages in Estero Bay over the 1988-2008 period are provided in Figure 5-

125, for comparison to the seagrass target for the combined segments of 3,662 acres 

(Janicki Environmental, 2009). 

Figure 5-125. Seagrass acreage in Estero Bay.

5.8.3 Relationship Between Chlorophyll a and Concentrations and Loads - 

Estero Bay

In addition to the time series plots, a series of bivariate plots were examined to 

investigate relationships between chlorophyll a and other potential explanatory 

variables. These plots included chlorophyll a versus TN concentration, TP 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1999 2003 2004 2006 2008

Seagrass 
5-123

concentration, TN loads, TP loads, and hydrologic loads (Figures 5-126 through 5-130, 

respectively). As can be seen in these plots, no strong linear relationships were 

identified. Additional bivariate plots were run between chlorophyll a and pulse 

residence time, 2-month, 3-month, 4-month, 5-month, and 6-month average loads (TN, 

TP, and hydrologic) with a similar conclusion of no strong linear relationships. 

Figure 5-126. Relationship between chlorophyll a and TN concentrations for Estero Bay.
5-124

Figure 5-127. Relationship between chlorophyll a and TP concentrations for Estero Bay.

Figure 5-128. Relationship between chlorophyll a and TN loads for Estero Bay.
5-125

Figure 5-129. Relationship between chlorophyll a and TP loads for Estero Bay.
5-126

Figure 5-130. Relationship between chlorophyll a and hydrologic loads for Estero Bay.

An alternative method was then employed with the intent on identifying a changepoint 

value that characterized the chlorophyll a distributions as distinct populations. The 

changepoint analysis was employed on a subset of data collected in Estero Bay 

representing the more open water areas within Estero Bay. Estero Bay is a large, 

shallow embayment with extensive mangrove islands and complex hydrology. The data 

used for analysis is displayed in (Figure 5-131). 

Based on this subset of data in Estero Bay, a changepoint was detected using TN loads 

from the Estero Bay watershed at a TN load value of 23.36 tons (Figure 5-132). The 

grand average chlorophyll a concentration was 5.08 μg/l and the conditional means 

were 4.36 μg/l and 6.60 μg/l as presented under the chlorophyll distribution boxplots of 

Figure 16.3.2. Thirty two percent of the data were above the 23.36 TN load 

changepoint.
5-127

Figure 5-131. Map of Estero Bay water quality sampling stations used in changepoint analysis.
5-128

Figure 5-132. Results of changepoint analysis for Estero Bay chlorophyll and Loads. 

5.8.4 Candidate Criteria - Estero Bay

The conditional mean of 5.9 μg/l for the chlorophyll distribution above the changepoint is 

not expected to be exceeded based on the empirical data given the TN loads from that 

distribution remain similar. This chlorophyll value is below all thresholds including the 

Reference Period threshold as well as the Regulatory threshold. Therefore, the 

proposed numeric nutrient criterion for Estero Bay is the expected annual average TN 

load of the higher condition mean. The empirical distribution of TN loads above and 

below the identified changepoint is presented in Figure 5-133. These distributions are 

clearly different in magnitude with a mean distribution below the changepoint of 9 tons 

per month and the mean above the changepoint of 78 tons per month. To develop the 

criterion value only the distribution of values above the changepoint is used since the 

conditional chlorophyll mean when TN loads are above 23 tons is 5.9 (μg/l). The Monte 

Carlo method was used to randomly select 12 samples from the simulated conditional 

distribution above the changepoint and calculate the annual sum of the TN load. Based 

on 1000 replicates, the expected annual TN load was 660 tons per year. The 95% 

confidence intervals were calculated as between 410 tons – 950 tons.
5-129

Figure 5-133. Histogram of TN loadings from Estero Bay corresponding to the water quality data 

with identified changepoint. 

Implementation of this proposed TN criterion will require accounting for the influence of 

inter-annual hydrologic variation and residence times on the target exceedance 

frequencies. Using the confidence interval provides one mechanism by which this might 

be accomplished. The effect of seasonality on these relationships is also not accounted 

for explicitly via the simulation; however, the resulting estimate is likely conservative 

given that higher loads during colder months would likely not elicit the same water 

quality response given the colder water temperatures and reduced photoperiod. 

A comparison of the candidate numeric TN concentration criteria based on Reference 

Period method is presented in Figure 5-134 for Estero Bay.
5-130

Figure 5-134. Comparison of the potential TN criteria to observed TN loads in Estero Bay.
6-1 

6.0 CONCLUSIONS AND RECOMMENDATIONS

In this document, candidate numeric nutrient criteria for the estuarine waters of the 

Charlotte Harbor National Estuary Program (CHNEP) have been developed and 

presented, including the estuarine bay segments in the Charlotte Harbor National 

Estuary Program’s jurisdiction. The methods employed in deriving these candidate 

criteria have been previously identified in a data analysis plan (Janicki Environmental, 

2010) and have been discussed in the previous sections of this report. These methods 

have been developed based on peer-reviewed literature, the many local scientists and 

natural resource managers studying southwest Florida estuaries, previous USEPA 

documents (USEPA, 2009), and reviews by its Science Advisory Board (SAB, 2010) on 

methods for establishing numeric nutrient criteria. 

The candidate numeric nutrient water quality criteria for the CHNEP jurisdiction are 

presented in Table 6-1. All candidate criteria are presented and it is anticipated that the 

water quality subcommittee of the CHNEP will determine which of the candidates 

should be the proposed criteria.

Table 6-1. Candidate numeric nutrient criteria based on the Reference Period, Optical 

Model, and Regulatory methods for the regions of CHNEP. 

Bay Segment Reference Period 

Method

Optical Model

Method

Regulatory

Method

Dona and Roberts Bays 0.47 mg/l n/a 1.05 mg/l

Upper Lemon Bay 0.69 mg/l 0.63 mg/l 0.85 mg/l

Lower Lemon Bay 0.48 mg/l 0.63 mg/l 0.85 mg/l

Tidal Myakka 1,330 tons/year 2,279 tons/year 2,759 tons/year

Tidal Peace 3,018 tons/year 9,619 tons/year 10,423 tons/year

Charlotte Harbor Proper 0.62 mg/l 0.81 mg/l 1.00 mg/l

Matlacha Pass 228 tons/year 437 tons/year 798 tons/year

Pine Island Sound 0.99 mg/l 1.14 mg/l 2.14 mg/l

Tidal Caloosahatchee 1,834-4,056 tons/year n/a n/a

San Carlos Bay 0.62 mg/l n/a n/a

Estero Bay 660 tons/year n/a n/a
7-1

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Appendix 1 - Water Quality Sampling Stations
Appendix 2 – Pulse Residence Time
Appendix 3 – Trend Tests Results
Appendix 4 – Changepoint Methods