the arthur r. marshall loxahatchee national wildlife refuge

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The Arthur R. Marshall Loxahatchee National Wildlife Refuge

Water Budget and Water Quality Models1

Jeanne C. Arceneaux Ehab A. Meselhe

Michael G. Waldon

Prepared for the US Fish and Wildlife Service,

Department of Interior

by Institute of Coastal Ecology and Engineering

University of Louisiana-Lafayette

Report #LOXA-07-004 June 2007

1 Modified from Arceneaux (2007)

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Table of Contents

List of Tables ................................................................................................................ v

List of Figures ............................................................................................................. viii

Chapter 1: Introduction .................................................................................................. 1 1.1 Background ................................................................................................. 1 1.2 Refuge Water Management .......................................................................... 5 1.3 Site Description ........................................................................................... 9 1.3.1 Vegetation....................................................................................... 9 1.3.2 Geology ........................................................................................ 11 1.3.3 Marsh Topography ........................................................................ 11 1.3.4 Canals ........................................................................................... 14 1.4 Objective of Study ..................................................................................... 15

Chapter 2: Literature Review ........................................................................................ 17 2.1 Introduction ................................................................................................. 17 2.2 Everglades Water Budget Modeling............................................................. 17

2.2.1 Lin (1979) ..................................................................................... 17 2.2.2 MacVicar et al. (1984)................................................................... 18 2.2.3 Richardson et al. (1990)................................................................. 19 2.2.4 Welter (2002) ................................................................................ 21

2.3 Previous Modeling Completed in Similar Wetlands ..................................... 22 2.3.1 Kadlec and Hammer (1982) and Kadlec and Knight (1996)........... 22 2.3.2 Mitsch (1988) and Mitsch and Reeder (1991) ................................ 24 2.3.3 Wang and Mitsch (2000) ............................................................... 25

2.4 Everglades Water Quality Modeling ............................................................ 25 2.4.1 Raghunathan et al. (2001).............................................................. 26 2.4.2 Munson et al. (2002) ..................................................................... 27 2.4.3 Fitz et al. (2002a) .......................................................................... 28 2.4.4 Walker (1995) ............................................................................... 29 2.4.5 Walker and Kadlec (2006)............................................................. 29

Chapter 3: Data Collection and Analysis ....................................................................... 31 3.1 Introduction ................................................................................................. 31 3.2 Precipitation................................................................................................. 32 3.3 Evapotranspiration....................................................................................... 37 3.4 Flows........................................................................................................... 39 3.5 Water Levels................................................................................................ 44 3.6 Water Quality .............................................................................................. 45

3.6.1 EVPA Monitoring Sites................................................................. 46 3.6.2 XYZ Monitoring Sites ................................................................... 47 3.6.3 Hydraulic Structures...................................................................... 48

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Chapter 4: Water Budget Model.................................................................................... 50 4.1 Introduction ................................................................................................. 50 4.2 Modeling Assumptions ................................................................................ 51 4.3 Model Predictions ........................................................................................ 52 4.4 Observed Parameters ................................................................................... 54

4.4.1 Precipitation .................................................................................. 54 4.4.2 Evapotranspiration ........................................................................ 56 4.4.3 Inflows and Outflows .................................................................... 57

4.5 Estimated Parameters................................................................................... 59 4.5.1 Exchange Flow.............................................................................. 59 4.5.2 Groundwater Recharge .................................................................. 60

4.6 Calibration................................................................................................... 61 4.6.1 Calibration Parameters .................................................................. 62 4.6.2 Calibration Results ........................................................................ 63 4.6.3 Calibration Performance Measures ................................................ 65

4.7 Validation.................................................................................................... 69 4.7.1 Validation Results ......................................................................... 69 4.7.2 Validation Performance Measures ................................................. 71

4.8 Results for Period of Record ........................................................................ 72 4.9 Regulation Schedule Analysis ...................................................................... 72 4.10 Discussion of Results................................................................................. 75 4.11 Case Study of Model Application............................................................... 76

CHAPTER 5: Water Quality Constituents, Model Selection, and Modeling Approach .. 84 5.1 Introduction ................................................................................................. 84 5.2 Constituents to be Modeled.......................................................................... 85

5.2.1 Chloride ........................................................................................ 85 5.2.2 Phosphorus.................................................................................... 88

5.3 Model Selection........................................................................................... 93 5.4 Water Quality Modeling Approach .............................................................. 96

CHAPTER 6: Chloride Water Quality Modeling........................................................... 99 6.1 Introduction ................................................................................................. 99 6.2 Chloride Excel Model.................................................................................. 99

6.2.1 Excel Model Setup ...................................................................... 100 6.2.2 Calibration .................................................................................. 102 6.2.3 Calibration Results ...................................................................... 104 6.2.4 Validation Results ....................................................................... 107 6.2.5 Discussion of the Chloride Excel Model...................................... 111

6.3 Chloride WASP Model .............................................................................. 111 6.3.1 Chloride WASP Model Setup...................................................... 111 6.3.2 Chloride WASP Model Calibration ............................................. 115 6.3.3 Chloride WASP Model Calibration Results ................................. 116 6.3.4 Chloride WASP Model Validation Results .................................. 120 6.3.5 Discussion and Further Analysis of the Chloride WASP Model... 123

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CHAPTER 7: Phosphorus Water Quality Modeling .................................................... 130 7.1. Introduction .............................................................................................. 130 7.2 Phosphorus WASP Model Setup................................................................ 130 7.3 Phosphorus WASP Model Calibration ....................................................... 132 7.4 Phosphorus WASP Model Calibration Results ........................................... 135 7.5 Phosphorus WASP Model Validation ........................................................ 138 7.6 Discussion and Further Analysis of the Phosphorus WASP Model............. 141

CHAPTER 8: Conclusions and Future Developments ................................................. 145 8.1 Water Budget Model Conclusions.............................................................. 145 8.2 Water Budget Future Developments........................................................... 146 8.3 Chloride Model Conclusions...................................................................... 147 8.4 Chloride Model Future Developments........................................................ 148 8.5 Phosphorus Conclusions ............................................................................ 148 8.6 Phosphorus Future Developments .............................................................. 149

Literature Cited .......................................................................................................... 150

APPENDIX A Removed Chloride and Phosphorus Outliers: ...................................... 160

APPENDIX B: Daily Chloride Excel Model Results................................................... 164

APPENDIX C: Daily Chloride WASP Model Results................................................. 169

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List of Tables

Table 3.1: Available rainfall data in the Loxahatchee Refuge for the POR (1995 to 2004).................................................................................................................... 33 Table 3.2: Availability of flow data in the Loxahatchee Refuge for the POR (1995 to

2004).................................................................................................................... 43 Table 4.1: Marsh and canal statistics in the Loxahatchee Refuge for the calibration

period January 1, 1995 to December 31, 1999 ...................................................... 69 Table 4.2: Marsh and canal statistics in the Loxahatchee Refuge for the validation period January 1, 2000 to December 31, 2004 ...................................................... 71 Table 4.3: Marsh and Canal Statistics for Complete POR............................................. 72

Table 4.4: Marsh and canal statistics for complete POR (1995 to 2004) using the

regulation schedule to predict outflows in the Loxahatchee Refuge....................... 75 Table 4.5: Comparison of the marsh modeled water budget statistics to the ELM v.2.1 model ................................................................................................................... 76 Table 4.6: Comparison of the marsh modeled water budget statistics to the SFWMM

model ................................................................................................................... 76 Table 5.1: Total annual chloride loads going in and out of the Refuge through hydraulic structures and the total percent of chloride retained in the Refuge ......... 87 Table 5.2: Total phosphorus loads going in and out of the Refuge through hydraulic

structures and the total percent of phosphorus retained in the Refuge.................... 91 Table 5.3: Comparison of the calculated inflow loads against the SFWMD’s loads

published in their annual reports for Florida Water Years 2002 to 2004................ 93 Table 5.4: Comparison of the calculated outflow loads against the SFWMD’s loads

published in their annual reports for Florida Water Years 2002 to 2004................ 93 Table 5.5: Distance of each cell from the Refuge canal and its area .............................. 97 Table 5.6: Location water quality stations in reference to the canal and interior cells used in calibration of the chloride and phosphorus models.................................... 98 Table 6.1: Initial and long term average concentrations for chloride in each cell......... 102

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Table 6.2: Chloride Excel model performance measures for the calibration period ...... 107 Table 6.3: Chloride Excel model performance measures for the validation period ....... 110 Table 6.4: Chloride Excel model performance measures for the POR.......................... 110 Table 6.5: Initial volumes for the canal and interior cells ............................................ 113 Table 6.6: Fraction of flows used in WASP................................................................ 114

Table 6.7: Areas and distance used to calculate dispersion in the WASP chloride model ................................................................................................................. 115 Table 6.8: Performance measures for the calibration period using the chloride WASP

model. ................................................................................................................ 119 Table 6.9: Performance measure for the validation period using the chloride WASP

model. ................................................................................................................ 122 Table 6.10: Performance measure for the POR using the chloride WASP model ........ 123 Table 7.1: Initial conditions for phosphorus and the average observed phosphorus

concentration for each cell. ................................................................................. 131 Table 7.2: Fraction of flows used in for calculating settling rate for each cell ............. 132

Table 7.3: Performance measures for the calibration period using the phosphorus WASP model...................................................................................................... 137 Table 7.4: Performance measure for the validation period using the phosphorus WASP model...................................................................................................... 140 Table 7.5: Performance measure for the POR using the phosphorus WASP model ..... 141 Table 7.6: Statistics in the canal comparing the phosphorus water quality model and the ELM v.2.1 phosphorus model ....................................................................... 142 Table 7.7: Statistics in the cell 1 comparing the phosphorus water quality model and the ELM v.2.1 phosphorus model ....................................................................... 143 Table 7.8: Statistics in the cell 2 comparing the phosphorus water quality model and the ELM v.2.1 phosphorus model ....................................................................... 143 Table 7.9: Statistics in the cell 3 comparing the phosphorus water quality model and the ELM v.2.1 phosphorus model ....................................................................... 143

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Table A.1: Chloride outlier values; and dates and values when there were more than recording ............................................................................................................ 161

Table A.2: Dates and values of days when there were more than one phosphorus reading at a inflow or outflow structure .............................................................. 162 Table A.3: Dates and values of days when there were more than one phosphorus reading at a inflow or outflow structure .............................................................. 163

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List of Figures

Figure 1.1: Satellite image and location of the Arthur R. Marshall Loxahatchee National Wildlife Refuge. Inset shows the image location within the State of

Florida.................................................................................................................... 1 Figure 1.2: Historic and altered flow patterns for the Everglades system. ....................... 2 Figure 1.3: Boundaries of the Loxahatchee Refuge......................................................... 4 Figure 1.4: Map of Water Conservation Areas (WCAs).................................................. 4 Figure 1.5: Water Regulation Schedule for WCA 1 ........................................................ 8 Figure 1.6: Plant communities located inside the Refuge ................................................. 9 Figure 1.7: Refuge vegetation map. .............................................................................. 10 Figure 1.8: Loxahatchee Refuge 2003 USGS topographic data..................................... 12 Figure 1.9: North to South ground profile of the Loxahatchee Refuge .......................... 13 Figure 1.10: West to East ground profile of the Loxahatchee Refuge............................ 13 Figure 1.11: Location of canals around the perimeter of the marsh ............................... 14

Figure 3.1: Rain gage locations in and around the Loxahatchee Refuge ........................ 33

Figure 3.2: Seasonal variation of average monthly rainfall in the Loxahatchee Refuge for the POR (1995 to 2004) .................................................................................. 35 Figure 3.3: Variation of total annual rainfall in the Loxahatchee Refuge for the POR

(1995 to 2004)...................................................................................................... 35 Figure 3.4: Spatial distribution of annual average rainfall in the Loxahatchee Refuge

from January 1, 1997, to December 31, 2004........................................................ 36 Figure 3.5: Seasonal variation of average monthly ET at STA-1W for the Loxahatchee

Refuge for the POR (1995 to 2004) ...................................................................... 38 Figure 3.6: Annual variation in total ET at STA-1W for the Loxahatchee Refuge for the POR (1995 to 2004)........................................................................................ 38 Figure 3.7: Location of hydraulic structures located in the Loxahatchee Refuge ........... 39

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Figure 3.8: Various inflow pump stations located in the Loxahatchee Refuge............... 40 Figure 3.9: Various outflow structures located in the Loxahatchee Refuge ................... 41 Figure 3.10: Various structures with bidirectional flows located in the Loxahatchee

Refuge.................................................................................................................. 42 Figure 3.11: Water level sites located in the Loxahatchee Refuge................................. 44 Figure 3.12: XYZ and EVPA water quality monitoring sites located inside the

Loxahatchee Refuge ............................................................................................. 46 Figure 3.13: Chloride and TP arithmetic means at Refuge XYZ transect stations with

increasing distance from the rim canal.................................................................. 48 Figure 4.1: Sketch of Water Budget double-box model................................................. 51

Figure 4.2: An example of one of the sixteen “Theissen Polygon Method” area distributions used for calculating average daily rainfall in the Loxahatchee

Refuge for the POR (1995 to 2004) ...................................................................... 55 Figure 4.3: Canal stages in the Loxahatchee Refuge for the calibration period January 1, 1995, to December 31, 1999 using the water budget model............................... 64 Figure 4.4: Marsh stages in the Loxahatchee Refuge for the calibration period January 1, 1995, to December 31, 1999 using the water budget model............................... 64 Figure 4.5: Canal stages in the Loxahatchee Refuge for the validation period January 1, 2000, to December 31, 2004 using the water budget model............................... 70 Figure 4.6: Marsh stages in the Loxahatchee Refuge for the validation period January 1, 2000, to December 31, 2004 using the water budget model............................... 70 Figure 4.7: Canal stage results using the regulation schedule to predict outflow for the

period January 1, 1995, to December 31, 2004 in the Loxahatchee Refuge ........... 74 Figure 4.8: Canal stage results using the regulation schedule to predict outflow for the period January 1, 1995, to December 31, 2004 in the Loxahatchee Refuge...... 74 Figure 4.9: A comparison of the reduction of inflow from STA1-W to the Refuge based on Alternatives 1 and Alternative 2 in respect to Alternative 0.................... 79 Figure 4.10: Comparison of Marsh stages using the water budget model to compare the Alternatives 1 and 2 against Alternative 0 ....................................................... 79 Figure 4.11: Time series of estimated marsh Stages for the three alternatives ............... 80

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Figure 4.12: Comparison of Canal stages using the water budget model to compare the Alternatives 1 and 2 against Alternative 0 ....................................................... 80 Figure 4.13: Time series of estimated canal Stages for the three alternatives................. 81 Figure 4.14: The total number of days when the water depth in the Refuge is greater than 0.8 ft, based on the stage results from the three alternatives........................... 82 Figure 4.15: The average number of consecutive days when the water depth in the

Refuge is greater than 0.8 ft, based on the stage results from the three alternatives ........................................................................................................... 83 Figure 4.16: The longest number of consecutive days when the water depth in the Refuge is greater than 0.8 ft, based on the stage results from the three alternatives ........................................................................................................... 83 Figure 5.1: Total annual chloride loads going in and out of the Refuge through hydraulic structures .............................................................................................. 87 Figure 5.2: The correlation between the net flow for the POR and the percent chloride

retained in the Refuge........................................................................................... 88 Figure 5.3: Schematic explaining how the composite phosphorus samples were filled to make a complete time-series ............................................................................. 90 Figure 5.4: Total annual phosphorus loads going in and out of the Refuge through

hydraulic structures .............................................................................................. 91 Figure 5.5: The correlation between the net flow for the POR and the percent of

phosphorus retained in the Refuge ........................................................................ 92 Figure 5.6: Location of EVPA and XYZ water quality monitoring sites in relation to the various cells .................................................................................................... 97 Figure 6.1: Schematic of cells used to calculate chloride concentrations...................... 100

Figure 6.2: Canal calibration results for the chloride Excel model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data...................................................................................... 105

Figure 6.3: Cell 1 calibration results for the chloride Excel model, representing the

average monthly observed data and its standard deviations; and the average monthly modeled data...................................................................................... 105

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Figure 6.4: Cell 2 calibration results for the chloride Excel model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data...................................................................................... 106

Figure 6.5: Cell 3 calibration results for the chloride Excel model, representing the


Figure 6.6: Canal validation results for the chloride Excel model, representing the


Figure 6.7: Cell 1 validation results for the chloride Excel model, representing the






Figure 6.10: Canal calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 117 Figure 6.11: Cell 1 calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 118 Figure 6.12: Cell 2 calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 118 Figure 6.13: Cell 3 calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 119 Figure 6.14: Canal validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 120

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Figure 6.15: Cell 1 validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 121 Figure 6.16: Cell 2 validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 121 Figure 6.17: Cell 3 validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 122 Figure 6.18: Modeled loads in the canal compared to the observed outflow loads from the canal structures. Solid line is a trendline with forced zero origin

generated by Excel .......................................................................................... 124 Figure 6.19: Observed (1/5/1995, to 1/12/1995 plotted without a line) and modeled

(1/11/1995 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal............................................................... 125

Figure 6.20: Observed (4/15/1996, to 4/25/1996 plotted without a line) and modeled










Figure 6.25: Observed (10/9/2001, to 10/16/2001 plotted without a line) and modeled (10/9/2001 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal ............................................. 128

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Figure 6.26: Observed (1/8/2002, to 1/15/2002 plotted without a line) and modeled (1/15/2002 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal............................................................... 128

Figure 6.27: Observed (12/4/2003, to 12/16/2003 plotted without a line) and modeled (12/4/2003 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal ............................................. 129 Figure 6.28: Observed (10/18/2004, to 10/21/2004 plotted without a line) and modeled (10/18/2004 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal ............................................. 129 Figure 7.1: Canal calibration results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 135 Figure 7.2: Cell 1 calibration results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 136 Figure 7.3: Cell 2 calibration results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 136 Figure 7.4: Cell 3 calibration results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 137 Figure 7.5: Canal validation results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 138 Figure 7.6: Cell 1 validation results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 139 Figure 7.7: Cell 2 validation results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 139 Figure 7.8: Cell 3 validation results for the phosphorus WASP model, representing the average monthly observed data and its standard deviations; and the average monthly modeled data......................................................................... 140 Figure 7.9: Modeled loads in the canal compared to the observed outflow loads from the canal structures.................................................................................. 142

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Figure B.1: Chloride Excel model results for the canal for the calibration period January 1, 1995, to December 31, 1999 ........................................................... 165 Figure B.2: Chloride Excel model results for the canal for the validation period January 1, 2000, to December 31, 2004 ........................................................... 165 Figure B.3: Chloride Excel model results for the cell 1 for the calibration period January 1, 1995, to December 31, 1999 ........................................................... 166 Figure B.4: Chloride Excel model results for the cell 1 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 166 Figure B.5: Chloride Excel model results for the cell 2 for the calibration period January 1, 1995, to December 31, 1999 ........................................................... 167 Figure B.6: Chloride Excel model results for the cell 2 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 167 Figure B.7: Chloride Excel model results for the cell 3 for the calibration period January 1, 1995, to December 31, 1999 ........................................................... 168 Figure B.8: Chloride Excel model results for the cell 3 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 168 Figure C.1: Chloride WASP model results for the canal for the calibration period

January 1, 1995, to December 31, 1999 ........................................................... 170 Figure C.2: Chloride WASP model results for the canal for the validation period January 1, 2000, to December 31, 2004 ........................................................... 170 Figure C.3: Chloride WASP model results for the cell 1 for the calibration period

January 1, 1995, to December 31, 1999 ........................................................... 171 Figure C.4: Chloride WASP model results for the cell 1 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 171 Figure C.5: Chloride WASP model results for the cell 2 for the calibration period

January 1, 1995, to December 31, 1999 ........................................................... 172 Figure C.6: Chloride WASP model results for the cell 2 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 172 Figure C.7: Chloride WASP model results for the cell 3 for the calibration period

January 1, 1995, to December 31, 1999 ........................................................... 173

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Figure C.8: Chloride WASP model results for the cell 3 for the validation period January 1, 2000, to December 31, 2004 ........................................................... 173

1

CHAPTER 1: Introduction

1.1 Background

The Arthur R. Marshall Loxahatchee National Wildlife Refuge (hereafter referred to as

the Loxahatchee Refuge or simply the Refuge) is a remnant of the Northern Everglades in

Palm Beach County, Florida, that once extended to Lake Okeechobee (USFWS, 2000).

The Refuge is approximately 143,238 acres (58,000 hectares) and is located seven miles

west of Boynton Beach, Florida (Figure 1.1).

±0 3 6 91.5

Miles Figure 1.1: Satellite image and location of the Arthur R. Marshall Loxahatchee National

Wildlife Refuge. Inset shows the image location within the State of Florida. Image adapted from SFWMD (2000a).

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The Loxahatchee Refuge is part of a large watershed known as the Kissimmee-

Okeechobee-Everglades System. Historically, the Kissimmee River discharged into Lake

Okeechobee, and during wet cycles, the lake would overflow its south bank, providing

additional flow to the Everglades (Douglas, 1947; Light and Dineen, 1994). This water

would sheet flow across the Everglades, but now, water flows through canals and

structures and through a series of water storage areas termed Water Conservation Areas

or WCAs (Loucks and McVoy, 2004). Today, the water not used for municipal water

supply and irrigation or lost to evapotranspiration is discharged to the Everglades

National Park (ENP) and ultimately flows to Florida Bay. Figure 1.2 shows the historic

and the current flow condition for the Kissimmee-Okeechobee-Everglades system.

Figure 1.2: Historic and altered flow patterns for the Everglades system. Adapted from

the Comprehensive Everglades Restoration Plan website, http://www.evergladesplan.org/index.cfm.

With the 1845 Swampland Act and the 1907 Everglades Drainage Act, excessive

drainage occurred in the Everglades to help establish the agricultural industry and

Loxahatchee Refuge

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encourage urban development in the area. In the late 1940s, the State of Florida in

cooperation with the U.S. Army Corps of Engineers (USACE) and other federal agencies

planned the construction of three impoundment areas (WCA 1, 2, and 3), bounded by

levees and connected by a series of canals, and placed them under the jurisdiction of what

is now the South Florida Water Management District (SFWMD) (Johnson, 1974; Light

and Dineen, 1994). In the early 1960s, construction of levees and canals circumscribing

WCA 1 was completed. In 1951, a license agreement occurred between the SFWMD and

the United States Fish and Wildlife Service (USFWS), under the Migratory Bird

Conservation Act; the Loxahatchee National Wildlife Refuge was established overlaying

Water Conservation Area 1 (WCA 1). The Refuge land is owned by the State of Florida,

but it is the responsibility of the USFWS to properly conserve, protect, and manage it

(USFWS, 2000).

The Refuge is now hydraulically isolated from the historic Kissimmee-Okeechobee-

Everglades Watershed, as it is completely enclosed within a levee system and a borrow

canal along the interior of the levee (Richardson et al., 1990). The marsh and interior

canal cover the approximately 140,000 acres of WCA 1. The remaining Refuge acreage

outside WCA 1 includes land owned by the USFWS, including four management

compartments A, B, C, and D (Figure 1.3).

The Refuge is bordered on the northwest by the Everglades Agricultural Area (EAA) and

primarily by urban development on the east. WCA 2A is located at the southwest of the

Refuge (Figure 1.4).

4

Figure 1.3: Boundaries of the Loxahatchee Refuge. Adapted from USFWS (2000).

Figure 1.4: Map of Water Conservation Areas (WCAs). Adapted from USFWS (2000),

courtesy of South Florida Water Management District.

5

1.2 Refuge Water Management

From a historic perspective regarding water control in the Everglades, Light and Dineen

(1994) indicated that the WCAs were designed to accomplish eight objectives: 1) receive

and store agricultural runoff from the EAA; 2) prevent water accumulated in the system

from overflowing into urban and agricultural areas; 3) recharge regional aquifers; 4)

prevent salt water intrusion; 5) store and convey water supply for agricultural, municipal

and industrial use, and for the ENP requirements; 6) receive controlled releases from

Lake Okeechobee; 7) protect wildlife and promote recreation; and 8) dampen the effect

of hurricane-induced wind tides by maintaining marsh vegetation in the system.

According to the Comprehensive Conservation Plan for the Loxahatchee Refuge

(USFWS, 2000) “the construction of the levees has had significant effects on the

hydrology, vegetation and wildlife in the refuge.” The changes in natural timing of water

levels affect wading birds’ feeding patterns, apple snail reproductive output, and alligator

nesting. Similarly, changes in the spatial distribution of water levels alter the distribution

of aquatic vegetation and tree islands. In addition, and particularly during the dry season,

lower water levels increase the potential for fire and damage to vegetation, soils, and

wildlife. The USFWS (2000) indicated that for consistency with the South Florida

Ecosystem Plan, the Refuge should be used to accomplish the following: 1) reduce exotic

species; 2) manage water quality and quantity through partnerships; 3) monitor and

inventory wildlife and habitats; 4) promote public awareness about the ecosystem; and 5)

provide wildlife-compatible recreation.

6

To control the water quantity and timing, the Refuge is managed under a water regulation

schedule; the current one was initiated in May 1995 after approximately five years of

analysis and negotiation. The Refuge regulation schedule is administered by the USACE

(U.S. Army Corps of Engineers, Jacksonville District 1994). The main purpose of the

water regulation schedule is to regulate the water level in WCA 1 in order to produce

maximum benefits for flood control, water supply, fish and wildlife, and prevention of

salt water intrusion. To meet these objectives, water levels in the Refuge are adjusted

during the year primarily by releasing water from the Refuge. The Refuge regulation

schedule is described in detail in the Comprehensive Conservation Plan for the Refuge

(USFWS, 2000) and is summarized below, along with a schematic diagram of the water

regulation schedule shown in Figure 1.5. The water regulation schedule is grouped into

four zones (Neidrauer, 2004).

• Zone A1 is the flood control zone from January through June. When water

levels reach this zone active water releases will be made through the S-10

spillway (and S-39 when agreed between USACE and SFWMD).

• Zone A2 is the flood control zone from July through December. In this zone,

water levels in the Refuge, which are linked with rainfall amounts and the

water level at Lake Okeechobee, are permitted to reach a maximum of

17.5 feet (ft) NGVD 29. Excess water is released from the S-10 and S-39

spillways. When additional water is needed for WCA 2A or other areas, it

is released from the Refuge depending on relative water level at Lake

Okeechobee. If Lake Okeechobee’s stage is above WCA 1’s stage or no

7

more than one foot below, then water supply releases from WCA 1 must

be preceded by an equivalent volume of inflow (Neidrauer, 2004).

• Zone B is the water supply zone. Water levels range from a minimum of 14.0 ft

NGVD 29 up to a maximum of 17.5 ft NGVD 29. When water levels in

the Refuge are within this zone, water releases are allowed, as needed

depending on the water level at Lake Okeechobee. If Lake Okeechobee’s

stage is above WCA 1’s stage or no more than one foot below, then water

supply releases from WCA 1 must be preceded by an equivalent volume

of inflow (Neidrauer, 2004). This is the zone considered to be most

beneficial to fish and wildlife of the Refuge (USFWS, 2000).

• Zone C is characterized when water levels drop to 14 ft NGVD 29 or less; when

this occurs the Refuge management should not allow any water supply

releases. If water supply releases do occur they must be preceded by an

equivalent volume of inflow.

According to the USFWS (2000), some of the benefits of the water regulation schedule

relative to earlier schedules include: 1) increased hydroperiod of interior marshes to avoid

annual dryout; 2) increased water depth during wet years in the northern portion of the

Refuge; 3) increased area of interior marsh which serves as nursery areas for aquatic

organisms; 4) improvements in timing in winter stage drawdown to benefit wading birds;

5) restoration of deep water habitats suitable for nesting Everglades snail kites; and 6)

greater storage within the central and southern Florida project system during wet and

normal rainfall years.

8

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

17.5

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Januar

y

Februar

yMarc

hApri

lMay Jun

eJul

yAugu

st

Septem

ber

October

November

December

Wat

er E

leva

tion

(fee

t NG

VD

29)

A1 A2 B

ZONE CNo net water releases due to drought

ZONE BWater releases as needed depending on water elevation at Lake Okeechobee

ZONE A1Active water releases due to flood conditions

ZONE A2

Water releases linked to amount of rainfall and water elevation at Lake

Okeechobee

Figure 1.5: Water Regulation Schedule for WCA 1. Adapted from USFWS (2000).

There are currently discussions of revising the Refuge regulation schedule to take into

account newer data and understanding of hydrological, ecological, and water quality

relationships. Analyses supporting management decisions concerning alternative

schedule revisions should utilize the models presented in this report.

Along with the changes in water quantity and timing, the changes in water quality are an

important threat to the Everglades ecosystem. High concentrations of nutrients

(specifically phosphorus) in runoff from agricultural areas cause proliferation of cattails

and other undesirable species that negatively affect the ecosystem’s balance. Other

negative impacts from increased nutrients include: increased soil phosphorus content;

changed periphyton communities; loss of native sawgrass communities; increased

9

organic matter in water; reduced dissolved oxygen; conversion of wet prairie plant

communities to cattail; and loss of important habitats for wading birds (Stober et al.,

1996).

1.3 Site Description

1.3.1 Vegetation

The Refuge landscape consists of a complex mosaic of wetland communities that grade

from wetter areas such as sloughs and wet prairies to sawgrass, brush, and finally tree

islands occurring at the dryer end of the scale (Figure 1.6) (USFWS, 2000).

Figure 1.6: Plant communities located inside the Refuge. Photographs a, b, and c taken

by J. Arceneaux; photograph d adapted from USFWS (2007).

(a) Sloughs (b) Wet Prairies

(c) Tree Islands

(d) Sawgrass

10

Sloughs are the deepest natural marsh communities in the Everglades with water depth

that may exceed 3 ft in the wet season; slough annual average depth is about 1 foot. In

contrast to sloughs, wet prairies have shallow water levels. They are the prevalent

vegetative community in the Refuge, with approximately 50 % land coverage (Figure

1.7) (USFWS, 2000).

Figure 1.7: Refuge vegetation map. Adapted from USFWS (2000).

Sawgrass accounts for about 25% of land coverage. It is present on all parts of the

Refuge including a vast area on the west side. The tree islands cover approximately 20%

of the Refuge interior. They are basically located at the northern portion of the Refuge,

ranging in size from less than 1 acre to more than 300 acres (USFWS, 2000).

11

In addition to the aforementioned species, cattails also grow on the Refuge. Cattails are a

native species and are naturally found around wading bird colonies, tree islands, and

alligator holes. Cattail growth is dependent on nutrients. Excessive cattail growth has

occurred along the perimeter of the canal as a response to the anthropogenic load of

nutrients in the incoming water (USFWS, 2000). According to Richardson et al. (1990),

almost all of the cattails are found within the first 0.621 miles (1000 meters) of the canal,

and most remaining cattails are found within the next 0.621 miles (1000 m); Childers et

al. (2003) documented additional expansion of cattail in the Refuge. Cattails are more

abundant in the west side of the Refuge.

1.3.2 Geology

The Refuge wetland communities occur on top of a bed of peat (Richardson et al., 1990)

from seven to nine feet deep (Scheidt et al., 2000; Stober et al., 1996). The peat is lightly

colored, fibrous and spongy, and reflective of high organic content (USFWS, 2000;

Stober et al. 1996).

1.3.3 Marsh Topography

The Refuge topography is characterized by a fairly flat interior marsh elevation and a

varying-section rim canal. The latest marsh elevation data for the Refuge are available

from the USGS on a 400 by 400 m grid. The horizontal and vertical data have an

accuracy of +/- 15 cm (Desmond, 2003). Figure 1.8 shows the bathymetric contours for

12

the Loxahatchee Refuge based on the USGS’s data. Results of this survey indicate that in

the Refuge the bathymetry contours (excluding the rim channel) range from 18.50 to

10.61 ft NGVD 29, with a mean elevation of about 15.17 ft (4.62 m) NGVD 29.

Figure 1.8: Loxahatchee Refuge 2003 USGS topographic data. Adapted from Meselhe

et al. (2006).

A North-South profile (Figure 1.9) shows that the Refuge has a very mild north to south

slope, which results in a slow southward flow movement of water. Lin (1979) indicates

that the flow through the heavily vegetated marsh is slower than the flow in the canals.

The North-South slope is estimated to be about an inch per mile. The West-East profile

shows mounds and depressions in the terrain, but maintains a relatively horizontal slope

(Figure 1.10).

13

10

11

12

13

14

15

16

17

18

0 2 4 6 8 10 12 14 16 18 20 22 24

Distance from Point A (miles)

Gro

und

Ele

vatio

n (f

t NG

VD

29)

Average Slope = 0.085 ft/mile

Overland Surface

Figure 1.9: North to South ground profile of the Loxahatchee Refuge. Adapted from

Meselhe et al. (2005).

10

11

12

13

14

15

16

17

18

0 2 4 6 8 10 12 14

Distance from Point C (miles)

Gro

und

Ele

vatio

n (f

t NG

VD

29)

Average Slope = 0.004 ft/mile Overland Surface

Figure 1.10: West to East ground profile of the Loxahatchee Refuge. Adapted from

Meselhe et al. (2005).

14

1.3.4 Canals

The Refuge is bordered by the L-7 and L-39 Canals to the west and south and by the L-40

Canal on the east (Figure 1.11).

Figure 1.11: Location of canals around the perimeter of the marsh.

All the water that is pumped into the Refuge goes into these canals and some of this

water moves through the canals around the perimeter and leaves the Refuge through the

southwestern and eastern structures. The rim canal bathymetric data were collected by

the University of Florida’s Institute of Food and Agricultural Sciences (Daroub et al.,

2002). For the western canals, the sediment surface elevations range between 7.0 and

-1.5 ft NGVD 29 and between 6.7 and -5.7 ft NGVD 29 for the L-40 Canal. The top

width ranges between 120 and 205 ft for the western canals, and between 88 and 173 ft

for the L-40 Canal. It is noted that modeling of sheet flow and water surface levels in the

wetlands of South Florida is very sensitive to changes in elevation due to the expansive

0 3 6 91.5 Miles ±

L-7 C

anal

Hillsboro

Canal (L-39)

L-40 Canal

15

and extremely low relief terrain. Therefore, vertical accuracy on the order of +/- 15

centimeters is required for the elevation data to be used as input to hydrologic models

(Desmond, 2003).

1.4 Objective of Study

According to the Comprehensive Conservation Plan for the Refuge (USFWS, 2000),

“Water quality, quantity and delivery timing affect the welfare of fish, wildlife and

plants… Because the Everglades is no longer a free-flowing system that relies on

temporal weather patterns to sustain it, humans must now attempt to provide water when

and where the system can most benefit.” The Refuge is impacted by elevated

concentrations of nutrients, particularly phosphorus, in pumped stormwater (Harwell et

al., 2005; USFWS, 2007b). Such nutrients enhance the growth of non-indigenous and

invasive species to the detriment of native species (USFWS, 2000). It is a priority for the

Refuge to better understand and minimize the impacts of this excessive nutrients loading.

Hence, the goal of this modeling effort is to provide a quantitative framework for

management decisions related to Refuge inflow and outflow quantity, timing, and quality.

Therefore, this report will present the methodology and results of simple water budget

and water quality models for the Refuge, which has the potential of providing the needed

management and scientific support related to these concerns. The simplified modeling

presented here is part of a larger project that will also develop more complex, 2-

dimensionsional models of hydrology and constituent fate and transport.

16

When fully calibrated and validated, the water budget and water quality models should

assist in answering questions and provide information such as those listed below (Brandt

et al., 2004).

• What is the impact of different management scenarios on the water distribution

inside the Refuge?

• Which management scenarios will cause portions of the Refuge to dry out and

for how long? In other words what is the impact of the management

scenarios on the hydroperiod?

• Does the water depth (duration and frequency) satisfy the needs of plants and

wildlife?

• What are the spatial and temporal distributions of phosphorus levels within the

Refuge?

• What are the impacts of management decisions and strategies on the water

quality?

• What are the impacts of alternative regulation schedules on the water quantity

and quality in the Refuge?

• What are the effects of the surface-groundwater interactions on the Refuge?

• How does the surface and ground water interact in the Refuge?

17

CHAPTER 2: Literature Review

2.1 Introduction

There have been various noteworthy efforts devoted to modeling the hydrology and water

quality of the Loxahatchee Refuge, alone or as a part of the greater Everglades. A great

deal can be learned from these models, but none of them meet the current management

needs for the Loxahatchee Refuge. This chapter briefly covers some of these modeling

efforts. Some similar modeling techniques that were used in the completion of this report

are also discussed.

2.2 Everglades Water Budget Modeling

2.2.1 Lin (1979)

Lin (1979) adapted and modified the Receiving Water Quantity Model to model the

WCAs in order to investigate the hydraulic impact of additional inflow under different

pumping scenarios. Lin (1979) modeled WCAs 1, 2A, and 3A with 20 link-nodes each.

The network system for WCA 1 contained 20 nodes and 57 channels. The calibration of

the model was based on a comparison of predicted and observed stages at selected gages.

The model was calibrated for the year 1974 and was later applied to the period 1962 to

1973. For WCA 1, modeled and observed values at gages S-6, 1-8, 1-7, and 1-9 were

compared. Gages S-6 and 1-8 are located in the existing canal system, while 1-7 and 1-9

18

are located in the central marshland of the Refuge. For the validation period, important

deviations were observed between the model results and the measurements. The

deviation for interior gages was far less than that seen in the canal system. Lin (1979)

recommended that the number of nodes in the network system should be increased in

order to provide a better representation of the real water body. Neither groundwater nor

water quality were modeled.

2.2.2 MacVicar et al. (1984)

MacVicar et al. (1984) presented the application of the South Florida Water Management

Model (SFWMM) to two planning areas, the Lower East Coast (LEC) and the Upper East

Coast (UEC). The WCA 1 was included in the LEC model that also included the other

WCAs, the Everglades Agricultural Area, and some other nearby areas. A two by two

mile node spacing was used to cover the 6,880 square mile area modeled. A time step of

one day was used. The model was able to simulate overland, channel, and groundwater

flow. McVicar et al. (1984) indicated that simplified mathematical formulations were

implemented in order to make the model computationally efficient. For example, the

canal routine developed for this model was a mass balance procedure that sums all the

inflows and outflows of a canal to determine the water surface position at the end of each

day. The canals were defined as continuous channel reaches with flow control structures

at the upstream and downstream ends. The overland flow was simplified using a

diffusion flow approximation based on Manning’s equation. According to MacVicar et

al. (1984), the model did simulate regional flooding in undeveloped areas, and also

19

indicated excessive groundwater drawdowns when they occurred, although it was unable

to provide detailed flood routing results for single events or define detailed depression

cones around municipal wells.

MacVicar et al. (1984) indicated that the period 1969 to 1971 was chosen as the

calibration period, and the period of 1973 to 1975 was selected as validation period. The

investigators reported a good agreement between simulated and recorded water levels at

two gages in WCA 1 (gages 1-8 and 1-7). They reported that evapotranspiration and

overland friction losses were the two major calibration parameters. Water quality and

mass transport were not simulated during this study.

The SFWMM continues to be developed and its period-of-record for simulation was

extended in order to support water resources management in the South Florida area

(SFWMD, 2003). A companion model, the Natural Systems Model (NSM), also

continues in development. The NSM is essentially the SFWMM with human alterations

of the system (e.g., canals, levees, and water control structures) removed, and topography

restored to an estimate of pre-development conditions.

2.2.3 Richardson et al. (1990)

Richardson et al. (1990) studied the distribution of water over space and time and how

vegetation was being structured on the Refuge by hydroperiod pattern. A hydrologic

model was developed to better understand the hydrologic characteristic of the Refuge.

20

For this task, topographic data and water depths were gathered and the percent covered

by each vegetation class was recorded. A flat pool of water in the Refuge was obtained

by holding water at the 17 foot level during the time that the grid survey was being

conducted. Marsh surface elevations were determined by subtracting measured water

depths at each of the grid locations from an assumed horizontal water level.

A hydrologic simulation model was constructed utilizing the Adaptive Environmental

Assessment Everglades Simulation Model (AEA Everglades Model) developed by Carl

Walters (Walters, 1990; Tait, 1990). Some modifications were made to the AEA

Everglades model to make it applicable to the Refuge; some of these modifications

included reducing the cell size, adjusting Manning’s roughness coefficient, tagging cells

located around the edge as canal cells, and using data from the Refuge. The stage of the

rim canal was not modeled, but rather inputted as a boundary. The input and output to

the canal were controlled using the historic monthly canal levels (data from SFWMD) by

adjusting the water depths in canal cells.

A sixteen-year period, 1970 through 1985, represented the standard base run of the

model. The simulations were compared to two stage stations, 1-7 and 1-9. Observed data

indicated that, during the 192 month time period, there were 33 and 11 months of

recession at the 1-7 and 1-9 gages, respectively. With water depths smaller than 0.075

feet set as dry, it was predicted that there were 30 months of drawdown at the 1-7 gage

and 14 months of drawdown at the 1-9 gage. The model slightly underestimated the 16-

year hydroperiod for gage 1-7 and slightly overestimated the hydroperiod at gage 1-9.

21

The model was later used for approximating spatial hydroperiods for the Loxahatchee

Refuge. The 16 year hydroperiod over the entire Refuge ranged from 70% to 98% (wet

period over total period) exhibiting an obvious north-south trend of increasing

hydroperiod with localized anomalies corresponding to topographic features. Mean

water depth for the study period ranged from 0.2 ft in the north to 3.2 ft in the south. It

was found that the north end of the Refuge had much greater variance in hydroperiod

than the south end. Richardson et al. (1990) stated that during dry years, the north end of

the Refuge is much more susceptible to staying dry for long periods, while in the south

the dry season is not as likely to completely dewater the marsh for months at a time.

2.2.4 Welter (2002)

Welter (2002) used the Regional Simulation Model (RSM) to simulate the hydrology of

the Loxahatchee Refuge. The model used a grid with 16,292 triangular cells with

average element size of 650 ft. Overland, canal, and groundwater flows were modeled.

Welter (2002) expressed that the groundwater portion of the model was simplified as

much as possible, because the overland processes seemed to be more important.

The RSM was calibrated over the period of record of 1988 to 1990, and validated for the

four-year period, 1991 to 1994. The model results showed the same trends observed in

the field measurements. However, some deviations were observed. Welter indicated that

“the most disappointing aspect of these results is that measured data shows a larger slope

in the canal’s water level than the model calculates.” He attributed this discrepancy to

22

inaccurate cross section data which, according to Welter, overestimated depths. Welter

also stated that “the limiting factor in this modeling effort is the sparse network of stage

monitoring stations in the Refuge.”

2.3 Previous Modeling Completed on Similar Wetlands

Many water quality constituents can be well modeled using a very simplified formulation

(Kadlec and Knight, 1996). Some constituents undergo no significant transformation

over their residence time within the modeled system. These constituents may be modeled

as conservative substances. That is, they are affected only by transport processes.

Disappearance of other constituents may be acceptably modeled using a first-order

disappearance rate analogous to a settling velocity (Bowie et al., 1985). Some

substances, including total phosphorus and total nitrogen may in some situations be well

modeled using a settling velocity with a minimum limiting concentration. The k-c*

model of Kadlec and Knight (1996) is an example of such a model formulation. Some

simplified models of wetland water quality constituents are briefly surveyed in this

section.

2.3.1 Kadlec and Hammer (1982) and Kadlec and Knight (1996)

Kadlec and Hammer (1982) presented a theoretical paper discussing the transport of

pollutant in wetland systems. They indicated that water flow in wetlands ecosystems

usually occurs in thin-sheet flows at slow rates, which are controlled by the ground slope,

23

water depths, type of vegetation and by the degree and type of channelization. Kadlec

and Hammer (1982) indicated that removal rates in wetland systems are fast in

comparison to typical biological processes, and can be represented by a first-order

reaction. Kadlec and Knight (1996) also suggested nitrogen and phosphorus removal in

wetland systems can be approximated by first-order models. They indicated that

corrections need to be made to account for non-ideal flow, infiltration, and atmospheric

inputs and outputs.

Kadlec and Knight (1996) introduce the k-c* model (Equation 2.1) which is an area

based, first order concentration or bacterial die-off model:

*)()(

CCkdAQCd

−−= (2.1)

where, Q is the volumetric flow rate in m3/day, C is the concentration in g/m3, k is the

removal rate constant in m/yr, C* is the background pollutant concentration in g/m3, and

A is area in m2. Assuming depth h is constant Equation 2.1 can be written as Equation

2.2:

**)( kCkCCCkdt

dhC+−=−−= (2.2)

The left side of Equation 2.2 is the derivative of the areal constituent mass, and the final

term on the right side in Equation 2.2 is analogous to a constant areal mass loading rate

24

(g/m2/yr). Kadlec and Knight (1996) conclude that this model is appropriate for wetland

treatment systems because surface area for constituent removal or for bacterial

inactivation does not increase proportionally to water volume as water covers the

vegetated zones. Kadlec and Knight (1996) listed some key assumptions of this model.

One such assumption is that there are no adaptation trends, as implied by a stationary

state for all active wetland storage; therefore the k-c* model cannot predict certain long-

term changes. They also assume that the model will not capture any rapid changes.

Therefore the k-c* model is best applied when there are intermediate changes or small

changes over a long period of time.

2.3.2 Mitsch (1988) and Mitsch and Reeder (1991)

Mitsch (1988) and Mitsch and Reeder (1991) stressed the importance of developing a

proper hydrologic model as the first step in producing a productivity and/or nutrient mass

balance simulation. Mitsch and Reeder (1991) developed a hydrologic-nutrient removal

model to estimate the fate of phosphorus in a wetland area adjacent to Lake Erie (one of

the North American Laurentian Great Lakes). The only state variable in the hydrologic

model was the volume of water in the marsh, which was affected by rainfall, inflow,

evapotranspiration and outflow. The TP model included incoming phosphorus,

macrophyte and plankton uptake, and sedimentation and resuspension of phosphorus.

The calibration of the TP model was done by varying a resuspension coefficient until the

model predicted phosphorus concentrations similar to field data. They also modeled

plankton and macrophyte biomass productivity.

25

2.3.3 Wang and Mitsch (2000)

Wang and Mitsch (2000) used a similar model to the one presented by Mitsch and Reeder

(1991) for the evaluation of phosphorus dynamics in a created riparian wetlands. The

hydrology module was updated to include seepage, and bank storage in the water volume

balance calculation, and periphyton community was included in the productivity model.

The authors indicated that simulated TP concentrations did not follow observed data well,

especially during times where there was no outflow or in low flow periods. They

conjectured that it was due to the fact that the model itself is a steady-state lumped

model, unable to capture influences of disturbance and random effects such as wind

stirring of sediments. The lack of an atmospheric deposition term may have also

introduced errors in the phosphorus budget calculations.

2.4 Everglades Water Quality Modeling

Water quality within the Everglades has been a central issue for management of this

ecosystem for decades (Richardson, 1990; USFWS, 2000). Models of water quality

constituents in the Everglades have been developed to improve understanding and to

support management decisions. This section covers some of the models that have been

developed to project aspects of water quality within the Everglades ecosystem.

26

2.4.1 Raghunathan et al. (2001)

Raghunathan et al. (2001) developed the Everglades Water Quality Model (EWQM) to

predict phosphorus fate and transport in the Everglades. The WCAs and the Everglades

National Park (ENP) were included in the model. The output from the SFWMM was

used to transport phosphorus between model cells and canals. As in the SFWMM, the

model used two-by-two mile grid-cells. A simplified relationship based on a single

apparent net settling rate coefficient was used to represent the combined effect of all

biogeochemical processes that control the dynamics of phosphorus in the water column.

This simplified relationship indicated a net deposition of phosphorus in the sediments.

An apparent net settling rate equal to 6.30 m/year was found for WCA 1 during the

calibration period. The model was simulated from 1979 to 1989. Model results indicated

that the interior of WCA 1 exhibits much lower concentration than actually found in the

areas near the rim canal. However, the rim canal was simulated with a single water

quality segment without nutrient concentration gradients (the EWQM assumed a constant

canal water depth of 3 m). Model results also suggested that reduction of phosphorus

concentrations leaving the EAA will result in lower concentrations entering the

Everglades National Park (Raghunathan et al., 2001). It was concluded that this model

proves to be a good tool for screening the effects of nutrient reduction options in the

regional scenario of the EAA-WCAs-ENP system; however, it lacks the level of detail

necessary to accurately model the phosphorus dynamics, and the temporal and spatial

distribution of water within the Loxahatchee Refuge.

27

2.4.2 Munson et al. (2002)

Munson et al. (2002) developed the Everglades Phosphorus and Hydrology (EPH) model

to simulate water movement and phosphorus dynamics in the water that flows from the

EAA through WCAs and into the Everglades National Park. The EAA-WCAs-ENP

system was modeled as a series of cells with water flowing from one cell to the next,

using a monthly time step. In this application, the Loxahatchee Refuge was modeled

with only three cells, cell 1 had a surface area of 250 ha representing the rim canal, cell 2

had a surface area of 46,952 ha representing the north-central portion, and cell 3 with

11,734 ha represented the southern part of the Refuge. The hydrologic processes

simulated by the EPH model included precipitation, evapotranspiration, inflow and

outflow. Total phosphorus in the water column was the only nutrient modeled in this

application.

Evapotranspiration parameters and stage-discharge relationship were adjusted during the

calibration process to obtain the best results for flows and water surface elevations. The

period of record of 1980 to 1988 was used for this purpose. The phosphorus removal rate

in each cell was adjusted in order to match simulated and observed concentrations.

During the calibration, the average deviations between simulated and observed values for

water depths and phosphorus concentrations were 7 and 6%, respectively. The model

was recently applied to simulate the impacts on annual average total phosphorus

concentrations in each cell as a result of the implementation of the management plan

mandated by the Everglades Forever Act. Model results indicate that reductions in input

28

phosphorus concentrations will have little impact on phosphorus concentrations for 85%

of the area of the WCAs and on the water entering the ENP.

2.4.3 Fitz et al (2002a)

Fitz et al. (2002a) presented the calibration of the Everglades Landscape Model (ELM) to

match the observed data on water stages and total phosphorus concentration in the water

column at about 60 point locations distributed throughout the greater Everglades using a

1 km x 1 km square grid. ELM simulates surface, canal, and groundwater flow, but it

only considers advective flow (dispersion is not directly modeled). Surface and

groundwater flows are solved using a finite difference, alternating direction explicit

technique, providing for propagation of water and water-borne constituents across space.

The simulation of phosphorus cycles includes uptake, remineralization, sorption,

diffusion, and organic soil loss/gain. Sixty gages were used for the calibration of water

stages (during the period from 1979 to 1995), but only three gages were located inside the

Loxahatchee Refuge (gages 1-7, 1-9 and 1-8T). The water quality data used in the

calibration was total phosphorus (TP) concentration sampled in the surface water column

during the period from 1979 to 1995. Of 57 monitoring sites, 21 were located inside the

Loxahatchee Refuge. A goodness of fit statistic indicated that for water levels, the ELM

v.2.1 simulated values explained 68% of variability in observed values. When each

simulated and observed depth weighted-seasonal mean surface water TP concentration (at

all stations) were compared, simulated values explained more than 50% of variability in

29

observed values (Fitz et al., 2002a). However, differences close to ten orders of

magnitude could be found at specific locations.

2.4.4 Walker (1995)

Walker (1995) presented the development of a mass-balance model for predictions of

long-term-average phosphorus removal in WCA 2. The model was driven by inflow

volumes, precipitation, evapotranspiration, phosphorus loads in the influent and

atmospheric deposition, and by a calibrated first-order settling rate. Walker (1995)

concluded that a settling rate of 8.9 to 11.6 m/yr was supported by peat-accretion and

water column data. He stated that over a long time period, accumulation of phosphorus

in plant biomass approaches zero as the ecosystem matures and approaches dynamic

equilibrium.

2.4.5. Walker and Kadlec (2006)

The Dynamic Model for Stormwater Treatment Areas (DMSTA) was developed by

Walker and Kadlec as an improvement of the total phosphorus models originally used in

Everglades stormwater treatment area (STA) design (Walker, 1995). The DMSTA model

has been applied to numerous wetlands and wetland treatment systems including STA-

1W located at the northwest boundary of the Refuge. Walker and Kadlec state that the

main goal of DMSTA is to develop and calibrate the simplest, highly aggregated model

that could mimic the major features of events driven behavior of treatment wetlands in

30

the runoff environment. DMSTA simulates daily water and mass balances in a user

defined series of wetland treatment cells. The model allows a maximum of six cells to be

linked in series or parallel. At the present time, DMSTA does not support bidirectional

flows. Water balance terms included in this model include inflow, bypass, rainfall,

evapotranspiration, outflow, seepage in, and seepage out. This model is coded in visual

basic and uses Microsoft Excel as the user interface. DMSTA is an advance over the k-

c* equation for modeling phosphorus within the STAs. By dynamically incorporating a

phosphorus storage state-variable, DMSTA is capable of providing greatly improved

projections of the transient behavior of phosphorus in wetlands. The model may be

calibrated using the settling rate, k and the c* value determined in the simpler k-c*

model. Based on experience in modeling a diverse set of wetland systems, multiple

parameter sets are suggested by the authors depending on wetland vegetation type. The

calibrated c* value ranged from 4 to 20 µg/L.

31

CHAPTER 3: Data Collection and Analysis

3.1 Introduction

As part of the Everglades, the Loxahatchee Refuge recently is a highly monitored area

and could be termed data-rich. Initial modeling efforts were devoted to data

identification, compilation, and processing (Meselhe et al., 2005). Many of the datasets

are spatially variable, while others are both temporally and spatially variable such as all

meteorological, hydrologic, and water quality parameters.

This chapter includes a brief summary of the data collected and analyzed for use in the

water budget and water quality models documented in this report. A detailed description

of the data acquisition and processing can be found in Meselhe et al. (2005), which

describes the selection of periods of record, the sources of the data, the compilation

process, and data quality of assurance. Meselhe et al. (2005) also concludes that some

additional data would be useful in improving model performance and credibility, and

recommends needed additional monitoring.

A ten-year simulation period from January 1, 1995, to December 31, 2004 was selected

for this modeling effort. This selection was based on the quality of the data collected

during this period, as well as on analysis of the temporally variable data showing

significant variability in precipitation and stage over this time period. It should be noted

that unless otherwise specified “year” in this report refers to a calendar year.

32

3.2 Precipitation

Rainfall is the predominant type of precipitation in South Florida. Based on data records

of varying lengths from a varying number of historical meteorological monitoring

stations, Abtew et al. (2005) concludes that South Florida is a high-rainfall region, with

an annual average rainfall of approximately 52.8 inches for a period of record from 1900

to 2000. Frontal, convective, and tropical system-driven rainfall events occur within this

region.

Daily rainfall data are available at different locations inside and close to the Refuge.

There are five weather stations inside the Refuge: S-5A, S-6, S-39, WCA1ME, and

LOXWS. One additional SFWMD station is located in the former Everglades Nutrients

Removal Project (ENRP), within what is now termed Storm Water Treatment Area 1

West (STA-1W). STA-1W is located adjacent to the northwestern boundary of the

Refuge (Figure 3.1).

These six rainfall measurement stations are operated by the SFWMD, and data are

available through their Environmental Database website DBHYDRO2. Table 3.1 shows

the availability of the rainfall data for the POR.

2 Available at www.sfwmd.gov/org/ema/dbhydro/

33

Figure 3.1: Rain gage locations in and around the Loxahatchee Refuge.

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Start Date End Date Total ContinuousS-5A 1/1/1995 12/31/2004 0 0S-6 1/1/1995 12/31/2004 0 0S-39 1/1/1995 12/31/2004 32 7STA1W 1/1/1995 9/30/2004 0 0WCA1ME 2/12/1996 12/31/2004 640 359LOXWS 12/31/1995 12/31/2004 216 85Gage 1 1/1/1997 12/31/2004 0 0Gage 2 1/1/1997 12/31/2004 0 0Gage 3 1/1/1997 12/31/2004 0 0Gage 4 1/1/1997 12/31/2004 0 0Gage 5 1/1/1997 12/31/2004 0 0Gage 6 1/1/1997 12/31/2004 0 0Gage 7 1/1/1997 12/31/2004 0 0Gage 8 1/1/1997 12/31/2004 0 0Gage 9 1/1/1997 12/31/2004 0 0Gage 10 4/1/2000 12/31/2004 0 0

Rainfall data are available for this period

Structure was not in operation during this period

Station Available Data

Missing Data Days from Available PeriodAvailable Data

Table 3.1: Available rainfall data in the Loxahatchee Refuge for the POR (1995 to 2004). Adapted from Meselhe et al. (2005).

34

Stations S-5A, S-6, and S-39 have daily average rainfall measurements since 1956, 1960

and 1963, respectively. The weather station WCA1ME has rainfall measurements since

1994, and weather stations LOXWS and ENRP have measurements since 1996.

There are ten additional rain gages located in and near the Village of Wellington adjacent

to the Refuge in the ACME Drainage District’s Northern Basin A and Southern Basin B

(Figure 3.1). Daily rainfall measurements from these gages are available since January

1997. Gage 10 was added to this rain gage network in April 2000, and its daily rainfall

data are available since then. Due to the location of the gages in reference to the Refuge,

only Gages 6 to 10, located in Acme Basin B, were used here for analysis.

The Refuge has two distinct seasons, wet and dry (Figure 3.2). The “wet season” runs

five months from June through October, and the “dry season” runs seven months from

November through May (USFWS, 2000). The “wet season” accounts for 66% of the

annual rainfall (Abtew et al., 2005). Accordingly, Meselhe et al. (2005) found that a

monthly rainfall analysis for the studied POR indicates that June is the wettest month

averaging 7.7 inches, followed by September with 7.5 inches. The driest months for the

POR were found to be January and December with 1.8 inches and 1.9 inches,

respectively.

Annual (calendar year) total rainfall (Figure 3.3) for the POR shows a steady distribution

for the first five years (1994 to 1999) with an annual value of about 58 inches/year. From

2002 to 2004, the annual rainfall dropped below 50 inches, with an average value of

35

about 46 inches/year. A severe drought occurred in 2000 with an annual total equal to

38.9 inches/year. The wettest year during the POR was in 1999 with an annual rainfall

total of 59.1 inches/year.

Figure 3.2: Seasonal variation of average monthly rainfall in the Loxahatchee Refuge for the POR (1995 to 2004). Adapted from Meselhe et al. (2005).

0

10

20

30

40

50

60

70

1994 1996 1998 2000 2002 2004 2006

Ann

ual R

ainf

all (

inch

es)

Figure 3.3: Variation of total annual rainfall in the Loxahatchee Refuge for the POR

(1995 to 2004). Adapted from Meselhe et al. (2005).

0

1

2

3

4

5

6

7

8

9

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mon

thly

Rai

nfal

l (in

ches

)

36

The spatial distribution of annual average rainfall in the Loxahatchee Refuge was

estimated for a period of record between January 1, 1997 and December 31, 2004 (Figure

3.4). This figure is based on the information of 8 active rain gages during the

aforementioned period (S-5A, WCA1ME, LOXWS, S-39, S-6, STA-1W, Gage 8, and

Gage 10). This period was selected because gages 8 and 10 started operating on January

1, 1997. As can be observed in Figure 3.4, the northeastern part of the Refuge received

more rainfall compared to the other areas. Conversely, the west and southwest received

the least amount of rain. The difference between the zones with the highest and the least

amount of rainfall is notable. This difference is about 19 inches of rain per year. It is

important to note that Meselhe et al. (2005) conducted a thorough evaluation of the rain

gages’ data and did not find reasons to avoid the use of any particular gage.

Figure 3.4: Spatial distribution of annual average rainfall in the Loxahatchee Refuge from January 1, 1997 to December 31, 2004. Adapted from Meselhe et al. (2006).

S-39

S-6

WCA1ME LOXWS

S-5A

STA1W (ENRP)

Gage 8

Gage 10

37

3.3 Evapotranspiration

Rainfall and evapotranspiration (ET) are the main drivers in the hydrologic balance of the

Everglades. The balance between rainfall and ET maintain the hydrology system in both

the wet and dry seasons (Abtew et al., 2005). According to Abtew et al. (2005) the

average annual ET for the Loxahatchee Refuge was approximately 51.1 inches for the

years 2003 and 2004.

ET data for the Refuge are available from the ENRP (STA-1W) site, where a lysimeter is

used to measure ET. Pan evaporation and potential ET data are also available from

station S-5A and LOXWS respectively, but were not used in modeling efforts. These

data are available through SFWMD’s Environmental Database, DBHYDRO. The

locations of these ET sites can be seen in Figure 3.1.

The seasonal variation of ET was estimated using site STA-1W for the POR (Figure 3.5).

As can be observed, ET is higher during the months of March to August with values

ranging from 4.5 inches to 6 inches. The average annual ET for the POR from station

STA-1W is approximately 52.1 inches with the range being between 49.3 inches and 56

inches (Figure 3.6).

38

0

1

2

3

4

5

6

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mon

thly

Eva

potr

ansp

irat

ion

(inc

h)

Figure 3.5: Seasonal variation of average monthly ET at STA-1W for the Loxahatchee

Refuge for the POR (1995 to 2004). Adapted from Meselhe et al. (2005).

0

10

20

30

40

50

60

70

1994 1996 1998 2000 2002 2004 2006

Ann

ual E

vapo

tran

spir

atio

n (i

nch)

Figure 3.6: Annual variation in total ET at STA-1W for the Loxahatchee Refuge for the

POR (1995 to 2004).

39

3.4 Flows

There are 19 hydraulic structures located around the perimeter canal, which play an

important roll in water management (Figure 3.7). The inflows and outflows associated

with these structures are important components of the water budget of the Refuge. The

flow data for the Refuge are available through the SFWMD’s Environmental Database,

DBHYDRO.

Figure 3.7: Location of hydraulic structures located in the Loxahatchee Refuge. Adapted

from Meselhe et al. (2005).

Sources of inflow into the Refuge include pump stations, S-6, S-5A, G-310, G-251, S-

362, ACME-1, and ACME-2 (via gate G-94D). Some of these pump stations are pictured

in Figure 3.8. At times, flows can be discharged from S-5A through bypass gates G-300

and G-301 directly into the Refuge. Similarly bypass of the S-6 discharge directly to the

!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

0 2 4 61 Miles ±

L-7 C

anal

Hillsboro

Canal (L-39)

L-40 Canal

S-5AG-300

S-6

G-251 G-310

S-5AS

S-10A S-10C

S-10D

S-39

S-10E

G-94C

G-94D / ACME-2

G-94A

G-94B

G-301

G-338

S-362 ACME-1

40

Refuge is possible through bypass gate G-338, but such bypass has not occurred since the

S-6 diversion. Pump station S-362 began discharging into the Refuge from STA1E in

fall 2004. The S-5A station pumps water from the West Palm Beach Canal, while pump

stations G-251 and G-310 pump water from the STA-1W, and the pump station S-6

pumps water to the Hilsboro (L-39 canal) (Meselhe et al., 2005).

Figure 3.8: Various inflow pump stations located in the Loxahatchee Refuge.

Photographed by J. Arceneaux.

(a) Pump Station S-362 (b) Pump Station G-251

(c) Pump Station G-310 (d) Pump Station S-6

41

Water is released from the Refuge through gated structures S-10E, S-10D, S-10C, S-10A,

S-39, G-94C, G-94A, and G-94B. Some of these structures can be seen in Figure 3.9.

The S-10 series consists of three spillways, S-10A, S-10-C, and S-10D (S-10B was

proposed but was never constructed), which function as flood control gates operated by

the USACE. Also included in the S-10 series is S-10E, which consists of three 6 ft

diameter culverts, and is operated as an outlet from the Refuge. The S-39 gate is

operated to make water supply releases from the Refuge during dry seasons and to also

release excess water to the ocean when water is not needed in WCA 2 and WCA 3

(Meselhe et al., 2005).

Figure 3.9: Various outflow structures located in the Loxahatchee Refuge. Photographed

by J. Arceneaux.

a) Spillway S-10D

b) Spillway S-39 c) Culvert G-94C

42

Some structures are bidirectional (Figure 3.10), with both inflows and outflows

occurring; these structures include S-5AS, G-338, G-301, and G-300 (Meselhe et al.,

2005).

Figure 3.10: Various structures with bidirectional flows located in the Loxahatchee

Refuge. Photographed by J. Arceneaux.

Table 3.2 shows the availability of the data at the various hydraulic structures. Not all 19

structures were in operation during the complete POR. The S-5A pump station

discharged into the Refuge until August 1999, when it was diverted to STA-1W.

Structures S-5AS and S-6 were diverted away from the Refuge in June 1999 and May

2001, respectively. Structures G-301 and G-300 started operating in August 1999.

Structure G-310 started operating in May 1999. During the POR, only one brief inflow

event occurred at the G-94C (Meselhe et al., 2005).

a) Spillway S-5AS b) Spillway G-301

c) Spillway G-300

43

Start End

S-5A Pump Station Inflow 1/1/1995 8/26/1999 1698 391.8 1,319,556 0

S-5AS Spillway Bidirectional 1/1/1995 6/7/1999 1618 112.8 0 362,004

G-300 Spillway Bidirectional 8/26/1999 12/31/2004 1954 2.4 9,302 0

G-301 Spillway Bidirectional 8/26/1999 12/27/2004 1950 28.4 109,845 0

G-310 Pump Station Inflow 7/7/2000 12/31/2004 1638 411.0 1,335,308 0

G-251 Pump Station Inflow 1/1/1995 12/31/2004 3652 118.6 859,095 0

S-6 Pump Station Inflow 1/1/1995 5/15/2001 2326 398.6 1,838,963 0

S-10E Culvert Outflow 1/1/1995 12/31/2004 3652 33.4 0 241,937

G-338 Culvert Inflow 1/1/1995 5/15/2001 2326 0.0 0 0

S-10D Spillway Outflow 1/1/1995 12/31/2004 3652 175.9 0 1,274,156

S-10C Spillway Outflow 1/1/1995 12/31/2004 3652 146.3 0 1,059,744

S-10A Spillway Outflow 1/1/1995 12/31/2004 3652 141.4 0 1,024,250

S-39 Spillway Outflow 1/1/1995 12/31/2004 3652 184.7 0 1,337,900

S-362 Pump Station Inflow 9/21/2004 12/31/2004 101 99.2 19,873 0

ACME # 1 Pump Station Inflow 1/1/1995 12/31/2004 3652 21.4 155,014 0

ACME # 2 Pump Station Inflow 1/1/1995 12/31/2004 3652 19.8 143,424 0

G-94C Culvert Bidirectional 1/1/1995 12/31/2004 3652 38.7* 0 280,329

G-94B Culvert Outflow 1/1/1995 12/31/2004 3652 4.7* 0 34,045

G-94A Culvert Outflow 1/1/1995 12/31/2004 3652 20.3* 0 147,046

5,790,380 5,761,411Total

Total Operative

Days during the POR

Daily Average

Flow (cfs)

Net Inflow Volume (Ac-ft)

Net outflow Volume (Ac-ft)Structure Type of Flow Type of Flow

Operational Dates

Table 3.2: Availability of flow data in the Loxahatchee Refuge for the POR (1995 to

2004). Adapted from Meselhe et al. (2005).

For the 10 year POR from 1995 to 2004, the yearly total inflow to the Refuge was

579,038 acre-ft, and the yearly total outflow was 576,141 acre-ft. Pumping stations G-

310, S-6, and S-5A present the highest mean of daily average inflows, with flows

averaging close to 400 cubic feet per second (cfs). The maximum daily average

discharge was equal to 4,779 cfs through pump station S-5A. Structures S-39 and S-10D

had the highest mean daily average outflow from the Refuge with flows close to 180 cfs.

The maximum daily average outflow from the Refuge, approximately 4,921 cfs, was

from spillway S-10A (Meselhe et al., 2005).

44

3.5 Water Levels

Precipitation, ET, seepage, and surface water management all affect changes in Refuge

water levels. There are five continuous recording stations located in the Refuge interior;

1-7, 1-9, 1-8T, Lox North, and Lox South (Figure 3.11). There is an additional site, 1-

8C, which is located in the perimeter canal (Figure 3.11). These data may be obtained

from SFWMD’s Environmental Database, DBHYDRO. These Refuge water level sites

are currently maintained by the USGS. Sites 1-7, 1-9, and 1-8C have been in operation

since 1954, while site 1-8T did not go into operation until 1979. Lox North and Lox

South were recently installed in 2001 (Meselhe et al., 2005).

Figure 3.11: Water level sites located in the Loxahatchee Refuge. Photograph by J.

Arceneaux.

For the POR, the arithmetic means of daily average water levels for the interior stations

(1-7, 1-8T, and 1-9) range between 16.55 ft and 16.26 ft NGVD 29, and the maximum

_̂_̂_̂

_̂

_̂

_̂

North

1-7

South

1-9

1-8T1-8C

±0 2 4 61 Miles

45

and minimum daily average stages are 18.12 ft and 13.94 ft NGVD 29, respectively. For

gage 1-8C, located in the perimeter canal, the arithmetic mean of daily average water

level is 16.31 ft NGVD 29, and the maximum and minimum daily average stages are

18.19 ft and 12.06 ft NGVD 29, respectively. Lox North has an average stage of 16.73 ft

NGVD 29, which is higher than the other stations. While Lox South has an average stage

of 16.10 ft NGVD 29, which is lower than the other stations (Meselhe et al., 2005).

Other stage data are available at the SFWMD’s Environmental Database, DBHYDRO

website for the inflow and outflow structures. It is important to recognize that these

water level observations are at times impacted by local influence of structure flows (Lin

and Gregg 1988).

3.6 Water Quality

Water quality data for the Loxahatchee Refuge are available from 5 different monitoring

efforts: 1) Everglades Protection Area (EVPA) water quality monitoring sites; 2)

Enhanced water quality monitoring sites; 3) District Transect monitoring sites, also

known as the XYZ sites; 4) water quality monitoring sites located at the hydraulic

structures; and 5) additional independent monitoring sites (Harwell et al., 2005; Meselhe

et al., 2005). Meselhe et al. (2005) did a complete data analyses for all 5 sources, and

based on the period of record from 1995 to 2004, only the data from the EVPA and XYZ

monitoring sites (Figure 3.12), and the hydraulic structures were used for modeling.

Also, the only constituents analyzed for modeling by Meselhe et al. (2005) were chloride

46

and total phosphorus (TP). The data from the EVPA monitoring sites and from the

hydraulic structures are available through SFWMD's environmental database,

DBHYDRO, and the XYZ data are available by request from the SFWMD.

Figure 3.12: XYZ and EVPA water quality monitoring sites located inside the

Loxahatchee Refuge.

3.6.1 EVPA Monitoring Sites

There are fourteen EVPA water quality monitoring sites located in the Refuge interior

that were active during the POR (Figure 3.12). These stations were designed to monitor

the physical, chemical, and biological quality of the Refuge. Most of the constituents are

measured monthly; however, the sampling frequency is irregular (Meselhe et al., 2005).

!

!

! !

!

! !

! !

!

!!

!!

#

#

#

# # #

## ##

#

±0 2 4 61Miles

Legend# XYZSites! EVPA

47

For TP, the sample size for the POR varies between 65 and 122 samples, with the

arithmetic average TP concentrations varying between 7.3 and 11.8 micrograms per liter

(µg/L) (Meselhe et al., 2005).

The chloride data from the EVPA sites were also analyzed. The sample size varied

between 41 and 112 data points per site for the POR, with the arithmetic site means

ranging between 13.5 and 67.6 milligrams per liter (mg/L). The arithmetic mean over all

EVPA sites of chloride concentration during the POR is equal to 31.8 mg/L (Meselhe et

al., 2005).

3.6.2 XYZ Monitoring Sites

There are eleven XYZ water quality monitoring sites located inside the Loxahatchee

Refuge, with two stations located in the rim canal and nine stations located inside the

marsh (Figure 3.12). According to the SFWMD (2000b), these stations were established

along a nutrient gradient in the southwestern corner of the Refuge for biological and

chemical sampling. Data from these stations are available beginning April 1996.

For TP, the sample size from these sites varies between 107 and 142 values per site, for

the POR. The arithmetic means for the POR ranges between 9.0 and 56.5 µg/L. The

highest values are at sites located in the rim canal, with the concentrations declining as

the distance from the rim canal increases.

48

Chloride data from the XYZ sites were also analyzed for the POR (Figure 3.13). It was

found that the sample size varies between 103 and 121 data points per station, with the

arithmetic means ranging between 40.4 and 148.6 mg/L. The arithmetic mean of chloride

during the POR is 92.7 mg/L. Chloride follows a pattern similar to that of TP, with the

concentrations declining as the distance increases from the rim canal. However, the

gradient of TP is steeper with TP concentrations decreasing to a fairly constant value of

about 10 µg/L within the first 1.5 km; whereas chloride concentrations decrease less

rapidly and seem to drop to a relative constant interior value of about 50 mg/L within the

first 3.2 km (Meselhe et al., 2005).

Figure 3.13: Chloride and TP arithmetic means at Refuge XYZ transect stations with

increasing distance from the rim canal. Adapted from Meselhe et al. (2005).

3.6.3 Hydraulic Structures

As mentioned in Section 3.4, there are 19 hydraulic structures located around the

perimeter canal of the Refuge (Figure 3.7). TP data are available from 16 of these sites

49

for the POR; only sites G-338, S-362, and G-94A do not have water quality monitoring

data available. Stations S-5A, G-310, and S-6 have both grab samples and composite

(usually flow proportional) TP samples, the rest of the stations only have grab samples

available. The composite data are for a 7 day period. Data gathered as grab samples had

a range of sample size between 81 and 534 samples per site for the POR, with a mean of

177 samples per station. The TP arithmetic means vary between 35.2 and 127.4 µg/L,

with the arithmetic mean for all the sites equal to 80.9 µg/L. For the TP data which were

gathered using composite samples the range of samples per site was between 160 and 314

for the POR. The TP arithmetic mean varies between 55.2 and 141.5 µg/L.

There are 14 hydraulic stations with data available for the POR. Those that do not have

data include G-300, G-301, G-94A, G-338, and S-362. Chloride data for station G-300

and G-301 were assumed to be equal to the S-5A data due to their close proximity. The

ranges of sample size of chloride for the POR are between 81 and 218 samples per site,

with a mean equal to 129 samples per station. The chloride arithmetic means vary

between 49.7 and 148.7 mg/L, with the arithmetic mean for all the sites equaling 113.2

mg/L.

50

CHAPTER 4: Water Budget Model

4.1 Introduction

It is a top priority for the Loxahatchee Refuge to ensure that appropriate water

management will produce maximum benefits for flood control, water supply, and fish and

wildlife. As mentioned in chapter 1, the main objective of this project is to develop

models that will provide quantitative support for making management decisions.

Therefore this chapter will cover the water budget model development, calibration and

validation, and results.

This water budget model evolved from a previous modeling effort that modeled the water

and constituent masses of the Loxahatchee Refuge; this model was developed by Dr.

William Walker (W.W. Walker, personal communication, 2004). Notable modifications

were introduced in order to fit the management needs of the Refuge. One particular need

of the Refuge is to predict the hydroperiods; therefore the model was derived to predict

temporal variations of water levels in the canal and marsh based on observed inflows,

outflow, precipitation, and evapotranspiration.

The model was implemented using Microsoft Excel with a daily time step. The

calibration period was selected as January 1, 1995, to December 31, 1999, and the

validation period from January 1, 2000, to December 31, 2004.

51

4.2 Modeling Assumptions

Initial model assumptions were made to insure that the model remained simple, but could

still efficiently predict the marsh and canal stages in the Refuge. An initial assumption

was made that the model would be implemented using a double-box (2 compartment)

model with canal and marsh compartments (Figure 4.1).

Figure 4.1: Sketch of Water Budget double-box model.

This setup, like Walker’s, models these two compartments separately, with the only

interaction being an exchange flow between the two compartments. This simple

modeling technique is reminiscent of the classical hydrological methods of level pool

routing (Chow et al., 1988) or cubature (Rantz, 1982).

Other assumptions include: 1) the water surface for both the canal and the marsh are flat;

2) the marsh is characterized by an average soil elevation of 15.16 ft NGVD 29 (4.62 m

Canal

CA = 996 acres

Canal Stage = CE

outQ

inQ

P P

Marsh

MA = 138,325 acres

Marsh Stage = ME

ET ET CG MG

MCQ

52

NGVD 29), which was obtained from the USGS bathymetry data; and 3) the surface area

in the marsh and the canal are constant. Initial water levels were assumed to match the

observed water levels for the first day of simulation. Therefore the observed water level

in the canal (Gage 1-8C) was 17.19 ft on January 1, 1995. The initial water level in the

marsh on January 1, 1995 was 17.15 ft, which is the average water level of gages 1-9 and

1-7 on this day.

4.3 Model Predictions

It is important for Refuge management to be able to determine and predict the

hydroperiods in the Refuge; therefore, for management purposes it was determined that

the best parameter for the water budget model to predict would be the stages in the marsh

and in the canal. The following equations were used to determine the canal ( CE ) and

marsh ( ME ) stages:

Canal Stage, CE : C

outMCinC

CA

QQQGETP

dtdE )( −−

+−−= (4.1)

and

Marsh Stage, ME : M

MCM

MA

QGETP

dtdE

+−−= (4.2)

53

where CE is the average stage in the perimeter canal in feet, ME is the average stage in

the marsh; CA and MA are the perimeter canal and marsh, respectively; P is the

precipitation; ET is the evapotranspiration; CG and MG are seepage in the canal and

marsh respectively; inQ is the external inflow to the perimeter canal, outQ is the outflow

from the perimeter canal; and MCQ is the flow from the perimeter canal to the marsh,

and vice versa.

The differential equations for canal and marsh stages are simulated using the Euler

numerical integration method with a one day time step. This method provides a fast

solution and is easily implemented using the available daily average time series data.

However one problem with this technique is that when net canal flow is large, stage

change over one day is so large that the assumption of “small” change in the integration

algorithm is not satisfied. This problem can result in failure of convergence and

instability. Here, a heuristic approach is used to stabilize the solution that is otherwise

unstable at times. This heuristic approach limits the magnitude of the canal stage, and

maintains conservation of water volume by shifting flow directly to the marsh. Such an

approach is reasonable because under these conditions flow between the marsh and canal

is likely being underestimated by the Euler Method with a daily time step. Denoting the

revised stage derivative with an asterisk, this heuristic scheme is as follows:

'max

*

CCCC E

dtdE

whendt

dEdt

dE≤= (4.3)

54

and

'max

'max

*

CC

CC

C

C Edt

dEwhenE

dtdEdt

dE

dtdE

>

= (4.4)

where max'CE is equal to 0.10 m/day. The additional flow into the marsh, QMC*, is

calculated using the following equations:

CCC A

dtdE

dtdE

QMC

−=

** (4.5)

and

M

MCMA

Qdt

dEdt

dEM

**+= (4.6)

4.4 Observed Parameters

4.4.1 Precipitation

Observed precipitation (P) data were obtained from the nine gages S-5A, S-6, S-39, STA-

1W, WCA1ME, LOXWS, Gage 6, Gage 8, and Gage 10 (Figure 4.2). When analyzing

55

the data, it was found that there were a few days during the POR where data was missing;

therefore, it was decided that using multiple Thiessen Polygons would provide the most

accurate spatial distribution of rainfall over the entire Refuge. In the “Thiessen Polygon

Method,” a weight is assigned to each station in proportion to its representative area

defined by a polygon (Gupta, 1989); the areas of the polygons were determined using

ArcGIS 9. For each day on which data are missing for one or more stations, the areas of

the polygons were altered so that the stations with the missing data were not included.

There were a total of 16 different scenarios; an example of one of these scenarios can be

seen in Figure 4.2. It was found that the average annual rainfall for the POR was

approximately 52.1 inches, with the maximum daily and monthly values for the POR are

about 6.5 inches and 16.6 inches, respectively.

Figure 4.2: An example of one of the sixteen “Theissen Polygon Method” area

distributions used for calculating average daily rainfall in the Loxahatchee Refuge for the POR (1995 to 2004).

56

4.4.2 Evapotranspiration

Evapotranspiration (ET) data was obtained from station STA-1W (ENRP) (Figure 4.2).

It has been observed that sites that go dry for even a few weeks out of the year have

considerably lower annual ET water losses (German, 1999). Therefore, when the marsh

stage approaches the average sediment elevation of 15.16 ft NGVD 29 (4.62 m NGVD

29), the measured potential ET is reduced below the observed value. The observed data

were modified using the following equation:

obsET ETfET *= (4.7)

where

=

ETETET H

HMinimumfMaximumf ,1,min ; minETf is the minimum reduction of

ET because of shallow depth = 20%; H is the marsh water depth in feet so that

),0( 0EEMaximumH M −= ; 0E is the marsh ground elevation = 15.16 ft 29 (4.62 m

NGVD 29), the average elevation of the Refuge interior (Desmond 2003; Meselhe et al.

2005); and ETH is the depth below which ET is reduced = 0.82 ft (0.25 m). Using a

linear reduction in ET over a small depth range as depth approaches zero is expected to

achieve more stable results than simple switching at zero depth. Some other models,

including SWAT (Arnold et al., 1998) and MODHMS3 use a similar approach. This

approach reduced the average annual ET from 52.1 inches/yr to 46.3 inches/yr for the

POR.

3 http://modhms.com

57

4.4.3 Inflows and Outflows

Inflow into the perimeter canal through hydraulic structures S-5A, S-5AS, G-300, G-301,

G-310, G-251, G-94C, ACME-1, and ACME-2 (G-94D) were used to create a daily time

series for the POR. It was found that the inflow from hydraulic structures accounted for

approximately 49.8% of the total inflows into the Refuge, with an annual average of

approximately 51.74 inches/yr (830 ft3/sec or 536 mgd).

Outflows from the rim canal through hydraulic structures S-5AS, G-300, G-301, S-10E,

S-10D, S-10C, S-10A, S-39, G-94C, G-94B, and G-94A were used to create a daily time

series for the POR. The average annual outflow from the Refuge through the hydraulic

structures was found to be 49.4 inches/yr (793 ft3/sec or 512 mgd).

The water budget model was set up to calculate outflows using the Refuge water

regulation schedule as an alternative to using historic values. Stage in the Refuge is

controlled through guidance from the current regulation schedule adopted in 1995. The

regulation schedule is discussed in detail in Chapter 1 and is summarized by a chart that

displays stage-dependent zones, termed Zone A1, A2, B, and C, whose boundaries

change throughout the year (Fig. 1.5). In the upper Zone, A1, the S-10 gates may

discharge at maximum capacity, and the S-39 may discharge at a rate agreed upon

between the Corps and SFWMD. Releases of water out of the Refuge in Zone A1

generally aim at returning the stage at least to the floor of the A1 Zone. In Zone A2,

releases are more constrained than in Zone A1, with consideration given to forecasts and

58

stage outside the Refuge boundary. In Zone B, water managers are constrained when

providing water supply releases from the Refuge but are given flexibility to otherwise

release water as needed for environmental purposes related to the Refuge and

downstream ecosystems. In Zone C, the lowest zone, no net water release from the

Refuge is allowed (USFWS, 2000).

For calibration of the water budget model, the historic outflow releases were used.

However, for modeling alternative scenarios of water management the release of water

must also be modeled as a function of modeled Refuge stage. Decisions on water

releases from the Refuge depend not only on information that is unavailable within the

Refuge model (stages downstream and in Lake Okeechobee, weather forecasts, and water

supply needs), but also depend on professional judgment of water managers. Thus, any

model of operations under the regulation schedule is challenging and will not precisely

reproduce historic values.

Here, water release, outQ , is optionally modeled as a function of position within the

regulation schedule zones (Figure 1.5). Regulation schedule zone is determined from

canal stage. In Zone C, below 14 ft canal stage, no discharge is assumed. In Zone B,

discharge is based on the fraction of Zone B at which the stage is located. Below a

threshold position in Zone B, )75.0(BOP , release is zero. Above this threshold,

discharge increases linearly with position, BP , to a value representative of the ceiling of

the B or A2 Zones and the floor of the A1 Zone, FloorAQ 1 (1.5 million m3/day). In Zone

A1, discharge increases linearly with stage position, 1AP , from the floor of the A1 Zone

59

to a discharge, 18Q (21.5 million m3/day), at a stage of 18 ft (5.4864 m). The equations

used to calculate the outflows based on the Refuge water regulation schedule are found

below in Equation 4.8.

( )

−−−+

−−=

1))4864.5/()((

2)1/()(,0

0

111181

001

AZoneEEEQQQ

AorBZonePPPQMax

CZone

Q

FloorAFloorATFloorAFloorA

BBBFloorARO

(4.8)

4.5 Estimated Parameters

4.5.1 Exchange Flow

The bidirectional flow between the marsh and canal is assumed to be controlled by the

stage difference between the two compartments. This was calculated using the “Power

Law Model” by Kadlec and Knight (1996). This equation is similar to a weir equation:

)(3MCMC EECHQ −= (4.9)

where 11877

1073.510210 −−=== dftxB

RWB

C π ; B is the calibrated transport

coefficient = 9.14 ft-1d-1; W is the average marsh width = 2.67 x 105 ft; R is the average

radius of the marsh (obtained assuming an approximated circular geometry) = 4.27 x 104

60

ft; ),0( 0EEMaximumH M −= ; ME and CE are the canal and marsh stages, and 0E is

the average marsh ground elevation of 15.16 ft NGVD 29 (4.62 m NGVD 29). Although

this simple equation is derived for a simpler geometry it appears to adequately describe

the bidirectional flows between the marsh and canal.

According to Kadlec and Knight (1996), this equation is applicable for wetlands due to

the fact the Manning’s “constant” is not constant for a wetland environment, and using a

model such as the “Power Law Model” would also describe the depth variability.

4.5.2 Groundwater Recharge

The rate of groundwater recharge in the canal or marsh is calculated from the head

difference between the Refuge and the boundary area (Lin and Gregg 1988). Therefore,

the seepage rates were determined using the following equation:

)( Biseepi EErG −= (4.10)

where MorCi = for canal or marsh, respectively; rseep is the seepage rate constant =

0.042 and 0.0001315 d-1 in the canal and marsh, respectively; and EB is the boundary

water surface elevation = 11.48 ft NGVD 29 (3.5 m NGVD 29). The seepage rate

constant for the both the canal and the marsh were calibration parameters.

61

It was found that groundwater seepage was of importance to the balance of the Refuge

hydrologic system, especially in the canal. Originally in order to maintain simplicity in

the model, only one seepage rate was calculated for the entire Refuge, and the stage

model calibrated adequately under this simple assumption. Later, it was found that the

seepage in the canal was needed to be much larger than that in the marsh in order to

explain the annual chloride budget. The significance of canal versus marsh seepage was

also adjusted during water quality model calibration.

4.6 Calibration

The model was calibrated using the data for the 5-year period January 1, 1995, to

December 31, 1999. Calibration compared the modeled canal and marsh stages to

observed stages. Parameters wee adjusted to obtain the best reproduction of the observed

data and statistics.

The marsh stage was compared to the average water levels recorded from gages 1-7 and

1-9 located inside the marsh (Figure 3.11). These gages have a long historical record and

continuous data over the entire POR therefore they were chosen for comparison against

the modeled data. For the marsh area the observed arithmetic mean of daily average

water level is 16.45 ft NGVD 29, and the maximum and minimum daily average stages

are 18.01 ft and 14.94 ft NGVD 29, respectively.

62

Stage gage 1-8C located inside the canal was used for calibration of the modeled canal

stages (Figure 3.11). For gage 1-8C the observed arithmetic mean of daily average water

level is 16.33 ft NGVD 29, and the maximum and minimum daily average stages are

18.19 ft and 12.06 ft NGVD 29, respectively. A constraint was set on the canal so that

when modeled or observed stage fell below 14 ft, a value of 14 ft was used. This was

done because the model is not expected to perform well below14 ft, and the canal stages

of interest are at and above 14 ft. This restraint value was set based on the proposed

water regulation schedule for the Refuge, which shows the water level in the Refuge

should not be allowed to drop below 14 ft NGVD 29. This constraint preserved model

simplicity, while permitting model calibration within the stage range of greatest interest.

4.6.1 Calibration Parameters

To calibrate the model certain parameters were adjusted to obtain the best fit and also the

best statistics. These parameters include: 1) the transport coefficient (B) in the “Power

Law Model,” which was used in calculating the exchange flow; 2) canal and marsh

seepage rate constants; and 3) the ET reduction factor.

The major calibration parameter was the transport coefficient (B) used in the “Power Law

Model” to calculate the exchange flow between the marsh and canal. It was found that

the value equal to 9.14 ft-1d-1 produced the best agreement between observed and

predicted values.

63

The seepage rate constants were initially calibrated for the canal and marsh as 0.06 per

day and 0.000004 per day, respectively. After the completion of the chloride water

quality model it was found that the constituent model was more sensitive to seepage, than

the water budget. Therefore, the original calibrated seepage rate constant in the canal

was decreased by 30%, to equal 0.042 per day, and the marsh seepage rate constant was

proportionally increased to 0.0001315 per day. These values are similar to those found in

literature (Linn and Gregg, 1988).

The ET reduction factor was also calibrated to be 20%. The range in which this value

was calibrated was based on personal communication with a Refuge employee.

4.6.2 Calibration Results

Figures 4.3 and 4.4 show the graphical comparison between the modeled and the

observed canal and marsh stages, respectively, for the 5-year calibration period, January

1, 1995, to December 31, 1999.

64

12

13

14

15

16

17

18

19

20

Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00

Sta

ge (

ft)

Modeled

Observed

Figure 4.3: Canal stages in the Loxahatchee Refuge for the calibration period January 1,

1995, to December 31, 1999 using the water budget model.

12

13

14

15

16

17

18

19

20


Sta

ge (

ft)

ModeledObserved

Figure 4.4: Marsh stages in the Loxahatchee Refuge for the calibration period January 1,


65

As seen in Figures 4.3 and 4.4, the observed and modeled values are in good agreement

for the calibration period. However, it can also be seen that the marsh results appear to

be in better agreement than in the canal. The model was unable to capture some of the

low stages seen in the observed data for the canal. One possible reason for this is that

there may have been observed outflows that occurred during this period that were not

recorded.

4.6.3 Calibration Performance Measures

Statistics are used to evaluate the performance measures that the model is capable of

producing. The statistics which were used to evaluate the calibration and validation

period of this model and other models presented in this report include: 1) bias; 2) root

mean square error (RMSE); 3) standard deviations of the modeled data, observed data,

and error between the modeled and observed data; 4) correlation coefficient (R); 5)

coefficient of determination, R2; 6) variance reduction; and 7) Nash Sutcliffe Efficiency

(Nash Sutcliffe, 1970). These statistics were also used in evaluating the ELM v.2.5 (Fitz

et al., 2002a) and SFWMM models (SFMWD, 2003).

1. Bias is the difference between the mean of the model prediction and the mean

observed values. Bias is calculated using Equation 4.11 (Montgomery et al., 2001).

OMBias −= (4.11)

66

where O is the mean of the observed stages over the entire period of study and M is the

mean of the modeled stages over the entire period of study.

2. The standard deviation (σ ) of the modeled and observed data, as well as the error,

also termed the residual, was determined using Equation 4.12 (Montgomery et al., 2001)

∑=

−=N

ii xx

N 1

2)(1

σ (4.12)

where, x represents either the observed, the modeled, or error between the observed and

modeled (error=observed-modeled); x is the mean of the modeled, the observed, or error

for the entire period; and N represents the number of values. Standard deviation carries

the dimensions of the value being analyzed, in this study the standard deviation of

observed, modeled, and the error of the observed and modeled stages is being analyzed,

therefore it takes the dimensions of feet.

3. RMSE is a weighted average of the absolute value of the model error; it was

calculated using Equation 4.13 (Legates and McCabe, 1999)

( )

N

MORMSE

N

iii

2

1

−

=∑

= (4.13)

67

where Oi represents the observed stage, Mi represents the modeled stages; N is the total

number of values. The RMSE value carries the dimension of the parameters being

analyzed, in this study they represent the stage therefore RMSE is in ft.

4. Variance reduction is one minus the ratio of the variance of the model residual to the

variance of the observed data.

2

1

−=

O

EReductionVarianceσσ

(4.14)

where the Eσ is the standard deviation of the error between the modeled and observed;

and Oσ is the standard deviation of the observed data. Variance reduction is typically

represented as a percent. Variance reduction is unaffected by bias, and quantitatively

measures how well the model follows variations in observed data.

5. The correlation coefficient (R) measures the linear association between the modeled

and observed data. R was calculated using Equation 4.15 and is dimensionless (Legates

and McCabe, 1999)

( )( )

( ) ( )

−

−

−−

=

∑∑

∑

==

=5.0

1

25.0

1

2

1

N

ii

N

ii

N

iii

MMOO

MMOO

R . (4.15)

68

6. Equation 4.16 was used to calculate the coefficient of determination (R2) which

represents the square of the correlation coefficient (Legates and McCabe, 1999)

( )( )

( ) ( )

2

5.0

1

25.0

1

2

12

−

−

−−=

∑∑

∑

==

=

N

ii

N

ii

N

iii

MMOO

MMOOR (4.16)

where the parameters are the same as those in Equation 4.15. R2 is dimensionless.

7. The Nash Sutcliffe Efficiency was calculated using Equation 4.17 (Legates and

McCabe, 1999)

∑

∑

=

=

−

−

−= N

ii

N

iii

OO

MO

EfficiencySutcliffeNash

1

2

1

2

)(

)(

0.1 . (4.17)

This value is also dimensionless. Efficiency reflects both model bias and reduction of

variance. It therefore has the value of combining these independent criteria into a single

goodness-of-fit measure. Efficiency has a maximum value of one, corresponding to a

perfect fit. A value of zero indicates that the model predicts no better than simply using

the average observed value. Negative efficiency values are often considered to indicate

that a model is not useful as a predictive tool. Nash Sutcliffe Efficiency can be

problematic when applied to observations with limited variation about their mean value.

69

Statistical Parameter Canal Calibration Statistics

Marsh Calibration Statistics

Bias (ft NGVD 29) 0.134 0.026 RMSE (ft NGVD 29) 0.458 0.251 Standard Deviation of Observed (ft NGVD 29) 0.718 0.466 Standard Deviation of Modeled (ft NGVD 29) 0.582 0.536 Standard Deviation of Error (ft NGVD 29) 0.438 0.250 Variance Reduction 62.9 % 71.2 % R (Correlation Coefficient) 0.793 0.885 R2 Value 0.629 0.783 Nash Sutcliffe Efficiency 0.594 0.709

Table 4.1: Marsh and canal statistics in the Loxahatchee Refuge for the calibration period January 1, 1995, to December 31, 1999.

The statistics in Table 4.1 show that the observed and predicted stages for the marsh are

in better agreement than the observed and predicted values for the canal. Based on the

bias results, the model slightly overestimated the observed data in both the canal and

marsh.

4.7 Validation

The model was validated for the 5-year period January 1, 2000, to December 31, 2004,

using the same calibrated parameters and model setup. The same observation gages were

also used to validate the modeled canal and marsh stages.

4.7.1 Validation Results

Figures 4.5 and 4.6 show the validation results for the canal and marsh, respectively.

Unlike in the calibration period results, it appears from Figures 4.5 and 4.6 that both the

70

canal and marsh stages are in equally good agreement. The restraint on the observed and

predicted canal stage of 14 ft can also be observed in the validation period.

12

13

14

15

16

17

18

19

20


Sta

ge (

ft)

ModeledObserved

Figure 4.5: Canal stages in the Loxahatchee Refuge for the validation period January 1,


12

13

14

15

16

17

18

19

20


Sta

ge (f

t)

ModeledObserved

Figure 4.6: Marsh stages in the Loxahatchee Refuge for the validation period January 1,


71

4.7.2 Validation Performance Measures

The validation period from January 1, 2000, to December 31, was also evaluated using

the performance measures discussed in Section 4.6.3. These results can be found in

Table 4.2 below.

Statistical Parameter Canal

Validation Statistics

Marsh Validation Statistics

Bias (ft NGVD 29) -0.165 -0.164 RMSE (ft NGVD29) 0.504 0.270 Standard Deviation of Observed (ft NGVD 29) 0.926 0.490 Standard Deviation of Modeled (ft NGVD 29) 0.836 0.521 Standard Deviation of Error (ft NGVD 29) 0.476 0.215 Variance Reduction 73.5 % 80.7 % R (Correlation Coefficient) 0.859 0.911 R2 Value 0.737 0.830 Nash Sutcliffe Efficiency 0.704 0.695

Table 4.2: Marsh and canal statistics in the Loxahatchee Refuge for the validation period January 1, 2000, to December 31, 2004

As can be seen in Table 4.2, it appears that both the marsh and canal showed good

agreement with the observed data. Based on the bias results the model slightly

underestimates the observed data in both the canal and the marsh, which is the opposite

of the results from the calibration period. Contrary to the calibration results the model

appears to have captured the low stages in the canal for this period, confirming the

assumption that there were possible observed outflow events that were not recorded.

72

4.8 Results for Period of Record

Performance measures were calculated for the 10-year POR from January 1, 1995, to

December 31, 2004 for both the canal and marsh areas (Table 4.3).


Statistics Marsh

Statistics Bias (ft NGVD 29) -0.015 -0.069 RMSE (ft NGVD 29) 0.481 0.261 Standard Deviation of Observed (ft NGVD 29) 0.836 0.487 Standard Deviation of Modeled (ft NGVD 29) 0.767 0.562 Standard Deviation of Error (ft NGVD 29) 0.481 0.252 Variance Reduction 66.9 % 73.3 % R (Correlation Coefficient) 0.823 0.895 R2 Value 0.678 0.800 Nash Sutcliffe Efficiency 0.669 0.713

Table 4.3: Marsh and canal statistics for complete POR

For the POR it can be seen from the statistics that the marsh performed slightly better

than the canal. Possible reasons for this variation include: 1) the area of the rim canal

was assumed constant; 2) the variability of the water levels is stronger in the canal than in

the marsh; 3) the emphasis during the calibration was to match the observed marsh stages

with the model predictions; and 4) water supply delivery flows through hydraulic

structures G-94A, G-94B, and G-94C, prior to 2000, were unavailable and set to zero.

4.9 Regulation Schedule Analysis

The major function of this water budget model is to allow Refuge management to

evaluate various scenarios. Therefore, the model was set up with the option of allowing

73

the model to calculate the estimated structure outflow based using the Refuge’s water

regulation schedule; this was discussed in detail in section 4.4.3.

Canal and marsh stages were calculated using the regulation schedule to predict outflows.

Water supply deliveries through the G-94 and S-39 structures were ignored in this

simulation. These results were compared to the observed stages; the results are shown in

Figure 4.7 and 4.8, for the canal and marsh, respectively. The simulation was run for the

entire POR from January 1, 1995, to December 31, 2004. The performance measures are

listed in Table 4.4.

It can be seen from the results that by using the regulation schedule to predict outflows,

rather than the historic outflows, the results are in better agreement between the observed

and modeled values in both the marsh and the canal. However, it can be seen in Figure

4.7 that the modeled canal stages did not drop like the observed data did, whereas when

using the historic data, the modeled stages follow the pattern.

74

12

13

14

15

16

17

18

19

20

Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04

Sta

ge (

ft)Modeled

Observed

Figure 4.7: Canal stage results using the regulation schedule to predict outflow for the

period January 1, 1995, to December 31, 2004 in the Loxahatchee Refuge

12

13

14

15

16

17

18

19

20

Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04

Sta

ge (

ft)

ModeledObserved

Figure 4.8: Canal stage results using the regulation schedule to predict outflow for the

period January 1, 1995, to December 31, 2004 in the Loxahatchee Refuge.

75

Statistical Parameter Canal Statistics

Marsh Statistics

Bias (ft NGVD 29) 0.005 -0.080 RMSE (ft NGVD 29) 0.458 0.253 Standard Deviation of Observed (ft NGVD 29) 0.836 0.487 Standard Deviation of Modeled (ft NGVD 29) 0.651 0.585 Standard Deviation of Error (ft NGVD 29) 0.458 0.241 Variance Reduction 70.0 % 75.6 % R (Correlation Coefficient) 0.839 0.915 R2 Value 0.704 0.838 Nash Sutcliffe Efficiency 0.700 0.729

Table 4.4: Marsh and canal statistics for complete POR (1995 to 2004) using the regulation schedule to predict outflows in the Loxahatchee Refuge.

4.10 Discussion of Results

The Water Budget Model proved to be credible when analyzed graphically and

statistically. The Water Budget Model while remaining simple still computes reasonable

canal and marsh stages in the Refuge. Statistically, the modeled marsh stages were in

better agreement with observed data than the canal. However, the canal modeling effort

also performed well with a Nash Sutcliffe Efficiency of 0.669.

The marsh statistics can be compared to those calculated by Fitz et al. (2002a) using the

ELM model (discussed in section 2.4.3). Fitz et al. (2002a) calculated statistics based on

the results from the ELM model compared to the observation data at stage gage locations

1-9, 1-8T, and 1-7. Statistics calculated for the SFWWM model (section 2.2.2) were also

compared to water budget modeled marsh stages. It should be noted that these models

were run for a different POR than the water budget model; however this comparison

76

illustrates a general performance of the results of similar models. The comparison of the

water budget model compared to these models can be seen in Tables 4.5 and 4.6.

Water

Budget Model Marsh

ELM v.2.1

Model WL

Gage 1-7

ELM v.2.1

Model WL

Gage1-9

ELM v.2.1

Model WL

Gage 1-8T Bias, m -0.021 0.06 0.00 0.04 R2 0.800 0.73 0.72 0.67 RMSE, m 0.079 0.16 0.15 0.23 Nash Sutcliffe Efficiency

0.713 0.33 0.50 0.06

Table 4.5: Comparison of the marsh modeled water budget statistics to the ELM v.2.1. model.

Water Budget Model Marsh

SFWMM Model WL Gage 1-7

SFWMM Model WL Gage 1-9

SFWMM Model WL Gage 1-8T

Bias, m -0.021 0.00 0.08 0.11 R2 0.800 0.71 0.72 0.73 RMSE, m 0.079 0.15 0.17 0.19 Nash Sutcliffe Efficiency

0.713 0.44 0.35 0.35

Table 4.6: Comparison of the marsh modeled water budget statistics to the SFWMM model.

4.11 Case Study of Model Application

The water budget model was used to predict stage, and compare Refuge alternatives in

the “Everglades Agricultural Area Regional Feasibility Study” (EAARFS), which was

initiated by the SFWMD to consider how flows and loads to the Everglades STAs and

planned reservoirs might be rerouted to improve treatment performance for removal of

total phosphorus (A.D.A. Engineering and SFWMD, 2005). Input flow files were

77

provided by Dr. William W. Walker. The purpose of the analysis presented here is to

illustrate the use of the model, and not to provide a definitive analysis of the project

alternatives.

The EAARFS considered two major alternatives termed Alternative 1 and Alternative 2,

relative to a no project alternative that is termed here Alternative 0. Both Alternatives 1

and 2 reduce the annual volume of inflow to the Refuge relative to Alternative 0.

EAARFS modeling used MIKE 11 to model the conveyance canals and Dynamic Model

for Stomwater Treatment Areas Version 2 (DMSTA 2) to model the reservoir and STA

performance. None of the EAARFS modeling explicitly addressed the effects of

alternative inflow volume changes on the “downstream” Everglades marshes that receive

the STA discharges, such as the Loxahatchee Refuge (A.D.A. Engineering and SFWMD,

2005). Therefore the simple water budget model discussed in this Chapter was used to

determine the effects on the hydroperiods of the Loxahatchee Refuge over the 36 year

period from May 1, 1965, to April 30, 2000. The period was analyzed using what is

termed as the South Florida Water Management Year, which is from to May 1 to April

30. This period was used to evaluate annual seasonal changes due to the variance in wet

and dry seasons.

Alternative 1 diverts inflow from the STA-1W/STA-1E complex primarily by the

construction of a pump station and some canal improvements. This alternative diverts a

portion of the water now entering STA-1W to other STAs south of the Refuge.

78

Alternative 2 diverts all Refuge inflow from STA-1W by routing the outflow south to

other treatment facilities. Thus, both alternatives reduce the volume of inflow to the

Refuge, with Alternative 2 having the greater reduction in flow (A.D.A. Engineering and

SFWMD, 2005). This reduction in inflow can be seen in Figure 4.9.

By simulating the future effects of the EAARFS alternatives on the Refuge, we have

assumed that no water supply deliveries will be provided from the Refuge over the

simulation period. Outflows from the Refuge are therefore determined in our simulations

based solely on the current Refuge regulation schedule (A.D.A. Engineering, and

SFWMD, 2005).

Using the water budget model the stages were calculated for both the marsh and canal

areas using the three different alternatives. Figures 4.10 and 4.11 shows a comparison of

the resulting marsh stages for Alternatives 1 and 2 compared to Alternative 0; figures

4.12 and 4.13 show a similar comparison for the canal stages.

79

Figure 4.9: A comparison of the reduction of inflow from STA1-W to the Refuge based

on Alternative 1 and Alternative 2 in respect to Alternative 0.

Figure 4.10: Comparison of marsh stages using the water budget model to compare the

Alternatives 1 and 2 against Alternative 0.

80

Figure 4.11: Time series of estimated marsh stages for the three alternatives.

Figure 4.12: Comparison of Canal stages using the water budget model to compare the

Alternatives 1 and 2 against Alternative 0

81

Figure 4.13: Time series of estimated canal stages for the three alternatives.

Using the estimated stages in the marsh, the hydroperiods were estimated to determine

the number of consecutive days when the Refuge water depth was greater than 0.8 ft.

The purpose of determining inundation periods is to provide ecologists and Refuge

management a basic understanding of the changes in water level and the affects they have

on the wildlife and plants in the Refuge. The average elevation of the Refuge is 15.158 ft

(4.62 m) NGVD 29, but the elevation used to calculate inundation periods was 16.0 ft

(4.88 m) NGVD 29 due to the fact that when the average Refuge elevation is used the

Refuge remains inundated throughout the year.

The inundation periods were analyzed based on the Florida water year from May 1 to

April 30 of each year. The total, average, and longest inundation periods were analyzed.

The total annual inundation periods refers to the total number of days that the depth of

82

water in the marsh was greater than 0.8 ft (Figure 4.14). The average annual inundation

period is the average number of consecutive days that the depth of water in the marsh was

greater than 0.8 ft (Figure 4.15). The longest annual inundation period allows the Refuge

management to know the longest number of consecutive days were the marsh water depth

is greater than 0.8 ft (Figure 4.16).

Figure 4.14: The total number of days when the water depth in the Refuge is greater than

0.8 ft, based on the stage results from the three alternatives.

83

Figure 4.15: The average number of consecutive days when the water depth in the Refuge is greater than 0.8 ft, based on the stage results from the three alternatives.

Figure 4.16: The longest number of consecutive days when the water depth in the Refuge

is greater than 0.8 ft, based on the stage results from the three alternatives.

84

CHAPTER 5: Water Quality Constituents, Model Selection, and Modeling Approach

5.1 Introduction

Along with the changes in water quantity and timing, changes in water quality are

introducing negative impacts to the Everglades ecosystem (USFWS, 2000). The

Everglades ecosystem is characteristically low in nutrients and is comprised of species

that have evolved under these conditions (Childers et al., 2003; USFWS, 2000). Nutrient

loading from urban areas and the EAA has significantly increased nutrient

concentrations, particularly phosphorus, in the WCAs (USFWS, 2000).

Wetlands respond to nutrient enrichment with characteristic increases in soil nutrients

and shifts in plant community compositions (Childers et al., 2003). Among the negative

effects from increased nutrients in the Everglades are: loss of native sawgrass

communities, conversion of wet prairie plant communities to cattails, invasion of exotic

plants, and loss of important habitats for wading birds (USFWS, 2000). Major efforts are

being made to reduce the nutrient load entering the Everglades ecosystem, for example

the construction of the STAs.

Development of a simple water quality model allows for Everglades’ scientists and

managers the opportunity to evaluate the effects of various scenarios and their impacts on

the water quality within the Refuge. These individuals can then identify areas of concern

and, if necessary, apply a more complex water quality model to gain a more detailed

85

understanding of their impacts. This chapter will present constituents that will be

modeled and the corresponding inflow and outflow loads, the model selection process,

and the water quality modeling approach.

5.2 Constituents to be Modeled

5.2.1 Chloride

Before the modeling of chloride began a simple mass balance estimating how much

chloride was coming into and leaving the Refuge through hydraulic structures was

completed. This allowed for a general estimate of how much chloride was apparently

being retained in the Refuge. The amount of chloride load retained in the Refuge refers

to the amount of chloride that remains in the Refuge, as well as the chloride that may

have left through other means of outflow, such as groundwater seepage or transpiration.

This simple mass balance was completed for the ten year period January 1, 1995, to

December 31, 2004.

Chloride data were downloaded from the SFWMD DBHYDRO database from 14 of the

hydraulic structures located around the perimeter of the canal. Chloride data were

available at the following structures S-5A, S-5AS, G-310, G-251, S-6, S-10E, S-10D, S-

10C, S-10A, S-39, ACME-1, ACME-2, G-94C, and G-94B (Figure 3.7). Chloride

samples were taken from these locations on a somewhat irregular basis. These data were

then filtered and analyzed removing any extreme (outlying) values. When there were

86

dates with more than one recorded concentration, the average of the two was used. A

table listing all outlier values that were removed, as well as any dates were multiple

concentrations were recorded can be found in Appendix A.

Linear interpolation between known concentrations was used to create a complete daily

time series at each structure. The hydraulic structures that had no data (G-300, G-301,

and G-94A), or a limited number of recorded concentrations (G-94C), used the data from

nearby stations. For example, G-300 and G-301 used the data recorded from S-5A; and

structures G-94A and G-94C used the data recorded from structure G-94B.

Once the chloride concentration time-series had been constructed for all stations, the

chloride load at each hydraulic station was able to be calculated using Equation 5.1:

LoadCQ =* (5.1)

where Q is in m3/day and C is in kg/m3; resulting in a Load in kg/day. The load time

series at each station were summed and separated into inflow (positive flows) and

outflow (negative flows) load time series. The total annual chloride retained inside the

Refuge could be calculated, (see Table 5.1) from the difference between inflow and

outflow loads divided by inflow load.

As can been seen in Table 5.1, the annual amount of chloride retained varies from 7.39 %

in 1995, to 49.13 % in 2000, with the 10-year average being around 26.66 % and the total

87

chloride retained over the 10-year period being approximately 25 %. This can also be

seen in the bar graphs shown in Figure 5.1.

Year LoadIN

kg/year LoadOUT kg/year

Difference kg/year

Percent Retained %

1995 147,853,910 136,925,206 10,928,704 7.39 1996 107,069,584 87,359,234 19,710,350 18.41 1997 119,601,977 70,856,138 48,745,349 40.76 1998 111,078,190 80,534,338 30,543,852 27.50 1999 109,418,942 94,312,865 15,106,077 13.81 2000 75,346,798 38,331,608 37,015,190 49.13 2001 46,268,615 32,423,886 13,844,729 29.92 2002 85,733,766 61,936,071 23,797,695 27.76 2003 72,656,556 56,942,900 15,713,656 21.63 2004 66,385,821 46,278,615 20,107,206 30.29

Table 5.1: Total annual chloride loads going in and out of the Refuge through hydraulic structures and the total percent of chloride retained in the Refuge.

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Tot

al A

nnua

l Chl

orid

e Lo

ad, k

g/ye

ar

Inflow Chloride Load

Chloride Outflow Load

Figure 5.1: Total annual chloride loads going in and out of the Refuge

through hydraulic structures.

88

It can be assumed that the majority of the percent chloride retained is associated with

groundwater seepage. Since we do not have any direct groundwater seepage data or any

knowledge of the percent of evapotranspiration that is transpiration, we can only assume

that when the net flow (Inflow – Outflow) volume is high then more water is consumed

through groundwater seepage or transpiration. Figure 5.2 shows the correlation between

net flow and percent chloride retained, which has a R squared value of 0.7327.

2004

2003

20022001

2000

1999

1998

1997

1996

1995

R2 = 0.7327

0

10

20

30

40

50

60

-250 -200 -150 -100 -50 0 50 100 150 200 250

Inflow-Outflow (hm3)

Perc

ent C

hlor

ide

Ret

aine

d in

Ref

uge

Figure 5.2: The correlation between the net flow for the POR and the percent chloride

retained in the Refuge.

5.2.2 Phosphorus

A similar balance of phosphorus loads was completed, much like the one discussed in

Section 5.2.1 on chloride. As in Section 5.2.1 the percent retained refers to the percent of

phosphorus that did not leave the Refuge through a hydraulic structure, therefore, it either

89

remained in the Refuge or exited the Refuge through some other means of outflow such

as groundwater seepage or transpiration.

Phosphorus data were downloaded from SFWMD’s DBHYDRO database for the

hydraulic structures located around the perimeter of the canal. Of the 19 hydraulic

structures 16 of them had phosphorus data for the period of record from January 1, 1995,

to December 31, 2004. These structures include: S-5A, S-5AS, G-300, G-301, G-310, G-

251, S-6, S-10E, S-10D, S-10C, S-10A, ACME-1, ACME-2, S-39, G-94C, and G-94B.

As with chloride, G-94C only had three days of data for this period and G-94A did not

have any data, therefore, the concentrations recorded at G-94B were used to fill these

structures. All of the phosphorus samples taken from the structures above were done so

by grab samples; although stations S-5A, G-310, G-251, and S-6 also had seven-day

composite samples taken. According to SFWMD and Refuge scientists when seven-day

composite samples are available it is best to use these data. All of the data were

processed and evaluated, removing any outliers and averaging the concentrations when

multiple recordings were recorded on a day. On days were the lab was unable to detect a

reading the phosphorus concentration was recorded as the negative of the detection level

(-0.004 mg/L), this value was divided by two and made positive (0.002 mg/L). A list of

these values can be found in the Appendix A.

Similar to chloride, phosphorus data were also recorded periodically; therefore,

phosphorus concentration time-series (mg/L) were generated for each hydraulic station.

90

For the stations where grab samples were available the data were filled linearly from

January 1, 1995, to December 31, 2004.

The process of applying values from a seven-day composite sample to each day was a

little more difficult. SFWMD scientists fill the time-series for such seven-day composite

samples as follows: on the date the sample is recorded that concentration is the

concentration for that date and the six days prior to that date. To fill the dates between

sixth day prior to the composite reading and the next composite samples the average of

the two samples is determined and that value is used to fill all the days between the two

readings. A simple schematic can be seen in Figure 5.3 to explain this method.

Figure 5.3: Schematic explaining how the composite phosphorus samples were filled to

make a complete time-series.

Once the daily time series were generated the total load could be calculated at each

hydraulic structure using Equation 5.1. Then, the total load going in and out of the

Refuge could be calculated by summing all of the daily loads from the individual

structures together. The results can be found in Table 5.2 and Figure 5.4.

Composite Value

Fill 6 Days Prior with this Value

6 Days

Take the Average of the 2 Composite Values and Fill

Missing Days Composite Value

91

Year LoadIN kg/year

LoadOUT kg/year

Difference kg/year

Percent Retained %

1995 104,473 95,165 9,308 8.91 1996 75,960 45,544 30,416 40.04 1997 115,186 37,675 77,511 67.29 1998 99,616 49,801 49,815 50.01 1999 87,434 74,720 12,714 14.54 2000 58,563 21,495 37,068 63.30 2001 21,331 14,895 6,436 30.17 2002 32,409 19,946 12,463 38.45 2003 33,916 20,000 13,916 41.03 2004 46,363 48,755 -2,392 -5.16

Table 5.2: Total phosphorus loads going in and out of the Refuge through hydraulic structures and the total percent of phosphorus retained in the Refuge.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

To

tal A

nn

ual

Ph

osp

ho

rus

Lo

ad, k

g/y

ear Inflow Phosphorus Load

Outflow Phosphorus Load

Figure 5.4: Total annual phosphorus loads going in and out of the Refuge

through hydraulic structures.

Opposed to the results seen in chloride, there appears to be more phosphorus retained in

Refuge. However, once again there is a correlation between net flow (Inflow – Outflow)

and the percent of phosphorus retained in the Refuge (Figure 5.5). We can assume that as

92

the net flow increase so does the amount of phosphorus that exits the Refuge through

groundwater seepage.

2004

20032002

2001

2000

1999

1998

1997

1996

1995

R2 = 0.5555

-10

0

10

20

30

40

50

60

70

80

-250 -200 -150 -100 -50 0 50 100 150 200 250

Inflow-Outflow (hm3)

Perc

ent P

hosp

horu

s R

etai

ned

in th

e R

efug

e

Figure 5.5: The correlation between the net flow for the POR and the percent of

phosphorus retained in the Refuge.

The calculated loads going in and out of the Refuge were compared to those recorded in

South Florida Environmental Report for the South Florida Water Years 2002, 2003, and

2004, that is produced by the SFWMD. To properly compare the results the total inflow

and outflow loadings were calculated based on the Florida Water Years; for example,

Florida Water Year 2002 is from May 1, 2001, to April 30, 2002. The comparison

between the calculated results and SFWMD’s results can be seen in Table 5.3 for the

inflow loads and Table 5.4 for the outflow loads. It should be noted that the outflow

loads posted by SFWMD did not include the loads from structures G-94A, G-94B, or G-

94C for Florida Water Years 2002 and 2003, and they did not include the loads for G-

94A in Florida Water Year 2004. Therefore, in order to properly compare the loads; the

93

loads from the G-94 structures were appropriately subtracted from the calculated yearly

loads.

Florida Water Year Calculated LoadIN

kg/year

SFWMD’s Loads kg/year

Difference kg/year

Percent Accuracy, %

2002 19,162 18,814 348 98.18 2003 43,706 43,409 297 99.32 2004 22,750 22,282 468 97.94

Table 5.3: Comparison of the calculated inflow loads against the SFWMD’s loads published in their annual reports for Florida Water Years 2002 to 2004.

Florida Water Year

Calculated LoadOUT

kg/year

Total Load from G-94 Structures

kg/year

Corrected Calculated LoadOUT

kg/year

SFWMD’s Loads

kg/year

Difference kg/year

Percent Accuracy,

%

2002 14,982 1,127 13,855 12,801 1,054 92.39 2003 25,964 3,756 22,208 21,628 580 97.39 2004 16,771 818 15,953 15,996 -43 100.27

Table 5.4: Comparison of the calculated outflow loads against the SFWMD’s loads published in their annual reports for Florida Water Years 2002 to 2004.

The calculated loads compare well, with the percent accuracy ranging from 100% to

92%. One possible reason for the minimal discrepancy in phosphorus total load could be

attributed to the filling of missing data. Although, emphasis was taken in attempting to

follow similar procedures as those followed by the SFWMD.

5.3 Model Selection

When first developing the water quality model it was important to find a modeling

program or technique that would allow for simple model setup and operation; but could

still be computationally efficient. Therefore, some previously developed modeling

94

programs and techniques were briefly analyzed to determine the one that could be

utilized for the water quality modeling in the Refuge.

One possible modeling technique that was evaluated was the use of Artificial Neural

Networks (ANN’s) as a way of forecasting water quality parameters. ANN’s are

mathematical models that consist of interconnected nodes, that can extract a complex non

linear relationship from a set of input and output data (Habib and Meselhe, 2006). It was

determined that although ANN’s have been successful in modeling water quality

constituents in the past, their predictive capabilities diminish when any changes would be

made to the physical settings in the Refuge. Only things like inflow and outflow could be

changed, therefore, limiting the scenarios that could be examined. ANN’s would have

been a good choice if the water quality model would have not had a lot of data, or if

future modeling would only be projecting within the range of calibration

Another modeling program that was looked at was the Dynamic Model for Everglades

Stormwater Treatment Areas (DMSTA) (section 2.4.5) by Walker and Kadlec (2006).

This model was developed for the U.S. Department of Interior and the U.S. Army Corp

of Engineers for use in modeling the water quality in the STAs located just northwest and

northeast of the Refuge, where flow is unidirectional. As canal-marsh flow in the Refuge

is bidirectional, this model was not pursued further.

The USGS Branched Lagrangian Transport Model (BLTM) was also considered as a

possible modeling program for water quality in the Refuge. The BLTM model was

95

developed to simulate the unsteady movement, dispersion, and chemical reactions of

various constituents that move through a series of one-dimensional channels (Jobson,

2001). An advantage of this model is that it is open source code, and can easily be

modified as needed. A disadvantage of BLTM is that it does not have any user friendly

pre or post processors, and one of the major objectives is to develop a model that can be

operated and modified easily by Refuge staff.

The modeling program that was chosen as the most suitable for the water quality

modeling effort was the U.S. Environmental Protection Agency’s (EPA) Water Quality

Analysis Simulation Program, Version 7.1 (WASP 7.1, hereafter referred to as simply

WASP). WASP is a dynamic compartmental model that allows users the ability to

interpret and predict water quality responses due to natural occurrence and man made

pollution. The flexible compartmental approach allows users to investigate one, two, and

three dimensional systems. The model includes the following data requirements: water

body hydrogeometry, advective and dispersive flows, settling and resuspension rates,

boundary concentrations, pollutant loadings, and initial conditions. The area being

modeled can be separated into multiple segments or compartments. The segment

volumes, connectivity, and type, such as surface water, must be known. Each segment or

compartment acts independently, with the water quality constituents modeled as spatially

constant within each segment. A possible limitation with this modeling program is that

WASP does not allow the cells to go completely dry (US EPA, 2006).

Some benefits of selecting this model is that it is free to the public, user friendly (does not

require any computer programming experience), has been widely applied, and although it

96

can be used for simple simulations it can also be used for more complex simulation in the

future. Another major advantage of using WASP is that it has a data preprocessor that

allows for quick development of input datasets, and a postprocessor that enables efficient

reviewing of model results.

WASP has a long history of application that even includes use in projects located in

Florida, such as the examining of the eutrophication of Tampa Bay, FL, and the

phosphorus loading in Lake Okeechobee, FL (US EPA, 2006).

5.4 Water Quality Modeling Approach

After reviewing the constituent data (sections 3.6 and 5.2), past modeling efforts within

the Refuge, and consulting with Refuge scientists, it was determined that the water

quality model would be best implemented by separating the Refuge into 4 cells (boxes).

These cells would consist of the canal, and three inner marsh cells. Based on the

distribution of chloride and phosphorus with distance away from the canal (Figure 3.13)

the cells were set so that the first marsh cell fell within the first kilometer from the canal,

the second marsh cell fell between one and four kilometers from the canal, and the third

marsh cell included the remaining interior marsh area. The areas of each cell can be

found in Table 5.5, and a sketch of the three interior cells and the XYZ and EVPA water

quality monitoring station locations can be seen in Figure 5.6.

97

Cell Number Distance from Canal Miles (km)

Area Acres

Canal 0 996 1 0.621 (1) 22,072 2 2.484 (4) 55,353 3 Remaining Interior 60,901

Table 5.5: Distance of each cell from the Refuge canal and its area.

Figure 5.6: Location of EVPA and XYZ water quality monitoring sites in

relation to the various cells.

Table 5.6 shows the relation of the various XYZ and EVPA monitoring stations to the

canal and interior cells. These monitoring stations were used for calibration of both

chloride and phosphorus. Because there were only two observation stations located

inside the canal, X0 and Z0, the outflow structure data from structures S39, S10-E, S10-

D, S10-C, S10-A, and G94-B were also included as observed canal concentrations. Cell

#*

#*

#*

#* #*#*

#* #* #*#*

#*

!

!

! !

!

!!

! !

!

!!

!!

±0 2 4 61 Miles

LOX3 LOX4

LOX5

LOX8LOX7

LOX6

LOX9LOX10

LOX11

LOX12LOX13

LOX14

LOX15LOX16

Z4Z3

Z2Z1

Z0Y4

X4

X3

X2X1X0

98

1 has a limited number of observation stations located within it. The stations that are

located within cell 1 are also all XYZ gages, that are located on the west side of the

Refuge where higher concentrations typically occur. Therefore, data from observation

stations LOX4 and LOX6 were used for the calibration of both cells 1 and cell 2.

Canal Cell 1 Cell 2 Cell 3

X0 X1 X2 X4 Z0 Z1 Y4 LOX3 S39 Z2 Z3 LOX5

S10-A LOX4 Z4 LOX7 S10-C LOX6 LOX4 LOX8 S10-D LOX6 LOX9 S10-E LOX10 LOX11 G-94B LOX12 LOX13

LOX14 LOX15

LOX16

Table 5.6: Location water quality stations in reference to the canal and interior cells used in calibration of the chloride and phosphorus models.

This model setup was used in the completion of the chloride and phosphorus models that

will be discussed in chapters 6 and 7.

99

CHAPTER 6: Chloride Water Quality Modeling

6.1 Introduction

Chloride is modeled here as a conservative tracer with flows determined in the Refuge

water budget model (Chapter 4). As a conservative, chloride, is assumed to not undergo

any significant chemical or biological transformations or degradations (Kadlec and

Knight, 1996), therefore, it was easily modeled here using both a simple spreadsheet

model (Microsoft Excel) and WASP 7.1. Both modeling techniques are discussed in this

chapter. This chloride model provides a better understanding of the transport of other

surface water constituents including nutrients throughout the Refuge, and additionally

provides insight supporting a better calibration of the water budget model.

6.2 Chloride Excel Model

Chloride was initially modeled using Microsoft Excel to calculate chloride concentrations

on a one day time-step for the canal and the three interior marsh cells for the calibration

period January 1, 1995, to December 31, 2004 and validation period January 1, 2000, to

December 31, 2004. This Excel model parallels the approach used in the Refuge water

budget model (Chapter 4).

100

6.2.1 Excel Model Setup

The water budget model discussed in chapter 4 was coupled with the simple spreadsheet

water quality model using the observed inflows, outflows, and precipitations; along with

adjusted evapotranspiration, and estimated canal and marsh seepage estimates. It was

assumed that mass entered the Refuge through inflows (M

inQ ), precipitation ( MP ), and

dry deposition ( MDD ); and left the Refuge by means of outflows (M

outQ ),

groundwater seepage in the canal and marsh ( MGS ), and transpiration ( MT ) (Figure

6.1). Mass was exchanged between cells through the advection of flow between cells

( MiQ _ where i represents the downstream cell). As in the water budget model the

exchange of flows was based on the corresponding inflows and outflows from each cell;

however unlike the water budget model when the net canal flow was large there was no

restriction that limits the magnitude of the canal stage.

Figure 6.1: Schematic of cells used to calculate chloride concentrations.

Canal

Cell 1 Cell 2

MP

E E E ME MT MT MGS MGS MGS

MoutQ

MP MP

MinQ

MQ1

MQ2

MQ3

MP MDD MDD MDD MDD

MC

CC

M1

C1

M2

C2

M2

C2

MT MGS E

Cell 3

Upstream Downstream

101

The spreadsheet model calculates concentrations by accumulating the daily change in

chloride mass (g) (Equations 6.1a and 6.1b)

1,_, −++++= tiMiMMMti MQTGPM (6.1a)

and

tcMoutMinMiMMMtc MQQQTGPM ,___, +−++++= . (6.1b)

Concentration is then calculated by dividing mass within the cell by the cell volume

(Equations 6.2a and 6.2b)

)(* 0_

,,

iii

titi EEA

MC

−= (6.2a)

and

)(* 0_

,,

ccc

tctc EEA

MC

−= . (6.2b)

Equations 6.1a and 6.2a apply to the ith marsh cell, while Equations 6.1b and 6.2b apply

to the canal. The subscript t is the day index, A represents the areas of the canal

(subscript c) and interior cells (subscript i) as listed in Table 5.4, iE is the canal or marsh

102

stage calculated from the water budget, and 0_iE is the marsh and canal elevations of

15.158 ft (4.62 m) and 1.641 ft (0.5 m), respectively.

The initial conditions for chloride concentrations were set using the average monthly

observed concentration from the XYZ and EVPA sites located in each interior cell for

January 1995, the starting month of the simulation, . The initial conditions for the canal

and three interior cells can be seen in Table 6.1. Also included in Table 6.1 is the

average chloride concentration for each cell based on the average monthly observed

values.

Canal Cell 1 Cell 2 Cell 3 Chloride Initial Condition, mg/L 89.6 71.5 30.00 12.19 Average Observed Chloride Concentration for the POR 112.57 94.82 54.64 27.34

Table 6.1: Initial and long term average concentrations for chloride in each cell.

The inflow chloride concentrations through the perimeter canal inflow structures were

obtained from the daily time-series which were calculated and discussed in section 5.2.1.

6.2.2 Calibration

The Excel model was initially calibrated on its own, but was later recalibrated using the

values that were found to result in the best calibration of the WASP model in order to

facilitate a comparison between the two model setups. Therefore, the values found in

calibrating the WASP model are the values that will be presented here for both the simple

Excel model and WASP model.

103

The calibration parameters for the chloride models include wet deposition, dry

deposition, and the percent of evapotranspiration that is transpiration. It is assumed that

transpiration transports chloride and other constituents into the root zone while

evaporation does not transport any constituents. The major calibration parameter in

modeling chloride was found to be the percent of transpiration fraction of

evapotranspiration. Through calibration it was estimated that approximately 35% of

evapotranspiration is transpiration. This value is relative to the range of 30% to 60%

suggested by Dr. Robert H. Kadlec (R.H. Kadlec, personal communication, 2006). It is

important to note that the percent of transpiration was calibrated over the entire Refuge,

although transpiration does vary considerably based on water depth and vegetation, and it

is reasonable to assume that percent of transpiration varies depending on vegetation type

and percent cover (German, 1999). The model is relatively sensitive to transpiration.

When calibrating wet and dry deposition in the Refuge it was important to remember that

the Refuge is unique in a part of the high nutrient water received from the control

structures remains in the rim canals without actually flowing through the interior of the

Refuge. Some high nutrient water moves into the Refuge, but evidence indicates that it

moves slowly and most acutely impacts only a limited habitat near the canals (USFWS,

2000). There are no known published references for dry deposition of chloride in the

Refuge. Therefore this parameter was simply calibrated based on recommended values

from experienced wetland modelers (W.W. Walker, personal communication, 2005).

Cells 2 and 3, the more interior cells, were more sensitive to the calibration value of dry

deposition of chloride than the canal and cell 1.

104

There was some difficulty in calibrating in order to get an overall agreement between

modeled and observed data between the various cells; therefore, the models were

calibrated by trying to achieve the best overall results in the canal and marsh, while

minimizing the biases in the canal and in cell 3.

6.2.3 Calibration Results

Figures 6.2 to 6.5 represent the graphical results of the chloride Excel model based on the

calibration parameter from the WASP model. Shown are the modeled and observed

monthly averaged values. Also shown are the standard deviations for the average

monthly observed values. The daily results can be seen in Appendix B. It should be

noted that it was observed that with the one day time step there was a lot of instability

and numerical dispersion especially in the canal. There were also months where there

were no observed data available within certain cells.

The model was also analyzed using the same performance measures used in the water

budget model (section 4.6.3). Performance measures were calculated for the canal, cell 1,

cell 2, cell 3, and also for the total marsh area which included cells 1, 2, and 3 together.

The results from these calculations can be seen in Table 6.2.

105

0

50

100

150

200

250

300


Chl

orid

e C

once

ntra

tion,

mg/

L

Monthly Average ObservedMonthly Average Modeled

Figure 6.2: Canal calibration results for the chloride Excel model, representing the

average monthly observed data and its standard deviations; and the average monthly modeled data.

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L

Observed Monthly AverageModeled Monthly Average

Figure 6.3: Cell 1 calibration results for the chloride Excel model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data.

106

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

LObserved Monthly AverageModeled Monthly Average


modeled data.

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tions

, mg/

L



modeled data.

107

Statistical Parameter Canal Calibrations

Statistics

Cell 1 Calibration Statistics




Bias, mg/L -5.458 -9.127 12.900 14.287 6.004 RMSE, mg/L 22.482 22.123 22.182 18.824 21.139 Standard Deviation of Observed, mg/L

26.470 26.756 19.189 8.625 33.777

Standard Deviation of Modeled, mg/L

24.022 20.912 16.270 13.666 24.146

Standard Deviation of Error, mg/L

21.993 20.331 18.200 12.368 20.328

Variance Reduction 31% 42% 10% -106% 64% R (Correlation Coefficient)

0.624 0.664 0.485 0.376 0.803

R2 Value 0.390 0.440 0.235 0.141 0.644 Nash Sutcliffe Efficiency

0.266 0.304 -0.359 -3.851 0.606

Table 6.2: Chloride Excel model performance measures for the calibration period.

6.2.4 Validation Results

The chloride Excel model was validated from January 1, 2000, to December 31, 2004

using the same model setup and parameters that were used for the calibration period.

These results can be seen in Figures 6.6 to 6.9.

The performance measures for the validation period and POR can also be seen in Tables

6.3 and 6.4, respectively.

108

0

50

100

150

200

250

300


Chl

orid

e C

once

ntra

tion,

mg/

LMonthly Average ObservedMonthly Average Modeled

Figure 6.6: Canal validation results for the chloride Excel model, representing the


-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L




109

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/




-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tions

, mg/

L




110


Validation Statistics

Cell 1 Validation Statistics





26.418 31.823 24.151 18.519 37.665


22.121 19.192 20.788 21.322 25.534


23.958 34.141 17.108 20.915 27.727

Variance Reduction 18% -15% 50% -28% 46% R (Correlation Coefficient)

0.525 0.171 0.720 0.396 0.677


0.002 -0.394 0.487 -0.891 0.458

Table 6.3: Chloride Excel model performance measures for the validation period.


Statistics for POR

Cell 1 Statistics for POR



Marsh Statistics for POR


26.412 30.035 24.258 15.475 36.519


23.005 20.321 19.437 18.694 25.308


23.066 28.159 18.329 17.179 24.462

Variance Reduction 24% 12% 43% -23% 55% R (Correlation Coefficient)

0.572 0.429 0.669 0.457 0.744

R2 Value 0.327 0.184 0.447 0.209 0.554 Nash Sutcliffe Efficiency 0.140 -0.49 0.327 -1.099 0.543

Table 6.4: Chloride Excel model performance measures for the POR.

111

6.2.5. Discussion of the Chloride Excel Model

In the chloride Excel model it is likely that there was significant numerical dispersion.

For the entire POR the model performed relatively well in the marsh as a whole (cells 1,

2, and 3); although, the model shows a negative Nash Sutcliffe Efficiency value. The

models all showed qualitatively a good repetition of the observed data catching the

overall trend of the data. The large biases are likely due to the fact that the chloride

Excel model was calibrated using the calibration values determined in the chloride

WASP model.

6.3 Chloride WASP Model

The chloride WASP model was setup using a 0.1 day time step for the calibration period

from January 1, 1995, to December 31, 1999 and the validation period January 1, 2000,

to December 31, 2004. WASP operates completely on the metric system, therefore all

values and input parameters mentioned in this section will use SI units. The equations

used by WASP are based on the basic principals of the conservation of mass. WASP

operates on a mass balance principle in each cell.

6.3.1 Chloride WASP Model Setup

WASP requires the input of the model segmentation (cells) geometry and their initial

conditions, system to be simulated, boundary conditions, source loads, exchanges

112

(dispersion), and flows. This input data along with the general WASP mass balance

equations and general kinetics equations then defines a special set of water quality

equations. These equations are numerically integrated by WASP.

The mass balance equations for a 1-dimensional stream used by WASP are shown in

Equation 6.3

( ) KBLxx ASSSAxC

AEACUx

ACt

+++

∂∂

+−∂∂

=∂∂

)( (6.3)

where A is the cross-sectional area, m2; C is the concentration of the water quality

constituent, mg/L; t is time in days; xU is the longitudinal, advective velocities in

m/day; xE is the longitudinal diffusion coefficients, m3/day; LS is the total of direct

loading rates in g/m3-day; BS is the boundary loading rates in g/m3-day; and KS is the

total kinetic transformation rate in g/m3-day. It should be noted that KS only applies to

modeling of phosphorus in this report.

Chloride was modeled using the eutrophication module in WASP. The eutrophication

module, rather than the toxics module, was selected so that later the more complex

phosphorus model in the eutrophication module could be implemented in the futures.

WASP models salinity as a conservative constituent. In the present study, the WASP

salinity state variable was chosen as the system in which chloride was modeled.

113

WASP requires initial volumes for each cell to be designated; this was done by assuming,

consistent with the water budget model, an initial water depth in the canal of 2 m and a

depth in the interior cells of 0.61 m. The water depth in the interior cells was calculated

by taking the observed water level in the marsh on January 1, 1995 of 5.23 m and

subtracting the average marsh elevation of 4.62 m. The assigned volumes are shown in

Table 6.5. The initial chloride concentrations were the same as those shown in Table 6.1.

The WASP parameter “fraction dissolved” for chloride was set at 100%.

Canal Cell 1 Cell 2 Cell 3

Volume, m3 8,066,971 54,509,080 136,701,113 150,402,747 Table 6.5: Initial volumes for the canal and interior cells.

Aerial loads were input into WASP based on calibrated wet and dry deposition. Wet

deposition was calibrated in mg/L and multiplied by the daily rainfall rate and area in

order to get a load in kg/day. Dry deposition was calibrated in g/m2-yr and multiplied

times the cell areas accordingly in order to get a load in kg/day; this dry deposition load

was assumed to be constant for each day during the modeled POR. A daily aerial load

time series was created by adding the daily wet and dry deposition rates.

Flows used in the modeling chloride were also taken from the water budget model

including inflow from canal structures ( inQ ), outflow from canal structures ( outQ ),

estimated canal and marsh seepages (GS ), and estimated exchange flow from the Power

Law Model ( MCQ ). WASP considers precipitation and evaporation as flows that do not

transport mass. As in the simple Excel model the percent of transpiration was also

calibrated and is modeled as a flow in WASP. All flows were input into WASP in

114

m3/sec. The flows were input according and distributed to and from the various cells

according to the fraction of flow going into or out of each cell. The fractions used for

each flow are expressed in Table 6.6.

The boundary inflow chloride concentration time series was input into WASP on a daily

time step in mg/L. This time series was obtained from that calculated in section 5.2.1.

Inflow to

Canal

Outflow from Canal

Exchange Flow

Marsh Seepage

Canal Seepage

Transpiration Precipitation

& Evaporation

Boundary to Canal

1

Canal to Boundary

1 1 0.00715

Canal to Cell 1 1 Cell 1 to Cell 2 0.840439 Cell 2 to Cell 3 0.443854

Cell 1 to Boundary

0.1559564 0.15842

Cell 2 to Boundary

0.3965825 0.39375

Cell 3 to Boundary

0.4438535 0.44068

Table 6.6: Fraction of flows used in WASP.

Dispersion was also used as a calibration parameter. WASP models dispersion as an

exchange function in m2/sec. In order to implement dispersion in WASP the user must

assign gross-cross sectional areas representative of the areas through which mixing

occurs; and mixing lengths which reflect the length over which mixing occurs. The cross

sectional areas were calculated using the perimeter of each cell and an estimated typical

depth of 0.5 m for the interior cells and a depth of 2 m for the canal. The lengths are

115

calculated using the center point of adjoining segments (cells). These values are shown

in Table 6.7. Dispersion was considered to be constant for the entire POR.

Area, m2 Distance, m

Canal – Cell 1 46,521.035 522 Cell 1 – Cell 2 42,949.0465 2,000 Cell 2 – Cell 3 31,268.145 4,500

Table 6.7: Areas and distance used to calculate dispersion in the WASP chloride model.

6.3.2 Chloride WASP Model Calibration

The parameters which were calibrated in the WASP chloride model and also used in the

chloride Excel model were percent transpiration, wet deposition concentration, dry

deposition rate, and dispersion. Dispersion was not implemented in the Excel model. As

mentioned earlier there was some difficulty in calibrating chloride based on when

improving the statistics in certain cells other cells statistics decreased. Therefore,

calibration was based on achieving the best overall statistics in the canal and marsh as a

whole (cells 1, 2, and 3 combined). It was also attempted to calibrate to minimize the

biases in the canal and cell 3.

The primary calibration parameter was found to be the percent of transpiration. It was

calibrated that the percent transpiration was approximately 35% of the total

evapotranspiration estimate.

116

Wet and dry depositions were also determined by calibration. It was found that the

model calibrated with a wet deposition concentration of 2 mg/L and a dry deposition of

0.5 g/m2-yr. Cell 3 was the most sensitive to the calibration of these parameters.

Longitudinal dispersion was also estimated by calibration. A range of 0.37 to 22 m2/hr

was typical in this area based on (Meselhe et al., 2005). Longitudinal dispersion was

calibrated to be equal to 22 m2/hr, although when calibrating it was found that dispersion

had very little effect in the canal and cell 1, and no effect in cells 2 and 3.

The observed concentrations used in calibration statistics were based on the XYZ, EVPA,

and outflow structure concentrations were aggregated to monthly averages (Meselhe et

al., 2005). Variability of samples was characterized by the monthly standard deviations

of values observed within the cell boundaries. There were some months where there

were no observed values within a cell; these months were eliminated from calibration

statistics.

6.3.3 Chloride WASP Model Calibration Results

The results from calibration can be seen in Figures 6.10 to 6.13; these plots represent the

modeled and observed data along with the standard deviations of the observed data.

Performance measures can also be found in Table 6.8. The performance measures can be

slightly misleading due to the gaps in data, and the minimal reading in some cells. For

example cell 1 has predominantly XYZ stations used for observation data, which tend to

117

have higher concentrations. Also due to the small range in data in cell 3 the statistics do

not represent the pattern that was achieved, that can be seen in the daily results which can

be found in Appendix C.

It was found when calibrating the chloride model that the modeled chloride

concentrations in the canal were low, therefore, it was determined that the water budget

model needed further calibration of the canal seepage rate. By lowering the canal

seepage rate by 30%, and proportionally adjusting the marsh seepage rate the bias in the

canal began to approach zero.

0

50

100

150

200

250

300


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure 6.10: Canal calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data.

118

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure 6.11: Cell 1 calibration results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data.

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L



modeled data.

119

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tions

, mg/



modeled data.

Statistical Parameter Canal Calibration Statistics






26.470 26.756 19.189 8.625 33.777


23.961 23.930 16.876 10.901 27.906


17.539 21.011 19.395 10.923 18.992

Variance Reduction 56% 38% -2% -60% 68% R (Correlation Coefficient)

0.782 0.667 0.430 0.318201 0.827


0.554 0.310 -0.243 -1.035 0.678

Table 6.8: Performance measures for the calibration period using the chloride WASP model.

120

The results show that both canal and marsh as an entirety perform well in modeling

chloride. The graphs show that the general pattern of concentration was obtained.

6.3.4 Chloride WASP Model Validation Results

The chloride WASP model validation results can be seen in Figures 6.14 to 6.17. Daily

validation graphs can be found in Appendix C. The performance measures for the

validation period and entire POR can be seen in Table 6.9 and 6.10.

0

50

100

150

200

250

300


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure 6.14: Canal validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data.

121

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure 6.15: Cell 1 validation results for the chloride WASP model, representing the


-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure 6.16: Cell 2 validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data.

122

-25

0

25

50

75

100

125

150

175

200


Chl

orid

e C

once

ntra

tions

, mg/


Figure 6.17: Cell 3 validation results for the chloride WASP model, representing the average monthly observed data and its standard deviations; and the average monthly

modeled data

Statistical Parameter Canal Validation Statistics





Bias, mg/L -8.402 -18.420 -8.121 -1.918 -9.463 RMSE, mg/L 23.037 40.357 20.311 20.672 28.562 Standard Deviation of Observed, mg/L

26.418 31.823 24.151 18.519 37.665


23.983 27.067 25.233 17.789 31.607


21.631 36.228 18.775 20.766 27.027

Variance Reduction 33% -30% 40% -26% 49% R (Correlation Coefficient)

0.635 0.256 0.712 0.340 0.709


0.227 -0.637 0.281 -0.268 0.422

Table 6.9: Performance measure for the validation period using the chloride WASP model.

123

Statistical Parameter Canal Statistics for POR





Bias, mg/L -5.308 -12.806 0.341 1.779 -3.536 RMSE, mg/L 20.471 32.513 20.761 17.043 24.337 Standard Deviation of Observed, mg/L

26.412 30.035 24.258 15.475 36.519


23.895 25.457 21.496 14.750 29.811


19.853 30.017 20.846 17.026 24.113

Variance Reduction 43% 0.13% 26% -21% 56% R (Correlation Coefficient)

0.693 0.431 0.591 0.353 0.754

R2 Value 0.480 0.182 0.350 0.125 0.569 Nash Sutcliffe Efficiency 0.394 -0.182 0.261 -0.224 0.555

Table 6.10: Performance measure for the POR using the chloride WASP model.

6.3.5 Discussion and Further Analysis of the Chloride WASP Model

The chloride WASP model showed better performance measures overall than the chloride

Excel model. The canal showed a vast improvement, likely related to more limited

numerical dispersion and improved stabilities. The canal, cell 2, and cell 3 all had

moderately low biases. Cell 3’s performance measures do not adequately represent the

successfulness of the model, however the graphical representation of modeled and

observed daily values are viewed in Appendix C; and it can be seen that both the overall

pattern and values were achieved.

The chloride WASP model was also analyzed by comparing the calculated outflow loads

discussed in section 5.2.1 to the canal modeled loads (this was calculated by multiplying

the daily canal modeled concentrations by the daily observed outflows and converting to

124

kg/day). The results showed a good correlation between the two (Figure 6.18) with a R2

value of 0.9285 and a Nash Sutcliffe Efficiency of 0.9254.

R2 = 0.9285 Efficiency = 0.925

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000

Observed (kg/day)

Mod

eled

(kg

/day

)

Figure 6.18: Modeled loads in the canal compared to the observed outflow loads from

the canal structures. Solid line is a trendline with forced zero origin generated by Excel.

The modeled chloride concentrations were also analyzed by plotting the modeled and

observed concentrations versus distance from perimeter canal. The distance from the

perimeter canal for each of the observed stations was determined, and the modeled

concentrations were plotted based on distance of the center of each cell in reference to the

canal. Therefore, the concentrations in cell 1 were set at 0.5 km, cell 2 at 2.5 km, and cell

3 at 10 km. Due to the observed chloride concentrations being recorded on an irregular

basis the concentrations from the observed stations were taken within a short period

either before or after the date of the analyzed model concentration date. Evaluations

125

were completed for one day of each year for the POR. These results can be seen in

Figures 6.19 to 6.28. From these figures it can be seen that the WASP chloride model

catches the overall trend in reduction of concentration with respect to distance.

Chloride Concentrations

1/11/1995( 1/5/1995 - 1/12/1995 )

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10

Distance from Canal, km

Chl

orid

e C

once

ntra

tion,

mg/

L

ObservedModeledCanal

Figure 6.19: Observed (1/5/1995, to 1/12/1995 plotted without a line) and modeled (1/11/1995 plotted with a solid line) chloride concentrations using the WASP model

versus distance from the canal.

Chloride Concentrations4/24/1996

( 4/15/1996 - 4/25/1996 )

020406080

100120140160180200

0 1 2 3 4 5 6 7 8 9 10Distance from Canal, km

Chl

orid

e C

once

ntra

tion,

mg/

L




126


( 6/3/1997 - 6/11/1997 )

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10


Chl

orid

e C

once

ntra

tion,

mg/

L



(6/3/1997 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal.


( 1/5/1998 - 1/13/1998 )

020406080

100120140160180200


Chl

orid

e C

once

ntra

tion

mg/

L




127


( 1/4/1999 - 1/12/1999 )

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10


Chl

orid

e C

once

ntra

tion

mg/

L



(1/4/1999 plotted with a solid line) chloride concentrations using the WASP model versus distance from the canal.


( 1/3/2000 - 1/11/2000 )

020406080

100120140160180200


Chl

orid

e C

once

ntra

tion,

mg/

l ObservedModeledCanal



128


( 10/9/2001 - 10/16/2001 )

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10


Chl

orid

e C

once

ntra

tion,

mg/

L





( 1/8/2002 - 1/15/2002 )

020406080

100120140160180200


Chl

orid

e C

once

ntra

tion,

mg/

l ObservedModeledCanal



129


( 12/4/2003 - 12/16/2003 )

020406080

100120140160180200220240260


Chl

orid

e C

once

ntra

tion

mg/

L ObservedModeledCanal




( 10/18/2004 - 10/21/2004 )

020406080

100120140160180200


Chl

orid

e C

once

ntra

tion,

mg/

l




130

CHAPTER 7: Phosphorus Water Quality Modeling

7.1 Introduction

High concentrations of nutrients, particularly phosphorus, in runoff from agricultural

areas cause proliferation of cattails and other undesirable species that negatively affect

the ecosystem’s balance in the Refuge. Therefore, the monitoring of phosphorus levels in

the Refuge has become a priority to the Refuge staff. Developing a model that efficiently

predicts the phosphorus concentrations in the Refuge gives the Refuge the ability to make

proper management decision. In order to meet these objectives, a simple phosphorus

model was implemented using WASP 7.1 and the k-c* model by Kadlec and Knight

(1996) (see section 2.3.1 for a description of this modeling technique).

7.2 Phosphorus WASP Model Setup

The phosphorus WASP model was setup using a 0.1 day time step for the calibration

period January 1, 2000, to December 31, 2004 and the validation period January 1, 1995,

to December 31, 1999. These calibration and validation periods were chosen based on

that there were more data for this period due to the increase in monitoring of phosphorus.

The Refuge was modeled using the modeling approach discussed in section 5.4.

Like chloride, phosphorus was modeled using the eutrophication module in WASP. The

same initial volumes were used assuming an initial depth in the canal and marsh of 2.0 m

131

and 0.61 m, respectively (Table 6.5). Phosphorus was modeled as carbonaceous

biological oxygen demand (CBOD), using the k-c* model by Kadlec and Knight (1996)

(Equation 2.2).

The initial phosphorus concentrations used in modeling can be found in Table 7.1, along

with the average observed monthly average phosphorus concentration for the POR.

Canal Cell 1 Cell 2 Cell 3 Phosphorus Initial Condition, mg/L 0.0341 0.0065 0.0144 0.0133 Average Observed Phosphorus Concentrations for the POR 0.0608 0.0241 0.0106 0.0111

Table 7.1: Initial conditions for phosphorus and the average observed phosphorus concentration for each cell.

Aerial loads were input into WASP based on calibrated wet and dry deposition and the

areal mass loading rate that was calculated from the calibration of the k-c* model..

Wet deposition was calibrated in mg/L and multiplied by the daily rainfall rate and area

in order to get a load in kg/day. Dry deposition was calibrated in mg/m2-yr and

multiplied times the cell areas accordingly in order to get a load in kg/day; this load was

assumed to be constant for each day during the modeled POR. The areal mass loading

rate was calculated in mg/m2-day and was also multiplied times the cell areas in order to

obtain a loading rate in kg/day. The areal mass loading rate was assumed to be a constant

value for each day during the POR. A daily aerial load time series was created by adding

the daily wet and dry deposition rates and the areal mass loading rates.

132

The same flow values used in modeling chloride were also used in the phosphorus WASP

model. The calibrated settling rate (m/yr) from the k-c* model is entered into WASP as a

flow by multiplying the rate times the total area of the Refuge. It should be noted that all

flows in WASP are inputted in m3/sec.

The flows were input accordingly and distributed to and from the various cells based on

the fraction of flow going into or out of each cell. The fractions used for each flow are

expressed in Table 6.6; the settling rate fraction of flows can be found in Table 7.2.

Settling Rate

Fraction of Flows Canal to Boundary 0.00715 Cell 1 to Boundary 0.15842 Cell 2 to Boundary 0.39375 Cell 3 to Boundary 0.44068

Table 7.2: Fraction of flows used in for calculating settling rate for each cell.

The boundary inflow phosphorus concentration time series was input into WASP on a

daily time step in mg/L. This time series was obtained from that calculated in section

5.2.2. Dispersion was set to the calibrated values found in the chloride model.

7.3. Phosphorus WASP Model Calibration

In the phosphorus WASP model the following parameters were used for calibration: 1)

wet deposition; 2) dry deposition; 3) settling rate (k); and 4) the c* concentration value.

133

As in the chloride models, calibration was difficult in achieving the best results in each

cell uniformly. Therefore, for phosphorus calibration it was aimed at achieving a low

bias in the canal and cell 3.

The model was primarily calibrated using the k-c* model. The recommended range for

settling rate based on Walker and Kadlec’s (2005) DMSTA2 model is between 16.8 and

52.5 m/yr. Through calibration it was found that the model responded best overall,

especially in the canal and cell 3, when the settling rate was set to 16.8 m/yr. Through

the use of the k-c* model in the DMSTA model, Walker and Kadlec (2005) found that

the c* value within the STAs ranged between 4 and 20 µg/L. The c* value was also

calibrated; it was initially assigned as 3 µg/L but through calibration it became aware that

the value would need to vary between the canal and interior cells. Therefore, the c* value

for the canal was calibrated to be 80 µg/L and the interior cells were calibrated to have a

value of 8 µg/L. The canal was not very sensitive to the c* value, ranges between 10 and

90 µg/L were tested and it was found that the model showed a minimal bias when 80

µg/L was used. Based on the calibrated settling rate and c* values an areal mass loading

rates in the canal and marsh were calculated to be 3.68 and 0.368 mg/m2-day,

respectively.

Wet and dry depositions were also calibrated for in the model. These parameters are an

important source of nutrients coming into the Refuge. According to Richardson et al.

(1990) the atmospheric deposition reported from 1979 through 1988 accounted for 25%

of the phosphorus entering the Refuge compared to 75% of the phosphorus entering via

134

S-5 and S-6 structures combined. Analysis of wet and dry deposition data is statistically

challenging (Ahn 1999a; Ahn 1999b; Walker and Jewell 1997). Measurements of

atmospheric deposition rates are complicated by numerous sources of contamination such

as ash, vegetation, insects, spider webs, and bird droppings that can cause positive bias.

Estimates of atmospheric phosphorus deposition have ranged from 17 to 96 mg/m2-yr for

different locations at South Florida (Walker, 1995). Most modeling approaches for the

Everglades have used a constant value for the atmospheric phosphorus deposition.

Walker (1995) assumed a constant value of 43 mg/m2-yr for an area adjacent to the

Refuge. Raghunathan et al. (2001) used a temporally and spatially constant value of 43

mg/m2-yr. For the phosphorus WASP model it was found that the model calibrated best

with a wet deposition concentration of 0.010 mg/L and a dry deposition of 40 mg/m2-yr.

The observed concentrations used in calibration were based on the XYZ, EVPA, and

outflow structure concentrations were aggregated to monthly averages (Meselhe et al.,

2005). The variability of samples was characterized by the monthly standard deviations

of values observed within the boundaries. As with chloride, there were some months

were there were no observed values within a cell, these months were eliminated from

calibration statistics. There were also quite a few months were there was only one value

recorded. These values were not thrown out due to the fact that they were consistent

throughout the cells, however, they did have an effect on the overall statistics in model.

135

7.4 Phosphorus WASP Model Calibration Results

The results from calibration can be seen in Figures 7.1 to 7.4; these plots represent the

modeled and observed data along with the standard deviations of the observed data.

Performance measures can be found in Table 7.3. As with chloride the performance

measures can be slightly misleading due to the gaps in data and the minimal number of

values in some cells.

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30


Phos

phor

us C

once

ntra

tion,

mg/

L


Figure 7.1: Canal calibration results for the phosphorus WASP model, representing the


136

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20


Phos

phor

us C

once

ntra

tion,

mg/


Figure 7.2: Cell 1 calibration results for the phosphorus WASP model, representing the


0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08


Pho

spho

rus

Con

cent

ratio

n, m

g/L

Observed Monthly Average

Modeled Monthly Average



137

0.00

0.01

0.02

0.03

0.04

0.05


Phos

phor

us C

once

ntra

tions

, mg/

L






Calibration Statistics





Bias, mg/L -0.0046 -0.0085 0.0009 -0.0010 -0.0028 RMSE, mg/L 0.0182 0.0270 0.0061 0.0058 0.0161 Standard Deviation of Observed, mg/L

0.0229 0.0245 0.0056 0.0056 0.0160


0.0194 0.0083 0.0019 0.0002 0.0054


0.0177 0.0258 0.0060 0.0057 0.0159

Variance Reduction 40% -11% -16% -3% 1% R (Correlation Coefficient)

0.6594 0.0085 -0.0606 -0.3233 0.1911


0.3580 -0.2366 -0.1884 -0.0633 -0.0190

Table 7.3: Performance measures for the calibration period using the phosphorus WASP model.

138

7.5 Phosphorus WASP Model Validation

The phosphorus WASP model validation results can be seen in Figures 7.5 to 7.8. The

performance measures for the validation period and entire POR can be seen in Table 7.4

and 7.5.

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30


Phos

phor

us C

once

ntra

tion,

mg/

L


Figure 7.5: Canal validation results for the phosphorus WASP model, representing the


139

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20


Phos

phor

us C

once

ntra

tion,

mg/


Figure 7.6: Cell 1 validation results for the phosphorus WASP model, representing the


0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08


Pho

spho

rus

Con

cent

ratio

n, m

g/L





140

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08


Phos

phor

us C

once

ntra

tions

, mg/

L





Statistical Parameter Canal Validation Statistics





Bias, mg/L 0.0056 0.0009 0.0023 0.0006 0.0009 RMSE, mg/L 0.0313 0.0171 0.0052 0.0063 0.0171 Standard Deviation of Observed, mg/L

0.0236 0.0165 0.0042 0.0063 0.0120


0.0303 0.0113 0.0021 0.0002 0.0087


0.0311 0.0172 0.0047 0.0063 0.0108

Variance Reduction -74% -8% -25% 0% 18% R (Correlation Coefficient)

0.3547 0.2840 -0.0119 0.0369 0.4904


-0.7946 -0.0856 -0.5462 -0.0069 0.0856

Table 7.4: Performance measure for the validation period using the phosphorus WASP model.

141

Statistical Parameter Canal Statistics for POR





Bias, mg/L 0.0005 -0.0037 0.0016 0.00003 -0.0008 RMSE, mg/L 0.0256 0.0225 0.0056 0.00618 0.0137 Standard Deviation of Observed, mg/L

0.0237 0.0208 0.0049 0.00599 0.0141


0.0272 0.0105 0.0021 0.0023 0.0074


0.0257 0.0223 0.0054 0.00620 0.0137

Variance Reduction -18% -15% -21% -7% 5% R (Correlation Coefficient)

0.469 0.1062 -0.0467 -0.1690 0.3156

R2 Value 0.2469 0.0113 0.0022 0.0286 0.0996 Nash Sutcliffe Efficiency -0.1817 -0.1851 -0.3153 -0.0109 0.0512

Table 7.5: Performance measure for the POR using the phosphorus WASP model.

7.6 Discussion and Further Analysis of the Phosphorus WASP Model

The phosphorus WASP model followed the relative trend in the canal and cell 1;

however, in cells 2 and 3 the transient changes in concentrations were not captured. The

bias was able to be reduced relatively close to zero in cell 3 for the POR. The canal

calibrated well, however, the validation results did not show the same conclusions. For

the entire POR the marsh as a whole performed better than the canal.

The phosphorus WASP model was also analyzed by comparing the calculated outflow

loads discussed in section 5.2.2 to the canal modeled loads (this was calculated by

multiplying the daily canal modeled concentrations by the daily observed outflows and

converting to kg/day). The results showed a good correlation between the two (Figure

142

6.18) with a R2 value of 0.6872 and a Nash Sutcliffe Efficiency of 0.6862. However, the

results were not as good as those seen in the chloride model.

R2 = 0.6872Efficiency = 0.6862

0

500

1,000

1,500

2,000

2,500

0 500 1,000 1,500 2,000 2,500

Observed (kg/day)

Mod

eled

(kg

/day

)

Figure 7.9: Modeled loads in the canal compared to the observed outflow loads from the

canal structures.

The statistics from the phosphorus water quality model were compared to results from the

ELM v.2.1 model (Fitz et al., 2002a). These comparisons can be seen in Tables 7.6 to

7.9.

Statistics

Phosphorus Model

Statistics for the POR

Canal

ELM v.2.1. Model

Statistics L40-1

ELM v.2.1 Model

Statistics L40-2

ELM v.2.1 Model

Statistics L-7

Bias, mg/L 0.0005 -0.009 -0.027 0.012 R2 0.2469 0.13 0.17 0.00 RMSE 0.0256 0.058 0.057 0.097 Nash Sutcliffe Efficiency -0.1817 0.00 -0.23 -0.57 Table 7.6: Statistics in the canal comparing the phosphorus water quality model and the

ELM v.2.1 phosphorus model

143

ELM v.2.1. Phosphorus Model Statistics

Statistics

Phosphorus Model Statistics

For the POR Cell 1

LOX 4

LOX 6

Bias, mg/L -0.0042 0.024 0.005 R2 0.0096 0.01 0.03 RMSE 0.0226 0.027 0.009 Nash Sutcliffe Efficiency -0.1867 -105.73 -2.69

Table 7.7: Statistics in the cell 1 comparing the phosphorus water quality model and the ELM v.2.1 phosphorus model.


Statistics

Phosphorus Model

Statistics For the POR

Cell 2

LOX 4

LOX 6

LOX 10

LOX 12

LOX 14

LOX 15

LOX 16

Bias, mg/L 0.0015 0.024 0.005 0.002 0.013 0.014 0.018 0.016

R2 -0.0577 0.01 0.03 0.08 0.12 0.08 0.00 0.00 RMSE 0.0056 0.027 0.009 0.011 0.014 0.016 0.023 0.019 Nash

Sutcliffe Efficiency

-0.2936 -105.73 -2.69 -0.28 -19.95 -18.06 -18.50 -10.14



Statistics

Phosphorus Model

Statistics For the POR Cell 3

LOX 3

LOX 5

LOX 7

LOX 8

LOX 9

LOX 11

LOX 13

Bias, mg/L 0.00002 0.00

1 -0.002 0.002 -0.002 -0.003 -0.004 -0.003

R2 0.0295 0.11 0.19 0.09 0.06 0.04 0.03 0.00 RMSE 0.00618 0.01

5 0.006 0.005 0.005 0.008 0.006 0.006

Nash Sutcliffe

Efficiency -0.0106 -0.33 -0.71 -2.41 -0.73 -0.15 -1.17 -0.54


144

When comparing the two models it can be seen that although the phosphorus WASP

model is cell oriented, it does show better statistical results than the ELM v.2.1 model. In

the canal, the phosphorus WASP model results show a lower bias than the ELM v.2.1

model and a relatively similar Nash Sutcliffe Efficiency.

145

CHAPTER 8: Conclusion and Future Developments

8.1 Water Budget Model Conclusions

The double-box water budget model has proven to be computationally efficient in

performing multi-decadal simulations within minutes. Also the simplicity of the water

budget model allows the Refuge managers to assess strategies (at least on a preliminary

basis) and make management decisions quickly and efficiently. The model allows for

rapid testing of the model sensitivity to parameters and supports quick tests of a broader

suite of management scenarios than can feasibly be examined using a more complex

model. Selected scenarios can later be verified using a more complex model.

The simple water budget model is capable of predicting temporal variations of water

levels in the canal and marsh. It can also help to quantify the different components of the

Refuge’s water budget, particularly the importance of seepage. There are no

measurements of overall seepage rate in the Refuge, therefore the simplified model can

be used to estimate seepage rates based on water balance.

Some limitations of the model are that no spatial variability within the Refuge is

modeled. For example, elevation differences between the northern and southern portions

of the interior marsh are not modeled. Another finding was that the model was

particularly sensitive to the area-average rainfall estimates and the seepage estimates. An

146

interesting finding was that the water budget model was relatively sensitive to the

assumed average marsh elevation.

The simple water budget model was also a valuable tool in filling time series of water

quality constituents such as chloride and phosphorus, particularly in the canal. The water

budget model also proved that by using “The Power Law Model” and simple geometry

the bidirectional flow between the marsh and canal could be appropriately estimated.

8.2 Water Budget Future Developments

Overall the simple water budget model is computationally efficient while maintaining its

simplicity, which was a major objective in the production of this model. However, as

with any model there are some minor improvements which can be made in future

developments that would allow the model to possibly perform more efficiently and

maintain the desired simplicity.

One possibility would be to allow the marsh elevation to be spatially variable, as

mentioned earlier the model showed some sensitivity to marsh elevation and by varying it

spatially it might allow for the model to have better efficiency. To do this the model

would have to be slightly more advanced, for example, the marsh areas would need to be

divided into multiple cells (boxes) such as a North, South, and midsection.

147

Additional modeling endeavors should include the completion of an uncertainty analysis

on the model parameters such as seepage coefficient, aerial average precipitation, and the

ET reduction coefficient. This would allow a better understanding of the parameters

which make up and drive the simple water budget model.

The model can also be used to continue to assess a variety of management scenarios and

alternatives. By continuing to assess various scenarios the model can be further

improved to meet certain needs that may arise.

8.3 Chloride Model Conclusions

Chloride was modeled as a tracer which allowed for a better understanding of the

transport of all constituents including nutrients in the Refuge. The chloride WASP model

can be used to rapidly test the affects of changes in flow on water quality within the

Refuge. The Excel chloride model also provides users with the ability to quickly test

calibration parameters and determine their relative sensitivity to the model.

This model is also helpful in testing and finalizing the calibration of the simple water

budget model. It allowed for better calibration in identifying the canal and marsh

seepage rates. The chloride model also proved to be essential in apportioning the percent

of evapotranspiration that was transpiration. The chloride model was rather sensitive to

this parameter especially in cell 3.

148

Richardson et al. (1990) indicated that there is a large central core area of water in the

interior of the Refuge whose nutrient composition is typical of rain water atmospheric

deposition, surrounded by an area with a higher nutrient composition affected by the

pumped inflows to the perimeter canal. However, this was not the case in this model. It

was found that although the interior cells were the most sensitive to the dry and wet

depositions, they were not predominately driven by these parameters. For example,

much of the chloride that came into cell 3 may have originated in the canal.

8.4 Chloride Model Future Developments

The modeled chloride results could be improved in the future by dividing the marsh into

more cells. This would provide the ability to adjust parameters that the model is sensitive

to such as the percent of evapotranspiration that is transpiration.

In the future the model should be run for the years 2005 to 2007 using data from the

additional 39 enhanced water quality stations which were recently installed (Meselhe et

al., 2005). The chloride model could also be extended to model other conservative or

semi-conservative constituents such as sulphate, total nitrogen, and calcium.

8.5 Phosphorus Conclusions

The phosphorus WASP model overall proved to be a helpful tool in better understanding

the mass transport of phosphorus within the Refuge. The model produced canal results

149

that were comparable to those found in the ELM v.2.1. model. All of the interior cells

showed better statistics than the ELM v.2.1 model.

From the phosphorus WASP model it was determined that the k-c* may be too simple of

a model. The model was able to capture the transients in the canal and cell 1, but it was

unable to do so in cells 2 and 3.

The model was also simulated using different c* and k values for each cell. The results

showed that the statistics were slightly improved, however the model was still unable to

capture the transients in the interior cells.

8.6 Phosphorus Future Developments

By completing the phosphorus model it was found that the k-c* model may be too

simple, therefore, it is suggested that future attempts in modeling phosphorus in the

Refuge be completed using a more complex model. WASP offers an eutrophication

model that uses a phosphorus cycle to directly model the constituent. This module was

not used in these modeling attempts because it was more complex and the main objective

of this report was to keep the models simple.

The phosphorus model may also be divided into multiple cells in the future.. The

phosphorus model can also be run for the period 2005 to 2007 using the observation data

from the 39 additional stations which were installed in 2004 (Meselhe et al., 2005).

150

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160

APPENDIX A: Removed Chloride and Phosphorus Outliers

161

Station Date Chloride Concentration mg/L

Reading Used, mg/L or

Eliminated Comments

28-May-98 88.599 S-5AS 28-May-98 89.779

89.189 Two Readings for this date - the average of the two was taken

10-Apr-95 120.967 S-39 10-Apr-95 101.829


G-251

12-Jul-99

830.67

Eliminated

This value was found to be an outlier the next highest value recorded was found to be 264.19

S-6

8-Jul-97

755.96

Eliminated

This value was found to be an outlier the next highest value recorded was 275 mg/L - Conductivity* was 1247 Siemens (giving an approximate Cl value of 157.9 mg/L) confirming the elimination.

S-10C

8-Jul-97

633.929

Eliminated

This value was found to be an outlier, the next highest value was recorded to be 167.494 mg/L. Conductivity* was 1105 Siemens (giving an approximate CL of 139.9 mg/L) confirming the elimination.

131.307 S-6

27-Mar-00 141.437


148.274 S-6

11-Apr-00 144.096


* A conductivity constant of 0.1266 was determined by averaging the daily chloride concentrations divided by daily conductivy values over for all of the stations over the POR. This constant times a daily conductivity value gives an estimated Chloride value.

Table A.1: Chloride outlier values; and dates and values when there were more than recording.

162

Station Date Phosphorus

Concentration mg/L

Reading Used, mg/L

or Eliminated Comments

28-May-98 0.039 S-5AS

28-May-98 0.038 0.0385 Two Readings for this date - the average of the two was taken

1-Jun-00 0.035 1-Jun-00 0.023 1-Jun-00 0.022

G-310

1-Jun-00 0.021

0.02525 Four Readings for this date - the average of the two was taken

8-Jun-00 0.016 8-Jun-00 0.016 8-Jun-00 0.017 8-Jun-00 0.016

G-310

8-Jun-00 0.017

0.0164 Four Readings for this date - the average of the two was taken

11-Apr-00 0.017

S-6 11-Apr-00 0.019


27-Mar-00 0.187

S-6 27-Mar-00 0.132


Table A.2: Dates and values of days when there were more than one phosphorus reading at an inflow or outflow structure.

163

Station Date Phosphorus

Concentration mg/L

Reading Used, mg/L

or Eliminated

Comments

10-Apr-95 0.006 S-39

10-Apr-95 0.027 0.027

Two Readings for this date - Used .027 mg/L because .006 mg/L was considered to be an extreme value - The previous data reading was .041 mg/L and the following data reading was .022 mg/L therefore it was appropriate to use .027 mg/L

0.017 G-251(G)

1-Jun-99

0.015 0.016

Two Readings for this date - the average of the two was taken

0.012 G-251(G)

6-Jul-99

0.013 0.0125


0.02 G-251(G)

7-Sep-99

0.021 0.0205


0.01 0.15

G-251(G)

2-Nov-99 0.015

0.0125

Three Readings for this date - the .15 was thrown out because of its extreme value - the average of the other two value was used

0.025

G-251(G) 18-Jan-00 0.022


Table A.3: Dates and values of days when there were more than one phosphorus reading at an inflow or outflow structure.

164

APPENDIX B: Daily Chloride Excel Model Results

165

0

25

50

75

100

125

150

175

200

225

250

275

300


Chl

orid

e C

once

ntra

tion,

mg/

L

X0 Z0 S39 S10E S10D S10C S10A G94B Modeld

Figure B.1: Chloride Excel model results for the canal for the calibration period January

1, 1995, to December 31, 1999.

0

25

50

75

100

125

150

175

200

225

250

275

300


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure B.2: Chloride Excel model results for the canal for the validation period January

1, 2000, to December 31, 2004.

166

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tions

, mg/

L

X1 Z1 Z2 LOX6 LOX 4 Modeled

Figure B.3: Chloride Excel model results for the cell 1 for the calibration period January 1, 1995, to December 31, 1999.

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tions

, mg/

L


Figure B.4: Chloride Excel model results for the cell 1 for the validation period January 1, 2000, to December 31, 2004.

167

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion

mg/

L

LOX4 LOX10 LOX12 LOX14 LOX15 LOX16 X2 Y4 Z3 Z4 LOX 6 Modeled

Figure B.5: Chloride Excel model results for the cell 2 for the calibration period January

1, 1995, to December 31, 1999.

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion

mg/

L

LOX4 LOX10 LOX12 LOX14 LOX15 LOX16 X2 Y4 Z3 Z4 LOX 6 Modeled

Figure B.6: Chloride Excel model results for the cell 2 for the validation period January

1, 2000, to December 31, 2004.

168

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion,

mg/

L

LOX3 LOX5 LOX7 LOX8 LOX9 LOX11 LOX13 X4 Modeled

Figure B.7: Chloride Excel model results for the cell 3 for the calibration period January

1, 1995, to December 31, 1999.

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion,

mg/

L

LOX3 LOX5 LOX7 LOX8 LOX9 LOX11 LOX13 X4 Modeled

Figure B.8: Chloride Excel model results for the cell 3 for the validation period January

1, 2000, to December 31, 2004.

169

APPENDIX C: Daily Chloride WASP Model Results

170

0

25

50

75

100

125

150

175

200

225

250

275

300


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure C.1: Chloride WASP model results for the canal for the calibration period January 1, 1995, to December 31, 1999.

0

25

50

75

100

125

150

175

200

225

250

275

300


Chl

orid

e C

once

ntra

tion,

mg/

L


Figure C.2: Chloride WASP model results for the canal for the validation period


171

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tions

, mg/

L


Figure C.3: Chloride WASP model results for the cell 1 for the calibration period January 1, 1995, to December 31, 1999.

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tions

, mg/

L


Figure C.4: Chloride WASP model results for the cell 1 for the validation period January 1, 2000, to December 31, 2004.

172

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion

mg/

L

LOX4 LOX10 LOX12 LOX14 LOX15 LOX16X2 Y4 Z3 Z4 LOX 6 Modeled

Figure C.5: Chloride WASP model results for the cell 2 for the calibration period


0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion

mg/

L

LOX4 LOX10 LOX12 LOX14 LOX15 LOX16X2 Y4 Z3 Z4 LOX 6 Modeled

Figure C.6: Chloride WASP model results for the cell 2 for the validation period


173

0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion,

mg/

L

LOX3 LOX5 LOX7 LOX8 LOX9 LOX11LOX13 X4 Modeled

Figure C.7: Chloride WASP model results for the cell 3 for the calibration period


0

20

40

60

80

100

120

140

160

180

200


Chl

orid

e C

once

ntra

tion,

mg/

L

LOX3 LOX5 LOX7 LOX8 LOX9 LOX11LOX13 X4 Modeled

Figure C.8: Chloride WASP model results for the cell 3 for the validation period


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