Evaluation of watershed control of two Central Florida lakes: Newnans Lake and Lake Weir

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Title:
Evaluation of watershed control of two Central Florida lakes: Newnans Lake and Lake Weir
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xvi, 259 leaves : ill. ; 29 cm.
Language:
English
Creator:
Williams, Sherry Brandt
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Subjects / Keywords:
Environmental Engineering Sciences thesis, Ph.D   ( lcsh )
Dissertations, Academic -- Environmental Engineering Sciences -- UF   ( lcsh )
watersheds
simulation modeling
spatial modeling
emergy
Spatial Coverage:
United States -- Florida -- Newnans Lake and Lake Weir
Coordinates:
29.65 x -82.22

Notes

Statement of Responsibility:
by Sherry Brandt Williams.
Thesis:
Thesis (Ph.D.)--University of Florida, 1999.
Bibliography:
Includes bibliographical references (leaves 253-258).
General Note:
Printout.
General Note:
Vita.

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University of Florida
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All applicable rights reserved by the source institution and holding location.
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aleph - 002531978
oclc - 43707672
notis - AMP7902
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AA00003998:00001


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EVALUATION OF WATERSHED CONTROL OF TWO CENTRAL FLORIDA
LAKES: NEWNANS LAKE AND LAKE WEIR










By

SHERRY BRANDT WILLIAMS


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


1999














ACKNOWLEDGMENT


Most important throughout this process was the love and support of my son,

Casey Cameron Cody Dakota Malachi.

I would like to thank all of my committee: Dr. T. L. Crisman, my chair, Dr. H. T.

Odum, my co-chair, and Drs. W. Viessman, C. Montague and F. Nordlie. Without their

support and unique insights, this study would not have been completed.

My sincere gratitude goes to Dr. Crisman for his patience and unfailing

encouragement throughout the past four years.

I am indebted to Dr. Odum for showing me a different perspective on

thermodynamics and scale of applications. Dr. Odum, like a spring in a desert, is a point-

source of energy in a thirsty wilderness, with a territory of influence as huge as a star.

I gratefully acknowledge Dr. M. T. Brown for discussions on this project and

spatial methods.

I thank all the people who encouraged me and critiqued my reasoning: Dr. Linda

Leigh, Debra Childs, David Brandt-Williams, Paula Palmer, Linda Tyson, Andrea

Kendall, Josh and Nadine Orrell, and Judy Fouts.

This study was aided by a National Science Foundation Minority Engineering

Doctorate Initiative Fellowship through the College of Engineering at the University of

Florida. B. J. Bukata, J. Buenfil and K. Jackson assisted in preparing maps.








The dissertation is dedicated to the memory of my parents, Winona and Richard

Brandt, who instilled incentives, means, and freedom to achieve.















TABLE OF CONTENTS


ACKNOW LEDGM ENTS .................................................................... ...................

LIST OF TABLES................ ...... .................................... ............................................... viii

LIST OF FIGURES ........................................................ ................................................x

ABSTRACT ............................................................................................................. xvi

CHAPTERS Rane

I INTRODUCTION ..............................................................................................

Concepts and Perspectives ............................................................................................3
Scale of Components..............................................................................................4
Energy Systems Diagram .........................................................................................6
Emergy and Empower Valuation.................................... ....... .................... 6
Transformity............................................................................................................8
Transform ity and Control.......................................................................................8
M ultistage Processes of M material Flows..................................... ............ ............... 9
Emergy per Mass and Concentration......................................................10
Empower Density.............................................................................................. 1
Spatial Organization...................................................................................... 1
Record of Lake Functions ............................................................ 12
Emdollars................................................................................................................12
Estimating Benefits of Lake M anagement.................................................. ..............13
Previous Studies..........................................................................................................14
Shallow Lake Limnology................................................................................ 14
Nutrient Dynamics and Loading ............................................................... 15
Spatially Distributed Surface Flow Models........................ ..........................16
Lake Valuation...................................................................................................... 18
Paleolimnology......................................................................................................20
W atersheds Evaluated ..................................................................................................21
Newnans Lake........................................................................................................21
Lake W eir ........................................................................................................23
Plan of Study............................................. .............................................................24









2 M ETHODS ..............................................................................................................25

Map Preparation and Data Sources .........................................................................25
Elevation and W atershed Delineation...... ................ ................... ................ 25
Land Use and Cover.................... ................................................................... 26
Soil Maps ..............................................................................................................27
Rain Data.... ....................... ............................. ...............................................27
Dynamic Simulation Models.....................................................................................28
System Diagram ................................................................................................ 28
Simulation Example.......................................................................................... 31
Emergy Evaluation ............................................................................................... 32
Emergy Tables.....................................................................................................32
Emergy Indices ...... ................ ............ ......... ..................... ................ 32

3 RESULTS...........................................................................................................40

PART 1: SPATIAL WATERSHED MODEL ............................................................40

Development of Material Flows ..... ...........................................................................40
Simulation of W ater Budgets in GIS ................................................... ............ 43
Infiltration and Runoff Calculations....................... .......................................46
Phosphorus Uptake, Adsorption and Deposition............................................47
Movement Between Cells ................................................................................ 51
Verification.......................................................................................................52
Development of Emergy Patterns............................................ ........................ 53
Empower Density Mapping ..................................................................... .. ...5.. 56
Emergy Accumulation Maps .......................................... ........................................56
Phosphorus Emergy Per Mass Ratios .......................................................................65
Results: Landscape Properties ........................................................ ....................65
W atershed Morphology, Newnans Lake ..........................................................66
W atershed Morphology, Lake W eir......................................................................66
Soils and Geology, Newnans Lake............. ............ ................................... 67
Soils and Geology, Lake W eir ............................................................................. 68
Land Use Changes, Newnans Lake .............................................................................68
Land Use Changes, Lake W eir.......................................................................... 9
Results: Non-point Source Runoff Profiles ............................................................79
W after Profiles............................................... ....................................................79
Water profiles for Newnans Lake ............................. ...............................91
Water profiles for Lake Weir.................................................................................92
Phosphorous Profiles for Newnans Lake..........................................................93
Phosphorous Profiles for Lake W eir.... ...................................................................93
Results: Emergy Patterns ........................................................................................... 121
Aerial Emergy Flux, Newnans Lake........................................................................121
Aerial Emergy Flux, Lake W eir ......................................................................... 121
Emergy Accumulation Profiles............. ................................... .................128








PART 2: DYNAMIC LAKE SIMULATION MODEL .........................................128

Development...........................................................................................................134
Aggregation and Interactions............................................................................134
Trophic State Simulation.............. ..................................................................139
Calibration Data .............................................................................................139
Simulation Results........................................................................................................140
Simulations Comparing Responses to TP Loading........................................... 140
Pulsing Simulation..................... .......................................... ....................... 145

PART 3: WATERSHED CLASSIFICATION AND MANAGEMENT................ 146

Material Loads and Ratios .................................... ...........................................146
Newnans Lake .....................................................................................................157
Lake Weir .............................................................................................................157
Emergy Loads and Ratios.......................................................................................... 157
Comparison Between Watersheds ......................................................... .......157
Comparison of Simulated Load and Empirical Phosphorus Data........................ 158
Emergy Accumulation Patterns............................................................................158
Intervention Strategies................................................................................................168
Newnans Lake ...................................................................................................... 168
Lake Weir.............................................................................................................168


4 SUMMARY AND RECOMMENDATIONS .................................................178

Summary....................................................................................................................178
Spatial Patterns in Lake Watersheds.......................................................................... 180
Patterns from Geologic Processes..................................................................... 181
Patterns from Human Development...................................................................182
Hierarchy of Lakes and Watersheds.................................................. ..................182
Cumulative Watershed Loading and Trophic Status ...................................... ............183
Emergy and Emergy per Mass Related to Lake Status..........................................185
The Watershed ........... ................................. .......... .......... 186
The Watershed-Lake Interface...............................................................186
Phosphorus ................ ..................................................................................... 187
Water.............................................................................................................. 187
Sediments .............................................................................................................187
Emdollars.................................................................................188
Use of Dynamic Simulations as Quality Indicators .........................188
Classification of Watersheds Using Material and Emergy Indices.......................... 189
Evidence for Watershed Intervention and Prioritization............. .........................190
Intrawatershed Modification of Non-Point Source Loading..................... ...190
Interwatershed Priorities................................................................................ 191
Recommendations ................. ....................... ......................................................192
Non-point Source Inputs .............................. ...............192









Development Density ............. ......................................................................193
Appropriate Scale for Stonnwater Management ...................... ............ 193
Use of Emergy as a Management Decision Tool ...............................................194
Conclusion............................................................ ..................................................195

APPENDICES

A GIS INFORMATION ......................................................................................... 197

B SOIL PROPERTIES........................................................................................... 199

C VERIFICATION OF SPATIAL MODEL ..........................................................206

D EMPOWER DENSITIES.......................................................................210

E EMERGY EVALUATIONS OF WATERSHEDS.......................................... 226

F TRANSFORMITIES .............................................................................................232

G IN-LAKE SIMULATIONS ...................................247


REFERENCES ................................................................................................. ................253

BIOGRAPHICAL SKETCH................... .................................................................259














LIST OF TABLES


Table Mae

2.1 Example of energy evaluation: annual production of one hectare of Bahia grass* (see
Figure 23) ................. .............................................................................................. 34

2.2. Description of information presented in an energy table......................................36

3.1 Impervious surface for different land uses................................................................48

3.2 Average phosphorous deposition rates.....................................................................48

3.3 Phosphorus quantities in sediment cores, Newnans Lake and Lake Weir (Gottgens &
Crisman, 1993; Crisman et al., 1992), values approximated from graphs................54

3.4. Empower densities for watershed land use, 1990........................................... 57

3.5 Empower densities for land use in 1950 and 1970 (natural areas are assumed the same
as 1990).............................................................................................................. 57

3.6 Emergy evaluation of Newnans Lake watershed/lake interface, 1970.......... ...........59

3.7 Emergy evaluation of Lake Weir watershed/lake interface, 1970 ..............................62

3.8 Summary emergy values for Newnans Lake and Lake Weir .......................................64

3.9 Land use areas for Newnans Lake..............................................................................83

3.10 Land use areas for Lake Weir ............................................................................... 88

3.11 Summary data for watersheds loads to Newnans Lake form spatial simulation........90

3.12 Summary data for watersheds loads to Lake Weir from spatial simulation..............90

3.13 Definitions of terms describing interactions and flows relevant to simulations.......133

3.14 Productivity, storage and turnover times for lake components from various literature
sources .............................................................................................................. 141

3.15 Summary data used as guide for initial simulations... .........................................142








3.16 Summary data for watershed loads to Newnans Lake form spatial simulation .......159

3.17 Summary data for watershed loads to Lake Weir from spatial simulation..............159

3.18 Comparison of watershed energy classification parameters .................................164

3.19 Summary emergy data for runoff to lake, Newnans Lake ..................................... 166

320 Summary emergy data for runoff to lake, Lake Weir ..........................................166














LIST OF FIGURES


Figure page

I.1 Components of a watershed-lake system on a graph of turnover time and territory ..5

12 Energy systems diagram of a lake watershed including an area ofurban settlement.....7

1.3 Watershed locations in the state of Florida...........-.................................... ............22

2.1 Energy systems symbols and definitions (Odum, 1994) ............................................29

22 Simulation example: aggregated water budget for a lake, values used for calibrating
coefficients and the differential equation...........................................-.................. 33

2.3 Emergy analysis example: a) definition of two indices emergy yield ratio and
investment ratio; b) simplified energy diagram for grass example in Table 2.2.......35

2.4 Diagram explaining solar transformity.................................................................... 37

3.1 Map layers used as data in spatial model and the hydrology functions derived from
each data set. Linking these maps with mathematical functions allows them to be
used as boundary conditions for solving continuity equations governing the flow of
materials through the watershed- ....................................................................42

3.2 Energy system diagram illustrating water budget in a single cell and movement into
next cell. Solid lines carry water. Dashed pathways are energy flows affecting
water. Et = evapotranspiration-.................................................... ........44

3.3 System diagram of the phosphorus model for each cell and its relation to water model
in Figure 3.2. Energy pathways without water or phosphorus are dashed. Water
paths are blue, phosphorus is green.......................................................................- 49

3.4 Diagram with empower pathways and emergy storage components included in
calculations for each cell ................. ............................................. ................. ...55

3.5 Diagram of inputs to lake, for use in energy evaluation in Table 3.6..........................58

3.6 Elevation contours in Newnans Lake watershed................................................69

3.7 Elevation contours on the floor ofNewnans Lake (SJRWMD, 1996) .....................70








3.8 Elevation contours in Lake W eir ........................................................................... 71

3.9 Lake Weir bathymetry (after Ott and Chazal, 1966) ...............-....... .....................72

3.10 Hydrological soil classification groups in Newnans Lake watershed, as defined by
Soil Conservation Service. Group D has high runoffpotential, C has moderate
potential, B has low runoffpotential and A has little to no runoff potential
(classification described in Chapter 2)-..--.....--...-...----------.......................73

3.11 Soil impedance distributions, categorized by permeability and capacity, with values
representing the fraction of an average rain event being retained within the soil
column, Newnans Lake ---...---......................... ...-......................--- 74

3.12 Map of subsurface geology formation, Newnans Lake (adapted from SCS, 1982)...75

3.13 Hydrological soil classification groups in Lake Weir watershed, as defined by Soil
Conservation Service. Group D has high runoff potential, C has moderate
potential, B has low runoff potential and A has little to no runoff potential
(classification described in Chapter 2)................................................................. 76

3.14 Soil impedance distributions, categorized by permeability and capacity, with values
representing the fraction of an average rain event being retained within the soil
column, Lake Weir..--..-.............-..-..... ...-....-.......-- -- ........-.........----.... ..---.......77

3.15 Map of subsurface geology formation, Lake Weir.-..-......--....-..... .......--..--..........78

3.16 Newnans Lake watershed land use, 1950.................................--.......--......-- .............80

3.17 Newnans Lake watershed land use, 1970-.................................-------..---.. --- 81

3.18 Newnans Lake watershed land use, 1990.-...-.............................---.........-----............--82

3.19 Highest land use changes in Newnans Lake watershed..---............-------------.......84

3.20 Lake Weir watershed land use, 1950 ...........-- .....-...............--------..-..-- .............85

3.21 Lake Weir watershed land use, 1970.--..-...--....-.......... ......-.---------------....--.86

3.22 Lake Weir watershed land use, 1990 ----.........------........ .......-------------- ..-- 87

3.23 Highest land use changes in Lake Weir watershed .......--...........---------------.............. ..89

3.24 Rank-order graph for water volume exported from each cell in Newnans Lake
watershed: a) curve generated by actual data; b) log-log representation of data-.....94

3.25 Effective watershed, 1950, Newnans Lake. Inner zone exports from 1E4 to 1E6
liters of water per year; middle zone exports on average 6000 1/yr; outer zone
exports less than 1000 1/yr. ...--....-- --..-----.................---......--. .------......95








3.26 Effective watershed, 1970, Newnans Lake. Inner zone exports from 1E4 to 1E6 liters
of water per year; middle zone exports on average 6000 I/yr; outer zone exports less
than 1000 /yr --------......................................-------.........................................................-..........------..97

3.27 Effective watershed, 1990, Newnans Lake. Inner zone exports from 1E4 to 1E6 liters
of water per year; middle zone exports on average 6000 /yr; outer zone exports less
than 1000 I/yr ...........................-........................---........---.............................---------..................----------99

3.28 Changes in area of watershed contributing stormwater to the lake betweenl950 and
1970, Newnans Lake watershed. Increases in effective watershed are red to yellow
(red being area of highest transport) and coincide with areas having an increase in
impervious surface. No change or decrease in transport range from dark green
(beneficial change) to light green (little or no change) with beneficial changes
coinciding with areas of reforestation................................................------------101

3.29 Changes in area of watershed contributing stormwater to the lake betweenl970 and
1990, Newnans Lake watershed. Increases in effective watershed are red to yellow
(red being area of highest transport). No change or decrease in transport range from
dark green (beneficial change) to light green (little or no change).......--................102

3.30 Rank-order graph for water volume exported from each cell in Lake Weir watershed:
a) curve generated by actual data; b) log-log representation of data.........-..........--- 103

3.31 Effective watershed, 1950, Lake Weir. Inner zone exports from 1E4 to 1E6 liters of
water per year; middle zone exports on average 6000 I/yr; outer zone exports less
than 1000 I/yr. ...............-...........-.......... .... .................. .. ............. 104

3.32 Effective watershed, 1970, Lake Weir. Inner zone exports from 1E4 to 1E6 liters of
water per year; middle zone exports on average 6000 1/yr, outer zone exports less
than 1000 /yr. ....... .............................................................................. 105

3.33 Effective watershed, 1990, Lake Weir. Inner zone exports from 1E4 to 1E6 liters of
water per year; middle zone exports on average 6000 I/yr; outer zone exports less
than 1000 I/yr ................................ ............ .............. ................... 106

3.34 Changes in area of watershed contributing stormwater to the lake betweenl950 and
1970, Lake Weir watershed. Increases in effective watershed are red to yellow (red
being area of highest transport). No change or decrease in transport range from dark
green (beneficial change) to light green (little or no change)......---........................107

3.35 Changes in area of watershed contributing stormwater to the lake betweenl970 and
1990, Lake Weir watershed. Increases in effective watershed are red to yellow (red
being area of highest transport). No change or decrease in transport range from dark
green (beneficial change) to light green (little or no change)............................ 108

3.36 Estimated phosphorus deposition, 1950, Newnans Lake watershed -.....................109








3.37 Estimated phosphorus deposition, 1970, Newnans Lake watershed .............-...... 110

3.38 Estimated phosphorus deposition, 1990, Newnans Lake watershed .............-...... 111

3.39 Estimated total phosphorus (TP) export profile for 1950, Newnans Lake. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake ..............112

3.40 Estimated total phosphorus (TP) export profile for 1970, Newnans Lake. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake.............. 113

3.41 Estimated total phosphorus (TP) export profile for 1990, Newnans Lake. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake...............114

3.42 Estimated phosphorus deposition, 1950, Lake Weir watershed......-..................1. 15

3.43 Estimated phosphorus deposition, 1970, Lake Weir watershed .......................... 116

3.44 Estimated phosphorus deposition, 1990, Lake Weir watershed..........................1. 117

3.45 Estimated total phosphorus (TP) export profile for 1950, Lake Weir. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake............118

3.46 Estimated total phosphorus (TP) export profile for 1970, Lake Weir. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake.............119

3.47 Estimated total phosphorus (TP) export profile for 1990, Lake Weir. Each band
represents 20kg TP/ha/yr exported from that area and reaching the lake ..............120

3.48 Empower density (E14 SEJ/m2/yr) distribution in Newnans Lake watershed in
1950. Black is highest density, progressively lighter areas have decreasing densities.
................. ............... .... .... ........ ....... ..........................................122

3.49 Empower density (E14 SEJ/m2/yr) distribution in Newnans Lake watershed in
1970. Black is highest density, progressively lighter areas have decreasing densities.
.... .... .......... ...- ......... ...................... ....... ............. ........ .......... 123

3.50 Empower density (E14 SEJ/m2/yr) distribution in Newnans Lake watershed in
1990. Black is highest density, progressively lighter areas have decreasing densities.
..............-.....-........... .... .... .. .................. .................... ..................... 24

3.51 Empower density (E14 SEJ/m2/yr) distribution in Lake Weir watershed in 1950.
Black is highest density, progressively lighter areas have decreasing densities......125

3.52 Empower density (E14 SEJ/m2/yr) distribution in Lake Weir watershed in 1970.
Black is highest density, progressively lighter areas have decreasing densities......126

3.53 Empower density (E14 SEJ/m2/yr) distribution in Lake Weir watershed in 1990.
Black is highest density, progressively lighter areas have decreasing densities......127








3.54 Water drainage network, post-development in Newnans Lake watershed, cumulative
energy, log sej .---........ ....... --........ .. .......-.... ............... ... ......... ... 129

3.55 Water drainage network, post-development in Lake Weir watershed, cumulative
emergy, log sej ----... ..--.--...-....-----------..----........--.............-.... ----. .....130

3.56 Post-development phosphorus emergy drainage network, Newnans Lake, log sej/g-....
............................ ....---........ ...-..................- .. .........-.....-..------- ...131

3.57 Post-development phosphorus emergy drainage network, Lake Weir, log sej/g-....132

3.58 Lake diagram illustrating complexity of interactions and food web hierarchy.........135

3.59 An aggregated in-lake energy systems diagram with components and pathways
included in the simulation of lake responses to changing phosphorus loads and
determination of dynamic trophic state indices--...............-............................------136

3.60 In-lake energy systems diagram and equations used to simulate response to changing
watershed inputs ---....--..-.................................--..........-.....-....-....................--..-..137

3.61 Steady state flows and storage for eutrophic simulation ....-.........----...-...-...--- 143

3.62 Simulation of eutrophic conditions with TP runoff increase after 3 years ........-....147

3.63 Simulation ofoligotrophic conditions with TP runoff increase after 3 years....--....148

3.64 Simulation ofhypereutrophic conditions with zooplankton ingesting less
phytoplankton and more organic matter ..--................-..........--.....--....-.......----...149

3.65 In-lake simulation using averaged eutrophic conditions for several lakes worldwide
with averaged environmental sources. No perturbations occur in inputs and the
model is run for 30 years ...--.......--........-........... .........-----...........-....---- ...150

3.66 In-lake simulation using averaged eutrophic conditions for several lakes worldwide,
but with higher environmental sources than shown in Figure 3.65 --.....~.~.......-..-.. 151

3.67 In-lake simulation using averaged eutrophic conditions for several lakes worldwide,
but with lower environmental sources than shown in Figure 3.65.........-----...........-- 152

3.68 Using in-lake simulation to explore hierarchy of pulsing control; phytoplankton
storage allowed to vary, all other variable higher in chain held constant-..--...-.--....153

3.69 Using in-lake simulation to explore hierarchy of pulsing control; producer storage
allowed to vary, all other variable higher in chain held constant.....--.................-- 154

3.70 Using in-lake simulation to explore hierarchy of pulsing control; producer and
zooplankton storage allowed to vary, all other variable higher in chain held
constant ............ ---.............................--- .................... ..-- ----.--.-- 155








3.71 Using in-lake simulation to explore hierarchy of pulsing control; all variables fully
interacting ...............................................................................................................156

3.72 Phosphorus and water loads to Newnans Lake from the watershed; a) TP loading, g;
b) water, m3; c) TP concentration, mg/1; d) TP to lake volume ratio, g/m3---..........160

3.73: Comparison of simulated TP loads and empirical water quality data, Newnans Lake;
a) TP and TSI data from Huber (1982) and Lakewatch (1998), TP/sediment ratios
from Gottgen and Crisman (1993); b) simulation values........---.......................- 161

3.74 Phosphorus and water loads to Lake Weir from the watershed; a) TP loading, g; b)
water, m3; c) TP concentration, mg/1; d) TP to lake volume ratio, g/m3 -----........162

3.75: Comparison of simulated TP loads and empirical water quality data, Newnans Lake;
a) TP and TSI data from Huber (1982) and Lakewatch (1998), TP/sediment ratios
from Gottgen and Crisman (1993); b) simulation values----...... ............ 163

3.76 Comparison of emergy flows to the watershed and lake with trophic state index for
both Newnans Lake and Lake Weir........- -....... ..............................................165

3.77 Comparison of simulated phosphorus emergy flows over time with empirical water
quality from Huber et al. (1982) and Lakewatch (1998).......................................167

3.78 Pre-development phosphorus emergy drainage network, Newnans Lake; log sej/g
........................... ..... ..... ....... ......... ........ ............ .. .. .... ..............170

3.79 Post-development phosphorus energy drainage network, Newnans Lake; log sej/g
......... --.................. ............................................................-. ...... ..... 171

3.80 Pre-development phosphorus emergy drainage network, Lake Weir, log sej/g.......172

3.81 Post-development phosphorus emergy drainage network, LakeWeir, log sej/g.......173

3.82 Placement of intervention based on points of highest mass loading, Newnans Lake;
purple areas indicate best siting...................-..................... ......--...... 174

3.83 Placement of intervention based on points ofemergy/mass greater than 2E12 sej/g,
Newnans Lake; purple areas indicate best siting ......................... ...... 175

3.84 Placement of intervention based on points of highest mass loading, Lake Weir;
purple areas indicate best siting ......---... --...-................... .....---........--.......... 176

3.85 Placement of intervention based on points of emergy/mass greater than 2E12 sej/g,
Lake Weir; purple areas indicate best siting....................................-.........177














Abstract of Dissertation Presented to the Graduate School of the
University of Florida in Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy

EVALUATION OF WATERSHED CONTROL OF TWO CENTRAL FLORIDA
LAKES: NEWNANS LAKE AND LAKE WEIR


By

Sherry Brandt Williams

December 1999


Chairman: T. L. Crisman
Co-Chairman: H. T. Odum
Major Department: Environmental Engineering Sciences

This dissertation relates lakes and watersheds by analyzing spatial patterns with

GIS and simulation models of lake inputs associated with non-point sources. The flow of

water and its constituents use energy transformations to organize landscape function and

structure. This organization was evaluated with measures of materials, energy and emergy

(a measure of real wealth based on prior work of nature and economy). Two Florida lakes

and their watersheds, Newnans Lake and Lake Weir, were studied.

The convergence of materials and energy makes these lakes centers of high emergy

in the watershed hierarchy. In these watersheds, there was also an area of concentration

in human settlements. The spatial chronosequence of watershed influence increased with








economic development. The extent of influence was determined by both soil type and

land use and was not concentric to the lake.

A spatial model estimated the average yearly total phosphorus input to Newnans

Lake from non-point sources at 4.3xl04 kg/yr. Using Vollenweider loading relationships,

phosphorus accounts for almost half the average algal chlorophyll concentration in

Newnans Lake. Estimated average yearly total phosphorus input to Lake Weir is

3.4x104 kg/yr, and accounts for all of the average chlorophyll concentration.

A simulation model of in-lake functions using oligotrophic calibrations responded

to increased phosphorus input with a 20% increase in total biomass. The simulation with

eutrophic calibration responded with a 10% increase. A hypereutrophic simulation

oscillated with frequency controlled by the fish zooplankton populations.

Simulated trophic state indices, using equations from Huber et al. (1982), was 78

for Newnans Lake and 38 for Lake Weir. This compares to a long-term observed index of

75 and 42, respectively.

Newnans Lake has higher emergy use in the watershed and lake,

2.5x1021 sej/yr and l.lxl1'9 sej/yr respectively. Lake Weir uses 9.2x10'9 sej/yr in the

watershed and 1.0xl0sO sej/yr in the lake. Newnans Lake watershed contributes

73 million Em$/yr to the lake about 8% of the total watershed real wealth and about

800 Em$ per visitor. Lake Weir's watershed contributes 1.3 million EmS/yr to the lake -

about 27% of the watershed and about 5 EmS per visitor.


xvii














CHAPTER 1
INTRODUCTION



Where the telescope ends, the microscope begins. Which of the two has the grander
view?
-Victor Hugo, 1862



Observation and intuition suggest an intimate connection between a lake and its

watershed. Lake ecosystems respond to economic activity and material flows from their

watersheds (Fluck et al., 1992a; Dierberg et al., 1988; Wetzel, 1983; Vollenweider, 1970).

Still, there are unresolved questions in limnology and landscape ecology concerning this

relationship (Lowe et al., 1997; Canfield, 1988). This dissertation evaluated watershed-

lake relationships using systems concepts, computer simulations, geographic information

methods, and the principles of energy hierarchy affecting spatial organization. Emergy

concepts (Odum, 1996) were used to classify watersheds and lakes and to evaluate

benefits of management alternatives.

As a single drop or torrential flood crest, water is a conduit for energy transfer

throughout the biosphere. Pervasive and awesome at any scale, water cradles life, sculpts

landforms and destroys economies. Water, carrying energy with it, is a tangible reality

that defines the productivity, structure and diversity of every ecosystem in its path, and

sets earth apart as a unique planet in the next larger scale, this solar system.








Water flows organize the land and lakes, using patterns of energy transformation

to create function and structure. Whereas water flowing downhill from the landscape to

the lake is the more familiar pattern, the lake also exerts an influence on its watershed, as

expected with symbiotic self-organization (Odum, 1994; Allen, 1986; Salthe, 1985).

Examples of this influence are the effect of the lake on the surrounding microclimate and

the economic development that accompanies recreational use.

Increasing human presence in watersheds alters land cover, use and ultimately

drainage patterns, thus affecting the quality, quantity, and timing of stormwater runoff.

Aquatic environments on the receiving end of this discharge may experience changes in

trophic state and shifts in species dominance (Cooke et al., 1983; Wetzel, 1983).

Predicting surface water changes that result from increased development in a watershed

may provide important management insights for avoiding negative impacts downstream.

Consequently, research is needed to improve prediction of the cumulative impact of

increasing watershed development on freshwater systems. This is especially critical as

developed and developing nations alike become increasingly dependent on surface water

resources.

This dissertation quantifies several important features of the watershed-lake

system using energy paths, and their systemic impacts. The elements of focus are the

study of runoff and its constituents within a watershed, the response of lakes receiving

the input, and, in the opposite direction, the effects of lakes on the watershed.

Numerous hydrological models estimate overall runoff quantity, nutrient loading

and timing changes, but do not provide watershed management criteria for optimum








retention strategies (Adamus and Bergman, 1995; Heidtke and Auer, 1993). Many

surface water quality indices, such as trophic state and total phosphorus, are static and

minimize the contributions and interactions ofmacrophytes, consumers and watershed

inputs (Canfield and Hoyer, 1992, Huber et al., 1982). Integrated long-term studies of

terrestrial-aquatic dynamics in subtropical areas are few, and simulations of watersheds

and lakes together using an overall system perspective and criteria of overall benefit are

largely absent.

Models providing a better understanding of these spatial and cumulative temporal

effects can be used by planners to reduce the negative impacts of watershed development.

Simulated models and benefit indices can direct development to less sensitive areas, assist

in prioritizing conservation of more sensitive areas, and identify critical locations for

water retention and quality monitoring.

Two Florida lakes (Newnans and Weir) of different depths and trophic state were

related to their watersheds. Patterns of development were analyzed, and nutrient, energy

and emergy budgets related. Suggestions were made for managing the watershed using

different scenarios of economic development that were consistent with system

organizational principles.


Concepts and Perspectives

The central question in this dissertation concerned the coupling of a shallow lake

and its watershed. A natural energy hierarchy is formed when both material and energy

flows from a landscape scale converge on the lake, but the watershed also has a hierarchy








of human settlements with an economy and concentration of information (Huang, 1998;

Odum, 1994).

One method for assessing this hierarchy is to quantify the impact of cumulative

watershed landscape changes on Florida's lakes. Changes in water influx from increasing

development create energy and material pulses from a large landscape and carry

constituents perhaps best left in upstream systems. These pulses and convergence of

materials inarguably affect the downstream surface waters where nutrients are

concentrated into a much smaller area. The following concepts were used in this study for

analysis and synthesis of these relationships.


Scale of Components

All systems have components at many scales, defined by turnover times,

territory, and energy consumption and output (Odum, 1994). A basic triadic structure

representing three fundamental and contiguous systems levels is the minimum sufficient

to study a process, its causes and its influence (Salthe, 1985). In Figure 1.1, the main

components of the watershed are represented from lower left to upper right according to

the scale of replacement time and territory of support and influence. Three levels

presented for evaluating the lake in relationship to its watershed are the lake itself, in the

middle, the surrounding natural systems, ranked below the lake, and the human economy,

information and structure within the watershed, ranked higher than the lake. This ranking

is proposed as a hypothesis.

















































Territory of support and influence



Figure 1.1. Components of a watershed-lake system on a graph of turnover time
and territory.









Energy Systems Diagram

The components of any system are organized as an energy hierarchy because energy

flows of many small processes converge and are transformed to make larger scale

processes (Odum, 1994). The food chain from phytoplankton to fishes is an example.

Components and processes of a system can be represented with an energy systems

diagram in which the main energy flows converge from left to right (Odum, 1994).

The energy systems diagram in Figure 1.2 represents the main components of the

watershed (Figure 1.1). Abundant lower quality energy (sun, wind, and rain) enters the

system from the left. Important inflows from the economy of the surrounding region are

delivered to the system in a more concentrated form and are shown entering from the

right. Examples of these flows are electricity, fuels or information.

A pathway represents an influence a component has on others. Natural areas

provide inputs to a city in the form of foods and aesthetic property values, among other

things. The city exerts control over the natural systems through recreational use,

development and management policy.


Emergy and Empower Valuation

Emergy can be used to evaluate the energy that has previously been required to

make a component or flow, and is calculated from data on energy flows that converge into

a product or process (Odum, 1996). Emergy is the available energy of one kind (solar

energy) previously consumed in energy transformations. Empower is the rate of flow of

emergy. Evaluating all pathways in solar emergy units (solar emjoules per time) is a way









































Figure 1.2. Energy systems diagram of a lake watershed including an area of urban settlement.








of putting all inputs on a common basis including human services and information.

Because it accounts for previous contributions, energy is useful for evaluating storage

such as those in soils and sediments (Odum, 1996).


Transformitv

Transformity is the ratio of energy to the energy available within any individual,

population, commodity, service or system (units: solar emjoule/Joule) (Odum, 1996;

Odum, 1994). Transformity can be used as an indicator of energy quality because it

measures what has gone into a unit of energy in the item, and because it increases with

each energy transformation (Odum, 1996). In an energy systems diagram, transformity

increases from left to right (Figures 1.1 and 1.2).

Transformities for many commodities and natural energy flows have already been

calculated and can be used to determine the amount of emergy that a similar item

contributes to a system. Transformities are dependent on the process used to create any

entity and show variation between studies. High values result when an inefficient process

is used as the basis for evaluation (Odum, 1996). Transformities should be selected from

studies of systems similar to the one being evaluated, or computed with representative

information.


Transformitv and Control

The energy hierarchy determines the scales at which controls of the system are

exercised (Odum, 1994; Salthe, 1985; Allen, 1982). Items with larger territories and

storage, control smaller scale functions with faster turnover times. Presumably, larger








entities occur as a result of more energy transformations and concentration, and can exert

influence over smaller items having more diffuse energy storage. Consequently, assuming

an efficient process, a joule of energy of high transformity also has more influence than a

joule of lower transformity. The hierarchy represented in Figure 1.2 depicts many higher

transformity items on the right returning controlling actions to exert large effects on items

to the left. For example, agencies with high transformity are part of the information

component, and exert considerable influence on a lake when water level stabilization plans

are implemented.


Multistage Processes of Material Flows

Materials such as nutrients circulate within a watershed system while receiving

some inflow from outside and releasing some outflow to the surroundings. Most of the

pathways in Figure 1.2 are accompanied by material flows.

Watersheds are naturally engineered, multi-step, cascading treatment processes for

materials draining toward a lake (Figure 1.2). Developed areas recycle materials to the

watershed in the form of runoff and its constituents, and the intervening natural terrestrial

systems sequester nutrients on the way to the watershed focal point, a lake in this case.

If these treatment stages are decreased or eliminated, the larger scale watershed process is

short-circuited, creating pulses and increased convergence of materials and their emergy

within the lake.








Some materials are also returned from the lake to the watershed, thus dispersing

nutrients and energy outwards. Migration of fish and birds and human use of the water or

lake products are examples of this reversed distribution.


EmerYv per Mass and Concentration

Since available energy is required to concentrate substances, the energy per mass

of any solution increases with the concentration of the element in the water, and is greater

than the chemical energy of the element by itself (Odum, 1996). One relevant example is

the phosphorus present in watershed runoff. Phosphorus is delivered in concentrated

forms, such as fertilizer or industrial reagents, to components high in the watershed

hierarchy agriculture and urban economies. The phosphorus not immediately used is

diluted by rain, irrigation or flushing water, and low concentration solutions are dispersed

to components lower in the hierarchy forests, wetlands and lakes.

The energy of a material can be calculated by multiplying the known mass

delivered to a system by the ratio of energy to mass. These ratios have, in many cases,

been evaluated in previous studies. This is particularly useful when the item is present in

the system, not from a process of energy concentration, but rather as a material in a

recycle pathway. Recycling materials disperse (right to left in Figure 1.2) in the energy

hierarchy. Because the original energy was used in the process of concentration, the

remaining energy requires a concentrated pulse to be useful as it disperses its influence

over a larger area (Odum, 1996; Odum, 1994).








As runoffmoves downwards through the watershed, the actual concentration of

runoff constituents may not change significantly. However, the spatial concentration of

water and phosphorus increases and carries with it the emergy of all the runoff and

constituents used in moving to the point of concentration. This includes the geopotential

emergy inherent in the watershed slope.


Empower Density

The amount of emergy flowing through a system over some unit time is ifs

empower (Odum, 1996). Spatial areas with convergence of emergy, such as cities, will

have a higher concentration of empower than areas using less emergy, such as forests

(Odum, 1996). By measuring the total emergy flux per unit area, a relative density value

is obtained, similar to measures of development density used by city planners. This

empower density (areal empower density) is useful in identifying the centers of energy

hierarchy.


Spatial Organization

Just as hierarchies of convergence are evident in flows of materials and energy

through food webs to fish populations, pathways of the energy hierarchy also form

converging patterns in space on a landscape scale (Lambert, 1999; Huang, 1998; Odum

1994). For example, waters from runoff converge into larger streams of increasing order,

and convergence of services, information and materials within the landscape concentrate

into cities. Large central cities are surrounded by many smaller towns and even smaller

villages and clusters of residence, interspersed with the agricultural and natural systems








providing environmental services. The smaller towns ship goods to the city, and the city

in turn exerts control over the smaller by returning information, services and dictates for

production.

Larger energy flow builds greater spatial structure (Odum 1994) evident in urban

centers, mountains and perhaps lakes. They are dependent upon inflows from the

surrounding landscape -the more structure built, the larger the support area required.


Record of Lake Functions

Short-term lake functions are influenced by both the inflow of constituents from

the watershed and recycling of nutrients stored in the sediments. Frequently, pulsing

storm events deliver large quantities ofemergy as water, kinetic energy, nutrients and

other terrestrial contributions. Wind energy is transformed into kinetic energy in the

water, scouring the bottom and resuspending sediment. The materials and energy stored

in these sediment components, therefore, constitute a history of contributions to the lake.


Emdollars

Production and use of real wealth by the economic system depends on availability

of environmental resources and services. These assets are measured by emergy and its

economic equivalent, emdollars (abbreviated Em$).

Emdollars are the part of the gross economic product associated with an emergy

flow or storage (Odum, 1996). The emdollar value of an item is determined from its

proportion of the emergy of the entire economy. Emdollars are, consequently, a measure








of the real wealth in the system, including not just monetary payment for human services,

but also the services provided by the environment.

The total energy consumed within a system divided by its economic production

provides an emergy/money ratio for an economy in a particular year. This ratio, when

divided into an energy value for natural resources under study, is useful in determining an

economic equivalent (Odum, 1996).


Estimating Benefits of Lake Management

The benefits of different management scenarios can be evaluated with emergy and

emdollars. More emergy production and use means more real wealth contribution to the

economy. Policies for lake and watershed management can be dedicated to maximizing

energy and emdollars, but emergy can also be used to examine the efficacy of other

objectives, for example longer term carrying capacities for lakes and watersheds.

As well documented in ecology, when two factors interact in production, output

is greatest when neither is limiting (Odum, 1994; Odum, 1983). One of these factors will

contribute more energy, while the other will have a higher transformity. The relationship

between light and phosphorous availability is an example. When the component higher in

the energy hierarchy (e.g. phosphorus) feeds a matching quantity of emergy back to the

unit inputting emergy at the lower level (e.g. light), system production is maximized with

more efficiency in emergy use, and limiting factors are balanced (Odum 1996).








Previous Studies

The following review of published studies cites many ways used previously to

relate lakes and watersheds in Florida and elsewhere.


Shallow Lake Limnolovg

Questions concerning the role of the watershed in eutrophication of shallow lakes

center around whether the response to increased availability of in-lake nutrients is greater

than the effect of watershed inputs. Studies to determine the importance of internal

loading contributions to eutrophication have been inconclusive (Hansen et al., 1997;

Schelske, 1989). Some studies of shallow lakes show a direct reduction in trophic state

variables with reduction in external loading (Scheffer, 1998; Lowe et al., 1997).

Shallow lakes (<3 m) have two unique properties that create phosphorus and

productivity dynamics differing from deeper temperate lakes. Thermal stratification is

short-term or absent, decreasing the amount of time that phosphorus is segregated from

the epilimnion (Scheffer, 1998). Further, less wind energy is necessary for resuspension

of bottom sediments, increasing the fraction of nutrients recycled into the upper water

column (Scheffer, 1998; Carper and Bachman, 1984).

However, resuspension of noncalcareous sediments can also provide adsorptive

sites for phosphorus, thereby reducing its availability, at rates varying with pH levels.

This interaction is particularly favored under oxygenated conditions often present during

mixing. (Hansen et al., 1997; Olila and Reddy, 1995)








Shallow lakes in Florida often do not develop a stable thermocline at any time in

the year (Whitmore et al., 1996) and are subject to frequent sediment resuspension

(Brenner et al., 1990). However, the majority of Florida lakes are softwater with

noncalcareous soils (Canfield et al., 1982). Consequently, productivity may not increase

due to in-lake resuspension, and watershed inputs may then still impact lakes with

significant sediment nutrient deposits.



Nutrient Dynamics and Loading

A connection between point-source nutrient loading and increasing eutrophication

in lakes has been documented in many cases (Scheffer, 1998; Cooke et al., 1993; Wetzel,

1983). Elimination of these inputs has provided varying degrees of reclamation success,

and initially, depth of the lake was thought to be the determining factor (Cooke et al.,

1993). However, recent studies in the Netherlands have shown reduction in

eutrophication of shallow lakes following decreases in point-source nutrients (Scheffer,

1998).

Vollenweider (from Scheffer, 1998 and Wetzel, 1983) constructed an empirical

mathematical model linking average phosphorus loading to a lake from the watershed to

the concentration of both phosphorus concentration (P, ) in the water column and algal

chlorophyll (Chl). Both are ratios of phosphorus loading (P) to retention time (Tr).

PwC =c Pi /(1 + T,-5) (1)

Chi = 0.55 Pi / (1 + Tr 0-s)0.76 (2)








Many studies have estimated watershed phosphorus loading to lakes based on

empirical coefficients of export from specific land uses (Reckhow et al., 1980; Huber et

al., 1982; Gottgens and Montague, 1987; Heidtke and Auer, 1993; Adamus and Bergman,

1995; Harper, 1996). Although the majority of the loading reduction emphasis has been

point-source loads, reduction of non-point source loads has become of greater interest

recently. Agricultural runoff appears to be a primary focus (Young, et al., 1989;

Srinivasan and Arnold, 1994).

Some studies have shown that increases in watershed development are

approximately proportional to phosphorus loading to lakes (Weibel, 1969), but another

large scale Florida study showed no correlation between the amount of land in

development and the overall trophic state of the lake (Huber et al., 1982). This is likely

due to other geological and soil conditions both at the point of runoff and in the

intervening distance to the lake, as shown in the pilot study for this project (Brandt-

Williams, 1995). This study shows that while the percentage of developed land use did

not correlate with trophic state or chlorophyll concentrations in seven Florida lakes,

phosphorus loads from non-point sources calculated from deposition, soil, and drainage

properties correlated strongly with both trophic state and chlorophyll.


Spatially Distributed Surface Flow Models

There are two primary approaches to incorporating spatial variation into runoff

and seepage models: stochastic and raster-based geographical information systems (GIS).

Stochastic approaches use probability density functions to translate the uncertainty of








randomized input data into probability distributions for the output response from the

model, and have been in use for some time (Chow et al., 1988). Recent research in

stochastic methods for spatially distributed hydrology models has focused on reducing

the number of simulations required to generate output curves (Braud et al., 1995; Kool et

al., 1994), and more recent use of neural networks may increase this method's

applicability to spatial variations.

Despite increasing ability of stochastic models to generate field data measures,

lack of specific mapping references hinders their use for appropriate remediation siting.

GIS models, while allowing greater flexibility in handling spatial variability, also involve

high levels of computational time. Therefore, a certain amount of parameter lumping is

still used. DeVantier and Feldman (1993) completed a review of lumped and distributed

models through 1993.

Three recent studies of interest attempt to limit parameter lumping, using either a

physics based approach or higher resolution spatial data. Julien et al. (1995) apply

Green-Ampt equations to each map cell to determine infiltration for an individual storm

event, and use two-dimensional Saint-Venant equations of continuity and momentum to

model flow between cells. Excess overland flow is automatically routed to connected

channels and modeled with kinematic wave functions. The model requires soil texture and

deficit data, Manning's roughness coefficients, basin connectivity and geometry, and rain.

Nutrient transport functions are not included.

Heidtke and Auer (1993) used a GIS-based non-point source loading model to

assess water quality in a New York lake. Empirical land use and soil parameters affecting








phosphorus runoffwere incorporated into a modified Universal Soil Loss Equation to

calculate an estimated load from each basin cell (1 hectare). Comparison to known

tributary loading showed similarities between the model and empirical evidence. A

suggestion for a method to compare to water quality was provided, but actual

comparisons were not tabulated.

Adamus and Bergman (1995), using empirical nutrient and runoff coefficients

determined in Florida from mean runoff and pollutant loads, presented distribution maps

for the entire St. John's River watershed. The results were based on average land use

densities and the four basic hydrological soil groupings. No correlation with water quality

was presented.


Lake Valuation

Classification of lakes usually involves division into three categories of

productivity: eutrophic (highly productive), mesotrophic (moderately productive) and

oligotrophic (unproductive). Numerous models, both quantitative and qualitative, have

been put forward as methods for classifying lakes and reservoirs and to assist in

determination of problem systems, as well as prioritization of reclamation efforts.

(Wetzel, 1983; Huber et al., 1982).

Early indices used presence or absence of indicator species to rank eutrophication,

and Nygaard's algal ratio was often used (Wetzel, 1983; Taylor, 1978). Nygaard's ratio of

typically eutrophic species to common oligotrophic species is not applicable, however, in








areas where the species used do not commonly exist (Taylor, 1978), and it is not a

measure of water quality perceived by the public (Kratzer, 1979).

One of the most commonly used multi-parameter indices is Carlson's Trophic

State Index (TSI), although his original intention was that each index (Secchi disc,

chlorophyll and total phosphorus in the water column) be used in relation to each other to

infer limiting factors and the presence of other light inhibitors. Carlson devised a log

transformation of empirical data available for temperate lakes so that a ten point

difference was directly proportional to a doubling (or halving) of algal biomass for each

parameter. (Carlson, 1970)

This TSI is insufficient for nitrogen limited lakes, uses relationships between

parameters established in temperate lakes, not Florida, disregards macrophyte

populations, and does not provide a single management index. Huber et al. (1982)

proposed a modification ofCarlson's TSI using a Florida lake data base that is now often

used in Florida studies. Several permutations were offered to account for phosphorus or

nitrogen limited systems, as well as nutrient balanced lakes. An index greater than 60 is

considered eutrophic; the split between oligotrophic and mesotrophic is still nebulous.

Macrophytes were not included.

A traditional valuation of lakes has always been the number of users or monetary

advantage to the local economy, both in recreational value and waterfront property taxes.

However, uses of oligotrophic and eutrophic lakes are very different, and monetary values

are generally inversely proportional to trophic state index values.








Paleolimnolov

Sediment cores from lakes have been suggested as a way to reconstruct a history

of lake productivity using both remains of organisms and phosphorus (Brenner et al.,

1993; Smol, 1992; Binford et al. 1986; Frey 1969). Using inferences from current water

chemistry and species communities and the connection to surficial sediments, longer term

function and structure are implied and can be used to determine the original trophic state

of the lake (Smol, 1992). Because much of sediment deposition in a lake originates in the

watershed, lake sediments contain a history of basin disturbance (Binford et al., 1986).

However, caution in interpreting the results in shallow, wind-stressed lakes is

advised (Whitmore et al. 1995) because of frequent sediment redistribution. Further,

shallow lakes are subject to photochemical oxidation of bottom sediments, limiting the use

of sedimentary pigments as a comparative tool (Flannery et al., 1991).

Both C/N ratios and total phosphorus (TP) in sediment cores have been used to

evaluate Newnans Lake. A study by Flannery et al. (1991) resulted in low and stable

C/N ratios, suggesting that Newnans has been eutrophic for some time. Whitmore et al.

(1998) found steadily increasing phosphorus deposition. Gottgens and Crisman (1993)

found differing levels of TP deposition dependent on position in the lake, with increasing

deposition near the inflow (north) and decreasing deposition near the middle and outflow

(south).

Lake Weirs core (Crisman et al., 1992) shows a sharp increase in TP accumulation

between 1970 and 1980. An equally steep decline in TP is exhibited between 1980 and

1990.








Watersheds Evaluated

Two lakes in Central Florida (Figure 1.3) were included in this study, Newnans

Lake, near Gainesville, and Lake Weir, near Ocala. Newnans Lake has a 20:1 watershed to

lake ratio, with a relatively flat, forested watershed and extensive cypress, bayhead and

mixed hardwood swamps surrounding the entire lake perimeter. Lake Weir has a 5:1

watershed to lake ratio, with a steeper watershed than Newnans. Weir's watershed was

predominantly citrus groves and pasture until the mid-1980s, and is now predominantly

residential and pasture.


Newnans Lake

Newnans Lake is located due east of Gainesville, Florida, in Alachua County (29040' N,

820 12' W). Newnans is part of the Oklawaha River basin and is located in the Central

Valley physiographic region (Canfield, 1981). The lake has a water surface area of 2,965

ha, and the elevational watershed has approximately 58,000 ha land area. The mean depth

is 1.6 m (Lassi and Schuman, 1996), and the estimated flushing rate is 0.6 years (Gottgens

and Crisman, 1993). The average fetch is approximately 2.41 km.

Two small creeks, Little Hatchett Creek and Hatchett Creek, are the main

tributaries flowing into the lake, and Prairie Creek is the single surface water outlet. Little

Hatchett Creek has an average annual flow rate of about 4 cfs, and Hatchett Creek's

annual flow is 18 cfs. Prairie Creek has a weir, and the range of flow is dependent on the

lake surface elevation. At the average elevation of 65 ft NGVD, outflow discharge is

about 20 cfs (Robison et al., 1997).
























Newnans Lake




Lake Weir


Figure 1.3. Watershed locations in the state of Florida.








Newnans is typically classified as a naturally eutrophic, softwater lake with a pH

near 7 (Canfield, 1981). Despite its eutrophic condition, Newnans' N: P ratio has risen

from 17 to 31, indicating balanced nutrients in the 1970s but some phosphorus limitation

in the 90s (Huber et al., 1982; Lakewatch, 1999). Newnans is highly colored and exhibits

high variability in this parameter (Canfield, 1981; Gottgens and Montague, 1987).

Newnans does not appear to develop a thermal stratification in the summer (Canfield,

1981).


Lake Weir

Lake Weir is located about 15 miles southeast of Ocala, Florida (290 01' N, 810 56'

W), in Marion County, Florida. It is located in the Oklawaha River basin in the Sumter

Upland physiographic region (Canfield, 1981). The lake surface area is about 2300 ha and

its elevational watershed covers about 12,100 ha. However, about 2400 ha is

depressional and does not contribute runoff to the lake. The mean depth is 7.1 m

(calculated from Ott and Chazal, 1966), and the longest fetch is about 2.26 km.

To the west, a canal and wetland area connect Lake Weir to Little Lake Weir. A

canal also connects the lake to a large hardwood swamp to the north (Marshall Swamp).

Lake Weir's average elevation is 57' NGVD, and Marshall Swamp is at about 50' NGVD.

Lake Weir is a mesotrophic lake with trophic state indices reported in the range of

41 to 54 (Canfield, 1981; Huber et al., 1982; Lakewatch, 1998). It is a softwater lake

with a pH around 7 and very little organic color (Canfield, 1981). Weir does develop a

10C temperature differential at certain times in the year (Canfield, 1981).








Plan of Study

This dissertation explores the relationships of lake and watershed using literature,

empirical data, spatial and temporal modeling, and emergy evaluation indices. The overall

organization and hierarchy of lake watersheds was studied using the following procedures:

1. Using methods of geographic information systems (GIS), a sequence of
historical maps (1950, 1970 and 1990) was constructed that included land
uses, geology and landforms, hydrological properties, nutrient storage and
flows, and energy characteristics.
2. The storage, budget and cycle of phosphorus were developed for the
watersheds and lakes. Simulation models related phosphorus to the influences
of the watershed and human settlement.
3. Emergy characteristics were evaluated for the main components of the
watershed and lakes including phosphorus, area concentration of emergy
flows, transformities, and other indices of energy transformation and
hierarchy.
4. Limnological characteristics of the lake ecosystems were related to the
watershed inputs including productivity, food chains, and the effect of
watersheds on lake classification. Responses were studied with a lake
simulation model.


Synthesis of these results was used to consider the position of lakes in the emergy

hierarchy of the earth, to understand the level of reciprocal control between a watershed

and a shallow lake, to examine spatial patterns that develop in changing watershed


systems, and to propose management alternatives.














CHAPTER 2
METHODS




MaD Preparation and Data Sources


A series of maps of land use, soil and rain for each watershed, for select time

periods, was used to explore changing energy, emergy, water and phosphorus inflow to

each lake. Three time periods 1950, 1970, 1990 were mapped and compared using a

geographical information system (GIS). MapFactory is a raster-based (cell or grid)

analysis GIS useful for simulating spatial movement defined by equations.


Elevation and Watershed Delineation

Elevations were digitized from USGS 7.5-minute topographical quadrants and

assumed constant throughout the 40 years of the time series analysis. All but one of the

quadrants was constructed on 5-foot intervals. The remaining 10-foot interval map was

kriged (mechanically interpolated using GIS) over the contours and the benchmark points

to produce 5-foot contour areas.

The watershed was delineated using a GIS command that spreads upwards from a

given point and stops when a downhill elevation is encountered. The elevation of each

study lake was used as the initial point of spread, and all uphill cells were considered part








of the larger basin within which the study lake was the focal point. All smaller lakes

within this basin were then used as points for upward spread to determine their individual

drainage areas within the larger lake basin. These smaller sub-basins were subtracted from

the larger basin, splitting the shared ridge between the study lake basin and the outlying

lake sub-basin. This final basin was considered to be the rain catchment area draining into

the study lake.


Land Use and Cover

The area ofindividual land use for each basin was configured from USGS

topographical quadrant maps (1966-1970 series). Land use for 1990 was determined

using 1988-1993 quadrant updates by USGS and aerial photos. Land use for 1950 was

interpreted from 1949 aerial photos using comparisons to similar areas of known land use

in 1968 photos. Groundtruthing to verify land use and to determine industry and

agricultural type was conducted extensively throughout both watersheds by visits to

existing sites, and via county records for historical sites.

Land use was divided into 15 categories:

1. open, vacant, or range lands (considered unmanaged turf)
2. golf courses (managed turf)
3. urban with residential, commercial, and institutional structures assumed to be
using a centralized waste water treatment system
4. outlying residential, commercial, and institutional on septic systems
5. industrial
6. mining
7. landfill
8. roads, parking lots and airport tarmacs
9. agriculture orchards (perennial)
10. agriculture row crops (annual)
11. forest








12. forested wetlands
13. herbaceous wetlands
14. lakes and ponds
15. streams.

Soil Maps

Soil coverages for each watershed were obtained by digitizing maps in the National

Cooperative Soil Survey for each county. The soil classes were then standardized and

aggregated for key parameters of interest hydrologicc capacity and clay content).

Soils were first grouped according to the four hydrological categories designated in

the United States Soil Conservation Service (USSCS) soil surveys. USSCS determines

groupings by the amount of water absorbed when thoroughly wet (USSCS, 1985) and

considers infiltration, vertical drainage and clay content. Group A refers to soil that has

low runoff potential, D has high nmoff potential, and B and C fall between these two

extremes. If a soil had two categories assigned because of potential drainage capability,

the pumping benefit was neglected, and the category with the highest runoff potential was

assigned.

Soil hydrology was also characterized and mapped by permeability (in/hr),

capacity (in/in) and depth to the first relatively impermeable horizon (<0.6 in/hr). The

use of these physical parameters is explained in the chapter on model development.


Rain Data

Rain data were obtained from the National Oceanic and Atmospheric

Administration (NOAA) for recording sites within each watershed. Because no

individual site had data for all the years under study, all available data were averaged for








1950, 1970 and 1990 for all sites recording rainfall within the watershed. Consequently,

rainfall was considered to be equal throughout the watershed.




Dynamic Simulation Models


Energy language symbols and their intrinsic mathematics (see Energy Systems

Symbols and Definitions, Fig 2.1), were used to develop temporal models of both a lake

system and its watershed. An energy system diagram was first constructed representing

the variables considered important in defining key interactions within the lake and

between the lake and its watershed. The resulting diagrams were translated into

mathematical equations representing changes in each variable over time, and these

equations were solved using a BASIC computer program.


System Diagram

The concept of constructing a system diagram and the hierarchy of arrangement

are discussed extensively in (Odum, 1994), but are briefly described below.

System frame. A rectangular box represents the boundaries selected.

Forcing functions. Any input that crosses the boundary is an energy source,

including pure energy flows, materials, information, the genes of living organisms,

services, as well as inputs that are destructive. All of these inputs are given a circular

symbol and are arranged around the outside border from left to right in order of

concentration with sunlight on the left and information and human services on the right











-EN--


P2


29

Energy circuit: A pathway whose flow is proportional to the quantity in the
storage or source upstream.

Source: Outside source of energy delivering forces according to a program
controlled from outside; a forcing function.

Tank A compartment of energy storage within the system storing a quantity as
the balance of inflows and outtlows; a state variable.

Heat sink- Dispersion of potential energy into heat that accompanies all real
transformation processes and storage; ross of potential energy from further use
by the system.

Interaction: Interactive intersection of two pathways coupled to produce an
outflow in proportion to a function of both;control action of one flow on
another, limiting factor action;work gate.

Consumer: Unit that transforms energy quality, stores it, and feeds it back
autocatalytically to improve inflow.



Switching action: A symbol that indicates an outside agent causing one or
more changes in a pathway or interaction


Producer: Unit that collects and transforms low-quality energy under control
interactions of high-quality flows.


Sef-limitig energy receiver: A unit that has a self-limiting output when input
drives are high because there is a limiting constant quality of material reacting
on a circular pathway within.

Box: Miscellaneous symbol to use for whatever unit or function is labeled.



Constant-gain amplifier: A unit that delivers an output in proportion to the
inputI but is changed by a constant factor as long as the energy source S is
sufficient


Transaction: A unit that indicates a sale of goods or services (solid line) in
exchange for payment of money (dashed line). Price is shown as an external
source.


Figure 2.1. Energy systems symbols and definitions (Odum, 1994).








Pathway line. Flows are represented by a line and include pure energy, materials,

and information. Money is shown with dashed lines. Lines without arrowheads flow in

proportion to the difference between two forces and represent a reversible flow due to

concentration gradients.

Outflows. Any outflow that still has available potential, materials more

concentrated than the environment, or usable information is shown as a pathway from

any of the three upper system borders, but is not shown exiting from the lower border.

Degraded or dispersed energy, with insufficient quantity or quality to do work in the

modeled system, is shown as very thin lines leaving at the bottom of the diagram with a

single arrow representing a heat sink.

Adding pathways. Pathways add their flows when they either join or enter the

same tank. Every flow in or out of a tank must be of the same type and measured in the

same units.

Intersection. Two or more flows that are different, but required for a process, are

drawn to an intersection symbol. The flows to an intersection are connected from left to

right in order of their transformity, the lowest quality one connecting to the notched left

margin. An example of this multiplicative interaction is the connection between light and

phosphorus required for photosynthesis.

Counterclockwise feedbacks. High-quality outputs from consumers, such as

information, controls, and scarce materials, are fed back from right to left in the diagram.

Feedbacks from right to left represent a loss of concentration because of divergence, with

the service usually being spread out to a larger area.








State variables. Storages of materials are shown as tanks within each system

compartment. Changes in the system can be recorded as fluctuating accumulations within

each tank. In simplified system diagrams, not to be confused with aggregated diagrams,

the actual simulation details, such as tanks and complex interactions flowing into each

tank, are often not presented. However, a state variable is always implied for every

process within the diagram.

Material balances. Since all inflowing materials either accumulate in system

storage or flow out, each inflowing material such as water or money needs to have

outflows drawn.

Agregated diagrams. Aggregated diagrams are simplified ftom the detailed

diagrams, not by omission of components, but by combining them in categories aggregated

with the purpose of answering a specific question.


Simulation Example

A simple one-tank simulation is used to explain the simulation methodology used

in this dissertation. A diagram of water inflow, outflow and accumulation (Figure 2.2)

illustrates arrangement of sources and material inflows and outflows.

The associated differential equation used to define the material balance and a

graphical representation of water accumulation over a two-year period are included

(Figure 2.2). The programming application QBASIC was used in this example and all the

simulations included in this dissertation, both to iterate the equation over a given time

interval and to plot the changes in accumulations with time.










Emern Evaluation


Emergy values were used to compare land uses and soils within each watershed, in-

lake functions, changing watershed systems over the forty year study period and

phosphorous in different solution concentrations from different sources. Empower

densities, transformities and storage energy were the primary indices used for comparison.


Emergy Tables

A sample emergy analysis table is presented in Table 2.1. The associated system

diagram is illustrated in Figure 2.3. This table represents flows per unit area and time

(J/ha/yr). An explanation of the information presented in each column of the table is

given in Table 2.2. Emergy analysis was used to evaluate the lake-watershed interface,

soils and land use in the watershed, and sediments in each lake.


Emergy Indices

Several emergy indices were used to draw inferences from emergy analyses of the

lake-watershed interface, economic use of the lake, and land use within the watershed.

Comprehensive descriptions of these indices and their uses are presented in Odum

(1996), but brief descriptions of these indices are given below.

The solar transformity of an item or flow is the solar emergy that would be

required to generate (create) a unit of that object or resource efficiently and rapidly.

Figure 2.4 shows the solar transformity defined as the solar emergy required to produce

one joule of another form of energy. Solar transformities of one or more products are










Rain
RN
4E7


Equation


Stream
Runoff

R 2-1


dW = RN + S k*W k2*W*I
dt


Calibration of coefficients
at steady state


k*W = 1.8E8
k2*W*L = 7E7


k = 1.8E8/5E7 = 3.6 yel.
k2 = 7E71(5E7*1.8E17) = 7.8E-18 J-1


Initial conditions


I= 1.8E17
RN= 4E7
S=1E8
W= 4E7


Figure 2.2. Simulation example: aggregated water budget for a lake, values used for
calibrating coefficients and the differential equation.


Time, t








Table 2.1. Example ofemergy evaluation: annual production of one hectare of
Bahia grass* (see Figure 2.3).
Unit Solar Solar
Data ENERGY ENERGY
Note Item Unit (units/yr) (sej/unit) (Ell sej/yr)

RENEWABLE RESOURCES (R)
1 Et J 5.43E+10 1.54E+04 8368

NONRENEWABLE ENVIRONMENTAL RESOURCES (N)
2 Net Topsoil Loss J 6.33E+07 7.38E+04 47
Sum of free inputs (sun, rain omitted) 8415

PURCHASED INPUTS (M, S)
3 Fuel J 2.82E+06 6.60E+04 2
4 Phosphate gP 7.38E+03 2.20E+10 1623
5 Nitrogen gN 1.55E+04 2.41E+10 3728
6 Lime g 3.73E+05 1.00E+09 3730
7 Labor J -- 0
8 Services $ 0
9 Sum of purchased inputs 9083

10 YIELD (Y) g, dry 3.63E+06
J 6.88E+10

11 TRANSFORMITY of yield 4.80E+08
12 EMERGY PER MASS of yield 2.50E+04


* Simplified from Appendix Table D.1
1. Includes contributions of sun, wind and rain
11. Total inputs divided by energy of yield
12. Total inputs divided by mass of yield









Materials
Fuels


= Feedback




Y = Yield


Emergy Yield Ratio = Y
F

Emergy Investment Ratio = F
I
(a)


N(Soil)
47-


842 [
R
(Environment)


9083
M (Fertilizer and Fuel)




- 17500


Emergy Yield Ratio = 17500/9083 = 1.93
Emergy Investment Ratio = 9083/842 = 10.79

(b)


Figure 2.3. Example of energy analysis : a) definition of two indices emergy yield ratio
and investment ratio; b) simplified emergy diagram for grass example in Table 2.2.








Table 2.2. Description of information presented in an emergy table.

Column Description of Information
One line item number: corresponds to the number of the footnote in the table
where raw data source is cited and calculations shown


Two name of the flow or item stored: shown on the aggregated diagram


Three raw data in joules, grams, or dollars: taken or calculated from various
sources


Four transformity in solar emjoules per unit (sej/joule; sej/gram; or
sej/dollar); see definition Table 2.4


Five solar emergy contributed by the flow or item stored: the product of
columns three and four


Six real wealth value in emdollars for a selected year: obtained by dividing
the emergy in column number five by the emergy/money ratio for the
selected year

















Non-renewable
Reserves \


Solar Transformity
of Tomatoes


1617 E13 sej/ha/yr
4.43 E10 J/ha/yr


Sum of
Solar Emergy
Sof Inputs


Solar Emergy of Inputs
Yield Energy



= 365,000 solar emjoules/Joule


Figure 2.4. Diagram explaining solar transformity.


Yield
Energy








obtained from each analysis. Solar transformities for main inputs from global climate are

obtained from world energy budgets, and transformities for sources to each system come

from previous analyses cited in each table's footnotes. Examples of energy sources with

abundant but low quality energy are the sun (transformity of 1 sej/J) and wind

(transformity of 300 to 1500 sej/J). Electricity from a coal plant requiring larger emergy

inputs to produce more concentrated energy has a transformity of 160,000 sej/J.

Empower density (aerial empower density) is a measure of the emergy utilized in

a unit area per unit time. It is a measure of the intensity of development and natural

resource use for the system under study.

The emergyyield ratio is the emergy of an output divided by the emergy of those

inputs to the process that are fed back from the economy (see Figure 2.3). This ratio

indicates whether the process contributes more to the economy than is purchased from it

for the processing. Ratios for typical agricultural products range from less than one to

six (Odum, 1996). Values less than one may be obtained when the yield is calculated

separately with a transformity from another source of data. In recent years, emergy

yield ratios of fossil fuels have ranged from three to twelve (Odum, 1996.

Emergy investment ratios relate the emergy fed back from the economy to the

emergy inputs from the free environment (Figure 2.3). These ratios indicate if a process

is economical in using the economy's investments in comparison to alternatives. To be

economical, the process should have a similar or lower ratio to its processes competing

for investment. If the ratio is less, the environment provides more to the process, costs

are lower, and its prices tend to be less so that the product competes in the market. If an








emergy investment ratio is higher than alternatives, the intensity of inputs invested from

the economy is greater, and impact on the environment is greater.

The emergy exchange ratio is the ratio of emergy received for emergy delivered in

a trade or sales transaction. For example, a trade of wood for oil can be expressed in

emergy units. The area receiving the larger emergy receives the larger real wealth and has

its economy stimulated more.

The emergy/money ratio is obtained by dividing the total emergy used by the

combined economy of man and nature in the country for that year by the gross national

product. This number becomes smaller as the country's economy becomes more

developed and more dependent on purchased goods and services from outside. A

developed country with low emergy/money ratio gains a net benefit from purchasing

products from less developed countries with a high emergy /money ratio.














CHAPTER 3
RESULTS



Results of this study are divided into three sections concerned with the two

models developed to quantify the connection between watershed and lake and the use of

simulations to develop management indices. In the first two sections, the development of

the model and data sources are presented first, followed by the simulation results. In the

third section, the derivation of indices and resulting values are presented together.



PART 1: SPATIAL WATERSHED MODEL


Development of Material Flows


A spatial model accounting for water and phosphorus export from different land

uses on specific soil types was developed. One of the objectives was to determine a

coarse level of aggregation at which long term watershed control of a shallow lake could

be evaluated. This serves two purposes. It makes a model of this magnitude manageable,

improving the potential for future use by water management agencies, and selects a scale

appropriate to the study of cumulative effects. Assessing the overall effect of ongoing

changes within the watershed was the priority, not prediction of drainage for every short-








term storm event This shorter time scale precludes the study of a larger system

hierarchy.

Theoretically, unique linkages of land use and geohydrology control water and

nutrient export that a given land use contributes to the lake. Further, the soils and natural

systems on the drainage path between one area and the lake can modify the contributions

of that area to the lake. Geographical information systems were used to track the flows

through the watershed in order to calculate the spatial patterns of energy and material that

develop and change as land uses in the basin change.

Geographical information systems (GIS) link a data set defining a specific

variable (attribute) to a set of geographically referenced points on a map. Examples of

attributes are rainfall and elevation, and a separate map (layer) is prepared for each

property. Grid-based GIS divide a map into unit areas (cells) that become referenced

points, each with a single attribute value. The seven attribute maps used in this model

were annual rainfall, land use categories, annual phosphorus deposition, land cover

(vegetated or impervious surface), soil hydrology, soil clay, and elevation.

Combining base map layers (Figure 3.1) creates new data sets describing the

linkage between base attributes. These base attribute maps were also reclassified or

related to each other by computation and used to represent the physical interactions that

occur as water and phosphorous move across the landscape. For example, land cover was

ranked by the percentage of impervious surface due to paved surfaces and roofs in each

cell, then used to simulate runoff from those surfaces. A flow chart of the map layer

computations described in the following sections is shown in Appendix A.














RAIN


TOTAL
PHOSPHORUS


COVER




SOILS




ELEVATION


Total Phosphorus availability


interception, impermeability
adsorption, infiltration, capacity

channelization


Figure 3,1, Map layers used as data in spatial model and the hydrology functions derived from each data set, Linking these maps
with mathematical functions allows them to be used as boundary conditions for solving continuity equations governing the flow of
materials through the watershed.









Simulation of Water Budgets in GIS

The transport of water, or any material, through a watershed is governed by the

conservation of mass. Consequently, the fate of water in any area can be summarized by

a fundamental continuity equation describing the water budget at any point in time.

Rain + Runin Infiltration Runoff- Evapotranspiration dS/dt = 0 (1)

where dS/dt is the rate of change of water quantity in each area.

Run-in from adjacent areas is dependent on the mass balance within that area.

The total runin volume is contingent upon the number of adjacent areas contributing, and

is determined by elevation differences between areas. Infiltration volumes are a function

of soil porosity and can be determined by the infiltration rate and maximum available

capacity. Runoff (export) occurs both when impervious surfaces preclude infiltration

and when soil capacity is exceeded.

This model calculated a water budget for each cell based on average annual

rainfall and average soil conditions. Overall storage was considered constant, and

therefore, dS/dt is negligible. The time increment for remaining terms in the mass

balance is one year. An energy system diagram illustrating the variables and interactions

affecting water for each cell is shown in Figure 3.2.

Algebraic manipulation of map layers was used to solve these mass balances

within each cell using the attributes governing infiltration and runoff. Water flow

between cells was modeled using functions provided by MapFactory. Drainage in

MapFactory simulates overland sheet flow and is dependent on the slope differential

between each cell. Total water volume reaching the lake was calculated by summing the

individual cell values at the perimeter of the lake.










Figure 3.2. Energy system diagram illustrating water budget in a single cell and movement into next cell. Solid lines carry water.
Dashed pathways are energy flows affecting water. Et = evapotranspiration.

Difference Equations


Surface water runs off or infiltrates


Sum of run-in from adjacent contributing
cells


Soil water in excess of average saturation
conditions either exceeds capacity and runs
off or is dispersed through evapotranspiration
or recharge of groundwater aquifer



Direction of export of all water leaving the
cell is dependent upon elevational differences
between cells


dSurfwater = Rain + RuninT kl Impervious Surfwater k2 Soilwater *
Surfwater k3 Surfwater

RuninT = Runoffcelll I + Runoffcell21 + ,, + Runoffcelli



dSoilwater = k2 Soilwater Surfwater X k4 Soilwater (Et + Recharge)
where X = 1 when soil capacity exceeded
where Et and recharge is assumed equal to water in excess of average
saturation conditions



Exportwaterij = Z k5 (kl Impervious Surfwater + k3 Surfwater
+ X k4 Soilwater)
where Z = 1 ifelevationl > elevation 2

















Impervious
(roads,
roofs)


El
Elevation


El E2


fk5 Adjacent cell



Runin from three cells
Export to one cell


Groundwater


r









Infiltration and Runoff Calculations

Water entering each cell was subject to one of two immediate consequences. It

fell either on a totally impervious surface such as a roof or road, or it fell on a vegetated

surface with variable infiltration capabilities.

Maps representing the percentage of impervious surface associated with each use

were derived from land use maps. Values from the literature for the fraction of rainfall

leaving each particular land use, based on average vegetation and impervious surface

(Table 3.1), were used to create this map. This percentage was used to calculate a split

between water leaving a cell without further interaction, as surface runoff, and a flow

available for further interaction with the soil.

Water falling on unpaved surfaces was subject to infiltration into available soil

spaces. If the soil capacity was exceeded, excess rainfall became runoff. The rainfall

rate subject to infiltration was determined using the average rain intensity (in/hr) in the

watershed for 2 year 60 minute events (Frederik, et al. 1977). Because this model tested

the efficacy of long-term averages, total annual rainfall (in) was assumed to be evenly

divided into these 60-minute events. The water flow exceeding soil capacity was

considered surface runoff and added to the runoff from impervious surfaces.

The amount capable of infiltrating was calculated from the soil permeability

(in/hr), unit capacity (in/m) and available volume (inches to impermeable soil horizon)

for each soil category (SCS, 1985). This was converted to the percentage of an average

rain event infiltrated. Calculation for each soil type is summarized in Appendix B.

Water that remained in each cell from infiltration was assumed to be drained from

the cell prior to the next rain event, either by evapotranspiration or recharge. This








assumption allowed the soil hydrology values to remain constant at their average

saturation levels throughout the year being modeled.


Phosphorus Uptake, Adsorption and Deposition

Annual phosphorus deposition was divided by total annual rainfall to determine

the concentration of phosphorus in solution for each cell. This concentration of

phosphorus was assumed to travel with water, either downward into the soil, where it

remained sequestered for plant uptake and diagenesis, or over the soil surface into the

next cell. However, before moving into the next cell, this surficial phosphorus was

subject to adsorption. The amount of clay in the soil was used to estimate the amount of

surficial phosphorus likely to adsorb in each cell. The percentage of phosphorus

adsorbed was assumed to be linearly related to the percentage of clay in the top 6-12" of

soil. The model of phosphorus flows and storage and their link to the water budget are

shown in Figure 3.3. Clay properties are presented in Appendix B.

Most phosphate runoff data available in the literature are in the form of empirical

averages for total phosphate concentrations in storm water runoff (Harper, 1996; Adamus

and Bergman, 1995; Heidtke and Auer, 1993; Gottgens and Montague, 1987; Huber et al-

1982). The values from various land uses with differing use densities are presented.

However, these studies are averaged over several study sites where the underlying soil

and geology are often different. As a result, concentration values account for and lump

many soil infiltration and impervious surface characteristics. Using these values for

phosphorous export would defeat the purpose of using a spatially specific model.

Consequently, rates of atmospheric deposition and fertilizer application rates from the








Table 3.1. Impervious surface for different land uses.

Land use % Impervious surface Reference

Agriculture 6 a
Range/Open 6 a
Commercial 77 a
Industrial 71 a
Residential 39 a
Water 0 a
Wetland 0 a
a. Brown and Tilley 1995


Table 3.2. Average phosphorous deposition rates.

Source Amount Reference

Dry atmospheric
agricultural 0.066 g/m2-yr a
non-agricultural 0.027 g/m2-yr a
Rain 0.167 g/m3 rain a
Orange groves 1.12E4 g/ha-yr b
Soybean cultivation 1.05E4 g/ha-yr b
Sod 7.63E3 g/ha-yr b
Residential landscape 3.36E3 g/ha-yr c
Urban landscape 3.0E3 g/ha/yr c
a. Huber, etal. 1982
b. Fluck 1992
c. Non-impervious surface assumed landscaped with sod
(level of fertilizer application, 50% of sod for residential
use, 75% for urban/commercial use)









Figure 3.3. System diagram of the phosphorus model for each cell and its relation to water model in Figure 3.2. Energy pathways
without water or phosphorus are dashed. Water paths are blue, phosphorus is green.


Phosphorus deposition on land surface in
solution with stormwater



Phosphorus leaving soil pore water either
from exceeding water capacity or
adsorbed to clay


Adsorbed sites made available from
phosphorus uptake by vegetation or
diagenesis


dPI = Prain + runin + Papplied k6*[Pl/Surfwatert=0] kl*Impervious Surfwater
k7*[Pl/Surfwatert=0] k3 Surfwater
k8*[Pl/Surfwatert=0] k2 Surfwater Soilwater

dP2 = k8*[Pl/Surfwatert=0] k2 Surfwater Soilwater X*k9* Soilwater P2
k0* Soilclay P2 Uptake



dPadsorbed = kl0* Soilclay P2 (Uptake + Diagenesis)


























Runoff








literature were used to determine phosphorous available for runoff(Table 3.2) assuming a

steady state.


Movement Between Cells

Downhill drainage of water and phosphorous was simulated using the DRAIN

function supplied by MapFactory. This function assumes that all material falling on a

cell surface is available for sheet flow with flow direction dependent upon slope

differentials between cells. The values from each cell are added to the adjacent cells into

which they flow, and convergent pathways with high cumulative loads become evident.

This function by itself is incapable of calculating the amount of material left

behind in an individual cell. It does, however, recognize material of varying amounts

within each cell. By using a map with an estimate of the actual contribution from that

cell to the lake, DRAIN becomes a tool capable of distinguishing differentials other than

slope between the cell and the lake. Hence, an important component of the spatial model

was the development of maps with the material export values resulting from the budget

for each cell (Figures 3.2 and 3.3). These maps are the basis for maps that represent

impediments to water and phosphorus leaving the surface of each cell. In GIS

nomenclature, these are referred to as cost or friction maps. To avoid confusion with

economic or hydrology terminology, they will be referred to as impediment maps in this

study.

Impediment maps use a percentage figure to increase the difficulty for an entity,

water or energy for example, to cross a cell. Theoretically, a cell with 100% export at a

completely vertical slope would have the minimum effective travel distance for the

quantity measured. Conversely, if nothing was exported and the cell was completely flat,








the effective travel distance would be at a local maxima. The impediment to each

material (water, phosphorus) leaving each cell was calculated from the mass balance

calculations presented in Figures 3.2 and 3.3.

Using a GIS command (SPREAD) that calculates travel from the destination

through each intervening specified impediment function, a measure of a distance from a

specific exporting cell to the lake perimeter was calculated. This distance is related to, but

not synonymous with, linear distance and becomes the effective proximity of that cell to

the lake. A different impediment map was prepared for each material simulated and each

year studied.

Total rainfall and phosphorus deposition was divided by the log of this effective

proximity value, and this value was used to represent the amount of material exported

from any specific cell actually reaching the lake. This new map was then used as the

basis for cumulative drainage into the lake.


Verification

Model results for total annual runoff were compared to runoff values

calculated using the Soil Conservation Abstraction Method. Runoff values from the

spatial model for Newnans Lake were within 8% of the SCS method. Weir estimates, on

the other hand, were twice the SCS value. Calculations are presented in Appendix C.

The efficacy of the model in linking distributed watershed loads to the lake was

evaluated in two ways. A simpler, one year regression analysis of seven Florida lakes

was completed (Brandt-Williams, 1995; Brandt-Williams & Brown, 1997). This study

compared phosphorus loading to total lake productivity, algal diversity and trophic state

indices for seven Florida lakes included in a 1973 study of lakes receiving sewage








effluent (Taylor, 1978). Estimated phosphorus loading was regressed versus several

water quality parameters. One month load using rainfall from the month immediately

prior to collection of data showed a significant and positive correlation with chlorophyll

concentrations (r2= 0.946). Annual loads regressed versus Huber et al. (1982) trophic

state indices resulted in a significant and positive correlation (r2=0.725) as well. An

abstract and key results of this study are presented in Appendix C.

The second verification method calculated the estimated phosphorus to

sediment ratio for each year in the chronosequence study and compared the value to

paleolimnological records presented in other studies (Kuntz, 1995; Gottgens and

Crisman, 1993). Sediment erosion quantities were calculated using a modified Universal

Soil Loss Equation in each cell. The resulting value was "drained" through the

watershed using a GIS function. Because sediment phosphorus is often reported as a

ratio of grams total phosphorous to kilograms sediment, the total simulated phosphorus

load was divided by the total simulated sediment load for the three time periods included

in this study. Table 3.3 presents this comparison.




Development of Emergy Patterns


Emergy values for inputs specific to each land use were used to create maps illustrating

concentration of energy within each watershed. One set of maps was created to depict

the base emergy flowing into and stored within each map cell on an annual basis. Another

showed the flow ofemergy through the watershed with a storm event. Figure 3.4 presents

a system diagram for inputs and storage included in these evaluations.












Table 3.3. Phosphorus quantities in sediment cores, Newnans Lake
and Lake Weir (Gottgens & Crisman, 1993; Crisman et al., 1992), values
approximated from graphs.


Core Date

c1900
1950
1960
1970
1980
1990


Newnans*
mg/g dry wt

8
14
27
28
30
30


* top core only


Weir
ug/cnrdyr

na
na
15
21
8
26









































Figure 3.4, Diagram with empower pathways and emergy storage components included in calculations for each cell,








Empower Density Mapping

Empower density for natural and developed areas were applied to the land use

maps for each time period included in this study. Values for the components included

were obtained from previous studies and evaluations completed for this study. Tables 3.4

and 3.5 list values ofemergy flow per hectare per year for each land use.

Emergy values for 1990 were used as the baseline evaluation for residential,

urban and industrial land uses. Values for agriculture were taken from energy analysis

completed between 1980 and 1989 (Fluck 1992). Some assumptions were made to

prorate all land use emergy values for 1970 and 1950. Emergy for all natural areas

remained constant because no monetary values are included in the analysis. Agriculture

emergy was the same for 1990 and 1970, but 10% less in 1950 to account for inflation

differences in service dollars. The emergy evaluation tables for residential, urban, and

agricultural commodities are presented in Appendix D.

Tables 3.6 (Newnans Lake) and 3.7 (Lake Weir) and Figure 3.5 provide the

details included in the evaluation ofemergy flow into the lake. Table 3.8 presents

summary emergy data for both lakes for all years evaluated. Additional calculations are

presented in Appendix E.


Emergy Accumulation Maps

The DRAIN command in MapFactory was used to show pathways of emergy

movement in water and phosphorus through the watershed. As water or phosphorus first

left a cell, its emergy was calculated by multiplying the mass by an emergy per mass ratio

appropriate for the dispersion process initiated. As this runoff converged on an








Table 3.4. Empower densities for watershed land use, 1990

Land use empower density Reference
E14 sej/halyr

Forested wetland 4.7 a
Forest 4.8 a
Grassland 8.4 b
Herbaceous wetland 11.0 c
Soybeans 19.5 b
Lake 19.6 b
Oranges 36.0 b
Rural residence, Alachua Co. 709.0 b
Mining 7030.0 c
Urban, Gainesville 20300.0 b
Industry estimate, Alachua Co. 3000000.0 b

a. Orrell 1997
b. Brandt-Williams 1999 this study)
c. Odum 1996

Table 3.5. Empower densities for land use in 1950
and 1970 (natural areas are assumed the same as 1990).

Land use empower density Reference
E14 sej/ha/yr

1950
Soybeans 17.6 b*
Oranges 32.4 b*
Rural residence, Alachua Co. 567.0 b+
Mining 5624.0 c
Urban, Gainesville 16240.0 b+
Industry estimate, Alachua Co. 2400000.0 b+
1970
Rural residence, Alachua Co. 638.0 b*
Mining 6327.0 b*
Urban, Gainesville 18270.0 b*
Industry estimate, Alachua Co. 2700000.0 b*

b. Brandt-Williams 1999 (this study)
c. Odum 1996
* 10% lower than 1990
+ 20% lower than 1990























































Figure 3.5. Diagram of inputs to lake, for use in emergy evaluation in Table 3.6.









Table 3.6. Emergy analysis of Newnan's Lake watershed/lake interface, 1970.


Unit Solar Solar
Data EMERGY EMERGY 1970 EM$
Note Item Unit (unitslyr) (sej/unit) (E15sej/yr) (E4US$)


Atmospheric inputs
A Insolation
B Wind shear
C Rain, chemical potential
D Transpiration emergents
E TP in Rain


Watershed inputs
F Stream, geopotential
G Stream, chemical potential
H Sediment
I Runoff, non-point
J TP in streams
K TP in runoff


J 1.78E+17 1 178
J 2.61E+14 1.50E+03 391
J 1.96E+14 1.82E+04 3574
J 1.03E+12 L54E+04 16
g 7.14E+06 2.00E+06 <1
Total atmospheric (sun omitted) 3981


1.38E+13 1.85E+03 26
1.60E+03 1.82E+04 <1
3.16E+12 7.30E+04 231
1.25E+15 6.3 1E+04 79077
3.70E+09 6.85E+09 25318
4.28E+07 6.85E+09 293
Total watershed 104945


Total emergylake/yr
Total emergy/ha/yr


Transformities
1 Phytoplankton
2 TP in water column
3 Water


6.59E+12 sej/g
2.90E+13 sej/g
6.16E+05 sej/J


Notes:


TP = total phosphorus
A Annual energy = (Avg. Total Annual Insolation J/yrXAreaXl-albedo)
Insolation: 6.90E+09 J/m2/yr
Area: 3.01E+07 m2


Albedo:


0.14


(Vishner, 1954)

(Odum, 1987)


Annual energy: 1.78E+17 J/yr
B Wind mixing energy =
(density, kg/m3)(drag coefficient)(geostrophic wind velocity3,m3/s3)(area)
u = wind velocity (m/s) = 3.58 m/s


2
5
45
<1
<1
50


<1
<1
3
99
32
4
131


108927
36








Table 3.6 continued

geostrophic wind velocity = 5.97 m/s
Energy = 1.3 kg/m3 1E-3 *212.77 m3/s3 3.14 E7 s/y 3.01E7 m2
Energy/yr = 261E+14 J/yr
C Rain, chemical potential = (rainm)lake aream2)(E6 g/m3)*G
Rain, m 1.32E+00 m
Lake area, m2 3.01E+07 m2
G, free energy, J/g 4.94E400 J/g
Energy/yr= L96E+14 J/yr
D Transpiration from emergent and floating macrophytes
14-2 ha cover (Huber et al, 1982)
7.30E+10 J/ha, estimated transpiration (Odum, 1996)
E Phosphorous in rain = area rainfall concentration
Area = 3.01E+07 m2
Rainfall= 1.4224 m/yr (-52 in, NOOA, 1995)
Concentration = 0.167 g/m3 (Brezonik, 1969)
Annual amount = 7.14E+06 g/yr
F Stream, geopotential, J/yr = (flow volume)(density)(dh)(gravity)
Hatchett Creek
flow,cfs = 18 cfs (SJRWMD, 1997)
dh,m= 76 m (Brandt-Williams, 1999)
Energy/yr = 18cfs*0.028317m3/ft3*3.1536E7sec/yr* E6g/m3*7 1.20E+13
Little Hatchett Creek
flow, cfs = 4 cfs (SJRWMD, 1997)
dh, m= 53 m (Brandt-Williams, 1999)
Energy/yr = 1.86E+12 J
G Stream, chemical potential = (volume flow)(density)(G)
G = (8.33J/mole/degX300Kyi8 g/mole)*ln[(1E6 S) / 965000] J/g
S, ppm = 5.9 (calculated from turbidity, SJRWMD, 1997)
Flow,cfs = 18 cfs
Energy/yr= 1.60E+03 J/yr
H Sediment = (Sediment kg/yr)*(IE3 g/kg)*(avg. % organic)*(5.4 Cal/g OM)*(4186 J/Cal)
Energy = (2.8E7 kg/yr)*(1E3 g/kg)*(0.5% Organic)*(5.4 Cal/g)*(4186 J/Cal)
= 3.16E+12 J/yr
I Runoff nonpoint = (volume/yr)(G) = (Volumem3)(4.82 J/g)(l E6 g/m3)
Volume= 2.60E+08 m3/yr
Energy/yr= 1.25E+15 J/yr
Transformity = 6.31E+04 sej/J
Transformity calculated from spatial simulation of total emergy at lake perimeter divided
by total volume of water converted to Joules









Table 3.6 continued

J Total phosphorus in streams
= (volume,cfs)(P,mg/l)(0283 l,m3/ft3X3 1536E7,seclyrX1E-3 g/mgXIE6 L/m3)
Volume ,cfs = 1.80E+01 cfs (SJRWMD, 1997)
Average concentration, mg/1 0.23 mg/l (SJRWMD, 1997)
Average TP mass = 3.70E+09 gfyr
Transformity = 182E+04 sej/g (Appendix D)
K Phosphorous in runoff from spatial model
Annual amount = 4.18E+07 gfyr
Transformity = 6.85E+09 sej/g
Transfonnity calculated from spatial simulation of total emergy at lake perimeter divided
by total mass of phosphorus

Transformities calculated from this analysis
1 Phytoplankton, g
= (avg. chlorophyll a concentration, g/m3)(lake volume, m3)(2g phytoplankton/g Chi a)
Avg Chl a = 0.231 g/m3 (Huber et al., 1982)
1.65E+07 g
2 TP in water column, g = (avg. TP in water column, mg/L)lake volume, m3)
Average concentration 0.105 mg/l (Huber et al., 1982)
Total g 3.76E+06
3 Water, J = (lake volume, m3)(IE6 g/m3X4.94 J/g)
Volume 3.58E+07 m3 (SJRWMD, 1997)
Energy stored 1.77E+14 J









Table 3.7. Emergy analysis of Lake Weir watershed/lake interface, 1970.


Unit Solar Solar
Data EMERGY EMERGY 1970 EMS
Note Item Unit (units/yr) (sej/unit) (E15sej/yr) (E4USS)

Atmospheric inputs
A Insolation J 1.58E+17 I 158 2
B Wind shear J 2.32E+14 1.50E+03 347 4
C Rain, chemical potential J 1.74E+14 1.82E+04 3171 40
D Transpiration emergents J 1-03E+12 1.54E+04 16 <1
E P inRain g 6.34E+06 200E+06 <1 <1
Total atmospheric (sun omitted) 3534 44

Watershed inputs
F Runoff, non-point J 3.22E+14 1.86E+04 6000 75
G Sediment J 8.70E+11 7.30E+04 64 1
H P in runoff g 5.26E+07 1.27E+10 668 8
Total watershed 6732 84

Total emergy/lake/yr 10265 128
Total emergy/ha/yr 4
Transformities
1 Phytoplankton, g 4.47E+12 sej/g
2 TP in water column, g 4.88E+12 sej/g
3 Water, J 1.09E+04 sej/J


Notes:
A


Annual energy = (Avg. Total Annual Insolation J/yrXAreaXl-albedo)
Insolation: 6.90E+09 J/m2/yr


(V


Area: 2.67E+07 m2
Albedo: 0.14 (
Annual energy: 1.58E+17 J/yr
B Wind mixing energy =
(density, kg/m3)(drag coefficientXgeostrophic wind velocity3,m3/s3)(area)
wind velocity (m/s) = 3.58 m/s
geostrophic wind velocity = 5.97 m/s
Energy = 1.3 kg/m3 IE-3 *212.77 m3/s3 3.14 E7 s/y 2.67E7 m2
Energy/yr = 2.32E+14 J/yr
C Rain, chemical potential = (rain,mX)ake aream2)le6gfm3)*G
Rain, m 1.32E+00 m
Lake area, m2 2.67E+07 m2


ishner, 1954)

Odum, 1987)









Table 3.7 continued


G, free energy, JIg 4.94E+00 J/g
Energylyr= 174E+14 J/yr
D Transpiration from emergent and floating macrophytes
14.2 ha cover
7.30E+10 J/ha, estimated transpiration
E Phosphorous in rain= area rainfall concentration
Area = 2.67E+07 m2
Rainfall= 1.4224 m
Concentration= 0.167 g/m3


(Huber et al.,
(Odum,



(-52 in, NOOA,
(Brezonik.


Annual amount = 6.34E06 g/yr
F Sediment =
Energy = (7.7E6 kg/yr)*(le3 g/kg)*(0.5% Organic)*(5.4 Cal/g)*(4186 J/Cal)
= 8.70E+11
G Runofl nonpoint = (volumeyr)(G) = (Volumem3X4.82 J/gX1 E6 g/m3)
Volume= 6.68E+07 m3/yr
Energy/yr= 3.22E+14 J/yr
Transformity= 1.86E+04 sej/J
Transformity calculated from spatial simulation of total energy at lake perimeter
divided by total volume of water converted to Joules
H Phosphorous in runoff from spatial model
Annual amount= 5.26E+07 gfyr
Transformity = 1.27E+10 sej/J
Transformity calculated from spatial simulation of total emergy at lake perimeter
divided by total mass of phosphorus

Transformities calculated from this analysis
1 Phytoplankton, g
= (avg. chlorophyll a concentration, g/m3)(ake volume, m3)(2g phytoplankton/g Chi a)
Avg Chl a = 0.006 g/m3 (Huber et al., 1982)
2.30E+06 g
2 TP in water column, g = (avg. TP in water column, mg/L)(lake volume, m3)
Average concentration 0.011 mg/1 (Huber et al., 1982)
Total g 2.11E+06
3 Water, J = (lake volume, m3X1E6 g/m3)(4.94 J/g)
Volume 1.91E+08 m3 (SJRWMD, 1997)
Energy stored 9.46E+14 J


1982)
1996)



1995)
1969)

























Table 3.8: Summary emergy values for Newnans Lake and Lake Weir

Value
Item Units Newnans Weir
Total emergy flow E15 sej/yr 108927 10265
Empower density E15 sej/ha/yr 36 4
Phytoplankton emergy/mass sej/g 6.59E+12 4.47E+12
Water transformity sej/J 6.16E+05 1.09E+04
TP emergy/mass sej/g 2.90E+13 4.88E+12








adjacent cell, the emergy was added to the emergy of the same entity in that cell. The

cumulative emergy reaching the lake perimeter was summed for all perimeter cells to

determine total emergy input to the lake.


Phosphorous Emergy Per Mass Ratios

Different phosphorus transformities were used for each cell depending on either

the estimated concentration of phosphorus in solution with rainfall or the source of

concentrated input, such as fertilizer. Curves for continuous transformities for

phosphorus solutions of known concentration were interpolated using previous

transformity evaluations for rock phosphate dissolved in rain water and reagent grade

phosphorus mixed with groundwater (Appendix F). Transformities for concentrated

phosphate products were calculated using standard emergy accounting methods

(Appendix F).

Final TP emergy per gram ratios were calculated in two steps. The total TP

emergy was drained through the watershed, providing cumulative emergy values at every

point in the watershed. These emergy values were divided by the grams of TP exported

from that cell, giving the energy per gram for TP within that specific cell. The average

emergy per gram used in evaluating emergy flows into the lake was determined by

dividing the total TP emergy flow by the TP load in grams.


Results: Landscape Properties


The following sections introduce maps illustrating basic watershed characteristics

(basin morphology, soils and geology, and land use). Results from the two watersheds

are presented together for immediate comparison. Elevation contours in the watershed are








presented separately from the bottom contours of each lake. The lake surface is shown at

average NGVD height in the elevation maps.

Soils for both watersheds are mapped using two different classifications. The first

is the standard Soil Conservation Service hydrological grouping, previously discussed in

Chapter 2. The second represents the total capacity for water infiltration as discussed in

the model development section of this chapter. Geological maps are for information only

and were not used in the simulation of the model.


Watershed Morphology. Newnans Lake

Figure 3.6 presents an elevation profile for Newnans Lake watershed. The range of

elevation for Newnans' basin is 16 m at the deepest lake point to 69 m at the northwest

edge of the basin. The steepest slope is 45%, and the average slope is 10%. Mean lake

level is 20 m (65' NGVD). The elevation map illustrates the relative flatness of the

watershed in the area immediately surrounding the lake (black to dark gray), with high

relief concentrated along the western edge of the basin.

Figure 3.7 is a bathymetric map prepared by the St. Johns River Water Management

District. This map illustrates the flat, shallow morphology of Newnans Lake. The depth

throughout most of the lake is less than 1.2 m (4 feet) with contours spread far apart.

There is a relatively small pool in the central eastern section reaching a maximum depth

of 3 m (10').


Watershed Morphology, Lake Weir

Figure 3.8 illustrates the elevation contours for the Lake Weir watershed. The

range of elevation for the basin is 9 m, near the center of the main lake, to 57 m along the








eastern ridge. The mean lake level is 18 m. The steepest slope is 45%, and the average

slope is 20%. Although lower in elevation overall than Newnans Lake, the lake is

deeper and the watershed steeper. However, Lake Weir's basin has several areas of

intermittent depressional relief evident by the spotty dark to light contours throughout

the lower map in Figure 3.11.

Figure 3.9 is a bathymetric map prepared by Ott and Chazal in 1966. The

majority of the lake bottom is approximately 7 m (25 feet) below the lake surface, with a

steep dropoff around the perimeter of the lake.


Soils and Geology, Newnans Lake

Soil hydrology distributions, determined by the Soil Conservation Service (SCS),

are shown in Figure 3.10, and Figure 3.11 depicts more detailed soil hydrology

distributions. Hydrologic soil class D is predominant in Newnans watershed, but soil

group A borders the western edge of the lake and the northern drainage into Hatchett

Creek. Soils classified by impedance to water transport (permeability times capacity)

show higher heterogeneity but generally follow the same distribution pattern as shown by

the SCS categories.

Figure 3.12 is a map of the underlying geological formations in Newnans'

watershed, which lies mainly within the Hawthorne formation and Plio-Pleistocene

Terrace deposits. The Ocala group surfaces in a small area of the southwest basin. The

Hawthorne formation is a highly variable mix of quartz sand, clay, carbonate and

phosphate overlying the Ocala group and ranges in thickness from a 200 feet to the east

of the lake to 160 feet near Gainesville. Plio-Pleistocene deposits are fine to medium

mixes of sand, silt and clay. The Ocala formation is 98% calcium carbonate. (SCS, 1982)








Soils and Geology. Lake Weir

Soil Conservation Service (SCS) soil hydrology distributions are shown in Figure

3.13, and Figure 3.14 depicts more detailed soil hydrology distributions. Hydrologic soil

class A is predominant in the Lake Weir watershed. Soil groups C and D border the

northeastern edge of the lake and the stream and wetland area north of Little Lake Weir.

Soils classified by impedance to water transport (permeability times capacity) generally

follow the same pattern of distribution as shown by the SCS categories.

Figure 3.15 is a map of the underlying geological formations in Lake Weir's

watershed. This basin lies mainly within the Ocala group and the Hawthorne Formation.


Land Use Changes. Newnans Lake

Land use within the Newnans watershed for 1950, 1970 and 1990 are presented in

Figures 3.16 through 3.18, respectively. Table 3.9 and Figure 3.19 provide area values

for specific land uses and illustrate the magnitude of changes.

Four significant changes in land use occurred between 1950 and 1970.

Residential and natural areas bordering Gainesville were incorporated into the city.

Large tracts of deforested areas, about 700 ha, around Hatchett Creek and in the

northwest watershed were reforested. Residential areas increased directly to the west of

Newnans Lake and along Waldo Road. The number and width of roads increased.

There are fewer differences evident between 1970 and 1990. The Gainesville

municipality increased in area near the far western edge of the watershed. Impervious

surface at the airport increased, as did industrial development of the same area. In

addition, existing residential clusters throughout the basin expanded in area and number

of residents.







































Meters above sea level
34.15

66.00

97.85

129.69

161.54


0 5,000 10,000
60,000 Met
1260,000


Figure 3.6. Elevation contours in Newnans Lake watershed.































































et m Lacmuw @am aft
ctphaIs- --m o smii
-Wi -mrmm


Figure 3.7. Elevation contours on the floor ofNewnans Lake (SJRWMD, 1996).












































meters above sea level
175

27.4

37.2

47.1

56.9


0 2,500 5,000
. etres
1:249,324


Figure 3.8. Elevation contours in Lake Weir.

















































Figure 3.9. Lake Weir bathymetry (after Ott and Chazal, 1966).


































D



m-












defined by Soil Conservation Service. Group D has high runoffpotential, C has moderate
water
0 5.000 10.000
1:260.000







Figure 3.10. Hydrological soil classification groups in Newnans Lake watershed, as
defined by Soil Conservation Service- Group D has high runoff potential, C has moderate
potential, B has low runoff potential and A has little to no runoff potential (classification
described in Chapter 2).





































Rain fraction retained
0.06000

029500

0.53000

0.76500

1.00000



0 5,000 10,000
Metres
1:260,000









Figure 3.11. Soil impedance distributions, categorized by permeability and capacity, with
values representing the fraction of an average rain event being retained within the soil
column, Newnans Lake.














































Miocene-Hawthore, Devils Millhopper
Miocene-Hawthome, Groveland Park
Pliocene, Bone Valley formation
Plio-Pleistocene
Miocene-Hawthorne, Statenville


0 5,000 10,000
=I M I IMetres
1:260,000





Figure 3.12. Map of subsurface geology formation, Newnans Lake (adapted
from SCS, 1982).





















d.


*

I-i',.


~1
I


M Water



Ic
-D
D


5,000

1:249,324


10,000
-= Metres


Figure 3.13. Hydrological soil classification groups in Lake Weir watershed, as
defined by Soil Conservation Service. Group D has high runoff potential, C has
moderate potential, B has low runoff potential and A has little to no runoff potential
(classification described in Chapter 2).


r


I I







































Rain fraction retained


- 0.1000

- 0.3250

- 05500

- 0.7750


L-I-- 1.0000


5,000

1:249,324


10,000
SMetres


Figure 3.14. Soil impedance distributions, categorized by permeability and capacity,
with values representing the fraction of an average rain event retained within the soil
column, Lake Weir.


tr









































- Ocala Group
SHawthorne Formation





0 5,000 10,000
SMetres
1:249,324


Figure 3.15. Map of subsurface geology formation, Lake Weir









Land Use Changes. Lake Weir

Figures 3.20 through 3.22 depict land use in the Lake Weir basin for 1950, 1970,

and 1990, respectively. Table 3.10 and Figure 3.23 provide area values for specific land

uses and illustrate the magnitude of changes.

The largest change in this watershed occurred between 1970 and 1990 and was the

conversion of orange groves throughout the watershed to range and residential land use, a

total loss of 2355 hectares in production. Between 1950 and 1970, residential land use

and agriculture increased throughout the watershed by approximately 400 ha, especially

near the lake perimeter. A major highway was also built to the west of Lake Weir (U.S.

Hwy. 441) between 1950 and 1970. A small section of urban area (Belleview) had

encroached at the far west of the basin by 1990.


Results: Non-point Source RunoffProfiles

This section presents the results from the simulation of water, phosphorus and

sediment movement through the watershed. Table 3.11 and 3.12 list overall material

flows calculated in the spatial simulation for Newnans and Weir, respectively.


Water Profiles

Water movement through the watersheds is presented four different ways. The

volume of water exported to the lake from each cell versus the number of cells exporting

that volume is illustrated by a rank-order graph. The area of watershed contributing the

largest amount ofstormwater to the lake is mapped for 1950, 1970 and 1990. These areas

of significant export were considered to be the "effective" watershed, as opposed to the

actual watershed determined solely by elevational differences. Changes in watershed


































Lakes/Ponds
Herbaceous wetland
Stream
SForested wetland
Forest
Open/Range
SUrban
Commercial
^ Residential
I industry
Orchard
SRow crop
iRoaing
M Roads


0 5,000 10,000
II Metres
1:260,000


Figure 3.16. Newnans Lake watershed land use, 1950.




































- Lakes/ponds
SHerbaceous wetland
Streams
Forested wetland
Forest
"] Open/Range
Railyard
SUrban
Commercial
M Residential
SIndustry
Orchards
SRow Crops


Roads


0 5,000 10,000

1:260,000


Figure 3.17. Newnans Lake watershed land use, 1970.



































Lakes/Ponds
Herbaceous wetland
Streams
SForested wetland
Forest
|J |Open/Range
Railyard
Urban
Commercial
- Residential
Industry
SOrchard
SRow crop


Roads


0 5,000 10,000
16I I Metres
1-260,000


Figure 3.18. Newnans Lake watershed land use, 1990.



















Table 3.9. Land use areas for Newnans Lake.

1950 1970 1990 Change, % Basin
By Land Use. ha
Water* 320 318 331 0.02%
Herbaceous Wetland 421 429 431 0.02%
Streams 628 629 588 -0.07%
Forested Wetland 7945 7990 7901 -0.08%
Forest 35086 35714 35330 0.44%
Open/Range 8226 6598 5813 -4.37%
Agriculture 200 143 129 -0.13%
Residential 911 1120 1341 0.78%
Urban 1060 1786 2550 2.70%
Industry 12 12 72 0.11%
Roads 410 481 732 0.58%

By Level of Development
Natural 44400 45080 44581 0.33%
Cleared 8226 6598 5813 -4.37%
Developed 2593 3542 4825 4.04%

* Newnans Lake not included




Full Text

PAGE 1

EVALUATION OF WATERSHED CONTROL OF TWO CENTRAL FLORIDA LAKES: NEWNANS LAKE AND LAKE WEIR By SHERRY BRANDT WILLIAMS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PIllLOSOPHY UNIVERSITY OF FLORIDA 1999

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UMI Number: 9956647 UMt UMI Microform9956647 Copyright 2000 by Bell & Howell Infonnation and learning Company. All rights reserved. This microfonn edition is protected against unauthorized copying under rltle 17, United States Code. Bell & Howell Information and learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI48106-1346

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ACKNOWLEDGMENT Most important throughout this process was the love and support of my Casey Cameron Cody Dakota Malachi_ I would like to thank all of my committee: Dr. T_ L. my chair, Dr. H. T. Odum, my co-chair, and Drs. W. Viessman, C. Montague and F. Nordlie. Without their support and unique insights, this study would not have been completedMy sincere gratitude goes to Dr. Crisman for his patience and unfailing encouragement throughout the past four years. I am indebted to Dr. Odum for showing me a different perspective on thermodynamics and scale of applications. Dr. Odum, like a spring in a desert, is a point source of energy in a thirsty wilderness, with a territory of influence as huge as a star. I gratefully acknowledge Dr. M_ T. Brown for discussions on this project and spatial methods. I thank all the people who encouraged me and critiqued my reasoning: Dr. Linda Leigh, Debra Childs, David Brandt-Williams, Paula Palmer, Linda Tyson, Andrea Kendall, Josh and Nadine Orrell, and Judy Fouts. This study was aided by a National Science Foundation Minority Engineering Doctorate Initiative Fellowship through the College of Engineering at the University of Florida. B. J. Bukata, J_ Buenfil and K. Jackson assisted in preparing maps. 11

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The dissertation is dedicated to the memory of my Wmona and Richard Brandt, who instilled incentives, means, and freedom to achieve. iii

PAGE 5

TABLE OF CONTENTS ACKN"OWLEDGMENTS ................................................................................................. Ji LIST OF TABLES .............. __ ............................................................................................ viii liST OF FIGURES ............................................................................................................. x ABSTRACT ................................................................................................................... xvi CHAPTERS I IN"TRODUCTION ................................................................................................... 1 Concepts and Perspectives ........................................................................................ 3 Scale of Components .......................................................................................... 4 EnergySystems Diagram ......................................................................................... 6 Emergy and Empower V aluatiOD. ........................................................................... 6 T ransformity ............................................................................................................ 8 Transformity and Control ........................................................................................ 8 Multistage Processes of Material Flows ............................................................... .9 Emergyper Mass and Concentration ..................................................................... 1 0 Empower Density .................................................................................................. 11 Spatial Organi7ation ................. __ ............................................................................ 11 Record of "Lake Functions ............................................................................... 12 Emdollars ............................................................................................................... 12 Estimating Benefits of Lake Management....................................................... 13 Previous Studies ....................................................................................................... 14 Shallow "Lake Limnology...................................................................................... 14 Nutrient Dynamics and Loading ......................................................................... 15 Spatially Distributed Surface Flow Models ....................................................... 16 Lake V aluatiOD._ .................................................................................................. 18 Paleolimnology................................................................................................... 20 Watersheds Evaluated .................................................................................................. 21 Newnans "Lake ............................................................................................... 21 Lake Weir ................................................................................................ .23 Plan of Study .................................................................................................... .24 tV

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2 METHODS .......................................................................................................... .25 Map Preparation and Data Sources .......................................................................... .25 Elevation and W atersheelineation. .................................................................... 25 Land Use and Cover ............................................................................................ .26 Soil Maps .............................................................................................................. .27 Rain. Data. .............................................................................................................. .27 Dynamic Simulation Models ...................................................................................... .28 System Diagram .................................................................................... .28 Simulation Example ................................................................................................ 31 Emergy Evaluation ...................................................................................................... .32 Emergy Tables ....................................................................................................... .32 Emergy IOOices ......... .32 3 RESUL TS .................................................................................................................. 40 PART I: SPATIAL WATERSHED MODEL ............................................................ 40 Development of Material Flows .................................................................................. 40 Simulation of Water Budgets in GIS ...................................................................... 43 Infiltration and Runoff Calculations ....................................................................... 46 Phosphorus Uptake, Adsorption and Deposition ................................................. 47 Movement Between Cells ...................................................................................... 51 V erification ............................................................................................................. 52 Development of EmergyPatterns ................................................................................ 53 Empower Density" Mapping .................................................................................. 56 Emergy Accumulation Maps ................................................................................. 56 Phosphorus EmergyPer Mass Ratios ................................................................... 65 Results: Landscape Properties ..................................................................................... 65 Watershed Morphology-, Newnans Lake ............................................................... 66 Watershed Morphology-, Lake Weir ....................................................................... 66 Soils and Geology-, Newnans Lake ......................................................................... 67 Soils and Geology-, Lake Weir ................................................................................ 68 Land Use Changes, Newnans Lake ........................................................................ 68 I.and Use Changes, Lak:e Weir ............................................................................... 79 Results: Non-point Source Runoff Profiles ................................................................. 79 Water Profiles ......................................................................................................... 79 Water profiles for Newnans Lake .................................................................... 91 Water profiles for Lak:e Weir ............................................................................ 92 Phosphorous Profiles for Newnans Lake ............................................................... 93 Phosphorous Profiles for Lak:e Weir ...................................................................... 93 Results: Emergy Patterns ........................................................................................... 121 Aerial Emergy Flux, Newnans Lak:e .................................................................... 121 Aerial Emergy Flux, Lak:e Weir ........................................................................... 121 Emergy Accumulation Profiles ............................................................................. 128 v

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PART 2: DYNAMIC LAKE SIMULATION MODEL .................................... 128 Development ................................................................................................. 134 Aggregation and Interactions ............................................................................ 134 T ropbic State S imulation. ............................................................................... 13 9 Calibration Data ................................................................................................ 13 9 Simulation Results ................................................................................................. 140 Simulations Comparing Responses to TP Loading ..................................... 140 Pulsing Simulation ................................................................................................ 145 PART 3: WATERSHED CLASSIFICATION AND MANAGEMENT ................ 146 Material Loads and Ratios ......................................................................................... 146 Newnans I.ake ..................................................................................................... 157 I.ake Weir ..................................................................................................... 157 Emergy Loads and Ratios .......................................................................................... 157 Comparison Between Watersheds .................................................................... 157 Comparison of Simulated Load and Empirical Phosphorus Data ........................ 158 Emergy Accumulation Patterns ............................................................................ 158 Intervention Strategies ................................................................................................ 168 N ewnans I.ake ..................................................................................................... 168 I.ake Weir ........................................................................................................ 168 4 SUMMARY AND RECOMMENDATIONS ..................................................... 178 Summary ............................................................................................................. 178 Spatial Patterns in I.ake Watersheds .......................................................................... 180 Patterns from Geologic Processes ....................................................................... 181 Patterns from Human Development .................................................................... 182 HierarchyofI.akes and Watersheds ........................................................................... 182 Cumulative Watershed Loading and Trophic Status ....................................... 183 Emergy and Emergy per Mass Related to Lake Status .............................................. 185 The Watershed ..................................................................................................... 186 The Watershed-I.ake Interface ............................................................................. 186 Phosphorus .......................................................................................................... 187 Water .................................................................................................................... 187 Sediments ............................................................................................................. 187 Emdollars ............................................................................................................. 188 Use of Dynamic Simulations as Quality Indicators .................................................. 188 Classification of Waters beds Using Material and Emergy Indices ............................ 189 Evidence for Watershed Intervention and Prioritization .......................................... 19O Intrawatershed Modification of Non-Point Source Loading ........................... 190 Interwatershed Priorities ..................................................................................... 191 Recommendations ...................................................................................................... 192 Non-point Source Inputs ..................................................................................... 192 VI

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Development I>ensity' .......................................................................................... 193 Appropriate Scale for Stonnwater Management ................................................. 193 Use ofEmergy as a Management Decision Tool ................................................. I94 Conclusion .......................................................................................................... 195 APPENDICES A GIS INFORMA.TION .......................................................................................... 197 B SOlI.. PROPERTIES ............................................................................. 199 C VERIFICATION OF SPATIAL MODEL .......................................................... .206 D EMPOWER DEN"SITIES ....................................................................................... .210 E EMERGY EVALUATIONS OF WATERSHEDS ............................................... 226 F TRANSFORMITIES ............................................................................................. 232 G IN-LAKE SIMULATIONS .................................................................................. .247 REFEREN"CES ................................................................................................................ .253 BIOGRAPIllCAL SKETCH ......................................................................................... .259 VIl

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LIST OF TABLES 2.1 Example of emergy evaluation: annual production of one hectare of Bahia grass. (see Figure 2.3) ........................................ ........................................................................ .34 22. Description of infonnation presented in an emergy table ........................................ .36 3.1 Impervious surface for different land uses ................................................................... 48 3 .2 Average phosphorous deposition rates ........................................................................ 48 3.3 Phosphorus quantities in sediment cores, Newnans Lake and Lake Weir (Gottgens & 1993; Crisman et al., 1992), values approximated from graphs ............... .54 3.4. Empower densities for watershed land use, I 990 ...................................................... .57 3.5 Empower densities for land use in 1950 and 1970 (natural areas are assumed the same as 1990) .................................................................................................................... 57 3.6 Emergy evaluation ofNewnans Lake watershedlIake interface, 1970 ......................... .59 3.7 Emergy evaluation of Lake Weir watershedlIake interface, 1970 ................................. 62 3.8 Summary emergy values for Newnans Lake and Lake Weir ........................................ 64 3.9 Land use areas for Newnans Lake ................................................................................ 83 3.10 Land use areas for Lake Weir ..................................................................................... 88 3.11 Summary data for watersheds loads to Newnans Lake form spatial simulation ........ 90 3.12 Summary data for watersheds loads to Lake Weir from spatial simulation ............... 90 3.13 Definitions of terms describing interactions and flows relevant to simulations ....... I33 3.14 Productivity, storage and turnover times for lake components from various literature sources .................................................................................................................... 141 3.15 Summary data used as guide for initial simulations .................................................. 142 viii

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3.16 Summary data for watershed loads to Newnans Lake form spatial simulation ....... 159 3.17 Summary data for watershed loads to Lake Weir from spatial simulation. .............. 159 3.18 Comparison of watershed emergy classification parameters .. 164 3.19 Summary emergy data for runoff to lake, Newnans Lake ........................................ I66 320 Summary emergy data for runoff to lake, Lake Weir ............................................... 166

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LIST OF FIGURES Figure 1_1 Components of a watershed-lake system on a graph of turnover time and territory __ 5 12 Energy systems diagram of a lake watershed including an area of urban settlement _____ 1 1.3 Watershed locations in the state ofFiorida ________________________________________________________________ 22 2_1 Energy systems symbols and definitions (Odwn, 1994) ____________________________________________ 29 22 Simulation example: aggregated water budget for a lake, values used for calibrating coefficients and the differential equation _________________________________________________________________ .33 2.3 Emergy analysis example: a) definition of two indices emergy yield ratio and investment ratio; b) simplified emergy diagram for grass example in Table 22 _____ ..35 2_4 Diagram explaining solar transformity _______________________________________________________________________ .31 3 _1 Map layers used as data in spatial model and the hydrology functions derived from each data set_ Linking these maps with mathematical functions allows them to be used as boundary conditions for solving continuity equations governing the flow of materials through the watershe
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3 _8 Elevation contours in lAlke Weir __________________________________________________________________________________ 11 3_9 Lake Weir bathymetry (after Ott and ChazaI, 1966) ___________________________________________________ 12 3_10 Hydrological soil classification groups in Newnans lAlke watershed, as defined by Soil Conservation Service_ Group D has bigh runoff potential, C has moderate potential, B has low runoff' potential and A has little to no runoff potential (classification descnbed in Chapter 2) ______________________________________________________________________ 13 3 _11 Soil impedance categorized by permeability and with values representing the fraction of an average rain event being retained within the soil Newnans lAlke _____________________________________________________________________________________________ 14 3_12 Map ofsubsur&ce geology formation, Newnans lAlke (adapted from 1982) ___ 15 3 _13 Hydrological soil classification groups in lAlke Weir as defined by Soil Conservation Service_ Group D has high runoff potential, C has moderate potential, B has low runoff potential and A has little to no runoff potential (classification descnbed in Chapter 2) ______________________________________________________________________ 76 3 _14 Soil impedance categorized by permeability and with values representing the fraction of an average rain event being retained within the soil I.ake Weir _____________________________________________________________________________________________ ______ 71 3 _15 Map of subsurface geology formation, lAlke Weir ________________________________________ __ .. _._. ____ 78 3.16 Newnans Lake watershed land 1950 ______________ _____ ._ .. ______ __________ __________________ ._. ____ 80 3 _11 Newnans Lake watershed land use, 1910 ________________________________________________________________ __ 81 3 _18 Newnans Lake watershed land use, 1990 ______ _____________________________________________________ ______ 82 3_19 Highest land use changes in Newnans lAlke watershed _________________ ______ ____ __ ... .... ______ 84 3.20 Lake Weir watershed land use, 1950 ______________ _______________________________ _________________ ... ______ 85 3 _21 Lake Weir watershed land use, 1910 ____ ____________ ___________________ ________________________ __ ._. ______ 86 3_22 Lake Weir watershed land use, 1990 ____________________ ________ _______________________________ ___ __ _____ 81 3 _23 Highest land use changes in Lake Weir watershed _____ ______________________ ________________________ 89 3 _24 Rank-order graph for water volume exported from each cell in Newnans Lake watershed: a) curve generated by actual data; b) log-log representation of data._. ____ 94 3.25 Effective watershed, 1950, Newnans lAlke_ Inner zone exports from lE4 to lE6 liters of water per year; middle zone exports on average 6000 Vyr, outer zone exports less than 1000 Vyr _________________________________________________________________________________ _____ 95 Xl

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3.26 Effective watershed, 1970? Newnans Lake. Inner zone exports from lE4 to lE6 liters of water per year; middle mne exports on average 6000 Vyr; outer zone exports less than 1000 Vyr ..................... ............................................. _._ ....... ........................... _.91 327 Effective watershed, 19907 Newnans Lake. Inner zone exports from lE4 to lE6liters of water per year; middle mne exports on average 6000 Vyr; outer zone exports less than 1000 Vyr ............. ... ........................... ... ......... __ ...... _._ .................................... .99 .28 Changes in area of watershed contributing storm water to the lake between1950 and 1910, Newnans Lake watershed_ Increases in effective watershed are red to yellow (red being area of highest transport) and coincide with areas having an incretie in impervious surface. No change or decrease in transport range from dark green (beneficial change) to light green (little or no change) with beneficial changes coinciding with areas of reforestation. ............ ............. ............. ................. _._ ....... 1 01 3.29 Changes in area of watershed contributing stormwater to the lake betweenl910 and 1990, Newnans Lake watershed_ Increases in effective watershed are red to yellow (red being area ofhighest transport)_ No change or decrease in transport range from dark green (beneficial change) to light green (little or no change} ____ ...... _._. ____ .......... 102 3.30 Rank-order graph for water volume exported from each cell in Lake Weir watershed: a) curve generated by actual data; b) log-log representation of data ....................... l 03 3.31 Effective watershed, 1950, Lake Weir. Inner zone exports from lE4 to IE6liters of water per year; middle zone exports on average 6000 Uyr; outer zone exports less than 1000 Vyr ......................................................................................................... 104 3.32 Effective watershed, 1910, Lake Weir. Inner zone exports from lE4 to lE6liters of water per year; middle mne exports on average 6000 Uyr; outer zone exports less than 1000 Vyr ........................................................................................................ 105 3.33 Effective watershed, 1990, Lake Weir. Inner zone exports from lE4 to lE6liters of water per year; middle mne exports on average 6000 Uyr; outer zone exports less than 1000 Vyr ........................................................................................................ 1 06 3.34 Changes in area of watershed contributing stormwater to the lake between1950 and 1910, Lake Weir watershed. Increases in effective watershed are red to yellow (red being area of highest transport). No change or decrease in transport range from dark green (beneficial change) to light green (little or no change) .................................... 101 3.3 5 Changes in area of watershed CODtnbuting storm water to the lake between1910 and 1990, Lake Weir watershed. Increases in effective watershed are red to yellow (red being area of highest transport). No change or decrease in transport range from dark green (beneficial change) to light green (little or no change) .................................... 108 3.36 Estimated phosphorus deposition, 1950, Newnans Lake watershed ...................... 109 XIl

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3.31 Estimated phosphorus 1910, Newnans Lake watershed ...................... 110 3.38 Estimated phosphorus 1990, Newnans Lake watershed ...................... 111 3.39 Estimated total phosphorus (TP) export profile for 1950, Newnans Lake. Each band represents 20kg TPlhafyr exported from that area and reaching the lake ............... 112 3AO Estimated total phosphorus (TP) export profile for 1910, Newnans Lake. Each band represents 20kg TPlhafyr exported from that area and reaching the lake ............... 113 3 Al Estimated total phosphorus (TP) export profile for 1990, Newnans Lake. Each band represents 20kg TPlhafyr exported from that area and reaching the lake ............... 114 3 A2 Estimated phosphorus 1950, Lake Weir watershed .............................. 115 3A3 Estimated phosphorus 1910, Lake Weir watershed .............................. 116 3.44 Estimated phosphorus 1990, Lake Weir watershed .............................. l11 3A5 Estimated total phosphorus (TP) export profile for 1950, Lake Weir. Each band represents 20kg TPlhafyr exported from that area and reaching the lake ............... 118 3.46 Estimated total phosphorus (TP) export profile for 1910, Lake Weir. Each band represents 20kg TPlhalyr exported from that area and reaching the lake ............... 119 3A1 Estimated total phosphorus (TP) export profile for 1990, Lake Weir. Each band represents 20kg TPlhalyr exported from that area and reaching the lake ............... 120 3 A8 Empower density (E14 SEJlm2Iyr) distribution in Newnans Lake watershed in 1950. Black is highest density, progressively lighter areas have decreasing densities ... ...... .............. .................. .......... ...... ...... .............................. .......... .... .......... .... ....... 122 3.49 Empower density (E14 SEJlm2Iyr) distribution in Newnans Lake watershed in 1910. Black is highest density, progressively lighter areas have decreasing densities ............................... ...... .......... .... .... .......... .... ........ ........ ........ ......... ....... ............ .... ... 123 3.50 Empower density (E14 SEJ/m2/yr) distribution in Newnans Lake watershed in 1990. Black is highest density, progressively lighter areas have decreasing densities ... ........ .......... ...... ..... ....... .... .......... .......... .... ........ .... ............ ........ .......... ........ ........ ... 124 3.51 Empower density (E14 SEIlm2Iyr) distnoution in Lake Weir watershed in 1950. Black is highest density, progressively lighter areas have decreasing densities ...... 125 3.52 Empower density (E14 SEIlm2Iyr) distnoution in Lake Weir watershed in 1970. Black is highest density, progressively lighter areas have decreasing densities ...... 126 3.53 Empower density (E14 SEJlm2Iyr) distnoution in Lake Weir watershed in 1990. Black is highest density, progressively lighter areas have decreasing densities ...... 121 xm

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3.54 Water drainage network, post-development in Newnans Lake watershed, cumulative emergy, log ............................................................................... __ ................... .. _129 3.55 Water drainage network, post-development in Lake Weir watershed, cumulative emergy, log sej .. __ ._ ...... .................. ............................................... __ ......... .. .. ...... _130 3.56 Post-development phosphorus emergy drainage network, NewnansLake, log sej/g ..... ... ...... .... ............ ...................... .............................................. ........... ............... ......... 13 1 3.57 Post-development phosphorus emergy drainage network, Lake Weir, log sejlg ...... 132 3.58 Lake diagram illusbating complexity of interactions and food web hierarchy .. __ ..... 135 3.59 An aggregated in-lake energy systems diagram with components and pathways included in the simulation of lake responses to changing phosphorus loads and determination of dynamic trophic state indices ...................................................... 136 3.60 In-lake energy systems diagram and equations used to simulate response to changing watershed inputs .................................................................................................... 137 3.61 Steady state flows and storages for eutrophic simulation. ....................................... 143 3.62 Simulation of eutrophic conditions with TP runoffincrease after 3 years .............. 147 3.63 Simulation of oligotrophic conditions with TP runofF increase after 3 years .......... 148 3.64 Simulation ofhypereutrophic conditions with zooplankton ingesting less phytoplankton and more organic matter ........................................ ....................... 149 3.65 In-lake simulation using averaged eutrophic conditions for several lakes worldwide with averaged environmental sources. No perturbations occur in inputs and the model is run for 30 years ................................................................ ....................... 150 3.66 In-lake simulation using averaged eutrophic conditions for several lakes worldwide, but with higher environmental sources than shown in Figure 3.65 ....................... 151 3.67 In-lake simulation using averaged eutrophic conditions for several lakes worldwide, but with lower environmental sources than shown in Figure 3.65 ......................... 152 3.68 Using in-lake simulation to explore hierarchy of pulsing control; phytoplankton storage allowed to vary, all other variable higher in chain held constant ................ 153 3.69 Using in-lake simulation to explore hierarchy of pulsing control; producer storages allowed to vary, all other variable higher in chain held constant ............................ 154 3.70 Using in-lake simulation to explore hierarchy ofpuIsing control; producer and zooplankton storages allowed to vary, all other variable higher in chain held constant .................................................................................................................. 155 XlV

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3.71 Using in-lake simulation to explore hierarchy of pulsing control; all variables fully interacting .................. "' ............................................ .. ",., .. ,., ................................. 156 3.72 Phosphorus and water loads to Newnans Lake from the watershed; a) TP loading, g; b) water, m3; c) TP concentration, mgII; d) TP to lake volume ratio, gfm3 ... m ___ ml60 3.73: Comparison of simulated TP loads and empirical water quality data, Newnans Lake; a) TP and TSI data from Huber (1982) and Lakewatch (1998), TPfsediment ratios from Gottgen and Crisman (1993); b) simulation values ........................................ 161 3.74 Phosphorus and water loads to Lake Weir from the watershed; a) TP loading, g; b) water, m3; c) TP concentration, mgII; d) TP to lake volume ratio, gfm3 ................ 162 3.75: Comparison of simulated TP loads and empirical water quality data, Newnans Lake; a) TP and TSI data from Huber (1982) and Lakewatch (1998), TPfsediment ratios from Gottgen and Crisman (1993); b) simulation values ........................................ 163 3.76 Comparison of emergy flows to the watershed and lake with trophic state index for both Newnans Lake and Lake Weir ........................................................................ 165 3.77 Comparison of simulated phosphorus emergy flows over time with empirical water quality from Huber et aI. (1982) and Lakewatch (1998) ........................................ 167 3.78 Pre-development phosphorus emergy drainage network, Newnans Lake; log sejfg ............... "'.'.' ..... "' .. ..................................... ..... ... ....................................... "" .... 170 3.79 Post-development phosphorus emergy drainage network, Newnans Lake; log sejfg ................. "' ............................................................................................................ 171 3.80 Pre-development phosphorus emergy drainage network, Lake Weir; log sej/g ....... 172 3.81 Post-development phosphorus emergy drainage network, LakeWeir; log sej/g ....... 173 3.82 Placement of intervention based on points of highest mass loading, Newnans Lake; purple areas indicate best siting .............................................................................. 174 3.83 Placement of intervention based on points ofemergyfmass greater than 2E12 sejfg, Newnans Lake; purple areas indicate best siting .................................................... 175 3.84 Placement of intervention based on points of highest mass loading, Lake Weir; purple areas indicate best siting ........................................................ ..................... 176 3.85 Placement of intervention based on points of emergy/mass greater than 2E12 sejfg, Lake Weir; purple areas indicate best siting ........................................................... 177 xv

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial FulfiJlrnent of the Requirements for the Degree of Doctor of Philosophy EVALUATION OF WATERSHED CONTROL OF TWO CENTRAL FLORIDA LAKES: NEWNANS LAKE AND LAKE WEIR Co-Cbairman: H. T. Odum By Sherry Brandt Williams December 1999 Major Department: Environmental Engineering Sciences This dissertation relates lakes and watersheds by analyzing spatial patterns with GIS and simulation models of lake inputs associated with non-point sources. The flow of water and its constituents use energy transformations to organize landscape function and structure. This organization was evaluated with measures of materials, energy and emergy (a measure of real wealth based on prior work of nature and economy). Two Florida lakes and their watersheds, Newnans Lake and Lake Weir, were studied. The convergence of materials and energy makes these lakes centers of high emergy in the watershed hierarchy. In these watersheds, there was also an area of concentration in human settlements. The spatial chronosequence of watershed influence increased with xvi

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economic development. The extent of influence was determined by both soil type and land use and was not concentric to the lake. A spatial model estimated the average yearly total phosphorus input to Newnans Lake from non-point sources at 4.3x104 kgIyr. Using Vollenweider loading relationships, phosphorus accounts for almost half the average algal chlorophyll concentration in Newnans Lake. Estimated average yearly total phosphorus input to Lake Weir is 3.4x10 4 kglyr, and accounts for all of the average chlorophyll concentration. A simulation model of in-lake functions using oligotrophic calibrations responded to increased phosphorus input with a 200/0 increase in total biomass. The simulation with eutrophic calibration responded with a 100/0 increase. A hypereutrophic simulation oscillated with frequency controlled by the fish -zooplankton populations. Simulated trophic state indices, using equations from Huber et al. (1982), was 78 for Newnans Lake and 38 for Lake Weir. This compares to a long-term observed index of 75 and 42, respectively. Newnans Lake bas higher emergy use in the watershed and lake, 2.5xl021 sej/yr and 1.Ixl019 sej/yr respectively. Lake Weir uses 92xlOl9 sej/yr in the watershed and l.OxI011 sej/yr in the lake. Newnans Lake watershed contributes 73 million Em$/yr to the lake about 8% of the total watershed real wealth and about 800 EmS per visitor. Lake Weir's watershed contributes 1.3 million Em$Iyr to the lakeabout 27% of the watershed and about 5 EmS per visitor.

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CHAPTER 1 INTRODUCTION Where the telescope ends,. the microscope begins. Which of the two has the grander view? -Victor Hugo, 1862 Observation and intuition suggest an intimate connection between a lake and its watershed. Lake ecosystems respond to economic activity and material flows from their watersheds (Fluck et al.,. 1992a; Dierberg et al.,. 1988; Wetzel. 1983; Vollenweider, 1970). Still, there are unresolved questions in limnology and landscape ecology concerning this relationship (Lowe et al., 1997; Canfield, 1988). This dissertation evaluated watershedlake relationships using systems concepts,. computer simulations, geographic information methods, and the principles of energy hierarchy affecting spatial organization. Emergy concepts (Odum, 1996) were used to classify watersheds and lakes and to evaluate benefits of management alternatives. As a single drop or torrential flood crest,. water is a conduit for energy transfer throughout the biosphere. Pervasive and awesome at any scale, water cradles life, sculpts landforms and destroys economies. Water,. carrying energy with it,. is a tangible reality that defines the productivity, structure and diversity of every ecosystem in its path, and sets earth apart as a unique planet in the next larger scale, this solar system. 1

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Water flows organize the land and lakes, using patterns of energy transformation to create function and structure. Whereas water flowing downhill from the landscape to the lake is the more familiar pattern, the lake also exerts an influence on its watershed, as expected with symbiotic seif-organimjon (Odum, 1994; 1986; Salthe, 1985). Examples of this influence are the effect of the lake on the surrounding microclimate and the economic development that accompanies recreational use. Increasing human presence in watersheds alters land cover, use and ultimately drainage patterns, thus affecting the quality, quantity, and timing of stormwater runoff. Aquatic environments on the receiving end of this discharge may experience changes in trophic state and shifts in species dominance (Cooke et aI., 1983; Wetzel, 1983). Predicting surface water changes that result from increased development in a watershed may provide important management insights for avoiding negative impacts downstream.. Consequently, research is needed to improve prediction of the cumulative impact of increasing watershed development on freshwater systems. This is especially critical as developed and developing nations alike become increasingly dependent on surface water resources. This dissertation quantifies several important features of the watershed-lake system using energy paths, and their systemic impacts. The elements of focus are the study of runoff and its constituents within a watershed, the response of lakes receiving the input, and, in the opposite direction, the effects of lakes on the watershed. Numerous hydrological models estimate overall runoff quantity, nutrient loading and timing changes, but do not provide watershed management criteria for optimum 2

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retention strategies (Adamus and Bergman, 1995; Heidtke and Auer, 1993). Many surface water quality indices, such as trophic state and total phosphorus, are static and minimize the contributions and interactions of macrophytes, consumers and watershed inputs (Canfield and Hoyer, 1992, Huber et al., 1982). Integrated long-term studies of terrestrial-aquatic dynamics in subtropical areas are few, and simulations of watersheds and lakes together using an overall system perspective and criteria of overall benefit are largely absent. 3 Models providing a better understanding of these spatial and cumulative temporal effects can be used by planners to reduce the negative impacts of watershed development. Simulated models and benefit indices can direct development to less sensitive areas, assist in prioritizing conservation of more sensitive areas, and identify critical locations for water retention and quality monitoring. Two Florida lakes (Newnans and Weir) of different depths and trophic state were related to their watersheds. Patterns of development were analyzed, and nutrient, energy and emergy budgets related. Suggestions were made for managing the watershed using different scenarios of economic development that were consistent with system organizational principles. Concepts and Perspectives The central question in this dissertation concemed the coupling of a shallow lake and its watershed. A natural energy hierarchy is formed when both material and energy flows from a landscape scale converge on the lake, but the watershed also has a hierarchy

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of human settlements with an economy and concentration of information 1998; Odum, 1994). 4 One method for assessing this hierarchy is to quantifY the impact of cumulative watershed landscape changes on Florida's lakes. Changes in water influx from increasing development create energy and material pulses from a large landscape and carry constituents perhaps best left in upstream systems. These pulses and convergence of materials inarguably affect the downstream surface waters where nutrients are concentrated into a much smaller area The following concepts were used in this study for analysis and synthesis of these relationships. Scale of Components All systems have components at many scales, defined by turnover times, territory, and energy consumption and output (Odum, 1994). A basic triadic structure representing three fundamental and contiguous systems levels is the minimum sufficient to study a process, its causes and its influence (Salthe, 1985). In Figure 1.1, the main components of the watershed are represented from lower left to upper right according to the scale of replacement time and territory of support and influence. Three levels presented for evaluating the lake in relationship to its watershed are the lake itself, in the middle, the surrounding natural systems, ranked below the lake, and the human economy, information and structure within the watershed, ranked higher than the lake. This ranking is proposed as a hypothesis.

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Forests, fields Lakes, wetlands, sediments economy and infonnation Territory of support and influence Figure 1.1. Components of a watershed-lake system on a graph of turnover time and territory. 5

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Energy Systems Diagram The components of any system are organized as an energy hierarchy because energy flows of many small processes converge and are transformed to make larger scale processes 1994). The food chain from phytoplankton to fishes is an example. Components and processes of a system can be represented with an energy systems diagram in which the main energy flows converge from left to right 1994). The energy systems diagram in Figure 1.2 represents the main components of the watershed (Figure 1.1). Abundant lower quality energy and rain) enters the system from the left. Important inflows from the economy of the surrounding region are delivered to the system in a more concentrated form and are shown entering from the right. Examples of these flows are electricity, fuels or information. A pathway represents an influence a component has on others. Natural areas provide inputs to a city in the form of foods and aesthetic property values, among other things. The city exerts control over the natural systems through recreational use, development and management policy. Emergy and Empower VallIation 6 Emergy can be used to evaluate the energy that has previously been required to make a component or flow, and is calculated from data on energy flows that converge into a product or process 1996). Emergy is the available energy of one kind (solar energy) previously consumed in energy transformations. Empower is the rate of flow of emergy. Evaluating all pathways in solar emergy units (solar emjoules per time) is a way

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Watershed-Lake System Geologic materials, land forms Lake, wetlands, sediments Figure 1.2. Energy systems diagram of a lake watershed including an area of urban settlement.

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of putting all inputs on a common basis including human services and information. Because it accounts for previous contnDutioDS9 emergy is useful for evaluating storages such as those in soils and sediments (Odum, 1996). Transformitv Transfonnity is the ratio of emergy to the energy available within any individual,. population,. commodity,. service or system. (units: solar em joule/Joule) (Odum,. 1996; OdWIl, 1994). Transfonnity can be used as an indicator of energy quality because it measures what has gone into a unit of energy in the item,. and because it increases with each energy transformation (Odorn,. 1996). In an energy systems diagram,. transformity increases from left to right (Figures 1.1 and 1.2). 8 Transfonnities for many commodities and naturaI energy flows have already been calculated and can be used to determine the amount of emergy that a similar item contributes to a system. Transfonnities are dependent on the process used to create any entity and show variation between studies. High values result when an inefficient process is used as the basis for evaluation (Odorn,. 1996). Transformities should be selected from studies of systems similar to the one being evaluated,. or computed with representative information. Transformity and Control The energy hierarchy determines the scales at which controls of the system are exercised (Odum, 1994; Salthe, 1985; Allen,. 1982). Items with larger territories and storages,. control smaller scale functions with faster turnover times. Presumably, larger

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9 entities occur as a result of more energy transformations and concentration, and can exert influence over smaller items having more diffiJse energy storage. Consequently, assuming an efficient process, a joule of energy of high transformity also has more influence than a joule oflower transformity. The hierarchy represented in Figure 1.2 depicts many higher transformity items on the right returning controlling actions to exert large effects on items to the left. For example, agencies with high transformity are part of the information component, and exert considerable influence on a lake when water level stabilization plans are implemented. Multislaie Processes of Material Flows Materials such as nutrients circulate within a watershed system while receiving some inflow from outside and releasing some outflow to the surroundings. Most of the pathways in Figure 1.2 are accompanied by material flows. Watersheds are naturally engineered, multi-step, cascading treatment processes for materials draining toward a lake (Figure 1.2). Developed areas recycle materials to the watershed in the form of runoff and its constituents, and the intervening natural terrestrial systems sequester nutrients on the way to the watershed focal point, a lake in this case. If these treatment stages are decreased or eliminated, the larger scale watershed process is short-circuited, creating pulses and increased convergence of materials and their emergy within the lake.

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10 Some materials are also returned from the lake to the watershed, thus dispersing nutrients and energy outwards. Migration of fish and birds and human use of the water or lake products are examples of this reversed distnoution. Emergy per MasS and Concentration Since available energy is required to concentrate substances, the emergy per mass of any solution increases with the concentration of the element in the water, and is greater than the chemical energy of the element by itseJf(Odum, 1996). One relevant example is the phosphorus present in watershed runoff. Phosphorus is delivered in concentrated forms, such as fertilizer or industrial reagents, to components high in the watershed hierarchy agriculture and urban economies. The phosphorus not immediately used is diluted by irrigation or flushing water, and low concentration solutions are dispersed to components lower in the hierarchy forests, wetlands and lakes. The emergy of a material can be calculated by multiplying the known mass delivered to a system by the ratio of emergy to mass. These ratios have, in many cases, been evaluated in previous studies. This is particularly useful when the item is present in the system, not from a process of energy concentration, but rather as a material in a recycle pathway. Recycling materials disperse (right to left in Figure 1.2) in the energy hierarchy. Because the original energy was used in the process of concentration, the remaining energy requires a concentrated pulse to be useful as it disperses its influence over a larger area (OdUlD, 1996; 1994).

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II As runoff moves downwards through the the actual concentration of runoff constituents may not change significantly. However, the spatial concentration of water and phosphorus increases and carries with it the emergy of all the runoff and constituents used in moving to the point of concentration. This includes the geopotential emergy inherent in the watershed slope. Empower Density The amount of emergy flowing through a system over some unit time is it's empower (Odum, 1996). Spatial areas with convergence of emergy, such as cities, will have a higher concentration of empower than areas using less emergy, such as forests (Odum, 1996). By measuring the total emergy flux per unit area, a relative density value is obtained, similar to measures of development density used by city plamIers. This empower density (areal empower density) is useful in identifYing the centers of energy hierarchy. Spatial Orpnization Just as hierarchies of convergence are evident in flows of materials and energy through food webs to fish populations, pathways of the energy hierarchy also form converging patterns in space on a landscape scale 1999; Huang, 1998; Odum 1994). For example, waters from runoff converge into larger streams of increasing order, and convergence of services, information and materials within the landscape concentrate into cities. Large central cities are surrounded by many smaller towns and even smaller villages and clusters of residence, interspersed with the agricultural and natural systems

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12 providing environmental services. The smaller towns ship goods to the city, and the city in tum exerts control over the smaller by returning information, services and dictates for production. Larger energy flow builds greater spatial structure (Odum. 1994) evident in urban centers, mountains and perhaps lakes. They are dependent upon inflows from the surrounding landscape -the more structure built, the larger the support area required Record of Lake Functions Short-term lake functions are influenced by both the inflow of constituents from the watershed and recycling of nutrients stored in the sediments. Frequently, pulsing storm events deliver large quantities of emergy as water, kinetic energy, nutrients and other terrestrial contributions. Wind energy is transformed into kinetic energy in the water, scouring the bottom and resuspending sediment. The materials and energy stored in these sediment components, therefore, constitute a history of contributions to the lake. Emdollars Production and use of real wealth by the economic system depends on availability of environmental resources and services. These assets are measured by emergy and its economic equivalent, emdollars (abbreviated EmS). Emdollars are the part of the gross economic product associated with an emergy flow or storage (Odum, 1996). The emdollar value of an item is determined from its proportion of the emergy of the entire economy. Emdollars are, consequently, a measure

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13 of the real wealth in the including not just monetary payment for human but also the services provided by the environment. The total emergy consumed within a system divided by its economic production provides an emergy/money ratio for an economy in a particular year. This ratio, when divided into an emergy value for natural resources under study, is useful in determining an economic equivalent (Odum, 1996). Estimating Benefits of Lake Management The benefits of different management scenarios can be evaluated with emergy and emdollars. More emergy production and use means more real wealth contribution to the economy. Policies for lake and watershed management can be dedicated to maximizing emergy and emdollars, but emergy can also be used to examine the efficacy of other objectives, for example longer term carrying capacities for lakes and watersheds. As well documented in ecology, when two factors interact in production, output is greatest when neither is limiting (Odum, 1994; Odum, 1983). One of these factors will contribute more energy, while the other will have a higher transformity. The relationship between light and phosphorous availability is an example. When the component higher in the energy hierarchy (e.g. phosphorus) feeds a matching quantity of emergy back to the unit inputting emergy at the lower level (e.g. light), system production is maximized with more efficiency in emergy use, and limiting factors are balanced (Odum 1996).

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Previous Studies The following review of published studies cites many ways used previously to relate lakes and watersheds in Florida and elsewhere. Shallow Lake Limnologv 14 Questions concerning the role of the watershed in eutrophication of shallow lakes center around whether the response to increased availability of in-lake nutrients is greater than. the effect of watershed inputs. Studies to determine the importance of internal loading contributions to eutrophication have been inconclusive (Hansen et al., 1997; Schelske, 1989). Some studies of shallow lakes show a direct reduction in trophic state variables with reduction in extemalloading (Scheffer, 1998; Lowe et al., 1997). Shallow lakes (<3 m) have two unique properties that create phosphorus and productivity dynamics differing from deeper temperate lakes. Thermal stratification is short-term or absent, decreasing the amount of time that phosphorus is segregated from the epilimnion (Scheffer, 1998). Further, less wind energy is necessary for resuspension of bottom sediments, increasing the fraction of nutrients recycled into the upper water column (Scheffer, 1998; Carper and Bachman, 1984). However, resuspension of noncalcareous sediments can also provide adsorptive sites for phosphorus, thereby reducing its availability, at rates varying with pH levels. This interaction is particularly favored under oxygenated conditions often present during mixing. (Hansen et al., 1997; Olila and Reddy, 1995)

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15 Shallow lakes in Florida often do not develop a stable thermocline at any time in the year (Whitmore et 1996) and are subject to frequent sediment resuspension (Brenner et al., 1990). However, the majority of Florida lakes are softwater with noncalcareous soils (Canfield et al 1982). Consequently, productivity may not increase due to in-lake resuspension, and watershed inputs may then still impact lakes with significant sediment nutrient deposits. Nutrient Dynamics and Loadini A connection between point-source nutrient loading and increasing eutrophication in lakes has been documented in many cases (Scheffer, 1998; Cooke et al., 1993; Wetzel, 1983). Elimination of these inputs has provided varying degrees of reclamation success, and initially, depth of the lake was thought to be the determining factor (Cooke et al., 1993). However, recent studies in the Netherlands have shown reduction in eutrophication of shallow lakes following decreases in point-source nutrients (Scheffer, 1998). Vollenweider (from Scheffer, 1998 and Wetzel, 1983) constructed an empirical mathematical model linking average phosphorus loading to a lake from the watershed to the concentration of both phosphorus concentration (P we) in the water column and algal chlorophyll (Chi). Both are ratios of phosphorus loading (Pu to retention time (Tr). Pwe=c*Pi/(l + Tro.s ) Chl = 0.55 Pi I (l + Tr 0.5)0.76 (1) (2)

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16 Many studies have estimated watershed phosphorus loading to lakes based on empirical coefficients of export from specific land uses (Reckhowet al., 1980; Huber et al., 1982; Gottgens and Montague, 1987; Heidtke and Auer, 1993; Adamus and Bergman, 1995; Harper, 1996). Although the majority of the loading reduction emphasis has been point-source loads, reduction of non-point source loads has become of greater interest recently. Agricultural runoff appears to be a primary focus (Young, et al., 1989; Srinivasan and Arnold, 1994 ). Some studies have shown that increases in watershed development are approximately proportional to phosphorus loading to lakes (Weibel, 1969), but another large scale Florida study showed no correlation between the amount ofland in development and the overall trophic state of the lake (Huber et al., 1982). This is likely due to other geological and soil conditions both at the point of runoff and in the intervening distance to the lake, as shown in the pilot study for this project (Brandt Williams, 1995). lIDs study shows that while the percentage of developed land use did not correlate with. trophic state or chlorophyll concentrations in seven Florida lakes, phosphorus loads from non-point sources calculated from deposition, soil, and drainage properties correlated strongly with both trophic state and chlorophyll. Spatially Distributed Surface Flow Models There are two primary approaches to incorporating spatial variation into runoff and seepage models: stochastic and raster-based geographical information systems (GIS). Stochastic approaches use probability density functions to translate the uncertainty of

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17 randomized input data into probability distributions for the output response from the model, and have been in use for some time (Chowet aL. 1988). Recent research in stochastic methods for spatially distributed hydrology models has focused on reducing the number of simulations required to generate output curves (Braud et al., 1995; Kool et aI., 1994), and more recent use of neural networks may increase this method's applicability to spatial variations. Despite increasing ability of stochastic models to generate field data measures, lack of specific mapping references hinders their use for appropriate remediation siting. GIS models, while allowing greater flexibility in handling spatial variability, also involve high levels of computational time. Therefore, a certain amount of parameter lumping is still used. DeVantier and Feldman (1993) completed a review of lumped and distributed models through 1993. Three recent studies of interest attempt to limit parameter lumping, using either a physics based approach or higher resolution spatial data. Julien et aI. (1995) apply Green-Ampt equations to each map cell to determine infiltration for an individual storm event, and use two-dimensional Saint-Venant equations of continuity and momentum to model flow between cells. Excess overland flow is automatically routed to connected channels and modeled with kinematic wave functions. The model requires soil texture and deficit data, Manning's roughness coefficients, basin connectivity and geometry, and rain. Nutrient transport functions are not included. Heidtke and Auer (1993) used a GIS-based non-point source loading model to assess water quality in a New York lake. Empirical land use and soil parameters affecting

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phosphorus runoff were incorporated into a modified Universal Soil Loss Equation to calculate an estimated load from each basin cell (1 hectare). Comparison to known tributary loading showed similarities between the model and empirical evidence. A suggestion for a method to compare to water quality was provided, but actual comparisons were not tabulated. 18 Adamus and Bergman (1995), using empirical nutrient and runoff coefficients determined in Florida from mean runoff and pollutant loads, presented distribution maps for the entire S1. John's River watershed. The results were based on average land use densities and the four basic hydrological soil groupings. No correlation with water quality was presented. Lake Valuation Classification of lakes usually involves division into three categories of productivity: eutrophic (highly productive), mesotrophic (moderately productive) and oligotrophic (unproductive). Numerous models, both quantitative and qualitative, have been put forward as methods for classifYing lakes and reservoirs and to assist in determination of problem systems, as well as prioritization of reclamation efforts. (Wetzel, 1983; Huberetal., 1982). Early indices used presence or absence of indicator species to rank eutrophication, and Nygaard's algal ratio was often used (Wetzel, 1983; Taylor, 1978). Nygaard's ratio of typically eutrophic species to common oligotrophic species is not applicable, however, in

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areas where the species used do not commonly exist (Taylor, 1978), and it is not a measure of water quality perceived by the public (Kratzer, 1979). 19 One of the most commonly used multi-parameter indices is Carlson's Trophic State Index (TSI), although his original intention was that each index (Secchi disc, chlorophyll and total phosphorus in the water column) be used in relation to each other to infer limiting factors and the presence of other light inhibitors. Carlson devised a log transformation of empirical data available for temperate lakes so that a ten point difference was directly proportional to a doubling (or halving) of algal biomass for each parameter. (Carlson, 1970) This TSI is insufficient for nitrogen limited lakes, uses relationships between parameters established in temperate lakes, not Florida, disregards macrophyte populations, and does not provide a single management index. Huber et al. (1982) proposed a modification of Carlson's TSI using a Florida lake data base that is now often used in Florida studies. Several permutations were offered to account for phosphorus or nitrogen limited systems, as well as nutrient balanced lakes. An index greater than 60 is considered eutrophic; the split between oligotrophic and mesotrophic is still nebulous. Macrophytes were not included. A traditional valuation of lakes has always been the number of users or monetary advantage to the local economy, both in recreational value and waterfront property taxes. However, uses of oligotrophic and eutrophic lakes are very different, and monetary values are generally inversely proportional to trophic state index values.

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20 Paleolimnology Sediment cores from lakes have been suggested as a way to reconstruct a history of lake productivity using both remains of organisms and phosphorus (Brenner et al., 1993; Smo!, 1992; Binford et al. 1986; Frey 1969). Using inferences from current water chemistry and species communities and the connection to surficial sediments, longer term function and structure are implied and can be used to determine the original trophic state of the lake (Smol, 1992). Because much of sediment deposition in a lake originates in the watershed, lake sediments contain a history ofbasin disturbance (Binford et al., 1986). However, caution in interpreting the results in shallow, wind-stressed lakes is advised (Whitmore et al. 1995) because of frequent sediment redistribution. Further, shallow lakes are subject to photochemical oxidation of bottom sediments. limiting the use of sedimentary pigments as a comparative tool (Flannery et al., 1991). Both CIN ratios and total phosphorus (TP) in sediment cores have been used to evaluate Newnans Lake. A study by Flannery et aL (1991) resulted in low and stable CIN ratios. suggesting that Newnans has been eutrophic for some time. Whitmore et ale (1998) found steadily increasing phosphorus deposition. Gottgens and Crisman (1993) found differing levels ofTP deposition dependent on position in the lake, with increasing deposition near the inflow (north) and decreasing deposition near the middle and outflow (south). Lake Weir's core (Crisman et al 1992) shows a sharp increase in TP accumulation between 1970 and 1980. An equally steep decline in TP is exhibited between 1980 and 1990.

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21 Watersheds Evaluated Two lakes in Central Florida (Figure 1.3) were included in this study, Newnans Lake, near Gainesville, and Lake Weir,. near Ocala. Newnans Lake has a 20:1 watershed to lake ratio, with a relatively Bat, forested watershed and extensive cypress, bayhead and mixed hardwood swamps surrounding the entire lake perimeter. Lake Weir has a 5: 1 watershed to lake ratio, with a steeper watershed than Newnans. Weir's watershed was predominantly citrus groves and pasture until the mid-1980s, and is now predominantly residential and pasture. Newnans Lake Newnans Lake is located due east of Gainesville, Florida, in Alachua County N, 82 12' W). Newnans is part of the Oldawaha River basin and is located in the Central Valley physiographic region (Canfield, 1981). The lake has a water surface area of2,965 ha, and the elevational watershed has approximately 58,000 ha land area. The mean depth is 1.6 m (Lassi and Schuman, 1996), and the estimated flushing rate is 0.6 years (Gottgens and Crisman, 1993). The average fetch is approximately 2.41 kIn. Two small creeks, Little Hatchett Creek and Hatchett Creek, are the main tributaries Bowing into the lake, and Prairie Creek is the single surface water outlet. Little Hatchett Creek has an average annual Bow rate of about 4 cfs, and Hatchett Creek's annual flow is 18 cfs. Prairie Creek has a weir, and the range offlow is dependent on the lake surface elevation. At the average elevation of 65 ft NGVD, outflow discharge is about 20 efs (Robison et aI., 1997).

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22 Lake Weir Figure 1.3. Watershed locations in the state of Florida.

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23 Newnans is typically classified as a naturally eutrophic, lake with a pH near 7 (Canfield, 1981). Despite its eutrophic condition, Newnans' N: P ratio has risen from 17 to 31, indicating balanced nutrients in the 1970s but some phosphorus limitation in the 90s (Huber et aI., 1982; Lakewatch, 1999). Newnans is highly colored and exhibits high variability in this parameter (Canfield, 1981; Gottgens and Montague, 1987). Newnans does not appear to develop a thermal stratification in the summer (Canfield, 1981). Lake Weir Lake Weir is located about 15 miles southeast of Ocala, Florida (29 01' N, 81 56' W), in Marion County, Florida. It is located in the Oklawaha River basin in the Sumter Upland physiographic region (Canfield, 1981). The lake surface area is about 2300 ha and its elevationaI watershed covers about 12,100 ha. However, about 2400 ha is depressional and does not contribute runotfto the lake. The mean depth is 7.1 m (calculated from Ott and Chazal, 1966), and the longest fetch is about 2.26 km. To the west, a canaI and wetland area connect Lake Weir to Little Lake Weir. A canal also connects the lake to a large hardwood swamp to the north (Marshall Swamp). Lake Weir's average elevation is 57' NGVD, and Marshall Swamp is at about 50' NGVD. Lake Weir is a mesotrophic lake with trophic state indices reported in the range of 41 to 54 (Canfield, 1981; Huber et aI., 1982; Lakewatch, 1998). It is asoftwaterlake with a pH around 7 and very little organic color (Canfield, 1981). Weir does develop a 1C temperature differential at certain times in the year (Canfield, 1981).

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24 Plan of Study This dissertation explores the relationships of lake and watershed using empirical data, spatial and temporal modeling, and emergy evaluation indices. The overall organization and hierarchy oflake watersheds was studied using the following procedures: 1. Using methods of geographic information systems (GIS), a sequence of historical maps 1970 and 1990) was constructed that included land uses, geology and landforms, hydrological nutrient storages and flows, and energy characteristics. 2. The storages, budget and cycle of phosphorus were developed for the watersheds and lakes. Simulation models related phosphorus to the influences of the watershed and human settlement. 3. Emergy characteristics were evaluated for the main components of the watershed and lakes including phosphorus, areal concentration of emergy flows, transformities, and other indices of energy transformation and hierarchy. 4. Limnological characteristics of the lake ecosystems were related to the watershed inputs including productivity, food chains, and the effect of watersheds on lake classification. Responses were studied with a lake simulation model. Synthesis of these results was used to consider the position of lakes in the emergy hierarchy of the to understand the level of reciprocal control between a watershed and a shallow lake, to examine spatial patterns that develop in changing watershed systems, and to propose management alternatives.

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CHAPTER 2 METHODS Map Preparation and Data Sources A series of maps ofland use, soil and rain for each watershed, for select time periods, was used to explore changing energy, emergy, water and phosphorus inflow to each lake. Three time periods 1950, 1970, 1990 were mapped and compared using a geographical information system (GIS). MapFactory is a raster-based (cell or grid) analysis GIS useful for simulating spatial movement defined by equations. Elevation and Watershed Delineation Elevations were digitized from USGS 7.5-minute topographical quadrants and assumed constant throughout the 40 years of the time series analysis. All but one of the quadrants was constructed on 5-foot intervals. The remaining 10-foot interval map was kriged (mechanically interpolated using GIS) over the contours and the benchmark points to produce 5-foot contour areas. The watershed was delineated using a GIS command that spreads upwards from a given point and stops when a downhill elevation is encountered. The elevation of each study lake was used as the initial point of spread, and all uphill cells were considered part 25

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26 of the larger basin within which the study lake was the focal point. All smaller lakes within this basin were then used as points for upward spread to determine their individual drainage areas within the larger lake basin. These smaller sub-basins were subtracted from the larger basin, splitting the shared ridge between the study lake basin and the outlying lake sub-basin. This final basin was considered to be the rain catchment area draining into the study lake. Land Use and Cover The area of individual land use for each basiIi was configured from USGS topographical quadrant maps (1966-1970 series). Land use for 1990 was determined using 1988-1993 quadrant updates by USGS and aerial photos. Land use for 1950 was interpreted from 1949 aerial photos using comparisons to similar areas of known land use in 1968 photos. Groundtruthing to verifY land use and to determine industry and agricultural type was conducted extensively throughout both watersheds by visits to existing sites, and via county records for historical sites. Land use was divided into 15 categories: 1. open, vacant, or range lands (considered unmanaged turf) 2. golf courses (managed turf) 3. urban with residential, commercial, and institutional structures assumed to be using a centralized waste water treatment system 4. outlying residential, commercial, and institutional on septic systems 5. industrial 6. mining 7. landfill 8. roads, parking lots and airport tarmacs 9. agriculture orchards (perennial) 10. agriculture -row crops (annual) 11. forest

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Z1 12. forested wetlands 13. herbaceous wetlands 14. lakes and ponds 15. streams. Soil Maps Soil coverages for each watershed were obtained by digitizing maps in the National Cooperative Soil Survey for each county. The soil classes were then standardized and aggregated for key parameters of interest (hydrologic capacity and clay content). Soils were first grouped according to the four hydrological categories designated in the United States Soil Conservation Service (UsseS) soil surveys. usses determines groupings by the amount of water absorbed when thoroughly wet (Usses, 1985) and considers infiltration, vertical drainage and clay content. Group A refers to soil that has low runoff potential, D has high runoff potential, and B and e fall between these two extremes. If a soil had two categories assigned because of potential drainage capability, the pumping benefit was neglected, and the category with the highest runoff potential was assigned. Soil hydrology was also characterized and mapped by permeability (inIbr), capacity (inlin) and depth to the first relatively impermeable horizon .6 inIhr). The use of these physical parameters is explained in the chapter on model development. Rain Data Rain data were obtained from the National Oceanic and Atmospheric Administration (NOAA) for recording sites within each watershed. Because no individual site had data for all the years under study, all available data were averaged for

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28 1950, 1970 and 1990 for all sites recording rainfal1 within the watershed. Consequently, rainfall was considered to be equal throughout the watershed. Ilynamic Simulation Models Energy language symbols and their intrinsic mathematics (see Energy Systems Symbols and Definitions, Fig 2.1), were used to develop temporal models of both a lake system and its watershed. An energy system diagram was first constructed representing the variables considered important in defining key interactions within the lake and between the lake and its watershed. The resulting diagrams were translated into mathematical equations representing changes in each variable over time, and these equations were solved using a BASIC computer program. Svstem Diagram The concept of constructing a system diagram and the hierarchy of arrangement are discussed extensively in (Odum, 1994), but are briefly described below. System frame. A rectangular box represents the boundaries selected. functions. Any input that crosses the boundary is an energy source, A including pure energy flows, materials, information, the genes of living organisms, services, as well as inputs that are destructive. All of these inputs are given a circular symbol and are arranged around the outside border from left to right in order of concentration with sunlight on the left and information and human services on the right.

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29 Energy circuit: A pathway whose flow is proportional to the quantity in the storage or source upstream. Source: Outside source of enemY delivering forces according to a program controUed from outside; a forcing function. Tank: A of energy storage within;, the system storing a quantity as the balance of inflows and outffows; a state variable. Heal sink: Dispersion of potential energy into heat that accomP.!lDies aU real transfonnation processes and storages; rOSS of potential energy from further use by the system. Interaction: Interactive intersection of two pathways coupled to an outflow in proportion to a function ofboth;control action of one flow on another; limiting factor action;work gate. Consumer: Unit that transforms energy quality. stores and feeds it back autocatalytically to improve inflow. Switching actiqn: A symbol tilat an outside agent causing one or more changes m a pathway or mteraction Producer: Unit that collects and transforms low-quality energy under control interactions of high-quality flows. Self-limiting energy receiver: A unit that has a self-limiting output when input drives.are lilgh beCause !be.re is a limiting constant quality of material reactIng on a cllCular pathway wlthm. Box: Miscellaneous symbol to use for whatever unit or function is labeled. Constant-gain amplifier: A unit that delivers an output in proportion to the inPJ!t.[ but is changed by a constant factor as long as the energy source S is suffiCient. Transaction: A unit that indicates a sale of goods or services (solid line) in exchange for payment of money (dashed line). Price is shown as an external source. Figure 2.1. Energy systems symbols and definitions 1994).

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30 Pathway line. Flows are represented by a line and include pure energy, materials, and information. Money is shown with dashed lines. Lines without arrowheads flow in proportion to the difference between two forces and represent a reversJ.ole flow due to concentration gradients. Outflows. Any outflow that still has available potential, materials more concentrated than the environment, or usable information is shown as a pathway from any of the three upper system borders, but is not shown exiting from the lower border. Degraded or dispersed energy, with insufficient quantity or quality to do work in the modeled system, is shown as very thin lines leaving at the bottom of the diagram with a single arrow representing a heat sink. Adding pathways. Pathways add their flows when they either join or enter the same tank. Every flow in or out of a tank must be of the same type and measured in the same units. Intersection. Two or more flows that are different, but required for a process, are drawn to an intersection symbol. The flows to an intersection are connected from left to right in order of their transformity, the lowest quality one connecting to the notched left margin. An example of this multiplicative interaction is the connection between light and phosphorus required for photosynthesis. Counterclockwise feedbacks. High-quality outputs from consumers, such as information, controls, and scarce materials, are fed back from right to left in the diagram. Feedbacks from right to left represent a loss of concentration because of divergence, with the service usually being spread out to a larger area.

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31 State variables. Storages of materials are shown as tanks within each system compartment. Changes in the system can be recorded as fluctuating accumulations within each tank. In simplified system diagrams, not to be confused with aggregated diagrams, the actual simulation detaiIs, such as tanks and complex interactions flowing into each are often not presented. However, a state variable is always implied for every process within the diagram. Material balances. Since all inflowing materials either accumulate in system storages or flow out, each inflowing material such as water or money needs to have outflows drawn. diagrams. Aggregated diagrams are simplified from the detailed diagrams, not by omission of components, but by combining them in categories aggregated with the purpose of answering a specific question. Simulation Example A simple one-tank simulation is used to explain the simulation methodology used in this dissertation. A diagram of water inflow, outflow and accumulation (Figure 2.2) illustrates arrangement of sources and material inflows and outflows. The associated differential equation used to define the material balance and a graphical representation of water accumulation over a two-year period are included (Figure 2.2). The programming application QBASIC was used in this example and all the simulations included in this dissertation, both to iterate the equation over a given time interval and to plot the changes in accumulations with time.

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32 Emem Evaluation Emergy values were used to compare land uses and soils within each inlake functions9 changing watershed systems over the forty year study period and phosphorous in different solution concentrations from different sources. Empower densities9 transformities and storage emergy were the primary indices used for comparison. Emergy Tables A sample emergy analysis table is presented in Table 2.1. The associated system diagram is illustrated in Figure 2.3. This table represents flows per unit area and time (J/halyr). An explanation of the information presented in each column of the table is given in Table 2.2. Emergy analysis was used to evaluate the lake-watershed interface, soils and land use in the watershed, and sediments in each lake. Emergy Indices Several emergy indices were used to draw inferences from emergy analyses of the lake-watershed interface,. economic use of the lake, and land use within the watershed. Comprehensive descriptions of these indices and their uses are presented in Odum (1996), but brief descriptions of these indices are given below. The solar transformity of an item or flow is the solar emergy that would be required to generate (create) a unit of that object or resource efficiently and rapidly. Figure 2.4 shows the solar transformity defined as the solar emergy required to produce one joule of another form of energy. Solar transformities of one or more products are

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7E7 Evaporation Equation r L8El7 J/yr Newnans Lake dW=RN+S -k*W -k2*w*r dt 1.8E8 Outflow m3/yr unless specified Calibration of coefficients k*W = 1.8E8 k = 1.8E8/5E7 = 3.6 yr-l 33 at steady state k2*w*r = 7E7 k2 = 1E7/(5E7*L8EI7) = 7.8E-18 J-l Initial conditions 1= 1.8E17 RN=4E7 S= IE8 W= 4E7 Time, t Figure 2.2. Simulation example: aggregated water budget for a lake, values used for calibrating coefficients and the differential equation.

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Table 2.1. Example of emergy evaluation: annual production of one hectare of Bahia grass (see Figure 2.3). Unit Solar Solar Data EMERGY EMERGY Note Item Unit (units/yr) (sej/unit) (Ell sej/yr) RENEW ABLE RESOURCES (R) 1 Et J 5.43E+IO 1.54E+04 8368 NONRENEWABLE ENVIRONMENTAL RESOURCES (N) 2 Net Topsoil Loss J 6.33E+07 7.38E+04 47 Sum of free inputs (sun, rain omitted) 8415 PURCHASED INPUTS (M, S) 3 Fuel J 2.82E+06 6.60E+04 2 4 Phosphate gP 7.38E+03 2.20E+IO 1623 5 Nitrogen gN 1.55E+04 2.4IE+IO 3728 6 Lime g 3.73E+05 1.00E+09 3730 7 Labor J 0 8 Services $ 0 9 Sum of purchased inputs 9083 10 YIELD(Y) g, dry 3.63E+06 J 6.88E+IO 11 TRANSFORMITY of yield 4.80E+08 12 EMERGY PER MASS of yield 2.50E+04 Simplified from Appendix Table D. L I. Includes contributions of sun. wind and rain II. Total inputs divided by energy of yield 12. Total inputs divided by mass of yield 34

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35 Materials ______________________________ Goods Emergy Yield Ratio = F Emergy Investment Ratio = F I N(Soil) 47 842 R (Environment) (a) Grass Labor & Services Economic '--....... Use 9083 M (Fertilizer and Fuel) 17500 Emergy Yield Ratio = 17500/9083 = 1.93 Emergy Investment Ratio = 90831842 = 10.79 (b) Y=Yield Figure 2.3. Example of emergy analysis: a) definition of two indices emergy yield ratio and investment ratio; b) simplified emergy diagram for grass example in Table 2.2

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36 Table 2.2. Description of information presented in an emergy table. Column One Two Three Four Five Six Description of Information line item number: corresponds to the number of the footnote in the table where raw data source is cited and calculations shown name of the flow or item stored: shown on the aggregated diagram raw data in joules,. grams, or dollars: taken or calculated from various sources transformity in solar emjoules per unit (sej/joule; sej/gram; or sejfdolIar); see definition Table 2.4 solar emergy contributed by the flow or item stored: the product of columns three and four real wealth value in emdollars for a selected year: obtained by dividing the emergy in column number five by the emergy/money ratio for the selected year

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Non-renewable Reserves Other Natural Modern Processes Direct Sun Solar Transformity of Tomatoes 1617 E13 sejnuvyr 4.43 EI0 Jnuvyr Used Energy Solar Emergy of Inputs Yield Energy 365,000 solar emjouleslJoule Figure 2.4. Diagram explaining solar transformity. Sum of Solar Emergy of Inputs ...... Yield 37 Energy

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38 obtained from each analysis. Solar transfonnities for main inputs from global climate are obtained from world energy budgets, and transformities for sources to each system come from previous analyses cited in each table's footnotes. Examples of energy sources with abundant but low quality energy are the sun (transformity of 1 sej/J) and wind (transformity of300 to 1500 sej/J). Electricity from a coal plant requiring larger emergy inputs to produce more concentrated energy has a transformity of 160,000 sej/J. Empower density (aerial empower density) is a measure of the emergy utilized in a unit area per unit time. It is a measure of the intensity of development and natural resource use for the system under study. The emergy yield ratio is the emergy of an output divided by the emergy of those inputs to the process that are fed back from the economy (see Figure 2.3). This ratio indicates whether the process contributes more to the economy than is purchased from it for the processing. Ratios for typical agricultural products range from less than one to six (Odum, 1996). Values less than one may be obtained when the yield is calculated separately with a transformity from another source of data. In recent years, emergy yield ratios of fossil fuels have ranged from three to twelve (Odum, 1996. Emergy investment ratios relate the emergy fed back from the economy to the emergy inputs from the free environment (Figure 2.3). These ratios indicate if a process is economical in using the economy's investments in comparison to alternatives. To be economical, the process should have a similar or lower ratio to its processes competing for investment. If the ratio is less, the environment provides more to the process, costs are lower, and its prices tend to be less so that the product competes in the market. If an

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39 emergy investment ratio is higher than alternatives, the intensity of inputs invested from the economy is greater, and impact on the environment is greater. The emergy exchange ratio is the ratio of emergyreceived for emergy delivered in a trade or sales transaction. For example, a trade of wood for oil can be expressed in emergy units. The area receiving the larger emergy receives the larger real wealth and has its economy stimulated more. The emergy/money ratio is obtained by dividing the total emergy used by the combined economy of man and nature in the country for that year by the gross national product. This number becomes smaller as the country's economy becomes more developed and more dependent on purchased goods and services from outside. A developed country with lowemergy/money ratio gains a net benefit from purchasing products from less developed countries with a high emergy Imoney ratio.

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CHAPTER 3 RESULTS Results of this study are divided into three sections concerned with the two models developed to quantify the connection between watershed and lake and the use of simulations to develop management indices. In the first two sections, the development of the model and data sources are presented first, followed by the simulation results. In the third section, the derivation of indices and resulting values are presented together. PART 1: SPATIAL WATERSHED MODEL Development of Material Flows A spatial model accounting for water and phosphorus export from different land uses on specific soil types was developed. One of the objectives was to determine a coarse level of aggregation at which long term watershed control of a shallow lake could be evaluated. This serves two purposes. It makes a model of this magnitude manageable, improving the potential for future use by water management agencies, and selects a scale appropriate to the study of cumulative effects. Assessing the overall effect of ongoing changes within the watershed was the priority, not prediction of drainage for every short40

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term storm event. This shorter time scale precludes the study of a larger system hierarchy. 41 Theoretically, unique linkages ofland use and geohydrology control water and nutrient export that a given land use contnoutes to the lake. Further, the soils and natural systems on the drainage path between one area and the lake can modifY the contnoutions of that area to the lake. Geographical information systems were used to track the flows through the watershed in order to calculate the spatial patterns of energy and material that develop and change as land uses in the basin change. Geographical information systems (GIS) link: a data set defining a specific variable (attribute) to a set of geographically referenced points on a map. Examples of attributes are rainfall and elevation, and a separate map Oayer) is prepared for each property. Grid-based GIS divide a map into unit areas (cells) that become referenced points, each with a single attnoute value. The seven attribute maps used in this model were annual rainfall, land use categories, annual phosphorus deposition, land cover (vegetated or impervious surface), soil hydrology, soil clay, and elevation. Combining base map layers (Figure 3.1) creates new data sets describing the linkage between base attnoutes. These base attribute maps were also reclassified or related to each other by computation and used to represent the physical interactions that occur as water and phosphorous move across the landscape. For example, land cover was ranked by the percentage of impervious surface due to paved surfaces and roofs in each cell, then used to simulate runoff from those surfaces. A flow chart of the map layer computations described in the following sections is shown in Appendix A.

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RAIN TOTAL PHOSPHORUS COVER SOILS ELEVATION Total Phosphorus availability interception, impenneability adsorption, infiltration, capacity ... ._" .. .. ,--..... channelization Figure 3.1. Map layers used as data in spatial model and the hydrology functions derived from each data set. Linking these maps with mathematical functions allows them to be used as boundary conditions for solving continuity equations governing the flow of materials through the watershed.

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43 Simulation of Water Budgets in GIS The transport of water, or any material, through a watershed is governed by the conservation of mass. Consequently, the fate of water in any area can be summarized by a fundamental continuity equation describing the water budget at any point in time. Rain + Runin InfiItration Runoff-Evapotranspiration dS/dt = 0 where dS/dt is the rate of change of water quantity in each area. Run-in from adjacent areas is dependent on the mass balance within. that area. (1) The total runin volume is contingent upon the number of adjacent areas contributing, and is determined by elevation differences between areas. Infiltration volumes are a function of soil porosity and can be determined by the infiltration rate and maximum available capacity. Runoff(export) occurs both when impervious surfaces preclude infiltration and when soil capacity is exceeded. This model calculated a water budget for each cell based on average annual rainfall and average soil conditions. Overall storage was considered constant, and therefore, dS/dt is negligIole. The time increment for remaining terms in the mass balance is one year. An energy system diagram illustrating the variables and interactions affecting water for each cell is shown in Figure 3.2. Algebraic manipulation of map layers was used to solve these mass balances within each cell using the attributes governing infiltration and runoff. Water flow between cells was modeled using functions provided by MapFactory. Drainage in MapFactory simulates overland sheet flow and is dependent on the slope differential between each cell. Total water volume reaching the lake was calculated by summing the individual cell values at the perimeter of the lake.

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Figure 3.2. Energy system diagram illustrating water budget in a single cell and movement into next cell. Solid lines carry water. Dashed pathways are energy flows affecting water. Et = evapotranspiration. Difference Equations Surface water runs off or infiltrates Sum of run-in from adjacent contributing cells Soil water in excess of average saturation conditions either exceeds capacity and runs off or is dispersed through evapotranspiration or recharge of groundwater aquifer Direction of export of all water leaving the cell is dependent upon elevational differences between cells dSurfwater = Rain + RuninT -k 1 '" Impervious'" Surf water -k2 '" Soil water '" Surf water -k3 '" Surf water RuninT = Runoffcelll1 + Runoffcell21 + ... + Runoffcellij dSoilwater = k2 '" Soil water '" Surfwater -X '" k4 '" Soil water -(Et + Recharge) where X = 1 when soil capacity exceeded where Et and recharge is assumed equal to water in excess of average saturati9n conditions Exportwaterij = Z '" kS '" (k 1 '" Impervious'" Surf water + k3 '" Surf water + X '" k4 '" Soil water) where Z = 1 if elevation 1 > elevation 2

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EI-E2 -m-I I k3 Runoff \ Adjacent cell \ \ -. --\' -tJ Vegetation \ I \ \ I I I T I I Groundwater Runin from three cells Export to one cell jj=1 0 ."""e.

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46 Infiltration and Runoff Calculations Water entering each cell was subject to one of two immediate consequences. It fell either on a totally impervious surface such as a roof or road, or it fell on a vegetated surface with variable infiltration capabilities. Maps representing the percentage of impervious surface associated with each use were derived from land use maps. Values from the literature for the fraction of rainfall leaving each particular land based on average vegetation and impervious surface (Table 3.1), were used to create this map. This percentage was used to calculate a split between water leaving a cell without further interaction, as surface runoff, and a flow '. available for further interaction with the soil. Water faIling on unpaved surfaces was subject to infiItration into available soil spaces. If the soil capacity was exceeded, excess rainfall became runoff. The rainfall rate subject to infiltration was determined using the average rain intensity (in/hr) in the watershed for 2 year 60 minute events (Frederik, et ale 1977). Because this model tested the efficacy of long-term averages, total annual rainfall (in) was assumed to be evenly divided into these 6O-minute events. The water flow exceeding soil capacity was considered surface runoff and added to the runoff from impervious surfaces. The amount capable of infiltrating was calculated from the soil permeability (in/hr), unit capacity (inrm) and available volume (inches to impermeable soil horizon) for each soil category (SCS, 1985). This was converted to the percentage of an average rain event infiItrated. Calculation for each soil type is summarized in Appendix B. Water that remained in each cell from infiltration was assumed to be drained from the cell prior to the next rain either by evapotranspiration or recharge. This

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assumption allowed the soil hydrology values to remain constant at their average saturation levels throughout the year being modeled. Phomhorus Uptake, Adsorption and Deposition 47 Annual phosphorus deposition was divided by total annual rainfall to determine the concentration of phosphorus in solution for each ceIL This concentration of phosphorus was assumed to travel with water, either downward into the soil, where it remained sequestered for plant uptake and diagenesis, or over the soil surface into the next cell. However, before moving into the next cell, this surficial phosphorus was subject to adsorption. The amount of clay in the soil was used to estimate the amount of surficial phosphorus likely to adsorb in each cell. The percentage of phosphorus adsorbed was assumed to be linearly related to the percentage of clay in the top 6-12" of soil. The model of phosphorus flows and storages and their link to the water budget are shown in Figure 3.3. Clay properties are presented in Appendix B. Most phosphate runoff data available in the literature are in the form of empirical averages for total phosphate concentrations in storm water runoff (Harper, 1996; Adamus and Bergman, 1995; Heidtke and Auer, 1993; Gottgens and Montague, 1987; Huber et aI. 1982). The values from various land uses with differing use densities are presented. However, these studies are averaged over several study sites where the underlying soil and geology are often different. As a result, concentration values account for and lump many soil infiltration and impervious surface characteristics. Using these values for phosphorous export would defeat the purpose of using a spatially specific model. Consequently, rates of atmospheric deposition and fertilizer application rates from the

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Table 3.1. Impervious surface for different land uses. Land use % Impervious surface Reference Agriculture 6 a Range/Open 6 a Commercial 77 a Industrial 71 a Residential 39 a Water 0 a Wetland 0 a a. Brown and Tilley 1995 Table 32. Average phosphorous deposition rates. Source Amount Reference Dry atmospheric agricultural 0.066 glm2-yr a non-agricultural 0.027 glm2-yr a Rain 0.167 glm3 rain a Orange groves 1.12E4 gIha-yr b Soybean cultivation 1.05E4 gIha-yr b Sod 7.63E3 gIha-yr b Residential landscape 3.36E3 gIha-yr c Urban landscape 3.0E3 gIhaIyr c a. Huber, et ale 1982 b. Ruck 1992 c. Non-impervious surface assumed landscaped with sod (level of fertilizer application, 500/0 of sod for residential use, 75% for urban/commercial use) 48

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Figure 3.3. System diagram of the phosphorus model for each cell and its relation to water model in Figure 3.2. Energy pathways without water or phosphorus are dashed. Water paths are blue, phosphorus is green. Phosphorus deposition on land surface in solution with stormwater Phosphorus leaving soil pore water either from exceeding water capacity or adsorbed to clay Adsorbed sites made available from phosphorus uptake by vegetation or diagenesis dPI = Prain + P runin + Papplied k6*[PIISurfwatert=01 kl *Impervious Surf water k7*[PIISurfwatert=01 k3 Surfwater k8*[PIISurfwatert=01 k2 Surfwater Soilwater dP2 = k8*[PIISurfwatert=01 '" k2 '" Surfwater '" Soil water X"'k9'" Soilwater '" P2 -k 1 0* Soil clay P2 Uptake dPadsorbed = klO* Soilclay P2 -(Uptake + Diagenesis)

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f;RunOff ---"I -......

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51 literature were used to determine phosphorous available for runoff (Table 3.2) assuming a steady state. Movement Between Cells Downhill drainage of water and phosphorous was simulated using the DRAIN function supplied by MapFactory. This function assumes that all material falling on a cell surface is available for sheet flow with flow direction dependent upon slope differentials between cells. The values from each cell are added to the adjacent cells into which they flow, and convergent pathways with high cumulative loads become evident. This function by itself is incapable of calculating the amount of material left behind in an individual cell. It does, however, recognize material of varying amounts within each cell. By using a map with an estimate of the actual contribution from that cell to the lake, DRAIN becomes a tool capable of distinguishing differentials other than slope between the cell and the lake. Hence, an important component of the spatial model was the development of maps with the material export values resulting from the budget for each cell (Figures 3.2 and 3.3). These maps are the basis for maps that represent impediments to water and phosphorus leaving the surface of each cell. In GIS nomenclature, these are referred to as cost or friction maps. To avoid confusion with economic or hydrology terminology, they will be referred to as impediment maps in this study. Impediment maps use a percentage figure to increase the difficulty for an entity, water or energy for example, to cross a cell. Theoretically, a cell with 100% export at a completely vertical slope would have the minimum effective travel distance for the quantity measured. Conversely, ifnothing was exported and the cell was completely flat,

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the effective travel distance would be at a local maxima. The impediment to each material phosphorus) leaving each cell was calculated. from the mass balance calculations presented in Figures 3.2 and 3.3. 52 Using a GIS command (SPREAD) that calculates travel from the destination through each intervening specified impediment a measure of a distance from a specific exporting cell to the lake perimeter was calculated. This distance is related to, but not synonymous linear distance and becomes the effective proximity of that cell to the lake. A different impediment map was prepared for each material simulated and each year studied. Total rainfall and phosphorus deposition was divided by the log of this effective proximity value, and this value was used to represent the amount of material exported from any specific cell actually reaching the lake. This new map was then used as the basis for cumulative drainage into the lake. Verification Model results for total annual runoff were compared to runoff values calculated using the Soil Conservation Abstraction Method. Runoffvalues from the spatial model for Newnans Lake were within 8% of the SCS method. Weir estimates, on the other hand, were twice the SCS value. Calculations are presented in Appendix C. The efficacy of the model in linking distributed watershed loads to the lake was evaluated in two ways. A simpler, one year regression analysis of seven Florida lakes was completed (Brandt-Williams, 1995; Brandt-Williams & 1997). This study compared phosphorus loading to total lake productivity, algal diversity and trophic state indices for seven Florida lakes included in a 1973 study of lakes receiving sewage

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S3 effluent (Taylor,. 1978). Estimated phosphorus loading was regressed versus several water quality parameters. One month load using rainfall from the month immediately prior to collection of data showed a significant and positive correlation with chlorophyll concentrations (r2 0.946). Annual loads regressed versus Huber et a1. (1982) trophic state indices resulted in a significant and positive correlation (r2=O.725) as well. An abstract and key results of this study are presented in Appendix C. The second verification method calculated the estimated phosphorus to sediment ratio for each year in the chronosequence study and compared the value to paleolimnological records presented in other studies (Kuntz, 1995; Gottgens and 1993). Sediment erosion quantities were calculated using a modified Universal Soil Loss Equation in each celL The resulting value was "drained" through the watershed using a GIS function. Because sediment phosphorus is often reported as a ratio of grams total phosphorous to kilograms sediment, the total simulated phosphorus load was divided by the total simulated sediment load for the three time periods included in this study. Table 3.3 presents this comparison. Development ofEmergy Patterns Emergy values for inputs specific to each land use were used to create maps illustrating concentration of energy within each watershed. One set of maps was created to depict the base emergy flowing into and stored within each map cell on an annual basis. Another showed the flow of emergy through the watershed with a storm event. Figure 3.4 presents a system diagram for inputs and storage included in these evaluations.

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Table 3.3. Phosphorus quantities in sediment cores, Newnans Lake and Lake Weir (Gottgens & Crisman, 1993; Crisman etaI., 1992), values approximated from graphs. Newnans* Weir Core Date mg/gdrywt uglcuifyr cl900 8 na 1950 14 na 1960 27 15 1970 28 21 1980 30 8 1990 30 26 top core only 54

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Natural Landscape Cells Lake Cells Developed Cells Figure 3.4. Diagram with empower pathways and emergy storage components included in calculations for each cell.

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56 Empower Density Mapping Empower density for natural and developed areas were applied to the land use maps for each time period included in this study Values for the components included were obtained from previous studies and evaluations completed for this study. Tables 3.4 and 3.5 list values of emergy flow per hectare per year for each land use. Emergy values for 1990 were used as the baseline evaluation for residential, urban and industrial land uses. Values for agriculture were taken from energy analysis completed between 1980 and 1989 (Fluck 1992). Some assumptions were made to prorate all land use emergy values for 1970 and 1950. Emergy for all natural areas remained constant because no monetary values are included in the analysis. Agriculture emergy was the same for 1990 and 1970, but 10% less in 1950 to account for inflation differences in service dollars. The emergy evaluation tables for residential, and agricultural commodities are presented in Appendix D. Tables 3.6 (Newnans Lake) and 3.7 (Lake Weir) and Figure 3.5 provide the details included in the evaluation of emergy flow into the lake. Table 3.8 presents summary emergy data for both lakes for all years evaluated. Additional calculations are presented in Appendix E. Emergy Accumulation Maps The DRAIN command in MapFactory was used to show pathways of emergy movement in water and phosphorus through the watershed. As water or phosphorus first left a cell, its emergy was calculated by multiplying the mass by an emergy per mass ratio appropriate for the dispersion process initiated. As this runoff converged on an

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Table 3.4. Empower densities for watershed land 1990 Land use Forested wetland Forest Grassland Herbaceous wetland Soybeans Lake Oranges Rural residence,. Alachua Co. Mining Urban, Gainesville Industry Alachua Co. a. Orrell 1997 empower density Reference E14 sejlhalyr 4.7 a 4.8 a 8.4 b 11.0 c 19.5 b 19.6 b 36.0 b 709.0 b 7030.0 c 20300.0 b 3000000.0 b b. Brandt-Williams 1999 this study) c. Odum. 1996 Table 3.5. Empower densities for land use in 1950 and 1970 (natural areas are assumed the same as 1990). Land use 1950 Soybeans Oranges Rural residence, Alachua Co. Mining Urban,. Gainesville Industry Alachua Co. 1970 Rural residence, Alachua Co. Mining Urban, Gainesville Industry Alachua Co. empower density Reference E14 sej/halyr 17.6 b* 32.4 b* 567.0 b+ 5624.0 c 16240.0 b+ 2400000.0 b+ 638.0 b* 6327.0 b* 18270.0 b* 2700000.0 b* b. Brandt-Williams 1999 (this study) c. Odum. 1996 10% lower than 1990 + 20% lower than 1990 57

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58 Figure 3.5. Diagram of inputs to lake, for use in emergy evaluation in Table 3.6.

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S9 Table 3.6. Emergy analysis of Newnan's Lake watershedllake interfac:e,. 1970. Unit Solar Solar Data EMERGY EMERGY 1970 EMS Note Item Unit (unitsfyr) (sej/unit) (E15sej/yr) (E4 USS) Atmospheric inputs A Insolation B Wind shear C chemical potential 0 Transpiration emergents E TP in Rain Watershed inputs F Stream, geopotential G Stream, chemical potential H Sediment I Runofl: non-point 1 TP in streams K TP in runoff Transformities 1 Phytoplankton 2 TP in water column 3 Water Notes: TP = total phosphorus 1 L78E+17 1 178 1 2.61E+14 L50E+03 391 1 L96E+14 L82E+04 3574 1 L03E+12 L54E+04 16 g 7. 14E-f06 2.00E-Hl6 <1 Total abnospheric (sun omitted) 3981 1 L38E+13 L85E+03 26 1 L60E+03 L82E+04 <1 1 3. 16E+12 7.30E+04 231 1 L25E+15 6.3IE+04 79077 g 3.70E+09 6.85E+09 25318 g 4.28E+07 6. 85E+09 293 Total watershed 104945 Total emergyJ1ake1yr 108927 Total emergyJhalyr 36 659E+12 sej/g 290E+13 sej/g 6.16E+05 sej/l A Annual energy = (Avg. Total Annual Insolation 1/yr)(Area)(I-albedo) 2 5 45 <1 <1 -50 <1 <1 3 99 32 4 131 136 Insolation: 6.90E+09 1/m1Jyr (Vishner. 1954) Area: 3.01E+07 m2 Albedo: 0.14 (Odum. 1987) Annual energy: L78E+171/yr B Wmd mixing energy = (density. kglm3)(drag coefficient)(geostrophic wind velocity3,m3/s3)(area) u = wind velocity (mls) = 358 mls

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60 geostrophic wind velocit = 5.97 m1s Energy = L3 kglm3 lE-3 *212..77 m3/s3 3.14 E7 sly 3.01E7 m2 Energy!yr= 2..61E+14 JIyr C Rain, chemical potential = (rain,m)(lake area,m2)(IE6 g1m3)*G D E F Rain, m L32E+OO m Lake area, m2 3.01E+07 m2 G,. free energy,. JIg 4.94E+OO JIg Energy!yr= L96E+14 JIyr Transpiration from emergent and floating macrophytes 142 hacover 7.30E+I0 JJha, estimated transpiration Phosphorous in rain = area rainfall concentration Area = 3.01E+01 m2 Rainfall = L4224 m!yr Concentratioo.= 0.161 g!m3 Annual amount = 7.14E-+06 g!yr Stream, geopotential,. Jfyr= (flow voiume)(density)(dh)(gravit) Hatchett Creek (Huber et at, 1982) (Odum,. 1996) (-52 in,. NOOA, 1995) (Brezonik,. 1969) flow,cfs = IS cfs (SJRWMD, 1997) dh, m = 16 m (Brandt-Williams,. 1999) Energy!yr = 18cfs*O.028311m3/ft3*3.1536E1sec/yr* lE6g/m3*7 L20E+13 Little Hatchett Creek flow, cfs = 4 cfs (SJRWMD, 1997) dh, m = 53 m (Brandt-Williams,. 1999) Energy!yr = L86E+12 J G Stream, chemical potential = (volume flow)(densit)(G) G = (8.331!mole/deg)(300"K)llS g/mole)*ln[(lE6 S) I 965000] JIg S,ppm= (calculated from turbidit, SJRWMD, 1997) Flow,cfs = 18 cfs Energy!yr = L60E+03 llyr H Sediment = (Sediment kglyr)*(lE3 glkg)*(avg. % organic)*(5.4 CalIg OM)*(41S61ICal) Energy = (2..SE7 kg!yr)*(lE3 g/kg)*(O.5% Organic)*(5.4 CalIg)*(41861ICal) = 3. 16E+12 J/yr I Runoff: nonpoint = (volumelyr)(G) = (Volume,.m3)(4.821Ig)(l E6 g/m3) Volume= 2..60E+OS m31yr Energy!yr= L25E+15 JIyr Transformit = 6.31E+04 sej/J Transformity calculated from spatial simulation of total emergy at lake perimeter divided by total volume of water converted to Joules

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61 Table 3.6 continued J Total phosphorus in s1reams = (volume.cfs)(p ,mgll)(O.02831,m3/ft:3)(3.1536E7 .secfyr)(1E-3 Wmg)(lE6 LIm3) Volume .cfs = cfs (SIRWMD. (997) Average 0.23 mWl (SIRWMD.I991) Average TP mass = 3.70E-tOO gIyr Transfonni1" = L82E-Hl4 sej/g (Appendix D) K Phosphorous in runoff from spatial model Annual amount = gIyr Transfonnity = 6.85E-tOO sej/g Transformity fiom spatial simulation of total emergy at lake perimeter divided by total mass of phosphorus Transfonnities calculated ftom this analysis 1 g = (avg. chlorophyll a concentration. g1m3)(lake volume. m3)(2g phytoplanktonlg ChI a) AvgChl a= 0.231 Wm3 (Huber et al 1982) g 2 TP in water column. g = (avg. TP in water column. mgILXlake volume. m3) Average concentration 0.105 mWl (Huber et al 1982) Total g 3. 3 Water. J = (lake volume. m3)(lE6 Wm3)(4.94 JIg) Volume m3 (SIRWMD. 1991) Energy stored L77E+14 J

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62 Table 3.1. Emergy analysis of Lake Weir watershedlIake interface7 1910. Unit Solar Solar Note Item Unit Data (unitslyr) EMERGY EMERGY 1910 EMS (sej/unit) (E15sejlyr) (E4 US$) Atmospheric inputs A Insolation B Windshear C chemical potential o Transpiration emergents E PinRain J J J J g L58E+11 1 2.32E+14 L50E+03 L14E+14 L82E+04 L03E+12 L54E+04 6.34E-t06 2J)OE+06 Total abnospheric (sun omitted) Watenhed inputs F RunotJ: non-point J 3.22E+14 L86E:Hl4 G Sediment J 8.70E+ll 7.30E+04 H P innmofI g 5.26E+07 L21E+IO Total watershed Total emergy/lake/yr Total emergy/halyr Transfonnities 1 g 4.41E+12 sej/g 2 TP in water g 4.88E+12 sej/g 3 Water,J L09E+04 sejlJ Notes: A Annual energy = (Avg. Total Annual Insolation JIyrXArea)(I-aIbedo) Insolation: 6.90E+09 IIm21yr Area: 2.67E+07 m2 Albedo: 0.14 Annual energy: 1.58E+11 JIyr B Wind mixing energy = 158 2 341 4 3171 40 16
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Table 3.7 continued free energy,.llg Eoergylyr= 4.94E-+OO Ilg 1. 74E+14 JIyr D Transpiration from emergent and floating macrophytes 63 14.2 ha cover 1.30E+I0 estimated transpiration (Huber et aL. 1982) (Odum,. 1996) E Phosphorous in rain = area rainfall conc:entration Area = RainfaIl= Concentration = Annual amount = F Sediment = 2.61E+07 m2 L4224m 0.167 g/m3 634E+06 gIyr (-52 NOOA,. 1995) (Brezonik,. 1969) Energy = (1.1 kglyr)*(le3 glkg)*(O.5% Organic)*(5.4 CaI/g)*(4186 IICal) = 8.10E+11 G Runoff: nonpoint = (volumelyr)(G) = ( V oIume,m3)( 4.82 J/g)(1 E6 g/m3) Volume= 6.68E+07 m3/yr Eoergy/yr= 3.22E+14 Ifyr TransfonniW = L86+04 sejlJ TransfonniW calculated from spatial simulation of total emergy at lake perimeter divided by total volume of water converted to loules H Phosphorous in runoff from spatial model Annual amount = 5.26+07 gIyr TransfonniW = 1.21E+1O sejlJ Transfonniw calculated from spatial simulation of total emergy at lake perimeter divided by total mass of phosphorus Transfonnities calculated from this analysis 1 Phytoplankton, g = (avg. chlorophyll a concentration, glm3)(lake m3)(2g phytoplankton/g Chi a) Avg Chl a = OJ)()6 glm3 (Huber et 1982) 2.30E+06 g 2 TP in water column, g = (avg. TP in water column, mgIL)(Iake m3) Average concentration 0.011 mgll (Huber et aL,. 1982) Total g 2. l1E+06 3 Water,. 1 = (lake volume,. m3)(IE6 glm3)(4.941'g) Volume L91E+08 m3 (SJRWMD,. 1997) Energy stored 9.46E+141

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64 Table 3.8: Summary emergy values for Newnans Lake and Lake Weir Value Item Units Newnans Weir Total emergy flow E15 sejlyr 108927 10265 Empower density E15 sejlha/yr 36 4 Phytoplankton emergy/mass sej/g 6.59E+12 4.47E+12 Water transformity sej/J 6. 16E+05 1.09E+04 TP emergy/mass sej/g 2.9OE+13 4.88E+12

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adjacent cell, the emergy was added to the emergy of the same entity in that cell. The cumulative emergy reaching the lake perimeter was summed for all perimeter cells to determine total emergy input to the lake. Phosphorous Emergy Per Mass Ratios Different phosphorus transfonnities were used for each cell depending on either the estimated concentration of phosphorus in solution with rainfall or the source of concentrated input, such as fertilizer. Curves for continuous transfonnities for phosphorus solutions of known concentration were interpolated using previous transformity evaluations for rock phosphate dissolved in rain water and reagent grade phosphorus mixed with groundwater (Appendix F). Transfonnities for concentrated phosphate products were calculated using standard emergy accounting methods (Appendix F). 65 Final TP emergy per gram ratios were calculated in. two steps. The total TP emergy was drained through the watershed, providing cumulative emergy values at every point in the watershed. These emergy values were divided by the grams ofTP exported from that cell, giving the emergy per gram for TP within that specific celL The average emergy per gram used in evaluating emergy flows into the lake was determined by dividing the total TP emergy flow by the TP load in grams. Results: Landscape Properties The following sections introduce maps illustrating basic watershed characteristics (basin morphology, soils and geology, and land use). Results from the two watersheds are presented together for immediate comparison. Elevation contours in the watershed are

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66 presented separately from the bottom contours of each lake. The lake surface is shown at average NGVD height in the elevation maps. Soils for both watersheds are mapped using two different classifications. The first is the standard Soil Conservation Service hydrological previously discussed in Chapter 2. The second represents the total capacity for water infiltration as discussed in the model development section of this chapter. Geological maps are for information only and were not used in the simulation of the modeL Watershed Momhology, Newnans Lake Figure 3. 6 presents an elevation profile for Newnans Lake watershed. The range of elevation for Newnans' basin is 16 m at the deepest lake point to 69 m at the northwest edge of the basin. The steepest slope is 45%, and the average slope is 10%. Mean lake level is 20 m (65' NGVD). The elevation map illustrates the relative flatness of the watershed in the area immediately surrounding the lake (black to dark gray), with high relief concentrated along the western edge of the basin. Figure 3.7 is a bathymetric map prepared by the St. Johns River Water Management District. This map illustrates the shallow morphology ofNewnans Lake. The depth throughout most of the lake is less than 1.2 m (4 feet) with contours spread far apart. There is a relatively small pool in the central eastern section reaching a maximum depth of3 m(lO'). Watershed MomholoK)', Lake Weir Figure 3.8 illustrates the elevation contours for the Lake Weir watershed. The range of elevation for the basin is 9 m, near the center of the main lake, to 57 m along the

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67 eastern ridge. The mean lake level is 18 m. The steepest slope is and the average slope is 20%. Although lower in elevation overall than Newnans the lake is deeper and the watershed steeper. However, Lake Weir's basin bas several areas of intermittent depressional relief evident by the spotty dark to light contours throughout the lower map in Figure 3.1 L Figure 3.9 is a bathymetric map prepared by Ott and Chazal in 1966. The majority of the lake bottom is approximately 7 m (25 feet) below the lake surface, with a steep dropoff around the perimeter of the lake. Soils and Geology, Newnans Lake Soil hydrology distributions, determined by the Soil Conservation Service (SCS), are shown in Figure 3.10, and Figure 3.11 depicts more detailed soil hydrology distributions. Hydrologic soil class D is predominant in Newnans watershed, but soil group A borders the western edge of the lake and the northern drainage into Hatchett Creek. Soils classified by impedance to water transport (permeability times capacity) show higher heterogeneity but generally follow the same distribution pattern as shown. by the SCS categories. Figure 3.12 is a map of the underlying geological formations in Newnans' watershed, which lies mainly within the Hawthorne formation and Plio-Pleistocene Terrace deposits. The Ocala group surfaces in a small area of the southwest basin. The Hawthorne formation is a highly variable mix of quartz sand, clay, carbonate and phosphate overlying the Ocala group and ranges in thickness from a 200 feet to the east of the lake to 160 feet near Gainesville. Plio-Pleistocene deposits are fine to medium mixes of sand, silt and clay. The Ocala formation is 98% calcium carbonate. (SCS, 1982)

PAGE 86

68 Soils and Geology. Lake Weir Soil Conservation Service (SCS) soil hydrology distributions are shown in Figure 3.13, and Figure 3.14 depicts more detailed soil hydrology distributions. Hydrologic soil class A is predominant in the Lake Weir watershed. Soil groups C and D border the northeastern edge of the lake and the stream and wetland area north of Little Lake Weir. Soils classified by impedance to water transport (permeability times capacity) generally follow the same pattern of distribution as shown by the SCS categories. Figure 3.15 is a map of the underlying geological formations in Lake Weir's watershed. This basin lies mainly within the Ocala group and the Hawthorne Formation. Land Use Changes. Newnans Lake Land use within the Newnans watershed for 1950, 1970 and 1990 are presented in Figures 3.16 through 3.18, respectively. Table 3.9 and Figure 3.19 provide area values for specific land uses and illustrate the magnitude of changes. Four significant changes in land use occurred between 1950 and 1970. Residential and natural areas bordering Gainesville were incorporated into the city. Large tracts of deforested areas, about 700 ha, around Hatchett Creek and in the northwest watershed were reforested. Residential areas increased directly to the west of Newnans Lake and along Waldo Road. The number and width of roads increased. There are fewer differences evident between 1970 and 1990. The Gainesville municipality increased in area near the far western edge of the watershed. Impervious surface at the airport increased, as did industrial development of the same area. In addition, existing residential clusters throughout the basin expanded in area and number of residents.

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o Meters above sea level .--....-34.15 66.00 97.85 129.69 L.;;...."'--_ 161.54 o 5,000 10,000 .... .. .. 1:260,000 Figure 3.6. Elevation contours in Newnans Lake watershed. 69

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70 N ''V''d Figure 3.7. Elevation contours on the floor ofNewnans Lake (SJRWMD, 1996).

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71 meters above sea level 175 27.4 372 47.1 '--"'--56.9 o 2,500 5,000 1_.' IMetres 1:249,324 Figure 3.8. Elevation contours in Lake Weir.

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12 R. LAKE WEIR 111.,;0.. Covill, JrU JW Jr.-JrU N 4 x.N JCM .. t: Jrg ... x,. S ......... IN __ I-IN!'. ill r .... --1(1-L ......... ............ 0.,-, is $7.1$ ........... ........... r lor .... Oca".f .. .., A,ril" .sea 0 4000 1000 FUT L I Figure 3.9. Lake Weir bathymetry (after Ott and 1966).

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"D .. C ':':"f?i.",,' B [:::J A .. pits .. water" o 5,000 10,000 1::( :::J_-=::J_-=:::::tII' _-===::f( Metres 1:260,000 73 Figure 3.10. Hydrological soil classification groups in Newnans Lake watershed. as defined by Soil Conservation Service. Group D has high runoff potential, C has moderate potential, B has low runoff potential and A has little to no runoff potential (classification described in Chapter 2).

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Rain fraction retained 0.06000 0.29500 0.53000 0.76500 "--"---1.00000 o 5,000 10,000 t:1 Metres 1:260,000 74 Figure 3.11. Soil impedance distributions, categorized by permeability and capacity, with values representing the fraction of an average rain event being retained within the soil column, Newnans Lake.

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.. Miocene-Hawthorne. Devils Mi1Ihopper .. Miocene-Hawthorne, Groveland Parle Pliocene, Bone Valley fonnation Plio-Pleistocene .. Miocene-Hawthorne, Statenville o 5,000 10,000 1:::1 :::J_-=::::::._-=:iI' _-.:==::::::fl Metres 1:260,000 Figure 3.12. Map of subsurface geology formation, Newnans Lake (adapted from SCS, 1982). 75

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.. Wafl:c c::J A 1"-''''',><.1 B .. C "0 -1/ 7 _. "" I ... ,., ... ... o 5,000 10,000 1:::1 ::JI_-=:::J_-=::j' ___ ==:=:::ll Metres 1:249,324 Figure 3.13. Hydrological soil classification groups in Lake Weir watershed, as defined by Soil Conservation Service. Group 0 has high runoff potential, C has moderate potential, B has low runoff potential and A has little to no runoff potential (classification described in Chapter 2). 76

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Rain fraction retained 0.1000 0.3250 05500 .. -0.Tl50 ----...-.-1.0000 o 5,000 10,000 1:::1 ::J_-=::::::._-=::iI' _-=====11 Metres 1:249,324 77 Figure 3.14. Soil impedance distributions, categorized by permeability and capacity, with values representing the fraction of an average rain event retained within the soil column, Lake Weir.

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.. Ocala Group .. Hawthorne Formation o 5,000 10,000 1:::1 ::J_-=::J_-==:j' .... _===::ll Metres l:249,324 Figure 3.1S. Map of subsurface geology formation, Lake Weir 78

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79 Land Use Changes. Lake Weir Figures 320 through 322 depict land use in the Lake Weir basin for 1950, 1970, and 1990, respectively. Table 3.10 and Figure 323 provide area values for specific land uses and illustrate the magnitude of changes. The largest change in this watershedoccured between 1970 and 1990 and was the conversion of orange groves throughout the watershed to range and residential land use,. a total loss of2355 hectares in production. Between 1950 and 1970, residential land use and agriculture increased throughout the watershed by approximately 400 ha, especially near the lake perimeter. A major highway was also built to the west of Lake Weir (U.S. Hwy. 441) between 1950 and 1970. A small section ofurban area (8elleview) had encroached at the far west of the basin by 1990. Results: Non-point Source Runoff Profiles This section presents the results from the simulation of water, phosphorus and sediment movement through the watershed. Table 3.11 and 3.12 list overall material flows calculated in the spatial simulation for Newnans and Weir, respectively. Water Profiles Water movement through the watersheds is presented four different ways. The volume of water exported to the lake from each cell versus the number of cells exporting that volume is illustrated by a rank-order graph. The area of watershed contributing the largest amount of stormwater to the lake is mapped for 1950, 1970 and 1990. These areas of significant export were considered to be the "effective" watershed, as opposed to the actual watershed determined solely by elevational differences. Changes in watershed

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.. LakesIPonds IIIJ Herbaceous wetland "Stream .. Forested wetland "Forest CJ OpenIRange "Urban .. Commelcial .. Residential "Industry "Orchard "Rowcrop "Mining "Roads o 5,000 10,000 1::1 ::::J_-=:::::J_-=:::::iII' _-===:::11 Metres 1:260,000 Figure 3.16. Newnans Lake watershed land use, 1950. 80

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.. Lakesfponds IiiI Herbaceous wetland "Streams .. Forested wetland "Forest c:::J OpenIRange iii RailyaId "Urban .. Commercial .. Residential "Industty "Orchards "RowCrops "Mining "Roads o 5,000 10,000 t:1 ::JI_-=::JI_-=::iI' _-===:=jl Metres 1:260,000 Figure 3.17. Newnans Lake watershed land use, 1910. 81

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Ii!III HeIbaceous wetland "Stn:ams .. Forested wetland [:::J OpeoIRange _Railyard "Urban .. Commercial .. Residential "Industry "Orchard "Rowcrop "Mining _Roads o 5,000 10,000 It: ::JI_-=::JI_-=::iI' _-===::jl Metres 1:260,000 Figure 3.18. Newnans Lake watershed land use, 1990. 82

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83 Table 3.9. Land use areas for Newnans Lake. 1950 1970 1990 Change, % Basin Water* 320 318 331 0.02% Herbaceous Wetland 421 429 431 0.02% Streams 628 629 588 -0.07% Forested Wetland 7945 7990 7901 -0.08% Forest 35086 35714 35330 0.44% OpenJRange 8226 6598 5813 -4.37% Agriculture 200 143 129 -0.13% Residential 911 1120 1341 0.78% Urban 1060 1786 2550 2.70% Industry 12 12 72 0.11% Roads 410 481 732 0.58% Level of Development Natural 44400 45080 44581 0.33% Cleared 8226 6598 5813 -4.37% Developed 2593 3542 4825 4.04% Newnans Lake not included

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rn 8000 4------------------------------------' 7000 -1---------------------------6000 -1---------------------------.s, 5000 .g: 4000 +-----------.c: 3000 -f--------------------------2000 -f------------------------T' 1000 -1--------= Roads Residential Urban Open/Range Figure 3.19. Highest land use changes in Newnans Lake watershed 81950 .1970 I?a 1990

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85 .. Water liIII Herb. Wetland .. Streams .. Forested Wetland .. Forest CJ Range/Open .. Commercial $ .. Residential .. Orchard .. 0 5,000 10,000 Roads I -I I Metres .. Row Crops 1:232,702 Figure 3.20. Lake Weir watershed land use, 1950.

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86 -} J -.. .... ... WatJ::c IiiiI Herb. Wetland Streams Forested Wetland Fon:st CJ OpenIRange UrbanlCommeIcial -Residential Orchanl $ -Road -Row Crop 5,000 lO,OOO 0 Mining I -I I MetIes 1:249,324 Figure 321. Lake Weir watershed land use, 1970.

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87 .-." \ ... era. -. ... .. Water a.. .. Herb. Wetland .. Stream .. Forested Wetland .. Fon:st CJ OpenIR.ange .. Urban/Commercial .. Residential .. Orchard .. RaId $ .. Row Crop .. Mining 0 5,000 10,000 i I I Metres 1:249,324 Figure 3.22. Lake Weir watershed land use, 1990.

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88 Table 3.10. Land use areas for Lake Weir. 1950 1970 1990 Changez % Basin Water* 242 242 233 -0.09% Herbaceous Wetland 218 156 168 -0.52% Streams 3 3 3 0.00% Forested Wetland 119 98 98 -0.21% Forest 1407 1346 1315 -0.95% OpenlRange 5747 5569 6641 9.15% Agriculture 1777 1910 253 -15.60% Residential 178 320 790 6.26% Urban 1 1 44 0.44% Industry 0 0 1 0.01% Roads 78 123 225 1.51% Level Natmal 1989 1845 1816 -1.77% Cleared 5747 5569 6641 9.15% Developed 2033 2354 1312 -7.38% Lake Weir not included

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89 10000 9000 8000 7000 6000 Residential .. 5000 Agriculture t 4000 fa OpenlRange -= 3000 2000 1000 0 1950 1970 1990 Figure 3.23. Highest land use changes in Lake Weir watershed

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Table 3.11. Summary data for watershed loads to Newnans Lake from spatial simulations. Pre-development 1950 1970 Phosphorous,g 3.87E+07 4. 18E+07 4.25E+07 Water,m3 2.66E+08 2.68E+08 2.68E+08 Sediments, kg 2.77E+07 2.79E+07 2.79E+07 Runoff, % of total rain 34.500/0 34.800/0 34.800/0 Table 3.12. Summary data for watershed loads to Lake Weir from spatial simuIations. Pre-develoEment 1950 1970 Phosphorous,g 2.42E+07 4.97E+07 5.26E+07 Water,m3 6.61E+07 6.66E+07 6.68E+07 Sediments, kg 7.59E+06 7.64E+06 7.66E+06 Runoff, % of total rain 30.21% 30.43% 30.51% 90 1990 4.28E+07 2.70E+08 2.81E+07 35.100/0 1990 3.40E+07 6.71E+07 7.70E+06 30.66%

PAGE 109

91 export to the lake are illustrated by mapping the quantitative difference in storm water runoff for each cell between 1950 and 1970 and between 1970 and 1990. Total estimated volumes of non-point source runofffor each year are also presented in Table 3.11. Water profiles for Newnans Lake The rank-order graph (Fig 3.24) shows an exponentially decreasing curve for Newnans Lake. In other words, there were a high number of map cells exporting less than 1,000 liters per year, and successively fewer cells exporting progressively higher amounts. This curve was generated by dividing cell data into 5,000 liter/cell increments and plotting the average of each increment. All three maps of the effective watershed (Figure 325 27) show the majority of the watershed exporting less than 1 I1m2/yr (1,000 liters per cell). The watershed zone closest to the lake exports from 11 I1m2/yr (lE4liters per cell) to 1,800 I1m2/yr (IE6liters per cell). The middle zone exports, on average, 7 I1m2/yr (6,000 liters per cell). Changes in export zones are most evident between 1950 and 1970 and mainly on the western shore of the lake. The high export zone immediately adjacent to the lake was approximately S.24E7 m 2 in 1950, 5.68E7 m 2 in 1970, and S.71E7 m 2in 1990. This translates to an 8% increase between 1950 and 1970 and a 3% increase from 1970 to 1990. The middle zone grew less than 1% between 1950 and 1970, but increased by 5% from 1970 to 1990. Areas of greatest export differences on a per cell basis occurred between 1950 and 1970 (Figure 3.28), with the highest runoff addition immediately west ofNewnans Lake. However, the entire northwestern section of the watershed increased in export. This is the area of development evident in the land use maps, and is also the steepest portion of

PAGE 110

92 the watershed. The watershed to the north and northeast contributed slightly less between 1950 and most likely due to the 700 ha reforestation visible in this region. The remainder of the watershed is unchanged. Changes between 1970 and 1990 (Figure 3.29) exhibit a patchy with most potentially adverse conditions to the far west of the watershed in the Gainesville area and around the airport. Water profiles for Lake Weir The rank-order graph (Fig 3.30) shows an exponentially decreasing curve for Lake Weir similar to Newnans curve. In other there were a high number of map cells exporting less than 1,000 liters per year, and successively fewer cells exporting progressively higher amounts. This curve was generated by dividing cell data into 5,000 liter/cell increments and plotting the average of each increment, in the same way as Newnans curve. All three maps of the effective watershed (Figure 3.31 33) show approximately half of the watershed exporting less than 1 lIm2/yr (1,000 liters per cell). The watershed zone closest to the lake exports from 11 lIm2/yr (1E4 liters per cell) to 1,800 lIm2/yr (IE6 liters per cell). The middle zone exports, on average, 711m2/yr (6,000 liters per cell). Changes in export zones are most evident between 1970 and 1990 and mainly in the southern portion of the watershed, but are not as apparent as changes in Newnans watershed with overall higher volume of runoff. The high export zone immediately adjacent to the lake was approximately 5.24E7 m 2 in 1950, 5.68E7 m 2 in 1970, and 5.71E7 m2in 1990. This translates to an 8% increase between 1950 and 1970 anda3%

PAGE 111

increase from 1970 to 1990. The middle zone grew less than 1% between 1950 and 1970, but increased by 5% from 1970 to 1990. 93 Areas of greatest export differences on a per cell basis occurred between 1970 and 1990 (Figure 334), with the highest runoff additions immediately north of Lake Weir and in the southern portion of the watershed. This is due to a switch in land use from citrus production to residential. Phosphorus Profiles for Newnans Lake Estimated deposition of phosphorus is shown in Figures 3.36-338. High levels of deposition are evident in agricultural, residential and urban areas due to fertilizer use. All sets of maps are for 1950, 1970 and 1990, respectively. Figures 3.39,3.40 and 3.42 depict the quantity of phosphorus estimated to reach the lake from that particular cell. Because this is a function of both deposition and effective distance, the spatial configuration of export cells is different than for the deposition maps. Phosphorus Profiles for Lake Weir Figures 3.42 and 3.43 show high levels of phosphorus deposition throughout the watershed in 1950 and 1970 when citrus production was active. Citrus production ceased in the mid-80s, when it was replaced by some forested areas, but mostly open range and residential. Deposition profiles for 1990 reflect that change (Figure 3.44). Figures 3.45 -3.47 depict the quantity of phosphorus estimated to reach the lake from that particular cell. Because this is a function of both deposition and effective distance, the spatial configuration of export cells is different than for the deposition maps,

PAGE 112

1.1990 1970 1950 I II r1'----r, ----" -------.--1"'----; I I Quaatity of Water Esported a I I I I I. 10, Quantity ofW.ter Esported b Figure 3.24. Rank-order graph for water volume exported from each cell in Newnans Lake watershed: a) curve generated by actual data; b) log-log representation of data. 94

PAGE 113

l.lEt03 Figure 3.25. Effective watershed, 1950, Newnans Lake. Inner zone exports from lE4 to lE6 liters of water per cell per year; middle zone exports on average 6000 I/yr; outer zone exports less than 10001/yr. liters per cell per year 2.7Et05 5.5E+05 8.2EtOS o 5,000 10,000 I -. I Metres 1:249,824 l.lE+06

PAGE 114

96 .. """". ; -,.'-

PAGE 115

1.lE+03 Figure 3.26. Effective watershed, 1970, Newnans Lake. Inner zone exports from lE4 to lE6 liters of water per cell per year; middle zone exports on average 6000 l/yr; outer zone exports less than 1000 l/yr. 2.7E+05 5.5E+05 8.2EtOS o 5,000 10,000 1 __ I Metres 1:249,324 I.lE+06

PAGE 116

-, .: .. 98

PAGE 117

1100 Figure 3.27. Effective watershed, 1990, Newnans Lake. Inner zone exports from 1 E4 to 1 E6 liters of water per cell per year; middle zone exports on average 6000 l/yr; outer zone exports less than 1000 l/yr. liters per cell per year 274850 548600 822350 o 5,000 10,000 1 __ I Metres 1:249,824 1096100

PAGE 118

100 .:+

PAGE 119

l 7' o 5,000 lO,OOO 1t::::J_-=:::J_-=::::iI' _-===:::il Metn:s 1:260,000 Figure 3.28. Changes in area of watershed contributing stormwaterto the lake between1950 and 1970, Newnans Lake watershedIncreases in effective watershed are red to yellow (red being area of highest transport) and coincide with areas having an increase in impervious surface. No change or decrease in transport range from dark green (beneficial change) to light green (little or no change) with beneficial changes coinciding with areas of reforestation. 101

PAGE 120

o 5,000 10,000 .. .. .. 1:260,000 102 Figure 3.29. Changes in area of watershed contributing stormwater to the lake between1970 and 1990, Newnans Lake watershed_ Increases in effective watershed are red to yellow (red being area of highest transport). No change or decrease in transport range from dark green (beneficial change) to light green (little or no change).

PAGE 121

I ------quantity of water exported (a) '-... 1M log quantity of water exported (b) Figure 3.30. Rank-order graph for water volume exported from each cell in Lake Weir watershed: a) curve generated by actual data; b) log-log representation of data 103

PAGE 122

7.8E+02 .!-:l ,-it ,.r' .' II --'j 'ii: o 5,000 10,000 I -' I Metres 1:249,324 liters per cell per year 2.7E+OS S,SE+OS 8.2E+OS Figure 3.31. Effective watershed, 1950, Lake Weir. Inner zone exports from lE4 to IE6liters of water per cell per year; middle zone exports on average 6000 I/yr; outer zone exports less than 1000 l/yr. I.IE+06

PAGE 123

7.8E-t{)2 .. ,. 'c,I' 1 .. 'i : :;"Ij 1 1 r )', ; 1 '.\ o 5,000 10,000 1 __ ; I Metres liters per cell per year 1:249,324 2.7E-t{)S S.SE-t{)S 8.2E-t{)S Figure 3.32. Effective watershed, 1970, Lake Weir. Inner zone exports from lE4 to lE6liters of water per cell per year; middle zone exports on average 6000 l/yr; outer zone exports less than 10001lyr. l.lEtQ6 ,....

PAGE 124

7.9E+02 .,',. l! .. $ o 5,000 10,000 I -! I Metres liters per cell per year 1:249,324 2.7E+OS 5.SE+05 8.2E+OS Figure 3.33. Effective watershed, 1950, Lake Weir. Inner zone exports from lE4 to lE6liters of water per cell per year; middle zone exports on average 6000 l/yr; outer zone exports less than 1000 l/yr. 1.1E+06 ""'"

PAGE 125

107 -i(.-'. .. o 5,000 10,000 1::1 ::::J_-==-_-=:j' __ ====fl Metres 1:249,324 Figure 3.34. Changes in area of watershed contributing stormwaterto the lake between 1950 and 1970, Lake Weir watershed. Increases in effective watershed are red to yellow (red being area of highest transport). No change or decrease in transport range from dark green (beneficial change) to light green Oittle or no change).

PAGE 126

it: -> <'",' .. o 5,000 10,000 t:1 :::J_-=====-_-==,' __ ====il Metres 1:249,324 Figure 3.35. Changes in area of watershed contributing stormwater to the lake between 1970 and 1990, Lake Weir watershed. Increases in effective watershed are red to yellow (red being area of highest transport). No change or decrease in transport range from dark green (beneficial change) to light green (little or no change). 108

PAGE 127

kglbafyr ,.-_-2.5 6.0 9.6 13.1 16.1 3"::-,. '>, o 5,000 10,000 t:1 _-===:::11 Metres 1:260,000 -.". -' -.,.,... ;:-,: -::,," ... ", ...... -. Figure 3.36. Estimated phosphorus deposition, 1950. Newnans Lake watershed. 109

PAGE 128

-fkglhalyr 2.5 6.0 9.6 13.1 16.7 ..! .... _, --..... ----. :..:;' .. -. __ :_ -; or o 5,000 10,000 tl= .... .. .... 1:260,000 Figure 3.37. Estimated phosphorus deposition, 1970, Newnans Lake watershed. llO

PAGE 129

. kglhalyr ...-...--2.5 6.0 9.6 13.1 16.7 .. :: ......... o 5.000 10.000 1t:::::J_-=::::::l_-=::::::iI' _-===::::il Metres 1:260.000 ... ,. "' .. '." .. .. ...... ") '. Figure 3.38. Estimated phosphorus deposition, 1990, Newnans Lake watershed. III

PAGE 130

kgTP/halyr ...-..._-0.0 0.9 l.3 1.8 / --, o 5,000 10,000 t:1 ::J_-==-_-=.' _-===::jl Metres 1:260,000 112 Figure 3.39. Estimated total phosphorus (TP) export profile for 1950, Newnans Lake. Each band represents 20kg TP/halyr exported from that area and reaching the lake.

PAGE 131

kglbalyr 0.4 0.9 1.3 1.8 : --. __ c.. o 5,000 10,000 t:1 ::::I_-=::::I_-=::::il _-===::::11 Metres 1:260,000 Figure 3.40. Estimated total phosphorus (TP) export profile for 1970, Newnans Lake. Each band represents 20kg TP/halyr exported from that area and reaching the lake. 113

PAGE 132

kglbalyc 05 Ll 1.6 22 o 5,000 10,000 cl 3 .. .. .... 1:260,000 Figure 3.41. Estimated total phosphorus (TP) export profile for 1990, Newnans Lake. Each band represents 20kg TPlhalyr exported from that area and reaching the lake. 114

PAGE 133

__ -2.5 6.0 9.6 13.1 16.7 o 5,000 10,000 t:1 _-===:::::::11 Metres 1:249,324 Figure 3.42. Estimated phosphorus deposition, 1950, Lake Weir watershed. 115

PAGE 134

...... ...--2.5 6.0 9.6 13.1 -.'16.7 o 5,000 10,000 IC::J_-=::::J_-=:::j' ___ ===:::ll Metres 1:249,324 Figure 3.43. Estimated phosphorus deposition, 1970, Lake Weir watershed. 116

PAGE 135

ro-'""_2.5 6.0 9.6 13.1 16.7 o 5,000 10,000 ft::::J_-=:::::J_-=::::::iI _-===:::i( Metres 1:249,324 Figure 3.44. Estimated phosphorus deposition, Lake Weir watershed. 117

PAGE 136

kgIhaIyr 0.00 1.79 3.58 5.37 7.16 118 I ... T,"l"Ji<: o 5,000 10,000 II:: ::I_-=:::I_-=::::iII1 _-===::fl Metres 1:249,324 Figure 3.45. Estimated total phosphorus (TP) export profile for 1950, LakeWeir. Each band represents 20kg TPlhalyr exported from that area and reaching the lake.

PAGE 137

kWbalyr 0.00 1.79 358 537 7.16 o 5,000 10,000 1::1 :::J_-=:::J--=:::::iI' _-===:::::11 Metres 1:249,324 Figure 3.46. Estimated total phosphorus (TP) export profile for 1970, LakeWerr. Each band represents 20kg TPlhalyr exported from that area and reaching the lake 119

PAGE 138

0.00 1.79 3.58 537 7.16 o 5,000 10,000 t:1 :JI_-=:::J_-=::j' __ ==::=jl Metres 1:249,324 Figure 3.47. Estimated total phosphorus (TP) export profile for 1990, LakeWeir. Each band represents 20kg TPlhafyr exported from that area and reaching the lake 120

PAGE 139

121 but in Weir's watershed the difference is less evident than in Newnans'. This is due to the much higher overall phosphorus deposition in Lake Weir's basin. Results: Emergy Patterns Aerial Emergy Flux, Newnans Lake Figures 3.48 3.50 illustrate the amount of emergy flow on an aerial basis for the years 1950,. 1970 and 1990,. respectively. These maps show two main areas of emergy concentration within the watershed -the Gainesville urban area and Newnans Lake. In 1950,. smaller clusters of concentration were evident where roads intersect and near roads leading to the lake. By 1970,. existing clusters had expanded,. especially the urban Gainesville area and those near the intersection of Waldo Road and Hatchett Creek. A new point of concentration on the lakes midwestern shore developed. A similar pattern of existing cluster expansion continued to 1990, with biggest changes further from the lake. Aerial Emergy Flux, Lake Weir Unlike Newnans Lake watershed,. Lake Weir's basin did not show a concentrated pattern of hierarchy in the 1950 or 1970 empower density maps (Fig. 3.51 and 3.52). Patches of agricultural areas with empower density slightly lower than the lake extended from the southern lake edge to the southeastern edge of the basin. Residential areas with higher emergy inputs than the lake were concentrated in three main areas: the northern lake perimeter, the middle of the eastern lake edge and southeast of the lake between two areas of agricultural production.

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E14 SEJ/m2Iyr CJ E:J .. .. .. 4.6 4.8 8.4 11.0 15.6 19.6 28.8 .. 567.0 .. 7030.0 .. 16240.0 .. 2400000.0 .. _, t --: .' .. o 5,000 10,000 I __ ; (Metres 1:260,000 .... Figure 3.48. Empower density (E14 SEIlm2Iyr) distribution in Newnans Lake watershed in 1950. Black is highest density, progressively lighter areas have decreasing densities. 122

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E14 SEJlm2lyr c::J 4.65 c:J fBI .. .. .. .. .. .. 4.80 8.40 11.00 19.50 19.60 36.00 638.00 7030.00 18270.00 2700000.00 o 5,000 10,000 1::1 ::::J_-=::J_-=::::::iI' _-===:::fl Metres 1:260,000 Figure 3.49. Empowerdensity (E14 SEJlm2Iyr) distribution in Newnans Lake watershed in 1970. Black is highest density, progressively lighter areas have decreasing densities l23

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E14 SEJ/m2!yr CJ 1":'",1 4.65 4.80 8.40 l.iiiI 11.00 ,. 19.50 19.60 36.00 709.00 7030.00 .. 20300.00 .. 3000000.00 '!'. -.. -. ", '>",',>'.',,,'c o 5,000 10,000 t:1 :I_-=:I_-=::1_-===::::t1 Metn:s 1:260,000 Figure 3.50. Empower density (E14 SEJ/m2/yr) distribution in Newnans Lake watershed in 1990. Black is highest density, progressively lighter areas have decreasing densities 124

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E14 SEJ/m2Iyr c::J 4.65 b'-'-'i ;1 4.80 mI 8.40 IIIiI 11.00 .. 15.60 .. l8.80 .. 35.00 .. 567.00 .. 16240.00 125 o 5,000 10,000 t:1 :::J_-=::J_-=::j' ___ ====il Metn:s 1:249.324 Figure 3.5L Empowerdensity (E14 SEJ/m2lyr) distribution in Lake Weir watershed in 1950. Black is highest density, progressively lighter areas have decreasing densities.

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E14 SEJ/m2Iyr CJ 4.65 4.80 8.40 IIi1 11.00 .. 19.50 .. 31.80 .. 40.00 .. 638.00 .. 7030.00 .. 18270.00 o 5,000 10,000 t:1 ::JI_-=::J_-=:j' ___ c:::::=::jl Metres 1:249,324 Figure 3.52. Empowerdensity (E14 SEJ/m2lyr) distribution in Lake Weir watershed in 1970. Black is highest density, progressively lighter areas have decreasing densities. 126

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E14 SEJ/m2Iyr CJ 4.65 c::J 4.80 8.40 11.00 IIIiiI 19.50 .. 31.80 .. 40.00 .. 700.00 .. 7030.00 .. 20000.00 .. 20300.00 o 5,000 10,000 1::1 ::JI_-=::J_-==iII' _-====il Mem:s 1:249,324 127 Figure 3.53. Empower density (E14 SEJfm2/yr) distributi,on in Lake Weir watershed in 1990. Black is highest density, progressively lighter areas have decreasing densities.

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128 By two main areas of the lake and a residential area, were apparent, a pattern similar to Newnans' watershed (Figure 3.53). emergy inputs have increased in the area immediately surrounding the lake because of high emergy residential and the main areas of convergence are separate from each other. Emergy Accumulation Profiles Cumulative emergy distribution for storm water in Newnans Lake show several areas of high accumulation in the steeper areas of the watershed to the northwest (Fig. 3.54), but a fairly evenly distributed network throughout the basin. Weir, on the other hand, shows high areas of accumulation throughout the watershed, but patchier networks (Fig. 3.55). Cumulative emergy distributions for phosphorus have different patterns of accumulation in both watersheds than delineated for the water profiles. Newnans Lake watershed has areas of high accumulation in a widely scattered mostly dependent on deposition (Figure 3.56). Accumulation networks in Weir's basin are more complex than Newnans' (Fig. 3.57), with areas of high accumulation all along the southern edge of the lake. PART 2: DYNAMIC LAKE SIMULATION MODEL Table 3.13 presents definitions of terms descnoing interactions and flows used in diagramming the lake system at different levels of aggregation.

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13 14 16 17 18 Figure 354. Water drainage network, post-development in Newnans Lake watershed, cumulative emergy, log sej. 129

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5 8 II 13 16 Figure 3.55. Water drainage network, post development in Lake Weir watershed, cumulative emergy, log sej. 130

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, -" / ""c "',-1.. ,;--," '-I ----I o 4 8 12 Figure 356. Post-development phosphorus emergy drainage network, Newnans Lake, log sej/g. 17 131

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132 'd: 0' 0 ,0, 0 o 4 8 12 17 Figure 3.57. Phosphorus emergy networks post-development in Lake Weir watershe
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Table 3.13. Definitions of terms descnoing interactions and flows relevant to simulations. Term Definition Autocatalytic unit A unit within a system that stores enough energy to internally feedback energy to increase its own energy consumption. Example: a fish expending energy to find higher quality food sources Calibration coefficients The proportion of the interactive flows used on any particular path and designated by k Drain Any interaction promoting loss from a tank Flow limited source A source delivering a regulated flow to a system that varies with the energy available to use that flow, but that cannot exceed the maximum regulated flow Flow limited path A material with rapid turnover times, relative to other storages in the system, is often treated as a flow whose delivery to system components is limited by availability and competition Symbol 133

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134 Development This in-lake simulation explored the hierarchy of control among trophic levels within the lake. A system diagram for a lake contains a complex network of interactions and food web hierarchy (Fig 3.58). This simulation model includes components considered to be most impacted by watershed nutrient inflow and most important to long term functional changes within the lake. Figure 3.59 presents an aggregated diagram, with letters used to descn"be pathways in the following section. Figure 3.60 provides mathematical equations and pathway coefficients used in the computer simulation. Various trophic state indices (TSI) were calculated based on different measures of component productivity, such as biomass storage and net productivity CAl. Aggregation and Interactions Phytoplankton were aggregated with epiphytic algae (PI); submergent, emergent and floating leaf macrophytes were aggregated with floating macrophytes (M). Bacteria was modeled as a flow without storage (B). Benthic invertebrates were aggregated with zooplankton (Z), and all fish (F) were incorporated into a single compartment. These divisions were based primarily on turnover times and similarity of main energy sources. Organic matter (Org) and phosphorus (P) in the water column were aggregated with sedimentary components, ultimately passing outside the system boundaries (H). A fraction of the detrital organic matter was both converted to phosphorus and resuspended on a regular basis (e) with the amount dependent on lake geometry and trophic status. Organic matter in the water column was modeled as a use of sunlight prior to availability to all producers except macrophytes (D). All producers were considered autocatalytic.

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Figure 3,58, Lake diagram illustrating complexity of interactions and food web hierarchy, w Vl

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Figure 3.59, An aggregated in-lake energy systems diagram with components and pathways included in the simulation of lake responses to changing phosphorus loads and determination of dynamic trophic state indices, -w Q'I

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Figure 3.60. In-lake energy systems diagram and equations used to simulate response to changing watershed inputs. Light energy remaining after use by floating vegetation Light energy remaining after dispersion by organics Light available for periphyton and phytoplankton Concentration of phosphorus and organics once in lake Floaters Phytoplankton Zooplankton Fish Phosphorus Organics Rates and concentrations RO = I/(l+kO*P*M) R2 = Rl/(l +k2*Org) R3 = R2/(1+k3"'P"'PI) PO = Pcrunin/h20 00= Ocruninl 000/h20 Pr = rainlk.1671h20 Mass/energy balanees dM = k4"'RO"'P"'M -kS"'F"'M -k6Z"'M -k7"'M kS"'M dPI = k9*R3"'P"'PI-klO*PI*F kll*PI*Z -k12*PIk13.PI dZ = k32 *Org*Z + k30*Z*M + k31 PI"'Z -k14*Z"'F -klS*Z dF = k27.F*M + k2S.F.PI + k29*F*Z -k16*F dP = phin phout phin = POtfr.(k7.M + k12*Pl t klS*Z t k16"'P) phout = k20*P*PI + k21.P*M + k22*P dOrg = orgin -k24*Org -k26*Org orgin = 00 t frorg*(k7.M + k12"'PI + klS*Z + k16"'F)

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.... w 00

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139 Since consumers aggregated in Z and F expend different amounts of energy in locating and ingesting different food sources, each was represented with separate autocatalytic interactions (G) within a single compartment. The energy fraction allotted to each of these three uptake interactions was based on estimates of the percentage that each food source contributed to the total intake. Trophic State Simulation Trophic state indices were calculated four different ways for comparison of early responses. Curves of instantaneous values are presented along with other system parameters in each simulation graph. Long term averages were calculated for the length of each simulation. 1. Gross productivity for all organisms, both autotrophs and heterotrophs, was calculated from the inflow to each individual storage 2. Net productivity was calculated from the inflow to each organism minus the metabolic portion of the outflow. 3. Biomass was calculated by summing the storages throughout the simulation. 4. A Huber TSI was calculated using the log transformation equations for Secchi disc, chlorophyll and phosphorus in the water column (Huber et al., 1982). Secchi depths were calculated from the amount of light remaining after use by the phytoplankton. A straight-line equation was derived from the relationship of remaining light in the oligotrophic simulation to an average Secchi depth for Weir and the same relationship from the eutrophic *simulation and Newnans. Calibration Data Numbers associated with production rates, biomass storage and turnover times were collected from the literature and actual data for the two lakes in this study. Lakes with similar trophic states or characteristics were selected for inclusion in average values for calibration of simulation coefficients (Table 3.14).

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140 Data from the literature were both averaged and adjusted to account for balancing of material flows between the components in the model. Most adjustments were made using life cycle turnover times and some basic assumptions about divisions of flows. For example, an assimilation efficiency of loo/o was used to value flows through each successive trophic leveL Table 3.15 presents a summary of these flows after they had been averaged and nonnalized for use as calibration points for the simulations. Figure 3.61 presents the flow and storage values used to calibrate this model for initial simulations. Calculation of coefficients for this and for all other simulations are given in Appendix G. Simulation Results Each lake was simulated using averaged data for oligotrophic, eutrophic and hypereutrophic conditions to demonstrate the impact of changing watershed inputs. The pulsing responses resulting from the hypereutrophic simulation were used to explore the cause of the pulse and the hierarchy of pulse control. Simulations Comparing Reswnses to TP Loading There were four main differences between calibrations for oligotrophic and eutrophic simulations. First, flows and storages were altered to reflect the levels present in empirical data (Table 3.15). Second, because the oligotrophic simulation was meant as a representation of Lake Weir, the lake volume was increased and the watershed area decreased from the eutrophic parameters, in proportion to the differences between Weir and Newnans. Third, the amount of nutrient resuspension was

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Table 3.14. Productivity, stomge, turnover times and uptake mtes from various litemture sources. Organism Comments Production! Biomass! Turnover/ Source units units units Algae eutrophic lOOOmgC/m2/d 300mgC/m3 Likens (1975) I 564mgClm2/d Wetzel (1966) mesotrophic 250-IOOOmgC/m2/d lOO-300mgC/m3 Likens (1975) 438mgClm2/d Goldman&. Wetzel (1963) oligotrophic 99.3mgC/m2/d Wetzel (1966) 50-300mgC/m2/d 20-IOOmgC/m3 Likens (1975) Macrophytes broad leaf 500-IIOOglm2/yr Kvet &. Husak (1978) (aquatic) gmsses 3450glm2/yr Klopatek (1974) submergent 4.6g1m2/d Wetzel (1983) emergent 2-7g1m2/d 2200glm3 ibid floating 4-12g1m2/d 630-1500glm3 ibid Zooplankton eutrophic 0.11-9.15g1m3/d 0.35g1m3 9.2-25d Winberg (1970) mesotrophic 1.12-3.09g1m3/d .07-.l4g1m3 9.l-14.3d Kajak (1970) oligotrophic .94-3.02g1m3/d 1 36-.43g1m3 22-29d Moskatenko & Votinsev (1970) Fish eutrophic I 34-614kglha Champeau (1997) mesotrophic 18-61 kglha Champeau (1997) -

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142 Table 3.15. Summary data used as guide for initial simulations. Production Biomass Turnover ilm2/dar iim3 dal! Pelagic algae 2-4 >0.6 2-10 Epiphytic Algae 0.12-34 1-177 10-60 Average 1.1-18 80 Submergent macrophytes 4.6 Emergent Dlacrophytes 2-7 2200 Floating macrophytes 4-12 630-1500 Average 5.5 1500 245 Zooplankton 0.11-9.15 0.35 9-25 Fish 0.0025 13-60(gfm2) 1825

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Figure 3,61, Steady state flows and storages for eutrophic simulation, -

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144 decreased in the oligotrophic model to simulate increased depth. Fourth, the phosphorous runoff coefficient from the watershed to the lake was 114 the input to the eutrophic lake. There was one main difference in calibration between the eutrophic and hypereutrophic simulations. A switch to blue-green algae as the domjnant phytoplankton species often accompanies this increase in eutrophic conditions (Brenner et a1., 1998; Wetzel, 1983), and this group is not a favored food by zooplankton, nor is assimilation by fish as high as other algal species. organic material becomes a more important food source. The hypereutrophic simulation has the algae consumption coefficient lowered and the organic consumption higher to reflect these conditions. The simulation using calibration values within eutrophic ranges exhibited a small increase in total biomass with increasing TP loading from the watershed, with macrophytes and fish showing the earliest response (Fig 3.62). The average Huber TSI before increased TP loading was 78 and 82 after loading. The hypereutrophic simulation exhibited a pulse in fish and zooplankton populations, but showed very little response to increased TP loading (Fig 3.63) The average Huber TSI before increased TP loading is 96 and is 98 after loading. The oligotrophic simulation showed the greatest change in conditions following TP load increases, with fish and phytoplankton showing early responses (Fig 3.64). The average Huber TSI before loading is 36 and jumps to 48 after loading. Gross and net productivity showed less than a 100/0 difference between all three simulation conditions, and was insensitive to TP loading. Average biomass was 1188 for hypereutrophic, 1000 for eutrophic and 548 for oligotrophic. Instantaneous biomass curves showed a fast response to TP loading. While the Huber TSI showed high

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variability dependent on seasonal fluctuations in TP inputs, overall sensitivity to increased loading was mixed. Pulsing Simulation 145 When the primary path of zooplankton productivity was switched from a balanced ingestion of phytoplankton and organic matter, a pulsing pattern between fish and zooplankton developed. Several sources and interactions in the simulation were varied to determine if the pulse was exogenous or endogenous. Figures 3.65 -67 present results from simulating different levels of available solar energy. Neither sun nor rain inputs are oscillating, and all other inputs are held constant. Figure 3.65 illustrates a pulsing response in fish and zooplankton for the first 3000 days, with decreasing amplitude and period. Macrophytes grow to a high level in the beginning but slowly decline to a much lower equihorium level in about 25 years. Figure 3.66, presenting results from higher solar input, exhibits a similar pulse in fish and zooplankton, but taking twice as long to reach equilibrium. Macrophytes exhibit a more pronounced oscillation and reach a high equihorium level after about 18 years. Figure 3.67 shows results from lower solar input. Again, fish and zooplankton develop a pulsing pattern, with initially higher amplitude and longer period than the two higher input simulations. Macrophytes increase initially but drop to an extremely lowequihorium level. Equilibrium for all components is reached in about 3 years. To determine the components interacting to create the pulsing behavior apparent in fish and zooplankton, the base model was simulated holding some components constant, while allowing others to vary. Phosphorus and organic tanks were always allowed to accumulate or drain, as they have no interactive inputs. Components allowed

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146 to vary were added one at a time in a step-wise beginning with phytoplankton. Figures 3.68 and 3.69 illustrate that phytoplankton and macrophytes alone or not pulse. Addition of zooplankton (Fig 3.70) as a varying component exhibits a single pulse. When fish are allowed to the repeated pulsing pattern occurs, with fish and zooplankton as the pulsing pair (Fig 3.71). PART 3. WATERSHED CLASSIFICATION AND MANAGEMENT Various ratios of material flows and emergy values were calculated to compare and contrast the two watersheds in this study. Predicted non-point source runoff, both water and phosphorous, were compared to water quality, physical characteristics of each lake and trophic state indices from other sources. Summary emergy values and changing emergy per mass ratios for these material flows are also presented. Predevelopment watershed simulations were also run to provide a basic understanding of watershed loads characteristic of long-term geomorphic parameters. Two key assumptions were used to create these land use maps. Firs4 the entire watershed was assumed to be covered in forest. areas surrounding water and streams were assumed to be forested wetlands if forested wetlands were still present nearby in any of the years included in this study. Material Loads and Ratios Loads and ratios of phosphorus and water exports from the watershed to the lake were calculated to put these values into a format familiar to lake management. They are also compared to water quality data collected as close to the periods of simulation as possible. Three different concentration ratios are presented. Runoff phosphorus

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__ ... __ .. ________ ___ .. .. _. __ ._ ___ .. _M_ .. _.. .. -_._-_._ ... ... -... .. Matrophytes TSI MIl Total HI_ Phytoplankton TP In W.ter Cohunn o 365 730 1095 o 365 730 1095 Zooplankton Fllh Figure 3,62, Simulation of eutrophic conditions with runoff increase after 2 years, """ :!:i

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_ ___ '. ___ __ MamJPhytes TSI" ToW ......... Phytoplankton TP"tn W"ter Cohlinn ----.--_._--------------o 365 730 1095 1460 1825 o 365 730 1095 1460 Zooplankton Fllb Figure 3.63. Simulation of oligotrophic conditions with runoff increase after 3 years. 1825 """' 00

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Macrophytes s ...... Toeal Blama. .,----Phytoplankton TP In Water Column o 365 730 1095 o 365 730 1095 Zooplankton Fllb .... Figure 3.64. Simulation of hyper eutrophic conditions with zooplankton using less phytoplankton :t

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350 '::I Macrophytes o 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Time days Figure 3.65. In-lake simulation using averaged eutrophic conditions for several lakes worldwide with averaged environmental sources. No perturbations occur in inputs and the model is run for about 30 years.

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350 b 'D i o 1000 2000 3000 4000 Fish 5000 Time days Phytoplankton 6000 7000 8000 9000 Figure 3,66, In-lake simulation using averaged eutrophic conditions for several lakes worldwide with lower environmental sources than shown in Figure 3,65, 10000 -

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70 i a o 1000 2000 3000 4000 5000 Time days 6000 Macrophytes Fish Macrophytes 7000 8000 9000 Figure 3.67. In-lake simulation using averaged eutrophic conditions for several lakes worlwide, but with higher environmental sources than shown in Figure 3.65. 10000

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T 8 --", I [ T I I t v---o 100 200 300 400 500 600 700 800 TIme days Figure 3.68. Using in-lake simulation to explore hierarchy of pulsing controL Phytoplankton storage is allowed to vary. All other variables higher in the chain are held constant. 153 900

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0 T T 0 0 N II) I o 200 400 600 800 1000 1200 1400 1600 Time days Figure 3.69. Using in-lake simulation to explore hierarchy of pulsing controL Producer storages are allowed to vary. All other variables higher in the chain are held constant. 154

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l V' I 8 N I o 200 400 600 800 1000 1200 1400 1600 Time days Figure 3.70. Using in-lake simulation to explore hierarchy of pulsing control. Producer storages and zooplankton are allowed to vary. Fish storage is held constant. 155

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g N o 200 400 800 TiDledays 1000 1200 1400 Figure 3.71. Using in-lake simulation to explore hierarchy of pulsing control; all variables are fully interacting. 156 1600

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157 concentrations in mgll can be used in Vollenweider's loading equations. Phosphorus to lake volume ratios can be compared to water column TP. Phosphorus to sediment ratios are commonly used in characterizing TP in sediment records. Newnans Lake Overall loading changes simulated from pre-development through 1990 are shown in Tables 3.16 for Newnans Lake. Newnans Lake shows a steady increase in TP and TP concentration ratios (Fig 3.72). Water showed a temporary drop in 1970 due to reforestation north of the lake, but was higher overall in 1990. These changes are similar to those in water quality data taken from Huber et aL (1982) and from more recent Lakewatch data (1998) (Figure 3.73). Phosphorus to sediment ratios are lower than those from cores taken by Gottgen and Crisman (1993) as expected (Fig 3.73), since stream inputs are greater than non-point source inputs (Table 3. 6). Lake Weir Lake Weir simulations show increasing storm water inputs to the lake, but much lower phosphorus after 1970 (Table 3.17). This is due to replacement of citrus production with range and residential land use. Phosphorus concentrations show a concomitant drop (Figure 3.74). Corresponding water quality data also show a drop in water column TP and overall trophic state index (Figure 3.75). Emergy Loads and Ratios Comparison Between Watersheds A direct comparison of emergy inflows at both the highest point of development and pre-development periods is presented as a means for ranking the two watersheds and

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158 comparing emergy to empirical water quality data (Table 3.18). Overall, Newnans Lake has higher emergy flows than Weir except in recreational use. Two interesting differences and similarities are evident. The emergy per mass in TP runoff is the same in pre-development time periods for both lakes, suggesting a baseline value of 6E4 sej/g for phosphorus. The transformity for runoff water is different for the two watersheds, but the same throughout time for each watershed. Watershed and lake emergy inputs for both lakes reflect the same relationship as the TSI (Figure 3.76). Log values of emergy flows are presented for better comparison. Comparison of Simulated Phosphorus Load and Empirical Phosphorus Data Emergy values for phosphorus runoff are higher for Lake Weir than Newnans Lake in both 1970 and 1990, but predevelopment TP runoff emergy is higher in Newnans (Tables 3.19 and 3.20). The direction of change in emergy values for TP loading are consistent with empirical data (Fig 3.77). Newnans Lake shows a slight increase across both sets of values, and Weir shows a larger decrease. Emergy Accumulation Patterns Little difference in the network pattern for phosphorus emergy is apparent between the pre-development era and highest level of development in Newnans Lake watershed (Fig 3.78 and 3.79). However, total emergy flows are five orders of magnitude lower. Lake Weir, on the other hand, exhibits less complexity in the pre-development period than in the period of highest phosphorus runoff {Fig 3.80 and 3.81). Total emergy flows are still increased by five orders of magnitude.

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159 Table 3.16. Summary data for watershed loads to Newnans Lake from spatial simulation. Pre-devel2l!ment 1950 1970 1990 Phosphorus,g 3. 81E+07 4. 18E+07 4.25E+07 4.28E+07 Water, m3 2.66E+08 2.68E+08 2.68E+08 2.70E+08 Sediments, kg 2.77E+07 2.79E+07 2.79E+07 2.81E+07 TP/Sed Ratio, mwg 1.40 1.50 L5J 1.52 Average P Concentration, mg/l 0.145 0.156 0.159 0.158 Runoff, % of total rain 3450% 34.80% 34.80% 35.10% TP loadllake volume Ratio, gin 1.08 1.17 1.19 1.20 TP loadllake uWcm2/yr 13.05 14.11 14.34 14.47 Table 3.17. Summary data for watershed loads to Lake Weir from spatial simulations. 1950 1970 1990 Phosphorus,g 2.42E+07 4.97E+07 5.26E+07 3.40E+07 Water, m3 6.61E+07 6.66E+07 6.68E+07 6.71E+07 Sediments, kg 7.59E+06 7.64E+06 7.66E+06 7.70E+06 TP/Sed Ratio, mglg 3.19 650 6.87 4.42 Average P Concentration, mg/l 0.37 0.75 0.79 051 Runoff, % of total rain 30.21% 30.43% 3051% 30.66% TP loadllake volume Ratio, gin 0.13 0.26 0.27 0.18 TP loadllake uWcm2/yr 10.40 21.35 22.61 14.62

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4.3E+07 4.2E+07 4.1E+07 e: 4.0E+07 3.9E+07 3.8E+07 -t--------r-.------.------""-T-------i 160 1830 1870 1910 (a) 1950 1990 fO\ 2.70E+08 ; 2.69E+08 i 2.68E+08 2.61+08 I I 2.66E+08+-------r---------.-------r------,i 1830 1870 1910 (b) 1950 1990 0.160......--------------------------.------.. 0.155 I a I 0.150 i I I 0.145 I 0.140 +--------.-------.-------,.....--------;1 1830 1870 1910 1950 1990 (e) L22 E L18 .! GI L16 I I L14 ,I C; L12 L10 E-L08 L06 +-------,.....--------r-------....,.......--------i 1830 1870 1910 (d) 1950 1990 Figure 3.72. Phosphorus and water loads to Newnans Lake from the watershed; a) TP loading, g; b) water, m3; c) TP concentration, mgll; d) TP to lake volume ratio,g/m3.

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100 80 60 40 20 o TP,.mgIL TP/Sed,m!if8
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162 6+07 i 5+07 i 4+07 I I ClIO 3+07 I 2+07 r f 1+07 I O+OO 1830 1870 1910 1950 1990 (a) 6.72E+07 6.70E+07 I I e 6.68E+07 t B 6.66E+07 = 6.64E+07 6.62E+07 6.60E+07 1830 1870 1910 1950 1990 (b) 1 0.8 ... 0.6 .8 0.4 : 0.2 0 1830 1870 1910 1950 1990 (e) 0.3 0.25 I i e 0.2 I :s 0.15 I I 0.1 I I 0.05 I 0 1830 1870 1910 1950 1990 (d) Figure 3.74. Phosphorus and water loads to Lake Weirftom the watershed; a) TP. g; b) water, m3; c) TP concentration, mgll; d) TP to lake volume ratio,glm3

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50 +---------------40 +---------------30 +----------------20 10 o TP. mgIL TSI
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Table 3.18. Comparison of watershed emergy classification parameters. Maximum Development Watershed emergy used, sej/yr Average empower density, sejlhalyr Emergy input to lake, sejlyr Emergy/perimeter lake, sej/mlyr Emergy/area lake, sej/halyr Emergy/mass TP lake, sej/g Emergy/mass runoffTP, sej/g Transformity lake water, sej/J Transformity runoff water, sej/J Recreational use, sej/yr Pre-Development Watershed emergy used, sej/yr Average empower density, sej/halyr Emergy/mass runoffTP, sej/g Transformity runoff water, sej/J Newnans 2.49E+22 4.28E+17 1.09E+20 3.33E+15 3.60E+16 8.31E+I0 6.85E+09 6. 15E+05 6.31E+04 2.62E+21 5.80E+19 9.91E+14 9.36E+04 6.32E+04 Weir 9.24E+20 7.64E+16 1.02E+19 2.01E+14 4.00E+15 1.46E+09 1.21E+I0 1.08E+04 1.86E+04 6.92E+21 2.42E+19 2.00E+15 9.36E+04 1.88E+04 164

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.Newnan .Weir I 80 70 60 50 40 30 20 10 o TSI watershed emergy. lake emergy.log sej recreational use. log logsej sej Figure 3.76. Comparison of emergy flows to the watershed and lake with trophic state index for both Newnans Lake and Lake Weir. 165

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Table 3.19. Summary emergy data for runoff to lake, Newnans Lake. Phomhorus Emergy sej Average emergy/mass, sej/g Water Emergy input, sej Average transformity, sej/J pre-development 3.62E+12 9.36E+04 S.09E+19 6.16E+04 1970 1990 2.86E+17 2.93E+17 6.S4E+09 6.S5E+09 8.15E+19 8.41E+19 6.31E+04 6.40E+04 Table 3.20. Summaryemergydata for runoff to lake, Lake Weir. pre-development 1970 1990 Phomhorus Emergy input, sej 2.26E+12 6.68E+17 4.32E+17 Average emergy/mass, sej/g 9.36E+04 1.27E+I0 1. 12E+I0 Water Emergy input, sej 5.94E+1S 6.OOE+1S 6.OOE+18 Average transformity, sej/J 1.87E+04 1.86E+04 l.S6E+04 166

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Newnans Lake 300 -t----------------------== 250 -t-----------------200 -t--------------150 +--------------100 +----==----50 o TSI TP u&'L E15 sej Lake Weir TP E8 sej/g 80 70 60+--------------------50 +-------------------40 30 20 10 o TSI TPugIL TP E16 sej -. TP E9 sej/g 111970 .1990 _1970 .1990 Figure 3.77. Comparison of simulated phosphorus emergy flows over time with empirical water quality from Huber et al. (1982) and Lakewatch (1998). 167

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168 Intervention Strategies Remediation of increasing phosphorus inputs was attempted by replacing existing land use in specific areas of each watershed with areas resembling retention ponds in holding capacity. Two strategies were used to optimize placement, and both were compared for amount of area required to reduce current loads to pre-development levels, as well as patterns of placement. One method uses areas of highest cumulative phosphorus loads to pinpoint mitigation sites. The second method uses emergy per mass ratios higher than the ratio found in industrial concentration of phosphorus (> 2E12 sej/g) to pinpoint appropriate holding areas for phosphorus runoff. Newnans Lake Using areas of highest drainage for phosphorus as preferred placement requires 385 hectares mitigation area to reduce Newnans phosphorus load to pre-development levels. The placement areas are concentrated along the western lake edge, between the lake and the residential areas (Fig 3.82). These areas are currently cypress swamp and lawn. The emergy per mass method requires 700 hectares ofNewnans watershed, but the areas are scattered throughout the watershed and require few changes in current developed land use (Fig 3.83). Lake Weir Using areas of highest drainage for phosphorus as preferred placement requires 280 hectares mitigation area to reduce Lake Weir's phosphorus load to pre-development levels. The placement areas are again concentrated along the lake edge, between the lake

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and the residential areas (Fig 3.84). However, the sites are more evenly distributed around the lake. 169 The emergy per mass method requires 560 hectares of Weirs watershed, but, again, the areas are scattered throughout the watershed, mostly near existing agricultural areas (Fig 3.85). They do not appear to require conversion of current developed land use.

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o .. 3 6 I "i I, } ... .... 8 Figure 3.78. Pre-development phosphorus emergy drainage network, Newnans Lake; log sej/g. 170 II

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o -, \\ .. "'-. .' .,. 4 .. -:: I 8 12 Figure 3.79. Post-development phosphorus emergy drainage network, Newnans Lake, log sej/g. 171 17

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, --:. o 3 5 8 11 Figure 3.80. Pre-development phosphorus emergy drainage network. Lake Weir; log sej/g. 172

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sr '-"I: o 4 8 12 Figure 3.81. Post-deve[opment phosphorus emergy drainage network, Lake Weir log sej/g 173 17

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o 5,000 10,000 .. .. .. 1:260,000 Figure 3.82. Placement of intervention based on points of highest mass loading, Newnans Lake; purple areas indicate best siting. 174

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o 5,000 10,000 cl .. .... .. 1 :260,000 Figure J .83. Placement of intervention based on points of emergy/mass greater than 2E 12 sej/g, Newnans Lake; purple areas indicate best siting. 175

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o 5,000 10,000 .. .. ...... 1:143,164 Figure 3.84. Placement of intervention based on points of highest mass loading, LakeWeir; purple areas indicate best siting. 176

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177 o 5,000 10,000 1:::1 ==_-=::::::J_-==iI' ___ Metres 1:143.164 Figure 3.85. Placement of intervention based on points of emergy/mass greater than 2Ei2 sej/g, LakeWeir, purple areas indicate best siting

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CHAPTER 4 SUMMARY AND RECOMMENDATIONS Summary This dissertation examines changing energy and material flows in watersheds and how they impact and shape the evolving productivity of the basin's focal poin4 in this case, lakes. Further, it evaluates emergent properties of concentrating material flows, the energy acting on them and the emergy embodied in them. Fox (1986), in discussing energy -driven systems states, "Energy flow is a necessary but not a sufficient condition for the living state of matter ... the living state is as much a consequence of special substances and their emergent properties as it is a consequence of energy flow." This study specifically examines two watersheds in central Florida with very different characteristics, and focuses on water and phosphorus inputs to the lakes. Phosphorus is usually the material limiting productivity in freshwater (Wetzel 1983), and water moving downhill provides the kinetic energy necessary to bring phosphorus into a lake. Phosphorus, as both material and energy transducer in production of evolutionary building blocks, represents a special substance with emergent properties sufficient for evolution (Fox (986). One goal of this research was to present the system of constraint evident from quantifying the hierarchy between a watershed and its lake. The downhill movement of energy and mass into a lake is an obvious causal mechanism, or feed forward (Salthe 178

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179 1985), but the extent of its control on the lake may not be dominant. The regulation of watershed activity by the lake, or is not as obvious, but is evident in use of the lake by humans for recreation, climate control and aesthetics and by migratory wildlife for food and habitat. The research focused on the quantity and influence expected from diffuse nonpoint source material contributions converging on a lake, and showed that the emergy per mass of both dispersed and concentrated in the watershed, is a dynamic property. Material, such as phosphorus, is applied in concentrated form to developed areas (agriculture, residential and urban), then diluted with stormwater, cycling it through the watershed. Concentration occurs when runoff from a much larger area is funneled to the lake edge, and all the energy used to move it accumulates (Odum, 1996). However, mass quantities alone often disguise the real power of the material in relation to other materials in the system. Phosphorus, usually a limiting nutrient, is measured in ug, while water is best dimensioned with cubic meters and sediment with metric tons. Emergy best defines the real power in phosphorus and provides a higher ranking by including the previous energy used in moving the element into a concentration useable by primary producers (Odum, 1996). This study proposed the use of dynamic system simulations and emergy as evaluative tools both for showing long term trends in watershed development and for providing early warnings for changes in lake productivity. Although not predictive, these simulations of a eutrophic and oligotrophic system showed comparative responsiveness to increased phosphorus loading.

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180 One application arising from the research was a methodology for prioritizing remediation and conservation both within a watershed and between watersheds with lakes that are exlnbiting increasing eutrophication. Because the runoff model is spatially specific and provides both patterns of high drainage and an expected loading from each area in the areas of proposed intervention can be tested for efficacy in reducing overall lake loading. Specific advances of interest in watershed-lake relationships are discussed in this chapter. I. Spatial patterns emerged that were both common to, or differentiated between, materials and watersheds. 2. A clear hierarchical relationship between systems within the watershed was established. 3. Simulated non-point source loading and observed lake productivity showed similar trends. 4. Dynamic properties of emergy per mass ratios emerged from time-series evaluation. 5. Simulations of in-lake loading impacts suggested differences between oligotrophic and eutrophic responses. 6. The use of watershed emergy and material ratios were representative of overall lake productivity. 7. Improvements in phosphorus loading were simulated using several decision strategies for placement of intervention ecosystems. These findings are discussed individually in the following sections. Spatial Patterns in Lake Watersheds Two primary spatial patterns are evident in this study: patterns established over millenniums by geologic and short-term development patterns created by humans. The former encompasses elevation and soil differences and establishes a baseline for watershed loading to the lake. These can provide information on the natural

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181 trophic state of the or at least the long-term runoff loads the lake bas organized around. The latter include increases in nutrient availability and impervious surfaces. These two factors combined increase the likelihood of cumulative loading to the lake in a persistent manner that is difficult to and can be expected to reinforce the level of connection between watershed and lake within a specific watershed. Patterns from Geologic Processes The Newnans Lake basin is very shallow with an average depth of about 1 meter. Water of this depth is completely available for evaporation (Chow et aL 1988). To retain a lake in a depression of this depth, stormwater inputs must therefore be greater than the volume. Newnans Lake is likely still a lake because its watershed bas a high percentage of soils with low permeability, few areas without a confining layer and relatively few depressional areas within its overall boundary, allowing large amounts of runoff to move unimpeded through the landscape. The spatial simulation shows that without development in the watershed, 37% of the watershed's rain reaches the lake. The total volume of runoff is 2.66E8 m3/yr. This corresponds to the 2.1E8 m3/yr runoff estimated with the Soil Conservation Service method for abstractions (Appendix C). This estimated runoff is 7 times higher than the calculated volume of the lake. Lake Weir, on the other exhibits a greater area of watershed in small depressional areas, particularly in the southeast quarter of the basin. Further, there is a greater percentage ofhigbly permeable with a larger percentage of the basin not occluded from the aquifer. We would expect the overall percentage of storm water runoff

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182 to be lower than. in Newnans, and in. fact, the model shows a 7% difference in the amount of rain that becomes runoff.. The simulation of Lake Weirs watershed, without development, has an estimated runoff load of30% of the total annual or about 6.6E7 m3/yr. This is about 34% of the total lake volume. Patterns from Human Development Patterns of drainage intensity varied in complexity and magnitude between the two watersheds. The amount of watershed contnDuting to the lake (effective watershed.) increased with urban or residential development near the lake. Each lake had a unique effective watershed, dependent on geology and soils, as originally hypothesized. Developments outside this area still contributed a substantial amount of runoff. However, the effective watershed in. each instance contributed a greater amount than the outlying areas combined. Both watersheds exhibited a tendency to concentrate development in two specific watershed areas the sandier portions of the lake and to a larger degree, the edges of the watershed where several watersheds adjoined. For the Gainesville area, this latter concentration was at the Newnans, Lake Alice and Paynes Prairie junction. For Lake Weir, there were three points of concentration: the Oklawaha River basin, the Withlacoochee River basin, and Lake Griffin. Hierarchy of Lakes and Watersheds Empower density establishes the hierarchy proposed in Chapter 1 (Figure 1.1 with urban and residential areas being of higher rank than the lakes. Concentration of

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183 materials and energy at the interface between watershed and lake place the lake much higher than the surrounding natural areas and somewhat higher in the hierarchy than agricultural areas. Although Newnans Lake is a higher emergy lake than Lake Weir, the same hierarchy is evident in both watersheds. Empower density values in the Newnans Lake watershed are similar to those calculated in a recent study of Alachua County 1999). Both studies show scattered areas of high empower density throughout the watershed due to agriculturaI and residential land uses. Both indicate a large concentration of emergy in Gainesville and in the lake. However, this study has a lower estimate of the Gainesville empower density and a higher estimate of the lake's empower than Lambert's evaluation. Differences in Gainesville's values are related to Lambert's inclusion of human services specific to each hectare, whereas the current study evaluated only inflows crossing the watershed boundary and averaged them for Gainesville as a whole. Lake empower differences are due to the dynamic simulation input from runoff in this study. Cumulative Watershed Loading and Lake Trophic Status Phosphorus loading simulated by the spatial model, while generaIly showing the same trend as development in both watersheds, was not directly related to land use changes, either in quantity or location. This finding is reinforced by the earlier runoff study (Appendix C) showing a high correlation between chlorophyll and phosphorus loading, but no correlation with land use. One key difference between Newnans Lake and Lake Weir arising from non-point source phosphorus estimates is that non-point sources are only about 1% of the total

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watershed exports from larger streams into Newnans Lake, but non-point inputs are essentially 1000,4 of the exports for Lake Weir. 184 Although non-point phosphorus (TP) and storm water loadings for Newnans Lake have increased over the overall increase is only 10% for TP and less than 2% for water between pre-development patterns and 1990 flows. The simulated TP loading and concomitant concentration values for the watershed appear to be stabilizing. Although only 4% of watershed area shifted from natural to developed between 1950 and this is an 86% increase in total developed area. According to Vollenweider phosphorus loading models 1983), input concentration values in post-development years would account for approximately 20ug1l of the observed lake chlorophyll concentrations. This level is considered within the eutrophic range (Wetzel, 1983), but is only about half of the long-term average (54 ugll, Huber et al., 1982) between 1957 and 1980, and about 10% of 1993-1998 values (231 ugll, Lakewatch, 1999). For a lake with high levels of evaporation, the TP concentration of inflowing runoff might not be as important as the concentration remaining in the lake. In other words, using the ratio of non-point TP loading to the total lake volume as the concentration in Vollenweider loading equations accounts for close to 80 ug/l. Lake Weir's simulation, on the other hand, while exhibiting a marked increase in overall runoff, shows a pronounced drop in TP loading between 1970 and 1990. This is directly due to conversion of citrus groves throughout the watershed to residential areas, resulting lower phosphorus deposition and increased impervious surfaces. The total

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185 watershed area in residential development in Weir's basin gained 8% during that period, while agricultural land use dropped by over 15 o/oOf total watershed area. According to Vollenweider TP loading input concentration values between 1950 and 1970 should have yielded chlorophyll concentrations of approximately 35ug/l. This is about six times the long-term average (6 Huber et 1982) between 1956 and 1981. The 1990 simulated input concentration indicates a drop to 27 or about twice 1991-1998 values (11 Lakewatch., 1999). if phosphorus input is compared to total lake volume, rather than. incoming a chlorophyll value closer to measured values results. A predicted value ofless than 20ug/L is still higher than the observed 6 to 12 range from repeated water quality testing (Huber et aL 1982; Lakewatch 1999). One very clear difference between the two watersheds emerges when simulated predevelopment material and emergy loads are compared to peak historical loading evaluated in this study 1990 for Newnans and 1970 for Lake Weir. While Newnans TP loading is only 10% higher than estimated predevelopment loads, Weir's highest loading is more than two times higher than estimated baseline loading. A change of this magnitude might be expected to lead to a higher trophic status. Historical evidence suggests that Weir was originally oligotrophic 1994), and current water quality data place it in the mesotrophic range (Lakewatch., 1999; Huber et al., 1982). Emergy and Emergy per Mass Related to Lake Status Emergy flows and the ratio of emergy flow to material inputs changed with time relative to changes in the lake. These changes were evaluated at three scales relevant to

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186 this study: total watershed inputs; watershed and atmospheric inputs to the lake; and at the individual material scales of water and TP. The Watershed Newnans Lake, with the larger watershed and high productivity. also had the highest emergy input to the watershed, both pre-development and post development. This is consistent with overall lake productivity and the level of concentrated development within the watershed. Weir, having lost its agricultural base to a freeze, is now developing a concentration hierarchy similar to Newnans' watershed, with two main areas of high emergy convergence the lake and a large residential development south of the lake. This may not impact productivity of the lake because, just as in Newnans, the second area of emergy concentration is outside the watershed area displaying highest export to the lake. The Watershed-lake Interface Water from runoff dominates the emergy flow to both lakes, and consequently. the emergy evaluation is not sensitive to changes in phosphorus input. Overall, emergy inflow to Newnans is not much different now than post-development. Weir. however, has doubled in emergy inputs. This suggests that Weir probably was originally at a lower trophic state than present, and that more influence to the lake is possible in this watershed. It further suggests that Newnans probably has always been eutrophic, but that non-point source inputs can still affect productivity.

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187 Phosphorus There are two key findings concerning phosphorus emergy in this study. Dilute phosphorus appears to have a lower emergy per mass limit of about 9E4 sej/g at a concentration of about 10 ppb. This is lower than the 9E9 sej/g ratio for oceanic upwelling phosphorus at a concentration of 50 ppb (Odum, 1996). Further, the average concentrated emergy per mass for both watersheds was very close to the ratio derived for the natural concentration of phosphate rock in Florida's wetlands -about 2E 1 0 sej/g of phosphorus. However, this higher ratio had assistance from addition of phosphorus concentrated by industry. Water Use of actual soil capacity up to the first level of confinement for determining runoff is another difference between the current spatial model and most models, which use Soil Conservation Service (SCS) hydrological classifications (Adamus and Bergman 1995; Heidtke and Auer, 1993). SCS classifications rank soils for ability to shed water, but they are neither a quantitative measure of capacity, nor an evaluation of sub-surface transport. Using the vertical distance to the confining layer may be acting as a transfer function for lateral subsurface flow, and the higher levels of water export predicted with this model may therefore be a measure of seepage. Quantity of seepage of this kind is an area of debate in Florida's sandy loamy watersheds (Deevey, 1992). Sediments Estimated sediment loading from Newnans watershed was higher than Lake Weir, with a higher estimated organic matter percent. The emergy use of sediments in

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188 Newnans Lake is 1.4xlOl&, and is 6.4xl016 in Lake Weir. However, sediment load is only a little more than 1% ofNewnans Lake total emergy use, and a little less than 1% in Lake Weir. Color from organic matter is an important component of function in Florida lakes (Crisman et al., 1999), and especially in Newnans Lake with high but variable observed color. This sediment simulation accounted only for average sediment loss based on soil type. This excludes an evaluation of the organic and humic acid contnoutions from surrounding cypress and hardwood wetlands. Emdollars Newnans Lake watershed contributes 73 million EmSlyr to the lake about 8% of the total watershed real wealth. This equates to about 800 EmS that each visitor receives when using Newnans Lake for recreation. Lake Weir's watershed contnoutes 1.28 million EmS/yr to the lake about 27% of the watershed total. This equates to about 5 EmS that each visitor receives when using Lake Weir for recreational purposes. When Weirs watershed was dominated by citrus, the total EmS/yr for agricultural production was lower than the inputs to the lake -1.23 million EmS for citrus versus 1.28 million EmS in the lake. Use of Dynamic Simulations as Quality Indicators Many schemes for assessing lake trophic state have been proposed (Carlson, 1977; Reckow, 1980; Huber et al., 1982). Most rely on long-term averages of several abiotic factors such as total nitrogen and phosphorus in the water column and algal

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189 biomass. Those using logarithm transformations to normalize data build in a resistance to fluctuations. Simulations using the main trophic levels within the lake and nonnaIiU
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190 Observed recreational use for each lake was inversely proportional to emergy and water quality values. This is expected based on Americans' preference for clear recreational water. Fishing incidence was not separated from the total user incidence, and implications from accessibility differences between lakes were not considered. Evidence for Watershed Intervention and Prioritization Emergy values discussed in Chapter 1 are presented here in comparison to empirical water quality measures for each lake, both to provide a ranking for the overall watershed and the lakes and to assess the emergy required for organization within each watershed. Intrawatershed Modification of Non-point Source Loading Because of its spatial specificity, the water and phosphorus budget model can be used as a development planning tooL Several features make this spatial model different from its predecessors. It uses deposition and allows unique characteristics of each area to determine mass balance and export, as opposed to export coefficients averaged over several landscape and geology features (Adamus and Bergman, 1995; Heidtke and Auer, 1993). Further, it estimates total runoff that a given area will export to the lake. In other words, it does not estimate the amount leaving the cell, but rather the actual amount reaching the lake after additional infiltration occurs along the way. This allows identification of those areas contributing the greatest amount of runoff and directs attention to areas needing major remediation. Two methods for siting intervention areas appeared effective in reducing overall simulated watershed phosphorus loads to lakes. Material convergence used drainage

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191 networks and the point of highest cumulative loads in the watershed to select areas for conversion to open ponds and high uptake wetlands. Emergy concentration was determined from cumulative emergy from phosphorus loads and the associated changing emergy per mass ratios. Using the material export method to determine intervention siting in the watershed required less area than the emergy method. However. most of the. remediation had to be in areas immediately adjacent to the lake. This required changes in zoning for lakefront property in order to be effective. Further, because this zone is subject to flooding during high water periods, the long-term benefit may be lost as material is transported to the lake. This interaction was not built into the model. Cumulative emergy simulations identified areas exporting phosphorus at transformities considered inappropriate for each lake. When watershed areas with emergy per mass ratios for runoff phosphorus higher than the highest concentrated phosphorus deposited in the watershed were blocked. the resulting phosphorus load matched pre-development loads. The emergy method required that more watershed area be converted to intervention sites. However. most are small areas scattered throughout the watershed. This has the advantage of decreasing the burden on individual landowners, and organizes the watershed in a way closer to the distribution of smaller wetlands normally found in undisturbed watersheds. Interwatershed Priorities Decisions regarding allocation of funding for reclamation efforts among lakes can be assisted using the baseline parameters calculated in this study. A watershed either with baseline loading indicative of a highly productive lake or with watershed changes

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192 not resulting in large changes in loading may not be as responsive to in-lake or watershed remediation as one with lower baseline loading. Comparison of emergy values both pre and post-development suggests that Lake Weir would benefit more from remediation efforts. Recommendations Non-point Source Inputs Regardless of the baseline level oflake productivity, non-point source phosphorus inputs will contribute to increasing eutrophication. Consequently, cessation of these inputs may eventually lead to a lower trophic state. Phosphorus, unlike water, shows a much higher level of change with development, both in quantity and emergy transformity in both watersheds. Its influence would be expected to be substantial based on emergy values alone. Lake Weir, without stream inputs, receives most of its water and nutrients as non point input from the watershed. Consequently, the change in watershed loading has had a significant impact. Although Newnans receives 90% more phosphorus from its stream inputs than from the watershed, it is unlikely that the natural source of this input has changed significantly over time. Therefore, the lake has organized around this input, and changes in the smaller non-point source loads have likely contributed to the rising trophic state. This study does not determine conclusively whether in-lake resuspension or watershed loading is the dominant factor in shallow lake function.

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193 Development Density There is a different level of development in given areas of a watershed that will maintain desired trophic status of a lake, and can be viewed as the trophic carrying capacity for its individual watershed. Further, the lag time between changes in development and eventual changes in trophic state can be viewed as the turnover time for the entire lake. The lake has organized around this level of inputs and will have a tendency to remain at that level until a certain level of change in its watershed is reached. However, the capacity cannot be given as a simple percentage of development, or at least cannot be determined with a study of only two lakes. Newnans, with 19.6% of the watershed developed, fanned or cleared in 1950 and 19.1% in 1990, has demonstrated an increase in eutrophication. Weir, with 80% cleared, farmed or developed by 1950 and 81% in 1990, experienced an anecdotal rise in eutrophication early in the century. However, between 1980 and now, the overall trophic state index of Lake Weir has dropped. Both of these water quality changes are in the opposite direction of development in the watershed, but they exhibit the same trend as simulated TP runoff. This would support the original hypothesis that watershed contributions to the lake are not solely dependent upon land use density. but rather are dependent on land use in a unique combination with soil and elevation. Appropriate Scale for Storm water Management Water drainage to a lake is determined more by larger scale geologic patterns than by small developmental changes in a watershed. Consequently, stonnwater should be managed at a watershed scale, not only at points of development.

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194 This is supported by the low level of change in both overall quantity delivered to the lake and the transfonnity. Water effects changed less than 2% over the long term in both watersheds despite very different impervious surface development. This may not be true in highly urbanized watersheds, and the control that point-of entry may have on patchy phytoplankton populations is one area of spatial organization not addressed in this study. Water management districts in Florida mandated on-site retention for storm water in 1984 (Adamus and Bergman, 1995), and about 2% ofNewnans' watershed was put into ponds between 1970 and 1990. However, as demonstrated in the model, stormwater runoffhas continued to increase. This is due to the fact that the majority of the water increase is to the far northwest of the watershed and lies outside the effective watershed delineated in this study. The establishment of watershed retention mitigation banks would insure both fair representation from all developers and more effective intervention expenditures. Use ofEmergy as a Management Decision Tool Comparison of pre-development to post-development emergy ratios of lake to watershed flows (or regional flows) suggests that priority for reclamation funds should be used for Lake Weir, if lower productivity in the lake is the primary cultural goaL If maximizing empower in the lake is a priority maximizing protein or blue-green production for commercial purposes, for example then current export of watershed emergy to the lake is a good strategy. However, holding rain and nutrients on land may maximize empower in the watershed leading to reduced trophic status in the lake and higher real wealth. in a larger region.

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195 Newnans phosphorus loading from the simulation ofhigber development is about 10% more than the baseline loading, while Lake Weir appears to be receiving 2000/0 more phosphorus in this half of the century than prior to clearing and cultivation. Cessation of Weir's non-point source runoff, combined with higher depth, might be expected to lead to meaningful reductions in algal biomass and TP in the water column. Further, Newnans Lake emergy flow is about 8% of the total watershed emergy, while Lake Weir's emergy is almost 30% of its watershed flow. Conclusion This qualitative watershed time-series study lays the groundwork for reclamation science at a watershed scale, not in a restorative sense, but in creating new larger scale remediation patterns in response to development pressures. The synthesis of dynamic emergy created by geologic, natural and human patterns in the watershed is useful in demonstrating the strong connection between watersheds and Florida lakes, regardless of their depth. Mapping phosphorus transfonnities results in a hierarchy of small and mid-sized areas of higher concentration that can be useful as retention areas on land, similar to the natural hierarchy of wetlands formed in many Florida watersheds. This is undoubtedly a modified network from a pre-development era, however it illustrates a way to incorporate the same ecological principles of spatial organization into watershed management. This study raises some questions about adaptive system strategies at the watershed scale. If water and nutrients are retained on land at the points of emergy convergence, what will new total watershed productivity be? Is this higher than the productivity and emergy with current export to the lake? If increased fish protein

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196 (regardless of species) is a higher priority than clear water,. would use of the fish at a watershed scale show that the current luxury loading of phosphorus to these systems is maximizing overall empower? This study demonstrates that using only the annual loading of nutrients to a lake as a management criterion may overlook the effect of time lags in developing certain watershed areas. Annual loads may also minimize the effect of cumulative loads from increasing overland input,. especially when channeled input is much larger. Results support the concept of purchasing strategic areas for retroactive rather than putting all intervention efforts into on-site retention. Tracking the overland process by which runoff travels to the lake is an important management tool,. and involves more than just the soil underlying the immediate contributing structure.

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APPENDIX A GIS INFORMATION

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Land Use I Soil Type TP Deposition Rain rt Lt Impervious Surface Elevation ... ... Water Runoff ,... ,... rt Soil Capacity r. r. TP runoff Rain rt Abstraction ... ... Soil --.. --.. Pen:1eability Lot Soil ... TP adsorption Clay --.. D Calculated map Figure A.I: A flow chart of the main steps in calculating the material balances around each cell, .... \0 00

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APPENDIXB SOIL DATA

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Table B, 1, Soil data, Newnans Lake, Soil Name Alachua Code Hydrology Depth to Clay % erosion organic % perm/capac K change Candler Fine Sand 2B,2C,47B A >82 <3 0,1 82 3. 13 0,15 1 Apopka Sand 6B,6C A 61 0,3 0,1 <2 Kanapaha sand, 0% 7B D 44 2-6 0,1 ,5-4 Millhopper Sand 8B, 8C,9B, 45 A 64 2 0,1 ,5 Riviera sand 11 D 32 1-6 0.1 ,1 Pelham sand 13 D 27 1 0,1 1 Pomona sand 14,25 D 43 1-6 0,1 1 Pompano Sand IS D?(A) >82 <5 0,1 1 Surrency sand 16 D 44 <10 0,1 1 Wauchula sand 17,18 D 28 <2 0,1 1 Monteocha loam sand 19 D 48 1 0,15 5 Tavares sand, 0-5% 20B A >82 0-4 0,1 ,5 Newnan sand 21 C 59 <5 0,1 1 Floridana sand, depressional 22 D 30 3 0,1 6 Mulatsand 23 D 26 2 0,1 1-4 Samsula muck 26 D 35 0 >20 Urban 27 Chipley sand 28 C?(A) >82 1 0,1 2 Lochloosa f, sand 29B,29C C 35 2 0,1 1-4 Kendrick sand 30B,30C A 24 1 0.1 <2 Blichton sand 31A, B, C,44B D 30 2 0,15 1-4 Bivans sand 32B,C,D D 10 3 0,1 1-4 Norfolk loamy f, Sand 33B,C B 9 2 0,2 ,5-2 Placid sand, depressional 34 D?(A) >82 <10 0,1 2 N Gainesville sand 35B,C A >82 4 0,15 2-4 8 Arrents. 0-5% 36

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B,t Soil Name Alachua Code Hydrology Depth to Clay % erosion organic % perm/capac K change Zolfo sand 37 C 60 1 0.1 .SI pits&' dwnps 38 BoIUle8u f. sand, 2% 39B A 29 2-8 0.15 .5.2 Pedro f. sand, 0% 4)B C ) 0.1 .5.2 Pedro-Jonesville, 0% 42B B 29 lS 0.1 .5 Jonesville-Cadillac-.Bonneau, 0% 46B A 29 2 0.1 .5 Myakka sand 48 D 24 <2 0.1 <2 Lochloosa f.sand, 0% 49A C 44 2 0.) 1-4 Sparr f. sand 50 C 48 ).5 0.1 <3 PIWlUller f. sand 51 D 42 1 0.1 1 Ledwith muck 52 D )7 0 0 30 Shenksmuck S3 D 21 0 0 >20 Emerelda f. sandy loam 54 D 18 6 0.)5 3)0 Lake sand, 0% SSB,S8B A >82 ) 0.) .5.) Wauberg sand 56 D 24 1)2 0.)5 )-4 Micanopy loamy f. sand, 2% S7B C 12 0 0.15 ).5 Poltsburg sand 59 D 52 <5 0.) <3 Udorthents, 0% 60 C 0 2 0.32 .5 Oleno clay, occ. flooded 61 D 0 46 0.37 1 Boardman loamy sand, 5% 62C D )4 1 0.15 <1 Tena Ceia muck 63 D >68 0 0 >60 Okkechobee muck 64 D >80 0 0 >60 Martel sandy clay loam 65 D )6 )5 0.32 )-6 Lynne sand 66 D 29 ) 0.) 1 Wacahoota loomy sand, 5% 67C D 32 ) 0.15 2-4 N 0 ,...

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Table B.2. Hydrologic capacity data Hydrologic Grouping A B C D Permeability Capacity Range Range inIhr infm 6-20 6-20 .2-20 .02-20 .03-.4 202

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Table B.3. Determination of impedance to water transport through spatial cells Alachua Code % infiltrate' % infiltrate2 % capacityJ % capacity4 confining layer Impedance Factof Impedance Factor? 2 YrStorm 100 Yr Storm 2 YrStorm 100 YrStorm YIN 2 YrStorm 100 YrStorm 2B, 2C, 47B 100 100 67 48 N 0.67 0.48 3B, 3C, 4B 100 100 82 ,58 Y 0.82 0,,58 ,5B 100 100 100 100 N 1.00 1.00 6B,6C 100 100 61 43 Y 0.61 0.43 7B 67 47 44 31 Y 0.29 0.1,5 8B, 8C,9B, 4,5 100 100 97 69 Y 0,97 0.69 11 100 100 ,53 37 Y 0,,53 0.37 13 100 100 36 25 Y 0.36 0,25 14,25 100 100 19 13 Y 0,19 0.13 1,5 100 100 100 100 N 1.00 1.00 16 67 47 79 S6 Y 0.53 0.26 17,18 67 47 2,5 18 Y 0.17 0.09 19 67 47 60 42 Y 0.40 0.20 20B 100 100 100 100 N 1.00 1.00 21 67 47 21 15 Y 0.14 0.07 22 100 100 73 52 Y 0,73 0,52 23 67 47 87 61 Y 0,58 0,29 26 100 100 100 100 Y 1.00 1.00 27 20 14 Y 0,20 0.14 28 100 100 100 100 N 1.00 1.00 29B,29C 67 47 52 36 Y 0.35 0.17 30B,30e 100 100 43 31 Y 0.43 0.31 31A, B, e,44B 100 100 40 28 Y 0,40 0.28 32B,C,D 20 14 SO 35 Y 0.10 0.05 33B,C 67 47 18 13 Y 0.12 0.06 34 100 100 100 100 N 1.00 1.00 35B,C 100 100 100 100 N 1.00 1.00 S 36 20 14 Y 0.20 0.14

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Table B,3 cQnlinued Alachua Code % infiltrate I % infiltrate2 %capacity3 %capacity4 confining layer Impedance Factof Impedance Factor' 2 YrStorm 100 YrStorm 2 YrStorm 100 YrStorm YIN 2 YrStorm 100 YrStorm 37 100 100 100 93 Y 1.00 0.93 38 100 100 Y 1.00 1.00 39B 67 47 39 27 Y 0.26 0.13 41B 100 100 36 25 Y 0.36 0.25 42B 100 100 36 25 Y 0.36 0.25 46B 100 100 39 27 Y 0.39 0.27 48 laO laO 16 11 Y 0.16 0.11 49A 67 47 57 40 Y 0.38 0.19 50 100 }OO 88 62 Y 0.88 0.62 51 67 47 42 30 Y 0.28 0.14 52 20 14 75 53 Y 0.15 0.07 53 100 100 100 100 Y 1.00 1.00 54 100 100 63 45 Y 0.63 0.45 55B,58B 100 100 100 100 N 1.00 1.00 56 100 100 30 21 Y 0.30 0.21 578 20 14 30 21 Y 0.06 0.03 59 100 100 52 37 Y 0.52 0.37 60 2 1 100 100 Y 0.02 0.01 61 2 1 100 100 Y 0.02 O.O} 62C 20 14 23 16 Y 0.05 0.02 63 100 100 100 100 N 1.00 1.00 64 100 100 100 100 N 1.00 1.00 65 20 14 80 56 Y 0.16 0.08 66 100 100 28 20 Y 0.28 0.20 67C 100 100 60 43 Y 0.60 0.43 1. The percentage of the average rain intensity in the tirst hour of a two year event that infiltrates based only on permeability. N 2. The percentage of the average rain intensity in the fmlt hour of a 100 year event that infiltrates based only on permeability.

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Table B.3 continyed 3. The percentage of the average rain intensity in the first hour of a two year event that could be contained based only on capacity. 4. The percentage of the avemge rain intensity in the first hour of II 100 year event that could be contained based only on capacity. 5. A layer is considered confining if permeability drops below O.2in1hr 6. Fraction in column one multiplied by fmction in column three. 7. Fraction in cohunn two multipUed by fraction in column four.

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APPENDIXC VERIFICATION OF SPATIAL MODEL A pilot study evaluating the relationship between non-point source phosphorus runoff and phytoplankton productivity and populations in lakes was conducted in 1995 as a project for a graduate GIS class in the Department of Environmental Engineering Sciences, University ofFioridL The abstract for this project is presented below. For further verification of the spatial simulation values, runoff for each watershed was estimated using the SCS Abstraction Method.. Newnans Lake simulations are within 8% of SCS estimates (Table C.I). Lake Weir simulations result are approximately twice the SCS estimates (Table C2). Land Use Analysis of Potential Non-point Source Phosphate Runoff into Seven Eutrophic Florida Lakes Using a Geographical Information System This project is a preliminary evaluation of the methods best suited for detailed GIS analysis of spatial factors affecting nutrient availability within a the probability of these nutrients reaching the lake and the correlation of predicted phosphate loading with measures of phytoplankton present within the lake. Forthe sake of initial simplification, the scope of this study has been limited to estimated phosphate runoff from aggregated categories of human land use. Seven eutrophic lakes receiving sewage 206

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207 input in central Florida were analyzed using Mapll, a GIS software application for the Macintosh. Land use, elevation and rainfall were the base maps for runoff estimation. Regressing the number of taxa versus combined amounts of developed areas resulted in a significant but negative relationship (r = 0.7439). One month load using rainfall from the month immediately prior to collection of data showed a significant and positive corre1ation with chlorophyll concentrations (r 0.946). Annual loads regressed versus Huber et aL (1982) trophic state indices also resulted in a significant and positive correlation (M.725). Regression of land use alone did not show a correlation with either chlorophyll or trophic state indices. Point-source TP loadings did not correlate with trophic state indices, either.

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Table C.I. Soil Conservation Service abstraction method calculations to determine expected runoff, Newnans Lake. Hl:droJosic Soil Groue A B C D Land Use % CN Product % CN Product % CN Product % CN Product Cultivated 0.12 72 8.62 0,00 81 0,00 0,22 88 19,55 0,03 91 2,57 Range 4,98 68 338,77 0,01 79 0,57 3.15 86 270,90 7,19 89 640,06 Forest 7,96 35 278,63 0,00 60 0,09 14,35 75 1076,31 42,94 80 3435.17 Commercial, Urban 1.38 89 122,86 0,00 92 0,00 0,29 94 27,44 0,33 95 31,12 Industrial 0,01 81 0,48 0,00 88 0,00 0,00 91 0,00 0,02 93 1.45 Residential 0,86 51 43,81 0,00 68 0,01 0,22 79 17,49 0,66 84 55,67 Pavement 95 0,00 95 0,00 95 0,00 95 0,00 HeroWL 0,06 30 1.93 0,00 58 0,00 0.15 71 10,45 0,48 78 37,80 ForestWL 0,47 25 11.84 0,00 55 0,00 1.31 70 91.91 12,80 77 985,51 15,85 806,93 0.01 0,67 19,70 1514,06 64,45 5189,34 Summa!! Calculations Weighted CN = 75.11 s= 3,31 in Pe= 0,66 in Tpe= 13,63 in Runoff.=: 2,OE+08 m3 Total Rain= 7,7E+08 % Runoff 26,2% All stonn events are considered to be typical 2 year one hour events for the region, The total average rain for the year is divided by this number to delennine the number of stonn events, and is used to delennine Tpe, i

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Table C.2. Soil Conservation Service abstraction method calculations to determine expected runoff, Lake Weir. Soil A B C D Land Use % CN Product % CN Product % CN Product % CN Product Cultivated 19.50 72 1403.83 0.00 81 0.13 0.63 88 55.62 0.12 91 10,95 Range 55.39 68 3766.54 0.06 79 4.66 2,37 86 203,79 0,77 89 68,61 Forest 12,20 35 426.99 0.01 60 0.71 0,95 75 71,49 1,01 80 80.53 Commercial, Urban 0,01 89 1.09 0.00 92 0.00 0,00 94 0,00 0,00 95 0,00 Industrial 0.00 81 0.00 0,00 88 0,00 0,00 91 0,00 0.00 93 0,00 Residential 2.57 51 131.28 0.01 68 0.40 0.40 79 31.23 0,29 84 24,01 Pavement 1.17 95 110.76 0.00 95 0.00 0,07 95 6,83 0,05 95 4,64 HerbWL 0,34 30 10.07 0.00 58 0,00 0,13 71 9,22 0,93 78 72,37 ForestWL 0.12 25 3.05 0,00 55 0.00 0,05 70 3,23 0,86 77 66,34 5853.60 5,91 381.42 327,45 Summ!!l: Calculations Weighted CN = 65,68 S= 5.22 in Pe= 0.32 in Tpe= 6.59 in Runoff= 2.0E+07 m3 Total Rain= 1.6E+08 m3 % Runoff 12.68% All stonn events are considered to be typical 2 year one hour events for the region. The total average rain for the year is divided by this number to N detennine the number of stonn events, and is used to detennine Tpe.

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APPENDIXD EMPOWER DENSITY EVALUATIONS

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211 Table D.L Emetgy evaluation of Bahia perba per year. Unit Solar Solar EmS Data EMERGY EMERGY Value Note Item Unit (unitsfyr) (sejlunit) (E13 sejlyr) (1989 $Iyr) RENEW ABLE RESOURCES I Sun J 5.93E+13 I 6 36 2 Rain J 630E+1O L80E+04 Il3 696 3 Et J 5.43E+1O I 54E+04 84 513 NONRENEWABLE STORAGES 4 Net Topsoil Loss J 633E+07 738E+04 0 2 Sum offtee inputs 84 516 PURCHASED INPUTS Operational inputs 5 Fuel J 2.82E+06 6.60E+04 0 0 6 Electricity J 222E+08 L60E+05 4 22 7 Potash gK 3.63E+04 LIOE+09 4 25 8 Lime g 3.73E+05 I.OOE+09 37 229 9 Pesticides g O.OOE+OO 150E+1O 0 0 10 Phosphate gP 738E+03 220E+1O 16 100 II Nitrogen gN 1.55E+04 2.4 IE+ 10 37 229 12 Labor I 6.79E+06 8.IOE+04 0 0 13 Services S 224E+OI L63E+12 4 22 Sum of purehased inputs 102 626 TRANSFORMITIES 14 Total dry g 3.63E+06 5.12E+08 186 Il42 15 I 6.88E+IO 2.7IE+04 Indices Note Name of Index Expression Quantity 16 Investment Ratio (p + S)/(N + R) 12 17 Yield Ratio Y/(p+S) 1.82 18 Emergy exchange ratio Y/S (SEIlS) NA 19 Emdollar Contribution to State (ba in production)*(y !ha)/(SEI/S) 5.98E+08 20 NonrenewablelRenewable (N+P)/R 12 21 Empower Density sejlhalyr L86E+15

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Table D.l-continued Notes, Table D.l 1 2 Sun,S Annual energy = Insolation: Area: Albedo: Annual energy: Rain,S Annual energy = iofyr: runoff coefficient: Annual energy: (Avg. Total AnImal Inwlation IIyrXArea)(l-aJbedo) 6.90E+09 Ifm2/yr (VISImer 1954) 1.00E+04 m2 0.14 Odum 1987) 5.93E+13 ( infyr)(Area)(0.0254 mrm)(1E6gfmJ)(4.94IfgXl runoft) 54 10000 7.00E-02 (AFSIRS Smajstda, 1990) 6.3E+I0 3 Evapotranspiration, S Annual energy = (IfacreX2.47acre1ba)(area) Ifacre: 220E+I0 (AFSIRS estimate. Smajstrla. 1990) 1 Annual energy: 4 Net ToplOit Lou, S Erosionrate = % organic in soil = Energy cont/g organic 5.43E+I0 7 g/m2fyr 0.04 5.40 kcallg Net loss oftopsoil = (fanned area)(erosion rate) [estim. 1995] [Pimentel et al., 1995. p.1118J Organic matter in topsoil used up= (total mass oftopsoil)(% organic) Energy loss= (loss of organic matter)(5.4 kcaVg)(4l86 Ilkcal) Annual energy: 6.33E+07 5 S per ha (includes diesel. lubricants) (gallons fuel) (1.51ES I/gal) Gallons: 1.87E+O 1 F AECM data (Fluck. 1992 ) Annual energy: 2.82E+06 6 Electricity, S KWh*3.6E6 IIKWh KWh: 6.15E+Ol FAECMdata(Fluck.1992) Annual energy: 222E+08 7 Potash, g K per ha (g fertilizer active ingredient)(78 gmol K/94 gmol K20) g: 4.38E+04 F AECM data (Fluck. 1992 ) Annual consumption: 3.63E+04 8 Lime, g per ha Annual coosumptiOD,. 3.73E+05 FAECM data (Fluck. 1992) 9 Pesticides, g per ha (includes pesticides. berbicides) 212

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Table D.l-continued Annualcomumptio,. O.OOE-fOO 1992) 10 Pbosphate,gPperba (g fertilizer active ingn:die1lt)(31 gmol P/132 gmol DAP) g: 3.14E+04 1992) Annual consumption: 7.38E+03 11 Nitrogm, g N per ha (g fertilizer active ingn:dielltX28 gmol Nl132 gmol DAP) g: 7.29E+04 FAECM data 1992 ) Annual consumption: L55E+04 12 Labor," (pers-homsIhafyr)*(3500 kcallday)*(4186J/Cal) / (8 pelS-hrsIday) pers-hours: 3.7lE-fOO FAECM data 1992) Annual energy: 6.79E+06 Trausformity: 8.lOE+04 (uneducated and Odum 1983) 13 Services, $ per ba SIyr: 2.24E+OI F AECM data 1992 ) Annual emergy ($ !yrXsejl$) 14 Yaeld -3240 Ib dry/acre FAECM data (Fluck. (992) DIyweigbt= 3.63E+06 g 15 Product ia "oates 18% protein. 3% fat. 7)0/0 (Pillsbwy 1993) Energy colltellt = 6.88E+I0 J 16 Investllleat Ratio P =[tems 5 +6+7+8+9+ 10 + II S= Items 12 + 13 N=Item4 R=Item3 17 Yaeid Ratio Y=Items 3 +4+5 +6+7 +8+9+ 10 + II + 12+ 13 L8 Emergy escbaaF ratio -NA F AECM data (Fluck, 1992) $. tota1Jba = NA 19 EmdoUar CoDtrihutio. to State hainproduction: 5.23E+05 FAECM data (Fluck, 1992) 20 NonrenewableJReaewable, See Note 16 21 Empower Deasity -sum of emergy per hectare peryear 213

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214 Table D2. Emergy Evaluation of Oranges, per ha. per year. Unit Solar Solar EmS Data EMERGY EMERGY Value Note Item Unit (unitslyr) (sej/unit) (El3 sej/yr) (l9S4$1yr) RENEW ABLE RESOURCES 1 Sun I 5.93E+13 1 6 25 2 Rain I 6.30E+1O LSOE+04 113 473 3 Et I 651E+1O 154E+04 100 418 NONRENEWABLE STORAGES 4 Net Topsoil Loss J 7.38E+04 5 21 Sum offtee inputs 105 439 PURCHASED INPUTS Operational inputs 5 Fuel I 228E+07 6.60E+04 0 1 6 Electricity I 4.68E+08 L60E+05 7 31 7 Potash gK 2.36E+05 LI0E+09 26 108 S Lime g 2.40E+05 LOOE+09 24 100 9 Pesticides g L79E+04 150E+IO 27 112 10 Phosphate gP LI2E+04 220E+IO 25 103 11 Nitrogen gN 3.0IE+04 2.41E+1O 73 302 12 Labor I 3.79E+OS S.10E+04 3 13 13 Services S 3.01E+02 2.40E+12 72 301 Sum of purchased inputs 257 1071 TRANSFORMlTlES 14 Total Yield, dry g 4.91E+06 731E+OS 362 1510 15 I 8.65E+IO 4. 19E+04 Indices Note Name of Index Expression Quantity 16 Investment Ratio (P + S)/(N + R) 2 17 Yield Ratio Y/(p+S) L41 18 Emergy exchange ratio Y/S (SEI/$) 03 19 EmdoUar Contribution to State (ha in production)*(yJba)l(SEI/S) 339E+08 20 NonrenewablelRenewable (N+P)/R 2 21 Empower Density sej/halyr 3.62E+15

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Table D.2 -continued Notes, Table Dol 1 2 San,J Annual energy = Insolation: Area: Albedo: Annual energy: Rain,J (Avg. Total Amwal Insolation JIyr)(Area){I-albedo) J/m2Iyr (VlShner 1954) LOOE+04 m2 0.14 Odum 1987) 5.93E+13 Annual energy = ( inIyr)(AIea)(0.0254 mfm)(lE6gfmJ)(4_94J/g)(l runoff) 54 Area, m2: 10000 runoff coefficient: 7.00E-02 (AFSIRS estimate. Smajstrla. 1990) Annual energy: 6.3E+I0 3 Annual energy = (J/acre)(2.47acreJbaXarea) Jfacre: 2.63E+I0 (AFSIRS estimate. Smajstrla. 1990) Area, ba: 1 Annual energy: 6.5lE+1O 4 Net TopllOU Loss, J Erosion mte = % organic in soil = Energy cont./g organic 70 gfm2fyr 0.04 5.40 kcalfg Net loss of topsoil = (fanned area)(erosionl3te) [estim.fromPimenteletal 1995] [Pimenteletal., 1995.p.l118] Organic matter in topsoil used up= (total mass of topsoil)(% organic) Energy loss= (loss of organic matter)(5.4 kcalfg)(4186 J/kcal) Annual energy: 5 Fuel. J per ha (includes diesel. gasoline. lubricants) (gallons fuel) (L5lES J/gal) Gallons: FAECMdata (Fluck. 1992) Annual energy: 6 Electrlcity,J KWh*3.6E6 J/KWh KWh: FAECMdata(Fluck.1992) Annual energy: 7 Potash, g K per ha (g fertilizer active ingredieot)(78 gmol Kl94 gmol K(0) g: F AECM data (Fluck. 1992 ) Annual consumption: 8 Lime, g per ha Annual consumption.. F AECM data (Fluck. 1992 ) 9 Pesticides, g per ha (includes pesticides. fungicides. herbicides) 215

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Table 0.2 -continued Ammal L79E-+04 FAECMdata (F1uck. 1992) 10 Phosphate, gP per'" (g fertilizer active ingredieDt)(31 gmol PI132 gmol DAP) g: 4.79E-+04 FAECMdata (Fluck. 1992) Ammal consumption: Ll2E-+04 II Nitrogen, g N per ha (g fertilizer active iDgredieDl)(28 gmol N/132 gmol DAP) g: L42E-+05 F AECM data (Fluck. 1992 ) Ammal consumption: 3.0 lE-+04 12 Labor,J (pers-homslbafyr)*(3SOO Ia:allday)*(4186J/Cal) / (8 pelSbrsIday) pers-hours: 2.07E-+02 F AECM data (Fluck, 1992 ) Ammal energy: 3.79E-+08 Traosformity: 8.10E-+04 (uneducated and Odum 1983) 13 Services, 5 per ha 51yr: 3.0lE-+02 FAECMdata(FIuck, 1992) Ammal emergy (5 Iyr)(sej/$) 14 Yidd 43785 Ib dry/acre F AECM data (Fluck. 1992) DIy weight = 4.9lE+06 gIba 15 Product ill Joules 8.6% protein. 9 L4% caJbohydrak (paul and Southgate. 1978) Energy content = 8.6SE+IO 1 16 IavestJDaat Ratio P = Items 5 + 6 + 7 + 8 + 9 + 10 + II S= Items 12 + 13 N=Item4 R=Item3 17 Yield Ratio Y=Items 3 +4+5 +6+7 +8 +9+ 10 + II + 12+ 13 18 Emergy exdluge rat 1984 S1750/acre F AECM data (Fluck. 1992) S. totallha. = 432E-+03 216 1994 $O.221lb tiesh (est. from FL Statistical AbstIact 1994) S. totallha. = 3.67E+03 19 EmdoUar ContrilMltioa to State 1984 ba in production: 2.2SE+05 F AECM data (Fluck. 1992) 1994 bainproduction: 2.07E+05 FL Statistical Absttact 1994 20 NonrenewablelRenewable, See Note 16 21 Empower Deasity -sum of emergy per hectare per year

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217 Table 03. Emergy evaluation of per ha per year. Unit Solar Solar EmS Data EMERGY EMERGY Value Note Item Unit (units/yr) (sej/unit) (E13 sej/yr) (1989 $lye) RENEWABLE RESOURCES I Sun. J 593E+13 I 6 36 2 Rain J 630E+IO L80E+04 1I3 696 3 Et J 650E+1O I 54E+04 100 614 4 Net Topsoil Loss J 633E+08 738E+04 5 21 Sum offiee inputs omitted) 105 635 PURCHASED INPUTS Operational inputs 5 Fuel J 151E+07 6.60E+04 0 I 6 Electricity J 2.96E+08 L60E+05 5 29 7 Potash gK 7.45E+04 LIOE+09 8 50 8 Lime g 3.73E+05 LOOE+09 37 229 9 Pesticides g 720E+03 150E+1O 11 66 10 Phosphate gP 2.1IE+04 220E+I0 46 284 11 Nitrogen gN 4.88E+04 2.41E+IO 1I8 721 12 Labor J 634E+07 8.lOE+04 I 3 13 Services S 2.1IE+03 L63E+I2 344 2IB Sum of purchased inputs 570 3495 TRANSFORMITIES 14 Total dry g NA 674 4130 15 J NA Indices Note Name of Index Expression Quantity 16 Investment Ratio (P + S)/(N + R) 5 17 Yield Ratio Y/(P +S) L18 18 Emergy exchange ratio YIS (SEllS) NA 19 Emdollar Contribution to State (ha in production)*(y lha)/(SEJI$) 2.0IE+07 20 NonrenewablelRenewable (N+P)IR 2 21 Empower Density sejlhalyr 6.74E+I5

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Table 0.3 -continued Notes, Table B.3 1 2 SaD,S Annual energy = Insolation: Area: Albedo: Annual energy: RaiD,J Annual energy = infyr: runoff coefficient: Annual energy: (Avg. Total AnnuallDsolation JIyr)(Area)(l-a1bedo) 6.90E+09 J/m2fyr (V1Shoer 1954) LOOE+04 m2 0.14 Odum 1987) 5.93E+13 ( infyr)(AIea)(0.0254 mrm)(1E6gfm3)(4.94J/g)(1 nmofI) 54 10000 7J)OE-02 (AFSIRS estimate, Smajstrla. 1990) 6.3E+l0 3 Evapotr.anspUnumoD,J Annual energy = (J/acre)(2.47acre1ha)(area) J/acre: 2.63E+l0 (AFSIRS Smajstrla. 1990) 1 Annual energy: 650E+l0 4 Net ToplOil Lou, S Erosion rate = % organic in soil = Energy cont/g organi< 70 gfm2fyr 0.04 5.40 kcallg Net loss of topsoil = (farmed area)(erosion rate) [estinL from Pimentel et 1995] [Pimentel et al, 1995. p.U18] Organic matter in topsoil used up= (total mass of topsoil)(% organic) Energy loss= (loss of organic matter)(S.4 kcalIg)(4186 J/kca1) Annual energy: 6.33E+08 5 Fuel, S per ha (includes diesel. lubricants) (gallons fuel) (L5lE5 J/gal) Gallons: 9.97E+01 FAECMdata (Fluck. 1992) Annual energy: LS1E+07 6 Electricity, S KWh*3.6E6 JIKWh KWh: 8.2lE+01 FAECMdata (Fluck. 1992) Annual energy: 2.96E+08 7 Potash, g K per ha (g fertilizer active ingredient)(78 gmo1 Kl94 gmol K20) g: 8.97E+04 FAECMdata (Fluck. 1992) Annual consumption: 7.45E+04 8 Lime, g per ha Annual consumption,. 3.73E+oS F AECM data (Fluck. 1992 ) 9 Pesticides, g per ha (includes pesticides. fungicides, helbicides) 218

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Table D.3 continued Ammal comumptio,. 7.20E+03 FAECM data 1992 ) 10 Phosphate, I P per ha (g fertilizer active ingmIiem)(31 gmol PI132 gmol DAP) g: 8.97E-+{)4 F AECM data (Fluck, 1992 ) Ammal comumption: 2.1lE-+{)4 11 Nitrogaa, I N per ha (g fertilizer active iogredient)(28 gmol N/132 gmol DAP) g: 2.30E+05 F AECM data (Fluck, 1992 ) Ammal conguoption: 4.88E-+{)4 12 (pers-hoUJSlhafyr)*0500 kcaVday)*(4186J/CaI) I (8 pelShrsfday) pers-hours: 3-46E+Ol FAECM data (Fluck, 1992) Ammal energy: 6.34E+07 Transformity: 8.10E-+{)4 ( uneducated Iabor.Odum and Odum 1983) 13 Services, S per ha $Iyr: 2.1lE+03 F AECM data (Fluck, 1992 ) 14 Y'leIdDryweigbt= g 15 Product is Energy content = J 16 P = Items 5 +6 +7 +8 + 9 + 10 + 11 s= Items 12 + 13 N=Item4 R=Item3 17 Yield Ratio =Items 3 +4+5 +6+7 +8 +9 + 10 + 11 + 12+ 13 18 Emergy esc ..... ce ratio -F AECM data (Fluck, (992) $. totallba = 19 EmdoUar Contn ..... tioa to State ha in production: 4.85E+03 F AECM data (Fluck, 1992) 20 NoareaewablelRalewable, See Note 16 21 Empower Density -sum of per hectare peryear 219

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220 Table D.4. Emetgy evaluation of per ha per year. Unit Solar Solar EmS Data EMERGY EMERGY Value Note Item Unit (unitsfyr) {sejlunit} (El3 sejlyr) (1989 $/yr) RENEW ABLE RESOURCES 1 Sun I 593E+13 1 6 36 2 Rain I 630E+10 L80E+04 ll3 696 3 Et I 6.15E+lO 1 54E+04 95 581 NONRENEWABLE STORAGES 4 Net Topsoil Loss I L81E+07 738E+04 0 1 Swn offiee inputs (sun, rain omitted) 95 581 PURCHASED INPUTS Operational inputs 5 Fuel I 7.01E+06 6.60E+04 0 0 6 Electricity I 291E+08 L60E+05 5 29 7 Potash gK 3.73E+04 L10E+09 4 25 8 Lime g 3.72E+05 LOOE+09 37 228 9 Pesticides g 7.01E+02 1.50E+10 1 7 10 Phosphate gP L05E+04 220E+10 23 142 II Nitrogen gN 238E+03 2.41E+lO 6 35 12 Labor J L03E+07 8.lOE+04 0 1 13 Services S L48E+02 L63E+12 24 148 Swn of purchased inputs 100 615 TRANSFORMITIES 14 Total Yield, dry g 4.04E+05 4.83E+09 195 ll97 15 J 9.86E+09 198E+05 Indices Note Name of Index Expression Quantity 16 Investment Ratio (p + S)/{N + R) 1 17 Yield Ratio Y/{p+S) 1.95 18 Emergy exchange ratio Y/S {SID IS) 4.1 19 Emdollar Contribution to State (ha in production)*{yIha)/{SEI/S) 3.81E+07 20 NonrenewablelRenewable {N+P)/R 1 21 Empower Density sej/halyr 1.95E+15

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Table D.4-continued Notes, Table D.4 I 2 Sun,J Annual energy = Insolation: Area: AJbedo: Annual energy: Rain,J Annual energy = inIyr: mootr coefficient: Annual energy: Total AnImal IMolation IIyr)(Area)(I-aIbedo) 6.90+09 I/m2Iyr (Vtslmer 1954) 1.00E+04 m2 0.14 Odum 1987) 5.93E+13 ( iDfyr)(Area)(0.0254 mrm)(1E6gfmJ)(4.94I/g)(I-nmofl) 54 10000 7.00E-02 (AFSIRS estimate, 1990) 6.3E+1O 3 Annual energy = (J/acre)(2.47acre1ha)(area) Ilaae: 2.49E+1O (AFSIRS estimate, 1990) I Annual energy: 6.15+10 4 Net Topsoil Loss, J Erosion late = % organic in soil = Energy cout/g organic 2 glm2Iyr 0.04 5.40 kcallg Net loss of topsoil = (fanned area)(erosion rate) [estim. fromPimenteletal., 1995) [Pimentel et aL. 1995, p.III8) Organic matter in topsoil used up= (total mass of topsoil)(% organic) Energy loss= Ooss oforgaoic matter)(5.4 kcallg)(4186 I/kcal) Annual energy: L8lE+07 5 Fuel, J per ha (includes diesel. lubricants) (gallons fuel) (1.5 IRS Ilgal) Gallons: 4.65+01 FAECM data (Fluck. 1992 ) Annual energy: 7.01E+06 6 Electricity, J KWh*3.6E6 IIKWh KWh: 8.25+01 FAECM data (Fluck. 1992) Annual energy: 2.91+08 7 Potash, g K per ha (g fertilizer active ingredieot)(78 gmol Kl94 gmol K20) g: 4.49E+04 FAECM data (Fluck. 1992 ) Annual comumption: 3.73E+04 8 g per ha Annual comumption.. 3.72E+05 F AECM data (Fluck. 1992 ) 9 Pesticides, g per ha (includes pesticides, fungicides, helbicides) Annual collSUJllPlion.. 7.01+02 F AECM data (Fluck. 1992 ) 221

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Table D.4-continued 10 Phosp'" & P perha (g fertilizer active iDgIedient)(31 gmol P/132 gmoIOAP) g: 4.49E+04 FAECM data 1992 ) Annual conmmption: L05+04 11 Nitro ... g N per ha (g fertilizer active ingredient)(28 gmol N/132 gmolOAP) g: Ll2E+04 F AECM data (Fluck,. 1992 ) Annual conmmption: 12 Labor,S (pers-howslbafyr)*(3500 kcaVday)*(418611Cal) / (8 peIS-brsIday) pers-hours: 5.61E-+OO FAECM data (Fluck,. 1992) Annual energy: L03E-+07 Transfonoity: 8.10E+04 ( unafucated.labor.Odum and Odum 1983) 13 Senrices, $ per ha $Iyr. L48E-+02 FAECM data (Fluck. 1992) Annual emergy ($/yr)(sej/$) 14 Yield 20 BU/acre F AECM data. (Fluck. 1992).60 lb",u (USFDA. 19) 70% water Oryweigbt= 15 Product in Soules (Steteos LivsmedelsverJe. 1988) 4.04E-+05 g 4()O/o 21% fat. 39% carbol (SteteDS UvsmedelsverJe. 1988) Energy content = 9.86E-+09 J 16 Investment Ratio P=Items 5 +6+7 +8+9 + 10+ 11 S= Items 12 + 13 N=Item4 R=Item3 17 Yield Ratio y = Items 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 18 Emergy exchange ratio $117/acnF AECM data (Fluck. 1992) $. totallba = 2.89E-+02 19 EmdoUar Coatribution to State hainproduction: 32375 FAECMdata(FIuck. 1992) 20 NonrenewablrJRenewable, See Note 16 21 Empower Density -sum of emergy per hectare peryear 222

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Table D.5. Emergy evaluation of Alachua Co. rural residence,. -o5ba avg. Note Item Unit RENEW ABLE RESOURCES 1 Sun J 2 Rain J Sum of free inputs (sun omitted) PURCHASED INPUTS Opel3tional inputs 3 Gas J 4Electricity J 5 Goods & SeIVices S Sum of purchased inputs Sum of all inputs Data (unitslyr) 2.97E+13 2.6lE+10 2.27E+08 6.18E+10 8.07E+03 Unit Solar Solar EMERGY EMERGY (sejfunit) (E13 sejfyr) 1 3 L80E-+04 47 47 6.60E-+04 2 L60E+05 990 L55E+12 1251 2242 2289 223 EmS Value (1990 Sfyr) 20 313 313 10 6597 8338 14945 15258 EMPOWER DENSITY 4-.58E+16 sejJbalyr Notes. Table 0.5 1 Sun,J Annual energy = Insolation: Area: Albedo: Annual energy: 2 Rain,J Annual energy = inlyr: Area. m2: runoff coefficient: Annual energy: 3 Gas.J (Avg. Total AnnuallDsolation JIyr)(Area)(1-aIbedo) 6.90E+09 J/m2Iyr (VlSbner 1954) 5.00E+03 m2 0.14 0dum1987) 2.97E+13 ( ioIyr)(Area)(0.0254 mrm)(lE6g!m3)(4.941Ig)(1 -runoft) 52 (SJRWMD. 1995) 5.00E+03 (FL Statistical Absttact. 1995) 2.OOE-Ol (SJRWMD.1995) 2.6lE+10 Alachua Co consumption*3.1 perlresideoce Consumption 486 gallperslyr (FL Statistical Abstract, 1995) 4 Electricity, J KWh/pers*3.1 pers*3.6E6 JIKWh KWh/pers: 5.54E+()3 (FL Statistical Abstract, 1995) Annual energy: 6. 18E+l0 5 Goods & Services (500/a of income) Average annual income per residence: 16138

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224 Table D.6. Emergy evaluation incorporated area peryear. Unit Solar Solar Em$Value Data EMERGY EMERGY (E4) Note Item Unit (unitslyr) (sej/unit) (E11 sejlyr) (l990$lyr) RENEW ABLE RESOURCES 1 Sun J L44E+17 U)OE+OO 1 10 2 Rain J 7.93E+13 1.80E+04 14 95 Sum offiee inputs (sun omitted) 14 95 PURCHASED INPUTS Operational inputs 3 Coal J 6.44E+15 4.00E+04 2575 17168 4 Gas J 6.40E+12 6.60E+04 4 28 5 Electricity J O.OOE+OO O.OOE+OO 0 0 6 Goods & Services $ L4lE+09 L55E+12 21857 145714 Sum of purchased inputs 24437 162910 Sum of all inputs 24451 163006 EMPOWER DENSITY LOIE+18 sejlhalyr Notes, Table 0.6 1 Sun,S Annual energy = Insolation: Area: Albedo: Annual energy: 2 Rain,J Annual energy = inIyr: Area. m2: runoff coefficient: Annual energy: 3 Coal.J (Avg. Total Annual Insolation JIyr)(Area)(l-aIbedo) 6.90E+09 J/m2Iyr (VISboer 1954) 2.43E+07 m2 0.14 Odum 1987) L44E+17 (inIyr)(Area)(O.0254 mrm)(lE6gfmJ)(4.94Ifg)(1 -runoft) 52 2.43E+07 (FL Statistical Abstract. 1995) (SJRWMD. 1995) 7.93E+13 (fotal coal purchased for electricity in FUelectricity produced)*GRU production Coal: 6.52E+14 (FL Statistical Abstract. 1995) Electricity: L40E+08 (FL Statistical Abstract. 1995) GRU production L3lE+06 (FL Statistical Abstract. 1995) 4 Gas. J (Alachua Co col1SUlllplionlAlachua Co popuIation)*GainesvilIe population Consumption 486 gallperslyr (FL Statistical Abstract. 1995)

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225 Table D.6 -continued county IS1.596 (FL Statistical 1995) G'vilIe SS075 (FL Statistical Abstract. 1995) 5 Electricity. J Produced within city oot considered an imput 6 Goods k Services (Gross sales in Alachua Col Alachua population)* Gainesville population Gross sales: 3.01E+09 $Iyr

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APPENDIXE WATERSHED-LAKE INTERFACE EMERGY EVALUATIONS

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227 Table E.L Emergy analysis ofNewnm's LakewatersbedJlake interface,. 1990. Unit Solar Solar Note Item Unit Data (unitslyr) EMERGY EMERGY 1970 EMS (sejlunit) (E15sejlyr) (E4 USS) Atmospberic inputs A Insolation I L78E+17 1 178 2 B Wmdshear I 2..61E+14 L50E-W3 391 5 C Rain,. chemical potential I L96E+14 L82E+04 3574 45 o Transpiration emergeots I L03E+12 L54E+04 16 <1 E TP in Rain g 7. 14E+06 2J)OE+06 <1 <1 --Total atmospheric (sun omitted) 3981 50 Watersbed inputs F Stream,. geopotential I L38E+13 L85E-W3 26 <1 G Stream, chemical potential J L60E-W3 L82E+04 <1 <1 H Sediment I 3. 16E+12 7.30E+04 231 3 I RunotI: non-point I L30E+15 6.31E+04 82U8 103 J TP in streams g 3.70E-+09 6. 85E-W9 25318 32 K TP in runoff g 4.28E-+07 6. 85E-W9 293 4 Total watershed 107987 135 Total emergyllakelyr 1U968 140 Total emergylh.tyr 37 Transformities 1 Phytoplankton 6.77E+12 sej/g 2 TP in water column 2.98E+13 sej/g 3 Water 6.33E-W5 sejfJ Notes: TP = total phosphorus A Annual energy = (Avg. Total Annual Insolation IIyr)(Area)(l-albedo) Insolation: 6.90E-+{)9 IIm2fyr (Vishner, 1954) Area: 3.0 lE-+{)7 m2 Albedo: 0.14 (Odum,. 1987) Annual energy: L 78E+17 Ilyr B Wmd mixing energy = (density, kgfm3Xdrag coefficient)(geostrophic wind velocity3,m3/s3)(area) u = wind velocity (m/s) = 3.58 mls

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228 Table E.I continued geostropbic wind velociw = 5.97 mls Energy = 1.3 kgfm3 IE-3 *212.77 m3/sJ 3.14 E7 sly 3.01E7 m2 Energy/yr= 2.61E+14Ilyr C Rain, chemical potential = area.m2)(IE6 gfm3)*G Rain, m 1.32E+OO m 3.01E+07 m2 G,. free energy,. Ilg 4.94E+OO I/g Energy/yr= 1.96E+14Ilyr o Transpiration from emergent and floating macrophytes 142 baeaver (Huber et aL. 1982) 7.30E+1O estimated transpiration (Odum, 1996) E Phosphorous in rain = area rainfall concentration Area = 3.01E+07 m2 Rainfall = 1.4224 mlyr (-52 in,. NOOA, 1995) Concentration = 0.167 g/m3 (Brezonik,. 1969) Annual amount = 7.14E+06 gIyr F Stream,. geopotential,. Ilyr= (flow volume)(densiw)(dhXgravity) Hatchett Creek flow,cfs = 18 cfs (SJRWMD, 1997) db, m = 76 m (Brandt-Williams,. 1999) Energy/yr= 18cfs*0.028317m3/ft3*3.1536E7sec/yr*IE6gfm3*7 1.20E+13 Little Hatchett Creek flow,cfs = 4 cfs (SJRWMD,I997) db, m = 53 m (Brandt-Williams, 1999) Energy/yr = 1.86E+12 I G Stream, chemical potential = (volume flow)(densiW)(G) G = (8.33I/mole/deg)(300"K.)l18 g/mole)*ln[(IE6 S) 1965000] Ilg S, ppm = 5.9 (calculated from turbidity, SJRWMD, 1997) Flow.cfs = 18 cfs Energy/yr = 1.6OE+03 Ilyr H Sediment = (Sediment kglyr)*(IE3 glkg)*(avg. % organic)*(5.4 CaIIg OM)*(4186 IICal) Energy = (2.8E7 kgfyr)*(IE3 g/kg)*(0.5% Organic)*(5.4 CaIIg)*(4186 IICal) = 3.16E+12 Ilyr I Runoff: nonpoint = (volumelyr)(G) = ( Vo1ume.m3)( 4.82 Ilg)(1 E6 gfm3) Volume= 2.70E-+{)8 m3/yr Energylyr = 1.30E+15 J/yr TransformiW = 6.3IE+04 sejlJ TransformiW calculated from spatial simulation orrotal emergy at lake perimeter divided by total volume of water converted ro Ioules

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229 Table E.I continued J Total phosphorus in streams = (volume.cfs)(p ,mg/lXO.02831,m3/ft3X3.1536E1.secIyrXIE-3 glmg)(IE6 Um3) Volume .cfs = L80E-+{)1 cfs (SJRWMD. 1997) Average concentration, mgll 023 mg/l (SJRWMD. 1997) Average TP mass = 3.10E+09 g!yr Traosformi1 = L82E-+{)4 sej/g (Appendix D) K Phosphorous in runoff from spatial model Annual amount = 4.28E-+{)1 glyr Transformi1 = 6.85E+09 sej/g Traosformi1 calculated from spatial simulation of total emergy at lake perimeter divided by total mass of phosphorus Transformities calculated from this analysis 1 g = (avg. chlorophyll a concentration, g/mJ)(lake volume. mJ)(2g phytoplanktonlg Chl a) Avg ChI a = 0.231 glm3 (Huberet al 1982) L65E-+{)1 g 2 TP in g = (avg. TP in mg/L)(Iake volume.m3) Average concentration 0.113 mg/l (Huberetal 1982) Total g 3.16E-+{)6 3 Water. J = (lake volume. m3)(1E6 glmJ)(4.94 JIg) Volume 3.58E+01 m3 (SJRWMD.I997) Energy stored L11E+14 J

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230 Table E2: Emergy analysis of Lake Weirwatershedllake interface, 1990 Unit Solar Solar Note Item Unit Data (unitslyr) EMERGY EMERGY (sej/unit) (E15sej/yr) Renewable Atmospheric iaputs A Insolation J 1.58E+17 I 158 B Windshear J 2.32E+14 1.50E+03 347 C Rain, chemical potential J L74E+14 L82E+04 3171 D Transpiration emergents J L03E+12 1.54E+04 16 E PinRain g 6.34E+06 2.00E+06 0 Total atmospheric (sun omitted) 3534 Watershed inputs F Runoff, non-point J 323E+14 1.86E+04 6000 G Pinrunoff g 3.40E+07 L27E+I0 432 Total watershed 6432 Total elDergyllakelyr 9965 Total emergy/halyr 4 Transfonnities 1 Phytoplankton, g 4.29E+09 2.32E+09 2 TP in water column, J 6.99E+09 L42E+09 3 Water, J 9.46E+14 L05E+04 Notes: A Annual eaergy = (Avg. Total AnIIuallnlOlation .J/yrXAra)(l-aibedo) Insolation: 6.90E+09 J/m2Iyr (VlShner 1954) Area: 2.67E+07 m2 Albedo: 0.14 Odum 1987) Annual energy: L58E+17 JIyr B Wind DliIing energy = (density, kgfDl3)(dng coeftident){geostropbk wind velocity3 .... 3/sl)(ara) u = wind velocity (mls) = 3.58 geostropbic wind velocity = 5.97 Energy = 1.3 kgfmJ ,. lE-3 *212.77 m3/s3 ,. 3.14 E7 sly 2.67E7 m2 Energylyr= 232E+l4 JIyr C Rain, chemical potaatial = (nin .... )(Iake area,ml)(1e6p3)-G Rain. m 132E+OO Lake area. m2 2.67E+07

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Table E.2 continued o m:e JIg Energyfyr= 4.94E+oo L74E+14 Truupintion fro .. eaaeqaat _d .aaoplayUs 14.2 hacover 231 1982) 7.3E10 JIba estimated tmnspirationlba (Odwn 1996) E Phospllor.s ia nia = area rainfall concentJation Area = 2.67E+07 m2 Rainfall = 1.4224 mfyr (-56 NOOA, 1995) ConceutJation = 0.167 g!mJ (Brezonik, 1969) Amwal amount = 6.34E-+{)6 g/yr F Runotr.lIOnpoiat = (vo .... elyrXG) = ( Volume,m3X4.82J/gX1 E6 gfmJ) Volume= 6.7lE+07 mJfyr Energyfyr= 3.23E+14 G Phospllorous ia nmofffrolll spatiallllodd Annual amount = 3.40E+07 g/yr Transfonnity = 1.27E+1O (Brandt-Williams, 1999) Transformities 1 Phytoplukton. -2*Chla 2 TP in water mhunn, J Average concentration o.on mg/l (Huber,et aL 1982) Totalg 2.0lE-+()7 Energy stored 6.99E-+()9 3 Water,J Volume L9lE-+()8 m3 Energy stored 9.46E+l4

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APPENDIXF TRANSFORMlTIES AND EMERGY PER MASS RATIOS Emergy per mass ratios for all phosphorus compounds used in this study are presented in this appendixFigures F. I and F -2 are graphs of emergy per mass ratios relative to concentrations of phosphorus in a water solution. These represent dilution processes. Two kinds of phosphorus solutions were evaluated. To compare the lowest emergy forms of the two components,. the emergy per gram of total solution for rock phosphorus dissolved in rainwater was calculated at increasing concentration of phosphorous in solution (FigureF-l and TableF.l). For comparison with runofffrom industrial or commercial land uses, the emergy for reagent phosphorus was diluted in ground water and used to determine the ratio (Figure F -2 and Table F.2). Conversely, the emergy per gram of phosphorus in both natural and industrial concentrating processes was also evaluated (Figure F.3 and Table F.3). The ocean was considered the natural emergy sink for phosphorus and was given an emergy/gram ratio of one (1). Values from other evaluations for phosphorus were converted to parts per billion and compared to the emergy per gram calculated in each study. 232

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233 .., 1ftIE+06 t IE+04 6: lE+02 e tIl IE+OO 1801 lE+02 lE+03 lE+05 IE+06 IE+07 IE+08 lE-+09 Phosphorous Concentration log ppb P Figure F .1. Phosphorus emergy per gram relative to concentrations of mined rock dissolved in rain water. Table F .1. Phosphorus emergy per gram relative to concentrations of mined rock dissolved in rain water as shown in Figure F .1. ppbP Emergy/gr.un solutionl gPIlH20 Total grams EmergyP2 EmergyH203 1 9.00E+04 0.000001 1000.00000 I L7SE+04 10 9.02E+04 0.00001 1000.00001 L7SE+05 100 9.1SE+04 0.0001 1000.0001 L7SE+06 1000 L08E+05 0.001 1000.001 L7SE+07 10000 2.68E+05 0.01 1000.01 L7SE+08 100000 L87E+()6 0.1 1000.1 L78E+09 1000000 L79E+07 1 1001 L78E+I0 10000000 L76E+oS 10 1010 L78E+ll 100000000 L62E+o9 100 llOO L78E+12 1000000000 S.90E+09 1000 2000 L78E+lJ I. Emergy per gram ofP solution is the smn of emergy per gram in the water and the phosphorous divided by the total grams of solution 2. Emergy/gram of phosphorous is the total grams ofP in solntionmultiplied by the transfonnity of phosphorous in mined rock 3. Emergy in HP in solution is the transfonnity of rain water multiplied by 1 liter 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07 9.00E+07

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234 lE+12 g lE+11 :.g lE+10 i'LE+09 lE+08 :DO lE+07 .2 lE+06 :: it LE+OS i LE+04 .... LE+03 LE+02 LE+Ol lE+OO lE+Ol 1+02 lE+03 lE+04 li-OS 1+06 lE+07 lE+08 IEi-09 Phosphorous Conc:eDbation log ppb P Figure F 2. Phosphorus emergy per gram relative to emergy/mass of industrial reagent in ground water Table F 2. Phosphorus emergy per gram relative to emergy/mass of industrial reagent in ground water as shown in Figure F 2 ppbP Emergy/gram soIutionl g PII H2O Total grams Emergyp2 1 2.07E+OS 0.000001 1000.00000 1 2.06E+06 10 2.26E+OS 0.00001 1000.00001 2.06E+07 100 4. 11E+OS OJ)OOI 1000.0001 2.06Ei-OS 1000 2.26E+06 0.001 1000.001 2.06E+09 10000 2.0SE+07 0.01 1000.01 2.06E+1O 100000 2J)6E+OS 0.1 1000.1 2.06E+n 1000000 2.0SE+09 1 1001 2.06E+12 10000000 2.04E+I0 10 1010 2.06E+13 100000000 l.S7E+11 100 noo 2.06E+14 1000000000 L03E+12 1000 2000 2.06E+1S EmergyH203 2.0SEi-OS 2.0SEi-OS 2.0SEi-OS 2.0SE+OS 2.0SEi-OS 2.0SEi-08 2.0SEi-OS 2.0SEi-OS 2.0SEi-OS 2.0SE+OS I. Emergy per gram ofP solution is the smn of emergy per gram in the water and the phosphorous divided by the total grams of solution 2. Emergy/gram of phosphorous is the total grams ofP in solution multiplied by the transformity of phosphorous in 100% COlllJllC[Cial grade reagent (see Appendix F) 3. Emergy in HP in solution is the transformity of grounciwatermultiplied by 1 liter

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ffi+13.------------------------------------------, ffi+12 ffi+U 'CIQ ffi+lO -....; :fffi-fOO IE-+()8 i IE-+()7 ,S' 1E-+()6 -ffi-+()S ... :1' ffi-t04 IE-+()3 1E-+()2 IE-+() I lE-+()2 IE-+()8 lE+IO Pbospborous Coocentmtion log ppb P 235 Figure F.3. Phosphorus emergy per gram relative to emergy/mass of natural and industrial concentration processes Table F.3. Phosphorus emergy per gram relative to emergy/mass of natural and industrial concentration processes as shown in Figure F.3 Item P concentration, ppb sej/gram Source Ocean 5.00E+OI 1 a Rain. Florida 2.00E+02 9.02E+04 a,b Newnans Lake. hypereutropbic LOOE+04 450E+09 c Coweeta vegetation LOOE+06 LOOE+IO d 10% wetland concentration process LOOE+08 L78E+IO a Industrial P205 L50E+08 2.18E+IO c Diammonium phosphate fertilizer 2.40E+08 3.02E+ll c 85%H3P04 2.70E+08 L45E+ll c 100%H3P04 320E+08 2.05E+ll c 100%P4 LOOE+09 2.06E+12 c a.Odum 1996 b. Brezonik 1969 c. Brandt-Williams 1999 (this study) d. Tilley 1999

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Table F.4. Emergy evaluation of 100% P.., per l000g production. Da1a Note Item Unit (units) NONRENFWABLESTORAGES I Water I 3.14E+04 Sum of free inputs (sun, rain omitted) PURCHASED INPUTS Operational inputs 2 Electricity I 125E+01 3 Silica g 150E+03 4 Cao g L60E+02 5 Coke g l.12E+03 6 Phosphate gP l.12E+04 Sum of purchased inputs TRANSFORMITIES 7 Notes 1 2 Total Yield gP l.OOE+03 Water,.J gal H2O 3785.43 cm3/gal density H2O Gal H2O: Density. ambient: Gibbs Energy: Total Energy: Transfonnity: Electricity, .J KWh*3.6E6 JIKWh 2.00E+OO I.OOE+OO g/cm3 4.94 JIg 3.74E+04 J 4.80E+04 sej/J KWh: 3.47E+OO Energy: 125E+07 UnitSoIar Solar EMERGY EMERGY (sejlunit) (E12seJ) 4.S0E+04 0 0 L60E+05 20 9.10E+OS 15 LOOE+09 2 2.05E+09 23 l.1SE+1O 1991 2056 2.06E+I2 2056 (estimated from Shreve 1945) (Odum. 1995) (estimated from Shreve 1945) Transfonnity: I.60E+05 sejIJ (coal plant; Odum 1996) 3 Silica, g 4 5 Feedstock g: 1.50E+03 (Sittig 1978) Emergy/gram 9.70E+08 sej/g (Odum 1996) assume equivalent to pelagic and abyssal sediment transfonnity CaO,g Feedstock g: Emergy per gram Coke,g Feedstock g: I.60E+02 I.OOE+09 l.12E+03 (Sittig 1978) (limestone; Odum 1996) (Sittig 1978) 236 EmS Value (1996 S) 0 0 11 12 1 19 1664 1714 1714

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Table F.4 continued 6 7 Emergy per gram Phosphate, ,p Feedstock g: Emergy per gram raeld gP4 2.05E+09 sej/g LI2E-f04. 3.90E+09 sej/g 1000 237 (Sittig 1978) (rock; Odum 1996)

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238 Table F 5. Emergy evaluation of 100% ( 32 % P),. per 908 kg H3P04. Unit Solar Solar Em$ Data EMERGY EMERGY Value Note Item Unit (units) (sejfunit) (E13 seJ) (1996 $) NONRENEWABLE STORAGES I Air J 3.76E+lO 2.l2E+03 80 2 Water J 3.74E+07 4.80E+04 2 Sum of me inputs (sun. rain omitted) 82 PURCHASED INPUTS Operational inputs 3 Electricity J 125E+I0 L60E+05 199 4 Labor J 131E+06 2A6E+07 3 5 Silica g 6.80E+05 9.70E+08 66 6 Coke g 3 AOE+06 2.05E+09 697 7 Phosphate gP 2.72E+06 L78E+I0 4842 Sum of purchased inputs 5807 TRANSFORMITIES 8 Total Yield gP 2.87E+05 2.05E+ll 5889 Notes I Air,J Drying ft3: 450,000 (Shreve, 1945) Furnace temperature: 1500 '1c Assume furnace pressure I atm Superheated air: enthalpy: 1046.6 kIlkg entropy: 8.22 kIlkg*T (Vasserman and Rabinovich, 1970) (Vasserman and Rabinovich, 1970) volume: 3829 dmJlkg (Vasserman and Rabinovich, 1970) F= H -t S =-L13E4kIlkg JI ft3 = ( F kIlkg) (l klif.3829 dmJ) (28.32 dmJ/I ft3)= 83577 J/ft3 J= 3.76E+1O Transformity: 2.12 E3 1996) assumes 02 as byproduct of gross photosynthesis 2 Water,J gal H2O 3785.43 cm3/gal deDSity H2O Gal H2O: Demity. ambient: LOOE+OO glcm3 Gibbs Energy: 4.94 JIg Total Energy: J Transformity: sej/J 664 15 679 1661 27 550 5808 40347 48392 49072

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Table F.5 continued 3 Electricity, S KWh*3.6E6 JIKWh KWh: 3.46-t03 Energy: Transformity: 4 Labor,S 1.2SE+I0 1.60E-t05 sejIJ 1945) (aver.age coal plant; Odum. 1996) (pers-hows)*(2S00 kcallday)*(41861/Cal) f (8 pers-lusfday) pers-hows: 1.00E-+-OO 1945) Energy: l.3lE-t06 Transformity: 2.46E-t07 sejIJ (high school graduate; Odum 1996) 5 Coke,g Feedsto<:k Ib: g: Emergy/gram: 6 Silica. g Feedstock Ib: g: 748 3.40E-t05 2.0SE-t09 sej/g 1270 (Shreve. 1945) 5.77E-tOS 1945) 1999) Emergy/gram: 9.70E+08 sej/g 1996) assume equivalent to pelagic and abyssal sediment transformity 7 Pbospbate, g P Feedstock Ib: g: Transmassity: 8 Yield 3970 1.80E-t06 3.9OE+09 sej/g kg H:J'04 908 Ratio P to H:J'O", 31gmol P/98 gmol HJlO4 g P: 2.87E+OS (Shreve. 1945) (rock; OdUlll, 1996) 239

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Table F.6. Emergy evaluation of85% HJlO4(21% P),. per 908 kg. Note Item Units PURCHASED INPUTS Operational inputs 1 Electricity I 2 Labor I 3 Silica g 4 Coke g 5 Phosphate gP Sum of purchased inputs TRANSFORMITIES 6 Total Yield gP Notes 1 Electricity, S KWh*3.6E61IKWh KWh: Energy: UnitSoIar Data EMERGY (units) (sej/unit) 1.25E+1O 1.60E+05 131E+06 2.46E+01 5.11E+05 9.10E+08 3.40E+06 2.05+09 1.80E+06 1.78E+I0 2.81E+05 1.45+11 (Shreve. 1945) Solar EMERGY (E13 seJ) 199 3 56 697 32044159 4159 Transfonnity: 3.46E+03 L25E+I0 L60E+05 sejIJ (average coal plant; Odum. 1996) 2 Labor,S (pers-hours)*(2500 kcallday)*(41861ICal) / (8 pers-hrsIday) pers-hours: LOOE+OO (Shreve. 1945) Energy: L31E-t()6 Transfonnity: 2.46E+07 sejIJ (high school graduate; 0duIn. 1996) 3 Coke,g Feedstock Ib: g: Emergy/gram: 4 Silica, g Feedstock Ib: g: Emergy/gram: 5 Phospbate, g P Feedstock Ib: g: Emergy/gram: 6 Yield 748 (Shreve. 1945) 3.40E+05 2.05E+09 sej/g assume equivalent to pelagic and abyssal sediment ttamformity 1270 (Shreve. 1945) 5.77E+05 9.70E+08 sej/g 3970 L80E-t()6 3.90E+09 sej/g (Odum. 1996) (Shreve. 1945) (rock; OdUDl, 1996) kg IDP04 908 (Shreve. 1945) Ratio P to H3P04: 31gmol P/98 gmol IDP04 g P: 2.87E+05 240 Em$ Value (1996 $) 1661 21 466 5808 26100 34662 34662

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241 Table F.7. Emergy evaluation of35% P205 (15 % P), per 908 kg. Unit Solar Solar EmS Data. EMERGY EMERGY Value Note Item Unit (units) (sejfunit) (E12 sej) (1996 $) NONRENEWABLE STORAGES Water I L40E+08 4.80E+04 7 6 Sum of free inputs (sun, rain omitted) 7 6 PURCHASED INPUTS Operational inputs 2 Electricity I L89E+08 1.60E+05 30 25 3 Labor I 1.05E+06 2.46E+07 26 22 4 H2SO4 (94%) g 8.85E+05 9. 12E+07 81 67 5 Phosphate gP 1.01E+06 3.90E+09 4173 3478 Sum of purchased inputs 4310 3591 TRANSFORMITIES 6 Total Yield gP 396E+05 2.18E+IO 8633 7194 Notes 1 Water, 3 gal H2O 3785.43 cm3/gal density H2O Gal H2O: 750E+03 (Shreve, 1945) Density, ambient: I.00E+OO gfcm3 Gibbs Energy: 4.94 JIg Total Energy: 1.4OE+08 J Transformity: 4.8OE+04 sejfJ (Odum. 1995) 2 Electricity, 3 KWh*3.6E6 J/KWh KWh: 5.25+01 (Shreve, 1945) Energy: 1.89E+08 Transformity: I.60E+05 sejfJ (average coal plant; Odum. 1996) 3 Labor, 3 (pers-hours)*(2500 kcalfday)*(4186J/Cal) 1(8 persbrsfday) pelS-hours: 8.00E-Ol (Shreve, 1945) Energy: 1.05E+06 Transformity: 2.46E+07 sejfJ (high school graduate; Odum. 1996) 4 H2SO4" Feedstock Ib: 1950 (Shreve, 1945) g: 8.85E+05 Emergy/gram: 9.12E+07 sej/g (PritchaJd. 1996) 5 Pbosphate, ,p

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Table F.7-continued Feedstock lb: g: Emergy/gram: 6 rleld 2350 l.07E-+()6 3.90E-t09 sejlg kgP20S 908 Ratio P to P20S: 62gmol PI 142 gmol PlOS g P: 3.96E-+()S 242 (945) (rock; 0cIum, 1996)

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Table F.8. Emergy evaluation of coke,. per 635600g. Data Note Item Unit (units) NONRENFWABLESTORAGES I Water I 3.74E+07 Sum offtee inputs (sun, rain omitted) PURCHASED INPUTS Operational inputs 2 Electricity I 3 Labor I 4 Sulfuric Acid g 5 Bituminous Coal g 6 Lime g Sum of purchased inputs TRANSFORMITIES 7 Notes 1 Total Yield Water,S g 324E+07 l.57E+07 l.14E+04 9.08E+05 9.08E+02 6.36E+05 gal H2O 3785.43 cm3/gal density H2O Gal H2O: 2.00E+03 Density. ambient: l.OOE+OO g/cm3 Gibbs Energy: 4.94 JIg Total Energy: 3.74E+07 J Transfonnity: 2 Electricity, S Unit Solar EMERGY (sej/unit) 4.80E+04 1.60E+05 2.46E+07 9.12E+07 I.OOE+09 l.OOE+09 2.05E+09 (Shreve 1945) (Odum. 1995) (pe1S-bourslhalyr)*(2500 kcaVday)*(4186J/Cal) 1(8 pelShrsIday) KWh: 9.00E+OO (Shreve 1945) Total Energy: Solar EMERGY (E12 seJ) 2 2 5 386 1 908 I 1301 1303 Transfonnity: 3.24E+07 1.60E+05 (average coal plant; Odum 1996) 3 Labor,S (pe1S-bours)*(2500 kcaVday)*(4186J/Cal) 1(8 pelS-hrsIday) pelS-bours: 1.20E+01 (Shreve 1945) Total Energy: 1.57E+07 243 EmS Value (1996 $) 1 I 4 322 I 757 I 1084 1086 Transfonnity: 2.46E+07 sej/J (high. school graduate; Odum 1996) 4 Sulfuric Add, g Feedstock: Emergy/mass: 1. 14E+04 (Shreve 1945) (L. Pritchard. 1996)

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244 Table F.8 -continued 5 Bitwniaoul Coal, C Feedstock: 9.08E+05 (Shreve 1945) Emergy/mass: l.OOE+09 sej/g 6 Lime,g Feedstock: 9.08E+02 g (Shreve 1945) Emergy/mass: l.OOE+09 sej/g (Odum 1996)

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Table F.9. Emergy evaluation ofDiammonium (Superphosphate) Fertilizer Note Item Units Units! Transfonnity Solar Emergy Production (sejlunit) (E16 sej) PURCHASED INPUTS Construction inputs 1 '84 $ 3.69E+05 2.20E+12 81 '81 $ 2.51E+06 2.70E+12 678 '79 $ 2.84E+05 3.50E+12 99 '75 S 1.56E+06 6.00E+12 936 Operational inputs 2 Fuel J 2.14E+14 4.80E+04 1,315 3 Electricity J L08E+15 L60E+05 4 Labor J 2.43E+12 2.46E+07 5 NH3 gN 2.18E+11 4.60E+09 127,880 6 P20s(35%) g L14E+12 9.31E+09 1,068,180 Sum of purchased inputs 1,222,421 Transformity with Services 1 Total Yield g 2.41E+12 5.01E+09 1,222,427 gP 5.53E+ll 2.21E+I0 gN 5.05E+ll 2.42E+10 Transformity without Services Total Yield g 2.41E+12 5.06E+09 1,220,633 gP 5.53E+ll 2.21E+I0 gN 5.05E+ll 2.42E+10 Noms: Production of2.4 E9 kg ofDiammooium Phosphate 1 Capital = (plant capital costs)/(life expectaDcy)/(% of capacity dedicated to DAP: 2 Fuel-2.6 E 6 tbenos natural gas (2.6 E6 thenns)(1.0S E8 Jltbenn) = 2.74 El41 3 Electricity -3.01 E8 KWh (3.01 E8 kwh)(3.6 E6 Jlkwh) = 1.08 ElS 1 4 Labor 1.86 E6 pers-Jus (1.86 E6pers-brs)(2S00kcallday)(4186 J/Kcal)(l(8 pers-hrsIday) = 2.43 E12 J Trausfonnity -high school gIaduate (Oelum 1996) 245

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TableF.9 continued 5 Ammonia-337Ell g (337 Ell g)(14g N/17g NH3 ) = 2_78 Ell gN 6 P205 35% 1.14 El2 g 7 Yield -2_4 E9 kg (NH..h(HPOJ Ratio P: 31gmol PI 132 gmol OAP Ratio N: 28gmol Nf 132 gmol OAP 246

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APPENDIXG IN-LAKE SIMULATION CALffiRATIONS This appendix contains calibration calculations for each simulation_ A QBASIC program for one simulation is included_ 247

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248 Table G.I. Coefficient calibration, eutrophic conditions. Sed = 0.1 Org= 5 P= 30 M= 1000 PI= 60 Z= 5 F= 50 I= 324E+OO Ro= 4.86E-OI Rl= 3.09E+OO R2= 3.02E+OO R3= LOOE+OO ko= 2156 l(Ro*P*M)= 1.89E-Ol kl= 150 I(RI*Sed)= 4.85E+02 k2= 74 1(R2*Org)= 4.91E+OO k3= 2016/(R3*P*PI)= LI2E+OO k4= 2.74E-04 k5= 0.02/F*M 4.00E-07 k6= 0.18/Z*M3.60E-05 k7-1.8 1M 1.80E-03 k8= 21M 2.00E-03 k9= 15 /R3*P*PI= 8.33E-03 klO= 3 IPI*Fl.OOE-03 kll= IIPI*Z= 3.33E-03 k12 61P1-l.OOE-01 k13= 51PI= 8.33E-02 kl4= 02IZ*F= 8.00E-04 k15= 0.13 IZ= 2.60E-03 kl6= 0.05/F= 9.20E-04 k20= 0.08 IP*PI= 4.61E-05 k21= L31P*M 4.33E-05 k22= 0.011P= 4.61E-04 k24= 0.04 Iz*Org= 1.60E-03 k26= O.04/Org= 8.00E-03 k21= O/F*M 4.00E-08 k28= o /F*Pl l.33E-06

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k29= 0.04 IF*Z Table G.l continued L60E-04 kJO= 03 IZ*M 6.00E-OS k31= 0.03 1P1*Z LOOE-04 kJ2 0 /org*Z L20E-04 fr= 0.8/(k7M+kl2Pl+kISZ+kl6F)= L02E-OI frorg= 0.06 /(k7M+kl2Pl+klSZ+kl6F)= 7.63E-03 249

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Table G.2. QBASIC in-lake simulation program REM Macintosh REM Lake: Generic REM graph subroutine GOSUB700 REM COEFFICIENTS kO = .000195 'albedo ki = LIlO3 'Iight:sediment inhibition k2 = .022239 'Iigbt:organic inhibition k3 = '()04929 'light use by phyto k4 = 4.1 I 52E-07 'macrophyte production k5 = 1.9048E-07 'macrophyte predation by fish k6 = .000058 lnacropb.yte predation. by zooplankton k7 = 2.52661E-03 "macrophyte feces and mortali1y k8 = .003 'macrophyte basal fimctions k9 = 3.I328E-05 phytoplankton production klO = .001 phytoplankton predation by fish kll = .0033 'phytoplankton predation. by zooplankton kl2 =.1 'phytoplankton mortality k13 =.0833 'phytoplankton basal fimctions kl4 = .0008 'zooplankton predation by fish kl5 = .00286 'zooplankton feces and mortality kl6 = .0054 'fish feces and mortality and pmJation by fish k20 =.0003316 'phosphate uptake by phyto k21 = 9.9461E-07 'phosphate uptake by macro k22 = .0000333 'phosphate sedimentation k23 = .02 'sediment settling k24 = .0274956 'organic consumption by zooplankton k26 = .0028 'organic sedimentation k27 = 8E-08 'fish utilization of macrophytes k28=.0000233 'fish utilization of phytoplankton k29=.OOOS 'fish utilization ofzoo k30=.OOOO2 'zooplankton utilization of macrophytes k31 =.000 133 'zooplankton utilization of phytoplankton k32=.0 12 'zooplankton utilization ofPOM kr3 = .0015835 REM initial conditions pl=60 m=900 p=20 sed=.1 org=5 h2o=2000 z=5 f=50 sc=.OOOOI oc=.OOOOI pc=.0002 a1 =400 aw=4000 rc=.0003 'phytoplankton 'macrophytes 'phosphate 'sediment 'organics 1akevolume 'zooplankton 'fish 'sediment in runoff 'organics in runoff phosphate in runoff 1ake SUIface area 'watershed area 'nmoff coefficient 250

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Table G.2 continued fr= .01018155# froIg = .08007962# to=O tend =3650 dt=.1 'fraction of phosphate in decomposition 'fraction of ocganic in decomposition FOR t = to TO tend STEP dt REM PLOITING subroutine REM scaling coefficients ts = t*63013650 IDS = m*430/(1 *1000) pIs = pI*430/(5*100) fs = f* 430/(10*100) zs = z*430/(5*100) ps = p*430/(2*50) PSET (18, 430ms) PSET (ts, 344 pIs) PSET (ts, 258 -f5) PSET (ts, 172 -zs) PSET (ts, 86 -ps) REM rate EQUATIONS rain = .OOO35*dt + .00035 *dt* SIN(t*.O 17) 'cumulative = 54 in i = 3800*dt + 800*dt*SIN(t*.0 11) rainlk =rain*al nmin = rc*rain*aw pO =pc*runin*IOOOlh2o sO = sc*runin*IOOOlh2o 00 = oc*runin* lOOOlh2o pr = rainlk* 1671h2o rO = i/(l +k0*p*m) rl=i!(l+k1 *sed) r2 =rll(l+k2*org) r3 = r2/(l +k3*p*pI) REM mass differentials dm = k4*rO*p*m -k5*f*m. -k6*z*m k7*m k8*m dpi = k9*r3*p*pI-klO*pl*f -kl1 *pl*z -kI2*p1-k13*p1 dz = kJ2 *org*z + kJO*z*m + kJl pl*z kI4*z*f -k15*z df= k27*f*m + k28*f*p1 + k29*f*z k16*f pbin = pO +fr*(k7*m + k12*p1 + k15 z + kI6*t) phout = k20*p*pl + k21 *p*m + k22*p dp = pbin -phout 'dsed = sO*runin -k23*sed orgin = 00 + frorg*(k7 *m + k12*p1 + k15*z + kI6*t) 251

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Table G.2 continued dorg = orgin k24*z*org k26*org REM accumulations m=m+dm*dt pI = pi + dpl*dt z=z+dz*dt f=f+df*dt p=p+dp*dt 'sed = sed + dsed*dt org = org + dorg*dt NEXrt 700 LINE (0,0)-(330,430).)3 LINE (O,86)-{330,86) LINE (0,172)-(330,172) LINE (0,258)-(330,258) LINE (0,344)-(330,344) LINE (0,430)-(330,430) RETURN 252

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REFERENCES Adamus, CL. and MI. Bergman, 1995. Estimating nonpoint source pollution loads with a GIS screening modeL Water Resources Bulletin 31 (4): 647-655. Allen, T.F H and T. B. Starr, 1982. Hierarchy: perspectives for Ecological Complexity. Chicago: University of Chicago Press. Binford. M W., E. S. Deevey, and T. L. Crisman, 1986. Paleolimnology: an historical perspective on lacustrine ecosystems. Annual Review of Ecology and Systematics 14:255-286. Braud, I., AC. Dantas-Antonio, and M.. Vauclin, 1995. A stochastic approach to studying the influence of the spatial variability of soil hydraulic properties on surface fluxes, temperature and humidity. Iournal of Hydrology 165:283-310. Brenner, M., T.I. Whitmore, M S. Flannery, and M.. W. Binford, 1993. Paleolimnological methods for defining target conditions in lake restoration: Florida case studies. Lake and Reservoir Management 7(2): 209-217. Brenner, M, M.. W. Binford, and E.S. Deevey, 1990. Lakes in Ecosystems of Florida (Myers, RL. and I. I. EweL eds.). Orlando: University of Central Florida Press. Brown, M.. T. and DR Tilley, 1995. South Dade Watershed Project Fmal Report: Data Inventory and Compilation, Evaluation of Stormwater Requirements, and Partial Ranking of Drainage Basins. Center for Wetlands and Water Resources, University of Florida. Canfield, DE., 1981. Chemical and Trophic State Characteristics of Lakes in Relation to Regional Geology. Final report to Cooperative Fish and Wildlife Unit, University of Florida. Canfield, DE., 1988. The eutrophication of Lake Okeechobee. Lake and Reservoir Management 4(2): 91-99. Canfield, DE. and M v. Hoyer, 1992. Aquatic Macrophytes and Their Relation to the Limnology of Florida Lakes. Tallahassee: Bureau of Aquatic Plant Management. Carlson, R.E., 1977. A trophic state index for lakes. Limnology and Oceanography 22:361-369. 253

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254 Carper, G. L. and R-W. Bachmann, 1984. Wmd resuspension of sediments in a prairie lake. Canadian Journal ofFlSheries and Aquatic Science 41: 1763-67. Champeau, T.R.., 1997. Fish community response to lake eutrophication: a case study. Florida Lake Management Conference Proceedings. Chapra, S.C., 1997. Surfiace Water-Quality Modeling. New York: McGraw-Hill Chow, V.T., DR. and L.W. Mays, 1988. Applied Hydrology. New York: McGraw-Hill. Cooke, GD., E.B. and S.A Peterson, P.R.. Newroth, 1993. Restoration and Management ofLake5 and Reservoirs. Boca Raton: Lewis. Crisman, TL., 1992. Natural lakes of the southeastern United States: Origin, structure and function in Biodiversity of Southeastern United Statest Aquatic Communities (C. Hackney, M. Adams, and W. Martin, ed5.). New York: Wiley. Crisman, T L., L.J. Chapman, and C.A Chapman, 1998. Predictors of seasonal oxygen levels in sma1l Florida lakes: the importance ofeolor. Hydrobiologia 368: 149-155. Crisman, TL., JR. Beaver, JX Jones, AE. Keller, AG. Neugard, and V. Nlakantan, 1992. Historical assessment of cultural eutrophication in Lake Weir, Florida. Palatka: St. Johns River Water Management District Division Special Publication, SJ92-SPI2. DeVantier, B. and A Feldman, 1993. Review of GIS applications in hydrologic modeling. Journal of Water Resources PJanning and Management 119 (2): 246-261. Dierberg, F .E., V.P. Williams, and W H. Schneider, 1988. Evaluating water quality effects of lake management in Florida. Lake and Reservoir Management 4(2): 101-111. Flannery, M. S., R. D. Snodgrass, and T. J. Whitmore, 1982. Deepwater sediments and trophic eonditions in Florida lakes. Hydrohiologia 92: 597-602. Fluck, R.C., C. Fonyo, E. Flaig, 1992a. Land-used based phosphorus balances for Lake Okeechobee, Florida drainage basins. Applied Engineering in Agriculture 8(6): 813-820. Fluck, R. C., B. S. Panesar, and C. D. Baird, 1992b. Florida Agricultural Energy Consumption Model. Final Report to the Florida Energy Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Fox, R.., 1985. Energy and the Evolution of Life. San Francisco: Freeman.

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255 Frey, D. F., 1969. The rationales of paleolimnology. Mitt. Intemat. Verein. LimnolI7:7-18. Goldman, C.R. and R. G. Wetzel, 1963. A study of the primary productivity of Clear Lake, Lake County, California. Ecology 44:283-294. Gottgens, I.F. and T L. Crisman, 1991. Newnans Lake, Florida: removal of particulate organic matter and nutrients using a short-term partial drawdown. Lake and Reservoir Management 7(1): 53-60. Gottgens, J.F. and T L. Crisman, 1993. Quantitative impacts of lake-level stabilization on material transfer between water and sediment in Newnans Lake, Florida. CanadianJournaI ofFish and Aquatic Science 50:1610-1616. Gottgens, I.F. and CL. Montague, 1987. Orange, Lochloosa and Newnans Lakes: a survey and preliminary interpretation of environmental research data. Palatka: Final Report to St. John's Water Management District, Project 15-200-33. Hansen, P. S., E. J. Pblips, and F. J. Aldridge, 1997. The effects of sediment resuspension on phosphorus available for algal growth in a shallow subtropical lake, Lake Okeechobee. Lake and Reservoir Management 13(2): 154-159. Harper, H. H., 1994. Storm water Loading Rate Parameters for Central and South Florida. Orlando: Environmental Research and Design, Inc. Heidtke, T.M. and M. Auer, 1993. Application of GIS-based nonpoint source nutrient loading model for assessment of land development scenarios and water quality in Owasco Lake, New York.. Water Science Technology, 28 (3-5): 595-604. Huang, S., 1998. Spatial hierarchy ofmban energetic system, in Proceedings of the International Workshop Advances in Energy Studies: Energy Flows in Ecology and Economy (Ulgiati, S., ed). Italy: MUSIS. Huber, W.C., PL. Brezonik, and J.P. Heaney, 1982. A Classification of Florida Lakes, Report ENV-05-82-1. Tallahassee: Florida Department of Environmental Regulation. Julien, P., B. Saghafian, and F. Ogden, 1995. Raster-based hydrologic modeling of spatially-varied surface runoff. Water Resources Bulletin 3 (3): 523-536. Kajak, Z., 1970. Analysis of the influence offish on benthos by the method of enclosures in Productivity Problems of Fresh waters (Z. Kajak and A. Dkowska, eels.). Warsaw: PWN Polish Scientific.

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256 KIopatec,1M, 1975. The role of emergent macrophytes in mineral cycling in a freshwater marsh in Mineral Cycling in Southeastern Ecosystems {F.G. Howen. 1. Gentry, and M.. Smith, eels.}. Washington D.C.: U.S. Energy Research Administration. Kool, 1., P. Huyakom, E. Sidicky, and Z. Saleem, 1994. A composite modeling approach for subsurface transport of degrading contaminants from lands-disposal sites. 10urnal of Contaminant Hydrology 17: 69-90. Kratzer, CR., 1979. Application of input-output models to Florida lakes. Master's Thesis, University of Florida. Kuntz, RC.,I994. Little Lake Weir: A Paleolimnological Investigation ofProgressive Cultural Eutrophication. Master's Thesis, University of Florida. LAKEWATCH website: www.ifas.ufledul-Iakewatch Lambert, D., 1999. A Spatial Emergy Model for Alachua County, Florida. Ph.D. University of Florida. Lasi, M. and Schuman, I., 1997. Orange Creek Basin Surfiace Water Management Plan. Palatka: St. 10hns River Water Management District. Likens, G.E., 1975. Primary production of inland aquatic ecosystems, in The Primary Productivity of the Biosphere (Leith, R, R. W. Whitaker, eds). New York: Springer-Verlag. Lowe, E., L. Battoe, M.. Coveney and D. Stites, 1997. The primacy of external P load reduction in restoration of hyper eutrophic shallow lakes with specific reference to Lake Apopka. Proceedings of Florida Lake Management Society Conference. Moskalenko, BX and KX Votinsev, 1970. Biological productivity and balance of organic substance and energy in Lake Baikal in Productivity Problems of Freshwaters (Z. Kajak and A. Dkowska, eds.). Warsaw: PWN Polish Scientific. National Oceanic and Atmospheric Administration website; www.ncdc.nOaaKOv/oVcIimateIclimate4atahtml. Odurn, E.P., 1983. Basic Ecology. Philadelphia: Saunders. Odum, H. T., 1994. Ecological and General Systems, an Introduction to Systems Ecology. WlWOt: University Press of Colorado. Odurn, H. T., 1996. Environmental Accounting, Emergy and Decision Making. New York: Wiley.

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257 Olila, O. G. and K. R.. Reddy, 1995. Influence of pH on phosphorus retention in oxidized lake sediments. Soil Science Society of America Iournal 59: 946-959. Orrell, 1.1., 1998. Cross Scale Comparison of Plant Production and Diversity, Master's Thesis, University of Florida.. Ott, E.R and LH.. Cbazal, 1996. 0caIi Country, Kingdom of the SUIL Ocala: Marion Publishers. Paul, A. and D. Southgate, 1978. McCance and Widdowson's The Composition of Foods. London: E1sevierlNorth-HoDand Biomedical Press. Pierce, A. (ed), 1995. Florida Statistical Abstract. Gainesville, FL: University Press of Florida. Pimental, D., C. Harvey, P. Resosudarmo, K. Sinclair, D. Kurz, M. McNair, S. Crist, L. Shprotz, L. Fitton, R Saffouri and R.. Blair, 1995. Enviommental and economic costs of soil erosion and conservation benefits. Science 267: 117-1121. Pritchard, L., 1992. The ecological economics of natural wetland retention oflead. Master's Thesis, University of Florida. Reckhow, K.H., 1979. Empirical lake models for phosphorus: Development, applications, limitations and uncertainty in Perspectives on Lake Ecosystem Modeling (D. Scavia and A.. Robertson, eels.). Ann Arbor: Ann Arbor Science Publishers. Robison, C.P., GB. Hall, C. Ware, and RD. Hupalo,I997. Water Management Alternatives: Effects on Lake Levels and Wetlands in the Orange Creek Basin. Palatka: St. Iohns River Water Management District Special Publication SI97-SP8. Romitelli, M. S., 1997. Energy analysis of watersheds. PhD. dissertation, University of Florida. Salthe, S. N., 1985. Evolving Hierarchical Systems: Their Structure and Representation. New York: Columbia University Press Schelske, C. L., 1989. Assessment of nutrient effects and nutrient limitation in Lake Okeechobee. Water Resources Bulletin 25(6): 1119-1130. Shreve, R.N., 1945. The Chemical Process Industries. New York: McGraw Hill Smajstda, A.. G., 1990. Agricultural Field Scale Irrigation Requirements Simulation Model (AFSIRS) V5.5. Department of Agricultural Engineering, Institute of Food and Agricultural Sciences, University of Florida.

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Smol, I. P., 1992. Paleolimnology: an important tool for effective ecosystem management. Joumal of Aquatic Ecosystem Health 1 :49-58. Srinivasan, R. and I.G. Arnold, 1994. Integration of a basin-scale water quality model with GIS. Water Resource Bulletin 30 (3): 453-462. Taylor, W D., 1978. Distribution of phytoplankton in Florida lakes. Las Vegas: U.S. Environmental Protection Agency. United State Soil Conservation Service, 1983. Alachua County Soil Survey. United State Soil Conservation Service, 1982. Marion County Soil Survey. 258 Vollenweider, R.A., 1970. Scientific fundamentals of the eutrophication of lakes and flowing waters, with particular reference to nitrogen and phosphorus as factors in eutrophication. Report to the O.E.CD., Paris. Weibel, S.R., 1969. Urban drainage as a factor in eutrophication in Eutrophication: Causes, Consequences, Correctives. Washington D.C.: National Academy of Sciences. Wetzel, R.G., 1975. PrimaIyproductioD, in River Ecology (Whitton, B.A, ed). Oxford: Blackwell Scientific Publishers. Wetzel, R.G., 1983. Limnology. Philadelphia: Saunders. Whitmore, T.I., M. Brenner, and C. L. Schelske, 1996. Highly variable sediment distribution in shallow, wind-stressed lakes: a case for sediment-mapping surveys in paleolimnological studies. Iournal of Paleolimnology 15: 207-221. Wmberg, G.G., 1973. The progress and state of research on the metabolism, growth, nutrition and production of fresh-water invertebrate animals. Hydrobiologia 9:77-84. Young, R.A, C.A Onstad, DD. Bosch, and W.P. Anderson, 1989. AGNPS: a nonpoint source pollution model for evaluating agricultural watersheds. Iournal of Soil and Water Conservation 44(2): 168-173.

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BIOGRAPIllCAL SKETCH Sherry Brandt-Williams was bom and raised in Florida during the time when some of the Dade pine stands and Everglade sloughs still existed in southern Dade County. She attended high school in Lake and there began a lifelong infatuation with Florida's lakes. After a long hiatus as an art major at Eckerd amateur naturalist, applied piano and music theory instructor, stained glass artisan and manager of a polymer furniture fabrication she received a Bachelor of Science in Chemical Engineering from the University of Florida. She worked as a research engineer for five years in the paper division of Procter & Gamble in Ohio, receiving a patent for a product invention. With a revived interest in protecting natural resources, Sherry returned to Florida and designed and built a home in a hydric hammock in Central Florida as a working example of universal design for the handicapped on a minimally disturbed site. At the same time she home-schooled her son until he was 10, using the environment as the classroom. She then returned to graduate school in Environmental Engineering Sciences at the University of Florida, studying ecological engineering and aquatic systems ecology and receiving a National Science Foundation Minority Engineering Doctorate Initiative Fellowship. After receiving her Ph.D., Sherry took a position as project coordinator for an ecological landscape characterization study with FDEP-Rookery Bay NERR. 259

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I certifY that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy_ Thomas L_ Proressor of Environmental Engineering Sciences I certifY that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy_ Howard T_ Odum, Co-Cbair Graduate Research Professor Emeritus of Environmental Engineering Sciences I certifY that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy Montague As -ate Professor of Environmental Engineering Sciences I certifY that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy_ ____ Warren Viessman Professor of Environmental Engineering Sciences

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I certitY that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate. in scope and quality. as a dissertation for the degree of Doctor of Philosophy_ Frank Nordlie Professor of Zoology This dissertation was submitted to the Graduate Faculty of the College of Engineering and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy_ December 1999 /l C-------I--M Iack Ohanian Dean, College of Engineering Dean, Graduate School