Environmental impact of unharvested forest buffer zones upon cypress-pond systems in coastal plains regions

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Title:
Environmental impact of unharvested forest buffer zones upon cypress-pond systems in coastal plains regions modeling analyses
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xvi, 239 leaves : ill. ; 29 cm.
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English
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Fares, Ali, 1960-
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Cypress swamp ecology -- Mathematical models   ( lcsh )
Wetland ecology -- Mathematical models   ( lcsh )
Soil and Water Science thesis, Ph. D   ( lcsh )
Dissertations, Academic -- Soil and Water Science -- UF   ( lcsh )
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bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1996.
Bibliography:
Includes bibliographical references (leaves 228-238).
Statement of Responsibility:
by Ali Fares.
General Note:
Typescript.
General Note:
Vita.

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ENVIRONMENTAL IMPACT OF UNHARVESTED FOREST BUFFER ZONES UPON
CYPRESS-POND SYSTEMS IN COASTAL PLAINS REGIONS: MODELING
ANALYSES




















By

ALI FARES
















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

1996
































I dedicate this work to my wife Samira, my mother

Hassnah, my father Ahmed, my brother, my sisters and my

children, who contributed to my success and well being.














ACKNOWLEDGMENTS


I wish to express my sincere gratitude to Professor

R.S. Mansell, my major professor, for his helpful support

and wise guidance during my program. Special appreciation

is extended to Professor S. Rao for his help and support. I

would also like to thank Dr. P. N. Kizza who introduced me

to the world of soil physics. Sincere gratitude is also

extended to Dr. W. D. Graham for involving me in her

research discussion sections that helped me considerably

during my research. Particular appreciation is extended to

Dr. H. Riekerk for his feedback and help. I would like to

express my appreciation to Dr. S.A. Bloom who helped me in

programming. Valuable comments from Mr. Dilip Shinde, a

good friend, were helpful. I thank him for his support and

kindness. I would like to thank Dr. A. Akpoji for his help

especially about solute transport.

I would like to express my deep appreciation to my

beloved wife, Samira, for her support and encouragement.

She sacrificed much of her time and comfort to succor me

during difficult periods of my life. Special acknowledgments

go to my parents, Ahmed, Hassna and Yagouta, my sisters,

Fatima, Salma, Zohra, Ahlam and Hamida, and my brother,

Abdel-Raouef, for their prayers, love and support.















TABLE OF CONTENTS


ACKNOWLEDGMENTS . . iii

LIST OF TABLES .... ............ vii

LIST OF FIGURES .. . ix

Abstract . .... . .. xv

CHAPTERS

1 INTRODUCTION . . 1

Research Background . .... .. .. 1
Hypotheses . . 5
Objectives . . 5
Structure of the Dissertation . 6

2 HYDROLOGICAL ASPECTS OF CYPRESS WETLANDS IN COASTAL
REGION PINE FORESTS AND IMPACTS OF MANAGEMENT PRACTICES
UPON THEM: LITERATURE REVIEW . 7

Introduction . . 7
Hydrology of Cypress Pond Flatwood Pine Systems .. 8
Precipitation (P) . .. 11
Precipitation Interception(I) ... .13
Evapotranspiration . 14
Evaporation of Intercepted Rainfall 16
Runoff . . 16
Dynamic Interaction Between Surface Water in
Ponds . .. 17
Current and Alternative Forest Management Practices 19
Buffer Zones . . 19
Best Management Practices (BMPs)in Cypress Pond
Flatwood Pine . .. .. 20
Evaluating Alternative Management Practices 21
Conclusions . . .24

3 THEORY OF WATER FLOW AND SOLUTE TRANSPORT ... .26

Estimating Evapotranspiration Using the Priestley-
Taylor Approach . . 33
Potential Evaporation . 37










Precipitation Interception . .. 37
Hydraulic Conductivity . 38
Boundary Conditions . ... .39
Initial Condition . ... .39
Surface Runoff . .. 40
Solute Transport in Two-Dimensions .. 40
Convection-Dispersion Transport Equation 41
Boundary Conditions for Solute Transport 43
Initial Conditions for Solute Transport 44
Numerical Solutions ................ 44
Water Flow Equation . .. 44
Solute Transport Equation . .. 45
Model Verification for Water Flow .. 47
Model Verification for Solute Transport 78

4 A POTENTIAL EVAPOTRANSPIRATION PREDICTION SUBMODEL
FOR MULTIDIMENSIONAL WATER FLOW IN SOILS .. 84

Introduction . . ... 84
Model Development . .. 88
Estimation of Input Parameters for the Model 88
Model Validation . . 93
Validation of the Net Radiation Component 93
Prediction of Pan Evaporation (PE) .. 97
Prediction of Pan Evapotranspiration 101
Conclusions . ... 106

5 EFFECTS OF HARVESTING SCENARIOS, ON THE HYDROLOGY AND
WATER QUALITY OF A CYPRESS-POND FLATWOOD PINE
SYSTEM . . 107

Simulated Evapotranspiration . .. 113
Annual Evapotranspiration . .. 113
Daily Evapotranspiration .. 116
Case#l: Mean PET&P weather Year (M&M) .117
Case#2: The "Hot" and "Dry" weather year
case (H&D) . 119
Case#3: The "Cold" and "Wet" weather
Year (C&W) . .. 121
Rainfall Interception . .. 121
Surface Runoff . . .. 124
Case#l: Mean PET&P Weather Year (M&M) 125
Case#2: "Cold" and "Wet" Year (C&W) .. 126
Case#3: "Hot" and "Dry" (H&D) wether Year 126
Pond Water Level and Water Table Depth in Flatwood 129
Harvesting Scenario#l (CONT) .. 129
Harvesting Scenario#2 (PINE) .. 134
Harvesting Scenario#3 (CYPR) .. 139
Harvesting Scenario#4 (PART) .. 139
Harvesting Scenario#5 (TOT) .. 144
Frequency of Groundwater Table Depth .. 144










Effects of Partial Harvesting upon Solute Transport
Within Cypress Pond Flatwood Pine Systems 157
Transport of a Non-Reactive Solute ... .157
Solute transport without plant uptake .157
Transport of non-Reactive solute with
plant uptake . 168
Transport of a Reactive Solute: An Herbicide 174

6 DISCUSSION . . ... 184

Evapotranspiration . .. 186
Annual Evapotranspiration . .. 186
Daily Evapotranspiration . .. 187
Case#l: Mean PET&P weather year (M&M) 188
Case#2: The "hot" and "dry" year 189
Case#3: The "cold" and "wet" year .. 190
Rainfall Interception . ... 191
Surface Runoff . . .. 192
Pond and Ground Water Elevations .. 194
Spatial Distributions of Pond and Ground Water
Levels . . 195
Solute Transport in the CPFS . 201
Nonreactive solute transport without plant
uptake . . 202
Nonreactive solute transport with plant uptake 203
Reactive solute without plant uptake 205

7 SUMMARY AND CONCLUSIONS . ... .207

APPENDIX . . ... 216

LIST OF REFERENCES . ... 228

BIOGRAPHICAL SKETCH . ... 238














LIST OF TABLES


Table page

2.1 Measured and/or estimated cypress pond flatwood pine
water budget components. ... 11

3.1 Input parameters for solute validation. .. .80

4.1 Simulated and calculated variation of A/(A + y) with
elevation and temperature . ... 95

5.1 Effects of harvesting scenarios on annual ET 116

5.2 Effects of harvesting scenarios and weather years on
rainfall interception ... 123

5.3 Rainfall interception for pine and cypress trees 124

5.4a Annual outflo as a function of weather and harvesting
scenarios . . 125

5.4b Percentage of ground water frequency. ... 147

5.5 Nonreactive solute, without plant uptake, partitioning
through the system for a M&M weather year. ... .158

5.6 Nonreactive solute, with plant uptake, partitioning
through the system for a M&M weather year. ... .168

5.7 Nonreactive solute, with plant uptake, partitioning
through the system for a wet year. ... 174

5.8 Reactive solute, without plant uptake, partitioning
through the system for a M&M weather year. ... .183

A.1 Van Genuchten parameters for soil water retention. 219

A.2 Vertical mean root length per surface area 220

A.3. The organic carbon content for the different soil
layers used . . ... 224








A.4. The sorption distribution coefficients for reactive
solute in the different soil layers used .... .225














LIST OF FIGURES


Figure page

2.1 Hydrological Components for cypress pond flatwood
systems. . ... .. .. 10

2.2 Absolute monthly rainfall and relative (% of annual
amount) for Gainesville, Florida ... .12

3.1 A cross-section of the simulated system .. .27

3.2 The water stress function for field crops and wetland
plants . ... .. .. 45

3.3 Transpiration coefficient for cypress trees. ..... .47

3.5. Measured and simulated water elevations for the C
wetland in 1992 . ... .51

3.6. Measured and simulated water elevations for the C
wetland in 1993 . . .. 52

3.7. Measured and simulated water elevations for the C
wetland in 1994 . ... .53

3.8. Measured and simulated water elevations for the N
wetland in 1992 . ... .54

3.9. Measured and simulated water elevations for the N
wetland in 1993 . ... .55

3.10. Measured and simulated water elevations for the N
wetland in 1994. .... . .56

3.11. Measured and simulated water elevations for the K
wetland in 1992 . ... .57

3.12. Measured and simulated water elevations for the K
wetland in 1993 . ... .58

3.13. Measured and simulated water elevations for the K
wetland in 1994 . ... .59

3.14. Simulated pond surface area for wet and dry years 62

ix









3.15. Measured and simulated
the N flatwoods in 1993

3.16. Measured and simulated
the N flatwoods in 1994

3.17. Measured and simulated
the C flatwoods in 1993

3.18. Measured and simulated
the C flatwoods in 1994

3.19. Measured and simulated
the K flatwoods in 1993

3.20. Measured and simulated
the K wetland in 1994

3.21. Measured and simulated
wetland C in 1993 .

3.22. Measured and simulated
wetland K in 1993 .

3.23. Measured and simulated
wetland N in 1993 .

3.24. Measured and simulated
of wetland C in 1993 .

3.25. Measured and simulated
of wetland K in 1993 .

3.26. Measured and simulated
of wetland N in 1993 .


ground water elevations for
. . 63

ground water elevations for
. . 64

ground water elevations for
. . 65

ground water elevations for
. . 66

ground water elevations for
. . 67

ground water elevations for
. . 68

plant transpiration for the
. . 71

plant transpiration for the
. . 72

plant transpiration for the
. . 73

flatwood pine transpiration
. . 74

flatwood pine transpiration
. . 75

flatwood pine transpiration
. . 76


3.27. Schematic of the physical system ... 80

3.28. Concentration profile for a reactive solute as
determined by numerical and analytical solutions 82

3.29. Concentration profile for a nonreactive solute as
determined by numerical and analytical solutions 83

4.1 Flow chart of the ETM model . ... .87

4.2 Extraterrestrial radiation and cloudless solar
radiation for a location at a 30-degrees latitude 96

4.3 Daily minimum and maximum temperature for "Hot",
"Mean", and "Cold" years at Gainesville, FL 98








4.4 Measured mean PE level, and simulated PE for a "mean"
year at Gainesville, FL . ... .100

4.5 Measured high PE level, and simulated PE for a "hot"
year at Gainesville, FL . 102

4.6 Measured high PE level, and simulated PE for a "cold"
year at Gainesville, FL . ... .103

4.7 Monthly measured ET and simulated PET for turfgrass at
Fort Lauderdale, FL . ... 104

5.1 Schematics for the five different harvesting
treatments. . . ... 108

5.2 Effects of the harvesting treatments and weather years
on annual ET. . . ... 115

5.3 Effects of harvesting treatments on ET for mean PET/P
year (M&M) . . 118

5.4 Effects of harvesting treatments on ET for a "Hot" &
"Dry" weather year . ... 120

5.5 Effects of harvesting treatments on ET for a "Cold" &
"Wet" weather year . ... 122

5.6 Effects of PET and P on cumulative outflow for a Mean
PET/P weather year (M&M) . ... 127

5.7 Effects of PET and P on cumulative outflow for a "Cold"
and "Wet" weather year (C&W) . .. 128

5.8 Effects of PET and P on cumulative outflow ("Hot" and
"Dry" year) . . ... 130

5.9 Effects of the three hypothetical weather years upon
the temporal distribution of daily pond water levels
for CONT . . .. ... 131

5.10 Influence of the three hypothetical weather years
upon the temporal distribution of daily ground water
levels for CONT . . 133

5.11 Comparison of the influence of the (M&M) mean PET/P
weather year upon the temporal distribution of daily
pond and ground water levels for CONT .. .135

5.12. Influence of the three hypothetical weather years
upon the temporal distribution of daily pond water
levels for the PINE harvest scenario ...... 136


xi










5.13. Influence of the three hypothetical weather years
upon the temporal distribution of daily ground water
levels for PINE . ... 138

5.14. Influence of the three hypothetical weather years
upon the temporal distribution of daily pond water
levels for CYPR . .. 140


5.15. Influence of the three hypothetical weather years
upon the temporal distribution of daily pond water
levels for PART . .. 141

5.16. Influence of the three hypothetical weather years
upon the temporal distribution of daily ground
water levels for PART . 143

5.17. Influence of the three hypothetical weather years
upon the temporal distribution of daily pond water
levels for TOTAL . ... 145

5.18. Influence of the three hypothetical weather years upon
the temporal distribution of daily ground water levels
for TOTAL . . .. 146

5.20. Schematics for the different partial harvesting
treatments . . ... 149

5.21. Surface and ground water levels for the CONTROL
treatment . . .. 152

5.22. Surface and ground water levels for the BUFFER
treatment . . .. 153

5.23. Surface and ground water levels for the EDGE treatment
. . 154

5.24. Surface and ground water levels for the STRIPS
treatment . . 155

5.25. Surface and ground water levels for the CENTER
treatment . . .. 156

5.26. Simulated distributions of a nonreactive solute for
the CONTROL treatment . .. 160


5.27. Simulated distributions of a nonreactive solute for
the BUFFER control . .. 163

5.28. Simulated distributions of a nonreactive solute for
the EDGE treatment . .. 164

xii










5.29. Simulated distributions of a nonreactive solute for
the STRIPS treatment . ... 164

5.30. Simulated distributions of a nonreactive solute for
the CENTER treatment . ... 166

5.31. Simulated distributions of a nonreactive solute with
uptake for the CONTROL treatment ... .167

5.32. Simulated distributions of a nonreactive solute with
uptake for the BUFFER treatment ... .170

5.33. Simulated distributions of a nonreactive solute with
uptake for the EDGE treatment ... .171

5.34. Simulated distributions of a nonreactive solute with
uptake for the STRIPS treatment ... .172

5.35. Simulated distributions of a nonreactive solute with
uptake the CENTER treatment . .. 173

5.36. Simulated distributions of reactive solute for the
CONTROL treatment . ... 177

5.37. Simulated distributions of a reactive solute for the
BUFFER control . ... .178

5.38. Simulated distributions of a reactive solute for the
EDGE treatment . ... .179

5.39. Simulated distributions of a reactive solute for the
STRIPS treatment . ... 180

5.40. Simulated distributions of a reactive solute for the
CENTER treatment . ... 182

6.1. Streamtraces for a total harvest treatment during C&W
weather conditions: day 60 . ... .199

6.2. Streamtraces for a control (unharvest) treatment during
M&M weather conditions: day 210 ... .200

A.1. Schematic of the different soil layers of the
simulated CPFFS . . 218

A.2. Daily rainfall for a "Wet" weather year at
Gainesville, Fl . ... .221


xiii










A.3. Daily rainfall for a "Mean" weather year at
Gainesville, Fl . ... 222

A.4. Daily rainfall for a "Dry" weather year at
Gainesville, Fl . ... 223

A.5. Spatial discretization for the simulated system 227














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

ENVIRONMENTAL IMPACT OF UNHARVESTED FOREST BUFFER ZONES UPON
CYPRESS-POND SYSTEMS IN COASTAL PLAINS REGIONS: MODELING
ANALYSES

By

Ali Fares

December 1996

Chairperson: Robert. S. Mansell
Major Department: Soil and Water Science

Hydrological and environmental impacts of tree harvest

in flatwood pine forests were investigated using modeling

analyses. A two-dimensional mathematical model was used to

predict surface and subsurface water flows and contaminant

transport in individual cypress-pond-flatwood-forest (CPFF)

systems. Published field data from individual cypress ponds

located in a CPFF in north central Florida successfully

validated the model.

Hydrology of the CPFF system is primarily controlled by

potential net water input (NWI = P-PET), simply defined as

the difference between precipitation (P) and potential

evapotranspiration (PET). Periods of groundwater recharge

in the flatwood forest tended to be associated with NWI < 0

whereas flow into the cypress pond tended to occur when NWI

> 0. The volume of surface water in the pond expanded

xv








during periods when NWI increased and shrank when NWI

decreased with time. Magnitudes of NWI within a given year

as well as between years were shown to be important to local

hydrology. A "mean" weather year based upon 91 years of

data provided a potential net water input of -155 mm yr-';

whereas "wet and cold" and "hot and dry" extreme weather

years provided +732 and -766 mm yr-', respectively. These

three weather years were used to simulate the hydrology for

unharvested, completely harvested and partially-harvested

CPFF systems. For a given weather year, harvest of mature

pine trees was shown to dramatically influence local

hydrology by decreasing NWI and increasing surface runoff.

Contaminant simulations for intact forest over annual

periods revealed that major contamination of water in the

cypress pond occurred only during the "cold and wet" weather

years. Negative net water inputs for "mean" and "hot and

dry" weather years greatly minimized the potential for

contamination of pond and runoff waters. Maximum potential

for contamination of cypress ponds occurred after complete

harvest of flatwood pine trees due to drastic decreases in

system ET. During a mean weather year, unharvested buffer

zones surrounding a pond during 75% harvest of the pine

forest provided significant control of contaminant movement

to the pond.














CHAPTER 1
INTRODUCTION


Research Background


Water managers have long recognized that major changes

in forest conditions greatly affect water use in local

streams and lakes. As early as 1600 BC, Emperor Yu of China

implemented a forest management program aimed at controlling

erosion and floods (Brown and Beschta, 1985). The slogan

for his national program was "to protect the river, protect

the forest." Early Greek scientists/philosophers similarly

realized that water flowing through a forest was much better

for drinking purposes than water in agricultural areas.

Increasing demand for wood fiber for paper products

and limitation of additional land areas for planting to

forest species has resulted in intensified forest management

practices (silviculture). Additionally, marginal lands

where an interface occurs between terrestrial and aquatic

ecosystems are sometimes used. Intensive management

practices in these relatively sensitive environments such as

the coastal flatwood landscape in north central Florida have

generated concerns about effects of forest practices on the

quality and quantity of surface water in adjacent wetlands

and ground water of the whole system. Harvesting

1










operations in forests such as thinning and partial cuts

cause a moderate increase in water yield, whereas clear-cuts

maximize flows out of the forest (Moore, 1988).

Augmentation of water flow naturally varies from site to

site and from year to year. Recently, alternative

management practices such as maintaining unharvested buffer

strips around surface water bodies (lakes, rivers and ponds)

have proven to be valuable agents in mitigating the impacts

of agricultural practices on surface and ground water

quantity and quality (Moore, 1988). However, relatively few

studies have examined the effectiveness of buffer zones for

mitigating the effects of silvicultural practices (Nutter

and Goskin, 1988).

Hydrologists are often requested to describe and

interpret the behavior of these forest systems. Although

some conclusions can be made using best engineering and

biological judgment, in many instances human reasoning alone

is inadequate to synthesize the conglomeration of factors

involved in analyzing complex hydrological problems. Field

experiments can be conducted to answer many of these

practical questions; however, such investigations are

commonly site specific, dependent upon climatological

conditions, and costly in time and resources.

Mathematical models based upon sound physical theory

can provide an alternative tool for assisting hydrologists

to meet the challenge of description and interpretation.










Hydrological modeling combines the subtlety of human

judgment with the power of a digital computer (Anderson and

Woessner, 1992). Mathematical models allow more effective

use of available data and can account for more complexity

(Hamilton, 1982). Such models have expanded current ability

to understand and manage water resources (Friedman et al.,

1984). Hamilton (1982) added that mathematical models, in

some cases, have increased the accuracy of estimates for

future events to a level beyond "best judgment" decisions.

Mathematical models are essential in performing complex

analyses and in making informed predictions concerning

consequences of a proposed action.

Models can be used in an interpretive sense to generate

insight into the controlling parameters in a site-specific

setting or as a framework for assembling and organizing

field data and formulating ideas about system dynamics

(Anderson and Woessner, 1992). Modeling provides an

excellent means to help organize and synthesize field data

but it is important to recognize that modeling is only one

component in a hydrological assessment and not an end within

itself. Reliable field data are essential when using a

model for predictive purposes. However, attempts to model a

system with inadequate field data can also be instructive as

they may serve to identify those areas where detailed field

data are critical to the success of the model (Wang and

Anderson, 1982.













Cypress swamps are the major deepwater forested

wetlands in the USA (Mitsch and Gosselink, 1986). These

swamps are intimately mixed with flatwood forest

plantations. Because of the perception that water and

nutrients move into the swamps from the entire surrounding

flatwood area, there is concern that forest management of

the uplands could result in changes in the water or nutrient

regimes in the swamps, which may eventually affect plant

community structure or biodiversity (Crownover et al.,

1995).

Field experiments have been conducted to evaluate

alternative forest management practices; however, such

studies are typically site specific, weather-dependent, and

costly in time and resources. Recent advances in computer

simulations of groundwater movement, solute transport, and

solute sediment interactions have provided useful research

tools to model the hydrogeologic complexity of wetland

systems (Richardson et al., 1994). Models offer practical

tools for optimizing two precious assets: time and money

(Salama et al., 1993). Modeling endeavors may lessen the

number of field experiments required to implement and

underscore major parameters and variables that most

influence this system. Models should also be utilized to

plan the design and operation of hydrological field

experiments in flatwood forests. Once hydrological











parameters for flatwood systems are specified, future field

work can be dramatically reduced and these parameters can be

realistically quantified, and the overall system response

can be determined.

Hypotheses


Two hypotheses provide the basis for this research

investigation. The first is that major aspects of water

flow and solute transport between cypress ponds and

surrounding flatwood pines forests can be simulated using a

computer model. The second hypothesis is that unharvested

forest buffer surrounding cypress ponds can be effectively

used to minimize the contamination of the pond water with

herbicides and fertilizers applied to the forest.

Objectives


This investigation is focused upon describing the

effective interaction of hydrological processes linking

individual cypress ponds and their surrounding flatwood pine

areas with respect to the use of unharvested forest buffers.

Specific objectives of this research include

1) To verify and utilize a mathematical model as a low-

cost means for describing complex water flow and chemical

transport fluxes within the interfacial transition zone

between a cypress pond and a surrounding flatwood forest.

2) To utilize the model to predict the hydrological and

water quality responses of a forest wetland system to an










unharvested buffer zone surrounding individual cypress

ponds.



Structure of the Dissertation


Six chapters are included in this dissertation. A

literature review presented in Chapter 2 provides i) a

comprehensive overview of the hydrology of the cypress pond

flatwood forest system (CPFFS) and ii) a synthesis of

information about current and alternative management

practices such as the use of unharvested buffer zones

proposed for forest wetland systems. Chapter 3 describes the

WETLANDS model and discusses the theory of twodimensional

numerical modeling of water flow, and solute transport in a

variably-saturated subsurface porous medium that is

dynamically linked to surface water bodies. Chapter 4

describes a prediction submodel based upon the Priestley-

Taylor equation for estimating the potential

evapotranspiration for multi-dimensional water flow in

soils. This submodel is utilized in conjunction with

WETLANDS model for water flow and solute transport in soil.

In Chapter 5, the WETLANDS model is utilized to evaluate the

effects of the tree-harvesting strategies, precipitation,

and potential evapotranspiration on the hydrology and the

solute transport within the CPFS. Chapter 6 covers the

discussion of simulation results and their practical

implications on the hydrology and water quality of the CPFS.














CHAPTER 2
HYDROLOGICAL ASPECTS OF CYPRESS WETLANDS IN COASTAL REGION
PINE FORESTS AND IMPACTS OF MANAGEMENT PRACTICES: LITERATURE
REVIEW


Introduction


Wetlands, as defined by the U.S. Fish and Wildlife

Service, are lands transitional between terrestrial and

aquatic systems where the water table is usually at or near

the surface or the land is covered by shallow water (Mitsch,

1979). Two important hydrologic factors, climate (rainfall

and evapotranspiration) and topography, interact to explain

the existence of wetlands in any landscape (Zoltai, 1988).

Depressions tend to collect water. Relatively level areas

do not have enough slope to create appreciable runoff and

low areas including floodplains receive runon water from

adjacent uplands in addition to periodic flooding.

Cypress (Taxodium ascendens Brongn.) ponds, also called

cypress swamps, the major deepwater forest wetlands (Mitsch

and Grosselink, 1986), are permanently wet depressions in

the landscape. Cypress ponds fluctuate in area and depth

during different seasons of the year. These ponds are

numerous in the flatwoods of much of the lower Atlantic and

Gulf Coastal Plain provinces of the southeastern U.S.











(Crownover et al., 1995). Improved understanding of the

complex interaction of hydrological processes in transition

zones between cypress ponds and associated flatwood pine

areas is a prerequisite to quantifying the environmental

impact of management practices on these systems. The use of

vegetative buffer zones or riparian strips has been

suggested to provide environmental protection for these

wetlands. Such buffer zones can be created by leaving

unharvested trees around individual cypress ponds during the

harvest of mature pine forests.

Cypress swamps and cypress swamp hydrology have been

the focus of several field investigations (Riekerk et al.,

1995; Sun, 1995). Some of these experiments targeted the

use of cypress swamps as waste water treatment localities

(Ewel and Odum, 1984; Gillespie, 1976; Heimburg, 1976).

Other field experiments have addressed the magnitude of ET

from the ponds and the impact of clearcutting upon the ET of

the area (Liu, 1996; Ewel and Smith, 1992). This chapter

provides a comprehensive review of the hydrology of the

CPFS, and a synthesis of information concerning current and

alternative management practices proposed for such systems.


Hydrology of Cypress Pond Flatwood Pine Systems


Determining water budgets for wetlands is an imprecise

task because of difficulties associated with determining

each of components and because of the climatically











controlled temporal fluctuations of wetland hydrology

(Carter, 1986). High uncertainty typically occurs in

experimentally determined water budgets for wetlands due to

difficulty in determining the magnitude of individual

inputs, outputs and changes in storage (Carter et al., 1979;

Winter, 1981). For some reported wetland studies, only one

water-budget component is measured or estimated.

Water entering, undergoing temporary storage, and

leaving a cypress pond flatwood pine system can be

quantitatively expressed in terms of a water budget (Hyatt

and Brook, 1984) (Fig. 2.1). The relation of water volume

changes in a pond to changes in water budget components for

the system per day can be expressed (Heimburg, 1984) as

follows:


AV
=Pp+Ps-ETp-ETs+ASsIu+RO (2.1)
At
where P, = rate of net rainfall falling on the pond (M3T );

Ps=rate of net rainfall falling on the flatwood area (M3T-1);

ETp= rate of ET losses from the pond (M3T-) ; ET, = rate of ET

losses from the flatwood (M3T-1); t= time period during which

the mass balance was calculated (T); AS,/, = is the variation

of combined water storage in groundwater and vadose zone

(M3T-1); RO = rate of surface runoff into (+) or out of (-)

the system (M3T-1); V is the volume of the pond filled with

water (M3). Some of the components of a cypress









10











4
3 3 6
i I






9
--lO O---l-------
%--------,--- ii-- -'L







1 Total Rainfall on Pond 6 Evapotransp ration From Surround ng
2 Net Rainfall on Pond 7 Runon Into the System
3 Evapotranspiration From Pond 8 Runoff Out of the System

4 Total Rainfall on Surrounding 9 Unsaturated Zone Water Storage
5 Net Rainfalt on Surrounding 10 Water Level in the Pond

Pine Tree y Cypress Tree 11 oWater in the Pond
12 Groundwater Storage


Figure 2.1 Hydrological components for cypress pond
flatwood pine systems











pond flatwood water budget as measured in the field are

summarized in Table 2.1.

Table 2.1. Measured and/or estimated cypress pond flatwood
pine water budget components.

Rainfall Evapotranspiration Runoff
(mm) (mm) (mm)
Reference Total Net Inte Pine Cyp- Mixed In Out
rcep ress Stand
tion
Heimburg 1070 770 300 n/a n/a 970 51 0
(1976)
Nessel & n/a 97.5 n/a n/a n/a 64 218 161
Bayley
(1982)
Riekerk 12
(1989) 1280 1080 200 n/a n/a 1110 n/a -
462
Ewel &
Smith 1420 660 n/a n/a n/a n/a n/a n/a
(1992)
Riekerk 900 158 795 974 1021 0 0
et al. 1059 + -
(1995); 841 218 73 61 1265 917 860
Sun,
(1995)

Precipitation (P)

More than half of the average annual rainfall (1370 mm)

(Dohrenwend, 1978) in north central Florida occurs during

the summer months. Minimal and maximal average monthly

rainfall amounts occur during November (44 mm) and in August

(208 mm), respectively. Measured long-term average monthly

rainfall amounts are presented (Dohrenwend, 1978) in Fig.

2.2. Rainfall in north Florida can be extremely variable




































Month of the Year


Figure 2.2 Absolute and relative (% annual amount)
monthly rainfall for Gainesville, Florida









13

from year to year, with standard deviations as great as 40%

of the mean (Dohrenwend, 1978). Rain accompanying winter

frontal storms is usually less intense than that associated

with convective (e.g. summer) activity and tends to be more

effective for recharge of soil and near-surface storage

because surface runoff tends to be minimized.

Total rainfall falling on cypress ponds represents

approximately 44% (960 mm) of total water received by the

pond (Heimburg, 1976). One quarter of the total rainfall is

intercepted by the vegetation and lost to the atmosphere;

thus, only 720 mm of the total annual rainfall typically

enters the pond as net rainfall.

Precipitation Interception (I)

During the initial stages of interception by a dry

canopy, much of the rain water is retained. There appears

to be a fairly well defined storage capacity for any given

canopy and, when this is exceeded, further intercepted water

(net precipitation) either drips from the canopy or runs

down the stems. Thus, the difference between gross and net

precipitation is designated as "interception loss".

Heimburg (1984) reported that interception by pond cypress

typically constitutes 25% of total rainfall. This is much

higher than the 12% recorded for north Florida slash pine

plantations by Voss (1975). In North Carolina, interception

was reported to be 13% of the incoming rainfall for a 10-

year-old loblolly pine (Pinus taeda) stand. The percentage











of rainfall intercepted increases with increasing plant

ground coverage as measured, for example, by the leaf area

index (LAI); thus, the intercepted rainfall by white pine in

North Carolina increased to 16% for a 35-year-old stand and

to 22% for a 60-year-old stand (Helvey, 1967). Waring et

al. (1981) found that interception is generally higher for

conifers (e.g. pines) than for deciduous forests, (e.g.

cypress). In a recent field study conducted in north

central Florida, Riekerk et al. (1995) reported that canopy

rainfall interception for an enclosed cypress wetlands was

52 T 11 mm, or 41% higher than for an open pine stand.

Evapotranspiration (ET)

Appreciable transpiration does not occur during winter

months for deciduous cypress trees; whereas coniferous pine

trees transpire year round. Bushes, vines and other

undergrowth species also contribute to ET, however.

Transpiration rates for vegetation tend to fluctuate

seasonally. Thus, water flow paths in the vicinity of

cypress ponds fluctuate in time and space. During wet

seasons (June-September) cypress ponds are recharged with

direct rainfall and subsurface flow from surrounding pine

flatwoods. However, during dry seasons subsurface water

moves laterally from the pond to the surrounding pines where

a significant but low ET demand remains.

In Florida, ET for slash pine (Pinus elliottii var.

Engelm.) has been estimated at 100 cm yr-1 or 80% of annual











rainfall (Riekerk, 1992). Golkin (1981) reported that

estimates of transpiration from cuvette-based measurements

in a 25-year old pine stand reached 110 cm yr-1. If we add

20 cm yr-1 for canopy interception (Riekerk and Korhnak,

1984), total ET amounts to 130 cm yr-1. Using a weighing

lysimeter, Riekerk (1985) found the average ET for a young

pine tree to equal 108 cm yr-1 or 84% of annual rainfall.

Brown (1981) calculated cypress transpiration at 108 cm

yr-1 using cuvette-based transpiration plus surface water

evaporation measurements in cypress wetlands. Canopy

interception, which was 15% of the annual rainfall (Ewel

and Smith, 1992), was not considered in these calculations.

The average ET of cypress trees with canopy interception

included is estimated at 84 cm yr -, which is less than that

of pine flatwoods (Riekerk, 1992). This difference can be

attributed to the deciduous and xeromorphic nature of

cypress foliage as compared to slash pine needles (Ewel and

Smith, 1992). During the summer when the cypress trees have

their leaves, ET is approximately 10% less than pan E; in

the winter, when cypress trees are without leaves, ET

reaches 50% less than PE (Ewel and Smith, 1992).

Riekerk et al. (1995) conducted a cuvette-based ET

study of an upland slash pine plantation with embedded pond

cypress. They found that annual ET by both forest types

ranged from 54 to 95% of annual rainfall and averaged 82

8%. Their data showed little difference in specific











transpiration by the two major tree species. Results of a

sensitivity analysis conducted recently by Liu (1996) with

an average LAI for cypress and slash pine trees predicted no

significant difference between ETs from both species. He

also predicted a maximum ET of about 1400 and 1200 mm yr'

from cypress wetlands and slash pine uplands, respectively.


Evaporation of Intercepted Rainfall


Coverage of the land surface by vegetation increases

surface roughness and enhances eddy transport of heat and

water vapor near the surface. Because of this physical

effect, evaporation of intercepted rain occurs at rates

higher than potential evaporation. Rutter et al. (1975)

found that evaporation of water intercepted on the canopies

of pine trees was 3 to 5 times greater than the potential

transpiration rate; this rate was in the range 0.2 to 0.5 mm

h-1 even in cloudy weather and in winter.



Runoff

In a full stand for a dry year, the yearly runoff out

of a cypress pond is nil; however, the amount of yearly

runoff can be as high as 30% of the total precipitation in a

wet year (Riekerk, 1989). In north central Florida,

average runoff was reported to be 240 mm yr-' for a young

pine plantation (Riekerk, 1985). Runoff measured by Tremwel

(1992) in Spodosols in the Lake Okeechobee Basin averaged











11-22% of total rainfall. Cypress ponds generally are

interconnected during wet seasons across the landscape,

though many are formed in isolated sinkhole depressions

(Spangler, 1984). The high hydraulic conductivity of

surface pine forest soils generates little surface runoff

except in saturated zones near cypress wetlands, where

runoff pathways may change depending on the degree of

flooding (Riekerk, 1992).

Clear-cutting a 40 year-old flatwood forest in north

central Florida significantly increased runoff by 150% or

150 mm/yr-' the first year after harvest (Riekerk, 1989);

however, this increase had dropped to 65% six years later.

Runoff occurrence is related to storm intensity and to depth

of the water table. The more intense the storm and the

higher the water table, the more likely runoff occurs

(Heimburg, 1984).

Dynamic Interaction Between Surface Water in Ponds and
Surrounding Groundwater

Three major scenarios have been observed in the field

for the dynamic coupling of water flow between cypress ponds

and groundwater levels in adjacent uplands. During the

first scenario (ground water discharge case) water may flow

into ponds from the surrounding area, making the pond a

focal point of surface drainage (forward flow) for excess

water (Crownover et al., 1995; Sun et al., 1995). In the

second case (ground water recharge case), water level in the

pond may be higher than the adjacent upland water table;











thus, water may flow away from the pond (reverse flow) to

irrigate surrounding forest vegetation (Crownover et al.,

1995; Heimburg, 1984). In the last scenario, water may

simply flow through the ponds (flow through case) in

response to local topographical gradients (Crownover et al.,

1995; Heimburg, 1984; Sun et al. 1995). Regional flow may

contribute to through flow.

During ground water recharge (reverse flow) conditions,

ET provides a dominant driving force for subsurface water

flow from the ponds into the subsurface of adjacent pine

flatwoods. Since groundwater flow typically changes

direction seasonally, solute transport should also follow

similar patterns. Thus, the hydrology of pond-flatwood

systems greatly influences the potential for chemical

contamination of ponds and subsequent drainage runoff

between ponds and nearby streams.

Heimburg (1984) reported that surface water levels in

cypress ponds tend to fluctuate much less than groundwater

levels in adjacent flatwood forests due to the porous nature

of the soil. For example, for 1 cm of water consumed from a

cypress pond by ET, the pond water level would decrease by 1

cm. However, within soil in a surrounding pine forest the

corresponding decrease in groundwater table depth can be

much greater than unity because of the finite porosity

(example: 0.40 cm3 cm-3) of the soil. Thus, water flow paths

in the vicinity of cypress ponds would be expected to be











complex in time and space. Even the frequency and

spatial/temporal distributions of rainfall must be

considered.


Current and Alternative Forest Management Practices


Clear cutting of mature (= 20-25 years) pine trees is

the conventional method practiced for forest harvest in the

southeastern Coastal Plain. Among the controversial aspects

of forest clearcutting is the potential for increased water

runoff induced by dramatically decreased ET. Nutrient loss,

erosion, flooding and subsequent degradation of surface and

groundwater quality may result from this sharp decrease in

ET (Hornbeck et al., 1975). One alternative to clearcutting

is progressive strip cutting, a form of clearcutting that

can be carried out in several cycles and over several years.

An alternative is to maintain designated buffer zones with

unharvested trees in the transitional area between pond

wetlands and flatwood forests so as to limit the flow of

runoff of water into the ponds. Cypress ponds often can be

considered simply as interconnected mini-wetlands that drain

coastal plain watersheds to local streams primarily as

surface runoff during wet seasons.


Buffer Zones

Buffer strips include vegetated filter strips (VFT),

buffer zones (BZ), or filter strips that provide bands of

planted or indigenous vegetation. They provide a means to











prevent or reduce the transport of sediment (primarily

during surface runoff), nutrients, and agrichemicals from

land management operations into aquatic environments.

Buffer strips are also used to mitigate the potential

adverse effects of local agricultural practices upon

adjacent surface waters (Comerford et al., 1992).

Buffer zones appear to be generally quite effective in

removing soluble nitrogen (N) from water moving through

these areas, largely by denitrification but also by

absorption by plant roots. More than 80% of N03-N is

commonly removed (Lowrance et al., 1984; Schipper et al.,

1989). Phosphorus (P) removal (uptake by plant roots and

sorption onto soil components) is more variable and

generally much less efficient than for N, having a range of

removal which varies between 0 to 90%. Other major plant

nutrients have not received the attention that N and P have,

mainly because of a perceived lack of importance in limiting

the production of terrestrial or aquatic ecosystems

(Comerford et al., 1992).



Best Management Practices (BMPs) in Cypress Pond Flatwood
Pine


Because cypress ponds are hydrologically sensitive to

forest activities such as harvesting of trees, BMPs are used

to regulate timber and pine harvesting activities in these

locations. Currently, harvested areas are classified into








21

three main categories (Florida Dep. of Agric. and Consumer

Serv., 1991): i) wetland areas less than 80 ha, ii) wetland

areas 80 ha and larger; and iii) units which contain five or

more small isolated wetlands, each less than 0.8 ha in size.

For the latter category, 20% of the number of isolated

wetlands must be retained unharvested until the regenerated

stands on the other 80% attain an average tree height of at

least 6 m (Florida Dep. of Agric. and Consumer Serv., 1991).

Special BMPs have been designated for regulatory areas

called Special Management Zones (SMZ). These SMZ areas are

specifically associated with identified surface water bodies

(lakes, ponds or streams). Such zones are designated and

maintained during silviculture operations. The purpose of

SMZs is to protect quality of surface water resources by

reducing or eliminating forestry-related inputs of sediment,

nutrients, logging debris, and chemicals that can adversely

affect the aquatic ecosystem. The SMZ has a specific width

that is prescribed by the size of the water body, the soil

type, and the slope percent.

Evaluating Alternative Management Practices

The impact of intensive forest management practices on

water quality and quantity can have both short- and long-

term effects. Simultaneously minimizing these detrimental

environmental effects on water resources and optimizing

forest productivity requires an adequate understanding of

the magnitude, timing and duration of hydrological responses









22

to such activities. Conducting field experiments to assess

the long-term effects of pine harvest on the hydrology of

cypress pond/flatwood areas is costly in time and resources,

site-specific, and can only provide answers after long

times. Mathematical models offer practical tools for

assessing long-term hydrological effects of harvesting

(Salama et al., 1993). Modeling endeavors can minimize the

number of required field experiments and underscore

important parameters and variables that most influence such

systems. A modeling approach also offers a means to

evaluate the relative importance of hydrological and solute

transport processes with advantages of minimizing cost,

time, and effort. Once this information is known, future

field work can be reduced significantly and realistic

quantification of these parameters and overall system

response can be estimated.

Recently, Salama et al. (1993) used the numerical model

MODFLOW (McDonald and Harbaugh, 1988) to predict the

effectiveness of different management options applied to

catchments in reducing groundwater discharge and water table

rise in low-rainfall saline drylands with hillslopes in

Western Australia. Results from their simulations revealed

that increasing ET by planting 25% of the catchment with

deeply rooted trees should effectively decrease groundwater

levels and, consequently, reduce the discharge of saline

groundwater at the base of hillslopes. Such predictions











agree with previous studies (Bell et al., 1990; Schofield,

1990) for high rainfall areas where 25-50% of reforestation

has been shown to be a requirement for the control of

groundwater discharge from hillslopes. Location of the

trees on hillslopes is also an important factor. Trees

planted in recharge areas are well known to reduce recharge

into aquifers through increased interception and

evapotranspiration, and direct water extraction from the

water table provided that plant roots are sufficiently long

to reach it.

Modeling the impact of different management practices

on the hydrology of cypress pond/flatwood systems requires a

multi-dimensional water flow and solute transport numerical

model. This model should be able to dynamically link

surface water in ponds and underlying groundwaters in

surrounding areas. WETLANDS, a modified form of the VS2DT,

Variably Saturated Two-Dimensional Transport model,

developed by the U.S. Geological Survey (Healy, 1990;

Lappala et al., 1987) was utilized for that purpose.

Modifications of VS2DT included i) incorporation of a

variable-geometry pond with variable water level; ii)

incorporation of a sub-model to estimate potential

evapotranspiration, using the Priestly-Taylor Equation and a

minimum set of daily weather data such as minimum and

maximum temperature, latitude and altitude of the location

and sunshine ratio; iii) incorporation of the capacity to











use different plant root distributions; and iv)

incorporation of a dynamic linkage in time between free

water in a pond and surrounding water in the porous

subsurface (soil water and groundwater) (Bloom et al.,

1995). Thus, a multi-dimensional water flow and solute

transport modeling effort was undertaken to simulate a

dynamic link between one or more surface water bodies to

surrounding subsurface (Fares et al., 1993; 1994 and 1995).

Conclusions



A need exists to improve current knowledge of wetland

hydrology for cypress pond/pine flatwood ecosystems. The

hydrology of cypress pond/flatwood pine ecosystems is

dominated by relative flat landscape; hence,

evapotranspiration (ET) and rainfall (R) provide the primary

hydrological outputs and inputs respectively. The

difference R ET will be used here as net water input.

High infiltration rates for water unsaturated sandy soils in

flatwood pine forests tend to minimize the generation of

surface runoff. Total-harvest scenarios for flatwood pine

forests introduce controversial aspects such as increased

water runoff induced by decreased ET and elevated

groundwater tables. Nutrient loss, erosion, flooding and

subsequent degradation of surface and groundwater quality

may result from total harvest. Alternative harvesting

strategies such as progressive strip cutting and locating












unharvested buffer zones in the transitional zone between

pond wetlands and flatwood forests have been proposed for

controlling water runoff into cypress ponds. Conducting

field experiments to assess the long-term effect of pine

tree harvesting on the hydrology of the cypress pond

flatwood area is costly in time and resources, and is

typically site-specific. Numerical hydrological models

offer an alternative approach to predict the effectiveness

of specific forest management strategies in controlling

water table fluctuations and subsequently minimizing

chemical contamination of surface water resources. A need

exists to utilize a multi-dimensional mathematical model

such as WETLANDS for the purpose of investigating

hydrological and environmental impacts for the use of

unharvested buffer zones in flatwood pine forests

surrounding cypress ponds in North Florida.














CHAPTER 3
THEORY OF WATER AND SOLUTE MODELING


A cypress pond flatwood forest system (CPFFS) is a

combination of a cypress pond and its surrounding flatwood

area (Fig. 2.1). In order to simplify the modeling process

of this complex, heterogeneous and dynamic environment three

simplifying assumptions were made. The first assumption was

that symmetry occurs along a vertical line passing through

the center of a circular pond with a maximum radius. The

second assumption was that a groundwater divide with zero

horizontal water flux was present along a vertical line at

the edge of the simulated physical system. The third

assumption is that the subsurface hydrologic properties are

homogeneous and isotropic within a given soil layer. Thus,

effective parameters averaged over the spatial domain of

interest must be used in all simulations. In effect, these

assumptions reduce the simulation domain to a large circular

cross-sectional area over the subsurface flow domain

surrounded a smaller circular pond. In other words,

regional groundwater flow was neglected. These two

assumptions require that only half of the system be

simulated (Fig. 3.1). In the model, both surface water in

the pond and subsurface groundwater flow are modelled by










































Figure 3.1. A cross-section of the simulated system.











simultaneously solving two coupled equations. The first

equation is a mass balance equation based upon all inputs

and outputs for water flow into and out of the pond

(Fig.3.1). Rates for water volume changes in the pond can

be expressed using the water budget as follows:

dV
dt Pp-Ip-Ep+G -Go-SF + SFi (3.1)


where Pp (1) = the rainfall rate on the pond (M3T"'); Ip (1-2)

= the rainfall interception rate by the cypress and pine

trees in the pond (M3T') ; Ep (3) = surface water evaporation

rate for the pond (M3T-) ; Gi (4) = the subsurface inflow

rate for the pond (M3T1) ; Go (5) = the subsurface outflow

rate for the pond (M3T"'); SFi (7) = the surface inflow rate

for the pond (M3'T) ; SF0 (6) = the surface outflow rate for

the pond (M3T"'); V the volume of the pond (M3); t the time

period when the mass balance is evaluated (T).

Equation (3.1) is coupled with Richards equation for

two-dimensional variably-saturated water flow (for

homogeneous and isotropic porous media) in the subsurface

porous medium surrounding the pond (Eq. 3.2).

ae aOg, aq,
at ax az Qwa, (3.2)



where qx and qz are the horizontal and vertical components

of water flux, respectively, and Q.ater is a sink term.

Equation (3.2) can be expanded and rewritten as follows:












C, (h) =a ( K(h) a + aK (h) -a -Qw (3.3)
-7at Ox ax- 8zKh az) az



where the water pressure head h(x,z) = H(x,z) z, H(x,z)[L]

is the hydraulic head, z is depth below the soil surface

[L], t is time [T], K(h) is the hydraulic conductivity [L T'
'] for a given pressure potential (h) value, Queter is a

transpiration sink term of dimension [L3 L-3 T-1] or simply

[T'1], C. [ L-1] is the specific water storage capacity (aQ/ah

= C.) for a given value of h.

The sink term, Quater' for water uptake by plant roots in

Eq. (3.2) depends upon the soil water pressure potential and

was defined by Feddes et al. (1978) as

Qwater(h) = a(h)S, (3.4)


where the water-stress-response function a(h) is a

prescribed dimensionless function (Fig. 3.2) of the soil

water pressure head (0 5 a(h) 1), and Sp is the potential

water uptake rate (T'1). The water-stress-response

function, a(h), is dependent upon plant type. Water uptake

by plants is considered to be zero (Qwater = 0) when the soil

water pressure head is less than or equal to the wilting

point pressure head (h4 = h = -15,000 cm of water). As

the soil water content increases such that the corresponding

pressure head increases above h the water uptake





















Water Stress Response Functions


Field Crops -***. Wetland Plants

Figure 3.2. The water stress function for field crops and
wetland plants.










increases linearly until it reaches an optimal value of S(h)

where h : h3. The pressure head range h S5 h S h3

corresponds to a range of volumetric water content 6 5

8(h) < 83 and a range of unsaturated hydraulic conductivity

K(h ) S K(h) 5 K(h,). The water uptake is considered

optimal between pressure heads, h2 and h3, whereas for heads

between hI and hz, for plants that are not adopted to

wetland environment, water uptake decreases linearly with

increasing h. Water saturation of soil pores occurs at a

pressure head hi = 0 cm water and water content of .sat. For

most field crops, Qwater becomes limiting for water content

near esat due to the development of anaerobic conditions for

plant roots. Pine flatwood and cypress trees are adapted to

shallow water table environments, having root systems

completely or partially under water during at least part of

the growing season. Therefore, water uptake by pine and

cypress trees was assumed to continue at optimum rates even

under water-saturated conditions.

The potential water uptake rate over a root distribution

zone in the subsurface flow domain was defined as:

Sp = b(x,z)LPT (3.5)

where L, is the width (L) of the soil surface associated

with the transpiration process, b(x,z) is the normalized

water uptake distribution (L2), and PT is the potential

transpiration rate (LT-1). The normalized water uptake

distribution function, also called the root effectiveness











function, describes the spatial variation of the potential

extraction term, Sp, over the root zone. Nimah and Hanks

(1973) linked the root effectiveness function to the weight

fraction of the roots in a depth interval relative to the

total weight of the roots. The summation of the root

effectiveness function over a given soil spatial domain

equals unity. Van Rees (1984) found that fifty-nine percent

of the total pine root mass was in the upper 40 cm of the

soil, while 25% occurred in the 1.4 m thick argilic horizon.

Consequently, for simulations reported in this dissertation

60% of pine root water uptake was assumed to occur in the

upper 40 cm of soil. Another 25% of water uptake was

assumed to result from roots located between soil depths of

40 and 140 cm and the remaining 15% of water was extracted

from the 140 cm to the bottom of the soil domain at 250 cm

depth.

Plant-root water extraction is determined by both the

potential transpiration, PT, and the capacity of the porous

media to satisfy this demand. If the soil profile is wet

and soil water pressure head occurs within the range h3 S h

< h2, then the rate of root extraction is equal to the PT.

In this case, the transpiration rate, T, is equal to the

potential transpiration, PT. However, if water flow in the

soil profile can not satisfy the imposed transpiration

demand, PT, the transpiration rate, T, will be less than the










PT. Consequently, the actual distribution of water uptake

within the soil will be as follows:

S(h,x,z) = a(h,x,z) b(x,z)LPT (3.6)

The potential transpiration used in equations (3.5 &

3.6) is applicable to plants with constant leaf coverage;

however, deciduous plants such as cypress trees and annual

plants have a variable leaf area index during the year.

Consequently, the potential transpiration must be adjusted

using a transpiration function (Hanks, 1992; Smajstrla,

1982) to describe the impact of a variable LAI on the PT.

More details are given about that transpiration function in

the next section.

Estimating Evapotranspiration Using the Priestley-Taylor
Approach

Priestley and Taylor (1972) used the following equation

to predict an average potential evapotranspiration PET

(mm/day) rate:


,n-G) (3.7
PET = -(37)
A + y




where y (J kg-') is the psychrometric constant (mb K-1 ), X

(J m3) is latent heat of vaporization; A (mb OK') is

gradient of the saturation vapor pressure-temperature curve

evaluated at the air temperature Ta, Rn (W m"2) is net solar

radiation, G (W m"2) is soil heat flux, a is the Priestley










and Taylor coefficient, which varies between 1.05-1.38

(Viswanadham et al., 1991). However, Black (1979)

recommended using a = 0.8. In the case of coniferous

forests with no intercepted water, values of a are generally

between 0.6 and 1.1 (Spittlehouse and Black, 1981).

The Priestley-Taylor equation is widely used to

estimate PET because it is computationally simple and

requires measurements of only mean air temperature and solar

radiation (Stagnitti et al., 1989). Additional information

on how to determine individual components of the Priestley-

Taylor equation and consequently, PET are given in detail in

Chapter 4. Given the high evaporation rates of the

intercepted precipitation on the vegetative canopies of pine

and cypress trees, 3 to 5 times greater than the PET rate

(Stewart, 1977), the assumption is made that it will not

reduce the actual ET from the trees themselves.

The evapotranspiration rate (ET) combines water losses

through both evaporation from soil or free water surfaces

(E) and transpiration (T) from vegetation. Ground cover

greatly influences the magnitude of ET. When plants cover

only a small portion of the soil surface, ET is dominated by

E. As plant cover increases, evaporation from the soil

surface or free water surface (in the pond), decreases and

the relative importance of transpiration increases. Several

empirical models (Tanner and Jury, 1976; Smajstrla, 1982)

related potential transpiration to leaf area development and

















was computed by multiplying PET with the transpiration

coefficient (Hanks, 1985), an empirical function to account

for the degree of leaf cover and consequently transpiration

variation. This transpiration coefficient should not be

confused with the crop coefficient, usually set equal to

0.7, that is used to convert measured pan evaporation from a

free surface water into PET (Hanks, 1992).

Recently, Liu (1996) showed that seasonal changes in

LAI for pine and cypress trees in CPFS showed different

patterns. Mature coniferous pine trees (30 years old) had a

LAI that varied around 3.5 (in 1993) throughout the study

period. However, deciduous cypress trees, lose their leaves

during part of the Fall and all the Winter. Figure 3.3

shows LAI measured by Liu for three cypress ponds in 1993.

Thus, a transpiration function identical to cypress trees

LAI was used in the WETLANDS model to adjust seasonal

variation in cypress PT through the year. In other words,

it was assumed that the potential transpiration rates for

the cypress trees followed similar patterns as the LAI. The

leaf area index for the pine trees was assumed to be

constant at 3.5 through the year for a mature pine stand.




























Cypress LAI & Transpiration Coefficient


Selected Days of the Year


1*.. Llu, 1996 --* Liu. 1996 Liu, 1996 Trmp. Coef

Figure 3.3. Transpiration coefficient for cypress trees.








37

Potential evaporation. Potential evaporation (PE) from

the soil surface can be estimated as a proportion of the

total evaporative potential (McCarthy and Skaggs, 1992;

McKenna and Nutter, 1984). Developed by Ritchie (1972) and

modified by McKenna and Nutter (1984) to apply to forest

conditions, the potential evaporation from the soil surface

is defined as follows:

PEso = (PET)*Exp(-0.4*LAI) (3.8)


Precipitation interception (I). The amount of water

present at saturation during rainfall is approximated by a

layer of water 0.2 mm thick over the upper surface of the

foliage (Rutter, 1975). The saturated interception

capacity, Isa, of the canopy is estimated as follows:

Iat = 0.2 LAI (mm) (3.9)


where LAI is the leaf area index of the canopy. Daily

interception (I) is calculated using

I sat p> Isat (3.10)
I =P P < I.t




where Isat(mm) is the saturated interception capacity, P (mm)

is the daily precipitation. Interception losses commonly

range from 15 to 40% of annual precipitation in coniferous

forests and from 10 to 25% in deciduous forests (Rutter,











1975). According to Monteith (1965), Rutter (1967) and

Stewart and Thom (1973) the ratio of evaporation rate of

intercepted water to transpiration rate is in the order of 4

or 5:1. Especially in forests, there is evidence that

evaporation of intercepted water and transpiration occur

simultaneously (Van der Ploeg and Benecke, 1981).

Hydraulic conductivity. Hydraulic conductivity for

variably-saturated soil has been described as a function of

soil water suction head h (or water content 0) using the

analytical model of van Genuchten (1980).


K=K l[l+(a*h)n] [ l-(a*h)"'-(l+(a*h)n)-"]2 (3.11)



where Ks is the hydraulic conductivity at water saturation

of the soil, m = 1-1/n is a constant, n is a constant, and a

is a constant. Values for n, m and a are obtained by

fitting the equation

8 e
S. = r-[l+(a*h)']- (3.12)




to experimental data (8 versus h) from the literature, where

Se is the effective degree of water saturation, 6O is the
residual water content and 6s is the water content at

saturation.








39

Boundary conditions. The solution of the flow equation

(3.2) requires knowledge of the boundary conditions along

each side of the 2-dimensional region of interest. The

different boundary conditions specified here are described

along the simulated system (Fig. 3.1).

The bottom of the system and the two vertical

boundaries were prescribed as no-flow boundaries. The upper

boundary of the system is a flux boundary that can receive

water as rainfall or lose it through evaporation. The

boundary conditions for the water flow were, therefore,

ah
-K(h)h + K(h)=qo(t,x) for,xe [C,D] (3.13)
8z


-K(h) = 0, forzE[D,E], [F,A] (3.14)



-K(h) = 0, for xE[E,F] (3.15)
az


-K(h) -K(h)K(h)((Kh) = qp(t,x,z) for, xe [C,G] (3.16)


where q%(t,x) is the rainfall flux imposed along the soil

surface at a given time t, qo (t,x,z) is the flux that is

either lost from the subsurface into the pond (qop < 0) or

entering the subsurface (qop > 0) from the pond as pond

water or rain falling over the exposed areas of the pond.

Initial conditions. The solution of the Richards'

water flow equation requires knowledge of the initial

distribution of the pressure head within the flow domain:










h(x,z,t) = h,(x,z) t=0 (3.17)

where h. is the initial pressure head.

Surface runoff. The upper surface of each soil column

receives or loses water according to the prevailing rainfall

or surface evaporation. Water reaching the soil surface

(rainfall) is partitioned into infiltration and excess

water. The excess water at the soil surface is handed to

next node on the left during the next time step. If excess

water occurs, the process is continued for the subsequent

nodes, until the excess water reaches the free surface water

of the pond. Thus, runoff is approximated as a one-

dimensional process directed toward the pond over the upper

surfaces of successive soil columns. This is a very

simplified implementation of the surface runoff; however,

more rigorous approaches are described in the literature.

Solute Transport


Solute transport in this system was simulated using the

advective-dispersive solute transport equation (Eq. 3.21) in

the subsurface coupled with a solute mass balance equation

for the solute present in the pond. The solute mass balance

equation for the pond is represented as follows:

dMP
dt = Ml + Mi MG-M + MsRi


where Mp = the total solute mass in the pond (M); MEp = the

rate for solute mass input into the pond as net

precipitation (P-I) over the pond (M T1); MGi & MGO = rates









for inflow & outflow subsurface solute mass for the pond,

respectively (M T1); Ms,, = the rate for solute mass entering

the pond with surface runoff from the flatwood (M T1); MFO=

the rate for solute mass leaving the pond with surface flow

out of the pond (M T'1); t = time when the mass balance is

evaluated (T).

Convection-Dispersion Transport Equation

The convection-dispersion equation describing solute

transport during variably-saturated, transient water flow

through a porous medium is developed by a combination of the

continuity (conservation of mass) and Darcy's equations and

can be written as

a aF aF
a (ec) x z Qsolute (3.19)



where 9 = volumetric moisture content, dimensionless; c =

concentration of chemical constituent, (ML)3); t = time,

(T); Fx is the horizontal component for convective-

dispersive solute flux, Fz is the vertical component for

convective-dispersive solute flux; and Qsolute a sourse/sink

term, (ML'3T"1). Expanding equation (3.19) results in


ac a(oDh h2ax oaDh) C c ac qac Q rQ(3.20)
at ax 9z ax 8z (-te sl3.20)


Where qx(L T"1) is the horizontal component for water flux,

qz(L T"1) is the vertical component for water flux. The










source/sink terms can be divided into two general

categories: solute mass introduced to or removed from the

domain by fluid sources and sinks; and mass introduced or

removed by chemical reactions occurring within the water and

the solid phase (Healy, 1990).

The first category of source/sink terms that have been

used in this study can be represented by


Qsoute= c* q (3.21)


where c* = mass concentration in a fluid source/sink, (ML-3);

q = strength of fluid source/sink, T'1. If q is positive,

the flow is into the system (injection well); however, when

q is negative, flow is out of the system (plant uptake). In

both cases (into or out of the system), the solute

concentration, c*, of the outflow or the inflow should be

specified. In the original code of VS2DT, the user can

specify only the inflow concentration; however, the

concentration of the outflow, c*, was set equal to the

ambient solute concentration at the location where flow is

leaving the system that is: c = c*, (Healy, 1990).

A second category of source/sink terms includes

sorption of the solute from the aqueous phase to the solid

phase and desorption in the reverse direction. Sorption and

desorption processes are physically and chemically driven.

If the sorptive process is rapid compared with the rate of

water flow, the solute may not come to equilibrium with the









sorbed phase, and a kinetic sorption model is need to
describe the process (Fetter, 1993). Groundwater movement

is usually a slow process; thus, reversible, instantaneous

adsorption (equilibrium controlled) is a practical

assumption. For all simulations reported in this
dissertation, solute in the soil solution and the sorbed
phase are assumed to be at equilibrium. The rate of change

of solute mass in the sorbed phase is defined as follows:

BS 8c
Qsolute = Pb8c st (3.22)


where S is the concentration of solute mass in the solid

phase, (MM1'); pb is the soil bulk density, (ML3). In this

dissertation, a linear sorption isotherm equation was used
in simulating the sorption of the reactive solute.

The linear sorption isotherm is defined by the

relationship as follows (Fetter, 1993):

Sp ac (3.23)


where kf is the Freundlich adsorption constant.

Boundary Conditions for Solute Transport

The boundary conditions applied to the simulated domain

in this study were as follows:

-eD '- ) + qxc = 0, z (D,E);(A,F) (3.25)


-e(D -- = 0, x e (E,F)


(3.26)












-e Dz + qc = qoci, x e (C,D) (3.27)


ac ac+
-0 Dx.a + z/ x) ,c+ qgc = gqc), (x z) E (G, C) (3.28)


where ci is the concentration of the incoming fluid; Di is

the dispersion coefficient in x or z direction, qo is the

incoming water flux, '* can be the incoming flux from the

pond or rainfall over the pond in case (q,* > 0); q,* can

also be from the subsurface into the pond (qo* < 0), Cip is

the concentration of the incoming fluid (qo* > 0) or the

concentration of the outgoing fluid (qg* < 0) which is equal

to the concentration of the boundary node.

Initial Conditions for Solute Transport

Solving the main solute equation requires specification

of the initial conditions for the flow domain:

c(x,z,0) = co(x,z) (3.29)




where co is a prescribed function of x and z. co can be

constant through the system or variable.

Numerical Solutions

Water Flow Equation

The water flow equation is a nonlinear, partial

differential equation that has no general closed-form or

analytical solution. Thus, numerical approximations to its











spatial and temporal derivatives must be made. These

approximations result in a set of simultaneous, nonlinear,

algebraic equations that must first be linearized and then

solved. The spatial derivatives in the flow equation were

approximated by a block-centered regular finite-difference

scheme. More detailed information about the numerical

technique used here is provided by Lappala et al. (1987).



Solute Transport Equation

The parabolic equation for solute transport was solved

using a finite-difference approximation. The spatial

discretization of the advective component of the solute

equation can be either central or backward differencing.

The time derivative of the solute equation can be

approximated either with a fully implicit (backward-in-time)

method or an implicit-explicit, centered-in-time (Crank-

Nicholson) approximation. More detailed information about

the numerical techniques used can be found in Healy (1990).

Selection of a given approximation method is problem

dependent (Healy, 1990). Backward-in-time differencing is

first-order accurate in terms of At; however, backward-in-

space differencing is first-order accurate in terms of Ax.

Although the Crank-Nicholson method is more accurate than

the fully implicit, it can produce oscillating results

around the true solution under certain conditions. Although

fully implicit time differencing eliminates such










oscillations, it can introduce numerical dispersion or

smearing of sharp fronts. Numerical dispersion can be

controlled by limiting the size of each time step. In order

to insure minimal numerical dispersion relative to physical

dispersion, Kipp (1987) recommended enforcement of the

following two conditions:

Ax
A << a,
2
(3.30)
IV/ At
< 2 L

where -L = longitudinal dispersivity of the porous media, L;

IvI = magnitude of the velocity vector, LT1'; x, is the

horizontal direction and t is the time.

A disadvantage of the central difference technique is the

numerical oscillation. Overshooting and undershooting near

sharp concentration fronts for the central difference

approximation induce these numerical oscillations. If the

following condition is met, numerical oscillation does not

occur:

I JI IIA 2 (3.31)
I1 hJ, IDhj



where Dhzz and Dhxx are the coefficients of hydrodynamic

dispersion in the horizontal and vertical directions,

respectively.

In practice, it is difficult to meet this condition and

a little more leeway is allowed especially in cases that do











not involve sharp concentration fronts (Healy, 1990). In

general, the implicit scheme is recommended in most cases.

It is essential to remember that numerical solutions are

approximations of reality both in time and space; thus, the

finer the grid, in space, and the smaller the time step the

better the resulting solution. On the other hand, finer

grid and smaller time steps may require tremendous computing

times.

Model Verification for Water Flow


Validation of the WETLANDS model was performed using

field data collected in the flatwoods of north central

Florida by other investigators (Riekerk et al., 1992, 1993,

1994; Sun, 1995; Liu, 1996). The research site is located

33 km northeast of Gainesville, FL. Long term studies

(monitored since 1991) were conducted for 3 ponds

(designated as C, K, N) and their surrounding flatwood pine

forest. Two main tree species dominated the system, cypress

(Taxodium ascendens) in the wetland along with slash pine

(Pinus elliotti) in the pine forest (Sun, 1995). Pond water

levels and groundwater table elevations in a flatwood pine

forest were recorded continuously with punch-tape recorders.

A plot 25 by 25 m was established for each wetland (Liu,

1996) to investigate ET for cypress and pine trees. There

was no surface inflow into pond C; however, during wet

periods, mainly during winter time (Sun, 1995), surface

inflow from adjacent upstream wetlands flowed into K and N












ponds. At the same time they were receiving inflow water

from one side, the ponds were loosing water as outflow from

another side. Thus, the estimated net inflow into these

ponds ranged from less than 1% to 4% (Sun, 1995). No

recording flumes were installed at the inlets of the K and N

ponds, but the surface inflow recording were calibrated by

periodic measurements of flow velocity and stage height at

the culverts. Elevation data from a 10 by 10 m grid system

was used to estimate the volume of each of the three ponds.

The annual rainfall of 1512 mm in 1992 was higher than

normal, but 1993 and 1994 were dry years with rainfall

amounts of only 1137 and 1240 mm, respectively. Detailed

information about the study can be found in Riekerk et al.

(1993, 1994, 1995); Sun (1995) and Liu (1996). In the

spring of 1994, cypress ponds N and K were harvested along

with flatwood pine adjacent to N pond. However, pond C and

its surrounding remained as an unharvested control.

The hydrology of these three different cypress ponds

and their surrounding flatwood area were simulated using

WETLANDS model. Most of the input parameters used in these

simulations were collected at the research site. Data for

soil water characteristic curves of the different soil

layers were used from another nearby flatwood site (Philips,

1989). Riekerk et al. (1993) observed that the hydrologic

influence of the cypress ponds in question appeared to be

about 25 m into the flatwoods landscape. Lateral and











vertical groundwater movement in the flatwood forest was

limited to areas adjacent to the pond (Sun, 1995). Given

these field observations, physical limits of the simulated

systems were set to 50 m from the edge of each pond. The

radius of each pond was determined based on the estimated

pond volumes, surface areas and depths determined by Sun

(1995). The validation process included comparing different

measured hydrological components to their comparable

simulated values for each wetland. Components included,

daily pond and ground water table elevations. Measured

daily pond water elevations data sets utilized were from

1992 to 1994. However, ground water table data cover the

period from April 1993 to December 1994. Transpiration data

for the same period (1993-94), measured at the leaf\needle

level, for both pine and cypress trees in the three

different cypress ponds, N, P and K were used as part of the

validation.

Measured ground and pond water elevations were

referenced to an arbitrary benchmark level of 30.4 m above

the mean sea level(Sun, 1995) for the research site.

Average daily water elevations of the three ponds in 1992,

1993 and 1994 were compared to appropriate simulated values

in Figures (3.5 3.13). Pond water levels were high for

all three ponds during most of the 1992 wet year and showed

less variability compared to the other two years 1993 and

1994. Lower levels occurred for the different ponds because










50

of low rainfall and high ET demands during the driest part

of the year, spring. Simulated and measured pond water

levels followed similar patterns. Mean percentage error MPE

which quantify differences between measured and simulated

data can be calculated in different ways. The obvious way

is to used both simulated and measured data based on the

bench mark (30.4 m MSL) as reported by Sun, 1995 & Riekerk

et al., 1996. However, the MPE can be determined after

modifying both measured and simulated data using a new

reference level which is the bottom of the simulated

physical system. Thus, pond and ground water levels will be

reported as elevations above the lower limit of the

simulated physical system (2.5 m from the soil surface).

Standard deviations (SD) and percentage mean differences

between measured and simulated pond water elevations were

calculated for each of the three ponds for each of the three

years based on the first type of MPE calculation:

Mean Percent Error = Meas. Elev. Simu. Elev. 100 (3.31)
Meas. Elev.

Values of the MPE and their corresponding standard

deviations were less than 1% indicating excellent








Measured & Simulated Pond Water
Elevations, Pond C; 1992


O dU.


o 29.

l 00
rn


75 125 175 225 275 325
Day of the Year


- Measured -....... Simulated


Figure 3.5. Measured and simulated water elevations for the C wetland in 1992. un
I-.








Measured & Simulated Pond Water
Elevations, Pond C; 1993


150 200
Day of the Year


Measured ............ Simulated

Figure 3.6. Measured and simulated water elevations for the C wetland in 1993.


C
c-
2 29.95-

W 29.85-









Measured & Simulated Pond Water
Elevations, Pond C; 1994


150 200
Day of the Year


Measured ........... Simulated

Figure 3.7. Measured and simulated water elevations for the C wetland in 1994. L
W


C
L2 29.


w 29.








Measured & Simulated Pond Water
Elevations, Pond N; 1992


Day of the Year


Measured .......... Simulated
Figure 3.8. Measured and simulated water elevations for the N wetland in 1992.








Measured & Simulated Pond Water
Elevations, Pond N; 1993


Day of the Year


Measured -.- Simulated
Figure 3.9. Measured and simulated water elevations for the N wetland in 1993.








Measured & Simulated Pond Water
Elevations, Pond N; 1994


Day of the Year


Measured ............ Simulated

Figure 3.10. Measured and simulated water elevations for the N wetland in 1994.








Measured & Simulated Pond Water
Elevations, Pond K, 1992


150 200
Day of the Year


Measured -...... Simulated

Figure 3.11. Measured and simulated water elevations for the K wetland in 1992.








Measured & Simulated Pond Water
Elevations, Pond K, 1993


Day of the Year


Measured --....... Simulated
Figure 3.12. Measured and simulated water elevations for the K wetland in 1993.








Measured & Simulated Pond Water
Elevations, Pond K, 1994
30.25
MPE = -0.348, SD = 1.3%
30.15-

30.05- -
N- Harvest
23.5 -



> 29.85-


29.65-

29.55- -
0 50 100 150 200 250 300 350
Day of the Year


Measured ........- Simulated
Figure 3.13. Measured and simulated water elevations for the K wetland in 1994.










performance of the model in simulating these CPFS. It is

obvious that these low levels of MPE are expected to

increase if the second type of MPE calculation was used.

Because 1993 was a dry year, all three ponds were dry for a

long period of the year, especially during spring and summer

seasons. Early spring of 1994 was dry; 1994 was dry;

consequently, water levels in the ponds decreased rapidly

from high levels during the winter time to a very low level

during the end of the month of March. During the months of

April and May of 1994, ponds N and K were harvested along

with the pine flatwood surrounding pond N; however, pond C

and its surrounding were left intact as a control.

Consequently, and as a result of combined effects of

harvesting and rainfall events, both N and K ponds had

higher water levels compared to the control C pond in early

June. Even though the simulated values followed the general

patterns of the measured pond water levels, differences

between simulated and experimental data were encountered.

Differences can be partially attributed to the disturbance

caused by the harvesting process upon the hydrology of these

systems since simulated results for the C pond (control) had

less variation from the measured values compared to that for

the other two ponds.

The maximum surface area of the wetland in the flatwood

system plays an important role in determining the runoff;

Riekerk (1992) suggested that the average annual runoff












decreased somewhat with an increase of percent cypress

wetlands area based on statistical data for 10 flatwood

watersheds. The surface area occupied by surface water in

the pond varies considerably through the year. Using

simulated pond water elevations and pond surface area and

volume estimation by Sun(1995), total surface area for water

in the N pond was simulated (Fig. 3.14) for wet (1992) and

dry (1993) years. During the first 150 days of both years,

the two ponds had a very similar variation in surface area.

Afterwards, however, two different patterns occurred for the

same pond at the end of the winter. In the case of the wet

year, the pond surface area was very close to its maximum

area for most of the year with relatively small decreases

occurring occasionally. During the dry year with a dry

summer, the pond was completely dry for more than two months

and consequently the pond surface area became zero. In both

cases, the end of year was marked by similar surface areas

for the pond. It is obvious from the responses of the wet

surface of this pond to two different precipitation regimes

that the pond size shrinks and expands within the same year

and also from year to year due to fluctuations in rainfall

and PET imposed on the flatwood system.

Daily ground water elevation in the flatwood forest

surrounding the different three ponds are reported in

Figures (3.15 3.20). Compared to pond water elevations,

ground water elevations had more variability in time. One








Simulated Pond-N, Surface Area


--
I)
o 0.8
(:
U)
-o
t-


o 0.4
a


0 100 200 300 400
Day of The Year


Wet 1992 .......... Dry 1993

Figure 3.14. Simulated pond surface area for wet and dry years.








Measured & Simulated Ground Water Table
Elevations, N Pond, 1993
28.6

MPE = 0.26%; SD = 0.41%

28.2



I 27.8- .

27 \h "4
27.4

\i


100


200 300
Day of the Year


Measured ........ Simulated
Figure 3.15. Measured and simulated ground water elevations for the N flatwoods in
1993.


/-7-----------7--------I








Measured & Simulated Ground Water Table
Elevation, Pond N, 1994


150 200
Day of the Year


Measured --..- Simulated
Figure 3.16. Measured and simulated ground water elevations for the N flatwoods in
1994.








Measured & Simulated Ground Water Table
Elevations, Pond C, 1993


29




S28.6




28.2




27.8-
100


200 300
Day of the Year


Measured ........... Simulated
Figure 3.17. Measured and simulated ground water elevations for the C flatwoods in
1993.


MPE = 0.65%; SD = 0.28%








Measured & Simulated Ground Water Table
Elevations, Pond C; 1994


Day of the Year


Measured --..... Simulated

Figure 3.18. Measured and simulated ground water elevations for the C flatwoods in
1994.









Measured & Simulated Ground Water Table
Elevations, Pond k, 1993


.o 29.4

U)
rn


Day of the Year


- Measured .....- Simulated


Figure 3.19. Measured and simulated ground water elevations for the K flatwoods in
1993.
.4








Measured & Simulated Ground Water Table
Elevations, Pond k, 1994


0 100 200 300
Day of the Year


Measured .......... Simulated
Figure 3.20. Measured and simulated ground water elevations for the K wetland in
1994.










obvious explanation for that variation is the low water

storage capacity of the subsurface soil in the flatwood

forest compared to the open water surface in the pond.

Since a gain or a loss of 1 mm of water in the pond

translates in a similar increase of decrease in its water

level; however, equal gain or loss from the flatwood of 1 mm

would result in a gain or a loss of several mm, 3 to 4 mm,

in the ground water table level (Sun, 1995, Heimburg, 1984).

The simulated data closely followed the measured data with

time. Notice however that the measured rise in water table,

especially after heavy precipitation, can be several times

higher than simulated groundwater elevation. Many

investigators have observed anomalously large rises in water

levels in observation wells in shallow unconfined aquifers

during and following heavy rainstorms (Sun, 1995; Heliotis

and DeWitt, 1987; Freeze and Cherry, 1979). Hooghoudt in

1947 named it the Wieringermeer effect after the

Wieringermeer polder in Holland (Heliotis and DeWitt, 1987).

Meyboom (1967) reported values of the ratio of water-level

rise to rainfall depth as high as 20:1. Several

explanations have been given for this phenomenon; however,

it is now recognized that this type of water-level

fluctuation results from air entrapment in the unsaturated

zone (Freeze and Cherry, 1979). After an intense rainfall,

an inverted zone of saturation is created at the ground

surface, and the rapidly advancing wet front traps air












theater table (Heliotis and DeWitt, 1987; Freeze and

Cherry, 1979).

In a field experiment conducted on the same NCASI

research site and during the same period (April 1993 to

March 1994), transpiration (T) at the leaf/needle level was

measured (Liu, 1996; Riekerk et al., 1995b). Liu (1996)

used a multi-species and multi-layer ET model, ETME, to

scale up transpiration from the leaf/needle micro level to

the macro stand level. These data were used here to

validate the transpiration generated during the verification

of the pond and ground water elevations discussed in the

previous section. The transpiration of plant species, used

for this validation, in this system have been divided into

two components. First, the transpiration of plant species

in the pond, mainly the cypress and pine trees (Fig. 3.21-

3.23), and secondly, the transpiration of the pine trees in

the flatwood forest (Fig. 3.24-3.26).

The upscaled transpiration data for both the pond and

the flatwood forest were highly variable compared to the

simulated transpiration data using the WETLANDS model.

However, the general trends in both data sets was similar.

Figure (3.21) shows the simulated and upscaled transpiration

data for N pond during 1993. Reasonable agreement occurred

between the two data sets, but the best agreement occurred

during the first 6 months of the year. Scatter in the

upscaled data show values as high as 4 mm of transpiration








Plant Transpiration in the C Pond, 1993


150 200
Day of the Year


a Liu, 1996 + Simulated
Figure 3.21. Measured and simulated plant transpiration for the wetland C in 1993.


E
E
c 3-
0

S2-

t-








Plant Transpiration in the K Pond, 1993


200
Day of the Year


0 Liu, 1996 + Simulated
Figure 3.22. Measured and simulated plant transpiration for the wetland K in 1993.








Transpiration of Cypress Pond Trees
N Pond, 1993


Day of the Year


+ Simulated 0 Liu, 1996
Figure 3.23. Measured and simulated plant transpiration for the wetland N in 1993.








Flatwood Pine Transpiration, C 1993


Day of the Year


n Liu, 1996 + Simulated
Figure 3.24. Measured and simulated flatwood pine transpiration of wetland C in
1993.







Flatwood Pine Transpiration, K, 1993


*I0 L
E 3- [0
E +0

O O O


2- 0 0 0 0





0 100 200 300 400
Day of the Year


0 Liu, 1996 + Simulated
Figure 3.25. Measured and simulated flatwood pine transpiration of wetland K in
1993.








Flatwood Pine Transpiration, N 1993


200
Day of the Year


a Liu, 96T + Simulated

Figure 3.26. Measured and simulated flatwood pine transpiration of wetland N in
1993.












one day and values as low as 1.5 mm on the next day.

Simulated T also showed scatter in the data but not as much

as in the upscaled T. Because T in the WETLANDS model, is

controlled by both imposed PT and the available water stored

in the soil, variability was encountered in the simulated T

values. Maximum rates of transpiration from the K pond were

very similar to those determined by Liu (1996). The highest

transpiration rates determined by the WETLANDS model and by

Liu (1996) were found during April/May. This same period

coincides with that reported in the literature (Ewel and

Smith 1992). The maximum rates of transpiration in all

three ponds were during the same period and had a very

similar magnitude (approximately 3.5 mm/day). These

transpiration data sets show a step increase during the

period between the calendar days 70 and 120 (Fig. 3.23 -

3.25). During this period, cypress trees typically develop

much of their leaves and reach their maximum leaf area

index. These results demonstrate the close link between T

and LAI.

Flatwood pine transpiration data from the WETLANDS

model and Liu (1996) have many similarities to transpiration

by pond plants. One obvious similarity is the high

variability of the upscaled data (Liu, 1996) data compared

to the WETLANDS data. Even though, the maximum T rates for

the flatwood pine trees (for both WETLANDS and Liu, 1996)

were similar (N and C ponds); these T values were different











at the end and at the beginning of the year. During these

low transpiration periods, transpiration occurs only from

the pine trees since the deciduous cypress trees are

dormant. The T data from the pine flatwood trees roughly

follow bell shape curves with near symmetry around the

middle of the year; however, curves of the cypress have

steep slopes at the beginning of the year but have

relatively small slopes at the end of the year.

Using some simplifying assumptions and a minimum input

data set, the ET submodel linked to the main flow equation

in WETLANDS gave realistic transpiration values through

different weather patterns and plant species. These

validation exercises using real field data demonstrated the

capacity of the WETLANDS model to realistically describe the

major hydrological behavior of this complex system. The ET

submodel successfully provided a realistic approximation of

transpiration, a major hydrological component of the cypress

pond pine flatwood system. The reasonable simulated results

for the main hydrological components of all the tested

cypress pond pine flatwoods in north central Florida testify

indisputably that the main hydrological components for this

system were realistically linked.

Model Verification for Solute Transport

The solute transport portion of the WETLANDS model was

verified using two test problems presented here. These two

tests were used to verify the mathematical accuracy of the











solute transport component of the WETLANDS model. Several

analytical solutions for simplified two-dimensional,

convective-dispersive solute transport problems are

available (Ogata, 1970; Cleary and Ungs, 1978) where steady

state unidirectional groundwater flow was assumed to occur a

in saturated, homogeneous, isotropic porous medium. For

solutes that undergo linear, instantaneous sorption the two-

dimensional solute transport equation becomes:

6 0 2c 2 sac Oc
D, c D c R (3.32)
ax2 Lz2 e az at

where DL, DT are the longitudinal and transverse dispersion

coefficients, respectively; qz is the vertical component for

water flux in flow direction, R = 1 + (kf pb)/6, and z and x

are the spatial coordinates parallel and orthogonal to the

flow direction. The initially, solute-free water saturated

medium is subject to a solute source, co, of length 2a at

the surface (z=0) where a is a constant. The source is

located symmetrically about the coordinate x = 0 (Fig.3.27).

Because of symmetry, only half of the system needs to be

simulated. The boundary conditions used were:


c(x,0,t) = co -asxsa
c(x,0, t) = 0 other values ofx (332)

lim Lac0; limz., ac=0
llm )ax 0(0 ) a0













*pptllotlon zone = 50 n









Figure 3.27. Schematic of the physical domain.


An analytical solution for this transport problem was

presented by Javandel et al (1984). Transport for reactive

and nonreactive solutes were simulated using the WETLANDS

model and calculated using the analytical solution. The

input parameters for the two cases are listed in the Table

3.1. The width of the source was assumed 100 m and the

depth was 100 m. Because of the symmetry, calculations were

carried out for only the quarter plane where x > 0 and z> 0.

Table 3.1. Input parameters for solute validation.

Parameter Nonreactive solute Reactive solute

V(m/day) 0.5 0.5
DT (m2/day) 1.0 1.0
DL (m2/day) 0.1 0.1
R 1.0 5.0

c,(kg/m3 ) 1.0 1.0


Figure 3.28 shows both the analytical and the numerical

solute concentrations throughout the simulated domain at day

365 from the beginning of the calculations for the reactive











solute case. Close agreement occurred between the

analytical and numerical results. Similarly, Fig. 3.29

shows the numerical analytical solute concentration through

the simulated profile at day 365 for a non-reactive solute

based on the input data presented in Table 3.1. Very close

agreement occurred between values obtained using the

analytical solution and the numerical simulation.

Successful validation of the WETLANDS model enables it

to be used as a reasonable tool to assist in solving

practical environmental problems. Of particular interest is

the impact of forest management practices upon the hydrology

and water quality in north Florida cypress pond flatwood

pine systems. It is important to mention that more rigorous

field data sets are needed for a realistic validation of the

solute part of the WETLANDS model. The second main

assumption used during model simulations was an assumption

of no regional flow. This simplified the simulated system

substantially. However, in some practical cases, where

regional flow is a major hydrological component of the

system, this assumption may be violated. Another assumption

assumed a circular pond and a circular cross-section of the

simulated system; this simple representation may result in

an over simplification of the real system.







Validation: Reactive solute: day 365
0-




S-20

S 0.7

)O 0.5
-40 0.5
0.3
E
O ---- 0.2


-60
a-




-80 -


Analytical Numerical

-100
0 20 40 60 80 100 120

Horizontal distance (m)
Figure 3.28. Concentration profile for a reactive solute as determined by numerical
and analytical solutions.







Validation: N onreactive solute: day 1 00


0




-20


E

S-40


U,

-60
E
o
-8

C -80
Q


.. Numerical Analytical


-1 0 0 I I I I i I i i I i i I I I I I I I
0 20 40 60 00 100 120
Horizontal distance (m)

Figure 3.29. Concentration profile for a nonreactive solute as determined by
numerical and analytical solutions.













CHAPTER 4
A SUBMODEL FOR PREDICTING POTENTIAL EVAPOTRANSPIRATION FOR
MULTI-DIMENSIONAL WATER FLOW IN SOILS


Introduction


Crop growth models and multi-dimensional water flow and

solute transport models can be used to optimize existing

crop management practices and/or evaluate possible

alternative practices in order to both increase crop

production and to minimize groundwater and surface water

degradation. These models require various combinations of

daily or hourly solar radiation (SR), pan evaporation (PE),

potential evapotranspiration (PET), and potential

transpiration (PET) data. Field measurement of these

parameters is usually limited to research contexts, when

small areas are subject to intensive study. Collection of

such data for long times, and for different locations and

environments, can be prohibitive due to excessive costs and

labor demands. Reliable mathematical submodels can serve as

realistic substitutes for estimating these parameters.

So-called "Weather generators" (Keller, 1987) can be

used to estimate SR, PE and PET using available

meteorological data as inputs. These generators may provide

reasonable data sets for exploring possible modeling

scenarios (Hook and McClendon, 1992). The Penman equation




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