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Estimating the Crop Coefficient of Bahiagrass Using Lysimeters and the FAO56 Penman-xxMonteith Equation

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Title:
Estimating the Crop Coefficient of Bahiagrass Using Lysimeters and the FAO56 Penman-xxMonteith Equation
Copyright Date:
2008

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Subjects / Keywords:
Arithmetic mean ( jstor )
Calibration ( jstor )
Crop coefficient ( jstor )
Crops ( jstor )
Evapotranspiration ( jstor )
Lysimeters ( jstor )
Rain ( jstor )
Rain gauges ( jstor )
Soil moisture ( jstor )
Weather stations ( jstor )

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University of Florida
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University of Florida
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4/17/2006

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ESTIMATING THE CROP COEFFICIENT OF BAHIAGRASS USING LYSIMETERS
AND THE FAO56 PENMAN-MONTEITH EQUATION















By

GEORGE WILLIAM TRIEBEL


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

George W. Triebel

































To my parents, Allen Triebel and Ardith Shealey















ACKNOWLEDGMENTS

I would like to acknowledge several individuals for their assistance and

contributions in the completion of this thesis. First, I would like to thank my advisor, Dr.

Michael D. Dukes, for giving me the opportunity to pursue this research as part of my

degree requirement and for helping to direct the scope of this work. I would also like to

thank my other supervisory committee members. Dr. Jennifer Jacobs gave me direction

as an undergraduate in the department of Civil and Coastal Engineering and became an

integral part of my supervisory committee. Dr. Kenneth Campbell was always around for

advice. Dr. Kirk Hatfield was one of the most approachable professors I have

encountered. He gave me the opportunity to work as a teaching assistant as an

undergraduate to help pay the bills. Another person I would like to thank is Dr. Suat

Irmak, who developed the design of this project. I helped him install most of the field

equipment used. The St. John's River Water Management District deserves thanks for

their support of this project. Xinhua Jia was also a great help with the data collection,

interpretation, and analysis involved in this project.

I would also like to give thanks to Danny Burch and Larry Miller, whose technical

expertise and assistance were greatly appreciated. The friendliness of all of the faculty,

staff, and students in this department made completing this research as enjoyable as

possible. Finally, I would like to thank my parents, Ardith Shealey, Allen Triebel, and

my stepfather, Steven Shealey. They all gave me the motivation and inspiration to make

all of my achievements possible.















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

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

LIST O F FIG U R E S .................... .. ................................... .... ........ ................ viii

ABSTRACT .............. ......................................... xi

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

State ent of Problem ........................................................ ..... .............. .. 1
R research O bjectiv es.......... ................................................................... ......... ....

2 REVIEW OF LITERATURE ......................................................... ..............2

Florida's W ater M anagem ent ............................................................................ ......2
H history of Lysim eters ..................................................... ................ .4
D e sig n ................... .................................... ............... ................ 5
O operational R equirem ents ................................................................ ...............6
Bahiagrass (Paspalum notatum) ........ ......................................................... 7
Evapotranspiration ................... ............. .......... ............................. .8
R reference Evapotranspiration ........................................ .......... ............... 9
Actual Evapotranspiration ............................................................................13
C rop C o efficient ..................................................... ................ 14

3 M E T H O D O L O G Y ........................................................................ .......................17

L o c atio n .........................................................................................1 7
L y sim e te rs ............................................................................................................. 1 8
D e sig n .......................................................................................1 8
Instrum entation .............................................................................20
M ain ten an ce ................................................................2 1
L oad C ell C alibration ............................................... ............... 22
R a in fa ll ................................................................2 4
W death er Station ................................................................2 5
C alcu latio n s ................................................................2 6




v









R reference ET U sing FA O 56 ........................................ .......... ............... 26
A ctual ET U sing Lysim eters ........................................ .......................... 27
Soil m oisture balance calculations .................................... ............... 27
Equivalent volume of water calculations ...................................................28
Equivalent w eight calculations.................................. ....................... 29
Change in storage calculations .............. .............................................. 29
Remaining soil moisture balance variables...............................................31
Actual ET Adjustm ent and Filtering ....................................... ............... 31
C rop C o effi cient ....................................................................... ..................... 3 3

4 RESULTS AND DISCU SSION ........................................... .......................... 43

R rainfall .................. ....................................................4 3
N et Radiation at the Lysim eter Site................................ ......................... ........ 44
Reference ET ................................................... 44
U sing Calculated N et Radiation ...................................................................... 46
U sing M measured N et R radiation .............. .................................................. .... 47
Difference in ETo between Using Measured and Calculated Net Radiation.......48
A ctual E T U sing L y sim eters ........................................................... .....................48
Crop Coefficient ......................... ........... .. .. ......... ..... ..... 53

5 RECOMMENDATIONS FOR FUTURE WORK ............................................. 75

Lysim eter D design .................. ...................................................... .. 75
R ainfall M easurem ent .............................................................. .....................75
D drainage ................................................................... 76
S o il M o istu re ................................................................................................. 7 7
Calculation Procedure ....................................................... ...... ................. 78
Evaporative A rea of the Lysim eters .................................. ...................................... 79
H erb icides an d P esticides ........................................ ............................................79

APPENDIX

A USING CALCULATED NET RADIATION TO CALCULATE DAILY
REFERENCE ET, DAILY ACTUAL ET, AND CROP COEFFICIENTS ...............81

B USING MEASURED NET RADIATION TO CALCULATE DAILY
REFERENCE ET, DAILY ACTUAL ET, AND CROP COEFFICIENTS ...............87

LIST OF REFEREN CES ............................................................................. 93

B IO G R A PH IC A L SK E TCH ..................................................................... ..................98
















LIST OF TABLES


Table page

3-1 Lysimeter instrumentation and description............................. .. .......... ........ 34

3-2 Lysimeter calibration results for June 9, 2003 ............... ............................... 35

3-3 Lysimeter calibration results for January 28, 2004 ............................................35

3-4 Lysimeter calibration results for January 19, 2005 ...............................................35

3-5 W weather station instrumentation and description...............................................36

4-1 Monthly rainfall comparison among lysimeter area collection sites......................59

4-2 Monthly rainfall comparison between surrounding area collection sites................59

4-3 Monthly total reference ET (mm) using calculated net radiation ..........................60

4-4 Monthly total reference ET (mm) using measured net radiation ...........................60

4-5 Difference between monthly total reference ET (mm) using calculated Rn and
measured Rn recorded at both weather stations (ETomeas-ETocalc).....................61

4-6 Monthly total actual evapotranspiration using lysimeters compared to reference
ET using calculated Rn measured at the lysimeter weather station......................61

4-7 Monthly crop coefficients ofbahiagrass using actual ET from lysimeters and
reference ET from the FAO56 method using calculated and measured net
radiation (reference ET calculated at the lysimeter weather station).....................62















LIST OF FIGURES


Figure p

3-1 Geographical location: Plant Science Research and Education Unit (PSREU)......37

3-2 Arial photograph of Phase I of the PSREU research facility: block layout and
the location of the lysimeters and weather station ................................................ 37

3-3 East/west section view of lysimeter design for one lysimeter .................................38

3-4 Layout and section construction drawings of lysimeters ......................................38

3-5 Southeast view of the installed lysimeters taken in July 2003 .............................39

3-6 Southwest view of the excavated site shown with the outer tanks resting on the
reinforced concrete foundation and PVC air pressure release ducts.....................39

3-7 Lysimeter calibration June 9, 2003: Flow meter shown while pumping water
into the inner tank of the easternmost lysimeter (L ). ....................................40

3-8 Lysimeter calibration on January 19, 2005: (2) 23.59 kg. weights are shown
resting on a sheet of plywood on the surface of the westernmost lysimeter (L3)....40

3-9 Location of all rainfall measurement devices at PSREU, W = weather station
tipping bucket, H = hobo tipping bucket, M = manual rain gauge ........................41

3-10 Northwest view of the weather station located adjacent to the lysimeters,
approximately 50 m east of the lysimeter site with instrumentation labeled ...........41

3-11 Northeast view of the supplementary reference ET weather station located
approximately 500 m southwest of the lysimeter site with instrumentation
lab eled .............................................................................. 4 2

4-1 Monthly cumulative rainfall measured at the lysimeter site using a tipping
bucket rain gauge mounted on top of the weather station, a manual rain gauge,
and a tipping bucket rain gauge recorded using a Hobo datalogger ......................63

4-2 Monthly cumulative rainfall measured at other sites at PSREU used for
com parative purposes ......... .. ................................... .. ........ .. ............ 63

4-3 Daily net radiation at the lysimeter weather station; net radiation is calculated
using FAO56 guidelines and measured using a net radiometer............. ...............64









4-4 Daily comparison of calculated net radiation and measured net radiation at the
lysim eter w weather station............................................................ ............... 64

4-5 Comparison of net radiation measured at the lysimeter site compared to net
radiation m measured at the supplementary site .................................. ............... 65

4-6 Plot of reference ET calculated using the Penman Monteith equation and
calculated net radiation at the lysimeter weather station.........................................65

4-7 Plot of reference ET calculated using the Penman Monteith equation and
calculated net radiation at the supplementary weather station..............................66

4-8 Comparison of VPD (kPa) measured at the lysimeter weather station and the
supplementary weather station .. .. .............................................66

4-9 Plot of reference ET calculated using the Penman Monteith equation with
measured net radiation at the lysimeter weather station .......................................67

4-10 Plot of reference ET calculated using the Penman Monteith equation with
measured net radiation at the supplementary weather station..............................67

4-11 Daily difference and monthly average difference between reference ET when
using calculated net radiation compared to measured net radiation at the
lysim eter w weather station ......... ................. .................................... ............... 68

4-12 Daily actual evapotranspiration measured by the lysimeters and daily rainfall
recorded at the tipping bucket at the lysimeter weather station ............................68

4-13 Daily actual evapotranspiration measured by the lysimeters compared to
equivalent evaporation from available energy from net radiation .........................69

4-14 Cumulative actual evapotranspiration, cumulative reference evapotranspiration
using calculated net radiation at the lysimeter weather station, and cumulative
rain fall .............................................................................. 6 9

4-15 Soil moisture profile in lysimeter 1 (east)............ ... ............. ............... 70

4-16 Soil moisture profile in lysimeter 2 (middle) .................... ......................... 70

4-17 Soil moisture profile in lysimeter 3 (west) ......... ............................................. 71

4-18 Average daily actual ET measured using lysimeters and daily reference ET
using calculated net radiation at the lysimeter weather station.............................71

4-19 Average daily actual ET measured using lysimeters and daily reference ET
using measured net radiation at the lysimeter weather station..............................72

4-20 Average daily and monthly crop coefficients using calculated net radiation
(reference ET calculated at the lysimeter weather station) ....................................72









4-21 Average daily and monthly crop coefficients using measured net radiation
(reference ET calculated at the lysimeter weather station) ....................................73

4-22 Difference in crop coefficient between using calculated and measured Rn to
com pute reference E T ..................................................................... ...................73

4-23 Differences in crop coefficient between calculated and measured Rn in reference
ET compared to daily values of calculated and measured Rn...............................74

4-24 Soil moisture content in tank 1 and overall Kc ................................ ............... 74

A-1 Daily actual ET, daily reference ET using calculated net radiation, daily Kc, and
average m monthly K c. ....................... .. .... .................. .. ............ .....81

B-l Daily actual ET, daily reference ET using measured net radiation, daily Kc, and
average m monthly K c. ....................... .. .... .................. .. ................. 87














Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering

ESTIMATING THE CROP COEFFICIENT OF BAHIAGRASS USING LYSIMETERS
AND THE FAO56 PENMAN-MONTEITH EQUATION

By

George William Triebel

December 2005

Chair: Michael D. Dukes
Major Department: Agricultural and Biological Engineering

Evapotranspiration (ET) is one of the most basic components of the hydrologic

cycle. Knowledge of this water balance term is essential for planning and operating

various water resource projects. Using ET measurements, a crop curve for bahiagrass

(Paspalum notatum) can be developed; the crop curve is critical to accurately determine

irrigation-water requirements for agricultural and developmental interests in the

southeastern United States. Daily crop evapotranspiration (ETc) rates for bahiagrass were

used to establish the crop coefficient curve that can be used in water management

practices and irrigation scheduling. The ET, was determined using three sensitive

weighing lysimeters, located at the Plant Science and Research Education Unit in Citra,

Florida. Daily reference evapotranspiration (ETo) was calculated using the FAO 56

Penman-Monteith method. The daily crop coefficient (K,) of bahiagrass was calculated

using daily values of ETc and ETo, and averaged on a monthly basis. The ETo was

calculated using estimated and measured net radiation (Rn) to determine the respective









monthly averages of K. Monthly averages of Kc can be used to determine an

approximate value of crop water use for bahiagrass under similar geography, climates,

and cultural conditions.

We used lysimeters to estimate the crop water use of bahiagrass from January 1,

2004 through December 31, 2004. We estimated monthly Kc values for bahiagrass using

the FAO Penman-Monteith method to estimate ETo and using lysimeters to measure ET,

over the course of the year. These values can be used to estimate irrigation requirements

and increase the efficiency of water use with respect to bahiagrass.














CHAPTER 1
INTRODUCTION

Statement of Problem

Development of monthly crop coefficients for bahiagrass (Paspalum notatum) is

important to accurately determine irrigation water requirements for agricultural and

developmental interests in the Southeastern United States. Knowledge of

evapotranspiration rates of bahiagrass in this region is the fundamental basis for

determining crop coefficients. Using lysimeters to measure crop water use, and

prescribed methods to compute reference evapotranspiration rates, the crop coefficients

can be calculated on a daily basis; and averaged on a monthly basis, for practical use

when calculating irrigation requirements.

Research Objectives

Three weighing lysimeters were used to establish monthly crop coefficients;

through the application of methods used to achieve this ultimate goal, several auxiliary

objectives were met. The goals set forth in this research are as follows:

* Measure daily actual evapotranspiration using three weighing lysimeters.

* Calculate daily crop coefficients and monthly average crop coefficients based on
actual and reference evapotranspiration.

* Show the impacts of using calculated net radiation to calculate reference
evapotranspiration using the FA056 guidelines compared to using measured net
radiation to calculate reference evapotranspiration.

* Show how the crop coefficients are affected by net radiation measurements.














CHAPTER 2
REVIEW OF LITERATURE

Florida's Water Management

Water management in Florida is based on historical experience and changing

philosophies about the conservation of natural resources and the environment. Florida

has more rainfall than any other state in the U.S. except Louisiana; Florida receives an

average of 1350 mm of rainfall per year while Louisiana receives an average of 1400 mm

per year (Purdum, 2002). The occurrence and location of rainfall does not always match

when and where it is needed, which affects irrigation scheduling. Irrigation timing and

amounts also depend on rainfall intensity, duration, and the location of interest as well as

plant needs.

Rapid population growth and continuous development are at the root of Florida's

impending water scarcity. Florida sits on top of one of the nation's most extensive

freshwater systems, possessing more available groundwater than any other state in the

U.S. The cavernous limestone aquifer system is a major part of the state's hydrologic

cycle and is a critical resource for the health of natural systems as well as a provider for

public freshwater supply. Florida's future depends on an ample supply of clean water as

a critical resource for all aspects of daily life. As increasing populations lead to higher

demands for water withdrawals, the ever-increasing usage contributes directly to

decreased water levels in lakes and wetlands, and ultimately impacts the health and

sustainability of the environment as well as the general public's water supply and private

interests.









Florida's population is currently estimated at 16 million people (1996). By the year

2020, it is expected to increase by 4 million people, to a total of approximately 20 million

people. Some figures exceed those projections and estimate populations as high as 21.8

million in the year 2020 (Marella, 1999). In addition to the resident population, an

estimated 45 million people visit the state every year. Population growth and water

demand do not always increase proportionally (FDEP, 2003); the rate of increased water

demand is expected to exceed the rate of population growth through the year 2020

(FDEP, 2003). Impending increases in water demand heighten the need for water

conservation and planning. Improved understanding and more efficient application of

agricultural water are needed to address these concerns.

Agriculture is the single largest user of water in Florida (and the world as a whole).

Two-thirds of all water withdrawn (from groundwater and surface water sources

throughout the world) is devoted to agricultural purposes (Postel, 1992). One way to

better manage available water is through efficient use of agricultural water. If major

improvements are made in this area, then water shortage issues may be more readily

addressed. Postel (1992) estimates only 40% overall efficiency of agricultural water use

worldwide, meaning that over half of the water used for agricultural purposes is wasted.

In the past century, population growth, industrial development, and the expansion of

irrigated agriculture have resulted in an enormous increase in the amount of water used.

Florida's Water Management Districts define a water resource caution area (WRCA) as

an area where water is either scarce or contaminated, and delineated these areas to

prevent misallocation of available water. These WRCAs now cover thousands of square

miles throughout the state (Purdum, 2002). By knowing the water use of specific crops,









agricultural water can be more effectively managed. Defining water-use characteristics

for specific crops allows for more accurate estimation of irrigation water requirements,

and in turn reduces water consumption.

History of Lysimeters

Lysimeters are the standard tool used to analyze relationships among soil, water,

and plants, as well as in water quality research. The first lysimeter to be used for water

use studies is attributed to De la Hire of France in 1688 (Kohnke et al. 1940, Aboukhaled

et al. 1982). Lysimeters are used to define water movement across a soil boundary. A

lysimeter consists of an isolated container of soil whose water movement characteristics

can be studied and measured directly. When a crop is planted at the ground level of the

lysimeter, water use (evaporation and transpiration, or evapotranspiration) can be

determined through water balance techniques.

Lysimeters used for evapotranspiration (ET) research are typically classified

according to their design and use as follows: monolithic or reconstructed soil profiles,

weighing or non-weighing designs, and gravity or vacuum drainage designs. Monolithic

lysimeters attempt to preserve existing vegetation and soil properties that can be

destroyed by the excavation and filling (Schneider and Howell, 1991) that is done when

using reconstructed soil profiles in the container. Weighing lysimeters determine

evapotranspiration directly, by the mass balance of the water; nonweighing lysimeters

indirectly determine ET using the volumetric soil-water balance in the container (Howell

et al., 1991). Vertical drainage through the soil column can be measured using either a

gravity drainage design, or a design that uses a vacuum system (if the lysimeter is

designed in such a way that gravity drainage is inconvenient).









Design

Advances in ET lysimetry have focused on duplicating field conditions in the

container as closely as possible to the surrounding field through the use of larger

lysimeters and more efficient vacuum pumping systems. Although some aspects of

lysimeter design are often duplicated or reused, Kohnke et al. (1940) warns that "no one

construction should be regarded as standard in a lysimeter and that a proper design can be

made only by having an accurate knowledge of both the purpose of the experiment and of

the pedologic, geologic, and climatic conditions." Pruitt and Lourence (1985) also

caution lysimeter users to critically evaluate all agronomic aspects, to ensure high-quality

ET data, since major errors in ET data are possible even with an accurate lysimeter.

The design of a lysimeter should always be appropriate to the type and scope of

research performed. However, some main elements are inherent in all lysimeter designs:

ET accuracy, shape and area, depth, soil profile characteristics, weighing mechanisms,

construction, and siting. Each design element is discussed in more detail as follows:

* ET accuracy: ET measurement accuracy is dictated by the planned measurement
interval (weekly, daily, hourly) and values ranging from 0.02 to 0.05 mm are
commonly cited as the resolution or precision of weighing lysimeter systems (Allen
et al., 1991).

* Shape and area: The shape and area of the container should be a direct reflection
of the expected crops to be studied and their root depths. Differences between
lysimeter surface area and crop geometry can bias the soil water evaporation and
crop transpiration relationship, but this may not critically affect ET measurements
for grass, alfalfa, or small grains or other broadcast planted crops (Howell et al.
1991).

* Depth: The depth of the lysimeter should be based on the rooting depth of the crop
which is to be studied. Van Bavel (1961) advised that lysimeter depth should
permit the development of normal rooting density and rooting depth and provide
similar "available" water profiles to the field profile.

* Soil profile characteristics: Although monolithic lysimeters may preserve the
exact physical, chemical, and vegetation characteristics of the surrounding area,









many weighing lysimeters have utilized reconstructed soil profiles for ET
measurements and, when carefully reconstructed, provided accurate ET data (Pruitt
and Angus, 1960; Van Bavel and Myers, 1962).

* Weighing mechanisms: Mechanical scales have been widely used in weighing
lysimeters since the 1950's and permit the precise measurement of the mass change
of water within the lysimeter (Howell et al., 1991). Load cell lysimeters measure
the total weight of the lysimeter; this leads to the accuracy of ET measurements
being dependant on the accuracy of the load cells and the area to mass ratio of the
lysimeter design.

* Construction: Most lysimeter soil containers are made using either steel,
reinforced fiberglass, or plastic as the primary construction material. The gap
between the inner and outer tank of a weighing lysimeter should be designed as
narrow as possible to prevent unnecessary wall heating while allowing for ample
clearance to avoid contact between the inner and outer tanks. The gap between
tanks must also be covered to prevent water intruding due to rainfall or irrigation.

* Location: Windward fetch must be accounted for when choosing a site for any
lysimeter. A sufficient distance of fetch consisting of the same vegetation and
moisture regimes as the lysimeter is necessary to ensure that the lysimeter is
representing the same environmental conditions as the entire field. Minimum fetch
distances should be determined based on the height at which weather recording
instruments are operating. For instance, if wind speed, humidity, and temperature
are being recorded at a 2 m height above ground surface, then a windward fetch of
100 to 400 m should be provided given suggested fetch ratios that vary from a
minimum of 1:25 (Allen et al., 1991) to as much as 1:200 (Jensen et al., 1990).

Operational Requirements

Potential lysimeter errors can be reduced using strict design and maintenance

regimes. Lysimeter operators and users of lysimeter data must be knowledgeable about

the constraints of proper environmental management for the lysimeter site and the

resulting interpretation of lysimeter data. The accuracy of lysimeter data depends on the

ability to achieve identical conditions between the lysimeter and the surrounding field. If

lysimeters are designed to meet specific requirements for the research performed and are

operated properly, then they can be utilized as precision tools to measure actual

evapotranspiration. Through proper use, precision weighing lysimeters are the most

practical research tool for direct measurement of daily evapotranspiration and an









effective approach to conduct crop coefficient studies (Howell et al., 1985; Yrisarry and

Naveso, 2000).

Bahiagrass (Paspalum notatum)

Bahiagrass has long been used in the southeastern United States as roadside turf,

and in the 1970's it became popular for use in Florida as a lawn grass due to its easy

planting, few pest problems, good drought tolerance, and low maintenance (Duble, 1989).

Bahiagrass is a warm-season grass that is well adapted to high rainfall areas; therefore its

use is widespread in the southeastern United States. Approximately 750,000 acres of

bahiagrass were maintained in Florida during the years 1991-1992, second only to St.

Augustinegrass (Stenotaphrum secundatum) in turfgrass usage (Hodges et al., 1994).

Water consumed for turfgrass irrigation in 1991-1992 in Florida was estimated at 1.75

billion gallons per day (Hodges et al., 1994). With the population of Florida increasing at

a rapid rate, a net inflow of 1,000 people per day (Shermyen, 1993), the need for efficient

water management practices and a firm grasp on specific water use characteristics in the

region is justified.

A literature review of ET research related to bahiagrass in humid environments did

not reveal a definitive knowledge of the water use of this warm season grass; the majority

of research that has been performed concentrates on select warm-season grasses such as

Bermudagrass and St. Augustinegrass and cool-season varieties such as fescue, bluegrass,

and ryegrass (Brown et al., 2001; Carrow, 1995). Since agricultural water is the main

consumer of Florida's freshwater withdrawals representing two-thirds of the total amount

of freshwater withdrawn in the state, then the efficiency of irrigation practices for

agricultural crops must be maximized to produce a substantial net benefit for water

conservation purposes. As Florida faces future water shortages due to the influx of









residents and constantly diminishing freshwater resources, research on crop water use for

agricultural purposes is necessary to benefit regional water management practices and to

provide a sound basis for irrigation scheduling.

Evapotranspiration

Evapotranspiration (ET) is the loss of water from a vegetated surface through the

combination of the evaporation of water from the soil or plant surface plus the

transpiration of water that is transported through the plant and released to the atmosphere

as water vapor. ET is a basic component of the hydrologic cycle. The knowledge of this

water balance term is essential for the planning and operation of water resource projects.

Evapotranspiration is the loss of water from a vegetated surface through the combination

of the evaporation of water from the soil or plant surface plus the transpiration of water

that is transported through the plant and expelled as water vapor. Evaporation and

transpiration are affected by solar radiation, air temperature, humidity, wind speed, and

soil moisture. Transpiration is also affected by soil moisture and crop characteristics. ET

is commonly expressed in units of either depth per time (e.g. mm day-) or energy per

unit area over a specified time (e.g. MJ m-2 day-'). Cuenca (1989) gives a description of

the evapotranspiration process as "the combination of water evaporated from the plant

and soil surface plus that amount of water which passes through the soil into roots,

through the stem of the plant, and to the leaves where it passes into the atmosphere

through small pores termed stomates."

An accurate estimation of evapotranspiration is important to water supplies (surface

and groundwater), water management, and the economics of multi-purpose water projects

(i.e. irrigation, power, water transportation, flood control, municipal and industrial water

uses, and wastewater reuse systems). The political implications of water use issues









affected by evapotranspiration include the negotiation of water compacts and treaties and

the litigation and adjudication of water rights in major river systems (Jensen et al., 1990).

Reference Evapotranspiration

The evapotranspiration rate from a reference crop surface, not short of water, is

called the reference crop evapotranspiration or reference evapotranspiration and is

denoted as ETo. The methods for reference ET calculation can be categorized into four

groups: combination, radiation, temperature and pan evaporation methods. Combination

methods, the most commonly used methods, include radiation (energy balance) and

aerodynamic (heat and mass transfer) terms. Typical combination methods are FAO

Penman (Doorenbos and Pruitt, 1977), Kimberly Penman (Wright, 1982), Penman-

Monteith (Allen et al., 1994a), the FAO Penman-Monteith (Allen et al., 1998) and the

ASCE-EWRI Penman-Monteith (Walter et al., 2002). Penman (1948) established the

modern reference evapotranspiration standard by separating the term into two

components that drive the process simultaneously: an available energy term, and a

mechanically derived term driven by atmospheric vapor transport. The combination of

these two terms was the first time net radiation was introduced into the physical modeling

of evapotranspiration.

Doorenbos and Pruitt (1977) defined reference ET to be the rate of

evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of

uniform height, actively growing, completely shading the ground and not short of water."

Allen (1994a, b) introduced the idea of a hypothetical reference crop so that crop

characteristics could be applied to the reference evapotranspiration definition. These

characteristics include a fixed crop height of 12 cm, a surface resistance (rs) of 70 s m-1

and albedo (a) of 0.23, and a constant latent heat of vaporization (k) of 2.45 MJ/kg. The









hypothetical reference crop was defined by Allen et al. (1998) as "an extensive surface of

green grass of uniform height, actively growing, completely shading the ground, and with

adequate water." Jensen (1990) defines reference ET as the rate at which readily

available soil water is vaporized from specified vegetated surfaces. A recent definition of

reference ET adds a minimum fetch specification to the requirements set by FAO56: "the

ET rate from a uniform surface of dense, actively growing vegetation having specified

height and surface resistance, not short of soil water, and representing an expanse of at

least 100 m of the same or similar vegetation" (Walter et al., 2005).

The standard method for determining ETo was established in 1990 during

consultation of experts and researchers from the Food and Agricultural Organization

(FAO) of the United Nations in collaboration with the International Commission for

Irrigation and Drainage (ICID) and the World Meteorological Organization, to review

methodologies on crop water requirements and to advise on procedures to use

meteorological data to estimate ETo (Allen et al., 1998). The method that was agreed

upon for calculating reference evapotranspiration is known as the FAO-56 Penman

Monteith (FAO56 PM) method. The guidelines presented by Allen et al. (1998) can be

used to compute crop water requirements for both irrigated and rainfall fed agriculture

and for computing water consumption by agricultural and natural vegetation. A recent

ASCE-EWRI standard method to calculate reference ET has been published (Walter et

al., 2005), but for daily calculations of reference ET on grass, the guidelines are identical

to the FAO56 method.

Equation 2-1 is used to calculate ETo on a daily time scale.









900
0.408A(R, -G)+y U0(e, e)
E T + 273
A A+ (1+0.34u2) (2-1)
ETo: reference evapotranspiration (mm)
T: mean daily air temperature (C) calculated as the average of the minimum
and maximum daily air temperatures
Rn: average daily net radiation (MJ m-2 day1)
G: average daily soil heat flux (assumed to be negligible in daily calculations
since it is very small compared to Rn)
u2: mean daily wind speed (m s-1) calculated as the average of each 30 min
wind speed reading over the course of a day
A: slope of the vapor pressure curve (kPa C1)
es: saturated vapor pressure (kPa)
ea: actual vapor pressure (kPa)

A can be calculated:

( 1727T
4098 0.6108exp 17.27
\T+ 237.3
A=
(T+237.3)2 (2-2)

y is the psychometric constant (kPa C-1):

cP
S= P =0.655 x103P
CA (2-3)
Cp: specific heat at constant pressure (1.013 10-3 MJ kg-1 oC-1)
P: mean daily atmospheric pressure (kPa)
s: ratio of the molecular weight of water vapor to dry air (0.662)
X: latent heat of vaporization (2.45 MJ kg-1)

es is the saturated vapor pressure (kPa) calculated as the average of the minimum and

maximum daily vapor pressures:

F 17.27T
e(Tmn)= 0.6108exp m
L Tmn + 237.3 (2-4)


e 17e 27Tmax
e(Tm) = 0.6108exp 17.27-a
STma + 237.3 (2-5)


es (kPa) is then calculated:









eo (Tma) + eO (Tmm)
es =
2 (2-6)

ea (kPa) is the actual vapor pressure calculated as a function of es and the minimum and

maximum daily relative humidity:

RHmax RHmn
eo(Tm n) max o (Tm )
S 100 100
ea =
2 (2-7)
RHmax: maximum relative humidity value during the 24-hour period
RHmin: minimum relative humidity value during the 24-hour period

Net radiation can be measured using a net radiometer or calculated as a function of

solar radiation according to the procedures outlined in Allen et al. (1998). The variables

used to estimate Rn on a daily basis are shown below:

extraterrestrial radiation, Ra is calculated:


Ra = 24(60) Gd, [(o)sin(p)sin(3)+ cos(p)cos(3)sin(o))]
Z (2-8)
Gs,: solar constant (0.0820 MJ m-2 day-)
dr: inverse relative distance between the earth and sun
p: latitude (rad)
8: solar declination (rad)
co: sunset hour angle (rad)

Net shortwave radiation, Rns is calculated:

R,s = (1-a)Rs (2-9)
a: defined albedo of grass (0.23)
Rs: measured incoming solar radiation (MJ m-2 day1)

Outgoing solar radiation, Rso is calculated:

Rs = (0.75+2x10 z)Ra (2-10)
z: station elevation in meters above mean sea level

This equation is used when no calibration has been performed for the as and bs

Angstrom values which will vary depending on atmospheric conditions. When









calibration values are not available, the above equation is used to calculate outgoing solar

radiation.

Net longwave radiation, R11 is calculated:


R,,l ( ma. K) mmnK)4 0.34- 0.14] 1.35R 0.35
L 2 Rso (2-11)
5: Stefan-Boltzmann constant (4.903 10-9 MJ K-4 m-2 day-1)
Tmax,K: maximum absolute temperature (K) during the 24-hour period
Tmin,K: minimum absolute temperature (K) during the 24-hour period.

Net radiation (Rn) can then be calculated using equations 2-9 and 2-11:

Rn = Rs- Rn (2-12)

Calculated net radiation is used in the recommended FA056 PM method, but

more accurate values for actual net radiation can be measured using a net radiometer.

The procedure outlined in the FAO 56 paper specifically states that net radiation should

be calculated as shown in the above procedure.

Actual Evapotranspiration

Actual evapotranspiration or crop evapotranspiration (ETc) is the actual amount of

water removed from a surface and delivered to the atmosphere through the processes of

evaporation and transpiration. This term has historically been difficult to measure

directly, but with the advent and technological advances within the field oflysimetry, the

term can be more readily measured. It is quite difficult to separate evapotranspiration

into evaporation and transpiration, so the combination of the two is widely used in water

balance studies.

Lysimeters have been considered the most reliable research tool for direct

measurement of crop evapotranspiration and have been regarded as the standard for all

other methods (Howell et al., 1985; Burman and Pochop, 1994; Burman et al., 1983).









Howell et al. (1985) states that weighing lysimeters are considered the most practical

research tools for the direct measurement of daily ETc.

The actual evapotranspiration rate of any crop that is planted in a weighing

lysimeter can be measured by monitoring the change in weight (or equivalently the

change water storage) of the soil container. Monitoring and measuring all inputs and

outputs of the lysimeter allows for the measurement of ETc. Positive changes in weight

indicate an addition of water to the soil container in the form of either rainfall or

irrigation while negative weight changes indicate the subtraction of water from the soil

container through either actual evapotranspiration, drainage, or runoff. Thus, a soil water

balance equation can be written:

AS = P + I-ET D- RO (2-13)
AS: change in soil water storage (g)
P: precipitation (g)
I: irrigation (g)
D: drainage (g)
RO: runoff(g)

AS can be considered analogous to the weight change of a weighing lysimeter over any

specified period of time as long as the growth of the crop does not contribute

substantially to the changing weight of the contents of the tank.

Crop Coefficient

A crop coefficient (K,) is a numerical factor that relates actual evapotranspiration

(ETc) of an individual, well watered crop to the reference evapotranspiration (ETo). The

crop coefficient accounts for the characteristics of a certain crop and its phenological

growth stages. Dimensionless Kc values are calculated (Doorenbos and Pruitt, 1975;

1977):


K, = ETE /ETo


(2-14)









Yrisarry (2000) states that precision weighing lysimeters are one of the most

effective methodologies for direct crop coefficient studies. Using known values of ET,

and an estimate ofETo, Kc can be solved for directly. A crop curve can be developed by

plotting Kc over any period of time. The crop curve is useful for most hydrologic water

balance studies, especially those involving irrigation planning and management, and for

the development of basic irrigation schedules. Average crop coefficients (on a monthly

basis) are usually more relevant and more convenient than the Kc computed using a daily

time interval (Allen et al., 1998).

Knowledge of a crop coefficient can be a useful tool to establish crop water usage

and can be used in the agricultural industry as a method to determine actual

evapotranspiration based on estimated values of reference evapotranspiration. Practical

crop water requirements are calculated using tabulated values of K, and calculated values

for ETo using one of many reference evapotranspiration equations, preferably the FA056

Penman-Monteith equation according to Allen et al. (1998). The crop water requirement

is analogous to the actual evapotranspiration of the crop and can be solved for by

rearranging equation 2-14 to the following form:

ET, = K, x ETo (2-15)

The knowledge of the crop water requirement on a monthly basis is critical for

proper irrigation management. Given a known actual evapotranspiration for a specific

crop and an estimated reference evapotranspiration for a reference crop (grass or alfalfa),

one can calculate the crop coefficient for that specific crop using equation 2-14. The

knowledge of specific monthly crop coefficients over a variety of crops and for a variety







16


of seasonal conditions can lead to the better understanding and management of irrigation

water resources.














CHAPTER 3
METHODOLOGY

Location

All field experimentation was performed at the University of Florida, IFAS Plant

Science Research and Education Unit (PSREU) located between Gainesville and Ocala

near Citra, Florida in Marion County (figure 3-1). The PSREU provides 445 hectares of

land for research. The facility is divided into two phases; Phase I of the PSREU research

facility is divided into 6 blocks (figure 3-2) with each block consisting of 400 smaller

subunits each having an area of about 0.31 ha and dimensions of 55 m of length by 55 m

of width. The research location for this project is located in block 6 of PSREU with

coordinates of 29.4061N latitude and longitude 82.16560 W and an average elevation of

20 m above sea level. The instrumentation for this research includes three weighing

lysimeters and a weather station located in the Northeast section of block 6 (figure 3-2).

At least 200 m of fetch is provided in all directions radial from the equipment to prevent

any disturbances that could jeopardize the accuracy of ET, measurements, while 100 m

of fetch is the minimum suggested fetch outlined by recent definitions of reference

evapotranspiration (Walter et al., 2005).

Block 6 is planted with Bahiagrass (Paspalum notatum) that is irrigated as

necessary using an overhead linear irrigation system. The irrigation system is operated

by the personnel at PSREU on a regular basis and at the request of the research team.

The field is typically irrigated twice a week when rainfall is absent (Irmak et al., 2003) to

ensure an actively growing field, and the grass is mowed to ensure that a 12 cm grass









height is maintained; these procedures are performed to satisfy the conditions of the FAO

56 definition of a reference surface (Allen et al., 1998). Mowing was typically performed

weekly during the active growing summer months and rarely, if at all, during the winter

months of the dormant stage.

The results of a particle size distribution test carried out on soil samples at the

PSREU showed a sand fraction always greater than 91%, a silt fraction always less than

9%, and a clay fraction always less than 4% (Gregory, 2004). The soil was therefore

classified as a sand according to the USDA soil textural classification (Soil Survey Staff,

1975).

Lysimeters

Three precision weighing lysimeters were installed in block 6 of the PSREU

research facility on May 28, 2003 with data collection initiated during June 2003. A

severe thunderstorm on August 4, 2003 resulted in a power surge that damaged the load

cells. The load cells were replaced on October 13, 2003 with additional surge protection

to prevent future power surges. The load cells in the lysimeters have been operational

since. ET data for this research spans January through December 2004.

Design

The three weighing lysimeters in block 6 of the PSREU are aligned in an east-west

direction; each lysimeter is separated by a distance of 3.35 m. The 3 lysimeters are

named from east to west as L1, L2, and L3 respectively (figure 3-3). The lysimeters

consist of inner and outer steel tanks, the inner tank having a surface area of 2.32 m2

(1.524 x 1.524 m) and a soil depth of 1.37 m (figure 3-4).

Each lysimeter is planted with bahiagrass at the same density and of the same type

(Pensacola variety) as the surrounding field (figure 3-5). An area (approximately 175 m2)









surrounding the lysimeters was sectioned off using signs (approximately 20 m from the

lysimeters in each direction) to prevent any unnecessary foot or vehicle traffic from

interrupting the sensitive weighing devices. The fetch extends at least 200 m beyond the

sectioned-off, no-vehicle-traffic area in all directions from the lysimeters. At the ground

level of the lysimeters the gap between the inner and outer tanks of each lysimeter is

covered by an approximately 20 cm strip of green painted rubber. This strip of rubber

protects the inside of the lysimeters from rusting by preventing any water from

interfering with the operation of the load cells. The rubber was spray-painted green from

its original black color to attempt to prevent the uneven distribution of heat caused by the

absorption of heat by the hotter black surface which could influence the

evapotranspiration of the grass. The protruding green, capped, PVC pipe in the near

corner of each lysimeter, as shown in figure 3-5, consists of a slotted pipe that extends to

approximately 10 cm above the bottom of the inner tank. This slotted pipe is used to

intermittently measure the depth of water in the inner tank after rainfall. The depth of the

water table in the lysimeter is used to estimate the duration of pumping needed to remove

all excess water from the inner tank.

The lysimeters were installed by first excavating the site to lay the necessary

reinforced concrete foundation on which they rest (figure 3-6). Figure 3-6 shows the

location of PVC pipes that serve as air ducts to release any pressure that may develop

under the weight of the concrete foundation, the backfilled soil, the soil inside the tanks,

and the steel tanks themselves. The reinforced concrete foundation consists of a double

mat of #6 60 grade rebar double matted and spaced 30 cm apart in a grid pattern. A

wooden frame was constructed around the perimeter of the foundation area, the rebar grid









pattern was laid out, and concrete filled the frame a minimum of 7.5 cm above, and 7.5

cm below the rebar. The concrete foundation (figure 3-5) rests on an 18 in layer of

compacted gravel which in turn rests on a 5 cm slab of concrete.

The lysimeters are designed so that drainage occurs when a pumping system that

originates at the bottom of the inner tank is initiated when suction is applied through a

porous filter plate. Copper tubing is used to transfer the water from the bottom of the

inner tank to an outlet approximately 20 cm above the ground surface. Water flows

through the porous plate at the bottom of the inner tank, through the copper pipe between

the inner and outer tanks, and eventually extending as a continuum of the copper pipe

approximately 20 cm above ground. The dual copper tubes that extend from the gap

between the tanks can be seen in figure 3-8. Drainage water is pumped by fixing a

threaded brass nozzle to each of the above ground copper pipes, connecting a flexible

plastic hose to the brass nozzle which extends to the inlet of a pump. When suction is

applied to the hose using a pump, the vacuum is distributed through the pipe to the

porous plate at the bottom of the inner tank of the lysimeter. The drainage from each

lysimeter is transferred to storage tanks where the volume of water that is drained can be

manually measured. The pump and storage tanks are situated on a portable trailer which

can be moved to accommodate mowing of the surrounding Bahiagrass and to provide

portability to the system.

Instrumentation

Table 3-1 lists and describes all instruments at the lysimeter site. The lysimeter site

has 2 data collection points. A CR-23X datalogger (Campbell Scientific, Logan, UT)

stores and collects all load cell readings from the lysimeters. A CR-10X datalogger

(Campbell Scientific Inc., Logan, UT) stores and collects all Vitel probe readings from









the soil profile of the lysimeters. Two rainfall collection devices occupy the lysimeter

site. A tipping bucket rain gauge is recorded by a Hobo (Onset Computer Corp.,

Pocasset, MA) datalogger, this gauge is located directly north of the westernmost

lysimeter. A second manual rain gauge is located directly north of the middle lysimeter.

Maintenance

To maintain the grass at a 12 cm height to satisfy the FAO 56 definition of a

reference surface, the site is mowed as needed. Mowing was typically performed up to

once per week during the rainy summer season and as infrequently as once per month in

the dormant winter season. The field around the lysimeter tanks is mowed using a riding

lawnmower inside an approximately 100 m x 100 m area designated by signage. The

areas immediately surrounding the lysimeter tanks themselves are carefully mowed with

a push-type lawnmower to avoid any unnecessary disturbance to the load cells that could

be caused by heavier machinery. The bahiagrass inside the lysimeters themselves is

maintained as close to a 12 cm height as possible by keeping the grass clipped using hand

shears. The grass inside the lysimeter is clipped to a 12 cm height. The perimeter of

grass is maintained to prevent the surface area of the grass from extending beyond the

walls of the inner tank. While it is not possible to control the grass from extending

slightly outside the confines of the inner tank, excessive protrusion is controlled by

routine clipping with the hand shears. Any grass that extends beyond the surface area of

the inner tank increases the effective area of the lysimeter and must be accounted for

when measuring ETc. The grass around the immediate perimeter of the lysimeter is

clipped with the hand shears as well to prevent disturbance of the load cells, and to

prevent grass from intruding into the surface area of the inner tank.









Load Cell Calibration

The load cells in the lysimeters were calibrated on three different occasions: June

9, 2003, January 28, 2004, and January 19, 2005. Calibration was performed by adding a

known amount of weight to the lysimeter and recording the resulting average of the four

load cells in each lysimeter in units of mV. The regression curve of the resulting data

points are then plotted to estimate the linear regression equation between the load and the

mV reading of the load cells. The load cells were wired to a datalogger (model CR-23X,

Campbell Scientific Inc., Logan, UT). The displayed mV output for each load cell is

directly proportional to the load applied. Load cell readings were recorded manually,

then averaged to obtain the resultant mV load of the entire tank. These averages were

plotted versus the corresponding volumes of water they represented to obtain a linear

regression.

The first lysimeter calibration on June 9, 2003 was performed on the empty inner

lysimeter tank just after installation. The results of the linear regression of this

calibration are shown in table 3-2. A known volume of water was pumped into each

lysimeter with the amount of water recorded using a flow meter as shown in figure 3-6.

The load applied to each lysimeter was measured according to the volume of water

pumped into the tank. The volume of water applied can then be converted to an

equivalent weight applied to the lysimeter according to the specific weight of water. The

multiple regression correlation coefficient (R2) value for each tank is shown to be 1.00.

The regression equations derived from each tank can be used to measure how much

weight rests on the load cells by inputting the recorded mV reading into the

corresponding regression equation and solving for the volume of water, or equivalently

the mass of the inner tank with its contents.









The second lysimeter calibration on January 28, 2004 was performed after the

lysimeters were planted with bahiagrass. Two plastic buckets were placed on the surface

of the lysimeter and the load cells were allowed to equilibrate to effectively zero the

calibration. The buckets were filled with water from a graduated cylinder incrementally

until the volume of water applied to the lysimeter reached 30 L. A linear regression was

performed on this data collection and the results are shown in table 3-3.

The third lysimeter calibration was performed on January 19, 2005, after all

evapotranspiration data had been collected for the previous year. This calibration was

performed by positioning a sheet of plywood in the center of the lysimeter, placing

known weights (in 23.59 kg increments) on the sheet of plywood, recording the resulting

average load cell outputs in mV, and using these data to perform a linear regression.

Once the plywood was placed in the center of each lysimeter, the load cells were allowed

to react to this increase in weight and that weight was used to effectively zero the load

cells for the linear regression analysis. Figure 3-8 shows the positioning of the weights in

the center of the lysimeter. The applied load was converted into an equivalent volume of

water for calibration purposes. The results for this calibration are shown in table 3-4.

The slope (a) in all of the regression equations represents the relationship between

the average mV reading of the load cells and the incremental load applied to the inner

tank of the lysimeter. The slopes of all the regression equations remained constant

throughout the duration of the research. For tank 1 the slopes (mV/mm H20) of each

regression equation for each calibration are 0.00041, 0.00041, and 0.00041 respectively.

For tank 2, the slopes (mV/mmH20) of each regression equation for each calibration are

0.00041, 0.00044, and 0.00039 respectively. For tank 3, the slopes (mV/mmH20) of









each regression equation for each calibration are 0.00038, 0.00039, and 0.00041

respectively. Rounded to 4 digits, the slope of the regression equation that was used in

the actual ET calculations for each tank is 0.0004 mV/mmH2O.

The intercept (b) in all of the regression equations represents the initial weight of

each tank before calibration; for the first calibration on June 9, 2003, the intercepts

represent the weight of the inner tanks themselves since it was not yet filled with soil or

planted with bahiagrass. The differences in intercepts for each calibration are the

consequence of small differences in the initial weights of each of the lysimeter tanks. For

the first calibration on June 9, 2003, the differences in intercepts represent small

differences in the weights of the inner tanks of each lysimeter due to small differences in

the amount of steel used to construct each tank. The intercepts in the first calibration

were used in the final calculation of actual ET because they represent the weight of the

unfilled tanks. The intercepts from the following two calibrations represent the weight of

the tanks plus vegetation, soil, and water.

Rainfall

Rainfall was measured at the lysimeter site using a tipping bucket rain gauge

recorded using the weather station data logger, a tipping bucket rain gauge recorded using

a Hobo event data logger, and a manual rain gauge. The tipping bucket rain gauges

record real time rainfall whereas the manual rain gauge collects data whenever the gauge

is recorded manually and emptied. The tipping bucket rain gauge recorded using the

weather station data logger is located at a height of 3.0 m above ground level on top of

the weather station mounting tower approximately 50 m east of the lysimeter site. The

tipping bucket rain gauge attached to the Hobo event data logger is located at a height of

0.3 m above ground surface, low enough to catch all irrigation as well as rainfall. The









manual rain gauge is located at a height of 0.25 m above the ground surface; the rainfall

recorded from this gauge was measured during every field visit so there is a time lapse

between actual rainfall and the measured value. Given this lapse, it is more useful to

compare rainfall from the manual gauge to the rainfall from the tipping bucket rain

gauges using cumulative monthly values rather than daily values. All rain gauges are

outlined with their respective heights in tables 3-1 and 3-5 which list the instrumentation

used at each site. The location of each rainfall measurement device used for this research

is mapped out in figure 3-9.

Weather Station

The weather station used to collect atmospheric variables for the reference ET

calculations is located approximately 50 m east of the lysimeters in the same bahiagrass

field. Measurements were recorded in accordance with the guidelines set forth in the

FAO 56 method of calculating reference ET. The bahiagrass surrounding the weather

station was kept as close as possible to a height of 12 cm to approximate a reference

surface and it was kept well watered through rainfall and irrigation when needed. The

atmospheric variables collected at the weather station during the research are air

temperature, relative humidity, solar radiation, net radiation, wind speed and direction,

soil heat flux, and soil temperature. The weather station at this location is shown in

figure 3-10 with the atmospheric measurement devices labeled accordingly.

Instrumentation at the weather station adjacent to the lysimeter site is outlined in

table 3-5. The weather station site was maintained at least once a week; data were

downloaded on a weekly basis to ensure that if there was an unexpected problem that it

would be found immediately and remedied. Data were collected using a CR-10X

datalogger (Campbell Scientific, Inc., Logan UT). Data were downloaded using a Palm









(palmOne, Milpitas, CA) handheld PDA and PConnect (Campbell Scientific, Logan, UT)

software which allows for portability and the convenience of not having to use a laptop in

field conditions.

A supplementary weather station is located approximately 500 m southwest of the

lysimeter site with all of the instrumentation necessary to perform another reference ET

calculation. This site was used for comparisons to the site adjacent to the weather station.

This additional weather station measures relative humidity and air temperature with the

same instrument as the lysimeter weather station. The station is equipped with the same

model net radiometer and pyranometer as the lysimeter weather station for radiation

measurements and the same model tipping bucket to record rainfall. Wind speed is

measured with a Vaisala WS425 Ultrasonic Wind Sensor (Vaisala, Helsinki, Finland).

All measurements are made at 15 minute intervals and these variables are used to

calculate daily reference ET on a daily basis; this weather station is shown in figure 3-11

below.

Calculations

Reference ET Using FA056

Reference ET was calculated using the methods outlined in the FA056 manual

using a daily time step. Reference ET was calculated 2 different ways. The first uses net

radiation measured from the net radiometer. The second uses net radiation calculated

from pyranometer readings according to FA056 guidelines. Since reference ET

calculations are highly dependent on the net radiation term, it is important that it be

accounted for accurately. This research was performed using the FA056 method of

calculating reference ET because it is considered the standardized method, superior to all

others (Itenfisu et al., 2003; Garcia et al., 2004).









Actual ET Using Lysimeters

Actual ET was calculated based on the soil moisture balance (Eqn. 2-13) for each

lysimeter. The daily actual ET was calculated by taking the difference in the equivalent

volume of water applied to the load cells at the beginning and ending of the day and

inputting this volumetric change (which can be converted to a change in terms of a depth

of water) as the daily change in storage for each lysimeter.

Soil moisture balance calculations

The rainfall and irrigation terms used in the soil moisture balance were measured

using the tipping bucket rain gauge located atop the weather station tower adjacent to the

lysimeter site and the drainage term is measured manually using a graduated cylinder.

Runoff is considered negligible since the infiltration rate for sandy soils in the area is

higher than any expected rainfall intensity. Infiltration rates for the soils in this location

average approximately 230 mm/hr (Gregory, 2004). The 100-year 24-hour design storm

intensity is 254 mm/hr, the 50-year 24-hour design storm intensity is 229 mm/hr, and the

25-year 24-hour design storm intensity is 203 mm/hr (Florida Department of

Transportation, 2003). It can be assumed that as long as rainfall does not approach the

50-year 24-hour design storm intensity of 229 mm/hr, then runoff will not occur. As long

as the rainfall intensity is less than the infiltration rate of the soil, runoff will not occur

and can then be removed from the lysimeter water balance equation. The resulting water

balance of each lysimeter is then reduced to

AS = P+I-ET -D (3-1)
AS: change in soil water storage (mm)
P: precipitation (mm)
I: irrigation (mm)
D: drainage (mm)









Actual ET from each lysimeter can then be calculated directly by rearranging Eq. 3-1 and

placing all known variables on the right side of the equation:

ET = P + I AS D (3-2)

The change in storage is calculated on a daily time step so that daily actual ET can be

observed, therefore, the weight of the lysimeters must be known at the start of the day

(0000 h) and the end of the day (2400 h). The difference of the weight of the tank from

the beginning and the end of the day is the daily change in storage. The weight of each

lysimeter is calculated using the equations derived during the lysimeter calibration and

the surface area of the evaporative vegetation. Since the correlation between the mV

voltage recorded by the load cells and the weight of the lysimeter is a linear relationship,

the amount of water entering or leaving the lysimeter at any time can be calculated using

the lysimeter calibration equations and the recorded load cell values.

Equivalent volume of water calculations

The readings of the four load cells in the tanks were averaged for every ten

minute reading to achieve one mV value which can then be converted to an equivalent

volume of water using the lysimeter regression equations. Since the lysimeter regression

equations were determined in terms of a volume of water added to the tank, the mV

reading was directly converted to a load of water applied or removed from the tank as

shown by rearranging the lysimeter regression equation to solve for the volume of water

represented by the mV reading recorded by the load cells (Equation 3-3).









V [avg(mV4)-b] 33)
eq a(3-3)
a
Veq: equivalent volume of water resting on the load cells (L)
avg(mV4): the average of the 4 mV readings recorded for a single 10 min time
step (mV)
b: intercept calculated from the lysimeter regression equation, represents the
initial weight of the lysimeter tank in terms of an equivalent volume of applied
water (L)
a: slope calculated from the lysimeter regression equation, represents the
relationship between the mV reading and applied volumetric load (mV L1)

Equivalent weight calculations

Since the load, x, is represented in terms of a volume of water in liters, then that

volume can be converted to an equivalent weight applied to the load cells using the

density of water (Equation 3-4).

We = Vq p, (3-4)
Weq: the equivalent weight of water applied to the load cells (kg)
Veq: the equivalent volume of water calculated from equation 3-3
p: the density of water at 50C (0.001 g/L)

Change in storage calculations

The volume of water entering or leaving the lysimeter (change in storage, AS) can then

be found by calculating the difference in equivalent volumes of water over any given

time period as shown in equation 3-5 below. The density of water was assumed to be at

5C for simplification purposes even though the water may be warmer in reality. If the

density of water at 350C were used (0.00094 g/L) at a hypothetical volume of water

totaling 5000 liters, compared to the density of water at 50C, it would only amount to a

change in 300 g, which can be considered negligible given that the weight of the

lysimeters ranged from around 4000 kg to 5500 kg over the period of research. This

change would only amount in an error of 0.0075 % which would not affect the results

significantly. A more accurate but time consuming method would be to monitor the









temperature change of the water within each lysimeter to determine the exact density.

This process would be time consuming and of negligible value.

AS Ve(t2) q (t) (3-5)
AS: change in storage over any time interval (L)
Veq(t2): the equivalent volume of the lysimeter at any time, t2 (L)
Veq(tl): the equivalent volume of the lysimeter at any time earlier than t2 (L)

To determine the daily evapotranspiration, daily change in storage is calculated over a 24

hour time step substituting the equivalent volume of water at 2400 hr for Vep(t2) and the

equivalent weight at time 0000 hr for Veq(tl) in equation 3-5. The change in storage over

all 10 min time intervals was also computed, for this time step the equivalent weight at

any t2 is substituted into equation 3-5, along with the equivalent weight at a time 10 min

earlier than t2 (tl).

The change in storage can be represented in terms of an equivalent depth of water

entering or leaving the lysimeter over any time period by using the area of the

evaporative surface to convert the equivalent weight to an equivalent depth as shown in

equation 3-6.

1*106mm3 1
ADepth = AS (3-6)
1L SA1,

ADepth: change in depth of water (mm) over a specified time interval

AS: change in storage (L) over a specified time interval (as calculated in 3-5)

SAiys: surface area of the evaporative area (2.48*106mm2)

1*106mm3
: unit conversion
1L

The surface area of the evaporative surface is measured as the extent that the grass grows

beyond the perimeter of the inner tank.









Remaining soil moisture balance variables

The daily change in storage is calculated using equations 3-3 through 3-6 and the

lysimeter calibration equations. The remaining variables in the lysimeter water balance

required to calculate ET, are rainfall (R), irrigation (I), and drainage (D). Rainfall is

measured by the various rainfall measuring devices on site (although actual values of

rainfall and irrigation entering the lysimeter are used for calculation purposes as

discussed later) and drainage is measured manually using a graduated cylinder whenever

pumping occurs.

Actual ET Adjustment and Filtering

The load cell readings are prone to noise interference, as well as the

overestimation of incoming rainfall. Noise in the load cell readings is encountered

frequently and is filtered out of the daily data and accounted for in the daily actual ET

calculation. If the load cells show that the lysimeter lost weight during the night, then

that weight loss was ignored in the daily actual ET calculation. Night was defined when

net radiation was negative; since ET does not occur at quantifiable amounts at night,

these values were excluded from actual ET calculations. Rainfall amounts were typically

observed to be higher in the lysimeter than the values obtained by tipping bucket rain

gauges due to runoff into the inner lysimeter tank from the surface of the green rubber

cover placed around the perimeter of the lysimeter. The amount of rainfall entering the

lysimeter for any given storm event or irrigation period can be obtained by observing the

change in weight of the lysimeter over that given time period. If the ETc during any

rainfall or irrigation event is assumed to be negligible, then the increase in weight of the

lysimeter can be attributed directly to incoming water; the amount of water that has

entered can be calculated by solving equation 3-6 using the change in storage over the









time interval in question, and solving for the representative depth of water that the

rainfall event produces in terms of the lysimeter surface area.

Incoming rainfall is overestimated by the lysimeters due to the rubber lining around

the perimeter of the inner and outer tank gap which causes an inflow of rainfall to the

tanks. The load cell readings were analyzed for each storm event. The load cell readings

were also compared to the recorded rainfall from the tipping buckets. During any storm

event the increase in weight recorded by the load cells is calculated for the duration of the

storm event defined by the duration of the event as recorded by the tipping buckets. This

increase in weight due to rainfall is used to determine daily actual ET calculation rather

than the amounts of rainfall recorded by the various rainfall measuring devices. Raw

lysimeter data from the load cells were filtered based on three criteria:

1. If rainfall was greater than zero, as recorded by the tipping bucket located at the

nearby weather station, then the duration for each storm event from the tipping

bucket was analyzed alongside the load cell data to determine the actual

increase in weight due to rainfall that each lysimeter experienced.

2. If the actual ET from any lysimeter, using the calculations outlined previously,

is greater than an acceptable maximum (6 mm/day), then the load cell data for

the day were analyzed to see if: a) there were any unexplained increases or

decreases in weight (e.g. if the lysimeter lost weight during the night, or if the

lysimeter gained weight at a period when no rainfall was recorded) or b) if any

of the observed load cell increases and decreases seemed to be unreasonable

(e.g. higher or lower than expected, or unnaturally high or low values).









3. If the standard deviation of the actual ET from all three lysimeters was found to

be unreasonable high (greater than 2), this suggested that either one or all of the

lysimeters were not achieving similar results. The filtering for this case was

treated similar to the filtering in the second criterion listed above, examining the

data for any unexplained or unreasonable changes.

If the actual ET data for any day did not meet the three criteria listed above after filtering,

then those data were excluded from the results and were not used in any monthly

comparisons or future crop coefficient calculations.

Crop Coefficient

Actual evapotranspiration is calculated using the lysimeters and adhering to the

above calculation procedures. Reference evapotranspiration is calculated using the

FA056 Penman-Monteith method. The crop coefficient is calculated as measured actual

evapotranspiration divided by estimated reference evapotranspiration as calculated using

equation 2-14. Since daily values of both actual and reference ET are calculated, daily

crop coefficients can also be calculated. Daily crop coefficients are determined by

dividing the average of the three lysimeter daily actual ET values by the daily reference

ET value using estimated net radiation per FAO 56 guidelines. Monthly crop coefficients

are calculated by averaging all of the daily crop coefficients for the month.









Table 3-1. Lysimeter instrumentation and description
Instrument and Description and use Units Height above
manufacturer ground surface
(m)
CR-23X Dataloger, Storage and collection of load cell N/A N/A


Campbell
Scientific
CR-1OX Datalogger,
Campbell
Scientific


data from lysimeters

Storage and collection of Vitel probe
data from the lysimeters


Gast Model 0823 Creates a vacuum which is applied
Pump to the lysimeters for drainage
SQB-10K Load (4) Load cells in the bottom corners
Cells (16) of each lysimeter's outer tank
used to measure the total load
applied by the inner tank that
rests on top of the 4 load cells;
each load cell is capable of
supporting 4,444 kg (10,000
lbs)
Surge Arrestors (31), (16) Surge protectors against
Citel Inc., lightning for each load cell (15)
Miami, Fl Surge protectors against
lightning for each Vitel probe
Clear-Vu Manual 10.5 cm diameter plastic rain gauge
Rain Gauge with an 30 cm rainfall capacity
Texas Electronics Tipping bucket rain gauge that
TE525WS measures precipitation in
Tipping Bucket increments of 0.0254 cm
Onset Hobo Event Data logger that records rainfall
Logger from the tipping bucket rain
gauge
Storage Tanks (4) (3) 302.8 L tanks used to store
drainage water and (1) 151.4 L
tank used to distribute a vacuum
which is applied to each of the
lysimeters
Stevens Water Vitel Measures the dielectric constant as it
Probes (20) relates to 4 recorded mV
readings which relates to the
soil moisture content; (5)
located in center vertical profile
of each lysimeter
CSI HFT3 Soil Heat Measures soil heat flux
Flux Plates (2)


N/A


N/A

mV








N/A



cm

cm


N/A


N/A




mV


N/A


N/A

-1.7








N/A



0.25

0.3


N/A


N/A




-0.05, -0.1,
-0.2, -0.5, -1.0


W m-2 -0.08









Table 3-2. Lysimeter calibration results for June 9, 2003
Lysimeter a (mV/mm H20) b (mV) R2
L1 (east) 0.00041 0.143 1.00
L2 (middle) 0.00041 0.173 1.00
L3 (west) 0.00041 0.153 1.00
*Linear regression of y on x (y=a*x+b) where y is the average mV output of the 4 load
cells in each tank and x is the applied load in mm of water


Table 3-3. Lysimeter calibration results for January 28, 2004
Lysimeter a (mV/mm H20) b (mV) R2
L1 (east) 0.00041 1.130 0.98
L2 (middle) 0.00044 1.121 0.99
L3 (west) 0.00039 1.087 1.00
*Linear regression of y on x (y=a*x+b) where y is the average mV output of the 4 load
cells in each tank and x is the applied load in mm of water


Table 3-4. Lysimeter calibration results for January 19, 2005
Lysimeter a (mV/mm H20) b (mV) R2
L1 (east) 0.00038 1.047 1.00
L2 (middle) 0.00039 1.032 1.00
L3 (west) 0.00041 1.018 1.00
*Linear regression of y on x (y=a*x+b) where y represents the average mV output of the
4 load cells in each tank and x represents the applied load in mm of water









Table 3-5. Weather station instrumentation and description
Instrument and Description and use Measurement Height above
manufacturer Units ground surface
(m)


N/A N/A


CR-10X
Datalogger,
Campbell
Scientific
Climatronics
CS800 Wind
Sensor
Kipp & Zonen
NR-LITE
Net
Radiometer



Texas Electronics
TE525WS
Tipping
Bucket
Li-Cor LI200X
Silicon
Pyranometer
Vaisala HMP45C
Probe
CSI HFT3 Soil
Heat Flux
Plates (2)
Thermocouples (2)


Storage and collection of
weather station data from
various instruments

3-cup anemometer and wind
vane that measures wind
speed and direction
Measures the energy balance
between incoming short-
wave and long-wave IR
radiation relative to
reflected short-wave and
outgoing long-wave IR
radiation (net radiation)
Tipping bucket rain gauge that
measures precipitation in
increments of 0.0254 cm

Measures incoming solar
radiation

Measures air temperature and
relative humidity
Measures soil heat flux


Measures soil temperature


2.2


0.6







3.0



3.0


1.5

-0.08


-1
m s
degrees from
North
Wm-2







cm



Wm-2


C, %

Wm-2


-0.02, -0.06



































Figure 3-1. Geographical location: Plant Science Research and Education Unit (PSREU)


Figure 3-2. Arial photograph of Phase I of the PSREU research facility: block layout
and the location of the lysimeters and weather station










GROUND LEVEL

6' PVC 1SC 40 PIPE
EMBEDDED NIN 8'" IN
GRAVEL B'ED (tyP
PUMP ~-~OSES
CANTILEVER
LOfA CELL
CDGCPETE B3ASE


FILTER
r: :, .[ :
n- ,_


Figure 3-3. East/west section view of lysimeter design for one lysimeter


LAYOUT PLAN
SCALE.,
In


.* IJ] .' I


*i, .1. a ~r,


SECTION A
SCALE: ,


Figure 3-4. Layout and section construction drawings of lysimeters. A) Plan view. B)
Section view.


_--3' x T3 x


.- I I -



-I-BEAM


-.1


BROIMI L: 1


X i'.; r: H VIC -,, .1 ,. R I R
4 2) L (E@ *& '. .r.r -Ll'^ i
QC. 3-' H3N CD-'i lit tLL IEe ^tT^^
































Figure 3-5. Southeast view of the installed lysimeters taken in July 2003


L1


--aw
~~4m '. -IIu


I-.


Figure 3-6. Southwest view of the excavated site shown with the outer tanks resting on
the reinforced concrete foundation and PVC air pressure release ducts






























Figure 3-7. Lysimeter calibration June 9, 2003: Flow meter shown while pumping water
into the inner tank of the easternmost lysimeter (L ).


Figure 3-8. Lysimeter calibration on January 19, 2005: (2) 23.59 kg. weights are shown
resting on a sheet of plywood on the surface of the westernmost lysimeter
(L3).












Plan* Scieie Rweareh rmI EducaiionUnit
Please 1 Field Layout


BInik


M Ebcb4
W Bloh4
H BlEx4


Figure 3-9. Location of all rainfall measurement devices at PSREU, W = weather station
tipping bucket, H = hobo tipping bucket, M = manual rain gauge


Figure 3-10. Northwest view of the weather station located adjacent to the lysimeters,
approximately 50 m east of the lysimeter site with instrumentation labeled









Wind Speed
Pyranometer


a %,.
-"-#-'. tli _


ipping
Bucket

'* ** I


Ci.


Tipping Bucket
^.. / j ..J .


Yet KHdiation



F


Figure 3-11. Northeast view of the supplementary reference ET weather station located
approximately 500 m southwest of the lysimeter site with instrumentation
labeled














CHAPTER 4
RESULTS AND DISCUSSION

Rainfall

Rainfall measured at the lysimeter site and the weather station site located 50 m to

the east of the lysimeters is summarized by month and collection device in figure 4-1.

Rainfall data were also collected at other sites at PSREU for comparison to values

recorded at the lysimeter site. The additional rainfall collection sites are shown in figure

3-8. The rainfall amounts recorded at sites other than the lysimeter sites were compiled

at 6 different collection areas with the monthly cumulative amounts shown in figure 4-2.

The regional average rainfall for north central Florida is 1320 mm (Satti and

Jacobs, 2004). The complete rainfall record of the tipping bucket rain gauge located on

top of the lysimeter weather station recorded 1874 mm of precipitation. This weather

station is high enough to exclude water from irrigation. As 2004 had a great amount of

hurricane activity, the recorded rainfall is reasonable as compared to the regional average

given by Satti and Jacobs (2004).

Differences in rainfall amounts among collection sites result from a difference in

irrigation amounts and timing of irrigation events. The manual rain gauges were

recorded at irregular intervals during each field visit and have errors associated with the

manual reading of rainfall amounts. The weather station tipping bucket rain gauges at

blocks 4 and 5 are located too high to catch all of the irrigation water from the overhead

linear irrigation in those plots. The Hobo tipping bucket rain gauges in those locations

are located low enough so that they catch all of the irrigation from the overhead linear









irrigation. Differences in these amounts can be seen from the monthly cumulative plots

shown in figures 4-1 and 4-2. These rain gauges do not affect the lysimeter water

balance used for actual ET calculations because rainfall was measured using the weight

increase for a rainfall event from the load cells. These rain gauges are shown to reflect

the amount of rain that fell in this region over the year.

Monthly total rainfall amounts are shown for the collection sites near the

lysimeters in table 4-1 and the collection sites at the additional weather station in table 4-

2. Blank spaces indicate the gauge was inoperable for all or part of that month or it had

not yet been installed at the site. 2004 was a wetter than average year given the regional

average. More rainfall produces more water available for evaporation, and making the

lysimeter operator's job of keeping the tanks at reasonable soil moisture levels more

difficult.

Net Radiation at the Lysimeter Site

The daily measurements of calculated and measured net radiation are shown in

figure 4-3. Calculated net radiation was higher than measured net radiation during the

winter months, and closer to the measured net radiation values during the summer

months. Calculated net radiation was also higher than measured net radiation when total

radiation for the day was low and was just the opposite when radiation for the day was

high. The comparison between calculated and measured net radiation is shown in figure

4-4. As net radiation increases, reference ET increases accordingly; a standard value for

reference ET must be known before any crop coefficient determinations can be made.

Reference ET

Reference ET values were calculated using two different net radiation values, the

first using net radiation calculated from solar radiation values per the FAO 56 outline,









and the second using measured net radiation from the net radiometer. A summary of

reference ET values are shown in tables 4-3 and 4-4. Reference ET calculated by

measured net radiation was found to be consistently lower than reference ET using

calculated net radiation at the weather station adjacent to the lysimeters (Jia et al., 2005)

and this was also found to be the case as well when analyzing net radiation data from the

supplementary weather station (figure 4-5). The highest monthly reference ET at the

lysimeter site using calculated net radiation occurred in May with 123.5 mm, the lowest

monthly value occurred in February with 62.7 mm. Lower reference ET values will

cause the crop coefficient values to be higher. The minimum reference ET value

encountered during this research was 0.61 mm on December 25, 2004 using calculated

net radiation. Using measured net radiation, the minimum reference ET value

encountered was 0.10 mm on October 11, 2004. Reference ET using calculated net

radiation was less than 1 mm during 7 out of the 366 days researched (1.9 %). Using

measured net radiation, reference ET was less than 1 mm for 21 days out of the 366 days

(5.7 %). Biased reference ET values can bias crop coefficient data if actual ET measured

by the lysimeters is not very close to reference ET. For low reference ET values, small

errors in actual ET values are amplified in crop coefficient calculations. If the average

difference in reference ET values is constant and this difference is compared on a day

when there was low net radiation the effects on the crop coefficient will be greater than if

it was a day with high net radiation. The difference in reference ET values will cause a

larger difference in the crop coefficient value on low-radiation days, therefore low

radiation days must be used with caution when using these days to calculate the crop

coefficient.









Using Calculated Net Radiation

Monthly plots of daily reference ET using the calculated net radiation per the

FA056 guidelines are shown in Appendix A. These standard reference ET values will be

used for crop coefficient calculations as they adhere to FA056 guidelines. Daily

reference ET values are for 2004 and monthly averages are shown in figures 4-6 and 4-7

for the lysimeter weather station and the supplementary weather station respectively. The

monthly reference ET shows an annual trend with higher values during the summer

months and lower values during the winter months at both sites. Figure 4-6 shows daily

reference evapotranspiration rate at the lysimeter weather station ranged from

approximately 1.5 mm/day in December to just over 4 mm/day for the month of May.

The highest daily reference ET encountered during the period of research at the lysimeter

weather station was 5.8 mm on August 31, 2004 and the lowest daily reference ET was

0.6 mm on December 25, 2004. The supplementary weather station (figure 4-7) had

daily reference evapotranspiration values ranging from a minimum monthly average of

approximately 1.6 mm/day in December and a maximum monthly average of

approximately 4.1 mm/day in June. The lowest and highest daily reference ET

encountered at the supplementary weather station during the period of research occurred

on the same days as at the lysimeter weather station. The highest recorded reference ET

at the supplementary weather station was 5.4 mm on August 31, 2004. The lowest daily

reference ET was 0.4 mm on December 25, 2004.

Monthly totals of reference ET using calculated net radiation are shown in table 4-

3. At the lysimeter weather station, the months with the lowest and highest recorded

reference ET was January with 64.0 mm May with 123.5 mm respectively. At the

supplementary weather station the months with the lowest and highest recorded reference









ET was December with 48.1 mm June with 121.1 mm respectively. The estimated

reference ET is consistently higher at the weather station adjacent to the lysimeters,

except for June and July. This difference can be explained by the higher measured vapor

pressure deficits (VPD) at the lysimeter weather station than at the supplementary

weather station during most of the period of research (Figure 4-8). The vapor pressure

deficit is the difference between saturated and actual vapor pressure and depends on

relative humidity measurements as well as minimum and maximum daily temperatures

and is used in the complete equation used to calculate reference ET (eqn 2-1). Since

calculated net radiation uses air temperature and actual vapor pressure in the calculation,

the use of the equations outlined by the FA056 guidelines to estimate net radiation may

be a source of error.

Using Measured Net Radiation

Monthly plots of daily reference ET using measured net radiation are shown in

Appendix B. Figures 4-9 and 4-10 show the yearly trend of reference ET by weather

stations using measured net radiation. The reference ET at the supplementary weather

station (Table 4-4) to be consistently lower for all months. Totals differ from 15.1 mm in

February to 30.2 mm in June. The difference between reference ET measurements at

both sites results from the differences in measured net radiation values by site as was

observed in figure 4-5. Figure 4-5 shows the difference in measured net radiation

recorded at both sites, this difference in net radiation explains the difference in reference

ET values; the higher the net radiation, the higher the reference ET.

Monthly reference ET totals using measured net radiation for both weather stations

are shown in Table 4-4. The weather station adjacent to the lysimeters had consistently

higher reference ET values than the supplementary weather station. This phenomenon









can also be seen in the daily comparison in figures 4-9 and 4-10. Since the weather

station adjacent to the lysimeters is in the same field and much closer than the other

weather station, the values obtained from the closer, adjacent weather station were used

to guarantee consistency and accuracy.

Difference in ETo between Using Measured and Calculated Net Radiation

Table 4-5 shows the difference in ETo at each station when using measured net

radiation compared to calculated net radiation. It can be seen that at the site adjacent to

the lysimeters, ETo is higher when using measured net radiation during every month

except June. At the supplementary weather station, ETo calculated using measured net

radiation was significantly higher during every month. Table 4-5 shows that there was a

8% difference between reference ET at the lysimeter weather station depending on

whether calculated or measured net radiation was used in the reference ET equation. At

the supplementary weather station there was a 24% difference between reference ET

using calculated net radiation as compared to measured net radiation. At both sites, using

measured net radiation to calculate reference ET resulted in lower values than using the

FAO 56 guidelines to calculate net radiation. At the lysimeter site, where crop

coefficients were calculated, the difference in reference ET based on net radiation

differences resulted in a 7% change in crop coefficient values. This means that when

using calculated net radiation, crop coefficients were 7% lower averaged over the course

of the year, compared to using measured net radiation.

Actual ET Using Lysimeters

A plot of daily actual evapotranspiration and rainfall is given in figure 4-12.

Monthly plots of daily actual ET are in Appendices A and B along with the reference

evapotranspiration data. Actual ET measurements were unable to be calculated for the









months of September and October due to hurricanes Frances (September 4th and 5th) and

Jeanne (September 26th and 27th) which saturated the lysimeters with 504 mm of rainfall,

approximately 40% of theyearly regional average rainfall. This excess water made

accurate measurements of actual ET impossible.

Actual evapotranspiration is limited by the amount of rainfall and soil moisture the

crop is exposed to and by the available energy received by the crop through radiation; as

a minimum requirement actual evapotranspiration should not exceed available energy or

rainfall. First stage evapotranspiration occurs when there is an adequate amount of water

near the soil surface such that ET is limited by available energy; second stage

evapotranspiration occurs during drying soil conditions where ET is limited by available

water in the soil coupled with available energy (Jacobs et al., 2002). Figure 4-13 shows

actual ET measured by the lysimeters compared to available energy in terms of

equivalent evaporation due to net radiation as well as the difference between these two

terms for each day in 2004. By converting net radiation values to equivalent evaporation

rates (mm d-1), actual ET can be compared to available energy. This conversion is given

in the FAO56 paper (Allen et al., 1998) as radiation expressed in MJ m-2 day- multiplied

by a conversion factor equal to the inverse of the latent heat of vaporization (1/? = 0.408).

Values for actual ET that fall below zero for the data series plotted as (Rn mm ETc) in

figure 4-13 should be used with caution. Actual ET exceeded available energy from net

radiation during 213 of the 284 days (75 %) where actual ET was used in crop coefficient

calculations. Cumulative rainfall for 2004 was shown to be higher than both cumulative

actual ET and cumulative reference ET for the duration of the year (figure 4-14).









Excess water in the lysimeters could cause higher than reasonable soil moisture

levels in the tanks, which may have increased the available water for evapotranspiration

and affect overall crop coefficients. If soil moisture inside the tanks was higher than the

surrounding field creating wet conditions (above the FA056 requirement for the

reference surface being well-watered), increasing available water and evaporation, higher

actual ET rates would result causing overall crop coefficients to be inflated. The soil in

the area is mapped as consisting of a Candler sand and a Tavares sand (Buster, 1979).

Both sands have a field capacity of 5.0 7.5% by volume in the upper 100 cm of the soil

profile (Carlisle et al., 1978). Soil moisture data for the three lysimeter tanks are

presented in figures 4-15, 4-16, and 4-17, for lysimeters 1 (East), 2, and 3 (West)

respectively. Unfortunately, due to the lack of user-friendly data output with the soil

moisture probes and the required program used to process the actual soil moisture values,

a yearly summary of soil moisture is choppy at best and excludes much of the summer

season when evapotranspiration is highest. For the given time periods it can be seen that

soil moisture levels are regularly, if not consistently, above the 5.0 7.5% field capacity

of the soil resulting in higher than the well-watered definition of a reference crop by

FA056 guidelines. Since the most roots actively involved in the water uptake process

are located in the upper 90 cm of soil (Smajstrla et al. 2000), and turfgrass is commonly

listed with root zone depths from 20 45 cm (Wu, 1985), it can be seen from figures 4-15

through 4-17 that soil moisture content is above the 5.0 7.5% field capacity at these

depths. The extra water in the soil profile increases available water and may enhance

evaporation. This would result in a high bias in actual ET as measured by the lysimeters

and the crop coefficient.









Errors are inherent in the measurement of actual ET since all of the measurements

are made by averaging the actual ET over the three lysimeters. The average daily ET for

the year was 3.25 mm, while the average standard deviation for each daily ETc

measurement is 0.11 mm. Errors made in the measurement of actual ET will propagate

to the crop coefficient calculations. Since reference ET is a calculated number, error

propagation will be based on the uncertainty in the actual ET measurements. Since the

uncertainty in actual measurements is (3.25 0.11 mm), and the average reference ET for

the year is 3.17 mm, then using the division rule for the propagation of uncertainty (6z/z

= 6x/x + 6y/y), the uncertainty of the crop coefficient is (1.03 0.03). Since all actual ET

measurements were not used in the crop coefficient calculations due to the filtering

process of the actual ET data, the average of the crop coefficient will be lower than this

reported value of uncertainty, but it does give an approximation of the amount of error

encountered.

A monthly summary of actual evapotranspiration compared to monthly reference

evapotranspiration using calculated net radiation as defined by FAO56 is shown in table

4-6. It can be seen from this table that actual ET is close to reference ET for most

months, which would produce a crop coefficient close to 1.0. Actual crop coefficient

values for these data are discussed later. Calculated crop coefficients are given in figure

4-20. The highest recorded monthly actual ET occurred during the month of May with a

total of 153 mm of ET; the lowest monthly actual ET occurred during the February with a

total of 45 mm of ET. The sum of actual ET for the year (excluding September and

October) totaled 988 mm of actual ET compared to 956 mm of reference ET. The









highest daily actual ET was 6.8 mm on May 22, 2004. The lowest daily actual ET was

0.0 mm on February 24, 2004.

Regional potential evapotranspiration for this region is given by Satti and Jacobs

(2004) as 127 cm. If the crop coefficient of 0.95 (Allen et al., 1998) for a cool-season

grass is used, the derived actual ET for the year would total 121 cm, or 1210 mm. This

research shows that the total actual ET for 2004 was 988 mm (excluding September and

October). If actual ET for October and September could be calculated and included in

the investigation, it can be assumed that the total ET for 2004 would be close to the

assumed regional average actual ET of 1210 mm, given that a crop coefficient of 0.95 is

assumed.

Extensive research has not been performed for warm-season grasses in the humid

Southeast region but Carrow and Duncan (2003) have researched a cool-season turfgrass,

tall fescue, in Griffin, GA, and found ET rates ranging from 2.2 mm d-1 to 3.8 mm d-1 for

twelve tall fescue ecotypes under turfgrass management conditions. This research was

performed in 1997 and 1998 during summer months, on four different occasions, for one

to two week periods at a time. The ET rates given are averaged over the four different

research periods. This research found that bahiagrass had an average yearly ET rate of

3.3 mm d-1, which agrees with the range given by Carrow and Duncan (2003); this

research also includes ET during winter months when actual ET rates were minimal so

the value of 3.3 mm may be low when compared to the ET values given by Carrow and

Duncan (2003) strictly during summer months.

A daily comparison of measured actual evapotranspiration with calculated

reference evapotranspiration using calculated net radiation as defined by FAO56 is









shown in figure 4-18. These evapotranspiration data were used to compute final crop

coefficients following the method prescribed by FAO56 guidelines. Figure 4-18 shows

the yearly trends of actual and reference ET and shows that during the winter months

actual ET is lower than reference ET, which would produce a crop coefficient less than

1.0. During the summer months, actual ET exceeds reference ET, which would produce

a crop coefficient greater than 1. Actual crop coefficients using these data can be seen in

table 4-7 and in figure 4-20.

A daily comparison of actual evapotranspiration with measured reference

evapotranspiration calculated using measured net radiation is shown in figure 4-19.

Figure 4-19 shows the yearly trends of actual and reference ET calculated using

measured net radiation that are much closer than those found when using reference ET

and calculated net radiation. While this is not the method prescribed by FAO56, this

analysis shows results vary based on the net radiation estimation method. The crop

coefficients resulting from this evapotranspiration data are shown in table 4-7 and in

figures 4-20 and 4-21.

Crop Coefficient

The ultimate goal of this research was to develop monthly crop coefficients for

bahiagrass in a specific climate. Monthly crop coefficients were developed for the year

2004 excluding September and October due to hurricane activity which disrupted

lysimeter results. Out of 366 total days in 2004 (leap year), 84 days were excluded from

crop coefficient calculation due to either hurricane interference or because the data from

that day did not meet the criteria outlined in chapter 3 concerning the adjustment and

filtering of actual ET data. Daily plots of the crop coefficients, namely the crop curve,

are shown in figures 4-20 and 4-21 using calculated net radiation and measured net









radiation respectively. Table 4-7 summarizes these monthly Kc averages along with the

difference between the two methods. The crop coefficient for each month except January

and June was higher when using measured net radiation rather than calculated net

radiation. This can be explained by figure 4-4, which shows that calculated net radiation

is consistently higher than measured net radiation. A study by Jia et al. (2005) found that

measured net radiation from the net radiometer at the supplementary weather station

approximately 500 m southwest of the lysimeters was 10-30% lower than net radiation

calculated using solar radiation following the FA056 method. Jia et al. (2005) found that

this difference in radiation caused a 6-20 % lower reference ET if measured net radiation

is used. The conclusion of the research performed by Jia et al. (2005) indicates that the

use of calculated net radiation from solar radiation measurements may be questionable.

The 6-20% difference in reference ET found by Jia et al. (2005) agrees well with the 1-

21% monthly difference in reference ET as shown in table 4-5 for the lysimeter weather

station.

Since the crop coefficient listed in the FA056 paper for warm-season grasses is

0.85 and crop coefficients in this research exceeded 1.0 during several months, it is

possible that the lysimeters were kept at wetter than recommended conditions leading to

additional available water and evaporation. An increase in actual ET leads to an increase

in the crop coefficient, which could explain crop coefficients of 1.08, 1.16, 1.19, and

1.09, for the months of April, May, June, and July respectively.

Since calculated net radiation is higher than measured net radiation and the

calculation for reference ET is highly dependant on net radiation calculations, reference

ET values using calculated net radiation will be higher than the reference ET using









measured net radiation as shown in figure 4-11 and table 4-5. As a result of this

difference in reference ET, the crop coefficient using calculated net radiation in reference

ET calculations will be lower when using measured net radiation.

Table 4-7 shows that the maximum monthly difference in crop coefficients

occurred in December, a crop coefficient difference ranging from -2.1 to 1.9% between

using calculated and measured net radiation to determine reference ET. When all of the

crop coefficient differences using each method are totaled, there is a -11.3% difference in

crop coefficients. The crop coefficient using calculated net radiation averaged 0.95 for

the year. The crop coefficient using measured net radiation averaged 1.00 for the year.

Small differences in crop coefficients can produce large differences in irrigation

requirements. For example, if the yearly averaged crop coefficients from both

calculations methods were used to calculate irrigation requirements for the month of

December, given that reference ET in December was 67.4 mm using calculated net

radiation, then with a crop coefficient of 0.87 using calculated net radiation compared to

a crop coefficient of 1.00 using measured net radiation, then the theoretical actual ET

would total 58.6 mm using the calculated net radiation Kc and 67.4 mm using the

measured net radiation Kc. Since the plant water requirement is the actual ET minus the

rainfall for that month, given that rainfall totaled approximately 65 mm in December,

then the crop water requirement would be met by 6.4 mm using the calculated net

radiation Kc, but there would be a 2.4 mm crop water requirement if the measured net

radiation Kc were used. Effective rainfall also plays a part in the crop water requirement,

given that if more than enough rainfall is available, there is a maximum amount of









rainfall that the plant can utilize, this is the effective rainfall. Only the amount of rainfall

that does not drain past the root zone and is utilized by the plant is considered effective.

FAO56 cites a crop coefficient of 0.95 for an unspecified cool-season turfgrass and

a crop coefficient of 0.85 for an unspecified warm-season turfgrass. A footnote to these

tabulated values reads that the cool-season turfgrass is one that is kept at a height of 6-8

cm (Allen et al., 1998), instead of the 12 cm required for the FAO56 definition of a

reference surface. This could explain some of the reason why crop coefficients in this

research were found to be unexpectedly high, with a shorter grass height there would be

less actual evapotranspiration which would result in the lower crop coefficient cited by

Allen et al. (1998). Assuming that the warm season turfgrass is kept at the same height

of 6-8 cm (which is not explicitly stated in FAO56), this could be part of the reason for

unexpectedly high crop coefficient values.

Monthly plots of daily crop coefficients calculated using the reference ET with

calculated net radiation are shown along with their respective monthly averages in

Appendix A. This is the FAO56 outlined method for calculating reference ET, and

should be used as the accepted crop coefficient values. The transferability of crop

coefficient values depends on the consistency of one method. If the FAO56 method is

used as a standard, crop coefficients can be compared using consistent methods. The

daily and monthly average crop coefficients using calculated net radiation are shown in

figure 4-20, differences between crop coefficients using the different net radiation

calculation procedures are summarized in table 4-7.

Monthly plots of daily crop coefficients calculated using reference ET with

measured net radiation are shown along with their respective monthly averages in









Appendix B. This method is not recommended by the FAO56 guidelines but is shown

for comparative purposes. The daily and monthly averages for crop coefficients using

measured net radiation are shown in figure 4-21. Monthly crop coefficients are shown in

table 4-7 along with the difference in crop coefficients depending on whether calculated

or measured net radiation was used to calculate reference ET.

Using calculated net radiation, the crop coefficient ranged from a low of 0.73 in

February to a maximum of 1.19 in June. Using measured net radiation the crop

coefficient ranged from a low of 0.81 in February to a high of 1.18 in May. On low

radiation days (i.e. cloudy/overcast), since calculated net radiation is usually higher than

measured net radiation according to figure 4-4, crop coefficients are affected to a greater

degree as compared to sunny, cloudless days when net radiation measurements are in

closer agreement.

Differences in the crop coefficients, depending on which method is chosen to

compute reference ET, are shown in figure 4-22 and tabulated on a monthly basis in table

4-7. Figure 4-22 shows that with the wide range of daily crop coefficients, especially

during the summer months, use of the standardized FAO56 method is critical. The large

differences of daily crop coefficients during the summer months can be attributed to the

differences in net radiation measurements on low radiation days as discussed earlier.

Figure 4-23 shows when the net radiation is low, net radiation values fall significantly

below the yearly trend, Kc differences tend to be higher. Many of the largest differences

were filtered out of the crop coefficient calculations resulting in a blank spot in the crop

curve. The days that were filtered out days also coincide with low radiation days as seen

in figure 4-23. The monthly average difference in crop coefficients is greatest during the









winter months when net radiation values are typically lower, resulting in differences

when calculating reference ET. The average difference in crop coefficients depending on

which net radiation method was used totaled a -11.3% difference over the course of the

year.

Figure 4-24 shows that the crop coefficient depends on the available soil moisture

for a 26 day period from November 13, 2004 through December 8, 2004. Higher soil

moisture levels inside the tanks lead to more available water for actual ET and an overall

higher Kc for that day. Figure 4-24 calculates the average soil moisture in tank 1 as a

weighted average of the vertical soil moisture profile in the tank. The general trend of

the soil moisture follows that of the crop coefficient. The crop coefficient was found to

be higher than previously published values, but this can be explained by the higher than

field capacity soil moisture levels in the tanks for much of the year (Figures 4-15 through

4-17).









Table 4-1. Monthly rainfall comparison among lysimeter area collection sites
Month H_6 (mm) M_6 (mm) W_6 (mm) H_5 (mm) M_5 (mm)


Jan 211
Feb 100 44 96
Mar 92 26 46
Apr 155 106 127 24
May 176 192 176 50 71
Jun 156 198 176 143
Jul 154 265 206 240
Aug 155 160 144 170
Sep 494 504 488 415
Oct 103 107 97 100 102
Nov 88 93 99 33 32
Dec 62 65 66 42 44
Sum 1239 1681 1874 879 1116
Descriptions: H_6 = Hobo rain gauge in Block 6, M_6 = Manual rain gauge in Bl
6, W_6 = Weather station tipping bucket in Block 6, H_5 = Hobo rain gauge in Bl
5, M_5 = Manual rain gauge in Block 5
* blank spaces indicate the gauge was either inoperable or not yet installed, this is
reflected in the difference between the yearly totals


Table 4-2.
Month


ock
ock


Monthly rainfall comparison between surrounding area collection sites
H 4 (mm) M 4 (mm) W 4 (mm) W F (mm)


Jan 44 44
Feb 120 142 143
Mar 20 51 55
Apr 112 29 25
May 211 67 67 70
Jun 163 153 159 140
Jul 124 231 231 269
Aug 209 155 154 157
Sep 580 533 565 418
Oct 99 90 97 115
Nov 74 32 34 34
Dec 82 46 46 38
Sum 1793 1307 1619 1508
Descriptions: H_4 = Hobo rain gauge in Block 4, M 4 = Manual rain gauge in Block
4, W_4 = Weather station tipping bucket in Block 4, WF = FAWN weather station
* blank spaces indicate the gauge was either inoperable or not yet installed, this is
reflected in the difference between the yearly totals










Table 4-3. Monthly total reference ET (mm) using calculated net radiation
ETo SUPP Difference
Month ETo LYS (mm) (mm) (mm) % Difference
Jan 65 54 -11 -17
Feb 63 52 -11 -17
Mar 114 100 -14 -13


98
124
116
113
115
112
94
82
67
1162
lysimeter weather station;


93
120
121
115
112
102
86
65
48
1067
SUPP=


-6
-3
-4


2 2
-3 -2
-10 -9
-8 -8
-17 -22
-19 -28
Avg
supplementary weather station


Table 4-4. Monthly total reference ET (mm) using measured net radiation
ETo SUPP Difference


Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sum
LYS=


ETo LYS (mm)
60
57
112
96
125
126
107
101
99
84
71
53
1090
lysimeter weather station;


(mm) (mm)
44 -26


42
83
74
97
96
84
77
74
62
47
35
815
SUPP:


% Difference
-44
-27
-26
-22
-22
-24
-21
-23
-25
-26
-34
-35
Avg = -28 %


supplementary weather station


11%


Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sum
LYS=









Table 4-5. Difference between monthly total reference ET (mm) using calculated Rn
and measured Rn recorded at both weather stations (ETomeas-ETocalc)
Lysimeter Supplementary
Month (mm) % Difference (mm) % Difference
Jan -4 -7 -10 -19
Feb -6 -9 -11 -20
Mar -3 -2 -17 -17
Apr -3 -3 -18 -20
May -2 -1 -23 -19
Jun 10 9 -25 -21
Jul -6 -5 -31 -27
Aug -14 -12 -35 -31
Sep -14 -12 -29 -28
Oct -10 -10 -24 -28
Nov -11 -13 -18 -28
Dec -14 -21 -13 -28
Avg -6 -7% -20 -24


Table 4-6. Monthly total actual evapotranspiration using lysimeters compared to
reference ET using calculated Rn measured at the lysimeter weather station
Month Actual ET (mm) Ref ET (mm)
Jan 61 65
Feb 45 63
Mar 95 114
Apr 121 98
May 153 124
Jun 145 116
Jul 129 113
Aug 113 115
Sep 112


Oct *
Nov 65
Dec 60
Sum 988
* Actual ET was not calculated due to Hurricanes


94
82
67
956 (excluding Sep and Oct)









Table 4-7. Monthly crop coefficients ofbahiagrass using actual ET from lysimeters
and reference ET from the FAO56 method using calculated and measured net
radiation (reference ET calculated at the lysimeter weather station)
Kc using Kc using
calculated net measured net
Month radiation radiation Difference % Difference
Jan 0.91 0.89 0.02 1.9
Feb 0.73 0.81 -0.08 -2.3
Mar 0.83 0.85 -0.02 -2.0
Apr 1.08 1.10 -0.02 -1.6
May 1.16 1.18 -0.02 -1.5
Jun 1.19 1.10 0.09 1.4
Jul 1.09 1.12 -0.03 -1.5
Aug 0.97 1.01 -0.04 -1.7
* Sep N/A N/A N/A N/A
* Oct N/A N/A N/A N/A
Nov 0.80 0.92 -0.12 -2.1
Dec 0.87 1.00 -0.13 -1.9
*2004 Average 0.95 1.00 Sum = -11.3 %
* September and October excluded due to hurricane activity









600


500
-E400
E
300
1 200
100


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-Weather Station Tipping Bucket at block 6 -Manual Rain Gage at block 6
Hobo Tipping Bucket at block 6

Figure 4-1. Monthly cumulative rainfall measured at the lysimeter site using a tipping
bucket rain gauge mounted on top of the weather station, a manual rain gauge,
and a tipping bucket rain gauge recorded using a Hobo datalogger


600
500
E 400
E
S300
4.-
r 200
100
0


flc~ I-"~


Jan Feb Mar Apr May Jun
-Weather Station Tipping Bucket at block 4
-Hobo Tipping Bucket at block 4
Manual Rain Gauge at block 5


Jul Aug Sep Oct Nov Dec
-Manual Rain Gauge at block 4
-Hobo Tipping Bucket at block 5
-Fawn Rain Gauge


Figure 4-2. Monthly cumulative rainfall measured at other sites at PSREU used for
comparative purposes


Y II


m


d


-


















E 10 A
J I EA




0 -
Jan Feb Mar Apr May Jun
daily measured Rn
-4th Order polynomial trend


Jul Aug Sep Oct Nov Dec
A daily calculated Rn
-4th Order polynomial trend


Figure 4-3. Daily net radiation at the lysimeter weather station; net radiation is calculated
using FA056 guidelines and measured using a net radiometer

18

16 -

14 -
S12
E






0 2 4 6 8 10 12 14 16 18
0 4 ^____ ___ ___ ______
0 2 4 6 8 10 12 14 16 18data point





Rn measured (MJ m-2 d1) using net radiometer


Figure 4-4. Daily comparison of calculated net radiation and measured net radiation at
the lysimeter weather station
the lysimeter weather station


















c' o data point
Ea 8 -1 to 1
SU 6 -Lineartrend

-,

E 0
0 2 4 6 8 10 12 14 16 18
Rn measured (MJ m-2 d1) at Supplementary Weather
Station


Figure 4-5. Comparison of net radiation measured at the lysimeter site compared to net
radiation measured at the supplementary site


E3

2

1

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


-Daily ETo using calculated Rn (mm)


- Monthly Average ETo (mm)


Figure 4-6. Plot of reference ET calculated using the Penman Monteith equation and
calculated net radiation at the lysimeter weather station




















E
E3
0-
2

1

0
Jan Feb Mar Apr May Jun


Jul Aug Sep Oct Nov Dec


-Daily ETo using calculated Rn (mm) Monthly Average ETo (mm)



Figure 4-7. Plot of reference ET calculated using the Penman Monteith equation and
calculated net radiation at the supplementary weather station


* data point
-1 to1
-Linear trend


0.0 0.5 1.0 1.5
VPD (kPa) @ Supplementary Weather
Station


Figure 4-8. Comparison of VPD (kPa) measured at the lysimeter weather station and the
supplementary weather station


w


w
CU



E
> c~
0
@ U)

CU




















I--
2m

1 -

0
Jan Feb Mar Apr May Jun


Jul Aug Sep Oct Nov Dec


-Daily ETo using measured Rn (mm) Monthly Average ETo (mm)


Figure 4-9. Plot of reference ET calculated using the Penman Monteith equation with
measured net radiation at the lysimeter weather station




6

5

34 -, ._,_ A .I. ___ll _,,


I--
2-


1-

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Daily ETo using measured Rn (mm) Monthly Average ETo (mm)



Figure 4-10. Plot of reference ET calculated using the Penman Monteith equation with
measured net radiation at the supplementary weather station












2.0


E 1.0
0
I-
w 0.5


W-0.5

o-1.0

-1.5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


EToRs EToRn (mm) Average Monthly Difference (mm)


Figure 4-11. Daily difference and monthly average difference between reference ET
when using calculated net radiation compared to measured net radiation at the
lysimeter weather station


2004 Total Annual
Rainfall = 1862 mm

II


0


60


E
120 E

4--
180


240


300


Jan Feb Mar Apr May Jun Jul Aug Sep Oct


Nov Dec


Rainfall (mm) ETc (mm) Monthly Avg ETc (mm)



Figure 4-12. Daily actual evapotranspiration measured by the lysimeters and daily
rainfall recorded at the tipping bucket at the lysimeter weather station













10


8


6
E
I-
LU 4


2


0


E
E
-5 I-
LU

-10 E
E

-15


-20


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

MRn ETc ETc (mm) -Available Energy from Rn (mm)



Figure 4-13. Daily actual evapotranspiration measured by the lysimeters compared to
equivalent evaporation from available energy from net radiation


2.0


I 1.5-


S- 1.0


05 ETc estimated
0.5
E based on ETo trend


0.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-Cumulative ETc
-Cumulative ETo
Cumulative Rainfall from lysimeter tipping bucket rain gauge



Figure 4-14. Cumulative actual evapotranspiration, cumulative reference
evapotranspiration using calculated net radiation at the lysimeter weather
station, and cumulative rainfall










40

35

30







10 ,
15



5 4-"

0 -r
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
S5cm 10 cm 20cm 50cm x 100cm


Figure 4-15. Soil moisture profile in lysimeter 1 (east)


40

35

30 -

S25 #A

S20 "

5o -





0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
S5cm 10cm 20cm 100 cm


Figure 4-16. Soil moisture profile in lysimeter 2 (middle)











45

40

35-
xx
Sx X

25 X
U,*
0 20
2x ^>!
.-= K il
S15 # m 10

10 *-

5 ----W


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
S5 cm 10 cm 20 cm 50 cm x 100 cm


Figure 4-17. Soil moisture profile in lysimeter 3 (west)

8 -

7 ,
6 ..

E^ a' : MIN 0
EA _E _m "m P -- IL 11y

; M" do M. M "- IN

2 ^K ... .- >.r -*.,- *-1,*




Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date
ETc (mm) ETo (mm) using calc Rn
-Poly. Trend of ETc -Poly. Trend of ETo


Figure 4-18. Average daily actual ET measured using lysimeters and daily reference ET
using calculated net radiation at the lysimeter weather station










8
7
6
-5
E
E4
.3 f, IF
II.. .U
2 -
1 1


Jan Feb Mar


Apr May Jun Jul Aug Sep Oct Nov Dec


* ETc (mm)


* ETo (mm) using measured Rn


-Poly. Trend of ETc


Figure 4-19. Average daily actual ET measured using lysimeters and daily reference ET
using measured net radiation at the lysimeter weather station


2.0
1.8
1.6
1.4
1.2
01.0
0.8
0.6
0.4
0.2
0.0


Jan Feb Mar Apr May Jun Jul Aug Sep


Oct Nov Dec


-Daily Kc using calc Rn Monthly Kc using calc Rn



Figure 4-20. Average daily and monthly crop coefficients using calculated net radiation
(reference ET calculated at the lysimeter weather station)


-Poly. Trend of ETo


I k
Ill. f I _A I. A
11 11 1 If INILA,
H'111)11
V1
I












2.0
1.8
1.6
1.4
1.2
,1.0
0.8
0.6
0.4
0.2
0.0


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

-Daily Kc using measured Rn Monthly Kc using measured Rn



Figure 4-21. Average daily and monthly crop coefficients using measured net radiation
(reference ET calculated at the lysimeter weather station)


o U.U

S-0.2

2 -0.4

o -0.6

-0.8 -

-1.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


6 0
8

4

2

0


-(Kc calc Rn) (Kc meas Rn)
- Average Monthly % Difference


- Average Monthly Kc Difference


Figure 4-22. Difference in crop coefficient between using calculated and measured Rn to
compute reference ET


I I


-


Y


I L il
i '--























-0.2 '

-0.4

-0.6
Jan Feb Mar Apr May Jun

(Kc calc Rn) (Kc meas Rn)
SDaily Calc Rn
Poly. Daily Calc Rn


10 I



Oz
-5
o.o



-10 -U
0


Jul Aug Sep Oct Nov Dec

Average Monthly Kc Difference
SDaily Meas Rn
Poly. Daily Meas Rn


Figure 4-23. Differences in crop coefficient between calculated and measured Rn in
reference ET compared to daily values of calculated and measured Rn


14

12

10
0-
8

S6

04
U) 4

2

0


2.0



1.5



1.0



0.5



0.0


rat
Date


---Average Soil Moisture -*-Crop Coefficient -Trend of Crop Coefficient


Figure 4-24. Soil moisture content in tank 1 and overall Kc













CHAPTER 5
RECOMMENDATIONS FOR FUTURE WORK

The lysimeters used in this research will be operational for years to come barring

any unforeseen interruptions. Vegetation other than bahiagrass will eventually be planted

in these same tanks as well as in the surrounding field. The selection ofbahiagrass for

this research was as much for the interest in the crop as it was to determine if the

lysimeters operate as anticipated and to determine the correct operational requirements

for the lysimeters and calculation procedure for evapotranspiration data for future crops.

In this way, depending on the needs of the user, the methods developed can be applied to

future research using the same or slightly modified instruments and methods.

Lysimeter Design

Some flaws are inherent with the design of the lysimeters that were used in this

research, but small design imperfections for any precision lysimeter design are bound to

occur due to the high accuracy and time consuming maintenance of these devices. The

design and construction of the lysimeters was performed prior to any realization of what

effects the design intricacies would have on actual evapotranspiration measurements.

Rainfall Measurement

The design specification that had the most influence on the operation of the

lysimeters was the rubber stripping that covered the gap between the inner and outer tank

of each lysimeter. It became clear after data processing that these rubber coverings

meant to keep water out of the gap between the inner and outer tank actually intercepted

rainfall and created an inflow of rainfall into the inner tank. This inflow of rainfall









caused rainfall amounts recorded by the lysimeters to be larger than the actual rainfall

that occurred because the surface of the lysimeter was effectively increased by the

surrounding rubber cover. Given that the rubber stripping surrounds the perimeter of the

inner tank with a width of 7.87 cm, that creates a maximum effective area approximately

60% greater than the area of the evaporative surface of the grass in the inner tank. The

increase in rainfall recorded by the lysimeters approached a 60% increase over actual

rainfall, but with random variation in wind speed and wind direction the effective area

that is added by the rubber stripping does not always contribute wholly to the inflow of

rainfall into the inner tank. Because the varying wind speed and wind direction is not

constant, the greater rainfall amounts recorded by the lysimeters cannot be directly

applied to evapotranspiration calculations simply by using a single rainfall coefficient.

To alleviate this problem, the rubber stripping that surrounds the inner tank should be

redesigned. One possible solution would be to construct a new barrier for the gap

between the inner and outer tanks that slopes slightly inward to deflect all rainfall caught

by the barrier to flow into or out of the inner tank. This would give the lysimeter

operator the ability to increase the effective area of the lysimeter for rainfall calculations

while keeping the effective area for evapotranspiration and drainage calculations the

same.

Drainage

Another design consideration is the efficiency of the pumping process. All

pumping was done while the lysimeter operator waited at the site and measured with a

graduated cylinder immediately after pumping by draining the storage tanks. This caused

problems because pumping was performed in the middle of the afternoon when

evapotranspiration is typically at a peak, so there are two sources of outflow from the









lysimeter in the forms of ET and pumping that cannot be distinguished easily. This

process would be more efficient by pumping the lysimeters during non-daylight hours

when ET is minimal, and by measuring the drainage from the storage tanks using a flow

meter attached to the nozzle on the outlet of the storage tanks. An automated timer

system was added to the design of the pumping system at the beginning of 2005 to

expedite the manual pumping process. Pumping can be performed in the early morning,

and scheduled up to a week in advance so that if rainfall is anticipated, pumping can be

performed without significant delay after a rainfall event so that standing water in the

inner lysimeter tank does not affect the evapotranspiration rate by the existence of extra

water present that is available for ET. This procedure would cut down on the afternoon

interference with actual ET measurement and expedite the pumping and measurement

procedure.

Soil Moisture

Soil moisture probes are located in each of the lysimeters at five different depths as

well as one set of probes outside the tanks in the surrounding field to monitor the amount

of water present in the inner tanks and at a location outside of the tanks. The soil probes

used in this research were extremely sensitive and the post processing program is not at

all user-friendly. The result was choppy soil moisture data, and large gaps of missing

data throughout the year. A more effective approach to this problem would be using a

different type of probe that measures soil moisture directly so the lysimeter operator

could tell how much water was present at the different levels in the inner tank and

determine the duration of pumping needed to attain soil moisture levels similar to the

surrounding field. This would also require an additional set of soil moisture probes

outside the tanks in the same field to compare soil moisture values of the lysimeters.









There were additional soil moisture probes installed outside the tanks during this research

but the first location had high concentrations of clay which affected initial soil moisture

readings. The second location was inundated with water after the high rainfall season

and was not a fair representation of the entire field. The additional recommended probes

could be wired to a data logger that outputs real-time soil moisture data so that the

lysimeter operator could see what the exact soil moisture was at that time and pump the

tanks accordingly. This would allow the lysimeter operator to maintain the soil moisture

levels in the lysimeter closer to those levels in the surrounding field.

Calculation Procedure

Evapotranspiration measurements were performed on a daily basis, but with the

precision of the lysimeter, calculations should be available for time scales as small as 30

minutes. The user would have to account for noise in the load cell readings when

applying calculation procedures to the raw data, but actual evapotranspiration data for

any time scale above 30 minutes can be achieved. Crop coefficients can be calculated for

any crop planted in the lysimeter using the same reference evapotranspiration equations

outlined in FA056 along with actual evapotranspiration data from the lysimeters.

An additional criteria for data suitability could be based on soil moisture. For

example, if the soil moisture inside and outside of the tanks is known, then a possible

criterion could be that the data is not used if the soil moisture inside the tanks is greater

than 10% different from the soil moisture outside of the tanks. This requirement would

prevent possible errors being made by calculating ET when the lysimeters do not truly

represent the surrounding field.









Evaporative Area of the Lysimeters

A major factor in calculating the actual evapotranspiration of any crop planted in

the lysimeters is the evaporative area of that crop. For bahiagrass, which produces full

ground coverage, the evaporative area is relatively straight forward, only the additional

evaporative area created by the grass extending over the edge of the inner tank must be

determined. For crops that do not have full ground cover, the evaporation of bare soil

may have to be accounted for to achieve accurate evapotranspiration measurements. This

would add more challenges to evapotranspiration calculations. The user would have to

consider the evapotranspiration of the crop, be able to accurately measure its respective

evaporative area, be able to account for evaporation from bare soil, and be able to

measure its evaporative area. If the evaporation from bare soil could be defined through

simple equations, then this term could be defined as a term in the lysimeter water balance

and the evapotranspiration of the crop could be measured directly as the only remaining

term in the lysimeter water balance equations.

Herbicides and Pesticides

Ants and weeds were common problems during this research that had to be

remedied as soon as possible to prevent measurement interruption. Red ants were

commonly found residing in the lysimeter tanks and had to be exterminated using store-

bought ant killer. Spreading of ant killer or other pesticides (that do not interfere with the

health of the crop) at regular intervals around the perimeter of the lysimeters could

prevent the frequency of ants or other pests. Weeds were also a problem in the

maintenance of the lysimeters that should be dealt with immediately. Creeping weeds

were commonly found in and around the lysimeter and were removed promptly to

prevent further spreading. Other various weeds were often found inside the lysimeter






80


tank and removed upon recognition to prevent further spreading as well as unwanted

evapotranspiration from vegetation other than the crop in question. Care should be used

when applying herbicide. The selection only occur when the user is certain that the

health of the crop will not be affected. A test should be performed on weeds outside of

the lysimeters before applying the herbicide inside the tanks to make sure the herbicide

will not cause any undesired effects.













APPENDIX A
ACTUAL EVAPOTRANSPIRATION, REFERENCE EVAPOTRANSPIRATION
USING CALCULATED NET RADIATION, AND CROP COEFFICIENTS


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Full Text

PAGE 1

ESTIMATING THE CROP COEFFICIENT OF BAHIAGRASS USING LYSIMETERS AND THE FAO56 PENMAN-MONTEITH EQUATION By GEORGE WILLIAM TRIEBEL A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by George W. Triebel

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To my parents, Allen Triebel and Ardith Shealey

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ACKNOWLEDGMENTS I would like to acknowledge several individuals for their assistance and contributions in the completion of this thesis. First, I would like to thank my advisor, Dr. Michael D. Dukes, for giving me the opportunity to pursue this research as part of my degree requirement and for helping to direct the scope of this work. I would also like to thank my other supervisory committee members. Dr. Jennifer Jacobs gave me direction as an undergraduate in the department of Civil and Coastal Engineering and became an integral part of my supervisory committee. Dr. Kenneth Campbell was always around for advice. Dr. Kirk Hatfield was one of the most approachable professors I have encountered. He gave me the opportunity to work as a teaching assistant as an undergraduate to help pay the bills. Another person I would like to thank is Dr. Suat Irmak, who developed the design of this project. I helped him install most of the field equipment used. The St. Johns River Water Management District deserves thanks for their support of this project. Xinhua Jia was also a great help with the data collection, interpretation, and analysis involved in this project. I would also like to give thanks to Danny Burch and Larry Miller, whose technical expertise and assistance were greatly appreciated. The friendliness of all of the faculty, staff, and students in this department made completing this research as enjoyable as possible. Finally, I would like to thank my parents, Ardith Shealey, Allen Triebel, and my stepfather, Steven Shealey. They all gave me the motivation and inspiration to make all of my achievements possible. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF TABLES ............................................................................................................vii LIST OF FIGURES .........................................................................................................viii ABSTRACT .......................................................................................................................xi CHAPTER 1 INTRODUCTION........................................................................................................1 Statement of Problem...................................................................................................1 Research Objectives......................................................................................................1 2 REVIEW OF LITERATURE.......................................................................................2 Floridas Water Management.......................................................................................2 History of Lysimeters...................................................................................................4 Design....................................................................................................................5 Operational Requirements.....................................................................................6 Bahiagrass (Paspalum notatum)...................................................................................7 Evapotranspiration........................................................................................................8 Reference Evapotranspiration...............................................................................9 Actual Evapotranspiration...................................................................................13 Crop Coefficient.........................................................................................................14 3 METHODOLOGY.....................................................................................................17 Location......................................................................................................................17 Lysimeters...................................................................................................................18 Design..................................................................................................................18 Instrumentation....................................................................................................20 Maintenance........................................................................................................21 Load Cell Calibration..........................................................................................22 Rainfall................................................................................................................24 Weather Station..........................................................................................................25 Calculations................................................................................................................26 v

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Reference ET Using FAO56...............................................................................26 Actual ET Using Lysimeters...............................................................................27 Soil moisture balance calculations...............................................................27 Equivalent volume of water calculations.....................................................28 Equivalent weight calculations.....................................................................29 Change in storage calculations.....................................................................29 Remaining soil moisture balance variables..................................................31 Actual ET Adjustment and Filtering...................................................................31 Crop Coefficient..................................................................................................33 4 RESULTS AND DISCUSSION.................................................................................43 Rainfall.......................................................................................................................43 Net Radiation at the Lysimeter Site............................................................................44 Reference ET..............................................................................................................44 Using Calculated Net Radiation..........................................................................46 Using Measured Net Radiation...........................................................................47 Difference in ET 0 between Using Measured and Calculated Net Radiation.......48 Actual ET Using Lysimeters......................................................................................48 Crop Coefficient.........................................................................................................53 5 RECOMMENDATIONS FOR FUTURE WORK.....................................................75 Lysimeter Design........................................................................................................75 Rainfall Measurement.........................................................................................75 Drainage..............................................................................................................76 Soil Moisture.......................................................................................................77 Calculation Procedure.................................................................................................78 Evaporative Area of the Lysimeters...........................................................................79 Herbicides and Pesticides...........................................................................................79 APPENDIX A USING CALCULATED NET RADIATION TO CALCULATE DAILY REFERENCE ET, DAILY ACTUAL ET, AND CROP COEFFICIENTS...............81 B USING MEASURED NET RADIATION TO CALCULATE DAILY REFERENCE ET, DAILY ACTUAL ET, AND CROP COEFFICIENTS...............87 LIST OF REFERENCES...................................................................................................93 BIOGRAPHICAL SKETCH.............................................................................................98 vi

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LIST OF TABLES Table page 3-1 Lysimeter instrumentation and description..............................................................34 3-2 Lysimeter calibration results for June 9, 2003.........................................................35 3-3 Lysimeter calibration results for January 28, 2004..................................................35 3-4 Lysimeter calibration results for January 19, 2005..................................................35 3-5 Weather station instrumentation and description.....................................................36 4-1 Monthly rainfall comparison among lysimeter area collection sites........................59 4-2 Monthly rainfall comparison between surrounding area collection sites.................59 4-3 Monthly total reference ET (mm) using calculated net radiation............................60 4-4 Monthly total reference ET (mm) using measured net radiation.............................60 4-5 Difference between monthly total reference ET (mm) using calculated Rn and measured Rn recorded at both weather stations (ET 0 meas-ET 0 calc).......................61 4-6 Monthly total actual evapotranspiration using lysimeters compared to reference ET using calculated Rn measured at the lysimeter weather station.........................61 4-7 Monthly crop coefficients of bahiagrass using actual ET from lysimeters and reference ET from the FAO56 method using calculated and measured net radiation (reference ET calculated at the lysimeter weather station).......................62 vii

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LIST OF FIGURES Figure page 3-1 Geographical location: Plant Science Research and Education Unit (PSREU)......37 3-2 Arial photograph of Phase I of the PSREU research facility: block layout and the location of the lysimeters and weather station...................................................37 3-3 East/west section view of lysimeter design for one lysimeter.................................38 3-4 Layout and section construction drawings of lysimeters.........................................38 3-5 Southeast view of the installed lysimeters taken in July 2003.................................39 3-6 Southwest view of the excavated site shown with the outer tanks resting on the reinforced concrete foundation and PVC air pressure release ducts........................39 3-7 Lysimeter calibration June 9, 2003: Flow meter shown while pumping water into the inner tank of the easternmost lysimeter (L1)..............................................40 3-8 Lysimeter calibration on January 19, 2005: (2) 23.59 kg. weights are shown resting on a sheet of plywood on the surface of the westernmost lysimeter (L3)....40 3-9 Location of all rainfall measurement devices at PSREU, W = weather station tipping bucket, H = hobo tipping bucket, M = manual rain gauge..........................41 3-10 Northwest view of the weather station located adjacent to the lysimeters, approximately 50 m east of the lysimeter site with instrumentation labeled...........41 3-11 Northeast view of the supplementary reference ET weather station located approximately 500 m southwest of the lysimeter site with instrumentation labeled......................................................................................................................42 4-1 Monthly cumulative rainfall measured at the lysimeter site using a tipping bucket rain gauge mounted on top of the weather station, a manual rain gauge, and a tipping bucket rain gauge recorded using a Hobo datalogger........................63 4-2 Monthly cumulative rainfall measured at other sites at PSREU used for comparative purposes...............................................................................................63 4-3 Daily net radiation at the lysimeter weather station; net radiation is calculated using FAO56 guidelines and measured using a net radiometer...............................64 viii

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4-4 Daily comparison of calculated net radiation and measured net radiation at the lysimeter weather station..........................................................................................64 4-5 Comparison of net radiation measured at the lysimeter site compared to net radiation measured at the supplementary site..........................................................65 4-6 Plot of reference ET calculated using the Penman Monteith equation and calculated net radiation at the lysimeter weather station..........................................65 4-7 Plot of reference ET calculated using the Penman Monteith equation and calculated net radiation at the supplementary weather station.................................66 4-8 Comparison of VPD (kPa) measured at the lysimeter weather station and the supplementary weather station.................................................................................66 4-9 Plot of reference ET calculated using the Penman Monteith equation with measured net radiation at the lysimeter weather station..........................................67 4-10 Plot of reference ET calculated using the Penman Monteith equation with measured net radiation at the supplementary weather station..................................67 4-11 Daily difference and monthly average difference between reference ET when using calculated net radiation compared to measured net radiation at the lysimeter weather station..........................................................................................68 4-12 Daily actual evapotranspiration measured by the lysimeters and daily rainfall recorded at the tipping bucket at the lysimeter weather station...............................68 4-13 Daily actual evapotranspiration measured by the lysimeters compared to equivalent evaporation from available energy from net radiation...........................69 4-14 Cumulative actual evapotranspiration, cumulative reference evapotranspiration using calculated net radiation at the lysimeter weather station, and cumulative rainfall......................................................................................................................69 4-15 Soil moisture profile in lysimeter 1 (east)................................................................70 4-16 Soil moisture profile in lysimeter 2 (middle)...........................................................70 4-17 Soil moisture profile in lysimeter 3 (west)...............................................................71 4-18 Average daily actual ET measured using lysimeters and daily reference ET using calculated net radiation at the lysimeter weather station................................71 4-19 Average daily actual ET measured using lysimeters and daily reference ET using measured net radiation at the lysimeter weather station.................................72 4-20 Average daily and monthly crop coefficients using calculated net radiation (reference ET calculated at the lysimeter weather station)......................................72 ix

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4-21 Average daily and monthly crop coefficients using measured net radiation (reference ET calculated at the lysimeter weather station)......................................73 4-22 Difference in crop coefficient between using calculated and measured Rn to compute reference ET..............................................................................................73 4-23 Differences in crop coefficient between calculated and measured Rn in reference ET compared to daily values of calculated and measured Rn..................................74 4-24 Soil moisture content in tank 1 and overall Kc........................................................74 A-1 Daily actual ET, daily reference ET using calculated net radiation, daily Kc, and average monthly Kc.................................................................................................81 B-1 Daily actual ET, daily reference ET using measured net radiation, daily Kc, and average monthly Kc.................................................................................................87 x

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineering ESTIMATING THE CROP COEFFICIENT OF BAHIAGRASS USING LYSIMETERS AND THE FAO56 PENMAN-MONTEITH EQUATION By George William Triebel December 2005 Chair: Michael D. Dukes Major Department: Agricultural and Biological Engineering Evapotranspiration (ET) is one of the most basic components of the hydrologic cycle. Knowledge of this water balance term is essential for planning and operating various water resource projects. Using ET measurements, a crop curve for bahiagrass (Paspalum notatum) can be developed; the crop curve is critical to accurately determine irrigation-water requirements for agricultural and developmental interests in the southeastern United States. Daily crop evapotranspiration (ET c ) rates for bahiagrass were used to establish the crop coefficient curve that can be used in water management practices and irrigation scheduling. The ET c was determined using three sensitive weighing lysimeters, located at the Plant Science and Research Education Unit in Citra, Florida. Daily reference evapotranspiration (ET 0 ) was calculated using the FAO 56 Penman-Monteith method. The daily crop coefficient (K c ) of bahiagrass was calculated using daily values of ET c and ET 0 and averaged on a monthly basis. The ET 0 was calculated using estimated and measured net radiation (R n ) to determine the respective xi

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monthly averages of K c Monthly averages of K c can be used to determine an approximate value of crop water use for bahiagrass under similar geography, climates, and cultural conditions. We used lysimeters to estimate the crop water use of bahiagrass from January 1, 2004 through December 31, 2004. We estimated monthly K c values for bahiagrass using the FAO Penman-Monteith method to estimate ET 0 and using lysimeters to measure ET c over the course of the year. These values can be used to estimate irrigation requirements and increase the efficiency of water use with respect to bahiagrass. xii

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CHAPTER 1 INTRODUCTION Statement of Problem Development of monthly crop coefficients for bahiagrass (Paspalum notatum) is important to accurately determine irrigation water requirements for agricultural and developmental interests in the Southeastern United States. Knowledge of evapotranspiration rates of bahiagrass in this region is the fundamental basis for determining crop coefficients. Using lysimeters to measure crop water use, and prescribed methods to compute reference evapotranspiration rates, the crop coefficients can be calculated on a daily basis; and averaged on a monthly basis, for practical use when calculating irrigation requirements. Research Objectives Three weighing lysimeters were used to establish monthly crop coefficients; through the application of methods used to achieve this ultimate goal, several auxiliary objectives were met. The goals set forth in this research are as follows: Measure daily actual evapotranspiration using three weighing lysimeters. Calculate daily crop coefficients and monthly average crop coefficients based on actual and reference evapotranspiration. Show the impacts of using calculated net radiation to calculate reference evapotranspiration using the FAO56 guidelines compared to using measured net radiation to calculate reference evapotranspiration. Show how the crop coefficients are affected by net radiation measurements. 1

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CHAPTER 2 REVIEW OF LITERATURE Floridas Water Management Water management in Florida is based on historical experience and changing philosophies about the conservation of natural resources and the environment. Florida has more rainfall than any other state in the U.S. except Louisiana; Florida receives an average of 1350 mm of rainfall per year while Louisiana receives an average of 1400 mm per year (Purdum, 2002). The occurrence and location of rainfall does not always match when and where it is needed, which affects irrigation scheduling. Irrigation timing and amounts also depend on rainfall intensity, duration, and the location of interest as well as plant needs. Rapid population growth and continuous development are at the root of Floridas impending water scarcity. Florida sits on top of one of the nations most extensive freshwater systems, possessing more available groundwater than any other state in the U.S. The cavernous limestone aquifer system is a major part of the states hydrologic cycle and is a critical resource for the health of natural systems as well as a provider for public freshwater supply. Floridas future depends on an ample supply of clean water as a critical resource for all aspects of daily life. As increasing populations lead to higher demands for water withdrawals, the ever-increasing usage contributes directly to decreased water levels in lakes and wetlands, and ultimately impacts the health and sustainability of the environment as well as the general publics water supply and private interests. 2

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3 Floridas population is currently estimated at 16 million people (1996). By the year 2020, it is expected to increase by 4 million people, to a total of approximately 20 million people. Some figures exceed those projections and estimate populations as high as 21.8 million in the year 2020 (Marella, 1999). In addition to the resident population, an estimated 45 million people visit the state every year. Population growth and water demand do not always increase proportionally (FDEP, 2003); the rate of increased water demand is expected to exceed the rate of population growth through the year 2020 (FDEP, 2003). Impending increases in water demand heighten the need for water conservation and planning. Improved understanding and more efficient application of agricultural water are needed to address these concerns. Agriculture is the single largest user of water in Florida (and the world as a whole). Two-thirds of all water withdrawn (from groundwater and surface water sources throughout the world) is devoted to agricultural purposes (Postel, 1992). One way to better manage available water is through efficient use of agricultural water. If major improvements are made in this area, then water shortage issues may be more readily addressed. Postel (1992) estimates only 40% overall efficiency of agricultural water use worldwide, meaning that over half of the water used for agricultural purposes is wasted. In the past century, population growth, industrial development, and the expansion of irrigated agriculture have resulted in an enormous increase in the amount of water used. Floridas Water Management Districts define a water resource caution area (WRCA) as an area where water is either scarce or contaminated, and delineated these areas to prevent misallocation of available water. These WRCAs now cover thousands of square miles throughout the state (Purdum, 2002). By knowing the water use of specific crops,

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4 agricultural water can be more effectively managed. Defining water-use characteristics for specific crops allows for more accurate estimation of irrigation water requirements, and in turn reduces water consumption. History of Lysimeters Lysimeters are the standard tool used to analyze relationships among soil, water, and plants, as well as in water quality research. The first lysimeter to be used for water use studies is attributed to De la Hire of France in 1688 (Kohnke et al. 1940, Aboukhaled et al. 1982). Lysimeters are used to define water movement across a soil boundary. A lysimeter consists of an isolated container of soil whose water movement characteristics can be studied and measured directly. When a crop is planted at the ground level of the lysimeter, water use (evaporation and transpiration, or evapotranspiration) can be determined through water balance techniques. Lysimeters used for evapotranspiration (ET) research are typically classified according to their design and use as follows: monolithic or reconstructed soil profiles, weighing or non-weighing designs, and gravity or vacuum drainage designs. Monolithic lysimeters attempt to preserve existing vegetation and soil properties that can be destroyed by the excavation and filling (Schneider and Howell, 1991) that is done when using reconstructed soil profiles in the container. Weighing lysimeters determine evapotranspiration directly, by the mass balance of the water; nonweighing lysimeters indirectly determine ET using the volumetric soil-water balance in the container (Howell et al., 1991). Vertical drainage through the soil column can be measured using either a gravity drainage design, or a design that uses a vacuum system (if the lysimeter is designed in such a way that gravity drainage is inconvenient).

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5 Design Advances in ET lysimetry have focused on duplicating field conditions in the container as closely as possible to the surrounding field through the use of larger lysimeters and more efficient vacuum pumping systems. Although some aspects of lysimeter design are often duplicated or reused, Kohnke et al. (1940) warns that no one construction should be regarded as standard in a lysimeter and that a proper design can be made only by having an accurate knowledge of both the purpose of the experiment and of the pedologic, geologic, and climatic conditions. Pruitt and Lourence (1985) also caution lysimeter users to critically evaluate all agronomic aspects, to ensure high-quality ET data, since major errors in ET data are possible even with an accurate lysimeter. The design of a lysimeter should always be appropriate to the type and scope of research performed. However, some main elements are inherent in all lysimeter designs: ET accuracy, shape and area, depth, soil profile characteristics, weighing mechanisms, construction, and siting. Each design element is discussed in more detail as follows: ET accuracy: ET measurement accuracy is dictated by the planned measurement interval (weekly, daily, hourly) and values ranging from 0.02 to 0.05 mm are commonly cited as the resolution or precision of weighing lysimeter systems (Allen et al., 1991). Shape and area: The shape and area of the container should be a direct reflection of the expected crops to be studied and their root depths. Differences between lysimeter surface area and crop geometry can bias the soil water evaporation and crop transpiration relationship, but this may not critically affect ET measurements for grass, alfalfa, or small grains or other broadcast planted crops (Howell et al. 1991). Depth: The depth of the lysimeter should be based on the rooting depth of the crop which is to be studied. Van Bavel (1961) advised that lysimeter depth should permit the development of normal rooting density and rooting depth and provide similar available water profiles to the field profile. Soil profile characteristics: Although monolithic lysimeters may preserve the exact physical, chemical, and vegetation characteristics of the surrounding area,

PAGE 18

6 many weighing lysimeters have utilized reconstructed soil profiles for ET measurements and, when carefully reconstructed, provided accurate ET data (Pruitt and Angus, 1960; Van Bavel and Myers, 1962). Weighing mechanisms: Mechanical scales have been widely used in weighing lysimeters since the 1950s and permit the precise measurement of the mass change of water within the lysimeter (Howell et al., 1991). Load cell lysimeters measure the total weight of the lysimeter; this leads to the accuracy of ET measurements being dependant on the accuracy of the load cells and the area to mass ratio of the lysimeter design. Construction: Most lysimeter soil containers are made using either steel, reinforced fiberglass, or plastic as the primary construction material. The gap between the inner and outer tank of a weighing lysimeter should be designed as narrow as possible to prevent unnecessary wall heating while allowing for ample clearance to avoid contact between the inner and outer tanks. The gap between tanks must also be covered to prevent water intruding due to rainfall or irrigation. Location: Windward fetch must be accounted for when choosing a site for any lysimeter. A sufficient distance of fetch consisting of the same vegetation and moisture regimes as the lysimeter is necessary to ensure that the lysimeter is representing the same environmental conditions as the entire field. Minimum fetch distances should be determined based on the height at which weather recording instruments are operating. For instance, if wind speed, humidity, and temperature are being recorded at a 2 m height above ground surface, then a windward fetch of 100 to 400 m should be provided given suggested fetch ratios that vary from a minimum of 1:25 (Allen et al., 1991) to as much as 1:200 (Jensen et al., 1990). Operational Requirements Potential lysimeter errors can be reduced using strict design and maintenance regimes. Lysimeter operators and users of lysimeter data must be knowledgeable about the constraints of proper environmental management for the lysimeter site and the resulting interpretation of lysimeter data. The accuracy of lysimeter data depends on the ability to achieve identical conditions between the lysimeter and the surrounding field. If lysimeters are designed to meet specific requirements for the research performed and are operated properly, then they can be utilized as precision tools to measure actual evapotranspiration. Through proper use, precision weighing lysimeters are the most practical research tool for direct measurement of daily evapotranspiration and an

PAGE 19

7 effective approach to conduct crop coefficient studies (Howell et al., 1985; Yrisarry and Naveso, 2000). Bahiagrass (Paspalum notatum) Bahiagrass has long been used in the southeastern United States as roadside turf, and in the 1970s it became popular for use in Florida as a lawn grass due to its easy planting, few pest problems, good drought tolerance, and low maintenance (Duble, 1989). Bahiagrass is a warm-season grass that is well adapted to high rainfall areas; therefore its use is widespread in the southeastern United States. Approximately 750,000 acres of bahiagrass were maintained in Florida during the years 1991-1992, second only to St. Augustinegrass (Stenotaphrum secundatum) in turfgrass usage (Hodges et al., 1994). Water consumed for turfgrass irrigation in 1991-1992 in Florida was estimated at 1.75 billion gallons per day (Hodges et al., 1994). With the population of Florida increasing at a rapid rate, a net inflow of 1,000 people per day (Shermyen, 1993), the need for efficient water management practices and a firm grasp on specific water use characteristics in the region is justified. A literature review of ET research related to bahiagrass in humid environments did not reveal a definitive knowledge of the water use of this warm season grass; the majority of research that has been performed concentrates on select warm-season grasses such as Bermudagrass and St. Augustinegrass and cool-season varieties such as fescue, bluegrass, and ryegrass (Brown et al., 2001; Carrow, 1995). Since agricultural water is the main consumer of Floridas freshwater withdrawals representing two-thirds of the total amount of freshwater withdrawn in the state, then the efficiency of irrigation practices for agricultural crops must be maximized to produce a substantial net benefit for water conservation purposes. As Florida faces future water shortages due to the influx of

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8 residents and constantly diminishing freshwater resources, research on crop water use for agricultural purposes is necessary to benefit regional water management practices and to provide a sound basis for irrigation scheduling. Evapotranspiration Evapotranspiration (ET) is the loss of water from a vegetated surface through the combination of the evaporation of water from the soil or plant surface plus the transpiration of water that is transported through the plant and released to the atmosphere as water vapor. ET is a basic component of the hydrologic cycle. The knowledge of this water balance term is essential for the planning and operation of water resource projects. Evapotranspiration is the loss of water from a vegetated surface through the combination of the evaporation of water from the soil or plant surface plus the transpiration of water that is transported through the plant and expelled as water vapor. Evaporation and transpiration are affected by solar radiation, air temperature, humidity, wind speed, and soil moisture. Transpiration is also affected by soil moisture and crop characteristics. ET is commonly expressed in units of either depth per time (e.g. mm day -1 ) or energy per unit area over a specified time (e.g. MJ m -2 day -1 ). Cuenca (1989) gives a description of the evapotranspiration process as the combination of water evaporated from the plant and soil surface plus that amount of water which passes through the soil into roots, through the stem of the plant, and to the leaves where it passes into the atmosphere through small pores termed stomates. An accurate estimation of evapotranspiration is important to water supplies (surface and groundwater), water management, and the economics of multi-purpose water projects (i.e. irrigation, power, water transportation, flood control, municipal and industrial water uses, and wastewater reuse systems). The political implications of water use issues

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9 affected by evapotranspiration include the negotiation of water compacts and treaties and the litigation and adjudication of water rights in major river systems (Jensen et al., 1990). Reference Evapotranspiration The evapotranspiration rate from a reference crop surface, not short of water, is called the reference crop evapotranspiration or reference evapotranspiration and is denoted as ET 0 The methods for reference ET calculation can be categorized into four groups: combination, radiation, temperature and pan evaporation methods. Combination methods, the most commonly used methods, include radiation (energy balance) and aerodynamic (heat and mass transfer) terms. Typical combination methods are FAO Penman (Doorenbos and Pruitt, 1977), Kimberly Penman (Wright, 1982), Penman-Monteith (Allen et al., 1994a), the FAO Penman-Monteith (Allen et al., 1998) and the ASCE-EWRI Penman-Monteith (Walter et al., 2002). Penman (1948) established the modern reference evapotranspiration standard by separating the term into two components that drive the process simultaneously: an available energy term, and a mechanically derived term driven by atmospheric vapor transport. The combination of these two terms was the first time net radiation was introduced into the physical modeling of evapotranspiration. Doorenbos and Pruitt (1977) defined reference ET to be the rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of uniform height, actively growing, completely shading the ground and not short of water. Allen (1994a, b) introduced the idea of a hypothetical reference crop so that crop characteristics could be applied to the reference evapotranspiration definition. These characteristics include a fixed crop height of 12 cm, a surface resistance (r s ) of 70 s m -1 and albedo () of 0.23, and a constant latent heat of vaporization () of 2.45 MJ/kg. The

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10 hypothetical reference crop was defined by Allen et al. (1998) as an extensive surface of green grass of uniform height, actively growing, completely shading the ground, and with adequate water. Jensen (1990) defines reference ET as the rate at which readily available soil water is vaporized from specified vegetated surfaces. A recent definition of reference ET adds a minimum fetch specification to the requirements set by FAO56: the ET rate from a uniform surface of dense, actively growing vegetation having specified height and surface resistance, not short of soil water, and representing an expanse of at least 100 m of the same or similar vegetation (Walter et al., 2005). The standard method for determining ET 0 was established in 1990 during consultation of experts and researchers from the Food and Agricultural Organization (FAO) of the United Nations in collaboration with the International Commission for Irrigation and Drainage (ICID) and the World Meteorological Organization, to review methodologies on crop water requirements and to advise on procedures to use meteorological data to estimate ET 0 (Allen et al., 1998). The method that was agreed upon for calculating reference evapotranspiration is known as the FAO-56 Penman Monteith (FAO56 PM) method. The guidelines presented by Allen et al. (1998) can be used to compute crop water requirements for both irrigated and rainfall fed agriculture and for computing water consumption by agricultural and natural vegetation. A recent ASCE-EWRI standard method to calculate reference ET has been published (Walter et al., 2005), but for daily calculations of reference ET on grass, the guidelines are identical to the FAO56 method. Equation 2-1 is used to calculate ET 0 on a daily time scale.

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11 )34.01()(273900)(408.022ueeuTGRETasno (2-1) ET 0 : reference evapotranspiration (mm) T: mean daily air temperature ( o C) calculated as the average of the minimum and maximum daily air temperatures R n : average daily net radiation (MJ m -2 day -1 ) G: average daily soil heat flux (assumed to be negligible in daily calculations since it is very small compared to R n ) u 2 : mean daily wind speed (m s -1 ) calculated as the average of each 30 min wind speed reading over the course of a day : slope of the vapor pressure curve (kPa o C -1 ) e s : saturated vapor pressure (kPa) e a : actual vapor pressure (kPa) can be calculated: 23.2373.23727.17exp6108.04098TTT (2-2) is the psychometric constant (kPa o C -1 ): PPcp310655.0 (2-3) c p : specific heat at constant pressure (1.013 10 -3 MJ kg -1 o C -1 ) P: mean daily atmospheric pressure (kPa) : ratio of the molecular weight of water vapor to dry air (0.662) : latent heat of vaporization (2.45 MJ kg -1 ) e s is the saturated vapor pressure (kPa) calculated as the average of the minimum and maximum daily vapor pressures: 3.23727.17exp6108.0)(minminminTTTeo (2-4) 3.23727.17exp6108.0)(maxmaxmaxTTTeo (2-5) e s (kPa) is then calculated:

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12 2)()(minmaxTeTeeoos (2-6) e a (kPa) is the actual vapor pressure calculated as a function of e s and the minimum and maximum daily relative humidity: 2100)(100)(minmaxmaxminRHTeRHTeeooa (2-7) RH max : maximum relative humidity value during the 24-hour period RH min : minimum relative humidity value during the 24-hour period Net radiation can be measured using a net radiometer or calculated as a function of solar radiation according to the procedures outlined in Allen et al. (1998). The variables used to estimate R n on a daily basis are shown below: extraterrestrial radiation, R a is calculated: ssrscadGRsincoscossinsin)60(24 (2-8) G sc : solar constant (0.0820 MJ m -2 day -1 ) d r : inverse relative distance between the earth and sun : latitude (rad) : solar declination (rad) s : sunset hour angle (rad) Net shortwave radiation, R ns is calculated: snsRR 1 (2-9) : defined albedo of grass (0.23) R s : measured incoming solar radiation (MJ m -2 day -1 ) Outgoing solar radiation, R so is calculated: asoRzR)10275.0(5 (2-10) z: station elevation in meters above mean sea level This equation is used when no calibration has been performed for the a s and b s Angstrom values which will vary depending on atmospheric conditions. When

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13 calibration values are not available, the above equation is used to calculate outgoing solar radiation. Net longwave radiation, R nl is calculated: 35.035.114.034.024min,4max,sosaKKnlRReTTR (2-11) : Stefan-Boltzmann constant (4.903 10 -9 MJ K -4 m -2 day -1 ) T max,K : maximum absolute temperature (K) during the 24-hour period T min,K : minimum absolute temperature (K) during the 24-hour period. Net radiation (R n ) can then be calculated using equations 2-9 and 2-11: nlnsnRRR (2-12) Calculated net radiation is used in the recommended FAO56 PM method, but more accurate values for actual net radiation can be measured using a net radiometer. The procedure outlined in the FAO 56 paper specifically states that net radiation should be calculated as shown in the above procedure. Actual Evapotranspiration Actual evapotranspiration or crop evapotranspiration (ET c ) is the actual amount of water removed from a surface and delivered to the atmosphere through the processes of evaporation and transpiration. This term has historically been difficult to measure directly, but with the advent and technological advances within the field of lysimetry, the term can be more readily measured. It is quite difficult to separate evapotranspiration into evaporation and transpiration, so the combination of the two is widely used in water balance studies. Lysimeters have been considered the most reliable research tool for direct measurement of crop evapotranspiration and have been regarded as the standard for all other methods (Howell et al., 1985; Burman and Pochop, 1994; Burman et al., 1983).

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14 Howell et al. (1985) states that weighing lysimeters are considered the most practical research tools for the direct measurement of daily ET c The actual evapotranspiration rate of any crop that is planted in a weighing lysimeter can be measured by monitoring the change in weight (or equivalently the change water storage) of the soil container. Monitoring and measuring all inputs and outputs of the lysimeter allows for the measurement of ET c Positive changes in weight indicate an addition of water to the soil container in the form of either rainfall or irrigation while negative weight changes indicate the subtraction of water from the soil container through either actual evapotranspiration, drainage, or runoff. Thus, a soil water balance equation can be written: RODETIPSc (2-13) S: change in soil water storage (g) P: precipitation (g) I: irrigation (g) D: drainage (g) RO: runoff (g) S can be considered analogous to the weight change of a weighing lysimeter over any specified period of time as long as the growth of the crop does not contribute substantially to the changing weight of the contents of the tank. Crop Coefficient A crop coefficient (K c ) is a numerical factor that relates actual evapotranspiration (ET c ) of an individual, well watered crop to the reference evapotranspiration (ET 0 ). The crop coefficient accounts for the characteristics of a certain crop and its phenological growth stages. Dimensionless K c values are calculated (Doorenbos and Pruitt, 1975; 1977): 0/ETETKcc (2-14)

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15 Yrisarry (2000) states that precision weighing lysimeters are one of the most effective methodologies for direct crop coefficient studies. Using known values of ET c and an estimate of ET 0 K c can be solved for directly. A crop curve can be developed by plotting K c over any period of time. The crop curve is useful for most hydrologic water balance studies, especially those involving irrigation planning and management, and for the development of basic irrigation schedules. Average crop coefficients (on a monthly basis) are usually more relevant and more convenient than the K c computed using a daily time interval (Allen et al., 1998). Knowledge of a crop coefficient can be a useful tool to establish crop water usage and can be used in the agricultural industry as a method to determine actual evapotranspiration based on estimated values of reference evapotranspiration. Practical crop water requirements are calculated using tabulated values of K c and calculated values for ET 0 using one of many reference evapotranspiration equations, preferably the FAO56 Penman-Monteith equation according to Allen et al. (1998). The crop water requirement is analogous to the actual evapotranspiration of the crop and can be solved for by rearranging equation 2-14 to the following form: 0ETKETcc (2-15) The knowledge of the crop water requirement on a monthly basis is critical for proper irrigation management. Given a known actual evapotranspiration for a specific crop and an estimated reference evapotranspiration for a reference crop (grass or alfalfa), one can calculate the crop coefficient for that specific crop using equation 2-14. The knowledge of specific monthly crop coefficients over a variety of crops and for a variety

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16 of seasonal conditions can lead to the better understanding and management of irrigation water resources.

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CHAPTER 3 METHODOLOGY Location All field experimentation was performed at the University of Florida, IFAS Plant Science Research and Education Unit (PSREU) located between Gainesville and Ocala near Citra, Florida in Marion County (figure 3-1). The PSREU provides 445 hectares of land for research. The facility is divided into two phases; Phase I of the PSREU research facility is divided into 6 blocks (figure 3-2) with each block consisting of 400 smaller subunits each having an area of about 0.31 ha and dimensions of 55 m of length by 55 m of width. The research location for this project is located in block 6 of PSREU with coordinates of 29.4061 o N latitude and longitude 82.1656 o W and an average elevation of 20 m above sea level. The instrumentation for this research includes three weighing lysimeters and a weather station located in the Northeast section of block 6 (figure 3-2). At least 200 m of fetch is provided in all directions radial from the equipment to prevent any disturbances that could jeopardize the accuracy of ET c measurements, while 100 m of fetch is the minimum suggested fetch outlined by recent definitions of reference evapotranspiration (Walter et al., 2005). Block 6 is planted with Bahiagrass (Paspalum notatum) that is irrigated as necessary using an overhead linear irrigation system. The irrigation system is operated by the personnel at PSREU on a regular basis and at the request of the research team. The field is typically irrigated twice a week when rainfall is absent (Irmak et al., 2003) to ensure an actively growing field, and the grass is mowed to ensure that a 12 cm grass 17

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18 height is maintained; these procedures are performed to satisfy the conditions of the FAO 56 definition of a reference surface (Allen et al., 1998). Mowing was typically performed weekly during the active growing summer months and rarely, if at all, during the winter months of the dormant stage. The results of a particle size distribution test carried out on soil samples at the PSREU showed a sand fraction always greater than 91%, a silt fraction always less than 9%, and a clay fraction always less than 4% (Gregory, 2004). The soil was therefore classified as a sand according to the USDA soil textural classification (Soil Survey Staff, 1975). Lysimeters Three precision weighing lysimeters were installed in block 6 of the PSREU research facility on May 28, 2003 with data collection initiated during June 2003. A severe thunderstorm on August 4, 2003 resulted in a power surge that damaged the load cells. The load cells were replaced on October 13, 2003 with additional surge protection to prevent future power surges. The load cells in the lysimeters have been operational since. ET data for this research spans January through December 2004. Design The three weighing lysimeters in block 6 of the PSREU are aligned in an east-west direction; each lysimeter is separated by a distance of 3.35 m. The 3 lysimeters are named from east to west as L1, L2, and L3 respectively (figure 3-3). The lysimeters consist of inner and outer steel tanks, the inner tank having a surface area of 2.32 m 2 (1.524 x 1.524 m) and a soil depth of 1.37 m (figure 3-4). Each lysimeter is planted with bahiagrass at the same density and of the same type (Pensacola variety) as the surrounding field (figure 3-5). An area (approximately 175 m 2 )

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19 surrounding the lysimeters was sectioned off using signs (approximately 20 m from the lysimeters in each direction) to prevent any unnecessary foot or vehicle traffic from interrupting the sensitive weighing devices. The fetch extends at least 200 m beyond the sectioned-off, no-vehicle-traffic area in all directions from the lysimeters. At the ground level of the lysimeters the gap between the inner and outer tanks of each lysimeter is covered by an approximately 20 cm strip of green painted rubber. This strip of rubber protects the inside of the lysimeters from rusting by preventing any water from interfering with the operation of the load cells. The rubber was spray-painted green from its original black color to attempt to prevent the uneven distribution of heat caused by the absorption of heat by the hotter black surface which could influence the evapotranspiration of the grass. The protruding green, capped, PVC pipe in the near corner of each lysimeter, as shown in figure 3-5, consists of a slotted pipe that extends to approximately 10 cm above the bottom of the inner tank. This slotted pipe is used to intermittently measure the depth of water in the inner tank after rainfall. The depth of the water table in the lysimeter is used to estimate the duration of pumping needed to remove all excess water from the inner tank. The lysimeters were installed by first excavating the site to lay the necessary reinforced concrete foundation on which they rest (figure 3-6). Figure 3-6 shows the location of PVC pipes that serve as air ducts to release any pressure that may develop under the weight of the concrete foundation, the backfilled soil, the soil inside the tanks, and the steel tanks themselves. The reinforced concrete foundation consists of a double mat of #6 60 grade rebar double matted and spaced 30 cm apart in a grid pattern. A wooden frame was constructed around the perimeter of the foundation area, the rebar grid

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20 pattern was laid out, and concrete filled the frame a minimum of 7.5 cm above, and 7.5 cm below the rebar. The concrete foundation (figure 3-5) rests on an 18 in layer of compacted gravel which in turn rests on a 5 cm slab of concrete. The lysimeters are designed so that drainage occurs when a pumping system that originates at the bottom of the inner tank is initiated when suction is applied through a porous filter plate. Copper tubing is used to transfer the water from the bottom of the inner tank to an outlet approximately 20 cm above the ground surface. Water flows through the porous plate at the bottom of the inner tank, through the copper pipe between the inner and outer tanks, and eventually extending as a continuum of the copper pipe approximately 20 cm above ground. The dual copper tubes that extend from the gap between the tanks can be seen in figure 3-8. Drainage water is pumped by fixing a threaded brass nozzle to each of the above ground copper pipes, connecting a flexible plastic hose to the brass nozzle which extends to the inlet of a pump. When suction is applied to the hose using a pump, the vacuum is distributed through the pipe to the porous plate at the bottom of the inner tank of the lysimeter. The drainage from each lysimeter is transferred to storage tanks where the volume of water that is drained can be manually measured. The pump and storage tanks are situated on a portable trailer which can be moved to accommodate mowing of the surrounding Bahiagrass and to provide portability to the system. Instrumentation Table 3-1 lists and describes all instruments at the lysimeter site. The lysimeter site has 2 data collection points. A CR-23X datalogger (Campbell Scientific, Logan, UT) stores and collects all load cell readings from the lysimeters. A CR-10X datalogger (Campbell Scientific Inc., Logan, UT) stores and collects all Vitel probe readings from

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21 the soil profile of the lysimeters. Two rainfall collection devices occupy the lysimeter site. A tipping bucket rain gauge is recorded by a Hobo (Onset Computer Corp., Pocasset, MA) datalogger, this gauge is located directly north of the westernmost lysimeter. A second manual rain gauge is located directly north of the middle lysimeter. Maintenance To maintain the grass at a 12 cm height to satisfy the FAO 56 definition of a reference surface, the site is mowed as needed. Mowing was typically performed up to once per week during the rainy summer season and as infrequently as once per month in the dormant winter season. The field around the lysimeter tanks is mowed using a riding lawnmower inside an approximately 100 m x 100 m area designated by signage. The areas immediately surrounding the lysimeter tanks themselves are carefully mowed with a push-type lawnmower to avoid any unnecessary disturbance to the load cells that could be caused by heavier machinery. The bahiagrass inside the lysimeters themselves is maintained as close to a 12 cm height as possible by keeping the grass clipped using hand shears. The grass inside the lysimeter is clipped to a 12 cm height. The perimeter of grass is maintained to prevent the surface area of the grass from extending beyond the walls of the inner tank. While it is not possible to control the grass from extending slightly outside the confines of the inner tank, excessive protrusion is controlled by routine clipping with the hand shears. Any grass that extends beyond the surface area of the inner tank increases the effective area of the lysimeter and must be accounted for when measuring ET c The grass around the immediate perimeter of the lysimeter is clipped with the hand shears as well to prevent disturbance of the load cells, and to prevent grass from intruding into the surface area of the inner tank.

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22 Load Cell Calibration The load cells in the lysimeters were calibrated on three different occasions: June 9, 2003, January 28, 2004, and January 19, 2005. Calibration was performed by adding a known amount of weight to the lysimeter and recording the resulting average of the four load cells in each lysimeter in units of mV. The regression curve of the resulting data points are then plotted to estimate the linear regression equation between the load and the mV reading of the load cells. The load cells were wired to a datalogger (model CR-23X, Campbell Scientific Inc., Logan, UT). The displayed mV output for each load cell is directly proportional to the load applied. Load cell readings were recorded manually, then averaged to obtain the resultant mV load of the entire tank. These averages were plotted versus the corresponding volumes of water they represented to obtain a linear regression. The first lysimeter calibration on June 9, 2003 was performed on the empty inner lysimeter tank just after installation. The results of the linear regression of this calibration are shown in table 3-2. A known volume of water was pumped into each lysimeter with the amount of water recorded using a flow meter as shown in figure 3-6. The load applied to each lysimeter was measured according to the volume of water pumped into the tank. The volume of water applied can then be converted to an equivalent weight applied to the lysimeter according to the specific weight of water. The multiple regression correlation coefficient (R 2 ) value for each tank is shown to be 1.00. The regression equations derived from each tank can be used to measure how much weight rests on the load cells by inputting the recorded mV reading into the corresponding regression equation and solving for the volume of water, or equivalently the mass of the inner tank with its contents.

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23 The second lysimeter calibration on January 28, 2004 was performed after the lysimeters were planted with bahiagrass. Two plastic buckets were placed on the surface of the lysimeter and the load cells were allowed to equilibrate to effectively zero the calibration. The buckets were filled with water from a graduated cylinder incrementally until the volume of water applied to the lysimeter reached 30 L. A linear regression was performed on this data collection and the results are shown in table 3-3. The third lysimeter calibration was performed on January 19, 2005, after all evapotranspiration data had been collected for the previous year. This calibration was performed by positioning a sheet of plywood in the center of the lysimeter, placing known weights (in 23.59 kg increments) on the sheet of plywood, recording the resulting average load cell outputs in mV, and using these data to perform a linear regression. Once the plywood was placed in the center of each lysimeter, the load cells were allowed to react to this increase in weight and that weight was used to effectively zero the load cells for the linear regression analysis. Figure 3-8 shows the positioning of the weights in the center of the lysimeter. The applied load was converted into an equivalent volume of water for calibration purposes. The results for this calibration are shown in table 3-4. The slope (a) in all of the regression equations represents the relationship between the average mV reading of the load cells and the incremental load applied to the inner tank of the lysimeter. The slopes of all the regression equations remained constant throughout the duration of the research. For tank 1 the slopes (mV/mm H 2 O) of each regression equation for each calibration are 0.00041, 0.00041, and 0.00041 respectively. For tank 2, the slopes (mV/mmH 2 O) of each regression equation for each calibration are 0.00041, 0.00044, and 0.00039 respectively. For tank 3, the slopes (mV/mmH 2 O) of

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24 each regression equation for each calibration are 0.00038, 0.00039, and 0.00041 respectively. Rounded to 4 digits, the slope of the regression equation that was used in the actual ET calculations for each tank is 0.0004 mV/mmH 2 O. The intercept (b) in all of the regression equations represents the initial weight of each tank before calibration; for the first calibration on June 9, 2003, the intercepts represent the weight of the inner tanks themselves since it was not yet filled with soil or planted with bahiagrass. The differences in intercepts for each calibration are the consequence of small differences in the initial weights of each of the lysimeter tanks. For the first calibration on June 9, 2003, the differences in intercepts represent small differences in the weights of the inner tanks of each lysimeter due to small differences in the amount of steel used to construct each tank. The intercepts in the first calibration were used in the final calculation of actual ET because they represent the weight of the unfilled tanks. The intercepts from the following two calibrations represent the weight of the tanks plus vegetation, soil, and water. Rainfall Rainfall was measured at the lysimeter site using a tipping bucket rain gauge recorded using the weather station data logger, a tipping bucket rain gauge recorded using a Hobo event data logger, and a manual rain gauge. The tipping bucket rain gauges record real time rainfall whereas the manual rain gauge collects data whenever the gauge is recorded manually and emptied. The tipping bucket rain gauge recorded using the weather station data logger is located at a height of 3.0 m above ground level on top of the weather station mounting tower approximately 50 m east of the lysimeter site. The tipping bucket rain gauge attached to the Hobo event data logger is located at a height of 0.3 m above ground surface, low enough to catch all irrigation as well as rainfall. The

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25 manual rain gauge is located at a height of 0.25 m above the ground surface; the rainfall recorded from this gauge was measured during every field visit so there is a time lapse between actual rainfall and the measured value. Given this lapse, it is more useful to compare rainfall from the manual gauge to the rainfall from the tipping bucket rain gauges using cumulative monthly values rather than daily values. All rain gauges are outlined with their respective heights in tables 3-1 and 3-5 which list the instrumentation used at each site. The location of each rainfall measurement device used for this research is mapped out in figure 3-9. Weather Station The weather station used to collect atmospheric variables for the reference ET calculations is located approximately 50 m east of the lysimeters in the same bahiagrass field. Measurements were recorded in accordance with the guidelines set forth in the FAO 56 method of calculating reference ET. The bahiagrass surrounding the weather station was kept as close as possible to a height of 12 cm to approximate a reference surface and it was kept well watered through rainfall and irrigation when needed. The atmospheric variables collected at the weather station during the research are air temperature, relative humidity, solar radiation, net radiation, wind speed and direction, soil heat flux, and soil temperature. The weather station at this location is shown in figure 3-10 with the atmospheric measurement devices labeled accordingly. Instrumentation at the weather station adjacent to the lysimeter site is outlined in table 3-5. The weather station site was maintained at least once a week; data were downloaded on a weekly basis to ensure that if there was an unexpected problem that it would be found immediately and remedied. Data were collected using a CR-10X datalogger (Campbell Scientific, Inc., Logan UT). Data were downloaded using a Palm

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26 (palmOne, Milpitas, CA) handheld PDA and PConnect (Campbell Scientific, Logan, UT) software which allows for portability and the convenience of not having to use a laptop in field conditions. A supplementary weather station is located approximately 500 m southwest of the lysimeter site with all of the instrumentation necessary to perform another reference ET calculation. This site was used for comparisons to the site adjacent to the weather station. This additional weather station measures relative humidity and air temperature with the same instrument as the lysimeter weather station. The station is equipped with the same model net radiometer and pyranometer as the lysimeter weather station for radiation measurements and the same model tipping bucket to record rainfall. Wind speed is measured with a Vaisala WS425 Ultrasonic Wind Sensor (Vaisala, Helsinki, Finland). All measurements are made at 15 minute intervals and these variables are used to calculate daily reference ET on a daily basis; this weather station is shown in figure 3-11 below. Calculations Reference ET Using FAO56 Reference ET was calculated using the methods outlined in the FAO56 manual using a daily time step. Reference ET was calculated 2 different ways. The first uses net radiation measured from the net radiometer. The second uses net radiation calculated from pyranometer readings according to FAO56 guidelines. Since reference ET calculations are highly dependent on the net radiation term, it is important that it be accounted for accurately. This research was performed using the FAO56 method of calculating reference ET because it is considered the standardized method, superior to all others (Itenfisu et al., 2003; Garcia et al., 2004).

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27 Actual ET Using Lysimeters Actual ET was calculated based on the soil moisture balance (Eqn. 2-13) for each lysimeter. The daily actual ET was calculated by taking the difference in the equivalent volume of water applied to the load cells at the beginning and ending of the day and inputting this volumetric change (which can be converted to a change in terms of a depth of water) as the daily change in storage for each lysimeter. Soil moisture balance calculations The rainfall and irrigation terms used in the soil moisture balance were measured using the tipping bucket rain gauge located atop the weather station tower adjacent to the lysimeter site and the drainage term is measured manually using a graduated cylinder. Runoff is considered negligible since the infiltration rate for sandy soils in the area is higher than any expected rainfall intensity. Infiltration rates for the soils in this location average approximately 230 mm/hr (Gregory, 2004). The 100-year 24-hour design storm intensity is 254 mm/hr, the 50-year 24-hour design storm intensity is 229 mm/hr, and the 25-year 24-hour design storm intensity is 203 mm/hr (Florida Department of Transportation, 2003). It can be assumed that as long as rainfall does not approach the 50-year 24-hour design storm intensity of 229 mm/hr, then runoff will not occur. As long as the rainfall intensity is less than the infiltration rate of the soil, runoff will not occur and can then be removed from the lysimeter water balance equation. The resulting water balance of each lysimeter is then reduced to DETIPSc (3-1) S: change in soil water storage (mm) P: precipitation (mm) I: irrigation (mm) D: drainage (mm)

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28 Actual ET from each lysimeter can then be calculated directly by rearranging Eq. 3-1 and placing all known variables on the right side of the equation: (3-2) DSIPETc The change in storage is calculated on a daily time step so that daily actual ET can be observed, therefore, the weight of the lysimeters must be known at the start of the day (0000 h) and the end of the day (2400 h). The difference of the weight of the tank from the beginning and the end of the day is the daily change in storage. The weight of each lysimeter is calculated using the equations derived during the lysimeter calibration and the surface area of the evaporative vegetation. Since the correlation between the mV voltage recorded by the load cells and the weight of the lysimeter is a linear relationship, the amount of water entering or leaving the lysimeter at any time can be calculated using the lysimeter calibration equations and the recorded load cell values. Equivalent volume of water calculations The readings of the four load cells in the tanks were averaged for every ten minute reading to achieve one mV value which can then be converted to an equivalent volume of water using the lysimeter regression equations. Since the lysimeter regression equations were determined in terms of a volume of water added to the tank, the mV reading was directly converted to a load of water applied or removed from the tank as shown by rearranging the lysimeter regression equation to solve for the volume of water represented by the mV reading recorded by the load cells (Equation 3-3).

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29 abmVavgVeq)(4 (3-3) V eq : equivalent volume of water resting on the load cells (L) avg(mV 4 ): the average of the 4 mV readings recorded for a single 10 min time step (mV) b: intercept calculated from the lysimeter regression equation, represents the initial weight of the lysimeter tank in terms of an equivalent volume of applied water (L) a: slope calculated from the lysimeter regression equation, represents the relationship between the mV reading and applied volumetric load (mV L -1 ) Equivalent weight calculations Since the load, x, is represented in terms of a volume of water in liters, then that volume can be converted to an equivalent weight applied to the load cells using the density of water (Equation 3-4). weqeqVW (3-4) W eq : the equivalent weight of water applied to the load cells (kg) V eq : the equivalent volume of water calculated from equation 3-3 : the density of water at 5C (0.001 g/L) Change in storage calculations The volume of water entering or leaving the lysimeter (change in storage, S) can then be found by calculating the difference in equivalent volumes of water over any given time period as shown in equation 3-5 below. The density of water was assumed to be at 5C for simplification purposes even though the water may be warmer in reality. If the density of water at 35C were used (0.00094 g/L) at a hypothetical volume of water totaling 5000 liters, compared to the density of water at 5C, it would only amount to a change in 300 g, which can be considered negligible given that the weight of the lysimeters ranged from around 4000 kg to 5500 kg over the period of research. This change would only amount in an error of 0.0075 % which would not affect the results significantly. A more accurate but time consuming method would be to monitor the

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30 temperature change of the water within each lysimeter to determine the exact density. This process would be time consuming and of negligible value. )()(12tVtVSeqeq (3-5) S: change in storage over any time interval (L) V eq (t 2 ): the equivalent volume of the lysimeter at any time, t 2 (L) V eq (t 1 ): the equivalent volume of the lysimeter at any time earlier than t 2 (L) To determine the daily evapotranspiration, daily change in storage is calculated over a 24 hour time step substituting the equivalent volume of water at 2400 hr for V ep (t 2 ) and the equivalent weight at time 0000 hr for V eq (t 1 ) in equation 3-5. The change in storage over all 10 min time intervals was also computed, for this time step the equivalent weight at any t 2 is substituted into equation 3-5, along with the equivalent weight at a time 10 min earlier than t 2 (t 1 ). The change in storage can be represented in terms of an equivalent depth of water entering or leaving the lysimeter over any time period by using the area of the evaporative surface to convert the equivalent weight to an equivalent depth as shown in equation 3-6. lysSALmmSDepth1*110*1*36 (3-6) Depth: change in depth of water (mm) over a specified time interval S: change in storage (L) over a specified time interval (as calculated in 3-5) SA lys : surface area of the evaporative area (2.48*10 6 mm 2 ) L mm110*136 : unit conversion The surface area of the evaporative surface is measured as the extent that the grass grows beyond the perimeter of the inner tank.

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31 Remaining soil moisture balance variables The daily change in storage is calculated using equations 3-3 through 3-6 and the lysimeter calibration equations. The remaining variables in the lysimeter water balance required to calculate ET c are rainfall (R), irrigation (I), and drainage (D). Rainfall is measured by the various rainfall measuring devices on site (although actual values of rainfall and irrigation entering the lysimeter are used for calculation purposes as discussed later) and drainage is measured manually using a graduated cylinder whenever pumping occurs. Actual ET Adjustment and Filtering The load cell readings are prone to noise interference, as well as the overestimation of incoming rainfall. Noise in the load cell readings is encountered frequently and is filtered out of the daily data and accounted for in the daily actual ET calculation. If the load cells show that the lysimeter lost weight during the night, then that weight loss was ignored in the daily actual ET calculation. Night was defined when net radiation was negative; since ET does not occur at quantifiable amounts at night, these values were excluded from actual ET calculations. Rainfall amounts were typically observed to be higher in the lysimeter than the values obtained by tipping bucket rain gauges due to runoff into the inner lysimeter tank from the surface of the green rubber cover placed around the perimeter of the lysimeter. The amount of rainfall entering the lysimeter for any given storm event or irrigation period can be obtained by observing the change in weight of the lysimeter over that given time period. If the ET c during any rainfall or irrigation event is assumed to be negligible, then the increase in weight of the lysimeter can be attributed directly to incoming water; the amount of water that has entered can be calculated by solving equation 3-6 using the change in storage over the

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32 time interval in question, and solving for the representative depth of water that the rainfall event produces in terms of the lysimeter surface area. Incoming rainfall is overestimated by the lysimeters due to the rubber lining around the perimeter of the inner and outer tank gap which causes an inflow of rainfall to the tanks. The load cell readings were analyzed for each storm event. The load cell readings were also compared to the recorded rainfall from the tipping buckets. During any storm event the increase in weight recorded by the load cells is calculated for the duration of the storm event defined by the duration of the event as recorded by the tipping buckets. This increase in weight due to rainfall is used to determine daily actual ET calculation rather than the amounts of rainfall recorded by the various rainfall measuring devices. Raw lysimeter data from the load cells were filtered based on three criteria: 1. If rainfall was greater than zero, as recorded by the tipping bucket located at the nearby weather station, then the duration for each storm event from the tipping bucket was analyzed alongside the load cell data to determine the actual increase in weight due to rainfall that each lysimeter experienced. 2. If the actual ET from any lysimeter, using the calculations outlined previously, is greater than an acceptable maximum (6 mm/day), then the load cell data for the day were analyzed to see if: a) there were any unexplained increases or decreases in weight (e.g. if the lysimeter lost weight during the night, or if the lysimeter gained weight at a period when no rainfall was recorded) or b) if any of the observed load cell increases and decreases seemed to be unreasonable (e.g. higher or lower than expected, or unnaturally high or low values).

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33 3. If the standard deviation of the actual ET from all three lysimeters was found to be unreasonable high (greater than 2), this suggested that either one or all of the lysimeters were not achieving similar results. The filtering for this case was treated similar to the filtering in the second criterion listed above, examining the data for any unexplained or unreasonable changes. If the actual ET data for any day did not meet the three criteria listed above after filtering, then those data were excluded from the results and were not used in any monthly comparisons or future crop coefficient calculations. Crop Coefficient Actual evapotranspiration is calculated using the lysimeters and adhering to the above calculation procedures. Reference evapotranspiration is calculated using the FAO56 Penman-Monteith method. The crop coefficient is calculated as measured actual evapotranspiration divided by estimated reference evapotranspiration as calculated using equation 2-14. Since daily values of both actual and reference ET are calculated, daily crop coefficients can also be calculated. Daily crop coefficients are determined by dividing the average of the three lysimeter daily actual ET values by the daily reference ET value using estimated net radiation per FAO 56 guidelines. Monthly crop coefficients are calculated by averaging all of the daily crop coefficients for the month.

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34 Table 3-1. Lysimeter instrumentation and description Instrument and manufacturer Description and use Units Height above ground surface (m) CR-23X Datalogger, Campbell Scientific Storage and collection of load cell data from lysimeters N/A N/A CR-10X Datalogger, Campbell Scientific Storage and collection of Vitel probe data from the lysimeters N/A N/A Gast Model 0823 Pump Creates a vacuum which is applied to the lysimeters for drainage N/A N/A SQB-10K Load Cells (16) (4) Load cells in the bottom corners of each lysimeters outer tank used to measure the total load applied by the inner tank that rests on top of the 4 load cells; each load cell is capable of supporting 4,444 kg (10,000 lbs) mV -1.7 Surge Arrestors (31), Citel Inc., Miami, Fl (16) Surge protectors against lightning for each load cell (15) Surge protectors against lightning for each Vitel probe N/A N/A Clear-Vu Manual Rain Gauge 10.5 cm diameter plastic rain gauge with an 30 cm rainfall capacity cm 0.25 Texas Electronics TE525WS Tipping Bucket Tipping bucket rain gauge that measures precipitation in increments of 0.0254 cm cm 0.3 Onset Hobo Event Logger Data logger that records rainfall from the tipping bucket rain gauge N/A N/A Storage Tanks (4) (3) 302.8 L tanks used to store drainage water and (1) 151.4 L tank used to distribute a vacuum which is applied to each of the lysimeters N/A N/A Stevens Water Vitel Probes (20) Measures the dielectric constant as it relates to 4 recorded mV readings which relates to the soil moisture content; (5) located in center vertical profile of each lysimeter mV -0.05, -0.1, -0.2, -0.5, -1.0 CSI HFT3 Soil Heat Flux Plates (2) Measures soil heat flux W m -2 -0.08

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35 Table 3-2. Lysimeter calibration results for June 9, 2003 Lysimeter a (mV/mm H 2 O) b (mV) R 2 L1 (east) 0.00041 0.143 1.00 L2 (middle) 0.00041 0.173 1.00 L3 (west) 0.00041 0.153 1.00 *Linear regression of y on x (y=a*x+b) where y is the average mV output of the 4 load cells in each tank and x is the applied load in mm of water Table 3-3. Lysimeter calibration results for January 28, 2004 Lysimeter a (mV/mm H 2 O) b (mV) R 2 L1 (east) 0.00041 1.130 0.98 L2 (middle) 0.00044 1.121 0.99 L3 (west) 0.00039 1.087 1.00 *Linear regression of y on x (y=a*x+b) where y is the average mV output of the 4 load cells in each tank and x is the applied load in mm of water Table 3-4. Lysimeter calibration results for January 19, 2005 Lysimeter a (mV/mm H 2 O) b (mV) R 2 L1 (east) 0.00038 1.047 1.00 L2 (middle) 0.00039 1.032 1.00 L3 (west) 0.00041 1.018 1.00 *Linear regression of y on x (y=a*x+b) where y represents the average mV output of the 4 load cells in each tank and x represents the applied load in mm of water

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36 Table 3-5. Weather station instrumentation and description Instrument and manufacturer Description and use Measurement Units Height above ground surface (m) CR-10X Datalogger, Campbell Scientific Storage and collection of weather station data from various instruments N/A N/A Climatronics CS800 Wind Sensor 3-cup anemometer and wind vane that measures wind speed and direction m s -1 degrees from North 2.2 Kipp & Zonen NR-LITE Net Radiometer Measures the energy balance between incoming short-wave and long-wave IR radiation relative to reflected short-wave and outgoing long-wave IR radiation (net radiation) W m -2 0.6 Texas Electronics TE525WS Tipping Bucket Tipping bucket rain gauge that measures precipitation in increments of 0.0254 cm cm 3.0 Li-Cor LI200X Silicon Pyranometer Measures incoming solar radiation W m -2 3.0 Vaisala HMP45C Probe Measures air temperature and relative humidity C, % 1.5 CSI HFT3 Soil Heat Flux Plates (2) Measures soil heat flux W m -2 -0.08 Thermocouples (2) Measures soil temperature C -0.02, -0.06

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37 Figure 3-1. Geographical location: Plant Science Research and Education Unit (PSREU) Figure 3-2. Arial photograph of Phase I of the PSREU research facility: block layout and the location of the lysimeters and weather station

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38 Figure 3-3. East/west section view of lysimeter design for one lysimeter Figure 3-4. Layout and section construction drawings of lysimeters. A) Plan view. B) Section view.

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39 Figure 3-5. Southeast view of the installed lysimeters taken in July 2003 Figure 3-6. Southwest view of the excavated site shown with the outer tanks resting on the reinforced concrete foundation and PVC air pressure release ducts

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40 Figure 3-7. Lysimeter calibration June 9, 2003: Flow meter shown while pumping water into the inner tank of the easternmost lysimeter (L1). Figure 3-8. Lysimeter calibration on January 19, 2005: (2) 23.59 kg. weights are shown resting on a sheet of plywood on the surface of the westernmost lysimeter (L3).

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41 Figure 3-9. Location of all rainfall measurement devices at PSREU, W = weather station tipping bucket, H = hobo tipping bucket, M = manual rain gauge Figure 3-10. Northwest view of the weather station located adjacent to the lysimeters, approximately 50 m east of the lysimeter site with instrumentation labeled

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42 Figure 3-11. Northeast view of the supplementary reference ET weather station located approximately 500 m southwest of the lysimeter site with instrumentation labeled

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CHAPTER 4 RESULTS AND DISCUSSION Rainfall Rainfall measured at the lysimeter site and the weather station site located 50 m to the east of the lysimeters is summarized by month and collection device in figure 4-1. Rainfall data were also collected at other sites at PSREU for comparison to values recorded at the lysimeter site. The additional rainfall collection sites are shown in figure 3-8. The rainfall amounts recorded at sites other than the lysimeter sites were compiled at 6 different collection areas with the monthly cumulative amounts shown in figure 4-2. The regional average rainfall for north central Florida is 1320 mm (Satti and Jacobs, 2004). The complete rainfall record of the tipping bucket rain gauge located on top of the lysimeter weather station recorded 1874 mm of precipitation. This weather station is high enough to exclude water from irrigation. As 2004 had a great amount of hurricane activity, the recorded rainfall is reasonable as compared to the regional average given by Satti and Jacobs (2004). Differences in rainfall amounts among collection sites result from a difference in irrigation amounts and timing of irrigation events. The manual rain gauges were recorded at irregular intervals during each field visit and have errors associated with the manual reading of rainfall amounts. The weather station tipping bucket rain gauges at blocks 4 and 5 are located too high to catch all of the irrigation water from the overhead linear irrigation in those plots. The Hobo tipping bucket rain gauges in those locations are located low enough so that they catch all of the irrigation from the overhead linear 43

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44 irrigation. Differences in these amounts can be seen from the monthly cumulative plots shown in figures 4-1 and 4-2. These rain gauges do not affect the lysimeter water balance used for actual ET calculations because rainfall was measured using the weight increase for a rainfall event from the load cells. These rain gauges are shown to reflect the amount of rain that fell in this region over the year. Monthly total rainfall amounts are shown for the collection sites near the lysimeters in table 4-1 and the collection sites at the additional weather station in table 4-2. Blank spaces indicate the gauge was inoperable for all or part of that month or it had not yet been installed at the site. 2004 was a wetter than average year given the regional average. More rainfall produces more water available for evaporation, and making the lysimeter operators job of keeping the tanks at reasonable soil moisture levels more difficult. Net Radiation at the Lysimeter Site The daily measurements of calculated and measured net radiation are shown in figure 4-3. Calculated net radiation was higher than measured net radiation during the winter months, and closer to the measured net radiation values during the summer months. Calculated net radiation was also higher than measured net radiation when total radiation for the day was low and was just the opposite when radiation for the day was high. The comparison between calculated and measured net radiation is shown in figure 4-4. As net radiation increases, reference ET increases accordingly; a standard value for reference ET must be known before any crop coefficient determinations can be made. Reference ET Reference ET values were calculated using two different net radiation values, the first using net radiation calculated from solar radiation values per the FAO 56 outline,

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45 and the second using measured net radiation from the net radiometer. A summary of reference ET values are shown in tables 4-3 and 4-4. Reference ET calculated by measured net radiation was found to be consistently lower than reference ET using calculated net radiation at the weather station adjacent to the lysimeters (Jia et al., 2005) and this was also found to be the case as well when analyzing net radiation data from the supplementary weather station (figure 4-5). The highest monthly reference ET at the lysimeter site using calculated net radiation occurred in May with 123.5 mm, the lowest monthly value occurred in February with 62.7 mm. Lower reference ET values will cause the crop coefficient values to be higher. The minimum reference ET value encountered during this research was 0.61 mm on December 25, 2004 using calculated net radiation. Using measured net radiation, the minimum reference ET value encountered was 0.10 mm on October 11, 2004. Reference ET using calculated net radiation was less than 1 mm during 7 out of the 366 days researched (1.9 %). Using measured net radiation, reference ET was less than 1 mm for 21 days out of the 366 days (5.7 %). Biased reference ET values can bias crop coefficient data if actual ET measured by the lysimeters is not very close to reference ET. For low reference ET values, small errors in actual ET values are amplified in crop coefficient calculations. If the average difference in reference ET values is constant and this difference is compared on a day when there was low net radiation the effects on the crop coefficient will be greater than if it was a day with high net radiation. The difference in reference ET values will cause a larger difference in the crop coefficient value on low-radiation days, therefore low radiation days must be used with caution when using these days to calculate the crop coefficient.

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46 Using Calculated Net Radiation Monthly plots of daily reference ET using the calculated net radiation per the FAO56 guidelines are shown in Appendix A. These standard reference ET values will be used for crop coefficient calculations as they adhere to FAO56 guidelines. Daily reference ET values are for 2004 and monthly averages are shown in figures 4-6 and 4-7 for the lysimeter weather station and the supplementary weather station respectively. The monthly reference ET shows an annual trend with higher values during the summer months and lower values during the winter months at both sites. Figure 4-6 shows daily reference evapotranspiration rate at the lysimeter weather station ranged from approximately 1.5 mm/day in December to just over 4 mm/day for the month of May. The highest daily reference ET encountered during the period of research at the lysimeter weather station was 5.8 mm on August 31, 2004 and the lowest daily reference ET was 0.6 mm on December 25, 2004. The supplementary weather station (figure 4-7) had daily reference evapotranspiration values ranging from a minimum monthly average of approximately 1.6 mm/day in December and a maximum monthly average of approximately 4.1 mm/day in June. The lowest and highest daily reference ET encountered at the supplementary weather station during the period of research occurred on the same days as at the lysimeter weather station. The highest recorded reference ET at the supplementary weather station was 5.4 mm on August 31, 2004. The lowest daily reference ET was 0.4 mm on December 25, 2004. Monthly totals of reference ET using calculated net radiation are shown in table 4-3. At the lysimeter weather station, the months with the lowest and highest recorded reference ET was January with 64.0 mm May with 123.5 mm respectively. At the supplementary weather station the months with the lowest and highest recorded reference

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47 ET was December with 48.1 mm June with 121.1 mm respectively. The estimated reference ET is consistently higher at the weather station adjacent to the lysimeters, except for June and July. This difference can be explained by the higher measured vapor pressure deficits (VPD) at the lysimeter weather station than at the supplementary weather station during most of the period of research (Figure 4-8). The vapor pressure deficit is the difference between saturated and actual vapor pressure and depends on relative humidity measurements as well as minimum and maximum daily temperatures and is used in the complete equation used to calculate reference ET (eqn 2-1). Since calculated net radiation uses air temperature and actual vapor pressure in the calculation, the use of the equations outlined by the FAO56 guidelines to estimate net radiation may be a source of error. Using Measured Net Radiation Monthly plots of daily reference ET using measured net radiation are shown in Appendix B. Figures 4-9 and 4-10 show the yearly trend of reference ET by weather stations using measured net radiation. The reference ET at the supplementary weather station (Table 4-4) to be consistently lower for all months. Totals differ from 15.1 mm in February to 30.2 mm in June. The difference between reference ET measurements at both sites results from the differences in measured net radiation values by site as was observed in figure 4-5. Figure 4-5 shows the difference in measured net radiation recorded at both sites, this difference in net radiation explains the difference in reference ET values; the higher the net radiation, the higher the reference ET. Monthly reference ET totals using measured net radiation for both weather stations are shown in Table 4-4. The weather station adjacent to the lysimeters had consistently higher reference ET values than the supplementary weather station. This phenomenon

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48 can also be seen in the daily comparison in figures 4-9 and 4-10. Since the weather station adjacent to the lysimeters is in the same field and much closer than the other weather station, the values obtained from the closer, adjacent weather station were used to guarantee consistency and accuracy. Difference in ET 0 between Using Measured and Calculated Net Radiation Table 4-5 shows the difference in ET 0 at each station when using measured net radiation compared to calculated net radiation. It can be seen that at the site adjacent to the lysimeters, ET 0 is higher when using measured net radiation during every month except June. At the supplementary weather station, ET 0 calculated using measured net radiation was significantly higher during every month. Table 4-5 shows that there was a 8% difference between reference ET at the lysimeter weather station depending on whether calculated or measured net radiation was used in the reference ET equation. At the supplementary weather station there was a 24% difference between reference ET using calculated net radiation as compared to measured net radiation. At both sites, using measured net radiation to calculate reference ET resulted in lower values than using the FAO 56 guidelines to calculate net radiation. At the lysimeter site, where crop coefficients were calculated, the difference in reference ET based on net radiation differences resulted in a 7% change in crop coefficient values. This means that when using calculated net radiation, crop coefficients were 7% lower averaged over the course of the year, compared to using measured net radiation. Actual ET Using Lysimeters A plot of daily actual evapotranspiration and rainfall is given in figure 4-12. Monthly plots of daily actual ET are in Appendices A and B along with the reference evapotranspiration data. Actual ET measurements were unable to be calculated for the

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49 months of September and October due to hurricanes Frances (September 4 th and 5 th ) and Jeanne (September 26 th and 27 th ) which saturated the lysimeters with 504 mm of rainfall, approximately 40% of the yearly regional average rainfall. This excess water made accurate measurements of actual ET impossible. Actual evapotranspiration is limited by the amount of rainfall and soil moisture the crop is exposed to and by the available energy received by the crop through radiation; as a minimum requirement actual evapotranspiration should not exceed available energy or rainfall. First stage evapotranspiration occurs when there is an adequate amount of water near the soil surface such that ET is limited by available energy; second stage evapotranspiration occurs during drying soil conditions where ET is limited by available water in the soil coupled with available energy (Jacobs et al., 2002). Figure 4-13 shows actual ET measured by the lysimeters compared to available energy in terms of equivalent evaporation due to net radiation as well as the difference between these two terms for each day in 2004. By converting net radiation values to equivalent evaporation rates (mm d -1 ), actual ET can be compared to available energy. This conversion is given in the FAO56 paper (Allen et al., 1998) as radiation expressed in MJ m -2 day -1 multiplied by a conversion factor equal to the inverse of the latent heat of vaporization (1/ = 0.408). Values for actual ET that fall below zero for the data series plotted as (Rn mm ETc) in figure 4-13 should be used with caution. Actual ET exceeded available energy from net radiation during 213 of the 284 days (75 %) where actual ET was used in crop coefficient calculations. Cumulative rainfall for 2004 was shown to be higher than both cumulative actual ET and cumulative reference ET for the duration of the year (figure 4-14).

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50 Excess water in the lysimeters could cause higher than reasonable soil moisture levels in the tanks, which may have increased the available water for evapotranspiration and affect overall crop coefficients. If soil moisture inside the tanks was higher than the surrounding field creating wet conditions (above the FAO56 requirement for the reference surface being well-watered), increasing available water and evaporation, higher actual ET rates would result causing overall crop coefficients to be inflated. The soil in the area is mapped as consisting of a Candler sand and a Tavares sand (Buster, 1979). Both sands have a field capacity of 5.0 7.5% by volume in the upper 100 cm of the soil profile (Carlisle et al., 1978). Soil moisture data for the three lysimeter tanks are presented in figures 4-15, 4-16, and 4-17, for lysimeters 1 (East), 2, and 3 (West) respectively. Unfortunately, due to the lack of user-friendly data output with the soil moisture probes and the required program used to process the actual soil moisture values, a yearly summary of soil moisture is choppy at best and excludes much of the summer season when evapotranspiration is highest. For the given time periods it can be seen that soil moisture levels are regularly, if not consistently, above the 5.0 7.5% field capacity of the soil resulting in higher than the well-watered definition of a reference crop by FAO56 guidelines. Since the most roots actively involved in the water uptake process are located in the upper 90 cm of soil (Smajstrla et al. 2000), and turfgrass is commonly listed with root zone depths from 20 45 cm (Wu, 1985), it can be seen from figures 4-15 through 4-17 that soil moisture content is above the 5.0 7.5% field capacity at these depths. The extra water in the soil profile increases available water and may enhance evaporation. This would result in a high bias in actual ET as measured by the lysimeters and the crop coefficient.

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51 Errors are inherent in the measurement of actual ET since all of the measurements are made by averaging the actual ET over the three lysimeters. The average daily ET for the year was 3.25 mm, while the average standard deviation for each daily ETc measurement is 0.11 mm. Errors made in the measurement of actual ET will propagate to the crop coefficient calculations. Since reference ET is a calculated number, error propagation will be based on the uncertainty in the actual ET measurements. Since the uncertainty in actual measurements is (3.25 0.11 mm), and the average reference ET for the year is 3.17 mm, then using the division rule for the propagation of uncertainty (z/z = x/x + y/y), the uncertainty of the crop coefficient is (1.03 0.03). Since all actual ET measurements were not used in the crop coefficient calculations due to the filtering process of the actual ET data, the average of the crop coefficient will be lower than this reported value of uncertainty, but it does give an approximation of the amount of error encountered. A monthly summary of actual evapotranspiration compared to monthly reference evapotranspiration using calculated net radiation as defined by FAO56 is shown in table 4-6. It can be seen from this table that actual ET is close to reference ET for most months, which would produce a crop coefficient close to 1.0. Actual crop coefficient values for these data are discussed later. Calculated crop coefficients are given in figure 4-20. The highest recorded monthly actual ET occurred during the month of May with a total of 153 mm of ET; the lowest monthly actual ET occurred during the February with a total of 45 mm of ET. The sum of actual ET for the year (excluding September and October) totaled 988 mm of actual ET compared to 956 mm of reference ET. The

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52 highest daily actual ET was 6.8 mm on May 22, 2004. The lowest daily actual ET was 0.0 mm on February 24, 2004. Regional potential evapotranspiration for this region is given by Satti and Jacobs (2004) as 127 cm. If the crop coefficient of 0.95 (Allen et al., 1998) for a cool-season grass is used, the derived actual ET for the year would total 121 cm, or 1210 mm. This research shows that the total actual ET for 2004 was 988 mm (excluding September and October). If actual ET for October and September could be calculated and included in the investigation, it can be assumed that the total ET for 2004 would be close to the assumed regional average actual ET of 1210 mm, given that a crop coefficient of 0.95 is assumed. Extensive research has not been performed for warm-season grasses in the humid Southeast region but Carrow and Duncan (2003) have researched a cool-season turfgrass, tall fescue, in Griffin, GA, and found ET rates ranging from 2.2 mm d -1 to 3.8 mm d -1 for twelve tall fescue ecotypes under turfgrass management conditions. This research was performed in 1997 and 1998 during summer months, on four different occasions, for one to two week periods at a time. The ET rates given are averaged over the four different research periods. This research found that bahiagrass had an average yearly ET rate of 3.3 mm d -1 which agrees with the range given by Carrow and Duncan (2003); this research also includes ET during winter months when actual ET rates were minimal so the value of 3.3 mm may be low when compared to the ET values given by Carrow and Duncan (2003) strictly during summer months. A daily comparison of measured actual evapotranspiration with calculated reference evapotranspiration using calculated net radiation as defined by FAO56 is

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53 shown in figure 4-18. These evapotranspiration data were used to compute final crop coefficients following the method prescribed by FAO56 guidelines. Figure 4-18 shows the yearly trends of actual and reference ET and shows that during the winter months actual ET is lower than reference ET, which would produce a crop coefficient less than 1.0. During the summer months, actual ET exceeds reference ET, which would produce a crop coefficient greater than 1. Actual crop coefficients using these data can be seen in table 4-7 and in figure 4-20. A daily comparison of actual evapotranspiration with measured reference evapotranspiration calculated using measured net radiation is shown in figure 4-19. Figure 4-19 shows the yearly trends of actual and reference ET calculated using measured net radiation that are much closer than those found when using reference ET and calculated net radiation. While this is not the method prescribed by FAO56, this analysis shows results vary based on the net radiation estimation method. The crop coefficients resulting from this evapotranspiration data are shown in table 4-7 and in figures 4-20 and 4-21. Crop Coefficient The ultimate goal of this research was to develop monthly crop coefficients for bahiagrass in a specific climate. Monthly crop coefficients were developed for the year 2004 excluding September and October due to hurricane activity which disrupted lysimeter results. Out of 366 total days in 2004 (leap year), 84 days were excluded from crop coefficient calculation due to either hurricane interference or because the data from that day did not meet the criteria outlined in chapter 3 concerning the adjustment and filtering of actual ET data. Daily plots of the crop coefficients, namely the crop curve, are shown in figures 4-20 and 4-21 using calculated net radiation and measured net

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54 radiation respectively. Table 4-7 summarizes these monthly Kc averages along with the difference between the two methods. The crop coefficient for each month except January and June was higher when using measured net radiation rather than calculated net radiation. This can be explained by figure 4-4, which shows that calculated net radiation is consistently higher than measured net radiation. A study by Jia et al. (2005) found that measured net radiation from the net radiometer at the supplementary weather station approximately 500 m southwest of the lysimeters was 10-30% lower than net radiation calculated using solar radiation following the FAO56 method. Jia et al. (2005) found that this difference in radiation caused a 6-20 % lower reference ET if measured net radiation is used. The conclusion of the research performed by Jia et al. (2005) indicates that the use of calculated net radiation from solar radiation measurements may be questionable. The 6-20% difference in reference ET found by Jia et al. (2005) agrees well with the 1-21% monthly difference in reference ET as shown in table 4-5 for the lysimeter weather station. Since the crop coefficient listed in the FAO56 paper for warm-season grasses is 0.85 and crop coefficients in this research exceeded 1.0 during several months, it is possible that the lysimeters were kept at wetter than recommended conditions leading to additional available water and evaporation. An increase in actual ET leads to an increase in the crop coefficient, which could explain crop coefficients of 1.08, 1.16, 1.19, and 1.09, for the months of April, May, June, and July respectively. Since calculated net radiation is higher than measured net radiation and the calculation for reference ET is highly dependant on net radiation calculations, reference ET values using calculated net radiation will be higher than the reference ET using

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55 measured net radiation as shown in figure 4-11 and table 4-5. As a result of this difference in reference ET, the crop coefficient using calculated net radiation in reference ET calculations will be lower when using measured net radiation. Table 4-7 shows that the maximum monthly difference in crop coefficients occurred in December, a crop coefficient difference ranging from -2.1 to 1.9% between using calculated and measured net radiation to determine reference ET. When all of the crop coefficient differences using each method are totaled, there is a -11.3% difference in crop coefficients. The crop coefficient using calculated net radiation averaged 0.95 for the year. The crop coefficient using measured net radiation averaged 1.00 for the year. Small differences in crop coefficients can produce large differences in irrigation requirements. For example, if the yearly averaged crop coefficients from both calculations methods were used to calculate irrigation requirements for the month of December, given that reference ET in December was 67.4 mm using calculated net radiation, then with a crop coefficient of 0.87 using calculated net radiation compared to a crop coefficient of 1.00 using measured net radiation, then the theoretical actual ET would total 58.6 mm using the calculated net radiation Kc and 67.4 mm using the measured net radiation Kc. Since the plant water requirement is the actual ET minus the rainfall for that month, given that rainfall totaled approximately 65 mm in December, then the crop water requirement would be met by 6.4 mm using the calculated net radiation Kc, but there would be a 2.4 mm crop water requirement if the measured net radiation Kc were used. Effective rainfall also plays a part in the crop water requirement, given that if more than enough rainfall is available, there is a maximum amount of

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56 rainfall that the plant can utilize, this is the effective rainfall. Only the amount of rainfall that does not drain past the root zone and is utilized by the plant is considered effective. FAO56 cites a crop coefficient of 0.95 for an unspecified cool-season turfgrass and a crop coefficient of 0.85 for an unspecified warm-season turfgrass. A footnote to these tabulated values reads that the cool-season turfgrass is one that is kept at a height of 6-8 cm (Allen et al., 1998), instead of the 12 cm required for the FAO56 definition of a reference surface. This could explain some of the reason why crop coefficients in this research were found to be unexpectedly high, with a shorter grass height there would be less actual evapotranspiration which would result in the lower crop coefficient cited by Allen et al. (1998). Assuming that the warm season turfgrass is kept at the same height of 6-8 cm (which is not explicitly stated in FAO56), this could be part of the reason for unexpectedly high crop coefficient values. Monthly plots of daily crop coefficients calculated using the reference ET with calculated net radiation are shown along with their respective monthly averages in Appendix A. This is the FAO56 outlined method for calculating reference ET, and should be used as the accepted crop coefficient values. The transferability of crop coefficient values depends on the consistency of one method. If the FAO56 method is used as a standard, crop coefficients can be compared using consistent methods. The daily and monthly average crop coefficients using calculated net radiation are shown in figure 4-20, differences between crop coefficients using the different net radiation calculation procedures are summarized in table 4-7. Monthly plots of daily crop coefficients calculated using reference ET with measured net radiation are shown along with their respective monthly averages in

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57 Appendix B. This method is not recommended by the FAO56 guidelines but is shown for comparative purposes. The daily and monthly averages for crop coefficients using measured net radiation are shown in figure 4-21. Monthly crop coefficients are shown in table 4-7 along with the difference in crop coefficients depending on whether calculated or measured net radiation was used to calculate reference ET. Using calculated net radiation, the crop coefficient ranged from a low of 0.73 in February to a maximum of 1.19 in June. Using measured net radiation the crop coefficient ranged from a low of 0.81 in February to a high of 1.18 in May. On low radiation days (i.e. cloudy/overcast), since calculated net radiation is usually higher than measured net radiation according to figure 4-4, crop coefficients are affected to a greater degree as compared to sunny, cloudless days when net radiation measurements are in closer agreement. Differences in the crop coefficients, depending on which method is chosen to compute reference ET, are shown in figure 4-22 and tabulated on a monthly basis in table 4-7. Figure 4-22 shows that with the wide range of daily crop coefficients, especially during the summer months, use of the standardized FAO56 method is critical. The large differences of daily crop coefficients during the summer months can be attributed to the differences in net radiation measurements on low radiation days as discussed earlier. Figure 4-23 shows when the net radiation is low, net radiation values fall significantly below the yearly trend, Kc differences tend to be higher. Many of the largest differences were filtered out of the crop coefficient calculations resulting in a blank spot in the crop curve. The days that were filtered out days also coincide with low radiation days as seen in figure 4-23. The monthly average difference in crop coefficients is greatest during the

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58 winter months when net radiation values are typically lower, resulting in differences when calculating reference ET. The average difference in crop coefficients depending on which net radiation method was used totaled a -11.3% difference over the course of the year. Figure 4-24 shows that the crop coefficient depends on the available soil moisture for a 26 day period from November 13, 2004 through December 8, 2004. Higher soil moisture levels inside the tanks lead to more available water for actual ET and an overall higher Kc for that day. Figure 4-24 calculates the average soil moisture in tank 1 as a weighted average of the vertical soil moisture profile in the tank. The general trend of the soil moisture follows that of the crop coefficient. The crop coefficient was found to be higher than previously published values, but this can be explained by the higher than field capacity soil moisture levels in the tanks for much of the year (Figures 4-15 through 4-17).

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59 Table 4-1. Monthly rainfall comparison among lysimeter area collection sites Month H_6 (mm) M_6 (mm) W_6 (mm) H_5 (mm) M_5 (mm) Jan 211 Feb 100 44 96 Mar 92 26 46 Apr 155 106 127 24 May 176 192 176 50 71 Jun 156 198 176 143 Jul 154 265 206 240 Aug 155 160 144 170 Sep 494 504 488 415 Oct 103 107 97 100 102 Nov 88 93 99 33 32 Dec 62 65 66 42 44 Sum 1239 1681 1874 879 1116 Descriptions: H_6 = Hobo rain gauge in Block 6, M_6 = Manual rain gauge in Block 6, W_6 = Weather station tipping bucket in Block 6, H_5 = Hobo rain gauge in Block 5, M_5 = Manual rain gauge in Block 5 blank spaces indicate the gauge was either inoperable or not yet installed, this is reflected in the difference between the yearly totals Table 4-2. Monthly rainfall comparison between surrounding area collection sites Month H_4 (mm) M_4 (mm) W_4 (mm) W_F (mm) Jan 44 44 Feb 120 142 143 Mar 20 51 55 Apr 112 29 25 May 211 67 67 70 Jun 163 153 159 140 Jul 124 231 231 269 Aug 209 155 154 157 Sep 580 533 565 418 Oct 99 90 97 115 Nov 74 32 34 34 Dec 82 46 46 38 Sum 1793 1307 1619 1508 Descriptions: H_4 = Hobo rain gauge in Block 4, M_4 = Manual rain gauge in Block 4, W_4 = Weather station tipping bucket in Block 4, W_F = FAWN weather station blank spaces indicate the gauge was either inoperable or not yet installed, this is reflected in the difference between the yearly totals

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60 Table 4-3. Monthly total reference ET (mm) using calculated net radiation Month ET 0 LYS (mm) ET 0 SUPP (mm) Difference (mm) % Difference Jan 65 54 -11 -17 Feb 63 52 -11 -17 Mar 114 100 -14 -13 Apr 98 93 -5 -6 May 124 120 -4 -3 Jun 116 121 -5 -4 Jul 113 115 2 2 Aug 115 112 -3 -2 Sep 112 102 -10 -9 Oct 94 86 -8 -8 Nov 82 65 -17 -22 Dec 67 48 -19 -28 Sum 1162 1067 Avg = 11 % LYS = lysimeter weather station; SUPP = supplementary weather station Table 4-4. Monthly total reference ET (mm) using measured net radiation Month ET 0 LYS (mm) ET 0 SUPP (mm) Difference (mm) % Difference Jan 60 44 -26 -44 Feb 57 42 -15 -27 Mar 112 83 -29 -26 Apr 96 74 -22 -22 May 125 97 -28 -22 Jun 126 96 -30 -24 Jul 107 84 -23 -21 Aug 101 77 -24 -23 Sep 99 74 -25 -25 Oct 84 62 -22 -26 Nov 71 47 -24 -34 Dec 53 35 -18 -35 Sum 1090 815 Avg = -28 % LYS = lysimeter weather station; SUPP = supplementary weather station

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61 Table 4-5. Difference between monthly total reference ET (mm) using calculated Rn and measured Rn recorded at both weather stations (ET 0 meas-ET 0 calc) Month Lysimeter (mm) % Difference Supplementary (mm) % Difference Jan -4 -7 -10 -19 Feb -6 -9 -11 -20 Mar -3 -2 -17 -17 Apr -3 -3 -18 -20 May -2 -1 -23 -19 Jun 10 9 -25 -21 Jul -6 -5 -31 -27 Aug -14 -12 -35 -31 Sep -14 -12 -29 -28 Oct -10 -10 -24 -28 Nov -11 -13 -18 -28 Dec -14 -21 -13 -28 Avg -6 -7 % -20 -24 Table 4-6. Monthly total actual evapotranspiration using lysimeters compared to reference ET using calculated Rn measured at the lysimeter weather station Month Actual ET (mm) Ref ET (mm) Jan 61 65 Feb 45 63 Mar 95 114 Apr 121 98 May 153 124 Jun 145 116 Jul 129 113 Aug 113 115 Sep 112 Oct 94 Nov 65 82 Dec 60 67 Sum 988 956 (excluding Sep and Oct) Actual ET was not calculated due to Hurricanes

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62 Table 4-7. Monthly crop coefficients of bahiagrass using actual ET from lysimeters and reference ET from the FAO56 method using calculated and measured net radiation (reference ET calculated at the lysimeter weather station) Month Kc using calculated net radiation Kc using measured net radiation Difference % Difference Jan 0.91 0.89 0.02 1.9 Feb 0.73 0.81 -0.08 -2.3 Mar 0.83 0.85 -0.02 -2.0 Apr 1.08 1.10 -0.02 -1.6 May 1.16 1.18 -0.02 -1.5 Jun 1.19 1.10 0.09 1.4 Jul 1.09 1.12 -0.03 -1.5 Aug 0.97 1.01 -0.04 -1.7 Sep N/A N/A N/A N/A Oct N/A N/A N/A N/A Nov 0.80 0.92 -0.12 -2.1 Dec 0.87 1.00 -0.13 -1.9 *2004 Average 0.95 1.00 Sum = -11.3 % September and October excluded due to hurricane activity

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63 0100200300400500600JanFebMarAprMayJunJulAugSepOctNovDecRainfall (mm) Weather Station Tipping Bucket at block 6 Manual Rain Gage at block 6 Hobo Tipping Bucket at block 6 Figure 4-1. Monthly cumulative rainfall measured at the lysimeter site using a tipping bucket rain gauge mounted on top of the weather station, a manual rain gauge, and a tipping bucket rain gauge recorded using a Hobo datalogger 0100200300400500600JanFebMarAprMayJunJulAugSepOctNovDecRainfall (mm) Weather Station Tipping Bucket at block 4 Manual Rain Gauge at block 4 Hobo Tipping Bucket at block 4 Hobo Tipping Bucket at block 5 Manual Rain Gauge at block 5 Fawn Rain Gauge Figure 4-2. Monthly cumulative rainfall measured at other sites at PSREU used for comparative purposes

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64 05101520JanFebMarAprMayJunJulAugSepOctNovDecRn (MJ m-2 day-1) daily measured Rn daily calculated Rn 4th Order polynomial trend 4th Order polynomial trend Figure 4-3. Daily net radiation at the lysimeter weather station; net radiation is calculated using FAO56 guidelines and measured using a net radiometer 024681012141618024681012141618Rn measured (MJ m-2 d-1) using net radiometerRn calculated (MJ m-2 d-1) data point 1 to 1 Linear trend Figure 4-4. Daily comparison of calculated net radiation and measured net radiation at the lysimeter weather station

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65 024681012141618024681012141618Rn measured (MJ m-2 d-1) at Supplementary Weather StationRn measured (MJ m-2 d-1) at Lysimeter Weather Station data point 1 to 1 Linear trend Figure 4-5. Comparison of net radiation measured at the lysimeter site compared to net radiation measured at the supplementary site 0123456JanFebMarAprMayJunJulAugSepOctNovDecET (mm) Daily ETo using calculated Rn (mm) Monthly Average ETo (mm) Figure 4-6. Plot of reference ET calculated using the Penman Monteith equation and calculated net radiation at the lysimeter weather station

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66 0123456JanFebMarAprMayJunJulAugSepOctNovDecET (mm) Daily ETo using calculated Rn (mm) Monthly Average ETo (mm) Figure 4-7. Plot of reference ET calculated using the Penman Monteith equation and calculated net radiation at the supplementary weather station 0.00.51.01.52.00.00.51.01.52.0VPD (kPa) @ Supplementary Weather StationVPD (kPa @ Lysimeter Weather Station) data point 1 to 1 Linear trend Figure 4-8. Comparison of VPD (kPa) measured at the lysimeter weather station and the supplementary weather station

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67 0123456JanFebMarAprMayJunJulAugSepOctNovDecET (mm) Daily ETo using measured Rn (mm) Monthly Average ETo (mm) Figure 4-9. Plot of reference ET calculated using the Penman Monteith equation with measured net radiation at the lysimeter weather station 0123456JanFebMarAprMayJunJulAugSepOctNovDecET (mm) Daily ETo using measured Rn (mm) Monthly Average ETo (mm) Figure 4-10. Plot of reference ET calculated using the Penman Monteith equation with measured net radiation at the supplementary weather station

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68 -1.5-1.0-0.50.00.51.01.52.0JanFebMarAprMayJunJulAugSepOctNovDecDifference in ETo (mm) EToRs EToRn (mm) Average Monthly Difference (mm) Figure 4-11. Daily difference and monthly average difference between reference ET when using calculated net radiation compared to measured net radiation at the lysimeter weather station 012345678910JanFebMarAprMayJunJulAugSepOctNovDecETc (mm)060120180240300Rainfall (mm) Rainfall (mm) ETc (mm) Monthly Avg ETc (mm) 2004 Total Annual Rainfall = 1862 mm Figure 4-12. Daily actual evapotranspiration measured by the lysimeters and daily rainfall recorded at the tipping bucket at the lysimeter weather station

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69 0246810JanFebMarAprMayJunJulAugSepOctNovDecET (mm)-20-15-10-505Rn (mm) ETc (mm) Rn ETc ETc (mm) Available Energy from Rn (mm) Figure 4-13. Daily actual evapotranspiration measured by the lysimeters compared to equivalent evaporation from available energy from net radiation 0.00.51.01.52.0JanFebMarAprMayJunJulAugSepOctNovDecCumulative Depth of Water (m) Cumulative ETc Cumulative ETo Cumulative Rainfall from lysimeter tipping bucket rain gauge ETc estimated based on ETo trend Figure 4-14. Cumulative actual evapotranspiration, cumulative reference evapotranspiration using calculated net radiation at the lysimeter weather station, and cumulative rainfall

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70 0510152025303540JanFebMarAprMayJunJulAugSepOctNovDec Soil Moisture (%) 5 cm 10 cm 20 cm 50 cm 100 cm Figure 4-15. Soil moisture profile in lysimeter 1 (east) 0510152025303540JanFebMarAprMayJunJulAugSepOctNovDec Soil Moisture (%) 5 cm 10 cm 20 cm 100 cm Figure 4-16. Soil moisture profile in lysimeter 2 (middle)

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71 051015202530354045JanFebMarAprMayJunJulAugSepOctNovDec Soil Moisture (%) 5 cm 10 cm 20 cm 50 cm 100 cm Figure 4-17. Soil moisture profile in lysimeter 3 (west) 012345678JanFebMarAprMayJunJulAugSepOctNovDecDateET (mm) ETc (mm) ETo (mm) using calc Rn Poly. Trend of ETc Poly. Trend of ETo Figure 4-18. Average daily actual ET measured using lysimeters and daily reference ET using calculated net radiation at the lysimeter weather station

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72 012345678JanFebMarAprMayJunJulAugSepOctNovDecET (mm) ETc (mm) ETo (mm) using measured Rn Poly. Trend of ETc Poly. Trend of ETo Figure 4-19. Average daily actual ET measured using lysimeters and daily reference ET using measured net radiation at the lysimeter weather station 0.00.20.40.60.81.01.21.41.61.82.0JanFebMarAprMayJunJulAugSepOctNovDecKc Daily Kc using calc Rn Monthly Kc using calc Rn Figure 4-20. Average daily and monthly crop coefficients using calculated net radiation (reference ET calculated at the lysimeter weather station)

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73 0.00.20.40.60.81.01.21.41.61.82.0JanFebMarAprMayJunJulAugSepOctNovDecKc Daily Kc using measured Rn Monthly Kc using measured Rn Figure 4-21. Average daily and monthly crop coefficients using measured net radiation (reference ET calculated at the lysimeter weather station) -1.0-0.8-0.6-0.4-0.20.00.20.4JanFebMarAprMayJunJulAugSepOctNovDecDifference in Kc02468101214% Difference (Kc calc Rn) (Kc meas Rn) Average Monthly Kc Difference Average Monthly % Difference Figure 4-22. Difference in crop coefficient between using calculated and measured Rn to compute reference ET

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74 -0.6-0.4-0.20.00.20.40.60.81.0JanFebMarAprMayJunJulAugSepOctNovDecDifference in Kc-20-15-10-505101520Calculated Net Radiation (MJ m-2 day-1) (Kc calc Rn) (Kc meas Rn) Average Monthly Kc Difference Daily Calc Rn Daily Meas Rn Poly. Daily Calc Rn Poly. Daily Meas Rn Figure 4-23. Differences in crop coefficient between calculated and measured Rn in reference ET compared to daily values of calculated and measured Rn 0246810121411/1311/1511/1711/1911/2111/2311/2511/2711/2912/112/312/512/7DateSoil Moisture (%)0.00.51.01.52.0Kc Average Soil Moisture Crop Coefficient Trend of Crop Coefficient Figure 4-24. Soil moisture content in tank 1 and overall Kc

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CHAPTER 5 RECOMMENDATIONS FOR FUTURE WORK The lysimeters used in this research will be operational for years to come barring any unforeseen interruptions. Vegetation other than bahiagrass will eventually be planted in these same tanks as well as in the surrounding field. The selection of bahiagrass for this research was as much for the interest in the crop as it was to determine if the lysimeters operate as anticipated and to determine the correct operational requirements for the lysimeters and calculation procedure for evapotranspiration data for future crops. In this way, depending on the needs of the user, the methods developed can be applied to future research using the same or slightly modified instruments and methods. Lysimeter Design Some flaws are inherent with the design of the lysimeters that were used in this research, but small design imperfections for any precision lysimeter design are bound to occur due to the high accuracy and time consuming maintenance of these devices. The design and construction of the lysimeters was performed prior to any realization of what effects the design intricacies would have on actual evapotranspiration measurements. Rainfall Measurement The design specification that had the most influence on the operation of the lysimeters was the rubber stripping that covered the gap between the inner and outer tank of each lysimeter. It became clear after data processing that these rubber coverings meant to keep water out of the gap between the inner and outer tank actually intercepted rainfall and created an inflow of rainfall into the inner tank. This inflow of rainfall 75

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76 caused rainfall amounts recorded by the lysimeters to be larger than the actual rainfall that occurred because the surface of the lysimeter was effectively increased by the surrounding rubber cover. Given that the rubber stripping surrounds the perimeter of the inner tank with a width of 7.87 cm, that creates a maximum effective area approximately 60% greater than the area of the evaporative surface of the grass in the inner tank. The increase in rainfall recorded by the lysimeters approached a 60% increase over actual rainfall, but with random variation in wind speed and wind direction the effective area that is added by the rubber stripping does not always contribute wholly to the inflow of rainfall into the inner tank. Because the varying wind speed and wind direction is not constant, the greater rainfall amounts recorded by the lysimeters cannot be directly applied to evapotranspiration calculations simply by using a single rainfall coefficient. To alleviate this problem, the rubber stripping that surrounds the inner tank should be redesigned. One possible solution would be to construct a new barrier for the gap between the inner and outer tanks that slopes slightly inward to deflect all rainfall caught by the barrier to flow into or out of the inner tank. This would give the lysimeter operator the ability to increase the effective area of the lysimeter for rainfall calculations while keeping the effective area for evapotranspiration and drainage calculations the same. Drainage Another design consideration is the efficiency of the pumping process. All pumping was done while the lysimeter operator waited at the site and measured with a graduated cylinder immediately after pumping by draining the storage tanks. This caused problems because pumping was performed in the middle of the afternoon when evapotranspiration is typically at a peak, so there are two sources of outflow from the

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77 lysimeter in the forms of ET and pumping that cannot be distinguished easily. This process would be more efficient by pumping the lysimeters during non-daylight hours when ET is minimal, and by measuring the drainage from the storage tanks using a flow meter attached to the nozzle on the outlet of the storage tanks. An automated timer system was added to the design of the pumping system at the beginning of 2005 to expedite the manual pumping process. Pumping can be performed in the early morning, and scheduled up to a week in advance so that if rainfall is anticipated, pumping can be performed without significant delay after a rainfall event so that standing water in the inner lysimeter tank does not affect the evapotranspiration rate by the existence of extra water present that is available for ET. This procedure would cut down on the afternoon interference with actual ET measurement and expedite the pumping and measurement procedure. Soil Moisture Soil moisture probes are located in each of the lysimeters at five different depths as well as one set of probes outside the tanks in the surrounding field to monitor the amount of water present in the inner tanks and at a location outside of the tanks. The soil probes used in this research were extremely sensitive and the post processing program is not at all user-friendly. The result was choppy soil moisture data, and large gaps of missing data throughout the year. A more effective approach to this problem would be using a different type of probe that measures soil moisture directly so the lysimeter operator could tell how much water was present at the different levels in the inner tank and determine the duration of pumping needed to attain soil moisture levels similar to the surrounding field. This would also require an additional set of soil moisture probes outside the tanks in the same field to compare soil moisture values of the lysimeters.

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78 There were additional soil moisture probes installed outside the tanks during this research but the first location had high concentrations of clay which affected initial soil moisture readings. The second location was inundated with water after the high rainfall season and was not a fair representation of the entire field. The additional recommended probes could be wired to a data logger that outputs real-time soil moisture data so that the lysimeter operator could see what the exact soil moisture was at that time and pump the tanks accordingly. This would allow the lysimeter operator to maintain the soil moisture levels in the lysimeter closer to those levels in the surrounding field. Calculation Procedure Evapotranspiration measurements were performed on a daily basis, but with the precision of the lysimeter, calculations should be available for time scales as small as 30 minutes. The user would have to account for noise in the load cell readings when applying calculation procedures to the raw data, but actual evapotranspiration data for any time scale above 30 minutes can be achieved. Crop coefficients can be calculated for any crop planted in the lysimeter using the same reference evapotranspiration equations outlined in FAO56 along with actual evapotranspiration data from the lysimeters. An additional criteria for data suitability could be based on soil moisture. For example, if the soil moisture inside and outside of the tanks is known, then a possible criterion could be that the data is not used if the soil moisture inside the tanks is greater than 10% different from the soil moisture outside of the tanks. This requirement would prevent possible errors being made by calculating ET when the lysimeters do not truly represent the surrounding field.

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79 Evaporative Area of the Lysimeters A major factor in calculating the actual evapotranspiration of any crop planted in the lysimeters is the evaporative area of that crop. For bahiagrass, which produces full ground coverage, the evaporative area is relatively straight forward, only the additional evaporative area created by the grass extending over the edge of the inner tank must be determined. For crops that do not have full ground cover, the evaporation of bare soil may have to be accounted for to achieve accurate evapotranspiration measurements. This would add more challenges to evapotranspiration calculations. The user would have to consider the evapotranspiration of the crop, be able to accurately measure its respective evaporative area, be able to account for evaporation from bare soil, and be able to measure its evaporative area. If the evaporation from bare soil could be defined through simple equations, then this term could be defined as a term in the lysimeter water balance and the evapotranspiration of the crop could be measured directly as the only remaining term in the lysimeter water balance equations. Herbicides and Pesticides Ants and weeds were common problems during this research that had to be remedied as soon as possible to prevent measurement interruption. Red ants were commonly found residing in the lysimeter tanks and had to be exterminated using store-bought ant killer. Spreading of ant killer or other pesticides (that do not interfere with the health of the crop) at regular intervals around the perimeter of the lysimeters could prevent the frequency of ants or other pests. Weeds were also a problem in the maintenance of the lysimeters that should be dealt with immediately. Creeping weeds were commonly found in and around the lysimeter and were removed promptly to prevent further spreading. Other various weeds were often found inside the lysimeter

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80 tank and removed upon recognition to prevent further spreading as well as unwanted evapotranspiration from vegetation other than the crop in question. Care should be used when applying herbicide. The selection only occur when the user is certain that the health of the crop will not be affected. A test should be performed on weeds outside of the lysimeters before applying the herbicide inside the tanks to make sure the herbicide will not cause any undesired effects.

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APPENDIX A ACTUAL EVAPOTRANSPIRATION, REFERENCE EVAPOTRANSPIRATION USING CALCULATED NET RADIATION, AND CROP COEFFICIENTS 024681012141/11/31/51/71/91/111/131/151/171/191/211/231/251/271/291/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc A 024681012142/12/32/52/72/92/112/132/152/172/192/212/232/252/272/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc B Figure A-1. Daily actual ET, daily reference ET using calculated net radiation, daily Kc, and average monthly Kc. A) January 2004. B) February 2004. C) March 2004. D) April 2004. E) May 2004. F) June 2004. G) July 2004. H) August 2004. I) September 2004. J) October 2004. K) November 2004. L) December 2004. 81

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82 024681012143/13/33/53/73/93/113/133/153/173/193/213/233/253/273/293/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc C Figure A-1 Continued 024681012144/14/34/54/74/94/114/134/154/174/194/214/234/254/274/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/Rs Daily Kc Monthly Kc D B Figure A-1 Continued

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83 024681012145/15/35/55/75/95/115/135/155/175/195/215/235/255/275/295/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc E Figure A-1 Continued 024681012146/16/36/56/76/96/116/136/156/176/196/216/236/256/276/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rs Daily Kc Monthly Kc F Figure A-1 Continued

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84 024681012147/17/37/57/77/97/117/137/157/177/197/217/237/257/277/297/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc Eto w/Rs Daily Kc Monthly Kc G Figure A-1 Continued 024681012148/18/38/58/78/98/118/138/158/178/198/218/238/258/278/298/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc H

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85 024681012149/19/39/59/79/99/19/19/19/19/19/29/29/29/29/2ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rs Daily Kc Monthly Kc I Figure A-1 Continued 0246810121410/110/310/510/710/910/110/110/110/110/110/210/210/210/210/210/3ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc J Figure A-1 Continued

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86 0246810121411/111/311/511/711/911/1111/1311/1511/1711/1911/2111/2311/2511/2711/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rs Daily Kc Monthly Kc K Figure A-1 Continued 0246810121412/112/312/512/712/912/1112/1312/1512/1712/1912/2112/2312/2512/2712/2912/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc L Figure A-1 Continued

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APPENDIX B ACTUAL EVAPOTRANSPIRATION, REFERENCE EVAPOTRANSPIRATION USING MEASURED NET RADIATION, AND CROP COEFFICIENTS 024681012141/11/31/51/71/91/111/131/151/171/191/211/231/251/271/291/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc A 024681012142/12/32/52/72/92/112/132/152/172/192/212/232/252/272/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc B Figure B-1. Daily actual ET, daily reference ET using measured net radiation, daily Kc, and average monthly Kc. A) January 2004. B) February 2004. C) March 2004. D) April 2004. E) May 2004. F) June 2004. G) July 2004. H) August 2004. I) September 2004. J) October 2004. K) November 2004. L) December 2004. 87

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88 024681012143/13/33/53/73/93/113/133/153/173/193/213/233/253/273/293/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc C Figure B-1 Continued 024681012144/14/34/54/74/94/114/134/154/174/194/214/234/254/274/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/Rs Daily Kc Monthly Kc D Figure B-1 Continued

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89 024681012145/15/35/55/75/95/115/135/155/175/195/215/235/255/275/295/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc E Figure B-1 Continued 024681012146/16/36/56/76/96/116/136/156/176/196/216/236/256/276/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rn Daily Kc Monthly Kc F Figure B-1 Continued

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90 024681012147/17/37/57/77/97/117/137/157/177/197/217/237/257/277/297/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc G Figure B-1 Continued 024681012148/18/38/58/78/98/118/138/158/178/198/218/238/258/278/298/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc H Figure B-1 Continued

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91 024681012149/19/39/59/79/99/19/19/19/19/19/29/29/29/29/2ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rn Daily Kc Monthly Kc I Figure B-1 Continued 0246810121410/110/310/510/710/910/110/110/110/110/110/210/210/210/210/210/3ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rn Daily Kc Monthly Kc J Figure B-1 Continued

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92 0246810121411/111/311/511/711/911/1111/1311/1511/1711/1911/2111/2311/2511/2711/29ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Daily ETc ETo w/ Rn Daily Kc Monthly Kc K Figure B-1 Continued 0246810121412/112/312/512/712/912/1112/1312/1512/1712/1912/2112/2312/2512/2712/2912/31ET (mm)0.00.20.40.60.81.01.21.41.61.82.02.2Kc Crop Coefficient Avg ETc ETo w/Rs Daily Kc Monthly Kc L Figure B-1 Continued

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LIST OF REFERENCES Aboukhaled, A., A. Alfaro, and M. Smith. 1982. Lysimeters. FAO Irrig. Drain. Paper No. 33. Rome, Italy. Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. FAO Irrig. Drain. Paper No. 56. Rome, Italy. Allen, R. G., W.O. Pruitt, and M.E. Jensen. 1991. Environmental requirements for lysimeters. pp. 170-181 in Allen, R. G., Howell, T. A., Pruitt, W. O., Walter, LA., and Jensen, M. E. (Editors). Lysimeters for Evapotranspiration and Environmental Measurements. Proceedings of the ASCE International Symposium on Lysimetry, Honolulu, HA, ASCE, New York, NY. Allen, R.G., M. Smith, A. Pereira, and L.S. Pereira. 1994a. An Update for the Definition of Reference Evapotranspiration. ICID Bull., 43(2):1-34. Allen, R.G., M. Smith, A. Pereira, and L.S. Pereira. 1994b. An Update for the Calculation of Reference Evapotranspiration. ICID Bull., 43(2):35-92. Brown, P.W., C.F. Mancino, M.H. Young, T.L. Thompson, F.J. Wierenga, and D.M. Kopec. 2001. Penman Monteith crop coefficients for use with desert turf systems. Crop Sci. 41:1197-1206. Burman, R., R.H. Cuenca, and A. Weiss. 1983. Techniques for estimating irrigation water requirements. In: Advances in Irrigation, Vol. II. D. Hillel (ed.) Academic Press, NY. pp. 336-394. Burman, R., and L.O. Pochop. 1994. Evaporation, Evapotranspiration and Climatic Data. Elsevier, New York, 278pp. Buster, T.P. 1979. Soil Survey of Marion County area, Florida. Soil Conserv. Service, Washington D.C. Carlisle, V.W., R.E. Caldwell, F. Sodek, L.C. Hammond, F.G. Calhoun, M.A. Granger, and H.L. Breland. 1978. Characterization data for selected Florida soils. Soil Science Research Report 78-1, University of Florida, IFAS, Gainesville, FL. Carrow, R.N. 1995. Drought resistance aspects of turfgrasses in the southeast: evapotranspiration and crop coefficients. Crop Sci. 35:1685-1690. 93

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94 Carrow, R.N., R.R. Duncan. 2003. Improving drought resistance and persistence in turf-type tall fescue. Crop Sci. 43:978-984. Cuenca, R.H. 1989. Irrigation system design-an engineering approach. New Jersey: Prentice-Hall. Doorenbos, J., and W.O. Pruitt. 1975. Guidelines for Prediction of Crop Water Requirements. FAO Irrig. Drain. Paper No. 24. Rome, Italy. Doorenbos, J., and W.O. Pruitt. 1977. Guidelines for Prediction of Crop Water Requirements. FAO Irrig. Drain. Paper No. 24 (revised). Rome, Italy. Duble, R.L. 1989. Southern Turfgrasses. TexScape, Inc.: College Station, TX. Florida Department of Environmental Protection (FDEP). 2002. Florida Water Conservation Initiative. http://www.dep.state.fl.us/water/waterpolicy/docs/WCI_2002_Final_Report.pdf November 20th 2004. Florida Department of Transportation (FDOT). 2003. Drainage Manual. Tallahassee, Fla.: Office of Design, Drainage Section. Garcia, M., D. Raes, R. Allen, and C. Herbas. 2004. Dynamics of reference evapotranspiration in the Bolivian highlands (Altiplano). Agr. Forest Meteorol. 125:67-82. Gibeault, V.A. 1978. Guides for developing an irrigation program for turfgrasses. In Proceedings of Scotts Turfgrass Research Conference. Vol. 5: 28-36. Grebet, P., and Cuenca, R.H. 1991. History of lysimeter design and effects of environmental disturbances. pp. 10-18 in Allen, R. G., Howell, T. A., Pruitt, W. O., Walter, LA., and Jensen, M. E. (Editors). Lysimeters for Evapotranspiration and Environmental Measurements. Proceedings of the ASCE International Symposium on Lysimetry, Honolulu, HA, ASCE, New York, NY. Gregory, J. H. 2004. Stormwater Infiltration at the Scale of an Individual Residential Lot in North Central Florida, Masters Thesis, University of Florida, Gainesville, Fl, August 2005. Hodges, A.W., J.J. Haydu, P.J. van Blokland, and A.P. Bell. 1994. Contribution of the Turfgrass Industry to Floridas Economy, 1991-1992: A Value Added Approach. University of Florida IFAS FRE Dept., Economics Report 94-1. 83 pp. Howell, T.A., R.L. McCormick, and C.J. Phene. 1985. Design and installation of large weighing lysimeters. Trans. ASAE 28(1): 106-112.

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95 Howell, T. A., A. D. Schneider, and M. E. Jensen. 1991. History of lysimeter design and use for evapotranspiration measurements. pp. 1-9 in Allen, R. G., Howell, T. A., Pruitt, W. O., Walter, LA., and Jensen, M. E. (Editors). Lysimeters for Evapotranspiration and Environmental Measurements. Proceedings of the ASCE International Symposium on Lysimetry, Honolulu, HA, ASCE, New York, NY. Irmak, S., A. Irmak, J.W. Jones, T.A. Howell, J.M. Jacobs, R.G. Allen, and G. Hoogenboom. 2003. Predicting daily net radiation using minimum climatological data. J. Irrig. Drain. E-ASCE., 129(4):256-269. Itenfisu, D., R.L. Elliott, R.G. Allen, and I.A. Walter. 2003. Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort. J. Irrig. Drain. E-ASCE, 129(6):440-448. Jacobs, J.M., S.L. Mergelsberg,, A.F. Lopera, and D.A. Myers. 2002. Evapotranspiration from a wet prairie wetland under drought conditions: Paynes Praire Preserve, Florida, USA. Wetlands 22(2):374-385 Jensen, M.E., R.D. Burman, and R.G. Allen. 1990. Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practices No. 70, ASCE, New York. Jia, X. M.D. Dukes, and J.M. Jacobs. 2005. Impact of net radiation estimation in accurate determination of reference evapotranspiration in central Florida. World Water Congress 2005. J. Irr. Drain. E-ASCE, (173):536. Jia, X., J.M. Jacobs, M.D. Dukes, and B. Wilder. 2005. Revision of AFSIRS Crop Water Use Model. SJRWMD Report, March 2005. Kohnke, H., F.R. Dreibelbis, and J.M. Davidson. 1940. A Survey and Discussion of Lysimeters and a Bibliography on their Construction and Performance. Misc. Publ. No. 372, USDA, Washington, D.C. 68p. Marella, Richard L. 1999. Water Withdrawal, Use, Discharge, and Trends in Florida, 1995. U.S. Geological Survey: Water-Resources Investigations Report 99-4002. purl.access.gpo.gov/GPO/LPS24255 November 20th 2004. Penman, H.L. 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. Ser-A, 193, 120-148. Phene, C.J., D.W. Meek, K.R. Davis, R.L. McCormick, and R.B. Hutmacher. 1985. Real time crop evapotranspiration and determination of crop coefficients. 122-129. In: Advances in Evapotranspiration. ASAE, Publ. 14-85. Postel, S. 1992. Last Oasis: Facing Water Scarcity. Norton: New York, NY. Pruitt, W.O. and D.E. Angus. 1960. Large weighing lysimeter for measuring evapotranspiration and dewfall. J. Agric. Eng. Res. 4:161-173.

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96 Pruitt, W.O. and F.J. Lourence. 1985. Experiences in lysimetry for ET and surface drag measurements. Pp. 51-69. In: Advances in Evapotranspiration, ASAE, St. Joseph, MI. Purdum, E.D. 2002. Florida Waters: A Water Resource Manual from Floridas Water Management Districts. http://www.sfwmd.gov/misce/fl_waters.html November 20th 2004. Satti, S.R. and J.M. Jacobs. 2004. A GIS-based model to estimate the regionally distributed drought water demand. Agric. Water Manage. 66:1-13. Schneider, A.D. and T.A. Howell. 1991. Large, monolithic, weighing lysimeters. pp. 37-45 in Allen, R. G., Howell, T. A., Pruitt, W. O., Walter, LA., and Jensen, M. E. (Editors). Lysimeters for evapotranspiration and environmental measurements. Proceedings of the ASCE International Symposium on Lysimetry, Honolulu, HA, ASCE, New York, NY. Shermyen, A. 1993. 1993 Florida Statistical Abstract. 27 th Edition. University of Florida, College of Business Admin., Bureau of Economic and Business Research. Smajstrla, A.G., F.S. Zazueta, G.A. Clark, and D.J. Pitts. 2000. Irrigation Scheduling with Evaporation Pans. Bulletin 254, Department of Agricultural and Biological Engineering, Florida Cooperative Extension Service, IFAS, University of Florida. Soil Survey Staff. 1975. Soil taxonomy: A basic system of soil classification for making and interpreting soil surveys. USDA-SCS Agric. Handb. 436. Washington, D.C.: U.S. Government Printing Office. U.S. Census Bureau. 2003. 2000 Census of Population and Housing, Summary File 4. http://ftp2.census.gov/census_2000/datasets/Summary_file_4/ Florida/ May 28th 2004. U.S. Dept. of Agric., Soil Conserv. Service. 1970. Irrigation Water Requirements. Tech. Release No. 21, (rev.), 92pp. U.S. Geological Survey, 1996. Comparison of Bowen-ratio, Eddy-correlation, and weighing-lysimeter evapotranspiration for two sparse-canopy sites in eastern Washington. USGS, Water Resources Investigation Report 96-4081. Tacoma, WA. Van Bavel, C.H.M. 1961. Lysimetric measurements of evapotranspiration in the eastern United States. Soil Sci. Soc. Am. Proc. 25:138-141. Van Bavel, C.H.M. and L.E. Myers. 1962. An automatic weighing lysimeter. Agric. Eng. 43:580-583, 586-588.

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97 Walter, I.A., R. G. Allen, R. Elliott, M. E. Jensen, D. Itenfisu, B. Mecham, T. A. Howell, R. Snyder, P. Brown, S. Echings, T. Spofford, M. Hattendorf, R. H. Cuenca, J. L. Wright, and D. Martin. 2005. The ASCE Standardized Reference Evapotranspiration Equation. http://www.kimberly.uidaho.edu/water/asceewri July 5th 2005. Wright, J.L. 1982. New evapotranspiration crop coefficients. J. Irrig. Drain E-ASCE, ASCE, 108(IR2):57-74. Wu, Lin. 1985. Matching irrigation to turfgrass root depth. California Turfgrass Culture, 35(1):1-2. Yrisarry, J.J.B., and F.S. Naveso. 2000. Use of weighing lysimeter and bowen-ratio energy-balance for reference and actual crop evapotranspiration measurements. Acta Hort. 537:143-150.

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BIOGRAPHICAL SKETCH George William Triebel was born on March 29, 1980 in Miami, FL. From Miami, he moved to Ocala, FL in 1981 then to Palm Harbor, FL in 1984, where he attended elementary school at Palm Harbor Elementary School until 1990. In 1991 he moved to Lakeland, FL and attended Scott Lake Elementary for 1 year, and Lakeland Highlands Middle School for 2 years. In 1998 he graduated from George Jenkins High School in Lakeland, FL, during which time he was dual-enrolled at Polk Community College. In July 1998 he enrolled at the University of Florida. After a year or two of uncertainty about which educational path he wanted to follow, he decided to pursue a degree in Civil Engineering. The Florida Bright Futures Scholarship funded his tuition for the duration of his undergraduate studies. He was inducted into Tau Beta Pi and Chi Epsilon engineering honor societies, was an active member of the student section of the American Society of Civil Engineers, was recognized for numerous Academic Deans List and Academic Honor Roll nominations, and the Whos Who of American College and University Students. He was employed by the University of Florida as a library assistant at the Marston Science Library, as a teaching assistant for a Hydraulics class in the Civil Engineering Department, and during a summer internship he was employed by Montgomery Watson Harza as an assistant engineer in their Cape Coral, Fl. Office. In December 2002, he graduated with honors from the University of Florida with a Bachelor of Science degree in civil engineering. 98

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99 In January 2003, he entered the University of Florida Graduate School to pursue a Master of Engineering degree. Initially studying civil engineering, after a year he transferred to the Agricultural and Biological Engineering department with a specialization in land and water resources and a minor in Civil Engineering. In December 2005 he received his Master of Engineering degree in agricultural and biological engineering from the University of Florida. George intends to apply his knowledge of engineering principles as a water resource engineer and eventually obtain his professional engineering registration. He has accepted a position with Montgomery Watson Harza, an international engineering consulting firm specializing in power, water, and wastewater issues. He began working with Montgomery Watson Harza in the Cape Coral, FL office as a water resources engineer on August 31, 2005.