estimation of the crop coefficient of bahiagrass using lysimeters and the fao56 penman-monteith
TRANSCRIPT
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.
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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
Florida’s 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
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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 ET0 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
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 (ET0meas-ET0calc) .......................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
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
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
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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
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 ETc was determined using three sensitive
weighing lysimeters, located at the Plant Science and Research Education Unit in Citra,
Florida. Daily reference evapotranspiration (ET0) was calculated using the FAO 56
Penman-Monteith method. The daily crop coefficient (Kc) of bahiagrass was calculated
using daily values of ETc and ET0, and averaged on a monthly basis. The ET0 was
calculated using estimated and measured net radiation (Rn) to determine the respective
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monthly averages of Kc. 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 ET0 and using lysimeters to measure ETc
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
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.
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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.
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3
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,
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).
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,
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 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
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 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
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
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 ET0. 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 (α) of 0.23, and a constant latent heat of vaporization (λ) of 2.45 MJ/kg. The
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 ET0 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 ET0 (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 ET0 on a daily time scale.
11
)34.01(
)(273
900)(408.0
2
2
u
eeuT
GRET
asn
o ++∆
−+
+−∆=
γ
γ
(2-1) • ET0: reference evapotranspiration (mm) • T: mean daily air temperature (oC) calculated as the average of the minimum
and maximum daily air temperatures • Rn: 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 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 • ∆: slope of the vapor pressure curve (kPa oC-1) • es: saturated vapor pressure (kPa) • ea: actual vapor pressure (kPa)
∆ can be calculated:
( )23.2373.237
27.17exp6108.04098
+
⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛
+=∆
TT
T
(2-2)
γ is the psychometric constant (kPa oC-1):
PPc p 310655.0 −×==
ελγ
(2-3) • cp: specific heat at constant pressure (1.013 10-3 MJ kg-1 oC-1) • P: mean daily atmospheric pressure (kPa) • ε: ratio of the molecular weight of water vapor to dry air (0.662) • λ: 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:
⎥⎦
⎤⎢⎣
⎡+
=3.237
27.17exp6108.0)(
min
minmin T
TTeo
(2-4)
⎥⎦
⎤⎢⎣
⎡+
=3.237
27.17exp6108.0)(
max
maxmax T
TTeo
(2-5)
es (kPa) is then calculated:
12
2)()( minmax TeTe
eoo
s+
= (2-6)
ea (kPa) is the actual vapor pressure calculated as a function of es and the minimum and
maximum daily relative humidity:
2100
)(100
)( minmax
maxmin
RHTeRH
Tee
oo
a
+=
(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:
( ) ( ) ( ) ( ) ( ) ( )[ ]ssrsca dGR ωδϕδϕωπ
sincoscossinsin)60(24+=
(2-8) • Gsc: solar constant (0.0820 MJ m-2 day-1) • dr: inverse relative distance between the earth and sun • φ: latitude (rad) • δ: solar declination (rad) • ωs: sunset hour angle (rad)
Net shortwave radiation, Rns is calculated:
( ) sns RR α−= 1 (2-9) • α: defined albedo of grass (0.23) • Rs: measured incoming solar radiation (MJ m-2 day-1)
Outgoing solar radiation, Rso is calculated:
aso RzR )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 as and bs
Angstrom values which will vary depending on atmospheric conditions. When
13
calibration values are not available, the above equation is used to calculate outgoing solar
radiation.
Net longwave radiation, Rnl is calculated:
( ) ( ) ( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛−−
⎥⎥⎦
⎤
⎢⎢⎣
⎡ += 35.035.114.034.0
2
4min,
4max,
so
sa
KKnl R
ReTT
R σ (2-11)
• σ: 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:
nlnsn RRR −= (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 (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 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).
14
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:
RODETIPS c −−−+=∆ (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 (Kc) is a numerical factor that relates actual evapotranspiration
(ETc) of an individual, well watered crop to the reference evapotranspiration (ET0). 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):
0/ ETETK cc = (2-14)
15
Yrisarry (2000) states that precision weighing lysimeters are one of the most
effective methodologies for direct crop coefficient studies. Using known values of ETc
and an estimate of ET0, 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 Kc and calculated values
for ET0 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:
0ETKET cc ×= (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 29.4061o N latitude and longitude 82.1656o 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 ETc 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
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 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)
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
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
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 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.
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 (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.
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 H2O) of each
regression equation for each calibration are 0.00041, 0.00041, and 0.00041 respectively.
For tank 2, the slopes (mV/mmH2O) of each regression equation for each calibration are
0.00041, 0.00044, and 0.00039 respectively. For tank 3, the slopes (mV/mmH2O) of
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/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
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
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).
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
DETIPS c −−+=∆ (3-1) • ∆S: change in soil water storage (mm) • P: precipitation (mm) • I: irrigation (mm) • D: drainage (mm)
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).
29
[ ]a
bmVavgVeq−
=)( 4 (3-3)
• 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 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).
weqeq VW ρ∗= (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 • ρ: the density of water at 5°C (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
5°C for simplification purposes even though the water may be warmer in reality. If the
density of water at 35°C were used (0.00094 g/L) at a hypothetical volume of water
totaling 5000 liters, compared to the density of water at 5°C, 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
30
temperature change of the water within each lysimeter to determine the exact density.
This process would be time consuming and of negligible value.
)()( 12 tVtVS eqeq −=∆ (3-5) • ∆S: change in storage over any time interval (L) • Veq(t2): the equivalent volume of the lysimeter at any time, t2 (L) • Veq(t1): 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(t1) 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 (t1).
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.
lysSAL
mmSDepth 1*1
10*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)
• SAlys: surface area of the evaporative area (2.48*106mm2)
• Lmm
110*1 36
: 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.
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 ETc 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
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).
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.
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 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)
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
35
Table 3-2. Lysimeter calibration results for June 9, 2003 Lysimeter a (mV/mm H2O) 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 H2O) 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 H2O) 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
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
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
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.
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
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).
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
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
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
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 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,
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.
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
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
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 ET0 between Using Measured and Calculated Net Radiation
Table 4-5 shows the difference in ET0 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, ET0 is higher when using measured net radiation during every month
except June. At the supplementary weather station, ET0 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
49
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 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/λ = 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).
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.
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 (δz/z
= δx/x + δy/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
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
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
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
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
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
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
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).
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
60
Table 4-3. Monthly total reference ET (mm) using calculated net radiation
Month ET0 LYS (mm) ET0 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 ET0 LYS (mm) ET0 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
61
Table 4-5. Difference between monthly total reference ET (mm) using calculated Rn and measured Rn recorded at both weather stations (ET0meas-ET0calc)
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
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
63
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rai
nfal
l (m
m)
Weather Station Tipping Bucket at block 6 Manual Rain Gage at block 6Hobo 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
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rai
nfal
l (m
m)
Weather Station Tipping Bucket at block 4 Manual Rain Gauge at block 4Hobo 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
64
0
5
10
15
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rn
(MJ
m-2
day
-1)
daily measured Rn daily calculated Rn4th 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
0
2
4
6
8
10
12
14
16
18
0 2 4 6 8 10 12 14 16 18
Rn measured (MJ m-2 d-1) using net radiometer
Rn
calc
ulat
ed (M
J m
-2 d
-1)
data point1 to 1Linear trend
Figure 4-4. Daily comparison of calculated net radiation and measured net radiation at the lysimeter weather station
65
0
2
4
6
8
10
12
14
16
18
0 2 4 6 8 10 12 14 16 18Rn measured (MJ m-2 d-1) at Supplementary Weather
Station
Rn
mea
sure
d (M
J m
-2 d
-1) a
t Lys
imet
er
Wea
ther
Sta
tion
data point1 to 1Linear trend
Figure 4-5. Comparison of net radiation measured at the lysimeter site compared to net radiation measured at the supplementary site
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET (m
m)
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
66
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET
(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
0.0
0.5
1.0
1.5
2.0
0.0 0.5 1.0 1.5 2.0VPD (kPa) @ Supplementary Weather
Station
VP
D (k
Pa
@ L
ysim
eter
Wea
ther
S
tatio
n) data point1 to 1Linear trend
Figure 4-8. Comparison of VPD (kPa) measured at the lysimeter weather station and the supplementary weather station
67
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET (m
m)
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
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET
(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
68
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Diff
eren
ce 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
0123456789
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ETc
(mm
)
0
60
120
180
240
300
Rai
nfal
l (m
m)
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
69
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET
(mm
)
-20
-15
-10
-5
0
5
Rn
(mm
) - E
Tc (m
m)
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
0.0
0.5
1.0
1.5
2.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Cum
ulat
ive
Dep
th o
f Wat
er (m
)
Cumulative ETcCumulative EToCumulative 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
70
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Soi
l Moi
stur
e (%
)
5 cm 10 cm 20 cm 50 cm 100 cm
Figure 4-15. Soil moisture profile in lysimeter 1 (east)
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Soi
l Moi
stur
e (%
)
5 cm 10 cm 20 cm 100 cm
Figure 4-16. Soil moisture profile in lysimeter 2 (middle)
71
0
5
10
15
20
25
30
35
40
45
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Soi
l Moi
stur
e (%
)
5 cm 10 cm 20 cm 50 cm 100 cm
Figure 4-17. Soil moisture profile in lysimeter 3 (west)
0
1
2
3
4
5
6
7
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date
ET
(mm
)
ETc (mm) ETo (mm) using calc RnPoly. 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
72
0
1
2
3
4
5
6
7
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ET
(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
0.00.20.40.60.81.01.21.41.61.82.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Kc
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)
73
0.00.20.40.60.81.01.21.41.61.82.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Kc
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
-0.2
0.0
0.2
0.4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Diff
eren
ce in
Kc
0
2
4
6
8
10
12
14
% D
iffer
ence
(Kc calc Rn) - (Kc meas Rn) Average Monthly Kc DifferenceAverage Monthly % Difference
Figure 4-22. Difference in crop coefficient between using calculated and measured Rn to compute reference ET
74
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Diff
eren
ce in
Kc
-20
-15
-10
-5
0
5
10
15
20
Cal
cula
ted
Net
Rad
iatio
n (M
J m
-2 d
ay-1
)
(Kc calc Rn) - (Kc meas Rn) Average Monthly Kc DifferenceDaily Calc Rn Daily Meas RnPoly. 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
0
2
4
6
8
10
12
14
11/13
11/15
11/17
11/19
11/21
11/23
11/25
11/27
11/29
12/1
12/3
12/5
12/7
Date
Soi
l Moi
stur
e (%
)
0.0
0.5
1.0
1.5
2.0
Kc
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 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
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
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.
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.
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
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
0
2
4
6
8
10
12
14
1/1
1/3
1/5
1/7
1/9
1/11
1/13
1/15
1/17
1/19
1/21
1/23
1/25
1/27
1/29
1/31
ET (m
m)
0.00.20.40.60.81.01.21.41.61.82.02.2
Kc -
Cro
p C
oeffi
cien
t
Avg ETc ETo w/Rs Daily Kc Monthly Kc
A
0
2
4
6
8
10
12
14
2/1
2/3
2/5
2/7
2/9
2/11
2/13
2/15
2/17
2/19
2/21
2/23
2/25
2/27
2/29
ET (m
m)
0.00.20.40.60.81.01.21.41.61.82.02.2
Kc -
Cro
p C
oeffi
cien
t
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
82
0
2
4
6
8
10
12
14
3/1
3/3
3/5
3/7
3/9
3/11
3/13
3/15
3/17
3/19
3/21
3/23
3/25
3/27
3/29
3/31
ET (m
m)
0.00.20.40.60.81.01.21.41.61.82.02.2
Kc -
Cro
p C
oeffi
cien
t
Avg ETc ETo w/Rs Daily Kc Monthly Kc
C
Figure A-1 Continued
0
2
4
6
8
10
12
14
4/1
4/3
4/5
4/7
4/9
4/11
4/13
4/15
4/17
4/19
4/21
4/23
4/25
4/27
4/29
ET (m
m)
0.00.20.40.60.81.01.21.41.61.82.02.2
Kc -
Cro
p C
oeffi
cien
t
Daily ETc ETo w/Rs Daily Kc Monthly Kc
D
B Figure A-1 Continued
83
0
2
4
6
8
10
12
14
5/1
5/3
5/5
5/7
5/9
5/11
5/13
5/15
5/17
5/19
5/21
5/23
5/25
5/27
5/29
5/31
ET (m
m)
0.00.20.40.60.81.01.21.41.61.82.02.2
Kc -
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p C
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Avg ETc ETo w/Rs Daily Kc Monthly Kc
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Figure A-1 Continued
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Figure A-1 Continued
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Figure A-1 Continued
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Figure A-1 Continued
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Figure A-1 Continued
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Figure A-1 Continued
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L
Figure A-1 Continued
APPENDIX B ACTUAL EVAPOTRANSPIRATION, REFERENCE EVAPOTRANSPIRATION
USING MEASURED NET RADIATION, AND CROP COEFFICIENTS
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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.
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
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Figure B-1 Continued
LIST OF REFERENCES
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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.
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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, Master’s 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 Florida’s 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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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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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.
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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.
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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 Dean’s
List and Academic Honor Roll nominations, and the Who’s 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.
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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.