IRRIGATION OF HIGH MAINTENANCE TURF USING THE ARIZONA

IRRIGATION OF HIGH MAINTENANCE TURF USING THE ARIZONA
IRRIGATION OF HIGH MAINTENANCE TURF USING THE ARIZONA
DEPARTMENT OF WATER RESOURCES WATER DUTY: EVALUATION OF
TURF PERFORMANCE AND THE POTENTIAL FOR SOIL SALINIZATION
by
Brian Stephen Whitlark
Thesis Submitted to the Faculty of the
DEPARTMENT OF SOIL, WATER AND ENVIRONMENTAL SCIENCE
In Partial Fulfillment of the Requirements
For the Degree of
MASTER OF SCIENCE
In the Graduate College
THE UNIVERSITY OF ARIZONA
1999
2
STATEMENT OF AUTHOR
This thesis has been submitted in partial fulfillment of requirements for an
advanced degree at The University of Arizona and is deposited in the University Library
to be made available to borrowers under rules of the Library.
Brief quotations from this thesis are allowable without special permission,
provided that accurate acknowledgment of source is made. Requests for permission for
extended quotation from or reproduction of this manuscript in whole or in part may be
granted by the head of the major department of the Dean of the Graduate College when in
his or her judgment proposed used of the material is in the interests of scholarship. In all
other instances, however, permission must be obtained from the author.
APPROVAL BY THESIS DIRECTOR
This thesis has 'een approved on the date shown below:
7//7
Paul W. Brown
Specialist, Soil, Water and Environmental Science
IKte
3
ACKNOWLEDGEMENTS
I would like to express my sincere thanks and utmost respect to Dr. Paul Brown.
We spent countless hours revising and reviewing this thesis, and I want to thank him for
his patience and time. I would also like to thank Dr. Donald Post and Dr. Tom
Thompson for their guidance and friendly comments during my career here at the
University of Arizona.
Thanks are also extended to Dr. Peter Wierenga -- always there to lend a helping
hand and provide keen advice. I want to thank Dr. James Riley for getting me started in
teaching. I am grateful to Teles Machibya for providing tremendous help in weekly data
analysis and installing the potable pump system. I thank Randy Post and Blake Elliot for
helping with weekly lysimeter data collection.
I want to sincerely thank two members of my family that aided in a portion of the
statistical analysis and mathematical computations. My Uncle, David Whitlark, was
instrumental in providing me with a method to statistically analyze the Kc data. My
father, Don Whitlark, helped with various computations throughout the study.
Lastly, but certainly not least, I want to send a sincere thank you to the faculty and
staff at the Karsten Desert Turfgrass Research Facility. Dr. David Kopec provided
helpful advice on turf management; namely the late winter overseeding process. Alison
Maricic was kind enough to allow me to utilize the laboratory for storing and analyzing
water samples. Dr. Ken Marcum provided advice on turfgrass salinity issues. And
finally, I want to thank Jeff Gilbert. I do not think I would have completed this thesis
without the help of Jeff. He went out of his way to help me whenever I needed it, and
was always there to share his experience and knowledge in the area of turfgrass and
irrigation management.
4
DEDICATION
I can't think of anyone who deserves this dedication more than my family. Not
only has their financial support been greatly appreciated, their emotional support has
brought me through the thick and thin. I truly am in great debt to them for their
tremendous support and guidance. Thank You! If I forget to show my appreciation in
the future, please refer to this manuscript...at least then I will know someone will have
referred to it!
5
TABLE OF CONTENTS
LIST OF FIGURES 7
LIST OF TABLES 11
LIST OF EQUATIONS 12
ABSTRACT 13
CHAPTER 1 INTRODUCTION 15
CHAPTER 2 LITERATURE REVIEW History of Water Use in Southern Arizona Turf Water Duties Within The Tucson AMA Evaporation and Turf Water Use Evapotranspiration Measurement Lysimetry Irrigation Water Assessments Soil Water Depletion Meteorological Assessment of ET Empirical Methods Reference ET and Crop Coefficients Turf Consumptive Use Studies Salinity and Turfgrass Culture Assessing Soil Salinity Direct Methods Time Domain Reflectometry 18
18
22
28
33
34
38
38
39
40
41
42
50
54
54
56
CHAPTER 3 MATERIALS AND METHODS Lysimeter Facility and Operation Turf and Irrigation Management Water and Salt Balance Procedure Subsurface Salinity Assessment Crop Coefficient (Kc) Assessment 64
64
70
76
78
81
CHAPTER 4 RESULTS AND DISCUSSION Water Balance Salt Balance Assessment of Salinity Profiles Turf Quality Biomass Accumulation and Turf Growth Rates 82
82
95
104
108
115
6
CHAPTER 4 RESULTS AND DISCUSSION (continued)
Crop Coefficients 122
CHAPTER 5 CONCLUSIONS Water Balance Salt Balance Turfgrass Quality Biomass Accumulation and Turfgrass Growth Rates Crop coefficients Scenario Testing 136
136
137
138
138
139
140
APPENDIX A IRRIGATION WATER ANALYSIS 142
APPENDIX B MONTHLY WATER BALANCE DATA 143
APPENDIX C TIME DOMAIN REFLECTOMETRY Comparison of ECa and ECw Comparison of Neutron Probe 0v and TDR Ov 156
156
158
APPENDIX D MANAGEMENT OF LYSIMETER DATA GAPS 163
APPENDIX E LYSIMETER PERFORMANCE
AND RECOMMENDATIONS Lysimeter Performance Lysimeter Recommendations 165
165
168
APPENDIX F CAMPBELL SCIENTIFIC
DATALOGGER PROGRAM 170
REFERENCES 197
7
LIST OF FIGURES
Figure
Page
1.
Percentage of water use in the Tucson Active Management Area supplied by mined
groundwater (GW), renewable groundwater and effluent during 1997. Figure
19
courtesy of Arizona Department of Water Resources 2.
Map showing the location and irrigation water source of the 32 golf courses within
the Tucson Active Management Area. Courtesy of the Arizona Department of
21
Water Resources 3.
Typical TDR output trace used to assess soil volumetric water content. Label A
identifies point where pulse signal enters the soil. Label B identifies point where the
58
reflected signal has returned to the soil entry point 4.
Typical TDR output trace used to assess bulk soil electrical conductivity. Arrows
60
labeled VT and V R depict the transmitted and reflected voltages, respectively. 5.
Plan view of Karsten Desert Turfgrass Research Facility in Tucson, AZ. Turfed
areas are shown in green. The lysimeters are located in the middle of the facility.
65
Labels W and E identify the west and east lysimeters, respectively 6.
Lysimeter scale response for day 170. The vertical axis depicts the depth of water
in mm relative to the midnight volume on day 170. Time of day is presented in
68
decimal format as the fraction of the current day of year 7.
Monthly inputs and outputs of water for the west and east lysimeters for the period
August 1997 to September 1998. Inputs (irrigation + precipitation) and outputs
84
(ETa + drainage) are presented as an equivalent depth of water in mm 8.
Percent difference in monthly turf ET between the west and east lysimeters plotted
as a function of the percent difference in monthly turf growth rate between the west
90
and east lysimeters 9.
Electrical conductivity (EC) of the drainage water obtained from the east and west
99
lysimeters over the course of the study 10.
Components of the salt balance for the east and west lysimeters during the period
August 1997 to September 1998. Monthly inputs of salt from irrigation in grams
are presented as positive values on the y-axis. Monthly outputs of salt from
drainage in grams are presented as negative values on the y-axis. 102
8
11.
Mean bulk soil electrical conductivity (ECa) obtained at depths of 0.5, 1.0 and 2.0 m
105
in the west lysimeter for the period 24 July through 17 Sep. 1998 12.
Mean bulk soil electrical conductivity (ECa) obtained at depths of 1.0 and 2.0 ni in
106
the east lysimeter for the period 24 July through 17 Sep. 1998 13.
NTEP quality ratings of west and east lysimeters over the course of the study.
109
Rating of 6 indicates acceptable quality turf
14.
Turfgrass quality and the total amount of water supplied to west lysimeter turf by
111
month for the period August 1997-September 1998 15.
Turfgrass quality and the total amount of water supplied to east lysimeter turf by
111
month for the period August 1997-September 1998 16.
Monthly mean turfgrass quality ratings from both lysimeters plotted as a function
of water supplied to the turf. Water supplied is presented as a percentage of ETa.
112
The line depicted represents the least squares regression line 17.
Mean monthly turfgrass quality ratings plotted as a function of bulk soil electrical
conductivity obtained at the 0.5 m depth in the west lysimeter. The line depicted is
114
the least squares regression line 18.
Mean monthly turfgrass quality ratings plotted as a function of bulk soil electrical
conductivity obtained at the 1.0 m depth in the east lysimeter. The line depicted is
114
the least squares regression line 19.
Turf biomass collected from the west and east lysimeters over the course of the
116
study. Biomass is presented in grams for both lysimeters ?O.
Monthly growth rates in g/m2 /day for turf grown on the east and west lysimeters
116
between August 1997 and September 1998 21.
Turfgrass growth rates and the total amount of water supplied to both lysimeters by
118
month for the period August 1997-September 1998 22.
Monthly mean turfgrass growth rates for both lysimeters plotted as a function of
water supplied to the turf Water supplied is presented as a percentage of ETa. The
120
line depicted represents the least squares regression line 9
23.
Mean monthly turfgrass growth rates plotted as a function of bulk soil electrical
conductivity obtained at the 0.5 m depth in the west lysimeter. The line depicted is
121
the least squares regression line 24.
Mean monthly turfgrass growth rates plotted as a function of bulk soil electrical
conductivity obtained at the 1.0 m depth in the east lysimeter. The line depicted is
121
the least squares regression line 25.
Comparison of the turf growth rates obtained from the previous study (1996) with
123
those obtained in the current study (1998) for the west lysimeter 26.
Comparison of the turf growth rates obtained from the previous study (1996) with
123
those obtained in the current study (1998) for the east lysimeter 27.
Mean monthly crop coefficients (Kcs) for overseeded intermediate ryegrass grown
on the east lysimeter. The seasonal mean Kc value is depicted by the horizontal
126
arrow 28.
Mean monthly crop coefficients (Kcs) for overseeded intermediate ryegrass grown
on the west lysimeter. The seasonal mean Kc value is depicted by the horizontal
126
arrow 29.
Mean monthly crop coefficients (Kcs) for bermudagrass grown on the east
127
lysimeter. The seasonal mean Kc value is depicted by the horizontal arrow 30.
Mean monthly crop coefficients (Kcs) for bermudagrass grown on the west
127
lysimeter. The seasonal mean Kc value is depicted by the horizontal arrow 31.
Mean daily crop coefficients (Kcs) by month for intermediate ryegrass grown on the
east lysimeter. Error bars represent the standard deviation of the mean daily Kc .129
32.
Mean daily crop coefficients (Kcs) by month for intermediate ryegrass grown on the
west lysimeter. Error bars represent the standard deviation of the mean daily Kc 129
33.
Mean daily crop coefficients (Kcs) by month for bermudagrass grown on the east
130
lysimeter. Error bars represent the standard deviation of the mean daily Kc 34.
Mean daily crop coefficients (Kcs) by month for bermudagrass grown on the west
130
lysimeter. Error bars represent the standard deviation of the mean daily Kc 35.
Coefficient of variation of mean daily crop coefficients computed using all days in a
131
month and only dry days (no rain) 10
36.
Daily values of turf ET from the east lysimeter versus daily ETo for the period
November 1997 through April 1998. The line depicted is the least squares
133
regression line with intercept forced through zero 37.
Daily values of turf ET from the west lysimeter versus daily ETo for the period
November 1997 through April 1998. The line depicted is the least squares
133
regression line with intercept forced through zero 38.
Daily values of turf ET from the east lysimeter versus daily ETo for the period May
through September 1998. The line depicted is the least squares regression line with
134
intercept forced through zero 39.
Daily values of turf ET from the west lysimeter versus daily ETo for the period May
through September 1998. The line depicted is the least squares regression line with
134
intercept forced through zero 40.
Soil solution salinity (ECw) plotted as a function of bulk soil salinity (ECa) for the
period August 1997 to September 1998 for the west lysimeter at the 1.0 m depth.
157
The line depicted represents the least squares regression line 41.
Soil solution salinity (ECw) plotted as a function of bulk soil salinity (ECa) for the
period August 1997 to September 1998 for the east lysimeter at the 1.0 m depth.
157
The line depicted represents the least squares regression line 42.
Soil solution salinity (ECw) plotted as a function of bulk soil salinity (ECa) for the
period August 1997 to September 1998 for both lysimeters at all depths. The line
159
depicted represents the least squares regression line 43.
Volumetric water content (theta) derived from time domain reflectometry (TDR)
plotted as a function of theta derived from neutron attenuation for the period August
1997 to September 1998 for both lysimeters at all depths. The line depicted
represents the least squares regression line 161
11
LIST OF TABLES
Table
Page
1.
Salinity tolerance of commonly used cool season and warm season turfgrasses in
55
southern Arizona
2.
Fertilizer application dates, rates and the type of fertilizer applied to both east and
72
west lysimeters over the course of the study 3.
Seasonal crop coefficients (Kcs) applied during summer (15 June-13 Oct.) and
winter (5 Nov.-4 June) seasons and Kcs applied during fall overseed (14 Oct.-4
75
Nov.) and spring transition (5 June-14 June) 4.
Components of the water balance by month in equivalent depth of water for the east
and west lysimeters for the period August 1997-September 1998. Totals represent
83
the sum of individual monthly values for the 12 months ending 30 Sept. 1998 5.
Maximum (Tmax) and minimum temperatures (Tmin), precipitation (ppt.) and
reference evapotranspiration (ETo) measured over the course of the study by the
AZMET weather station located at the Karsten Desert Turfgrass Research Facility.
Temperatures are reported as monthly means and precipitation and ETo are reported
as totals. Departure from normal (+ / - Normal) compares monthly values to mean
values for the period 1987-1997 86
6.
Total salt inputs (through irrigation) in grams of salt applied, total salt outputs
removed through drainage over the course of the study, calculated from October
1997 to September 1998. Values indicate totals for the study period. Total water
applied indicates the summation of rainfall and irrigation. The leaching fraction
indicates the total drainage divided by the total water applied 96
7.
Month-by-month assessment of the salt balance for the east lysimeter over the
course of the study. Monthly totals indicate either salt input through irrigation, salt
output removed through drainage or the net balance between inputs and outputs. 101
8.
Month-by-month assessment of the salt balance for the west lysimeter over the
course of the study. Monthly totals indicate either salt input through irrigation, salt
output removed through drainage or the net balance between inputs and outputs .101
9.
Monthly crop coefficients computed using ETa data obtained from the east and west
lysimeters, and reference ET data provided by the Arizona Meteorological Network.
124
12
Equation
LIST OF EQUATIONS
Page
1.
Water Balance 33
2.
Modified water balance (solved for ET) 33
3.
Energy balance 39
4.
Modified energy balance (solved for latent heat) 40
5.
Bowen ratio 40
6.
Crop coefficient 41
7.
Leaching fraction 52
8.
Leaching requirement 53
9.
Dielectric constant 57
10.
Volumetric water content 57
11.
Expression of bulk soil electrical conductivity related to the bulk liquid phase
59
conductivity and the bulk surface conductivity 12.
Bulk soil electrical conductivity related to the saturation percentage 59
13.
Derivation of bulk soil electrical conductivity from TDR output 59
14.
Relationship between ECa and ECw for unsaturated soils 61
15.
Empirical relationship for the transmission coefficient 62
16.
Time domain reflectometry pulse travel time 80
17.
Statistical model used to assess Kc values 81
18.
East lysimeter rainfall adjustment 163
19.
West lysimeter rainfall adjustment 163
13
ABSTRACT
Water is an essential resource that requires careful management at all golf courses
located in southern Arizona. The Arizona Department of Water Resources, through its
enforcement of irrigation water duties, is forcing the golf industry to reduce water usage.
The objective of this study was to evaluate turfgrass performance and the potential for
soil salinization, when high maintenance desert turf systems are irrigated in accordance
with the present Tucson area water duty of 1.4 ha-m/ha/yr (4.6 ac-ft/ac/yr). Two large
weighing lysimeters supporting year-round turf systems consisting of bermudagrass
(Cynodon dactylon x transvalensis (L.) pers.) overseeded with intermediate ryegrass
(Lolium multiflorum x perenne) were irrigated at rates not to exceed the ADWR water
duty using either low salinity (EC = 0.25 dS/m) groundwater or higher salinity effluent
water (EC = 1.0 dS/m). Irrigation treatments were initiated in August 1997 and
continued through September 1998 and consisted of applying water daily at rates set by
applying appropriate crop coefficients to values of reference evapotranspiration generated
by an on-site weather station. Soil moisture and salinity regimes were monitored weekly
using the lysimeter subsurface sampling system and time domain reflectometry (TDR).
Water percolating below the root zone was quantified and sub-sampled to facilitate
assessment of leaching fractions and total lysimeter salt balance. For the year ending 30
Sept. 1998, each lysimeter received —1729 mm (68 in.) of water comprised of 1296 mm
(51 in.) of irrigation water and 433 mm (17 in.) of precipitation. Turfgrass
evapotranspiration (ET) totaled 1419 mm (56 in.) for the lysimeter irrigated with
14
groundwater (east lysimeter) and 1466 mm (58 in.) for the lysimeter irrigated with
effluent (west lysimeter). Approximately 421 and 311 mm of drainage water was
removed from the east and west lysimeters, respectively, establishing leaching fractions
of 0.24 in the east lysimeter and 0.18 in the west lysimeter. Salts accumulated in both
lysimeters over the course of the study; however, the substantial amount of drainage did
not allow for salts to accumulate to harmful levels. Turfgrass performance, as quantified
by turf quality and growth was acceptable or better during most months of the study.
Crop coefficients (Kcs) were slightly higher than previous years, however, Kcs compared
favorably to previous research at the study site. Summer Kcs averaged 0.79 and were
significantly higher than winter Kcs that averaged 0.73. Turf irrigated with effluent
produced better quality turf and used slightly more water than turf irrigated with
groundwater. These data indicate that the present ADWR water duty of 1.4 ha-m/ha/yr
(4.6 acre-ft/acre/yr) is adequate to replace turfgrass evapotranspiration and provide for
leaching of salts when rainfall exceeds normal amounts, assuming no water loss due to
irrigation and plumbing inefficiencies.
15
CHAPTER 1
INTRODUCTION
Water is an essential production input at all golf courses located in southern
Arizona. Golf courses in the Tucson area require large quantities of irrigation water due
in large part to the arid climate which allows for year round production of turf yet
provides limited precipitation to offset high rates of evaporative demand. Both economic
and regulatory pressures are driving the golf industry to conserve water. Economic
pressures result from the increasing cost of water, while the regulatory pressures come
from the Arizona Department of Water Resources (ADWR) through its implementation
and enforcement of the 1980 Groundwater Management (GWMA). The primary
objective of the GWMA is to reduce groundwater overdraft in the five Active
Management Areas (AMAs) -- geographical areas designated for intense management
due to the large imbalance between the groundwater consumed and the dependable
supply. Total water use in the Tucson AMA presently exceeds annual groundwater
recharge by approximately 50 percent. This chronic overuse of groundwater has reduced
the static levels of local aquifers by approximately 50 m and increases the threat of
land subsidence.
Groundwater overdraft in the Tucson and other AMAs has led ADWR to develop
water conservation plans for all industries and municipalities that use substantial amounts
of groundwater. An important aspect of the conservation plans established for Tucson
area turf facilities is the water duty which represents the maximum amount of
16
groundwater a turf facility may use for irrigation. The present water duty for Tucson turf
facilities provides 1.4 ha-m/ha (4.6 acre-ft/acre) of water per year to an 18 hole golf
course with 36.5 ha of turf There is growing pressure from the public and ADWR to
reduce this duty further in future years. The golf industry and developers claim that the
current duty is inadequate and a further reduction in the water duty will reduce turf
quality and/or turf acreage, and possibly lead to soil salinization.
Recent research conducted at the Karsten Desert Turf Facility in Tucson lends
credence to industry concerns (Brown and Kopec, 1997; Brown et al. 1996). These
studies indicate the water duty is adequate to support high quality turf provided the entire
duty can be applied to the turf (no inefficiencies in application) and rainfall runs near
normal. However, these studies were unable to address industry concerns regarding: (1)
application inefficiencies inherent in all commercial irrigation systems, (2) the adequacy
of the duty to provide sufficient water for salinity management and (3) the adequacy of
the duty in dry years.
The study described in this thesis was designed to address the industry concerns
regarding the adequacy of the present turf water duty to (1) generate acceptable turf
performance in both wet and dry years and (2) provide adequate water to facilitate the
necessary leaching of salts from the turf root zone. Large weighing lysimeters supporting
year round green turf were irrigated with local groundwater and effluent at rates not to
exceed ADWR's water duty of 1.4 ha-m/ha/yr. Turf performance, turf water
requirements and the soil water and salinity balances were monitored to address the
17
aforementioned industry concerns regarding the adequacy of current turf water duties.
The results presented in this thesis summarize the findings from the first year of a
planned three year study.
18
CHAPTER 2
LITERATURE REVIEW
History of Water Use in Southern Arizona
Groundwater management in Arizona has been a recurring problem since the
1930s. The major problem lies in the imbalance between the amount of water consumed,
and the amount that is naturally or artificially recharged. About 60% of the total water
consumed in Arizona comes from underground aquifers, and, each year, Arizonans
consume approximately 305,750 ha-m (2.5 million acre-ft) more groundwater than is
recharged. Within the Tucson AMA, total water demand has climbed to 38,402 ha-m
(314,000 acre-ft) per year. More than 50% of that water demand is served by mined
groundwater (Figure 1; ADWR, 1997). Groundwater overdraft has lowered the static
level of underground aquifers by as much as 52 m (170 ft) since 1940, causing land
subsidence and water quality problems (ADWR, 1984).
In order to deter groundwater overdraft, the Arizona legislature enacted the
Groundwater Management Act (GWMA) in 1980. The GWMA established five Active
Management Areas (AMA's) including Tucson, Pinal, Phoenix, Santa Cruz and Prescott.
These are geographical areas where serious groundwater overdraft is occurring. The
GWMA created the state agency Arizona Department of Water Resources (hereafter
referred to as "the Department" or ADWR) to develop and implement all state water
legislation and secure long term water supplies for Arizona's AMA's (Eden and Wallace,
1992). In the Tucson, Phoenix, Prescott and Santa Cruz AMA's, ADWR's primary
19
Renewable GW (42.60%)
134,000 AF
Mined GW P.79%)
169,000 AF
10,138 AF of CAP water delivered to
agriculture through in-lieu recharge is legally
accounted for as mined groundwater.
Figure 1. Percentage of water use in the Tucson Active Management Area
supplied by
mined groundwater (GW), renewable groundwater and effluent during 1997. Figure
courtesy of Arizona Department of Water Resources.
20
management objective is to achieve "safe-yield" by 2025 through a series of water
management plans. The management goal for the Pinal AMA is to preserve the
agricultural industry for as long as feasible while preserving groundwater for future nonirrigation uses.
Safe-yield is defined as "a groundwater management goal which attempts to
achieve and thereafter maintain a long term balance between the annual amount of
groundwater withdrawn in an Active Management Area and the annual amount of natural
and artificial recharge in the AMA." (ADWR, 1984). The Department is presently
employing the second management plan which is scheduled to end in 1999; the third
management plan will be implemented in the year 2000. Within these management plans
are conservation programs for agriculture, municipalities, and industrial use. Turf-related
facilities are classified as industrial water users; thus turf facilities must comply with
plans set forth under the Industrial Conservation Program.
The 32 golf courses located within the Tucson AMA (TAMA) used 1,976 ha-m
(16,153 acre-ft) of water (groundwater and effluent) to irrigate 1.309 ha (3,233 acres) of
turf in 1995. Thirty five percent of the total water demand was satisfied with effluent
water which was supplied to thirteen golf facilities. Figure 2 depicts the locations of golf
facilities within the TAMA (ADWR, 1997). The Department reported the average
application rate for all golf courses in 1995 was 1.42 ha-m/ha (4.7 acre-ft/acre) per year;
golf courses using only groundwater for irrigation used on average 1.52 ha-m/ha (5 acreft/acre; David Johnson, ADWR, 1998, personal communication). Water use by the golf
21
Figure 2. Map showing the location and irrigation water source of the 32 golf courses
within the Tucson Active Management Area. Courtesy of the Arizona Department of
Water Resources_
22
industry is expected to grow in subsequent years. The Department projects an additional
23 golf courses will be built within the TAMA by 2025, increasing water use by turfrelated facilities to approximately 3,914 ha-m (32,000 acre-ft), compared to the 1,976 ham used in 1995. Up to 85% of total water demand by turf facilities is projected to be met
by alternative water sources in 2025, the primary one being effluent.
Turf Water Duties Within The Tucson AMA
The first management (FMP) plan instituted by ADWR covered a ten year period
ending in 1990. The Industrial Conservation Program of the FMP included a catch all
conservation requirement stating,
For industrial uses including industrial uses within the exterior boundaries of the
service area of a city, town, private water company or irrigation district, the
program shall require use of the latest commercially available conservation
technology consistent with reasonable economic return. (ADWR, 1984)
Conservation requirements were to be met by 31 Dec. 1986. One such
requirement involved a cap on the amount of water per unit area allocated to golf courses
within the TAMA. This water allotment is referred to as a water duty. The initial water
duty instituted for the FMP was 1.52 ha-m/ha (5.0 acre-ft/acre) per year for existing turfrelated facilities and 1.46 ha-m/ha (4.8 acre-ft/acre) per year for new turf-related facilities
(ADWR, 1984). An existing turf-related facility was one in existence or in operation
before the effective date of the FMP. Additional water (1.76 ha-m/ha; 5.8 acre-ft/acre)
was allocated for filling/refilling surface water bodies. Also, turf-related facilities had the
23
option to apply for a modification of the total water allotment if technical difficulties
caused by using effluent water were evident. The Department used five factors to derive
turf facility water duties for the FMP (ADWR, 1984):
1.
Number of acres in water-demanding landscaping;
2.
Number of acres of water surface;
3.
Type of vegetation grown;
4.
Germination requirements of new turf;
5.
Efficiency of water application.
The Department points out that the most important factor with respect to water
use is the number of acres with water demanding landscape. Although ADWR does not
regulate the total acres a turf facility manages, they do regulate the total water allocation.
A new turf-related facility under the FMP would receive 3.06 ha-m (24.8 acre-ft) of water
per golf hole. This total water allotment was derived from the product of 1.46 ha-m/ha
(4.8 acre-ft) x 2 turf-ha (5 turf-acres) per golf hole plus 0.10 ha-m (0.8 acre-ft) allocated
for surface water. Most golf courses have an eighteen hole layout approximating ninety
acres of irrigated turf. Thus, ADWR regulations allowed for approximately 55 ha-m (446
acre-ft) of water for golf courses regulated as new turf-facilities under the FMP (David
Johnson, ADWR, 1998, personal communication). Golf course designers and managers
realize the implications the number of irrigated acres have on the amount of water applied
per unit area of turf. Hence, the trend for golf courses in the southwest has been to
subscribe to the "target golf' design philosophy which involves reduced turf area
24
compared to golf courses using traditional designs. A reduction in turfed acreage allows
for greater amounts of water applied per unit area.
The Department listed the type of vegetation and the water requirements for new
vegetation as factors impacting the determination of the water duty for the FMP. Most
turf-related facilities within the Tucson AMA manage bermudagrass as the dominant
grass species. Since bermudagrass has a winter dormancy period, many facilities
overseed with ryegrass to provide for year-round green turf. The Department did not
openly recognize the water requirements of ryegrass in developing the water duties,
however, bermudagrass consumptive use was recognized and listed as 1.1 ha-m/ha (3.5
acre-ft/acre). Newly turfed areas were to receive additional water for germination and
establishment (overseed areas do not qualify as new turf). Existing facilities received
1.82 ha-ni/ha (6.0 acre-ft/acre) and new facilities received 1.76 ha-ni/ha (5.8 acre-ft/acre)
for newly turfed areas in addition to their annual allotment.
The efficiency of the water applied was also listed as a factor affecting the
derivation of the water duty. The Department realizes that irrigation systems vary in
efficiency, however, additional water is not allocated for turf managers to account for
irrigation non-uniformities and plumbing losses.
Absent from the above list is (1) the excess water required for establishment of
winter overseeded turf and general turf maintenance, and (2) the excess water needed to
obtain an adequate leaching fraction. ADWR has a provision to provide additional water
for leaching. The provision is based on standard procedures for computing a leaching
25
fraction, however, the provision requires that the irrigation water contains a minimum
dissolved salt content of 1000 mg/liter. The leaching fraction requirement will be
discussed in more detail under salinity concerns.
The second management plan (SMP) was instituted in 1990 and will continue
through the year 1999 (ADWR, 1991). Development of the SMP involved a reevaluation
of the water duties set by the FMP. The section of the SMP related to turf water duties
was developed based on a review of scientific literature, surveys, and on site visits.
Sources for the literature review primarily came from southern California and southern
Arizona including Erie et al., 1981; Kneebone and Pepper, 1982, 1984; and Marsh et al.,
1980a. In reviewing the aforementioned sources, the following information was
considered.
•
Actual water figures collected from over 400 turf-related facilities
regulated under the first management plan
•
The consumptive use of the grass species most frequently used
•
Water application efficiency achievable with currently available
technology
•
Evaporative losses from lakes based on pond-evaporation data
•
Management practices and technologies currently in place
•
Conservation potential associated with additional technologies, practices
and design alternatives
•
Germination requirements for establishing new turf
Again, absent from this information is (1) additional water required for winter overseed
establishment and general turf maintenance, and (2) excess water needed to obtain an
26
adequate leaching fraction. Leaching allotments remained unchanged and were available
only if the TDS of irrigation water exceeded 1000 mg/l.
In most cases water duties were reduced during the SMP. Existing and new
Tucson area facilities that qualified for the first management plan were required to meet a
maximum water allotment of 1.4 ha-m/ha (4.6 acre-ft/acre) per year by the year 2000,
whereas new turf facilities that qualified for the second management plan had to meet the
1.4 ha-ni/ha per year water duty by the year 1992 (ADWR, 1991). An added contingency
to the SMP involved segregating low water use landscaped areas from high water use turf
areas. The SMP allowed 0.18 ha-m (1.5 acre-ft) of water for low water use landscaped
areas. Also, as an incentive to increase effluent use, the Department provides allotment
modifications (incentives) based on the percent water use met by effluent. The effluent
incentive states that each 0.123 ha-m (1.0 acre-ft) of effluent used by the turf facility for
landscape watering purposes counts as 0.117 ha-m (0.95 acre-ft) of water if effluent
accounts for 50-89% of the total water used, or the total allotment (whichever is
less) for
the year. The Department counts each 0.123 ha-m of effluent used by the turf facility as
0.111 ha-m (0.90 acre-ft) of water if the total effluent used by the turf facility for the year
is greater than 90% of their total water use or their total water allotment, whichever is
less. Although the effluent incentive does not change the total water allotment, a
using >90% effluent water to meet their water demands could apply up to
acre-ft) per year as opposed to 1.4 ha-m (4.6 acre-ft) per year.
facility
1.55 ha-m (5.1
27
The third management plan (currently in draft form) will be implemented in the
year 2000 and continue through 2010 (ADWR, 1998). Water duties projected for the
third management plan (TMP) remain at 1.4 ha-m/ha (4.6 acre-ft/acre) for turfed acres,
1.76 ha-ni/ha (5.8 acre-ft/acre) for water surface acres and 0.46 ha-ni/ha (1.5 acre-ft/acre)
for low water use landscaped areas. The maximum annual water allotment is the same as
the SMP at 2.93 ha-m (23.8 acre-ft) of water per golf hole.
Similar to the previous plans, the draft of the TMP includes "allotment additions"
which allow for turf facilities to receive additional water for specific cases. These
specific cases include allotments for a reduction in turfed acreage, establishment of newly
turfed area (does not qualify as winter overseeding), revegetation of low water use plants,
filling bodies of water, leaching of salts and allotment modifications based on effluent
use. Due to the underutilization of effluent, the effluent incentive was modified in the
third management plan. The maximum water allotment did not change, however, each
0.123 ha-m of effluent water used will count as 0.09 ha-m (0.7 acre-ft) when turf facilities
are in compliance with the maximum annual water allotment (ADWR, 1998).
It is debatable whether some of these special case water allotments should be
included in the maximum annual water allotment. The Department stresses the need for
increased use of effluent and Central Arizona Project (CAP) water in the future if
conservation requirements are to be met. Long-term use of effluent and CAP water will
likely lead to build-up of salts and will require additional water to flush those salts below
the root zone. The Department recognizes that further research is required to quantify the
28
long-term impacts of using water high in salts and to determine its affects on future water
application rates.
Evaporation and Turf Water Use
It is evident that water conservation is imperative to secure long term water
supplies to ensure future growth and continued commercial success of southern Arizona's
golf industry. It is therefore important to understand the water requirements and salinity
tolerance of high maintenance turf systems in order to implement water duties that
encourage water conservation yet provide acceptable turf quality.
Evapotranspiration (ET) is defined as the loss of water from vegetation through
the combined processes of soil evaporation and plant transpiration. Effective irrigation
management therefore requires an understanding of ET in order to determine the amount
and timing of irrigation. Consumptive use includes ET plus the water retained in the
plant tissue (Jensen et al., 1990). Under most circumstances the water in the plant tissue
is nominal relative to daily ET and does not change appreciably from one day to the next,
thus ET and consumptive use are essentially equal and the terms are often used
interchangeably.
Both environmental and biological factors combine to influence ET (Beard,
1985). Environmental factors include soil water content and meteorological conditions
which provide the energy and transport mechanisms necessary to evaporate and transfer
water vapor from the plant-soil system. Biological factors include the plant type and the
stage of development.
29
Soil moisture is the most important environmental factor impacting ET since
without soil moisture there is no water available for evapotranspiration (Kramer and
Boyer, 1995). When soil moisture is non-limiting, ET is a function of meteorological
factors or evaporative demand and biological factors such as size and type of plant,
however, when soil moisture levels decline below field capacity ET becomes much more
dependent on the soil moisture availability. Studies by Kneebone and Pepper (1982,
1984) have shown that turf ET is directly related to available soil moisture.
Four meteorological variables - - net radiation, air temperature, windspeed and
relative humidity impact evaporation and thus contribute to evaporative demand. Net
radiation is derived from a combination of solar and thermal components and represents a
key source of energy necessary for evaporation. The solar component of net radiation is
made up of incoming radiation from the sun termed solar radiation minus that portion of
solar radiation that is reflected from the surface. Net thermal radiation is described as a
balance between the incoming longwave radiation emitted by the earth's atmosphere
outgoing longwave radiation which is emitted by the earth's surface (Kramer and
and
Boyer,
1995). Net radiation is simply the sum of the solar and thermal components.
On most days solar radiation is the dominant component of net radiation; thus,
solar radiation is the primary meteorological factor affecting ET when soil moisture is
increased
available (Kneebone et al., 1992). Feldhake et al., (1983) found that turf ET
in
linearly with solar radiation. Solar radiation is most intense during the summer months
is at its peak during this period.
the northern hemisphere, especially in the desert, thus ET
30
Air temperature and horizontal wind movement provide the means by which
sensible heat is transferred between the leaf and the environment. Sensible heat
represents a source of energy for evaporation and moves to or from vegetation by the
processes of diffusion, convection and advection (Kramer and Boyer, 1995). Diffusion
occurs in unstirred air due to differences in air temperature between the plant and
surrounding air. Convection refers to the sensible heat transfer by the bulk movement of
air due to temperature-induced density differences in the air. Advection occurs due to
bulk movement of air caused by horizontal wind movement (Rosenberg, 1974). Wind
can also decrease the leaf boundary layer which increases the rate at which water is lost
from the plant and transported to the surrounding air (Kramer and Boyer, 1995).
Humidity impacts ET by establishing the vapor gradient from the leaf surface to
the atmosphere. This gradient is referred to as the vapor pressure deficit (VPD), and is a
function of canopy and air temperature, and atmospheric vapor pressure. High
temperatures and low atmospheric humidities result in high VPDs (Brown, 1999a), which
in turn lead to accelerated rates of ET. Lower temperatures and higher humidities reduce
VPD and lessen ET.
The biological factor that has the greatest potential influence on ET is the plant
type or plant species. Grass species can use upwards of 12.5 mm per day when grown
under high evaporative conditions and provided with ample soil moisture (Kneebone and
Pepper, 1984), whereas native desert plant species can live on rainfall alone
(approximately 30 cm per year). Differing grass species vary in consumptive use values.
31
In Tucson, AZ, annual consumptive use values for Tifgreen bermudagrass, St.
Augustinegrass and tall fescue were determined to be 1635 mm, 1657 mm and 1817 mm,
respectively, under non-limiting soil-moisture conditions (Kneebone and Pepper, 1982).
Cool season grasses typically have higher rates of ET of than warm season grasses when
grown under similar conditions (Biran et al., 1981; Kneebone and Pepper, 1982; and
Feldhake et al., 1983). Feldhake et al. (1983) determined that Kentucky bluegrass and
tall fescue which are cool season grasses utilized over 20% more water than
bermudagrass and buffalograss which are warm season grasses. Biran et al. (1981)
studied the effects of mowing height, irrigation frequency and soil moisture on ET and
found that the single most important factor affecting ET was whether the grass was cool
season or warm season. They observed the ET of the cool season grasses were 45%
higher than ET for warm season grasses.
Another biological factor affecting ET is the relative growing activity of the plant.
A dormant plant with little or no green foliage will use considerably less water than an
actively growing plant. Bermudagrass and buffalograss represent typical southwest
turfgrasses that display a distinct seasonal (winter) dormancy that impacts ET. Under a
deep and infrequent irrigation regime (irrigation applied only when turf showed
symptoms of wilt), mean annual consumptive use for bermudagrass over a three year
period was 879 mm (Garrot and Mancino, 1994). However, when bermudagrass is
overseeded with a cool season grass to sustain green turf during winter months, annual
consumptive use values in excess of 1500 mm have been reported (Brown et al., 1998;
32
Kneebone and Pepper, 1982; Kopec et al. 1991). Grass that is growing vigorously will
have higher ET rates than a slow growing grass. K_rogman (1967) observed a direct
relationship between ET and turf growth rates, which resulted from increased rates of
nitrogen (N) fertility. Likewise, Feldhake et al. (1983, 1984) reported that Kentucky
Bluegrass produced higher growth rates and used 13% more water when subjected to an
adequate N fertility regime of 4 kg of N/1000 m 2 /month compared to a deficit level of 4
kg of N/1000 m2 /year.
Other biological factors affecting ET include turf height and density. Increased
mowing heights will increase ET (Shearman and Beard, 1973, Biran et al., 1981, and
Johns et al., 1983). Biran et al. (1981) found that increasing the mowing height from 3 to
6 cm resulted in a significant increase in water consumption in cool season grasses and a
slight increase in warm season grasses. Water consumption for cool season grasses
increased by 29 and 25% for tall fescue and perennial ryegrass, respectively. Shearman
and Beard (1973) determined that an increase in mowing height from 0.75 to 2.5 cm
increased the ET by 50% in Pencross' creeping bentgrass.
A reduction in turf density may actually increase ET rates. As turf density
declines surface roughness increases, thus decreasing the leaf boundary layer resulting in
a subsequent increase in turf ET (Kramer and Boyer, 1995). In Kentucky bluegrass,
shoot density was determined to be negatively correlated with ET, while the vertical leaf
extension rate was positively correlated with ET (Shearman, 1986). Ebdon and Petrovic
(1998) determined that turf with a lower ET rate was associated with more horizontal leaf
33
orientation, narrower leaf texture, more lateral shoots per plant, shorter leaf blades and
sheath, and more leaves per shoot than the higher water use turf.
Evapotranspiration Measurement
Various methods exist for measuring ET (Kneebone et al., 1992). Common
methods include (1) utilizing lysimeters to measure the amount of water lost by
evaporation and transpiration, (2) measuring the amount of water applied to field crops,
(3) measuring the change in soil water content over a period of time, and (4)
meteorological techniques. The first three methods listed utilize the water balance
method to measure ET. The water balance equation is given below:
P + I — ET — R — D = ASWS [Eq.1]
where P is precipitation, I is irrigation, R is runoff, D is drainage out of the soil profile
and ASWS is the change in soil water storage. Rearrangement of Equation [1] provides
the following:
ET = P + I —D — R — ASWS [Eq.2]
which allows computation of ET provided one can quantify P, I, D, R and ASWS.
the most
Quantitation of all the variables on the right side of Equation [2] will provide
accurate appraisal of ET, however, if terms R, D and ASWS are
assumed negligible, an
al.,
estimation of ET can be obtained simply by quantifying terms I and P (Marsh et
1980a).
34
Lysimetry
A lysimeter is a receptacle that contains soil as the medium by which a particular
plant species can be irrigated and fertilized in a contained system in order to assess water
use requirements (Tanner, 1967). Lysimeters have the potential to measure every
parameter in the water balance equation, thus they serve as an excellent tool for
measuring ET. Lysimeters have been used extensively to provide information on water
quality (Kirkham et al., 1984), root growth (Dugas et al., 1985), and validation and
quantitation of evapotranspiration (Tovey et al., 1969; Kneebone and Pepper, 1982,
1984).
There are two general types of lysimeters in use today: percolation and weighing
lysimeters (Kneebone et al. 1992). Percolation lysimeters rely on the change in water
storage, or the inflow minus the outflow to determine ET. The inflow-outflow method
relies on measurement and quantitation of the inflow: precipitation (P) and irrigation (I),
and the outflow: drainage (D) to estimate ET. Percolation lysimeters measure
ET most
effectively over time periods of a week or longer due to the lag time between irrigation
and the resulting leachate obtained from percolation.
Tovey et al. (1969) used percolation lysimeters and employed the inflow-outflow
method to measure actual ET of lawngrass and bermudagrass. Percolation lysimeters
were used by Kneebone and Pepper (1984) to measure luxury water use on
and the effect of very high water application rates on consumptive
bermudagrass
use. Gee et al. (1994)
and deep percolation. A
used percolation lysimeters to monitor shallow water movement
35
variant of the standard percolation lysimeter is one that uses Marriotte siphons to deliver
subirrigation. Kneebone and Pepper (1982) used this technique to evaluate consumptive
use of turfgrasses. The subirrigation technique relies on measurement of the loss of water
from the Marriotte siphon container rather than the difference between water applied and
water lost to drainage.
In contrast to percolation lysimeters, weighing lysimeters are capable of
quantifying accurate ET measurements over much shorter time intervals. Weighing
lysimeters measure changes in mass that can be attributed directly to plant water use.
Weighing lysimeters were introduced in order to perform precise measurements of
precipitation, irrigation, evaporation and drainage events (Tanner, 1967)
The simplest weighing lysimeters (micro-lysimeters) consist of pots weighed at
regular intervals under different water regimes. Micro-lysimeters (-20 cm in depth) can
be used to measure potential or maximum ET rates. These micro-lysimeters are
commonly constructed of PVC pipe and are filled with calcined clay or a fine silica sand
to facilitate drainage, yet provide for adequate soil moisture-holding capacity (Kopec
et
al., 1988). Rogowski and Jacoby (1977) measured and observed distribution patterns of
ET using micro-lysimeters in order to develop a model for predicting ET in the field.
Micro-lysimeters were used by Kopec et al. (1988) to measure ET of tall fescue turf
ET
under field conditions. Johns et al., (1983) used mini-lysimeters to measure potential
of St. Augustinegrass utilizing the water balance method.
36
A slightly more complicated weighing lysimeter was utilized by Feldhake et al.
(1983, 1984) to measure turf ET where lysimeters were constructed of large PVC pipe
and inserted into PVC cylinders set into the turf. Even more complicated (and more
expensive) weighing lysimeters are constructed of an inner and an outer lysimeter box
fabricated from steel. The inner lysimeter box rests on a scale to provide for frequent
monitoring of lysimeter mass changes. Sensitivity of these lysimeter systems can
typically resolve better than 0.05 mm (0.002 in.) of water. Dugas et al. (1985) used
sensitive weighing lysimeters to study crop root densities and compare lysimeter ET to
calculated Penman ET. Weighing lysimeters were installed at the Department of
Energy's Hanford site in Washington state to determine the water balance at hazardous
waste sites, and to measure ET and calculate crop coefficients (Kirkham et al., 1984).
Lysimeters, while considered an excellent tool for measuring ET, are prone to
error, if not managed or operated correctly. Some sources of error include evaporative
and vegetative areas on the lysimeter, height of the lysimeter rim above the surrounding
area, thickness of the rim, height and density differences between the lysimeter vegetation
and surrounding vegetation, inadequate fetch surrounding the tanks, and shallow
lysimeter soil profiles (Allen et al., 1991)
An accurate appraisal of the evaporative area on the lysimeter is essential to
determine ET. Evapotranspiration measurements will be erroneously high if lysimeter
vegetation is allowed to overhang beyond the lysimeter rim (Allen at al., 1991). For
example, the mini-lysimeters used by Kopec et al. (1988) had a radius of 10 cm. If
37
lysimeter vegetation was allowed to grow beyond the lysimeter rim extending the
effective radius (that which is covered by vegetation) by only 1 cm, a 17% error in ET
measurement would result. Overhang errors typically decrease when lysimeter surface
area increases; thus, most weighing lysimeters are designed with a large surface area.
A lysimeter rim that is too tall or too wide can block wind, absorb heat or reflect
radiation to the lysimeter vegetative surface and thus impact ET (Allen et al., 1991).
Increased vegetation height on the lysimeter surface can increase ET compared to
surrounding vegetation by increasing radiative transfer. A decrease in the density of
lysimeter vegetation relative to surrounding vegetation may increase ET due to a decrease
in the leaf boundary layer (increased wind transfer). Inadequate fetch results in increased
advective transfer (edge-effect).
Shallow lysimeters can affect rooting depth, soil-water availability and thermal
conditions of the soil (Marek et al., 1988; Allen et al., 1991). Deep weighing lysimeters
have been developed to avoid these sources of error. Dugas et al. (1985) developed
lysimeters 2.7 m in depth to provide adequate room for root development. Large
weighing lysimeters , 2.5 m in diameter and 4.0 m deep were installed at the University
of Arizona Karsten Desert Turfgrass Research Facility to assess water use and deep
percolation in a deep soil profile. (Young et al., 1996).
Lysimeters have been used extensively in the past and will continue to play an
integral role in determining ET of crops in the future. Environmental conditions must be
taken into account when managing and interpreting lysimeter data. The problems which
38
beset lysimeters can be deterred by proper design, installation, management and
operation.
Irrigation Water Assessments
Evapotranspiration can be estimated through quantitation of applied irrigation
water. In this case, terms P and I of Equation [2] are quantified, while terms R, D, and
ASWS are assumed to be negligible. Evapotranspiration is assumed to equal the quantity
of water applied to the turf. Accurate irrigation is required to minimize R and D and the
technique is best suited for monthly and seasonal time-frames to minimize the impact of
changes in ASWS on ET. In irrigated turf systems, a significant portion of the applied
water may drain from the upper soil profile, which is neglected in this method, thus ET
evaluation is not as quantitative as those methods that measure all components of the
water balance equation. Marsh et al. (1980) used this procedure to assess consumptive
use of two warm season grasses and two cool season grasses in California; tensiometers
were used to control irrigation and minimize D.
Soil Water Depletion
The most widely used method of measuring ET is to monitor the change in soil
water content over time (Meyer and Gibeault, 1986, 1987; Frank et al. 1987).
Evapotranspiration is estimated from Eq. [2] by making soil water depletion
measurements to obtain ASWS and assuming R and D, I and P are known or negligible.
Soil water depletion techniques are commonly used to estimate ET of row crops,
39
especially in arid regions where rain is uncommon and irrigations are infrequent.
Typically, soil water measurements are obtained two to three days after irrigation, then
again one day prior to the following irrigation. The lag time between irrigations is
commonly one to three weeks, which allows ample time for additional drainage to occur,
however, this method often neglects drainage during this period. Several researchers
have used this method to estimate turf water use. Erie et al. (1981) used this method to
determine consumptive use of bermudagrass while Garrot and Mancino (1994) used this
procedure to determine crop coefficients of bermudagrass. Frank et al. (1987) used this
technique to determined the ET of crested wheatgrass, intermediate wheatgrass, and
western wheatgrass in order to assess the water use efficiency of the varieties.
Meteorological Assessment of ET
The most commonly used meteorological method for measuring turfgrass ET
involves use of the energy balance method:
+ H +1E + G + aA --= 0 [Eq.3]
where 1Z, is the net radiation, H is the sensible heat exchange with the atmosphere, lE is
the latent heat exchange with the atmosphere, G is the heat exchange with both vegetation
and soil, and aA is the energy utilized in photosynthesis and respiration processes within
plants (Beard, 1985). Transpiration occurs due to energy supplied by the atmosphere that
creates a free energy gradient in the water from the evaporating surface of the leaf
through the plant and down to the root hairs (Rosenberg, 1974). The energy supplied by
the atmosphere satisfies the latent heat parameter of the energy balance equation. The
40
energy utilized in photosynthesis and respiration (aA) is usually very small compared to
the other components, thus aA is commonly ignored. Using the convention that lE is
positive when there is a loss of lE from the surface, Equation [3] can be rearranged to
give:
lE = + H - G [Eq. 4]
Measurements of Rn, H and G can be used in conjunction with Eq. [4] to obtain lE which
is transformed to ET by multiplying lE by the latent heat of vaporization.
A more common means of using Eq. [4] to estimate ET involves the introduction of
the Bowen Ratio, [2. (Bowen, 1926). The Bowen Ratio is a measure of the ratio of
sensible to latent heat flux (H/lE). Substitution of p into Eq. [4] provides:
lE = (Rn - G) / (1 - p) Eq. [5]
The p is obtained by measuring the thermal and vapor gradients above the crop canopy.
Measurements of (3, Rn and G provide an effective meteorological procedure for
estimating ET.
Most meteorological methods of ET assessment require that measurements be made
in large fields to ensure measured meteorological parameters reflect the underlying
surface. Thus, energy balance methods are best suited for measuring ET of homogeneous
vegetative surfaces grown under non-limiting soil moisture conditions (Tanner, 1967)
Empirical Methods
Commonly, ET derived from experimental studies is compared or referenced to
meteorological strategies for estimating and predicting ET. Empirical modeling
41
procedures are a function of meteorological demand which differs from region to region,
thus local calibration is essential to obtain an accurate estimate of ET (Brown, 1999b).
Empirical methods are grouped into combination methods (i.e. Penman-Monteith,
Businger-van Bavel), radiation (Jensen-Haise), temperature (Blaney-Criddle), and
evaporation pan (U.S. Weather Bureau Class A and Bureau of Plant Industry; Kneebone
and Pepper, 1982). The measurement frequency to estimate ET ranges from seasonal to
daily measurements depending on the empirical modeling approach being used.
The most common empirical model for estimating ET today is the Penman
equation (Penman, 1948). The Penman equation utilizes meteorological data to generate
estimates of reference crop ET (ETo). The four weather parameters used by the Penman
equation are (1) solar radiation, (2) wind, (3) temperature, and (4) humidity. Numerous
variations of the Penman equation exist to estimate ETo (Brown, 1999).
Reference ET and Crop Coefficients
Reference crop ET (ETo) is commonly determined from a continuous stand of
cool season grass, 8-15 cm in height, grown under non-limiting soil moisture conditions
(Brown, 1999). The ETo must then be modified to provide ET estimates for each plant
species that differs from the reference crop. Plant species will differ in stage of
development, height, density and overall physiology, which will affect ET, as previously
discussed. A crop coefficient (Kc) is used to convert ETo to a "crop-specific" ET (ETa)
ETa = Kc * ETo [Eq. 6]
42
Actual turf ET will commonly be less than ETo due to limited soil moisture, difference in
turf species and lower mowing height, thus turf Kcs less than one are common.
Turf Consumptive Use Studies
Estimates of consumptive use and Kc values vary widely due to regional climatic
differences, variance in turf height and species, fertilization rate, irrigation method, and
different procedures for estimating ETo.
Climatic differences dominate the factors affecting ET. Bermudagrass ET, grown
in an arid environment was reported to use, on average, 8.7 mm/day (Kneebone and
Pepper, 1982). In contrast, much lower ET rates (4.0 mm/day) were reported for
bermudagrass grown in North Carolina (Van Bavel, 1966). In Texas, St. Augustinegrass
used 12.2 mm of water per day (Johns et al., 1983), whereas in Arizona, ET from St.
Augustinegrass averaged 9.6 mm/day (Kneebone and Pepper, 1982). Although cultural
management techniques in the above studies accounted for some variance in water use,
climatic differences were the primary reason for the wide range of ET rates.
Variance in turf species can also have an impact on turf ET. A study by Qian et
al. (1996) in Manhattan, KS determined the mean consumptive use of 'Meyer'
zoysiagrass (Zoysia japonica Steud.), `Midlawn' bermudagrass [Cynodon dactylon (L.)
Pers. x transvaalensis Burtt-Davy] and 'Mustang' tall fescue (Festuca arundinacea
Schreb.) was 5.9, 4.9 and 6.6 mm/day, respectively in a one-year study. A three year
study by Marsh et al., (1980a) at the California South Coast Field Station determined the
consumptive use for warm season grasses to be lower than cool season grasses. Annual
43
consumptive use values for warm season grasses totaled 693 mm for bermudagrass and
635 mm for St. Augustinegrass, whereas cool season grass consumptive use values were
1072 mm and 991 mm for tall fescue and Kentucky bluegrass, respectively.
Plant nutrition also impacts turf ET. Improved plant nutrition increases leaf area,
and leaf vertical elongation rate, thus increasing turf water use. A study by Danielson et
al. (1981), determined that Kentucky bluegrass used 10% more water when supplied with
an adequate amount of N fertilizer as compared to the same grass without any application
of fertilizer.
In Colorado, Feldhake et al. (1984) showed that Kentucky bluegrass (Poa
pratensis L. var. `Merion'), grown under deficit N fertility conditions (4kg N/1000 m 2 /yr)
utilized 13% less water and experienced a decrease in maximum ET compared to the
same turf grown under adequate N levels of (4kg N/1000 m 2 /month). The authors
attribute lower ET under deficit N fertilization to slower growth (shorter grass) rates and
lighter green turf that reflects more incoming solar radiation (higher albedo).
Higher mowing heights typically produce higher rates of ET. Increased leaf
radiation
surface area resulting from higher mowing heights increases absorption of solar
al. (1983) found that
and lessens boundary layer resistance, thus increasing ET. Johns et
ET of St. Augustinegrass grown in a controlled environment chamber increased by 30%
when mowing height was increased from 5 to 8 cm. Biran et al. (1981) found that
bermudagrass grown in growth chambers under conditions representative of
semiarid
44
climates used 23% more water when mowing height changed from 3 to 6 cm.
Consumptive use of ryegrass, grown under the same conditions increased by 25%.
Amount and frequency of irrigation can also have significant affects on turf ET.
Bermudagrass (Cynodon dactylon 'Suwannee') consumptive use was 16% lower when
irrigated two to three times in a 2-week span (dry treatment), compared to six to eight
irrigations per 2-week period (wet treatment). Every irrigation event for both treatments
supplied water between 20 and 40% in excess of soil water depletion, thus the wet
treatment received considerably more water than the dry treatment over the 9-week study
period. The same study showed a decrease of 24% in consumptive use for perennial
ryegrass (Lolium perenne Tennfine') under the dry treatment compared to the wet
treatment (Biran et al., 1981).
Total consumptive use of kikuyugrass grown in the arid climate of Israel ranged
from a low of 324 mm to a high of 789 mm depending on the amount and frequency of
irrigation. Irrigation amounts were designed to refill the soil profile and irrigation
frequencies ranged from every 30 days to every 7 days. Consumptive use increased by
60% when irrigation frequency changed from once every thirty days to once every seven
days. Differences in consumptive use between fertilized and non-fertilized plots were
less than 10% (Mantell, 1966).
significant
The above discussion emphasizes that climatic differences can have
a
impacts on ET rates from turfgrasses. In order to normalize ET over differing climates,
universal reference crop ET (ETo) is used in conjunction with crop coefficients (Kcs).
45
Assuming the reference crop is managed similarly and the method to calculate ET is the
same, Kcs are applicable across different climates. However, ET estimation procedures
vary from region to region, as does the management of the reference crop, thus restricting
Kc use to areas where local ETo data is accessible.
Procedures to estimate ET have shown significant error when compared to one
another. Evapotranspiration estimates derived from a Class A pan (Kps) in Irvine,
California ranged from 0.52 to 0.63 for warm and cool season grasses, respectively.
However, Kcs for the same grasses were determined to be 0.65 and 0.79, respectively
when referenced to ETo computed from weather data collected by CIMIS (California
Irrigation Management Information System) at the same study site (Meyer and Gibeault,
1986).
much
Crop coefficients for bermudagrass grown in Reno, Nevada differed by as
as 17% depending on the method by which ETo was estimated. The seasonal
bermudagrass Kc for evaporation from a from Class A pan was .73, whereas the Kc
calculated from the Penman method was .90 (Tovey et al., 1969).
coefficients for five
A study recently completed in Tucson, Arizona develop crop
data
different forms of the Penman equation used in concert with meteorological
This study
measured from an on site AZMET (Arizona Meteorological Network).
with
developed both summer and winter Kcs for bermudagrass turf overseeded
1996. Winter Kcs
intermediate ryegrass during a three year period between 1994 and
46
ranged from a low value of 0.55 to a high of 0.86, and summer Kcs ranged from 0.65 to
0.86, depending upon the form of Penman equation used (Brown et al., 1998).
The aforementioned studies demonstrate the diverse array of factors that combine
to influence actual turf ET and estimation of ETo and Kcs. Actual turf ET and estimates
of ETo vary primarily due to:
•
regional climatic differences
•
variance in turf species
•
fertility management
•
varying turf height
•
different watering regimes
•
different procedures and models for estimating ETo
A summary of these studies emphasizes that locally derived meteorological data
employed with appropriate use of a model to estimate ETo is essential; otherwise,
significant errors in water application will result.
Of greatest relevance to this investigation are those data
derived either locally or
from regions with a very similar climate. Also of great importance are those studies that
with a
implement similar turf management systems; that is, bermudagrass overseeded
cool season ryegrass. Under the extreme environment of Kuwait, short
grass
(Al-Nakshabandi,
(bermudagrass) was grown in lysimeter tanks to measure potential ET
1983). Consumptive use values ranged from 4 mm per day to 11 mm
averaged 7.3 mm per day over an entire year. The cumulative
per day and
annual consumptive use
47
was reported to be 2650 mm. Estimates of bermudagrass ET via the Penman equation
(2193 mm) and Jensen-Haise (2088 mm) method were considerably lower than the actual
ET values, producing crop coefficients well in excess of 1.0. Turf Kcs in excess of 1.0
suggest possible errors in ET assessment. The high ET estimates can be attributed in part
to the relatively small fetch area (10 m x 10 m) surrounding the lysimeter, which leads to
high levels of sensible heat advection from surrounding areas. Turf height was
maintained fairly high at 5-10 cm, which may cause higher ET rates. Also, the lysimeter
tanks were raised 10 cm above the soil surface which can increase the radiant energy load
and sensible heat advection, thereby increasing turf ET (Allen et al., 1991).
Kneebone and Pepper (1982) measured consumptive use of bermudagrass,
zoysiagrass, St. Augustinegrass and tall fescue in Tucson, Arizona using lysimeters that
employed a Marriotte siphon, subsurface irrigation system. The Marriotte siphon system
was used to deliver water to the bottom of the lysimeters to maintain water tables at
certain heights, according to high or low management regimes. High and low
management regimes included maintaining water tables at 35 and 45 cm below the soil
surface, respectively throughout the first year, and 30 and 40 cm for the following year.
High management plots were fertilized monthly with urea at 4.9 g N1m 2 compared to
bimonthly treatments at the same rate for low management. High management plots were
overseeded each fall with annual ryegrass to simulate high maintenance turf on golf
fairways. The mean annual consumptive use values for bermudagrass and zoysiagrass
were 1654 mm and 1310 mm for high and low management, respectively. The authors
48
concluded that consumptive use of warm season grasses ranged from 50-80% of Class A
pan evaporation depending on management intensity, whereas the consumptive use of
cool season grass ranged from 60-85% of pan evaporation. These ET estimates could be
inaccurately high because the subsurface irrigation system maintained a high water table,
(35 cm below soil surface at high management) thus creating a non-limiting soil moisture
environment. As previously discussed, soil moisture significantly impacts ET. The
subsurface irrigation system employed by Kneebone and Pepper did not produce a typical
diurnal variation in soil moisture that would be expected from sprinkler irrigation, thus
some differences in ET estimates could be expected between this study and other studies
employing sprinkler irrigation.
Consumptive use studies were conducted in Tempe and Mesa, Arizona by Erie et
al., (1981). Consumptive use was determined from periodic gravimetric soil samples
taken at one-foot intervals throughout the root zone. Bermudagrass consumptive use was
determined to be 1105 mm during the period between mid April to mid October
(approximately 6 mos.). These estimates appear somewhat higher than those obtained
over similar periods in other studies and may result from a high mowing height or errors
in assessment of drainage. Turf was moved every four weeks at a height of 3.8 cm (1.5
in) (Garrot and Mancino, 1994), creating more surface area and thus higher ET rates.
Gravimetric soil samples were taken throughout the root zone, however, it is unclear if
adjustments were made for water lost below the root zone, which is inevitable with the
49
flood irrigation system used in the study. Failure to account for drainage would result in
an overestimation of ET.
Kopec et al. (1991) determined water use and Kc values from small bucket
lysimeters for bermudagrass and perennial ryegrass in Tucson, Arizona. Reference ET
was determined by a modified Penman method using automated weather station data
(AZMET) located approximately 10 km from the study site. Average consumptive use
values determined over four years were 678 mm for perennial ryegrass (October-April),
and 871 mm for bermudagrass, resulting in an average annual water application of 1525
mm (61 in.). Under optimal growing temperatures for ryegrass, Kc values ranged from
.73-.77, while bermudagrass Kcs ranged from .72-.75 under summer desert conditions.
Brown et al. (1996) examined the quality and growth rate of Tifway
bermudagrass in Tucson, AZ irrigated with tertiary effluent water daily and every three
days at levels of 60, 70 and 80% of ETo. Management was employed to produce fairway
quality turfgrass, and the bermudagrass was overseeded with perennial ryegrass each
winter. Bermudagrass overseeded with perennial ryegrass irrigated at 60% of ETo did
not produce acceptable quality turf. Acceptable quality turf resulted when irrigation was
applied at 70-80% of ETo, suggesting an appropriate Kc for both winter and summer turf
rests in the range of 0.7 to 0.8.
In more recent study, Brown et al. (1998) determined crop coefficients for Tifway
bermudagrass overseeded with "Froghair" intermediate ryegrass using large weighing
lysimeters irrigated with local groundwater and tertiary treated wastewater. Actual turf
50
ET determined from the lysimeters was compared with ETo computed from five different
forms of the Penman equation. As previously discussed, different forms of the Penman
equation produced a wide variance in Kcs. However, the modified Penman equation used
in Arizona by AZMET showed the lowest coefficient of variation in terms of month-tomonth changes in Kc over the course of the year. Bermudagrass Kc averaged 0.76 for the
period May through September, while the Kc for overseeded ryegrass averaged 0.72 for
the winter months (October-April).
Salinity and Turfgrass Culture
It is evident from the previous section that large amounts of irrigation are required
to maintain high maintenance turf in arid regions such as southern Arizona. As the
domestic and industrial demand for potable water have increased, so to have water costs
and legislation restricting water use. Thus, turf facilities are being forced to water under
deficit irrigation regimes or turn to alternative water sources. This poses a problem of
salinity that is becoming increasingly relevant in arid regions.
Salinity danger arises under conditions of: (1) insufficient drainage; (2) irrigation
with water containing high levels of soluble salts; (3) deficit irrigation; and (4) arid
regions where soils contain appreciable amounts of soluble salts. Salt-affected soils can
be classified under saline or sodic soils, or a combination of the two. Saline soils have an
electrical conductivity > 4 dS/m as measured by saturated extract (ECe), and an
exchangeable sodium percentage (ESP) < 15. Sodic soils have an ECe <4 dS/m, and an
ESP > 15. Saline-sodic soils have both an ECe > 4, and an ESP > 15. (Singer and
51
Munns, 1991). Saline soils generally have a pH of 8.5 or less and are well flocculated,
whereas sodic soils have a pH above 8.5, and exhibit poor structural characteristics,
which can significantly reduce infiltration rates (Harivandi et al., 1992).
Salt affected soils negatively impact germination and growth of turfgrass. Visual
symptoms of plant stress are typically the earliest indicators of physiological changes or
adaptations, yet their evaluation tends to be subjective and error prone. Salt stressed
plants exhibit symptoms similar to phosphorus deficiency, including smaller, darkergreen leaves, decreased shoot:root ratio, and decreased tillering (Maas and Hoffman,
1977). High levels of salinity can lead to a delay, or slow development of seed
germination (Beard, 1982). As salt damage progresses, leaf tips may die, leaves may fall
from the plants, and turf density may decrease (Turgeon, 1996). Rooting depth may
decrease, however, root growth may increase relative to shoot growth in certain salttolerant turfgrasses (Marcum, 1994). Devitt (1989) determined that total root length and
root density decreased with increased salinity in bermudagrass. Peacock et al. (1993)
determined that bermudagrass, under salt stress, exhibited decreased stomatal
conductance, stolon length, biomass, and reduced leaf water potential.
Reductions in growth and yield of turfgrasses associated with salinity are due to
osmotic effects (Hanson et al., 1993). Reductions in osmotic potential of soil water
decreases the soil water potential, thus reducing the availability of soil water to the plant.
Low osmotic potentials produced by excessive salinity cause the plant to divert valuable
energy normally used for plant growth to mechanisms required to alter cell osmotic
52
potential. Lower cell osmotic potentials can decrease tissue water content, top growth,
root density, leaf turgor, and photosynthetic rate (Dudeck et al., 1993; Devitt et al., 1993).
While osmotic stress is most important in overall growth reductions, excessive
accumulation of ions in plant tissue can also be detrimental. Sodium (Na*), chlorine (C1
-
), and boron (B ) can accumulate in plant leaves causing toxic effects. Sodium can
k
compete with other cations, changing the overall plant nutritional balance. Dudeck and
Peacock, (1985) determined that tissue Na+ and Cl contents increased with increasing
-
salinity, while calcium, magnesium, and potassium decreased. Excessive accumulation
of Na+ and Cl can result in leaf tip burn and decreased turf density (Marcum, 1994).
-
Boron toxicity can be seen on tips of older leaves as yellowing and drying (Ayers and
Westcott, 1989).
The aforementioned salinity problems can be avoided, or lessened by employing
intelligent management practices. A few management strategies include: (1) application
of irrigation water over and above ET to facilitate leaching; (2) providing adequate
drainage to ensure leaching is possible; and (3) selection of salt-tolerant turfgrasses.
Leaching involves applying water in excess of ET, in order to prevent salt
accumulation in the root zone. The fraction of water applied that percolates below the
root zone is known as the leaching fraction (LF) (Ayers and Westcot, 1989).
(LF)
depth of w ater leach ed below t he root zo ne
depth of w ater appli ed at the surface
[Eq. 7]
The larger the leaching fraction, the less salt accumulation in the root zone.
53
A related calculation is the leaching requirement (LR), which estimates the LF
required to sustain crop yield performance for a given EC of irrigation water (ECiw;
Ayers and Westcot, 1989):
LR =
ECiw
5 (ECe) - ECiw
[Eq. 8]
where ECe is the soil salinity in dS/m associated with a particular crop yield level and
ECiw is in units of dS/m. Ayers and Westcot (1989) indicate bermudagrass will produce
maximum yields when ECe is 6.9 dS/m or less. To achieve 100% yield with
bermudagrass utilizing irrigation water of 1 dS/m, Equation [8] indicates the leaching
requirement should be 3% (i.e., three percent of the applied water must pass through the
root zone in order to leach enough salts to sustain 100% yield potential).
LR =100 x
ldSm -1
— 3%
,
5 0.9dSm -1 ) — ldSm -1
Ryegrass, which is slightly less salt tolerant requires a leaching requirement of 4% to
achieve 100% yield using the same irrigation water.
It is important to utilize salt tolerant turfgrasses in areas of salinity danger. The
relative tolerance to salinity varies widely with plant species. In the past twenty years,
a
research has identified many salt-tolerant turfgrasses (Harivandi et al., 1992). Although
few species can tolerate highly saline conditions, their characteristics may not be
danger is
desirable for high maintenance turf systems. In arid regions, where salinity
most likely, warm season grasses such as bermudagrass, Seashore paspalum,
and St.
season grasses
Augustinegrass are used on golf courses during summer months. Cool
54
such as annual ryegrass, perennial ryegrass, and tall fescue are used during winter
months. Harivandi et al. (1992) rated these grasses for salinity tolerance (Table 1). The
warm season grasses common to arid turf systems are all rated as tolerant to salinity
stress. The cool season grasses are slightly less tolerant to salinity with perennial
ryegrass rated moderately tolerant, annual ryegrass rated moderately sensitive and tall
fescue rated moderately tolerant.
Assessing Soil Salinity
Direct Methods
Numerous methods exist for determining soil salinity. Numerical values for
classification of saline and sodic soils and the relative tolerances of turfgrasses are
commonly derived from measurements of ECe by the saturation extract method. Plant
response is highly correlated with the ion concentration of the soil solution, rather than
the salts contained in the soil (Marcum, 1994). Thus the EC of the soil water (ECw) will
affect the physiological functions of the plant. For this reason, the saturation extract is
the most widely used method of assessing soil salinity. The saturated paste involves
collecting a soil sample from the field, drying the sample, and saturating with distilled
water. After a saturated paste is achieved, the soil water sample is extracted via vacuum
extraction, centrifugation, or gravity filtration (Bower and Wilcox, 1965). Various
methods exist for creating the soil water paste. One method involves saturating the soil
to the maximum amount without any water loss (field capacity). This method is rather
subjective, thus soil pastes using 1:1 or 1:5 soil:water ratios are being used.
55
Table 1.
Salinity tolerance of commonly used cool season and warm season turfgrasses in southern
Arizona.
Warm-season
Cool-Season
Name
R
Name
R
Annual
M
Bermudagrass
T
Seashore
T
Perennial
M I
Tall fescue
M
T
St.
'
i
--The rating reflects the general difficulty in establishment and maintenance at
various salinity levels. It in no way indicates that a grass will not tolerate higher salinity
levels with good growing conditions and optimum care. The ratings are based on soil
(ECe) levels of: Sensitive (S) = <3 dS/m, moderately sensitive (MS) = 3-6 dS/m,
moderately tolerant (MT) = 6-10 dS/m, tolerant (T) = >10 dS/m (adopted from Harivandi
et al., 1992).
56
Electrical Conductivity can also be measured by direct extraction of the soil water
via porous suction cups. Various problems are associated with this method. The soil
water obtained by solution samplers may not be representative of the entire water-filled
pore space (Ward et al., 1994). The solution may extract the water from large pores
(under low tension), and exclude the water in smaller pores (held under high tension).
The water in the small pores held at the solid/liquid interface is considered immobile,
thus the ion mobility associated with this water is far less than ions located in the central
pore water. The decreased ion mobility associated with the water held in small pores can
affect ECw measurement (Mualem and Friedman, 1991). Also, solution samplers are not
effective when soil matric potentials are low (low 0v) (Heimovaara et al., 1995). The
measurement of ECw via suction samplers is similar, but does not equal the ECe
measured from saturation extract (Rhoades et al., 1989). The ECe value is considered to
be somewhat lower (-2x) than the ECw, (Ayers and Westcot, 1989) due to dilution of
ions with distilled water.
Time Domain Reflectometry
Time domain reflectometry (TDR) is a less tedious method of assessing soil
salinity. Numerous authors have found TDR to produce rapid, reliable, and routine
et al., 1982;
measurements for in situ volumetric water content (Topp et al., 1980; Topp
Dasberg and Dalton, 1985), and more recently, techniques have been developed to obtain
probes
both bulk soil electrical conductivity (ECa) and water content using the same TDR
(Dalton et al., 1984; Topp et al., 1988 and Zegelin et al., 1989).
57
In theory, measurement of water content and bulk soil electrical conductivity
using TDR is independent of soil texture, structure, density, and temperature, and no
calibration is required. A cable tester (TDR) measures the propagation velocity of a highfrequency pulse through parallel metal probes inserted in the soil. The time interval
required for the electromagnetic pulse to travel from the point at which the probe enters
the soil to the end of the probes and reflect back to the point of entry in the soil varies
with transmission line length and the dielectric constant (K). The expression to define K
is given:
K = [ct/21] 2[Eq. 9]
where c is the velocity of light in a vacuum, t is the transit time, and 1 is the path length
(Dalton et al., 1990).
Changes in K are primarily dictated by changes in 0„, regardless of soil type.
Topp et al. (1980) developed an empirical relationship between K and O v :
Ov = -5.3 * 10' + 2.92 * 10' K - 5.5 * 10 4 K2 + 4.3 * 10 6 K3 [Eq. 10]
This empirical equation is still widely used, however, a site specific calibration curve is
needed to obtain very accurate results for peat and heavy clay soils (Dalton and Poss,
1990). The travel time is measured in distance units on the cable tester screen (Figure 3).
This distance can be read directly from the screen or digitized and transferred to a
computer. Direct readings can be subjective and error prone. A computer algorithm can
be used to determine the tangent of the two distance points to accurately calculate the
58
V
o
a
I
Time (ns)
1
Figure 3. Typical TDR output trace used to assess soil volumetric water content. Label
A identifies point when pulse signal enters the soil. Label B identifies point where the
reflected signal has returned to the soil entry point.
59
dielectric constant (Yanuka et al., 1988; Nadler et al., 1991). Numerous computer
programs are now available to determine moisture content and salinity (e.g., Evett, 1994).
The TDR measures bulk soil salinity independent of soil moisture. The water content
affects the horizontal distance of the TDR trace, (Figure 3) whereas, the salinity of the
soil affects the attenuation of the signal (Figure 4). The bulk soil salinity (ECa) is
comprised of the bulk liquid-phase conductivity (ECb) associated with the salts contained
in the liquid-filled pores, and the bulk surface conductivity (ECs), which is associated
with the salts contained at the solid/liquid interface (salts derived from exchangeable
cations adsorbed on the cation exchange sites of clay surfaces). The relationship is given
by Rhoades et al., 1976):
ECa = ECb + EC s [Eq. 11]
Rhoades et al. (1990) reported that ECs values for typical arid land soils of the
southwestern United States may be estimated from the water saturation percentage (SP)
of mineral soils. The following equation relating ECs to SP was developed for Arizona
and southern California soils by Rhoades et al (1990).
ECs = 0.019 SP — 0.434 [Eq. 12 1
The bulk soil electrical conductivity (ECa) is given by Dalton et al. (1984) in terms of the
transmitted voltage (V T), the reflected voltage (V R) (Figure 4), the length of the
waveguide (L) and the dielectric constant (K):
ECa =
/ 120nL (ln * V T/V R) [Eq. 13]
60
—
V
o
1
t
a
g
e
ITime (ns) I
Figure 4. Typical TDR output trace used to assess bulk soil electrical conductivity.
Arrows labeled VT and VR depict the transmitted and reflected voltages, respectively.
61
The transmitted voltage is the voltage that enters the waveguide, and V R is the
voltage reflected from the end of the probe. The soil salinity attenuates the transmitted
voltage pulse,
VT,
and reduces the amplitude of the reflected voltage V R (Dalton et al.,
1990). Values of VR can represent different amplitudes depending on where the signal is
read, (i.e., after partial reflection, from the start of the probe, after reflection at the end of
the probe and reflectance over a long time) (Figure 4). Nadler et al., (1991) determined
that V R should be measured after a very long time, when it approaches a constant value,
thus avoiding multi-reflection interferences caused by impedance discontinuities.
The ECa value is very low compared to ECw values obtained by solution
samplers and ECe via saturation extract. The low ECa results because rock material and
soil solids have low electrical conductivities and thus act as insulators. Several models
have been developed to convert ka to the more useful value of the saturation extract
ECe or soil solution ECw. Rhoades et al. (1976) developed a model to estimate ECw
from ECa using Equation [11] and the assumption that ECb is a linear function of ECw.
Rhoades et al., (1976) reworked Equation [11] to determine the relationship between ECa
and ECw for unsaturated soils:
ECa ECw ev T (0v) + ECs [Eq. 14]
where ev, T(0v), and ECs are the soil volumetric water content, soil water transmission
coefficient, and the soil solid phase conductivity, respectively. The transmission
coefficient ( 1) accounts for the tortuous nature of the current flow through the geometric
62
matrix of the soil pores and the decrease in mobility of ions at the solid/liquid interface.
The transmission coefficient can be described by the empirical equation:
T = a Ov + b [Eq. 15]
where a and b are constants and 0v is the volumetric water content. Constants a and b
will vary due to soil mineralogy and texture.
The Rhoades model is but one of many developed to estimate ECw from ECa.
Other models developed include Mualem and Friedman (1991), Heimovaara et al. (1995)
and Persson (1997). These models serve as useful predictive tools for monitoring
changes in soil salinity over time, however, the models do not provide accurate
estimations of ECw over wide ranges of soil salinity.
Achieving a strong relationship between ECa and ECw is difficult due to the
different media by which each parameter is obtained. Bulk soil salinity is measured via
TDR, which measures the conductivity of both the mobile and immobile soil solution
plus the soil solid phase. In contrast, only the mobile portion of the soil solution is
measured by solution samplers when obtaining ECw. Therefore, an accurate relationship
solutions
between ECa and ECw can only be made when the mobile and immobile soil
are in diffusional equilibrium (Persson, 1997).
An additional source of error relating ECa to ECw occurs when either ECa
or
ECw values are very low. The relationship between ECa and ECw at very low salinity
values is non-linear (Nadler and Frenkel, 1980). An independent study by
(1989) revealed that ECw values measured with an ac bridge
Zegelin et al.
compared favorably with
63
ECa values derived from TDR (within ± 10%) provided ECa exceeds 1.0 dS/m. A study
by Persson (1997) concluded the accuracy of ECa and ECw measurements increases with
ECa. Error associated with the ECw estimation was <10%, provided ECa values were
higher than 0.6 dS/m. Dasberg and Dalton (1985) determined ECa values below 0.18
dS/m did not compare favorably with ECw values obtained from the same samples.
64
CHAPTER 3
MATERIALS AND METHODS
Lysimeter Facility and Operation
This study was conducted in the field at the Large Weighing Lysimeter Facility
located at the University of Arizona Karsten Desert Turfgrass Research Facility (KDTRF)
in Tucson, AZ. The field research plots at KDTRF are located on 2.2 ha of bottom land
that resides just south of the Rillito River at an elevation of 717 m above mean sea level.
The soil at the KDTRF is classified as an Agua sandy loam (coarse-loamy over sandy
or
sandy-skeletal, mixed, calcarious, thermic Typic Torrifluvent).
The Large Weighing Lysimeter Facility is centrally located within the KDTRF
and 4.0 m
(Figure 5). Two large, cylindrical weighing lysimeters, 2.5 m in diameter
deep, filled with a Vinton fine sand (sandy, mixed, thermic Typic Torrifluvent), were
used in this study. The lysimeters, labeled (W) and (E) in Figure 5,
referred to as the west and east lysimeters, respectively. The
remove coarse fragments in excess of 2 mm prior to
will hereafter be
Vinton soil was sieved to
placement in the lysimeters. The soil
was then packed to a uniform bulk density of 1.5 Mg/m 3 and 1.49 Mg/in
3
in the west and
of the
east lysimeters, respectively. Particle size analyses conducted on samples
lysimeter soil revealed the Vinton soil was 90% sand (35%
fine sand, 35% medium
sand), 7% silt and 3% clay on a weight basis. Saturated hydraulic conductivity of the
65
65m
86m
95m
90m
IN her Station
Karsten Facility
North 11
Figure 5. Plan view of the Karsten Desert Turfgrass Research Facility, Tucson, AZ.
Turfed areas are shown in green. The lysimeters are located in the middle of the facility.
Labels W and E identify the west and east lysimeters, respectively. The arrows and
associated distances represent the distance from the lysimeters to the turf facility
boundary. Map not drawn to scale.
66
lysimeter soil was determined to be 150 cm/day using the constant head method of Klute
and Dirksen (1986).
The lysimeters are located within a uniform 918 in' block of irrigated turf (Figure
5). The area outside the immediate vicinity of the lysimeters consists of irrigated turf
plots interspersed with plot alleyways and cart paths and thus does not provide a uniform
turf fetch which is preferred for evapotranspiration studies. Turf occupies approximately
two-thirds of this surrounding area; the remaining area is allocated to the alleyways and
paths. The length of fetch in the upwind direction is most critical for ET studies. Wind
flow at the KDTRF is periodic in nature, moving up river (from W or NW) during the day
and flowing down river (from E) at night. Fetch lengths west and east of the lysimeter
area measured 86 m and 95 m, respectively.
The two lysimeters rest on truck scales (Model FS-8, Cardinal Scale, Webb City,
MO) equipped with loadcells (Model Z-100, Cardinal Scale) which can resolve changes
in lysimeter mass of approximately ±100 g (0.02 mm depth of water) under controlled
calibration conditions. However, potential errors from electronic drift, wind and
mechanical limitations suggest a realistic daily resolution is between 0.5 and 1.0 kg (0.10.2 mm of water). Loadcell calibration was checked twice over the course
of the study by
comparing loadcell output following addition and subtraction of known weights.
al.,
Additional details on lysimeter construction and layout are described in Young et
(1996).
67
Both loadcells are connected to a datalogger (Model CR-7, Campbell Scientific,
Inc., Logan, UT) programmed to sample the lysimeter mass once every ten seconds and
compute and output mean values of lysimeter mass at ten-minute intervals (see Appendix
F). Data are retrieved from the CR-7 using a personal computer and the communications
software Telcom (Version 6E, Campbell Scientific, Inc., copyright 1990). Data are
downloaded to a disk and subsequently entered into a spreadsheet program (Quattro Pro,
version 7.0-Pro) for ET analysis.
Evapotranspiration is determined on a daily basis by analyzing the diurnal pattern
of lysimeter mass change. Changes in mass in kg are converted to an equivalent depth of
water in mm by converting the mass change to an equivalent volume of water in crn3 then
dividing by the surface area of the lysimeter in crn2 . The resulting equivalent depth of
water in cm is multiplied by 10 to convert the units to mm. An example follows for a
mass change of 29 kg which occurred on day 170 (19 June, 1998) for the east lysimeter
(see Figure 6).
Mass change = 29 kg = 29000 cm 3 water
Lysimeter Area = 49087 cin2
Equivalent Depth of Water = 29000 crn 3 / 49087cm' = 0.59 cm = 5.9 mm
is
Figure 6 depicts lysimeter mass change for day 170. The vertical axis in Figure 6
converted to units of mm of water using the conversion from kg of mass change to mm of
water described above, and the midnight mass value is assigned a value of 0 mm. Day
170 runs from point (A) through point (F) in Figure 6. The abrupt increase in
lysimeter
68
0 I.LI.
63
in
c'
IL
2 OL I.
a
I--
>.
co
0
9*OL I.
V
2
a$
E
'7)
>,
›,
06
U)
ctl
o
to
—
co
d
c
co
I
ci)
E
p
U)
c
o
o.
u)
V OL I.
cu
cL
a)
To
t.)
0
,_
a)
a1
E
'7)
4
>,
_1
Z OL I.
0
(Ni
I
C
.
n
q- '
--
cd
0
—
—
(5
ô
0
ci
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co
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-a-
00
WW
o
ti! Jalem Jo todaa
'T
CO
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69
mass results from irrigation (points B to C). Removal of drainage water produces the
abrupt decrease in lysimeter mass (points D to E). The more gradual loss of lysimeter
mass between points C and D and E and F represents ET. Evapotranspiration is assumed
to be negligible during irrigation events because irrigation is applied over a period of
thirty minutes or less during the pre-dawn hours. Linear interpolation is used to estimate
ET during daytime irrigation events and when drainage water is removed. The mass
change during such events is estimated by determining the slope of the line representing
the change in ET per unit time during the period thirty minutes (three data points) prior to
and thirty minutes following the irrigation or drainage event. The average slope of these
two lines is computed and used to estimate the rate of ET per unit time during the
irrigation or drainage event. The ET is then estimated by multiplying the rate of ET per
unit time by the total time elapsed during the event.
Total ET for the day is determined by summing the changes in equivalent depth of
water as depicted by points A to B (+0.26 mm), C to D (-2.55 mm), and E to F (-4.48
mm) to the estimated ET occurring between points D to E (-0.41 mm). The resulting
summation is -7.18 mm. ET is reported as a positive value when there is a loss of water;
thus, ET is reported as 7.18 mm.
Drainage water reaching the bottom of the lysimeter is extracted via a vacuum
pump attached to a series of ceramic suction candles (25.4 mm OD, 610 mm long, Soil
Measurement Systems, Tucson, AZ) located 25 cm above the bottom of the lysimeters.
The vacuum pump applies a suction of approximately 150 kPa for a period of four hours
70
daily to remove the drainage water. Drainage water is stored in containers situated on the
tank scale, thus daily drainage events do not impact lysimeter mass. Drainage water is
removed and quantified weekly using both volumetric and gravimetric procedures.
Volumetric procedures involve reading the depth gauge on each drainage tank, then
converting the depth to volume using the linear calibration equation for the depth gauge.
Gravimetric procedures simply use the change in lysimeter mass obtained before and
after the drainage water is removed from the tank (see Figure 6). Drainage volumes are
converted to an equivalent depth of water using the procedure described previously. A
subsample of the drainage water is acquired during each drainage event. Subsamples are
placed in a plastic container and then stored in a freezer at -28 °C for later analysis.
Turf and Irrigation Management
Tifway (Tifton 419) bermudagrass, (Cynodon dactylon x transvalensis (L.) pers.),
sprigged during the summer of 1994 over the entire lysimeter facility (lysimeters and
was
surrounding turf), served as the summer turf surface for the study. The bermudagrass
mowed two to three times per week at a height of 1.9-2.2 cm. Both lysimeters and the
surrounding turf block were overseeded on 13 Oct. 1997 with `Froghair' intermediate
was allowed
ryegrass (Lolium multiflorum x perenne). Prior to overseeding, bermuda turf
to increase in height (approx. 4 cm), then was mowed to about lcm (scalped). Thatch
was then removed by raking the surface and disposing of the resulting material.
Intermediate ryegrass was overseeded at a rate of 733 kg/ha (15 lbs/1000 ft 2 ) and worked
into the soil surface to ensure soil contact. During the two-week period required
for
71
germination and establishment of the overseeded turf, daily irrigation amounts of
approximately 4.1 mm/day were applied using six application periods to ensure the soil
surface remained moist. Once established, overseeded turf was mowed one to two times
per week at a height of 2.2-2.5 cm.
The lysimeters were overseeded again on 26 Jan. 1998 due to a thinning stand of
ryegrass. Froghair intermediate ryegrass was again used, however, the seed was pregerminated in water to hasten the germination process. The turf was de-thatched in order
to open the canopy to ensure proper seed-soil contact. Each lysimeter was top-dressed
with soil identical to that contained in the lysimeters (Vinton soil). Topdressing was
applied at a depth of 3.2 mm (1/8 in.) over the surface of each lysimeter. Irrigation was
applied at a daily rate of 2.7 mm/day using three application periods to ensure adequate
surface soil moisture for germinating seed. This irrigation schedule ran from 27 January
through 4 February.
Lysimeter turf was fertilized every two to four weeks using liquid fertilizer (see
Table 2 for fertilizer rates and dates of application). The fertilization regime before and
N.
after 2 Feb. 1998 differed to facilitate a companion study on the fate of fertilizer
Applications of fertilizer N prior to 2 Feb. 1998 were made using ( 15 NH 4 ) 2 SO 4 .
Potassium, phosphorus and micronutrients were supplied prior to 2 Feb. 1998 using 1/2
strength Hoaglands solution (Hoagland and Arnon, 1950) containing 5.5 g of potassium
72
Table 2.
Fertilizer rates and dates applied throughout the study.
Date
97/07/07
Fertilization Rates and Dates Applied
Rate
Fertilizer
West
East
Kg/ha
Kg/ha
12.1
24.3
(NI-14)2SO4
°
97/07/29
(NH4)2 S
4
18.2
15.2
97/08/18
(NH4)2SO4
24.3
15.2
97/09/10
97/10/21
97/10/22
97/10/24
98/10/28
98/10/29
97/11/07
97/11/14
97/11/26
97/12/14
97/12/29
98/01/12
98/02/02
98/03/09
98/04/05
98/04/24
98/05/12
98/06/05
98/06/24
98/07/11
98/08/08
(NH4)2SO4
P and K
P and K
24.28
2liters
2liters
21.25
2liters
2liters
18.21
2liters
18.21
2liters
18.21
2liters
12.2
12.2
12.2
12.2
24.4
24.4
24.4
24.4
24.4
15.18
2liters
2liters
15.18
2liters
2liters
12.14
2liters
12.14
2liters
12.14
2liters
12.2
12.2
0
6.3
5.1
7.8
10.5
15.3
11.7
,
(NH4)2SO4
P and K
P and K
(NH4)2SO4
P and K
(NH4)2SO4
P and K
(NH4)2SO4
P and K
Peters Solution
Peters Solution
Peters Solution
Peters Solution
Peters Solution
Peters Solution
Peters Solution
Peters Solution
Peters Solution
73
After 2 Feb. 1998 fertilization was accomplished by applying 24 kg/ha
(0.5 lb./1000 ft2 ) of a standard soluble 20-20-20 liquid fertilizer (Peter's Solution; Grow
More Inc., Gardena, CA) at regular intervals (Table 2). A total of 2 1 of fertilizer solution
was applied to each lysimeter on each date of fertilization. Similar amounts of N were
applied to both lysimeters over the course of the study. However, the amount of fertilizer
N applied to each lysimeter was adjusted to compensate for differences in N
concentration in the respective irrigation waters (See Appendix A for complete water
analysis). At each fertilization event, the amount of N required by the turf was reduced
by the amount of N supplied in the irrigation water since the previous fertilization.
The turf quality on each lysimeter was rated weekly using the USDA National
Turfgrass Evaluation Program (NTEP) standards by which turf color, density, and overall
quality were assessed independently on a 1-9 scale. An NTEP rating of 6 or higher
represents a turf of acceptable quality or better.
Turf growth rate was determined by collecting turf clippings during mowing
events and drying to a constant weight at 40°C. Growth rate in units of g/m2 /day
was
computed by dividing total clipping dry weight by the surface area of the lysimeter and
the number of days between mowing events.
Irrigation water was supplied to each lysimeter by four low trajectory Rain Bird
1804 Series pop-up sprinkler heads (Rain Bird Sales, Inc. Glendora, CA) installed in a
was monitored
square pattern with a head spacing of 3.65 m (12 ft). Precipitation rate
periodically using metal catch cans and averaged 5.3 cm/hr. System operating pressure
74
was maintained at approximately 207 l(Pa (30 psi) utilizing pressure regulators. Irrigation
uniformity was determined on a regular basis using Christiansen's Coefficient
(Christiansen, 1942) and ranged from 89 to 95%. Irrigation was controlled by a Rainbird
Maxi-5 irrigation control system. Irrigations were applied in the pre-dawn hours to
minimize (1) problems with wind-induced distortion of sprinkler patterns and (2) losses
due to evaporation. Irrigation amounts were not adjusted for irrigation nonuniformity.
Each lysimeter received irrigation water from a different source. The west
lysimeter was irrigated with tertiary effluent water from the Pima County wastewater
treatment plant. The ECiw (EC of irrigation water) of the effluent water was
approximately 1.0 dS/m and contained approximately 13 ppm (mg/1) NO 3 -N. The east
lysimeter received potable groundwater, which has an ECiw of approximately 0.25 dS/m
and a NO 3 - N concentration of 3 ppm (mg/1) (see Appendix A for water analysis).
Irrigation treatments consisted of applying each water source to a single lysimeter at
annual rates not to exceed the 1.4 halm/ha turf water duty presently required by ADWR
in the Tucson AMA. Irrigation was applied daily with application rates determined by
applying the crop coefficients presented in Table 3 to values of reference
evapotranspiration (ETo) computed by the Maxi-5 weather station located approximately
10 m south of the lysimeters. The Maxi-five system computes ETo using the procedure
described by Jensen and Haise (1963). These ETo values were reduced by 10% prior
applying the Kcs in Table 3 to make ETo compatible with values generated by AZMET
(Brown, 1998). Irrigation was diminished or eliminated by the Maxi-Five Irrigation
75
Table 3.
Crop coefficients (Kcs) applied during summer (15 June-13 October) and winter (5
November-4 June) seasons and Kcs applied during fall overseed (14 October-4
November) and spring transition (5 June-14 June).
Kc
Time Period
0.76
15 Jun- 13 Oct.
1.10
14 Oct. - 4 Nov.
0.72
5 Nov - 4 June
1.00
5 Jun. - 14 Jun.
76
System following precipitation events. Treatments were initiated on 21 July 1997 and
continued through 30 Sept. 1998. Difficulties with regulating the pressure of the
irrigation system resulted in somewhat inaccurate water applications prior to mid-August
1997.
The Kcs in Table 3 generate an annual water application of approximately 1.55
ha-m/ha (5.1 acre-ft/acre) in a normal year if precipitation is not considered. On average,
precipitation adds approximately 0.3 ha-m/ha, which should lower application rates
below 1.4 ha-m/ha if precipitation is used efficiently. Plans were developed to reduce the
Kcs if rainfall and weather conditions indicated annual water use would exceed 1.40 ham/ha. However, the ensuing year produced above normal precipitation and plans to
reduce Kcs were never implemented.
Water and Salt Balance Procedure
All parameters of the water balance were assessed daily over the course of the
study. Irrigation was calculated from lysimeter mass gain during irrigation events.
Precipitation was estimated from lysimeter mass gain during rainfall events. Lysimeter
measured precipitation was used in lieu of rainfall measured by the AZMET weather
station due to greater accuracy obtained with lysimeter measured precipitation [see
was
Appendix D (Equations 17 and 18) for raingage precipitation adjustment]. Drainage
determined from the loss in lysimeter mass during drainage events, and
evapotranspiration was determined from lysimeter mass changes as described previously.
Runoff was assumed negligible in this study. Daily values of each component of the
77
water balance were collected and stored in a spreadsheet program (Microsoft Excel,
version 7.0), and then summarized at the end of each month. Water balances for each
month are presented in Appendix B. An annual water balance was developed for the
period October 1997 to September 1998 by summing the water balance parameters from
each of these twelve months. The change in soil water storage was determined for the
entire year (October 1997 - September 1998) by computing the difference between the
total inputs to each lysimeter (irrigation and precipitation) and the total outputs from each
lysimeter (turf ET and drainage).
The salt balance of each lysimeter was assessed on a monthly basis by quantifying
the amount and EC of drainage and irrigation waters, respectively. The EC of drainage
and irrigation water was measured using a YSI Model 35 Conductance Meter (Yellow
Springs Instrument Co., Yellow Springs, OH). The cell constant (k) was 1.0/cm and
probe resolution was ±0.01 dS/m using the appropriate display range. Cell accuracy was
determined using ten prepared solutions ranging from 0.33 dS/m to 15.24 dS/m. Cell
calibration confirmed the cell accuracy of ±1%. Water samples were thawed and allowed
to equilibrate at room temperature prior to measurement.
Salt input to each lysimeter was determined by multiplying ECiw by 640 to obtain
the total dissolved solids (TDS) in mg/1, then multiplying TDS by the volume of
irrigation water applied during each irrigation. An example follows for 3.0 cm of applied
effluent irrigation water (ECiw 1.0 dS/m):
TDS Eciw * 640 1.0 dS/m * 640 640 mg/1
78
Volume of Water = lysimeter area * depth of irrigation
=49087 cm' * 3 cm = 147,261 cm' = 147.26 1
Total Salt Input = 640 mg/1* 147.26 1= 94,247 mg = 94.2 g
The water contacting the lysimeters from rainfall was assumed to have negligible TDS,
thus salt iput from rainwater was assumed to be zero. The amount of salt removed in the
drainage water was determined by computing the grams of salt per drainage event. An
example follows for a drainage event that occurred on 3 Sep. 1998, where 34.8 1 of
drainage water was quantified with an EC of 2.14 dS/m.
TDS2- drainage water EC * 640
=
--- 2.14 dS/m * 640 mg/1= 1369.6 mg/1
-
Volume Drained = 34.8 1
Total Salt Lost = 1369.6 mg/1* 34.8 1= 47662 mg = 47.7 g
The balance of salt applied versus salt removed in drainage was determined on a
monthly basis by computing the difference between the grams of salt applied and the
grams of salt removed. The annual salt balance for the period October 1997 to September
1998 was computed by subtracting the total mass of salt drained from the lysimeters from
the total mass of salt applied in irrigation water. The leaching fraction was computed for
the entire year (October 1997 - September 1998) by dividing the total drainage water by
the total amount of water applied (irrigation + rain).
Subsurface Salinity Assessment
The lysimeters are equipped with 96 external sampling ports that facilitate
installation and utilization of solution samplers, tensiometers, thermocouples and time
79
domain reflectometry (TDR) probes. The sampling ports on each lysimeter are divided
into three vertically aligned sets. Adjacent sets of ports are separated by an angular
distance of 120 0 . In the vertical dimension, sampling ports are separated by 50 cm over
the depth range of 100 cm to 350 cm. An additional set of sampling ports was available
at a depth of 50 cm in the west lysimeter to facilitate monitoring near the soil surface.
Time domain reflectometry was used to assess vertical profiles of volumetric soil
water content (0v) and bulk soil electrical conductivity (ECa) at weekly intervals.
Horizontal TDR probes were available in sampling ports at each depth increment. The
TDR probes (Dynamax, Inc., Houston TX) consist of three parallel 0.3 cm OD
waveguides, 20 cm in length and spaced 3 cm apart. The probes were connected through
RG 58/U, 50S 2 coaxial cable to a TDR cable tester (Model 1502, Tektronix Corp.,
-
Beaverton, OR). Cable tester settings were set as follows for the Ov measurements: 0.5
Ft/div for horizontal control, 100 mp/div for vertical control and 0.99 for the cable
dielectric constant. The vertical control was compressed to 200 mp/div to obtain the
transmitted voltage, V T , and the reflected voltage, V R , necessary for assessment of ECa.
A personal computer was used to sample and analyze the output from the cable tester via
the computer serial port. The computer program, TACQ, (Time Domain Reflectometry
(TDR) Automatic Data Collection Program, Copyright 1993, Vadose Zone Equipment
Company, version of 7-24-94, provided by Steven Evett, USDA-ARS) was used to
collect and analyze TDR traces for travel times, apparent dielectric constants and soil
volumetric water contents. This program measures the path length of the signal in
80
distance units (d), and using a known pulse velocity (V) calculates the travel time (t) in
nano-seconds (Dalton et al., 1984):
t= d/V [Eq. 16]
The pulse transit time is determined from the point at which the pulse signal enters the
soil (point A in Figure 3) to the point at which the transmitted pulse signal has returned to
the point of entry in the soil (point B in Figure 3). Direct measurements may introduce
error into locating the points of inflection, thus the TACQ computer program is used to
determine these points by locating the local maximum and minimum points on the output
trace. Equations [9] and [10] are then used to yield the dielectric constant (K) and
volumetric water content (ev), respectively.
K = [ct/2L] 2 [Eq. 9]
ev = -5.3 * 10 -2 + 2.92 * 10 -2 K - 5.5 * 10 4 K2 + 4.3 * 10 6 K3[Eq. 10]
where c and L are the speed of light and probe length, respectively.
Bulk soil electrical conductivity was analyzed manually using the method
described by Dalton et al., (1984) which requires measurements of the transmitted and
reflected voltage (Figure 4; VT and V R). Bulk soil electrical conductivity was computed
from Equation [13] using V T , V R , K obtained from Equation [9] and probe length
(L).
ECa = K 1/2 / 1207cL (ln VT V R) [Eq. 13]
It should be noted that VR was determined after it approaches a constant value
(Figure 4), according to Nadler et al., (1991).
81
Crop Coefficient (Kc) Assessment
Crop coefficients were computed on a monthly basis by dividing the monthly sum
of the lysimeter ET by the sum of ETo as computed by AZMET. Seasonal Kcs for
summer (May-Sep) and winter (Nov-Apr) were calculated as the average of the monthly
Kc values during the appropriate time period. To assess day-to-day variation in Kcs, Kc
values were calculated each day by dividing lysimeter ET by ETo. From these data, a
mean Kc value and standard deviation were computed on a monthly basis.
The Kc values were also analyzed to determine statistical significance of seasonal
and locational (east vs. west lysimeter) affects on Kc. To accomplish this, a regression
model was used. The daily Kc values were entered into a spreadsheet program
(Microsoft Excel, version 7.0) for each lysimeter and for both summer and winter
seasons. Next, a dummy code was assigned to season (summer = 1, winter = 0) and to
location (west = 1, and east = 0). These codes were then regressed (as independent
variables) against Kc using the model described by Johnston (1984):
Kc = + a i L i + p i s, + u [Eq. 17]
Statistical model usewhere the parameter u represents the expected Kc for the east
lysimeter during the winter season, oc i represents the differential effect for L i (west
lysimeter location Kc) compared with L2 (east lysimeter location Kc), the f3 i parameter
measures the differential effect for S i (summer season Kc) compared with S2 (winter
season Kc) and u is the random error in the model.
82
CHAPTER 4
RESULTS AND DISCUSSION
The objectives of this study were to evaluate the adequacy of the ADWR water duty
to meet turfgrass ET requirements, to provide for fairway quality turf, and to evaluate any
potential for salinity damage to turfgrass under this irrigation regime. These objectives
were assessed by evaluating the lysimeter water balances, salt balances, turf quality,
biomass accumulation, turf growth rates, and crop coefficients.
Water Balance
The components of the water balance for each lysimeter are presented by month in
Table 4 and Figure 7. While August and September 1997 are presented in Table 4, the
water balance for both months was not considered representative of the irrigation regime
employed by this study due to residual effects from the previous study. Irrigation was
applied in the previous study to create non-limiting soil moisture conditions, thus
three months prior
irrigation was supplied well above ETa (approx. 120% of ETa). The
to October 1997 served as a transitional period during which the soil water dynamics of
the lysimeters approached a new equilibrium representative of the irrigation regime
soil
employed in this study. High levels of drainage and large decreases in lysimeter
water storage in August and September 1997 provide ample evidence of lysimeter
consistent
adjustment to the new irrigation regime. October drainage decreased to levels
with monthly drainage measured later in this study; thus, October was selected
as the
83
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84
Water Inputs (Irrigation + Rain) and Outputs
(ETa + Drainage ) for Both Lysimeters
r-.
33
O)
D.
(0
0
West Inputs
o
a)
0
Q.
IL
.
)
-3
• East Inputs 0 West Outputs 0 East Outputs
Figure 7. Monthly inputs and outputs of water for the west and east
lysimeters for the period August 1997 to September 1998. Inputs
(irrigation + precipitatioin) and outputs (ETa + drainage) are
presented as an equivalent depth of water in mm.
85
month where the water balance first reflected the irrigation regime used in this study.
The twelve months from October 1997 - September 1998 were therefore chosen for
assessment of the lysimeter water balances in this study.
Weather was one factor that significantly influenced the lysimeter water balance
during the course of this study (Table 5). Weather conditions during the first two months
of the study (October and November) were typical of the fall season in Tucson. The
expected El Nirio winter weather pattern developed in December bringing below normal
temperatures and ETo, and rainfall that exceeded normal by approximately 50%. Above
normal temperatures and dry weather returned in January, only to be replaced again by
the cool, wet El Nirio weather pattern in February. February rainfall totaled 73% above
normal while ETo ran 29% below normal. Wet weather continued through April,
producing below normal temperatures and ETo. Rainfall in March and April exceeded
the normal by 42% while ETo was reduced 11% relative to normal. The summer season
and
began cool and dry. Only 0.24 mm of precipitation was reported in May and June
temperatures in both months averaged below normal. The summer monsoon season
developed in July. July rainfall exceeded normal by 58% and the increased cloudiness
continued in August;
and humidity reduced July ETo 8% below normal. Monsoon flow
however, August rainfall was 37% below normal and ETo totaled near normal.
September 1998 concluded the period of study with slightly above normal precipitation
and normal levels of Eto.
86
Table 5.
Maximum (Tmax) and minimum temperatures (Tmin), precipitation (ppt.) and reference
evapotranspiration (ETo) measured over the course of the study by the AZMET weather
station located at the Karsten Desert Turfgrass Research Facility. Temperatures are
reported as monthly means and precipitation and ETo are reported as totals. Departure
from normal (+ / - Normal) compares monthly values to mean values for the period 19871997.
Date
Tmax
°C
08/97
09/97
10/97
11/97
12/97
01/98
02/98
03/98
04/98
05/98
06/98
07/98
08/98
09/98
37
36
28
24
16
20
17
22
24
31
36
37
37
36
+/Normal
Tmax
°C
0
+1
-2
+1
-3
+1
-4
-1
-4
-2
-2
-1
0
+1
Tmin
°C
23
21
11
4
1
2
3
6
7
12
16
22
23
19
+/Normal
Tmin
°C
+2
+3
0
0
0
+1
+1
+2
0
0
-1
0
+2
+1
Rain
mm
46.5
50.8
8.1
15.0
74.2
4.8
97.3
37.1
12.5
0.0
0.0
85.6
29.0
23.6
+/Normal
Rain
mm
+0.5
+35.2
-10.7
-3.5
+38
-30.9
+71
+10.8
+4
-6.1
-0.8
+50
-17
+8.1
ETo
mm
198.9
176.8
163.8
90.7
53.6
73.9
67.3
127.5
182.4
237.2
274.3
220.5
213.6
188.0
+/Normal
ETo
mm
-4.3
-11.2
+16.5
-0.7
-7.4
+2.8
-26.7
-17.3
-20.8
-6.6
+5.1
-18.3
+10.4
-1.0
87
Clearly, the most striking meteorological feature of the year of study was the
above normal winter rainfall caused by the El Nirio weather pattern. This wet winter was
the main reason twelve-month rainfall totals exceeded normal by —30% and annual ETo
totaled 3% below normal. Temperatures followed a pattern typical of wet years with
maximum temperatures running slightly below normal, but minimum temperatures
running above normal.
Irrigation is an important component of the water balance of any irrigated turf
system. An important objective of this study was to apply the same amount of water to
each lysimeter, and Table 4 reveals this goal was accomplished. Differences in applied
irrigation between lysimeters varied by less than 4% in all months except for December
1997, and March and June 1998. In December 1997, the east lysimeter received 18%
more water from irrigation than the west lysimeter. This 18% difference actually
translates to <3 mm in irrigation water due to the very wet December weather. The
difference in irrigation in March 1998 can be attributed in part to a leaking sprinkler
which reduced the precipitation rate of the west lysimeter irrigation system by about 10%
for the month. The difference in June irrigation was due to a malfunction in the potable
pump system supplying water to the east lysimeter, resulting in two days where no
irrigation was applied to the east lysimeter.
The overall pattern of irrigation followed a typical seasonal pattern with the
lowest amounts applied in the months of December through February, and the highest
amounts applied during the spring and summer months. Winter irrigations were reduced
88
due to above normal precipitation. Overall, the total amount of irrigation applied
between 1 Oct. 1997 and 30 Sept. 1998 was 1,294 and 1,297 mm for the west and east
lysimeters, respectively.
Precipitation was measured by monitoring mass changes of the lysimeter during
precipitation events. Rainfall measured by the lysimeters was always slightly higher
(-15%) than that measured by the AZMET weather station (see Appendix D). The
lysimeters provide a more accurate estimate of precipitation because (1) the lysimeter
mass gain is a direct measurement of precipitation, (2) the surface area of the lysimeter is
far greater than the limited collection area of the raingage, and (3) the raingage is
mounted 3 m above the ground and is prone to loss of precipitation due to wind
turbulence near the gage. As expected, rainfall events recorded from the mass gain by
each lysimeter were very similar. The difference in precipitation between the east and
west lysimeters was always less than 1 mm and usually less than 0.5 mm.
Total rainfall measured by the west and east lysimeters was 435 mm and 431 mm,
respectively. Periods of high rainfall occurred during the winter months of December,
February and March. These three months contributed over 50% of the precipitation for
the 12 months ending 30 September 1998. However, monsoon rains produced the highest
monthly precipitation total - - —99 mm in July 1998. Low amounts of precipitation
occurred during the summer months of May and June, when rainfall during the twomonth period totaled 0.2 mm, and in January when precipitation totaled —6 mm.
89
Turf ET followed a typical seasonal pattern with minimal ET during the winter
months and peak ET during the summer months. Comparatively, ET from the east and
west lysimeters varied slightly over the course of the study. Total annual ET was 1466
mm and 1419 mm on the west and east lysimeters, respectively. While it is difficult to
assess statistically whether the 3% greater ET observed on the west lysimeter is real,
there are some compelling reasons for believing the difference is significant. Perhaps the
most important factor supporting higher ET on the west lysimeter is the fact that turf
growth rates were higher on the west lysimeter. This was most evident during the
summer months of May and June when ET from the west lysimeter exceeded that of the
east lysimeter by —10%. During these months turf growth rates on the west lysimeter
exceeded those of the east lysimeter by 26%.
Figure 8 depicts the percent difference in monthly lysimeter ET (west vs. east) as
a function of the percent differences in turf growth rate. It is evident in Figure 8 that
higher growth rates are related to higher turf ET. The slope of the regression line in
Figure 8 was subjected to a statistical analysis which tested the null hypothesis that
the
slope did not differ from zero. The null hypothesis was rejected at p < 0.05 adding
further support to the fact that higher ET rates are related to higher rates of
biomass
for
accumulation. Higher ET and growth rates have been reported on the west lysimeter
associated with the
the past four years. Brown et al. (1998) suggested improved nutrition
Several
use of effluent as a water source was the cause of higher growth rates and ET.
90
14
rn
cvl
111
12
•
y = 0.24x + 0.30
42
0.)
.,>
r 2 = 0.71
10
ra
Tu
iI
r)
Ec
—1 VI
0
0
. _.
0
LL.1
e
._
0
a,
an
=na
c
c)
-
cu
u
,..
a)
-30
-20
-10o
10
20
30
•
•
o_
Percent Change in Growth Rate—West Lysimeter Relative to East Lysimeter
Figure 8. Percent difference in monthly turf ET between the west and
east lysimeters plotted as a function of the percent difference in
monthly turf growth rate between the west and east lysimeters.
40
50
91
other studies have observed similar relationships between increased turfgrass growth rates
and ET (Krogman 1967; Feldhake et al. 1983, 1984).
A second possible reason for higher ET in the west lysimeter may relate to the
difference in thatch in the two lysimeters. Higher turf growth rates have generated more
thatch in the west lysimeter. Visual observations indicated the heavier thatch retarded
infiltration of irrigation water, thus leaving more water at/near the surface where it would
more readily evaporate.
The seasonal and/or annual ET totals measured during this study were slightly
less than totals obtained from previous Arizona studies. Kneebone and Pepper (1982)
found the mean annual consumptive use of bermudagrass overseeded with ryegrass was
1,654 mm when the grass was well watered and fertilized for optimal performance.
Kopec et al. (1991) determined the annual consumptive use of bermudagrass overseeded
with ryegrass to be 1,525 mm. Erie et al., (1981) determined the consumptive use for
bermudagrass during the summer months was 1,005 mm. Bermudagrass ET obtained
from this study ranged from 868 mm (east lysimeter) to 906 mm (west lysimeter). The
lower ET totals observed during this study resulted in part from the wet year which
lowered overall evaporative demand. Other likely causes for lower ET observed from
this study were the lower mowing heights employed in this study and differences in
irrigation procedure. Lower mowing heights often result in lower water use rates
(Shearman and Beard, 1973; Biran et al., 1981; and Johns et al., 1983), while different
92
irrigation procedures can alter the soil evaporation component of ET (Kneebone and
Pepper, 1984).
Drainage during the period of study varied in response to total water inputs as
expected. Drainage decreased during the fall period (October - November) due to
minimal rainfall, then increased to higher levels during the winter months (January April) as water inputs greatly exceeded ET. Lack of rainfall in May and June
significantly reduced drainage in June. Monsoon rains in July and August resulted in a
subsequent increase in east lysimeter drainage during July and west lysimeter drainage in
July and August.
For the year ending 30 September 1998, drainage from the east lysimeter totaled
421 mm or 26% more than the 311 mm of drainage obtained from the west lysimeter.
The large difference in drainage results from two factors: (1) a higher drainage rate on the
east lysimeter due to residual effects of the previous study and (2) a lower turf ET rate on
the east lysimeter. Approximately 56% of the difference in total annual drainage
occurred during the months of October and November of 1997 when east lysimeter
drainage far exceeded that of the west lysimeter. Subsequent lysimeter analyses of soil
water measurements revealed the water content of the east lysimeter was slightly higher
than the west lysimeter at the onset of the study and thus was the reason for higher levels
of drainage. The higher soil moisture of the east lysimeter early in the study is believed
to be a residual effect of the previous study where irrigation rates were not controlled
with the same accuracy as achieved in this study. The remaining 46% of the difference in
93
drainage was due to lower ET on the east lysimeter. Similar amounts of water were
applied to both lysimeters during this study; however, annual ET on the east lysimeter
was 47 mm below ET on the west lysimeter. The soil water balance equation indicates
that such a scenario should lead to the increased level of drainage observed in the east
lysimeter.
The monthly change in soil water storage for both lysimeters (Table 4) reflects the
difference between the inputs (irrigation + rain) and outputs (ET + drainage) and thus
fluctuates between positive and negative values. Soil water storage declines during
periods with limited rainfall (e.g. May and June 1998) and rises during periods with
significant rainfall (e.g. February 1998). For the year ending 30 September 1998 the
change in soil water storage was -48 and -111 mm for the west and east lysimeters,
respectively. Negative storage values for the year indicate lysimeter soil moisture was
still adjusting to the new irrigation regime imposed at the start of this study. Assuming a
lysimeter soil depth of 3750 mm, the net annual decrease in soil water storage of -48 and
-111 mm equate to an average decline of Ov of 1.3% and 2.9% for the west and east
lysimeters, respectively. It is unlikely the higher soil moisture in the east lysimeter
impacted the ET and performance of the turf because soil moisture in the root zone
should have adjusted quite quickly. However, the residual effects of the previous study's
irrigation regime clearly impacted drainage and leaching characteristics.
For the year ending 30 September 1998 both lysimeters received — 1,730 mm (68
in.) of water comprised of 1,300 mm (51 in.) of irrigation water and — 430 mm (17 in.)
94
of precipitation (Table 4). According to historical data collected by AZMET over the
past twelve years (Table 5), normal precipitation for the study site averages 274 mm (10.8
in.) annually. Thus, precipitation during the period from October 1997 to September
1998 exceeded normal rainfall by 156 mm (6.1 in.). Reference evapotranspiration totaled
1,893 mm (74.5 in.) during the period of study, which is 63 mm (2.5 in.) below historical
ETo data derived at the KDTRF. Actual turf ET totaled 1,466 mm (1.46 ha-m) for the
west lysimeter and 1,419 mm (1.42 ha-m) for the east lysimeter during the period of
study (Table 4). These ET values are slightly lower than normal, which can be attributed
to the excess precipitation and the increased cloud cover encountered during this study.
Drainage totaled 311 mm and 421 mm for the west and east lysimeters, respectively.
These drainage values were quite high due to (1) residual soil water draining from the
previous irrigation regime, and (2) excessive rainfall that percolated below the turf
rooting zone. Clearly, the drainage of residual water from the previous study overstates
the drainage one would expect from the irrigation regime imposed by this study. If one
assumes the change in soil water storage represents this residual drainage, then a good
estimate of true drainage under this irrigation regime is obtained by adding the change in
soil water storage to the drainage measured in this study. Such a computation provides
drainage estimates of 263 min and 311 mm for the west and east lysimeters, respectively.
The overall water balance shows that during a year where precipitation exceeded
normal by 156 mm, total water inputs from irrigation and rainfall exceeded turf water use
by more than 250 mm. The amount of irrigation water applied totaled —1295 mm which
95
is —8% below the water duty imposed by ADWR. Given the level of drainage observed
in this study, one can conclude the ADWR water duty was sufficient to provide for turf
ET and leach salts from the rooting zone during this wetter than normal twelve-month
period. However, it is important to recognize that total irrigation applied represents 92%
of the ADWR water duty which leaves only 8% of the water allocation for use in general
turf maintenance, or to offset losses due to irrigation and plumbing inefficiencies.
Salt Balance
One objective of this study was to evaluate the potential for soil salinization when
irrigating at or below the ADWR water duty. Table 6 presents an assessment of the salt
balance in each lysimeter for the year ending 30 Sept. 1998. This period was chosen in
order to best represent the irrigation regime employed in this study. Important aspects to
consider when determining a salt balance are (1) the electrical conductivity of the applied
irrigation water (2) the total amount of water supplied in irrigation and precipitation, and
(3) the total amount and electrical conductivity of the drainage water. From these data,
the amount of salt applied through irrigation and the total amount of salt removed by
drainage can be estimated, and the leaching fraction calculated. It is evident in Table 6
that both lysimeters received similar amounts of irrigation and rainfall. However, the
amount of salt applied to the west lysimeter (4,054 g) was approximately four times
higher than that applied to the east lysimeter (1,017 g) due to the different EC of the
irrigation water. The EC of the irrigation water was 1.0 dS/m and 0.25 dS/m for the west
and east lysimeters, respectively. However, if rainfall is included and assumed to have an
96
Table 6.
Total salt inputs (through irrigation) in grams of salt applied, total salt outputs removed
through drainage over the course of the study, calculated from October 1997 to
September 1998. Values indicate totals for the study period. Total water applied
indicates the summation of rainfall and irrigation. The leaching fraction indicates the
total drainage divided by the total water applied.
Grams of Salt
Applied
East West
1,017 4,054
Grams of Salt
Drained
East
West
1,870
871
Total Water
Applied (mm)
West
East
1,729
1,729
Drainage Water
(mm)
West
East
311
421
Leaching
Fraction
West
East
0.24
0.18
97
EC of 0.0 dS/m, the effective EC of the water received by the lysimeters was 0.75 dS/m
and 0.18 dS/m for the west and east lysimeters, respectively.
High amounts of drainage were obtained from both lysimeters with the east
lysimeter drainage (421 mm) exceeding that from the west lysimeter (311 mm) by 26%.
Annual leaching fractions, computed by dividing drainage by total water inputs, also were
quite high at 0.24 and 0.18 for the east and west lysimeters, respectively. The discussion
of lysimeter water balances clearly showed that residual effects from the previous study
increased drainage in this study above what could be expected from the irrigation and
precipitation regime encountered between 1 Oct. 1997 and 30 Sept. 1998. When drainage
was adjusted to remove the effects of the previous study, the annual totals were 311 mm
and 268 mm for the east and west lysimeters, respectively. These adjusted totals suggest
more realistic leaching fractions for the study were 0.18 for the east lysimeter and 0.16
for the west lysimeter. The high leaching fractions were due to a combination of factors
including (1) water inputs from irrigation and precipitation were substantially in excess of
turf ET, and (2) high hydraulic conductivity of the sandy lysimeter soil which supported
rapid downward movement of excess soil water that accumulated during precipitation
events.
The salt balance of both lysimeters was positive for the period of study. Inputs of
salt to the east lysimeter totaled 1,017 g while drainage water removed only 871 g. The
salt balance of the west lysimeter was even more positive with salt inputs totaling 4,054 g
and salt removal accumulating to 1,870 g. The accumulation of salt in the east lysimeter
98
is minimal and reflects both the low salt content of the irrigation water and the high level
of leaching. The accumulation of salt in the west lysimeter seems quite high considering
the significant leaching fraction established. However, this high rate of salt accumulation
can partly be explained by the change in soil moisture regimes that occurred during the
transition from the previous study to the current study. In the previous study, excessive
irrigation rates provided for leaching fractions near 0.50, whereas the leaching fractions
for this study were reduced by more than half to — 0.18. The change in leaching fractions
dictated by this study has created a higher potential equilibrium status with respect to soil
salinity and thus explains the accumulation of salts in both lysimeters. Dean et al. (1996)
observed that 18 months were required after a change in irrigation before soil salt profiles
reached steady state. One might expect the time required to reach a steady state salt
balance to be even longer in the 4 m deep lysimeters. Implications of the transition to a
new soil water equilibrium status are (1) the ratio of the salt applied to the amount of salt
drained will decrease, and (2) the EC of the drainage water will rise over time until the
new equilibrium is established, then the salinity of the drainage water will remain fairly
constant.
The trend toward increasing EC in the drainage water is clearly shown in Figure
9, where the drainage water EC is plotted over the course of this study for both
lysimeters. Since the onset of the study, drainage water EC has increased by 40% and
18% in the west and east lysimeters, respectively. Ayers and Westcott (1989) provide
procedures for estimating the soil water EC for various leaching fractions and irrigation
99
Drainage Water EC over Time
East & West Lysimeter
2.4
. A ..1 alb& A
1.9
I
ll
0.9
l
..;
0.4
r- "
%
‘.•.„,
A
1
MA
vw Y7
,•
•
‘.."•.. .... ..... „..•, • ••••".••. •••
•
. nsA
v
••..,
•• n '
ii ii II I} i IIIIIIMMIIIIIIMM MIIIIIiIIMM HMI ill
to
N CO CO CO
0) 0)
to
to
(;)
co
c6
2 <
6 e,
ON
WEST
4
EAST
Figure 9. Electrical conductivity (EC) of the drainage water obtained
from the east and west lysimeters over the course of the study.
100
water salinities. They indicate drainage water EC should approach 5.6 dS/m in the west
lysimeter when the ECiw = 1.0 dS/m and the leaching fraction is 18%, and 1.0 dS/m in
the east lysimeter when the ECiw = 0.25 dS/m and the leaching fraction is 22%. As
previously indicated, rainfall effectively reduced the EC of the irrigation water by 25%.
If precipitation levels that occurred during this study were to continue, drainage water in
the lysimeters should continue to increase to levels approximating 4.2 dS/m and 0.8 dS/m
in the west and east lysimeters, respectively. The EC of the west lysimeter drainage
water was 2.1 dS/m as of 30 Sept. 1998, suggesting soil profile adjustment to the new
irrigation regime is not complete. In contrast, the EC of the east lysimeter drainage was
0.8 dS/m at the conclusion of the study - - a value that would imply a near equilibrium
salt balance is already established in the east lysimeter. It is important to note than
normal fluctuation in precipitation will eliminate the possibility of developing a true
equilibrium in the lysimeters. For example, if precipitation levels were to decrease to
normal and leaching fractions decline to about 0.10, the drainage water EC from the
lysimeters should increase to levels closer to 10.0 dS/m for the west and 2.5 dS/m for the
east lysimeter.
Tables 7 and 8 and Figure 10 present a month-by-month assessment of inputs,
outputs and the net balance of salts in each lysimeter for the period between October
1997 and September 1998. Both the tables and the Figure clearly show the overall salt
balance differed significantly among the two lysimeters. The salt balance of the east
lysimeter was negative during the fall and winter (October through March) because: (1)
101
Table 7. Month-by-month salt Balance over the course of the
study for the east lysimeter. Monthly totals indicate either salt
input through irrigation, salt output removed through drainage or
the net balance between inputs and outputs.
Salt Balance -- East Lysimeter
Salt Input Salt Output Net Balance
Month
(+ or -)
grams
grams
Aug-97
Sep-97
Okt-97
Nov-97
Des-97
Jan-98
Feb-98
Mar-98
Apr-98
Mei-98
Jun-98
Jul-98
Aug-98
Sep-98
138.3
75.1
93.2
59.7
9.1
40.6
5.6
65.9
79.5
143.3
181.9
114.3
115.4
108.1
329.3
155.7
93.7
102.2
44.9
65.2
44.5
67.6
70.5
78.3
44.7
79.0
115.6
64.4
-191.0
-80.6
-0.5
-42.4
-35.8
-24.6
-38.9
-1.8
9.0
65.0
137.3
35.3
-0.2
43.7
Table 8. Month-by-month salt Balance over the course of the
study for the west lysimeter. Monthly totals indicate either salt
input through irrigation, salt output removed through drainage or
the net balance between inputs and outputs.
Salt Balance -- West Lysimeter
Salt Input Salt Output Net Balance
Month
(+ or -)
grams
grams
Aug-97
Sep-97
Okt-97
Nov-97
Des-97
Jan-98
Feb-98
Mar-98
Apr-98
Mei-98
Jun-98
Jul-98
Aug-98
Sep-98
541.8
310.6
372.9
248.4
27.8
157.5
22.8
235.6
324.3
574.7
788.6
439.7
443.2
418.2
564.7
263.2
177.6
154.4
141.7
191.5
189.5
161.1
181.6
181.2
96.8
98.9
150.5
144.7
-22.8
47.4
195.3
94.0
-114.0
-34.0
-166.6
74.4
142.7
393.5
691.8
340.9
292.6
273.6
102
Salt Inputs (Irrigation) and Outputs (Drainage) for
Both Lysimeters
(3.>
1
A
I
3
8
3.0
-3 u_a)
33
D.
CO
,M1A../
g800
CV)
L.
cn
..S.
600
. g
400
1
-..
13= 200
0
-
OS
--n—r 1
.17. -200
..
1111
4 iiii ki.„
4
.-400
0=
c
v1
Cl)
(
-£300
El Salt
Input(East)
0 Salt Input(West)
• Salt Output(East)
0 Salt Output(East)
Figure 10. Components of the salt balance for the east and
west lysimeters during the period August 1997 to September 1998.
Monthly inputs of salt from irrigation in grams are presented as
positive values on the y-axis. Monthly outputs of salt from drainage in
grams are presented as negative values on the y-axis.
103
water supply was well above ET values, producing increased drainage, and (2) significant
rainfall reduced salt input as nearly salt-free rainfall replaced irrigation as the source of
water. In contrast, the salt balance in the west lysimeter was positive from September
through November and during March. The west lysimeter salt balance turned negative
only during the winter months of December through February when salt inputs decreased
due to low irrigation rates and drainage increased as a result of heavy precipitation.
The normal combination of low precipitation and high evaporative demand
resulted in high levels of irrigation in April, May and June 1998. Salt inputs increased
dramatically and drainage slowed, producing a positive salt balance in both lysimeters.
Arrival of the monsoon rains in July lowered irrigation rates due to rainfall and reduced
evaporative demand. However, the salt balance for both lysimeters remained positive
through September 1998, with the exception of the east lysimeter in August when inputs
and outputs were essentially equal.
While Tables 7 and 8 represent but one year of data, the results provide some
clear guidance on when leaching is most likely to occur. The winter months are clearly a
good time for leaching because ETa is low and rainfall is common. Natural leaching can
be expected during those winter months when rainfall is above normal or excessive
irrigation is applied. Another obvious time for leaching is during overseeding when
increased irrigation is required to germinate the winter grass. It appears the increase in
October irrigation that accompanied overseeding did create a leaching event in the east
104
lysimeter. However, there is no strong indication that irrigation during overseeding
produced an increase in leaching from the west lysimeter.
The most difficult time to leach is during the summer months when ETa is high.
It is evident in Tables 7 and 8 that during extended dry periods such as in May and June,
salts can be expected to accumulate due to high rates of irrigation needed to replace turf
ET. However, the annual monsoon weather pattern in July and August typically brings
increased atmospheric humidity and greater rainfall, thus providing potential leaching
events. The amount of salt removed from both lysimeters increased during the summer
months due to an increase in drainage. In most months, however, the lysimeter salt
balances remained positive suggesting the summer rains did not purge salts from the
lysimeters. One must be careful to not interpret the positive salt balances as indicating
summer rains have no leaching potential. As indicated earlier, the salt balance of the
lysimeters is in a readjustment phase. Once the adjustment is complete, summer rains
will likely produce negative salt balances in the lysimeters. In situ measurements of soil
salinity using time domain reflectometry provide evidence of summer leaching and are
described below.
Assessment of Salinity Profiles
Time domain reflectometry provided an effective means for evaluating the
changes in salinity throughout the soil profile over time. Figures 11 and 12 depict the
salinity profiles at depths of 0.5, 1.0 and 2.0 m for the west lysimeter and 1.0 and 2.0 m
for the east lysimeter during the course of the study. Figures 11 and 12 represent the bulk
6
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L6 - An N - 9Z
- L6 - A 0 N -171•
— L6 - 4310 - 1.2
- L6 - 4)10 - L
— L6 - 1,10 - 20
— L6 - daS - 6
- 2.6 - d 8 S - S0
— L6 -6 nV - LZ
L6 -6 nV - LO
•
I
op
o)
sC7I
cn 0 N N
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D 6 o o
ci 6 6 ci ci 6
6 6 6 6 ci ci
N 0)
• d-
00 0 o
00
•:1-
CID
WISP B03
L6-Inr-tz
107
soil salinity (ECa) at varying depths, where each depth represents the mean ECa derived
from the three angular sampling positions on the lysimeters. As expected, the most
discernible changes in ECa occurred above one meter where plant roots are actively
extracting soil water. Fluctuations in salinity over time reflect dry periods requiring high
levels of irrigation which result in increases in salinity (if not applied in excess of turf
ET) and significant rainfall periods which result in leaching events (decreases in salinity).
Comparatively, the most significant changes in ECa over time were observed in the west
lysimeter where salt loading from irrigation was highest.
Bulk soil salinity in both lysimeters followed a slight decreasing trend from
August through October 1997, perhaps reflecting impacts of excessive irrigation from the
previous system. As drainage decreased ECa increased in November and into the early
part of December 1997. In the latter part of December, rainfall (76 mm) significantly
decreased the ECa in both lysimeters. Minimal rainfall in January 1998 (only 5 mm) did
not provide significant leaching, and ECa increased slightly in both lysimeters. However,
excessive February rainfall provided substantial leaching and reduced ECa in both
lysimeters.
The next significant increase in ECa occurred in June on the west lysimeter,
following two dry months where turf water requirements were met by irrigation. Bulk
soil EC increased 53% at 0.5 m relative to values observed during the wet winter period
of early March. The ECa data appear to reflect and/or confirm the findings presented
earlier (Table 8) which show very positive monthly salt balances in May and June. A
108
similar rapid increase in ECa was not observed in the east lysimeter. The failure to
observe such an increase in ECa likely reflects the lower net increase in salts in the east
lysimeter and the fact that ECa measurements were not available in the east lysimeter at
the 50 cm depth.
Monsoon rainfall began on 5 July, and ECa at 50 cm in the west lysimeter
declined precipitously as salts leached below the 0.5 m region. This salt laden pulse
reached the 1.0 m depth about ten days later, producing a very substantial peak in ECa at
1.0 m in mid-July. As the July rains continued, the pulse of salts moving with the
percolating water moved beyond the 1.0 m region and produced an increase in ECa at the
2.0 m depth in late July and early August. Although the monsoon season did provide
substantial leaching, Figures 11 and 12 show that ECa in the west lysimeter did not
decrease to levels comparable to those in the wet winter months. These results are in
complete agreement with the salt balance data presented in Tables 7 and 8. While the
monthly balances show an increase in the amount of salts leached in July and August, the
net salt balance of the lysimeters remained positive throughout the summer.
Turf Quality
An important objective of this research was to determine whether irrigating at
levels equal to or below the ADWR water duty would produce acceptable quality turf.
On average, turfgrass quality was acceptable or better during most of the study (Figure
13). There were three periods where turf quality slipped below acceptable levels for the
study period. The first period followed overseeding in October and was not unexpected.
109
Quality Ratings Over Time for the East and West Lysimeter
9
7
i
n Accep.
4—
3
N- 1"--
0)0)
D
>
0 0
(3?
0
(;)
co
03 CO
CO CO CO CO CO CO CO CO CO CO CO 03 CO 03 CO CO CO CO
i a? Cri ai ai
cl? ai ai a? ai a) CY) ai ai a
a.' ci)
Ci) CL CL
C C5
.C1
-‘5 735
w w w
"
cs_
sa
sa
as
°)
z 0 —)
6
0!) 6
4
6
°? °? °?
u_
2 < < 2
(;-)
N
o
West
4
< <
0 N
(.6
6
0 N
East
Figure 13. NTEP quality ratings of west and east lysimeter turf over the course of
the study. Rating of 6 indicates acceptable quality turf
110
A second period of decreased turf quality occurred in late November and December,
when temperatures turned too cold to support adequate ryegrass growth. A nitrogen
deficiency was also evident during this period as indicated by a slight yellowing of the
older leaves on the turf plant. This period of poor turf quality lasted for nearly a month
and was resolved when the lysimeters were re-seeded in mid-January 1998. The third
period of significant turf quality reduction occurred during the transition from ryegrass to
bermudagrass in early July. Summer transition to bennudagrass is often slow at the
KDTRF due to cold night temperatures that develop as a result of cold air drainage from
higher elevation areas surrounding the facility. Summer transition at KDTRF is often
delayed until the nights warm in response to the monsoon.
Mean turf quality ratings over the course of the study were 6.2 and 5.8 for the west
and east lysimeters, respectively. The quality ratings of west lysimeter turf were
significantly higher than quality ratings for the east lysimeter turf (p = 0.029), a finding
consistent with previous studies. The improved quality of the west lysimeter turf is
presumably due to improved plant nutrition that results from daily irrigation with effluent
water.
The quantity of water supplied to the turf did not impact turf quality. Figures 14
and 15 provide mean turf quality ratings and the amount of water supplied to the turf as a
percentage of ETa for each month of the study. Turfgrass quality did not improve when
a
greater percentage of water was applied, suggesting water was not a limiting factor. This
fact is clearly shown in Figures 16, where monthly quality ratings show no
111
Turfgrass Quality vs Supplied Water
West Lysimeter
—8
300 —
—7
250 —
6
cTs
- 200 —
ô
150
—
!NT
73
a 100 —
l
=CL
7
•
(/)
—2
50 —
o
0
I
Aug- Sep- Old- Nov- Des- Jan- Feb- Mar- Ap - Mel- Jun- Jul-98 Aug- Sep98
98
98
98
98
98
98
97
98
97
97
97
97
—
--M—Quality
= Supplied Water
Figure 14. Turfgrass quality and the the total amount of water
supplied to west lysimeter turf by month for the period August 1997-
September 1998.
Turfgrass Quality vs Supplied Water
East Lysimeter
—8
300 —
—7
250 —
-6
200 —
-5
—4
150 —
—3
100 —
—2
50 —
o
—1
•
Aug- Sep- Old- Nov- Des- Jan- Feb- Mar- Apr- Mel- Jun- Jul- Aug- Sep98 98
97 97 97 97 97 98 98 98 98 98 98 98
C=I Supplied Water
4-- Quality
—
Figure 15. Turfgrass quality and the the total amount of water
supplied to east lysimeter turf by month for the period August 1997-
September 1998.
0
112
Turfgrass Quality vs. Supplied Water
8—
7—
*
6—
•
•
5—
•
4—
3—
2—
y = -0.09x + 6.13
r2 = 0.00
1—
0
0
50
100
150
200
250
Water Supplied (%ETa)
Figure 16. Monthly mean turfgrass quality ratings from both
lysimeters plotted as a function of water supplied to the turf. Water
supplied is presented as a percentage of ETa. The line depicted
represents the least squares regression line.
300
350
113
distinguishable relationship with the level of supplied water. It is important to realize that
the level of water supplied to the turf was not below the expected turf ET in most months,
thus water stress induced reductions in quality should not have been expected.
Under the objectives of this study, it was important to determine whether bulk soil
salinity (ECa) levels would hinder turfgrass quality. In order to investigate this objective,
turfgrass quality was regressed against ECa for the east and west lysimeters (Figures 17
and 18). Bulk soil EC data in Figures 17 and 18 are from the 0.5 m depth in the west
lysimeter and the 1.0 m depth in the east lysimeter. These depths were chosen in order to
best represent ECa near the turf root zone. Figures 17 and 18 suggest that no discernible
relationship existed between turf quality and ECa. The least squares regression lines
presented in Figures 17 and 18 were subjected to a statistical analysis which tested the
null hypothesis that the slopes did not differ from zero. In both cases, the null hypothesis
was not rejected, indicating the regression line slopes did not differ from zero. Thus it
was concluded that ECa did not accumulate to levels capable of hindering turfgrass
quality. The lack of relationship between turf quality and ECa was not surprising. Water
inputs were typically in excess of ETa during the course of the study, which helped
minimize the buildup of salt in the root zone. The EC of the lysimeter drainage
water
also suggests there should be no relationship between ECa and turf quality. Drainage EC
was well below levels deemed dangerous to turfgrass performance (Ayers and Wescott,
1989).
114
Turfgrass Quality vs. Bulk Soil Salinity
West Lysimeter (0.5 m depth)
8—
• •
••
y = -25.89x + 6.74
•
••
12 = 0.02
7 -6—
•
••
•
•
5 —
4 —
3
0.000
0.005
0.025
0.020
0.015
0.010
0.030
Bulk Soil EC dS/m
Figure 17. Mean monthly turfgrass quality ratings plotted as a
function of bulk soil electrical conductivity obtained at the 0.5 m depth
in the west lysimeter. The line depicted is the least squares
regression line.
8
Turfgrass Quality vs. Bulk Soil Salinity
East Lysimeter (1.0 m depth)
-
7—
0
y = 98.06x + 4.14
"'" "
12=0.04
6
.
47
0
0 0.
5 —
4 —
3
0
0.005
0.01
0.015
0.02
Bulk Soil EC dS/m
Figure 18. Mean monthly turfgrass quality ratings plotted as a
function of bulk soil electrical conductivity obtained at the 1.0 m depth
in the east lysimeter. The line depicted is the least squares
regression line.
0.025
115
Biomass Accumulation and Turf Growth Rates
Biomass accumulation and growth rate provide a second means of assessing
turfgrass performance under the irrigation regime employed in this study. Biomass
accumulation, as determined by collecting, drying and weighing turf clippings after each
mowing event is presented in Figure 19. The two large peaks in biomass accumulation
occurred during the de-thatching and scalping process required prior to the fall and winter
overseed events. Figure 20 depicts the mean monthly turf growth rates obtained from
both lysimeters over the course of the study. Biomass collected during overseed
preparation was removed from the data presented in Figure 20. Figure 20 indicates
bermudagrass growth rate declined in September 1997 prior to overseeding in October.
In October and November, growth rate increased significantly due to ryegrass
germination and growth. Turf growth rate and stand density then decreased considerably
in December due primarily to cold temperatures. Reseeding of the lysimeters in midJanuary resulted in improved growth rates in late January and in February. Growth rates
continued to increase into March as temperatures were favorable for ryegrass growth. In
April, growth rates decreased slightly, but rebounded in May to levels similar to that of
March.
The slow transition from ryegrass to bermudagrass caused growth rates to
decrease in June. Similar to previous years, growth rates increased in July and August
during the annual monsoon weather pattern, and then declined as temperatures cooled in
September.
•
• 2
116
Collected Biomass Over Time
1600
Fall
Overseed
1400 —
1200 —
1000
—
S
800
•
600 —
•
•
cl;)
a' )
OD
(3)
OD
CO
Cr.)
al )
Q
oo
cr?
3
—
cr)
0
6 z
6
--
o
- 3
(44
17N
OD
II)
7
0
Clipping Weights East
—Clipping Weights West
Figure 19. Turf biomass collected from the west and east lysimeters
over the course of the study. Biomass is presented in grams
for both lysimeters.
Turfgrass Growth Rate
East and West Lysimeters
6—
Aug- Sep- Old- Nov- Des- Jan- Feb- Mar- Apr Mei-
97
97
97
97
97
98
• Growth Rate-West
98
98
98
98
98
98
D Growth Rate-East
Figure 20. Monthly growth rates in g/m2 /day for turf grown on the east
and west lysimeters between August 1997 and September 1998.
98
117
Comparatively, the turf on the west lysimeter had a higher growth rate than the east
lysimeter turf over the course of the study. Turf on east lysimeter grew at a higher rate
only during the months of January and August 1998. The total biomass accumulation
during the period (October 1997-September 1998) was 5,498 g and 6,314 g for the east
and west lysimeters, respectively. Average turf growth rates for the east and west
lysimeters were 1.87 g/m2/day and 2.22 g/m 2 /day, respectively over the course of the
study.
Turf growth rates were compared with the supply of water provided to the turf as
a means of assessing whether water was a factor impacting growth (Figure 21). One
would expect that low amounts of water supplied to the turf would result in decreased
growth rates. However, the turf received water in excess of its ET requirement in every
month during the study except April, thus water stress induced reductions in turfgrass
growth rates were not expected. Turfgrass growth rates were lowest during the months
September of 1997, December and January. Figure 21 suggests that growth rates in
September were low due to the low amount of water applied. However, September is
typically a month where temperatures decrease and bermudagrass growth slows.
Ryegrass growth rate was low in December 1997 and January 1998 due to a weakening
turf stand and cooler temperatures.
If water stress was limiting growth one would expect ample or even excessive
water applications to produce the highest growth rates. This phenomenon, termed luxury
rates
water use, was shown by Kneebone and Pepper (1984). Periods of high growth
118
Turfgrass Growth Rate vs Supplied Water
– 350
6.00 –
Aug- Sep- Okt- Nov- Des- Jan- Feb- Mar- Ap - Mel- Jun- Jul Aug- Sep97 97 97 97 97 98 98 98 98 98 98 98 98 98
Growth Rate-West
—•—WEST Supplied Water
Growth Rate-East
-
— EAST Supplied Water
Figure 21. Turfgrass growth rates and the total amount of water
supplied to both lysimeters by month for the period August 1997September 1998.
119
occurred in November 1997 and March, May, July and August 1998. However, the water
supplied during these months was not comparatively higher than other months (Figure
21). An increase in water application did prove beneficial during overseeding events.
The relationship between growth rate and water supply was further evaluated by
comparing growth rate against the level of supplied water reported as a percentage of ETa
(Figure 22). The least squares regression line presented in Figure 22 implies a reduction
in turf growth rate with higher rates of supplied water. However, a statistical evaluation
of the regression line determined that the slope was not significantly different from to
zero (the null hypothesis was rejected), indicating this negative relationship between
growth and water supply is likely not real.
Regression analysis was used to determine if growth was related to ECa. Monthly
turf growth rates were regressed on ECa (Figures 23 and 24). Bulk EC data for the
lysimeters were again taken from the 0.5 m depth in the west lysimeter and the 1.0 m
depth in the east lysimeter. Results from these analyses reveal no relationship between
growth and ECa. Statistical assessment of the slopes of the regression lines revealed
neither slope was significantly different from zero.
It is not surprising that growth rate appears unrelated to ECa. Bulk soil EC and
soil solution EC peaked in early summer at 0.03 dS/m and 3 dS/m, respectively. The soil
solution EC would convert to an extract ECe value of — 1.5 dS/m. Ayers and Westcott
(1989) indicate ECe values of 6.9 dS/m are required to reduce bermudagrass yield.
120
Turfgrass Growth Rate vs Supplied Water
6y = -0.75x + 3.04
r2 = 0.09
5-
en
4-
-
3 -
m
2 -
t
1-
m
181
It
II
1 -
Ei
0
0
o
50
100
150
200
250
300
Supplied Water (% of ETa)
Figure 22. Monthly mean turfgrass growth rates from both lysimeters
plotted as a function of water supplied to the turf. Water supplied is
presented as a percentage of ETa. The line depicted represents the
least squares regression line.
350
121
Turfgrass Growth Rate vs. Bulk Soil Salinity
West Lysimeter (0.5 m Depth)
6—
5—
•
y = 17.16x + 1.86
r = 0.00
2
4—
••
• •
3—
2 —
•
•
1 —
•
0
0.000
•
0.005
0.025
0.020
0.015
0.010
0.030
Bulk Soil EC dS/m
Figure 23. Mean monthly turfgrass growth rates plotted as a function
of bulk soil electrical conductivity obtained at the 0.5 m depth in the
west lysimeter. The line depicted is the least squares regression line.
Turfgrass Growth Rate vs. Bulk Soil Salinity
East Lysimeter (1.0 m Depth)
6—
ro
•
"E
"i3)
5—
y = -265.49x + 6.43
12
= 0.11
cu 4 —
rts
2
•m
u'
:th
3
-
2
—
•%<<to
•
1—
• •
o
0
0.005
0.01
0.015
0.02
Bulk Soil EC dS/m
Figure 24. Mean monthly turfgrass growth rates plotted as a function
of bulk soil electrical conductivity obtained at the 1.0 m depth in the
east lysimeter. The line depicted is the least squares regression line.
0.025
122
Harivandi et al., (1992) lists bermudagrass as tolerant to salinity stress (Table 1),
indicating that bermudagrass is difficult to establish or maintain at ECe values > 10dS/m.
Soil salinity during this study did not reach these levels, thus bermudagrass yield did not
decline due to salinity stress.
Turf growth rates were compared with those obtained by Brown et al. (1998) to
further evaluate whether irrigating within the limits of the ADWR water duty would
hinder turf growth. Growth rates reported by Brown et al. (1998) were for turf irrigated
well above turf ET and the ADWR water duty. Growth rates observed during the current
study exceeded those from the previous study during vigorous growth periods except in
the month of April (Figures 25 and 26). The lower growth rates observed during the
month of April for this study could be attributed to abnormally cool temperatures. Mean
temperature in April 1998 was 16°C. Comparatively, mean temperature was 4°C higher
during the previous study (April 1996). The higher growth rates during this study
indicate the irrigation regime employed in this study coupled with above normal rainfall
did not hinder turfgrass growth. Improved nitrogen fertility and better cultural
management of the turf are possible reasons for the improved turf performance in this
study.
Crop Coefficients
Turfgrass crop coefficients (Kcs) are presented in Table 9 for each month of the
study. Crop coefficients ranged from a low of 0.63 in December 1997 to a high of 0.83 in
123
Growth Rate--Previous Study vs Current Study
West Lysimeter
3.5
0.5
Bermudagrass
• Previous Study
<
Mar
D Current Study
Figure 25. Comparison of the turf growth rates obtained from the
previous study (1996) with those obtained in the current study (1998)
for the west lysimeter.
Growth Rate--Previous Study vs Current Study
East Lysimeter
3.00
1.00
0.50
Jul
Bermudagrass
• Previous Study
CI Current Study
Figure 26. Comparison of the turf growth rates obtained from the
previous study (1996) with those obtained in the current study (1998)
for the east lysimeter.
124
Table 9.
Monthly crop coefficients computed using ETa data obtained from the east and west
lysimeters, and reference ET data provided by the Arizona Meteorological Network.
MONTHLY CROP COEFFICIENTS
ARIZONA PENMAN EQUATION
DATE
EAST LYS. WEST LYS.
Aug-97
0.80
0.83
Sep-97
0.78
0.81
0.71
Oct-97
0.68
0.79
Nov-97
0.78
0.66
0.63
Dec-97
0.66
0.68
Jan-98
0.80
0.83
Feb-98
0.77
0.75
Mar-98
0.75
0.72
Apr-98
0.83
0.77
May-98
0.80
0.70
Jun-98
0.79
0.79
Jul-98
0.82
0.83
Aug-98
0.75
0.74
Sep-98
125
August 1997 and May 1998. The low Kcs obtained in December and January coincide
with the period of declining turf quality and thus may underestimate the Kcs appropriate
for higher quality turf. It is important to note, however, that low Kcs were observed
during these same months in the previous study. Brown et al. (1998) reported December
Kcs ranged from 0.60 to 0.71 over a three-year period. It therefore appears factors other
than low turf quality can reduce Kcs at this time of year. Other possible reasons for low
winter Kcs may be turf response to frost and/or low temperature conditions, or a
weakness in the Penman estimates of ETo at that time of year.
Seasonal Kc values were developed by averaging the monthly Kcs for the
respective winter and summer periods. Winter Kcs were 0.73 and 0.74 for the east and
west lysimeters (Figures 27 and 28), respectively and compared favorably with the winter
Kc value of 0.72 obtained by Brown et al. (1998). Summer Kcs averaged 0.77 for the
east lysimeter (Figure 29) and 0.80 for the west lysimeter (Figure 30), again supporting
previous research that determined the summer Kc to be 0.77.
Overall, Kcs obtained in this study were slightly higher than Kcs obtained in the
previous study (Brown et al. 1998). Whether the higher Kcs are truly different in a
statistical or practical sense is debatable; however, both weather and higher turf growth
rates resulting from improved nitrogen nutrition could produce an increase in Kcs. The
relationship between higher turf growth rates and higher ET was described in an earlier
section of this chapter. Turf growth rates in this study were higher than those reported in
the previous study, and may be the cause of the higher Kcs. Brown et al. (1998) also
126
WINTER CROP COEFFICIENT: EAST LYSIMETER
ARI7ONA PENMAN FOUATION
1.00
1!!!!!!!!!!!"!
0.80
0.73
3 0.60
u.
tu
0
c.)
0.
0.40
201n.,
O
ce
0.20
0.00
Nov-97
Des-97
Jan-98
Feb-98
Mar-98
Apr-98
TIME PERIOD 1997 1998
-
Figure 27. Mean monthly crop coefficients (Kcs) for overseeded
intermediate ryegrass grown on the east lysimeter. The seasonal
mean Kc value is depicted by the horizontal arrow.
WINTER CROP COEFFICIENT: WEST LYSIMETER
ARIZONA PENMAN EQUATION
1.00
0.80
2
u_
77.!!!!!!!Zr
0.74
•-nn11•1=1.
0.60
u_
Ui
O
C.)
a. 0.40
0.20
1n11n•=1, .
j11n1•n•n•
0.00
Nov-97
Des-97
Jan-98
Feb-98
Mar-98
TIME PERIOD 1997 1998
-
Figure 28. Mean monthly crop coefficients (Kcs) for overseeded
intermediate ryegrass grown on the west lysimeter. The seasonal
mean Kc value is depicted by the horizontal arrow.
Apr-98
127
SUMMER CROP COEFFICIENT: EAST LYSIMETER
ARIZONA PENMAN EQUATION
1.00
0.80
—7
•••••••••••••
0.77
0.60
0.40
0.20
1
0.00
Mei-98
Jun-98
Jul-98
Aug-98
Sep-98
TIME PERIOD 1997-1998
Figure 29. Mean monthly crop coefficients (Kcs) for
bermudagrass grown on the east lysimeter. The seasonal
mean Kc value is depicted by the horizontal arrow.
SUMMER CROP COEFFICIENT: WEST LYSIMETER
ARIZONA PENMAN EQUATION
1.00
i—
z
-10•
0.80
ei
E, 0.60
u_
-
0
0.40
O
re
o
0.20
0.00
Mei-98
Jun-98
Jul-98
Aug-98
TIME PERIOD 1997-1998
Figure 30. Mean monthly crop coefficients (Kcs) for
bermudagrass grown on the west lysimeter. The seasonal
mean Kc value is depicted by the horizontal arrow.
Sep-98
0.80
128
found that wet weather conditions produced higher Kc values because the AZMET
Penman Equation underestimates ETo during cloudy periods. This tendency was again
observed in this study and is best examined in the wet month of February. Crop
coefficients obtained in February were the highest of the winter season and approached
the peak Kcs for the entire year which occurred in August 1998 - - another humid, cloudy
month.
Daily Kcs were examined to provide some assessment of the day-to-day
variability in Kcs. Means and standard deviations of the daily Kcs were computed for
each lysimeter and season, and are presented in Figures 31-34. It is clear from the
standard deviations presented in Figures 31-34 that daily Kcs are more variable in winter
than summer. Coefficients of variation (CVs) for monthly Kcs, computed by dividing Kc
standard deviations by the mean monthly Kc values, clearly reveal the differences in
winter and summer Kc variation (Figure 35). Winter Kc CVs averaged 0.21 and 0.19 for
the east and west lysimeters, respectively, whereas summer Kc CVs averaged only 0.08
for the east and 0.09 for the west lysimeter. Some of this greater variation in daily Kcs is
due to weaknesses in the Penman estimates of ETo during wet conditions. This
weakness
is evident during the wet month of February when Kc CVs reached their highest levels
of
similar increase
the year at 0.33 and 0.26 for the east and west lysimeters, respectively. A
in Kc CVs is evident in July when rainfall was well above normal.
Lower day-to-day
month
variation in Kcs is evident during dry months. Crop coefficient CVs in May, a
with no precipitation, averaged about 0.05.
129
Winter Crop Coefficient
East Lysimeter
0.73
Feb-98
Mar-98
Apr-98
Figure 31. Mean daily crop coefficients (Kcs) by month for
intermediate ryegrass grown on the east lysimeter. Error bars
represent the standard deviation of the mean daily Kc.
Winter Crop Coefficient
West Lysimeter
0.74
Nov-97
Dec-97
Jan-98
Feb-98
Mar-98
Figure 32. Mean daily crop coefficients (Kcs) by month for
intermediate ryegrass grown on the west lysimeter. Error bars
represent the standard deviation of the mean daily Kc.
Apr-98
130
Summer Crop Coefficient
East Lysimeter
0.77
May-98
Jun-98
Jul-98
Aug-98
Sep-98
Figure 33. Mean daily crop coefficients (Kcs) by month for
berrnudagrass grown on the east lysimeter. Error bars
represent the standard deviation of the mean daily Kc.
Summer Crop Coefficient
West Lysimeter
0.80
May-98
Jun-98
Jul-98
by month for
Figure 34. Mean daily crop coefficients (Kcs)
bars
bermudagrass grown on the west lysimeter. Error
Kc.
mean
daily
represent the standard deviation of the
131
Comparison of the Monthly Coefficient of Variation for Each
Lysimeter
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
ClEast Lys.
• West Lys.
0 East Lys. (no rain)
0 West Lys. (no rain)
Figure 35. Coefficient of variation of mean daily crop coefficients
computed using all days in a month and only dry days (no rain).
132
To further investigate the impact of rain on Kc stability, CVs were computed for
monthly Kcs developed using only dry days (days with rain excluded). Figure 35
provides the comparison of the CVs obtained from all days with CVs computed using
only dry days. The CVs based on dry days decline significantly in months with
substantial rainfall (December, February, March and July).
Regression analysis was used to further assess whether Kcs differed among
lysimeters and seasons. Daily values of turf ET from each lysimeter were regressed on
daily totals of ETo for the winter and summer periods. Figures 36 and 37 present the data
and the resulting least squares regression lines for the winter months, and Figures 38 and
39 provide the same information for the summer months. The y-intercept was set equal
to zero for all regression lines to facilitate assessment of line slopes (dETa/dETo) which
provide another means of estimating seasonal Kc values. A good linear relationship was
obtained between turf ET and ETo during the winter months (Figures 36 and 37). Winter
Kcs based on regression line slopes were 0.72 and 0.74 for the east and west lysimeters,
respectively. These values agree quite closely with seasonal Kcs developed from
monthly totals of turf ET and ETo (Figures 27 and 28).
The relationship between turf ET and ETo was a bit more scattered during
the
summer months as evidenced by the lower r2 associated with the least squares regression
and
lines (Figures 38 and 39). Summer Kcs based on regression line slopes were 0.76
developed from this
0.80 for the east and west lysimeters, respectively. Again, the Kcs
133
Lysimeter Turf ET vs Reference ET
East Lysimeter -- Winter Regression (Nov-Apr)
7-
o
7
1
8
Figure 36. Daily values of turf ET from the east lysimeter versus
daily ETo for the period November 1997 through April 1998.
The line depicted is the least squares regression line with
intercept forced through zero.
Lysimeter Turf ET vs Reference ET
West Lysimeter -- Winter Regression (Nov-Apr)
Reference ET (mm)
Figure 37. Daily values of turf ET from the west lysimeter versus
daily ETo for the period November 1997 through April 1998.
The line depicted is the least squares regression line with
intercept forced through zero.
9
10
134
Lysimeter Turf ET vs Reference ET
East Lysimeter -- Summer Regression (May-Sept)
9.0
•
8.0
7.0 n••
6.0 •••
5.0 •n
4.0
3.0
2.0
1.0 n•
0.0
10.0
8.0
6.0
4.0
2.0
0.0
12.0
Reference ET (mm)
Figure 38. Daily values of turf ET from the east lysimeter versus
daily ETo for the period May through September 1998.
The line depicted is the least squares regression line with
intercept forced through zero.
Lysimeter Turf ET vs Reference ET
West Lysimeter -- Summer Regression (May-Sept)
9
8
7
6
5
4
3
2
1
0
o
2
4
6
8
10
Referecne ET (mm)
Figure 39. Daily values of turf ET from the west lysimeter versus
daily ETo for the period May through September 1998.
The line depicted is the least squares regression line with
intercept forced through zero.
12
135
regression analysis agree quite closely with Kcs developed from monthly totals of turf ET
and ETo (Figures 29 and 30).
Observed differences in Kcs among seasons and lysimeters were evaluated using
the regression procedure described by Johnston (1984). The difference of 0.05 between
summer (0.78) and winter Kc values (0.73) was found to be highly significant at p <
.0003. However, assessment of the Kc difference of 0.017 between the west (0.75) and
east lysimeters (0.73) was less clear cut. The Kc difference was significant only if p was
greater than .012, suggesting the difference was only marginally significant.
136
CHAPTER 5
CONCLUSIONS
This study was designed to address industry concerns regarding the adequacy of
the present ADWR water duty to provide (1) sufficient water for culture of acceptable
quality turf produced under high maintenance conditions in both wet and dry years and
(2) adequate water to leach the necessary amount of salts below the turf root zone. Soil
water and salinity balances, turf quality and growth rates, and turf water requirements
were monitored in order to address the aforementioned industry concerns regarding the
adequacy of the ADWR water duty.
Water Balance
Water balance data indicate that during a year where precipitation exceeded
normal by 156 mm, total water inputs from irrigation and rainfall exceeded turf water use
by more than 250 mm. Turf irrigated with effluent (west lysimeter) used 3% more water
then turf irrigated with local groundwater (east lysimeter), presumably due to (1) higher
turf growth rates resulting from improved plant nutrition and (2) greater thatch
accumulation which retarded water infiltration, thus leaving more water in the thatch
where it could more readily evaporate. The amount of irrigation water applied totaled
—1295 mm which is —8% below the water duty imposed by ADWR. Given the level of
drainage observed in this study, one can conclude the ADWR water duty provided
sufficient water to satisfy turf ET and leach salts from the root zone during this wetter
137
than normal twelve-month period. However, it is important to recognize that total
irrigation applied represents 92% of the ADWR water duty which leaves only 8% of the
water allocation for use in general turf maintenance, or to offset losses due to irrigation
and plumbing inefficiencies.
Salt Balance
Both lysimeters received similar amounts of irrigation and rainfall, however, salt
input to the west lysimeter was about four times higher than that applied to the east
lysimeter due to differences in total dissolved solids of the two irrigation waters. A year
of excessive rainfall and residual effects from the previous study resulted in leaching
fractions of 0.24 and 0.18 for the east and west lysimeters, respectively. Although
leaching fractions were high, the salt balance of both lysimeters remained positive. The
positive salt balances reflect the adjustment of soil salinity to a new equilibrium status
caused by the current irrigation regime which applied less water to the lysimeters
(relative to previous study). Drainage water electrical conductivity at the conclusion of
the
the study indicates salt accumulation in the west lysimeter is still increasing, however,
EC of east lysimeter drainage water implies a near equilibrium salt
balance is already
established. Salinity profiles indicated salt accumulation is most likely to occur during
summer months when turf water requirements are high. Leaching is most
efficient during
winter months when turf water requirements are low and rainfall is common. Assessment
of the salinity profiles also indicate that late summer monsoon rainfall
and overirrigation
during establishment of overseeded winter turf provide potential leaching events.
138
Turfgrass Quality
On average, turfgrass quality was acceptable or better during most of the study.
Turf irrigated with effluent rated higher in quality than turf irrigated with groundwater; a
result observed in previous studies. Improved plant nutrition resulting from daily
irrigation with effluent water is the likely cause for this improvement in turf quality. Turf
quality declined due to slow germination following overseeding, during periods with cold
temperatures and during spring transition. The level of water supplied to the turf was not
below the expected turf ET in most months, thus turf quality was not hindered due to lack
of soil moisture. Bulk soil salinity did not accumulate to levels capable of reducing turf
quality.
Biomass Accumulation and Turfgrass Growth Rates
Biomass accumulation and turf growth rates were monitored as a second means of
assessing turf performance under the irrigation regime employed in this study. Growth
rates were highest following overseeding, during spring when temperatures increased to
levels favorable for ryegrass growth and during summer monsoon. Growth slowed
during periods of freezing/cold temperatures and spring transition. Turf
irrigated with
effluent grew faster than turf irrigated with groundwater - - a finding that agrees with
of ET in most
previous research at the study site. Water was supplied to the turf in excess
Growth rates
months, thus growth rates did not decline due to water induced stress.
obtained during this study were higher than those obtained in the previous lysimeter study
139
that utilized higher irrigation rates, indicating the irrigation regime employed in this
study, coupled with above normal precipitation, did not hinder turfgrass growth. Bulk
soil EC and soil solution EC did not accumulate to levels capable of jeopardizing turf
growth rates.
Crop Coefficients
Crop coefficients (Kcs) averaged 0.73 and 0.74 for winter ryegrass irrigated using
groundwater and effluent, respectively. Bermudagrass Kcs averaged 0.77 when irrigated
with groundwater and 0.80 when irrigated with effluent. These Kcs compared favorably
but were slightly higher than Kcs obtained in previous research at the study site. Possible
reasons for the higher Kcs in this study are (1) turf growth rates were higher than those
reported in the previous study and (2) wet weather conditions which cause the AZMET
Penman Equation to underestimate ETo. Summer Kc values were statistically higher
(+0.05) than average winter Kc values (p = .0003). Turf irrigated with effluent produced
higher Kc values (+0.017) than turf irrigated with groundwater, however this relationship
was considered only marginally significant. The fact that higher Kcs were observed with
effluent irrigation suggests irrigation with effluent may result in higher turf water
use
superior
rates than similar turf irrigated with potable water. The higher growth rates and
turf quality (both of which were shown to produce higher water
use rates) on the west
effluent
lysimeter strengthen the argument of higher water use from turf irrigated with
water.
140
Although these results represent but one year of a planned three-year study, the
findings show that the water duty set by ADWR provides sufficient water for production
of acceptable turf and leaching of salt during a year of above normal precipitation. The
remaining two years of the study will address the adequacy of the ADWR water duty
during years with different and likely lower levels of precipitation.
Scenario Testing
Although findings from this study represent data collected during a year of
excessive precipitation, one can use historical ETo and precipitation data to project the
adequacy of the ADWR water duty in normal and dry years. Assuming an annual Kc of
0.74, one can estimate an ET value for bermudagrass turf overseeded with ryegrass in a
normal and dry year. Normal annual ETo for the past twelve years as given by AZMET
is 1956 mm (77 in.), and using an annual Kc of 0.74 gives an estimated annual turf ET
value of 1448 mm (57 in.). The driest year in the AZMET record, 1989, produced an
annual ETo value of 2184 mm (86 in.). Multiplying the 1989 ETo value by 0.74 provides
a dry season estimation of turf ET of 1626 mm (64 in.).
The ADWR water duty totals to —1400 mm, thus the ETa during a normal year
and a dry year could exceed the water duty by —48 and —226 mm, respectively. Normal
rainfall for the past twelve years as given by AZMET was 273 mm (10.7 in.), and for a
dry year (1989), rainfall totaled 176 mm (6.9 in.). Therefore, during a normal year of
precipitation, 18% of the average rainfall would be needed in addition to the ADWR
water allotment to provide sufficient water to meet ETa requirements. Data from this
141
study indicated the turf utilized —33% of the total annual precipitation, thus the value of
18% does not seem unreasonable. During a dry year, however, ETa could exceed the
ADWR water duty by —226 mm, which is higher than the total precipitation expected
during a dry year (176 mm); thus, compliance with the ADWR water duty would result in
deficit irrigation.
These computations, though only approximations, show that irrigation within
ADWR water duty is achievable under a normal year of precipitation and ETo. However,
under a dry year, irrigation within ADWR guidelines will be a difficult task for turf
managers. It should be strongly noted that these values are extrapolations based on one
year of data, thus these values are not based on actual data and should not be construed
thereof.
It is also important to note that the water use requirements presented above were
computed based on the assumption that 100% of the water applied will reach the turf.
The water duty allocated by ADWR represents the amount of water recorded by the
pumping station, not the amount of water the turf receives. Some loss of water is
unavoidable during transit from the pumping station to the turf due to inefficiencies
inherent in all irrigation systems (e.g. leaks, evaporation). From the perspective of the
turf facility, these inefficiencies represent a loss of water for irrigation. Thus,
turf facility
compliance with ADWR water duties may relate to facility's ability to minimize these
system inefficiencies.
142
APPENDIX A
IRRIGATION WATER ANALYSIS
The table below provides the components of the water analysis for the lysimeter
irrigated with reclaimed water (west lysimeter) and the lysimeter irrigated with
groundwater (east lysimeter).
Attribute
Groundwater
Reclaimed
Bicarbonate
*2.12 meq/1
*4.88 meq/1
Calcium
*1.77 meq/1
*3.68 meq/1
Magnesium
*0.18 meq/1
*0.90 meq/1
Sodium
*1.03 meq/1
*6.36 meq/1
Nitrate-N
*0.05 meq/1
*0.21 meq/1
Sulfate-S
0.31 meq/1
4.13 meq/1
Boron
0.03 meq/1
0.01 meq/1
Chloride
0.59 meq/1
2.96 meq/1
Total Salts
160 mg/1
640 mg/1
PH
Conductivity
6.8
7.5
0.25 dS/m
1.0 dS/m
SAR
1.0
3.3
Adj. SAR
1.4
6.9
, • ,- ,
values reported from the most current water analysis teoruary
143
APPENDIX B
MONTHLY WATER BALANCE DATA
The following data represent each of the lysimeter monthly water balances during
the period October 1997 to September 1998. Irrigation, rainfall, evapotranspiration (ET)
and drainage events are recorded and summed on a daily basis. Monthly summation of
these data are presented in mm at the bottom of each table.
144
October 1997
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Drainage Date Irrigation
ET
Irrigation Rain
Date
4.01
0.22
1
4.27
0.10
1
4.67
3.02
4.01
2
4.34
2.91
4.22
2
5.32
4.14
3.26
3
10.90
3.76
3.38
3
4.95
4.33
4
4.50
4.12
4
4.93
4.04
5
4.62
4.05
5
4.94
4.14
6
4.74
4.20
6
2.57
0.69
0.00
7
2.34
0.65
0.00
7
4.16
0.00
8
3.73
0.00
8
3.79
3.24
9
3.63
2.93
9
6.46
5.42
0.00
10
19.21
4.83
0.00
10
4.79
3.77
11
4.32
3.77
11
3.60
4.42
12
3.46
4.38
12
3.73
6.99
13
3.69
7.17
13
4.03
10.47
14
3.82
11.69
14
3.56
3.85
15
3.93
4.18
15
5.46
2.89
16
6.29
3.60
16
9.33
4.50
3.40
17
8.15
4.25
3.72
17
3.17
4.35
18
2.99
4.49
18
3.41
3.73
19
3.25
3.25
19
3.10
3.91
20
3.09
4.06
20
3.67
3.40
4.16
21
3.60
3.38
4.15
21
1.83
3.79
3.48
22
1.72
3.50
3.32
22
2.26
0.06
2.36
23
2.19
0.12
2.00
23
9.74
4.75
8.44
24
9.02
3.94
6.91
24
3.78
2.76
25
3.56
2.89
25
2.98
3.88
26
3.05
3.83
26
1.36
4.42
27
1.41
4.39
27
2.27
5.31
28
1.97
5.15
28
2.83
4.44
29
2.86
4.32
29
0.66
4.08
4.56
30
6.50
3.96
4.52
30
4.08
4.16
31
3,96
4.12
31
Drainage
ET
Rain
Irrigation
Drainage
ET
Irrigation Rain
31.51,
116.82
10.96
118.99
53.78
118.91 10.56 112.07
Sum(mm)
145
November 1997
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Date
Irrigation
Drainage
Rain
ET
Irrigation
Date
4.08
1
4.16
3.96
4.12
1
4.08
4.16
3.96
2
4.12
2
4.08
4.16
3.96
3
4.12
3
2.13
6.15
4
2.13
4
6.27
2.79
2.42
5
2.72
2.34
5
1.20
1.72 2.34
4.07
6
7.10
1.72 2.38
3.73
6
3.70
8.23
7
3.70
6.50
7
3.70
3.11
8
3.70
3.11
8
3.02
3.85
9
2.74
3.22
9
2.47
0.00
10
2.24
0.00
10
0.65
6.23
2.69
11
6.03 0.60
2.61
11
0.39
0.00
12
0.40
0.00
12
1.56
0.00
13
1.60
0.00
13
8.50
2.87
4.60
0.00
14
11.73
4.64 2.57
0.00
14
1.99
0.20
15
2.00
0.18
15
2.24
0.20
16
2.18
0.14
16
2.06
0.20
17
1.91
0.16
17
1.99
0.55
18
2.04
0.49
18
5.85
2.04
1.91
19
15.40
2.06
1.81
19
2.37
3.02
20
2.26
2.77
20
2.40
2.97
21
2.43
2.53
21
2.62
3.05
22
2.65
3.05
22
2.49
3.30
23
2.87
3.30
23
2.84
3.56
24
2.87
3.56
24
2.40
3.81
25
2.43
3.81
25
8.52
1.90
5.74
26
13.32
2.13
6.82
26
2.23
0.90
2.06
27
0.90 2.31
2.06
27
1.68
1.98
28
1.66
1.87
28
1.75
1.91
29
1.72
1.83
29
1.05
1.81
30
0.99
1.75
30
31
31
Drainage
Irrigation Rain ET
Drainage
ET
Irrigation Rain
24.07
70.31
13.45
79.27
47.55
76.27 13.29 71.17
Sum(mm)
146
December 1997
EAST Lysimeter
Irrigation
Date
,
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Sum(mm)
,
WEST Lysimeter
mm
Drainage
Drainage Date Irrigation Rain ET
Rain ET
0.00
12.79
1.83
1
0.00
1.82 12.61
0.00 13.75 0.07
2
0.06
0.00 13.71
1.43
0.00
3
1.10
0.00
1.94
0.00
4
1.85
0.00
8.23
1.70
0.00
5
10.19
2.04
0.00
1.36
0.00 5.54
6
1.16
0.00 5.52
0.00 15.14 0.00
7
0.00 15.15 0.00
0.00 9.02 0.59
8
0.00 8.98 0.24
1.79
0.00
9
1.69
0.00
1.59
0.00
10
1.49
0.00
1.81
0.00
11
1.73
0.00
2.26
2.16
0.00
12
3.73
2.19
0.00
1.16
0.00
13
1.12
0.00
1.61
0.00
14
1.55
0.00
1.32
0.00
15
1.36
0.00
1.61
0.00
16
1.34
0.00
1.45
0.00
17
1.36
0.00
3.42
1.51
0.00
18
2.49
1.77
1.93
2.55 2.24 0.66
19
2.76 2.18 0.52
0.90
0.00
20
0.77
0.00
1.10
1.81
0.98
21
1.23
1.79
1.08
0.08
18.68
0.00
22
0.00 18.63 0.08
1.02
0.00
23
0.73
0.00
0.45
7.35
0.00
24
0.00 7.46 0.51
1.20
0.00
25
0.98
0.00
0.98
0.00
26
1.04
0.00
1.14
0.00
27
0.96
0.00
9.15
1.06
0.00
28
6.82
1.41
0.00
0.83
3.50
29
1.02
4.12
1.16
0.00
30
1.08
0.00
1.63
0.00
31
1.49
0.00
Drainage
Irrigation Rain ET
Drainage
ET
Irrigation Rain
23.06
35.31
86.32
8.86
23.23
11.71 86.03 33.87
mm
•
147
January 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Date
Irrigation
Rain
ET
Drainage
Date Irrigation Rain ET
1.20
0.00
3.63
1.42
0.00
1.30
0.00
1.07
1.03
1.30
1.13 2.16
1.83
0.00
0.59
1.24
1.89
0.87
1.92
8.21
1.36
0.81
0.93
2.13 2.65
1.10
0.00 1.20
1.59
1.89
1.56
0.95
1.54
2.09
1.72
1.06
8.25
1.79
1.71
1.68
2.17
1.66
1.96
1.87
1.84
1.73
1.96
2.08
1.77
1.57
1.94
8.11
1.71
1.98
2.21
2.04
1.88
1.98
1.90
2.02
1.82
7.86
0.86
0.88
3.15
2.12
7.27
2.37
2.31
1.99
2.38
Drainage
Irrigation Rain ET
36.06
51.82 6.01 50.30
Sum(mm)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.00
0.00
0.00
0.94
0.94 2.18
0.00
1.88
1.89
0.90
1.94 2.59
0.00 1.20
2.16
0.92
1.78
0.86
1.94
1.87
1.92
1.83
1.90
1.94
1.91
2.00
1.89
1.85
1.83
7.54
0.84
1.98
2.49
2.32
Irrigation Rain
50.26 5.97
Drainage
1.22
1.36
1.30
1.10
1.18
1.79
1.41
0.72
1.32
0.72
1.28
1.43
1.45
1.23
1.64
1.76
1.49
1.60
1.65
1.67
2.33
1.55
1.58
2.28
1.90
2.00
1.84
0.78
3.09
2.32
1.94
ET
48.93
2.34
4.91
7.15
7.68
7.60
Drainage
29.68
148
February 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Drainage Date Irrigation
Irrigation Rain ET
Date
2.43
1
2.57
2.49
2.34
1
2.27
2
2.66
2.22
2.60
2
2.06 4.32 2.95
3
2.06 4.32 2.98
3
0.75
0.00 19.81
4
0.73
4
0.00 19.81
1.85
0.00 0.25
5
1.20
0.00 0.25
5
8.31
2.44
0.00
6
1.96
2.10
0.00
6
2.30
7
0.00
2.42
0.00
7
0.00 8.89 0.53
8
0.00 8.89 0.61
8
0.00 12.95 0.53
9
0.00 12.95 0.64
9
2.04
0.00
10
2.16
0.00
10
2.36
0.00
11
2.55
0.00
11
2.38
0.00
12
2.53
0.00
12
4.44
1.73
0.00
13
5.03
1.56
0.00
13
1.77
0.00
14
2.10
0.00
14
0.00 13.46 2.22
15
1.96
0.00 13.46
15
2.47
0.00
16
2.69
0.00
16
0.39
19.56
0.00
17
0.51
19.56
0.00
17
0.73
4.32
0.00
18
0.76
4.32
0.00
18
1.98
0.00
19
2.00
0.00
19
7.86
0.43
7.87
0.00
20
7.84
0.00 7.87 0.48
20
1.32
0.00
21
1.57
0.00
21
2.36
0.00
22
2.61
0.00
22
2.85
0.00
23
3.02
0.00
23
2.40
2.79
0.00
24
0.00 2.79 2.51
24
1.94
3.05
0.00
25
0.00 3.05 2.00
25
2.61
0.00
26
2.83
0.00
26
9.86
3.48
0.00
27
10.25
3.86
0.00
27
2.67
0.00
28
2.93
0.00
28
29
29
30
30
31
31
Drainage
Irrigation Rain ET
Drainage
Irrigation Rain ET
30.47
54.18
97.27
7.29
25.08
7.15 97.27 55.87
Sum(mm)
149
March 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Irrigation
Rain
Date
Rain
ET
Drainage
Irrigation
Date
2.74
1
1.79
2.51
1.80
1
2.70
2.77
2
2.47
2
2.30
2.94
2.80
3
2.79
2.28
3
3.63
2.63
4
3.59
4
3.04
3.63
3.63
5
3.59
4.14
5
4.75
3.80
3.59 0.41
6
5.46
3.38
4.05 0.41
6
3.44
0.77
3.61
7
0.77 3.73
4.26
7
2.45
2.49
8
2.61
2.96
8
2.84
2.61
9
2.88
3.07
9
2.85
2.67
10
2.93
3.14
10
3.54
0.18
3.81
11
3.85 0.14 3.54
11
3.96
3.75
12
3.66
4.24
12
8.54
2.67
4.62
13
7.15
2.43
5.24
13
1.54
5.87
2.57
14
1.55
5.87
2.73
14
0.00 0.86 2.08
15
1.96
0.00 0.88
15
2.34
0.00
16
2.24
0.00
16
2.19
1.89
17
2.11
2.16
17
3.32
1.83
18
3.30
2.08
18
3.34
0.00
19
3.23
3.24
19
10.23
4.86
2.79
20
6.91
4.65
2.87
20
4.18
3.77
21
4.01
4.14
21
4.20
3.91
22
3.97
4.30
22
3.89
4.71
23
3.67
5.30
23
5.21
3.66
24
5.15
4.09
24
6.01
3.75
25
5.91
4.09
25
0.49
5.52 11.31
26
0.73
11.12
4.71
26
4.83
3.25
0.00
27
13.95
3.52
0.00
27
4.40
0.00 4.17
28
0.00 4.18 4.75
28'
0.00 16.13 0.57
29
0.62
0.00 16.13
29
1.62
0.00 4.10
30
1.32
0.00 3.81
30
2.87
0.00
31
2.95
0.00
31
Drainage
Irrigation Rain ET
Drainage
ET
Irrigation Rain
28.35
97.55
43.80
75.17
33.47
84.08 43.31 95.75
Sum(mm)
150
April 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
Drainage Date Irrigation Rain ET
Irrigation Rain ET
Date
3.26
0.00 12.83
1
3.12
0.00 12.88
1
3.14
0.00 0.90
2
3.08
0.00 0.90
2
9.70
3.75
0.00
3
10.63
3.59
0.00
3
3.99
0.00
4
3.91
0.00
4
4.32
0.00
5
4.44
0.00
5
3.30
0.26
0.00
6
3.32
0.00 0.26
6
3.54
1.12
2.57
7
3.57
1.12
2.28
7
3.91
0.00
8
3.87
0.00
8
7.21
3.97
0.00
9
8.17
4.36
0.00
9
3.62
1.68
10
3.54
1.61
10
5.34
3.88
11
5.35
3.88
11
3.75
7.19
12
3.59
5.54
12
4.30
4.95
13
3.12
4.33
13
4.38
0.00
14
4.46
0.00
14
3.28
0.00
15
2.95
0.00
15
4.43
3.59
16
4.35
3.52
16
8.47
5.02
7.40
17
8.78
4.58
7.33
17
4.80
4.89
18
4.42
4.75
18
4.68
8.92
19
4.80
8.68
19
5.05
4.64
20
5.02
4.83
20
4.80
5.59
21
4.62
5.52
21
6.03
5.49
22
5.72
5.40
22
6.82
5.53
23
6.57
5.82
23
6.87
6.95
6.64
24
6.99
6.25
6.64
24
4.14
0.37
5.99
25
4.07
6.42 0.39
25
3.30
4.69
26
3.08
4.67
26
4.86
3.84
27
4.60
3.69
27
6.20
4.73
28
6.24
4.85
28
5.93
5.62
29
5.79
5.74
29
5.54
5.66
30
5.55
5.95 _
30
31
31
Drainage
Irrigation Rain ET
Drainage
Irrigation Rain ET
32.25
136.40
15.48
103.49
34.57
101.45 15.55 131.93
Sum(mm)
151
May 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Date
Irrigation
Rain
ET
Drainage
Date Irrigation Rain ET
5.47
5.54
7.40
6.13
5.68
5.92
5.87
6.06
5.62
5.99
5.56
4.95
5.87
5.60
4.73
11.35
4.66
5.81
5.58
4.79
6.93
5.85
6.01
6.84
6.52
5.32 0.24
5.34
4.97
4.95
4.73
6.29
5.58
5.93
6.15
5.72
5.73
6.15
6.01
6.68
6.05
6.62
4.59
6.07
6.32
6.97
5.34
_
6.36
6.27 .
5.74
6.72
5.24
5.70
6.13
5.46
6.93
6.52
8.19
6.19
6.72
5.97
6.58
6.34
6.46
6.66
6.27
5.70
6.48
Drainage
ET
Rain
Irrigation
39.55
182.45
0.24
182.87
Sum(mm)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Drainage
5.91
6.13
5.96
6.09
6.07
5.24
5.62
4.66
5.85
7.24
6.67
6.57
5.69
5.64
6.38
6.53
6.20
6.49
6.61
5.28
6.82
6.88
6.47
6.06
7.08
7.34
5.79
7.41
7.46
7.62
6.96
5.26
6.93
5.66
5.19
5.34
4.91
5.68
4.89
6.46
5.62
4.73
5.22
6.78
5.25 0.24
5.14
4.52
5.66
5.50
5.78
6.33
6.94
6.69
6.19
5.19
5.15
6.74
6.43
5.98
6.14
7.09
5.82
7.07
6.80
6.57
6.79
7.02
Drainage
Irrigation Rain ET
30.06
183.40 0.24 196.72
152
June 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Irrigation
Drainage Date
Irrigation Rain ET
Date
7.20
1
8.02
6.32
6.56
1
7.83
8.04
2
6.93
6.40
2
7.59
8.38
3
6.82
7.66
3
6.85
5.68
6.94
4
2.89
5.92
6.54
4
6.72
9.88
5
5.64
10.67
5
7.67
8.16
6
6.88
8.56
6
7.28
10.01
7
6.58
9.15
7
6.43
9.49
8
5.65
9.47
8
7.56
8.92
9
6.51
8.86
9
5.30
9.82
10
4.93
9.07
10
5.50
6.08
8.05
11
7.37
6.39
8.05
11
8.21
9.49
12
7.19
9.31
12
7.97
10.60
13
6.26
10.53
13
7.40
8.38
14
6.26
9.02
14
7.17
8.04
15
6.21
7.62
15
8.30
7.37
16
7.43
7.31
16
7.74
8.15
17
6.61
7.97
17
3.12
8.00
7.46
18
7.29
5.83
7.68
18
7.08
7.25
19
6.69
7.46
19
7.94
6.84
20
7.09
7.64
20
8.10
8.06
21
6.99
0.00
21
5.87
0.00
22
5.89
0.00
22
7.09
16.15
23
6.45
9.78
23
2.36
7.87
7.96
24
4.16
6.98
8.23
24
8.09
7.82
25
6.84
7.90
25
7.63
8.02
26
6.74
8.33
26
7.12
8.77
27
6.15
8.58
27
7.80
8.25
28
6.90
7.52 28
8.16
9.31
29
6.82
8.33
29
6.87
8.03
30
6.70
8.05
30
31
31
Drainage
Irrigation Rain ET
Drainage
Irrigation Rain ET
17.83,
219.75
0.00
251.66
21.61
232.25 0.00 194.60
Sum(mm)
153
July 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Irrigation
Date
ET
Drainage
Irrigation Rain
Date
4.09
7.55
7.98
1
7.57
3.89
8.17
1
6.47
5.40
2
6.52
5.97
2
6.33
8.31
3
6.15
8.41
3
5.03
7.19 0.16
4
4.85
7.46 0.18
4
1.39
6.03 27.38
5
1.30
6.68 27.22
5
1.49
0.00 15.58
6
1.59
0.00 15.36
6
4.36
18.80
0.00
7
4.69
18.46
0.00
7
5.09
0.00 0.84
8
5.56
0.00 0.84
8
1.91
5.46
0.00
9
5.36
5.67
0.00
9
5.81
0.00
10
6.05
0.00
10
5.62
1.75 2.67
11
5.87
1.69 2.67
11
7.80
5.24
12
7.33
5.54
12
7.48
7.03
13
7.42
7.37
13
7.62
7.05
14
7.50
7.35
14
7.29
6.91
15
7.12
7.68
15
2.12
8.55
7.25 0.31
16
3.57
8.26
0.26
7.09
16
6.59
7.76 2.81
17
6.75
8.17 2.81
17
4.99
1.41
5.42
18
5.07
1.32
5.26
18
6.10
1.79
4.40
19
6.18
1.73
4.77
19
5.72
5.54
20
5.85
5.77
20
4.38
3.71
4.83
21
4.44
3.69
5.22
21
1.36
1.67 11.45
22
1.40
1.79 11.43
22
3.97
5.03
0.51
0.00
23
9.17
4.74
0.00 0.47
23
6.27
0.00
24
6.07
0.00
24
6.07
2.75
25
5.91
2.79
25
6.46
6.40
26
6.40
6.46
26
7.03
6.29
27
6.98
6.25
27
7.03
6.27
28
7.13
6.74
28'
5.23
7.05
29
5.41
7.35
29
4.46
6.02
5.54
30
13.91
6.07
5.56
30
3.48
6.27 11.92
31
3.25
6.32 11.82
31
Drainage
ET
Rain
Irrigation
Drainage
'Irrigation Rain ET
16.55
175.10
99.34
140.33
35.90
145.86 98.26 175.10
Sum(mm)
154
August 1998
WEST Lysimeter
EAST Lysimeter
mm
mm
Irrigation Rain
Date
1
2
3
4
5
6
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.00
1.89
5.64
6.46
0.00
1.79
6.52
6.40
5.62
1.73
4.87
3.67
4.60
5.95
0.00
3.85
6.52
2.73
5.54
5.38
4.64
7.60
6.42
6.97
5.60
5.72
5.72
6.29
4.85
6.80
7.54
ET
147.31
5.26
5.02
6.46
6.72
6.74
6.33
5.67
5.32
4.90
4.13
5.88
5.18
5.95
5.10
6.07
6.06
4.45
5.32
6.22
5.62
6.53
6.65
6.10
5.17
5.44
5.81
5.65
5.20
7.02
6.70
5.46
7.84
0.22
3.99
3.42
1.00
9.94
3.32
0.59
0.16
0.20
3.54
Irrigation Rain
Sum(mm)
Drainage
34.22
ET
178.13
Date Irrigation Rain
14.26
12.06
9.94
8.64
Drainage
44.90
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
ET
Drainage
5.30
4.97
6.40
6.65
6.76
6.24
6.02
5.19
5.07
4.06
5.85
4.98
6.00
5.19
5.99
5.89
4.34
5.26
5.96
5.92
6.61
6.05
6.11
4.94
5.87
5.56
6.00
5.01
6.61
4.89
4.45
0.00
1.81
5.46
6.11
8.09
0.00
5.44
1.67
6.27
0.08
6.48
3.99
5.36
1.87
3.36
4.69
3.26
6.13
1.00
4.30
5.36 10.80
0.00
3.50
3.30
6.17
2.59
5.36
6.17
0.57
5.58
0.16
4.13
7.19
6.09
6.64
0.20
5.40
5.50
6.78
5.46
3.77
6.54
4.79
5.96
7.89
Drainage
ET
Irrigation Rain
24.52,
141.43 35.32 174.14
155
September 1997
WEST Lysimeter
EAST Lysimeter
mm
mm
Drainage
ET
Rain
Irrigation
Date
Drainage
Irrigation
Rain
ET
Date
5.70
0.10
1
5.07
0.10
1
5.30
5.90
2
6.60
5.66
2
7.06
4.86
5.15
3
4.28
8.16
5.90
3
2.36
5.32
5.22
4
2.38
6.05 5.46
4
1.98
0.00 18.03
5
1.94
0.00 17.97
5
4.83
0.00
6
4.56
0.00
6
5.36
0.00 0.20
7
5.28
0.00 0.22
7
5.56
0.90
8
5.41
0.94
8
5.09
5.28
9
5.06
5.32
9
4.48
5.10
5.42
10
7.54
4.63
5.58
10
3.84
4.30
11
3.72
4.36
11
4.81
3.81
12
4.76
3.63
12
4.88
4.53
13
4.86
4.73
13
5.15
5.54
14
5.10
5.74
14
5.42
9.19
15
5.33
9.02
15
5.58
3.38
3.95
16
5.28
3.38
4.42
16
5.71
4.05
4.56
17
5.80
3.78
4.38
17
4.71
5.34
18
4.77
5.60
18
4.79
5.46
19
4.81
5.64
19
5.87
4.83
20
5.81
5.19
20
4.55
6.29
21
4.50
6.42
21
4.35
5.32
22
4.42
5.60
22
4.22
4.73
23
4.33
4.93
23
4.92
4.77
4.36
24
3.87
4.91
4.64
24
4.60
5.64
25
4.41
5.79
25
4.42
5.38
26
4.45
5.46
26
3.97
5.48
27
4.24
5.62
27
4.38
5.52
28
4.28
5.81
28
4.76
5.48
29
4.80
5.72 29
4.79
5.79
30
4.65
5.79
30
31
31
Drainage
Irrigation Rain ET
Drainage
ET
Irrigation "Rain
22.17,
140.05
26.93
133.47
25.37
138.04 27.03 138.42
Sum(mm)
156
APPENDIX C
TIME DOMAIN REFLECTOMETRY
Time domain reflectometry (TDR) was used to assess bulk soil salinity (ECa) and
volumetric water content Ov using the procedures described in Chapter 2. Independent
assessments of soil water EC (ECw) were determined from soil water samples obtained
from the lysimeter solution samplers while independent measurements of Ov were
obtained using neutron attenuation. Bulk soil EC and ECw were compared over the
course of the study as were measurements of 0v made using TDR and neutron
attenuation. The results of these comparisons are provided below.
Comparison of ECa and ECw
Bulk soil EC is useful to determine changes in soil and soil water electrical
EC of the
conductivity over time. However, the standard for assessing soil salinity is the
the
saturated extract (ECe), or the soil water salinity itself. In order to determine
relationship between the bulk soil salinity (ECa) and the soil water salinity (ECw),
the
two were compared at three different depths (1.0, 2.0 and 3.0 m) for both the east and
west lysimeters.
ECw at any given
Overall, a strong relationship did not exist between ECa and
west lysimeter and is
depth. The best relationship occurred at the 1 m depth in the
the best fit to
presented in Figure 40. A third order polynomial provided
Figure 40. A more typical comparison of ECw versus ECa
the data in
is provided in Figure 41
157
ECw vs. ECa for the West Lysimeter -- 1.0 m depth
4-
y = 250149x 3 - 26367x 2 + 948.87x - 8.82
= 0.71
3.5 3-
•
2.5
-o
LU
•
•
•
2 -
••
•
•
n••
1.5
1
0.5 T
0
0.005
0.01
0.015
I
I
I
I
I
0.025
0.02
0.035
0.03
I
I
0.04
0.045
0.05
ECa dS/m
Figure 40. Soil solution salinity (ECw) plotted as a function of bulk
soil salinity (ECa) for the period August 1997 to September 1998 for
the west lysimeter at the 1.0 m depth. The curve depicted represents
the least squares regression curve.
ECw vs. ECa for the East Lysimeter 1.0m depth
1.2 1
y = 5E+06x 3 - 246850x 2 + 4471.2x - 26.161
0.8
LLI
r2 = 0.23
0.6
0.4
0.2
O
0.000
0.005
0.010
0.015
0.020
ECa
function of bulk
Figure 41. Soil solution salinity (ECw) plotted as a
1998 for
soil salinity (ECa) for the period August 1997 to September
curve depicted represents
the east lysimeter at the 1.0 m depth. The
the least squares regression curve.
0.025
158
where ECw appears unrelated to ECa at the 1.0 m depth in the east lysimeter. The closest
relationship between ECa and ECw was obtained in the west lysimeter at the 1.0 m depth
(r2 =0.71) where overall salinity levels and the change in salinity with time were greatest.
Poor ECa versus ECw relationships were obtained at low levels of salinity, and especially
where salinity does not change a great deal over time.
A much better relationship developed between ECa and ECw when data from
both lysimeters and all depths were combined (Figure 42). The combined data set offers
a greater range of ECa and ECw values. A relationship between ECa and ECw is
apparent in Figure 42, however, the r2 value (0.68) is somewhat lower than observed in
other studies. The low soil salinity values encountered in this study may explain the poor
relationship between ECa and ECw.
Rhoades et al. (1976) observed a good relationship between ECa and ECw when
ECw ranged from 2.5 and 56 dS/m (ECa ranged from 0.31 to 1.62 dS/m). However,
Nadler et al. (1982) showed that models were deficient at salinity levels below the range
tested by Rhoades et al. Persson, (1997) showed similar error at low salinity levels using
Rhoades model, and three other models used to compute ECw from ECa.
Comparison of Neutron Probe Av and TDR Ov
A recent alternative to measuring the change in soil water storage over time with
the neutron probe or gravimetric analysis is time domain reflectometry (TDR). Time
is less
domain reflectometry provides a smaller sampling volume than the neutron probe,
159
Soil Solution Salinity vs. Bulk Soil Salinity
54.5
y = 151.41x -1.69
r2 = 0.68
4-
•
3.5 32.5 2 1.5 1
0.5
0.00
0.01
0.01
0.02
0.02
0.03
0.03
0.04
0.04
0.05
ECa dS/m
as a function of bulk
Figure 42. Soil solution salinity (ECw) plotted
to September 1998 for
soil salinity (ECa) for the period August 1997
both lysimeters at all depths. The line depicted represents
the least squares regression line.
0.05
160
destructive than gravimetric soil sampling and is able to measure the change in soil water
storage near the soil surface (Dasberg and Dalton, 1985).
Profiles of lysimeter Ov were obtained using both TDR and neutron attenuation on
9 days during the course of this study. Measurements of Ov were obtained at intervals of
0.5 m over the depth range of 0.5 m to 3.5 m. Figure 43 compares Av obtained from TDR
with Ov derived from neutron attenuation. A least squares regression equation was fit to
the data in Figure 43. Both the non-zero intercept and the slope of 1.16 suggest the two
procedures for measuring Ov do not provide similar values of Ov. On average, Ov
measured with TDR was 0.02 m 3 /m3 lower than Ov measured by neutron probe. This
discrepancy among the methods could be due to several factors, including: (1)
differences in the soil volume sampled, (2) differing sampling locations for the TDR and
neutron attenuation, and (3) inaccurate calibration of the neutron probe.
Differences in sampling volume may have caused bias between Ov measured with
TDR and neutron attenuation. The TDR sample volume is restricted to an area
approximately within the three-wire probes, whereas the neutron probe measures sample
in a
volumes ranging from a radii of approximately 0.16 m in a saturated soil to 0.7 m
soil near oven dryness (Van Bavel et al., 1956).
Sampling locations also differed for TDR and neutron attenuation. Time domain
from the
reflectometry probes are located at each of the three angular positions 500 mm
locations for
inner lysimeter wall and the probes are 200 mm in length, thus the sampling
TDR range from 500 mm to 700 mm from the inner lysimeter
wall. In contrast, the
161
TDR Volumetric Water Content vs. Neutron Probe
0.15
•
y= 1.17x - 0.04
0.14
r2 = 0.48
0.13
0.12 -
-
•to •
0.11 —
•••
•
• •
•
0.10
0.09
I
0.08
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
Neutron Probe - Theta
Figure 43. Volumetric water content (theta) derived from time domain
reflectometry (TDR) plotted as a function of theta derived from neutron
attenuation for the period August 1997 to September 1998 for both
lysimeters at all depths. The line depicted represents
the least squares regression line.
0.16
162
sampling location for neutron attenuation extends from the neutron access tube located
directly in the center of the lysimeters in a spherical pattern. Differences in Av based on
sampling location could create systematic errors; however one would expect these
differences to eventually impact turf performance. Turf performance did not vary over
the lysimeter.
Site specific calibration of the neutron probe is necessary to obtain accurate
measurements of Ov. Inaccurate calibration may result in measurement error, which may
have been the case in this study. Calibration of the neutron probe involved recording
count ratios through the entire lysimeter soil profile on 12 dates and then comparing the
mean change in profile count ratio to the change in soil water storage (ASWS) as given by
the lysimeter mass change between any two sampling dates. This calibration method is
better suited for studies where ASWS is large. Unfortunately, the ASWS was not large in
this study and the probe calibration may well be in error.
163
APPENDIX D
MANAGEMENT OF LYSIMETER DATA GAPS
Lysimeter data were lost or in error on 17 days between October 1997 and
September 1998. Missing data occurred on 2, 12 and 3 days in the months of October
1997, November 1997 and March 1998, respectively. The water balance equation,
rearranged to solve for ET, (Equation [2]), was used to calculate the ET during these
periods of missing data:
ET = P + I —D — ASWS [Eq. 2]
For each data gap, the amount of irrigation (I) applied was estimated from
irrigation run times or by multiplying the applicable Kc by ETo if run times were not
available. Any precipitation (P) that fell during data gaps was recorded by the on-site
weather station. Raingage precipitation was adjusted upward using Equations [18] and
[19] based on previous work that indicated lysimeter precipitation always exceeded
precipitation recovered by the raingage.
East Lysimeter Rain [mm] = 1.148 (raingage rainfall (mm)) + 0.093 [Eq. 18]
West Lysimeter Rain [mm] = 1.149 (raingage rainfall (mm)) + 0.081 [Eq. 19]
in
Drainage (D) was quantified manually and converted to an equivalent depth
soil water storage
mm of water and runoff (R) was assumed equal to zero. The change in
(ASWS) was determined from the change in lysimeter weight during periods of missing
data. ET was then computed from these values.
164
The above procedure did not provide acceptable values for missing data during 12
and 13 Nov. 1997. It was assumed an incorrect irrigation amount was the reason the
procedure failed; thus, on these days ET was estimated by multiplying the mean monthly
Kc value by the ETo. The water balance equation was then solved for I using known
values of P, R, and ASWS and estimated ET.
165
APPENDIX E
LYSIMETER PERFORMANCE AND RECOMMENDATIONS
Lysimeter Performance
During the past two years we have struggled with many aspects of lysimeter
operation. Effective evaluation of the ADWR water duty required an efficient irrigation
system. An initial test of the lysimeter irrigation system (June 1997) revealed unstable
water pressure as a cause for inconsistent irrigation of the east lysimeter. A shallow well
pump system was installed to ensure adequate water pressure in an attempt to solve the
problems with east lysimeter irrigation.. Following installation of the pump, frequent
precipitation tests were run using low lying soil cans to decrease water loss due to
receptacle deflection, thus decreasing potential for error. The irrigation coefficient of
rates
uniformity (CU) was quite good for both lysimeters (>90%), however, precipitation
were not equal. A difference in static pressure between the two lysimeter irrigation
systems was identified as the cause of differing precipitation rates. In-line pressure
regulators were installed at 0.207 MPa (30psi) in the irrigation lines of each lysimeter on
and the CU for both
22 Aug. 1997 and by 4 Sep. 1997, precipitation rates were equal,
lysimeters was 93%.
easy task. Thatch
Growing high maintenance turf on the lysimeters is not an
build-up has created a slight mound on the lysimeter, which has made
It is important to mow the surrounding turf and lysimeter turf
mowing difficult.
at the same height to avoid
circular pattern on the lysimeter,
ET measurement error. If mowing is done following a
166
traffic stress due to the weight of the mower wheels becomes evident on the turf To
avoid this, turf immediately surrounding the lysimeter is mowed prior to mowing the
lysimeter turf. The mower can then be driven across the lysimeter and the surrounding
turf, while only clipping the lysimeter turf One must be careful, however, not to scalp
the lysimeter turf, as the lysimeters are slightly raised above the surrounding area.
Serious consideration should be given to re-leveling the lysimeter surfaces at the
conclusion of this study.
In order to decrease any heterogeneity in the soil, these lysimeters were filled with
vinton sand (93% sand). However, this soil also has a very low cation exchange capacity
and turf nutrient deficiencies will result unless frequently fertilized. Previous studies
have indicated that improved turf performance is associated with effluent irrigation.
However, irrigation with different water sources presents the problem of a bias in
nutrition between the west and east lysimeters. West lysimeter fertilizer applications are
decreased to account for the nitrogen in effluent water. However, greater daily nutrition
from effluent has produced increased growth, quality, ET and thatch build-up compared
to the east lysimeter.
Thatch build-up has increased on both lysimeters to produce a slight mound. A
in ET
lysimeter crop that is raised above the surrounding area can cause errors
measurement (Allen et al., 1991). Fortunately, turf is maintained at very low mowing
heights (-2.5 cm), thus minimizing error.
167
Turf growing over the lysimeter rim creates two sources of measurement error for
ET. Growth beyond the lysimeter rim expands the evaporative surface of the lysimeters
and leads to overestimation of ET. Growth of turf onto adjacent soil areas can inhibit
lysimeter movement, thus affecting change in lysimeter mass. Thatch, rhizomes and
roots can get stuck or grow under and through the nylon flashing covering the tank rim,
which can also inhibit tank movement. Regular trimming of turf along the outer rim of
the lysimeters is required to minimize these errors. Also, the nylon flashing should be
replaced every four to six months in order to deter this potential error.
During overseeding, ryegrass seed is planted to produce green turf during winter
months. Unfortunately, the local bird population loves to feed on ryegrass seed. Thus,
during the fall overseeding process in 1997, Jeff Gilbert and I built a "bird-away"
structure for each lysimeter. The structure consisted of PVC pipe connected to form a
square area of approximately 13.3 m 2 (144 ft 2 ) and 36 cm high with a nylon net strewn
across to keep birds away.
Water has appeared on the lysimeter floor following significant rainfall events. A
small amount of water seeps in from under the lysimeter well floor, however, the majority
of water entered the facility from an electrical conduit that supplies power to the facility.
south and 25
A break in this conduit was discovered in an in-ground electrical box 20 m
has since been sealed, and
m west of the lysimeter well stairway. This electrical conduit
soil levies were constructed to inhibit water from entering the box. Facility flooding has
not been a problem since.
168
When heavy rains occur or when excessive irrigation is necessary, timely removal
of lysimeter drainage water is essential to avoid leakage from the lysimeters. If water
within the lysimeters accumulates to a height above the lower lysimeter sampling ports,
hydrostatic pressure can force open the stoppers resulting in lysimeter water loss. Thus it
is essential to ensure the drainage pumps are running properly, and drainage is removed
frequently.
During monsoon, lightning can be a problem. In early September 1997, lightning
struck very near the lysimeter well. An electrical surge destroyed the computer, and the
datalogger program and memory were scrambled. Two surge protectors have since been
installed to protect the computer and datalogger.
One other problem we encountered and solved was a mysterious change in
lysimeter weight upon entering and exiting the lysimeter well. The lysimeter well door
must be left open while the lysimeter ventilation system is functioning, otherwise air
pressure will build up and slightly lift the tanks (enough to affect lysimeter weight
change).
Lysimeter Recommendations
few concerns need to
In order to better evaluate the lysimeter salinity profiles, a
closer to the soil
be addressed. Installation of time domain reflectometry (TDR) probes
zone salinity
surface at depths of 0.15 m and 0.35 m would significantly improve root
zone
measurement. Previous research lends support for the need to monitor upper root
salinity. Garrot and Mancino (1994) determined that —55 % of soil water
use by turf
169
plants came from the top 0.30 m of soil. The east lysimeter needs TDR probes installed
at these depths, and at 0.5 m for consistency with the west lysimeter. Finally, it would
seem wise to use the same irrigation water on both lysimeters. Our data have shown that
irrigation with effluent water will increase growth rates and turf quality compared to the
same soil and turf system irrigated with potable water. Use of the same water might
eliminate the difference in turf quality and water use observed in this study and allow two
replicate treatments for any future study.
170
APPENDIX F
CAMPBELL SCIENTIFIC DATALOGGER PROGRAM
The following provides the Campbell CR7 datalogger program used to obtain
lysimeter mass change data.
Program:
Flag Usage:
Input Channel Usage:
Excitation Channel Usage:
Continuous Analog Output Usage:
Control Port Usage:
Pulse Input Channel Usage:
Output Array Definitions:
Table 1 Programs
* 1
01: 2
Sec. Execution Interval
Resolution
01: P78
1: 1
2:
High Resolution
P89 If X<=>F
01:32 X Loc
02: 1
03:0
04: 99
F
Call Subroutine 99
171
3:
P86 Do
01: 1
4:
Call Subroutine 1
P86 Do
01: 2
Call Subroutine 2
05: P86 Do
01: 3
06: P92
Call Subroutine 3
If time is
01:0minutes into a
02: 10
minute interval
03: 98
Call Subroutine 98
07: P92
If time is
01:0minutes into a
2: 10
minute interval
3: 10
Set high Flag 0 (output)
08: P77 Real Time
01: 111 Day,Hour-Minute, Seconds
09: P71
Average
01:3Reps
02:
3
10: P71
Loc
Average
01:7Reps
172
02: 7
Loc
11: P92
If time is
01:0minutes into a
2: 360 minute interval
3: 99
Call Subroutine 99
Page 2 Table 1
12: P
End Table 1
* 2
Table 2 Programs
01: 60
Sec. Execution Interval
01: P78
High Resolution
1: 1
2:
P86 Do
Call Subroutine 4
01: 4
P86 Do
3:
01: 5
4:
Resolution
Call Subroutine 5
P86 Do
1: 7
05: P92
Call Subroutine 7
If time is
01:0minutes into a
2: 60
minute interval
03:10Set high Flag 0 (output)
173
6: P77 Real Time
01: 111 Day,Hour-Minute,Seconds
7: P71
Average
1: 19
Reps
2: 14
Loc
08: P
* 3
End Table 2
Table 3 Subroutines
01: P85
Beginning of Subroutine
1: 99 Subroutine Number
2: P89 If X<=>F
01:32 X Loc
02:1
03:0F
04:30 Then Do
3: P30 Z=F
01:1F
02:32Z Loc :
4: P95 End
05: P30 Z=F
01:5000 F
02:36Z Loc :
174
6:
P30 Z=F
01:0
F
02:35
Z Loc :
Page 3 Table 3
7:
P6
Full Bridge
1: 1
Rep
2: 6
500 mV slow Range
03: 1
IN Card
04:1
IN Chan
05:1
EX Card
06:1
EX Chan
7: 1
Meas/EX
8: 2500 mV Excitation
09:1
Loc :
10: 2423.8 Mult
11:0Offset
08: P6
Full Bridge
1: 1
Rep
2: 6
500 mV slow Range
03: 1
IN Card
04:2
IN Chan
175
05:1EX Card
06:2EX Chan
7: 1
Meas/EX
8: 2500 mV Excitation
09:2Loc :
10:2488 Mult
11:0Offset
9: P95 End
10: P85
Beginning of Subroutine
01: 98 Subroutine Number
11: P35 Z=X-Y
01:3X Loc
02:1Y Loc
03:33Z Loc :
12: P35 Z=X-Y
01:4X Loc
02:2Y Loc
03:34Z Loc :
13: P93
Case
01:32Case Loc
14: P83
If Case Location < F
176
01:-99 F
02: 95
Call Subroutine 95
15: P83
If Case Location < F
01:99
F
02: 97
Call Subroutine 97
16: P95 End
17: P95
End
Page 4 Table 3
18: P85
Beginning of Subroutine
01: 97 Subroutine Number
19: P89 If X<=>F
01:33 X Loc
02:3
>=
03: 120 F
04: 96
Call Subroutine 96
20: P89 If X<—>f
01:34 X Loc
02:3
>=
3: 120 F
4: 96
Call Subroutine 96
21: P95 End
177
22: P85
Beginning of Subroutine
01: 96 Subroutine Number
23: P21 Analog Out
01:1EX Card
02: 1
CAO Chan
03:36 mv Loc
24: P22
Excitation with Delay
01:1EX Card
02:8EX Chan
3: 0
Delay w/EX (units=.01sec)
4: 200 Delay after EX (units=.01sec)
05: 0
25: P21
mV Excitation
Analog Out
01:1EX Card
02: 1
CAO Chan
03:35 mv Loc
26: P30 Z=F
01:-999 F
02:32
Z Loc :
27: P95 End
28: P85 Beginning of Subroutine
178
01: 95 Subroutine Number
29: P89 If X<=>F
01:33
X Loc
02:4
3: 120 F
4: 94
Call Subroutine 94
30: P89 If X<=>F
01:34 X Loc
02:4
3: 120 F
4: 94
Call Subroutine 94
Page 5 Table 3
31: P95
End
32: P85
Beginning of Subroutine
01: 94 Subroutine Number
33: P21
Analog Out
01:1
EX Card
02: 1
CAO Chan
03:36 mv Loc
34: P22 Excitation with Delay
01:1
EX Card
179
02:8 EX Chan
3: 0
Delay w/EX (units=.01sec)
4: 200 Delay after EX (units=.01sec)
05: 0
mV Excitation
35: P21
01:1
Analog Out
EX Card
02:1CAO Chan
03:35 mv Loc
36: P30 Z=F
01:1F
02:32Z Loc :
37: P95 End
38: P85
01: 1
39: P6
Beginning of Subroutine
Subroutine Number
Full Bridge
1: 1
Rep
2: 4
50 mV slow Range
03:1
IN Card
04: 1
IN Chan
05:1
EX Card
06: 1
EX Chan
180
7: 1
Meas/EX
8: 2500 mV Excitation
09:3
Loc :
10: 2423.8 Mult
11:0Offset
40: P89 If X<=>F
01:3
X Loc
02:4
<
03: -99998 F
04:30 Then Do
Page 6 Table 3
41: P6
Full Bridge
01:1
Rep
02:5
150 mV slow Range
3: 1
IN Card
4: 1
IN Chan
05: 1
EX Card
06:1
EX Chan
7: 1
Meas/EX
8: 3000 mV Excitation
09:3
Loc :
181
10: 2423.8 Mult
11:0Offset
42: P89 If X<=>F
01:3
X Loc
02:4
03: -99998 F
04:30 Then Do
43: P6
Full Bridge
01:1
Rep
02: 6
500 mV slow Range
03:1IN Card
04: 1
IN Chan
05:1EX Card
06:1EX Chan
7: 1
Meas/EX
8: 2500 mV Excitation
09:3
Loc :
10: 2423.8 Mult
11:0Offset
44: P95 End
45: P95 End
01:
182
46: P95 End
47: P85
2
Beginning of Subroutine
Subroutine Number
48: P6
Full Bridge
1: 1
Rep
2: 4
50 mV slow Range
03:1IN Card
04:2IN Chan
05:1EX Card
06:2 EX Chan
7: 1
Meas/EX
8: 2500 mV Excitation
09:4Loc :
10:2488 Mult
11:0 Offset
Page 7 Table 3
49: P89 If X<=>F
01:4X Loc
02:4<
03: -99998 F
04:30 Then Do
183
50: P6
Full Bridge
1: 1
Rep
2: 5
150 mV slow Range
03: 1
IN Card
04:2
IN Chan
05:1
EX Card
06:2 EX Chan
Meas/EX
7: 1
8: 3000 mV Excitation
Loc :
09:4
10: 2488 Mult
11:0Offset
51: P89 If X<=>f
X Loc
01:4
2: 4
<
3: -99998 F
04:30 Then Do
52: P6
Full Bridge
1: 1
Rep
2: 6
500 mV slow Range
03:1
IN Card
184
04:2IN Chan
05:1EX Card
06:2 EX Chan
7: 1
Meas/EX
8: 2500 mV Excitation
09:4
Loc :
10:2488 Mult
11:0Offset
53: P95 End
54: P95 End
55: P95 End
56: P85
01: 3
Beginning of Subroutine
Subroutine Number
57: P17 Panel Temperature
01:1IN Card
02:6
Loc :
Page 8 Table 3
58: P14 Thermocouple Temp (DIFF)
1: 1
Rep
2: 2
5000 uV slow Range
03:1IN Card
185
04:3IN Chan
5: 1
Type T (Copper-Constantan)
6: 6
Ref Temp Loc
07:5Loc :
8: 1
Mult
9: 0
Offset
59: P14 Thermocouple Temp (DIFF)
01:7Reps
02: 2
5000 uV slow Range
03:1IN Card
04:4IN Chan
05: 1
Type T (Copper-Constantan)
06:6 Ref Temp Loc
07:7Loc :
8: 1
Mult
9: 0
Offset
60: P95 End
61: P85
01: 4
62: P6
Beginning of Subroutine
Subroutine Number
Full Bridge
01:8Reps
186
02: 1
1500 uV slow Range
03:2IN Card
04: 1
IN Chan
05:1EX Card
6: 1
EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 14
Loc :
10: 1
Mult
11:0Offset
63: P89 If X<=>F
01:14 X Loc
2: 4
3: -99998 F
04:30 Then Do
Page 9 Table 3
64: P6
Full Bridge
01:8Reps
02: 2
5000 uV slow Range
03:2IN Card
04:1IN Chan
187
05:1EX Card
06:1EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 14
Loc :
10: 1
Mult
11:0Offset
65: P89 If X<—>F
01:14X Loc
02:4
03: -99998 F
04:30 Then Do
66: P6
Full Bridge
01:8Reps
02:315 mV slow Range
03:2IN Card
04:1IN Chan
05:1EX Card
6: 1
EX Chan
7: 1
Meas/EX
08: 5000 mV Excitation
188
9: 14
Loc :
10: 1
Mult
11:0Offset
67: P89 If X<=>F
01:14 X Loc
02:4
03: -99998 F
04:30 Then Do
68: P6
Full Bridge
01:8Reps
02: 4
50 mV slow Range
03:2IN Card
04:1IN Chan
05: 1
EX Card
06:1EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 14
Loc :
10: 1
Mult
11:0Offset
69: P95 End
189
70: P95 End
71: P95 End
Page 10 Table 3
72: P95 End
73: P85
01: 5
Beginning of Subroutine
Subroutine Number
74: P6
Full Bridge
01:4Reps
02: 2
5000 uV slow Range
03:2
IN Card
04:9
IN Chan
5: 1
EX Card
6: 1
EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 22
Loc :
10: 1
Mult
11:0Offset
75: P89 If X<—>F
01:22 X Loc
02:4
<
190
03: -99998 F
04:30 Then Do
76: P6
Full Bridge
01:4Reps
02: 2
5000 uV slow Range
03:2IN Card
04:9IN Chan
05:1EX Card
6: 1
EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 22
Loc :
10: 1
Mult
11:0Offset
77: P89 If X<=>F
01:22 X Loc
02:4
<
03: -99998 F
04: 30 Then Do
78: P6
Full Bridge
01:4Reps
191
02: 3
15 mV slow Range
03:2IN Card
04:9IN Chan
05:1EX Card
6: 1
EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 22
Loc :
10: 1
Mult
11:0Offset
Page 11 Table 3
79: P89 If X<=>F
01:22 X Loc
02:4
<
03: -99998 F
04: 30 Then Do
80: P6
Full Bridge
01:4Reps
02: 4
50 mV slow Range
03:2IN Card
04:9IN Chan
192
05:1EX Card
6: 1
EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 22
Loc :
10: 1
Mult
11:0Offset
81: P95
End
82: P95 End
83: P95 End
84: P95 End
85: P85
01: 7
86: P6
Beginning of Subroutine
Subroutine Number
Full Bridge
01:6Reps
02:1
1500 uV slow Range
03:3IN Card
04: 1
IN Chan
05:1EX Card
06:3EX Chan
07: 1
Meas/EX
193
8: 5000 mV Excitation
9: 26
Loc :
10: 1
Mult
11:0Offset
87: P89 If X<=>F
01:26 X Loc
02:4
<
03: -99998 F
04:30 Then Do
Page 12 Table 3
88: P6
Full Bridge
01:6Reps
02: 2
5000 uV slow Range
03:3IN Card
04: 1
IN Chan
05: 1
EX Card
06:3EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 26
Loc :
10: 1
Mult
194
11:.0Offset
89: P89 If X<=>F
01:26 X Loc
02:4
03: -99998 F
04:30 Then Do
90: P6
Full Bridge
01:6Reps
02: 3
15 mV slow Range
03:3IN Card
04: 1
IN Chan
05: 1
EX Card
06:3EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 26
Loc :
10: 1
Mult
11:0Offset
91: P89 If X<=>F
01:26 X Loc
02:4
195
03: -99998 F
04:30 Then Do
92: P6
Full Bridge
01:6Reps
02: 4
50 mV slow Range
03:3IN Card
04: 1
IN Chan
05: 1
EX Card
06:3EX Chan
7: 1
Meas/EX
8: 5000 mV Excitation
9: 26
Loc :
10: 1
Mult
11:0Offset
93: P95 End
94: P95 End
95: P95 End
Page 13 Table 3
96: P95 End
97: P
* 4
End Table 3
Mode 4 Output Options
196
1: 1
(Tape OFF) (Printer ON)
2: 2
Printer 9600 Baud
* A
Mode 10 Memory Allocation
1: 36
Input Locations
2: 64
Intermediate Locations
* C
Mode 12 Security
1: 00
Security Option
2: 0000 Security Code
197
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