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University MicrOfilms
International 300 N. Zeeb Road Ann Arbor, MI48106
8522812
Hassan, Hesham Mahmoud
ESTIMATION OF EVAPOTRANSPIRATION AND IRRIGATION UNIFORMITY FROM SUBSOIL SALINITY
The University of Arizona
University Microfilms
International 300 N. Zeeb Road, Ann Arbor, MI48106
PH.D. 1985
ESTIMATION OF EVAPOTRANSPIRATION
AND IRRIGATION UNIFORMITY
FROM SUBSOIL SALINITY
by
Hesham Mahmoud Hassan
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF SOILS, WATER AND ENGINEERING
In Partial Fulfillment of the Requirements For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
1 985
THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE
As members of the Final Examination Committee, we certify that we have read
the dissertation prepared by __ ~H~e~s~ha~m~M~a~h~m~o~u~d~H~a~s~s~a~n __________________ ___
and recommend that it be accepted as fulfilling the dissertation requirement
for the Degree of --~~~~~~~~~~--------------------------------
Date IT I
~~)7'h-Date
Date 7
Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copy of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.
Dissertation Dir ctor iJ.t"-'l l 'it ( 'I 'ff5 te"
STATEMENT BY AUTHOR
This dissertation 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 dissertation 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 or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.
SIGNED:
To my wonderful family --
my Father my Mother my sister, Sahera my brothers--Shaker, Hazim,
Kanim, Moayad, Nowval, Rayiath and Yahya
iii
ACKNOWLEDGMENT
The author expresses his deepest respect and appreciation to
his major professor, Dr. A. W. Warrick, for his ideas and guidance
throughout this work.
Appreciation is extended to Dr. W. R. Gardner and Dr. A. D.
Matthias for their ideas, suggestions, and guidance throughout this work.
Special thanks to Dr. G. R. Dutt for his assistance and guid
ance. Also, the author thanks Dr. J. L. Thames and Dr. P. f:folliott
for reviewing the dissertation.
Thanks also to all faculty, staff members of the Soils, Water
and Engineering Department at The University of Arizona. I also would
like to thank Dr. J. E. Watson and Sheri A. Musil for their help and
cooperation.
I would also like to give grateful acknowledgment to the Iraqi
Government for its financial support.
Most importantly, I wa.nt to give special thanks to my wonderful
family, especially to my father, my mother, my sister Sahera, and my
brothers--Shaker, Hazim, Kanim, Moayad, Nowval, Yahya, and Rayiath-
for instilling in me the motivation to further my education goals and
for their love and invaluable encouragement.
Special thanks are extended to Joan Farmer for typing this
dissertation.
iv
TABLE OF CONTENTS
LIST OF TABLES . . • .
LIST OF ILLUSTRATIONS
ABSTRACT.
1. INTRODUCTION AND LITERATURE REVIEW
Irrigation Uniformity .•...• Water and Salt Movement Soil Salinity and Measurement Salt in Drainage Water. Evapotranspiration. Spatial Variability . . • Geostatistics. . . . . . Objecti ves of the Study
2. MATERIALS AND METHODS
Description of Field Sites Field 1: Safford Agricultural Center Field 2: Maricopa Agricultural Center Field 3: Howard Wuertz Farm
Soil Sampling .••...•.•• A nalyses of Soil Samples
Methods of Calculation. . • . . . . . • Uniformity and Related Calculations B laney-Criddle Calculations Soil-water Extract Model Sample Variograms. • •
3. RESULTS AND CALCULATIONS
Basic Data ...•.. Salt in the Profiles Irrigation Water .••. Hydrochemistry Data Discussion. . . . . .
v
Page
vii
xi
xiii
1
1 5
13 17 21 27 28 33
34
34 34 38 43 43 47 47 48 48 49 49 55 56 56
58
58 . . . . 58
95 95
100
T ABLE OF CONTENTS--Continued
Prediction of Salinity Composition in the Soil Profile .• . . . . • • . . . .
Input Data and Model Descr'iption Discussion. • . • . • • • • • . . •
Irrigation Uniformity, Efficiency, and Leaching 'Fraction . • .
Irrigation Uniformity. • Irrigation Efficiency . • • Leaching Fraction . • . • Ages of Salt and Water Discussion. • • • . • . .
Evapotranspiration. . . • • Evapotranspiration Calculated by using
the Blaney-Criddle Method. . . • . Comparison of the Blaney-Criddle and Other
Evapotranspiration Estimations • Discussion. • .
Geostatistical Data. . . . .
4. SUMMARY AND CONCLUSIONS.
APPENDIX A: BASIC DATA FOR FIELD 1 REPRESENTED
vi
Page
103 105 105
110 111 122 129 132 137 142
142
152 157 159
164
BY OPEN CIRCLES IN FIGURE 2. • . . • 170
APPENDIX B: VARIATION IN THE CALCULATED CATIONS AND ANIONS BY USING THE SOIL-WATER EXTRACT MODEL AND 5:1 WATER-SOIL EXTRACT OF THE THREE FIELDS .••••••••...••..•.•.• 176
APPENDIX C: V ARIA TION IN THE RA TIO OF SO 4/ Cl FOR THE THREE FIELDS AND THE CALCULATED IRRIGATION UNIFORMITY FROM THE CONCENTRATION OF Cl, HC0 3 , AND S04 •
REFERENCES
180
186
LIST OF TABLES
Table Page
1. Climatological data for 1983 for Safford Agricultural Center, Field 1 . . . . . • . • . • • . • . . 36
2. Physical characteristics of the soil profiles of Field 1 37
3. Quality of irrigation water applied in the three fields studied . . . . . . . . . . . . . . . . . . . . . . . 39
4. Climatological data for 1983 for Maricopa Agricultural Center, Field 2 • • •• .•....• • . . • • • 41
5. Physical characteristics of the soil profiles of Field 2 42
6. Climatological data for 1983 for Casa Grande, Field 3 45
7. Physical characteristics of the soil profiles of Field 3 46
8. The salt concentration of the soil (meq/L), moisture 2 content, the salt content calculated per unit area (kg/m ), the ratio of I/ET and DII for each depth at each site of Field 1 (Safford) . • • • . • . . • 59
9. The salt concentration of the soil (meq/L), moisture 2 content, the salt content calculated per unit area (kg/m ), the ratio of IIET and D/I for each depth at each site of Field 2 (Maricopa). . . . . . . • . 63
10. The salt concentration of the soil (meq/L), moisture 2 content, the salt content calculated per unit area (kg/m ), the ratio of IIET and D II for each depth at each site of Field 3 (Casa Grande). • . . . . • . . . . . . . 67
11. Mean eX), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by depth for Field 1 . • . • • . . . . . . . • . . . 75
12. Mean (X), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by site for Field 1 . . • . . . .. .....•..... 76
vii
Table
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
LIST OF TABLES--Continued
Mean eX), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by depth for Field 2 . . . . . . . . . . . . . . . . . . .
Mean eX), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by site for Field 2 . . . . . . . . . . . . . . . . . . .
Mean (X), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by depth for Field 3 . . . . . . . . . . . . . . . . . . .
Mean (X), standard deviation (0), and coefficient of variation (CV) of salt concentration (meq/L) by site for Field 3. . . . . . . . . . . . . . . . . . .
2 2 Cumulative salt (kg/m ) and cumulative water (kg/m ) by depth for Sites 5, 9. and 10 (Field 1) • • . . . . .
Cumulative salt (kg/m2
) and cumulative water (kg/m2
) by depth for Sites 1, 6, and 11 (Field 2) . . . . . . .
Cumulative salt (kg/m2
) and cumulative water (kg/m2
) by depth for Sites 2.1, 2.4, 5, 8, 9, and 12 (Field 3)
The number of irrigations during the growing season and the amount of water applied (irrigation and rainfall for the three fields . . • . • . • . . • • . • . . • . .
Chemical characteristics of the 5: 1 water-soil extract for Site 5, Field 1 . . . • . . . • . . . • • . •
Chemical characteristics of the 5: 1 water-soil extract for Site 7, Field 2 . . • . . . . . • . . . . . .
Chemical characteristics of the 5: 1 water-soil extract for Sites 2.1 and 2.4, Field 3 .•.....•....
Calculated concentration (meq/L) of chemical constituents within the soil profile at the final moisture content for Site 5 of Field 1 . • . . . . • . . . • . . . • . . . . . .
Calculated concentration (meq/L) of chemical constituents within the soil profile at the final moisture content for Site 7 of Field 2 . . . . . . . . . . • . . . . . . . . .
viii
Page
78
79
81
82
89
92
93
96
97
98
99
106
107
LIST OF T ABLES--Continued
Table
26. Calculated concentration (meq/L) of chemical constituents within the soil profile at the final moisture content for
ix
Page
Sites 2.1 and 2.4 of Field 3 . • . . • • • • . • • • • • •. 108
27. Calculated ratio of irrigation to evapotranspiration (IIET) from the concentration of CI, 5°4 , and H C03 by depth for Site 5 of Field 1. . . . • • • . . . . • • . • • • • .. 113
28. Calculated ratio of irrigation to evapotranspiration (IIET) from the concentration of CI, 504' and HC0 3 by depth for Site 7 of Field 2. • . . . . • • • . • . • • • • • • .. 114
29. Calculated raio of irrigation to evapotranspiration (I/ET) from the concentration of CI, 504' and HC0 3 by depth for Sites 2.1 and 2.4 of Field 2 . • . • . . . . • • . . .. 115
30. Mean (X), standard deviation (0'), coefficient of variation (CV), Christiansen's uniformity (UC), lower-quarter distri bution (D U ), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 1. • . . . • . . . . . . . • . . • . • .. 116
31. Mean (X), standard deviation (0'), coefficient of variation (CV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for Field 1 . . . . . . . . . . . . . . . . . . . . . . . . 117
32. Mean (X), standard deviation (0'), coefficient of variation (CV), Christiansen's uniformity (U C), lower-quarter distribution (DU), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 2. . . • • . . . . . • . . • . . . . . .. 118
33. Mean (X), standard deviation (0'), coefficient of variation (gV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for Field 2 . . . . . • . . . . . . • • . . . . . . • . . • 119
34. Mean (X), standard deviation (0'), coefficient of variation (CV), Christiansen's uniformity (UC), lower-quarter distribution (DU), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 3. . . . • . . • . . . . . . . . . • . . •. 120
LIST OF T ABLES--Continued
Table
35. Mean eX), standard deviation (a), coefficient of variation (CV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for
36. Calculated ages (years) of salt and water at a depth of 1. 5 m ( Field 1). · · · . · · · · · · · · · ·
37. Calculated ages (years) of salt and water at a depth of 1. 5 m (Field 2). · · · . · · · · · · · · ·
38. Calculated ages (years) of salt and water at a depth of 1.5 m (Field 3). · · · · · · · · · · · · ·
39. Calculated crop evapotranspiration (ET) of Field 1 by use of the Blaney-Criddle equation · · · · · ·
40. Calculated crop evapotranspiration (ET) of Field 2 by use of the Blaney-Criddle equation · · · · · · ·
41. Calculated crop evapotranspiration (ET) of Field 3 by use of the Blaney-Criddle equation · · · ·
42. Mean ET II, time period prior to sampling date, and the estimated ET by using the Blaney-Criddle (ETBC)
134
135
136
· · · 144
· · · 146
· · · 148
equation and estimated ET from salt data of Field 1 153
43. Mean ET II, time period prior to sampling date, and the estimated ET by using the Blaney-Criddle (ETBC) equation and estimated ET from salt data of Field 2 154
44. Mean ET II, time period prior to sampling date, and the estimated ET by using the Blaney-Criddle (ETBC) equation and estimated ET from salt data of Field 3 . 155
45. Calculated semi variance values of em' EC s ' I/ET, ET II, DII, As, and Aw of Field 1 . . . 161
46. Calculated semi variance values of em' EC s ' I/ET, ET II, DII, As, and Aw for Field 2 162
47. Calculated semi variance values of em' EC s ' I/ET, ET II, D II, As, and Aw for Field 3 163
LIST OF ILLUSTRATIONS
Figure Page
1. Usual method of plotting a semivariogram; a. Spherical model; b. Comparison of the exponential and spherical models with same range and sill; c. The linear model with nugget effect and without a distinct sill; and A d. The linear and generalized linear model '( (h) = ph . 31
2. Location of study area and the sampling sites at the University of Arizona Safford Agricultural Center (Field 1) • • • • • • . . . . . • . . • • • . . . • . 35
3. Location of study area and the sampling sites at the University of Arizona Maricopa Agricultural Center (Field 2) . • . . . • • • • • . . . . . . • • 40
4. Location of study area and the sampling sites at the Howard Wuertz Farm, Casa Grande, Arizona (Field 3) 44
5. Mean value of salt concentration in the soil (meq/L) and standard deviation for each depth of Field 1
6. Mean value of salt concentration in the soil (meq/L)
77
and standard deviation for each depth of Field 2 • • • •• 80
7. Mean value of salt concentration in the soil (meq/L) and standard deviation for each depth of Field 3 • . . .. 83
8. Variation of soil solution conductivity (dS ~1) water content (kg kg 1 ) for Site 2 of Field 1.
9. Variation of soil solution conductivity (dS ~1) water content (kg kg 1 ) for Site 5 of Field 1.
10. Variation of soil solution conductivity (dS ~1) water content (kg kg1 ) for Site 9 of Field 1 .
11. Variation of soil solution conductivity (dS ~1) water content (kg kg 1 ) for Site 2 of Field 2.
12. Variation of soil solution conductivity (dS fiJI) water content (kg kg 1 ) for Site 4 of Field 2.
xi
and
· and
· and
· and
· and
· . · 84
· . 84
· · 85
86
· 86
Figure
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
LIST OF ILLUSTRA TIONS--Continued
Variation of soil solution conductivity (dS m1) and
water content (kg kg 1 ) for Site 11 of Field 2
Variation of soil solution conductivity (dS fil) and water content (kg kg 1 ) for Site 2.1 of Field 3 ...
Variation of soil solution conductivity (dS m1) and
water content (kg kg 1 ) for Site 2.4 of Field 3 ...
Cumulative salt (kg 1m2) and cumulative water (kg 1m2) for Site 5 of Field 1. . . . . . . . . . . . . . .
2 2 Cumulative salt (kglm ) and cumulative water (kglm ) for Site 11 of Field 2 . . . . . . . . . . . . . .
Cumulative salt (kg/m2
) and cumulative water (kg/m2
) for Site 2.1 (away from trickle line) of Field 3 ....
Cumulative salt (kg 1m2) and cumulative water (kg 1m2) for Site 2.4 (next to trickle line) of Field 3. . . . .
Mean value of the calculated ET II ratio and standard deviation for each depth of Field 1 . . . . . . . . .
Calculated ET II ratio by depth for Sites 2, 5, and 9 of Field 1 . . . . . . . . . . .
Contour map of the calculated ET II ratio at the soil surface of Field 1 . . . . . . . . . .
Contour map of the calculated ET II ratio at a depth of 1.5 m, Field 1 . • . . . .
Mean value of the calculated ET II ratio and the standard deviation for each depth of Field 2. .
Calculated ET II ratio by depth for Sites 2, 4, and 11 of Field 2 . . . . . . . . . . .
Mean value of the calculated ET II ratio and the standard deviation for each depth of Field 3. .
Calculated ET II ratio by depth for Sites 2.1 and 2.4 of Field 3 ............ .
xii
Page
87
87
88
90
91
94
94
123
124
125
125
126
127
128
129
xiii
LIST OF ILLUSTRA TIONS--Continued
Figure Page
28. Mean value of the calculated D II ratio and the standard deviation for each depth of Field 1 . . • • . . . . . • 130
29. Mean value of the calculated D II ratio and the standard deviation for each depth of Field 2 . • • • . • • . • • 131
30. Mean value of the calculated D II ratio and the standard deviation for each depth of Field 3 • . • . . . • 132
3l. Calculated crop ET by use of the Blaney-Criddle formula for Field 1 · · · · · · · · · · · · · · · · · · 150
32. Calculated crop ET by use of the Blaney-Criddle formula for Field 2 · · · · · · · · · · · · · · · · · · 150
33. Calculated crop ET by use of the Blaney-Criddle formula for Field 3 · · · · · · · · · · · · · · · · · · 151
ABSTRACT
Irrigation uniformity, efficiency, leaching fraction, salt and
water ages, and evapotranspiration rate were estimated from subsoil
salinity data for three cotton fields in Arizona. The estimation of
these parameters was based on the assumption of steady-state water and
salt flow through the crop root zone. The levels of salt concentration
in the irrigation water were 21.3, 11.5, and 11.6 meq/L for Fields
1, 2, and 3, respectively. Two of these fields were furrow irrigated,
and the third was subsurface drip irrigated. Each field was sampled
for salt concentrations to a depth of 1.5 m at 10-15 sites. A total of
514 soil samples were collected.
Significantly lower salt concentrations were observed in the
soil profiles in Fields 1 and 2 compared to Field 3, but lower variations
in the salt concentrations were observed in Field 3 compared with
Fields 1 and 2. These variations in salt concentration could be due to
restricted water movement within the soil profile caused by stratified
soil. Since a soil-water extract model indicated little or no chemical
precipitation of salt within the soil profile, there was no need to cor
rect the data for chemical effects.
The calculated irrigation uniformity was highest in Field 3 and
lowest in Field 1. This may be related to more accurate land leveling
in Field 2 than Field 1. The irrigation efficiencies were 83.0%, 89.0%,
and 80.0% for Fields 1, 2, and 3, respecti vel y. The correlation
xiv
xv
coefficient between the ages of salt and water was 0.98, 0.99, and 0.97
for Fields 1, 2, and 3, respectively. Leaching fraction was highest in
Field 3 and lowest in Field 2.
Mean actual ET calculated from the Blaney-Criddle method were
273, 314, and 308 mm for Fields 1, 2, and 3, respectively. Mean ET
calculated from the salinity data were 1,250, 1,590, and 1,140 mm for
Fields 1, 2, and 3, respectively. Statistically significant correlation
coefficients were, however, found between both methods of estimating
ET. These values were 0.97, 0.86, and 0.93 for Fields 1, 2, and 3,
respecti vely.
CHAPTER 1
INTRODUCTION AND LITERATURE REVIEW
Many irrigation waters contain dissolved salts that become con
centrated by evapotranspiration as the irrigation water passes through
the root zone. The concentration of these salts is often monitored
routinely. Where this is the case, it is then possible to estimate the
previous leaching history of a field, temporally and spatially. This
can be done quantitatively, if the amount of irrigation water applied is
known. If not, relative values can be calculated. Irrigation uniformity
or evaporation and salination rates are closely related, and a knowledge
of one gives a knowledge of the other. In this chapter, I will review
the literature concerning the primary research done in these areas.
Irrigation Uniformity
Irrigation uniformity is based on the distribution of infiltration
throughout the field. The effects of uniformity of water infiltration on
crop yield have been analyzed and examined from different approaches
by different scientists. Timing and uniformity of irrigation should be
considered in water management practices. To have a good uniformity
and timing, it is necessary to have quantitative measurements. Decreas
ing uniformity generally corresponds to decreases in yield for a given
application of water. If the water contains a significant amount of
1
2
dissolved salts, water in excess of evapotranspiration must be applied
for leaching requirement. It generally increases with increases in
concentration of dissolved salts in the water.
The uniformity of the soil in a field is important because it
influences the choice of the method of irrigation. A low value of
irrigation uniformity indicates that losses due to deep percolation
occurred.
Clemmens and Dedrick (1981) presented two methods for estima-
ting irrigation distribution uniformity in level basins. They described
the distribution uniformity of applied water as a function of the rate
of advance to opportunity time and the exponents of the infiltration
and water advance functions. The advance exponent had a relatively
minor effect on the results compared to the infiltration exponent. The
distribution uniformity can be calculated directly from the infiltration
function, if the distribution uniformity is assumed to be a function of
time and the advance exponent.
Hanson and Howell (1983) evaluated irrigation uniformity of a
level basin irrigation system. The system consisted of 48 ha with six
8-ha basins served by an above-ground, concrete-lined ditch. The
3 -1 flow rate was 0.57 m s • Furrow irrigation was also investigated.
They found that by increasing the furrow flow rate a more rapid
advance resulted. They also found that furrow irrigation improved the
irrigation performance, but soil cracking resulted in increased water
infiltration in a large portion of the furrows. By using plant stress
3
measurements to integrate the stress effects of soil water depletion and
soil salinity, irrigation scheduling was accomplished. The groundwater
could contribute between 10% and 40% of the crop water requirement as
calculated by the estimation of evapotranspiration along with measure
ments of soil water content.
Warrick (1983) studied the interrelationship of various irrigation
uniformity coefficients for six separate statistical distributions of infil
trated water. Christiansen's Uniformity Coefficient (U C) is the ratio of
the average depth of water infiltrated minus the average deviation from
this depth, divided by the average depth infiltrated. The Lower Quarter
Distribution Uniformity Coefficient (DU) is the ratio of the average
lower-quarter depth of irrigation water infiltrated to the average depth
of irrigation water infiltrated. Both UC and the DU were related ana
lytically to the coefficient of variation (CV). The six distributions
chosen were the normal, log normal, a specialized power function,
f3 and y. In all cases studied, approximate forms of UC and DU were
found to be valid for CV up to about 50%:
UC = 1 - 0.8 CV
DU = 1 1.3 CV
Three examples using the specialized power function were given for
level basin irrigat:on based on a surface water advance proportional to
a power of time and an intake rate which everywhere approached a con
stant value before recession.
Gardner, Warrick, and Halderman (982) found that the great
est wa'(er use efficiency occurs when irrigation is less than that needed
for optimum growth if water is limited and the variability is high.
Only modest improvement of water use efficiency can be achieved for
values of CV below 0.5, but maximum efficiency is still achieved by
under irrigation.
4
Warrick and Gardner (1983) studied crop yield as affected by
spatial variations of soil and irrigation. Analytical expressions and
Monte-Carlo simulations were used to calculate yields for an assumed
linear response function. Uniformity of water was expressed as a
coefficient of variation ranging from 0 to 2. They found that variation
in either irrigation or soil uniformity changed the results, but irriga
tion uniformity was usually more important for surface systems. This
result was found to be unaffected by taking the irrigation and soil
uniformities to be correlated.
Feinerman, Knapp, and Latey (1984) studied the effects of
irrigation water salinity and uniformity on average crop yields. Eco
nomically optimal water application was analyzed under steady-state and
transient salinity conditions. At all levels of water salinity under both
steady-state and transient conditions, decreasing uniformity of infiltrated
water resulted in decreasing yield at a given water application rate.
Maximizing water applications increased under conditions of increased
irrigation water salinity, decreased uniformity of infiltrated water, and
decreased water price. Increased salt concentration and decreased
infiltrated water uniformity caused profits to decrease.
5
Water and Salt Movement
Soil is a continuous porous medium. As salt moves in soil by
convection and diffusion, the amount added to or removed from the
soil solution is determined by evaporation and chemical activities.
These include the solute uptake by plant roots as well as precipitation
and dissolution.o
In the soil, the solute can be transported by either
thermal motion within the soil solution (diffusion) or by viscous move-
ment of the soil solution (convection). The diffusion process depends
on the concentration gradient of the ion and occurs from points of high
concentration to low concentration.
Fick's first law described the diffusion rate (J) of solute par-
ticles in a uniform aqueous medium as proportional to the concentration
gradient (dc/dx), J = - D dc/dx. The coefficient of proportionality
(D) is called the diffusion coefficient. When a solute moves by diffu-
sion in the soil-water phase (Olsen and Kemper, 1968) Fick's first law
can be modeified as:
where c is the concentration of material per unit volume of solution,
D is the diffusion coefficient of the solute in pure water, (L/L ) 2 is o e
the tortuosity factor, y is a term used to account for the retardation
effect or anion exclusion on flow in the vicinity of negatively charge
soil particles ,0 and (l is the term used to account for reduction in water
viscosity.
6
When a dissolved ion is carried by moving water, the process
is known as mass flow or convection. The effect of infiltration, redis
tribution, or evapotranspiration on water content causes the dissolved
salt to move with the water. This movement of salt depends on the
macroscopic ·flow velocity, which depends on the size and shape of
the pores, with faster flow in large pores than in small pores. The
equation which describe·s the macroscopic convective transport of a
solute takes into account two modes of transfer: average flow velocity
and mechanical dispersion. The total amount of solute transported by
convection across a unit area in the direction of flow with steady
water movement through a uniform soil of uniform water content is
given as:
J h = - (e) Dh (V) del dx + V (e) c
where Jh
is the total amount of salt movement, V is the average in
terstitial flow velocity, and Dh is the mechanical dispersion coefficient.
In saturated porous media, Dh is dependent on the average flow veloc
ity and the characteristics of the medium.
Wierenga (1982) reviewed solute movement through one
dimensional soil profiles by using a convective-dispersive equation
given by:
7
with c being the solute concentration, t the time, x the distance, V the
pore water velocity, R the retardation factor, and D the dispersion
coefficient. In the model, Wierenga assumed that both chemical and
physical equilibrium exist within the displacing solutions and the orig
inal soil solution. He showed that there is considerable evidence that
water and solutes can move through soils without complete mixing
between invading solution and the original soil solution. During dis
placement with new water, he assumed that a fraction of the soil water
is immobile, and that solute most likely moves in or out of this immobile
water slowly by diffusion. The presence of immobile water allows for
applied solutes to move through porous media at a faster rate than
anticipated. It also follows that solutes cannot be leached efficiently
out of porous media once they have equilibrated with the entire liquid
phase, resulting in extensive tailing. This implies long leaching times
are required before complete removal of all solutes from the porous
medium is attained. The amount of immobile water depends on the
physical properties of the porous medium, degree of saturation, and
the prevailing flux.
Ghuman, Verma, and Prihar (1975) experimentally verified con
cepts regarding the displacement and profile spread of surface salt with
applied water and the leaching efficiency. Different amounts and rates
of water application and different initial soil water contents were used
In soil column experiments. A 2.0-cm interval was used to determine
both salt and water profiles. They found that in initially dry soil, the
salt front coincided with the water front, while in initially moist soil the
8
salt lagged behind the water front. After infiltration and redistribu-
tion, a salt peak occurred at a depth above which total water storage
equaled infiltration. As the water content increased, the salt spread
inore within the soil profile. Less frequent water application caused
deeper salt displacement. They showed that under field conditions
slower rates of water application may not increase the leaching effici-
ency of water. This was in agreement and tested earlier work by
Warrick, Biggar, and Nielsen (1971) who found that the maximum val-
ues of solute concentration agreed with field data over a 17-h infiltra-
2 -1 tion period when the apparent diffusion coefficient was 2.07 em min .
Also, the advance of a solute front introduced as irrigation water was
shown to be highly dependent on the moisture content maintained at
the soil surface during infiltration and is nearly independent of the
initial soil moisture content.
Thomas and Phillips (1979) studied the current views of infil-
tration of water into soil based on nearly complete displacement of soil
water by incoming water. They found that there is an effect on water
and solute distribution by a rapid flow through macropores. Also, they
found that flow of water through macropores is important in soil and
groundwater recharge and in salt movement through soils.
Letey and Kemper (1969) studied the equations that describe
the movement of solution and salt through a soil system in the presence
of hydrostatic and osmotic pressure gradients:
J v = -Lp"i/ P + LpD
"i/1T
J D = LDp P - LD "i/1T
J IC = (- L + LD )"i/p + (-LD + L D)"i/1T ssp p p
where J is the difference in velocity between water and salt, J is the v s
9
flux of salt, P is the hydraulic pressure, 1T is the osmotic pressure, C is s
the average solute concentration, L is a phenomenological coefficient,
Lp is the hydraulic conductivity, and LDp
is the coefficient relating water
flux to osmotic pressure. The Onsager reciprocal relation LpD = LDp
was
found to be valid for a clay-water-silt system. Letey and Kemper
(1969) found that the value of LpD appeared to be slightly dependent on
the osmotic gradient, and this can be caused by the clay adsorbing
more water with time. Also, LpD increases as water content in the
clay increases, and it appears to be directly related to "i/1T. The values
of Lp are somewhat variable, and do not appear to be correlated with
any of the other parameters. In clay loam and sandy loam at soil-
water suctions ranging from 0.08 to 15 bars, movement was compared
in response to various hydraulic pressure gradients (Letey and Kemper,
1969) . They found that the amount of water moved by osmotic pressure
gradients was less than 4.0% of the water moved by hydraulic gradients
of equal magnitude at suctions less than 0.5 bars.
Ortiz and Luthin (1970) studied the effect of soil anisotropy on
the displacement of salts in a tile-drained field being leached by ponded
water. The displacement is assumed to be piston-type, with no hydro-
dynamic dispersion or diffusion considered. An impermeable barrier is
assumed to exist at a gi ven depth. No salt precipitation or chemical
10
reaction are also assumed. The position of the displacement front at
different times was calculated from a computer program using point
values of the stream and potential function. The results indicated
that the distribution of vertical flow at the surface improved with the
value of the horizontal to vertical permeability rates. This means that
leaching in the region halfway between drains will be better for an
anisotropic soil than for an isotropic one.
Miyamoto and Warrick (1974) mathematically analyzed steady
state piston-type displacement into drain tiles by using an impermeable
material to partially cover the ground surface. They found that the
flow rate into the drain decreases with the extension of soil covers for
a given ponded water depth. Also, the displacement front into the
drains advanced much faster near the drains than in the region midway
between them. The analysis is useful for predicting the displacement
patterns for localized initial solute distributions. They showed that the
volume of water required for leaching is, however, about half of that
with a soil cover. Therefore, the time required to complete the salt
leaching from the equivalent depth of drains is less with the soil
covers.
Raats (1974) studied the steady upward and downward flow of
water in the presence of root uptake by plants. The specific calcula
tions were based upon an exponential increase of hydraulic conductivity
with the pressure head and an exponential decrease of the uptake rate
with depth, and this related to flux distance given by Rawlins (1973).
The downward flow was expressed as a function of infiltration rate.
11
Upward flow was expressed as a function of the infiltration and trans
piration rates, and the hydraulic conductivity of the saturated soil.
Also, Rawlins evaluated the salt distribution associated with the steady
downward flux. He calculated the depth-time trajectories of elements
of water as a function of their initial position, average soil water con
tent, the uptake distribution, and the rates of irrigation, evaporation,
transpiration, and drainage. The salt mass balance was reduced to a
linear, first-order partial differential equation. He assumed that a
maximum uptake rate occurred at the soil surface and exponentially
decreased with depth. He calculated a step increase and step decrease
of leaching fraction and the response of salinity sensors at various
depths. Raats (1969) did a theoretical analysis of steady gravitational
convection from a line source of salt in a saturated or in a uniformly
partially saturated soil. The source strength of the line, the diffusion
coefficient of salt in the soil, and hydraulic conductivity affected the
flow of the water solute mixture and salt distribution. His analysis
agreed with experimental observations. If the hydraulic conductivity
is large, the gravitational convection is significant as indicated by the
theory and experiment. An example of such a situation is a coarse
textured soil with a water content that is not too small.
Bresler and Hanks (1969) presented a numerical solution of simul
taneous salt and water flow in unsaturated soil. They neglected the
effect of diffusion on salt distribution in the computation. They ob
tained reasonable results for non-interacting solutes as indicated by
comparison with the salt distribution which was measured after one
wetting and drying cycle.
12
Dutt (1962) used the Debye-Huckel theory to predict the
equilibrium concentrations of the ions in the solution and ions adsorbed
by exchange when a solution of Ca or Mg salts is used. He found that
the deviation between the calculated and observed concentrations of
Ca and Mg of the systems containing the lower total salt concentrations
is therefore due in part to the influence of the soil particles on the
soil solution.
Three model options for testing the prediction of salt transport
and precipitation di,~solution were studied by Robbins, Wagenet, and
Jurinak (1980). The transport model predicts relative crop growth
and water uptake as affected by soil moisture and salinity. They com
pared the predicted values for EC, SAR, Ca, Mg, Na, K, CI, S04' and
HC0 3 concentrations by the three options with the experimental data
obtained from a lysimeter study were only satisfactorily predicted
when both chemical precipitations and cation exchange were considered
for a gypsiferous and a nOl1gypsiferous soil irrigated with high,
medium, and low CaS04 water at 10% and 25% leaching fractions.
Perroux, Smiles, and White (1981) presented an analysis for
constant-flux infiltration of water in soil based on the flux-concentration
relation. They compared the analysis with laboratory experiments on
constant-flux infiltration into columns of fine sand and silty clay loam.
They showed that the effect of gravity is small for the early stage and
13
a sufficient prediction of moisture profile development can be made by
using the simpler absorption analysis of infiltration.
Amoozegar-Fard, Warrick, and Fuller (1983) described solute
movement through soil by a variety of mathematical models. The
breakthrough curve can be used to describe the movement of an ion
at a given depth. Such a curve resulting from a step input of a
solute is often of sigmoidal shape. They evaluated two unknown
parameters to fit a breakthrough curve. A procedure for estimating
these two parameters using a small calculator was described.
Allison and Hughes (1983) used natural tracers as indicators
of soil water movement in a temperate, semiarid region. Chloride con
centrations of soil water have been used to show that the mean annual
amount of deep drainage increased from less than 0.1 to 3 mm/yr. The
environmental concentration of tritium in soil water beneath the vegeta
tion is consistent with the hypothesis that some relatively recent water
had penetrated to depths of at least 12 m along channels occupied by
living roots. They found that no water was found at depths greater
than 2.5 m where the native vegetation had been cleared.
Soil Salinity and Measurement
Soil salinity may occur in soils having distinctly developed pro
file characteristics or in undifferentiated soil material such as alluvium.
The chemical characteristics of soil classed as saline are mainly deter
mined by the kinds and amounts of salts present. The amount of
soluble salts present controls the osmotic pressure of the soil solution.
If the total quantity of salts in the irrigation water is high, salts
accumulate in the root zone and affect crop yield.
14
The electrical conductivity (EC) measurement is based on the
amount of electrical current transmitted by a salt solution under stan
dardized conditions, it increases as the salt concentration of the solu-
tion is increased. To make such a measurement, a sample of solution
is placed between two electrodes, and an electrical potential is imposed
across the electrodes. Under these conditions, the resistance of the
solution is measured and converted to reciprocal resistance or conduc
tance. The basic unit of resistance is an ohm, and the reciprocal resis
tance is mho, which is equivalent to siemens (S) in SI units. The
result is then multiplied by a IIcell constant II having units of cm -1.
the resultant EC value has units of mho/cm, or dS/m. Soil salinity
can be measured by several methods. In the laboratory, the deter
mination of soluble salts consists essentially of two steps: (1) the
preparation of a soil-water extract and (2) the measurement of the salt
concentration of the extract. The choice of a method for preparing a
soil-water extract and for measuring its salt concentration depends
upon the purpose of the determination and the accuracy required.
While in situ, a different method is used to measure the EC.
The salinity sensor probe permits continuous and nondestructive mea
surements of EC values at specified points in an irrigated field. The
sensors are imbedded in a porous ceramic matrix, and have a response
time as the salinity level of ambient solution is changed. The salinity
sensor can be used for irrigation scheduling, particularly where
15
irrigation frequency is dictated to minimize salt stress for the growing
crop. Some problems do exist, however, when the soil-water potential
drops below -2 bars, the sensor becomes less sensitive to soil solution
EC. Also, the calibration curves can change after a few seasons.
Enfield and Evans (1969) assessed the electrical conductivity of
soil water in situ by de :..)ping a transducer. The transducer was
operated in the range of field moisture using platinum electrodes. In a
range of 5°C to 45°C temperature, the accuracy of the transducer was
10% when measured with an associated solid-state meter in the 1 to 20
dS/m range at 25°C. To achieve 63% of equilibrium in aqueous solu
tions, the time required was less than 2 h. Wesseling and Oster
(1973) developed a theory to describe sensor response to changes in
soil salinity. They experimentally verified the theory in laboratory
solutions and in soils. They have shown that the response of the sen
sor was adequately described by a single response factor. Rhoades
and Ingvalson (1971) presented a field method -for assessing soil salinity
and soil resistance. Measurements using this method were made with an
array of four electrodes placed on the soil surface and a geophysical
Megger-Type earth resistance meter. They showed an excellent rela
tionship between determined soil conductivities and soil salinities.
Halvorson and Rhoades (1974) examined the use of soil conductivity
values calculated from resistance measurements obtained with the four
probe Wenner electrodes to identify potential saline-see p areas and to
estimate soil salinity in the field. They found significant correlations
between apparent soil conductivity (EC a ) and electrical conductivity of
saturation extracts (EC ) showing that the four-probe soil resistance e
method can be used to estimate field salinity. Shain berg, Rhoades,
16
and Prather (1980) measured the EC of eight soils as a function of the
solution EC. In the low range of salt concentration, the soil EC
increased nonlinearly with respect to the equilibrium solution EC,
while a straight-line relationship was obtained at high salt concentra-
tion. When the EC method is used to measure soil salinity, the effect
of exchangeable sodium percentage on the EC curve parameter is slight
and is not significant.
Nadler and Dasberg (1980) measured soil salinity with an in
situ salinity sensor, a four-electrode salinity probe, a four-electrode
Wenner array, and a multi-electrode probe in an experimental plot
irrigated with 3.1 dS/m CaCI2
. They obtained good agreement among
salinity, soil extract (1: 1), and EC measurements with the salinity
probe. Good electrode-soil contact was required for the salinity probe
limited to the higher soil-water contents. On the other hand, the
Wenner array functions at lower contents, and the result is influenced
by soil layering. Nadler and Frenkel (1980) studied soil electrical
conductivity (ECa ) by the four-electrode method as a function of soil
water electrical conductivity (ECw ) in the laboratory by using six soil
types. They found that at salinity greater than 4 dS/m, a linear rela-
tionship occurred between ECa and ECw . At very low salinity levels
the contribution of the surface conductance was not constant and has
a higher contribution than ECw to the measured ECa .
17
Rhoades and Corwin (1981) used a new electromagnetic device
for measuring soil electrical conductivity. With this device, readings
of field salinity are obtained without any soil-to-instrument contact.
The device is well suited for field investigation of soil salinity.
Rhoades (1981) calibrated soil electrical conductivity with salinity for
12 soil types according to soil properties. He found that the calibra
tion slope was highly correlated with water-holding capacity and satu
ration percentage. The intercepts were highly correlated with clay
content.
Salt in Drainage Water
The processes of water extraction by plants, water movement
both up and down in the profile, the degree of water saturation in the
profile, and the chemoical interaction of dissolved, exchangeable and
precipitated salts determine the water-ion environment of the plant
roots. A s the plant extracts water from the soil, most of the salts are
left behind. This can increase the concentration of salt in the remain
ing soil solution, lower the water potential, and diminish plant growth.
It is apparent that the plant cannot use all the saline water in its root
zone without leading to prohibitively high salt concentrations. Thus, in
order to maintain satisfactory plant growth, some of the salt must be
removed by drainage from the profile.
Rhoades et al. (1971) studied salt in irrigation drainage water
from synthesized waters of eight ri vers in the western section of the
United States with alfalfa in a controlled lysimeter experiment. They
showed that the leachate compositions were affected by the composition
18
of the river water used for irrigation, the leaching fraction, the pres
ence or absence of CaC03
, whether or not the drainage water is open
to the atmosphere, and the time of year.
Oster and Rhoades (1975) calculated the salt composition of
drainage water by using a computer simulation model from irrigation
water compositions, leaching fractions, aragonite and gypsum solubilities,
and measured partial pressure of CO2
, They compared the calculated
compositions with measured values obtained from lysimeters filled with
Pachappa soil, cropped with alfalfa, and irrigated with eight synthetic
river waters. One-to-one relationships were obtained between predicted
and measured composition when a linear regression analysis included Na
and SO 4 concentrations, sodium adsorption ratio (SAR), electrical conduc
tivity (EC), and salt burden. Due to mineral dissolution at high leaching
fractions, there is a gain in salt load of drainage water. By using crop
tolerance data for alfalfa and the hydraulic conductivity of several soils,
they evaluated the quality of irrigation water at the minimum leaching
fraction. Based on salt tolerance data, leaching fractions between 0.05
and 0.1 should be safe for alfalfa for six of the eight waters.
By using large-ring infiltrometers, Leffelaar and Sharma (1977)
experimentally determined the leaching curve with respect to desaliniza
tion and desodification of a highly saline-sodic soil. Different theoreti
cal models were tested by comparing the calculated and experimental
leaching curves. They found good agreement between theoretical and
experimental curves up to 10% of the initial salinity. The experimental
data fitted the empirical relationships on the lines of Reeve's (1957)
19
equation. Leffelaar and Sharma calculated that there was no need for
the application of any amendment such as gypsum to reclaim these soils
from the desodification leaching curve. The infiltration data were
collected to calculate the amount of water evaporating during reclamation
and to predict the time required for reclamation.
Jury, Frenkel, Devitt, and Stolzy (1978) analyzed experimental
data for four soil types in 23 lysimeters containing wheat or sorghum,
irrigated with three synthesized levels (2.2, 3.9, 7.1 dS/m) of irrigation
water. Soil salinity was used to calculate salt balance. Ion balance was
determined from saturation extracts. After 500 days of the experiment,
50% of the salt was precipitated, which was twice the expected amount
at the time the salt concentration of the root zone reached steady-state.
The Cl approached a steady-state value. when the drainage equaled
one pore volume displacement through the root zone. Daily irrigation
with high water salinity caused the water uptake in all lysimeters to
occur in the top 20 cm. Lai, Jurinak, and Wagenet (1978) examined the
adsorption of Na, Mg, and Ca under different total cation concentrations
and input pulse volumes. They showed the degree of separation
decreased with total concentration but was not affected significantly by
pulse volume. A satisfactory agreement was found between the theoret
ical model and experimental results.
Jury, Frenkel, and Stolzy (1978) studied transient soil solution
concentrations and salt precipitation rates in the root zone. They found
concentrations and rates to be influenced by the ion composition and
concentration of applied water, the soil exchange complex, the water
20
uptake distribution, and infiltration rate. Three kinds of infiltration
water were used to estimate the ionic composition of soil solution, the
rates of gypsum and CaC03 precipitation, and the time to reach a
steady-state for a given irrigation. They showed that the adjusted
solution concentration for exchange interaction was the quantity of salt
in a given time. This resulted in a slower solution concentration and
an altered composition of Ca, Mg, Na, and 504 ion concentration.
They found that up to 1,600 days were required to reach steady-state
through the top 150 cm for a 0.5 leaching fraction. Also, they found
that the concentration of sulfate and the degree of saturation with
gypsum were strongly dependent on the extent of precipitation. The
transient duration depended on the diffusion and dispersion.
Jury and Pratt (1980) estimated the salt burden of irrigation
drainage waters by using steady-state and transient models that included
chemical reactions as well as salt transport of four waters and three
leaching fractions for depths up to 450 cm. The steady-state model
predicted a greater tendency toward mineral dissolution than occurred
when Ca ions were brought into solution through ion exchange, but
the salt balance was calculated more accurately than from the propor
tional model, which tends to precipitate Ca salts and underestimate
the salt burden of waters, which tends to dissolve native CaC03
in
the soil. They found that with a saline irrigation water a leaching
fraction of 0.1, the steady-state and transient model mass emission
predictions were 22% and 36% less, respectively, than the estimates
from the proportional model.
21
Suarez (1981) studied the relation between the SAR and PH to c
predict SAR of the drainage water (SARdw
) better than existing empir-
ical equations. At any fixed P CO when the Ca concentration 0"£ the 2
drainage water is assumed to be constant, a satisfactory prediction of
the drainage water SAR occurs due to the model's relative insensitivity
to Ca concentrations. In the absence of gypsum precipitation, the SAR
prediction of drainage water or soil-water composition required P CO2
'
irrigation water composition, HC03
/Ca ratio, and the leaching fraction.
From the derived equation and a table accounting for ionic strength
and HC0 3 /Ca ratio, SARdw can be calculated simply and accurately.
Suarez (1982) estimated the root zone salinity and ion composi-
tion and compared it to crop tolerance data. He obtained reasonably
accurate estimates by using the graphical solutions to the equation
which described CaC03 and gypsum equilibria. The graphical method
can be used to predict the effect of reduced leaching on the change in
salt precipitation and on the salt burden in irrigation return flows.
Also, the procedure permits calculation of the amount of CaC03
and
gypsum precipitated in the soil as a function of leaching fraction for
any type of irrigation water.
Evapotranspiration
Evapotranspiration (ET) is defined as the combined processes
by which water is transferred from the earth's surface to the atmos-
phere. It includes evaporation of liquid or solid water from soil and
plant surfaces plus transpiration of liquid water through plant tissues
expressed as the latent heat transfer per unit area or its equivalent
depth of water per unit area. The ET can be influenced by salt
frost protection, and soil temperature control. The major objective in
selecting managment procedures to control salinity is to improve soil
water availability to the crop. This requires more frequent irrigations,
additional leaching, pre plant irrigation, and seed placement. Signifi
cant changes in management that may need to be made are changing
the irrigation method, altering the water supply, planting salt tolerant
crops, land-grading, and installing artificial drainage.
The ET can be estimated by either direct measurement or from
climatological and crop data. The ,direct measurement is based on the
water balance. This involves periodic determination of the soil mois
ture of the root zone as well as maintaining a record of the amount of
rainfall, irrigation, and drainage water. In the field this can be done
by using a lysimeter. The other method of field measurement is done
by applying meteorological equations. These are based on mass trans
fer, which requires vapor pressure and wind speed measurements at
one or more heights above the crops. By collecting all the above infor
mation and applying it to the energy balance equation, the ET can be
estimated. On the other hand, ET can be estimated from climatic data
that requires weather records. Either daily or long period ET values
can be estimated.
The reference crop ET can be estimated by several methods
based on combination theory and humidity, radiation, and temperature
data. The Penman method is one of the methods used to estimate
23
reference crop ET, and it is the most accurate for a very wide range
of climatic conditions. Estimations obtained by this method are reliable
for periods of 1 day to 1 month. Another method for estimating ET,
which is based on climatic data, is the Jensen-Haise (Jensen and Haise,
1963) method and is classified as a solar radiation method. It is based
on the elevation 'and long-term temperature, and is valid for periods of
5 days to 1 month. The Blaney-Criddle (Blaney and Criddle, 1962)
method is another method based on the principle that ET is proportional
to the product of day-length percentage and mean air temperature.
Estimates have been considered to be valid for monthly periods.
Tanner (1960) used the vertical energy balance method to
obtain reliable estimates of evapotranspiration on an hourly basis under
large variations of thermal stratification. He found this method to be
accurate, provided measurements were made close to a reasonably
homogeneous surface and time-sampling procedures were followed. The
energy balance method was used to measure the radiation exchange at
the surface and found to be a promising method for obtaining daily
estimates of ET, provided either periods of positive and negative net
radiation are considered or a reasonable estimate of the 24-h Bowen
ratio can be developed. He found that there is very little vertical
transfer of sensible heat to the surface in humid regions, so that the
ET will approximate the daily net radiation under potential ET condi
tions. By consideration of the complete energy balance, he also showed
that small plots are not adequate for estimating ET. Energy balance
measurements on corn, including the net radiation measurement, indicated
that the heat exchange at the surface was an appreciable fraction of
the total heat exchange even in mature corn at high populations.
24
Willis (1960) studied evaporation from two-layered soil systems
with varying depths to the water table under steady-state conditions.
He found relatively small differences in evaporation between a fine
textured soil overlying a coarse soil and a fine-textured soil of homoge
neous profile. He concluded that when the water table is relatively
deep, the presence of inhomogeneities may be of little consequence and
a weighted-average capillary conductivity curve might give satisfactory
results in calculating estimated evaporation rates.
Gardner and Gardner (1969) measured evaporation from col
umns of Rago loam and McGrew loamy sand to which water had been
added at several rates ranging from 0.15 cm/ day to 10.2 cm every 20
days. They found that 100% of the total applied water was lost by
evapora.tion for the smallest and most frequent addition to 31.2% for
10.2 cm of water added to the soil every 20 days. The loss tended to
approach a constant value that was less than the potential loss when
the amount added was increased for a given evaporation period. The
losses from soil with two different potential evaporations were compared
with predicted curves by using dimensionless variables and compared
with a theoretical solution of the diffusi vity equation for finite media.
Black, Gardner, and Thurtell (1969) reported a study of evaporation,
drainage, and change in water storage for a bare Plainfield sand.
They used a lysimeter under natural rainfall conditions. They found
that drainage was a function of water storage and that evaporation was
proportional to the square root of time following a heavy rain at any
stage. This relation was predicted from the flow theory involving
capillary conductivity, diffusivity, and moisture characteristics.
25
Qashu (1969) estimated ET losses from five lysimeters with dif
ferent desert plant species. Soil-water regimes, infiltration, and run
off in lysimeters with vegetative cover were estimated by prediction
equations. He found good estimates of measured quantities from calcu
lated soil-water contents and runoff for lysimeters with five different
plants.
Nimah and Hanks (1973) developed a model to predict water
content profiles, ET, water flow from or to the water table, root extrac
tion, and root-water potential under transient field conditions. Hydrau
lic conductivity and soil-water potential as a function of water content,
rooting depth and limiting root-water potential, potential evaporation
and potential transpiration are required to appply this model. They
showed that this model gave significant changes in root extraction,
ET, and drainage due to variations in pressure head-water content
relations and root depth. A small influence on estimated ET, drainage,
and root extraction occurred due to variations in limiting root-water
potential. Nimah and Hanks tested the model in the field by using
alfalfa as a test crop. They showed good agreement for water content
depth profiles 48 h after any water addition. The poorest agreement
for all crops tested occurred immediately after irrigation. The com
puted cumulative upward water flow from the water table was 4.80 cm
as compared to 0.0 cm measured for the whole of 116 days.
26
Heilman and Kanemasu (1976) conducted a field study to evalu
ate an ET model that used the diffusion resistance to calculate heat
transport from thermal diffusion (rH
) in the energy-balance equation
and to assess the effect of replacing the resistance for heat with that
for momentum. They compared the latent heat flux (LE) estimated by
the model with lysimetric measurements of LE. They found that LE
values estimated by their model were within 4% and 15% of lysimetric
measurements for soybean and sorghum, respectively, when using rH
•
Using the aerodynamic resistance (rD
), LE estimated for soybeans was
25% greater but for sorghum only 10% greater than when using rH
•
Also, significant errors occurred if the momentum resistance was used
in the model instead of the resistance for heat.
Davis, Nightingale, and Phene (1980) studied consumptive
water requirements of trickle-irrigated cotton. The results showed
that yield differences were slight for plots receiving 38 and 19 cm of
preplant irrigation. When the preplant irrigation was in excess of 19
cm, some water drained below the root zone. Without preplant irriga
tion the yield decreased, even when comparable quantities of post
emergence trickle irrigation was applied. Maximum yield was produced
under conditions of 67 cm of water applied. Shih, Rahi, and Harrison
(1982) studied ET from rice in relation to water-use efficiency by using
field lysimeters. They showed that the mean daily ET values were
6.5, 6.8, and 4.5 mm/day for a spring, summer, and fall crop, respec
tively. The mean total ET values were 800, 740, and 450 mm for
spring, summer, and fall crops, respectively. To produce 1 kg of
rough rice grain, 875 kg of water was required.
Spatial Variability
Many researchers have carried out work in spatial variation
27
studies as the diversity of physical and chemical properties of the soil
has prompted. Examples of these are spatial variations of salt and
spatial variability of soil physical properties.
Biggar and Nielsen (1976) measured the solute distributions
within the soil profile during the leaching of water-soluble salts
applied to the soil surface at six depths to 182.4 cm within 20 subplots
of a 150-ha field. They found that the estimate of the pore water
velocity within each subplot was log normally distributed and in agree-
ment with the volumetric measures of water infiltration rates. The
number of observations required to yield an estimate of the mean pore
water velocity within a prescribed accuracy was shown to depend upon
the nature and extent of spatial variability of the field soil. The
apparent diffusion coefficient, also found to be log normally distributed,
and the pore water velocity were examined and interpreted in terms of
solute distributions.
Wagenet and Jurinak (1978) studied the variability of the EC
in 1: 1 or saturation extract for 35 sampling sites in a Mancos shale
watershed within a 777-km2
area of the Price River Basin in Utah.
Soil samples were collected at 0 to 0.25,0.25 to 7.5, and 7.5-15.0 cm
depths. They found that the EC were distributed log normally about
the mean value of 35 observations. For all three depths at 35 sites,
28
for the lognormal statistical plots, the coefficient of determination was
1. They found that the variance in the EC values increased with
depth.
Sharma and Luxmoore (1979) represented the effect of soil
spatial variability on the water-balance equation of a grassland water
shed near Chickasha, Oklahoma by using a simulation model. They
found that the ET and surface runoff decreasd with an increase in the
scaling factor and a deep drainage increase. They found that the
effects of spatial variability on monthly water balance are highly depen
dent on the soil-plant-weather combination. Also, greater surface run
off was predicted by the normal distribution than the lognormal distri
bution.
Amoozegar-Fard, Nielsen, and Warrick (1982) used a Monte Carlo
simulation to obtain solute concentration and solute movement properties
as affected by the variability of pore water velocity and apparent diffu
sion coefficient. They found sharp differences within the solute profiles
when the deterministic value of the pore water velocity is used as com
pared to average salt profile for 2,000 random values of pore water
velocity. The variability of the diffusion coefficient is much less than
pore water velocity for deeper depths. Also, Amoozegar-Fard et al.
found significant quantities of both water and solutes continue to move
through soil profiles after infiltration ceases.
Geostatistics
Geostatistics is a relatively new method developed by George
Matheron in France in the early 1960s for ~nalyzing spatial variability.
29
The mathematical basis of the method is called "Theory of Regionalized
Variables." The simplest application is that of producing the "best"
estimate of the unknown value of ore grade at some location within a
deposit. The application to problems in geology and mining has led to
the mor~ popular name "geostatistics" by D. G. Krige (1966). The
method can be used wherever a continuous measure is made on a sample
at a particular location in space or time. The experimental mean differ
ence in grade can be written as m* = lIn L [g(x) - g(x + h)], where
g is the grade, x is the position of one sample in the pair, x + h the
position of the other sample at a distance h from x, and n the number
of pairs.
The variance of the differences is known as the semivariogram
y(h) defined as y(h) = (l/2) var[g(x) - g(x + h)]2 at which "var" is
the variance of the argument. The vector h is the lag. Under the
~>;ero drift assumption E[g(x + h)] = E [g(x)] and the above equation
will be y(h) = E[(g(x + h) - g(x)]2, where E is the "expected value."
An estimate of y is defined as 2y* = [l/n(h)] L [g(x -I- h) - g(x)]2.
Spatial dependence may depend on separation distance only or
on both distance and direction. If the variogram is a function of dis-
tance only, it is called isotropic, otherwise anisotropic (Warrick et al.,
1985) . Spatial variations with interdependence are commonly described
with a semivariogram. It considers a set of values g(x1), g(x2), and
g(xn ) at Xl' x 2 ' and x n ' where each location defines a point in 1-,
2-, or 3-dimensional space. The value need not be for an exact point,
but rather represents a defined "support volume" which is centered at x.
30
The semivariogram, y, describes the variance of the expected
difference in value between pairs of samples with a given relative
orientation. The semivariogram can be plotted in a graph within two
dimensions, the horizontal axis which represents the distance between
the pairs of samples and the value of the semivariogram along the
vertical axis. The values of both y and h start at zero as shown in
Figure 1a. The distance at which samples become independent of one
another is the range of influence (a) corresponding to the zone of
infleunce of variance on sampling. The range represents the maximum
separation for which two samples will be correlated. The value of y at
which the graph levels off is called the sill of the semivariogram and is
denoted by C at which the similarity between samples decreases and
the values become independent. The value of the variogram at distance
(h) = 0 is called the nugget value. A positive nugget value can have
a physical meaning such as measuring error.
The most popular semivariogram models in practice are the
spherical and the linear models as shown in Figure 1. The spherical
model can be expressed mathematically as:
3 3 y(h) = C[3h/2a - (/2) (h la )] when 0 < h < a
y(h) = C when h > a
This model was originally derived on theoretical grounds and has been
widely applicable in practice. The exponential model with a sill, which
has found some applications, can be written: y(h) = C[l - exp(-h/a)].
a y (h)
--------!....----h a
c
y(h)
Co+C=
C I I I I ______ ...1
Co 1 '------1 ___ h
Y (h) b
~----~------~-----h
y (h)
d
.... ~ ,~
~------------------h
Figure 1. Usual method of plotting a semivariogram; a. Spherical model; b. Comparison of the exponential and spherical models with same range and sill; c. The linear model with nugget effect and without a distinct sill, and d. The linear and generalized linear model y(h) = ph>-.
31
32
This model rises more slowly from the origin than the spherical and
never quite reaches its sill as shown in Figure lb. The linear model,
which has no sill, can be written as y (h) = ph, with p the slope of
the line. The generalized linear model is considered as an extension
of this model at which y(h) = phCt
, where Ct lies between 0 and 2
(but must not equal 2). Figure lc shows the application of this model
for various values of Ct. Also, the de Wijsian model has no sill and the
semivariogram is linear if plotted against the logarithm of the distance.
The model can be written as y(h) = 3Ct log(h).
The spherical model with a very small range 'of influence can
exist and describe the semivariogram of a purely random phenomena.
The nugget effect is y(h) = 0, y(h) = C when h> O. The nugget o
appears in the graph as an intercept when the semivariogram does not
pass through the origin, giving a discontinuity as shown in Figure Id.
The semivariogram must be zero at a distance zero with completely
random phenomena and increases with increasing a distance to a
maximum.
Hajrasuliha et al. (1980) studied the spatial variability of soil
salinity in southwestern Iran using geostatistics. They sampled the
soil from three sites of 150, 440, and 455 ha. Spatial variation was
found for EC for separation distances less than 800 m. The data for e
Site 1 were randomly distributed at separatjon distances 80 to 320 m.
In Site 3, a range did not exhibit, but a nugget effect exhibited that
may be related to the spacing of the data points. Russo (1984) used
a geostatistical approach to investigate the spatial variability of three
33
soil properties: the saturated hydraulic conductivity, the soil charac
teristics, and the dispersivity, as well as the initial salinity, using
actual measured data in a 187-ha plot. He found that 107 h of con tin-
uous leaching were required to obtain an average salinity of 5 dS/m
from the soil surface to a depth of 0.40 cm. Theoretically, the amount
of water for leaching required to obtain EC = 5 dS/m uniformly through
out the field can be reduced to 4,038 m3 ha -1, with a reduction of
38%.
Objectives of the Study
The primary objectives of this study were to use soil salinity
to determine:
1. Irrigation uniformity,
2. Irrigation efficiency and leaching fraction in the soil,
3. Historical ET rates from irrigated fields and to compare
these rates with those calculated by the Blaney-Criddle
method.
In each case the soil salinity is viewed as a natural tracer which
reveals past irrigation practices.
CHAPTER 2
MATERIALS AND METHODS
Description of Field Sites
Field studies were conducted at three irrigated cotton fields in
Arizona. Field 1 is located near Safford (Graham County), Field 2 is
located near Maricopa (Pinal County), and Field 3 is located near Casa
Grande (Pinal County).
Field 1: Safford Agricultural Center
During April and May of 1983 soil samples were collected at the
University of Arizona Safford Agricultural Center. The farm is located
near the Gila River in the southeastern part of the state about 241 km
from Tucson at an 884-m elevation. Figure 2 shows the sampling sites in
the field. The climate is arid and characterized as hot and dry. The
range of extreme temperature is approximately -2°C during the winter
to 40°C during the summer. The total annual rainfall ranges from 300
to 400 mm and is evenly distributed between the winter rains and a
summer monsoon season. The climatological data for 1983 as shown in
Table 1 were obtained from the weather station at the center.
The soil at the field site is a Pima Variant of Fine-Silty, Mixed,
Thermic, Typic Torrifluvents (Post, Hendricks, and Hart, 1977). The
physical characteristics of the soil profile of Field 1 are shown in Table 2.
34
~ I LONE STAR ~
o 50 leo m i:::::====
98 S_
10 7.
68
4.
20
5 a
3 •
28
1 •
lOa
30
o 5 10 m
35·
Figure 2. Location of study area and the sampling site at the University of Arizona Safford Agricultural Center (Field 1)
36
Table l. Climatological data for 1983 for Safford Agricultural Center, Field 1
Mean Minimum Total Temperature RH Rainfall
Month (OC) (%) (mm)
January 7.3 30 31
February 7.8 34 0
March 12.3 32 0
April 13.4 20 2
May 20.5 10 0
June 25.0 9 2
July 28.4 23 82
August 28.3 29 13
September 36.3 35 96
October 18.7 37 96
November 11.5 35 29
December 7.6 36 29
37
Table 2. Physical characteristics of the soil profiles of Field 1
Soil Depth Sand Silt Clay (m) (%) (%) (%)
0.00-0.02 28.7 50.3 18.8
0.02-0.15 34.3 46.9 16.5
0.15-0.30 68.8 21.1 9.3
0.30-0.45 30.8 54.4 17.4
0.45-0.60 29.3 52.6 15.5
0.60-0.75 59.3 4.4 35.1
0.75-0.90 71.2 18.2 9.7
0.90-1.05 69.3 20.5 9.9
1. 05-1. 20 64.3 21.4 14.2
1.20-1.35 95.9 2.3 1.4
1. 35-1. 50 94.2 4.7 1.6
38
Cotton was grown at the field site from 1977 to 1980, sorghum in 1981,
and cotton again in 1982 and 1983.
Two kinds of water were used to irrigate the field by furrows.
They were from the Safford water system and from a well located on
the farm. The EC of the well water is 2.13 dS/m as shown in Table 3
and that of the city 0.42 dS/m. Most of the irrigation supply was from
the well.
Field 2: Maricopa Agricultural Center
In July of 1983 samples were taken at the University of Arizona
Maricopa Agricultural Center. The center is located in a desert plain
of soutl:-central Arizona at an elevation of 427 m. It is about 161 km
northwest of Tucson, as shown in Figure 3. The climate is arid and is
characterized as hot and dry during the summer season. The range of
temperatures is approximately -6°C during the winter to 44°C during
the summer. The total annual rainfall averages 210 mm, more of which
occurs during the summer season than during the winter and fall sea
sons. Climatological data shown in Table 4 were obtained from the
Climatological Data of Arizona (1983).
The soil at the field site is calcareous. The family name is
Coarse, Loamy, Over Sandy or Sandy Skeletal, Mixed (calcareous),
Thermic, Typic Torrifluvent. The physical characteristics of the soil
profile of Field 2 are shown in Table 5. Cotton was grown at the field
site from 1970 to 1983. The water EC was 1.15 dS/m and was pumped
on site for furrow irrigation (see Table 3).
39
Table 3. Quality of irrigation water applied in the three fields studied
Parameters Field 1 Field 2 Field 3
EC (dS / m) 2.13 1.15 1.16
pH 7.5 7.8 7.5
Ca (mg/L) 85 73 30
Mg (mg/L) 14 11 10
Na (mg/L) 320 125 230
Cl (mg/L) 325 117 125
S04 (mg/L) 165 136 266
HC0 3 (mg/L) 340 142 132'
C03
(mg/L) 0.0 0.0 0.0
SAR 8.5 4.0 9.3
I-- 366 m -l --33 m--
'7
W-~6
MARICOPA
011
IS/\/j,,\ -, 'r~n7\,1A~H I
I I
W-3 I ,." W-2S0
: r 0 , I I
I ,
o 1oi-5 I
I
~-<l(J
I \ ) I I
I -.N I SMI TH ENKE I bl~ I ~(
a. I z
a I Vl
a: w
W-21 d I W-2 6 0
11- 23 0 W-IO z cr
HONEYCUTT ROAD
HARTMAN ROAD
0 PHOENIX MARICOPA
40
Figure 3. Location of study area and the sampling sites at the University of Arizona Maricopa Agricultural Center (Field 2)
41
Table 4. Climatological data for 1983 for Maricopa Agricultural Center, Field 2
Mean Minimum Total Temperature RH Rainfall
Month (OC) (%) (mm)
January 10.4 34 14
February 11.4 40 0
March 16.1 38 0
April 16.6 19 4
May 24.1 10 0
June 28.2 9 0
July 32.9 18 4
August 31.3 28 52
September 30.6 31 62
October 22.2 33 35
November 14.6 29 11
December 11.2 40 51
42
Table 5. Physical characteristics of the soil profiles of Field 2
Soil Depth Sand Silt Clay (m) (%) (%) (%)
0.00-0.02 30.2 49.3 18.0
0.02-0.15 29.5 51.3 18.1
0.15-0.30 34.2 44.2 20.7
0.30-0.45 46.6 23.9 27.2
0.45-0.60 41.3 22.0 34.9
0.60-0.75 45.9 22.2 32.0
0.75-0.90 50.2 34.6 14.5
0.90-1. 05 71.5 15.7 12.5
1. 05-1. 20 81.2 19.7 6.3
1. 20-1. 35 71.6 14.6 17.3
1.35-1.50 34;3 35.1 30.3
43
Field 3: Howard Wuertz Farm
In A pril of 1984 samples were collected in a drip-irrigated
cotton field in Pinal County owned by Mr. Howard Wuertz. The field
is located in a flat desert area in south-central Arizona at an elevation
of about 427 m as shown in Figure 4.
The field site was furrow irrigated from 1970 to 1981. The EC
of the well water was 1.62 dS/m (see Table 3). In the spring of 1981
subsurface drip-irrigation lines were installed at a depth of 0.3 m.
The climate is arid and is characterized as having hot, dry
summers and moderate winters. The annual range of temperature is
approximately 10°C during the winter. to 45°C during the summer. Casa
Grande has two rainfall seasons: one during the winter months and
the other during the summer. The total annual rainfall ranges from 200
to 250 mm. The climatological data shown in Table 6 were obtained from
the Climatological Data of Arizona (1984).
The family names of the soils at the field site are Fine-Loamy
Mixed, Hypothermic, and Typic Natrargid. The physical characteristics
of the soil profile of Field 3 are shown in Table 7. Cotton was grown
at the field between 1972 and 1984. The cotton plants were about 0.5-
cm tall at the time of the study.
Soil Sampling
At Field 1, 13 sampling sites were established. The sampling
sites were within three furrows, starting from the ditch side and
extending to the other side of the field. The distance between the
sampling sites along the field was almost 30 m apart, and 5 m apart
43
Field 3: Howard Wuertz Farm
In April of 1984 samples were collected in a drip-irrigated
cotton field in Pinal County owned by Mr. Howard Wuert z. The field
is located in a flat desert area in south-central Arizona at an elevation
of about 427 m as shown in Figure 4.
The field site was furrow irrigated from 1970 to 1981. The EC
of the well water was 1.16 dS/m (see Table 3). In the spring of 1981
subsurface drip-irrigation lines were installed at a depth of 0.3 m.
The climate is arid and is characterized as having hot, dry
summers and moderate winters. The annual range of temperature is
approximately 10°C during the winter to 45°C during the summer. Casa
Grande has two rainfall seasons: one during the winter months and
the other during the summer. The total annual rainfall ranges from 200
to 250 mm. The climatological data shown in Table 6 were obtained from
the Climatological Data of Arizona (1984).
The family names of the soils' at the field site are Fine-Loamy
Mixed, Hypothermic, Typic Natrargid. The physical charactersitics of
the soil profile of Field 3 are shown in Table 7. Cotton was grown at
the field between 1972 and 1984. The cotton plants were about 0.5-cm
tall at the time of the study.
Soil Sam piing
At Field I, 13 sampling sites were established. The sampling
sites were within three furrows, starting from the ditch side and
extending to the other side of the field. The distance between the
sampling sites along the field was almost 30 m apart, and 5 m apart
5 500 1000 m
2
3
•• 12 11 5
6
8
o 50 100 m
CASA GRAND
Figure 4:. Location of study area and the sampling sites at the Howard Wuertz Farm (Field 3)
44
45
Table 6. Climatological data for 1983 for Casa Grande, Field 3
Mean Minimum Total Temperature RH Rainfall
Month (OC) (%) (mm)
January 11.1 36 9
February 12.1 44 0
March 16.1 42 0
April 18.6 21 36
May 27.4 l3 16
June 29.1 16 NA a
July 30.4 30 84
August 30.7 28 106
September 29.6 32 46
October 20.6 27 35
a. Not a vaila ble.
46
Table 7. Physical characteristics of the soil profiles of Field 3
Soil Depth Sand Silt Clay (m) (%) (%) (%)
0.00-0.05 70.0 14.0 16.0
0.05-0.10 70.0 14.0 16.0
0.10-0.15 65.0 20.0 15.0
0.15-0.20 65.0 17.0 18.0
0.20-0.25 66.0 l3.0 21.0
0.25-0.30 68.0 12.0 20.0
0.30-0.45 70.0 10.0 20.0
0.45-0.60 68.0 10.0 22.0
0.60-0.75 70.0 8.0 22.0
0.75-0.90 63.0 14.0 23.0
0.90-1.05 55.0 20.0 25.0
1.05-1.20 60.0 20.0 20.0
1. 20-1. 35 80.0 10.0 10.0
1.35-1.50 70.0 14.0 16.0
47
across the field as shown in Figure 2.' The bucket auger (No. R-HEO,
Art's Machine Shop, America Falls, ID) used in sampling the soil had a
diameter of 7.6 cm. The soil was sampled at IS-cm increments to a
depth of 150 cm for 10 sites and to a 300-cm depth for 3 sampling sites
as represented by the open circles in Figure 2. This resulted in 183
samples. At Field 2, 11 sampling sites were established within a single
furrow of 366-m length (see Figure 3). The distance between sites was
33 m. The same auger used in Field 1 was used in sampling. The soil
was sampled at IS-cm increments to a depth of 150 cm. This resulted
in 121 samples.
Fifteen sampling sites were established in the subsurface drip
irrigated Field 3. Four sampling sites were established from the center
of a I-m furrow. The sampling sites were located on both sides of the
drip line and were 15 cm apart. Another 7 sampling sites were estab
lished along the same furrow. The distance between the sampling sites
along the furrow was about 33 m. Two sampling sites were established
in both sides of this furrow at a distance of 15 m across the field as
shown in Figure 4. The soil was sampled at all sites in S-cm increments
to 30 cm and at IS-cm increments to a depth of 150 cm with the auger
used previously at Safford and Maricopa. This resulted in 210 samples.
Analyses of Soil Samples
Preparation of Soil Samples
All soil samples were taken to the Department of Soils, Water
and Engineering at The University of Arizona, Tucson, where the
moisture content was determined by oven-drying at 105°C for 24 h.
48
The oven-dried samples were put in a desiccator to cool, then reweighed.
The soil samples were then crushed with a rubber mallet and passed
through a 2-mm sieve.
Extraction
The soil samples were run for EC by using 5: 1 water extracts.
Five parts of water were added to each part of soil. Each sample was
put in aI, OOO-ml flask and then put on a shaker for 1 h. The resulting
suspensions were filtered (No.1, ll-cm paper) and the extract was
retained for analysis.
Electrical Conductivity Determination
The extract EC was measured using a Horizon Model 1484 EC
meter (Ecology Co., 7435 N. Oak Ave., Chicago, IL). Separate 5: 1
extracts were used for determining Ca, Mg, N a, Cl, SO 4' H C03 , and
C03
for Site 5 of Field 1, Site 7 of Field 2, and Sites 2.1 and 2.4 of
Field 3. Fifty grams of soil and 250 m1 of deionized water were used.
Atomic absorption spectrophotometry (Jarrel-Ash, Div. of Fisher Scien
tific, Phoenix, AZ} was used for Ca and Mg determinations. Sodium in
the soil extracts and in the leachates was determined by flame photometry.
Chloride was determined by using a Reitemeir titration with
silver nitrate (0.005 N) and potassium chromate as an indicator. Car
bonate and bicarbonate were determined by using phenolphthaline and
methyl orange as an indicator and sulfuric acid as a standard solution.
Sulfate was determined by using the spectrophotometer at 470 nm.
Exchangeable cations were extracted with 1 N ammonium acetate and the
49
sodium acetate method was used for cation exchange capacity measure-
ments. The pipette method for the soil part.icle analysis was used.
Methods of Calculation
Uniformity and Related Calculations
The EC was obtained for all soil sample extracts and the EC
was read as "pmhos/cm." That number was divided by 1,000 to get the
results in dS/m. In order to estimate the salinity of the soil samples,
I used the equation:
meq salt/L original soil solution = (10)(5) EC/e m
To get the amount of salt per unit area of the soil, I assumed
a bulk density of the soil to 1. 4 g I cm3
, and the formula was:
M = c m EC x
where M is the amount of salt (kg) per unit (m2
) for each depth
. -5 -1 -1 -2 interval (m), c is the constant (640)(10 )(mg L )(mg L m ), m
is the mass of the soil (kg), EC is the electrical conductivity of the
extract, and x is the ratio of kg of water to kg of soil for the
extract.
Example 1: Salt concentration and salt per unit area
I will demonstrate how the above formula was used to calculate
results for Tables 8, 9, and 10. To calculate the first row of Table 8
for Field 1, I have EC = 0.650 dS/m and the em = 0.18. Now,
meq salts/L original solution = (0)(5) EC/e m
meq salts/L original solution = (0)(5)(0.65)/0.18
= 180.6 meq/L
The M for Field 1 is
M = c m EC x = (640)00-5 )(2.8)(0.65)(5)
-2 = 0.06 kg m Uorthe 0.0- to O. 02-m depth
interval} .
The above results for meq/L and M for Field 1 are shown in the first
50
row of Columns 5 and 6, respectively, of Table 8. The same calcula-
tion was done for Fields 2 and 3, as shown in the first row of Columns
5 and 6 of Tables 9 and 10, respectively.
An overall water and salt balance requires
I - D - ET = ds/dt and
DC = IC. o 1
where I is the amount of irrigation water, D is the amount of water
drainage through the profile, ET is the amount of water lost by soil
ET Co is the salt concentration in the drainage from the soil root
zone, Ci is the salt concentration of irrigation water, and dsl dt is
the change of storage for a period of time.
Two assumptions are made for the above equations:
1. The change in water storage will be negligible for the time
period (e. g ., one mon th) con sidered .
51
2. The water going into the soil times its salt concentration will
be equal to the water going out of the soil root zone times its
salt concentration (no precipitation or dissolution).
This results in:
I - D - ET = ° and
D/I = C.le 1 a
Rearranging the water balance equation gives
1 - ET II = D/I
Use of the second salt balance relationship for D/I results in
1 - ET/I = Ci/Co or
I1ET = Col (C - C.) a 1
If the ET is essentially equal at all points in the field, the irrigation
uniformity can be calculated. It follows from
ET/I = (C - C.)/C a 1 a
The CV of the ratio I1ET was used to calculate the UC and the
DU. If ET is constant, then
Var(I ) = VAR [(ET)(I/ET)]
Var(I ) = ET2 Var(I/ET)
CV(I) = [Var(I)]O.5(f = (ET/I) SD(I/ET)
= SD(I1ET)/(I/ET)
= CV(I/ET) .
52
The UC and DU are approximately 1 - 0.8 CV and 1 - 1.3 CV,
respectively. The relationships are valid for CV < 0.25 (Warrick,
1983) .
Example 2: Calculations of UC and DU for Field 1
For Field 1, the CV = 0.07 as shown in Table 30 (p. 116)'
which gives
U C = 1 - 0.8 CV = 1 - 0.8 (0.07) = 0.94
and
DU = 1 - 1.3 CV = 1 - 1.3(0.07) = 0.91
The above results are shown in the first row of Columns 5 and 6 of
Table 30.
Moisture content at the depth d was converted to mass of
water per unit area in the soil profile by
= 1,000 e m
-2 where Mw is the amount of water per unit area kg m , em is the
gravimetric moisture content of the soil samples, Pb
is the bulk density
of the soil (1.4 Mg/m 3 ) and tid is the depth increment of the sample
(m) •
Also, the number of years needed for wat~r to accumulate to a
certain depth of the soil profile in the root zone were calculated as
follows:
1. Calculate the average DII = L (DII)/N,
2. Find the application of water corresponding to a certain
depth = (D/I)(I+R) p , and w
3. Calculate the age of water = application water corresponding
to certain depth/average water in an irrigation.
where N is the total number of samples, R is the amount of rainfall,
and Pw is the density of water.
To calculate an age (year) of salt at an arbitrary depth, the
53
measured amount of salt per unit area was used at an arbitrary depth
along with the estimated rate of salt applied by rainfall and irrigation.
The salt concentration of irrigation water (kg m -3) was required for
this calculation. Because the concentration of the rain was very small
-3 (0.013 kg m ) in southern Arizona. I assumed this contribution to
soil salinity was negligible. The following equation was used to cal-
culate the age of salt at an arbitrary depth (d):
Ms = lCi
where Ms is the mass of salt applied per year, I is the amount of
water applied per irrigation, and C j is the salt concentration of irri-
gation water.
As = Mst at arbitrary depth/Ms
where As is the age (year) of salt and Mst is the total mass of salt to
an arbitrary depth.
Example 3: To calculate the amount of water per unit area
and the ages of salt and water for Field 1
The moisture content for each site is shown in Tables 8, 9,
54
and 10. By using the previous equation for calculating the amount of
By using this method, I can calculate the semivariogram for all
the parameters in Tables 45, 46, and 47 for Fields 1, 2, and 3, respec-
tively. Because of the limited number of samples, semivariograms were
not drawn.
CHAPTER 3
RESULTS AND CALCULATIONS
Basic Data
Salt in the Profiles
For this study, about 514 soil samples were collected from the
three cotton fields. The data for the three fields are shown in Tables
8, 9, and 10, respectively. Included are the salt concentration of the
soil solution (meq!L), moisture content, and the salt content calculated
per unit area. Also given are the calculated results for the ratio of
irrigation (1) to evapotranspiration (ET), and the ratio of drainage
(D) to irrigation. The site locations are given in Figures 2, 3, and
4, respectively. The open circles in Figure 2 represent the sampling
sites of the previous study at Field 1 with the results given in Appen
dix A.
In Table 11 the mean (X), the standard deviation (0), and
the coefficient of variation (CV) of the salt concentration of the soil
.solution are given for each depth of Field 1. The concentration of
the irrigation water was taken as 21. 3 meq!L. There is some variation
of the soil salinity by depth (Table 11). The range of the X values
of the salt concentration in the soil is 98.2 to 202.4 meq!L. There is
also a variation in the salt distribution by depth; the range of the CV
is between 20% to 68%. The overall X and 0 , and CV shown in Table
12. The range in X values is 102.9 to 243.0 meq!L and the 0 value
58
59
Table 8. The salt concentration of the soil (meq/L), moisture content, the salt content calculated per unit area (kg/m 2 ), the ratio of I/ET and DII for each depth at each site of Field 1 (Safford)
Table 9. The salt concentration of the soil (meq/L), moisture content, the salt content calculated per unit area (kg/m2 ), the ratio of I1ET and D/I for each depth at each site of Field 2 (Maricopa)
Table 10. The salt concentration of the soil (meq/L). moisture content. the salt content calculated per unit area (kg/m2). the ratio of I/ET and D II for each depth at each site of Field 3 (Casa Grande)
104 ECe Salt a Cum.
Depth 8m Cone. Salt Salt (m) (S/m) (kg/kg) (meq/L) (kg/m2) (kg/m2) I/ET DII
Table 26. Calculated concentration (meq/L) of chemical con-stituents within the soil profile at the final moisture content for Sites 2.1 and 2.4 of Field 3
Undo b Undo b
Ca Mg Depth
Na CI S04 HC0 3 R-Naa R-Caa R-Mga CaS04 MgS0 4 SAR
water in Field 1, but at the same time there was a variable distribution
of this ratio within the soil profile. A maximum value occurs at the
surface layer that indicates the concentration of CI was lower than the
concentration of 504
, That could be due to the mobility and a higher
solubility for CI than for 504
, Both the CI and 504
were dissolved in
the soil profile and leached with the drainage water because the ratio
does not pass the limit of the same ratio of the irrigation water. For
Field 2, the ratio of the calculated concentration of 504 ' Cl was more
uniform than with Field 1. Some leaching occurred from a depth of
0.15 to 0.45 m of both CI and 504
, There was a zone from 0.60- to
1.20-m depth at which the ratio became more than the ratio of the irri
gation water, and this suggests that there was some sorption of sulfate.
For Field 3, Figures C-3 and C-4 show the variation of the ratio of
S04/CI vs. soil depth for Sites 2.1 and 2.4, respectively. The value
of Cl concentration at the soil surface is higher tha~ the value of 504
because the chloride is more soluble in the water and the mobility is
high enough such that the chloride moves upward with the water dur
ing the evaporation processes from the soil. There is some leaching
from the soil surface up to either 0.45 m for the side next to the drip
line, or 0.75 m for the side away from the drip line. This ratio for
the water is almost 1: 1, so that from a depth of 0.60 to 1.50 m there
is a zone passing the limit of the ratio S04/CI of the water for the
site next to the drip line and at O. 90-m depth for the side away from
the drip line. This suggested that there was more sorption of sulfates
no
than of chloride at this depth because the concentration of CI decreased--
the same pattern that occurred in Field 2.
The correlation coefficient between the concentration of salt in
the soil profile and the concentration of CI was low in Field 1 (49.%).
The correlation coefficient between the salt concentration and the concen-
tration of SO 4 in the soil profile was 46.1%. This could be an indication
that the salt composition was not affected by the ovarall salt level. The
correlation coefficient between the salt concentration and the concentra-
tion of CI and SO 4 in the soil profile for Field 2 were 13.0% and 29.0%,
respectively. In Field 3, there was a high correlation (92.0%) between
the salt concentration and the concentration of CI for the site away from
the drip line. For the site adjacent to the line, the value was 55.0%.
The correlation coefficients between the salt concentration in the soil
profile and the concentration of SO 4 for' Site 2.1 were higher than for
Site 2.4. The values were 38.0% and 3.0% for the site away from and
the site next to the drip line, respectively.
From the input data of the three fields, I recognized some precip-
itation of the bicarbonate in all fields. The concentration of the cations
and anions in the soil is shown in Tables 22, 23, and 24, and these con-
cent rations were used as an input for the model. The variation between
the concentration of cations and anions vs. soil depth is shown in Appen-
dix B.
Irrigation Uniformity, Efficiency, and Leaching Fraction
The calculations of the irrigation uniformity, efficiency, and
leaching fraction are based upon steady-state water flow and salt
III
equations. The ratio of irrigation water applied (I) to the amount of
water evaporated from the soil and the plant (ET) was calculated from
IIET = Col (Co-Ci), with Co the salt concentration of soil water in the
soil (meq/L) and Ci the salt concentration in irrigation water (meqlI).
If the amount evapotranspired is relatively uniform on an areal basis
and the salt remains in solution, the irrigation uniformity in the field
can be examined by using Col (Co-Ci ) values. For these conditions,
the CV for I will be exactly the CV of Col (Co-Ci ). The irrigation
uniformity is approximated as 1. 0 - 0.8 CV.
For irrigation (application) efficiency, the ratio of ET to the
average of the irrigation was used. Thus I used the reciprocal of the
IIET to give a site-specific efficiency of water utilization as ET II =
Similarly, the leaching fraction can be calculated. The equation
for leaching fraction is DII = C./(C -C.). The data for irrigation unifor-1 0 1
mity, efficiency, and leaching fraction for each depth and by site for
Fields 1,2, and 3 are shown in Tables 30-31 (pp. 116-117), Tables 32-
33 (pp. 118-119), and Tables 34-35 (pp. 120-121), respectively. The
values can be based on either the overall salinity or on individual
solutes.
Irrigation Uniformity
A key factor in the evaluation of irrigation is the uniformity.
The uniformity is a consequence of both the irrigation system itself and
the soil variability (Warrick, 1983). The ratio of I/ET results were
112
calculated from the concentrations of CI, H C0 3 ' and SO 4 in the irriga
tion water {initial concentration}. These are shown in Tables 27,28,
and 29. The ratio of I/ET vs. soil depth, as calculated by the concen
tration of CI, HC0 3 , and S04 for Sites 5,7,2.1, and 2.4 of Fields 1,
2, and 3, is shown in Figures C-6, C-7, C-8, and C-9, respectively.
For Field 1, the averages eX) of I/ET are 1.14 to 1.29. The
CV value ranges are 4.0% to 13.0% as shown in Table 30 for all depths
of the field. The X and CV ranges calculated by site are shown in
Table 31 and are 1.12-1.32 and 3.0%-13.0%, respectively. As the CV
values were less than 0.25, the approximate formulae of UC and DU
were applied: UC = 1 - 0.8 CV and DU = 1 - 1.3 CV. The values
for UC are on the order of 0.91-0.97. Likewise, DU values are 0.83
to 0.95, respectively.
In Field 2, the X and CV of the ratio of VET are shown in Tables
32 and 33 for all depths and by site. The X of the ratio of VET varies
from 1.10 to'1.17, and the CV value v'aries from 2.0% to 5.0%. This is
equivalent to UC values of 0.96-0.99 and the DU value of 0.94-0.98.
The X and CV values for each site are 1.08-1.16 and 2.0%-4.0%. The
variation in CV is not large and UC is larger than the DU, as it must
be based on the approximation formulae.
For Field 3, the range of X and CV for the ratio of I/ET for
all depths at each site from the field are 1.12-1.33 and 3.0%-7.0%,
respectively (Tables 34 and 35). The X and CV values for each site
are 1.19-1.31 and 3.0%-8.0%. The values for both UC and DU decrease
113
Table 27. Calculated ratio of irrigation to evapotranspiration (I/ET) from the concentration of Cl, SO 4' and HC0 3 by depth for Site 5 of Field 1
Depth (m)
0.15
0.30
0.45
0.60
0.75
0.90
1.05
1.20
1.35
1. 50
Cl
1.14
1.04
1.03
1.02
1. 03
1.01
1.01
1.06
1.03
1.02
I/ET
S04 HC03
1.17 1.18
1.11 1.06
1.10 1.05
1.06 1.02
1.04 1.01
1.02 1.01
1.02 1.01
1.18 1.11
1.05 1.03
1.03 1.03
114
Table 28. Calculated ratio of irrigation to evapotranspiration (I1ET) from the concentration of Cl, S04' and HC0 3 by depth for Site 7 of Field 2
Depth (m)
0.15
0.30
0.45
0.60
0.75
0.90
1.05
1.20
1.35
1.50
Cl
1.07
1.08
1.03
1.07
1.13
1.19
1.14
1.09
1.04
1.04
I/ET
S04 HC0 3
1.09 1.16
1.11 1.15
1.04 1.06
1.08 1. 33
1.16 1.15
1.21 1.17
1.15 1.21
1.11 1.08
1.05 1.11
1.05 1. 03
115
Table 29. Calculated ratio of irrigation to evapotranspiration (IIET) from the concentration of CI, S04' and HC0 3 by depth for Sites 2.1 and 2.4 of Field 2
Table 30. Mean 00, standard deviation (0), coefficient of variation (tV) , Christiansen's uniformity (UC), lower-quarter ciistri-bution (DU), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 1
I/ET Leaching Fraction
Depth -(m) X 0 CV UC DU IE X CV
0.02 1.24 0.08 0.07 0.94 0.91 0.81 0.24 0.34
0.15 1. 29 0.07 0.05 0.96 0.93 0.78 0.29 0.23
0.30 1.22 0.05 0.04 0.97 0.95 0.82 0.22 0.22
0.45 1.17 0.04 0.04 0.97 0.95 0.85 0.17 0.26
0.60 1.14 0.06 0.05 0.96 0.93 0.88 0.14 0.42
0.75 1.15 0.09 0.08 0.94 0.90 0.87 0.15 0.59
0.90 1.17 0.09 0.07 0.94 0.91 0.85 0.17 0.51
1.05 1.21 0.13 0.11 0.91 0.86 0.83 0.21 0.64
1.20 1.24 0.17 0.13 0.89 0.83 0.81 0.24 0.70
1.35 1.23 0.14 0.12 0.91 0.85 0.81 0.23 0.62
1. 50 1.25 0.13 0.11 0.92 0.86 0.80 0.25 0.53
Combined 1.21 0.05 0.04 0.94 0.90 0.83 0.21 0.21
117
Table 31. Mean eX), standard deviation (0), coefficient of variation (CV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for Field 1
Leaching I/ET Fraction
Site X 0 CV IE X CV
1 1.19 0.05 0.04 0.84 0.19 0.26
2 1.32 0.17 0.13 0.76 0.32 0.53
3 1.23 0.14 0.11 0.81 0.23 0.60
4 1.30 0.10 0.08 0.77 0.30 0.33
5 1.23 0.07 0.06 0.81 0.23 0.31
6 1.24 0.07 0.06 0.81 0.24 . 0.29
7 1.16 0.07 0.06 0.86 0.16 0.42
8 1.15 0.07 0.06 0.87 0.15 0.44
9 1.12 0.07 0.06 0.89 0.12 0.60
10 1.15 0.04 0.03 0.87 0.15 0.24
Combined 1.21 0.07 0.06 0.83 0.21 0.32
118
Table 3"2. Mean (X) , standard deviation (0), coefficient of variation (CV) , Christiansen's uniformity (UC), lower-quarter c:!istri-bution (DU), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 2
I/ET Leaching Fraction
Depth (m) X 0 CV UC DU IE X CV
0.02 1.14 0.06 0.05 0.96 0.94 0.88 0.14 0.40
0.15 1.17 0.04 0.03 0.98 0.96 0.85 0.17 0.21
0.30 1.15 0.02 0.02 0.99 0.98 0.87 0.15 0.12
0.45 loll 0.02 0.02 0.98 0.97 0.90 0.11 0.22
0.60 1.10 0.03 0.03 0.98 0.97 0.91 0.10 0.28
0.75 1.10 0.03 0.02 0.98 0.97 0.91 0.10 0.26
0.90 loll 0.04 0.04 0.97 0.95 0.90 0.11 0.38
1.05 1.13 0.05 0.05 0.96 0.94 0.88 0.13 0.39
1. 20 1.12 0.04 0.04 0.97 0.95 0.89 0.12 0.36
1.35 1.13 0.04 0.04 0.97 0.95 0.88 0.1'3 0.33
1. 50 1.13 0.04 0.03 0.97 0.96 0.88 0.13 0.30
Combined 1.13 0.02 0.02 0.97 0.96 0.89 0.13 0.17
119
Table 33. Mean eX) , standard deviation (CJ), coefficient of variation (CV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for Field 2
Leaching l/ET Fraction
Site X CJ CV IE X CV
1 1.15 0.04 0.04 0.87 0.15 0.27
2 1.12 0.02 0.02 0.89 0.12 0.14
3 1.16 0.06 0.05 0.86 0.16 0.36
4 1.11 0.03 0.03 0.90 0.11 0.28
5 1.16 0.03 0.02 0.86 0.16 0.17
6 1.12 0.04 0.04 0.89 0.12 0.34
7 1.11 0.03 0.03 0.90 0.11 0.31
8 1.14 0.04 0.03 0.88 0.14 0.27
9 1.12 0.03 0.03 0.89 0.12 0.26
10 1.12 0.03 0.03 0.89 0.12 0.30
11 1.08 0.03 0.03 0.93 0.08 0.43
Combined 1.13 0.02 0.02 0.89 0.13 0.19
120
Table 34. Mean (X), standard deviation (0), coefficient of variation (CV), Christiansen's uniformity (UC), lower-quarterqistri-bution (DU), il-rigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data for each depth of Field 3
Leaching Fraction
I/EI' Depth (m) X 0 CV UC DU IE X CV
0.05 1.12 0.03 0.03 0.98 0.96 0.89 0.12 0.25
0.10 1.18 0.05 0.04 0.97 0.94 0.85 0.18 0.29
0.15 1.22 0.05 0.04 0.97 0.95 0.82 0.22 0.22
0.20 1.24 0.04 0.03 0.98 0.96 0.81 0.24 0.16
0.25 1.25 0.05 0.04 0.97 0.95 0.80 0.25 0.21
0.30 1.26 0.07 0.05 0.96 0.93 0.79 0.26 0.26
0.45 1.26 0.04 0.03 0.98 0.96 0.79 0.26 0.15
0.60 1.29 0.05 0.04 0.97 0.95 0.78 0.29 0.18
0.75 1.29 0.08 0.06 0.95 0.92 0.78 0.29 0.27
0.90 1.31 0.08 0.06 0.95 0.92 0.76 0.31 0.27
1.05 1.33 0.08 0.06 0.95 0.92 0.75 0.33 0.25
1. 20 1.32 0.09 0.07 0.95 0.91 0.76 0.32 0.27
1. 35 1.29 0.08 0.06 0.95 0.92 0.78 0.29 0.28
1.50 1.28 0.06 0.05 0.96 0.94 0.78 0.28 0.21
Combined 1. 26 0.06 0.05 0.96 0.94 0.80 0.26 0.22
121
Table 35. Mean eX), standard deviation (cr), coefficient of variation (CV), irrigation efficiency (IE), and leaching fraction (X and CV) calculated from salt data by site for Field 3
I/ET
Site x cr
1 1.31 0.11
2.1 1.21 0.06
2.2 1.19 0.06
2.3 1.24 0.05
2.4 1.29 0.06
3 1.20 0.05
4 1. 28 0.10
5 1.26 0.06
6 1. 25 0.04
7 1. 31 0.10
8 1.25 0.04
9 1.25 0.06
10 1.26 0.07
11 1.28 0.10
12 1. 31 0.09
Combined 1.26 0.04
CV IE
0.08 0.76
0.05 0.83
0.05 0.84
0.04 0.81
0.05 0.78
0.04 0.83
0.08 0.78
0.05 0.79
0.03 0.80
0.08 0.76
0.03 0.80
0.05 0.80
0.06 0.79
0.08 0.78
0.07 0.76
0.03 0.80
Leaching Fraction
x CV
0.31 0.34
0.21 0.30
0.19 0.32
0.24 0.22
0.29 0.21
0.20 0.25
0.28 0.37
0.26 0.24
0.25 0.15
0.31 0.33
0.25 0.17
0.25 0.25
0.26 0.28
0.28 0.35
0.31 0.29
0.26 0.15
122
as the CV increases and the variations of the points within each depth
and each site are close to each other.
Irrigation Efficiency
Because CI is not adsorbed in the soil system, the average con
centration of the CI ions below the root zone were calculated for the soil
solution of Site 5 of Field 1, Site 7 of Field 2, and Sites 2: 1 and 2.4 of
Field 3. Different irrigation efficiencies (25.0%, 50.0%, 75.0%, 90.0%,
and 99.0%) were used to predict the differences in the CI ion concentra
tion. To establish the change in water analysis with time, there was a
detectable change in the water analysis between 1981 and 1984 for Field 1.
The differences in the CI ion concentration vs. different values of irriga
tion efficiency are shown in Figure C-10. The ranges of CI ion concen
tration at different values of irrigation efficiency for the three fields were
as follows: 25-101, 9-36, and 10-39. The mean values of the irrigation
efficiencies calculated by salt data gave good agreement with the theo
retical results calculated by using the soil-water extract model (Dutt et
aI., 1972). This suggests that there is no need to correct the results
calculated by using the overall salt data in the three fields.
The mean value of the irrigation efficiency for Field 1 is 83.0%
and the range is between 78.0%-88.0% for all depths (Tables 30 and 31).
The variation of irrigation efficiency within the depth is shown in Figure
20. Also shown by bars are the cr to each side of the mean. The CV
is 3.7% for all depths, 4.9% by site. The mean values for efficiency
are 76.0% to 89.0%, with the average value of 83.0%. The calculated
123
CALCULATED MEAN ET/I RATIO
0.6 0.8 1.0 I I - I
~
0.3 - ... -.....
~ 0.6 E - ...... -:::I: ..... t-e.. I.J.J Cl
....I 0.9 - .....0- -
0 en ..........-.
1.2 I- .....0- -.....-
1.5 - .......... -I I I
Figure 20. Mean value of the calculated ET/I ratio and stan-dard deviation for each depth of Field 1
irrigation efficiencies for Sites 2, 5, and 9 of Field 1 are shown in
Figure 21. There are some variations of the calculated irrigation effi-
ciency from the soil surface to the O. 90-m depth for both Sites 2 and
5. On the other hand, the calculated irrigation efficiency increased
from the soil surface to a O. 90-m depth at Site 9. I am most concerned
about the calculated irrigation efficiency at deeper depths between 0.90
to 1.50 m, which are below or in the lower part of the root zone. The
irrigation efficiency is low at Site 2 for deeper depths and intermediate
at Site 5.
124
CALCULATED MEAN ET/I RATIO
0.5 0.6 0.7 O.B 0.9 0
.,~-~~
0.3 • Site 2 .. Site 5 • Site 9
E 0.6 -:J: t-o.. LLJ 0 0.9 -l ...... 0 V')
1.2
1.5
Figure 21. Calculated ET II ratio by depth for Sites 2, 5, and 9 of Field 1
The comparison of the calculated irrigation efficiency at the soil
surface and at the 1.S0-m depth of Field 1 is represented by a contour
map (Figures 22 and 23). The calculated irrigation efficiency at soil
surface areas next to the ditch and away from the ditch is shown.
Slightly higher values of efficiency were calculated for these areas and
were on the order of 80.0% to 85.0%. The irrigation efficiency at the
middle of the field was less with a value of 70.0% to 80.0%. This
variation of the irrigation efficiency at the 1.50-m depth is shown in
Figure 23. The same pattern of the calculated irrigation efficiency was
observed at the 1.S0-m depth as observed at the soil surface of Field 1,
Figure 22. Contour map of the calculated ET II ratio at the soil surface of Field 1
~
E
:I: 0.65 u 10 t-
o 0.90 0.85 U) z 0 ...J Cl;
w 5 u z Cl; t-V>
0
20 30 40 50 60
DISTANCE FROM DITCH (m)
Figure 23. Contour map of the calculated ET II ratio at a depth of 1.5 m, Field 1
125
126
except the variation in magnitude. Also, higher irrigation efficiencies
were on the order of 85. 0% ~o 90.0%. A lower value occurred at the
middle of the field on the order of 65.0% to 75.0%.
In Field 2, the mean value for irrigation efficiency is 89.0%. The
range of the irrigation efficiency was between 85.0% and 91.0% when the
data were evaluated for all depths (Table 32). The CV is 2.04%. Figure
24 shows the mean irrigation efficiency vs. depth, and the bars are the
a to each side of the mean. The efficiency is 88.0% at the surface layer
and then decreases to 85.0% at the 0.15-m depth. The efficiency then
started to increase from the 0.15-m depth to the 0.45-m depth. After
that, it became more uniform with increasing depth. This uniformity
CALCULATED MEAN ETII RATIO
0.6 0.8 1.0
-0.3
0.6 E
:x: • f-Cl.. w 0.9 Cl • ....J
0 V) •
1.2 •
• 1.5 ----.-
Figure 24. Mean value of the calculated ET II ratio and the standard deviation for each depth of Field 2
127
may be related to the uniformity of the soil texture. The range of this
value is between 86.0% and 93.0% by site (Table 33). The mean value
is 89.0%, which is not different from the values for all depths. The
CV is a little higher than the previous result of Field 1.
The calculated irrigation efficiency vs. soil depth of Field 2 for
Sites 2, 4, and 11 are shown in Figure 25. There is some variation of
the calculated irrigation efficiency with increasing soil depth.
In Field 3, the range of the irrigation efficiency values with
depth were 75.0% to 89.0% (see Tables 34 and 35). The mean value is
80 and the CV is 2.0%. The result of efficiency vs. depth are shown
with the bars representing the (J to each side of the mean in Figure
26. Higher efficiencies occur at the soil surface and decrease to
CALCULATED ETII RATIO
0.5 0.6 0.7 0.8 1.0 0
0.3 • Site 2 .r. Site 4 II Site 11
~ 0.6 :::t: l-e.. w C
~ 0.9 c Vl
Figure 25. Calculated ET II ratio by depths for Sites 2, 4. and
11 of Field 2
CALCULATED MEAN ETII RATIO
0.6 0.8 1.0
0.3
~ 0.6 E
:J: t-o.. u.J Cl
..J 0.9 I e 0 V'l
1.2 •
• 1.5 -
Figure 26. Mean value of the calculated ET II ratio and the standard deviation for each depth of Field 3
become more uniform with increasing depth. That may be related to
128
the frequency of the irrigation and the variation of soil texture. When
I ran the results for all sites taken together, the mean value for the
efficiency was 80.0% and the CV was 3.1%. These values (80.0% and
3.1 %) are similar to the Field 2 values, but the CV is less than the
Field 2 value for combined depths.
The calculated irrigation efficiency for both Sites 2.1 and 2.4
vs. depth are shown in Figure 27. There are variable irrigation effi-
ciencies for both sites with increasing soil depth. The region with which
I am most concerned is the 0.90- to 1.50-m depth, which is below the
0.3
e 0.6
~ c.. w o ...J
o V1
0.9
1.2
1.5
0.6
o Site 2.1
• Site 2.4
CALCULATED ET/I RATIO 0.7 0.8 0.9
_..0 ...0--q---b..
')l
'{ I
P , , ~
129
Figure 27. Calculated ET II ratio by depth for Sites 2.1 and 2.4 of Field 3
root zone. In Site 2.1, the calculated irrigation efficiency varies some-
what throughout the 0.90- to 1.50-m depth. The average value is 79.0%,
which is slightly higher than the calculated irrigation value for Site
2.4. Irrigation efficiency was uniform throughout the 0.90- to 1. 20-m
depth at Site 2.4, then the value increased from the 1.20-m depth to
the 1.50-m depth.
Leaching Fraction
As mentioned in the previous section, the leaching fraction is
approximately Ci / (Co-Ci ). The mean and CV of the leaching fraction
for each depth and by site are shown in Tables 30-35.
130
For Field I, the mean and the CV values of the leaching fraction
for each depth and site are shown in Tables 30 and 31. The values of
the leaching fraction range between 0.14 and 0.29 when grouped by depth.
The same values by site are 0.12 and 0.32. The combined mean for the
leaching fraction is 0.21. The coefficient of variation values are from
22.0% to 70.0% for specific depths and 24.0% to 60.0% by sites. The over-
all CV values by depth and site are 21.0% and 32.0%, respectively.
Figure 28 shows this variation of the leaching fraction for Field 1,
where the bars indicate the' (J on both sides of the mean. The maximum
calculated leaching fraction value occurs at a O.15-m depth, then the
values decrease to 0.60 m. They generally increase again from a depth
of 0.60 to 1. 20 m.
CALCULATED MEAN 0/1 RATIO
0.08 0.16 0.24 0.32 0.40
• 0.3 -
• ~
E 0.6 f- -:I: f-e.. w Cl 0.9 f- • --l
0 Vl
1. 2 f- • •
1.5f- -
Figure 28. Mean value of the calculated D II ratio and the stan:.... dard deviation for each depth of Field 1
131
In Field 2, the mean and the CV of the leaching fraction by depth
and by site are shown in Tables 32 and 33. The mean values of the
leaching fraction by depth are 0.10 to 0.17 and 0.08 to 0.16 by sites.
The overall mean value is 0.13. The CV values are 12.0% to 40.0% by
depth, and 14.0% to 43.0% by site. The combined CV value within each
depth and site are about the same, 17.0% and 19.0%. The mean values
of the leaching fraction and the CJ by bars vs. depth of the soil are
shown in Figure 29.
In Field 3, the mean values of the lepching fraction and the CV
are shown in Tables 34 and 35. The mean values are 0.12 and 0.33 for
each depth and 0.19 to 0.31 by site. The overall mean is 0.26.
CALCULATED MEAN 0/1 RATIO
0.08 0.16 0.24 0.32 0.40
• 0.3 ----
E 0.6 ...... • :I: I- • a. w
0.9 Cl • -' 0 • Vl
1.2 • .
1.5
Figure 29. Mean value of the calculated D II ratio and the standard deviation for each depth of Field 2
132
The CV values for the leaching fraction for each depth are between 15.0%
and 29.0% and 15.0% to 37.0% by site, respectively. The total CV for
each depth and by site of the field is 22.0%-15.0%. Figure 30 shows
the variation of the mean values of the leaching fraction and the (J as
bars to each side of the mean vs. depth of the soil.
Ages of Salt and Water
Based on the steady-state water flow and salt balaqc~ equations,
a simple procedure was used to get an estimation of the salt and water
ages at the 1.50-m depth. The procedure for estimating these ages is
based on the amount of water applied to the field as irrigation water
and rainfall. The concentration of the dissolved salt for the irrigation
CALCULATED MEAN D/I RATIO
0.08 0.16 0.24 0.32 0.40 I I I I
• • • 0.3 l- • -•
0.6 ~ -E ~
:c t-a. 0.9 - • -L.LJ 0
....J • ...... 0 Vl 1.2 -
• 1.5 - • -
I I I I ,
Figure 30. Mean value of the calculated D II ratio and the stan-dard deviation for each depth of Field 3
133
water is required for estimating the age of salt. The age of water is
based on the ratio of the drainage water to the irrigation water obtained
from the salt data. The moisture content of the soil samples as well as
the amount of water applied to the field are required for the estimation
of the water age at the 1.50-m depth. Ideally, the two ages should be
the same. The calculated ages of salt and water at the 1.50-m depth
are shown in Tables 36, 37, and 38.
For Field 1, the ages of salt and water are shown in Table 36.
The minimum value of the salt age is 1.31 yr at Site 4 and the maximum
value is 4.03 yr at Site 9. The minimum water age is 0.54 yr at Site 2
and the maximum value is 2.22 yr at Site 9.
Table 37 shows the ages of salt and water for Field 2. The
minimum value of salt age is 0.99 yr at Site 2, and the maximum is 2.92
yr at Site 11. Thus, salt took 1. 27 yr to reach the 1. 50-m depth at
Site 5, and the water took 1.17 yr to reach the 1.50-m depth. For
Site 11, located away from the ditch, the water took 2.7 yr to reach
the 1. 50-m depth, and the salt took 2.92 yr to reach the 1. 50-m
depth.
The ages of salt and water for Field 3 are shown in Table 38.
There are some differences between the ages of salt and water within
the profile. The· minimum value of the salt age is 1.62 yr at Site 5,
and the maximum value is 2.67 yr at Site 3. The age of water is more
uniform, and the minimum value of the water age is 0.91 yr, while the
maximum value is 1.52 yr.
134
Table 36. Calculated ages (years) of salt and water at a depth of 1. 5 m (Field 1)
Cumulative Moisture Salt Water Content Age Age
Site (kg/m2) (yr) (yr)
1 207 1. 73 0.97
2 195 1.43 0.54
3 221 1. 75 0.86
4 187 1.31 0.56
5 233 1.68 0.90
6 273 1.92 1.01
7 252 2.56 1.41
8 196 2.12 1.17
9 299 4.03 2.22
10 214 2.17 1.27
135
Table 37. Calculated ages (years) of salt· and water at a depth of 1. 5 m (Field 2)
Cumulative Moisture Salt Water Content Age Age
Site (kg 1m2) (yr) (yr)
1 229 1.24 1.17
2 150 0.99 0.95
3 224 1. 33 1.07
4 252 1.81 1. 75
5 245 1. 27 1.17
6 195 1.47 1.25
7 255 1.87 1.77
8 240 1.43 1.31
9 248 1. 71 1. 58
10 284 2.08 1.81
11 283 2.92 2.70
136
Table 38. Calculated ages (years) of salt and water at a depth of 1.5 m (Field 3)
Cumuiative Moisture Salt Water Content Age Age
Site (kg/m2) (yr) (yr)
1 319 1.64 0.94
2.1 301 2.28' 1.31
2.2 294 2.25 1.42
2.3 269 1. 79 1.12
2.4 308 1. 75 0.97
3 331 2.67 1.52
4 334 1.92 1.09
5 260 1.62 0.92
6 277 1.80 1.07
'l 333 1.81 0.99
8 248 1.66 0.91
9 275 1. 74 1.01
10 278 1. 76 0.98
11 302 1. 78 0.99
12 327 1. 70 0.97
137
Discussion
Because there was some precipitation of bicarbonate in the soil
profile at Field 1, the ratio of I/ET as calculated by the CI, HC03
, and
SO 4 data were more uniform throughout the soil profile as shown in
Figure C-6. The ratio of I/ET calculated from .total dissolved salt data
was higher than the ratio of I/ET calculated from CI, HC03
, and SO 4
concentrations. In both Fields 2 and 3, the I lET was calculated by
considering CI, H C03 , and SO 4 in the irrigation water and in the soil,
and the total dissolved salts in the irrigation water and in the soil (see
Tables 28 and 29). Figures C-7, C-8, and C-9 show the I/ET vs. soil
depth of Sites 7, 2.1, and 2.4, respectively. There was no difference
between the I/ET based upon the steady-state water flow and salt bal
ance equations (total dissolved salts) and the one calculated from CI or
SO 4. This suggests that there was no need to correct the data calcu
lated from the total dissolved salt. There was, however, some differ
ence in the ratio of IIET calculated when considering the effect of HC0 3
and the one that had been calculated from the total dissolved salts.
This could be due to easily dissolved salts of both CI and S04 in water,
and if there was any variation in these two anions, the data would have
to be corrected for this effect. The HC0 3 is originally added to the
soil from the irrigated water applied becasue it is more soluble in water.
Figure C-9 shows the ratio of IIET vs. soil depth at the site away from
the drip line. There was no big difference between the I/ET calculated
from steady-state water flow and salt balance equations and from the
effect of concentrations of CI, HC0 3 , and SO 4. A t the same time, the
138
the I/ET calculated from the conceT'tration of HC03
was low compared to
the site next to the drip line from the soil surface to a depth of 0.90 m.
At 0.90-to 1.50-m depth, there was some variability in the ratio ofI/ET.
In general, the mean values of the ratio of I/ET for Field 1 are
high next to the ditch. The amount of irrigation was much greater than
ET. The I/ET ratio decreased at the middle of the field, perhaps because
of a small depression due to poor lveling. The ratio was more uniform
at Field 2, apparently because of better irrigation scheduling and good
land preparation. The ratio of I lET in Field 3 varies at each site.
This may be related to a plugging drip line.
The efficient use of irrigation water is an obligation of each user.
Even though the water in general is used carefully, efficiency will vary
from locality to locality. The usual goal for irrigation is to store the
water in the root zone. All the water cannot be stores as soil moisture
as some loss always occurs as runoff and deep percolation. Some factors
that can contribute to large losses and low efficiency are: insufficient
leveling, shallow soils underlain by gravels of high permeability, small
irrigation streams, long irrigation runs, and excessive single applications.
The depth of water applied in each irrigation is a dominant fac
tor influencing efficiency of application. Even if water were spread uni
formly over the land surface, excessive depths of application would
result in low efficiencies.
In the three fields, there are some variations in the Cl concentra
tion below the root zone a t the different values of irrigation efficiency.
These variations are illustrated in Figure C-IO. Good agreement between
139
the theoretical and experimental results were obtained. This suggests
that the assumption of no salt precipitation in the soil profile was valid.
The calculated irrigation efficiency from salt data seems to be in the same
range by taKing under consideration the values of the Cl concentration
below the root zone as compared with theoretical values of irrigation
efficiency. For Field 1, the concentration of Cl at various irrigation effi
ciencies was calculated for 1981 and 1984. There was some variation
between the theoretical irrigation efficiency and the calculated values.
The calculated irrigation efficiency is smaller under the subsur
face drip irrigation in Field 3 than in either Fields 1 or 2. Figures 20,
24, and 26 indicate a higher efficiency for Field 2 than for Field 1. The
high value of irrigation efficiency may be related to the nonuniform distri
bution of the applications. Also, the high values of irrigation efficiency
may be related to inadequate irrigation of certain parts of the field.
Higher values of irrigation efficiency appearing at certain depths may be
related to heavy soil texture. The variation of the irrigation efficiency
at the soil surface and at the 1.50-m depth followed the same pattern.
This variation in the calculated irrigation efficiency suggested that there
is a depression at the middle of the field and that water is lost through
deep percolation and evaporation from the soil surface.
The effectiveness of the leaching fraction depends on the size
distribution of the water-filled pores. The value of the leaching frac
tion is also affected by the structure and swelling properties of the
soil and the water application schedule.
140
In Field 1, there is more variation in the leaching fraction by
depth than by site. In Field 2, the variation of the leaching fraction
follows the same pattern of Figure 28 for Field 1 except for a differ
ence in magnitude and more uniformity. Maximum values of the leach
ing fraction occurs at the O. 30-m depth.
In Field 3, there are low values of the leaching fraction at the
soil surface with maximum values at the 1.05-m depth. The variation
is related to the amount of water that reaches the soil from the drip
line. The leaching fraction increases with soil depth to 1.05 m and then
decreases from 1.05 m to 1.50 m. The salt leaches downward through
the root zone as the water moves from the drip line down to 1.05 m.
There is a smaller leaching fraction for Field 2 than for Fields
1 and 3. This variation is related to the irrigation management in each
field and the irrigation schedule. Also, the overall heterogeneity may
affect this variation of the leaching fraction for each field.
The variation of the salt and water ages of Field 1 are interde
pendent. The age of the salt is dependent upon the amount of water
applied to the soil and on the quality of the applied water. The salt took
4.03 yr to reach the 1. 50-m depth. Because of the low net input of
water, the salt takes a long time to reach that depth. Site 10, located
away from the ditch, takes 1.27 yr for the water to reach the 1.S0-m
depth and 2.17 yr for the salt to reach the 1. 50-m depth. Less time is
needed for Site 10 compared with the time at Site 9. The correlation
coefficient is 99.0% between the ages of salt and water. The
141
relationship may be expressed as Y = 0.612 X - 0.175, with X the age
of salt and Y the age of water.
In Field 2, the salt movement was calculated to be slower than the
water. The reason may be related to the amount of water and quality
that has been used for irrigation. Also, there is some variation of cal-
culated ages of salt and water in Field 2. The reason for this variation
of age may be related to .the lower amount of water that reached Site 11
compared with Site 6 located at the middle of the field and next to the
ditch. If there is a coarse-soil texture, the water will not take as much
time to reach a certain point of the soil profile. The correlation coeffi-,
cient between the ages of water and salt is 99.0%. The linear relation-
ship can be written as Y = 0.930 X - 0.029.
In Field 3, there is some variation of both salt and water at the
site next to the drip line and away from the drip line. The reason is
that water movement below the drip line allows the salt to mov~ downward
below the root zone. The water takes 0.97 yr to reach the 1.50-m
depth compared to l. 31 yr away from the drip line. The correlation
coefficient between the age of water and the age of salt is 97.0%. A
lower correlation coefficient at Field 3 relative to Fields 1 and 2 was
observed. (The lienar relationship is Y = 0.618 X - 0.080.) The
uniform age of water is probably related to the uniform distribution of
water under the drip line. The salt takes less time to reach the 1.50-m
depth under the drip line compared to the same depth for the site
which is away from the drip line.
142
Evapotranspiration
For optimum irrigation timing, it is necessary to know the ET rate
as well as how much water is in the profile. Several methods of measuring
ET have been used. Some techniques indicate only potential ET, while
others measure actual ET, which mayor may not be the potential rate.
Some of these methods are based upon empirical formula, while others
depend on approximate single climatic parameters (see Chapter 1).
Evapotranspiration Calculated by using the Blaney-Criddle Method
Based upon the Blaney-Criddle equation (Blaney and Criddle,
1962), developed for estimating consumptive use of irrigated crops in
the western section of the United States, the ET is calculated for the
3 years previous to the sampling dates of each field. The method uses
air temperature as an index of the energy available for ET and day-
length as the major independent variables. Actually, this leads to an
estimate of the potential ET, but knowing the crop coefficient the crop
ET per month can be calculated assuming nondeficit irrigation. The
crop coefficients reflect differences in roughness, advection, and net
radiation as affectp.d by the structure of the crop during its various
stages of growth. They may also reflect differences in the methods of
water application, or physiological differences among species. In gen-
eral, the crop coefficient s increase with the height of the vegetation.
Estimates of crop coefficient values were taken from Erie, French, and
Harris (1965). Also, the crop ET is affected by natural factors such
as climate, soils, and topography. The climate factors include rainfall,
143
solar radiation, temperature, humidity, wind movement, and length of
growing season. The approach assumes a vaila ble soil water is not
limiting.
For this study, the ET was calculated for 1981, 1982, and 1983,
and part of '1984. The mean temperature in degrees Celsius was taken
from the Climatological Data of Arizona (1981, 1982, 1983, and 1984).
The formulae as proposed in Chapter 2 were used to calculate the Blaney
Criddle factor (f). The relative humidity, sunshine hours, and an esti
mated daytime wind are required to calculate the reference crop ET for
the month. .Tables 39, 40, and 41 show the crop ET for each month
of 1981, 1982, 1983, and part of 1984, respectively.
Table 39 shows the crop ET for each month of the 3 years
previous to the sampling date in Field 1. The minimum value for crop
ET was 1. 0 mm/month, which occurred in January of 1982, 1983, and
1984. The maximum values for crop ET are 273, 242, and 242 mml
month, which occurred in August of 1981,1982, and 1983. Figure 31
shows the variation of the crop ET (mm/month) vs. the time in (month)
of 1981, 1982, 1983, and part of 1984.
In Field 2, Table 40 shows the crop ET for each month of
1981, 1982, 1983, and part of 1984. Minimum value is 1.0 mm/month,
which occurred in 1981, 1982, and 1984. Maximum values ranged from
309 to 314 mm/month, which occurred in July and August of 1983 and
1982, respectively. The variation of the crop ET vs. the time per
month of 1981, 1982. 1983. and part of 1984 are shown in Figure 32.
The crop ET followed the same pattern of the crop ET for Field 1
144
Table 39. Calculated crop evapotranspiration (ET) of Field 1 by use of the Blaney-Criddle equation
Mean ET ET Temperature 0 crop Month (OC) P f (mm/day) (mm/mo. )
1981
January 8.8 0.24 2.9 1.5 1
February 10.3 0.25 3.2 2.0 1
March 11.8 0.27 3.6 2.6 2
April 18.3 0.29 4.8 6.7 20
May 21.9 0.31 5.6 8.4 26
June 28.2 0.32 6.7 8.8 63
July 28.5 0.31 6.5 6.4 198
August 27.7 0.30 6.2 8.0 273
September 24.7 0.28 5.4 5.8 178
October 18.0 0.26 4.2 4.9 118
November 12.8 0.24 3.3 3.8 31
December 8.2 0.23 2.7 1.9 12
1982
January 7.0 0.24 2.7 1.3 1
February 9.6 0.25 3.1 1.9 1
March 12.0 0.27 3.7 2.7 3
April 16.4 0.29 4.5 6.5 20
May 20.3 0.31 5.4 7.9 25
June 24.8 0.32 6.2 9.1 65
July 28.0 0.31 6.5 7.5 233
August 27.7 0.30 6.2 7.1 242
September 24.9 0.28 5.5 7.0 214
October 17.0 0.26 4.1 4.8 116
November 11.0 0.24 3.1 3.0 24
December 6.8 0.23 2.6 1.3 8
Table 39. --Continued
Month
January
February
March
April
May
June
July
August
September
October
NovembeT
December
January
February
March
April
May
June
Mean Temperature
(OC)
7.1
9.3
11.6
13.4
20.5
25.0
28.4
28.3
26.3
18.7
11.5
7.6
7.3
7.8
12.3
15.0
23.0
26.8
p
1983
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
1984
0.24
0.25
0.27
0.29
0.31
0.32
145
ET ET o crop
f (mm/day) (mm/mo. )
2.7 1.3 1
3.1 1.9 1
3.6 2.6 2
4.1 4.5 14
5.4 6.5 20
6.2 9.1 65
6.5 7.5 233
6.3 7.1 242
5.6 6.2 190
4.3 5.2 125
3.2 2.8 23
2.6 1.9 12
2.7 1.3 1
2.9 2.0 1
3.7 3.5 3
4.3 3.5 11
5.8 7.5 23
6.5 8.7 63
146
Table 40. Calculated crop evapotranspiration (ET) of Field 2 by use of the Blaney-Criddle equation
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
Mean Temperature
(OC)
12.5
13.2
15.1
21.7
24.5
31.9
33.6
32.7
29.1
19.9
15.2
11.2
9.9
13.2
14.2
19.7
23.8
28.4
32.2
32.7
28.2
19.6
14.0
9.7
p
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
0.23
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
ET ET o crop
f (mm/day) (mm/mo. )
1981
3.3 2.1 1
3.5 2.4 1
4.0 3.2 3
5.2 6.5 20
6.0 7.8 24
7.3 9.8 132
7.3 8.7 281
6.9 8.8 314
6.0 6.9 207
4.5 4.7 114
3.6 5.2 42
3.0 2.2 14
1982
3.0 1.8 1
3.5 2.5 1
3.9 3.0 3
5.0 6.0 18
5.9 7.3 23
6.1 8.2 111
7.1 9.6 309
6.9 8.0 285
5.9 6.8 204
4.4 5.2 126
3.5 2.5 20
2.9 2.3 14
Table 40. --Continued
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
Mean Temperature
(OC)
10.3
12.1
14.3
16.6
24.1
28.2
32.9
31.3
30.6
22.2
14.6
11.2
10.4
11.4
16.1
18.6
27.4
29.1
p
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
0.24
0.25
0.27
0.29
0.31
0.32
ET o
f (mm/day)
1983
3.1 1.9
3.4 2.3
4.0 3.0
4.5 5.2
5.9 7.5
6.7 8.6
7.2 9.6
6.7 7.8
6.2 7.0
4.7 4.9
3.5 3.2
3.0 2.3
1984
3.1 1.9
3.3 2.6
4.2 3.8
4.8 5.2
6.4 8.2
6.8 9.0
147
ET crop (mm/mo. )
1
1
3
'.6
23
116
309
278
210
118
26
14
1
2
4
16
25
122
148
Table 41. Calculated crop evapotranspiration (ET) of Field 3 by use of the Blaney-Criddle equation
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
Mean Temperature
(OC)
13.3
14.1
15.5
22.1
24.8
31.7
33.3
32.6
29.3
21.1
16.0
12.3
19.9
23.6
27.7
31.8
31.9
27.9
19.2
14.3
9.7
p
1981
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
1982
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
ET o
f (mm/day)
3.4 2.5
3.6 2.8
4.1 3.4
5.3 6.5
6.0 8.1
7.2 9.6
7.2 8.4
6.9 9.3
6.0 6.8
4.6 6.2
3.7 4.3
3.1 2.6
4.7 5.8
5.8 7.4
6.6 8.9
7.0 9.2
6.8 7.8
5.8 6.5
4.4 5.4
3.5 2.4
2.9 2.2
ET crop (mm/mo. )
1
2
3
20
25
130
271
300
204
150
35
16
17
23
120
297
278
195
131
20
14
Table 41.--Continued
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
Mean Temperature
(OC)
11.0
12.2
14.7
16.8
24.1
27.9
32.4
30.5
29.8
21.9
14.7
11.6
10.4
11.8
16.2
18.4
27.3
29.6
p
0.24
0.25
0.27
0.29
0.31
0.32
0.31
0.30
0.28
0.26
0.24
0.23
0.24
0.25
0.27
0.29
0.31
0.32
149
ET ET o crop
f (mm/day) (mm/mo. )
1983
3.1 1.9 1
3.4 2.4 1
4.0 3.1 3
4.6 5.3 16
5.9 7.4 23
6.7 8.8 119
7.1 9.4 303
6.6 7.8 278
6.1 7.0 210
4.7 5.2 126
3.5 3.2 26
3.1 2.0 12
1984
3.1 1.8 1
2.4 2.9 2
4.2 3.9 4
4.5 5.0 15
6.4 8.5 26
6.9 9.0 122
150
400 o 1981 .6 1982
300 It 1983 0 1984
.c ....., c a E ....... E
200 E
I-W
c.. 0 ~ u 100
May July August October
TIME (month)
Figure 31. Calculated crop ET by use of the Blaney-Criddle formula for Field 1
400
o 1981 .6 1982
300 o 1983 .c o 1984 ....., c a E ....... ~ 200 I-W
c.. 0 ~ u 100
July August October December
TIME (month)
Figure 32. Calculated crop ET by use of the Blaney-Criddle formula for Field 2
151
except the difference in the magnitude. By looking at Figure 31, it
can be noted that the crop ET generally increased steadily with time.
In Field 3, the results of crop ET by using the Blaney-Criddle
formulae ar~ shown in Table 41. The calculation periods are for 12
months for 1981, 1982, 1983, and part of 1984. Minimum value for crop ET
is 1. 0 mm/month for January of 1984, 1983, and 1981, respectively. The
maximum values occurred in August of 1981 and in July of 1982 and 1983,
which are ranged 297, 300, and 303 mm/month, respectively. The varia-
tion of crop ET vs. time per month of 1981, 1982, 1983, and part of 1984
was plotted from April to December except for 1984, the data were plotted
from April to June as shown in Figure 33. The same pattern followed for
crop ET as shown in Figure 31, except the variation in magnitude of the
crop ET for both fields.
400
o 1981 300 ... 1982
~ • 1983 ..., c [J 1984 0 E ...... E E
200 I-w a. 0 c::: L>
100
July August October December
TIME (month)
Figure 33. Calculated crop ET by use of the Blaney-Criddle equation for Field 3
Comparison of the Blaney-Criddle and Other Evapotranspiration Estimations
152
Based on the steady-state water flow and salt balance equations,
the ratio of ET to irrigation water (ET/i) was calculated for all depths of
the three fields, taking into consideration the ages of salt at the 1.50-m
depth. The amount of water applied per season can be used for estima-
ting the total amount of ET per time period. Tables 42, 43, and 44 show
the ET by using the salt data (ET salt) for Fields 1, 2, and 3.
For Field 1, Table 42 shows the mean value of the ratio of ET II
for all depths at each site and the estimated ET salt in mmltime period.
The mean value of ET II ranged from 0.76 to 0.89, and the overall mean
is 0.87. The estimated value of ET salt ranged from 746 to 2,263 mml
time period prior to soil sampling, and the overall mean is 1,249 mm/time
period. The variation from the mean is 447 mm/time period, and the
CV is 0.36. The variation of the ET depends on the variation of the
mean value of the ratio ET II. It depends on the amount of salt that
reaches a certain point in the soil profile. Also, the amount of irriga-
tion water and rainfall may vary from season to season.
In Field 2, the mean value of ET II for all depths at each site
and the estimated ETsalt are shown in Table 43. The mean value of ET II
for all depths at each site is 0.86 to 0.93, and the overall mean is 0.89.
The estimated ET salt values are between 955 and 2,920 mm/time period,
and the overall meari value is 1,588 mm/time period. The CJ is 546 mml
time period, and the CV is 0.34, which is less than the CV of Field 1.
The low CV value indicate~ a consistency for the ET values based on
the salt data. This variation of estimated ET is related to the variation
153
Table 42. Mean ET/I, time period prior to sampling date, and the estimated ET by using the Blaney:-Criddle (ET BC) equation and estimated ET from salt data of Field 1
Time Period Prior to ET ET
salt Sampling Date BC Site Mean ET/I (yr) (mm) (mm)
1 0.84 1. 73 1,151 1,076
2 0.76 1.43 990 802
3 0.81 1. 75 1,329 1,053
4 0.77 1.31 989 746
5 0.81 1.68 1,151 1,011
6 0.81 1.92 1,602 1,146
7 0.86 2.56 1,956 1,633
8 0.87 2.12 1,909 1,364
9 0.89 4.03 3,735 2,263
10 0.87 2.17 1,911 1,396
Combined 0.87 2.07 1,672 1,249
154
Table 43. Mean ET II, time period prior to sampling date, and the estimated ET by using the Blaney-Criddle (ET BC) equation and estimated ET from salt data of Field 2
Time Period Prior to ET
BC ET
Sampling Date salt Site Mean ET/l (yr) (mm) (mm)
1. 0.87 1.24 1,598 1,165
2 0.89 0.99 1,427 955
3 0.86 1. 33 1,579 1,238
4 0.90 1.81 1,640 1,761
5 0.86 1. 27 1,579 1,182
6 0.89 1.47 1,583 1,418
7 0.90 1.87 1,754 1,819
8 0.88 1.43 1,582 1,355
9 0.89 1. 71 1,598 1,649
10 0.89 2.08 2,793 2,006
11 0.93 2.92 2,907 2,920
Combined 0.39 1.65 1,822 1,588
155
Table 44. Mean ET II, time period prior to sampling date, and the estimated ET by usirig the Blaney-Criddle (ET
BC) equation and
estimated ET from salt data of Field 3
Site Mean ET II
1 0.76
2.1 0.83
2.2 0.84
2.3 0.81
2.4 0.78
3 0.83
4 0.78
5 0.79
6 0.80
7 0.76
8 0.80
9 0.80
10 0.79
11 0.78
12 0.76
Combined 0.80
Time Period Prior to
Sampling Date (yr)
1.64
2.28
2.25
1. 79
1. 75
2.67
1.92
1.62
1.80
1. 81
1.66
1. 74
1. 76
1. 78
1. 70
1. 88
ETBC
(mm)
1,305
2,235
2,235
1,778
1,778
2,640
2,075
1,305
1,778
1,778
1,500
1,500
1,778
1,778
1,500
1,780
ETsalt (mm)
951
1,432
1,437
1,097
1,031
1,691
1,140
977
1,094
1,050
1,009
1,058
1,062
1,057
986
1,138
156
of the nonuniform salt distribution in the soil profile and perhaps vari
ation in rainfall from season to season.
In Field 3, the mean value of ET II for all depths at each site
and the estimated ET salt value are shown in Table 44. The mean value of
ET II for all depths at each site ranged from 0.76 to 0.84, and the over
all mean is 0.80 with the estimated ET value between 951 and 1,691 mml
time period. The C1 from the mean is 211 mmltime period, and the CV is
is 0.19. The CV value of Field 3 is slightly less than the CV values
at Fields 1 and 2. This variation in the estimated ET value suggests
that the irrigation schedule that has been used is good to prevent the
salt accumulation in the soil profile.
The estimated crop ET values, calculated by use of the Blaney
Criddle formula (ETBC
)' are shown in Tables 39,40, and 41 for 1981,
1982, 1983, and part of 1984 for each field. These values are required
to compare the estimated ET salt with ET BC. Tables 42, 43, and 44
show the ratio of ET II, time period prior to sampling date, the estimated
ET BC and ET salt for the three fields. Knowing the ages of salt helps
us to go back from the sampling date and add all ET BC' which is equal
to the amount of ET for all the season.
In Field 1, the ratio of ET II, ET BC' and ET salt are shown in
Table 42. The ages of salt vary from one site to another, perhaps fol
lowing the variation in soil texture and structure, as water moves more
slowly in the heavy soil than in the coarser soil. The ages of salt are
dependent on the number of irrigations and the schedule of the irriga
tion that were used in this field.
157
Table 43 shows the ET II for Field 2 along with the ages of
salt, the ET BC' and ET salt for all depths at each site. The number
of irrigations are more generally uniform in this field.
For Field 3, the ET II, the ages of salt, the ET BC value, and
the estimated ET salt are shown in Table 44. The ages of salt vary
from Site 1 to Site 4 and then is more uniform from Site 5 to Site 12.
Also, the ages of salt are dependent on the number of irrigations and
the schedule that has been used in this field.
Discussion
In Field 1, minimum value of crop ET occurred in April for
each year. The crop ET increased steadily and then leveled off from
July to August. For 1982 and 1983, the crop ET declined gradually
from August to harvest time. This variation is related to the amount
of water needed by the plant. When the plants are young, the amount
used is small; then use increases with plant growth until a peak is
reached followed by a tapering to harvest time. Also, this variation
of crop ET may be related to the increase of solar radiation from April
into the summer. The variation of wind movement and the length of
the growing season may affect the variation of crop ET. The variation
of crop ET of Field 2 followed the same pattern of Field 1. The
increase in crop ET started from May of each year and reached a peak
in July of 1982 and 1983, while for 1981 the peak occurred in August,
and followed the pattern of a low th'cough reproductive stage.
In Field 3, the crop ET also increased steadily from May and
reached a peak in July of 1982 and 1983, then leveled off from July to
158
August. The maximum value for 1981 reached a peak ·in August. The
crop ET declined after A ugust and dropped to the minimum again by
the end of December.
Overall, there is some variation between crop ET for the three
fields. The crop ET in Field 1 is less than the crop ET for either
Fields 2 or 3. This is related to the variation in the climatological
data such as the mean daily temperature, daily sunshine, wind move
ments, and relative humidity.
The CV of the estimated ET salt value for the three fields
reveals a more consistent estimated ET salt for Field 3 than for Fields
1 and 2. The variation of the estimated ET salt value is related to the
amount of the salt that accumulated or leached from the soil profile,
which depends on the amount of water that was applied in each field
per season. The most important factors that control the leaching frac
tion are the irrigation schedule and the irrigation amount for each field.
The estimated· ET BC value ranged from "989 to 3,735 mm/time
period. The estimated ET salt ranged from 746 to 2,263 mm/time period
and both values of ET occurred at Sites 4 and 9 in Field 1. The cor
relation coefficient suggested that there is a good correlation between
both ET values. The correlation coefficient is 97.0%, and the best
fitting line can be written as: Y = 0.523 X + 359 with X the ET BC and
Y the ET salt' In Field 2, the variation of the estimated ET BC ranged
from 1,427 to 2,907 mm/time period. The ETsalt values ranged from
955 to 2,920 mm/time period, which occurred at Sites 2 and 11. The
correlation coefficient of Field 2 is less than that of Field 1, but not
bad. The correlation coefficient is 86.0%. The line best fitting the
data can be stated as: Y = 0.912 X - 74.33.
159
In Field 3, the estimated ETBC
ranged from 1,305 to 2,640 mml
time period. On the other hand, the estimated ET salt ranged from 951
to 1,691 mm/time period. Evapotranspiration values are uniform for
both Fields 1 and 2. The correlation coefficient (93.0%) is higher than
the correlation coefficient of Field 2. The linear relationship can be
stated in this form: Y = 0.527 X + 19l.
Overall, the estimated ET BC and ET salt gave a good correl~tion
for Fields 1 and 3, and the correlation was less for Field 2. These
variations might suggest that there are some external factors controlling
these affects. These factors may be the climatological data such as the
variation of the air temperature, wind movements, hourly sunshine, and
the rainfall that varied from one place to another and from season to
season and from day to day. An these factors affect plant growth.
On the other hand, the variation of salt distribution depends mainly
on the original amount of the salt in the soil of each field, the quality
of irrigation water that had been used in each field, and the irrigation
schedule. All of these factors affected the estimated ET salt values and
reflect the variation in the estimated values of the ET salt for the three
fields.
Geostatistical Data
The experimental semivariances of this study are shown in
Tables 45, 46, and 47. Included are the depth of the soil, number of
couples, average separation between the samples and the values of
160
semivariance for em' ECs ' IIET, ET/I, D/I, As, and Aw. Because of
the limited number of samples, variograms were not drawn.
The main question to be answered by the semivariogram values is
whether spatial independence exists for the fields and parameters studied.
Qualitatively, this is examined by checking whether y increases as a
function of average separation distance.
In nearly every case, the values of yare about the same for
all separations for a given depth and property. This suggests the
properties were randomly distributed on the scale for which the measure-
ments were made. Possible exc~ptions include most of the properties
for the O. 9-m depth of Field 1. For example, the water content values
-4 increase from about 3 x 10 for short separations to values of 1.3 to
-3 1. 5 (10) for larger separation distances.
161
Table 45. Calculated semi variance values of em' EC s' l/ET, ET/I, DII , A s' and Aw of Field 1
.... c: 0 0 Experimental Semi variance (y) ... <I) 11) "= III III OIl<ll~
.0- <II ... E E g. ... <II .......
III 0. e EC I/ET ET/I 011 As Aw :I 0 > III zt.) <Ul m s
0.90-m DeEt h
4 5 2.9 x 10-4 625 9.4 x 10-4 5.5 x 10-4 9.4 x 10-4
6 9 3.8 x 10-4 2,394 2.6 x 10-3 1.3 x 10-3 2.6 x 10-3
17 17 3.5 x 10-4 6,055 1.1 x 10-2 5.3 x 10-3 1.1 x 10-2
11 30 4.2 x 10-4 4,923 7.2 x 10-3 3.8 x 10-3 7.2 x 10-3
3 38 1.5 x 10-3 17,089 1.1 x 10-3 7.0 x 10-3 1.0 x 10-2
4 47 1.3 x 10-3 5,045 2.3 x 10-3 1.7 x 10-3 2.3 x 10-3
1 .05-m DeE! h
4 5 2.4 x 10-4 967 LOx 10-2 5.7 x 10-3 1.9 x 10-2
6 9 9.4 x 10-4 4,468 4.5 x 10-3 2.1 x 10-3 4.5 x 10-3
17 17 5.0 x 10-4 4,969 1.9 x 10-2 8.1 x 10-3 2.0 x 10-2
11 30 4.1 x 10-4 3,811 2.3 x 10-2 9.0 x 10-3 2.3 x 10-2
3 38 2.8 x 10-4 21,719 3.6 x 10-2 1.7 x 10-2 3.6 x 10-2
4 47 1.7 x 10-3 1,692 4.4 x 10-4 3.6 x 10-4 4.4 x 10-4
1.20-m DeEth
4 5 2.4 x 10-4 2,449 5.0 x 10-2 1.5 x 10-2 5.0 x 10-2
6 9 1.6 x 10-3 8,051 9.2 x 10-3 4.9 x 10-3 9.2 x 10-3
17 17 2.0 x 10-3 5,718 2.6 x 10-2 9.5 x 10-3 2.6 x 10-2
11 30 9.2 x 10-4 4,004 3.3 x 10-2 LOx 10-2 3.3 x 10-2
3 38 7.5 x 10-4 20,075 5.7 x 10-2 2.1 x 10-2 5.7 x 10-2
4 47 7.3 x 10-4 4,825 5.2 x 10-3 2.9 x 10-3 5.2 x 10-3
1.35-m DeEth
4 5 9.3 x 10-4 1,759 4.0 x 10-2 1.2 x 10-2 4.0 x 10-2
6 9 1.2 x 10-3 2,230 2.9 x 10-3 1.6 x 10-3 2.9 x 10-3
17 17 9.1 x 1074 2,565 1.6 x 10-2 5.6 x 10-3 1.6 x 10-2
11 30 8.6 x 10-4 3,959 2.7 x 10-2 9.0 x 10-3 2.7 x 10-2
'3 38 6.8 x 10-4 14,201 4.4 x 10-2 1.6 x 10-2 4.4 x 10-2
4 47 9.3 x 10-4 2,362 1.7 x 10-3 9.5 x 10-4 1.7 x 10-3
1.50-m DeEth
4 5 6.7 x 10-3 254 8.9 x 10-3 2.3 x 10-3 8.9 x 10-3 1.7 x 10-2 5.0 x 10-2
6 9 3.9 x 10-3 18,256 1.5 x 10-2 5.8 x 10-3 1.5 x 10-2 1.7 x 10-1 2.0 x 10-1
17 17 5.1 x 10-3 5,634 1.5 x 10-2 5.2 x 10-3 1.5 x 10-2 1. 0 x 10-1 1.5 x 10-1
11 30 LOx 10-2 1,087 2.1 x 10-2 6.7 x 10-3 2.1 x 10-2 4.1 x 10-2 7.2 x 10-2
3 38 7.4 x 10-3 48,234 5.7 x 10-2 2.3 x 10 -2 5.7 x 10-2 5.6 x 10- 1 5.7 x 10- 1
4 47 1.7 x 10-2 8,739 2.5 x 10-3 1.4 x 10 -3 2.5 x 10-3 8.9 x 10-2 7.2 x 10-2
162
Table 46. Calculated semivariance values of em' EC s ' I1ET, ET II, D/I. As, and Aw for Field 2 .... ~ 0 0
(y) s.. III ~ ''::: Experimental Semi variance ~~ tlOrJ~
.Do. '" s.. E E ::l
s.. rJ~ ~ 0. S EC ::l 0 :> ~ l/ET 'ET/I Dll As Aw zU <en m s
0.90-m DeEth
10 34 8.1 x 10-4 946 l.3x 10-3 9.5 x 10-4 1.3 x 10-3
9 67 8.7 x 10-4 624 9.3 x 10-4 6.5 x 10-4 9.3 x 10-4
8 101 2.7 x 10-4 1,151 l.5x 10-3 1.0 x 10-3 1.5 x 10-3
7 134 5.6 x 10-4 845 9.4 x 10-4 7.6 x 10-4 9.4 x 10-4
6 168 1.1 x 10-3 1,228 l.5x 10-3 1.0 x 10-3 1.6 x 10-3
5 201 6.0 x 10-4 2,532 2.9 x 10-3 2.1 x 10-3 2.9 x 10-3
1.05-m DeEth
10 34 5.4 x 10-4 1,211 2.9 x 10-3 l.6x 10-3 2.8 x 10-3
9 67 3.6 x 10-4 798 l.2x 10-3 6.8 x 10-4 l.2x 10-3
8 101 4.6 x 10-4 1,438 2.6 x 10-3 1.5:11 10-3 2.6 x 10-3
7 134 5.0 x 10-4 1,119 2.0 x 10-3 1.2 x 10-3 2.0 x 10-3
6 168 5.5 x 10-4 1,408 2.4 x 10-~ 1.4 x 10-3 2.4 x 10-3
5 201 5.4 x 10-4 2.626 4.3 x 10-3 2.5 x 10-3 2.3 x 10-3
1.20-m DeEth
10 34 5.1 x 10-4 805 1.3 x 10-3 8.5 x 10-4 1.3 x 10-3
9 67 3.7 x 10-4 1,451 2.3 x 10-3 1.4 x 10-3 2.3 x 10-3
8 101 4.3 x 10-4 875 1.6 x 10-3 9.9 x 10-4 l.6x 10-3
7 134 5.0 x 10-4 836 1.1 x 10-3 7.6 x 10-4 1.1 x 10-3
6 168 3.6 x 10-4 1,244 2.2 x 10-3 1.3 x 10-3 2.2 x 10-3
5 201 3.9 x 10-4 1,157 1.9 x 10-3 1.1 x 10-3 1.9 x 10-3
1 .35-m DeEt h
10 34 5.8 x 10-4 1,228 l.Rx 10-3 1.4 x 10-3 l.8x 10-3
9 67 2.4 x 10-4 873 9.9 x 10-4 6.9 x 10-4 9.9 x 10-4
8 101 5.5 x 10-4 1,738 1.8 x 10-3 1.4: 10.3 1.8 " 10-3
7 134 2.9 x 10-4 1,566 l.9x 10-3 1.4 x 10-3 l.9x 10-3
6 168 l.6 x 10-4 405 7.8 x 10-4 5.5 x 10-4 7.8 x 10-4
5 201 4.6 x 10-4 1,917 2.2 x 10-3 1.6 x 10-3 2.2 x 10-3
l.50-m DeEth
10 34 8.6 x 10-4 759 l.3x 10-3 7.5 x 10-4 l.3x 10-3 9.3 x 10-2 5.3 x 10-2
9 67 3.1 x 10-4 780 9.9 x 10-4 6.0 x 10-4 9.9 x 10-4 l.6x 10-1 8.0 x 10-2
8 101 8.0 x 10-4 1,020 l.6x 10-3 8.8 x 10 -4 l.6x 10-3 l.6x 10-1 6.6 x 10-2
7 134 3.3 x 10-5 642 1.1 x 10-3 6.3 x 10 -4 1.1 x 10-3 1.5 x 10-1 6.9 x 10-2
6 168 7.5 x 10-5 305 5.9 x 10-4 3.4 x 10 -4 5.9 x 10-4 2.8 x 10-1 1.4 x 10-1
5 201 7.9 x 10-4 1,514 2.5 x 10-3 1.4 x 10 -3 2.5 x 10-3 3.2 x 10-3 1.1 x 10-1
163
Table 47. Calculated semivariance values of 8m , EC s ' I/ET, ET/I, D/I, As' and Aw for Field 3 .... !:: 0 0 Experimental Semivariance (y) '" III
cu'Z QJ QJ bO",,~ .0- '" '" E E g. "'''' ....... QJ 0-
em EC :l 0 > QJ s IIET ET/I DII As zt) <til
0.90-m DeElh
6 0.5 4.3 x 10-4 129 3.2 x 10-3 1.1 x 10-3 3.2 x 10-3
24 30 7.9 x 10-4 170 7.5 x 10-3 2.4 x 10-3 7.5 x 10-3
7 47 7.5 x 10-4 137 8.3 x 10-3 2.3 x 10-3 8.3 x 10-3
17 68 7.0 x 10-4 218 LOx 10-2 3.2 x 10-3 1.0 x 10-2
28 101 6.2 x 10-4 163 7.0 x 10-3 2.0 x 10-3 7.0 x 10-3
11 133 6.4 x 10-4 96 4.1 x 10 -3 1.3 x 10-3 4.1 x 10-3
1.05-m DeEt h
6 0.5 4.9 x 10-4 76 2.3 x 10-3 9.7 x 10-4 2.3 x 10-3
24 30 4.0 x 10-4 241 6.2 x 10-3 2.0 x 10-3 6.2 x 10-3
7 47 4.9 x 10-4 150 1.1 x 10-2 2.8 x 10-3 1.1 x 10-2
17 68 2.8 x 10-4 503 1.0 x 10-2 3.3 x 10-3 LOx 10-2
28 101 5.0 x 10-4 184 8.7 x 10-3 2.3 x 10-4 8.7 x 10-3
11 133 3.1 x 10-4 153 3.7 x 10-3 1.2 x 10-3 3.7 x 10-3
1.20-m DeEth
6 0.5 8.3 x 10-4 124 3.3 x 10-3 9.6 x 10-4 3.3 x 10-3
24 30 6.9 x 10-4 332 9.4 x 10-3 3.0 x 10-3 9.4 x 10-3
7 47 3.1 x 10-4 54 2.5 x 10-3 8.1 x 10-4 2.5 x 10-3
17 68 7.5 x 10-4 547 1.2 x 10-2 3.9 x 10-3 1.2 x 10-2
28 101 4.7 x 10-4 123 3.8 x 10-3 1.3 x 10-3 3.8 x 10-3
11 133 7.0 x 10-4 299 1.1 x 10-2 3.2 x 10-3 1.1 x 10-2
1.35-m DeEth
6 0.5 9.2 x 10-5 209 2.7 x 10-3 1.1 x 10-3 2.7 x 10-3
24 30 6.9 x 10-4 411 7.3 x 10-3 2.8 x 10-3 7.3 x 10-3
7 47 9.5 x 10-4 483 7.8 x 10-3 3.1 x 10-3 7.8 x 10-3
17 68 1.1 x 10-3 638 9.0 x 10-3 3.5 x 10-3 9.0 x 10-3
28 101 3.3 x 10-4 274 5.4 x 10-3 1.9 x 10-3 5.4 x 10-3
11 133 6.2 x 10-4 365 6.5 x 10-3 2.5 x 10-3 6.5 x 10-3
1. 50-m DeEt h
6 0.5 3.3 x 10-4 82 1.8 x 10-3 6.3 x 10-4 1.8 x 10-3 4.3 x 10-2 1.5 x 10-2
24 30 7.5 x 10-4 251 3.9 x 10-3 1.4 x 10-3 3.9 x 10-3 2.5 x 10-2 6.4 x 10-3
7 47 1.1 x 10-3 50 7.6 x 10-4 2.5 x 10-4 7.6 x 10-4 1.4 x 10-3 1.2 x 10-3
17 68 5.5 x 10-4 354 4.9 x 10-3 l.~x 10-3 4.9 x 10-3 5.1 x 10-2 1.4 x 10-2
28 101 7.5 x 10-4 64 1.5 x 10-3 5.0 x 10':4 1.5 x 10-4 2.1 x 10-2 9.7 x 10-3
11 133 9.1 x 10-4 272 6.0 x 10-3 2.1 x 10 -3 6.0 x 10-3 1.7 x 10-2 7.6 x 10-3
CHAPTER 4
SUMMARY AND CONCLUSIONS
Three fields were chosen to study the uniformity of irrigation,
irrigation efficiency, leaching fraction, and evapotranspiration. This
study was based on the assumption of steady-state water and salt flow
through the crop root zone. Fields 1 and 2 were furrow irrigated for
the previous 3 years with water having salt concentrations of 21.3 and
ll.S meq/L, respectively. In Field 3, trickle lines were buried at the
0.30-m depth for the previous 3 years, and the salt concentration of
the irrigation water was 11.6 meq/L.
In Field I, the salt concentration in the soil profile tended to
be low near the soil surface, high in the middle (0.45-1.20 m), and
lower in the. deeper depths. That variation could be due to restricted
water movement within the soil profile caused by stratified soil. The
mean value of the salt concentration in the soil profile for all depths
was 98-202 meq/L. There is some variation in the mean value, and the
coefficient of variation ranged from 10.0% to 68.0%. The same pattern
occurred at Field 2 with a lower mean salt concentration of 88-134 meq/L.
The variation from the mean for Field 2 was lower than for Field I, and
the CV values were from 10.0% to 36.0%. In Field 3, the mean salt
concentration distribution in the soil profile tended to be high near the
soil surface and more uniformly distributed in the lower depth near the
trickle line. That variation could be due to the upward movement of
164
165
water in the soil through evaporation at which the salt reached near
the surface of the soil. At lower depths (below the trickle line), the
salt moves downward with frequent water application. The mean values
of the salt concentration in the soil were 69-155 meq/L. The CV values
were 13.0% to 28.0%, which is lower than both Fields 1 and 2.
Based on the steady-state water flow and salt balance equations,
the variation of irrigation uniformity, efficiency, leaching fraction, and
the ages of both salt and water were estimated for the three fields.
The ratio of the amount of water applied !O each field to the amount of
water lost as evapotranspiration (I/ET) was examined. If the amount
of water lost to the atmosphere was higher than the amount of water
applied, a lower value of this ratio resulted.
In Field 1, the mean values of the ratio (I lET) for all depths
and by site were 1.14-1.29 and 1.12-1.32. The variation from the mean
as indicated by CV values was 4.0% to 13.0%. The variation of salt con
centration indicated that the uniformity of irrigation varied from one
site to another at the same field. Approximate values of Christiansen's
(UC) and lower quarter distribution (DU) uniformities were 0.91-0.97
and 0.83-0.95, respectively. In Field 2, the mean values of the I/ET
ratio were 1.10-1.17 by depth and 1.08-1.16 by site. Lower variation
from the mean value occurred compared to Field 1 with a range of val
ues of CV of 2.0% to 9.0%. The mean values were lower than Field 1,
perhaps due to a higher ET than the amount of water applied in the
field. The UC and DU values for Field 2 were higher than for Field 1
and ranged from 0.96-0.99 and 0.94-0.98, respectively. The mean
166
values of I/ET at Field 3 were 1.12-1.33 and 1.19-1.31 by depth and
by site. The CV values indicated that the variation from the mean
value was less than those of either Field 1 or 2, and the values of the
CV were 3.0% to 7.0%.
The unif.ormity values are unrealistically high. A primary fac
tor may be that the ET tends to adjust from site to site in accordance
with available water. The consequence would be a nonconstant ET and
a lower variance for I/ET than for I alone.
In general, for the first two fields the area next to the ditch
received more water, so that the amount of applied water over evapo
transpiration was larger compared with the other sites. The more fre
quent application of water for Field 3 resulted in a more uniform salt
distribution and irrigation uniformity compared to the furrow method in
Fields 1 and 2. Th'~ water applied by furrows allowed the water to
distribute vertically and laterally 3.nd cause the salt to move far away
from the surface. Under high temperatures, the salt can move upward
by evaporation. This is what happened at Fields 1 and 2. The irriga
tion uniformity was higher in Field 2 than in Field 1, probably because
the land was more adequately leveled.
Irrigation efficiency varied from one site to another due to the
amount of water that reached that site. The mean values of the irriga
tion efficiency of the whole field were 83.0%, 89.0%, and 80.0%, and
ranged from 78.0% to 88.0%, 85.0% to 91.0%, and 75.0% to 89.0% for Fields
1, 2, and 3, respectively. A low application efficiency water use under
the drip line was observed compared to the slightly higher efficiency in
167
Fields 1 and 2. Higher efficiencies indicate a greater percentage of
water use by the crop in its growth and transpiration, and the lower
amounts indicate more deep percolation of surface runoff.
The variation of the leaching fraction can be recognized in the
three fields by examination of the range of the mean values for all
depths and sites. There was a higher leaching fraction in Field 3 with a
lower variation than Field 1, but 1.0% higher than Field 2. Comparing
Fields 1 and 2, there was a higher leaching fraction at Field 1 than at
Field 2, but the variation from the mean was lower at Field 2 than at
Field 1. Even though the salt concentration of the irrigation water at
Field 1 was higher than Field 2, the leaching fraction was better at
Field 1 than at Field 2. A t the same time, there was some variation in
the time for both salt and water to reach the depth below the root zone
(l.SO-m depth). The correlation coefficients were 99.0%,99.0%, and
97.0% for Fields 1,2, and 3, respectively.
The ET of the crop depends on several factors. Some are related
to the crop itself such as the stage of the growth, the length of the
growing season, and the amount of water applied during the growing
season. In addition, climatological factors affect the crop ET, the mean
daily temperature, daily sunshine, wind movement, and relative humidity.
According to the above factors, the variation in the crop ET vs. time
has been examined. Minimum crop ET occurred at the beginning of the
growing season. An increase occurred with plant growth, and the peak
was followed by a tapering until harvest time. The increase in solar
radiation is a factor in the increase of crop ET during summertime. In
168
Field 1, the maximum value of crop ET was 273 mm/month, while the
maximum value of crop ET at Field 2 was 314 mm/month. Likewise,
the maximum crop ET at Field 3 was 303 mm/month. All the maximum
vallies for the three fields occurred during the summertime.
In addition, the crop ET was estimated from salt data and
agreed reasonably well with the Blaney-Criddle calculati~ns. In Field 1,
the mean value of the estimated ET was almost 1,250 mm/time period
with a CV value of 0.36. The mean value of the estimated crop ET in
Field 2 was 1,590 mm/time period with a CV value of 0.34. On the
other hand, the mean value of the estimated crop ET in Field 3 was
1,140 mm/time period with a CV value of 0.19. The estimated crop ET
values by using salt data were higher than the crop ET by using the
Blaney-Criddle calculations, but a significant correlation coefficient was
found between both methods. The correlation coefficient values were
97.0%, 86.0%, and 93.0% for Fields 1, 2, and 3, respectively. These
variations in the crop ET suggested that there are some factors that
control plant growth such as the climatological factors which affect the
estimation of crop ET by both methods. Besides that, the variation of
salt distribution depends mainly on the original amount of the salt in
the soil of each field, the quality of irrigation water that has been
used in each field, and the irrigation schedule.
The reliability of this method is dependent on the:
1. Estimate of salts (quantity and quality) in irrigation water,
2. Amount of irrigation water applied (for ET estimates, not for
efficiency) .
169
3. Calculations of salt source and sink strengths in the soil, and
4. Depth of water table. Under high water table conditions, salts
would move up, and the method may not be reliable.
On the other hand, the practical aspects of this method are that:
1. Data are generally available or easily obtained by soil sam
pling, and
2. The method is inexpensive to apply.
APPENDIX A
BASIC DATA FOR FIELD 1 REPRESENTED BY OPEN CIRCLES IN FIGURE 2
170
171
Table A-I. Calculated salt concentration (meq/L), moisture content per unit area, ratio of Irrigation (1) to evapotranspiration (ET), and the ratio of drainage (D) to irrigation (1) of Field 1
104 EC Soil Cumulative
Depth e EC Salt 2 Salt2 ( m) (S/m)e m (meq/L) (kg/m ) (kg/m ) IIET D/I ET/I
Figure A-I. Variation of soil solution conductivity (dS iii 1 ) and water content (kg kg 1 ) for Site 1 of Field 1
0.6
E 1.2 :J: t-o. UJ 0 1.8 -' 0 Vl
2.4
3.0
CONDUCTIVITY (dS m1)
20
P" /
I
30 ~"
'"
40
..sf
• Soil solution conductivity
o Water content
---------;IJ '" __ .. --0'
01 __ -:'-:: -------
If'" ' 'b,.
~' _----0" .,1>-----
'. 'Q.
0.08 0.16 0.24 0.32
WATER CONTENT (kg kgl)
Figure A-2. Variation of soil solution conductivity (dS iii1 ) and water content (kg kg 1 ) for Site 2 of Field 1
173
0.6
E 1.2 ~
:I: l-e.. w Cl
-' 1.8
0 V'l
2.4
3.0
CONDUCTIVITY (dS ml)
10 20 30 40
• Soil solution conductivity
o Water content
------0. __ ""'I.
... ..b « ... ---~
~---.......... "'q
b
0.08 0.16 0.24 0.32
WATER CONTENT (kg kgl)
Figure A-3. Variation of soil solution conductivity (dS m1 ) and water content (kg kg 1 ) for Site 3 of Field 1
~
E
:I: l-e.. w Cl
-' 0 V'l
0.6
1.2
1.8
2.4
3.0
CALCULATED ET/I RATIO
.Si te 1 aSite? • Site 3
174
Figure A-4. Calculated ET II ratio for each depth for Sites 1, 2, and 3 of Field 1
CALCULATED D/I RATIO
0.6
E 1.2 ~
:::c I-a. w Cl
-' 1.8 Cl Vl
2.4
3.0
Figure A-5. Calculated D/l ratio for each depth for Sites 1, 2, and 3 of Field 1
175
APPENDIX B
VARIATIONS IN THE CALCULATED CATIONS AND ANIONS BY USING THE SOIL-WATER EXTRACT
MODEL AND 5:1 WATER-SOIL EXTRACT OF THE THREE FIELDS
176
177
CALCULATEO Na (meq/L)
0.3
I-U
E 0.6 c:( - ex I-
:c x I- w a. w -' 0 0.9 0 -' Vl
I 0 ex Vl w
I-
1.2 c:( :3:
1.5
Figure B-1. Variation in the calculated Na concentration by using the model (Dutt et al., 1972) and 5: 1 water-soil extract for site 5 of Field 1
CALCULATED Cl (meq/L)
40 80 120 160 200
0.3
I-~ u E 0.6
c:( ex I-
:c x I- w a.
-' w 0 0.9 >-<
0 -' Vl
I
0 ex Vl w
I-
1.2 c:( :3:
1.5
Figure B-2. Variation in the calculated Cl concentration by using the model (Dutt et al., 1972) and 5: 1 water-soil extract for Site 5 of field 1
178
CALCULATED Na (meq/L)
120 240 360
0.3
I-U - 0.6 « ex:
E l-X
:c w I- -' c.. w
0.9 0 0 V)
I -' ex: 0
Lu l-
V) « 3
1.2
1.5
Figure B-3. Varia.tion in the calculated Na concentration by using the model (Dutt et al., 1972) and 5: 1 water-soil extract for Site 7 of Field 2
CALCULATED C1 (meq/L)
0.3 ~ - 0.6 l-E u «
ex: MODEL :c l-I- X c.. w w 0 0.9 -' -' 0
V)
0 I V) ex:
w I-
1.2 « 3
1.5
Figure B-4. Variation. in the calculated Cl concentration by using the model (Dutt et al., 1972) and 5: 1 water-soil extract for Site 7 of Field 2
179
CALCULATED Na CONCENTRATION (meq/L)
0.3
f-U
E 0.6 <:(
~ 0:: f-
::I: X f- \..LI a. \..LI
0.9 -l
Cl 0
-l Vl I
0 0:: Vl \..LI
1.2 f-<:( :3
1.5
Figure B-5. Variation in the calculated Na concentration by using the model (Dutt et al., 1972) and 5: 1 water-soil extract for Site 2.1 of Field 3
CALCULATED Cl CONCENTRATION (meq/L)
20 60 80 90 100
0.3 f- MODEL ~ u
E <:(
0.6 0:: f-
::I: x f- \..LI
a. -l \..LI Cl 0.9 0 -l
Vl. I
0 0:: \..LI Vl f-
1.2 <:( :3
1.5
Figure B-6. Variation in the calculated Cl concentration by using the model (Dutt et aI., 1972) and 5: 1 water-soil extract for Site 2.1 of Field 3
APPENDIX C
V ARIATION IN THE RATIO OF 504 ' Cl FOR THE THREE
FIELDS AND THE CACULATED IRRIGATION
UNIFORMITY FROM THE CONCENTRATION
OF Cl, HC03
, AND 504
180
181
CALCULATED S04/C1 RATIO
0.3
a: w
E 0.6 t-c:t
~ 3:
::I: Z t- o c... w t-o 0.9 c:t
(,!) ....J
a: 0 a: Vl
1.2
1.5
Figure C-l. Variation of the calculated concentration SO 4/ CI ratio with soil depth of Site 5 of Field 1
CALCULATED S04/C1 RATIO
0.4 0.8
0.3
E 0.6 a: ~ w ::I:
t-c:t
t- 3: c... w z 0 0.9 0
....J t-
o c:t Vl
(,!)
a: 1.2 a:
1.5
Figure C-2. Variation of the calculated concentration SO JCI ratio with soil depth of Site 7 of Field 2
0.3
E 0.6 ~
:J: I-Cl.. UJ 0 0.9 -' 0 Vl
1.2
1.5
CALCULATED S04/C1 RATIO
0.4 0.8
c::: UJ l-e( 3:
z 0
1.2 1.4
CD Site 2.1 o Site 2.4
Figure C-3. Variation of the calculated concentration 504/ Cl ratio with soil depth for Sites 2.1 and 2.4 of Field 3
-' ...... 0-W E
.... W
I.J.. o Z o -l-e( c::: IZ UJ W Z o w
2.0 • Site 2.1 o Site 2.4
1.6
1.2
- ..... 0.8 . ... ..
..... 048
ELECTRICAL CONDUCTIVITY (dS ~1)
182
Figure C-4. Relationship between the electrical conductivity of the soil solution and the concentration of the Cl in the soil for Sites 2.1 and 2.4 of Field 3
183
I I I
1.8 f- -G Site 2.1
...J o Site 2.4
....... CT C1J E
1.4 -f-<:r
0 V)
I..L. 0
:z LO f- .. -0
..... • ex: cr: • ..... :z
~ w • u . • :z 0.6 f- • -0 . • u •• - I· -.
I I I
0 4 8 12 16
ELECTRICAL CONDUCTIVITY (dS m1)
Figure C-5. The relationship between the electrical conductivity of the soil solution and concentration of S04 in the soil for Sites 2.1 and 2.4 of Field 3
-E
:::c ..... e. w 0
...J
0 V)
0.3
0.6
0.9
1.2
1.5
CALCULATED IIET RATIO
1.2 1.6
o Original I/ET ratio
• Calculated by Cl concentration
o Calculated by HC03
concentration
a Calculated by S04 concentration
Figure C-6. Variation of the calculated I!ET ratio by depth for Site 5 of Field 1
184
CALCULATED I/ET RATIO
0.3
E 0.6 ~
:I: t-o.. UJ 0.9 Cl
-1
0 V)
1.2 o Original I/ET ratio
• Calculated by Cl concentration
1.5 o Calculated by HC0
3 concentration
• Calculated by 504
concentration
Figure C-7. Variation of the calculated I/ET ratio by depth for Site 7 of Field 2
CALCULATED I/ET RATIO
1.6
0.3
0.6
0.9
s: 1. 2
1.5
o Original I/ET ratio
• Calculated by Cl concentration
o Calculated by HC03
concentration
a Calculated by 504 concentration
Figure C-8. Variation of the calculated I/ET ratio by depth for Site 2.1 of Field 3
0.3
E 0.6 :r: l-e.. w 0.9 0
-' 0 Vl
l.2
l.5
CALCULATED IIET RATIO
o Original I/ET ratio
o Calculated by Cl concentration
o Calculated by HC03
concentration
• Calculated by 504 concentration
185
Figure C-9. Variation of the calculated I/ET ratio by depth for Site 2.4 of Field 3
-' ....... CT C1J E
z 0
I-c:x: 0:: I-z w U z 0 u
u
30
60
90
120
150
% IRRIGATION EFFICIENCY
25 50 75 100 125
1984
Figure C-I0. The relationship between Cl concentration in the soil at different irrigation efficiency
REFERENCES
Allison, G. B., and M. W. Hughes. 1983. The use of natural tracers as indicators of soil-water movement in a temperate semi-arid region. Journal of Hydrology 60: 157-173.
Amoozegar-Fard, A., D. R. Nielsen, and A. W. Warrick. 1982. Soil solute concentration distribution for spatially varying pore water velocities and apparent diffusion coefficients. Soil Sci. Am. J. 46:3-8.
Amoozegar-Fard, A., A. W. Warrick, and W. H. Fuller. 1983. A simplified model for solute movement through soils. Soil Sci. Am. J. 47:5:1047-1049.
Biggar, J. W., and D. R. Nielsen. 1976. leaching characteristics of a field. 12:78-84.
Spatial variability of the Water Resources Research
Black, T. A., Gardner, W. R., and G. W. Thurtell. 1969. The prediction of evaporation, drainage, and soil water storage for a bare soil. Soil. Sci. Soc. Amer. Proc. 33:655-660.
Blaney, H. F., and W. D. Criddle. 1962. Determining consumptive use of irrigation water requirements. Tech. Bull. 1275. U. S. Department of Agriculture, Washington, DC.
Bresler, E., and R. J. Hanks. 1969. Numerical method for estimating simultaneous flow of water and sa.lt in unsaturated soils. Soil Sci. Soc. Amer. Proc. 33:827-831.
Bresler, E., B. L. McNeal, and D. L. Carter. soils: Principles--dynamics--modeli ng. Verlag.
Clemmens, A. J., and A. R. Dedrick. 1981. Estimating distribution uniformity in level basins. Transactions of the ASAE 24:5:ll7-ll8.
Climatological Data of Arizona. 1981-1984. Annual summaries. Climatological Data Center, National Oceanic and Atmospheric Administration, Asheville, NC. Vols. 85-88, respectively.
186
187
Davis, K. R., H. or. Nightingale, and C. J. Phene. 1980. Consumptive water requirement of trickle irrigated cotton: I. Water use and plant response. Paper No. 80-2080 presented at the 1980 Summer Meeting of the American Society of AgriCultural Engineers, San Antonio, TX (June 15-18).
Dutt, G. R. 1962. Prediction of the concentration of solutes in soil solutions for soil systems containing gypsum and exchangeable Ca and Mg. Soil Sci. Soc. Am. Proc. 26:341-343.
Dutt, G. R., M. J. Shaffer, and W. J. Moore. 1972. Corr:puter simulation model of dynamic bio-physicochemical processes in soils. Technical Bulletin 196. Tucson: Dept. Soils, Water and Engineering, Agricultural Experiment Station, The Univ. of Arizona.
Enfield, C. G., and D. D. Evans. 1969. Conductivity instrumentation for in situ measurement of soil salinity. Soil Sci. Soc. Amer. Pro~ 33:787-789.
Erie, L. J., French, O. F., and Harris, K. 1965. Consumptive use of water by crops in Arizona. Technical Bulletin 169. Tucson: Agricultural Experiment Station, College of Agriculture, The Univ. of Arizona.
Feinerman, E., K. C. Knapp, and J. Letey. 1984. Salinity and uniformity of water infiltration as factors in yield and economically optimal water application. Soil Sci. Soc. Am. J. 48:477-481.
Gardner, H. R., and W. R. Gardner. 1969. Relation of water application to evaporation and storage of soil water. Soil Sci. Soc. Amer. Proc. 33:192-196.
Gardner, W. R., A. W. Warrick, and A. D. Halderman. 1982. Soil variability and measures of irrigation efficiency. Paper No. 80-2106 presented at the 1982 Summer Meeting of American Society of Agricultural Engineers, University of Wisconsin-Madison (June 27-30).
Ghuman, B. S., S. M. Verma, and S. S. Prihar. 1975. Effect of application rate. initial soil wetness. and redistribution time on salt displacement by water. Soil Sci. Soc. Amer. Proc. 39:7-10.
Hajrasuliha, S., N. Baniabbassi, J. Matthey, and D. R. Nielsen. 1980. Spatial variability of soil sampling for salinity studies in southwest Iran. Irrig. Sci. 1:197-208.
188
Halvorson, A. D., and J. D. Rhoades. 1974. Assessing soil salinity and identifying potential saline-seep areas with field soil resistance measuremetns. Soil Sci. Amer. Proc. 38:576-581.
Hanson, B. R., and T. A. Howell. 1983. Low energy level basin irrigation in the San Joaquin Valley. Irrigation Efficiency: Soil Salinity Measurement. Davis, CA: Univ. of California. USDA Science and Education Admin., Research Work Unit /Project Abstract. CRIS ID No. 326670/RMS.
Heilman, J. L., apd E. T. Kanemasu. 1976. An evaluation of a resistance form of the energy balance to estimate evapotranspiration. Agonomy Journal 68:607-611.
Jensen, M. E. (Ed.). 1980. Design and operation of farm irrigation systems. St. Joseph, MI: American Society of Agricultural Enginf'ers. ASAE Monograph No.3.
Jensen, M. E., and H. R. Haise. 1963. Estimating evapotranspiration from solar radiation. Proc. Am. Soc. Civ. Engr. J. Irrig. and Drain. Div. 89:15-41.
Jury, W. A., H. Frenkel, D. Devitt, and L. H. Stolzy. 1978. Transient changes in the soil-water system from irrigation with saline water: II. Analysis of experimental data. Soil Sci. Soc. Am. J. 42:585-590.
Jury. W. A., H. Frenkel, and L. H. Stolzy. 1978. Transient changes in soil-water system from irrigation with saline water: I. Theory. Soil Sci. Soc. Am. J. 42:579-584.
Jury, W. A .• iilnd P. F. Pratt. 1980. of irrigation drainage waters.
Estimation of the salt burden J. Environ. Qual. 9:1:141-146.
Krige, D. G. 1966. Two-dimensional weighted moving average trend surfaces for ore--evaluation. J. S. Afr. Inst. Min. Metal. 66:13-38.
Lai, Sun-ho, J. J. Jurinak, and R. J. Wagenet. 1978. Multicomponent cation adsorption during convective-dispersive flow through soils: Experimental study. Soil Sci. Soc. Am. Proc. 42: 240-243.
Leffelaar. P. A., and Raj Pal Sharma. 1977 . Leaching of a highly saline-sodic soil. J. Hydrol. 32: 203-218.
Letey. J .• and W. D. Kemper. 1969. Movement of water and salt through a clay-water system: Experimental verfication of Onsager reciprocal relation. Soil Sci. Soc. Amer. Proc. 33: 25-28.
189
Miyamoto, S., and A. W. Warrick. 1974. Salt displacement into drain tiles under ponded leaching. Water Resources Research 10: 2: 275-278.
Nadler, A., and S. Dasberg. 1980. for measuring soil salinity.
A comparison of different methods Soil Sci. Soc. Am. J. 44 :725-728.
Nadler, A., and H. Frenkel. 1980. Determination of soil solution electrical conductivity from bulk soil electrical conductivity measurements by the four-electrode method. Soil Sci. Soc. Am. J. 44:1216-1221.
Nimah, M. N., and R. J. Hanks. 1973. Model for estimating soil water, plant, and atmospheric interrelations: I. Description and sensitivity. Soil Sci. Soc. Amer. Proc. 37: 522-532.
Olsen, S. R., and W. D. Kemper. 1968. Movement of nutrients to plant roots. Adv. Agron. 30:91-151.
Ortiz, J., and J. N. Luthin. 1970. Movement of salts in ponded anisotropic soils. Journal of the Irrigation and Drainage piv., ASCE, Proceedings of the American Society of Civil Engineers, no. IR 3, pp. 257-264.
Oster, J. D., and J. D. Rhoades. 1975. Calculated drainage water compositions and salt burdens resulting from irrigation with river waters in the western United States. J. Environ. Qual. 4:1:73-79.
Perroux, K. M., D. E. Smiles, and 1. White. 1981. Water movement in uniform soils during constant-flux infiltration. Soil. Sci. Soc. Am. J. 45:237-240.
Post, D. F., D. M. Hendricks, and J. M. Hart. University of Arizona Experiment Station: Soil Sci. Series 77-1.
1977 . Soils of the Safford. A g. eng.
Qashu, H. K. 1969. Infiltration and water depletion in lysimeters. Soil Sci. Soc. Amer. Proc. 33:775-778.
Raats, P. A. C. 1969. Steady gravitational convection induced by a line source of salt in a soil. Soil. Sci. Soc. Amer. Proc. 33: 4:483-487.
Raats, P. A. C. 1974. Steady flows of water and salt in uniform soil profiles with plant roots. Soil Sci. Soc. Amer. Proc. 38:717-722.
190
Rawlins, S. L. 1973. Principles of managing high frequency irrigation. Soil Sci. Soc. Amer. Proc. 37:626-629.
Reeve, R. C. 1957. requirements. 10.175-10.187.
The relation to salinity to irrigation and drainage 3rd Congress on Irrigation and Drainage 5:
Rhoades, J. D. 1981. Predicting bulk soil electrical conductivity versus saturation paste extract electrical conductivity calibrations from soil properties. Soil Sci. Soc. Am. J. 45: 42-44.
Rhoades, J. D., and D. L. Corwin. 1981. Determining soil electrical conductivity--Depth relations using an inductive electromagnetic soil conductivity meter. Soil Sci. Soc. Am. J. 45:255-260.
Rhoades, J. D., and R. D. Ingvalson. 1971. Determining salinity in field soils with soil resistance measurements. Soil Sci. Sco. Amer. Proc. 35:54-60.
Rhoades, J. D., J. D. Oster, R. D. Ingvalson, J. M. Tucker, and M. Clark. 1974. Minimizing the salt burdens of irrigation drainage waters. J. Environ. Quality 3: 4: 311-316.
Robbins, C. W., R. J. Wagenet, and J. J. Jurinak. 1980. A combined salt transport-chemical equilibrium model for calcareous and gypsiferous soils. Soil Sci. Soc. Am. J. 44:1191-1194.
Russo, D. 1984. A geostatistical approach to solute transport in heterogenous fields and its applications to salinity management. Water Resources Research 20:9:1260-1270.
Shain berg,!., J. D. Rhoades, and R. J. Prather. 1980. Effect of exchangeable sodium percentage, cation exchange capacity, and soil solution concentration on soil electrical conductivity. Soil Sci. Soc. Am. J. 44:469-473.
Sharma, M. L., and R. J. Luxmoore. 1979. Soil spatial variability and its consequences on simulated water balance. Water Resources Research 15:1567-1573.
Shih, S. F., G. S. Rahi, and D. S. Harrison. 1982. Evapotranspiration studies on rice in relation to water use efficiency. Transactions of the ASAE 25:3:702-707, 712.
Suarez, D. L. 1981. Relation between pHc and sodium adsorption ratio (SAR) and an alternati ve method of estimating SAR of soil or drainage waters. Soil Sci. Soc. Am. J. 45:469-475.
191
Suarez, D. L. 1982. Estimating soil solution composition for reduced leaching management and efficient reclamation. Riverside, CA: USDA Salinity Laboratory.
Tanner, C. B. 1960. Energy balance approach to evapotranspiration from crops, Soil Sci. Soc. Amer. Proc. 24:1:1-9.
Thomas, G. W., and R. E. Phillips. 1979. Consequences of water movement in macropores. J. Environ. Qual. 8:2:149-152.
Wagenet, R. J., and J. J. Jurinak. 1978. Consequences of water salt content and a Mancos shale watershed.' Soil Sci. 126: 342-349.
Warrick, A. W. 1983. Interrelationships of irrigation uniformity terms. Journal of Irrigation and Drainage Eng. 109:3:317-331.
Warrick, A. W., J. W. Biggar, and D. R. Nielsen. 1971. Simultaneous solute and water transfer for unsaturated soil. Water Resources Research 7:1216-1225.
Warrick, A. W., and W. R. Gardner. 1983. Crop yield as affected by spatial variations of soil and irrigation. Water Resources Research 19:1:181-186.
Warrick, A. W., D. E. Myers, and D. R. Nielsen. 1985. Geostatistical methods applied to soil science. In C. A. Black, D. D. Evans, J. L. White, L. E. Ensminger, and F. E. Clark (Eds.), Methods of soil analysis: Physic:".1 and mi neralogical properties, including statistics of measurement and sampling (Part 1). Madison, WI: American Society of Agronomy, In(:., Publisher.
Wesseling, J., and J. D. Oster. rapidly changing salinity. 557.
1973. Response of salinity sensors to Soil Sci. Soc. Amer. Proc. 37:553-
Wierenga, P. J. 1982. Solute transport through soils: Mobile-immobile water concepts. Proceedings of the Symposium on Unsaturated Flow and Transport Modeling, Seattle, WA, March 23-24. NUREG/CP-0030, PNL-SA-10325.
Willis, W.O. 1960. Evaporation from layered soils in the presence of a water table. Soil Sci. Soc. Amer. Proc. 24:4:329-242.
