United States
Environmental Protection
Agency
Hobert S. Kerr Environmental Research EPA 600 2-79-148
Laboratory August 1979
Ada OK 74820
Research and Development
&EPA
Irrigation
Practices and Return
Flow Salinity in
Grand Valley
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a .maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ENVIRONMENTAL PROTECTION TECH-
NOLOGY series. This series describes research performed to develop and dem-
onstrate instrumentation, equipment, and methodology to repair or prevent en-
vironmental degradation from point and non-point sources of pollution. This work
provides the new or improved technology required for the control and treatment
of pollution-sources to meet environmental quality standards.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/2-79-148
August 1979
IRRIGATION PRACTICES AND RETURN FLOW SALINITY
• IN GRAND VALLEY
by
Gaylord V. Skogerboe
David B. McWhorter
James E. Ayars
Department of Agricultural and Chemical Engineering
Colorado State University
Fort Collins, Colorado 80523
Grant No. S-800687
Project Officer
James P. Law, Jr.
Source Management Branch
Robert S. Kerr Environmental Research Laboratory
Ada, Oklahoma 74820
ROBERT S. KERR ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ADA, OKLAHOMA 74820
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DISCLAIMER
This report has been reviewed by the Robert S. Kerr Environmental
Research Laboratory, U. S. Environmental Protection Agency, and approved for
publication. Approval does not signify that the contents necessarily reflect
the views and policies of the U. S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute endorsement or
recommendation for use.
ii
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FOREWORD
The Environmental Protection Agency was established to coordinate admin-
istration of the major Federal programs designed to protect the quality of our
environment.
An important part of the Agency's effort involves the search for informa-
tion about environmental problems, management techniques and new technologies
through which optimum use of the Nation's land and water resources can be
assured and the threat pollution poses to the welfare of the American people
can be minimized.
EPA's Office of Research and Development conducts this search through a
nationwide network of research facilities.
As one of these facilities, the Robert S. Kerr Environmental Research
Laboratory is responsible for the management of programs to: (a) investigate
the nature, transport, fate and management of pollutants in groundwater; (b)
develop and demonstrate methods for treating wastewaters with soil and other
natural systems; (c) develop and demonstrate pollution control technologies
for irrigation return flows; (d) develop and demonstrate pollution control
technologies for animal production wastes; (e) develop and demonstrate tech-
nologies to prevent, control or abate pollution from the petroleum refining
and petrochemical industries; and (f) develop and demonstrate technologies to
manage pollution resulting from combinations of industrial wastewaters or
industrial/municipal wastewaters.
This report contributes to the knowledge essential if the EPA is to meet
the requirements of environmental laws that it establish and enforce pollution
control standards which are reasonable, cost effective and provide adequate
protection for the American public.
William C. Galegar
Director
Robert S. Kerr Environmental
Research Laboratory
111
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PREFACE
This report is the first in a series of two reports resulting from U.S.
Environmental Protection Agency Grant No. S-800687, "Irrigation Practices,
Return Flow Salinity and Crop Yields." This report focuses upon the prediction
of subsurface irrigation return flow salinity. The second report, "Potential
Effects of Irrigation Practices on Crop Yields in Grand Valley," focuses upon
the impact of various irrigation practices in determining crop yields, with
particular emphasis on corn and wheat. These reports have been used as input
to another research project conducted in Grand Valley and largely funded by
the U.S. Environmental Protection Agency under Grant No. S-802985, "Implementa-
tion of Agricultural Salinity Control Technology in Grand Valley."
Three reports have been produced under Grant No. S-802985. The first
report, "Implementation of Agricultural Salinity Control Technology in Grand
Valley," describes the design, construction and operation of a variety of
salinity control technologies implemented on farmers' fields. The second report,
"Evaluation of Irrigation Methods for Salinity Control in Grand Valley," is
concerned with the evaluation of furrow, border, sprinkler and trickle irriga-
tion as individual salinity control alternatives. The third report of this
series, "'Best Management Practices' for Salinity Control in Grand Valley,"
develops the methodology for determining the cost-effectiveness of individual
salinity control measures, as well as a complete package of salinity control
measures that should be implemented in the Grand Valley.
iv
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ABSTRACT
This study was undertaken to evaluate the effects of the volume of leach-
ate on the quality of the leachate. A numerical model of salt transport
developed by Dutt et al. (24) was used in the study. Field data were collected
on 63 research plots located in the Grand Valley and used to test and cali-
brate the model. The model was used in a series of hypothetical simulations
designed to provide the required information.
From the calibration of the moisture flow model using infiltration data,
water content profiles, and storage change data, it was concluded that water
flow could be adequately modeled for the Grand Valley. The functional rela-
tions used for hydraulic conductivity and soil-water diffusivity and the
method of averaging the values of the hydraulic parameters were developed
during the course of the study.
From comparisons of simulated and field data used in evaluating the'chem-
istry model, it was concluded that total dissolved solids (TDS) concentrations
were adequately modeled but that individual ionic species concentrations were
not. Comparison of calculated and measured data indicate that the
CaS04-CaC03-Ca(HCOo)2 system is not properly modeled for the soils in the
Grand Valley. .
Data for single growing season simulations using 7- and 14-day irrigation
schedules and 2%, 5%, 20% and 40% leaching increments, coupled with data from
a 6-year simulation using a 14-day irrigation interval and 20% leaching incre-
ment, indicate that the salt concentration of the leachate at the bottom of
the soil profile is independent of the volume of leachate. The TDS profile
calculated at the beginning and end of the growing season show the concentra-
tion of salt in the profile below the root zone to be relatively constant.
This region acts as a buffer and caused the salt concentration of the return
flow to be relatively constant. This means the reductions in salt loading
are directly proportional to reductions in the volume of return flow.
This report was submitted in fulfillment of Grant No. S-800687 by Colo-
rado State University under the sponsorship of the U.S. Environmental Protec-
tion Agency. This report covers the period of February 18, 1974 to June 17,
1977 and was completed as of August 31, 1978.
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CONTENTS
Foreword iii
Preface iv
Abstract y
Figures viii
Tables x
List of Symbols xii
Acknowledgements xiv
1. Introduction !
2. Conclusions 5
3. Recommendations 8
4. Experimental Design 10
Study Area 10
Locating a Project Site 12
Design of Irrigation and Drainage Systems . . 15
Construction of Plots 28
Installation of Vacuum Soil Moisture Extractors 32
Treatments • • • 38
Data Collection and Instrumentation 40
5. Soil Moisture and Salt Transport Models 44
Solutions of Water Flow Equation 44
Sink Strength 50
Soil Properties 51
Salt Transport pZ
6. Model Description • £°
Moisture Flow Program ~°
Biological-Chemical Program °°
7. Model Results !*
Moisture Flow Model '*
Chemical Model
Simulation of Hypothetical Cases
8. Prediction of Return Flow Salinity
Geology and Subsurface Hydrology
Prediction of Salt Load
14*}
References
Appendices lt;i
A. Soil properties and evapotranspiration data iai
B. Simulated data '54
C. Analysis of field data Jo2
D. Listing of Program SORPT 163
E. Listing of Biological-Chemical Program 166
vii
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FIGURES
Number Page
1 Geology of the Grand Valley 11
2 Map of the Grand Valley showing areas of positive site location 13
3 Pictures of the Giddings rig and jetting rig 14
4 Map showing location of the Matchett farm 16
5 Map showing the fields used for the study area 17
6 Plots in Field I 19
7 Plots in Field II . . 20
8 Plots in Field III 21
9 Plot cross-section with drain details 22
10 Plan view of drainage system detail 23
11 Typical manhole installation 25
12 Irrigation system control valves 27
13 Flow measurement structures containing 30° V-notch weir for
measuring" quantity of irrigation water applied to each plot . . 29
14 Pictures showing use of the grade rod 30
15 Placement of grade stakes in trench 31
16 Placement of rolled curtain on the trench floor 31
17 Unrolling of curtain and placement against trench wall .... 33
18 Sealing the PVC curtain at corners 33
19 Method of sealing the curtain around the drainage pipe .... 34
20 Field installation soil moisture vacuum extractors 36
21 Hydraulic ram used to auger and shape holes for lysimeter pans. 37
22 Construction of lysimeter pans 37
23 Housing for vacuum units 39
24 Vacuum units 39
25 Grid system used for one-dimensional finite differencing ... 47
26 Spacial division of soil-plant water system along a flow line . 59
27 Grid system used for finite differencing Richards' equation . . 61
28 Saturation domains used for fitting Su and Brooks parameters . 66
29 Generalized block diagram of Moisture Flow model 69
30 Generalized block diagram of Biological-Chemical Program ... 71
31 Generalized block diagram of subroutine XCHANGE 72
32 Soil-water characteristic used in study 82
33 Moisture content profiles in Plot 30 used to calibrate the flow
model 86
34 Moisture content profiles in Plot 25 used to calibrate the flow
model • • • • 87
35 Computed and measured concentrations of Mg++, Na+ and Ca in
soil solution at a depth of 1.1 m in Plot 23 93
36 Computed and measured concentrations of S04=, HC03, and Cl~ in
soil solution at a depth of 1.1 m in Plot 23 94
viii
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Number ...,,*. *
37 Computed and measured IDS concentrations in soil solutions at
a depth of 1.1 m in Plot 25 ........ * ..........
38 Cumulative leachate as a function of cumulative infiltration
calculated by hypothetical simulations using a 7-day irrigation
39 Cumulative* leachate as a function of cumulative infiltration
calculated by hypothetical simulations using a 14-day irriga-
tion interval ............... •• • • • • : • • • •
40 Chloride concentration profiles calculated by hypothetical
simulations using 7-day irrigation interval ..... .....
41 Chloride concentration profiles calculated by hypothetical
simulations using a 14-day irrigation interval .........
42 IDS and chloride concentrations as a function of cumulative
leachate at a depth of 2.1 m calculated by hypothetical
simulations using a 7-day irrigation interval •••••••••
43 IDS and chloride concentrations as a function of cumulative
leachate at a depth of 2.1 m calculated by hypothetical
simulations using a 14-day irrigation interval . . . . . . • •
44 (TDS-C1) concentration as a function of cumulative leachate
at a depth of 2.1 m calculated by 6-year hypothetical simula-
tions using 20% leaching increment and 14-day irrigation
interval ...................... • : : j *
45 IDS and chloride concentration profiles at day 293 calculated
by a 6-year hypothetical simulation using 20% leaching mere-
ment and 14-day irrigation interval ........ ......
46 Chloride concentration profiles for second year of 2-year
simulation calculated by hypothetical simulations using a
14-day irrigation interval, 20% leaching increment and 2
winter conditions ..... .......... • • • • • • • •
47 Chloride concentration profiles at day 293 calculated by
hypothetical simulations using a 14-day irrigation interval,
20% leaching increment and 2 winter conditions . . . . . . • •
48 Natural washes, canals and boundary of irrigated lands in tne
Grand Valley ......................... 127
49 Cobble aquifer cross-section ...... • • • • • • • • • • •
50 Monitoring network for the Grand Valley Salinity Control
Demonstration Project ........... • • • • • • ', ' ' '
51 Calcium-magnesium ratios for selected ground and surface water
samples in the Grand Valley ..... . • • • • • • • • • • • •
52 Location of wells installed by the Agricultural Research 14Q
Service in western Grand Valley ..... ...........
ix
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TABLES
Number Page
1 Moisture Content Profiles at a Depth of 1.52 to 2.13 meters
for Selected Plots 80
2 Parameters Used in Hydraulic Conductivity and Diffusivity
Functions 83
3 Moisture Content Profiles From Plot 30 Used for Model Calibra-
tion 85
4 Moisture Content Profiles From Plot 25 Used for Model Calibra-
tion 88
5 Simulated Volumetric Moisture Content at 2.13 meters Using
14-day Irrigation Schedule and 20 Percent Leaching Increment . 89
6 1975 Irrigation Water Analysis (ppm) 90
7 Initial Chemical Profile and Soil Data for Plot 23, Matchett
Farm, 1975 91
8 Irrigation Treatments on Plot 23 in 1975 Used to Calibrate
Chemical Model 92
9 pK Analysis of Soil Solution Extract at 1.1 m on Plot 23,
Matchett Farm, 1975 97
10 Concentrations Calculated at 1.1 m Depth With Gypsum - 25 meq/
100 gm in all Horizons . . 98
11 pK Values for Selected Ions 98
12 Plot 23 Concentration at 2.13 m Predicted Using Pc02=7 matn1 • • 10°
13 Chemical Composition of Drainage Water From Field IT, Matchett
Farm, 1975
14 Cumulative Infiltration for 150-day Hypothetical Simulations
Using 7-day and 14-day Irrigation Schedules 103
15 Cumulative Leachate at 2.1 m for 150-day Hypothetical Simula-
tions Using 7- and 14-day Irrigation Schedules 103
16 Leaching Fractions Computed for 7- and 14-day Irrigation
Schedules 103
17 Variation of Volume of Solution in Soil Segment at the Lower
Boundary for Simulations Used in the Study HI
18 TDS Concentration and Chloride Concentration in Cumulative
Leachate at 2.13 m for 6-year Hypothetical Simulation Using
14-day Irrigation Schedule and 20% Leaching Increment 115
19 Chloride Concentration Profiles for 6-year Simulation Using
14-day Irrigation Schedule and 20% Leaching Increment '
20 Average Water Equivalent Depth Used for Winter Simulations . .
21 Concentration of Salts in Soil Solution, Matchett Farm, 1976 .
22 Total Dissolved Soilds of Drainage Water From Field III,
Matchett Farm, 1975 ';?'
23 Salinity of Natural Wash Discharges in the Grand Valley ....
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Number
24Salinity of Open Drains in the Grand Valley Salinity Control
Demonstration Project Area
25 Location, Depth and Top Elevation of Two-inch Diameter Wells
In the Grand Valley Salinity Control Demonstration Project .
26 Selected Salinity Data for CSU Well No. 12 Located Near the
Intersection of 31 and F Roads in the Grand Valley Salinity
Control Demonstration Project Area : • • • •
27 Selected Salinity Data for Wells Located Along D Road in the
Grand Valley Salinity Control Demonstration Project
28 Selected Salinity Data for Wells Installed by the Agricultural
Research Service (SEA) in Western Grand Valley I
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LIST OF SYMBOLS
Symbol Description*
a domain of saturation associated with concave portion of soil-water
characteristic
A area (L2)
A(Z) plant root extraction term (L)
b domain of saturation associated with convex portion of soil-water
characteristic
c solute concentration (m/L3)
C specific water capacity (1/L) .
D diffusion-dispersion coefficient (L2/T)
D(e) soil-water diffusivity (L2/T)
Etf evapotranspiration = volume per utvM area ^
E.T evapotranspi rat ion = volume per unit area (L)
FR1 solution flux computed using Darcy's law (L/T)
g acceleration due to gravity (L/T2)
h soil-water pressure head (L)
H piezometric head (L)
i finite difference index
I infiltration rate (L/T)
j finite difference index
K(e) hydraulic conductivity as function of water content (L/T)
KS saturated hydraulic conductivity (L/T)
Ksp solubility product for chemical species
nrf shape factor used in Su and Brooks representation of soil-water
characteristic
_P > volume of precipitation per unit area (L3/L2 = L)
*The units given in parenthesis are: m for mass, L for length, and T for
time.
Et and ET are used interchangeably.
TA1though this symbol usually represents mass, it has been used in the
text as a shape factor to conform with the original work by Su and Brooks.
xii
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Symbol Description
Pa Pascal (m/LT2)
p
P^ bubbling pressure (m/LT )
P capillary pressure (m/LT )
?
P^ inflection capillary pressure (m/LT )
q solution flux (L/T)
S water-content saturation
S effective saturation
c *
Sr residual saturation
t time (T)
v volumetric flux (L/T)
V. volume of intercepted water (L3)
1 o
V, volume of leakage (L )
u o
Vf volume of runoff (L )
Vs volume of water stored in a partially saturated zone (L3)
^
Vw volume of ground water storage (L )
3 volume of water stored in soil segment (L)
YI monovalent activity coefficient
Y2 divalent activity coefficient
6 volumetric water content (L3/L3)
6R water content at residual saturation (L3/L3)
es water content at saturation (L3/L3)
X pore-size distribution index
P density of solution (m/L3)
Z depth (L)
xiii
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ACKNOWLEDGEMENTS
The extreme 1972-1973 winter conditions in Grand Valley prevented the
site selection process from being undertaken until the last half of March
1973. The cooperation and public-spirited attitude of the landowner,
Mr. Kenneth Matchett, in leasing the necessary site was deeply appreciated.
A high degree of cooperation and support facilitated the undertaking and com-
pletion of the construction process.
In order to get construction under way required the swift cooperation and
efforts by the Project Officer, Dr. James P. Law, Jr.; the Colorado State
University (CSU) College of Engineering purchasing agent, Mr. 0. K. Warren;
and Mr. Ronald Jaynes, salesman for Grand Junction Pipe and Supply Company,
who submitted the low bids for the drainage and irrigation system materials.
The construction of the drainage system was accomplished by Smith Welding and
Construction Company of Grand Junction. Mr. Delbert Smith, President, was
extremely cooperative in meeting the special construction requirements of this
project. The construction, field installation, and successful operation of
the vacuum soil moisture extractors resulted from the conscientious efforts of
Mr. John Brookman of CSU.
Numerous project personnel worked long and hard hours durin'g the construc-
tion of facilities and during cultivating, planting and field data collection.
The assistance in the collection of field data by Messrs.. George Bargsten,
John Bargsten, Robert Evans and Berry Treat and the remaining staff and field
personnel of the Grand Junction office is deeply appreciated. In addition,
the diligent efforts of Ms. Barbara Mancuso and Mr. Sam Marutzky in the lab-
oratory were very important to the project.
Much of this report resulted from the efforts of James E. Ayars in com-
pleting a Ph.D. dissertation. The authors wish to thank the other members of
Mr. Ayars1 committee; Dr. Arnold Klute and Dr. Harold Duke, for their exten-
sive review of this work. Also, the discussion with Dr. Sterling Olsen and
Dr. John Laronne have been very helpful.
Finally, the authors appreciate very much the efforts of Ms. Diane English
and Ms. Mary Lindburg in typing the drafts and final copy of this report.
Gaylord V. Skogerboe
David B. McWhorter
James E. Ayars
xiv
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SECTION 1
INTRODUCTION
BACKGROUND
The Colorado River Basin typifies the problems and the future needs for
river management. The Colorado River currently provides irrigation water to
seven states: Colorado, Wyoming, Utah, Arizona, New Nexico, California, Nevada,
as well as to the Republic of Mexico. In addition to agricultural uses, the
Colorado River also provides water to the cities of Los Angeles, San Diego,
Denver, and many others.
Holburt (40) estimates that unless salinity control measures are insti-
tuted, the salinity levels at Imperial Dam, the lowest diversion point in the
United States, will have increased from their current 870 parts per million
(ppm) to over 1300 ppm by the turn of the century. To maintain the current
concentration of salinity, roughly 2.7xl09 kilograms (kg) of salt will have
to be removed yearly from the Colorado River to offset the projected growth
in the basin.
These growth projections were made before the energy shortage raised the
spectre of supplying large quantities of water to various energy complexes;
water which would be taken from the headwaters of the Colorado River and be
of the highest quality possible. The challenge facing agriculture in the
Colorado River Basin is to minimize return flow while maintaining a productive
industry.
PROBLEM
The Colorado River Basin lies in the arid and semi-arid west and exem-
plifies the problems of production faced by irrigated agriculture in arid
areas. As irrigation was introduced to virgin lands and an irrigated agricul-
ture developed, leaching of salts from the soils occurred. As new irrigation
projects were developed, the return flows increased and the salinity loading
of the river increased due to two factors. The first, salt-loading, is due
to the mineral dissolution occurring in the soil profile. The second effect,
concentration of salts, is the result of the consumption of pure water through
evaporation and transpiration.
In the Colorado River Basin there are several irrigated valleys which
contribute large salt loads to the river. One of the largest of these is the
Grand Valley located in western Colorado. Irrigation started in the Grand
Valley in the 1880's and developed over the years until roughly 30,350
1
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hectares (ha) were developed for irrigation. Of the total developed land,
about 12,000 ha have been damaged due to salinization and urbanization.
As irrigation developed on the higher lands away from the river, excess
water from deep percolation, low soil hydraulic conductivity and a soil of
marine origin combined to destroy the productive capability of the land.
High water tables near the river contributed to the upward movement of water
which evaporated from the soil surface leaving a deposit of salt, thus taking
the land out of production.
Studies have been conducted in the Grand Valley since 1908 on methods to
alleviate the high water tables and restore the land to a productive state.
The most recent series of studies began in 1968 with the Grand Valley Salinity
Control Demonstration Project. In this study, seepage of water from the
canals and laterals in the demonstration area was investigated. The resulting
seepage data* along with hydraulic and hydrologic data for the region, were
used to estimate salt loading of the Colorado River due to irrigation in the
Grand Valley. Skogerboe and Walker (75) found that the diversion of water
into the canals of Grand Valley's irrigation system amount to 27,420 cubic
meters (m3) of water diverted for each hectare under cultivation of which
10,050 m3 was spilled. They estimated a salt loading of 6.35xl05 to 9.07xl05
metric tons of salt annually from the Grand Valley. The salt originates in
the marine soils of the valley and in salt lenses found in the Mancos shale
which underlies.this region. It is dissolved by percolation water from irri-
gation and seepage from canals and laterals and is carried to the river. The
final step in the investigation was to line portions of the canals studied
and again estimate losses due to seepage. From these data, the effect of a
program of canal lining was evaluated and estimates of the cost of control
were made.
On-farm water management practices were studied next. These studies
included installing drainage for salinity control and irrigation scheduling
to improve water management. It was believed that drainage would intercept
return flows from irrigation before they reached chemical equilibrium with
the underlying shale. Since concentrations of deep percolation beneath the
soil profile are about 3000 ppm salt while salinity levels leaving the shale
are as high as 9000 ppm salts,, it was theorized that a significant reduction
in salt load could be made by intercepting the subsurface return flow before
it picked up additional salt from the underlying shale. Due to the low
hydraulic conductivities of the soil, the required spacings for the subsurface
drains are 30 meters (m). This means that' parallel relief drains as a salin-
ity control measure are quite expensive (1).
Irrigation scheduling was investigated to evaluate the effect of supply-
ing water as needed to meet crop needs plus the required leaching fraction.
These studies indicated that, at the time of the study, irrigation scheduling
for salinity control had only a marginal effect because of poor on-farm con-
trol of water. However, irrigation scheduling was found to be essential in a
program of total water management in the valley which has as its goal the
reduction of saline return flows (81).
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PROJECT OBJECTIVES
Previous studies conducted on methods of salinity control in the Grand
Valley assumed that the concentration of salt jn the subsurface return flow
was dependent of the volume of the return flow. This implied that any method
which reduced the volume of return flow would effect a similar reduction in
the salt load. The current study was designed to evaluate the validity of
this assumption.
A total of eight objectives were outlined for this research project:
1. Evaluate the effects of various irrigation practices
and chemical quality of return flows.
2, Evaluate the effects of various irrigation practices on crop yields
and fertilizer requirements.
3. Demonstrate that improved farm management of irrigation water can
reduce the mineral content of return flows.
4. Demonstrate that improving the chemical quality of irrigation
return flows through better farm irrigation practices is profitable due to
increased crop yields and reduced fertilizer expense.
5. Provide a better understanding of the manner in which water quality
degradation takes place as a result of irrigation.
6. Develop recommendations regarding irrigation systems, methods, and
practices which will minimize the chemical quality of return flows while main-
taining a good crop environment and maximum benefits from the consumed water.
7. Develop procedures for projecting the findings of this study to
basin-wide evaluations.
8. Provide useful information for future salinity studies concerned
with farm management.
This particular report addresses-objectives 1, 3, 5, 7, and 8. An
accompanying report, "Potential Effects of Irrigation Practices on Crop Yields
in Grand Valley," will address the remaining objectives. The results of these
two reports were utilized in preparing the reports, "Evaluation of Irrigation
Methods for Salinity Control in Grand Valley" and "'Best Management Practices'
for Salinity Control in Grand Valley" under EPA Grant No. S-802985. The
results of this particular report regarding the methodology for soil moisture-
chemistry simulation has been incorporated into an "Evironmental Planning
Manual for Salinity Management in Irrigated Agriculture" under EPA Grant No.
R-804672.
SCOPE
Before a valley-wide action salinity control program can be implemented
in Grand Valley, it becomes essential that salt load reductions occurring in
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the Colorado River can be predicted as a result of reducing subsurface irriga-
tion return flows by constructing physical facilities and improving water
management practices to insure that the program will be cost-effective.
In order to relate chemical quality to reduced subsurface return flows
this particular study focused upon the adaptation and evaluation of a numerical
model which could be used to characterize the salt transport occurring in the
soils of the Grand Valley. A numerical model developed by Dutt et al (24)
which is currently being used by the Bureau of Reclamation, USDI, was selected
for use in the study. The method of calculating the value of hydraulic con-
ductivity and diffusivity used in the difference equation of the soil-water
flow program was changed from that found in Dutt's model (24). Also, the
functional relationships used to calculate hydraulic conductivity and diffus-
ivity were changed. The soil-water flow and soil-chemistry data used in the
evaluation of the model were collected as part of an on-going study in which
the effect of irrigation on crop yields and salinity of deep percolation was
investigated.
The research was conducted on 63 research plots located on a 9.3 ha site
in the Grand Valley. Eight irrigation treatments, four crops, and two fertili-
zation treatments were used to generate the moisture flow and salt transport
data required to calibrate the numerical model.
_ Once the evaluation was completed, the model was used to simulate a
hfUfS ?• hyP°^etical irrigation treatments. The irrigation schedules in the
hypothetical simulations used either 7- or 14-day irrigation intervals and a
depth of irrigation equal to the evapotranspiration in the interval plus a
leaching increment which ranged from 1% to 40% of estimated evapotranspiration
The evapotranspiration for the simulations was estimated using meteorological
data collected in the Grand Valley in conjunction with the irrigation schedu -
ing program of the Agricultural Research Service (42). Data from these simu-
lations were used to evaluate the effect of tlie volume of return flow on ihe
concentration of ionic species in the soil solution, both in the soil profile
and leaving the soil profile. If the soil solution became saturated with a
particular ionic species, then further salt pickup could be prevented as the
return flow moved over the shale bed.
Data from these studies were used to evaluate the effects of on-farm
irrigation water management on return flow quality and quantity. These results
could then be used in conjunction with the field data collected under the
Grand Valley Salinity Control Demonstration Project in order to predict the
impact of constructing new irrigation facilities and improved irrigation prac-
tices upon the salt load reaching the Colorado River. In turn, sufficient
field data has been collected throughout the Grand Valley under EPA Grant No.
S-802985 to allow the results found in the demonstration project area to be
expanded to valley-wide predictions. The final objective of the research
reported .herein was to develop an irrigation return flow model (later referred
to as soil moisture-chemistry simulation) which can be used as a tool in
water resources planning and management.
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SECTION 2
CONCLUSIONS
1. From the calibration of the moisture flow model using infiltration data,
water content profiles and storage change data, it was concluded that the
water flow through the soil profile could be adequately modeled for the Grand
Valley. Several modifications were made to Dutt's original program before the
above conclusion could be made.
2. The functions originally used to calculate hydraulic conductivity, K(e),
and soil-water diffusivity, D(e), in the model did not permit accurate compu-
tation of soil-water flux at water contents close to full saturation for the
conditions of this study. A functional relationship developed by Brooks and
Corey (9) was used in the program to calculate hydraulic conductivity. The
function used in the model to calculate soil-water diffusivity was developed
using the Brooks-Corey (9) relationship for K(e) and the Su-Brooks (78)
relationship for the soil-water characteristic.
3. The method used to compute the average values of hydraulic conductivity,
K(e), and soil-water diffusivity, D(e), required to solve the difference form
of Richards' equation was also changed. The average values of K(e) and D(e)
were originally computed using the average water content of the two nodes being
considered. The averaging in the flow model was modified so the conductivity
is now calculated by using the moisture content at each node and then the cal-
culated conductivities are averaged. The diffusivity is now calculated as an
integrated average diffusivity between the water contents at adjacent nodes.
4. After making the changes described above, it was possible to predict
infiltration, water content distributions and changes in storage that agreed
satisfactorily with field measurements. Since the model assumed a homogeneous
profile, it was necessary to calibrate the flow model so as to incorporate
the var ability of field properties into the simulations. The soil-water
characteristic was calculated as an average from water-content pressure head
data gathered through the entire depth of the soil profile in a small area of
the test site. This average characteristic was then used to calculate K(e)
and D(e) in the calibration simulations.
5. From comparisons of simulated and field data used in evaluating^
chemistry component of Dutt's model, it was concluded that TDS concentrations
were adequately modeled but that individual ionic species concentrations were
not The simulations used to compare computed and field chemistry data were
made using field data for initial and boundary conditions in both the chemistry
and flow models. Field data on the chemical composition of the soil solution
-------
extracted at a depth of 1.1 m for a 30-day period was used to compare with
calculated salt concentrations.
. ______ , _ . . _ ^ w g wiiwiiwiMMB^sif** \* \f M U^_ IV l> I I W I I • I III l# lid L
were greater than theoretical maximum values expected for this soil system. A
study of the CaS04-CaC03-Ca(HC03)2 equilibrium equations indicated that the
solubility product of Ca(HC03)2 calculated using ion activities was incorrect.
The solubility of calcium bicarbonate [Ca(HC03)2] was then calculated based on
the partial pressure of carbon dioxide (C02). A reasonably good agreement
between computed and measured total dissolved solids (IDS) was obtained using
a value of 7 mi Hi-atmospheres (matm) for the partial pressure of CO? in the
simulations. c
7. Data for single growing season simulations using 7- and 14-day irrigation
schedules and 2%, 5%, 20%, and 40% leaching increments, coupled with data from
a 6-year simulation using a 14-day irrigation schedule and 20% leaching incre-
ment, indicate that the salt concentration of the leachate is independent of
the volume of leachate. TDS profiles calculated at the beginning and end of
the 6-year simulation show the concentration of salt in the profile below a
depth of 122 centimeters (cm), which is the bottom of the root zone in the
simulation, to be relatively constant.
8. Since chloride ions (CT) are relatively inert in soils, CT concentration
profiles were used to evaluate the calculation of salt transport by the model
Concentration profiles for the hypothetical simulations indicate that salt
transport is modeled adequately at least on a qualitative basis.
9. Simulations were made for a winter condition which included the addition
of pure water. The Cl- concentration profiles calculated from this simulation
show the effectiveness of pure water in reducing Cl- concentrations This
simulation also shows the necessity for properly accounting for precipitation
when computing leaching fractions based on Cl" concentrations. The simulation
shows that the leaching fraction would be overestimated if precipitation is
not included in the computation.
10. These studies showed that- the salinity concentration of the deep percola-
tion losses were independent of the volume of deep percolation, because the
concentration of salt below the root zone produces a saturated gypsum and lime
condition which is relatively constant. Groundwater chemistry data also show
that the concentration of salt in the cobble aquifer, although double the con-
centration of deep percolation immediately below the crop root zone, is still
relatively constant owing to the solubility limits of the major salts. Thus,
the salt loading due to irrigation return flow can be calculated from a know-
ledge of water balance for the Grand Valley. The reductions in salt loading
which reach the Colorado River will be directly proportional to reductions in
subsurface irrigation return flows (seepage and deep percolation losses).
11. The results of this study show that a strong emphasis should be placed on
achieving high irrigation application efficiencies in a salinity control pro-
gram for the Grand Valley in order to minimize deep percolation losses. Also,
-------
improvements in present irrigation methods and practices in the valley should
be sought that will result in more uniform irrigation applications. Consequent-
ly, advanced irrigation methods such as sprinkler or trickle irrigation, or
automation of surface irrigation methods, are highly desirable because of their
potential for more uniform irrigation applications while reducing deep percola-
tion losses.
12. These research results can be incorporated into the detailed water budgets
(hydro-salinity model) for the Grand Valley Salinity Control Demonstration
Project, which in turn can be used in combination with the inflow-outflow
analysis for the entire valley, in order to predict the impact of any proposed
salinity control technologies upon the salt load in the Colorado River.
13. Based upon the results of this study and EPA funded research conducted in
Ashley Valley by Utah State University (93), it is expected that other irri-
gated areas in the Upper Colorado River Basin having soils derived from
erosion and weathering of the Mancos shale formation would also exhibit a
nearly constant salinity concentration of the deep percolation losses immedi-
ately below the crop root zone.
14. The soil moisture-chemistry model used in this study has general utility
and can be used in other irrigated areas. This model has been incorporated
into an "Environmental Planning Manual for Salinity Management in Irrigated
Agriculture" under EPA Grant No. R-804672.
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SECTION 3
RECOMMENDATIONS
1. Although it has been shown that the groundwater (subsurface) return
flows to the Colorado River are in chemical equilibrium, this research was
not able to describe the higher level (second order) chemical reactions that
are taking place during the movement of water through the shallow groundwater
aquifer. To describe such complex phenomena will require the best expertise
available in the fields of soil chemistry and water chemistry. Such knowledge
would be beneficial in extending our capability to model and predict the
chemical changes occurring during the movement of subsurface irrigation return
flows.
2. These research results should be incorporated into the development of
best management practices for the Grand Valley. The effectiveness of each
proposed salinity control technology in reducing the salt load in the Colorado
River can now be evaluated using the results of this study.
3. A strong emphasis should be placed on achieving high irrigation applica-
tion efficiencies and more uniform irrigation applications in the salinity
control program for the Grand Valley to be implemented by the U.S. Bureau of
Reclamation (USBR) and the Soil Conservation Service (SCS). Advanced irriga-
tion methods, such as sprinkler or trickle irrigation, or automation of surface
irrigation methods, should be incorporated into the best management practices
because of their potential for more uniform irrigation applications while
reducing deep percolation losses.
4. These research results should be incorporated into the irrigation sched-
uling program presently being conducted by the USBR in the Grand Valley. The
irrigation scheduling program should make every attempt to minimize deep per-
colation losses through improved irrigation methods and practices, as well as
insuring that irrigation is terminated as soon as possible so as to maximize
the available soil moisture storage for winter precipitation.
5. The economic advantages to farmers in adopting more advanced irrigation
methods, such as sprinkler or trickle, should be documented in a style that
is meaningful to farmers. These irrigation methods have definite advantages
for reducing the salt loads reaching the Colorado River. Salt loads will be
reduced primarily because of significant reductions in deep percolation losses
early in the season. Increased fertilizer use efficiency resulting from
reduced deep percolation losses should also be included in this documentation.
-------
6 The results of this study, and EPA funded research conducted in Ashley
Valley by Utah State University (93), show that other irrigated areas in the
Upper Colorado River Basin having soils derived from erosion and weathering
of the Mancos shale formation should be investigated to determine whether they
also exhibit a nearly constant salinity concentration of the deep precolation
losses immediately below the crop root zone. If this is the case, then the
development of best management practices for each irrigated area in the Upper
Colorado River Basin will be a much simpler task.
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SECTION 4
EXPERIMENTAL DESIGN
STUDY AREA
The geologic formations throughout the Colorado River Basin were laid by
an inland sea which covered the area. After the retreat of the sea, the land
masses were uplifted and subsequent erosion has created the mountains and
plateaus as they are today. As shown in Fig. 1, the upper formations are
sandstones and marine shales which are underlain by the marine Mancos Shale
and the Mesa Verde formations. These formations occur in about 23% of
the basin in such locations as the Book Cliffs, Wasatch, Aquarius and Kaipar-
owits Plateaus, the cliffs around Black Mesa and areas in the San Juan and
Rocky Mountains. The Grand Valley was created by erosion, which cut through
the upper formations creating the valley in the Mancos Shale. This formation
is the main source of the salt contribution to the Colorado River. Due to
its marine origin, the shale contains lenses of salt which are easily dis-
solved as water moves over the shale beds. Water moving over and through the
shale originates as leakage from the canals, laterals and over-irrigation.
Since the overlying soil is derived from the shale, it is also high in salts
and contributes significantly to the salinity of return flows.
The desert climate of the area has restricted the growth of native vege-
tation, thereby causing the soils to be very low in nitrogen content due to
the absence of organic matter. The mineral soil is high in lime, carbonates,
gypsum and sodium, potassium, magnesium and calcium salts. Although natural
phosphate exists in the soils, it becomes available too slowly to supply the
needs of cultivated crops. Other minor elements such as iron are available,
except in areas where drainage is inadequate. The soils in the Grand Valley
are of relatively recent origin and contain no definite concentration of lime
or clay in the subsoil as might be expected in weathered soils.
The climate is marked by a wide seasonal range of temperature with sud-
den or severe weather changes occurring infrequently. The ring of mountains
around the valley moderates weather changes but also contributes to the rel-
atively low annual precipitation of approximately 20 cm. Moisture is removed
from the air masses originating in the Pacific Ocean or Gulf of Mexico as
these air masses move over the mountains. Precipitation during the growing
season is minimal and comes from thunderstorms which develop over the western
mountains. The valley location, coupled with west to east valley breezes, pro-
vides some spring and fall frost protection resulting in an average growing
season of 190 days from April to October. Temperatures range as high as 40°C,
with summer temperatures normally in the middle to low 30's in the daytime
10
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GRAND MESA
CENOZOIC
UIMCOMPAHGRE UPLIFT
m (EOCENE)
ME SO/OlC
(CRETACEOUS)
y^ (JURASSIC)
(TRIASSIC)
ARCHCZOIC
Figure 1. Geology of the Grand Valley.
-------
and about 20°C at night. Relative humidity is usually low during the growing
season, which is common throughout the semi-arid Colorado River Basin.
LOCATING A PROJECT SITE
The effects of an ancient sea are evident from the large amount of Mancos
shale prevalent in the area. Nearly all of the valley is underlain by this
shale at varying depths below the present shallow alluvial soil surface. The
shale is at or near the ground surface in the area along the north side of
the valley and along the south bank of the Colorado River. The lands along
the north side of the valley were considered desirable for a possible project
site (Fig. 2).
The site requirements for the project were quite restrictive. An area
of approximately 10 ha was required for the research plots and buffer zones.
The field had to be located in an area such that all subsurface flows pres-
ently crossing the area could be intercepted and removed. A smooth, fairly
level topography over the farm land with slopes not exceeding 1% was necessary
for furrow irrigation to be used successfully. However, a drainage channel,
either natural or man made, was needed nearby and of sufficient depth to allow
the water removed by the subsurface drains to leave the area under gravity
flow. For construction purposes, a continuous layer of shale underlying the
area at a depth of between 6 and 12 feet was required. Preferably the slope
of the shale would not exceed the slope of the ground surface.
The first step in the location procedure was to carefully study the aer-
ial photographs of the valley to locate fields of suitable size that were con-
tained in the desired area. Land lying above and below the Government High-
line Canal was considered. Virgin, as well as cultivated, lands were initially
considered; however, it was soon decided that, due to the lack of soil devel-
opment and the possibility of higher salt levels in the unfarmed lands, the
virgin lands would not be suitable for the study area.
Having thoroughly studied the photographs, a field survey of the area
was undertaken. Starting at the upper end of the valley, each field was
located and evaluated using the criteria previously mentioned. Many of the
possible sites were eliminated because they lacked suitable drainage outlets,
sufficient water supplies, or were too saline to grow the required crops.
Changes in land use since the date of the aerial photos also eliminated some
of the possibilities. Several possible sites were found during the field
survey that had not been evident on the photos. Following the field survey,
the sites which met the surface requirements were probed to determine the
depth to the underlying shale layer. This was accomplished using the Giddings
Soil Sampling Rig shown in Fig. 3. The Giddings unit consists of a small
gasoline engine which powers a hydraulic pump. The unit is capable of oper-
ating either a 4-inch screw auger or a 2-inch coring tube to depths of 8 m.
Since the primary interest at this time was in determining the depth to shale,
the 4-inch screw auger was used. These preliminary holes were dug mainly on
public rights-of-way such as in borrow pits or on canal banks beginning in
mid-March. The purpose of this was twofold: first, the severity of the winter
(one of the coldest on record in Grand Valley) did not allow access to the
12
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!
^-Grond Valley
^
COLORADO
Grand Valley Salinity
Control Project
Boundary of Irrigated
Area
Area in Valley where Shale
is near Soil Surface
Figure 2. Map of the Grand Valley showing areas of positive site location.
-------
Figure 3. Pictures of the Giddings rig and
jetting rig.
14
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fields as early as planned; and secondly, contact with the owners was not
considered desirable until it could be ascertained that the field might be
suitable for this project. Initially, many of the sites were thought to be
suitable. However, as the drilling process was*begun, it was soon discovered
that in most areas the shale layer was more undulating than had been expected.
Approximately 100 holes were drilled to depths ranging from 1 to 8 m before a
site was located.
Upon locating the site which was ultimately used, the process of mapping
the shale elevations in detail was begun. The field was staked using a stand-
ard 30.5 m by 30.5 m (100 ft by 100 ft) pattern. Using an engineer's level,
the ground surface elevation above mean sea level at each stake was determined.
These elevations were also used in preparing topographic maps of the area.
The depth to the shale layer was then determined using the jetting technique.
Since the shale is similar to a layer of soft rock material, the pipe, which
is being jetted into the ground, cannot penetrate the shale layer. Therefore,
by measuring the length of the pipe which entered the ground and subtracting
this from the ground surface elevation, the elevation of the shale layer can
be determined. The pipe is then removed from the ground and the process
repeated at the next station. The jetting technique is more accurate than
drilling because it is difficult to tell exactly when the shale is encountered
using a drill rig. Having completed the topographic maps o-f both the shale
and the ground surface, a preliminary design of the project was prepared.
This not only included a tentative layout of the plots, but also the tentative
location of the drains and the irrigation lines. Upon deciding that the site
was suitable, negotiations were begun on March 28, 1973, with the land owner
for a lease agreement.
DESIGN OF IRRIGATION AND DRAINAGE SYSTEMS
The intensive study area was constructed on 9.3 ha of land owned by
Kenneth Matchett. The farm is located north of the city of Grand Junction and
just below the Government Highline Canal (Fig. 4). A natural waste channel
known as Indian Wash runs along the east boundary of the area, then turns to
the west and cuts diagonally across the top of the land which was used for the
study area (Fig. 5). The wash averages approximately 8 m in depth and is cut
into the shale, thereby effectively intercepting any subsurface flows origina-
ting in the lands above and seepage losses from the Government Highline Canal.
Water is supplied to the area by a lateral which is operated by the
Grand Valley Water Users Association. Because of this lateral, the required
acreage is divided into three fields instead of one as was originally planned.
However, having three fields does have the advantage of better accessability
to the plots. Also, there are four points of water diversion, thereby pro-
viding more flexibility in the supply of irrigation water.
The depth to shale over the fields ranged mostly between 2 and 4 m with
isolated areas as shallow as 0.4 m and as deep as 7 m. The deep areas were
not used for plots. The plane of the shale slopes to the southwest with some
undulation. However, it was possible to construct the system with all of the
perforated drain lines lying on top of the shale with only a minimum of
15
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City of
Grand Junction
Scale
Figure 4. Map showing location of the Matchett farm.
16
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Field* I
Scolt
•••^
0 50 100 ZOOfMt
0 50 100 nwt«rs
Figure 5. Map showing the fields used for the study area.
17
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excavation into the shale for the main outlet lines.
Having located a suitable site for the project and upon the closing of
the lease agreement, work was begun on the final design of the system. Since
the area was divided into three fields because of the lateral, the first prob-
lem was to lay out the plots to use the ground most effectively and to avoid
the areas of deep shale. The final drawings showing plot boundaries for the
three fields are shown in Fig. 6, 7, and 8. The reader should note that these
figures show the proposed boundary locations. The final curtain locations are
offset slightly because the curtains were attached to the trench walls. Also,
due to higher than anticipated construction costs, only plots 11, 12, 13, 14,
15, and 16 were constructed on Field I during the spring of 1973, with plots
1 to 10 being constructed during the early spring of 1974.
The plots on Field III which are 12.2 m (40 ft) wide and either 61, 91.5,
or 152.4 m (200, 300, or 500 ft) long were constructed to evaluate the effects
of long period contact with shale on the chemical water quality of subsurface
irrigation return flows. In these areas, the depth to shale ranges from 0.4
to 1.2 m (1.3 to 4 ft).
Plastic barriers were installed between each of the plots and "sealed" to
the shale as indicated in Fig. 9. A drainage line was installed across the
lower end of each plot as indicated in Fig. 10. Water applied to these plots
percolated normally through the soil until it encountered the shale. The flow
path was then along the top of the shale until the drain was reached, which
collected and conveyed the water from the field to a collection box where
quality and quantity samples could be taken. The variations in plot lengths
allowed comparison of the change in water quality with the distance the water
traveled in contact with shale. These data were then compared with that col-
lected from the standard plots.
Drainage System
The drainage system for this project was unique in that the usual factors
of depth, spacing, and size of the drains were not the limiting constraints
in the design of the system. These factors were adequately met by the criteria
required for the plots. The depth of the drain was dictated by the fact that
the drain must be placed on the shale barrier (Fig. 9). The spacing and size
of the lines were limited by the plot size. In a 30 m by 30 m plot, the
greatest distance that water must travel to.a drain was 15 m and the drainage
pipe had the capacity to convey the water draining from such a small area.
The deciding factors in the choice of the type of pipe used for the drain
lines were ease of installation and cost. The fact /that a plastic curtain had
to be used to divide the plots (Fig. 9 and 10) pointed to the need for a pipe
that was easy to install because of congestion in the trenches, while the
large footage of pipe required that the material be low in cost. The new
plastic drainage pipe materials were found to fit both requirements. The
10-cm (4-in) diameter pipe came in 75-m (250 ft) rolls which made it easier to
install than short lengths of tile or cement pipe would have been. The price
for this material was 59 cents per m (18 cents per ft), which was considerably
less than the cost of clay or cement pipe.
18
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Roadway.
Scale
0 5O 100 feet
8
13
15
50 meters
10
12
14
Figure 6. Plots 1n Field I.
19
-------
o
*
T3
a
o
a:
Op
17
2 1
25
29
18
22
26
30
IS hale Too
Deep for
Plots
37
41
45
38
42
46
19
23
27
31
33
35
39
43
47
20 1
24
28
32
34
36,
40
44
48
v
t
1
1
Scale
0 50 100 feet
0 50 meters
1
— Drain Line
to
Indian Wash
'
o Manhole
^-Roadway
Figure 7. Plots in Field II,
20
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o
•o
o
o
cr
49
50
51
52
53
54
59
60
55
57
56
58
61
62
63
Scale
—i '
50 100 feet
50 meters
Figure 8. Plots in Field III.
21
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ro
ro
Wafer
table
Shale
Figure 9. Plot cross-section with drain details.
-------
— PVC Sorrier
— Perforated drainage line to collect water from plot
•
— Solid wall drainage line to transport water from plot
to measuring station
Figure 10. Plan view of drainage system detail
23
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Originally, plans called for using polyethylene film for the membrane to
divide the plots. However, upon further research into the materials available
it was found the PVC vinyl was much better suited to the requirements of this
project. The PVC is much stronger than the polyethylene film with the same
thickness. The problem of connecting the sheets of material in the field was
also solved since the PVC material could be bonded together using solvent
cement, whereas the polyethylene had to be taped. Furthermore, the cement
bond on the PVC was much stronger and more water tight. After considering the
suitability and cost of the various materials, the decision was made to use
PVC vinyl membrane with a 10 mil thickness.
Selection of the proper gravel filter material for encasing the drain
lines was possibly the key to the successful operation of the entire drainage
system. The soils in the project area are classified as Billings Clay loam,
which is a very fine-grained soil. The filter material surrounding the per-
forated drainage pipe had to be selected so that a minimum amount of these
fine materials would be permitted to pass through the filter and into the
drain line. Five gravel sources of sufficient volume were found in the valley:
(a) 2-cm (0.75 in) washed crusher waste; (b) 4-cm (1.5 in) washed crusher
waste; (c) pit run; and (d) two different sources of unwashed 2-cm (0.75 in)
crushed material. The pit run, or uncrushed, gravel in the Grand Valley con-
tained a large percentage of very large cobble rocks ranging from 15 to 30 cm
(6 to 12 in) in diameter. Samples were taken from each of the sources and a
standard mechanical analysis performed on each sample. After carefully com-
paring the particle size distribution curves of the filter material with that
of the soil, a 2-cm (0.75 in) unwashed crushed material located at the upper
end of the valley was selected.
The slope of the drain lines was dictated by the slope of the shale layer.
However, the minimum slope required to prevent salt accumulation was calculated
to be 0.5 m per 100 m. Whenever the slope of the shale exceeded the minimum
slope required, which was the case at most locations, the drain lines were
laid at the slope of the shale. When the slope of the shale was less than the.
minimum required slope, the trenches were excavated at a slope of 0.5%.
Since the only barrier between adjacent plots was the vinyl membrane,
when the drain line from one plot passed through the membrane it was in another
plot and, therefore, had to be a closed conduit to prevent v/ater from moving
either in or out of the conduit. Original plans called for a pipe of reduced
size to be used to carry the water from each plot to a centrally located water
monitoring station. After considering the total length of pipe required and
its cost, the use of a number of smaller monitoring stations was found to be
the most desirable method.* Since the Indian Wash waste channel runs close to
the east boundaries of Fields I and III, a number of control box structures
were used along the wash.
The location of Field II presented a different problem. A system for
using concrete manholes was developed to cope with this problem (Fig. 11).
By setting the base and the outlet of the manhole below the grade of the incom-
ing drain lines, as shown in Fig. 11, the water entered the manhole and fell
freely to the floor. This free outfall made it possible to collect both qual-
ity and quantity samples of the water being removed from each plot. The water
24
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_
=-4=
Plot 21 22 23 24
o o o.o
Collector
Drain
Figure 11. Typical manhole Installation.
25
-------
then entered a 15-cm (6 in) diameter pipe which collected the water from each
manhole and transported it to Indian Wash. The use of the manholes eliminated
the need for several miles of pipe. The manholes chosen for use were standard
concrete sewer manholes 122-cm (48 in) in diameter, which provided ample room
in which to conduct the sampling procedures.
Irrigation System
The furrow method of irrigation was used in the study area. Due to the
nature of this study, the delivery system used had to meet certain requirements
which included: (a) the water applied must be accurately measured; (b) tail- '
water runoff must be minimized or eliminated; (c) the water application must
be carefully controlled; (d) there must not be any water applied to the plots
that is not measured including seepage losses, leakage, or water running from
the plot above; and (e) flow rates in the furrows must be small due to the
short length of run (30 m). The delivery of water to each plot with zero
losses required that a system of lined or closed conduits be used. A network
'of lightweight aluminum gated pipe was found to be ideally suited to this
purpose.
The system was designed so that a line of gated pipe was laid across the
upper end of each plot. Since none of the fields were more than four plots
wide, the ability to water one plot on each line per day allowed a complete
irrigation every four days, which was more than sufficient. Calculations from
previous studies conducted by the authors showed that a flow rate of 4 liters
per minute (1/m) [1 gallon per minute (gpm)] in each furrow was adequate for
runs of 30 m. Using row spacings of 75 cm (30 in), which are fairly standard
in this area, a total of forty furrows on each pipeline could be irrigated at
once. Therefore, the design capacity of the system in liters per minute
equaled forty times the number of lines served. It was .found that 15-cm (6-in)
diameter pipe was needed for the gated pipe lines since pipes of smaller
diameters have a tendency to leak around the gates. Supply lines of 20-cm
(8-in) diameter were required to carry the needed flows under the available
hydraulic head.
The ability to control the flow rate entering each line of gated pipe
from the main supply line was very critical. This was accomplished by using
a hydrant valve assembly to connect the gated pipe to the supply line as
shown in Fig. 12. A "butterfly" valve placed immediately downstream from each
turnout was used to control the amount of head available at the hydrant.
Flow Measurement
/
The accurate measurement of the irrigation water applied to each plot
was of utmost importance. This measurement also posed one of the more diffi-
cult problems encountered on this project. ' During the first year, the flow
rate into each furrow was determined volumetrically using a 2-1 (0.5 gal)
container and a stop watch. While this method was highly accurate, the time
needed to perform this task for approximately 400 to 500 furrows daily became
enormous.
26
-------
HWJMJlJi^^^k^^^^^^.. 4
Figure 12. Irrigation system control valves.
27
-------
Prior to the' 1974 irrigation season, flow measurement structures were
constructed. As mentioned earlier, a 20-cm aluminum main line was used to
deliver water along one side of each field. At the upper end of a series of
plots, a combination control valve and riser were used, with the water dis-
charging from the riser into a weir box. The weir box contained a gravel -
filled screen which reduced the flow turbulence. The water then passed over a
30° V-notch weir which had been rated. After flowing over the weir, the
water discharged into a lateral of 15-cm gated pipe which conveyed the water
across the top of a series of plots (usually four). This system of flow
measurement is illustrated in Fig. 13.
CONSTRUCTION OF PLOTS
After completing the design and obtaining the materials, construction on
the drainage system was begun on May 9, 1973. In order to gain experience in
handling the plastic curtains, the 12 m by 30 m plots in the shallow portions
of Field III were undertaken first. A small bucket type wheel trencher was
used in this shallow area. The trench was dug so that the bottom was slightly
below the top of the shale layer. The loose material in the bottom of the
trench was removed by hand to provide a smooth flat surface. The curtain was
then secured inside the trench. Sufficient curtain material was left at the
bottom of the trench to be laid across the trench floor and covered with the
moist clay soil. Workmen compacted this soil to "seal" the plastic curtain
to the shale layer. The trenches were then backfilled, making certain that
the curtain remained in place. Two large hydraulic crawler backhoes were
required to excavate the deeper trenches. Since most of the trench work was
to depths of 2 to 5 m, the trenches had to be "shelved" so that the top was
much wider than the bottom in order to prevent the banks from caving.
To obtain the proper grade to the trenches, lines of hub stakes were set
on a 3 m (10 ft) offset from the center line of the trench. The elevation of
the hubs was then determined using an engineer's level. A grad rod, consisting
of two boards, a hinge and a carpenter's level, as shown in Fig. 14, was used
to determine when the trench had been excavated to the proper depth. The
trenches were cut 15 cm (0.5 ft) below the depth specified for the drain
invert placement. This was necessary to provide for a clay layer of 7.5 cm
(0.25 ft) on top of the curtain and a 7.2 cm (0.25 ft) layer of gravel filter
material underneath the drain. After placement of the 10-cm diameter plastic
drainage pipe, additional gravel was placed on the sides and over the top of
the pipe to ensure that the drainage pipe was completely surrounded with the
gravel filter.
Upon completion of the trenching operation, wooden stakes were driven
into the side of the trench at the elevation of the invert of the drainage
pipes (Fig. 15). The plastic curtain was then laid in the bottom of the trench
and a strip about 30 cm wide along the bottom edge covered with compacted clay
soil. The entire curtain was coiled and placed on the floor of the trench as
shown in Fig. 15. The gravel and pipes were then laid into position with the
gravel completely surrounding the pipeline. Upon completion of the drain
installation, the curtain was then unrolled upward from the bottom of the
trench (Fig. 16) and secured to the wall of the trench, opposite from the soil
28
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• f * • t \
>.» > . » ' • -
• f ++v**r»1fr*
» -*
*f' ' •
' •» "
'.« • ,'
, -»**•«*' »V
(a) Watering of corn plots using gated pipe.
(b) Measurement of water applied to the plots using a V-notch weir.
Figure 13: Flow measurement structures containing 30° V-notch weir for
measuring quantity of irrigation water applied to each plot,
29
-------
.'. *****
• ^_
• . *•
•
"'*-
Figure 14. Pictures showing use of the grade rod.
30
-------
>.,-
»." •':**s
Figure 15. Placement of grade stakes in trench.
Figure 16. Placement of rolled curtain on the trench floor.
31
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bank, using large nails (Fig. 17). A means of supporting the curtains across
the open ends of the trenches while completing the backfilling operation was
needed. This was accomplished by suspending the curtain with baler twine
which was connected to wooden stakes driven into the wall of the trenches as
shown in Fig. 18. This method also provided a means of holding the curtains
in position during the glueing operation.
The drainage lines, used to convey the water from the plots to the mea-
suring stations, had to pass through the plastic curtain upon leaving the plot,
Since this required making a hole through the curtain, a possible point of
leakage of water between plots was introduced, which had to be sealed. This
was solved by glueing another piece of PVC material around the hole through
the curtain and allowing it to extend perpendicular to the curtain, forming
a "boot" around the pipe (Fig. 19). By wrapping this boot around the pipe and
securing it with plastic materials, a virtually leak-proof seal was formed.
Whenever possible, the pipes running from the plots to the measuring
stations were installed using a continuous section of pipe. However, this
was not always possible since the distance was sometimes greater than 75 m
(250 ft). In standard drainage systems, a water-tight conduit is not needed;
therefore, the couplers used with the Certiflex pipe are not water tight.
When water-tight joints were required, the couplers were coated with a tar-
like asphaltic mastic that completely sealed the joint.
The backfilling operation was performed in much the same manner as on
the 12-m plots except that the deeper trenches produced much larger soil banks.
This larger bank required that a D-8 dozer be used to backfill t'he trenches.
The curtain was again held in place by a workman until the fill dirt was in
place. The corner areas of the plots, where the curtain was exposed from
all sides, presented a special backfilling problem. Unless dirt was evenly
deposited on all sides of the curtain, the weight of the soil would tear the
curtain material. The use of a small tractor-mounted backhoe proved very
successful for this purpose. A laborer assisted in the careful placement of
the backfill material against the curtain. The dirt was placed carefully on
all sides until the curtain was completely buried. The remainder of the
trench was then filled using the dozer.
INSTALLATION OF VACUUM SOIL MOISTURE EXTRACTORS
To aid in modeling the ground water and salt movement in the soil, data
were required on the total flux of solute and soil solution leaving the root
zone, as well as that leaving through the drains. Since the root zone is an
unsaturated zone, the soil-water is under suction and a vacuum is required to
collect a soil moisture sample. This can be accomplished by applying a vacuum
to a ceramic tube which has been isolated from the total soil mass by a box
which is open only to percolation from the ground surface. The total flux of
soil-water can be collected and measured. Knowing the vertical contribution
of flow, a more accurate water balance can be computed for the entire ground-
water system. Sources of salt contribution are also more accurately identi-
fied by knowing the solute concentration leaving the root zone. The vacuum
32
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>•*•.
Figure 17. Unrolling of curtain and placement against trench wall
Figure 18. Sealing the PVC curtain at corners.
33
-------
Figure 19. Method of sealing the curtain around the drainage pipe.
34
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lysimeter used in this study was patterned after the equipment developed by
Duke and Haise (21).
The equipment represents an extension of the techniques used for on-site
collection of the soil solution described by Reeve and Doering, (67) and Brooks
et al. (8). The early studies simply used porous ceramic cups connected to
vacuum lines. The intent of these investigators was to extract soil water at
various depths to investigate soil salinity variations with depth. Since
total flux was not being measured, the pan required to collect percolating
water was not required. In the current application, the cups were replaced
by a 135-cm (4.5 ft) "string" of porous ceramic tubes which were enclosed
within a pan. These "strings" were made by joining four 30-cm (1 ft) long by
1.27-cm (0.5 ft) diameter tubes with 5 cm pieces of polyethylene tubing. Glue
inside the tubing and clamps on the outside insured that the joint would not
leak when a vacuum was applied [Fig. 20(a)]. Each "string" had a fitting on
each end which was used for connecting the tubing needed to collect samples
and to flush the ceramics with chemicals in order to prevent the growth of
microorganisms. The ceramic strings were treated with 0.1 N hydrochloric acid
and flushed with deionized water prior to being installed for use in the field.
Two ceramic strings were placed in each lysimeter pan which was construc-
ted of sheet metal and measured 150 cm long by 12.7 cm wide and 17.8 cm deep.
When ready for installation, the ceramics were placed in this pan and covered
with soil. The candles were placed 5 to 8 cm above the bottom of the pan so
that they were surrounded by soil. The soil was mounded above the upper edge
of the pan.
A heavy gauge rectangular rubber pillow was glued to the bottom of the
pan. Inflating the pillow after installation of the unit in the field pushed
the pan up against the soil above it and the mounded soil in the pan ensured
a positive contact between the lysimeter and soil matrix above. A schematic
of a completed lysimeter pan is given in Fig. 20 (b). Four pan lysimeters
were placed in each of two test plots. They were located at the corners of
a 3 m by 4 m rectangle which had been centered in the plot as shown in Fig.
20 (c).
The pan lysimeters were installed during the construction of the test
plots. First, a rectangular pit (3m by'4.5 m by 1.5 m) was excavated in the
center of the plot. Then, holes to house the lysimeter pans were augered
into the sides of this pit roughly 1 to 1.3 m below the ground surface. These
holes were augered using a hydraulic ram (Fig. 21) which was mounted on a
steel frame. The ram was then used to push a box-shaped bit into the soil
which formed a shaped hole the size of the lysimeter pan (Fig. 22). After
the four holes were shaped and the pans inserted, the flush lines, air pillow
lines, collector lines, vacuum lines and collection bottles were placed two
to a well, which was 0.6 m in length and made of 30-cm diameter plastic pipe.
Various sizes of polyethylene and plastic tubing were used to make the con-
nections of the lysimeter pans to the collection bottles, to the vacuum unit,
to the flush lines and to the air supply. Vacuum, air, flush and dump lines
running to the edge of the test field were housed in 3-cm polyethylene tubing.
After completing the installation, the site was backfilled.
35
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Ceramic tube
jClomp.
Fitting
• vinyl tubing
(a) Ceramic Candle Detail
Top view
Side view
Flush line
Collector line
End view
.Lysimeter pan
Ceramic candle
jOutlet to collector
bottle
-Air pillow.
—Lysimeter pan
Air pillow
(b) Lysimeter Pan Detail
3m
4m
4m
I
3m
(c) Plan View of Lysimeter Pan Locations
in Research Plot
Figure 20. Field installation soil moisture vacuum extractors,
36
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Figure 21. Hydraulic ram used to auger and shape holes for lysimeter pans,
Figure 22. Construction of lysimeter pans.
37
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Vacuum was applied continuously to the ceramics and samples were collec-
ted weekly or more frequently as required. The proper operating vacuum was
attained by using two tensiometers; one placed in the lysimeter pan and one in
the soil at the same depth in a region close to the pan. The vacuum was then
adjusted so approximately the same soil suction was present on each tensiometer
The ceramic strings were connected to a 4-1 (1-gal) jar which collected
and held the soil solution between sampling periods. There were collection
bottles for each pan. The 4-1 (1-gal) jar was connected to a vacuum source
located at the edge of the test field (Fig. 23). A vacuum unit (Fig. 24)
consisted of a vacuum pump, vacuum tank, manometer and pressure switches to
control and maintain the vacuum in the collectors. One vacuum unit was used
to run a set of four pans.
TREATMENTS
Crops
The agricultural industry in the Grand Valley is comprised mainly of
fruit crops, pears, peaches, cherries, and field crops such as corn, small
grains, sugar beets, alfalfa, and other hay crops. The crops selected for use
in the experiment were corn, alfalfa, wheat, and a crested wheat grass. These
were selected because they represent the predominant crops and require a min-
imum amount of special equipment for production. The alfalfa was planted as
a permanent stand in Field I (Fig. 6), corn was planted in Field II (Fig. 7)
and wheat in the north one-half of Field III (Fig.'8). The south one-half of
Field III was planted with a permanent stand of Jose Tall Wheat grass. '
Fertilization Treatment
The fertilization treatments were designed to ensure a good stand of the
crop and to evaluate nutrient losses due to excess irrigation. After an
initial fertilization to establish the crop, the alfalfa received no additional
fertilizer. The wheat crop received the recommended quantities of nitrogen,
potassium and phosphate based on a nutrient analysis of the surface soils in
the test area. The recommendation was based on a yield goal of 32.656 mg/ha
for wheat and comes from the Colorado State Publication, "Guide to Fertilizer
Recommendation in Colorado" (51). The Jose Tall Wheat grass in Field III-S
received a uniform application of fertilizer based on soil analysis and yield
goals found in the fertilizer guide.
The corn test plots received fertilization such that two levels of nitro-
gen were achieved in the soil. The goal was to achieve an equivalent of
either 100 ppm nitrogen or 50 ppm nitrogen in the surface soils on a plot.
To do this, the surface soils were analyzed for nutrients and then, based on
existing nitrate levels, one-half of the plots was selected to be fertilized
to 50 ppm nitrogen, while the other one-half was fertilized to 100 ppm nitro-
?en; * y™s?n?,.the approximation (51) that 10 ppm nitrogen is roughly equiva-
lent to 40 kg/ha of nitrate-nitrogen in the top 30 cm of soil, the nitrogen
required to achieve a specified fertilization level was computed. The potas-
sium and phosphate fertilizations were based on the surface soil analysis and
38
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Figure 23. Housing for vacuum units.
Figure 24. Vacuum units,
-------
recommendations found in the fertilization guide [Ludwick and Soltanpour (51)]
The treatment design was random in that no specific pattern of plots within
the test area was chosen for the fertilization treatment.
Irrigation Treatment
The irrigation treatments were developed based on the levels of depletion
and replenishment of available water in a plot. The available water, as used
in this study, was defined as the soil water stored between 33 kilopascals (kPa}
and 1.5xl(P kPa. The water content at 33 kPa and 1.5xl03 kPa was computed as
a percentage of the dry soil weight. The levels of depletion selected were
70% and 50% of available moisture. Four levels of replenishment, 75%, 100%,
150%, and 200% of the depleted moisture were used. This resulted in a total
of eight irrigation treatments. Specific irrigation treatments assigned to a
plot depended on both plot location and fertilizer treatment. For operation
purposes, replicate plots were irrigated using the same schedule. This meant
that replicates could not be supplied by the same lateral.
In Field I, the irrigation treatments were replicated because there were
sixteen test plots. The plot assignments were made based solely on the oper-
ational requirements. In Field II, all eight irrigation treatments were repli-
cated four times, twice on the plots containing 50 ppm nitrogen and twice on
those plots containing 100 ppm nitrogen. This design used all available test
plots in Field II. Field III-N contained only ten plots and each irrigation
treatment was used once. Two treatments were replicated using the remaining
plots. The plots in Field III-S were used to evaluate the pickup of salts by
water moving over the shale layer. The irrigation treatment for.these plots
was different from the rest of the research area. Irrigation water was applied
when the crop required moisture and was run for either 24 or 48 hours on a plot.
Initiation of Irrigation
Since the design of the irrigation treatment was based on depleted soil-
water, a method was established to monitor this depletion. Because of the
large number of test plots, it was impossible to monitor all plots for their
existing water content. Plots having the same irrigation treatments were
paired and one plot of each pair was monitored for depletion. Monitoring was
accomplished using a neutron probe. The decision to irrigate was based on the
depleted water computed as the difference between the 33 kPa water content
(field capacity) for that plot and the existing water content. Depletions
were computed from graphical plots of soil moisture with depth, using a plan-
imeter to measure the area on the curves between the field capacity moisture
and the existing moisture profile. Once a monitor plot was depleted to the
desired level, both plots were irrigated.
DATA COLLECTION AND INSTRUMENTATION
Data needed as input to the model and for use in calibrating the model
were collected. This included irrigation depths and timing, soil-water stor-
age, soil-water fluxes, drainage, chemical analysis of soil-water, evapotran-
spiration data and chemical analysis of soil profiles. Other data needed to
40
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extend the results of this report to predicting changes in salinity of sub-
surface irrigation return flows in the Grand Valley Salinity Control Demon-
stration Project area and the entire Grand Valley are presented in Section 8.
Irrigation
The depth of irrigation for a specific plot was computed based on the
assigned irrigation treatment and the available moisture on the plot. After
the decision to irrigate was made, the irrigation was scheduled as soon as
practicable. Irrigation water was delivered to the plots via 20-cm aluminum
pipe strung along one edge of a field. From the 20-cm line the water came up
through a riser into a weir box containing a 30° V-notch weir; where the flow
was measured. After flowing over the weir, the water dropped into a lateral
of 15-cm gated pipe which conducted the water to the upper end of the plot.
Plots were isolated by constructing an earthen berm around the perimeter of
each plot. As a practical matter, the rate and duration of flow for an irri-
gation treatment were specified for a plot prior to initiating the irrigation.
Soil Moisture Measurements
Soil moisture measurements were required to monitor moisture levels for
irrigation, compute water stored with each irrigation and estimate soil
hydraulic properties. These measurements were made gravimetrically and witn
neutron attenuation equipment, see van Bavel et al. (87).
Neutron probes used in the study were field-calibrated to the soils and
access tubes used in this project (87). Neutron access tubes centered in each
quadrant (4 tubes per plot) of all the test plots were used to establish an
average moisture profile. Neutron readings taken at 6 in. intervals beginning
at 6 in. below the soil surface over the entire soil depth were taken in each
quadrant of a plot and then averaged to give a single profile. A count inter-
val of 0.5 min per reading was used instead of the 1 and 2 mm counts usually
preferred. Rogerson (72) found that for practical purposes 0.5 mm counts are
adequate for probe systems with 100 millicuries (me) Americium-Bery lium (AmBe)
sources, which were the sources used in this study. Moisture profiles of each
plot were made the day before irrigation and four days subsequent to an irri-
gation with the difference in moisture profiles being the water stored from
that irrigation.
Vacuum Extractors
Data on the total flux of soil solution leaving the root zone was gathered
using two sets of vacuum extractors developed by Duke and Haise (21). Since
the root zone is generally a partially saturated zone, the soil water is under
suction and vacuum is required to extract it from the soil. The vacuum was
applied through a ceramic tube that was isolated from the total soil mass by
a box that could only receive percolation from the ground surface.
Four lysimeter pans (comprising one unit) were placed in each of two test
plots in Field II and located at the corners of a 3 m by 4 m rectangle centered
in the plot as shown in Fig. 20. The pans were installed during the construc-
tion of the test plots and were connected by polyethylene tubing to control
41
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units housed at the edqe of Field II. The polyethylene tube was used to suDDlv
vacuum, air pressure and provide dump lines to collect accumulated samples.
Extracted water was held in 3.9 1 jars buried in wells in the test plots which
were emptied as required. Each pan was equipped with a separate collection
bottle.
The control unit consisted of a vacuum pump, vacuum tank, manometer and
pressure switches to control and maintain the vacuum at a specific level on
the ceramic tube. One vacuum unit was used to run a set of four lysimeter
pans. Water samples collected were used to estimate total flux below the root
zone and as samples for water quality analysis.
Drainage
The collection boxes for the drains from Fields I and III-N were located
in Indian Wash on the east side of the test area. These boxes were compart-
mentalized (one drain per compartment) and fitted with 30° V-notch weirs so
discharge from each drain could be measured. The depth and duration of flow
were measured. The drains were checked daily and depth of flow was measured
twice each day if flowing. The drains from the plots in Field II and their
outfall in the manholes in Field II also were checked each day and flows were
measured twice each day as required. Water for quality analysis was collected
at the same time that flows were being measured.
Evapotranspiration
A weather station consisting of a Class A evaporation pan, a tipping
bucket rain gage, an anemometer, a recording hygrothermograph, two grass
lysimeters and a pyranometer were located in Field III-S for use in evapo-
transpiration studies. These instruments provided daily values of humidity,
maximum and minimum temperature, net daily solar radiation, evaporation from
free water surface, rainfall, and daily evapotranspiration from a well-watered
grass. By using the pan evaporation and a crop coefficient (84), the daily
loss of soil moisture was estimated and used in scheduling irrigations.
The grass lysimeters consist of a 1.2 m by 1.2 m by 0.5 m box containing
a layer of coarse gravel covered by a layer of soil on which sod was grown.
Water was supplied to the sod in the box lysimeter from a reservoir. A con-
stant water level was maintained in the sod box using a float valve. The drop
in water level in the reservoir supplying the sod box was recorded using a
water stage recorder.
Soil and Water Chemistry
Soil chemical profiles were determined annually for each plot by taking
soil samples at 30-cm intervals through the first 1.8 m of the profile and
then at 60-cm intervals from 1.8 m until shale was encountered. Within each
plot, samples were taken from the center of the upper and lower one-half of
the plot, composited by depth, and prepared for laboratory analysis.
In addition to the water samples gathered from the vacuum extractor and
drains, water samples were taken daily from the irrigation supply lateral for
42
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chemical analysis. Analyses conducted by the project laboratory included
determinations of pH, electrical conductivity (EC), total dissolved solids
(IDS), and the concentration of the following ions: Calcium (Ca ), Magnesium
(Mg++), Sodium (Na+), Potassium (K+), carbonate (,C03=), bicarbonate (HCO?-),
chloride (Cl~), sulfate ($04-), and nitrate (N0o~). Additional chemical
studies were done at the Colorado State University Soil Testing Laboratory to
determine soil texture, percent of organic matter, lime, total nitrogen,
cation exchange capacity, and concentration of gypsum in the soils in the
study area.
Soil Properties
Soil-water characteristic curves for the research plots were developed
from undisturbed soil samples using a pressure plate apparatus. Two undis-
turbed samples were taken at 30-cm intervals through a 2.1-m soil profile.
New samples were taken for use with each value of pressure used to compute
the characteristic curves. Fourteen values of moisture content were averaged
at each value of pressure head (ranging from 29 cm of water pressure to 1.5x
103 kPa) used to construct the characteristic curves. Saturated flow through
short columns of undisturbed soil and values of hydraulic conductivity from
previous studies (1) were used to estimate saturated hydraulic conductivity.
Bulk densities for the soil in the research area were calculated from the
dried soil samples used to develop the soil moisture characteristic curves.
43
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SECTION 5
SOIL MOISTURE AND SALT TRANSPORT MODELS
Salt transport studies should include not only a consideration of the
movement of salts or dissolved constituents, but also the displacement of the
solvent as well. Biggar and Nielsen (3) have stated that "such considerations
become particularly important in irrigated agriculture when it is desirable to
know the concentration arid location of a dissolved constituent in the soil
profile, the reactions of constituents with each other, and the soil matrix
during the displacement and transport of water and solutes to plant roots."
The research described in this report considered salt transport and
solution displacement. A field study was conducted in the Grand Valley of
Colorado where data were collected to calibrate a numerical model which
describes the salt and solvent transport process occurring in the soils in
the Grand Valley.
To meet the objectives of the research, the solution flow segment of the
model simulated transient one-dimensional infiltration and redistribution,
and evapotranspiration by crops. The boundary condition in the field at the
soil surface was that imposed by intermittent irrigation from a gravity sys-
tem. It was also required that the model calculate the dissolution and pre-
cipitation of salts and cation exchange of ions commonly found in soils and
compute the transport of these ionic species in response to the solution
displacement computed in the solution flow segment. In the remaining portions
of this section the literature pertinent to each of the components of the
model is reviewed.
SOLUTIONS OF WATER FLOW EQUATION
The equation describing the vertical flow of water in soils is
where 9 is volumetric water content, t is time, z is depth, H is piezometric
head, and K(e) is hydraulic conductivity as a function of water content. This
formulation without the sink term is attributed to Richards (70) and is com-
monly called the Richards' equation. The equation is valid for flow in
saturated and unsaturated flow regimes. Water is added or subtracted from
the soil at "points" in some problems and a sink or source term (S) is used
to handle these cases. Many investigators have found it more convenient to
44
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write Equation 1 with 6 as the dependent variable, a form known as the water
content form, i.e.,
where D(e) is the diffusivity. (However, the water content form is applicable
to partially saturated flow only because D(e) is not defined in saturated
soils.)
Analytic Solutions to Richards' Equation
Analytic solutions have found considerable application as tools for inves-
tigating and understanding particular aspects of flow phenomena. However,
they have a very limited applicability for direct use in this study because of
their lack of generality imposed by limiting assumptions. These assumptions
are not generally satisfied in the field problem of interest in this research.
The analytic solutions do, however, play a role in model studies because they
provide a standard for comparison against which numerical models can be checked,
It is for this reason that several solutions for one-dimensional flows are
described briefly.
Few exact solutions to the Richards' equation exist due to the nonlinear-
ity of the equation. The water content form of the equation for horizontal
and vertical infiltration has been studied extensively, experimentally and
mathematically. Philip (65), Brutsaert (13,15) and Parlange (62,63) have
developed analytic solutions for Richards' equation.
Philip (65) developed numerical solutions for horizontal imbibition and
vertical infiltration of water. For vertical infiltration, the solution is
given as an infinite series
Z = £ f (e) tn/2 (3)
n=l n
where Z is depth to a particular water content, t is time. The coefficient
fn(e) is calculated from a knowledge of diffusivity and conductivity functions.
Brutsaert (13,15) also used Richards' equation which had been transformed
into an ordinary differential equation using the Boltzmann transformation to
arrive at his solutions. He developed functional forms for the conductivity
and soil moisture characteristic and substituted an approximation for the
transformed terms on the right-hand-side of the equation. He was then able
to integrate the equation and arrive at an analytic solution for 6 versus
depth.
Parlange (62,63) transformed the water content form of Richards' equation
into an equation with Z as the dependent variable and approximated the water
content profile by integration while neglecting the unsteady-state term. The
unsteady-state term was calculated using this approximation and was reinserted
into the differential equation which was then integrated to derive a second
45
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approximation (52). Numerical comparisons of water content profiles calculated
using Parlange's method with Philip's analysis were quite good as was Brutsaert's
comparison for cumulative infiltration.
Gardner et al. (33) developed an approximate solution to the Richards'
equation for redistribution behind a wetting front. By assuming functional
forms for conductivity and diffusivity as power functions of water content,
and assuming that the matric potential is proportional to exp(-Be), where B 1s
a constant, they solved the equation by separation of variables with the solu-
tion assumed to be of the form e = T(t)Z(z) where t is time and z is depth.
Solutions were given for cases of redistribution with and without gravity terms
included, and good agreement was attained between the theory and experimental
results for stored water and drainage from column studies.
Numerical Solutions to Richards' Equation
Because the complexity of the flow system often makes an analytic solution
to the Richards' equation impossible, recent investigators have turn to numer-
ical methods to study flow systems. The object of a numerical method is to
solve a differential equation using an equation which approximates the original
equation. Numerical methods to solve Richards' equation were developed many
years ago (44), but only with the advent of high speed digital computers did
they become feasible as a method to solve complex problems. Currently, finite
difference techniques are probably the most widely used numerical method. The
finite element method and dynamic simulation languages have been investigated
and, due to their versatility, will probably gain more acceptance in the future.
A finite difference technique was used in this investigation to model soil-
water flow.
When using finite differences, the derivatives in the equation are approx-
imated by Taylor series expansion of the dependent variable as a function of
the independent variable. Depending on the expansion used, the differencing
is known as a "forward-difference," "backward-difference," or "central-
difference." When the function f is expanded into a Taylor series about x in
the positive direction, f(x+Ax), the expression for df/dx is
df _ f(x+Ax)-f(x) , , ,.
dx " AX 0(**' (4'
where O(AX) represents the remaining terms in the series. The forward approx-
imation for df/dx is given by dropping the O(AX) term. The backward approx-
imation is derived in the same fashion as the forward approximation except
that the function is expanded in the negative direction f(x-Ax). When the
Taylor series expansion for f in the negative direction is subtracted from the
Taylor series for f in the positive direction, the resulting expression is the
central-difference approximation for the derivative. A geometrical interpre-
tation can be given to these differences. The approximation can be represented
by the slope of the line connecting the two values of the function used to
describe the difference. If the approximation of the function is being made
at a point x, then the "backward-difference" is represented by the slope of
the line between x and X-AX. The "forward-difference" is represented by the
slope of the line between x and X+AX, and the "central-difference" is
46
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represented by the slope of the line between X-AX and X+AX. Approximations
for both first and second derivatives can be made using Taylor series
expansions.
To apply a numerical technique, the algebraic equations which approximate
the differential equation are solved at a series of points or nodes which
denote the time and space domains. For instance, in one dimension, domains
are represented as a rectangular grid system with the indices i and j (Fig.
25) denoting the principal axes of the system. The j index indicates the time
domain and the i index corresponds to the space domain.
j
Figure 25. Grid system used for one-dimensional finite differencing.
The difference equations for the nodes between the boundaries, along with
the equations for the boundary conditions, create a system of n algebraic
equations in n unknowns. The series of algebraic equations used to approxi-
mate the Richards' equation in one-dimension form a tridiagonal matrix for which
many solution schemes' have been developed (69).
The solution techniques which have been developed are classified as
either implicit or explicit. The implicit methods solve the equations simul-
taneously for each new time interval using a value of the variable at eacn
node from the previous time interval. The implicit method provides a stable
but not necessarily accurate solution regardless of the size of the time
interval used to advance the solution.
Because of the approximation, the unknown in the "
equation is given explicitly in terms of three known values for a
time step. Thus, the terminology explicit arises and the solution is capable
of being marched forward in time. For this technique *> be convergent and
stable, the time increments can be no larger than one-half the square of the
space increments (69). In many practical situations this criterion can be
very restrictive and can require large amounts of computer time for very short
simulation periods. However, it also requires less storage than implicit
techniques and solution methods for problems in subsurface hydrology, the
reader is referred to Remson et al . (69).
One of the earliest and most widely known numerical solutions of the
Richards' equation was developed by Hanks and Bowers (37). They solved the
pressure head form of the equation, including a gravity term, for infiltration
47
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into a layered soil using a Crank-Nicholson numerical technique. They con-
sidered the critical part of the solution of the system of difference equa-
tions to be the selection of the hydraulic parameters, K (conductivity), and C
(specific moisture capacity), and At, the time interval. The parameters K and
C were considered constant for a given time interval but were allowed to vary
with time.
The time interval varied and was dependent upon the infiltration of a
constant volume of water. The relationship was:
where Q is a constant volume approximated as Q = 0.035AZ, where Az is depth
increment and IJ-1/2 is the infiltration rate from the previous time step.
The superscript j indicates the time step being used in the computation.
The hydraulic conductivity at each grid for each time step was estimated
from a difference form of the definition of diffusivity
K(e) = D(e) $ (6)
where the diffusivity had been estimated as an integrated average. This
averaging was done to minimize the effect of water content changes on the com-
puted value of K(e), since small changes in water content can cause large
changes in hydraulic conductivity. Since the diffusivity D(e) does not vary
as widely as K(e) with moisture content, they found that better results in
their simulation were obtained when using an average D(e) to compute an aver-
age K(e).
Hanks and Bowers (37) obtained better results when the specific moisture
capacity was calculated using a value of moisture content estimated at the end
of the time interval. The expression used for this estimation is
ej+1 (estimated) = (QJ - e^'"1 ) B + 0^ (7)
where B is a constant equal to 0.7 or t/(t+3-l/3), whichever is greater. The
moisture content, pressure head and diffusivity data were entered as tabular
data. Good agreement was achieved between calculated and experimental water
content profiles for horizontal infiltration when compared with Philip's (66)
work. The work of Hanks and Bowers has been used extensively in the develop-
ment of the flow model used in this investigation.
Hysteresis of the soil moisture characteristic has been considered by
several investigators (25,38,73,77,92) and has been found to be a significant
factor in calculating all phases of flow, i.e., infiltration, redistribution
and drainage. Also air entrapment by infiltrating water has been shown to
significantly affect the advance of the water front (70). The effects of
hysteresis and air entrapment were not included in the current study since
48
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such a detailed description of flux was not needed to complete the analysis
for this research project.
Characterization of soils is a major problem encountered in modeling
soil-water systems. In most studies (39,62,73), it has been assumed that the
soils were homogeneous and isotropic throughout the profile. If layered soils
were to be modeled, then the properties were considered uniform through each
layer (36). Wang and Lakshminarayana (90) used numerically averaged field data
for the entire profile for the conductivity water content relationship and the
soil-water characteristics. Comparisons between computed and field measured
water content profiles in a nonhomogeneous soil were good.
Freeze (26,27) investigated saturated-unsaturated flow systems in both
one and three dimensions. The models were used to analyze the interaction
between surface water and groundwater as influenced by partially saturated
flow in basin-wide hydrologic response studies. For the one-dimensional case,
the pressure-head form of Richards' equation was solved using a recurrence
relation developed by Richtmyer (71). The solution was initiated at the
bottom boundary and proceeded to the surface boundary. The procedure applies
as long as the soil is partially saturated. At saturation the recursion
relationships are no longer defined and an alternate solution is required.
The functional relationships for the hydraulic parameters and soil-moisture
characteristic, including hysteresis, are entered as tabular values.
Bhuiyan et al. (2) and van der Ploeg (89) have used a dynamic simulation
language to model vertical and horizontal infiltration in one-dimension as
well as two- and three-dimensional infiltration problems. Using this method,
the flux is calculated through a series of soil layers with conservation of
mass principles and Darcy's law. Water content is calculated by integrating
net flux using a fourth order Runga-Kutta scheme (2). The method gave excel-
lent comparisons for horizontal infiltration studies when compared to Philip's
numerical studies. The method is easily programmed and mathematically
straight-forward which makes it easy to use.
Soil Moisture Extraction
The models discussed have not included the sink term as part of the
solution. In investigations where a sink was included, the focus of the study
was the sink, its functional form, and how it could be incorporated into the
numerical solution for moisture flow. Plant roots are the most important
water sink in the soil profile. The first approach to simulating water extrac-
tion by roots, termed microscopic, considers flow to a single root while the
second approach, labeled macroscopic, considers evapotranspiration as a sink
distributed over the total depth of the root zone.
Gardner (31), Molz et al. (57), and Cowan (16) have used a microscopic
model to study the effect of soil water availability on transpiration by
plants. Gardner's (31) idealized root model consists of an infinitely long
cylinder of uniform radius and water absorbing properties placed in an infinite
two-dimensional medium in which flow occurs in the radial direction only.
Studies of root models such as the one proposed by Gardner (31) are necessary
in developing an understanding of the microscopic aspects of flow in soil-water
49
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systems. However, the description of the macroscopic or bulk flow of the soil
solution is required for the research in this study.
The next type of model to be considered treats the root-zone extraction
process as a whole without considering flow to individual roots. Molz and
Remson (56) have labeled these macroscopic extraction models. A macroscopic
root model developed by Gardner (32) distributed the roots through the soil
profile and determined the water uptake pattern based on soil hydraulic proper-
ties. To apply the model, the root zone was segmented into layers, osmotic
effects were neglected, and gravity was accounted for in terms of head. The
total withdrawal (q) is computed for a cross-sectional area. Other macroscopic
models are constructed from the Richards' equation coupled with a sink term
and the resulting equations are solved with the sink included. Molz and Remson
(56) and Nimah and Hanks (59) have developed models of this type which differ
in the functional expression of the sink.
Molz and Remson (56) developed a model which was a function of a fixed
rooting depth and pattern and plant transpiration rate. They approximated the
distribution of root extraction as 40%, 30%, 20%, and 10% of the total tran-
spiration coming from each successively deeper quarter of the root zone. The
depth of the root zone remained fixed throughout the simulation. Soil moisture
flux computed with the model compared well to experimental values of flux
measured in a steady-state system, in which Birdsfoot trefoil (Lotus con-iaul-
atus var. Tennuifolius) was being grown in Pachappa fine sandy loam. Molz and
Remson (56) and Nimah and Hanks (59) have proposed macroscopic models which
are functions of moisture content, root depth and distribution, and crop
transpiration rate. In each of the above models, the sink term was finite
differenced and solved as part of the Richards' equation. All the models
mentioned require that the magnitude of the sink (rate of withdrawal of water
by the root system) be specified.
\
SINK STRENGTH
The magnitude of the sink strength is usually correlated with a value of
evapotranspiration. The measurement of evapotranspiration was divided into
three categories by Tanner (80) and provided convenient groups for considera-
tion of the methods used to calculate evapotranspiration. .
The first method considers a water balance for the region to be studied.
Mathematically the balance is given as:
ET = P - (v^.+V^AV^AVjJ/A (8)
where P is the volume of precipitation or applied water per unit area, and V
represents volume elements of moisture accounting for intercepted water (i),
leakage (L), runoff and drainage (r), stored water above the water table (s),
and ground-water storage (w), and area of interception (A). The size of the
region which can be studied varies from an entire watershed to a lysimeter.
In general, as the area under Investigation 1s reduced, the accuracy of the
estimates improves because the measured variables begin to more closely
50
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reflect the environment of a specific area. Lysimeter studies are very precise
since the variables in the water balance can be controlled and measured accu-
rately. Field studies have less precision because several of the variables
must be estimated. The problem in field studies ,is to accurately estimate
changes in storage and total drainage losses. Soil moisture changes can be
estimated using either gravimetric methods or neutron scattering techniques;
the second method is preferred since the same soil mass is measured each time
and relatively large masses are considered. This ability or inability to
measure or estimate accurately the drainage component from the profile can
seriously affect the accuracy of the evapotranspiration estimate. Soil-water
depletion studies, coupled with measurements of soil suction, have been used
by Reicosky et al. (68) to estimate the uptake of water by plant roots.
The second classification of equations are those which use micrometeor-
ological data. In this group are the equations which have been developed
using mass transfer and wind profile theories or energy balances. Also
included in this category are the equations which combine profile and energy
balance methods. The assumptions basic to all the equations are steady-state
adiabatic conditions, one-dimensional transport (no horizontal gradients),
and a homogeneous surface. These conditions are difficult to achieve, and
factors have been developed to account for deviation from the assumed condition.
There still is the problem of deciding where to measure the variables and how
many measurements to make. This becomes particularly difficult when measuring
the environment around agricultural surfaces. Combination equations by Penman
and van Bavel (64,86) are used frequently in evapotranspiration studies.
The remaining methods are empirical equations which have been developed
by relating specific climatological parameters to evapotranspiration. Param-
eters used in the development of these equations include radiation, tempera-
ture, vapor pressure, humidity and percentage of monthly daylight hours.
These equations have been developed for specific climatic conditions and their
applicability is limited to these conditions. The Jensen-Haise and Blaney-
Criddle equations (41), which are examples of empirical formulas, were devel-
oped in the western United States and are best suited for use in regions with
a climate similar to this area. Correlation of pan evaporation and crop
evapotranspiration is another method for estimating E+ (evapotranspiration).
Again, this is site-specific, but has the advantage of being easily measured
and applied. The equations mentioned predict potential evapotranspiration,
thus requiring an adjustment for actual evapotranspiration. This adjustment
can be made using crop coefficients which account for crop growth stage.
SOIL PROPERTIES
Characterization of the soil hydraulic properties and soil moisture char-
acteristics is probably the most difficult part of modeling, particularly in
a field study. Stable (77) attributed much of the error in his comparison
between field data and computed results to the difficulty inherent in measuring
conductivity and diffusivity over the entire range of moisture content occur-
ring in the field. One alternative is to create a hypothetical soil with
"reasonable" properties and use this data to conduct a theoretical study (92).
Since the current research was a field investigation, the collection of soil
51
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data from the research plot was part of the study. A knowledge of the conduc-
tivity versus moisture content relationship and the soil moisture character- ^
istic is sufficient to develop the parameters needed for a flow model. The
techniques which are currently used to measure these properties fall into one
of two categories: In-situ or laboratory.
In-Situ Method
To calculate soil properties using in-situ methods, water flow and soil
water suction data are collected in the field and used to solve the Richards'
equation in one dimension. The hydraulic conductivity can be calculated once
the soil-water flux and head are known. In-situ methods are attractive con-
ceptually since the properties are measured in conditions which are representa-
tive of the soil profile and in large volumes of soil which are relatively
undisturbed. In-situ methods have been used by several investigators (18,39,
58), but were of no use in the current investigation. The inability to measure
small changes in water content and suction occurring in the soils in the test
plots prevented the use of these methods in characterizing the soils.
Laboratory Methods
Extensive literature exists on laboratory methods to measure hydraulic
conductivity, diffusivity and soil moisture characteristics. The hydraulic
properties are measured by experiments which have been devised to collect
data to solve Richards' equation (11,12,30) or Darcy's law (9). Other inves-
tigators have explored the use of the soil moisture characteristic as a means
to estimate hydraulic conductivity from the implied pore-size distribution-
data (9,10,35,55). Bruce (10) and Green and Corey (35) have evaluated these
equations and found them acceptable provided a matching factor is used to
match the computed value of conductivity to a measured value of conductivity
at a specific moisture content, usually at or near saturation. Brutsaert (14)
applied probability laws to the pore-size distribution to arrive at permeabil-
ities, while Brooks and Corey (9) developed a power relationship for the
permeability as a function of capillary pressure based on extensive experimen-
tal data.
Pressure plate (85) and hanging water column devices (45) have been used
to develop the moisture characteristic needed to complete either of the above
studies. Sample sizes and the use of disturbed samples are the major criti-
cisms of laboratory methods. Collection of a number of samples sufficient to
characterize a field is particularly important since, as Nielsen et al. (58)
concluded, "The most important laboratory measurements for predicting the
soil-water behavior in the field are the soil-water characteristic curve and
a steady-state hydraulic conductivity." Steady-state methods for measuring
hydraulic conductivity using short columns and other techniques have been
discussed in detail by Klute (45).
SALT TRANSPORT
Salt transport occurs in soils as part of a miscible displacement process
resulting from irrigation water or precipitation infiltrating and displacing
52
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the soil solution. The salt transport process can be described using the
diffusion convection equation. The equation in one dimension is
+S (9)
where D is an apparent diffusion coefficient which accounts for diffusion and
dispersion, c is solute concentration, v is volumetric flux given by Darcy's
law, and S is a sink term for the chemical species. The other parameters have
the same definition as in the Richards' equation. When solute concentration
changes occur at "points" in the system as the result of precipitation, dis-
solution or cation exchange, the source or sink term on the right-hand side of
equation (9) handles these cases. As the displacement occurs, mixing of the
two solutions occurs and a zone develops which is a mixture of the solutions.
Within this zone in nonreactive porous media, mixing is the result of two
phenomena which occur simultaneously. The first effect, mechanical dispersion,
occurs because of the nonuniform velocity distribution in soils due to the
variation in the shape and size of the pore spaces. The second effect, dif-
fusion, is the mixing due to random motion of ions occurring in response to
chemical potential gradients (3, 28). Even though the processes occur simul-
taneously, the effects of the processes cannot be superimposed and are generally
treated as a single process because each is affected by the geometry of porous
media, the properties of the fluid and water flux. Ion exchange between the
soil solution and soil matrix, and dissolution and precipitation of species
occur in soils and complicate the mathematical description of the transport
process.
Chromatographic Theories
Initially, investigators tried to adapt the chromatographic theories used
in column separations in the chemical industry to soil systems. Frissel and
Poelstra (29) have discussed these theories and their application in much
detail. The theories can be broken into two classifications; rate and plate.
The rate theories were developed assuming a kinetic exchange process.
Theories of de Vault and Hiester and Vermeulen have been used to study trans-
port in soils (29). Generally, rate theories have not been satisfactory for
use in soil systems and all the flow and exchange parameters are required for
the successful application of these methods.
Plate theories have been used by several investigators (24, 83, 88) with
varying degrees of success. Dutt's model (24), which is used in the current
study, is based on plate theory. The plate theory uses the height of the
plate as the unit of calculation. The plate height is defined as the distance
required for the mobile phase to come to equilibrium with the stationary phase.
Application of plate theories requires an experiment to determine the plate
height for each flow system. This is a limitation since each flow rate
requires a different plate height. In some cases (24), the plate height has
been fixed for convenience1 sake to complete the computations.
Thomas and Coleman (83) investigated the leaching of ferti-lizer salts in
soils using a chromatographic equation. They found poor agreement between
concentrations of fertilizer salt found in the soil and those predicted by the
model. They attributed the poor agreement to the lack of adequate data to
53
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describe the soil characteristics. Van der Molen (88) studied the reclamation
of Dutch soils which had been inundated with sea water using Glueckaufs (34)
theory and found good qualitative agreement.
Lai and Jurinak (50) developed a numerical solution of a material balance
equation which included a nonlinear exchange function. The isotherms were
developed from column studies. They found from comparisons of numerical :
results with column studies that better agreement was obtained using nonlinear
exchange isotherms. They also found that applicability of the equilibrium
assumption used in the analysis depended on the flow velocity of the fluid and
the cation exchange properties of the soil.
Bresler (5) and Terkletaub and Babcock (82) have developed plate models
for use in investigating the movement of non-interacting solutes in response
to irrigation water. Bresler (5) developed a linear model based on conserva-
tion of mass principles which he used to study the vertical downward flow of
non-adsorbed ion species. Input data required for application of Bresler's
model included the soil moisture characteristic, initial salinity and water
content in each layer and the quantity and quality of applied water. Bresler
(5) found good agreement between measured and predicted Cl~ profiles for a
series of field experiments using varying irrigation treatments.
Terkletaub and Babcock (82) developed an algebraic expression to model
the mixing process occurring during infiltration of a solution containing a
non-interacting ionic species. They found a reasonably good prediction of
concentration profiles when compared to column studies using ten sections.
They also found that increasing the number of sections used in the computation
had a marginal effect in improving the accuracy of the simulation.
The mixing cell concept is another technique which has been used to model
dispersion in porous media. It is assumed that the solution in the cell is
completely mixed and has a uniform concentration. The simple cell model is
developed using the material balance equation
dC.
Ci-l " Ci = dT" 1=1,2,3,...,N (10)
where C-j is the concentration of a component, T is dimensionless time, and N
is the number of cells. The advantages of.the model are: (a) a serial solution
of ordinary differential equations is required rather than a solution of a
boundary-value partial differential equation; and (b) transport phenomena,
chemical reactions or flow profiles can be easily added without changing the
mathematical form or difficulty (19). It does not predict the observed tail-
ing and asymmetry for pulsed systems. To account for this behavior, more
complex models which include stagnant zones have been developed (49).
Numerical Solutions
In addition to the methods previously discussed, many investigators (6,7,
76,91) have attempted to solve the diffusion convection equation. These
solutions are generally numerical solutions. Analytic solutions are possible,
i.e., when pure diffusion is considered (28).
54
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Warrick et al. (91) arrived at an approximate analytic solution for the
diffusion-convection equation which describes the simultaneous transfer of a
non-interacting solute and water during infiltra'tion. He assumes one-
dimensional steady flow in homogeneous soils. The finite difference method of
Hanks and Bowers (37) was used by Warrick (91) to simulate the water infiltra-
tion. Warrick felt that comparisons of predicted moisture contents and con-
centration profiles and field measured data were reasonable considering the
lack of homogeneity in the field.
Bresler and Hanks (7) combined the flow model of Hanks and Bowers (37)
and the salt model of Bresler (5) to develop a new model capable of describing
salt transport of non-interacting solutes in unsaturated soils under transient
conditions. They found that the computed concentration profiles had shapes
which were similar to profiles found in the experimental columns used for
comparison.
In the solution of the diffusion convection equation, the magnitude of
diffusion-calculated by the solution is often much smaller than the dispersion
(numerical dispersion) due to differencing of the convective term. Bresler
(6) eliminated the numerical dispersion by including higher than second order
differences. He found agreement in the shape and concentration values between
calculated and field measured water and salt profiles. Bresler (6) concluded
that the apparent agreement suggests that macro-scale theoretical approaches
were generally satisfactory for analysis and prediction.
Davidson et al. (17) solved the transport equation including a sink term
for simultaneous transport of water and exchangeable solutes through soil
under transient flow conditions. Water movement was simulated using an
implicit-explicit technique and the salt transport equation was solved using
an explicit method. Dispersion was calculated using the methods described by
a Freundlich relation. Separate equations were used to describe either adsorp-
tion or desorption. Equilibrium conditions were assumed to exist between
exchanging phases.
In addition to Dutt et al. (24), whose model is used in this study and
discussed in detail in Section 6, King and Hanks (43) have also developed a
salt transport model. King and Hanks (43) developed a detailed transport
model which combined the water and salt flow model of Bresler and Hanks (7)
and the inorganic chemistry model of Dutt et al. (24). The moisture flow
model of Hanks et al. (38) was modified to include a plant root extraction
A(Z) term. The moisure flow equation solved by King and Hanks (43) was
If = fz [K(6) If1 + A(Z) (11)
where e is volumetric water content, K(e) is hydraulic conductivity, Z is
distance, t equals time, H is piezometric head and A(Z) is plant root extrac-
tion term.
55
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The transport of salts in one dimension was expressed as
(e
it
where e is volume water content, c is concentration of solute, v is solution
flux, Z equals depth and t equals time.
In the derivation of Eq. 12, it was assumed that dispersion was absent
and no sources or sinks existed. The sink or source was treated implicitly as
a change of concentration of the salts present at each depth due to chemical
reactions occurring at that depth. The dispersion, which occurred in the
results of the simulations, was due to the method of computing salt flow.
The change in salt concentration within a depth increment was calculated using
the net solution flux. The concentrations of the influent and the solution
remaining in the depth increment were averaged to give the concentration for
the space and time increment.
Reactions included in King and Hanks' model (43) were: (a) the dissolution
or precipitation of gypsum; (b) the formation of undissociated calcium and
magnesium sulfates (CaSCty and MgSCty); (c) the dissolution or precipitation of
lime; and (d) cation exchange reactions for Ca++ and Mg++ and Na . The shapes
and values of field measured moisture profiles and computed moisture profiles
beneath an established stand of alfalfa compared quite well. The comparisons
of the profiles for computed and measured values of concentrations of ionic
species were found to be better for TDS than for individual species. With the
exception of King and Hanks' model, most of the transport models-discussed
considered either the transport of non-interacting solutes or adsorption of a
single solute. For the research in this investigation, it was required that
the chemistry portion of the transport model calculate: (a) the dissolutions
or precipitation of gypsum and/or lime; and (b) cation exchange reactions for
Ca++, Mg++ and Na+.
The fundamental chemical reactions and the stoichiometric relations
describing the aforementioned reactions have been known and studied for some
time. The application of computers to solve a system of equations which
describe a combination of these reactions has occurred only recently. Several
researchers (22,23,60,61,79) have investigated the chemistry of soil systems
which included gypsum and lime equilibria and cation exchange reactions.
Dutt (22) predicted the equilibrium concentration of Mg"1"1" and Ca++ in the
soil solution and adsorbed phase in a Ca++-Mg++-soil containing excess gypsum.
The concentration of Mg++ and Ca++ were predicted for the case of wetting the
soil with either distilled water or a solution containing Mg++ and/or Ca++
salts. The equations used to describe the exchange of the cation and the dis-
solution of gypsum were solved by a computer using a series of successive
approximations. Comparisons of measured and calculated values of Ca++ and
Mg++ concentrations in the soil solution were generally good. Dutt and
Doneen (23) used a computer to solve the equations to predict the concentra-
tions of Ca++, Mg++ and Na+ in saturated extracts of soils undergoing salini-
zation with waters containing Cl" and 50^" salts and one or more of the
cations, Ca++, Mg++ and Na*.
56
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Tanji (79) developed a computational scheme to predict ion association
and solubility of gypsum in simple and mixed aqueous electrolyte systems.
This computed model accepted as input data nonequilibrium solute concentrations
and considered simultaneously the Debye-Huckel theory, the solubility product
of gypsum and the dissociation constant of CaSO/j, MgS04, and sodium sulfate
(NaSCfy) to predict equilibrium concentrations without prior measurement in
the equilibrium state (79). Predicted cation activities and solubility of
gypsum were in agreement with values found in the study.
Oster and McNeal (60) used three models to compute the variation of soil
solution composition with water content for partially saturated soils. The
calculation began using laboratory data on the composition of the soil-
saturation extract, the cation exchange capacity of the soil, the percent water
at saturation, the field water content and the estimated partial pressure of
carbon dioxide in the laboratory atmosphere during the analytical determina-
tions. These data were used to calculate the concentrations and activity
coefficients of ion and ion pairs and the degree of supersaturation with
respect to calcite and gypsum. Sulfate-gypsum equilibria and HC03~-C03=-pH
equilibria were computed as subgroups. Cation exchange was not included. The
composition of the exchange phase was then initialized. Saturation-extract
data were related to field-water contents by multiplying the calculated dis-
sociated concentrations of each dissolved species by the ratio of the water
content at saturation to the field water. The calculations used to calculate
the concentrations in saturation extract were then repeated with cation
exchange included. Calculated values of electrical conductivity compared well
with field measured values.
Oster and Rhoades (61) used irrigation water compositions, leaching
fractions, aragonite and gypsum solubilities and the partial pressure of C02
to calculate drainage water compositions. The model assumed: (a) steady
conditions for chemical equilibria; (b) soil solution was saturated by lime;
(c) water was in equilibrium with 0.13 atmospheres (atm) C02; and (d) the
Debye-Huckel theory applied to mixed salt solutions when ion pair chemistry
was considered. The initial input to the model was the concentration of salt
in the drainage water obtained by concentrating the salts in the irrigation
water using the experimental leaching fractions. Equilibrium drainage water
compositions were determined by successive calculations of the concentrations
of each chemical species using appropriate equilibrium constants (61). Com-
parisons of measured and calculated concentrations of salts in drainage water
from lysimeters maintained at steady-state leaching were reasonably good. The
model was used to predict the salinity, sodic and pollution hazards of irriga-
tion waters in terms of minimum leaching fractions needed to maintain satis-
factory salinity and sodic levels.
In each of the models presented, the major problem encountered in the
simulation was developing the set of chemical reactions and constants which
properly described the soil system under investigation. Many of the reactions
discussed in the review of literature were included in the Dutt et al. (24)
chemistry model, i.e., Ca++-Na+ exchange, dissolution and precipitation of
gypsum and lime.
57
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SECTION 6
MODEL DESCRIPTION
The salt transport model used in this study was developed by Dutt et al.
(24) and has subsequently been modified by the Bureau of Reclamation, USDI.
Salt transport is computed by assuming that soluble species move freely with
water contained in the segment. The mass of salt moved into a segment from
adjacent segments is computed by multiplying solute concentrations (assumed
constant for any segment) by the appropriate flow volumes (24). The model
is composed of two primary components. The first component computes soil
solution flux using the Richards' equation. The flux from the first component
is input to the second section and is used to compute the .flow volume in the
chemical transport model. The second submodel computes the concentration of
the solution with depth needed to complete the transport calculation.
The spatial division of the soil-piant water system used in the computa-
tions is shown in Fig. 26. The segment sizes used in the simulations differ
between models; and an interfacing program has to be written to adjust for
these differences.
MOISTURE FLOW PROGRAM
Mathematical Basis
Soil homogeneity, air entrapment, hysteresis, thermal and chemical gra-
dients all affect the flow of water through soil. Incorporation of these
factors into a model requires extensive data and a degree of complexity not
warranted in a study of the scope being considered here.
In the flow program, the Richards' equation was used to solve one-dimen-
sional flow assuming a homogeneous soil profile, isothermal conditions, no
air entrapment and no hysteresis.
For the one-dimensional case with distance measured as positive downward,
Richards' equation is
36 _ j!
It ~ 3
—
9Z
(13)
Substituting the sum of suction head (h) and elevation head (z) for the
piezometric head (H) and completing the differentiation, Eq. 13 becomes
58
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MOISTURE FLOW PROGRAM
BIOLOGICAL-CHEMICAL PROGRAM
en
LOWER BOUNDARY
ROOT ZONE
HORIZONS
CHEMISTRY
HORIZONS
Figure 26. Spacial division of soil-piant-water system along a flow line.
(After Dutt et al., 24)
-------
-1)] • (14)
Equation 14 is transformed to an equation with e as the dependent vari
able by first applying the chain rule of differentiation to the gradient of
suction,
. 9z de 8z
and defining the diffusivity D(e)
D(e) = K(e) $ .
After substitution of these expressions into Richards' Eq. 14, the result is
A sink term (S) was added to the right-hand side and the equation used in the
model is
where 6 is volumetric moisture content, D(e) is soil moisture diffusivity,
K(&) is hydraulic conductivity, S is a sink term (volume of water consumed
per unit volume of soil per unit time), t equals time, and z is the space
coordinate in vertical direction.
Solution Technique
The finite difference approximation used in the model is
eW"-1
9j 9J _ rn1'-1/2 l*i +fl1-1 a1 A1'-1 \ 9A,*1-1 n1'-1/2
t ' LDj-l/2 (6j+l+ej+rej "9j }- 2AzKj+l/2"Dj-l/2 '
where the superscript "i" specifies the time step used to evaluate the vari-
able and the subscript "j" specifies the depth increment used to evaluate the
variable.
Using the grid system in Fig. 27, the combination of superscripts and
subscripts in Eq. 19 specifies the node and the value of water content used
in tfoe calculation. The value of water content being calculated is specified
as ej and integer or fractional values of the indices specify the step size
used to select the values of water content needed for the calculation. For
example, e]+l specifies the value of moisture for the next time step at depth j
60
-------
A,
D
E
i-I.H B
, Kj C
M,i+l
F
i,H
',j
i.jfl
, t*'.H
i+'ii
i+l.j+l
I
Figure 27. Grid system used for finite differencing of Richards' equation.
The finite difference approximation used for Richards' equation computes
the moisture content for the center of the grid as the average of the moisture
content occurring at the nodes of the grid, ABCD and CDEF in Fig. 27. The
approximation is backwards in time, which means that values of moisture con-
tent from the previous time step are used to calculate values for the present
time.
When the algebraic Eq. 19 is applied to each grid point, along with the
equations for the boundaries, a system of n equations in n unknowns is formed.
When the system of equations is put into a matrix, a tridiagonal matrix is
created which can be solved efficiently.
An implicit solution method developed by Richtmyer (71), which is a
special adaptation of the Gauss elimination procedure, uses a series of recur-
sion relationships and is the solution method used in the model. The algebraic
Eq. 19 to be solved by the model can be written in the form
* B.a. - C,e, , = D, (20)
where A, B, and C are the coefficients of the water contents given on the left-
hand side of the matrix equation. D is the right-hand side of the matrix
equation and contains known values of moisture content. Richtmyer (71) pro-
vided a solution to the system of equations as
6j •
(21)
where
BJ
Ao
- VM
J I1
(22)
61
-------
D. + C.F
j
. .
F1 = B - C E J 1 ] • (23)
J b L
Equations 22 and 23 and the condition E0=0 and F0=0 are used to complete the
solution. Ej and Fj can be calculated inductively in order of increasing j
(j=0,l,...,k). The value of Uj+] is given for j=k by the right-hand boundary
condition (in the model this corresponds to the lower boundary). The value
for 9j in Eq. 21 can be calculated inductively in order of decreasing j
Initial and Boundary Conditions
The previous discussion shows that the initial moisture content distri-
bution and the upper and lower boundary conditions must be specified. The
initial soil moisture profile can be uniform or nonuniform. The saturated
water content and lower limit of available water are also required.
Attempts to accurately simulate the boundary conditions in the field
resulted in a modification by the authors of the original program. The bottom
boundary was originally specified as a constant moisture content. If water
content at complete saturation is used, then the boundary condition represents
a water table fixed at that position. If no water table exists, the moisture
content can be specified. In any case, to complete the solution, the moisture
content must be known at the lower boundary.
Little or no drainage water from tile drains located in the test area was
evidence that a water table condition did not exist in the area being modeled.
Neutron probe data indicated that the moisture content at the lower boundary
was not constant. Plots of the moisture profiles showed that relatively
uniform values of moisture content existed below a depth of 1.5 m. This
indicated that the gradient of piezometric head was near unity in this region.
Dutt's program was modified to permit the moisture content at the bottom
boundary to vary in response to flow through the soil profile by forcing the
gradient of piezometric head to be unity. This was done by adding a node
below the bottom boundary of the profile and assigning the moisture content
of the bottom node to the extra node. The moisture content of the bottom node
is now computed using the same recursion relations as are used in the solution
of the remaining internal nodes. As a result, the moisture content can fluc-
tuate in response to the drainage and redistribution occurring in the soil
profile.
The upper boundary condition can be specified to simulate infiltration,
evaporation and zero flux. The evaporation and zero flux conditions can be
simulated by applying the sink term to the first node inside the upper bound-
ary. Infiltration is calculated as the flux between the boundary node and
second node using the diffusivity form of Darcy's equation:
q = K(9) - D(9) de/dz (24)
62
-------
In the model, when infiltration is calculated, the upper boundary is
given as moisture content until the water ponded on the surface has been infil-
trated. Dutt et al . (24) stated that the use of the moisture content boundary
condition was not expected to introduce significant error in the infiltration
computation. Philip (66) found that an error in* the calculated values of
infiltration rate and cumulative infiltration of 2% /cm of ponded water
resulted if the depth of ponding was not considered. If the model is used to
simulate the infiltration under conditions where the depth of ponding is min-
imal, or ponding does not occur, then the approximation of Dutt et al . (24)
is adequate.
Time Step
The simulation is advanced in time using time increments selected in two
ways. When infiltration is not occurring, the -time interval is specified as
input data. When infiltration is occurring, the time interval used is computed
internally using a relationship suggested by Hanks and Bowers (37), i.e.,
Ati
i+1 = ^ (25)
where At is the interval for the next time step, AZ is the segment size,
and FR1 is the largest value of flux occurring between any two nodes for
the previous time step. Flux (FR1) is calculated using the diffusivity form
of Darcy's law.
Hydraulic Parameters
The hydraulic parameters used in the model are assumed to be single
valued functions of moisture content (no hysteresis). Data available for
computing the soil hydraulic parameters included a soil moisture characteris-
tic and an estimate of saturated hydraulic conductivity. Attempts to collect
in-situ field data and laboratory studies of steady flow in short columns
were unsuccessful. As a result, the Brooks-Corey (9) relationship for con-
ductivity was selected for use in this study. The relationship used is
2+3X
K(Se) - Ks(Se) X (26)
where Ks is the saturated hydraulic conductivity, X is the pore-size distri-
bution index, and Se is the effective saturation. The effective saturation
(Se) is defined by
se = f^4|-) ' (27)
where 9R is water content at residual saturation and 6- is water content at
saturation. Substituting Eq. 27 in Eq. 26, the expression for the conductiv-
ity becomes
63
-------
K(6) = KS I ver 'er < eies (28)
\ O
K(e) = 0
which is the form used in the model. The pore-size distribution index t\] is
the negative slope of the straight line drawn through a plot of log *
as
is
The value of conductivity in the difference Eq. 19 is given by Kj+1/2.
The value indicated is that which occurs midway between nodes j and 1+1 Thp
value for K was calculated by evaluating the function at each node (j and II?)
and then averaging the computed values, i.e., Kj+1/2 = (Ki+K.-+1)/2 This
method of computing the value for the conductivity' is another modificat on
the authors applied to Dutt's model. *•«»«. ion
During the preliminary testing of the moisture flow model, it was found
that accurate simulation of field infiltration data and water content profiles
was not possible for the case under consideration. The functions K(e) and
D(e) used to compute flow were studied to determine their effect on the cal
culation of infiltration and flow. Originally in Dutt's (24) model, the con-
ductivity function (Eq. 28) was computed using the average value of water
content of adjacent nodes. For example, if the conductivity is calculated
tor two nodes which have volumetric water contents of 0.45 and 0 20 usina thn
average value 0 325, the conductivity is 0.075 cm/day. If the conductivity J|
calculated as the average of the values of conductivity at each node, the con!
.ductivity is 10 cm/day. Because of the nonlinearity of the conductivity^water
content relationship, averaging water contents before computing conductivities
gives too much weight to the lower water contents. uunaucnvities
Infiltration is computed in the model using the diffusivity form of
Darcy's law. One can expect, therefore, that the time of infiltration will
be sensitive to the flux computations. Using the above example of nodes with
water contents of 0.45 and 0.20 and Ax=15 cm, there is a 17% difference in
the calculated value of flux, assuming the same value for the term fD(e) ae/axl
in the flux calculations. If K(e) is 0.075 cm/day, the flux equals 53 cm/dav
and if K(e) is 10 cm/day, the flux equals 63 cm/day. Therefore, the method
of computing K(e) has a significant effect in the flow computation After
the method of computing K(e) and D(e) (which will be discussed later) was
changed, it was possible to more nearly simulate field data. Computed infil
tration times and water content profiles were roughly equal to measured values.
Hanks and Bowers (37) found, in their model studies, that better results
for horizontal infiltration into homogeneous soil were obtained when compared
to Philip's work, if the specific water capacity (C) was selected using a
value of moisture content estimated to occur at the end of the time step
This procedure was adopted to calculate the diffusivity. Hanks and Bowers'
(37) water content equation is
64
-------
i+1 / i i-1\ M «i
e' = (e.-e')Y + e.
where Y equals 0.7 or t/(t+3-l/2), whichever is larger.
The diffusivity has been previously defined as
(29)
(30)
where K is the conductivity and dh/de is the derivative of the capillary pres-
sure head with respect to water content. For the diffusivity to be consistent
with the theory used to develop the conductivity relationship, the value for
the derivative dh/de should also be computed using Brooks-Corey theory. Pres-
sure head can be derived from the Brooks-Corey theory using the functions:
se -
(31)
s = i.o
where Pb is bubbling pressure, Pc is capillary pressure and Se is effective
saturation. The equation for capillary pressure head is
!c = !b
pg pg
(32)
Without modification, these functions cannot be used to define dh/de, since
the derivative of the function is not continuous over the entire range of
pressure. A functional relationship proposed by Su and Brooks (78), which
gives pressure head as a function of saturation over the entire pressure range,
was used therefore instead of the Brooks-Corey relation.
The relation proposed by Su and Brooks (78) is a combination of two
Pearson Type VIII distributions that were found to match a soil moisture
characteristic. The function is
s-s
-m
bm/a
(33)
where Pr is capillary pressure, Pi is capillary pressure at inflection point,
S is water content saturation, Sr is residual saturation, m is shape factor
of the curve, a is the domain of saturation associated with concave portion
of the curve and b is the domain of saturation associated with convex portion
of the curve. The relation of the domains specified by the constants a, b,
and Sr is given in Fig. 28. If b in Eq. 33 goes to zero, Eq. 33 becomes
pc • Pi
S-S
-m
(34)
65
-------
Figure 28. Saturation domains used for fitting Su and Brooks1 parameters.
For this case, a = l-Sr, m = I/A, Pc = Pb and Eq. 33 reduces to
P P
_C = _b re i-l A
pg pg L ej
(35)
which is the Brooks-Corey equation for capillary pressure head.
Values of moisture content, not saturation, are calculated by the model
It becomes necessary, therefore, to make Eq. 33 a function of moisture con-
tent (e). This conversion was accomplished by defining the saturation as
S =
(36)
where e is the moisture content at saturation. This expression was substi-
tuted into Eq. 33 to give the pressure head as a function of moisture content
The resulting equation was differentiated with respect to e to give dh/de, i.e'.
dh = _ Pi m
de ~ pg e a
bm/a
es-e
v.
9-6.
• (37)
Equations 28 and 37 for conductivity and the gradient of pressure head
are substituted into the definition for diffusivity. The resulting relation-
ship defining diffusivity is
66
-------
m n bm/a
/2+3X N
(—— -m)
(e-ej (e-e)bH 1(^)^11 . (38)
The value for diffusivity (Dj+j,) used in the difference form of
Richards' equation corresponds to a value of moisture midway between nodes.
The method used to calculate the value of diffusivity is another modification
of the Dutt et al. (24) program. Originally, the model of Dutt et al. (24)
computed an average water content between 2 nodes and used the average value
of water content to compute the diffusivity. We replaced Dutt's average D(e)
with an integrated average value. The change was required to properly model
infiltration. Hanks and Bowers (37) found that cumulative infiltration was
changed markedly for small changes of diffusivity computed at water contents
near saturation. Their infiltration studies showed the need for weighted
diffusivity values which include the effect of the diffusivity at saturation
on the average value of diffusivity. Since the diffusivity - water content
relationship is also nonlinear, averaging the water contents at two adjacent
nodes prior to computing D(e) does not properly weight the value of D(e) at
higher levels of saturation. Using the example for two nodes at water con-
tents 0.45 and 0.20, the integrated average value of D(e) is 3194 cm^/day,
while the value of D(e) for e equal to 0.325-is 6.35 cm<7day. Calculating
flux without considering the contribution from gravity (q=D de/dx) with AX=
15 cm, for water content equal to 0.325, the computed flux is 0.1 cm/day and
with D(e) computed as an integrated average, the computed flux is 53 cm/day.
For this example, using water contents of 0.45 and 0.20 at adjacent
nodes, if the average value of water content 0.325 is used to compute K(e)
and D(e), the calculated values of flux between these two nodes will be 0.175
cm/day. Using the K(e) and D(e) functions included in the model by the
authors, the computed value of flux is 63 cm/day. Even though the example
used shows an extreme case, it serves to point out the importance of properly
accounting for the water content when computing the hydraulic functions K(9)
and D(e).
The integrated average diffusivity was computed using the expression
ei+l
n/^\ r D(e)d6 (39^
D(e) = / Q '.e ^y'
ave e. ej+l 6j
J
[a form used by other investigators (37, 77)]. The integration is completed
numerically, using Simpson's rule, between the values of moisture content
6j and e.., occurring at adjacent nodes. If the moisture content is less
tnan thejvalue of moisture content at residual saturation (er), the integra-
tion is divided into two parts. For Q<$r the integrated average diffusivityis
67
-------
r D2
D(0)de + / D(e)de
and the value of the integral for water contents below residual saturation is
zero. The diffusivity is not defined at saturation (e=e,). Therefore the
upper limit of the integration is a value of e = es - AS where AO is small
Subroutine CONUSE
Subroutine CONUSE is called by the main program described above to provide
values for the sink term (S). The value of evapotranspiration (ET) used for the
sink is either input data given as semi-monthly or daily (ET), or semi-monthlv
values computed within the program using the Blaney-Criddle formula. The sink
is a macroscopic root model which is distributed according to a user supplied
JW ^1™"- Jn th1'- W°rk' the distribution of t"e sink was given as 408, 30%
20%, and 10% in 30 cm increments. Water is withdrawn from the root zone in
proportion to the fixed distribution. Extraction is assumed to occur accord-
ing to this fixed distribution until the lower limit of available water content
is reached. The limit simulates the water content below which extraction by
roots cannot occur. The model has no mechanism to increase withdrawal from
wetter Portions of the root zone as do the models of Nimah and Hanks (59)
Gardner (32), and Molz and Remson (56), and thus, it lacks some realism avail-
aoie in other models. For studies which include the presence of a water table
this could represent a serious weakness. There are two other subroutines
included in this program which are used as bookkeeping routines to record the
results of the simulation and control the flow of data required for the simula-
tion. The generalized program is given in block form in Fig. 29.
BIOLOGICAL-CHEMICAL PROGRAM
This section is a summary of the work of Dutt et al. (24) and is provided
as source material. For a complete discussion of the chemistry and related
works, the reader is referred to Duttetal. (24).
of c,Jihe !>1o]091cal-c[iem1cal model, as constructed, includes two major areas
of so 1 chemistry. The first area, nitrogen chemistry, was developed usina
reaction kinetics so that the nitrogen transformations including Slcrob !l
?c*lvjty;.f?"ld bj Deluded. While nitrogen is an important element affect inq
soil fertility and plant nutrition, it will not be considered as a pollutant
in this study The major pollutants in the Grand Valley are salts, and for
this reason only the salt chemistry is considered.
The other area of chemistry considered, inorganic chemistry, includes
reactions involving ion exchange, solution-precipitation of slightly soluble
salts and formation of undissociated ion-pairs. In contrast to the nitroaen
species, the equations describing these reactions are based on equilibrium
chemistry, since the reaction times involved are assumed to be on the order
68
-------
<
Q
I
O
-------
of minutes or seconds (times which are less than the residence time of water
in a soil segment).
The chemical component of the Dutt et al. (24) model was developed
assuming that water flow and content are independent of any chemical process.
However, chemical process (dissolution, precipitation, etc.) depended on water
flow and water content in a soil segment. From a computation standpoint, the
water flow can be simulated independent of the chemistry and the results of
the simulations used in the chemical component. The mixing cell concept is
used with the water flow data to simulate solute dispersion and movement. It
is assumed that: (1) complete mixing occurs at each increment in time and
space; (2) each chemical process is independent of other processes over a time
step with respect to availability of component masses; and (3) the rate of
change of mass for each component is constant over a time step.
A generalized block diagram of the biological-chemical program is given
in Fig. 30. The program consists of three control routines (MAIN, EXECUTE,
COMBINE), five computational subroutines (TRNSFM, UPTAKE, XCHANGE, FL, EQEXCH)
and several subroutines which serve as accounting and input-output devices.
The routines of interest in this discussion include, MAIN, EXECUTE, COMBINE,
XCHANGE, FL (flow) and EQEXCH (equilibrium exchange).
The program sequence begins with program MAIN reading control and input
data and printing the same data, if desired. From MAIN, control is transferred
to routine EXECUTE which initiates the computations in the biological-chemical
program for each-time step, monitors application of fertilizer and organic mat-
ter and reads daily moisture flow values which were computed by the moisture
flow program. EXECUTE calls the COMBINE subroutine which controls the computa-
tion of chemical analyses for each depth increment and updates the masses of
salt in storage in a segment using moisture flow data from routine FL.
Routine XCHANGE includes chemical reactions in base saturated soils which
affect the solute composition of percolating waters. The primary assumptions
used in this routine are: (1) that the reaction rates of the chemical process
considered are much less than the residence times; and (2) that water entering
a segment equilibrates with any remaining solution, the slightly soluble salts
and exchangeable ions on the exchange complex. A generalized block diagram of
the logic of this routine is included as Fig. 31. During the initial time step,
the subroutine EQEXCH calculates the exchangeable ion concentration from the
initial soil analysis. The iteration process implied in Fig. 31 represents a
method of successive approximations which is used to solve the equations de-
scribing the chemical reactions. The computation is initiated with an approxi-
mation of the concentration of an ionic constituent and is completed when the
equilibrium constants of the involved reactions are satisfied within a
tolerance established by the program user.
The chemical constituents and the mathematical relationship used to
describe the chemical reactions included in the program are given below. The
justification and development of these relationships can be found in Dutt
et al. (24).
70
-------
X
u
<
UJ
fSTART BIOLOGICAL-A
V CHEMICAL PROGRAMS
PROGRAM MAIN
READ CONTROL AND INPUT DATA
STORE INITIAL SOIL-CHEM DATA
PRINT CONTROL AND INPUT DATA
(OPTIONAL)
SUBROUTINE EXECUTE
MAKE ANY FERTILIZER AND/OR
ORGANIC MATTER APPLICATIONS
INITIALIZE OR UPDATE SOIL
TEMPERATURES (WEEKLY)
READ MOISTURE FLOW DATA FROM
MAGNETIC TAPE
SUBROUTINE COMBINE
FOR EACH SEGMENT;
CALL EXCHANGE SUBROUTINE
CALL NITROGEN SUBROUTINE
CALL SOLUTE REDISTRIBUTION
SUBROUTINE
CALL PLANT-N UPTAKE SUBROUTINE
SUM CHEMISTRY CHANGES AND
UPDATE VALUES IN STORAGE
PRINT OR WRITE SPECIFIED VALUES
Q.
UJ
XX
1
(STOP BIOLOGICAL- A
CHEMICAL PROGRAM/
Figure 30. Generalized block diagram of Biological-Chemical Program.
(After Dutt et al., 24)
71
-------
f ENTER J -
CALL EQEXCH (FIRST TIME)
1
CALCULATE CaC03 SOLUBILITY CONSTANT AT
SPECIFIED MOISTURE CONTENT
i
CONSIDER SOLUBILITY REACTION CaS04 x2H20
Co •"+ S0^+ 2 H20
CONSIDER UNDISSOCIATED ION PAIR REACTION
CoS04 ^Ca+*-
1
CONSIDER THE EXCHANGE REACTION
2No++ Co- R =Ca** + 2Na~R
CONSIDER THE EXCHANGE REACTION
* + Co-R :£Co*
i
CONSIDER THE EXCHANGE REACTION
^ +NO-R ^ No* + NH4-R
CONSIDER UNDISSOCIATED ION PAIR REACTION
MgS04 ZT
CONSIDER THE SOLUBILITY REACTION
Coco
= Co •*••*•+ 2 HCO;
c
RETURN TO COMBINE IF EOUI->
LIBRIUM CONSTRAINTS SATISFIEDy
Figure 31. Generalized block diagram of subroutine XCHANGE.
(After Dutt et al., 24)
72
-------
Subroutine XCHANGE
Solubility and Precipitation of Gypsum --
Gypsum is a slightly soluble salt found in many soils in the western
United States and often included as a soil amendment in reclaiming sodic soils.
It is found in the soils of the Grand Valley and is of interest in this study.
The equilibrium equation for gypsum is
CaS04 x 2H20 J Ca++ + S04= + 2H20 . (41)
The equilibrium concentrations for Eq. 41 in soil-water systems given
either initial concentrations or approximations of the constituent concentra-
tions are calculated using
x2 + Bx + C = 0 (42)
where x equals the change in concentration of Ca and S04~ to reach equilib-
rium. The coefficients are given as
B = CCa + CS04
p -» p I p I I/ O
~ fa <^n ~ 9P/v
\jO oU/i O'/ y
-------
- KSP/KD
t
Ca - Mg Exchange -- ++ ++
The equation used to calculate the Ca - Mg exchange process is
Ay2 + By + C = 0 (47)
where y is the change in concentration of Mg and Ca to reach equilibrium.
The constants and coefficients are defined as
A '
B = *(% + KMg-CaN'ca> + CCa = KMg-CaCMg
6 is the liters of water per grams of soil; KMg-Ca is the Ca-Mg exchange
constant; and N1 is the approximation of initial concentration of
exchangeable ion indicated by the subscript.
Ca - Na Exchange —
The Gapon equation was used to describe the Na+-Ca++ exchange. The equa
tion for the equilibrium condition is
Ax4 + Bx3 + Cx2 + Dx + E = 0 (48)
wherp x equals change in concentration of Ca++ to reach equilibrium.
B =
cCa + NNaB> ' 4KCa-Na8NCa '
D = "Na^/z'^Ca + NNaB> + 2KCa-NaNCaCNa(26NCa + CNa'
F = N'2pi v _ 1/2 pi2Ni2
fc NNaLCaYl/2 KCa-NaLNaNCa
where Y-]/2 = Y1/Y2 Yl = monovalent activity coefficient.
Dissociation of CaC03 in Water —
The dissociation of CaCOo is given as
CaC03 t Ca++ + C03= . (49)
74
-------
Dutt et al. (24) state that the C03= concentration is a function of C02 par-
tial pressure and HCOV" is usually the predominant form of (£3 occurring in
soil-water systems. The following reaction is considered in the model:
H9CO. + CaCO, Z Ca++ + 2HCO ~ (50)
£ O o O
with
2
aCaaHCO~
KK=. - - (51)
H2C03
or
KK = ^- (52)
K2
where a is the activity coefficient of subscripted ion; KK is the thermodynamic
equilibrium constant; K, and K2 are the first and second acid dissociation
constants for H2C03; and, KSP is the thermodynamic solubility product.
If an equilibrium system is at constant C02 pressure and the activity
of the uncharged species is unity, Eq. 51 becomes
Ki HoCOo
_ 2 3
where Y| is the monovalent activity coefficient; 72 1S tne divalent activity
coefficient; and C is the equilibrium concentration of subscripted species.
The equation describing the dissociation of CaCOs was developed by substitut-
ing the stoichiometric relations
CCa ' CCa + Z (54)
CHC03 ' ^003 + 2Z (55)
I
into Eq. 53. Where Cx are concentrations of species before equilibria existed
or approximate concentrations of indicated species, and Z is the change in
moles to reach equilibrium. The resulting equations are
AZ3 + BZ2 + CZ + E = 0.0 (56)
where,
A = 4.0; B=4.0(C+C); OC 4.0C
Dutt et al. (24) investigated the change in CaCOo solubility with changes
in soil moisture and included this representation in the model. Through
laboratory determination of Ca++, C03= and HC03" concentration in extracts
75
-------
at three moisture levels (saturation, 100% and 500%) from six calcareous
soils, a functional relationship was derived to describe the solubility. The
derived relationship was then assumed to hold at field moisture levels and was
used in the model.
Activity Coefficients—
Debye-Huckel theory was used in the model to calculate activity coeffi-
cients. To calculate single ion activities the equation is
where Z is the valence of ion i and
n 9
v = 1/2 _£ C.Zf,
and n is the total number of species present. Only two activity coefficients
are needed since only mono and divalent ion species are considered in the
model.
Subroutine EQEXCH
by
/c« Putt et al- ^ 1ncluded 1n EQEXCH the effects of sulfate as an ion
(S0|-) and undissociated CaSO* and MgS04 on the exchangeable Na+, Ca++, and
Mg-1"1- in the system. The total sulfate, Ca++ and Mg++ in solution are given
CS04T = CS04 + CCaS04 + CMgS04 <58)
CCaT = CCa + CCaS04 ' (59)
CMgT = CMg + CMgS0
The thermodynamic equilibrium constants for equilibrium between the undis-
sociated species in solution and the appropriate ions are
KCaS04 = CaS04/CaS04 (61 )
KMgS04 = "9S04/MgS04 (62)
Combining Eqs. 59, 60, 61, and 62, the concentrations of CaS04 and MgS04 can
be calculated. When these expressions are entered into Eq. 58 and assuming
the divalent activity coefficients of MgS04 and CaS0/i equal, the equation
necessary to calculate the concentration of Ca++ and Mg++ is derived. The
equation is
Ax3 + Bx + Cx + D = 0 (63)
76
-------
where x = C
" ~ YO I (KpaQn + KM cn ) + YO(CM_T + Cr T
taoU, MgbU/, d MgT CaT
i ^r
C = K
CaS04KMgS04 + Y2 [ CMgTKCaS04 + CCaTKMgS04 " CS04T(KCaS04 + KMgS04)]
D = " CS04TKMgS04KCaS04
The Ca-Mg exchange is given by
= K (64)
aMg ' %
where N is the concentration of the subscripted exchangeable ion.
The Gapon equation
aNa _ NNa
-
is used for the Na-Ca exchange. The total concentration of exchangeable ions
(Nj) then is
NT = NNa + NCa + NMg • (66>
Using Eqs. 64, 65, and 66, the equation for exchange of calcium is
NTar Ko K-i aM
., _ i ta t , I Mq , i te-i\
1 . (67)
aNa aCa
Once the activity coefficients, ionic concentrations for an equilibrium
extract for Caf+, Mg++, Na+ and the total exchangeable bases are known, the
exchangeable Ca++ can be calculated and, in turn, the exchangeable Na+ from
Eq. 65 and the exchangeable Mg++ from Eq. 66. In practice the exchange capac-
ity is assumed to equal Nj.
Exchangeable NH4+ is computed using
77
-------
CNH. NNH
ST K° t (68)
with K assumed equal to 0.22.
The equilibrium exchange routine was tested (24) using the experimental
data of Paul, Tanii and Anderson. Plots of measured values for exchangeable
Ca++, Mg++ and Na* against calculated values showed a good correlation between
the experimental and calculated results. The favorable correlation between
the observed and calculated values indicated the procedure for calculating
the exchangeable ions is of use in the model (24).
The preceding discussion has outlined the chemical reactions and the
equations considered in the model. Once these computations have been made for
all segments for a time interval, the time is incremented. The moisture move-
ment for the next time is read, and the new values for the equilibrium concen-
trations are computed.
Subroutine FL
The mixing cell concept is used to calculate salt transport in the model.
The soluble species are assumed to move with the soil solution and to be at
their equilibrium concentrations throughout the entire length of the cell.
The length of the cell corresponds to the segment size used for the computa-
tions and remains constant. Flow data from the moisture flow program supply
this subroutine with the volume of water remaining in the segment and the
volume of water transferred between the segments for each time step.
Subroutine FL combines concentration and flows to compute the incremental
transfer of salts into or out of a segment. Once the transfer is complete,
the value of the mass of ion in storage per segment is computed. After the
transfer and update of salt mass is completed, time is incremented and the
solution proceeds.
The lower boundary condition of the flow model assumes that the solute
concentration in the water adjacent to the lowermost segment is the same as
in the lowermost soil segment for the last time step. Surface inputs are
simulated by assuming that surface additions of chemicals mix completely with
the applied water. The infiltrating water and its dissolved constituents are
then treated as inputs to the first segment.
Input data required to run this model include chemical analysis of irri-
gation water, chemical analysis of soil profile, fertilization and organic
matter treatment and soil temperature when nitrogen chemistry is considered.
The required soil chemical analysis includes concentrations of NH4+, N03~,
UREA, Ca++, Na+, Mg++, HC03=, Cl", C03=, and gypsum plus the exchange capacity,
bulk density, the presence of lime and the moisture content of the saturation
extract. The soil analysis is required for each horizon identified within
the soil profile. The irrigation water analysis includes the concentrations
of NH4+, N03-, Ca++, Na+, Mg++, HC03~, CT, C03% and S04=.
78
-------
SECTION 7
MODEL RESULTS
This section is divided into three topics. The first topic discusses
the calibration and adaptation of the moisture flow model. The second topic
deals with the comparison of the chemical model with field data and the se-
lection of the parameters used for the final simulations. The last topic
presents the results of the simulations of hypothetical irrigation treatments
usjd to evaluate the effect of irrigation on salt transport.
MOISTURE FLOW MODEL
The flow model, which was discussed in Section 6, computes flow in one
dimension assuming homogeneous isotropic soils, isothermal conditions and no
hysteresis. Data required as initial input to run the program include: (1)
upper and lower boundary conditions; (2) an initial soil moisture distribution;
(3) the hydraulic conductivity and diffusivity as functions of water content;
and (4) values for the crop evapotranspiration, root distribution, and rooting
depth.
To calibrate the flow model, the upper boundary conditions were formu-
lated to simulate the depth of water applied, duration of application and
frequency of application that were observed in the field during selected
irrigation intervals. The desired lower boundary condition required a modi-
fication of the original program. The lower boundary condition was originally
given in the model as a fixed moisture content, which could be used to sim-
ulate a water table or any moisture content desired by the user. Lack of
drainage water from the test plot and neutron probe data taken on the test
plot indicated that a water table condition did not exist in the area being
modeled. Field data given in Table 1 for the moisture content profile over
the 1.5 to 2.13 m depth exhibited fairly uniform values. This uniformity of
moisture content indicated that the nydraulic gradient which existed in the
field was probably close to unity. The data in Table 1 show that the values
of moisture content over the 1.5 to 2.13 m depth interval fluctuated slowly
over the season. A method of treating the boundary condition was developed
which forced a unit gradient to exist at the lower boundary between the bot-
tom node and an imaginary node. This boundary condition permitted the mois-
ture content at the bottom boundary to vary with time in response to irriga-
tion. The suitability of the modified boundary condition and some alternative
boundary conditions will be discussed in conjunction with the calibration.
79
-------
TABLE 1. MOISTURE CONTENT PROFILES AT A DEPTH OF 1.52 TO 2 13
METERS FOR SELECTED PLOTS
Plot
17
18
19
21
25
27
Date
6/9
8/6
8/18
9/3
9/15
7/17
7/23
8/5
8/15
9/2
9/18
7/8
7/14
7/22
8/8
8/18
8/25
9/10
6/20
6/24
7/11
7/19
7/29
8/8
8/12
8/27
9/4
9/15
7/9
7/18
7/25
8/12
8/15
6/19
6/23
7/18
8/22
0
1.52m
0.32
0.35
0.35
0.33
0.33
0.30
0.30
0.30
0.32
0.29
0.29
0.30
0.32
0.31
0.32
0.31
0.31
0.30
0.32
0.32
0.36
0.36
0.36
0.36
0.33
0.35
0.35
0.35
0.33
0.36
0.34
0.35
0.35
0.28
0.28
0.31
0.32
9
1.83m
0.30
0.31
0.31
0.31
0.31
0.29
0.31
0.32
0.32
0.30
0.30
0.31
0.33
0.33
0.34
0.32
0.32
0.31
0.29
0.29
0.31
0.31
0.30
0.30
0.30
0.32
0.30
0.33
0.29
0.33
0.32
0.32
0.32
0.32
0.32
0.34
0.34
6
2.13m
0.31
0
0
0
0
0
0
0
0
0
0
0.
.32
.32
.31
.31
.31
.31
.33
.33
.32
.32
33
0.33
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
35
32
34
34
33
33
34
34
32
35
35
35
36
32
32
35
35
Plot Date
29 6/21
6/27
7/10
7/20
30 7/30
8/11
. 8/18
8/28
9/4
9/11
31 6/17
7/4
7/28
8/18
33 6/19
6/24
7/9
7/17
7/21
7/28
34 6/21
6/28
7/23
8/1
8/26
35 6/17
6/23
7/21
7/27
7/30
8/5
8/19
8/28
39 6/18
6/27
7/9
7/17
7/25
8/4
8/12
8/24
1
0
0
0
0
0
0
0
0
0
0
0
0
e
.52m
.31
.32
.34
.34
.35
.35
.34
.36
.32
.35
.30
.36
0.35
0.36
0.29
0.33
0.34
0.34
0.35
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
35
28
31
30
31
31
30
35
33
35
33
33
33
33
30
35
33
33
34
34
34
34
1
0
0
0
0
0
0
0
0
0
0
e
.83m
.29
.31
.32
.32
.32
.32
.32
.33
.31
.32
0.32
0.38
0.33
0.36
0.30
0.30
0.33
0.33
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
33
33
32
34
33
34
34
32
36
34
34
34
34
34
34
33
33
34
34
31
35
34
34
9
2.13m
0.33
0.33
0.34
0.34
0.35
0.35
0.36
0.36
0.35
0.37
0.36
0.40
0.36
0.39
0.33
0.35
0.38
0.38
0.37
0.37
0.32
0.33
0.33
0.33
0.36
0.34
0.34
0.35
0.35
0.35
0.35
0.35
0.35
0.35
0.35
0.36
0.36
0.31
0.34
0.38
0.38
80
-------
The sink strength equaled the estimated daily values of evapotranspi-
ration for the corn grown in the test plot. Evapotranspiration estimations
were made using pan evaporation data which had been modified by the crop coef-
ficient to account for the crop growth stage. The assumed extraction pattern
for the roots was 40% from the top foot, 30% from the second foot, 20% from
the third foot and 10% from the fourth foot of the soil profile. The 4-ft
rooting depth was assumed fixed for the entire season.
The initial moisture distribution was specified using field data collected
with neutron probe equipment. The initial moisture content profile used for
calibration corresponded to the field moisture content which existed at the
beginning of the calibration period.
The hydraulic conductivity and diffusivity functions used in the study
were Eqs. 26 and 38, which were developed using empirical relationships derived
from the soil-water characteristic. The hydraulic conductivity function was
developed from the Brooks-Corey relationships and the diffusivity was developed
using both Brooks-Corey and Su-Brooks representation of the soil -water char-
acteristic. To complete the development of the hydraulic properties, the
parameters in the Brooks-Corey empirical representation of the soil -water
characteristic and in the Su-Brooks equations were determined by fitting to
field data. The Brooks-Corey representation of the soil moisture character-
istic is defined by Eq. 31, where Se is the effective saturation defined by
Eq. 27, er is water content at residual saturation, es is water content at
full saturation and e is water content. The values of A, S, and Pb/pg (bub-
bling pressure head) were calculated using a computer program (SORPT) developed
at Colorado State University by Dr. A. T. Corey. Values of capillary pressure
head and corresponding values of saturation taken from the measured soil -water
characteristic were used by program SORPT to calculate x, Sr, and Pb/pg. The
graphical representations of the field data and the Brooks-Corey curves are
given in Fig. 32. Field data for the soil-water characteristic are given in
Appendix A. The computed parameters for the Brooks-Corey equations represent
the shape and values of field water content well over the concave portion of
the curve and diverge over the convex portion of the curve, as would be
expected.
The definition of diffusivity {D = K [d(Pc/pg)]/de} requires that both
the hydraulic conductivity and the derivative of the capillary pressure be
known. The Brooks-Corey functions can not be used to define the diffusivity
entirely, because the derivative of the function is not continuous over the
full range of capillary pressure head. The expression for capillary pressure
head developed by Su and Brooks was used to compute the derivative of the
capillary pressure head.
The equation 'for the capillary pressure head used in the study
'c - l t r \-m / 1-S xbm/a . _ e
;pg ~ pg ( ~T~ '• ( T > • s ' e
81
-------
1000
900
800
• Field Data
— Brooks-Corey Theory
— Su-Brooks Theory
s^—1
•8
1-0
Figure 32. Soil-Water characteristic used in study.
82
-------
was fitted to the soil-water characteristic by a trial and error process.
First, the inflection pressure head (P^) was selected and then, from the
relationship for the parameters
a + b + Sr = 1.0- (70)
in Fig. 28, the values for a and b were calculated. The value of m was
computed by selecting a value for S, and its corresponding value of capillary
pressure head, Pc/pg, substituting them in Eq. 69 and solving for the value
of m which satisfied the equation. The first approximate characteristic was
checked by entering values of saturation, S, calculated values of a, b and m,
and an estimate of P^ into Eq. 69 and computing values of P_/pg. The com-
puted values of Pc/pg were compared to the corresponding values of Pc/pg at
the same water content from the field soil-water characteristic. Values of
P-J were adjusted and the above process was repeated until the fit was con-
sidered satisfactory. The graphical representation of the function used in
this study is given in Fig. 32. The value of residual saturation, Sr, used
in Eq. 69 was computed at program SORPT. Hanks and Bowers (36) found that
values of _diffusivity at or near saturation were most important in calculating
infiltration. Therefore, the convex section of the characteristic was given
the most weight in fitting the curve. This gave the best approximation of
the derivative (d(P /Pg)/de) in the regions of higher saturation, and, we
hope, the best values for the diffusivity. The curve fits the field data
quite well over the convex section of the curve, but diverges on the concave
side of the inflection point (to where K(e) and D(e) are quite low),
The values of parameters, for the Brooks-Corey and Su-Brooks functions
used in this study, are given in Table 2.
TABLE 2. PARAMETERS USED IN HYDRAULIC CONDUCTIVITY AND DIFFUSIVITY
FUNCTIONS
Brooks-Corey
X = 0.651
Sr = 0.538 9r = 0.242
Su-Brooks
a = 0.24
b = 0.222
es = 0.45
The calibration of the model was necessary to implicitly incorporate the
variability of field soil properties in the simulation. Field variation of
properties occur both horizontally and vertically throughout the profile.
The variation can be measured by extensive sampling and testing in the field.
However, this was not done in the current study. Instead, the soil-water
characteristic was calculated using undisturbed soil samples taken in only a
small area on a single plot and the saturated hydraulic conductivity, Ks, was
adjusted until calculated infiltration depth and time agreed with field
observation.
83
-------
The soil-water characteristic was calculated using two undisturbed soil
samples taken at each 30 cm depth through the profile. Fourteen samples were
used at each value of pressure head tocalculate the water content. The cal-
culated values of water content at a given pressure head were averaged to give
a single representative water content. By averaging in this manner, the soil-
water characteristic incorporated, in an approximate way, the vertical vari-
ability occurring in this region of the field. The area selected to gather
data for the characteristic is similar to the remainder of the field with
respect to soil type, and it is believed that the soil-water characteristic
should be reasonably representative of the average characteristic for the
field.
The average soil-water characteristic was used to develop the hydraulic
functions K(e) and D(e), except for the value Ks, which was selected during
the calibration procedure. Field observations of water content profiles and
irrigation data were used with different values of Ks in a series of simula-
tions to select a value for Ks.
The procedure was to select a value for Ks (the only hydraulic parameter
remaining unspecified) and to calculate the cumulative infiltration and dis-
tribution of water content in the soil. The calculated time required to infil-
trate a prescribed depth of water was compared to the observed time required
in the field. Also, the calculated and observed water-content distributions
were compared during infiltration and in the subsequent period of redis-
tribution. The observed water-content distribution was an average one;
obtained by averaging measured water contents for corresponding depths at four
locations in the field plot.
The above procedure was repeated several times, and the value of Ks was
determined which gave the most satisfactory agreement between calculated and
observed water-content distributions, infiltration, and changes in soil-water
storage. Even though a completely objective method for expressing the optimum
agreement for all three comparisons was not derived, it was possible to select
Ks so that all three comparisons were considered satisfactory, as will be
shown in subsequent paragraphs.
Any effects on infiltration and soil-water distribution caused by spatial
variability of the soil-water characteristic and not included in the char-
acteristic used in the calculations was lumped into the adjusted value of Ks
by this procedure. Strictly speaking, therefore, it is not certain that
either the soil-water characteristic or Ks are actually the appropriate aver-
ages. On the other hand, the fact that, by adjusting Ks only, satisfactory
comparisons for water balance, water distribution, and infiltration strongly
suggests that the soil-water characteristic and Ks used in the calculation
are nearly the correct, spatially weighted parameters.
The initial soil moisture distribution, field moisture distribution four
days after irrigation, and corresponding data from the calculations for the
values of Ks used in the calibration are given in Table 3. Infiltration data
for the test plot are also given in Table 3. A value of. KS = 20 cm/day was
found to yield calculated infiltration times that most nearly matched the
measured infiltration time. The moisture profiles for the field data and the
84
-------
TABLE 3. MOISTURE CONTENT PROFILES FROM PLOT 30 USED FOR MODEL
CALIBRATION
Volumetric Moisture Content
Depth
(cm)
0
15
30
46
61
76
92
107
122
137
152
168
183
198
214
229
244
Initial
Field
Moisture
(1 day before
irrigation)
0.248
0.268
0.253
0.217
0.191
0.240
0.289
0.260
0.283
0.246
0.282
0.330
0.322
0.326
0.329
Final
Field
Moisture
(4 days after
irrigation)
0.32
0.34
0.32
0.28
0.27
0.29
0.31
0.29
0.31
0.26
0.29
0.33
0.33
0.33
0.33
Final
Model
K =20r^-
s day
0.298
0.310
0.321
0.328
0.332
0.332
0.275
0.236
0.283
0.269
0.282
0.247
0.288
0.309
0.316
0.320
0.323
Final
Model
v — i c^m
Nc l;3,ia>,
s oay
0.302
0.315
0.326
0.334
0.337
0.337
0.246
0.236
0.283
0.269
0.282
0.246
0.287
0.312
0.318
0.321
0.320
Final
Model
I/ =-|f£!!L_
s day
0.308
0.323
0.334
0.342
0.344
0.341
0.205
0.230
0.280
0.269
0.282
0.246
0.286
0.314
0.321
0.323
0.325
Time of Infiltration
(Days)
0.2
0.2
0.3
0.4
Total Change in Storage
(cm)
5.0
4.3
4.5
6.37
Simulated Date - Day 170-175
Et = 1.48 cm
Depth of Irrigation 9.65 cm Day 171
simulation with Ks = 20 cm/day are plotted in Fig. 33. While the profile
shapes do not match exactly, the fit is reasonable considering the soil
is not homogeneous and hysteresis was not included in the calculations. The
change in storage was computed using the plot of moisture content versus
depth in Fig. 33. The field change in storage was 5 cm of water and the
storage change for the simulation was 4.3 cm,using a value of Ks = 20,cm/day.
Field data from another plot were selected and used with a value of Ks = 20
cm/day. The initial data are presented in Table 4 and the graphical presen-
tation is given in Fig. 34. Again, the moisture distribution is not an exact
match, but it is reasonable. In this simulation, water storage change in the
field was 8.61 cm and the model simulated a storage change of 8.26 cm. On
the basis of these simulations, a value of Ks = 20 cm/day was selected for
use in the final simulations.
85
-------
6 - Volumetric Moisture Content
0 -10 -20 -30 -40
•50
30-5
61-0
91-5
~ 122-2
E
152-5
183-0
213-5
244-0
• Initial Field Data and
Model Data-Day 170
o Field Data - Day 175
a Model Data-Day 175
Figure 33. Moisture content profiles in Plot 30 used to calibrate
the flow model. •
86
-------
9 - Volumetric Moisture Content
•10 -20 -30 ' -40
30-5-
61-0
91-5
^ 122-2
152-5
183-0-
213-5
r ,
1 1
1
If
\
50
\
\
• Initial Field Data and
Model Data - Day 190
o Field Data-Day 199
o Model Data - Day 199
Figure 34. Moisture content profiles in Plot 25 used to calibrate
the flow model.
87
-------
TABLE 4. MOISTURE CONTENT PROFILES FROM PLOT 25 USED FOR MODEL
CALIBRATION
Depth
(cm)
15
30
45
61
76
91
106
122
137
152
167
183
198
213
Volumetric
Initial
0.283
0.333
0.323
0.331
0.325
0.325
0.333
0.339
0.331
0.302
0.292
0.318
0.318
Moisture
Field
Final
0.332
0.355
0.344
0.356
0.352
0.342
0.352
0.356
0.364
0.350
0.331
0.339
0.357
Content
Model
Final
0.315
0.324
0.331
0.337
0.342
0.346
0.349
0.352
0.355
0.358
0.360
0.362
0.365
0.367
Time of Infil-
tration (Days)
0.2
0.2
Change in Stor-
age (cm)
8.61
8.26
Simulation Dates - Day 190-199
KS = 20 cm/day
Depth of Irrigation 10.44 cm Day 191
8.48 cm Day 193
Et = 7.92 cm
The field moisture profiles plotted in Figs. 33 and 34 show that the
assumption of a unit hydraulic gradient existing at the lower boundary condi-
tion was quite good. The agreement between the field moisture profiles and
the simulated profile in Figs. 33 and 34 indicates that a unit gradient lower
boundary condition was a good representation of the actual boundary condition.
The effect of the unit gradient boundary condition on values of moisture con-
tent at the lower boundary was checked for a 150-day simulation period. Data
for the moisture content at 2.13 m from a simulation using a 14-day irrigation
interval and a 20% leaching increment are given in Table 5. The depth of the
irrigation was calculated as the sum of the water depleted by evapotranspira-
tion during the 14 days preceding the irrigations plus the leaching incre-
ments. Data in Tables 1 and 5 show that the fluctuations in moisture content
at 2.13 m for field and simulated data are small.
Other boundary conditions considered were: (1) fixing the value of mois-
ture content at the lower boundary; and (2) specifying a time varying moisture
content for the lower boundary. Field data indicated that the moisture
88
-------
TABLE 5. SIMULATED VOLUMETRIC MOISTURE CONTENT AT 2.13 METERS USING
14-DAY IRRIGATION SCHEDULE AND 20 PERCENT LEACHING INCREMENT
Day
144
155
170
190
210
6
0.35
0.34
0.33
0.32
0.32
Day
230
250
270
293
e
0.33
0.33
0.33
0.33
content changed at the lower boundary during an irrigation season and a fixed
value of moisture content would not be an accurate representation of the field
situation.
Changing moisture content with time was also considered as a lower bound-
ary condition. This method would provide an accurate representation of field
conditions provided that the moisture content on the boundary was known as a
function of time. A condition specifying a value of moisture content at the
lower boundary as a function of time has one serious drawback, however. The
values of moisture content at the lower boundary will not be known as a func-
tion of time unless they are measured under all conditions used in the
simulation. This would require extensive experimentation and obviate the need
for the model in the first place. The simulations of moisture flow used to
calibrate the model indicated that the soils in the Grand Valley could be
adequately modeled with the present program.
CHEMICAL MODEL
The chemical model calculates the chemistry of the soil solution and the
transport of the salts. Computation of salt transport uses the moisture flow
data generated by the moisture flow model. The data requirements for the
chemistry subroutines in the model are: (1) the irrigation water chemical
analysis; (2) the number and depth of the chemistry horizons in the soil pro-
file; (3) the initial soil analysis of each horizon; and (4) fertilization
and irrigation dates. The soil analysis required for each chemistry horizon
includes_the concentrations of N03-, MH4+, urea, Ca++, Ma+, Mg++, HC03-,
CT", C0^~, and _S04= ions. Additional soil properties required include: (1)
the cation exchange capacity of the soil; (2) the concentration of gypsum in
the soil; (3) the bulk density of the soil; and (4) the presence of lime.
The irrigation water analysis includes the same ions as does the soil analysis
except for urea. If the partial pressure of C02 and the exchange constants
for the Ca++-Mg++ and Ca++-Na+ exchanges are known, these values can be used
in the chemistry portions of the model. Otherwise, estimates are supplied in
the model for the Ca++-Na+ and Ca++-Mg++ exchange constants. The partial
pressure of C02 is not needed to run the model; it is an optional data
requirement.
89
-------
The chemistry model was developed assuming that all chemical reactions
reached equilibrium instantaneously. Since the reaction times for the pro-
cesses considered in the model (ion exchange, solution-precipitation of slight-
ly soluble salts, and formation of ion pairs) are on the order of seconds or
minutes (13), the assumption of instantaneous equilibrium should be good.
The validity of the equilibrium assumption as it applies to gypsum will be
discussed later in this section.
Dutt et al. (24) validated the nitrogen portions of the model, but made
no attempt to verify salt predictions of the model. Previous work indicated
that the approach for the salt chemistry sections of the model was adequate.
Comparison of observed soil chemistry with predictions from the chemical
model was accomplished as a single plot for which the available data included:
(1) the initial soil chemistry for the soil profile; (2) the chemical analysis
for a set of soil solution samples taken daily or at least weekly; (3) the
initial and final soil moisture profiles; and (4) the irrigation treatment.
Data from plot 23, taken from one of the vacuum extractors units, were used
for the comparison.
The chemistry model uses a single chemical analysis for the irrigation
water. Therefore, an average analysis of the water used for irrigation of
the test plots in 1975 was used both for the calibration of the model and the
hypothetical simulations. The average chemical analysis of the irrigation
water used in the model and the analysis of June and October irrigation water
for 1975 are given in Table 6. The data show a wide range of values for Cl~
and S04= concentrations.
TABLE 6. 1975 IRRIGATION WATER ANALYSIS (ppm)
Average
Ca++ -
Na+ -
HC03" -
Cl" -
43.5
47.25
134.0
61.0
June
34
17
139
38
Oct.
63
110
176
160
Average June
so4=
Mg++
Total
- 57.3
- 0.0
- 10.3
= 353.35
16
0
7
Oct.
182
14
19
The soil profile was divided into seven chemistry segments each 30-cm thick.
The initial soil properties and soil chemical analyses were assumed uniform
throughout each 30-cm segment. The segments were subdivided into segments
15 cm thick (using the field data for the 30-cm segments) to provide the com-
putational segments used for the simulations and calibration of the model.
The initial chemical profile and soil properties used to run the model for
the investigation into its validity and, later, the hypothetical simulations
are listed in Table 7. The irrigation, evapotranspiration and initial soil
moisture data (taken from field data) used are presented in Table 8.
90
-------
TABLE 7. INITIAL CHEMICAL PROFILE AND SOIL DATA FOR PLOT 23, MATCHETT
FARM, 1975
Profile Chemical Analysis
HZN or Ca
segment meq/1
1
2
3
4
5
6
7
24.
9.
15.
31.
25.
27.
24.
95
68
52
04
76
60
70
Na
meq/1
8.02
9.43
8.23
1.57
7.15
6.61
6.50
Mg
meq/1
7.56
3.86
3.68
4.28
2.53
4.93
6.29
HC03
meq/1
8.10
3.48
2.02
2.43
2.47
2.36
1.55
Cl -
meq/1
4.39
8.84
6.29
3.96
4.05
4.82
3.36
C03
meq/1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
S04
meq/1
30
7
22
28
34
36
28
.05
.90
.75
.55
.76
.00
.00
N03
meq/1
0.
0.
0.
0.
0.
0.
0.
03
21
32
18
27
02
13
Soil Properties
HZN or
segment
1
2
3
4
5
6
7
Lime
yes
yes
yes
yes
yes
yes
yes
Gypsum
meq/1 00 qm
1
1
1
5
1
15
21
Cation exchange
capacity meq/1 00 gm
14
15
13
16
16
16
15
The data used for comparison covered a 30-day period from June 15 to July
14 (day 166-196). The computed concentrations of Ca++, Mg++, Na+, HC03,
$04=, Cl~ and TDS were compared to the soil solution extracted at 1.1 m depth.
No drainage occurred from the drains which surround Plot 25 during the time
period used in the comparison. Plot 25 had a treatment of L-5-4, which means
low fertility (50 ppm of nitrogen in top 30 cm of soil), 50% allowable soil
moisture depletion between field capacity (1/3 bar) and permanent wilting
point (15 bars), and each irrigation to be 200% of the allowable depletion.
However, it was not always possible to have sufficient irrigation water infil-
trate into the soil in order to apply 200%. For the 1975 irrigation season,
a total of 59.4 cm of water was applied (including rainfall), which was about
18 cm less than required to satisfy the experimental design. Water balance
computations for the time period of June 15 to August 25, which encompasses
the time period of the irrigations, showed that estimated evapotranspiration
(43.7 cm) plus increased soil moisture storage (19.5 cm) exceeded the depth
of water applied by 3.8 cm, which explains why there was no drainage except
for a small event of 0.036 cm on July 14. The computed concentrations are
presented graphically in Figs. 35 to 37 for the simulation period. The
computer program is written so that TDS values are calculated as the sum of
the concentrations of the ions in the soil solution samples extracted at a
depth of 1.1 m in plot 23 for the time period of interest are presented in
F1gs. 35 to 37 and in Table C-l.
91
-------
ro
TABLE 8. IRRIGATION TREATMENTS ON PLOT 23 IN 1975 USED TO CALIBRATE CHEMICAL MODEL
Irrigation Treatment (H-3-2)
Irrigation Data Initial Moisture Distribution
Date 1975 Depth Depth
(Julian) (cm)
171 11.43 30.
174 11.58 45.
191 7.95 61.
192 2.62 76.
Total for 4 „ „ q.
Irrigations 33'58 91 '
106.
122.
5
7
0
2
5
7
0
Vol . Depth Vol .
0.
0.
0.
0.
0.
0.
0.
30 137.2 0.26
30 152.5 0.28
25 167.6 0.30
25 183.0 0.31
31 198.25 0.31
32 213.50 0.34
29
Evapotranspi ration
Date Et
(Julian) (cm)
166
167
168
169
170
171
172
173
174
175
0
0
0
0
0
0
0
0
0
0
.28
.28
.20
.20
.15
.18
.15
.23
.36
.48
Data
Date E. Date E.
(Julian)(cm)(Julian)(£m)
176
177
178
179
180
181
182
183
184
185
0
0
0
0
0
0
0
0
0
0
.48
.33
.48
.51
.53
.48
.38
.38
.25
.36
186
187
188
189
190
191
192
193
194
195
196
0.51
0.41
0.64
0.43
0.25
0.30
0.51
0.43
0.43
0.41
0.33
H - High fertility, 100 ppm of nitrogen in top 30 cm of soil.
3 - Allowable moisture depletion of 30% below field capacity as measured by difference between field
capacity (1/3 bar) and permanent wilting point (15 bars).
2 - Replace 100% of depleted moisture so that soil moisture content after irrigation is at field
capacity.
crop - Corn.
-------
1000
-900'
O_
~ 800
o
0 700
300
CO
20O
-o- o o-
9
D
J L.
-o o
JJ_J L
168
Figure 35
172
176 ISO 184
Julian Day
188
192
-i 1
196
• Simulation
o Field Data
o Simulation with
R._ =7matm
CC/2
Computed and measured concentrations of Mg , Na and Ca in soil
solution at a depth of 1.1 m in Plot 23.
-------
11 ^1500
40 MOO
1300
_ 500
_O- O O O O O O-
J 1 , I
I I I I I I I
I I I I I I I
2001
168 172 176 I8O 164 188 192
Julian Day
196
• Simulation
a Field Data
o Simulation with
7matm
Figure 36. Computed and measured concentrations of SO ~ HC(L and CT in soil solution at a depth of
l.l m in Plot 23. to r
-------
3800
10
190
3000-
280Cfe6~J JTO"" 174"^178182^ '86
Julian Day
Figure 37. Computed and measured TDS concentrations in soil solutions at a depth
• Simulation
a Reid Data
o Simulation with
194
198
of 1.1 m in Plot 25.
-------
Inspection of the data presented in Figs. 35 to 37 shows that predicted
values of Mg++, HC03-, Na+, 504=, Ca++, and IDS are within 25% of the field
values, while the predicted values for Cl- vary up to 100% from the measured
values. With the exception of HC03- and Mg++ ions, the predicted values are
generally greater than the field values. These graphs reflect a calibration
of the computer model and indicate the expected accuracy of any model pre-
dictions; however, some additional improvements will be made in the model as
described in the following pages. The graphs of the Ca++ and $04= ions and
TDS show sharp drops in concentration early in the simulation and then a
tendency to level off. This effect probably results from the simulated
chemical system adjusting to an equilibrium condition between the initial
soil chemistry and the soil solution. The initial drop in the Ca++ and S0a=
would probably be eliminated by equilibrating the soil solution with the
soil matrix before beginning the simulation. The lack of agreement points
to the importance and need for further improvements in this soil chemistry
model.
For soils containing gypsum, the upper limit of the concentration of
Ca++ should be 630 to 650 ppm. This concentration is controlled by the sol-
ubility of gypsum. A saturated gypsum solution at 25 degrees C contains 30 5
meq/liters (85), which means the concentration of Ca++ at saturation is 610
ppm. Lower soil temperatures and the salts in the soil solution increase the
solubility of CaS04 and increase the upper limit of Ca++ concentrations in the
saturated solution. For the soil in the test plot, the program computed Ca++
concentrations of over 770 ppm. The analysis of the soil solution extracted
in plot 23 (Table C-l) was used as a check to determine whether 'the concen-
tration of Ca++ in the soil solution in the field was being controlled by
the solubility of gypsum.
The check was made using a computer program developed by Dr. S.R. Olsen
and Dr. H.R. Duke, Scientific Educational Administration-Agricultural Research
currently stationed at Colorado State University. The program computes the
activity of each ion species in the solution and provides the negative
logarithm (pK) of the computed activity for each species, K. If the Ca++
concentration is being controlled by the gypsum and is in an equilibrium
condition, the pK of CaSCty should be 4.61, which is the pK value of pure
CaS04. The pK analysis, using Dr. 01 sen's program, of the soil solution
(Table C-l) collected from plot 23 during the test period is given in Table
9. It can be seen from the CaS04 data that the concentration of Ca++ in the
soil solution is in equilibrium with and being controlled by the gypsum in
the soil.
One possible explanation of the discrepancy between field and simulation
data was that the Ca++ concentration was not controlled by the gypsum solubil-
ity product, due to the absence of gypsum in the soil. If gypsum were not
present, the Ca++ concentrations would be affected by the loss of water or
other reactions occurring in the soil. To be sure the Ca++ concentration was
controlled by the solubility product of gypsum, a simulation was made using a
value for the gypsum concentration in the soil of 25 meq/100 gm of soil for
all the chemistry horizons. The data for this simulation are presented in
Table 10. The data show that the calculated values of Ca++ concentration
were not improved, while the agreement between the field and predicted con-
centrations for the other ions did not differ significantly from the initial
96
-------
simulation results. The check indicated that the value used for the concen-
tration of gypsum in the soil was not the problem; therefore, additional
investigation was required.
Other reactions considered in the model which include Ca++ are cation
exchange and the dissociation of CaC03- Dutt et al. (24) state that the
HC03- is usually the predominant form of 003= occurrinq in the soil-water
system. The reaction used in the model for the C03= was
H2C03 + CaC03 t Ca++ + 2HC03~ . (71)
The system of equations used to describe the reaction(s) is given in Section
6. As part of the development of the equations describing the HCOs- system,
Dutt et al. (24) proposed an equation to describe the solubility product of
Ca(HC03)2 as a function of moisture content. The solubility of Ca(HC03)2
is computed in the program as the product of the activities of the Ca++ and
HC03" ions present in the soil solution. It is then modified using Dutt
et al.'s (24) experimentally derived relationship for the solubility as a
function of moisture content.
The effect of the value of the solubility product of Ca(HC03)2 used in
the simulation on the computed Ca++ concentrations was investigated using the
measured and simulated data for plot 23. The simulated value for the Ca(HC(h)2
solubility can be compared to field values by using the pK values of the Ca+*
and HCC>3~ ions. The pCa and pHC03 values for the field data for plot 23 are
given in Table 9. The ion concentration data from the simulation using the
initial soil analysis (Table 7)were used to calculate the pCa and pHCC>3 values
for the simulations. The pK values for Ca++, HCO§ S04=, and Mg++ for the
simulated and measured data are given in Table 11.
TABLE 9. pK ANALYSIS OF SOIL SOLUTION EXTRACT AT 1.1 m ON PLOT 23,
MATCHETT FARM, 1975
Julian
Date
169
171
172
174
176
180
185
185
197
2.
2.
2.
2.
2.
2.
2.
2.
2.
pCa
3013
2590
2883
2839
2710
3180
2888
2786
3272
pMg
2.9167
2.8696
2.7873
2.8763
2.7909
2.7733
2.8956
2.8196
2.8824
Pso4
2.2849
2.3583
2.3159
2.3524
2.4585
2.3295
2.3355
2.3566
2.3480
pHC03
2.2976
2.2493
2.3522
2.3506
2.3198
2.4213
2.3801
2.4119
2.3294
Pco3
4.
5.
4.
4.
4.
4.
4.
4.
5.
9366
0783
8812
5796
6488
8503
9091
9409
0584
pCaC03
7.2280
7.3374
7.1696
6.8635
6.9199
7.1684
7.1979
7.2196
7.3856
pMgC03
7
7
7
7
7
7
7
7
7
.7434
.9480
.6686
.4559
.4398
.6236
.8047
.7605
.9408
pCaS04
4.5862
4.6173
4.6043
4.6363
4.7295
4.6476
4.6244
4.6353
4.6752
97
-------
TABLE 10. CONCENTRATIONS CALCULATED AT 1.1 m DEPTH WITH GYPSUM
25 meq/100 gm IN ALL HORIZON.
JuTian
Date
166
168
170
172
174
176
178
180
182
184
186
188
190
192
194
196
Ca
ppm
974
830
826
771
772
770
772
776
779
781
784
786
786
779
779
781
Na
ppm
60
60
62
121
134
212
220
224
227
227
229
231
231
229
247
253
Mg
ppm
101
75
75
74
74
79
80
81
81
82
82
81
82
81
83
84
HC03
ppm
302
291
296
275
292
282
299
309
316
322
326
332
336
329
293
303
Cl
ppm
333
339
345
429
450
491
487
490
494
496
500
505
510
497
488
484
S04
ppm
1938
1521
1526
1656
1665
1725
1727
1730
1727
1727
1727
1726
1721
1718
1755
1756
TDS
ppm
3078
3116
3130
3326
3387
3559
3585
3610
3624
3635
3648
3662
3666
3633
3645
3661
TABLE 11. pK VALUES FOR SELECTED IONS
Simulation Field
Ca++
Mg"1"*"
HC03-
$04=
2.1929
2.9943
2.4393
2.2390
2.2839
2.8763
2.3500
2.3524
Us ng the pK values from Table 11, the pCa(HC03)2 calculated by the
simulation was 7.0715 and the field value was 6.985?. These pCa(HOh)?
values correspond to solubility products of KSD = 8.48x10-8 for the slm
and Ksp = 1.04x10-7 for the field data. The simulation predicts a lower
solubiTity than exists in the field. The'value of KSD computed frcmtll
field data was inserted into the program as a fixed vaPlue? Sfec?ed by
moisture content, and the simulation was rerun. The predicted values of r
from the run using the field value of Ca(HC03)2 solubimy was larqe? ?han
the Ca" concentrations predicted in the orig^al simulation ven' thou h
the solubility of gypsum (2.4x10-5 is significantly larger than the solubil
ity of Ca(HC03)2, the predicted values of Ca++ concentration are sensi? ve
to the va ue of the Ca(HC03)2 solubility product used. Therefore, the problem
T,J° I?1?^ Yalue f?r the solubl'my of Ca(HC03)2 which is character's? ?
of the field. Apparently, calculated field values for the solubility product
can not be used in the model at this time. Dutt et al. (24) have provided
another option to calculate the Ksp of Ca HC03)2.
98
-------
Dutt et al. (24) assume "that at a given moisture content the H2C03 con-
centration is constant at equilibrium, which is equivalent to assuming a
constant CO? partial pressure at a constant moisture content." One option in
the chemistry model specified a fixed value for the partial pressure of carbon
dioxide (CO?) for the soil solution and- this fixes the solubility product of
the Ca(HC03)2.
A value of 3 mm 1 -atmospheres was used with the initial data in Table
7 to evaluate the effect of specifying the partial pressure of C02 on the
computed Ca++ concentration. The computed values of Ca++ concentrations were
lower than the values presented in Fig. 35. After discussions with
Dr. Sterling Olsen, a value of 7 matm for the C02 partial pressure was
selected as being representative of the soil system in the Grand Valley.
A 30-day simulation was made using the C02 partial pressure of 7 matm.
The results are presented in Table B-l and have been plotted in Figures 35
to 37. The use of a fixed value of C02 partial pressure improved the com-
parison between the field values and predicted values for the Ca++ concentra-
tion and had no effect on the comparison between the values of Na+, Mg++,
and Cl" concentrations. The comparison of HC03 concentrations is now quite
poor, however. In this instance, the value of 'the solubility product was
lower than the values used in previous simulations. The agreement between
field and predicted values of TDS concentrations was improved when the C02
partial pressure was fixed. The comparison between the computed and measured
= concentrations was poorer in this simulation.
Apparently, the reactions included in the model do not adequately describe
the CaS04, CaC03-Ca(HC03J2 system for the soils in the Grand Valley. However,
the Ca++, S04= and HO^- concentrations appear to occur in the proper propor-
tions so that TDS computations are valid even though the concentrations of
Ca++, S04=, and HC03" individually are incorrect. King and Hanks (43) used
the salt portion of Dutt et al.'s (24) model in their studies and found that
the TDS calculations were fairly good, but that the computations of the
concentrations for single ion species were not adequate.
Comparisons of the data for Ca++, .HC03", and S04= concentrations for
field and simulated data (Tables C-l and B-l) show the predicted values of
Ca++ and S0,= to be higher than field values, and HC03- concentrations for
the field data being higher than predicted. The sums of the average concen-
trations of Ca++, HCOa- and S04= ions in Tables B-l and C-l are 2556 ppm for
the field data and 2624 ppm for the simulated data, a difference of 3%.
While the predicted concentrations of Na++ and Mg++ fit field data fairly
well (Fig. 25) the predicted Cl" concentrations vary considerably from the
field data.
The discussion has centered on comparisons of ion concentrations,
computed and field, occurring at a depth of 1.1 m in the soil profile.
However, the solution concentrations of interest in the final simulations
are for the return flow at a depth of 2.13 m. As previously indicated, no
drainage water was collected from plot 23, but chemical analyses of drainage
water from other test plots are available.
99
-------
Ion concentrations for the soil chemical profile occurring between
depths of 1.2 to 2.1 m in all the field test plots are nearly equal regard-
less of irrigation treatment. Comparison of concentrations occurring from
1.2 to 2.1 m depth between plots shows that the values are nearly equal
throughout the field. If the ion concentrations are the same throughout
the field between depths of 1.2 to 2.1 m, then a reasonably good comparison
should exist between concentration values computed using plot 23 and the
field data for plot 23 or other plots. Comparison of the data simulated
using a C02 partial pressure of 7 matm presented in Table 12 and field data
in Table 13 show poor comparisons for individual ion concentrations, while
TDS concentrations agree reasonably well. Based on the simulations used in
the comparison of ion concentrations at 1.1 m and 2.13 m, a partial pressure
of 7 matm was selected for use in the hypothetical simulations that follow.
TABLE 12. PLOT 23 CONCENTRATION AT 2.13 m PREDICTED USING PCQ2=7 matm
Date
166
168
170
172
174
176
178
180
182
184
186
188
190
192
194
196
Ca
ppm
818
731
730
717
703
659
648
646
645
644
643
642
642
643
641
641
Na
ppm
221
218
220
213
207
197
199
200
200
200
203
203
203
204
204
204
Mg
ppm
142
118
118
115
112
104
103
102
102
102
102
102
102
102
102
102
HC03
ppm
188
122
122
123
125
131
133
134
134
134
134
134
134
135
134
135
Cl
ppm
236
247
249
236
245
294
324
337
346
350
355
358
362
363
365
365
S04
ppm
2136
1790
1786
1785
1834
1969
2010
2056
2042
2038
2047
2047
2048
2044
2056
2056
TDS
ppm
3741
3226
3225
3189
3226
3354
3417
3475
3469
3470
3484
3486
3491
3491
3502
3503
TDS-C1
ppm
3505
2979
2976
2953
2981
3060
3093 '
3138
3123
3120
3129
3128
3129
3158
3137
3138
SIMULATION OF HYPOTHETICAL CASES
After the calibration of the moisture flow and chemistry models was com-
pleted, the chemistry and flow models were used as a single model to evaluate
the effect of the volume of leachate on the salt concentration of the soil
solution leaving the profile at the lower boundary. These simulations were
undertaken to test the impact of a very small leaching fraction (e.g., 20%)
and a large leaching fraction (40%). The long-term salinity Impacts were
tested by running the simulations for a six-year time period. The effect of
winter precipitation on salt movement through the soil profile was also sim-
ulated The hypothetical simulations in this part of the study were made
using the initial chemistry profile data from Plot 23 (Table 7) and widely
differing irrigation treatments. The irrigation treatments used were fixed
100
-------
TABLE 13. CHEMICAL COMPOSITION OF DRAINAGE WATER FROM FIELD II,
MATCHETT FARM, 1975.
_. . CaMgNaHC03 ClSO^IDS Date
p'ot ppm ppm ppm ppm ppm ppm ppm collected
25 612 88 147 616 268 1505 3464 7/14
28 619 90 151 624 274 1553 3436 7/14
28 644 114 228 622 278 1459 3532 7/15
28 573 95 187 436 247 1536 3392 7/16
29 634 102 152 754 308 1512 3608 7/14
29 653 125 234 736 323 1536 3720 7/15
29 653 131 237 826 320 1464 3748 7/15
32 607 112 736 736 296 1488 3580 7/14
33 636 172 223 501 304 1728 3804 7/25
33 597 166 159 118 79 2237 3736 8/08
33 481 109 131 490 198 1344 3144 8/25
33 525 99 138 432 178 1542 3040 8/26
34 603 118 155 634 293 1704 3104 7/14
34 572 162 179 476 294 1771 3756 7/24
34 592 162 136 459 265 1728 3300 7/25
34 601 18 223 458 211 1824 3928 7/28
34 575 29 136 94 78 1632 3816 8/08
35 560 118 126 573 238 1627 3460 7/14
40 482 121 205 252 255 1230 3125 6/24
40 506 125 186 389 180 1716 3492 7/22
40 593 106 196 379 137 1548 3456 7/31
40 611 102 185 365 131 1680 3368 8/15
40 619 85 144 420 172 1752 3064 8/20
41 613 88 150 450 171 1567 3428 6/22
41 544 90 137 423 192 1512 3148 6/26
41 570 100 152 490 177 1630 3276 7/15
41 607 93 148 336 148 1560 3016 7/25
41 566 79 125 309 140 1414 2936 7/24
41 525 99 144 315 150 1358 3124 8/22
42 688 110 200 529 230 1584 3348 6/22
42 578 99 162 455 198 1272 3304 6/29
42 659 99 168 490 174 1555 3308 7/15
42 569 121 179 521 189 1541 3512 7/17
42 590 107 184 388 198 1598 3408 7/19
42 578 93 148 348 162 1502 3180 7/25
42 578 93 136 307 112 1656 3136 7/26
43 494 106 181 407 221 1266 3292 6/24
43 545 119 150 476 85 1716 3508 7/14
43 594 108 184 379 189 1080 3252 7/19
43 547 107 168 386 60 1675 3420 7/21
44 589 100 166 451 206 1302 3264 6/24
44 603 106 166 492 186 1541 3384 7/15
101
-------
irrigation schedules with varying depths of applied water. Daily 7-, 14-,
and 28-day irrigation intervals were considered for use in the simulations.
The depth of irrigation was set equal to the cumulative evapotranspiration
occurring in the interval prior to irrigation plus an additional leaching
increment equal to a percentage of the computed crop evapotranspiration. The
leaching increments considered were 1%, 2%, 5%, 10%, 20%, and 40% of the com-
puted evapotranspiration.
Simulations were made for a corn crop with a 150-day growing season
beginning on May 24 and ending on October 20 (day 144-293). The crop was
assumed to have a 120-cm rooting depth with a constant root distribution for
the entire simulation period. The root distribution was assigned as a
percentage of the total extraction with 40% occurring in the top 30 cm, 30%
in the second 30 cm, 20% in the third 30 cm and 10% in the fourth 30 cm of
soil.
The initial moisture distribution for the purpose of tha simulations
was assumed to be at 50% depletion of the available water, where available
water is the difference between field capacity (1/3 bars) ,and permanent
wilting point (15 bars). The initial moisture profile used in the simula-
tions is given in Table A-2. The available water was defined as the water
stored in the soil between a suction of 30 and 1500 kPa. From field data
for the research plots, the value of available water used in the study was
13 cm of water in 1.2 m of soil.
Evapotranspiration (Et) was computed using the method described by Kincaid
and Heerman (42). The equations and measured climatic data used to compute Et
are given in Appendix A. The 7-day and 14-day irrigation schedules used in
the study are listed in Appendix A.
Simulations were made using daily irrigations, but the data were not
included in the final analysis. Daily values of irrigation equalled daily Ef-
values plus the leaching increment. The sum of Et plus the leaching increment
was consistently less than a depth of 1 cm. When daily irrigations were sim-
ulated, the computed depth of infiltration differed from the planned depth for
a given day. Because of the poor representation of infiltration in this case
a daily schedule for irrigations was not used in the study.
The problem with modeling a small depth of infiltration is a result of
the method used to compute infiltration. The upper boundary is specified as
a saturated water content and the infiltration is computed using the flux
between the upper two nodes. The depth of infiltration is equal to the flux
multiplied by the time increment. The defining relation for the time interval
is
. J+l _ 0.035AX ,_-.
At FF~ (72)
where FR1 is the largest value of flux occurring in the previous time interval.
Except when infiltration is occurring, the flux between any two nodes in the
system will be small and the resulting time step will be relatively large
(the maximum time interval used in the moisture flow calculations is 0.01 day
102
-------
and was established as part of the input data). Therefore, the time step used
to initiate infiltration will be large. The use of a large time step and the
large flux values which occur during infiltration tends to over-predict infil-
tration in cases when the depth of infiltration is small. The infiltration
computations do not create a significant error in the computed depth of infil-
tration when the depth of irrigation is large.
A 28-day schedule was also considered in the study, but it is not
reported here. Estimates of water extracted by evapotranspiration between
scheduled irrigations indicated that most of the available water would be
removed between some irrigations. This irrigation practice would not
normally occur in the field where irrigation water is abundant, and for that
reason it was not included in the final analysis.
Irrigation intervals of 7 and 14 days and planned leaching increments of
2%, 5%, 20%, and 40% were used in the simulations needed for the study. Values
for the total cumulative infiltration and leachate at 2.1 m resulting from the
7- and 14-day schedules for leaching increments of 2%, 5%, 20%, and 40% are
given in Tables 14 and 15 for the 150-day irrigation season simulations.
TABLE 14. CUMULATIVE INFILTRATION FOR 150-DAY HYPOTHETICAL
SIMULATIONS USING 7- AND 14-DAY IRRIGATION SCHEDULES
Irrigation
Frequency
(days)
7
14
Cumulative Infiltration (cm)
Leaching Increments
2%
80.30
71.55
5%
61.58
74.10
'20%
91.46
84.70
40%
107.46
98.14
TABLE 15. CUMULATIVE LEACHATE AT 2.1 m FOR 150-DAY HYPOTHETICAL
SIMULATIONS USING 7- AND 14-DAY IRRIGATION SCHEDULES
Irrigation
Frequency
(days)
7
14
Cumulative Leachate
Leachinq Increment
2% 5% 20%
cm)
40%
8.17 9.19 19.25 33.8
7.84 8.95 17.74 30.9
TABLE 16. LEACHING FRACTIONS COMPUTED FOR 7- AND 14-DAY IRRIGATION
SCHEDULES
Leaching
Increment
2%
5%
20%
40%
Leaching
7-Dav
Actual
0.102
0.110
0.210
0.310
Adjusted
0.027
0.039
0.178
0.310
Fractions
14-Dav
Actual
0.109
0.121
0.209
0.315
Adjusted
0.026
0.040
0.186
0.285
103
-------
Leaching fractions at a depth of 2.1 m were calculated by two methods
using data from Tables 14 and 15 and Figs. 38 and 39. The computed values of
leaching fraction given in Table 16 are labeled actual and adjusted. The
values labeled actual were calculated as the ratio of cumulative leachate at
2.1 m to cumulative infiltration. The leaching fractions labeled adjusted were
computed using the data for cumulative infiltrations and leachate in Figs. 38
and 39. If the same boundary conditions were used to simulate water flow for
many years, a plot of cumulative leachate vs. cumulative infiltration would
become roughly linear. The slope of the linear portion of the plot would be
equal to the leaching fraction for the simulation. The value of the adjusted
leaching fraction is the slope of the line drawn through the linear segment of
the data in Figures 38 and 39, and represents the long term leaching fraction.
Comparison of the data labeled actual and the planned leaching increments
shows that with the exception of the 20% leaching increment, the planned values
of the leaching fractions were not achieved. If the planned values of leaching
had been attained, the values of the "actual" leaching fraction and the planned
leaching increment would have been equal. The comparison shows that higher
values of leaching were attained from the 2% and 5% leaching increments than
were planned and that the leaching value was lower than planned for the 40%
leaching increment.
The cumulative leachate was plotted versus the cumulative infiltration
in Figs. 38 and 39 for each leaching increment and irrigation frequency used
in the study. The plots in Figs. 38 and 39 show a sharp initial rise in the
cumulative leachate values and then a transition to an approximately linear
relationship.
The leaching fractions represented by the slopes of the linear portion of
the plots of cumulative infiltration and cumulative leachate are given in
Table 16 as the adjusted values of the leaching fraction. Comparison of the
data in Table 16 shows the values of the adjusted leaching fraction to be
much closer to the planned leaching increments for the 2% and 5% values for
both the 7- and 14-day schedules. Comparison of the value of the planned in-
crements and adjusted values for the 20% and 40% leaching fractions for the
7-day schedule shows a larger difference in value for the 20% than the one
calculated as the actual value. The value of the adjusted leaching fraction
is the same as the actual value for the 40% leaching increment and 7-day
schedule. For the 14-day irrigation schedule, the adjusted leaching fractions
for the 20% and 40% leaching increments are lower than the previously calcu-
lated "actual" value.
In the field, changes in soil moisture due to evapotranspiration and
excess applications of irrigation water contribute to variations in the
leaching fraction from day-to-day. Therefore, the concept of a leaching
fraction is most appropriately applied over a long period of time. Storage
in the profile and variation in computed flux due to the approximations used
in the model also contribute to the differences between planned and computed
leaching fractions.
104
-------
o
tn
40
so
oJ20
O
Figure 38
Leochmg Increment
• 2%
a 5%
• 20%
o 40%
10
20
30 40 50 60 70
Cumulative Infiltration (cm)
80
90
100
110
Cumulative leachate as a function of cumulative infiltration calculated by
hypothetical simulations using a 7-day irrigation interval.
-------
40
Leaching Increment
• 2%
a 5%
• 20%
o 40%
o
30
o
-C
0>
5
o
a
•i^*
~0
Figure
10 20 30 40 50 60
Cumulative Infiltration (cm)
70
80
90
100
39.
Cumulative leachate as a function of cumulative infiltration calculated by
hypothetical simulations using a 14-day irrigation interval.
-------
The first objective of this research was to measure the effect of the
volume of return flow on the quality of return flow. The previous discussion
of the leaching fraction points out the difficulty in characterizing the con-
cept of leaching fraction. For the purposes of this research, a wide range
of leachate was desired. Since the cumulative leachate data plotted as a
nearly linear function over two-thirds of the simulation time (Figs. 38 and
39), the adjusted values obtained from the slope of the curves in Figs. 38
and 39 between a cumulative infiltration of 20 to 100 cm were used to character-
ize the leaching.
If an instantaneous equilibrium is assumed, the soil solution will always
be in equilibrium with the salts in the soil, providing the salt exists in the
profile, regardless of the volume of water passing through the soil. This
means that the volume of leachate alone might not be the only significant
parameter to use in evaluating the effect of the volume of leachate on the
quality of the return flow. Another factor to be considered in relation to
the salt concentration would be the water content in the soil segment. Inspec-
tion of the water content profiles for the soil below a depth of 1.2 m
indicated that the water content values are nearly equal in this region.
Therefore, the water contents at the lower boundary are representative of the
water content in the soil profile below a depth of 1.2 m.
The values for the volume of solution in the last computation segment
(bottom boundary) of the chemistry model are given in Table 17. Inspection of
the results in Table 17 show about a 15% variation in the volume of soil
solution in the final segment. The range of-the volumetric water content at
the lower boundary is 0.30 to 0.35. This range of water content probably
encompasses values which are representative of field water contents below
1.2 m for the test plots, as well as areas where a shallow water table does
not exist.
The calculated variation in water content for the lower boundary in the
hypothetical simulations is large enough to evaluate the effect of water con-
tent on the salt concentration of the leachate. This is true because the con-
centration of salts in the leachate moving below the root zone is equal to
the concentration occurring in the last soil segment. Therefore, any concen-
tration changes due to the variation of water content should be reflected in
the concentration of the leachate. The'effect of moisture content on the
concentration of salts in the return flow will be discussed in later sections.
Chloride Transport
The transport characteristics of the model can be evaluated qualitatively
using profiles of Cl" concentrations. Several investigators (3,54,91)
have used CT ions to study transport processes in soils since they are non-
reactive in soils. Profiles of Cl" concentrations for the 2%, 20% and 40%
leaching increments and the 7- and 14-day schedules have been plotted in
Figs. 40 and 41. The profiles were drawn for the Julian dates 157, 199, 255,
and 293. Comparing the peak concentrations for each leaching increment in
both the 7- and 14-day irrigation schedules shows that the peak concentrations
decrease with increasing values of leaching increment. For the larger
107
-------
leaching fractions, proportionately less water is extracted bv evapotranspira-
tion from the applied water than for the small leaching fractions. This
means that the ionic concentration has been increased less by the larger
leachiny increments than for the smaller ones.
All of the profiles of Cl" concentrations in Figs. 40 and 41 show an
increase in peak concentration and an increase in depth to the peak concentra-
tion with time. The increases in Cl" concentration result from the concentrat-
ing effect of evapotranspiration of the applied irrigation water. Evapotran-
spiration removes pure water from the solution and leaves the salts. The net
effect is an increase in the concentration of salts. The movement of the
peak results from the transport of the salts in the soil solution by infiltra-
tion of irrigation water, redistribution and drainage of the soil solution.
For both the 7- and 14-day irrigation schedules, the depth of penetration
of the peak concentration is greatest for the largest leaching increment.
Qualitatively, this would be expected. Excess water from the higher leaching
increments moves deeper into the soil profile, since more water is available
for redistribution. As the excess water moves, it transports the peak con-
centration deeper into the profile. Comparison of the depth to peak of the
concentration profile for each leaching increment shows that the profiles
were leached deeper with a given leaching increment for the 7-day irrigation
schedule than for the 14-day schedule. For example, using the 2% leaching
increment for the profile on day 255, the depth to the peal< concentration is
approximately 68 cm for the 14-day schedule and 83 cm for the 7-day schedule.
Deeper penetrations of the peak chloride concentrations, using frequent small
irrigations, have been reported by other investigators (3, 54). Results for
the transport of CT computed by the model indicate that salt transport is
modeled in a manner which corresponds qualitatively to results described in
experimental work on transport phenomena (3, 54).
TDS Studies
The TDS values at 2.1 m were plotted versus the cumulative leachate
values (Figs. 42 and 43) for the 150 days, the four leaching increments and two
irrigation frequencies used in the simulation. (Note: The scales or cumula-
tive leachate in Figs. 42 and 43 have been extended over the range of 1 to 10
cm.) The data for both irrigation intervals show the same increasing values
of TDS as a function of increasing values of cumulative leachate. Data for
all leaching fractions are included in the initial portions of the curve.
Since the values of cumulative leachate for the 2% and 5% leaching increment
were less than 10 cm, only the data from the 20% and 40% leaching increment
extend beyond 10 cm.
Some insight into the cause of the increase of the TDS concentration is
available from the data for the Cl~ concentrations vs the cumulativp leachate
(Figs. 42 and 43). These data show the Cl" concentrations rising, leveling
off, and then showing a second increase in concentration.
108
-------
• Doy 157
A Doy 199
a Doy 255
o Day 293
30-5 61-0
91-5 122-0 152-5 183-0 213-5
Depth (cm)
Figure 40. Chloride concentration profiles calculated by hypothetical
simulations using 7-day irrigation interval.
109
-------
700
600
500
400
300
200
100
800
700
600
~ 500
* 400
.2 30°
S 200
0
o
c
o
u
100
I 100
1000
900
800
700
600
500
400
300
200
100
0
• Doy 157
A Day 199
O Day 255
o Day 293
2% L.R
30.5 61.0 91.5 122.0 152.5 183.0 213.5
Depth (cm)
Figure 41. Chloride concentration profiles calculated by hypothetical
simulations using a 14-day irrigation interval.
110
-------
TABLE 17. VARIATION OF VOLUME OF SOLUTION IN SOIL SEGMENT AT THE
LOWER BOUNDARY FOR SIMULATIONS USED IN THE STUDY
Volume (cm3/soil segmentY"
Leaching Increment
Date 2% 5% 20% 40%
7-Day Schedule
157 5.11 5.12 5.12 5.15
171 4.92 4.93 5.02 5.05
185 4.80 4.81 4.86 4.88
199 4.71 4.72 4.75 5.07
213 4.65 4.65 4.84 5.11
227 4.59 4.60 4.94 5.18
241 4.55 4.57 4.96 5.17
255 4.52 4.57 5.01 5.14
269 4.50 4.61 4.98 5.12
283 4.48 3.69 4.94 5.08
293 4.49 4.71 4.92 5.03
14-Day Schedule
157
171
185
199
213
227
241
255
269
283
293
5.11
4.90
4.79
4.70
4.64
4.59
4.55
4.52
4.40
4.47
4.46
5.12
4.90
4.79
4.70
4.64
4.59
4.56
4.53
4.54
4.63
4.69
5.12
4.90
4.79
4.76
4.88
4.92'
5.00
5.02
5.00
4.98
4.91
5.13
4.95
4.90
5.02
5.07
5.05
5.07
5.08
5.08
5.08
5.09
The Cl" and other ions moving through the soil are concentrated as
water is removed by evapotranspiration. Repeated applications of irrigation
water increase the mass of salts and transport the salts through the soil.
As the salts are concentrated, reactions occur in the soil solution and
between the salts in the solution and the soil matrix. Examples are Ca++-Na+
exchange, ion pair formation, and precipitation. Chlorides, however, do not
participate in these reactions and changes in Cl~ concentrations are due to
changes in irrigation water flux and the concentrating effect of the loss of
pure water from the_root zone. Since Cl~ ions are essentially inert in a
soil system, the Cl" concentrations were plotted against cumulative leachate
(Figs. 42 and 43). This presentation more accurately reflects the results
of the chemical reactions that occur. For example, Figs. 42 and 43 show that
much of the increase in TDS values, particularly for the 20% and 40% incre-
ments, was due to the concentration of Cl" in the soil solution.
Ill
-------
The data for (TDS-C1) in Fig. 42 and 43 show an initial rise to a peak
value and then a slight decrease. The data follow the same trend and have
approximately the same values of (TDS-C1) concentrations as a function of
cumulative leachate for each of the leaching increments used. The data seem
to indicate that the concentration of salts in the leachate is independent
of the volume of leachate. Since the data in Table 17 show a range of
volumetric moisture content from 0.30 to 0.35 (corresponding to a solution
volume of 4.5 to 5.2 cubic cm per soil segment), the salt concentration as
computed by the model is relatively insensitive to moisture content.
The question does arise, however, as to the effect of the computed
concentration of soil solution in the upper one-half of the profile on the
salt concentrations of the leachate. To answer this, a simulation was ex-
tended for six years. A 14-day irrigation interval with a 20% leaching
increment was used in the extended simulation. The data for this simulation
are presented in Tables 18 and 19. A total of 482 cm of water was infiltrated
during the simulation which resulted in 80 cm of leachate.
The (TDS-C1) concentration at a depth of 2.13 m is plotted in Fig. 44.
The data show the same pattern as was evidenced in Figs. 42 and 43. The
concentrations rise to a peak value followed by a gradual decline and finally
end in a constant value. The rise of (TDS-C1) reflects the transport of salt
from the profile above 2.13 m. The plot of the Cl" profiles for the first,
third and sixth years of the simulation show a steady advance of the peak
chloride concentration (Fig. 45). The profile for year 6 is nearly a steady-
state profile. The_steady-state profile was calculated using the leaching
fraction and the Cl" concentration of the irrigation water,
DTW CIW
CDW ' CIW D^ = LT. <73>
where Cg^ is the concentration of the drainage water; Cju is concentration of
the irrigation water; D™ is depth of irrigation water; DQU is depth of drain-
age water; and L.F. is teaching fraction. The actual leaching fraction for
the simulation was 0.166 and the Cl" concentration was 61 ppm. For a steady-
siata.sy^iem, ihe £]" concentration at the lower boundary should be 367 ppm
and the computed value was 390 ppm. Since the hypothetical simulation was a
perturbation on the field soil system, an extended simulation was required
for the system to reach a steady-state condition. Once the steady-state condi-
tion was achieved, the data show uniform values of salt concentration.
After 63 cm of leachate, the (TDS-C1) concentrations were 3028 ppm and
the concentrations varied by less than 0.1% in the last 17 cm of leachate in
the simulation. This is contrasted to a 5% variation in (TDS-C1) con-
centration which occurred in the first 19 cm of leachate in the simulation.
From the simulation results plotted in Figs. 42 to 45, it was concluded that
the concentration of salts in the return flow is independent of the volume of
leachate.
112
-------
3SOO
3500
23400
a
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2 400
&
o 300
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tz>
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._ V ^^
• &\
• 2%
A 5%
n 20%
o 40%
i i i • i i , i i i
345678910203040
Cumulative Leachote (cm)
Figure 42. IDS and' chloride concentrations as a function of cumulative
leachate at a depth of 2.1 m calculated by hypothetical
simulations using a 7-day irrigation interval.
113
-------
3600
3500
f 3400
0.
g 3300
H
3200
500
5 400
a.
a.
3 300
200
3200
— 3100
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0
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.
o
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A 5%
a 20%
o 40%
,,,111111 1
3" 4 5 6 7 8 9 10 20 30 40
Cumulative Leochote (cm)
Figure 43. IDS and chloride concentrations as a function of cumulative
leachate at a depth of 2.1 m calculated by hypothetical
simulations using a 14-day irrigation interval.
114
-------
TABLE 18. IDS CONCENTRATION AND CHLORIDE CONCENTRATION IN CUMULATIVE
LEACHATE AT 2.13 m FOR 6-YEAR HYPOTHETICAL SIMULATION USING
14-DAY IRRIGATION SCHEDULE AND 20% LEACHING INCREMENT
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
157
171
185
199
213
227
241
255
269
283
293
157
171
185
199
213
227
241
255
269
283
293
Cumulative
Infiltration
(cm)
8.33
11.45
16.46
23.50
31.64
40.55
51.25
60.49
68.36
75.11
80.55
88.52
91.64
96.77
103.71
111.84
120.80
131.49
140.73
148.60
155.36
160.79
168.76
171.88
177.01
183.95
192.18
201.04
211.73
220.97
228.84
, 235.60
241.03
Cumulative
Leachate
(cm)
Year 1 of 6
5.02
6.70
7.48
8.05
8.92
10.11
11.93
12.86
13.66
14.31
14.75
Year 2 of 6
19.20
19.96
20.65
21.21
22.16 •
23.25
24.85
26.02
26.76
21 Al
27.91
Year 3 of 6
32.37
33.12
33.81
34.37
35.22
36.41
38.01
39.18
39.97
40.63
41.07
Cl
ppm
269
290
298
302
297
299
302
317
323
327
327
357
338
348
353
351
362
378
405
421
434
438
525
504
522
531
531
546
562
594
608
618
620
TDS
ppm
3276
3318
3336
3352
3352
3365
3391
3416
3432
3444
3444
3501
3468
3479
3484
3476
3471
3484
3513
3527
3541
3539
3620
3582
3599
3610
3606
3612
3627
3657
3673
3684
3684
TDS-C1
ppm
3007
3028
3038
3050
3055
3066
3089
3099
3109
3117
3117
3144
3130
3131
3131
3125
3109
3106
3108
3106
3107
3101
3095
3078
3077
3079
3075
3066
3065
3063
3065
3066
3064
(continued)
115
-------
TABLE 18. (Continued)
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
157
171
185
199
213
227
241
255
269
283
293
157
171
185
199
213
227
241
255
269
283
293
Cumulative Cumulative
Infiltration Leachate
(cm) (cm)
249.00
252.12
257.25
264.19
272.33
281.28
291.97
301.21
309.08
315.84
321.27
329
332
337
344
353
361
372
381
389
396
401
409
413
418
425
433
442
452
462
470
476
482
Year 4 of 6
45.53
46.28
46.97
47.53
48.38
49.57
51.17
52.34
53.13
53.79
54.23
Year 5 of 6
58.69
59.44
60.12
60.69
61.54
62.73
64.33
65.50
66.29
66.95
67.39
Year 6 of 6
71.85
72.60
73.29
73.85
74.71
75.89
77.49
78.69
79.45
80.10
80.55
Cl
ppm
656
612
618
620
604
596
584
595
596
591
587
563
519
518
515
497
484
466
471
470
467
463
446
411
413
412
398
390
382
391
392
392
390
IDS
ppm
3724
3664
3672
3674
3652
3642
3627
3638
3643
3637
3636
3610
3552
3552
3550
3530
3512
3491
3498
3501
3499
3492
3479
3435
3439
3438
3422
3414
3406
3415
3418
3421
3418
TDS-C1
ppm
3068
3052
3054
3054
3048
3046
3043
3043
3047
3046
3043
3047
3033
3034
3035
3033
3028
3025
3027
3031
3032
3029
3033
3024
3026
3026
3024
3024
3024
3024
3026
3029
3028
116
-------
TABLE 19. CHLORIDE CONCENTRATION PROFILES FOR 6-YEAR SIMULATION USING
14-DAY IRRIGATION SCHEDULE AND 20% LEACHING INCREMENT
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
15
30
46
61
76
91
107
122
137
152
168
183
198
213
15
30
46
61
76
91
107
122
137
152
168
Cl concentration (ppm)
203
297
604
675
459
450
333
329
332
344
341
331
244
236
122
188
332
569
699
725
583
480
375
351
344
337
312
302
95
104
133
199
329
614
820
890
631
472
396
Year
234
Day 144
83 83 83
86 86 86
104 102 102
179 157 157
301 204 203
529 271 257
734 361 302
845 539 377
742 652 430
589 702 485
472 697 546
403 648 601
366 576 629
358 528 659
Day 199
111 111 111
118 118 118
126 126 126
161 156 156
216 188 188
366 252 247
564 315 289
794 458 363
773 556 392
680 644 443
554 683 504
453 663 563
391 607 607
353 532 620
Day 293
95 95 95
104 104 104
130 130 130
172 172 172
214 212 212
295 276 276
403 320 314
644 422 387
706 452 371
719 524 390
676 598 432
5
83
86
102
157
203
257
297
354
371
384
409
448
493
566
111
118
126
156
188
245
287
352
356
372
392
424
469
515
95
104
130
172
212
276
314
383
356
356
368
6
83
86
102
157
202
257
297
352
362
361
364
377
397
448
111
118
126
156
188
245
287
350
352
357
361
368
387
412
95
104
130
172
212
276
314
383
356
350
356
(continued)
117
-------
TABLE 19. (Continued)
Cl concentration (ppm)
Depth
(cm)
Year
1
2
3
4
5
6
Day 293 (Continued)
183
198
213
15
30
46
61
76
91
107
122
137
152
168
183
198
' 213
359
340
327
96
103
131
193
310
551
763
865
716
549
441
384
353
343
590
504
438
96
103
129
169
207
278
376
571
676
712
692
628
550
487
641
695
621
Day 365
96
103
129
169
206
263
308
393
440
499
566
618
638
642
485
538
587
96
103
129
169
206
263
303
365
374
386
416
460
571
573
388
420
463
96
103
129
169
206
263
303
363
365
359
366
380
387
451
361
370
390
96
103
129
169
206
263
303
363
363
357
356
358
366
388
Winter Simulations
The data and analyses in the previous sections have been based on simula-
tions made for a single growing season, or multiple growing seasons, without
considering the effects of winter precipitation between irrigation seasons on
salt transport below the root zone. One simulation using the 20% leaching
increment and 14-day irrigation interval was extended over the winter months
and through a second growing season. Two conditions were assumed for the
winter portion of the simulation. The first condition assumed no water was
applied during the winter months and no evapotranspiration occurred during the
same period, which corresponds with the simulations described above wherein
only the growing season was considered. The second condition assumed pure
water (rainfall) was applied on the first day of each month during the winter,
and again no evapotranspiration was assumed to occur. The water applied for
each month in the winter was equal to the water equivalent resulting from the
average depth of precipitation for the given month. The average water equiv-
alent for each of the winter months was estimated from the Climatological
Records for the Grand Valley. The data used in the simulation are given in
Table 20.
118
-------
3200r
a.
a.
•
*•
*3KX>H _• '•••* •
o
I
w~ -~ • A ^^ A
•• •*• •
3000
••. %
(0 20 30 40 50 60 70 80 90
Cumulative Leachate (cm)
Figure 44. Total dissolved solids and chloride concentrations as a function of cumulative
leachate at a depth of 2.1 m calculated by 6-year hypothetical simulations using
20% leaching increment and 14-day irrigation interval.
-------
ro
o
15
30
46
61
76
91
107
»•»»
J! 122
f 137
152
168
183
198
213
Figure
IDS (PPM)
1000 2000 3000
Cl (PPM)
4000 0 200 400 600
800
JOOO
o Year I
A Year3
• Year 6
45.
IDS and chloride concentration profiles at day 293 calculated by a 6-year
hypothetical simulation using 20% leaching increment and 14-day irrigation interval,
-------
TABLE 20. AVERAGE WATER EQUIVALENT DEPTH USED FOR WINTER SIMULATIONS
Month
Nov.
Dec.
Jan.
Depth
(cm)
1.55
1.45
1.62
Month
Feb.
March
April
Depth
(cm)
1.75
1.90
2.00
Two sets of Cl" concentration profiles were plotted for these series
of simulations. In the first set, Cl" profiles were plotted for days 157,
199, 255, and 293 of the second year of the simulations for both conditions
used (Fig. 46). In the second set, the plot (Fig. 47) shows the Cl profile
on day 293 of the first and second year for each of the winter conditions
simulated.
The effect of winter precipitation on the Cl concentration profile
can be seen in Fig. 47. Below a depth of 75 cm the winter precipitation was
quite effective in reducing the Cl" concentration. The effectiveness of
the winter precipitation results from the fact that the water contains no
salts and the additional water maintained a larger water content over the
winter. The larger water content in the soil contributed to the redistribu-
tion of the water and transport of chlorides.
Comparison of the Cl" concentration profiles given in Fig. 46 shows a
steady advance of the peak concentration through the soil profile. The data
in Fig. 46 for the CT advance during the second growing season show the bene-
fit of the addition of the 10 cm of pure water. In the simulation where the
pure water was added, by day 293 of the second season, the peak concentration
had advanced 30 cm further than the simulation which did not include the pure
water. Also, the peak concentration was reduced by 70 ppm for the simulation
including the pure water as compared with the simulation which excluded the
addition of precipitation.
These simulations serve to dramatize the effect of small quantities of
pure water on leaching and transport of salts. For the simulations including
winter precipitation, the pure water represented about 6% of the total applied
water. The improvement in the efficiency of leaching by rain water has been
noted by other investigators (3,54).
The Cl~ concentration profiles computed by including winter precipitation
show one problem that arises in trying to use Cl~ concentrations to estimate
leaching fractions. The leaching fraction can be estimated as the ratio of
the Cl" concentration of the applied water to the Cl concentration of the soil
solution below the root zone. This calculation assumes that the Cl" concen-
trations below the root zone represent a long-term average of the leaching
from the upper portion of the profile. An idealized concentration profile
would show gradually increasing Cl" concentration with depth to the bottom of
the root zone and then a uniform concentration to the bottom of the profile.
This is the shape of the Cl~ profiles in the last year of the 6-year simulation
(Fig. 45). Apparently, the Cl" concentrations in this simulation have reached
a steady state.
121
-------
0
ro
30-5
61-0-
91-5
o 122-0
ex
9)
O
Cl Concentrotion (PPM)
200 400 600 t 800 0 200 | 400 t 600
water added during
winter simulation
152-5-
1830
2I35L
Figure 46.
no water added during
winter simulation
• Day 157
A Day 199
a Day 255
o Day 293
Chloride concentration profiles for second year of 2-year simulation calculated by
hypothetical simulations using a 14-day irrigation interval, 20% leaching increment
and 2 winter conditions.
-------
0
100
200
300
Cl (PPM)
4OO 500
600
TOO 800 900
ro
to
30-5
61-0
91-5
-a 122-0
I
o
152-5
1830
213-5
Figure 47-
o Day 293 Year I
• Day 293 Year 2 - no water
added during winter simulation
a Day 293 Year 2 - water added
during winter simulation
Chloride concentration profiles at day 293 calculated by hypothetical
using a 14-day irrigation interval, 20% leaching increment and 2 winter
-------
Comparing the Cl concentrations at the bottom of the root zone at day
293 for year 6, and on the same day of the second year of the simulation which
included the winter precipitation, shows the effect of the addition of pure
water. The concentration profiles are roughly equal to a depth of 61 cm.
Between a depth of 61 cm and 122 cm, the concentration where winter precipi-
tation is considered is significantly lower after only two years than after
six years when winter precipitation in not included. There is almost a 30%
difference in concentrations at a depth of 120 cm with an addition of precipi-
tation equal to only 6% of the total water applied to meet evapotranspiration
and leaching_requirements. If the leaching fraction were estimated using
simulated Cl concentrations including winter precipitation, the leaching
fraction would be over-estimated. Presumably, this would be the case in
field sampling as well. As the volume of pure water included in the simula-
tion is increased in relation to the irrigation water applied, the effect of
pure water on the concentration profiles become even more significant.
TDS Profiles
The TDS profiles for day 293 of the first year and the sixth year in the
b-year simulation are plotted in Fig. 45. The data show that leaching is
occurring in the region to a depth of 122 cm. This corresponds to the depth
of the root zone used in the simulation. Below this depth, the concentration
of salt is fairly constant. Irrigation water dissolves salts, such as
gypsum and lime, and transports the ions through the profile until the concen-
tration due to evapotranspiration causes precipitation. The region below
the root zone acts as a buffer zone and controls the concentration of salts
leaving the profile. Because of this buffering, the concentration of the
leachate at 2.13 m remains relatively constant.
124
-------
SECTION 8
PREDICTION OF RETURN FLOW SALINITY
The knowledge gained from the model results can be combined with the
monitoring data collected in the Grand Valley Salinity Control Demonstration
Project, as well as data collected by the Agricultural Research Service in
the Grand Valley, to provide a picture of subsurface irrigation return flows
and their corresponding salinity concentrations.
GEOLOGY AND SUBSURFACE HYDROLOGY
The general geologic characteristics (Fig. 1) of the Grand Valley have
been briefly described in Section 4 of this report. The purpose of the
additional discussion in this subsection is to provide a better back-
ground for understanding the irrigation return flow phenomenon in the valley.
The Grand Valley is underlain by the Mancos shale, a "dark-gray (black
when wet) clayey and silty or sandy, calcareous gypsiferous" deposit of
marine origin and upper Cretaceous in age (74). In the portion of the valley
lying north of the Government Highline Canal (Fig. 48), Mancos shale is an
exposed erosional surface. Almost no irrigation is practiced in this
portion of the valley. Intermittent ridges of Mancos shale are exposed in
the area bounded, approximately, by the Government Highline Canal on the
north and the Grand Valley Canal on the south. These shale ridges have a
general north-south trend and represent remnants of a shale terrace that has
been dissected by southward flowing streams that began in the Book Cliffs.
The southern extremities of these ridges (approximately the Grand Valley
Canal) are the remnants of the shale cliffs that once formed the northern
bank of the Colorado River (74).
With time, the Colorado River migrated southward in an approximately
horizontal plane until it reached its present position. During this period,
the river deposited what is now a cobble aquifer that extends from the present
river location northward to, approximately, the Grand Valley Canal (Fig. 49).
Migration of the Colorado River to the south decreased the gradient of south-
ward flowing tributaries, and the valley was gradually filled with alluvial
deposits transported by the tributaries. These tributary deposits buried the
Colorado River bedload and flood plain deposits (74). It is the tributary
alluvium, deposited during the Quaternary, that forms the source of most of
the irrigated soils in the valley. In recent time, local washes have again
cut into the alluvial deposits and into the Mancos shale at many loca-
tions. Recent downcutting into the Mancos shale bedrock is most prevalent
125
-------
ro
\
/
Boundary of Irrigated Area
Grand Valley Salinity Control
Demonstration Project
Approximate Extent of
Cobble Aquifer
See Cross-Section
of Cobble Aquifer
Seal* in Milt*
1012345
Scale in Kilometers
Figure 48. Natural washes, canals and boundary of irrigated lands in the Grand Valley.
-------
o
o
Legend
l Fine Grovel
-;! Silty Clay Loom Soils
\ Cobble Aquifer
N
j
j Tight Clay ( Discontinuous)
tr-I-I-I-3 Mancos Shale Bedrock
IS3
Orchard
Mesa
-o
Scale I Mile
Horizontal Seal*
Figure 49. Cobble aquifer cross-section.
-------
near the north edge of the irrigated region where the tributary deposits are
relatively thin.
The alluvial deposits overlying the cobble aquifer and/or the Mancos
shale are saline clays and silts derived mainly from Mancos shale in the Book
Cliffs area and from shaly members of the Mesa Verde Group. Where the cobble
aquifer is absent, the clay soils are in contact with a weathered shale zone
below which is the unweathered Mancos shale. The weathered shale can be
recognized by its brownish-gray to brown color as compared to the darker qrav
of the unweathered shale. The weathered shale also exhibits joints, dis-
integration and separation along the bedding planes. These features account
for the permeability of the weathered shale.
The cobble aquifer that underlies the tributary alluvium in much of the
irrigated region of the valley is, locally, under artesian pressure, and the
water table aquifer in the overlying alluvium is a perched aquifer. The two
aquifers are not hydraulically independent, however, since there is sufficient
permeability in the confining layer to permit interchange of waters. At some
locations, the confining layer is apparently absent and there is direct
hydraulic connection between the tributary alluvium and the cobble layer.
Ground water in the Quaternary alluvium exists because of seepage from
canals and laterals and deep percolation from irrigation. This ground water
acts as source for recharge of the cobble aquifer, particularly along the
northern boundary of the cobble (74). Apparently the cobble is also recharged
upstream by the Colorado River. Deep percolation from irrigation and seepage
from the canals and distribution system return to the Colorado River only
after passing through the soil formed from the Quaternary alluvium. The sub-
surface return flow, after passing through the soil, may then take one of
several routes to the river. These routes include passage directly into nat-
ural washes or man-made drains with little or no contact with the Mancos Shale
movement through the weathered zone of the shale and into the washes or drains'
and movement into and through the cobble aquifer to the washes, drains or
river. The quality of these return flows depends upon the particular route
taken as discussed in the following subsections.
Quality of Surface Waters
For purposes of general background, some of the chemical analyses of the
irrigation water supply used in the research reported here are presented 1n
Table 6. This water supply comes directly from the Government high line Canal
which is only 300 feet north of Field I. The data show that the water is of '
good quality for purposes of irrigation. The variation in TDS throughout the
irrigation season is roughly 300 to 700 ppm. Commonly, the canals divert
water from the Colorado River beginning on April 1 and terminating on October
O I •
Quality of Subsurface Waters
Soil Chemistry Test Plots —
In the previous sections of this document, it is reported that the dis-
solved solids concentration in the drainage water at the bottom of the soil
profile at the Matchett farm site generally fell within the range of
128
-------
TABLE 21. CONCENTRATION OF SALTS IN SOIL SOLUTION, MATCHETT FARM,
1976 (All concentrations in ppm)
DepthPlot Number~
(cm) 1 23456789 10 11
0-30 1820 5308 3816 2836 1540 6588 10616 7680 11296 6612 4052
30-60 2052 1268 1928 1640 3080 7460 3044 2500 2232 9948 5576
60-90 2104 1292 2676 2464 4732 3548 3464 - 3276 3648 3736
90-120 3000 2588 3160 2704 4704 4748 3156 3260 3204 - 3348
120-150 2916 3272 3140 2828 3404 3716 3344 4988 3668
150-180 3192 3092 3148 3148 3080 3840 3760 3868 3240
180-240 2576 2992 3256 2904 3192 3520 3156 3276 3196
240-300 2976 3148 3260
12 13 14 15 16 17 18 19 20 21 22"
0-30 4948 7112 4572 6386 8124 8876 8088 2992 9084 1656 3235
30-60 1108 3404 8884 1472 1840 3028 2936 6576 6952 1096 920
60-90 3224 - 3364 4224 3208 2644 3028 4444 3700 940 1252
90-120 4488 3440 3792 3828 3388 4032 2448 3500 3672 3872 1720
120-150 6608 4112 3868 3308 3680 3708 3316 3848 4564 3944 5088
150-180 3224 4020 3092 3412 3336 3824 2904 3376 3160 2928 5408
180-240 3444 3608 3028 3548 3604 3008 3144 2968 2824
240-300 4200 3576
23 24 25 26 27 28 29 30 31 32 33
0-30 5876 3340 2536 4344 2602 7660 6248 6772 2000 7784 2032
30-60 1276 1936 2684 1812 1324 3396 1852 2232 900 2520 824
60-90 1480 3884 2844 2412 1876 3728 2120 1856 1440 2512 1040
90-120 4840 3308 3568 3612 2096 3796 4055 5024 1700 2496 1328
120-150 3420 3064 3208 2896 2760 3460 3600 3408 1820 3776 2176
150-180 - 3004 3104 3060 1964 2856 3424 2964 1648 2900 5237
180-240 2852 4268 2744 2660 2940 3132 2840 3112 2864 2944
240-300 3100 2940 3370 2828 3112 3444
34 35 36 37 38 39 40 41 42 43 44
0-30 7172 2500 5076 1820 2048 2548 6372 6648 2116 5988 3590
30-60 1880 1140 1912 1120 1084 1156 2368 1628 1084 1916 1124
60-90 6660 1908 4820 4972 944 2532 2732 3704 1248 1267 992
90-120 5276 1164 3400 3648 1260 3888 3176 4164 4600 2416 2928
120-150 3272 3292 3212 3488 3944 4164 4224 3144 2580 2944 2736
150-180 3260 - 2976 3040 2732 3096 5008 2844 2736 2736
180-240 3056 2716 4000 2960 2836 2956 2844 2488
240-300 3096 3040 2752 2180
~~ (continued)
129
-------
TABLE 21. (Continued)
Depth
(cm)
0-30
30-60
60-90
90-120
120-150
150-180
45
6844
1132
1208
-
2956
2440
cT:
46
1268
2764
5312
2772
47
1596
1296
3040
3108
48
2288
1364
3008
Plot Number
49 50
1124
672
2452
3340
3252
1092
1040
3060
3728
3444
51
1304
860
4084
2976
52
2196
1076
1268
2892
3036
53
1136
1280
3020
2932
3272
54
5676
4124
3312
55
2924
1664
4556
3636
62 63
0-30 1192 1946 1176 2926 3160 3220 3204 3160
30-60 1256 5324 1012 2972 2828 2956 2880 3328
60-90 4104 3104 2784 3108
90-120 3408
3000 to 3900 ppm (see Table 13). Table 21 contains the dissolved solids con-
centrations of the soil solution as a function of depth for all of the plots
at the Matchett experimental site. These data were collected in the fall of
1976. It is apparent that the concentrations in the lower part of the profile
again fall withtn the range of 3000 to 3900 ppm. The significance of this
observation is that the concentration remains in a rather narrow range even
under a wide variety of irrigation and cropping treatments over a rather large
sampling area. Again, this tends to verify the conclusion, derived from the
model,^that the concentration of waters leaving the soil profile (at -2 m) is
insensitive to the rate or volume of deep percolation. Thus, the salt load
leaving the soil profile is proportional to the volume of deep percolation
and can be reduced most effectively by reducing the deep percolation.
Some of the test plots in Field III were constructed with lengths of
approximately 60 m (200 feet), 90 m (300 feet), and 150 m (500 feet) (see
Fig. 8). The TDS for some of the drainage samples collected from Field III
are listed in Table 22. These data for the grain plots (49 to 58) correspond
roughly with the data in Table 13, which means that no additional knowledge
is gained regarding the salt pickup phenomena that are taking place as subsur-
face irrigation return flows continue their movement from a depth of 2 m in
the soil profile, continue downward until reaching the Mancos shale bed, then
moving overland until reaching the cobble aquifer, where it is displaced back
into the Colorado River (Fig. 49). In contrast, the drainage water from the
grass plots (59-63) showed very little quality degradation as compared with
the salinity of the irrigation water supply. Unfortunately, Field III was
underlain by fractured shale, whereas Fields I and II did not have this prob-
lem. As a consequence, large deep percolation loss rates were required
before any subsurface flows would enter the drainage pipes that were located
around the inside periphery of each plot. This was especially true for plots
59 to 63.
130
-------
TABLE 22. TOTAL DISSOLVED SOLIDS OF DRAINAGE WATER FROM FIELD III,
MATCHETT FARM, 1975
Plot
49
50
52
58
TDS
ppm
2908
3344
2960
2376
Date
collected
8/28
9/16
9/11
9/16
Plot
59
60
62
63
TDS
ppm
956
472
588
544
Date
collected
8/01
7/31
7/27
8/08
Natural Washes and Open Drains
There are a number of natural washes that traverse the Grand Valley (Fig.
48). These washes originate in the Book Cliffs north of the Grand Valley.
Thunderstorm activity, principally during the months of July and August,
results in flood flows transported by these washes in a generally southerly
direction until they reach the Colorado River. These natural washes are used
extensively for discharging canal spillage and tailwater runoff from irrigated
lands. Summer flows and corresponding salinity concentrations reflect the
usage of these natural washes as irrigation waste channels. Winter flows in
these washes consist largely of subsurface flows into these channels, which
have much higher salinity concentrations. These characteristics are illus-
trated in Table 23. These natural wash discharges frequently have salinity
concentrations that are 50% greater than the usual salinity concentrations
encountered below the crop.root zone at a depth of 2 m.
TABLE 23. SALINITY OF NATURAL WASH DISCHARGES IN THE GRAND VALLEY
Natural
Wash
Lewis
Indian
Persigo
Hunter
Adobe
Little Salt
Big Salt West
Big Salt East
12/17/75
EC, ymhos
4580
6090
5370
4720
4650
4650
3940
3740
1/07/76
EC, ymhos
4480
5880
5510
5030
4870
4800
4020
3930
1/22/76
EC, ymhos
4430
5920
5420
4850
4460
4530
3840
4020
2/05/76
EC, ymhos
4350
5730
5360
4710
4580
4360
3560
3890
3/03/76
EC, ymhos
4180
5090
4810
4340
4260
2850
3420
3660
The monitoring network for the Grand Valley Salinity Control Demonstra-
tion Project is shown in Fig. 50. Some selected salinity data for open drains
are listed in Table 24 to illustrate the variation in salinity concentrations
in natural washes and open drains during the irrigation season as compared to
131
-------
CO
no
Legend
• Piezometers
® 2" Wells
*• Canal Rating Section
(J) Drainage
Measurement *
Drains
Area Boundary
Stub Ditch
i
Government
Highline
Canal "*~~
Scale I Mile
Figure 50. Monitoring net*^ for tne Grand Va,,ey Sa),nny Contro]
-------
TABLE 24. SALINITY OF OPEN DRAINS IN THE GRAND VALLEY SALINITY
CONTROL DEMONSTRATION PROJECT AREA
Date
03/27/72
04/25/72
06/06/72
07/03/72
08/07/72
09/04/72
10/03/72
11/07/72
12/05/72
01/08/73
02/05/73
03/05/73
04/02/73
05/02/73
06/01/73
07/02/73
08/07/73
09/04/73
10/03/73
11/08/73
12/05/73
01/10/74
02/01/74
03/05/74
04/06/74
05/01/74
06/06/74
07/08/74
08/06/74
09/02/74
10/01/74
11/05/74
12/06/74
01/07/75
03/05/75
04/08/75
05/06/75
06/02/75
07/07/75
08/04/75
09/01/75
10/01/75
11/05/75
12/03/75
Flume No. 4
EC
ymhos
2567
2268
1602
2108
2732
2613
3299
6763
6728
6678
6891
6624
6550
1841
1170
1062
1336
1533
1671
4912
5712
5626
5314
5036
5867
1459
1126
1351
1576
1635
1719
5184
6501
5200
6149
5461
1882
1207
1065
1329
1695
2100
6234
6258
TDS
ppm
1872
1664
1216
1704
2328
1980
2584
6764
6852
7060
7128
6836
6796
1592
1008
908
892
1088
1256
4702
6213
5208
6716
6328
6552
944
927
1008
1164
1488
1352
6360
6152
6624
6404
6672
1360
832
796
948
1128
1536
6064
6260
Flume
EC
ymhos
3065
2571
3193
2391
2428
4221
2338
6689
6624
6689
5472
6592
6630
1642
1378
1453
1732
2460
2060
4815
5611
5626
5475
5425
5580
2367
1488
2208
3065
1986
2565
5184
5959
5175
6337
5566
1597
1494
1314
1961
2223
1900
6222
6264
No. 6
TDS
ppm
2412
1548
2768
1972
1912
3804
1644
6576
6724
6860
5536
6872
6808
1420
1216
1092
1268
1880
1752
4676
6513
5888
6656
6884
6672
1648
1380
2428
2828
1532
2232
6644
6328
6844
6752
6684
1132
1316
968
1388
1500
1136
6095
6284
Flume No. 8
EC
ymhos
7248
1773
1391
2571
2276
2714
2342
7421
7234
7189
7332
7210
7175
2587
1802
1816
2376
2861
2314
5225
6262
6068
5788
5899
6511
2334
1448
1676
1931
2452
2041
6117
6773
5815
6816
5671
2133
1496
2242
3203
2517
2213
6966
6962
TDS
ppm
7476
2000
1080
2160
1832
2256
1700
7456
7448
7492
7596
7608
7368
2288
1584
1460
1980
2364
2028
5660
7241
6696
7328
7416
7356
1724
1273
1881
1584
2104
1620
7376
7112
7388
7308
6116
1664
1248
1716
2588
1752
1616
6836
7060
Lewis Wash
EC
ymhos
5452
909
515
823
1165
1256
1228
4438
4960
4500
5109
5055
5415
846
565
511
853
1129
1131
1251
4174
4105
4247
3587
4694
690
506
837
1016
1279
1256
3802
4200
3959
4853
4515
1031
630
522
899
1199
1258
3685
4391
TDS
ppm
512
376
576
904
740
680
4216
4824
5196
5120
5096
5368
556
468
296
364
628
728
903
4581
3700
4820
4052
4948
440
447
652
700
868
860
4140
4216
4640
4936
4680
648
544
260
552
664
776
3252
4320
Indian
EC
ymhos
5816
5824
5072
5740
5273
5137
5032
5095
5142
5201
5129
4951
4551
4913
4748
5356
5876
4960
5647
4939
5202
2198
2774
5342
4665
5440
4883
1819
696
1413
1655
1512
1836
4541
5003
Wash
I US
ppm
4458
6004
5056
5812
6320
6064
5904
5452
5584
5800
5472
5771
4560
5724
5948
5676
5768
6013
5520
5756
5792
1732
2620
4544
5028
5320
5152
1348
940
1076
1132
936
1332
4168
4736
133
(continued)
-------
TABLE 24. (Continued)
Date
01/05/76
02/02/76
03/01/76
04/06/76
05/04/76
06/01/76
07/06/76
08/02/76
09/03/76
10/01/76
11/03/76
Flume
EC
umnos
6492
6450
6406
6502
1370
1128
1678
1744
1756
1747
6200
• •
No. 4
IDS
ppm
6121
6424
6420
6444
776
744
1204
1240
1112
1100
6184
Flume
EC
ymhos
6384
6414
6411
5610
2943
2081
3016
2372
2208
1989
5400
No. 6
IDS
ppm
6196
6416
6404
5424
2244
1584
2508
1836
1183
1352
5160
Flume
EC
ymhos
7161
7021
6984
7124
4673
2686
1975
2193
2660
2785
6800
No. 8
IDS
ppm
6784
6968
7052
7084
3948
2084
1468
1628
1908
2108
6780
Lewi s
EC
umhos
4851
4740
4980
3081
860
632
803
1234
1303
1187
4110
Wash
IDS
ppm
4608
4508
4736
2536
476
356
480
772
664
620
3820
Indian
EC
ijmhos
1 i •—
5320
5480
5544
5273
1720
1319
1712
1367
2190
2098
4116
• »^— ^^_
Wash
TDS
PPm
5164
5284
5364
4916
1176
956
1252
892
1640
1512
3708
• •
"re .essent1a11* subsurface flows from groundwater.
Groundwater
in the last sixty years. The following is quoted from
"An important groundwater body in the Grand Valley is a gravel
aquifer approximately parallel to the Colorado River. This water s
S!?pr ^hJh?°UrC
-------
l.5r
*>
o>
E
0 0.
Irrigation Season Measurements
Drains
Miller
5-
Wells)
Miller
(6Wells)
Gravel Pits
15 of 5) y
/X**Mancos shale
Bethel (4 Samples)
Corner
o
Area I
Skogerboe
Winter Measurement
1915
1 % ' ' ;I ' ' -1 •
1955 1971 1973 Mar. July-Aug. Nov. May
Year 1972 1972 1972 1973
Figure 51. Calcium-magnesium ratios for selected ground and surface
water samples in the Grand Valley. (Taken from S.R. 01 sen
as reported by Kruse, 46)
135
-------
drains east of Grand Junction showed a Ca/Mg ratio of 0.58 during the
winter when the canals were dry. Water from several gravel pits east
and west of Grand Junction had a Ca/Mg ratio of 0.55. Water extracts
of several shale samples had a Ca/Mg ratio of 0.5 as shown in Fig. 51.
Water from a well within the city limits of Grand Junction has a Ca/Mg
ratio of 0.66. This well is pumped continuously. Water from drains
west of Grand Junction showed a Ca/Mg ratio of 1.2 during the winter
season. Water from 12 wells east of Grand Junction had a Ca/Mg ratio
of 0.50.
"Water in the gravel aquifer shows essentially a constant Ca/Mg
ratio since 1915. This ratio appears to be constant because the water
is in equilibrium with three solid phases, i.e., calcium carbonate
gypsum (CaSO/pZ^O), and magnesite (MgC03J, and the partial pressure of
C02 is near 0.011 atmospheres (in air PC02=0.0003 atm). The water is
supersaturated with respect to calcite or aragonite if the pH is above
7; so the actual form and composition of the calcium carbonate present
is unknown.
"Most of the water samples were in equilibrium and saturated with
magnesite and gypsum. This criteria appeared to be necessary in most
cases in order for water from other sources to show a Ca/Mg ratio similar
to the water in the aquifer at Bethel Corner.
"Data for water in various wells north of the gravel aquifer indi-
cate a characteristic Ca/Mg ratio of near 0.5 is reached by this water
before it enters the gravel aquifer. This result indicates that the
solid phases (gypsum, magnesite, and calcium carbonate) are present in
shale and the alluvial material over the shale, but not necessarily in
the surface soil material 0-3 feet in depth.
"Although the Ca/Mg ratio of water in the aquifer appears to be
controlled by the solid phases present, the system has one degree of
freedom to allow a soluble salt to vary in concentration, such as
Na2$04 or NaCl. The data indicate that such concentrations tend to
vary within a narrow range rather than a wide range. These results
will require further study for confirmation; but the data indicate
tentatively that a reduction in the volume of water entering the aquifer
will cause a proportional reduction in the salt load to the river."
The monitoring network shown in Fig 50 includes numerous 2-inch diameter
wells which reach the underlying Mancos shale formation. The location, depth
and top elevation of these wells is listed in Table 25. The cross-section
shown in Fig. 49 is taken along 31 Road which runs north-south and is parallel
but 30 miles east of the Utah-Colorado state line. Selected salinity data for
a 2-inch well shown in Fig. 49 is listed in Table 26. [The complete data is
reported by Binder et al. (4).] This well is located near the upper portions
of the irrigated lands. The TDS varies from roughly 6000 to 8000 ppm, which
again is approximately twice the salinity concentration encountered at a depth
of 2 m below the ground surface of croplands. Most of the data listed in
Table 26 show that the TDS in ppm exceeds the EC in ymhos.
136
-------
TABLE 25. LOCATION, DEPTH AND TOP ELEVATION OF TWO-INCH DIAMETER WELLS
IN THE GRAND VALLEY SALINITY CONTROL DEMONSTRATION PROJECT
csu
Well
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Location
29 & D Roads
30 & D Roads
31 & 0 Roads
31 & D Roads
31 & D Roads
32 & D Roads
3110 E.25 Road
3110 E.25 Road
3110 E.25 Road
32 & G.V. Canal
3250 F Road
31 & F Road
31 & F.5 Road
30 & F Road
2950 E. Road
29 & D.5 Road
31 & D.5 Road
Well
Depth
(ft)
28.6
31
34
22
40
39.5
56
50
45.5
41 .
77
56
57
50
56
42
43
Elevation
(ft)
4603.65
4610.87
4622.13
4622.08
4622.22
4633.40
4676.94
4676.97
4676.82
4667.11
4717.74
4715.89
4750.33
4689.49
4641.07
4618.29
4641.36
TABLE 26. SELECTED SALINITY DATA FOR CSU WELL NO. 12 LOCATED NEAR THE
INTERSECTION OF 31 AND F ROADS IN THE GRAND VALLEY SALINITY
CONTROL DEMONSTRATION PROJECT AREA
Date
Collected
10/30/69
11/26/69
02/05/70
06/08/70
05/12/71
06/22/71
07/20/71
08/02/71
08/19/71
08/31/71
09/14/71
09/23/71
09/28/71
10/12/71
10/27/71
11/09/71
11/23/71
12/08/71
12/21/71
EC
ymhos
7012
6985
6626
6672
6800
7300
7000
6726
7281
7141
7027
7141
7050
7061
6438
6707
6924
6793
6806
TDS
ppm
8240
7440
8016
7492
7344
7596
7572
7532
7532
7612
7764
7204
7208
7020
7100
7080
Date
Collected
01/05/72
02/01/72
04/25/72
05/02/72
05/08/72
06/06/72
07/05/72
08/01/72
09/04/72
10/03/72
11/15/72
12/05/72
01/02/73
01/29/73
03/05/73
04/02/73
06/11/73
07/02/73
08/07/73
09/04/73
EC
ymhos
6804
6938
6793
7094
6999
7061
6992
6925
6804
6756
6773
6935
6750
6847
6657
6948
5930
6086
6008
6150
TDS
ppm
7144
7168
7292
7684
7424
7472
7348
7476
7260
7044
6592
7248
7172
7180
7280
7408
7380
7300
7256
7284
Date
Collected
11/28/73
02/01/74
03/06/74
06/06/74
06/24/74
07/30/74
08/26/74
10/02/74
11/05/74
12/06/74
01/07/75
03/05/75
04/01/75
05/06/75
06/03/75
07/08/75
08/05/75
09/02/75
10/03/75
EC
ymhos
5342
5599
5689
5580
8126
6767
6212
5705
5301
6837
5495
6701
5267
6383
6304
6236
6490
6467
6468
TDS
ppm
5852
6924
6984
7060
7012
7964
7536
7148
7015
7352
6832
7040
6740
6776
5708
6228
6236
6156
6412
137
(continued)
-------
TABLE 26. (Continued)
Date
Collected
11/07/75
12/05/75
01/07/76
02/04/76
EC
ymhos
6397
6522
6456
6362
IDS
ppm
6352
6372
6376
6320
Date
Collected
03/01/76
03/29/76
05/06/76
06/03/76
07/06/76
EC
ymhos
6400
6425
6482
6331
6325
TDS
ppm
6216
6240
6396
6536
6488
Date
Collected
08/02/76
09/09/76
10/06/76
11/03/76
EC
ymhos
6426
6479
6978
6300
TDS
ppm
6440
6484
6788
6168
Salinity data for the 2-inch wells located along D Road (Fig. 50) are
listed in Table 27. The TDS of these wells varies roughly from 5500 to 9000
ppm. There are numerous TDS measurements that exceed 8000 ppm. The salinity
concentrations in the wells along D Road are only slightly greater than the
salinity levels shown in Table 26 for CSU Well No. 12, which is located two
TABLE 27. SELECTED SALINITY DATA FOR WELLS LOCATED ALONG D ROAD IN
THE GRAND VALLEY SALINITY CONTROL DEMONSTRATION PROJECT AREA
Date
Collected
05/25/71
07/06/71
08/02/71
09/23/71
10/27/71
11/23/71
12/21/71
02/01/72
03/21/72
03/27/72
04/25/72
05/30/72
06/27/72
07/25/72
08/29/72
09/26/72
10/31/72
11/27/72
12/18/72
01/29/73
02/26/73
03/26/73
04/27/73
05/30/73
CSU Wei
29 & D
. EC
ymhos
6067
6231
6252
6185
6222
5965
6136
6284
6166
6230
6210
5285
5211
1 No. 1
Roads
TDS
ppm
6088
6148
6300
6292
5916
5960
5544
5776
6100
5872
6080
5992
6116
CSU Wei
30 & D
EC
ymhos
7600
7264
7585
7257
7453
7674
7718
7465
7491
7491
7610
7813
7575
7534
7650
7444
7593
7837
7580
7697
7613
6718
6560
1 No. 2
Roads
TDS
ppm
8262
7816
7964
7872
7664
8332
7556
7860
8184
8180
8140
8284
7920
7512
7684
7980
7656
6284
8068
7844
8064
7828
8128
CSU Wei
31 & D
EC
ymhos
6191
6311
6023
6366
6278
6298
6362
6314
6321
6376
6400
6475
6462
6426
9294
6240
6373
6251
6251
6321
5591
5424
1 No. 4
Roads
TDS
ppm
6364
6544
6308
6248
6312
6212
6424
6508
6424
6500
6572
6592
5204
6888
6360
5824
6421
6476
6248
6388
6460
6564
CSU Wei
32 & D
EC
ymhos
7896
8414
7905
8316
8229
8340
8190
7836
8191
8446
8400
8268
8464
8364
8163
8321
8176
8243
8307
8300
6806
6857
1 No. 6
Roads
TDS
ppm
8804
8640
8524
9096
8440
8904
9100
8404
8880
9040
8964
9060
7332
8664
8760
8208
6323
8776
8688
8772
8640
8724
138
-------
TABLE 27. (Continued)
Date
Col 1 ected
06/25/73
07/30/73
08/27/73
09/26/73
10/31/73
11/28/73
12/21/17
01/24/74
03/22/74
04/23/74
05/29/74
06/24/74
07/30/74
08/26/74
09/24/74
10/29/74
11/27/74
12/19/74
01/22/75
02/26/75
03/24/75
04/22/75
05/27/75
06/30/75
07/28/75
08/25/75
09/24/75
10/29/75
11/26/75
12/29/75
01/26/76
02/23/76
03/29/76
04/27/76
05/25/76
06/28/76
07/28/76
08/23/76
09/22/76
10/28/76
11/03/76
CSU Wei
29 & D
EC
ymhos
5406
5436
5580
5493
5643
5500
5483
4961
5585
5526
5406
5322
6270
5472
5287
5145
5317
5818
5433
5269
5234
5313
6166
5987
5884
6049
6020
6049
6033
6049
6064
6014
5928
5946
5819
5893
5980
5851
6266
5300
1 No. 1
Roads
TDS
ppm
6016
6584
6248
5808
5868
6128
6006
4852
6216
5988
6004
6092
6268
6224
6152
6172
6160
5984
6068
6148
5888
6108
5892
5832
5816
5804
5640
5692
5576
5620
5552
5508
5072
5688
5456
5540
5508
5400
5940
5048
CSU Well
30 & D
EC
ymhos
6507
6801
7061
6371
6763
6651
6579
6058
7299
6826
6834
6845
8066
6756
6220
6595
6635
7528
6614
5694
6406
5895
7573
7904
7401
7927
7589
7666
7800
7747
7718
7733
7579
7684
7638
7666
7405
7794
7162
6597
7444
1 No. 2
Roads
TDS
ppm
7876
8004
8208
7324
6956
7988
7200
5825
8276
7092
8304
7332
8480
8732
8340
8124
6160
7920
7844
6540
7872
6852
8096
8072
8296
8004
7448
7708
7788
7712
7596
7608
7648
7636
7612
7728
7368
7280
6976
6476
7512
CSU Well
31 & D
EC
pmhos
5481
6688
5806
5484
5899
5501
5293
4969
6068
5831
5490
5694
6310
5767
5495
5356
5137
6344
5270
5279
5162
5330
5743
6192
6038
5997
6142
5957
6454
6212
6049
6096
5991
5904
6059
5684
5893
6171
5593
5416
5800
1 No. 4
Roads
TDS
ppm
6256
6364
6576
6036
6040
6424
5920
4776
6464
6256
6288
6348
6408
6484
6468
6380
5748
6340
6128
6016
6060
6180
5736
6164
6032
5728
5520
5528
6024
5972
5728
5664
5676
5604
5912
5484
5524
5652
5296
5019
5660
CSU Well
32 & D
EC
ymhos
6902
7015
7084
6879
7556
6769
6373
6481
6952
6934
6609
6610
8013
6902
6324
6281
6507
7316
6304
5776
6271
7314
7592
No. 6
Roads
TDS
ppm
8432
8684
8672
7856
7156
8528
6748
5968
8680
8436
8604
7528
8836
8540
8516
8012
7844
7736
7688
6704
7976
7808
8108
139
-------
DH-I
• Drill Hole Location
Figure 52. Location of wells installed by the Agricultural Research
Service in western Grand Valley.
140
-------
TABLE 28. SELECTED SALINITY DATA FOR WELLS INSTALLED BY THE AGRICULTURAL RESEARCH SERVICE (SEA)
IN WESTERN GRAND VALLEY
ARS
Well
No.
2
2
2
2
2
12
12
12
12
12
15
15
15
15
15
18-L
18-L*
18-L
18-L
18-L*
20- L
20-L
20-L
20-L
20-L
Date
Collected
06/25/75
08/07/75
10/12/75
12/17/75
03/16/75
06/25/75
08/07/75
10/12/75
12/17/75
03/16/75
06/25/75
08/07/75
10/12/75
12/17/75
03/16/75
06/25/75
08/07/75
10/12/75
12/17/75
03/16/75
06/25/75
18/07/75
10/12/75
12/17/75
03/16/75
PH
7.76
7.82
7.82
7.71
7.47
7.91
7.89
7.83
7.78
7.74
7.65
7.65
7.69
7.68
7.48
7.59
7.76
7.86
7.29
7.71
7.57
7.71
7.68
7.40
7.46
EC
ymhos
7,630
8,120
12,610
12,860
10,310
18,500
18,420
21,280
21,790
20,150
15,900
14,520
16,190
17,370
16,560
5,530
5,110
5,480
6,530
5,510
4,810
4,210
4,260
4,490
4,500
Ca
14.97
15.47
22.50
22.34
16.88
13.97
13.97
18.28
20.49
16.16
17.30
16.97
20.11
22.52
18.55
9.65
10.48
14.18
15.51
12.35
18.46
13.67
16.95
16.80
13.78
Mg
24.18
32.90
55.89
52.04
45.43
49.34
50.16
53.41
49.60
51.53
117.19
119.24
122.29
127.81
120.48
13.16
13.16
14.58
20.39
15.30
13.65
12.50
11.50
12.18
12.45
Na
78.26
95.65
167.83
164.13
116.90
379.35
373.91
456.30
405.17
393.46
189.13
208.70
228.15
241 . 61
228.98
46.29
43.70
49.22
58.65
48.33
25.43
23.70
24.80
26.74
27.96
meq/1
K
0.407
0.537
0.80
0.73
0.59
0.621
0.660
0.82
0.74
0.67
0.767
0.794
0.97
0.89
0.84
0.849
0.852
1.02
1.21
0.92
0.286
0.269
0.25
0.26
0.30
HC03
10.70
13.50
19.60
18.00
14.70
23.00
23.00
23.40
21.76
20.24
18.60
18.40
18.40
18.20
18.20
10.80
11.80
12.36
11.96
11.80
9.00
8.40
10.20
8.60
10.00
Cl
15.72
19.90
26.00
24.10
17.88
33.04
30.66
32.84
30.60
28.40
23.04
21.56
22.00
21.82
21.54
6.64
6.86
6.88
7.30
6.32
19.24
19.30
18.36
18.14
16.94
N03
1.135
1.443
1.39
1.43
1.27
0.795
0.438
0.647
0.784
1.22
35.47
34.775
36.26
33.14
26.70
0.795
0.623
0.824
3.02
1.03
0.758
0.623
0.893
0.877
1 .03
S04
86.25
111.74
200.31
191.50
145.71
365.63
406.96
463.75
416.47
402.66
235.94
280.97
282.19
282.06
258.66
50.63
49.72
57.81
70.87
54.33
25.62
24.72
23.03
24.99
26.00
*L, 4" casing, shallow
-------
The Scientific Education Administration (SEA), Agricultural Research has
drilled a number of wells in the western portion of the Valley near the town
of Fruita. The locations of these wells are shown in Fig. 52. Data from some
of these wells (46,47,48) are listed in Table 28 for comparison with results
cited in Tables 26 and 27. Some of the results are comparable (e.g., wells
ARS(SEA)18-L and ARS(SEA)20-L). Some of the data included in Table 28 was
selected because it represented the highest levels of salinity concentration
encountered in the valley (e.g,, wells ARS(SEA)2, ARS(SEA)12, and ARS(SEA)IS)
These wells have much higher Na+ concentrations than the other wells. Thus,
as subsurface irrigation return flows move through the groundwater reservoir
additional Na+ is taken into solution. Since the soil moisture movement at a
depth of 2 m is already saturated with gypsum, but the gypsum levels are even
higher in the cobble aquifer, this would imply that secondary chemical reactions
are taking place which allow additional sodium to be taken into solution.
Unfortunately, these secondary chemical reactions are not described in the
soil chemistry model used in this study.
PREDICTION OF SALT LOAD
The fact that the TDS concentrations in the drainage water at the
bottom of the soil profile and the groundwater in the cobble aquifer, although
markedly different, are relatively insensitive to the rates and volumes of
discharge makes the prediction of salt load under various management or abate-
ment alternatives a simple task. In other words, the salt load reaching the
Colorado River is directly proportional to the volume of subsurface irrigation
return flows because the salinity concentrations remain approximately constant
below the crop root zone and in the cobble aquifer. The problem of predicting
the subsurface return flow salinity is, therefore, reduced to determining the
flow routes and discharge volumes for each flow route, which can then be
combined with the salt concentrations corresponding to each flow route in
order to calculate the salt load reaching the Colorado River.
142
-------
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150
-------
TABLE A-l.
APPENDIX A
SOIL PROPERTIES AND EVAPOTRANSPIRATION DATA
SOIL PROPERTIES FOR BILLINGS SILTY CLAY LOAM, MATCHETT FARM
SOIL MOISTURE CHARACTERISTIC
, Pc/pg %
(cm water)
28
59
114
332
504
800
6
volume
44
41
33.3
30.6
28.0
26.6
S
0.98
0.91
0.73
0.68
0.62
0.62
Se
0.95
0.80
0.41
0.31
0.18
0.13
Bulk Density =1.64 gm/cc
Saturated Moisture Content es = 0.45
Empirical Parameters
Brooks and Corey
X = 0.651
Sr = 0.538
= 41 .0 cm water
Su and Brooks
= 96 cm water
a = 0.24
b = 0.222
m = 0.428
TABLE A-2. INITIAL SOIL MOISTURE DISTRIBUTION USED FOR SIMULATIONS
Depth
(cm)
0.0
15.2
30.5
45.7
61.0
76.2
91.5
106.7
e
(vol)
0.19
0.19
0.28
0.33
0.33
0.33
0.34
0.35
Depth
(cm)
122.0
137.2
152.5
167.7
183.0
198.2
213.5
9
(vol)
0.35
0.33
0.32
0.33
0.34
0.34
0.36
151
-------
TABLE A-3. EQUATIONS USED TO CALCULATE EVAPOTRANSPIRATION
From - Scheduling Irrigations Using a Programmable Calculator -
ARS-NC-12, February 1974, ARS-USDA
Polynomial Constants for Crop Curves
KCQ - crop coefficient
CO
Corn A = -1.583
B = 2.756
C = -0.4276
D = 0.213
Corn A = 275x10
B = 4688x10
C = 9.0
D = 0.915
-8
-7
log[l
Cr
Before effective cover
r = fraction time from planting
to effective cover
After effective cover
r = number of days beyond effective
cover date
100(1 - Dp/Dt)]
"s log (101)
D = soil water depletion
Dt = total available water in root zone at field capacity
Dpi = Dpi-l
Kc + KcoKs
KcEtp
Etr - Ri
K = adjusted Et for losses due to surface evaporation
Etr
K = 0.8 first day
0.5 second day
0.3 third day
- Kc>Etp
Follow irrigation or rainfall
= 0
K = 0.9 or more for 3 days after irrigation E
c t
Et = potential evapotranspiration computed using Penman formula
R.. = rainfall
152
-------
TABLE A-4. IRRIGATION SCHEDULE FOR THE CORN CROP USED IN THE SIMULATION
14-DAY IRRIGATION SCHEDULE AND 7-DAY IRRIGATION SCHEDULE
14-DAY IRRIGATION
Date
5/24
6/07
6/21
7/05
7/19
8/02
8/16
8/30
9/13
9/27
10/11
Date
5/24
5/31
6/07
6/14
6/21
6/28
7/05
7/12
7/19
8/02
8/09
8/16
8/23
8/30
9/09
9/13
9/20
9/27
10/04
10/11
Julian
Day
144
158
172
186
200
214
228
242
256
270
284
Julian
Day
144
151
158
165
172
179
186
193
200
214
221
228
235
242
249
256
263
270
277
284
Up
(cm)
6.60
2.62
3.81
5.72
6.86
7.44
8.86
8.86
7.49
6.38
5.16
7 -DAY
Dp
(cm)
6.60
1.70
2.13
2.03
2.48
3.12
3.40
3.71
3.78
4.24
5.00
4.39
4.62
4.80
4.04
3.78
3.48
3.15
2.89
2.44
SCHEDULE
Dp plus Leaching Increment (cm)
1%
6.67
2.65
3.85
5.78
6.93
7.51
8.95
8.95
7.56
6.44
5.21
2%
6.73
2.67
3.89
5.83
7.00
7.59
9.04
9.04
7.64
6.51
5.26
IRRIGATION
5%
6.93
2.75
4.00
6.01
7.20
7.81
9.30
9.30
7.86
6.70
5.42
10%
7.26
2.88
4.19
6.29
7.55
8.18
9.75
9.75
8.24
7.02
5.68
20%
7.29
3.14
4.57
6.86
8.23
8.93
10.63
10.63
8.99
7.66
6.19
40%
9.24
3.67
5.33
8.01
9.60
10.42
12.40
12.40
10.49
8.93
7.22
SCHEDULE '
Dp plus Leaching Increment (cm1
1%
6.67
1.72
2.45
2.05
2.50
3.15
3.43
3.75
3.82
4.28
5.05
4.43
4.67
4.85
4.08
3.82
3.51
3.18
2.92
2.46
2%
6.73
1.73
2.17
2.07
2.50
3.18
3.47
3.82
3.86
4.32
5.10
4.48
4.71
4.90
4.12
3.86
3.55
3.21
2.95
2.50
5%
6.93
1.78
2.24
4.12
2.60
3.28
3.57
3.90
3.97
4.45
5.25
4.61
4.85
5.04
4.24
3.97
3.65
3.31
3.03
2.56
10%
7.26
1.87
2.34
2.23
2.73
3.43
3.74
4.08
4.16
4.66
5.50
4.83
5.08
4.28
4.44
4.1.6
3.83
3.46
3.18
2.68
20%
7.92
2.04
2.56
2.44
2.98
3.74
4.08
4.45
4.54
5.09
6.00
5.27
5.54
5.70
4.85
4.54
4.18
3.78
3.47
2.93
40%
9.24
2.38
2.98
2.84
3.47
4.37
4.76
5.19
5.29
5.94
7.00
6.1
5
6.47
6.72
5.66
5.29
4.87
4.41
4.05
3.42
Corn planted May 24, 1975.
153
-------
APPENDIX B
SIMULATED DATA
TABLE B-1. SIMULATION DATA FOR PLOT 23, MATCHETT FARM WITH P,
DAY 166-196, 1975 C
= 7 matm
Concentrations
Julian
Date
166
168
170
172
174
176
178
180
182
184
186
188
190
192
194
196
TABLE B-2
Ca
ppm
915
790
785
739
736
737
738
740
741
742
744
745
747-
739
746
747
Na
ppm
60
58
61
120
132
209
216
220
222
223
225
227
226
225
243
249
computed at a
Mg
ppm
100
72
72
71
71
77
77
78
78
78
78
79
79
78
81
81
. SIMULATION DATA FOR
Concentrations
Julian
Date
166
168
170
172
174
176
178
180
182
184
186
188
190
192
194
196
Ca
ppm
975
820
826
771
772
769
773
776
779
781
784
786
786
779
779
781
Na
ppm
60
57
61
121
134
212
220
224
227
227
229
231
231
229
247
253
HC03
ppm
123
111
113
119
120
122
122
123
123
123
123
123
122
123
123
123
PLOT 23,
computed at a
Mg
ppm
101
75
75
73
74
79
80
81
81
82
82
82
83
81
83
84
HC03
ppm
304
290
296
275
292
282
299
309
316
322
326
332
336
329
293
303
depth of
Cl
ppm
333
339
345
429
450
491
487
490
494
496
500
505
510
497
488
484
MATCHETT
depth of
Cl
ppm
333
338
345
429
450
491
487
490
494
496
500
505
510
497
488
484
1 .1 meters
S04
ppm
1938
1529
1557
1687
1701
1761
1867
1770 '
1769
1768
1771
1771
1767
1760
1792
1795
FARM, DAY
TDS
ppm
3469
2919
2942
3165
3210
3397
3407
3421
3427
3430
3441
3450
3451
3422
3472
2379
166-196, 1975.
1.1 meters
S04
ppm
1938
1521
1526
1656
1665
1725
1727
1730
1727
1727
1727
1726
1721
1718
1755
1756
TDS
ppm
3711
3113
3129
3325
3387
3558
3586
3610
3624
3625
3648
3662
3667
3633
3624
3661
154
-------
TABLE B-3. IDS CONCENTRATIONS AND CHLORIDE CONCENTRATIONS IN CUMULATIVE
LEACHATE AT 2.13 m FOR HYPOTHETICAL SIMULATION USING 7-DAY
IRRIGATION SCHEDULE
Julian
Date
157
171 .
185
199
213
227
241
255
269
283
293
Julian '
Date
157
171
185
199
213
227
241
255
269
283
293
2%
Cumulative
Infiltration
(cm)
9.36
13.59
18.79
26.59
34.19
43.40
52.63
61.72
69.12
76.72
80.32
5%
Cumulative
Infiltration
(cm)
9.60
14.00
19.19
27.23
35.11
44.77
54.25
63.55
74.14
77.90
81.58
LEACHING INCREMENT
Cumulative
Leachate
(cm)
3.86
5.66
6.54
7.05
7.37
7.60
7.76
7.90
8.01
8.10
8.17
LEACHING INCREMENT
Cumulative
Leachate
(cm)
4.08
5.89
6.82
7.35
7.67
7.92
8.11 .
8.29
8.53
8.91
9.19
Cl
ppm
260
278
290
297
301
305
307
310
313
315
312
Cl
ppm
262
280
291
299
303
306
311
310
308
302
304
TDS
ppm
3256
3296
3318
3332
3338
3351
3355
3356
3357
3362
3357
TDS
ppm
3254
3295
3320
3334
3344
3350
3358
3357
3360
3350
3354
TDS-C1
ppm
2996
3018
3028
3035
3037
3046
3048
3046
3044
3047
3045
TDS-C1
ppm
2992
3015
3029
3035
3041
3044
3047
3047
3050
3048
3050
(continued)
155
-------
TABLE B-3. (continued)
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
20%
Cumulative
Infiltration
(cm)
10.61
15.63
22.36
30.82
39.87
51.09
61.61
72.35
81.19
88.43
91.46
40%
Cumulative
Infiltration
(cm)
12.40
17.98
26.12
36.04
46.47
59.42
72.14
84.44
94.83
103.41
107.40
LEACHING INCREMENT
Cumulative
Leachate
(cm)
5.02
7.26
8.54
9.18
9.91
11.27
13.01
14.83
16.66
18.26
19.25
LEACHING INCREMENT
Cumulative
Leachate
(cm)
6.23
8.90
10.32
12.34
15.24
18.52
22.25
25.97
29.18
32.07
33.76
Cl
ppm
269
284
300
309
305
303
312
315
327
336
343
Cl
ppm
278
295
311
306
317
326
346
373
400
429
447
TDS
ppm
3276
3320
3349
3373
3375
3385
3411
3424
3446
3471
3467
TDS '
ppm
3296
3344
3385
3404
3424
3441
3468
3480
3492
3512
3527
TDS-C1
ppm
3007
3036
3049
3064
3070
3082
3099
3109
3119
3135
3124
TDS-C1
ppm
3018
3049
3074
3098
3107
3115
3122
3107
3092
3083
3080
156
-------
TABLE B-4. IDS CONCENTRATIONS AND CHLORIDE CONCENTRATIONS IN CUMULATIVE
LEACHATE AT 2.13 m FOR HYPOTHETICAL SIMULATION USING 14-DAY
IRRIGATION SCHEDULE
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
2%
Cumulative
Infiltration
(cm)
7.09
9.85
13.56
19.48
26.58
34.07
43.10
52.15
59.83
66.42
71.51
5%
Cumulative
Infiltration
(cm)
7.74
10.54
14.41
20.49
27.62
35.45
44.78
54.07
61.93
68.67
74.10
LEACHING INCREMENT
Cumulative
Leachate
(cm)
3.87
5.51
6.30
6.77
7.07
7.29
7.45
7.58
7.69
7.78
7.84
LEACHING INCREMENT
Cumulative
Leachate
(cm)
4.46
6.13
6.93
7.40
7.70
7.92
8.09
8.24
8.39
8.65
8.95
Cl
ppm
260
279
288
296
299
303
308
310
312
313
314
Cl
ppm
264
284
294
300
306
309
311
313
313
307
303
TDS
ppm
3256
3291
3314
3328
3338
3343
3354
3356
3362
3366
3368
TDS
ppm
3264
3006
3324
3342
3352
3362
3361
3370
3374
3361
3361
TDS-C1
ppm
2996
3012
3026
3032
3039
3040
3046
3046
3050
3053
3054
TDS-C1
ppm
3000
3022
3030
3042
3046
3053
3050
3057
3061
3054
3058
(continued)
157
-------
TABLE B-4. (continued)
20% LEACHING INCREMENT
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
Julian
Date
157
171
185
199
213
227
241
255
269
283
293
Cumulative
Infiltration
(cm)
8.33
11.45
16.46
23.50
31.64
40.55
51.25
60.49
68.36
75.11
80.55
40%
Cumulative
Infiltration
(cm)
9.52
13.22
18.60
26.64
36.15
46.59
59.04
71.49
82.00
90.94
98.14
Cumulative
Leachate
(cm)
5.02
6.70
7.48
8.05
8.92
10.10
11.93
12.86
13.66
14.31
14.75
LEACHING INCREMENT
Cumulative
Leachate
(cm)
6.16
8.00
9.23
10.93
13.47
16.22
19.79
23.44
26.64
29.38
30.94
Cl
ppm
269
290
298
302
297
299
302
317
323
327
327
Cl
ppm
278
297
304
303
312
323
343
364
388
409
420
TDS
ppm
3276
3318
3336
3352
3352
3365
3391
3416
1 3432
3444
3444
TDS
ppm
3299
3344
3367
3383
3407
3445
3470
3496
3508
3508
3520
TDS-C1
ppm
3007
3028
3038
3050
3055
3066
3089
3099
3109
3117
3117
TDS-C1
ppm
3021
3047
3063
3080
3095
3122
3127
3132
3120
3099
3100
158
-------
TABLE B-5 CHLORIDE CONCENTRATION PROFILES (ppm) FOR HYPOTHETICAL SIMULATIONS USING
14-DAY IRRIGATION SCHEDULE
2%
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
LEACHING INCREMENT
Day
157
195
302
525
659
578
510
411
360
345
346
344
334
287
260
Day
199
146
239
439
730
787
727
508
426
359
350
344
338
308
296
Day
255
111
155
211
435
767
1108
903
670
347
347
345
338
312
310
Day
293
96
108
149
277
547
1012
1061
869
409
347
341
336
312
314
20% LEACHING INCREMENT
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
Day
157
161
282
482
621
582
522
432
374
351
348
343
335
296
267
Day
199
122
188
332
669
699
724
583
480'
375
351
344
337
312
302
Day
255
105
131
151
241
403
665
786
791
597
460
389
355
336
317
Day
293
88
97
118
162
246
439
645
796
712
575
463
396
358
327
40% LEACHING INCREMENT
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
Day
157
145
256
439
587
579
532
448
388
356
349
344
337
303
279
Day
199
110
155
252
433
587
675
614
533
422
373
354
343
325
303
Day
255
100
117
125
161
226
358
493
618
613
564
499
436
392
364
Day
293
82
92
104
124
151
213
302
433
519
560
556
578
467
420
-------
TABLE B-6. CHLORIDE CONCENTRATION PROFILES (ppm) FOR HYPOTHETICAL SIMULATIONS USING 7-DAY
iKKltiAriON SCHEDULE
2% LEACHING INCREMENT
ueptn
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
uay
157
150
270
480
643
596
528
422
366
345
347
343
333
287
360
uay
199
97
152
306
601
768
774
539
438
363
349
346
338
309
297
Day
255
101
116
157
312
623
1052
956
719
348
348
346
338
313
310
Day
293
71
87
116
201
401
814
1035
972
553
379
343
330
308
312
20% LEACHING INCREMENT
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
Day
157
130
246
440
604
596
540
441
380
349
346
344
335
296
270
Day
199
94
119
215
427
624
734
627
513
383
351
344
340
318
309
Day
255
97
104
123
176
290
532
723
803
634
485
399
361
359
315
Day
293
83
85
102
145
217
392
602
787
724
600
489
411
368
343
40% LEACHING INCREMENT
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
Day
157
112
214
381
546
583
550
464
397
358
349
344
337
304
278
Day
199
94
104
156
288
459
616
630
576
459
390
360
345
328
300
Day
255
93
94
108
132
169
260
383
534
583
571
521
462
410
374
Day
293
71
78
84
105
134
186
260
377
467
529
546
526
486
447
-------
TABLE B-7. CHLORIDE CONCENTRATION PROFILES (ppm) FOR DAY 293 OF SECOND YEAR
IN A 2-YEAR HYPOTHETICAL SIMULATION USING 20% LEACHING INCREMENT
AND 14-DAY IRRIGATION SCHEDULE
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
No Winter
Precipitation
88
98
117
148
179
229
284
401
494
573
620
621
584
527
With Winter
Precipitation
88
98
117
145
166
188
193
227
262
326
406
483
540
564
TABLE B-8. TDS CONCENTRATION PROFILES (ppm) FOR 6-YEAR SIMULATION
Depth
(cm)
15
30
46
61
76
91
107
122
137
152
168
183
198
213
Day 293
Year 1
569
615
999
3638
3932
4166
3922
3734
3396
3320
3395
3390
3469
3444
Year 6
567
595
668
790
898
1071
3224
3598
3554
3445
3384
3347
3371
3418
161
-------
TABLE C-l.
APPENDIX C
ANALYSIS OF FIELD DATA
CHEMICAL ANALYSIS OF SOIL SOLUTION EXTRACTED AT A DEPTH OF
1.1 METERS IN PLOT 23 ON THE MATCHETT FARM IN 1975
Julian
Date
169
171
172
174
176
180
185
191
197
Ca
ppm
647
674
656
627
620
601
629
616
593 ,
Mg
ppm
106
89
111
86
101
113
84
96
102
Na '
ppm
250
185
263
163
174
262
162
176
203
S04
ppm
1788
1519
1704
1488
1224
1613
1548
1508
1533
HCOa
ppm
416
466
370
365
398
313
342
374
379
Cl
ppm
309
285
346
254
397
296
229
259
303
TDS
ppm
3516
3218
3450
2983
3217
3198
2994
3250
3597
TABLE C-2,. pK ANALYSIS OF DRAINAGE WATER SAMPLES COLLECTED ON MATCHETT
FARM, 1975
Plot
Plot
Julian
Day
28
196
210
29
196
196
2.
2.
2.
2.
pCa
2822
3389
2904
2873
2.
2.
2.
2.
pMg
7691
8546
7415
7156
Pso4
2.3805
2.3324
2.3636
2.3880
PHC03
2.1250
2.2724
2.0538
2.0051
pco3
4.8540
5.1014
4.7828
4.8341
pCaC03
7.1363
7.4404
7.0732
7.1215
pMgC03
7.6232
7.9561
7.5243
7.5497
pCaS04
4.6628
4.6713
4.6541
4.6754
Plot 33
206 2.3074 2.6079 2.3235 2.2234 5.4524 7.7599 8.0603 4.6309
220 2.3628 2.6516 2.1795 2.8410 5.6700 8.0329 8.3217 4.5424
Plot 34
208
211
212
216
220
Plot 35
205
213
230
252
2.3459
2.3585
2.3562
2.3863
2.3334
2.3627
2.3330
2.3429
2.3622
2.
2.
2.
2.
3.
2.
2.
2.
2.
6186
5781
5880
6090
3693
6741
7642
7048
7450
2.2470
2.3007
2.2850
2.2651
2.2687
2.3054
2.2879
2.2813
2.3407
2.
2.
2.
2.
2.
2.
2.
2.
2.
2686
2639
2713
2790
9293
2602
3063
2852
2176
5.0976
5.1929
5.1003
5.3080
5.9583
5.0892
5.3353
5.4142
5.0466
7.4435
7.5514
7.4566
7.6943
8.2918
7.4519
7.6683
7.7572
7.4089
7
7
7
7
9
7
8
8
7
.7162
.7711
.6884
.9170
.3277
.7633
.0995
.1191
.7917
4.5929
4.6592
4.6412
4.6515
4.6022
4.6681
4.6209
4.6242
4.7030
162
-------
APPENDIX D
LISTING OF PROGRAM SORPT
PROGRAM SORPT
PROGRAM SOHPT ( INPUT. OUTPUT)
DIMENSION 5(20) .PCI20)
HEAL LtLl
C
C • • DEFINITION OF SYMBOLS ••••••••••••••••••••
C
C S«SATUP»TION.
c PC»CAPILLARY PRESSURE.
C SRsRgSIOUAL SATURATION.
C L»LAM80A.
C R«SQUARC OF CORRELATION COEFFICIENT.
C PP«HU8BLING PRESSURE.
C E»ETA.
c RK»RELATIVE PERMEABILITY.
C FPC« (PS/PC) »»L
C***»»oft***«»»»«*«**». .**.»».».*•.
C
POINT 1
I FORMAT (lHlt/1
READ 2S(2)
FPC2«(l./PC2)»ll./PC2)
SR»(FPC2»Sl-FPCl»S2)/(FPr2-FPCl)
IF(S« .LT. 0.0) SR«0.
PRINTlOltSR
101 FORMAT(»F1RST SR CMECKa*.F10.*)
C
C NEXT ESTIMATION OF L»M«OA USING ESTIMATED "ESIOUAL SATURATION.
C
PCLl»ALOfi(PCl)
PCLN»ALOr,(PC(N) )
SEL1«ALOG((S(1)-SR)/(1.-SR) )
SELN»*LOG((S(«l)-SR)/(l.-SR) )
L-- ( SELN-SEL 1 > / CLl I
J-0
K»0
OL«0.1
JR«0
Rl«0. '
R3-0.
SS»0.
SS2*0.
no 6 I*I«N
SI-S(I)
SS«SS»SI
SS2«SS2»SI»sr
PBINT 10?. L
6 CONTINUE
C
C CALCULATION OF CORRELATION COEFFICIENT.
C
7 CONTINUE
10? FORMAT(»FIPST CHECK ON L».F10.*I
163
-------
SFPC=0.
SPS«0.
SFPC2«0.
oo a 1*1,H
FPCI«tl./PC(I))••!_
SFPC*SFPC»FPCI
SFPC2*SFPC2»FPCI«FPCI
SPS=SPS»F»ci»S
c
C FINO 8FST FIT L4MBCU.
c
IF(JP ,FO.
IFljP .EQ.
3) P3»P
IF(« .£0. 2) 60 TO 22
IF(03 .NF.
IF(P? ,NE.
32«S
L1»L
L*L1-OL
GO TO 7
IF(01 .N£.
L«L1»OL
GO TO 7
0.) 00 TO IS
0.) 00 TO 13
0.) GO TO 14
14
15 IF( .LE. 0.0) GO TO Id
GO TO 1ft
1* IF(01 .GT, P.3) '30 TO 1">
no TO 20
19 P3«»2
JP»1
Ll»Ll-OL
L«L1-OL
^0 TO 7
20 S1.R2
L«LI«OL
GO TO 7
16 C«(Pl«P3-2
a»(P3-Q2-C»OL »OL)/OL
L«L1
DL«OL/10.
»3»0.
GO TO 7
C
C
C FIND HEST PESIOUAL SATURATION AND BUBBLING PRESSURE.
22 P,»(SPS-SS»SFPC/N)/(SS2-SS»SS/N)
PRINT 103. SS
PPINT 10»tN
PP-INT 105.SFPC
PRINT 106. R
103 FORM»T(»«5S"».F10.S)
104 FCR««T(»N««.F10.5)
105 FORMAT(*SFPC»».F10.5)
106 FOPM»T(»P,«».F10.5)
SR»SS/N-SFPC/N/a
P8«l./(P«SFPC/*(-8»SS/'N)»»(l./L)
C
c FIND ET*.
c
164
-------
E«3.»C*?.
PRINT 31.ION
31 FORKATUM ,» NR «.I5«///)
PRINT 32
32 FORMATUM t20Xt»S».20X.»PC»»20Xt»KR«.20X,«FPC».//>
C
c FIND RELATIVE PERMEABILITY AND FPC.
c
00 33 I«1.N
«K«((S(I)-SB)/(l.-SP)»«»(E/L)
FPC«(PR/PC(I))««L
PRINT 34.SH) tPCii) «RK«FPC
33 CONTINUE
34 FOR»*T (iu»Fio.»,iax.Fio.».iax.ei5. *. IOX.FIO.*./)
PRINT 35.E.L.SR»P9.R
35 FCfi"»T(///tlOXt»ETA« ».F10.3.9X.»LAMBDA« «,F10.3«10X«»SR* »«F10.*«
110X.»Pq« •.F10.2.//.10X,»CO«R£LATION«
NRaNR-1
IFINP ,NE. 0> 60 TO 100
STOP
END
165
-------
APPENDIX E
LISTING OF BIOLOGICAL-CHEMICAL PROGRAM
PROGRAM MO/STRE
PROGRAM MOISTRE
1 (INPUT. OUTPUT. PUNCH. TAPE*, TAPE10)
C««»«»VERSION FOR U.S.S.K.. APRIL 20.1971.
£***•**» MODIFIED FOR GARRISON DIVERSION UNIT MAR. 10, 1972 SSSStSS?
<;•••«• MODIFIED FOR CYBER 70-28. MAY 2.197*
C»«»« MODIFIED TO CONFORM TQ OOOJM£NTAT!ON, JAN. 15.1975 QQC
DIMENSION HOP(=» .Z<60) .MQN(50) •04TEI50) .AMT(50) .TMF_(50) ,SF<60) ,
2TO(60)»TN<60),FN<60).ANT<60j.K<60>.0<60>»S(60).E(60),F(60>.Ut60>.
3UHO66) ,KP3(6> ,TH(60) tAOENTtSOJ
DIMENSION AIOI9) *••*«••!
COMWON/PROP/KSAT,DSAT,C1.C2'C3.C«,TS .TP8.SR MOO 1
COMMON/PPOPl/OTS.DOSAT
COMMON/XYZ/IOTE,"
CCMMON/C^CK/ICrrECK, ICROP
CCMMON/AJST/Q
INTEGER o«Pi»p2»o,Apps«DAT=-,YEAP» DAY,CROP,TM£,AA»q8,cc,AOENT,
1START
PEAL K,IR,K?3,XSATD.KSAT MQO
C READ PRINT OPTIONS. HUN PARAMETERS, rfATER APPLICATIONS, PROFILE DATA
»EAO 9156, IPUNCH,I*ESTfl,ISAVE,ITENTH,INFlL5,LLSTRT.MMSTOP»ISTOP.
i IDEF.FCAP.IPOPT,ICRCP
READ ioo.AA.pa.cc.LL»MM,R8C'T9c,YEAP, CROP.M, [>ELX,TS,TM,TO.SMDOC
C SEAD SOILS DATA FOR COMPUTING HYDRAULIC PROPERTIES, COMPUTE MOD 1
C CONSTANTS H00 .
iFdPOPT.EO.n CALL CHAR M0n }
IF(IPCPT.NE.l) CALL ACHAP
READ 996*1. ICOOE,IYEAR,APPS.(A(D,OATE
C ----- COMPUTATION OF TIME OF WATCH APPLICATIONS
START=0
DO 29 L»1,APPS
AMT(L)=AMT(LC2.54
29 TM£(L)sOAYtDATE(L) .START,
C ----- ESTABLISH MOISTURE DISTRIBUTION AND CONSTANTS
OELTM=1./M
CALL PROP(TS,TS»TD.KSATO.OSATO
DELT»OELTM
QaHOR(0)/OELX*l.l
c IF SBC EQUALS o THF.N BBC is TRANSIENT
IF(89C.E0.2)RRC=TS
IF(T«C.EQ.l)TBCaTM
IF(TBC.E(3.2)T8C»TS
IR=1000.
HEOaCL=CHECK=ETS»ETaCI=FN
-------
SF (J)sQ.O
TN«TN(J)
15 CONTINUE
no so J*I.Q
Z(J)»0.0
IFtO.EQ.Q) TNMJ*1>=TN(J)
20
16
.Q>
$$S$SS$5
SSSSSSS5
5S1SHSS
sssstsss
CCNTINUF
DO 16 Jsl.O
COMST*CONST»TN(J)
•CCNST»CONST-0.5»(TN<1) *TN<0>
c ----
8491
!F
HF.A.O 9182, (FN(J) ,J«1»0)
READ 9182. (ANT(J).Jal.O)
REAO 9182. .J»l.<3)
READ(IO) (FN(J).jBl.O)
RFaO<10) (ANTiJ).J»1.Q>
R-.AO(IO) (Z(J).j3l«0>
8492 efiINT 9155
PRINT 9151.LYEAR,MONTH,IDTE.II.CL.CHECK,IR.L.HEO.CONST,CI.ETS.ET,
1 CNA.CNl,OEFAMC
PRINT 9152. (TN(J».Jal.Q)
PRINT 9153. (FN(J)»J»1.0)
PRINT 9154* (ANTIJ)«J*1«Q)
PRINT 9172. (Z(J).J*1«0)
8493 CONTINUE
C ---- POSITION TAPE 5 TO CORRECT POSITION FOR FIRST WRITE IF THIS IS A
C ---- HF3TART RUN AND UNIT 5 IS EQUIPED TO A SAVED MAG TAPE
IF(IRESTR.E0.1.ANO.ISAVE.EQ.1> GO TO 8700
1FCIRESTR.EQ.2.ANO.ISAVE.EQ.U GO TO 8700
GC TO 8710
8700 REWIND 5
8733
IFUMI.LE.O) 60 TO 8734
00 8733 Isl.IMI
CALL SKIP(5)
CONTINUE
873<. IF(LL.EO.LLSTRT) GO TO B710
8701 00 8705 Ial.10
REAO(5) IDUMYtKDAY
IF(EOF<5) 18800.8705
8705 CONTINUE
IF (LLSTRT-KOAY-1 18802. 871 0.8701
C ---- SET INDICES OF YEARLY LOOP
8710 ILC*LLSTRT
IHI»MM
IF(yEAR.rQ.ISTOP) IHIsMMSTOP
C— — DAYS WITHIN TOTAL RUN LENGTH LOOP
7700 DO 32 II*ILn.IhI
ooc
1
••••••13
(SSSSSS8
•«•»**•!
•••••••1
•••••••I
sstsssse
SSSS5S10
SSSSSS10
SSSS1S10
SSSffflO
SSTSSS10
tsttssio
SSSStilO
SSSSSS10
SSSfSSlO
SSSSSS10
sstssssi
•••*•••!
•••••••1
••••••I
ssistsu
••••••12
••••••12
sstsssi
SSfSSfl
SSS$$il
ssstssi
SSfSSSl
SSSfSSSl
SSSSSSS4
••••••12
••••••12
SSSSSSS1
sststss?
SSSSSSS7
SSSSSSS7
USSSSSSl
167
-------
1 = 0
°EFAM=O.O
OEFA"0=0.0 S$tSJ$S8
ISTCT=0 «
ISTCTO = 0
XT=IO.»«<-10.)
CALL THFOATEtSTART.H)
c— — MOTE THAT THIS PROGRAM CAN ONLY BE RESTARTED ON FIOST OR SIXTEENTH.
. IF ( IOTE.FQ.1.0R. IOTE.EO. 16. OR .KFLAG.EQ. 0 .OR. I CROP. EQ. 3) 11 ,8500
11 00 3 J = 1.Q
CALL CONUSE(CfiOP»OELX,j,U(J) .II.ILO.IHI)
3 CONTINUE
U(2)=1.5«U<2) ' «
IF(KFLAG.EQ.O) GO TO 3500
PRINT 9151, YEAR,MONTH,IDTF,H,CL«CHECK,I*,L»HED, CONST, CI,ETS,ET,
1 CNA,CNI,OEFAMC
PRINT 9152, (TM(J) ,jal,Q) ««««««<,i
PRINT 9153, (FN(J) ,J=1.0) •«.*». «l
PRINT
6332 CNA = 0.0 ...... jf
C IF(HED.LE.O.O)HEDaAMT(L) ' *««««* in
L=L»1 IU
IPslOO.
IRslOOO.
PRINT 102.II.HED
C ----- TIME INTERVALS WITHIN EACH DAY LOOP
34 DO 21 J=1,Q
21 TO(J)=TN(J)
C ----- COMPUTE SIZE OF TIME INTERVAL, OELT
1 = 1*1
DELTO=OELT
IF(X.GE.O.l) X=10.»«(-10.)
DELT=AMIN1 (OELX*0.035/IR,DELTM)
IF(HED.GT. 0.01. AND. KSATD»DELT.GT. HED) DELT=HED/KSATD
IR=10.«»(-10.)
IF(X»OELT.LE. 0.1)00 TO 4
OELT=0.1-X
4 CONTINUE
X = X»OELT
XT=XT*OELT*10.*»(-10.)
Y = 0.7
C ----- EXAMINE UPPER BOUNDARY CONDITIONS
IF(HED.GT.O.Ol) GO TO 17 »»*»»«1()
C ----- NOTE—FIRST OF TWO PLACES STATEMENT FUNCTIONS ARE REFERENCED.
ZKCON=(TO(1) »TO(2) )/2.
IFdPOPT .NE.l) GO TO 670
CALL PROP (ANT(l) ,TN(1) »TD,K(1) ,0(1) )
CALL PROP (ANT(2) ,TN(2) f TD,K(2) ,0(2) )
K(l)s(K(l) »K (2) )/2.
CALL AOIFIANTI1 ) ,ANT(2) ,0(1) )
GO TO 668
670 CALL APROP(ANTd) . ZKCON. TD.K ( 1 ) ,0(1) )
668 CONTINUE
E(l)=1.0
168
-------
If (0< 1 ) . LE. 0.0) 666* 6ft 7
66* F(l)sO.O
GO TO 1««
667 F(l)s-K
GO TO 1«
17 TN(1)*TPC DOC
E<1)*0.0
P(1)=TRC HOC
ZKCONs(TO(l)»TO(2> ) /2. •*••••«!
IF(IPOPT.NE.l) GO TO 671
CA|_L P«OP (ANT(l) ,TN<1) ,TD»K<1) .0(1 ) )
CALL P"OP ,TD,K (2) ,0(2) >
CALL AOIF(ANTU) ,ANT<2> .0(1) >
GO TO 672
671 CALL APROP(ANTU) »ZKCON»TD.K (1) ,0(1) )
672 CONTINUE
C— — -COMPUTE E AND F FOR EACH NOOE(J) FOQM SURFACE TO DRAIN
IS N=l
Pl=2
35 00 5 J»P1»P2
S(J)=U(J)«DELT/OELX
IF(J.EQ.O) TO(J»1)=TO(J)
c ----- NOTE — SECOND OF TwO PLACFS STATEMENT FUNCTIONS ARE REFERENCED.
ZKCON=(TO(J)»TO(J*1) 1/2. **••*•• 1
IF (IPOPT.NE.1) GO TO 673
CALL PROP (ANT(J) ,TN(J) •TO.K(J) ,0(J) )
CALL POOP ( ANT (j«n ,TN
8«1.»A»C
¥«A»TO(J«1)« (l.-A-C)«TO(J) »C«TO(J-1)»(K(J-1)-K(J) )»
10ELT/(2.«DELX«*2)
IF-TD
MsW-S(J)
Z(J)>2(J) *(U(J)«OELT-S(J)«OELX)
GO TO 76
75 xaW-S(J)
76 ETs£T»S(J)«0£LX
-1) )/(B-C*E(J-l) )
5 CONTINUE
IFIN.GE.OIGO TO 8
N*N*1
P1»P2»1
P2»HOR«N)/OELX»1.
GO TO 35
— --- COMPUTE THETA AND FLUX FOR EACH NOOE(J) F«OM DRAIN TO SURFACE
8 J»Q
TN(J)sEIJ) *TO( J*1)»F (j)
TMJ*1)=TN(J)
ANT(J)=TN(J) *Y»(TN(J)-TO(J) )
IF(ANT(J).GT.TS)ANT(J)=TS
IF(ANTtJ) .LT.TO)ANT(J)aTD
ANT( J*1)=ANT(J)
IF(flBC.EO.TS) TN(Q)=TS
IF(BBC.EO.TM) TN(0)*TM
J=Q-1
169
-------
TN( J)ȣ .GT.TS) ANT(j)=TS
IF(ANT(J) .LT.TO) ANT< JlsTD
FN(J)= •TO-TO(J»/<2.«OELX)) )«OELT
F
FRsASS(FP)
IRzAMAXl
Jsj-1 *
IF (J.GT.O)GO TO 48
CL»CL*FN(0-1)
IF(Q.GE.7) CL3*CL3*FN<7)
ETS«ETS*FN<1)
iFfFNin .LE. o.o. AND. HEO.LE. o.oi) GO TO 8793 *««•». i4
CIsCI»FN(l)
HEO»HEO-FN(1)
C IF(HEO.LE.O.O)HED*0.0 •«•»». 10
SO TO 23 ......13
8793 CM=CNI-FN(l) ««,,»»13
CNAsCNA-FN(l) ...... {3
00 TO ?3 ...... 3
23 CONTINUE
C ----- *BIT£ ON TAPE 5 OW PRINT THETA AND FLUX AT 0.1 DAY INTERVALS
IFtX.LT.O.UGO TO 2
WRITE (5) YEAR,II,XT,CI.CL«HEO.ETStOEFAMC.(J.TN(J).Z(J) tSF(J), SSSSftSfl
1 U(J).J»1,Q) .
IF(ITENTH.NE.l) GO TO 8712 nor
PRi*T i" ;«
PRINT 121.YEAR,IIfXT.MONTH,IOTE.CL»CHECK,ETS,ET,OIF.CNA.CNl,HEO. DOC
CI«OEF4MC«I
9161 FORMAT <4X.»CL AT3.5 FEET THIS TENTH OAY*,F10.3>
IF(ISTCT.NE.O) "PINT 9166tISTCT •*..„•«)
9166 FORMAT (10X, .UNSTABLE SOLUTION SITUATION ENCOUNTERED •, I8t * TIM ...... .9
1ES THIS TENTH DAY.) ......»q
IF(ICTDF.NE.O) PRINT 9170. ICTDF.OEFAM S«««<««A
9170 FORMAT (10X, -OEFICIT MOISTURE SITUATION ENCOUNTERED », 18, • TlMESSSSfSSfi
IS THIS TENTH DAY. AMOUNT IS •, F6.2. • CM*) Slf<«t«A
8712 CONTINUE ..",!?!
ISTCTO»ISTCTO»ISTCT ...... *f
I5TCT"° .......c
ICTOFO«ICTDFD»ICTDF «««««««
OEFAMO»OEFAMO»OEFAH
DO 6 J»1.Q
Z(J)aO.O
6 SF(J)«0.0
i
2 IFIXT.LT. 1.0)00 TO 34
C- — —COMPUTE "CHECK" TO VERIFY CL
CONSTlsO.O
00 19 Jal.O
19 CCNST1»CONST1«TN(J)
CONST 1 "CONST 1-0.5»(TN(1)»TN(Q»
OIF«(CONST1-CONST)*OELX
CHECK-ETS-DIF-ET
C ----- WRITE FIN«L VALUES FOR LAST (I) IN DAY (II) AS INPUTS FOR DAY (H«l)
C WRITE <4> (TN(J).FN(J) ,CL »CHECK. IR.L.HED, ANT ( J) ,J«1,Q)
C— — -PRINT ONE OF TtaO OPTIONS FOR DAILY OUTPUT
IF(89.EQ.1)GO TO 52
PRINT 103
PRINT 121»YEAR.II.XT.MONTH,lDTEtCLtCHECK,ETS,ET,OIFiCNA,CM.HEO. OOC
1 CI.OEFAMC.I nor
<5° TO 31 ......*,
170
-------
52 PRINT 103
PRINT 121 .YEAR, II,XT,MONTH.IDTE,CL.CHECK,ETS,ET,bIF,CNA.CNI,HEO. OOC
1 CI.OEFAMC.I OOC
PRINT 9160,CL3
9160 FORMAT(4X,»CL AT 3.5 FT».F10.3)
PRINT 105.«I*1.8>
PRINT 119
READ 9863, (MON(I> tDATEIII lADENTd) tlal.APPS)
IF (IDEF.EQ.l) AMT(1)»SUMOEF
PRINT 9864,(MON(I).DATEID(AMT(I).AOENT(I).lal.APPS)
00 8739 L»1»APPS
AMT(L)aAMT(L)*2.5*
TM£(L>aOAY(OATE(L),START,MON(L)1
SSSStSt3
SSSSffSS
8739
KFLAG»0
GO TO 7700
9300 IF(IPUNCH.EQ.2) GO TO 9301
IF (IPUNCH.NE.n GO TO 99
;—. PUNCH RESTART DATA AT END OF RUN
PUNCH 9181, YEAR,MONTH.IOTE.II.CL.CHECK,IR,L.HEO
PUNCH 9182. CONST,CI.ETS.ET,CNA.CNI
PUNCH 91A2. DEFAMC
PUNCH 91fl2« (TN(J).,Jal,Q)
(FN(J),Jal,Q)
(ANT(J),J«1,Q)
PUNCH 9182,
PUNCH 9192,
PUNCH 9182. (Z(J)<
GO TO 99
9181 FORMATdS, 13. 13. 14. 3E13.6. 13. E13.6)
9182 FORMATI6E13.6)
9301 REWIND 10
WBITEUO) YEAR,MONTH,IOTE»II,CL,CHECK,IR,L,HEO,CONST,CI,ETS.ET.
I CNA.CNI.OEFAMC
$SStSSS3
SSSSSSS3
stsssssa
SSfSfSSS
sstssssi
SStSSf 11
SSSSSSS1
SSSSSSS3
tfSSSSSl
SfSSSSSl
SSfSSSfl
tssstssi
SSSSSSf I
SSSSSSS1
SSSSftSl
sssstssi
SStSSiSl
SSSSftS7
SSSSSSS7
SSSSSSS1
SSSSfSSl
SSSSSSS1
SSSSSSS1
sstssssi
sssstssi
SSSSSSfl
SSSfSSSl
sssstssi
SSSSSSS1
SSSSSSS1
SSSSSSS1
SSSSSSS1
SSSSSSS1
OOC
DOC
»«****«1
SSSSSSS1
sssssssa
•••••••1
SSSSSSS8
SSSSSS10
SSSSSSS1
••••••13
SSSSSSIO
SSSSSSIO
SSSSSSIO
171
-------
(TN(J) tjsl«0) SSSSSS10
nnr
aaos PRINT -oWJ
8803 FCRMAT(5X, » OAY READ FROM TAPE 5 EQUALS OR IS GREATER THAN STAPTI 12
ING OAY, EXECUTION TERMINATED ») **«»«.13
<5C TO 99 •••«»«i|
C PRINT RUN PARAMETERS AND INITIAL CONDITIONS.
in KFLAfisl ««»»»,»)
inAA.EQ.H9.12 f
9 PRINT 100 *
PRINT HO
PRINT 'ISa.IPUNCH.IRESTR.ISAVE.ITENTH.INFlLS.LLSTRT.MMSTOPflSTOP.
l IOEF.FCAP.IPOPT
PRINT 119
PRINT 120.(TME(J),MON(J),OAT£!J),AMT(J),AOENT(J),J=LAPPS)
PRINT 112
PRINT 10«i, (TH(J) ,J»1,Q)
PRINT 127
PRINT 107
PRINT lll.AA,BB»CC«LL»MM,6BC»TBC»YEAR, CPOP.M,APPS.OELX.TS,TM,TO,
ISM
PRINT 113
PRINT 114, !IOENT,HOR!N) ,N=1,0)-
IF(CROP.NE.3.0R.ICROP.EQ.3)GO TO 12
PRINT 115
PRINT llf.,(KP3(I),l3l,6)
PRINT 117
PRINT 118,(UH(D,1=1,24)
PRINT 108
GO TO 12
100 FCRMAT(5I5.2F5.0,3I5,5X.5F5.0> nnr
101 FORMAT(2X,lAa,lF10.0)
102 FORMAT!/,2X,'WATER APPLIED. DAY NUMBER *,I4,*. AMOUNT * »F7.2,
103 FORMAT (/, X. 129H YEAR II XT MON DTE CL CHECK DOC
1ETS ET OIF" CNA CNI HED CI DOC
2 OEFAMC NSTEPS) ^P
104 FORMAT(45X»F6.5)
105 FORMAT(QX,10F12.6)
106 FORMAT(20X,I2,1X,I2,5X,F10.0,39X,A1)
107 FORMAT! 9X,' AA BB CC LL MM BBC TBC YEAR CROP M AP
IPS DELX TS TM TO SM»)
108 FORMAT(lHl)
109 FORMAT(1H1,3X,'PARAMETERS, CONSTANTS, AND INITIAL CONDITIONS USED
UN THIS REPORT.*)
110 FORMAT!/,3X,» NOTE DIFFUSIVITY AND CONDUCTIVITY RELATIONS
1HIPS MUST 8E INSERTED INTO SOURCE DECK.',/)
Ill FORMAT(9X,515»2F5.2,415«5F5.2)
112 FORMAT!/,7X,'INITIAL SOIL MOISTURE CONDITIONS.*)
113 FORMAT!/.7X,»SOIL IDENTIFICATION AND HORIZON DEPTHS.')
114 FORMAT(9X,'IDENTIFICATIONS »,A8»*. DEPTH* »,F5.1)
115 FORMAT!/,7X.*CONSUMPTIVE USE DATA.')
116 FCRMAT<9X,'PERCENTAGE OF ROOTS FOUND IN EACH OF TOP 6 FEET.',
16F10.3)
117 FOHMATI9X,'CONSUMPTIVE USE CONSTANTS READ IN FROM DATA CARDS (IN
1 CM./ 15 DAYS) FOR SEMIMONTHLY PERIODS.')
llfl FORMAT(11X,24F5.2)
119 FORMAT!/,7X,'WATER APPLICATION DAYS. DATES, AND AMOUNTS.')
120 FORMAT(9X,'DAY NUMBER*.14,7X,»OATE *,12,'/'i12,7X,'AMOUNT»'.F6.2,»
1 CM. SOURCE » «,A1)
121 FORMAT (X, 214, F6.3, 13, 14, X, 10F10.4, 16) • QOC
172
-------
126 FORMAT(25X."UNSTABLE SOLUTION SUSPECTED",HO,FH.6«110,F20.4)
127 FORMAT!/,7X."*UN PARAMETERS, AND BOUNDARY CONDITIONS.*)
129 FORMAT (12)
<)151 FORMAT!/, x, "RESTART DATA FOR YEAP *, 15. * MONTH » 12, • DAY • DOC
1, 12. » DAY MO. », I3/ X, » CL= «. E13.6. « CHECK= «, F.13.6, • IR=*S1S1111
2 «» F13.6, " L= »» 13, * HEO = «. E13.6/, X, • CONST* « £13.6, • CSSSSISIl
31= », E13.6, • ETS= ",E13.f>. • ET= », E13.6/ X, • CNA= », £13.6, 1S1SSS11
4 • CNI = •» E13.6, • OEFAMC= «, E13.61 SSSSSSSH
9152 FORMAT! X, « TNI J),J=1,Q»/, 7<9E13.6./1) »««*»««1
9153 FORMAT! X. • FN(J) ,J=1,Q»/, 7<9E13.6./1) »•«««««!
915* FORMAT! X, "ANT .1 = 1,12) =
SO.80,0.50,3.00,2.65,0.44.0.00,0.00,0.00,0.00,0.00,0.00,0.12).
C CONSUMPTIVE USE CONSTANTS FOR MlLO (FIRST HALF OF MONTH)
«<(K61(I) ,1 = 1,121"
SO.00,0.00,0.00,0.00,0.00,0.14,1.29.2.20,2.20.0. 10.0. 10. 0.10),
c—_—CONSUMPTIVE USE CONSTANTS FOR MILO (SECOND HALF OF MONTH)
1((KB2II) ,1 = 1,12) =
SO. 00»0. 00,0.00,0.00,0.00,0.74,1.67,2.20,2.20,0.10,0.10,0.10),
c PERCENT OF DAYLIGHT HOURS FOR FIRST HALF OF MONTH
«<(PI (I),1 = 1,12) =
S3.31.3.65,4.04,4.47,4.84,5.03,4.94,4.63•<*.22,3.SO,3.42,3.22) ,
C PERCENT OF DAYLIGHT HOURS FOR SECOND HALF OF MONTH
$((P2(I).I=1,12)=
13. 54, 3.17,4.31,4.4715.16.5.03,5.27,4.94,4.22.4.06,3.42,3.43)
C ROOT DISTRIBUTION WITH DEPTH FOR BARLEY
DATA <(KP(I),1 = 1,6)30.40,0.24,0.19.0.13.0.04,0.00)
C ROOT DISTRIBUTION WITH DEPTH FOR MILO
OATA(IKP1(1),1=1,61=0.31,0.22,0.14,0.09,0.08.0.0*1
DATA(ICHECK=0)
DATA (FACTOR=15.,16.,15.,13.,15.,16.,15.,15.,15..16.,15.,15.,I5., ••*»*«*2
116. ,15.,16..15..15.,15.,16.,15.,15.,15.,16.) »«»»»*«2
C—— COMPUTE DEPTH IN FEET MQQ 2
0=OELX»(J-1)/30.48 MOO 2
C-—--REAO U OFF DATA CARDS IF CROP=3.
IFICROP.NE.31GO TO 11
IF(J.NE.2)GO TO 9
ICOUNT=MONTH«2-0.5
173
-------
60 TO 201
E.l^) ICOUNT=MQNTH«2*0.5
9 IFdCHECK.EfJ.lJGO TO 20
ICHECK*!
N=LL-1
IF(^CHECK.EO.l) GO TO 7000
BEAD loo. (KP3(i> ,i3i,6>
00 15 I=lt6
IS *P2(I.3> a KPJ(I)
JCHECK»1
CALL 4JST(KP3»OELX. ADJUST)
ADJUSTsAC>JUST«OELX/30.48
7000 CONTINUE
IFUl (I)
19 CONTINUE
20 UsUKICOUNT)
GO TO 21
201 CONTINUE
C READ NUMOEP OF DAYS OF ET
LM«(MM-LL>»1
»EAD 106.ICOOE. IYEAR
106 FOHM«T(2I5)
READ IOS.JUKD.I
105 FCRMATI16F5.3)
PRINT I07» ICOOE«IY£AR
107 FORMAT (7X. 'CONSUMPTIVE USE CONSTANTS READ
ICHES PER OAY, ICOOE» «.I5. • lYEARa »,I5)
PPIMT lOfl. (Ul (I) .lal.LM)
lOfl FORMAT(11X.10F5.3)
7001 00 7002 1=1. LM
Ul(I)aUl(I)«2.54
UH(I)»U1 (I)
7002 CONTINUE
21 CONTINUE
IFCICROP.NE.3) U»U1(ICOUNT)
U»U1
SSSSSSS1
SSSSSSS1
MOO 2
MOD 2
tsstsssi
IN FROM
• 1YEAR:
OATA CARDS
< •» 15)
IN
IN»»«»»«»I
»****«* 1
««*««o*l
*«*»***1
»»•»•••« 1
**•*•* J
I.LMI
IN FROM OATA CARDS IN IN
IF(ICROP.EQ.3.ANO.CROP.E0.3)
GO TO 7
TO ADJUST (FEET)
11 00 12 1*1.6
KP2(I.l) * KP(I)
12 KP2(I.2> » KPl(I)
C ---- CONVERT OELX (CM)
AOJUST»OELX/30.48
laMONTH
C—— BRANCH ACCORDING TO CROP
GO TO (1.2)»CHOP
C --- — BRANCH ACCORDING TO HALF OF MONTH
1 IF(IOTE.LE.15)3.4
2 IFdOTE.LE. 15)5.6
C ----- COMPUTE CONSUMPTIVE USE FROM CONSUMPTIVE
3 U»KA1 (I)»(MEANT1 (I)»P1 (I)/100.)*2.S4
C ----- COMPUTE CONSUMPTIVE USE FROM CONSUMPTIVE
4 U»KA2(I)»(MEANT2(I)«P2(I)/100.)«2.54
C— --- COMPUTE CONSUMPTIVE USE FROM CONSUMPTIVE
5 UaKRKI)*(MEANTl(I)«PI(I)/100.)»2.54
C— — COMPUTE CONSUMPTIVE USE FROM CONSUMPTIVE
6 U»K«52 ( I ) * (ME ANT2 ( I ) «P2 ( 1) /1 00 . ) «2.54
M00
MOO
USE FORMULA
S 80 TO 7
USE FORMULA
S GO TO 7
USE FORMULA
S GO TO 7
USE FORMULA
——ADJUST CONSUMPTIVE USE FOR LENGTH OF TIME INTERVAL
7 CONTINUE
IF(ICROP.E0.3 .AND.CROP.FQ.3) GO TO 200
174
-------
IFIIOTE.LE.15)47,48
47 KIS=(MONTH»ai-1 »««»««»2
60 TO 4Q »«**»*«2
4R KIS = VONTM»2 «•»»*«••£
49 U=U/F»CTOR(KIS) «»«««««2
C ADJUST CONSUMPTIVE USE FOR SIZE OF DEPTH SEGMENT AND ROOT OISTPI8-
C——UTION
200 CONTINUE
IFOOT*0+1 MOO 2
IFIIFOOT.GT.6) U*0.0 MOO 2
IFIIFOOT.LE.6) U*U«KP2(IFOOT,CROP)'ADJUST MOD 2
RETURN
100 FORMAT(6F10.0>
END
INTEGER FUNCTION DAY
INTEGER FUNCTION DAY
CCMMON/XYZ/IDTE,MONTH.UH,KP3
IFIM.GE.l.ANO.M
1
2
3
4
5
f>
7
a
9
10
11
12
IFJM.GT.3l.
IFIM.GT.59.
IF1M.GT.90.
IF(M.GT.120
IFIM.GT.151
IF(M.GT,131
IF(M.GT.212
IF(M.GT.243
IFJM.GT.273
IFtM.GT.304
IF(M.GT.334
IOTE»M
IDTF.aM-31
IOTE*M-5<)
IOTE*M-90
IDTE»M-120
IOTE«M-151
IDTE«M-1<31
IOTE»M-212
IDTE«M-243
IDTE«M-273
IDTE«M-304
IOTE«M-334
END
AND.
AND.
AND.
•
•
•
•
•
•
•
•
AND
AND
AND
AND
AND
AND
AND
AND
.LE.31) GO TO
M.
M.
M.
.M
.M
.M
.M
.M
.M
.M
.M
LE.
LE.
LE.
.LE
.LE
.LE
.LE
.LE
.LE
.LE
.LE
59) GO TO
90) GO TO
120) GO TO
.151)60 TO
.181)60 TO
.212)00 TO
.243)60 TO
.273)60 TO
.304)60 TO
.334)60 TO
.365)60 TO
MONTH
MONTH
«
*
MONTH*
MONTH
a
MONTH*
MONTH
a
MONTH*
MONTH*
MONTH
MONTH
MONTH
MONTH
X
*
*
a
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
175
-------
SUBROUTINE CHAR
SUBROUTINE CHAR CHtR 1Q
CHAR 20
PROGRAM TO i»EAO INPUTS AND COMPUTE CONSTANTS FOR EVALUATING THE CHAR 30
CONDUCTIVITY AND OIFFUSIVITY BY BROOKS COREY THEORY USINQ
SPECIAL GENERALIZED FORM FO SUBROUTINE PROP
CCMMON/PROP/KSAT,DSAT,Cl.C2.C3.C4,TS.TP8.SR
CQMMON/PPOP1/OTS.OOSAT
COMMON/PROP2/8ET»ALP«AIRINF.ALM»EMP
REAL KSAT
READ INPUTS
»EA09001,KSAT,BEMP.AIBENT«TPB«SR.DTS.OOSAT
READ 9001,ALM»ALP«B£T,AIRINF.EMP
COMPUTE SATURATED OIFFUSIVITY AND CONSTANTS
OSAT»REMP*AIRENT»KSAT/(OTS-SRr
C-
C-
C-
C-
C-
C ----
C --- -
C1«KSAT/((TS-SR)«»C3)
C2»OSAT/<
C— - LIST OUTPUT
PRINT9500.K5AT.REMp,AIRENT.SR»ALP>BET«EMP.AIRINF.ALw.Cl.C3
SETURN
c— - FORMATS
9001 FOR«AT<8F10.5)
9500 FOBMAT(1M1,49X,35M UNSATURATEO FLOW PROGRAM (MQISTRE) //t5X«
1 6QH CONDUCTIVITY RELATIONS TO BE BASED ON THE MET
SHOO OF 8POOK AND COREY//, SX t 12H INPUTS AR£-/.9Xi25H SATURATED CONO
3UCTIVITY "«F10.4,7H CM/DAY/, 9X, 25M EMPIRICAL CONSTANT B »•
4F10.4/,9X, 2SH AIR-ENTRY POTENTIAL "tF10.2t 3H CM/,
i9x, 25H RESIDUAL SATURATION »«FIO.S//.SX,
563H OIFFUSIVITY RELATIONS COMPUTED USING FUNCTION OF SU AND BROOKS
6X/i5X,I2H INPUTS ARE-/,9X,25M A *.F10.5/,«X.2
*5H q «tFl0.5/.9X,25H EMPIRICAL CONSTANT M •
1»F10.5/,9X.2?H INFLECTION POTENTIAL «,F10.St3H CM/,9X,25H LAMBDA
2 «»F10.5//.5Xtl9M RELATIONSHIP USEt)-//.9X,21ri CONO
3UCTIVITY(THETA)*,F1*.5.13H • ITHETA •• .F7.3, 9H ) CM/DAY//)
ENO
SUBROUTINE PROP
—— NOMENCLATURE
C
C
C
C
c
c-
c-
c-
c-
c-
c-
c-
c-
c-
c-
c-
c-
c—
c
SUBROUTINE PROP(Z,ZK»TD»K»0)
— PROGRAM TO COMPUTE CONDUCTIVITY AND OIFFUSIVITY USINB BROOKS AND
— COREY METHOD
PROP 10
PROP 20
- Z «VOL.MOISTURE CONTENT AT WHICH OIFFUSIVITY COMPUTED
- ZK "VOL.MOISTURE CONTENT AT WHICH CONDUCTIVITY COMPUTED
- TO «VOL.MOISTURE CONTENT BELOW WHICH PROPERTIES ASSUMED
- TS «VOL.MOISTURE CONTENT AT SATURATION
- KSAT "CONDUCTIVITY AT SATURATION (CM/DAY)
- OSAT «OIFFUSIVITY AT SATURATION (CM2/DAY)
- Cl-C* "CONSTANTS IN EQUATIONS
• K "CONDUCTIVITY AT CONTENT ZK (CM/DAY)
- 0 "OIFFUSIVITY AT CONTENT Z (C»«2/OAY)
- EQUATIONS
• K«ZK)»CI»(ZK-SR)««C3
-D(Z)"C2*(Z-SR)**C*
COMMON/PROP/KSAT,OSAT,Cl*C2«C3,C4«TS
-------
11 KaO.O
GO TO 100
10 IFIZK.GE. TS) GO TO 20
IF(ZK.LT.SR) GO TO 11
K=C1»<*OEL»0.5
ADJUSTsl./U
RETURN
END
C
C
c
c
c
c
100
110
c
10
20
30
40
50
60
70
80
•JO
AJST
AJST
AJST
AJST
AJST
AJST
AJST
AJST
AJST
AJST 100
AJST 110
AJST 120
AJST 130
AJST 1*0
AJST 150
AJST 160
AJST 170
AJST 180
AJST 190
AJST 200
AJST 210
AJST 220
AJST 230
AJST 240
AJST 250
AJST 260
AJST 270
AJST 280
AJST 290
177
-------
SUBROUTINE ADIF
SUBROUTINE AOIF(TZtTKtO)
COMMON/PROP/KSAT.DSAT.Cl.C2.C3,C4,TS tTP8.SR
COMMON/PPOPl/DTStODSAT
COMMON/P«OP2/BET,ALP»AIRINFtALMfEMP
^EAL KSAT
IF(TZ.GT.SR.AND.TZ.LT.TS.ANO.TK.LE.SR) GO TO ft
IF(T*.GT.SR.AND.TK.LT.TS.ANO.TZ.LE.SR) GO TO 7
IF(TZ.EO.TS.ANO.TK.LE.S«) 50 TO 5
IF(TK.EO.TS.ANO.TZ.LE.SR) GO TO 6
HO TO 10
5 TZsTS- .0001
GO TO 4
6 TK=TS-0.0001
GC TO 7
*• CALLSIMP(TZ.SS.AVO)
0=Avn/
-------
SUBROUTINE SIMP
SUBROUTINE SI*P(SZ.SK,SVD>
CCMMON/PPQP/«)*»(BE
CONSTsA»B
C THIS SECTION COMPUTES INTEGRAL VALUES USING SIMPSON
Hs(SZ-SK)/2.1
£NDS=FUMC(SZ> »FUNC(SK)
FCURsFl)Nr(SK*M)
OLOINTaH/3.0«(ENOS»4.0«FOURI
C EVALUATION LOOP
25 HaH/2.0
Nx2»N
FOURsO.O
T=SK*H
00 2* 1=1. N
FOUR=FOUP»FUNC(T)
2f> T=T»H»H
INTE6aM/3.0»(ENOS+2.0«TWO*4.0«FOUR)
C CHECK FOR CONVERGENCE OR EXCESSIVE NUMBER OF ITERATIONS
IF(ABS(OLOINT-INTEG) .LT.l.OE-6. OR. N.GT. 10000) GO TO 10
OLOINTsINTEG
00 TO 25
10 SVO=CONST»INTEG
RETURN
END
PROGRAM USCHEM
PROGRAM USCHEM
C I
C——UNSATURATEO CHEMISTRY PROGRAM USSR VERSION 1.2.0—NOV 197*
C . 3
DIMENSION XC7.2S) 5
6
COMMON/BYPAS/NPYPAS.IOYSTR,IOYSTP.ILOtIHI.INFlLltICONT1tJPAS 8
COMMON/AflLE/TITLE(lO).SHONTH,MM,O.IPRINT,JPRINT.INKtIPUNCH.ISTOP, 9
lITESTtIREADPtIMASStIAOO(25».IORNAP(25).HO»(9),TOTN(99), YEAR . 10
2AIRR(9)tIRR(2S)iTT(60).FERT(7).OFERT(3),NORGIN,NFERTIN.NTEHPIN. 11
3ITOTtJTOTiIRTOT,NT {i
COMMON/XX2/Al«A2tA3tAN03<25 ).ANM3(25 ).UREA(25 ),ORN 18
f!2!.^^*!2^',^1^*'?5.1'^40'25 '»HC03(25 >iCU(25 »,C03<25 ).SO»(25 19
llr.l* T if? f* >«SAS(25 ).XX5(25 ),CASO(25 ),AQSO(25 ).BNH*(25 ). 20
*EC(25 )tCNl(25 ),SAMT(25 >,RN<25 ),RC<2S ),T£M(25 ),CAL(25 1,0,SRO 21
lP«XTRACT.SOMN03.THOR<4),TO.IOAY,U9(25).CH.CMl,lReHUN.IS«CH,CUMSUM,
179
-------
ISljMOUT.RFDUCF. «IIK(25) . A2E<25)«PP<10)
COMMON/ 1/-XTRCT (25) , AKCS<25> «AKCM<25)
COMMON/TRNIT/U<25),ACTCA<25)«IOPN,ISETN<25)
COMMON/SALT/SEPATIO<25> tSflYPAS
COMMON/C02/PC02(25)«IPC02
INTEGER 0tOtSTAPT.CHOP.TO*SMONTH,YEAR,TITLE*SBVPAS
SEAL MOISIN.MOISOUT
DATA
-------
) (TT(I) .I«1,TO) 49
800 CONTINUE 90
91
9001 CONTINUE 92
c- ---- SEAO IRRIGATION WATER ANALYSIS 93
BEAD 100. ANH3«i> .ANoaui .CAUI .ANAUI ,AMG»SAMT(1)«0.0 98
CALL UNITSK1) 99
AIRR<1)*ANH3{1) SAIRR(2)«AN03(1) SA IPR ( 3) *CA ( I ) SA IRB (4) xANA ( 1 ) 100
AIBB<5)«AMG(1) $AIRR<6)sHC03(l) SAIRR ( 7 > =CL < 1) JAIRR (8) *C03 ( 1 ) 101
AIRR(9)»S04<1) 10?
103
c ---- -COMPUTE TOTAL NUMBER OF COMPONENT HORIZONS 10*
Q«HOR(0)/OELX*1.1
IF(SBYPAS.EO.l) RE ADI 100. (SEBATIO(N9) tU(N9) ,ACTCA(N9) «N9a2tQ!
IF(ISN.EQ.l) RITAO 1101. (ISETN(N9) <.M9>2tQ)
IF(ITEST.EO.U782t783 . 106
782 BEAO T84t (CMH20KJ) tMOISIN(J) .MOISOUT(J) ,TEN(J) .U(J) .J»l,0) 107
ion
C—— PRINT HEADING 109
783 IF(IBERUN.EQ.O) P»INT 201 110
111
C ----- SET COUNTERS 112
N«2 tL«l JKl » 1 113
lit
C ----- CALL OUTPT TO ZERO INITIAL VALUES 115
CALL OUTPT(Kl) 116
IFdRERUN.EO. 0)22. 701 117
118
C ----- BEAD INITIAL SOIL ANALYSES 119
22 BEAH 100«ANH3(1) .AN03 ( 1 ) .UREA ( 1 ) »CA ( 1 ) .ANA ( 1 ) , AMGI1 ) .HC03 ( 1 120
1) .CL(l) »C03(1) » 504(1) «EC(1) .XX5I1) «CAL(1) .HD(1) tSAMT(l) tCNl (1) 121
123
c ----- PRINT INITIAL SOIL ANALYSES 12*
PRINT 200tL»ANH3(l) tAN03(l> tUREA(l) .SAMT(l) tCA(l) t ANA(l) tAMGd ) • 125
1HC03(1) .CL(1) «C03(1). 504(1) 126
READ 10 1 , XTRCT c i > .PCOZ 1 1 ) . AKCS < i > , AKCM 1 1 1
127
C— --- COMPUTE SEGMENT NUMRER OF COMPONENT HORIZON 12B
KK»HOR(L)/OELX»1.1 129
130
C ----- STORE INITIAL SOIL ANALYSES IN PROPER COMPONENT ARRAYS 131
00 23 J»N»KK 132
ANH3 (J)«ANM3( 1) $AN03(J)=AN03(1) SUREA ( J) «UR£A ( 1 ) 133
CA(J)>CA(1) SANAIJ)BANA(l) SAMG C03 ( 1 ) 135
so4< j)>so4 $ec(j> «ec< u sxxs«xxs(i) 136
CAL(J)aCALU) $BO(J)a80(U SSAMT (J) «SAMT ( 1 ) 137
CN1(J)»CN1(1) 138
XTRCT (J) «XTBCT ( 1 ) SPC02 ( J) »PC02 ( 1 )
IF(IREK.EQ.O) GO TO 23
AKCS(J)«AKCS( 1)
AKCM(J)»AKCM(1)
23 CONTINUE 140
141
142
C ----- CHECK FOR LAST SEGMENT 143
IFtKK.EO. 0)20.21 144
145
c — —BESET COUNTERS U6
21 N»KK*1 147
L«L*1 148
GO TO 22 149
150
C ----- PRINT HEADING 151
20 PRINT 202 152
GO TO 703 153
701 CONTINUE
155
181
-------
c— — FCR 4 RERUN. READ PROM TAPES OH FROM CARDS 156
IF(IPEACP.EQ.O) 157
1REAO (3) ICOUNT.NFERTIN.NORGIN.NTEMPIN. .C03(J) «S04(J) .EC ( J) »XX5 ( J) »CAL( 159
2J) »SO(J) . SAMT (J) ,CN1 (J) . ORN(J) tRN(J) .RC ( J) .E5(J) .C5( J) .SAS(J) >CASO 160
3(J) .AGSO(J) ,3NH4(J) «XTRCT
IFUREAOP.NE.O) 16?
1REAO 505. ICOUNT.NFERTIN.NORGIN.NTEMPIN. »AZE(J> .IIK (J) • 166
4PC02(J) . AKCS(J) ,AKCM 200
fl02 CONTINUE 201
202
IF(NBYPAS.EO.l) GO TO 9003 203
C— --- STORE ORGANIC APPLICATIONS ON TAPE 10 204
DO 803 I=I.JTOT 205
REAO 100. (OFERT(J) .J»l»3) 206
WRITF(IO) (OFERT(J). J»l*3) ' 207
803 CONTINUE 208
9003 CONTINUE 209
210
C- — —SET SEGMENT ONE VALUES EQUAL TO ZERO 211
16 ANH3 ( 1 ) »AN03 ( 1 ) =CA ( 1 ) «ANA ( 1 ) «AMG < 1 > *HC03 I 1 ) 3UREA ( 1 ) *CL ( 1 ) *C03 ( 1 ) » 212
1S04(1)«0.0 213
IF( IRERUN.NE. 0)508.720 214
508 REWIND 8 215
REWIND 9 216
REWIND 10 217
IF(NTEMPIN.EQ.O) GO TO 522 218
IF(NBYPAS.EO.l) GO TO 9004 219
220
C— --- SPACE TAPES FOREWARO THE PROPER NO. OF RECORDS 221
DC 510 lal.NTEMPIN 222
510 REAO (8) 223
224
9004 CONTINUE 225
182
-------
522 IF(NFERTIN.EQ.O) GO TO 550
c ----- SPACE TAPE9 FOREWARD THE PROPER NO. OF RECORDS III
oo sii i»i, NFERTIN
511 READ (9»
550 IF (NORGIN. EO.O) GO TO 513
IF(N9YPAS.E0.1) GO TO 9005
c ----- SPACE TAPEIO FOREWASO THE PROPER NO. OF RECORDS
00 512 Isl, NORGIN
512 »EAO (10)
9005 CONTINUE
00 TO 513
720 REWIND A !*?
REWIND o
REWIND 10
NFERTIN = NORGIN a NTEMPIN a 0 74?
513 CONTINUE
ISWCH = 1 .,
IF(IPHINTJ.NE.O) CALL PPNT1 ( IPRINT I , IPRINT J) £47
248
C ----- CALL SUBROUTINE TO EXECUTE PROGRAM FOR EACH DAILY TIME INTERVAL 2*9
CALL EXECUTE 250
C—— CHECK FOB END OF RUN
' ENOFILE 2
IF (MOO (IDAY.IDYSTP).EQ. 0)726, 721 25*
726 IF(YEAR.EO.ISTOP) GO TO 721 2S5
c ----- RESET COUNTERS
ICOUNT = 0 SYEAR * YEAS » 1 SLL » 1
lLOsIOYSTR •
ILAP » ILO
IHLIOYSTP
IF(YEAR.EO.ISTOP) IHI»MM
IF(ICONTl.EQ.O) GO TO 720
REWIND 10
c ---- READ IRRIGATION WATER APPLICATION DATES FOR NEXT YEAR
READ IO*.IRTOT, (IRR.KSI,IRTOT>
C— — "EAO LAST ORGANIC-N APPLICATION FOR NEXT YEAR
READ 100.(OFERT(J),J«l,3>
IOCOU « JTOT - 1
JPAS * 0
00 1321 I*lf IOCOU
1321 READ (10)
WfllTF.(lO) (OFERT(J),J»1.3)
REWIND 10
GO TO 720 264
721 CONTINUE
C 721 ENDFILE 2
C ENOFILE 15
NTE«PIN«NTEMPIN-l •
ICOUNT»ICOUNT-1 269
C ----- EITHER PUNCH A RERUN DECK OR WRITE RERUN (RESTART) DATA ON TAPP3 27?
IF(IOAY.EQ.IDYSTP) ICOUNT * NFERTIN » NORGIN * NTE-PIN « 0 ->T»
IF(IPUNCH.EQ.O) 502,503 ,„
502 REWIND 3 |^
*«RITE (3) ICOUNT, NFERTIN. NORGIN, NTEHPIN, (ANM3(J) ,AN03(J) ,UREA(J) 27S
1»CA(J>,ANA(J>,AMG(J),HC03(J>,CL(J),C03(J),SO*(J),£C(J),XX5(J),CAL( 276
2J),P.O(J),SAMT(J),CN1(J),ORN(J),RN(J).RC(J) «E5(J),C5(J) iSA5(J),CASO ?77
3(J),AGSO(J),8NH*(J),XTRCT(J),A.METLIMU),AZE(J),IIK(J), ?7a
*PC02(J),AKCS
-------
C 561 REWIND Z 284
c REWIND 3 2Q5
C ---- PRINT RESTART DATA 286
561 PRINT 9100. IDAY.YEAR.ICOUNT.NFEOTIN.NORGIN.NTEMPIN.O 397
PRINT 910U (ANH3U) ,AN03(J).UREA(J) 288
ItCA(J) ,ANA(J) .AMG(J) ,HC03(J> »CL(J> .C03(J) .S04(J) .EC (J) .XX5 < J) ,C*L( 289
2J> .eO(J>.SAMT .ftN.C5.CA(J) ,ANA(J> .AMG(J) .HC03U) .CL(J) «C 294
303
COMMON/YYY/START tIDTE«MONTH»111»LL 6
COMMON/AFG/ENH3»II.LLL«IOP.ANETLIM(25) 7
COMBINE
C THIS SUBROUTINE CALLS THE COMPUTATIONAL SUBROUTINES AND ASSEMBLES COMBINE
C THEIR DELTA VALUES COMBINE
COMMON/XXX/DELXtOELTtMMtWTART.80(25 ).TEN(25 >.CHECK(25 ).MOISIN 8
1(25 >tCMH201(25 1.MOISOUTI25 ),AN03(25 ).ANH3(25 ).UREA(2S ),ORN 9
2(25 )»CA(25 )tANA(25 ).AMG(25 ).HC03(25 J.CU135 ).C03(25 ).304(25 10
3>.ES(25 ),C5(2S >.SAS(2S ).XX5(25 ).CASO(2S ).AGSO(2S ).BNH4(2S ), n
4EC(25 )tCNl(25 ).SAMT(25 >.RN(25 ).RC(25 ).T£M(25 J.CALI25 ).O.CRO 12
IP.SPACE(36).ISWCH.CUMSUM.SUMOUT.REDUCE
COMMON/GIRL/UHEA1.UREA2.DNH31.ONH32.DN031.ON032.CA1,ANAl.AMG1. U
1HC031.CL1»C031.S041,KKK.PPPP(4)
COMMON/C02/PC02125)tIPC02
184
-------
1*.
DIMENSION CON VERT (35) .£XNrt3<25) «EXCA(25> .Ex AN A (35) .EXA»G<25> , 17
10F|_N03<25) »OELNH3(25) »DELORGN<25) .OELUPEA (2*1 .EXHC03I25I .£XC03(25) 18
2.EXS04I25) .EXCL<25> .EX8NH*(25) .F|_N03<25) ,FLNM3(25) .FLUPEA125) ,FLCA 1 .FLCL(2S) .Fl_C03<25> .FuSO*(2S) .' 20
4PLN03I25) .PLNH4<25) .DEL8NH4I2S) .ANEM (25) «ANET2(2S) ,ANET3(2S) .ADD I 21
5T(25) .ADOITK25) .DELRNC25) .OELRCC25)
23
INTEGER Q.SBYPAS
25
REDUCE » i.o
NOW * 2
IF(ISEGST.EO.l) NOW a 1
IFACT a REDUCE 28
ISET * IFACT * 2 SF a 1.0 29
IF(II.EQ.LLL) Ka2 30
31
c... —COMPUTE DELTA VALUES FOR EACH SOIL SEGMENT 32
50 DO 1 IafcOw.0 33
34
c- — —CALL SHUT-OFF SUBROUTINE 35
C CALL CHK(L1.L2»L3.I.EXNH3(I) .EXCA(I) .EXANA(I) .F.XAMGd) ,OELN03U) . 3) 57
GO TO 334
333 CALL SALTBP(CONV£RT(I) , ANH3 < U.BNH4< I > ,1)
EXNH3II) « EX8NH4II) a 0.0
334 IF(IOP.FQ.O) GO TO 206
C 59
C ----- AGAIN COMPUTE LIME IN SYSTEM EXCLUSIVE OF SOLID STATE SO
ASUM2 a CA(I)*2.497 . CASO ( I ) 'CMH201 < I ) »100 .09E3 » ES ( I ) *100. 09E6* bl
1CONVE«T(I) * XX5U)*100.09£6»CONVERT(I) 62
C 63
c ----- ADO OR SUBTRACT ANY DIFFERENCE IN LIME TO SOLID STATE LIME STORAGE 64
ANETLIM(I) = ANETLIM(I) * ASUMl - ASUM2 65
C 66
c ----- COMPUTE POROSITY OF SOIL SEGMENT. ASSUME PARTICLE DENSITY is 2.45 67
POP « 1. - BD(I)/2.65 «,«
C 69
O --- -COMPUTE UG OF CAC03 WHICH CAN PRECIPITATE IN PORE SPACE 70
APOR a OELX«POR»2.828E6 71
C 72
C ----- COMPARE UG OF LIME PRECIPITATED WITH UG OF CAC03 NECESSARY TO 73
c ----- FILL THIS SPACE 74
C—— ASSUME DENSITY OF CAC03 (CALCITE) IS 2.82B 75
c . 76
C ----- IF PORE SPACE HAS BEEN EXCEEDED. PRINT DAY, SESMENT. MASS OF CAC03 77
c ----- WHICH CAN PRECIPITATE IN PORE SPACE. AND MASS OF CAC03 WHICH HAS 78
C—— PRECIPITATED 79
IF(ANETLIMd).GE.APOR) PRINT 20 1 , 1 1 1 , I , APOR , 80
UNETLIM(I) ,j
185
-------
206 CONTINUE a2
IFUl.NE.O) EXNH3d)aEXCAd)=EXANAd>=FXAMGd>»£XHC03d)=EXCr>3d>» S3
1EXSC4 I 1) =EX3NH4< I)=EXCL(D=0.0 84
85
IF(N9YPAS.FQ.1> GO TO 9008 .DELRNd) .OELPCd) »II) 89
90
9008 CONTINUE • 91
IFdFL9YPA.EO.lt SO TO 3000 92
c CALL Th£ FLOW SU9«OUTINE 93
CALL FLd«FLN03dt ,FLNH3d) tFLUREAd) .FLCAd) .FLANAdl 94
l.FLAMGd)tFLHC03 »FLCL(I)»FLC03d>.FLS04II)) 95
«000 CONTINUE 96
IF(II.NE.l) (50 TO 20 97
IF(ISET.LE.tFACT) GO TO 20 98
99
IF (NaYOiS.EQ.l) GO TO 9009 100
c——CALL THF PLANT NUTRIENT UPTAKE SUBROUTINE 101
IFdOTE.eQ.UOP.IDTE.EQ.16.OH.IDftY.eo.LLl CALL UPTAKEd.PLN03d>• 102
lPLNM4d) .OELT.OELX) 103
20 CONTINUE
M CON a AN03(I)/CMH201(I)
CON1 a AkH3d)/CMH201 (1) 111
112
c TEST FOR LOW NOS CONCENTRATION 113
IFCCON.LT.0.2)62.63 ' 11*
62 ftODITd) = 0.0 115
GO TO ^4 116
63 AODITdl = PLN03ID 117
118
C —— TEST FOR LOW NH4 CONCENTPATION 119
6* IF(CONl.LT.0.2)65i66 120
65 ADDITTd) » 0.0 121
GO TO 67 122
66 AQOITKI) = PLNH41II 123
67 CONTINUE 124
125
c COMPUTE NET CHANGES FOR NH*, UREA* AND N03 126
ANETld) = DELMH3(I> » FLNH3(I) * EXNH3d> » AODITKI) 127
ANET2d)3 RELUPEA(I) * FLUPEAd) 128
ANET3«I)» DELN03- * AOOITd} 129
130
9009 CONTINUE 131
C TEST TO DETERMINE IF SEGMENT ONE IS BEING CONSIDERED 132
IF(KKK.EO.1)77.1 133
77 SNH31=0*H31 $SN031*ON03l $SREA1=URE&1 SSAl»CAl *SMAl«ANAl 134
SMGl=AMGl »SC031=HC03l *SL1»CLI JS031»C031 $R041«S041 135
1 CONTINUE 136
137
136
139
C TEST TO DETERMINE IF ADDITIONAL ri"E STEPS ARC BEING USED 140
IFdSET.LE.IFACT) GO TO 16 141
1*2
IF(NRYPAS.EO.l) GO TO 9010 ' 143
C -TEST TO DETERMINE IF MASS IN SYSTEM KILL 3E EXCEEDED 14*
DO 5 1*2.0 1*5
IF(ANM3(I) * ANETld),LT.0.0) GO TO 14 146
IF(UPEAd) • ANET2CI) .LT.0.0) GO TO 14 147
IF(AN03d) * ANET3d).LT.O.O) GO TO 14 14*
5 CONTINUE 1*9
GO TO 16 150
151
C USE SMALLER TIME STEPS IF NECESSARY 152
14 ISET » 1 IF * IFACT 1S3
154
9010 CONTINUE . 155
C UPDATE THE MASSES IN STORAGE 156
16 00 6 I*NQta«0 157
ANM3CI) * ANH3(I) » ANETldJ/F SUPEA (I) » UREA (I) » ANET2d)/F 158
186
-------
C ----- CALL SUBROUTINE TO OUTPUT LEACHATE VALUES
CALL OUTPT(K)
229
C ----- CALL "ASS BALANCE ROUTINE FOR NITROGEN
IF(NRYPAS.EO.l) GO TO 9013
IF(ISWCH.EO.l.ANO.II.EO.JPPINT) CALL MCHECK
9013 CONTINUE 233
33*
c ----- RETURN TO SUBROUTINE EXECUTE
RETURN
2J8
100 FORMAT(I5,UE9.3) "9
FORMATUX»THE SOIL POPOSITY EQUALED ZERO DUE TO PRECIPITATED LI*F. 3*1
ION DAY NO.».I5./1X»OEPTH SEGMENT N0.«, I5»/10X«POROSITY ALLOWS«2X, 242
2£10.3.3X»UG OF LIME TO PRECIPITATE', 5X ,E10 .3«2X»UG OF LI*E HAVE 3*3
3PRECIPITATED«) *
SUBROUTINE XCHANGE
SUBROUTINE XCHANGE ( J»EXNH3.EXCAtEXANA, EX AMG.EXHC03.EXC03«EXSO*.EXCX CHANGE
1L.CXPNH4) XCHANGE
XCHANGE
C --- --THIS IS THE EXCHANGE SUBROUTINE XCHANGE
XCHANGE
CCHMON/IP/CAS(25) ,AMGS(25>
XCHANGE
COMMON/AION/U
COMMON/TPNIT/ISTR(25) .ACTCA (25) . IOPN, ISETN (25)
COHMON/XXX/DELX.DELT,MM,STARTtBO(25 )tTEN(25 1.CHECKI35 ) .MQISIN XCHANGE
1(25 >»C«H203<25 ).MOISOUT(2S ).ANOZ(25 ),AMHZ(2S ).UP£A(25 ) .ORN
2(25 )«CZ<25 ),ANZ(25 ).AMZ{25 ),HCOZ(25 ).CY(25 )»COZ{25 )«SOZ(25 XCHANG1
3)«EZ(25 ).CX(25 ).SAZ(25 ),XXZ(25 ) »CASZ (25 ).AGSZ(25 ).BNHZ(25 1.XCHANG1
*F.Y(25 ),CN1(25 )»SAMT(25 ).RN(25 )«RC(25 >,rEM(25 ),CAZ(25 ) .Q.CROXCHANGl
IP.XTPACT«SUMN03,THOR(4) «TO, IDAY,U3 (25) .CH.Chl . IRERUN.SPC (*) , I IK (25
n,AZ£(2S>
COMMON/1/XTRCT(25) .AKCS(25) ,AKCM(25)
COMMON/C03/PC02 (25) « IPC02
XCHAN61
DIMENSION CMH20U25) XCHANG1
XCHANG1
OATA(TES»1.E-100)
XCHANG1
C— --- SET EXCHANGE CONSTANTS
OA • AKCS(J)
0 • AKCM(J)
•SET SEGMENT VOLUMES XCHANG1
CVH201 (J)»CMH202(J) XCHANG2
•COMPUTE MOISTURE CONTENT ON A PERCENT BASIS XCHANGZ
Bl « CMH201 (J)/(BD(J)*OELX) XCHANG2
81 * 81*100. XCHANG2
XCHANG2
C—— COMPUTE SEGMF.NT VOLUMES RASED ON INITIAL SOIL ANALYSES XCHANG2
IF(CHECK(J) .EO.0.0) CMH20l(J>"XTRCT(J>«OELX*BD(J> *a«« 2
XCHANG2
C— --- CONVERT UNITS FROM UG/SEGMENT TO MOLES/LITER XCHAN62
XCHANG2
c- ---- RESET STORAGE LOCATIONS FOR USE IN THIS ROUTINE XCHANGS
1005 ANH4 * ANHZ(J)/CMM201 (J)/l*000. XCHAN33
A « CZ(J)/CMH201 (J)/*0080. XCHANG3
S • ANZ(J)/CMH201 (01/22990.
F m AMZ(J)/CMH201(J)/24320.
HC03 « HCOZ(J)/CMH201(J)/61000.
C03 « C07(J)/C-H201(J>/60000. XCHANG3
187
-------
AN031I) = AN03 * ANET3U1/F SCA < I ) = CA ( 1 > * FLCA < I > /F * EXCA( 159
111 160
ANA(I) = ANA(I) » FLANA/F * EXANA(I) SAMG < I ) s AMG ( I } » F|_AMG < I 161
11/F *EXAMG 162
HC03(I1 = hC03(I> • FLHC03(I)/F * EXHC03CI) SCL ( I ) = CL(I) * FLCL 163
1(I)/F * FXCL *OELRN( I)/F SRC 1 1 > =RC ( I > «DELRC < I ) /F
31 IF(I.EQ.2)36,37 ,80
C ----- UPDATE MASSES CONTAINED ON SOIL SURFACE
36 ANrl3(l) s ANH3U) - SNH3 1 /F$ AN03 (1 ) = AN03 < I t - SNQ31/F 1
UPEA(l) = UREA(l) - SREA1/F*CA(1) = CA ( 1 ) - SA1/F 192
ANA(l) = ANAU) - SNAl/FSAMGfl) a AMG ( 1 ) - SMG1/F 183
HC03(1) = MC03(1) - SC031/F $CL 1 1 ) = CL ( 1 J - SL1/F ig*
C03(l) s C03(l) - S031/FJS04(1) * S04(l» - R041/F 195
37 CONTINUE HI
187
IF(NP.YPAS.EQ.l) GO TO 9011
C--— CHECK AND CORRECT FOR ANY NEGATIVE VALUES
IF(«NH .0.0 . 0 . 0 . AOOIT ( I ) .0,0 • 0 .
10tAN03(I-l ) .CONVERT (I) ,3)
IMUREAd) .LT.0.0) CALL NEGN(UREA(D . 0 . 0 . 0 . 0 . 0. 0 . 0. 0 , 0 .0 tUREA ( 1-1 )
IF(I.EQ.O) 30.9011
170
C— — KEEP TRACK OF TOTAL-N LEACHED FROM SYSTEM
30 SUMOUT = SUMOUT MON033 * ONH32 * UREA2J/F
SUMS (1) s SUMS<1) » ON032/F
SUMS(2) = SUMS(2) » DNM32/F 175
SL'MS(3) s SUMS(3) » UREA2/F 17ft
9011 CONTINUE
IF(ORN .EQ.O.ANO.II.EQ.JPRINT)2t6 216
217
c — "-PRINT VALUES FOP. THE COMPONENTS AND SEGMENT VOLUMES 213
C -----
-------
H « CY(J)/CMH20l(Jl/35460.
G a SOZ(J)/CMH201 (J)/9MOO.
&N03 • AN07
CCaA»G-(2.4E-5)»EXP
«»SQRT(BB«BB-4.0»CC)
Xa(-eB»R)/2.0
CAS134.P97E-3-CASO
OEL»9»XXT-CAS1
IF(nEL-X)27t28.28
X3XXT»B
XXTaQ.O
CASlaO.O
A>A»X
G>G*X
(FA)
XCHANG7
XCHANGS
XCHANGfl
XCHANGS
XCHANG8
XCHANG6
XCHANGS
XCHANGS
XCHANG8
XCHANG9
XCHANG9
XCHANG9
XCHANG9
XCHANG9
XCHANG9
XCHANG9
XCHANG9
XCKANG9
189
-------
U=SORT(2.0«
37 CASO=CASO»X1
A=A-X1
G=G-X1
GO TO 44
IS IF 1,1,6
f> If (A) 1,1,7
1 IF (CASO) 44.44,7
28 AsA»X
r,=G»X
XXTaXXT-X/B
CASO=CASO»CASI
XXT=XXT-CAS1/B
4* A2*A
IF (S) 80, 131, SO
191 IF(SAT)80,515.80
"0 IJ = 2
404 IF(SAT-ET)402.403,403
ZsSAT/10.
GO TO 5
2«ET/10.
21 = 2
5 EXaEXP ((-2.341«U)/U.o«U)>
AA=-4.0«OA«OA«fl»8
«Ba4.0«R»(EX»2.0«DA»OA«ET«B*OA«OA»S)
fll Z2=-(«(AA»Z»38)»2*CC)«Z*00)«Z*eE)
ZZZ3(((4
T0 515
™ 515
83
55?
551
55(1
510
z=z»zz
IF(AeS(ZZZ)-. 001)33. 83, 81
A=A*3»Z
IF(A)510,510,512
SATsSAT-?.»Z
ET=ET*Z
512
513
514
515
A=A-P«Z
2=-Zl
GO TO fll
IF (S) 550,550t513
ET=ET-Z
IF (ET)551,551.514
SATsSAT«2.0«Z
IF (SAT) 552.552,515
Rfl=A»e»(CT*D«ET) *0«F
AA»B»(l.O-0)
CC»(A»CT-0»F»ET)
RaSQPT(8?«eR-4.0»AA*CC)
V=<_qB«R)/<2.0»AA)
ET«ET-V
CT=CT*Y
A4«A
AA s S«(1.0-ONM4)
BB » ANH4 * B»(SAT»DNH4»PNH4) * ONH**S
190
XCHANIO
XCHANlO
XCHANIO
XCHANIO
XCHANIO
XCHANIO
XCHANIO
XCHANIO
XCHANII
XCHANl 1
XCHANII
XCHANl 1
XCHANII
XCHANII
XCHANII
XCHANII
XCHANII
XCHANII
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN12
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN13
XCHAN14
XCHAN14
XCHAN14
XCHANU
XCHAN14
XCHANU
XCHAN14
XCHANU
XCHANU
XCHANU
XCHAN15
XCHAN15
XCHAN15
XCHAN15
XCHAN15
XCHAN15
XCHAN1S
XCHAN15
XCHAN15
XCHAN15
XCHAN16
XCHAN16
XCHAN16
XCHAN16
XCHAN16
XCHAN16
XCHANU
XCHAN16
XCHAN16
XCHAN16
XCHANl 7
-------
CC * ANH4«SAT - ONH4»S«BNH4
RaSQRT(PR*SB-4.0*AA»CC)
Y=(.qB«P)/<2.0*AA>
QKH4 a RNH4 - Y
SAT * SAT * v
AKH4 a ANH4 » R*Y
S a S - R»Y
IF(G)790»790.791
791 IF(F)790.790.792
793 AA«EXP(-9.366«U/(1.*U>)
RBs-(5.9E-3*AA*F»AA»G>
CCa»A»F»G-5.9E-3»AGSO
XXXX=9B«fl8-4.0»AA«CC
IF(XXXX)793«7V3.794
793 XlaO.O
GO TO 7«5
794 X1»(-R8-SOPT(XXXX))/(2.0»AA)
79s AGSO=AGSO»XI
F = F.-Xl
G«G-X1
790 CCNTTNUF
GO TO (600.601).IK
601 AA'4.0
96«4.»(HC03»A)
CCaMC03»*2«4.»A*HC03
00»A»HC03»*2-ZE*EXP (7.033«U/(1.*U))
IF(HC03-A)61i61»62
61 Zs-HC03/4.
GO TO 6SO
A? Za-A/2.
650 Zl'Z
63 ZZ = -( ( (»A»Z««»e)»Z»CC)«Z»00)
ZZZ»((3.0«AA«Z*2.0«eB)»Z»CC)
IF(ABS(ZZ).UT.TE5.0R.»HS(ZZZ).LT.TES) GO TO 600
zz*zz/zzz
IF(ARS(ZZ).LT.TES.O».ARS(Z) .LT.TES1 GO TO 600
ZZZ-ZZ/Z
z«z»zz
IF(ABS(ZZZ)-.001)64,64.63
64 A=A*Z
HC03=MC03»2.»Z
IF(HC03)752»752»651
7S2 HC03»HC03-2.«Z
A«A-Z
Za-Zl
GO TO 63
651 IF(A) 752.752.753
753 CAL»CAL-Z
600 IFIIK.E0.2) GO TO 606
ZX»(A»HC03««2«EXP(-7.033«U/(1.»U)))
IF(ZX-ZE)60b«605«605
605 IK«2
AZE(J» « (81 •«1.68)»ZX
606 OEL»A-Al
IF(OEL*CHl)24»4A«48
46 IF(OEl-CHl>49,49,24
49 OEL>A-A2
IF(OEL»CH1)24,50.50
50 IF(DEL-CHH51.51.24
51 OEL»A-A3
IF(OEL«CH1)24,52.52
52 IF«OEL-CHl)fl.8t24
ft OEL»A-A4
IF (DEL»CH1)24.66.66
66 IF(OEL-CH1)67.67.24
1000 CONTINUE
67 CONTINUE
IF
-------
F * (F • AGSO) »CMH201 < J) »24320.
HC03 » *C03«CMM201 »«1000.
H » H«c*w201 (J>«35*60.
CC3 = C03«C«H201 (J)«60000.
G = (G « AGSO » CASO)»CMH201(J>»96100.
IF (CHECK (J).F.Q. 0.0)400. 401
*00 AKHZ(J) 3 ANH4 $CZ(J) a A
AN2(J) s S $AMZ(J) r (r
.HCOZ(J) = HC03 SCOZ(J) * C03
CV(J) = H SSOZ(J) » G
9NHZIJ) = PNH4.
CHECK(J)=CWF.CK(J)*1.
401 CONTINUE
IIK(.J)
C ----- COMPUTE
IK
DELTA
VALUES FO*
COMPONENTS
SEXC* * *
HC03 - HCOZU, $EXC03 = C03 - COZ(J»
CXCL « H - CV(J) SEXSO* « G - SOZ(J)
EXBNH4 = PNH4 - BNHZ(J) =>O<(J>
EZ(j) = ET $CX(J) . CT
SAZ(J)=SAT SXXZ(J)aXXT
CAZ(J)»CAL $EY(J)«EC
ISTR(J) s U*«2
CAS(J)aCASZ(J)«CMH201(J)»136180.
*MGS ( j) 3AGSZ ( j) »CMH201 ( J) • 120420 .
c ----- PETURN TO SURROUTINE COMBINE
RETURN
1001 STOP
EMO
XCHAN24
XCHA.N24
XCHAN24
XCHAN2*.
XCHAN24
XCHAN24
XCHAN24
XCWAN24
XCHAN25
XCHAN25
XCHAN25
XCHAN25
XCHAN25
XCHAN25
XCHAM26
XCHAN26
XCHAN26
XCHAN26
SUBROUTINE EQEXCH
DA * 1.414/01
ONH4s0.22
CASO=O.O
«
. 0
*2 ACT2aEXP(-9.366»U/(1.0»Ul)
IF (SO) 1000,713.712
712 AA»ACT2»ACT2
flR»«CT2»(10.8E-3*(ACT2«(AMG*CA-SO»)
800 Z-SO/2.
850 Zl-Z
863 ZZ=-(((AA*Z»OB)»Z»CC)»Z«00)
ZZZa((3.0»AA»Z*2.0»88)»Z»CC)
ZZ«ZZ/ZZZ
ZZZ»ZZ/Z
Z»Z*ZZ
IF (ABS(ZZZ)-. 001)840. 840. 863
«40 SOT«SO
SO»Z
IF(SO)710.710.711
710 SC»SOT
Z,21
GO TO 963
192
EQEXCH
EQEXCH1
tQEXCHl
EQEXCH1
EQEXCH1
EQEXCH2
EOEXCH2
EQEXCH2
EQEXCH2
EOEXCH3
EOEXCH3
EOEXCH3
EQEXCH3
-------
711
41
40
713
CASX=SO«CA«ACT2/ (4.9E-3*ACT2«SO>
CX=CA-CASX
AGSX=SO»AMG»«CT2/ (S.SE-3»ACT2»SO)
AHX=AM6-AGSX
UU=SQPT (2.«(CX»AMX«SO»C03> »0.5» ( SOS»MC03*CL« ANH4»AN03 ) )
IF (APS (UU/U-1.) -l.OE-4) 40.40.41
U=UU
SO=SOT
GO TO 42
CASO«CASX
AGSO=AGSX
CA=CX
A*G«AMX
ACT1=SQPT(ACT2)
ACTM=SQRT (ACT1 )
ACT^sSQRT (ACT«4)
CA=CA«2.
AMG=AM«»2.
E5 = F,C/< (ACTM«SOS/(OA»SQRT(ACT1«CA) ) ) »1 . » .AN03(25 ).ANH3(25 ).UREA(25 ).ORN
2(25 )«CA(25 ).ANAI25 )»AMG(25 ).HC03(25 1.CLI2S ).C03(25 ).504(25
3).E5(25 1.C5I25 I.SA5125 ).XX5(25 I.CASOI25 ).AGSO(25 ).BNH4(25 J.EXECUT1
4EC125 ).CN1(2S ).SAMT(25 ).RN(25 ),RC(25 ),TEM(25 ).CAL(25 ).Q.SROEXECUT1
1P.XTRACT,SUMN03.THOR(4).TO.IOAY.U (25) .CH»CHl.I RERUN.ISWCH.CUMSUW.EXECUT1
EXECUTE
EXECUTE
EXECUTE
EXECUTE
SfSS A
EXECUTE
SSfS
EXECUTE
EXECUTE
EXECUT1
EXECUT1
EXECUT1
EXECUT1
EXECUT1
EXECUT1
EXECUT1
1SUMQUT
DIMENSION X(7.25)
INTEGER 0,0.START,CROP.TO.SMONTH,YEAR
REAL HOISIN.MOISOUT
C POSITION TAPE1 (INPUT FROM MOISTURE FLOW PROGRAM) TO PROPER
C RECORD
IF(ITEST.NE.O) GO TO 9007
IF(ICONTl.EQ.l) GO TO «000
REWIND 1
fl009 NRECalLO-IOYSTR
IF(NREC.LE.O) GO TO 9007
DO 9004 Isl.NREC
00 9003 IK=1.LLL
READ(l) II
9003 CONTINUE
9004 CONTINUE
GO TO 9007
8000 IF(JPAS.EQ.l) GO TO B002
JPASal
REWIND 1
NSKIP«INFIL1-1
EXECUT2
EXECUT2
EXECUT2
EXECUT2
EXECUT2
EXECUT2
SSSS A2
SSSS 82
SSSS C2
SSSSAC2
SSSS 02
SSSS E2
SSSS
SSSS
SSSS
SSSS
ssss
ssss
ssss
ssss
ssss
ssss
F2
62
H2
J2
K2
L2
M2
N2
02
P2
193
-------
<30 TO <3009
00 4001 Is
CAUL SKIP(l)
3001 CONTINUE .
GC TO *009
8002 READIl) II
IF(F.OF<1> >9007.8003
H003 PRINT 809*. Y£A«
800* FORMAT (/. 5X» * ERROR- END OF FILE NOT FOUND ON TAPE 1 AT START
1 YEAR NO. ». IS/. SXt » EXECUTION TERMINATED «>
CALL EXIT
900? CONTINUE
C— — LL a STARTING DAY» MM = TERMINATION DAV
OC * IsILOilHl
IF(\«YPAS.€Q.l) GO TO 9010
IFC-ODU .IMASS) .EO.O) ISWCH a 1
<»010 CONTINUE
c
C
C
C
C
C
STCPP o«rcr INTERNAL VALUES ON TAPEIS
*PITF (15)
1 «AN03(J) tUREAIJ) tCA( J) «ANA ( J) t AM6( Jk iHC03( J) «C
1) »C03(.J) »SO*( J) «EC< Jl »XX5 »80(J) tSAMT(J) »CN1 < J) »ORN(J)
2«N(J1 « «CASO«J) «AGSO(J)
CALU SUBROUTINE TO COMPUTE DAY OF MONTH
CALL THEOATE(STAPT,I,SMONTH,0>
IDAV = I
C ----- CHECK FOR FERTILIZER APPLICATION DATE
00 3 Kal,lTOT
IF(I.EQ.IAOO(K) )30l»3
3 CONTINUE
GO TO 5
c— — «EAO FERTILIZER APPLICATIONS F«OM TAPE 9
301 RCAO<9) OEPTH«AANH3«AAN03»AURCA«ACA « ANH3(J> » SAVE1
AN03(J) s 4N03(J) * SAVES
UREA(J) a UR£A(J) « SAVE3
CA(J)aCA{J)*SAVE*
C03
-------
302 CUMSO*aCUMS04«SAVE10
5 IF(NRYPAS.FQ.l) SO TO 9006
DO 8 K=1,JTOT
c CHECK FOB ORGANIC-N APPLICATION DATE
IF(I.EO.IORNAP(K))7,a
7 CONTINUE
c -REAO ORGAMC-N APPLICATION
READ .GT.O.O)790«795
—CHECK TO SEE IF THIS IS AN IRRIGATION BAY
00 792 L» » 1«IRTOT
IF(I.EQ.IRRAIRRO)*CMH201 (1)
SAVE*a4IRR(3)»CHH201(l)
SAVE**AIRR(5)»CMH201(1)
SAVEA"AIRR(7)*CMH201(1)
SAVE10«AIRR(9)*CMH201<1>
ANH3<1)aANH3(1)»SAVE1
CA(l)aCA(l)»SAVE4
ACSIl)«AMG(1)»SAVE6
CLU)«CL<1)»SAVE8
S04(l)aS04(l)*SAVE10
—STORE ACCUM AMOUNTS OF COMPONENTS
$SAVE2«AIRR(2)»CMH20i<1)
$SAVE5«AIRR(*)'CMH201(1)
SSAVE7aAIR«(6)
$SAVE9
SAN03(1)'AN03 <1>"SAVE2
SANA <1)*ANA(1)»SAVE5
SHC03(1)"HC03(1)«SAVE7
SC03(1)*C03(1)«SAVE9
SSSSB13
EXECU13
EXECU13
CXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU14
EXECU15
EXECU15
EXECU15
EXECU15
EXECU1S
EXECU15
195
-------
5CUMCA=CUMCA
CL'MANA=CUMANA»SAVE5
CUMnCn3=CU"nC03+SAVE7 SCUMCL=CUMCL»SAVE8
CUMC03=CUMC03»SAVES $CUMSO*=CUMSO*»SAVE10
PRINT 207,CVM201tl> •!
GO TCI 795
792 CONTINUE
795 CONTINUE
.IF<»00.401
400 PRINT 206.YEAB.I.II
PRINT 205
*01 CONTINUE
C- CALL COMPIME SUBROUTINE
CALL COMflINE(IOAY.IPRINT,JPRINTl
10 CONTINUE
4 CONTINUE
RETURN
ExECUlS
FXECU15
EXECUIS
EXECU15
EXECU16
EXECU16
T SCU13
EX£CU13
EXECU13
EXECU16
205
204
207
FOP-!*AT< 1X«PREOICTEO AMOUNTS (UG/SEGMENT OF SOIL) — ( SESVOLSCC
1P./SEO SOIL)*//2X»SEG*
3X»RNH4-N»3X»SEGVCL»iSX»ESP»U«PC02(ATM)»)
FOBM«T(//'U»YEAfi» •,I*»10X«OAY= « , I*., 10X«TJME INTERVAL" »
I.I*)
FO«M4T(///10X*AN IRRIGATION OF«»F6.1»«CM WAS APPLIED ON DAY
EXECU16
EX6CU16
tXECU16
EXECU16
EXECUlft
WATEEXECU17
EXECU17
•X»CL
EXECU17
SUBROUTINE OUTPT
SUBROUTINE OUTPT(K)
C—.—THIS SUBROUTINE WRITES PREDICTED TOTAL AND DELTA AMOUNTS FOR THE
C——COMPONENTS AND VOLUMES ON TAPE2 (UNITS AP£ EXPRESSED IN UG/UNIT
C——AREA AND ML/UNIT AREA).
DIMENSION AMT(IO).AMTl(10).DEL<10)
INTEGER Q.O.START.CROP.TO
INTEGER YEAP
Rf-AL MQISOUT
COMCON/SABLE/SUMS(3)
COMMON/XXX/OF.LX.OELT.HS.wTART.80(25 ).TEN(25 ).CHECK(25
1(25 >«CWH201(25 )»MOISOUT(25 ).AN03(25 ).ANH3(25 ).UREA(25
2(25 I.CAI25 )»ANA(25 ).AMG(25 ).HC03<25 1.CL125 ).003(25 )
3).£5(25 )»C5(25 J.SA5125 ).XX5(25 ).CASO(25 ).AOSO(25 ).8NH4(25 )
4EC(25 ).CN1(25 ).SAMT(25 ).PN(2S ).«C<25 ).TEM(25 )»CAL(25 ).0 C»
1P,XTRACT.SUMN03.THOR(*>.TO.IDAY.U(25).CH.CHl.IPERUN
COMMON/AP.LE/TITLE<10).SMONTM,MM,0,IPRINT.JPRINT,INK.IPUNCH.ISTOP«
1ITEST.IREADP.IMASS.IADD(25).IORNAP(25),HOR(9),TOTN(99), YEAR .
2AIRR(9)»rR"(25>.TT(60)iFERT(7).OFEPT(3).NOROIN.NFERTIN.NTEMPIN,
3ITOT»JTOT»IRTOT»NT
COMMON/IP/CAS(25)»AMGS(25)
C-.._-ESTAeLISH STATEMENT FUNCTION
SUBA(X.Y) » X»Y
IF(K.EO.l) 1.2
C ZERO INITIAL VALUES
1 SUMOUT » SUMOUT1 = 0.0
00 3 1*1.10
3 AMT(I) » AMTKI) « 0.0
GO TO 5
2 Y a CMH201(Q)
Z » MOISOUT(O)
196
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OOL-TPT
OUTPT
ZZZZ
2222
2222
222
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUT"T
OUTPT
OUTPT
1
1
I
1
1
1
I
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
-------
Y » Z/Y
IF(Y.GT.l) Y«0.99
C—... SUM THE COMPONENTS
AMT(l) « SUMS!I)
AMTI21 « SIJMS(2)
AMTO) * SUMSI3)
A > SUBA(CAS(Q)«Y)
f»«SUBA(AMOS(0)tY)
AMT«4)
AMT(S)
ACT (7) i
AMT(8) 1
AMT(9) <
AMT(IO)
AMTI4)
AMT(51
AMT(6)
AMT<7)
AMT(fl)
AMTO)
• AMTdOl
SUBAICAIO) «Y)
SU«A(ANA«J( (V)
SURA(AUGCO) *Y>
SUBA(HC03(Q) »Y
SUQAIC03CQ) tY)
» SU8A(S04(Q) »
c
4
f>
C
c
SUM THE VOLUMES OUT
SliMOUT « SUMOUT » MOISOUT(Q)
IF4.5
-COMPUTE OELTA VALUES F0« COMPONENT?.
00 6 1-1.10
OEL(I> • AMT(I) - AMTltl)
COMPUTE DELTA VALUE FOR VOLUME OUT
OELN « SUMOUT - SUMOUT1
WRITE SUMMATIONS AND OEUTA VALUES ON TAPEZ
»B1TE(2) YEAPtIOAYtSUMOUT»06LN.«A«TtI)»OEL(I)«t«l»10)
RESET VALUES FOR DELTA DETERMINATION*
OC 7 I«1.10
AMTKI) a AMT(T)
SUMQUT1 * SUMOUT
C—— RETURN TO MAIN PROGRAM
RETURN
100 FORMAT(lXtl2E10.3>
END
OUTPT 3
OUTPT
OUTPT 3
OUTPT 3
OUTPT 3
OUTPT 3
OUTPT 4
OUTPT 4
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT
OUTPT 5
OUTPT 5
OUTPT 5
OUTPT 5
OUTPT 5
OUTPT
OUTPT
OUTPT
OUTPT 5
OUTPT 5
OUTPT 6
ZttZ 6
OUTPT «.
OUTPT 6
OUTPT 6
OUTPT 6
OUTPT 6
OUTPT 6
OUTPT 6
OUTPT 6
OUTPT 7
OUTPT 7
OUTPT 7
OUTPT 7
5
5
5
INTEGER FUNCTION DAY
INTEGER FUNCTION DAYIK«H>
L « o
GO TO U*Zt3t4tS*6t7«8«9tlO
12 OAY-K-L
1 OAY«K-L»31
2 OAY»K-L«62
3 OAY«K-L»90
4 OAY»K-L»121
5 OAY»K-L*15l
6 OAY.K-L»182
7 DAY«K-L»212
» DAY»K-L»2*3
9 DAY>K-L»274
10 OAY«K-L»304
11 OAV«K-L*335
13 DAYBK-LO6S
END
.Iltl2t;3) «
S RETURN
S RE' j»N
$ RETURN
S RETURN
% PETURN
s SETURN
HE TURN
«ETURN
RETURN
RETURN
RETURN
RETURN
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
DAY
1
1
1
1
1
1
1
1
1
197
-------
SUBROUTINE /DAY
SUqeQUTIME IDAY(SMONTH.SOAY.MONTH,!DTE»JOAY.K)
JD/U » OAY(SOAY.SMONTH)
JJDAY s OAftlDTE.MONTH)
JOAY * JJOAY - JOAY » K
IF (jQAr.t.E.0) l»2
JDAY * JOAY » 365 » K
RETURN
END
[DAY
IOAY
IOAY
IDAY
IDAY
13AY
IDAY
IOAY
12
1
2
3
4
5
6
7
ft
<5
10
II
SUBROUTINE THEDATE
SUBROUTINE THEOATE(K«L»SMONTHiKl)
YY/ OtIOTEtMONTH
SMONTHtQAY
JOAY * OAY(K.SHONTH)
M » JO*,'' » U - Kl - 1
IF(M.6£.l .ANO.H.LE.31) GO TO 13
IF (M.GT.31.ANO.M.LE.62) SO TO 1
IF (M.6T.62.4ND.M.LE.90) GO TO 2
IF(H.6r.90.ANO.M.LE.121) GO TO 3
IFfW. dr. 121. ANO.M.LE. 151)50 TO 4
IFIM.6T. 151. ANO.M.LE. 183)60 TO 5
If GO TO 11
MONTH«12
IOTE*«-3) MONTHal
IOTE=»||-1?'.
!DTE*M-?12
IDTE=H-243
IDTE=M-274
IOTC=h-304
IOTE««-33S
EN'O
MONTH«3
MONTH=5
MONTH»6
HONTH=7
MONThs8
MONTH»10
MONTH-11
THEDATE
\ RETURN
S RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
RETURN
THEDATE
THEDATE
THEDATE
THEDATE
THEOATE
THEDATE
THEOATI
THEDATl
THEDATl
THEDATl
THEOATI
THEOATI
THEDATl
THEOATI
THEDATl
THEDATl
THEOAT2
THEDAT2
THEDAT2
THEDAT2
THEDAT2
THEOAT2
TMEOAT2
THEDAT2
THEDAT2
THEDAT2
THEDAT3
THEDAT3
SUBROUTINE UNITS
UNITSKJ)
--THIS SUBROUTINE CONVERTS UNITS PROM MEO/L TO UG/SEGMENT. OR
--UG/SEGMENT TO MEQ/L AT ENTRY POINT UNITS2
COMHON/XX»ChECK<25
) .MOISIN
ORN
C—
*EC(25 1
IP
-CONVERT
ANH3(J>
AN03»AN03(25 ),ANH3(2S )»UREA<25 ),ORN UNITSI
)»ANA<25 ).*HG(25 )>HC03(2S )»CL(25 )tC03(25 )«SO*(25 UNITSll
).C5t25 >.SA5<25 )»XX5f25 )tCASO(25 ).AGSO(25 ).BNH*(25 ).UNITSll
CH1(?5 >tSAMT(25 J.RNI25 ».RC(25 ».TEM(25 )tCAL(25 ),Q*CROUNITS11
UNITSll
UNITSll
FRO* MEQ/LITEfl TO UG/SEGMENT UNITSll
= ANH3(J)*CMH20UJ)»14.0 UNITSll
s AN03»28.0 UNITSll
Cflf J) <*CMH201 (J) *?0,0* UNITSll
-------
HC03U) a HC03(J)«CMH201(J)«61.0
CC3(J) a C03(J)»CMH201(J)«30.0
CL(J) « CL(J)»CMH201(J)«35.46
S(H a S04(J)»CMH20l(J)«48.05
ORN(J) a ORN(j)*90(J)«OEUX
SAMT(J) a SAMT(J)«90(J) *DEl_X
RETURN
ENTRY UNITS?
c CONVERT FRO" UG/SEGMENT TO MEG/LITER
ANH3IJ) a ANH3(J)/(CMH201
UPEA(J) a U»EA(J)/(CMH201(J)»28.0)
CA(J) a CA(J)/(CMH201(J)»20.04)
ANA(J> = ANA(J)/(CMH201(J)»22.99>
A*G(J) = »MG(J)/(C»«h201 » SO*(J)/(CMM20l(J)"48.05)
ORN(J)aORN(J)/f<0(J)/OEUX
RETURN
END
UNITS12
UN1TS12
UN1TS12
UNITS12
UNITS12
UNITS12
UNITS12
UNITS12
UNITS13
UNITS13
UNITS13
UNITS13
UNITS13
UNITS13
UNITS13
UNITS13
UNIT513
UNITS13
UNITSl*
UNITS!*
UMITSU
UNITS1*
UNITS1*
SUBROUTINE FL
1*
IS
4
5
6
7
SUBROUTINE FL(J«FLN03.FLNH3tFLUREA.FLCA«FLANAiFLAMO,FLHC03iFLCL«
1FLC03.FLS04)
CCMMON/XXX/DELX.OELT,MM,STARTt80(25» tTEN(25».CHECK(25).MOISIN
1(25).ORMOIS(25)tMOISOUT(25).HN03(25).BNH3(2S>iBPEA(25)«ORN
2(25)«8A(25)«RNA(25).BHG125)<8C03t2SI tXX5(25).CASO(25)tA6SO(25)tRNM4(25) .
4EC(25)fCNl(25)«SAMT(25> »PN(25)tRC(25).TEMC25).CAL125)tQtCRO
5P.XTPACT.SUMN03.THORH) tTO.IDAYtUS(251,CH.CH1.IRSRUN.ISWCM.CUMSU",
6SUMOUT,PSPA(60)
COMMON/RIRL/UREAl«URE*2«ONH3ltONH32.DNO3l.ON032.CA1, ANA1*
lAMGlfHC031»CUliC03ltSC4l.KCOUNT.LSETl,LSET2«LSET3
DIMENSION ANH3(25).AN03I25)»UR£A<25)»CA(25)tANA(25)iAMQ(25),h>C03(2
15) .CL125)fC03(25)»504(25)
INTE5ES 0
REAL MOISIN»HOISOUT
IF(J.ME.?) GO TO I
DO lf> 1 = 1.(3
ANH3(I> a BNH3(I)
1) lANA(I)s8NA(I)
2 $C03(I) a 903(1)
CONTINUE
ORMOIS(Q»1)
ANH3(Q«1) a
UREA(Q*1) a
ANA(0»1) a
SAN03(I)a8N03(I)
JAMQ(I)aBMQ(I) SHC03(I)*8C03(I)
SS04(I) a B04II)
SCL ( I
1)
a ORMQIS(Q)
ANH3(0) SAN03(Q«1) a AN03(0)
UREA(O) $CA(Q*1) a CA(0)
ANA(O) $AMQ(Q«1) a AMG(Q)
HC03IO) $CU(0»1) a CL(Q)
C03(Q) $SOA(Q»1) a 504(0)
0.0
HC03(Q*1)
C03(Q*1)
CONTINUE
IF(ORMOISd).LT.O.O) ORMOISIU a
IF(MOISIN(J).LT.0.0) 2.3
COEFIN a MOISIN(J)/ORMOIS(J)
GO TO 4
IF(OPMOIS(J-1).GT.O.O) GO TO 14
COEFIN a 0.0
GO TO 15
COEFIN m MOISIN(J)/ORMOIS(J-1)
CONTINUE
IF (COISOUTtJ).LT.0.0)5*6
COEFOUT a MOISOUT(J)/ORMOIS(J»1)
GO TO 7
COEFOUTa MOISOI)T(J)/ORMOIS(J)
IF(COEFTN.LT.O.O)8.9
199
-------
fl K a J
GO TO 10
9 Ks J-l
10 IFICOEF-OUT.ur.O.m U» 12
11 L = J*l
SO TO 13
12 L * J
13 KCCUNT = K SIF GO TO 101
OM031 = COEFIN«AHn3(K)
GO TO 102
101 DN031 = DM032
102 DN032 » COEFOUT»1N03(L)
IF(J.NF,.2.ANO.LSET1.E0.1) 00 TO 103
SO TO lOfc
103 ONH31 s ONH32
10* OKH32 = COEFOUT»»NH3(L)
lF(J.NF.2.ANO.t.SET3.EQ.l) 60 TO 105
URgAl » COEFIN'UREA(K)
GO TO 106
105 UREAl = URE42
1Q6 UP.EA2 = COEFOUT»OfJE» (L)
CAl » CO£FIN*C»(K) SCA2 = COEFOUT*C*(L)
ANA1 = COEFIN«AN»(K! SANA2 = COEFOUT»«NA!L>
k*G\ = COEFIN*9.i"i5(»c) 5&M62 = COEFOUT»JMS (L>
MC031 = COEFIN*HC03
S0*l » COEFIN«SO*(K) SSO»2 * COEFQUT»SO*
FLM03 = ON031 - ON032
FLNH3 = HNH31 - ONH32
FLURE* « UPEAI - usEA2
FLCA .= CAl - CA2
FLANA « ANAI - ANAS
FLAMG = AMGI - AMG2
FLHC03 = HC031 - HC032
FLCL = CLl - CL2
FLC03 s C031 - C032
FlSOA » S041 - S042
LSET1 = LSET2 - 0
RETURN
END
SUBROUTINE PRNT
SUflBOUTlNE PflNTdPRlNri.IPBIMTj) P"NT 2
PflNT 3
C ----- THIS SUBROUTINE PRINTS CONTROL AND INPUT DATA PRNT *
PHF4T j
COHfON/AeLE/TITLEJlOI,SMONTH,MM,O.IPRINT.JPRIHT.INK,rPUNCM.ISTOP» PRNT 6
lITEST,IREAOP.IMASS«IAOD<2S>,tORNAP<25>.HOR(<51iTOTN<9<»>, YEAR » 9<}99 7
2AlRP<9»,IR9(25!,TT(60l,FE»T(7>,OFeRT(3UNORGIN.NFERTtN,NTEMP!N. »«NT 8
3ITOT,JTOT,IRTOT,NT "J|J *
COMI-ON/XXZ/Al,A2tA34X ^T 1U
CQMMON/YYY/START,IDTE.-ONTHfI»LL ""' Ji
PKl«CBOP KKNI I*1
25 ),TEN(2S ).CHECK(2S I.MOISIN P«MT 13
. ».MOISOUT«S ),AN03(25 J,*W3(3S ),U»EA(25 I.OPN PRNT 1*
2 29 IcM» .»NA 25 > .AH6(25 ),HC03(25 I.CL(2S I.C03I25 ).SO«{25 P«NT IS
3 ,F5 25 ,C5 2S ) 545(25 ),XX5<25 ),CASO(25 )rAGSO(25 ),BNH*I25 > .PBWT 16
SAMTI25 I.RNC2S J.RC(25 I,TEM<25 ),CAL(2S 3,0,SPOPP^T 17
.CH.CM1 ,IflERUN.lS«CM.CUHSUM,P«NT 10
1SUMOU7,PEOUCE
INTESEP TITUE«SMONTH»START,0»TO»YE>*
200
-------
c POINT TITLE
PRINT 100»TITLE
IF< IPRINTI.EQ.2) GO TO 1
r — -PRINT BASIC CONTROL CARD PARAMETERS
PRINT 101. SMONTH.XTPACT, START, CPOP«UL»PK»«M«PK1,OF.LX.CH,OELT.
ICHltO. A 1.TO.A2.ISTOP.YEAP. REDUCE
c PRINT 1-0 CONTROL PARAMETERS
PRINT 102, IPRINT.IREAOP, JPRINT, ITEST.INK, I^ASS.IREBUN .IPRINTI,
1IPUNCH»IP9INTJ
1 RETURN
ENTRY PRNT1
C_ ___ C tf T O P AftF
••"•""aRlr* ~ ** O ~.
PRINT 103
c— ••••PRINT TF^PERATURE HORIZONS
PPINT 104, (THOR(J) «J=1 »TO>
PRINT 109
REWIND 8
C-——— -PRINT TEMPERATURES
DO 10 J»ltNT
READ (8) >TOT)
PRINT 115
00 3 I=liJTOT
READ (10) (OFERT(J) ,J«1 t3)
FCRN s OFERT(3)*CONV
c PRINT ORGANIC APPLICATIONS
3 PRINT 113, I,OFERT(1) ,OFERT(2) ,FORN
REWIND 10
C__— PRINT COMPONENT HORIZON DEPTHS
PRINT 106» (HOP(J) »J«l«0)
PRNT 22
PRNT 23
PRNT 24
PRNT 25
PRNT 26
PRNT 27
PRNT 28
PRNT 29
PPNT 30
PRNT 31
PRNT 32
DRNT 33
PRNT 34
PRNT 35
PRNT 36
PRNT 37
PRNT 38
PRNT 39
PRNT 40
PRNT 41
PRNT 42
PHNT 43
PRNT 44
PRNT 45
PRNT 46
PRNT 47
PRNT 4
-------
PRINT 103
RETURN
100
101
102
103
104
105
106
107
198
109
no
in
112
in
1U
us
r(1H1//.3SX,10AB//)
FORMAT(56X«CONTROL CARD SUMM*RY«/57X*(BASIC PARAMETERS)*//35X
1«STARTIMG MONTH =«t15,10X«XTRACT =».F5.1,/35X
1'STiRTlNG DAY s»,IS,10X*C»OP ".1S/35X
2«»ELATIVE STARTING DAY =»i15.10X«UPTAK£(N03) »*.F5.2»/35X
3-RELATIVF TFPMIN DAY =«t15,10X»UPTAKE(NH4) «,F5.2,/35X
4»SOIL SEGMENT SIZE »»,F5.1,« CM»7X,»CONV£RQ1 *»,F5.2,/35X
<;»TIMF INTERVAL SIZE **.F5.2.» OAYS*SX»CONVEPG2 =*»F5.3/35X
(S»NO. OF COMPONENT HRZNS=».15.10X»CHECK1 »«iF5.1/35X
7»KO. OF TEMP HP2NS **»15,10X»CH£CK2 ".F5.1/35X
«*ISTOP »*tIS,10X*YEAR *<
FORM»T(55X*(I-0 CONTROL PARAMETERS)»//35X
1«IPHINT »»iI5,10X»IREAOP
2»JPPINT *»tI5.10X»ITEST
3*INK »»»I5,10X»1MASS
**,I5,10X*IPRINTI
",I5,10X»!PRINTJ
»*,I5/35X
",I5/35X
»««IS/35X
FCRM4T(//15X»NEEKLY TEMPFRATURE OATA»13X«HORI20N DEPTH(CM)*
1/46X,6(3X»F6,1))
FORMAT(20X.I3,2X«TEMPERATURe»CA(25 ),ANA(25 I.AMGC25 ),HC03(25 )»CL(25 ),C03(25 ),S04(25
3),£5(25 ),C5<25 ),SA5(25 J,XX5(25 >,CASO(25 >,A6SO(25 ).SNH4(2S >
4EC<25 ),CN1(25 )iSAMT(25 ),«N(25 ),RC<25 ),TEM(25 ).CAL(25 ),0,S«i
1P,XTRACT,SUMN03,THOR(4),TO,IDAY,U (25),CH,CH1»IRERUN
COMMON/XX2/A1,A2,A3,X
REAL HOISIN, MOISOUT
DIMENSION X<7.25)
LI « L2 » L3 » 0
IF - ANH3(J)>,LT.Al>4,2 '
IFUf)S - CA(J)).LT.A1)5,2
IF(ASS(X(6,J) - ANA(J)}.LT.A1)6,2
IF(ABS(X(7,J) - AMG
-------
7 Ll « 1
2 IF(NflYPAS.FQ.l) 00 TO 9007
IFfARSIDELN03).LT.A2.AND.AflS AN031J) tX(2.J) > ANH3IJ)
X(3.J) a UREAIJ) SXI4.J) a ORN(J)
X(5.J) > CA(J) SXI6.J) * ANA(J)
XI7.J) * AMQ(J)
RFTURN
END
CMK
• * **
CHK
CHK
CHK
CHK
CHK
CHK
CHK
• •»*
CHK
CHK
CHK
CHK
CHK
CHK
CHK
CHK
29
A29
30
31
32
33
34
35
36
A36
37
30
39
40
41
42
43
44
SUBROUTINE SKIP
SUBROUTINE SKIP(IUNIT)
c—
c—
c—
c—
c-
10
74-28
.— PPOQPAM TO SKIP FROM 'RESENT LOGICAL FILE TO NEXT LOGICAL FILE
— tUNlTaLOGICAL UNIT NUMBER
CEAO(IUNIT) IDUM
IF(FOF(IUNIT))20,10
20
ESO
SKIP 10
SKIP 20
SKIP 30
SKIP 40
SKIP 50
SKIP 60
SKIP ao
SKIP 90
SKIP 100
SUBROUTINE BACK
c—-
c
c
c
c
c—
10
SUBROUTINE BACK(IUNIT)
CYRER 74-2B
PROGRAM TO BACK FflQM PRESENT LOGICAL FILE TO END OF PREVIOUS
LOGICAL FILE (IE.JUST BEFORE ENO-OF-FILE MARK)
IUMT«LOGICAL UNIT NUMBER
BACKSPACE IUMIT
REAOUUNIT)
IF(EOF(IUNIT))30t20
20 BACKSPACE IUNIT
(30 TO 10
30 BACKSPACE IUNIT
RETURN
END
BACK 10
BACK 20
BACK 30
BACK 40
BACK 41
BACK 50
BACK 60
BACK 40
BACK 90
BACK 100
BACK 110
BACK 120
BACK 130
BACK 140
203
-------
TECHNICAL REPORT DATA
(Pic-asc read Instructions on the reverse before completing)
i. REPORT NO.
EPA-600/2-79-148
2.
4. TITLE AND SUBTITLE
IRRIGATION PRACTICES AND RETURN FLOW SALINITY IN GRAND
VALLEY
5. REPORT DATE
August 1979 issuing date
6. PERFORMING ORGANIZATION CODE
3. RECIPIENT'S ACCESSION-NO.
7. AUTHOR(S)
Gaylord V. Skogerboe, David B. McWhorter, and James E.
Ayars
8. PERFORMING ORGANIZATION REPORT NO
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
Agricultural and Chemical Engineering Department
Colorado State University
Fort Collins, Colorado 80523
1BB770
11. CONTRACT/GRANT NO.
Grant No. S-800687
12. SPONSORING AGENCY NAME AND ADDRESS
Robert S. Kerr Environmental Research Laboratory
Office of Research and Development
U. S. Environmental Protection Agency
Ada, Oklahoma 74820
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/600/15
15. SUPPLEMENTARY NOTES
216 pages, 52 fig., 42 tab., 93 ref.
16. ABSTRACT
This study was undertaken to evaluate the relationships between leachate volume and
chemical quality. A numerical model of soil moisture and salt transport was used.
Field data were collected on 63 research plots located in the Grand Valley, Colorado.
From the calibration of the moisture flow model using infiltration data, water content
profiles and storage change data, it was concluded that soil moisture- flow could be
adequately modeled for the Grand Valley. From comparisons of field and simulated data
used in evaluating the soil chemistry model, it was concluded that TDS concentrations
were adequately modeled but that individual ionic species concentrations were not. Th
TDS profile calculated at the beginning and end of the growing season show the salt
concentration in the profile below the root zone to be relatively constant. This
region acts as a buffer and causes the salt concentration of the return flow to be
relatively constant. This means the reductions in salt loading are directly propor-
tional to reductions in the volume of return flow.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Fluid infiltration, Irrigation, Saline
soils, Salinity, Seepage, Water distribu-
tion, Water loss, Water pollution, Water
Quality
Colorado River, Furrow
irrigation, Grand Valley,
Irrigation practices,
Return flow, Salinity
control
98C
•3. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
218
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
204
*US. GOVERNMENT PRINTING ?f FICE: l»7» 657 080 8375
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