Environmental P/qtectior^Technology Series
Pesticide Transport and Runoff
Model for Agricultural Lands
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20460
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PESEARCK EXPORT I KG SERIES
Research reports of the Office of Research and
Monitoring, Environmental Protection Aqency, have
been grouped into five series. These five broad
'categories were established to facilitate further
development and application of environmental
technology. Elimination of traditional grouping
was consciously planned to foster technology
transfer aiid a maximum interface in related
fields. The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
H. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ENVIRONMENTAL
PROTECTION TECHNOLOGY series. This series
describes* ^research perfoJif to develop and
t^KOT,"
demontrate j ^^troxnentKOT," 'equipment and
methodolog?/ to rs^i^r or prevent environmental
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 sourtoes to meet environmental quality
standards.
EPA REVIEW NOTICE
This report has been reviewed by the Office of Research and
Development, EPA, and approved for publication. Approval does
not signify that the contents necessarily reflect the views
and policies of the Environmental Protection Agency, nor does
mention of trade names or commercial products constitute
endorsement or recommendation for use.
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PESTICIDE TRANSPORT
AND RUNOFF MODEL
FOR AGRICULTURAL LANDS
EPA-660/2-74-013
December 1973
By
Norman H. Crawford
Principal Investigator
and
Anthony S. Donigian, Jr.
Project Manager
Contract No. 68-01-0887
Program Element 1BB039
Project Officer
Dr, George W. Bailey
Southeast Environmental Research Laboratory
Environmental Protection Agency
Athens, Georgia 30601
Prepared for
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL 'PROTECTION AGENCY
WASHINGTON, D,C. 20460
For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402 - Price $2.40
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ABSTRACT
The development and testing of a mathematical model to simulate
the loss of pesticides from agricultural lands is presented. The
Pesticide Transport and Runoff (PTR) Model is composed of submodels
concerned with hydrology, sediment loss, pesticide-soil interaction,
and pesticide attenuation functions. The Model 'piggybacks' the
applied pesticide onto the movement of water through the soil profile
and the loss of water and sediment from the land surface. The
pesticide-soil interaction is based on the Freundlich
adsorption-desorption isotherm, which provides the division between
the adsorbed and dissolved states of the pesticide. Attenuation
functions of volatilization and degradation are provided but were not
tested due to lack of data.
An extensive sampling and data-gathering program at the EPA
Southeast Environmental Research Laboratory (Athens, Georgia) provided
observed data on pesticide loss from experimental plots in the
Southern Piedmont. Comparison of si moated and recorded runoff and
sediment loss showed considerable agreement. Simulated pesticide loss
agreed reasonably well with recorded values for those pesticides
completely adsorbed on sediment particles. The Freundlich adsorption
model did not accurately predict the division between the adsorbed and
dissolved states for those pesticides which are transported by runoff
and sediment loss. Recommendations for future work include further
calibration and testing of the PTR Model, and additional refinement of
the pesticide adsorption/desorption and attenuation functions. The
regulation of pesticide releases to the environment are explored as
possible eventual uses of the PTR Model.
This report was submitted in fulfillment of Contract No.
68-01-0887 by Hydrocomp, Inc. under the sponsorship of the
Environmental Protection Agency. Work completed as of December, 1973.
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CONTENTS
Page
Abstract i
List of Figures
List of Tables
Acknowledgments
Section
I. Conclusions 1
II. Recommendations 3
III. Introduction 5
The Pesticide Problem 5
Pesticide Regulation 7
IV. Mechanisms of Pesticide Loss and Transport in the
Environment 10
Pesticide Cycling in the Environment 10
Mechanisms of Loss from Agricultural Lands 10
V. .Pesticide Transport and Runoff Model Components 13
Hydrologic Model 13
The LANDS Subprogram 17
Modification to HSP LANDS 20
Model Description and Operation 22
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CONTENTS (Continued)
Sediment Loss Program
The Erosion Process 23
Sheet Erosion 24
Sediment Loss Simulation 26
Pesticide Adsorption-Desorption Model
Mechanism of Pesticide Adsorption-Desorption ... 27
Model Description 28
Pesticide Volatilization and Degradation Model 32
Volatilization of Soil-Incorporated Pesticides. . . 32
Volatilization of Surface Applied Pesticides. ... 35
Pesticide Degradation 38
VI. PTR Model Structure and Operation 39
Model Structure 39
Model Operation 39
Model Input and Output
Model Input 40
Model Output 43
Parameter Evaluation and Calibration Procedures .... 48
Operational Parameters . . , 59
LANDS Parameters 59
Watershed and Pesticide Application Parameters . . 59
SEDT Parameters 60
Pesticide Adsorption-Desorption and VOLDE6
Parameters 61
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CONTENTS (Continued)
Page
Conclusion 61
VII. Experimental Program* and Modeling Methodology 63
Experimental Program 63
Modeling Methodology 65
VIII. PTR Model Results and Discussions 67
General 67
PI Watershed Results
Calibration - Runoff and Sediment Loss 67
Pesticide Loss 75
P3 Watershed Results
Simulation with PI Parameters 86
Sources of Error and Data Problems 86
Discussion of Simulation Results 89
IX. Recommendations for Future Research 97
X. References 100
XI. Appendices 105
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FIGURES
No.
1
2
3
4
5
6
8
9
10
11
12
13
14
15
16
17
Page
Pesticide Cycling in the Environment 11
Flowchart of Pesticide Movement in PTR Model 14
Assumed Soil Depths for Pesticide Storage 15
The Hydro!ogic Cycle 16
LANDS Flowchart 19
Cumulative Frequency Distribution of Infiltration Capacity
Showing Infiltrated Volumes, Interflow and Surface Detention 21
Source-Zones Superimposed on the Infiltration Capacity
Function 21
Pesticide Adsorption-Desorption Model 30
PTR Model Structure and Operation 41
Location of Experimental Watersheds 64
PI Watershed: Monthly Summary of Rainfall, Runoff, and
Sediment Loss, (1972-73)
69
PI Watershed: Storm of July 28, 1972 71
PI Watershed: Storm of July 31, 1972 72
PI Watershed: Storm of August 10, 1972 73
PI Watershed: Storm of December 14, 1972 , 74
Wl Watershed: Monthly Runoff Volumes, (1941-42) 76
Wl Watershed: Storms of July 11, 1974 and May 15, 1942 .' . . 77
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FIGURES (Continued)
No. Page
18 Wl Watershed: Storm of August 16 and 17, 1942 78
19 PI Watershed: Monthly Pesticide Loss (1972-73) 79
20 PI Watershed: Paraquat in Water and Sediment During
Storm Events 81
21 PI Watershed: Diphenamid in Water and Sediment During
Storm Events 82
22 PI Watershed: Rate of Sediment and Pesticide Loss on
Sediment - July 28, 1972 83
23 PI Watershed: Rate of Sediment and Pesticide Loss on
Sediment - August 10, 1972 84
24 PI Watershed: Rate of Diphenamid Loss in Water, July
28 and August 10, 1972 85
25 P3 Watershed: Summaries of Rainfall, Runoff, and Sediment
Loss (1972) 87
26 P3 Watershed: Monthly Summaries of Pesticide Loss (1972) . . 88
27 LANDS Flowchart 107
28 Schematic Frequency Distribution of Infiltration Capacity
in a Watershed 110
29 Cumulative Frequency Distribution of Infiltration Capacity . . Ill
30 Application of Cumulative Frequency Distribution of
Infiltration Capacity in HSP Ill
31 Mean Watershed Infiltration as a Function of Soil Moisture . . 112
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FIGURES (Continued)
No. Page
32 Cumulative Frequency Distribution of Infiltration Capacity
Showing Infiltration Volumes, Interflow and Surface
Detention 114
33 Interflow c as a Function of LZS/LZSN 114
34 Components of HSP Response vs. Moisture Supply 115
35 Surface Denention Retained in the Upper Zone 116
36 HSP Overland Flow Simulation H9
37 HSP Overland Flow Simulation 119
38 Hydrograph Simulated (0.26 square miles) 120
39 Hydrograph Simulation (18.5 square miles) 120
40 Infiltration Entering Groundwater Storage 122
41 Groundwater Flow 123
42 Potential and Actual Evapotranspiration 125
43 Examples of Hydrograph Response with Indicated Corrections
to INFILTRATION Parameter 128
44 Example of the Response to the INTERFLOW Parameter 129
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TABLES
No. Page
1 Hydrologic Model (LANDS) Parameters 18
2 PTR Model Subprograms 40
3 Sequence of Input Data 42
4 PTR Model Input Parameters in 'NAMELIST' Format 44
5 Sample Input and Format for Evaporation, Temperature, and
Wind Data 45
6 PTR Model Rainfall Input Data Format 46
7 Sample Output: LANDS and SEDT Calibration Run 47
8 Sample Output: Pesticide Calibration Run 49
9 Sample Output: Production Run-Daily Output Interval 50
10 Sample Output: Calibration Run - Monthly Summary 52
11 Sample Output: Production Run - Monthly Summary 53
12 PTR Model Input Parameter Description 54
13 PTR Model Input Parameter Attributes 57
14 Parameter Values and Initial Conditions From PI Calibration . . 62
15 1972 Summary of Rainfall, Runoff, Sediment and Pesticide Loss
From the PI Watershed 70
16 Rainfall, Runoff, and Sediment Loss for SP3 and P3 Watersheds . 90
17 1972 Summary: Paraquat Simulation on PI Watershed 92
18 1972 Summary: Diphenamid Simulation on PI Watershed 93
19 Pesticide Mass-Balance on PI Watershed On October 30, 1972 . . 95
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ACKNOWLEDGEMENTS
A number of people have contributed to the satisfactory
completion of this project. Primary acknowledgement must go to the
assistance and data provided by the Pesticide Runoff Modeling staff
under the direction of Dr. George Bailey at the EPA Southeast
Environmental Research Laboratory (SERL) in Athens, Georgia. For
their efficient establishment and operation of the extensive field
experimental program, Dr. Bailey and his staff received EPA bronze
medals for their dedication to the project; a fitting witness to the
support provided in this modeling effort.
The USDA Southern Piedmont Conservation Research Center (SPCRC)
in Watkinsville, Georgia co-sponsored the experimental data gathering
program and provided the test watersheds. Dr. Ralph Leonard and
others .on the staff of SPCRC were instrumental in supplying hydrologic
data and assistance throughout the project. For their efforts in the
development and operation of the runoff sampling equipment, Dr.
Leonard and his staff received certificates of recognition from the
ARS Incentive Awards Program. i
Associated researchers of SERL provided support on model
development, particularly in regards to adsorption/desorption and
pesticide attenuation processes. Dr. Walter Farmer (University of
California - Riverside) supplied the basic theory on pesticide
volatilization and provided data for model testing. Dr. James
Davidson (Oklahoma State University) advised on the mechanism of
pesticide adsorption-desorption and vertical movement, and reviewed
the proposed model algorithms.
Many individuals on the Hydrocomp staff were involved in the
project. ' Dr. Norman H. Crawford (principal investigator) provided
the guiding philosophy and general direction of the modeling effort.
Ray K. Linsley advised on methodology and hydrologic problems. The
project manager, Mr. Anthony Donigian Jr., was also responsible for
project administration and completion of the final' report. Mr. W.
Henry Waggy assisted in model development and programming. Mr. James
Hunt was involved with program development and model calibration,
while Mrs. Joan Eyster provided assistance in data reduction and
drafting. Clerical assistance throughout the project was supplied by
Ms. Sherri Ellis, Miss Dea Bell, Mrs. Carol Mendoza and Miss Carol
Sinclair.
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SECTION I
CONCLUSIONS
1. The PTR Model was developed to simulate the transport of
pesticides in solution and on sediment. The Model uses physically -
based submodels to calculate runoff volumes and sediment
concentrations. Initial model tests show good results for transport
of pesticides on sediment and fair-to-good results for transport in
solution.
2. Surface runoff from agricultural lands in the Southern
Piedmont can be simulated with reasonable accuracy with the PTR Model.
The hydrologic submodel has been used extensively in other studies,
and past experience indicates that similar simulation accuracy for
runoff volumes can be expected in other geographical regions.
3. Simulation of monthly sediment loss agrees adequately with
recorded volumes; however, sediment concentrations during storm events
vary somewhat from the observed values. The general nature of the
sediment submodel shows promise of applicability to other regions,
although experience is limited at the present time.
4. The PTR Model has demonstrated the capability of providing
reasonable estimates of surface runoff and sediment loss from
agricultural watersheds in the Southern Piedmont. These routes are
the major modes of transport of pesticides and other non-point source
pollutants to waterbodies. Consequently, further refinement of the
pesticide functions (adsorption/desorption, volatilization, and
degradation) will upgrade the capability of the model to predict the
pesticide input to waterbodies from surface washoff. Moreover, the
PTR Model can provide the basis for the simulation of other non-point
source pollutants (nutrients, fertilizers, etc.), and thus estimate
the water quality of surface runoff from agricultural lands.
5. The loss of paraquat from the experimental watershed is
simulated reasonably well by assuming complete adsorption onto
sediment particles. Pesticides with a similar attraction to soil
particles would likely produce similar results.
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6. The single-valued (reversible) Freundlich adsorption isotherm
appears to be inadequate in simulating the division between adsorbed
and dissolved phases of diphenamid in runoff from the watersheds.
This was also evidenced by the inability to simulate the observed
vertical movement of the pesticides.
7. The observed variations in pesticide concentrations during
runoff events appears to be of little consequence in predicting total
pesticide loss; total mass movement of pesticide (grams/minute) past
the gage during a storm event is a more valid comparison between
simulated and observed pesticide loss.
8. Although simulated and recorded pesticide amounts remaining
on the watersheds agreed reasonably well, concentrations within the
soil profile were in error. The assumed depths of the soil zones is
largely responsible for this discrepancy because the pesticide
concentration is dependent on the total mass of soil in the zone.
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SECTION II
RECOMMENDATIONS
From the results and conclusions of the present research effort,
and considering future uses of the PTR Model in pesticide regulation,
the following recommendations are presented:
1. Continuous simulation of pesticide transport, as opposed to
static steady-state investigations, has been shown to be a valid
methodology for performing a materials balance of pesticides applied
to agricultural lands. The dynamic nature of continuous simulation
allows the full accounting of: (a) pesticides remaining on the land
surface, (b) pesticide concentrations and volume lost during storm
events, and (c) accumulated amounts of pesticide lost to the aquatic
ecosystem during a growing season. Consequently this approach to the
investigation of pesticide transport warrants further refinement.
2. An understanding of the mechanisms of surface runoff and
sediment loss is paramount to the study of the importance of non-point
source pollutants on water quality. The PTR Model has demonstrated
the capability of representing these mechanisms. Consequently, the
coupling of the PTR Model with additional pollutant attenuation,
adsorption, and degradation functions could provide the structure for
modeling the transport of plant nutrients, fertilizers, animal wastes,
and other non-point source pollutants. The effects of silviculture!
and agricultural management techniques on water quality could be
evaluated through the PTR Model by their effect on the transport
mechanisms of the non-point source pollutants. Development of such
submodels needs to be undertaken in order to realize the full
potential of the Model as a management tool.
3. For further refinement of the existing PTR Model, future
research needs to be concerned with:
a. additional testing and calibration of the hydrologic model to
more accurately evaluate model algorithms and land surface
parameters.
b. calibration and possible refinement of the sediment loss
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model to better reproduce recorded sediment concentrations and to
gain experience with the sensitivity of the sediment loss
parameters.
c. refinement and testing of the adsorption-desorption model to
better determine the division between the adsorbed and dissolved
phases of pesticides which are transported on both sediment and
water. The inclusion of a nonsingle-valued adsorption-desorption
model warrants further investigation. These refinements are
critical to the reliable prediction of pesticide lost in water
and on sediment during storm events.
d. additional development and testing of the volatilization and
degradation models on actual field data, so that an accurate
pesticide materials balance can be performed. The effects of
environmental factors on these mechanisms needs to be
investigated.
4. To determine the general applicability of the PTR Model, the
following tasks are recommended:
a. Calibration and testing of the Model for runoff and sediment
loss on watersheds in various regions of the country. This would
allow investigation of changes in parameter values with varying
soil and climatic characteristics, and would demonstrate the
behavior of the Model under varying conditions.
b. Evaluation of model performance on watersheds ranging from 20
to 200 hectaries in order to determine required improvements in
the Model for larger watersheds. This would provide insight into
the effects of channel processes on runoff and sediment loss, and
demonstrate the efficacy of existing model algorithms to simulate
the hydrologic and erosion processes.
5. If the PTR Model is to be considered as a tool for regulating
the release of pesticides, the following areas need to be
investigated:
a. determination and definition of control-size watersheds in
various regions of the country which would be most amenable to
pesticide release regulations.
b. classification and grouping of pesticides according to
toxicity, transport, and persistence characteristics
c. establishment of maximum seasonal releases of pesticides
within each classification which would not inflict serious
consequences on man or the aquatic ecosyste. The fractions of
applied pesticides which reach waterbodies could then be
evaluated by the PTR Model and provide a basis for regulating
pesticide releases.
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SECTION III
INTRODUCTION
In 1962, the publication of Rachel Carson's "Silent Spring"
exposed a problem which had been smoldering for a number of years
within the confines of the academic and scientific communities -- the
indiscriminate use of toxic pesticides in the environment. The
vituperative dialogue which resulted between the ecologically and
agriculturally minded sectors of society has since gradually
diminished. At the present time, it appears that the pesticide
situation has attained a more rational and more productive level.
Enforcement of pesticide registration has been expanded at both the
state and federal levels. Certain persistent and extremely toxic
pesticides have been banned except in emergencies involving the public
health. Studies on the transport, attenuation, accumulation and toxic
effects of pesticides have proliferated and are continuing by various
governmental agencies. To adequately understand problems posed by
pesticide usage, it is necessary to comprehend the arguments put
forward ,by both sides of the conflict. In this way, the requisite
data and research can be determined and instigated so that a
methodology can be developed to equitably regulate pesticide releases
to the environment. Such a regulatory system must necessarily
consider the values of all sectors of society concerned with the
pesticide problem.
THE PESTICIDE PROBLEM
Pesticide is a general term for all forms of insecticides,
herbicides, fungicides, fumigants, nematocides, algacides,
rodenticides, etc. A pesticide is often described as a substance used
to control objectionable forms of life. Ecologists prefer to use the
term "biocide", denoting the destruction of life forms. Many
governmental agencies will employ the term "economic poison" to stress
the monetary gains obtainable. In any case, the total production of
these chemicals in 1971 amounted to 600,000 tonnes 1 involving
nearly 1,000 registered chemicals produced in over 32,000 formulations
However, of the 1971 production, 65% (385,000 tonnes) can be
attributed to only 24 different pesticidal chemicals l , i.e. a
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relatively small number of pesticides is produced in large quantities.
In 1939, the discovery of the insecticide! properties of DDT was
hailed as a great advance in the science of public health. Swiss
chemist Paul Muller was awarded the Nobel Prize in 1948 for his part
in the discovery. With the advent of low-cost production methods, the
production of DDT climbed steadily in the post World War II period.
This fact initiated intensive research into chemicals with structural
properties similar to DDT, known as the chlorinated hydrocarbons. It
was this group of pesticides which absorbed the brunt of the attack by
Rachel Carson in Silent Spring. The persistence and biomagnification
of these pesticides in the environment are the main factors which have
led to the decline in their use. The detection of DDT in remote
regions of the globe, and the discovery that marine fish are almost
universally contaminated with DDT residues 3 are testimony to these
problems of persistence and biomagnification. In some cases,
pesticidal chemicals will lodge and accumulate in fat tissues. Since
lower animals serve as food for the higher animals, the chemical
content of the fat tends to increase in concentration as one moves up
the food chain. The discovery of DDT in mother's milk "* indicates
that man, the finale of the food chain, is not immune to this problem.
There is no need to cite the numerous accidents and catastrophies
which have resulted due to pesticidal contamination of the
environment. This has been done quite eloquently elsewhere 4' 5' 6 .
The problem of pesticide contamination is a unique environmental
problem in that pesticides are deliberately introduced into the
environment for beneficial purposes and/or monetary gains. They are
not waste products as is characteristic of the large majority of
environmental contaminants. Consequently, both the opponents and
advocates of pesticide usage are numerous and steadfast in their
beliefs. In the past, this has raised the argument to an emotional
pitch, and, at present, continues to complicate the issue.
The effects of pesticide contamination are both short and long
term. The short term effects often involve kills of non-target
organisms due to ingestion of toxic levels of the pesticide. The
long-term effects can be classified as carcinogenic, teratogenic
(birth defects), mutagenic (genetic alteration), etc. Pesticides that
accumulate in fat tissues can be spread throughout the organism when
the energy in the fat tissues is required by the organism in times of
hunger or stress. Thus, the time lag between the exposure and the
onset of symptoms can be quite variable. The loss or reduction in
certain species can have a considerable impact on the ecosystem.
Ecologists generally agree that the presence of highly diversified
species within an ecosystem increases its stability. Thus, the loss
of individual species upsets predator-prey relationships causing
imbalance and instability in the ecosystem. In sum, the effects of
pesticide contamination are varied and complex, and are a major topic
of research at the present time.
To equitably present the problem of pesticide contamination, one
cannot ignore the benefits obtained from and the need for pesticides
for weed and insect control. Although it is unlikely that a ban on
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pesticide usage would lead to starvation in the U. S., such an action
would have severe economic implications. Production would decrease
and prices of agricultural products would increase as the food supply
diminished. It has been suggested that more agricultural land be
brought into production to offset production losses from a ban on
pesticide usage 7 . There is no guarantee that unchecked pests would
quickly spread to the new land in production. On the contrary, this
is highly likely. In many undeveloped countries, the situation is
entirely different. Starvation and disease are still major problems
even with all available agricultural land in production. Pesticides
are invaluable in many such countries. It would be sheer folly to try
to convince the underdeveloped countries that pesticide usage is
harmful and that restrictions should receive consideration. With
regard to other problems facing these countries, pesticide
contamination has very low priority. In other words, concern for
environmental pollution is strictly a luxury of the affluent
countries.
The inescapable conclusion is that chemical pesticides will
remain a permanent tool of the agriculturist and the public health
scientist for some time to come. The programs of Integrated Pest
Management of the U. S. Department of Agriculture emphasize the need
to use a variety of control methods. This would include chemical
pesticides, although on a much lower level than at present. Thus, it
is clear that pesticide regulations must be designed to balance the
detrimental and beneficial effects of pesticide usage. The recent
shift in pesticide usage away from the chlorinated hydrocarbons to the
less persistent pesticides is an encouraging trend. However, the less
persistent carbonates and organophosphates which are gaining in
popularity, are also more toxic. Consequently, it is important to
understand the routes of pesticide loss from the field and
the transport processes to the aquatic environment. Such an
understanding, along with a knowledge of the effects of pesticides
on non-target species, can provide an equitable basis for
regulating the amount of pesticide to be released to the environment.
PESTICIDE REGULATION
Prior to October , 1972, federal authority on the regulation of
pesticides was derived from the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA) of 1947 requiring registration of all
pesticides shipped in interstate commerce. Registration applications
were filed with the Environmental Protection Agency (EPA) and renewed
at 5 year intervals. EPA retained the authority to suspend or cancel
registrations if use of the pesticide 1 presented an 'imminent
hazard1, of 2 resulted in injury to man or animals, when applied in
accordance with label directions. This authority has been exercised a
number of times. In 1971 registration cancellation proceedings were
initiated against companies producing DDT, Mirix, 2,4,5-T, aldrin, and
dieldrin. The subsequent ban on nearly all uses of DDT demonstrated
the extent of authority of the EPA administrator under FIFRA in cases
of extreme risk to man and the environment. The establishment of
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statutory limits on pesticide residues in foods remains an additional
EPA regulatory tool.
In October, 1972, the Federal Environmental Pesticide Control Act
(FEPCA) was written into law (PL92-516). FEPCA substantially
increases federal authority over pesticide regulation from that
provided under FIFRA. FEPCA allows the EPA Administrator to classify
registered pesticides according to general or restricted use, or both.
Moreover, FEPCA requires that all pesticides be registered; thus,
requiring that state registration laws conform to present Federal law.
Stop sale and seizure orders for pesticides in violation of the new
legislation are also authorized. In sum, the level of pesticide
regulation has advanced considerably with the new law.
With the passage of FEPCA, individual state pesticide regulatory
agencies must revamp their existing regulations to assure conformance.
At present, state pesticide laws vary in both severity and scope of
regulation. With the increase in concern for environmental
contamination, recent years have witnessed a proliferation of state
pesticide laws. Generally these regulations involve one or more of
the following:
1. Pesticide registration.
2. Permits for sale and/or use of pesticides.
3. Licensing of pesticide applicators.
4. Prohibition of certain hazardous pesticides, or, compilation
of acceptable ones.
5. Regulations for handling and transport of pesticides, and
disposal of pesticide containers.
Due to its inherent nature, the enforcement of pesticide regulations
is performed most easily at the state or local levels. However, the
non-uniformity of state regulations has led to jurisdictional disputes
and enforcement problems.
Obvious by their absence are regulations stipulating the amount
of pesticides which can be applied without detrimental external
effects. The reasons for this are largely technological. Research is
presently being conducted on the physical, chemical, and biological
effects of various pesticide concentrations on plant and animal
species. The complex interactions of pesticides when released to the
environment have hindered the quantitative definition of the
mechanisms involved in pesticide transport and attenuation.
Laboratory research, aided by field experimentation, is progressing on
these topics.
The present research effort is a study of the loss of pesticides
from agricultural lands and an attempt to model the mechanisms by
which pesticides reach a nearby watercourse. Available evidence
points to surface runoff and sediment loss as the primary modes by
which pesticidal contamination inflicts waterbodies. The reliable
modeling of these mechanisms along with the pesticide interactions on
soil and water can result in estimates of the fraction of applied
pesticide reaching the stream channel. Such information, accompanied
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with estimates of allowable pesticide concentrations in the
environment, can provide a basis for establishing allowable pesticide
application rates. These rates could be determined for each of the
various edaphic and climatic regions of the country. Such a
methodology would allow regulation of pesticide use so as to minimize
the detrimental environmental effects and still achieve the purposes
of pesticide application.
A reliable pesticide transport model could provide the basis for
formulation and evaluation of various management systems to restrict
and control pesticide movement to receiving waters. Adjustment of
model parameter values would represent the effects of proposed systems
and techniques while analysis of model output (e.g., pesticide loss)
would provide the basis for system evaluation. In this way, the
relative efficiacy of terracing, coutour farming, pesticide
regulations, and combinations of these and other management systems
could be analyzed and evaluated. The end result would be a greater
understanding of the proposed management system.
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SECTION IV
MECHANISMS OF PESTICIDE LOSS AND TRANSPORT IN THE ENVIRONMENT
PESTICIDE CYCLING IN THE ENVIRONMENT
Upon release to the environment, pesticides move by a myriad of
transport mechanisms. Figure 1 attempts to portray the various
pathways by which pesticides cycle through the environment. The term
'cycle' is quite appropriate here. Initially applied by Man,
pesticide residues return to the human body through the ingestion of
meat and harvested crops. The complexity of the maze of pathways
shown in Figure 1 emphasizes the need to approach the problem at its
origin -- the initial pesticide application. The President's Council
on Environmental Quality has recognized that multi-media pollution
problems, such as pesticides which involve air, land and water, are
best analyzed by a materials balance approach P A full accounting
of the movement and loss of pesticides, subsequent to application, is
necessary. Knowing the quantities of pesticide transported and
retained by the various media, one could then attack the major
mechanisms responsible for the detrimental effects of pesticide use.
Figure 1 demonstrates that performing a materials balance on pesticides
is not a simple matter. The mechanisms of transport and loss from the
various media must be quantitatively understood, so that amounts of
pesticide in transport can be predicted. Climatic, hydrologic, and
edaphic conditions of the region plus the chemical properties of the
pesticide will have an effect on the various mechanisms. Although
many of these mechanisms are not completely understood at the present
time, research is underway to explain the transport behavior of
pesticides upon release to the environment.
MECHANISMS OF LOSS FROM AGRICULTURAL LANDS
Pesticides are introduced into the environment by a variety of
sources. Agricultural applications are obviously the major source.
Spillage and accidents, industrial effluent, and municipal sewage are
other instruments of pesticide pollution. These are either
non-deliberate releases or point sources of pesticides, both of which
must be handled in a different manner than applications to
agricultural lands. A number of states have passed legislation
- 10 -
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Figure 1. Pesticide cycling in the environment
PESTICIDE APPLIED
Drift
SPRAY GRANULES
PELLETS. FUMIGANTS
Degradat ioi:
loss
Injection
pellets, etc.
Inject! on,
soil incorpation
Volatility
Codistillation
L_Absorption ". - ' . . '."
Degradation loss
Degradation loss
PESTICIDE
RUNOFF
Hyd
r o c o m p
-------
regulating the intrastate transport of pesticides, and the disposal of
pesticide containers 9' 10 These two areas have proven to be a
substantial source of spillage and accidential releases of pesticidal
chemicals. Enforcement and regulation of these components is easier
because of their point-source nature.
Because agricultural applications of pesticides constitutes the
major release of pesticides to the environment, this area has received
the greatest attention in the curbing of pesticide pollution.
Following application, pesticide loss from agricultural lands occurs
through surface runoff, sediment loss, volatilization, organisms
(plant and animal) uptake, and degradation (microbial, photochemical,
chemical). The relative significance of the various mechanisms is
highly dependent on environmental conditions and pesticide properties.
Movement of pesticides to a water course is of primary environmental
concern because of possible effects on the aquatic ecology. Other
than direct application, surface runoff and sediment transport have
been recognized as the major routes to the aquatic environment11'12
Fortunately, a very small portion of the amount of applied
pesticide will reach a watercourse. In general, the large majority of
the application amount will volatilize or degrade by chemical,
photochemical, and microbial agents. In some cases, pesticides will
degrade into other chemicals more toxic and/or detrimental to the
ecology of the area. Volatilization during and after application
accounts for the escape of pesticides to the atmosphere. Upon
entering the atmosphere, the area! extent of pesticide contamination
is essentially unlimited. Atmospheric transport has been recognized
as a major mechanism of widespread DDT contamination 13
In summary, the movement of pesticides from agricultural lands is
the major component of pesticidal contamination of the fresh water
aquatic environment. Performing a materials balance on pesticides
applied to agricultural lands would help to understand, qualitatively
and quantitatively, the mechanisms involved in the loss of the applied
pesticides. This report is part of a research effort designed to
evaluate and model these mechanisms. The general goal is to estimate
the amount of pesticide lost from agricultural levels that will reach
and contaminate a watercourse. The remainder of the report describes
the accompanying modeling program and preliminary results of that
research effort.
- 12 -
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SECTION V
PESTICIDE TRANSPORT AND RUNOFF MODEL COMPONENTS
Figure 2 illustrates the movement of applied pesticides as
visualized by the Pesticide Transport and Runoff (PTR) Model. Four
pesticide storage zones within the soil profile are assumed. Figure 3
presents the assumed depths of the various pesticide storage zones -
surface zone, upper zone, lower zone, and groundwater zone. The
assumed zone depths are necessary to specify the mass of soil involved
in the pesticide-soil interactions.
The PTR Model estimates the loss of pesticides from the land
surface by simulating the mechanisms of surface runoff, sediment loss,
pesticide adsorption-desorption, and pesticide volatilization and
degradation. This chapter describes the various loss mechanisms and
submodels included within the PTR Model. Initially the hydrologic
model responsible for the determination of surface runoff and soil
moisture storage is discussed. The sediment loss model estimates
sediment production from the land surface based on input rainfall and
surface runoff provided by the hydrologic model. The division of
applied pesticide among the various phases (adsorbed, dissolved, and
crystalline) is determinated by the pesticide adsorption-desorption
model. This model, in conjunction with the hydrologic and sediment
loss models, determines the amount of pesticide removed from the land
surface by surface runoff and sediment loss. This section concludes
with a discussion on the modeling of the loss of pesticides by
volatilization and degradation. The actual structure and operation of
the PTR Model is presented in Section VI.
HYDROLOGIC MODEL
The Hydrologic Cycle
The hydrologic cycle can be defined as follows: "The circuit of
water movement from the atmosphere to the earth and return to the
atmosphere through various stages or processes such as precipitation,
interception, runoff, infiltration, percolation, storage, evaporation,
and transpiration. Also called water cycle"13. Figure 4
- 13 -
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Figure 2. Flowchart of pesticide movement in PTR Model
Soil
I incorporated
latil izationy
beg radatiory
Volatilization)
Degradation/
/Pesti
\Applic
cide\
ation/
/Application ]
V^Morfe J
HSur)
Pesti
Sto
Surface
Applied Pesticide on
/ Sediment
' _^y/r
ace jy^Surface^< Pesticide
rirln I Pncitlrjrln ' reSllCIQe
rage * \lnteract ion^/ Crystals
/ x Pesticide in
/ Overland Flow
/ Infiltration
_ Upper Zone ./Upper ZoneN Pesticide in
Pest c de I Pesticide I , ,. •
Storage * ^Interaction/ Interflow
-+ to Stream
-> to Stream
f to St rea r
Percolat ion
,ower Zone\
Pesticide J
nteraction/
Losses to
Ground water
Legend
-Data Input
— Function
— Storage
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 14 -
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Figure 3. Assumed soil depths for pesticide storage
en
i
BERTH
(Parameter
(tiinput
variable)
^
liirf-irr
............ ....... .._, _ _. • ..:.. ........ .
/ [•;. ':/' V !;V .':) ^-•"•t"\ •-: " :' >'-' ! v'rV-^v-^'.-V-:';--' " Y. ? -v''.*---; V •:V-v-"'V:;>'"'.:---:>-:-:;:'>' ' uPPer Zor
-------
Figure 4. The hydrologic cycle
11 I I 1 I 1 1 I I 1 M
Precipitation
Interception
w
Evapotranspiration
t
, Groundwater
PESTICIDE
RUNOFF
H y d r o c o
m p
- 16
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schematically shows the interactions and mechanisms that comprise the
hydro!ogic cycle. The hydrograph of streamflow from a watershed is
the end product of variable time and areal distributions of
precipitation, evapotranspiration, soil moisture conditions, and
physical watershed characteristics. Streamflow eventually reaches
large water bodies, such as rivers, lakes, and oceans, evaporation
from which is the major source of atmospheric moisture. The cycle is
completed when atmospheric moisture condenses and returns to the land
surface in the form of precipitation.
Since the movement of water is cyclical, without permanent
sources or sinks, a pollutant entering any phase of the cycle can be
transported by the water; hence, other phases of the cycle can be
contaminated. Thus, an understanding and an ability to simulate the
hydrologic cycle is instrumental in the study of the cycling of
pollutants in the environment. Since pesticide movement from the land
surface is the major mechanism of aquatic contamination, the present
study is concerned largely with the land surface components of the
hydrologic cycle.
The LANDS Subprogram
Within the PTR Model, the LANDS subprogram simulates the
hydrologic response of the watershed to inputs of precipitation and
evaporation. LANDS simulates runoff continuously through a set of
mathematical functions derived from theoretical and empirical
evidence. It is basically a moisture accounting procedure on the land
surface for water in each major component of the hydrologic cycle.
Parameters within the mathematical functions are used to characterize
the land surface and soil profile characteristics of the watershed.
These parameters must be selected, tested and modified when LANDS is
applied to a new watershed. Calibration is the process whereby the
parameters are modified as a result of a comparison of simulated and
recorded streamflow data for the watershed. Section VI on model
structure and operation will describe the calibration process.
The mathematical foundation of the LANDS subprogram is derived
from the Stanford Watershed Model (SWM) developed at Stanford
University by Crawford and Linsley lk . Subsequent improvements and
refinements in the simulation algorithms of the SWM have been
incorporated into the Hydrocomp Simulation Program (HSP). HSP is
composed of three components whose functions involve data management
and handling (named LIBRARY), hydrologic response of the land surface
(also named LANDS), and kinematic channel routing of the land surface
runoff (named CHANNELS). The LANDS subprogram of the PTR Model, with
certain modifications is identical to the HSP LANDS module; moreover,
- 17 -
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the calibration parameters, listed in Table 1, are identical. For the
sake of brevity, the basis and description of the mathematical
functions of LANDS have been abstracted from the HSP Operations Manual
and have been included in Appendix A. Figure 5 presents the flowchart
of LANDS showing the interactions of the various land surface
components that are described in Appendix A. Modifications to HSP
LANDS which are incorporated into the PTR Model are explained
subsequently.
Table 1. HYDROLOGIC MODEL (LANDS) PARAMETERS
A - A fraction representing the impervious area in a segment.
EPXM - The interception storage parameter, related to vegetal cover
density.
UZSN - The nominal storage index for the upper soil zone.
LZSN - The nominal lower zone soil moisture storage parameter.
K3 - Index to actual evaporation (a function of vegetal cover).
K24L & K24EL - Parameters controlling the loss of water from
groundwater storage. K24L is the fraction of groundwater recharge
that percolates to deep groundwater tables. K24EL is the fraction
of the segment area where shallow water tables put groundwater
within reach of vegetation.
INFIL - This parameter is a function of soil characteristics defining
the infiltration characteristics of the watershed.
INTER - This parameter defines the interflow characteristics of the
watershed.
L - Length of overland flow plane.
SS - Average overland flow slope.
NN - Manning's "n" for overland flow.
IRC & KK24 - The interflow and groundwater recession parameters.
KV - The parameter KV is used to allow a variable recession rate for
groundwater discharge.
- 18 -
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Figure 5. Lands flowchart
KEY
PRECIPITATION \
POTENTIAL \
.JVAPOTRANSPIIIATIOH/
PESTICIDE
RUNOFF
Hydrocomp
- 19 -
-------
Modification to HSP LANDS
The major concern in modifying the HSP LANDS module for pesticide
transport was the desire to accommodate the expected area! variation
in pesticide concentration over the land surface. It is generally
accepted in hydrology that infiltration is time and area dependent;
infiltration capacity will vary even within small watersheds with
reasonably homogeneous soil characteristics. This area! variation in
infiltration results in source areas, or zones, with low infiltration
capacity within the watershed, contributing a large component of
overland flow. Sediment loss will vary with the area! variation in
overland flow. Since overland flow and sediment loss are the major
mechanisms of pesticide transport to the water course, the low
infiltration source areas will also experience a greater loss of
pesticide than the remainder of the watershed. Consequently, the
pesticide concentration on the land surface will vary, in spite of an
initially uniform application. The pesticide concentration within the
soil profile will also vary as a function of the volume of
infiltration. Obviously, the extent of pesticide areal variation
depends upon the solubility and transport characteristics of the
specific pesticide applied, and upon topography and watershed
characteristics. Natural hydrologic conditions and watershed
characteristics are sufficiently non-uniform to justify the above
described mechanisms leading to areal variations in infiltration and
pesticide concentrations.
HSP LANDS employs a cumulative frequency distribution on
infiltration capacity to account for the areal variation. Figure 6
graphically presents the infiltration function of HSP LANDS described
in Appendix A. A mean infiltration capacity, f, is calculated and a
linear approximation to the actual cumulative distribution is assumed.
Interflow is determined as a function of infiltration and lower zone
moisture storage. It is evaluated in Figure 6 as a second linear
cumulative distribution denoted by f (c-1) (see Appendix A for a full
description). Since the X-axis is unity (i.e. 100% of watershed
area), the area of each wedge in Figure 6 represents the portion of
the moisture supply allocated to each component. During any time
interval, the available moisture supply is distributed to surface
detention, interflow detention and infiltration. Overland flow and
interflow are determined as losses from surface detention and
interflow detention respectively. Lower zone moisture storage and
groundwater components are derived from the infiltration component.
The LANDS subprogram of the PTR Model employs the same
infiltration function as HSP LANDS, with one modification. The
watershed is divided into five zones, each representing 20% of the
total area. The zonal division is based on infiltration capacity.
Schematically, Figure 7 shows that zone 1 will infiltrate much less
water than zone 5; conversely, zone 5 will provide less overland flow
than zone 1. Thus, the areal variation in infiltration capacity is
approximated. Zones with lower infiltration capacity will serve as
the major source areas for overland flow and sediment and pesticide
- 20 -
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MOISTURE
SUPPLY
(mm) X
Increment to
Surface
(-Detention
Increment to
Interflow
-Detention „
7 (c-1)
INFILTRATION
CAPACITY
(mm)
IF AREA WITH INFILTRATION CAPACITY
LESS THAN OR EQUAL TO INDICATED VALUE
Figure 6. Cumulative frequency distribution of infiltration
capacity showing infiltrated volumes, interflow
and surface detention
MOISTURE
SUPPLY
(mm)
_ Zone 1 Zone 2 Zone 3/^Zone 4 Zone 5
INFILTRATION
CAPACITY
(mm)
20
40
60
80
100
% OF AREA WITH INFILTRATION CAPACITY
LESS THAN OR EQUAL TO INDICATED VALUE
Figure 7. Source-zones superimposed on the infiltration
capacity function
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 21 -
-------
loss. Generally, zones with high infiltration will contain more
pesticide in the soil profile because of the greater amount of
infiltrated water.
Conceptually, the zones are not necessarily concentric,
continuous, or contiguous. Each is connected directly to the stream
channel by the overland flow plane. As with any simulation model,
this source zone concept is an approximation. It is an attempt to
portray mechanisms which are known to occur, but are impossible to
simulate in detail.
Model Description and Operation
The LANDS subprogram operates on a 5 or 15 minute time interval
at the discretion of the user. Daily potential evapotranspiration and
time-interval precipitation are required inputs. During each time
interval, precipitation, in inches, is input and first encounters the
interception function, as shown in Figure 5. Interception is a
storage function dependent on vegetation and crop canopy. For
agricultural lands, this will vary over the growing season. The
change in crop canopy is administered by the MAIN program of the PTR
Model which oversees input-output operations, time accounting, and
general management of the component submodels. The MAIN program will
be described in a subsequent section.
Once interception storage is filled, any remaining precipitation
is added to the moisture supply of the infiltration function. The
infiltration function, performs the basic division of available
moisture into the components of surface detention, interflow
detention, and infiltration. Surface detention includes the
components of overland flow and an increment to upper zone soil
moisture storage. Interflow detention is a delay mechanism
controlling the release of interflow to the stream. Infiltration and
percolation from the upper zone provide the means by which moisture
reaches lower zone storages. From lower zone storage, the available
moisture proceeds to active groundwater storage from which the
groundwater component of streamflow is derived. Other than
evapotranspiration, inactive groundwater (groundwater recharge) is the
only other means of release of water from active groundwater storage.
The mathematical functions describing the divisions involved in the
LANDS subprogram are fully explained in Appendix A. The necessary
input parameters are also defined.
Other than streamflow and losses to inactive groundwater,
evapotranspiration is the only remaining loss component in the
moisture balance performed in LANDS. Evapotranspiration occurs at
different rates from each of the various moisture storages shown in
Figure 5. Daily potential evapotranspiration values are input and
transformed to hourly values by an empirical diurnal variation.
Actual evapotranspiration is calculated on an hourly basis from
interception, upper zone, and lower zone storages, and on an average
daily basis from groundwater storage. From interception storage,
- 22 -
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evapotranspiration occurs at the potential rate. Any remaining
potential is satisfied initially from the upper zone and then from the
lower zone, depending on existing moisture conditions.
Evapotranspiration from groundwater storage is controlled through an
input parameter, K24EL, delineating the percent of watershed area
where the groundwater table is close enough to the surface to allow
evapotranspiration to occur (e. g. marshes, swamps, deep-rooted
vegetation). Appendix A also describes in detail the functions
simulating evapotranspiration from the various moisture storages.
The basic algorithms of the LANDS subprogram, as part of the
Stanford Watershed Model, and the HSP, have been tested on over 200
watersheds in various regions of the country. However, its use on
watersheds in the range of 4 - 20 hectares has been limited. Also, the
modifications, described above, and its use in simulating pesticide
transport are new endeavors. Problems arising from these new
applications, and results of model testing are discussed in Section
VIII - Model Results and Discussions.
SEDIMENT LOSS SUBPROGRAM
The Erosion Process
The process of erosion has been in operation throughout the
history of the earth. The monuments of its glory surround the globe
and shape its surface. Man marvels at the dichotomy of the minuteness
of the Colorado River within the majestic environment it has shaped.
Yet, despite the millions of acres and centuries of geologic evidence,
the mechanism of erosion has not been mastered to the point where soil
loss can be reliably predicted. Perhaps this will remain another of
Mother Nature's secrets to which man's ingenuity can only achieve an
approximation. However, attempts to understand and simulate the
process continue in order to attain the best approximation possible.
Erosion is generally thought to consist of the detachment of soil
particles, and movement of the particles to a channel in which they
are transported to their ultimate destination. Our present
understanding of the process involves its breakdown into the three
mechanisms of sheet erosion, gully erosion, and channel erosion.
Sheet erosion refers to the relatively uniform loss of topsoil across
the soil surface. The impact of falling raindrops is an important
process in this mechanism, detaching soil particles, or fines, from
the soil aggregates. The fines are then available to be picked up and
transported by overland flow.
Initially occurring as sheet flow, the overland flow will soon
begin to concentrate into small rivulets due to the topography of the
surface. Gully erosion becomes operative when the flow turbulence
creates local forces sufficient to dislodge particles from the sides
and head of the gully. As the gully grows deeper and wider, the
momentum and inertia of the flow become significant factors in shaping
the streambed and stream course. Here, channel erosion begins to
- 23 -
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influence the direction of the stream resulting in changes in the
stream cross-section and meandering of the streambed.
In reality, the erosion process is gradual and continuous from
the raindrop impact and overland flow pickup to the transport of
sediment in the stream. There is no definitive dividing line between
the mechanisms of sheet, gully and channel erosion. This conceptual
model of the erosion process helps our overall understanding.
Sheet Erosion
With regard to pesticide loss from agricultural lands, our
interest in the erosion process is directed to the mechanism of sheet
erosion. Included in this mechanism is the formation of small rills
or rivulets which signify the primitive stages of gully erosion.
Although gully erosion is significant in total sediment loss, the
occurrence of gullies will not have a significant effect on pesticide
loss because pesticide application is generally restricted to the
surface or top few inches of soil. In this region, sheet erosion,
including small rills, is the critical mechanism.
The hydrologic factors governing sheet erosion include the impact
of raindrops and the occurrence of overland flow. As raindrops strike
the land surface, soil aggregates are broken down and detached soil
particles are dispersed in all directions. On a sloping surface, the
raindrop splash will tend to move particles downhill. The angle of
incidence of the raindrops will also be a function of wind velocity
and direction. In any case, the soil movement by raindrop splash is
quite small. Its main effect is the production of soil fines which
are transported downslope by overland flow. The soil fines are either
picked up from the surface by the overland flow or are added to the
flow by the soil splash. In this way, the soil fines are
transported downslope until a reduction in velocity allows the
particles to settle. Subsequent overland flow will pick up the
deposited particles and transport them another distance downslope.
This process occurs repeatedly until the soil particles reach a major
gully or stream channel where they become part of the sediment load of
the stream.
The quantitative evaluation of the above processes is far from
obvious. Theoretically, one would expect the soil detachment due to
raindrop splash to be dependent on soil properties and on the kinetic
energy of the raindrop in its collision with the soil surface. The
kinetic energy, in turn, is a function of the mass and terminal
velocity of the raindrop. Since the mass is the accumulated depth of
rainfall, one could determine the theoretical kinetic energy if
terminal velocities were known. Wischmeier and Smith 16 have shown
that raindrop terminal velocities will vary with drop diameter and
rainfall intensity. Natural rain storms will generally include a
range of drop sizes and rainfall intensities. Moreover, wind velocity
and crop canopy will have a significant effect on these factors.
- 24 -
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Consequently, a strict theoretical approach would be impractical for
field conditions.
Up to the present time, gross soil loss estimates have been based
on either the Musgrave equation 17 or the Universal Soil Loss (USL)
equation, developed by the Agricultural Research Service of the U. S.
Department of Agriculture. These equations are similar in form, and
both contain empirical components relating to soil credibility, crop
cover and management, overland slope and length and rainfall. The USL
equation 18 is presented as follows:
A = B x F
SL x P
(1)
where: A = average annual soil loss, tons per acre
B = relative soil credibility factor
F = relative farming practice factor
G = relative cropping-management factor
SL = relative slope-length factor
P = rainfall erosion factor
The above equation yields the average annual soil loss per acre for
the watershed. The factor which attempts to account for
hydrologic conditions is P, the rainfall erosion factor, which is
determined by:
P = (E I30)/100
(2)
where:
E = Total storm kinetic energy, in foot-tons/acre-inch.
130 = Maximum 30-minute intensity during the storm, in/hour.
Wischmeier and Smith 16 have evaluated E as a function of rainfall
intensity, and have further determined that the factor E Ion
demonstrated the best correlation for soil loss per storm among the
factors tested. For this reason, the EI30 factor has been employed to
evaluate P, the rainfall erosion factor in the USL equation. Negev
19 has noted that neither the Musgrave equation nor the USL equation
contains a factor specifically accounting for the effects of runoff on
soil loss. Since the sheet erosion mechanism depends on the
occurrence of overland flow, applying these equations to a specific
short time interval can lead to gross errors. Attempts to simulate
the sheet erosion process on small watersheds must employ a short time
interval. For this reason, neither the Musgrave nor the USL equations
are applicable to continuous simulation of sediment loss from small
watersheds.
25
-------
Sediment Loss Simulation
The simulation of sheet erosion as performed by the sediment
subprogram is based on a sediment model developed by Moshe Negev at
Stanford University 19 . Negev simulated the entire spectrum of the
erosion process including sheet, gully, and channel erosion.
Simulation was performed on an hourly basis. The accumulated daily
sediment loads from Negev's model compared well with recorded values
on an 81 square mile watershed in California. The simulated suspended
sediment loads also agreed reasonably well with the recorded values.
The sediment subprogram of the PTR model employs Negev's
functions to simulate the mechanism of raindrop impact and overland
flow pick up and transport of soil particles. As opposed to Negev's
model, rill formation and erosion is assumed to be included within
sheet erosion process. On the small test watersheds, gully erosion
did not appear to be significant, and consequently was not included
within the model.
The production of soil fines from raindrop splash is modeled per
unit area for each time interval as follows:
JRER
RER(t) = (l-COVER(T)) x KRER x PR(t) (3)
where: RER(t) = soil fines produced during time interval, T.
COVER(T) = percent vegetal cover as a function of the relative
time within the growing season.
KRER = coefficient of soil properties.
PR(t) = precipitation during time interval, t.
JRER = exponent.
The term 'soil fines' is not defined according to particle size
within the Model. Negev assumed that the 'wash load1 portion of total
sediment loss included particles less than 0.062 mm in diameter 19.
The 'soil fines' would include the finer particles of the wash load,
i.e., approximately the silt and clay fraction. Implicitly, the soil
fines can be defined as those particles disrupted and detached by the
force of raindrop impact.
The soil fines produced by the raindrop impact are immediately
available for transport by overland flow if overland flow is occurring
within the time interval. If overland flow is not occurring, such as
during the initial or final stages of the storm period, the soil fines
accummulate on the soil surface. Thus, a reservoir of fines is
deposited on the surface available for pick up and transport by
subsequent overland flow. The pick up of deposited soil fines is a
function of the overland flow occurring during the time interval and
the total amount of deposited fines on the surface. This mechanism is
modeled per unit area for each time interval by the relationship:
- 26 -
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SER(t) = KSER x SRER(t-l) x ROSB(t) JSER (4)
where: SER(t) = fine pick up during time interval, t.
KSER = coefficient of pick up.
SRER(t-l) = reservoir of deposited fines existing at the
beginning of time interval, t.
ROSB(t) = overland flow occurring during time interval, t.
JSER = exponent.
Thus, the total contribution to sediment loss during any time interval
is the sum of the soil fines production from raindrop impact plus the
fines pickup by overland flow. The subprogram also includes soil
washoff component from impervious areas, although this is not
significant for agricultural lands.
The source-zone concept described in Section V allows the
calculation of the overland flow component from each zone within the
watershed. The zones with lower infiltration will experience greater
overland flow and, thus, greater sediment loss. The overland flow
component from each zone is used to calculate the soil fines pickup
for that zone. Consequently, during each time interval, the sediment
production from each zone is determined from the above equations. The
sum of the zonal contributions is the total sediment load to the
stream. This zonal concept attempts to simulate the areal variation
in sediment loss due to the overland flow variation.
PESTICIDE ADSORPTION - DESORPTION MODEL
Mechanism of Pesticide Adsorption-Desorption
The interactions between pesticides and the soil to which they
are applied are undoubtedly the most critical mechanisms in the
determination of pesticide loss from agricultural lands. Bailey and
White20 have concluded that pesticide adsorption-desorption, the
attraction of pesticidal molecules to soil particles, directly or
indirectly effects all factors known to influence the fate and
behavior of pesticides in soil systems. Pesticide adsorption provides
the route of transport to stream channels for many pesticides.
Moreover, the strength of this adsorption will affect the importance
of surface runoff as a mechanism of transport for soluble pesticides.
The mechanisms of volatilization, degradation, organism uptake, and
movement of pesticides are all affected by the adsorption-desorption
interactions which occur. Consequently, in order to attempt to
simulate the loss of pesticides from the soil, one must adequately
understand this complex phenomenon.
- 27 -
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A number of factors determine the extent of pesticide
adsorption-desorption in a soil-water system. King and McCarty 21
have classified these factors into three groups relating to (1) the
nature of the soil, (2) the nature of the pesticide, and (3) the
influence of environmental factors. Soil characteristics of major
importance include clay content, major clay mineral type, organic
matter content, and cation exchange capacity. Surface charge density
and surface area will also have an effect on pesticide adsorption.
The significant pesticidal properties include molecular weight,
chemical structure, water solubility, acidity or basicity, and
polarity. In general, the extent of pesticide adsorption will depend
upon the specific combination of soil and pesticide properties in any
given situation. Also, since many of the determining factors are
interrelated, the individual effect of separate soil and pesticide
characteristics is extremely difficult to investigate. An elaboration
of the possible effects of all these factors is beyond the scope of
this report. A number of articles 20' 22' 23 have summarized the
existing state of knowledge and research on the determinant factors of
pesticide adsorption-desorption. The reader is referred to these
references for additional information.
The influence of environmental factors is believed to be less
significant than pesticide and soil properties in the
adsorption-desorption reaction. Soil temperature, soil moisture
content, and climatic conditions are usually considered to be the
major environmental factors. Although rainfall, runoff, and other
climatic conditions are critical determinants of pesticide loss from
the soil, their importance is limited in the actual pesticide-soil
interaction.
Because of the lack of quantitative relationships describing the
effects of the above mentioned factors on pesticide adsorption, the
model simulating this process is based on a number of assumptions.
Testing the validity of these assumptions from the model output was a
major portion of the present research effort. The model and
accompanying assumptions are described subsequently.
Model Description
The algorithm currently in use to model pesticide adsorption and
desorption on soil is as follows:
X/M = K C + F/M (5)
where: X/M = Pesticide adsorbed per unit soil, mg/gm,
F/M = Pesticide adsorbed in permanent fixed state per unit soil,
F/M is less than or equal to FP/M, where FP/M is the
permanent fixed capacity of soil in mg/gm for pesticide.
This can be approximated by the cation or anion exchange
capacity for that particular soil type.
- 28 -
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C = Equilibrium pesticide concentration in solution, mg/ml
N = Exponent;
K = Coefficient;
24 25
This model was derived from the work of Weber and Weed ' " ,
Faust and Zarins 26 and others. It combines the standard Freundlich
model x/m = K C (1/N) with an empirical term. The empirical term
describes pesticide which is adsorbed so strongly that the surrounding
dissolved pesticide concentration is too small to be measured
experimentally. Weber and Weed 2Lf have shown that this initially
adsorbed pesticide will not desorb under repeated washing; hence, this
initial adsorption is called the permanently fixed pesticide.
Pesticide adsorbed into the permanent fixed state will be assumed
unavailable for desorption. The permanent fixed state is assumed to
have a maximum capacity, FP/M, which must be determined from
experimental adsorption isotherms for the pesticide-soil combination
under consideration. All available dissolved pesticide is assumed to
be adsorbed into the permanent fixed state until FP/M is reached. The
remaining dissolved pesticide is then subject to reversible
equilibrium adsorption as governed by Freundlich equation. The model
is presented graphically in Figure 8.
The basic assumptions underlying this model are as follows:
(1) Permanent fixed adsorption is irreversible.
(2) Single-valued reversible adsorption occurs above the FP/M
concentration.
(3) Reaction is pH independent.
(4) No competing ion effect occurs.
(5) Adsorption is time independent, i.e. equilibrium is assumed.
Assumption 1 appears to be substantiated by the work of Weber and
Weed 2k . For the majority of pesticides, the permanently fixed
portion will be negligible. However, the existing model can
accommodate those pesticides, such as paraquat, which are so strongly
adsorbed to soil particles that their mode of transport from the land
surface is exclusively by sediment loss.
Assumption 2 states that the division of the pesticide between
the water and sediment phases will follow the same curve in both the
adsorption and desorption cycles, i.e. a single-valued, reversible
adsorption-desorption relationship is assumed. Davidson and McDougal
Van Genuchten, Davidson, and Wierenga 28 , and Davidson, Mansell, and
Baker 29 , have shown that the reaction is non-reversible for a
number of pesticides. The end result of assumption 2 is that
predicted pesticide concentration on sediment would be less than
recorded values. The field data is inconclusive.
- 29 -
-------
Figure 8. Pesticide adsorption-desorption model
E
o>
01
E
O
z
O
O
CO
Q
O
r: FP
? M
CO
LLI
a.
PESTICIDE SOLUTION CONC.(C), mg/ml
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 30 -
-------
However, the existing laboratory evidence suggests that the addition
of a non-reversible adsorption-desorption model warrants further study
and consideration (see Section IX - Recommendations for Future
Research).
The third and fourth assumptions are more the result of necessity
than of experimental evidence. On the contrary, both the pH and the
availability of competing ions will affect the adsorption-desorption
reaction. The lack of quantitative relationships describing these
effects is the reason for the assumptions. However, the existing
model clearly requires laboratory determination, of the Freundlich
adsorption constants for the specific pesticide and soil system.
Consequently, the adsorptton-despfl-ptton constants will inherently
correct for the pH and competing ion conditions of the specific soil
system. Variations fn ph over the growing season are thought to be
negligible. Also, adsorption sites are considered much more prevalent
than available pesticide ions. Therefore, the third and fourth
assumptions appear to be reasonable, and will likely introduce minimal
error in the final results.
Assumption/5 states that the adsorption-desorption reaction is
essentially instantaneous when the time -required to reach equilibrium
is compared with infiltration times through soils. Thus, in
accordance with the Freundlich isotherm, equilibrium conditions are
assumed to exist within any time interval. Time intervals employed in
the model include five or fifteen minutes during storm events, and
daily intervals otherwise. Hornsby and Davidson 30 have shown that
under saturated conditions, adsorbed and solution phases of
fluometuron are not in equilibrium at an average infiltration rate of
5.5 cm/hr. (2.165 in/hr.). whereas equilibrium does exist at 0.59
cm/hr ( 0.23 in/hr). Obviously, the range of infiltration values will
depend on the pesticide and specific soil system. However, in natural
soil systems, infiltration rates will generally fall near the lower
and middle values of this range. Consequently, the existence of
equilibrium conditions appears to be a reasonable assumption that may
be violated infrequently during some storm events. Further study on
the significance of these events is needed as more data becomes
available.
In summary, a model is only an approximation to real-world
conditions. The above assumptions have been adopted to provide a more
manageable framework to the problem of pesticide adsorption in natural
soil systems. These assumptions are based on mechanisms and factors
which are known to be operative under laboratory conditions, but whose
significance under field conditions is unknown. The vagaries of
nature have yet to be reproduced in laboratory experiments. This is a
familiar problem in the application of laboratory research to natural
conditions. The results of continuing testing of the PTR Model should
provide a basis for evaluating the validity of the above assumptions
under natural field conditions. Those assumptions found to be invalid
will provide the basis for further Model improvements.
- 31 -
-------
PESTICIDE VOLATILIZATION AND DEGRADATION MODEL
The loss of pesticides by volatilization and degradation is
generally the major component in the establishment of a materials
balance of the applied pesticide. A number of studies 31> »
have shown that these mechanisms are operative and generally account
for the largest portion of pesticide loss subsequent to application.
The relative significance of volatilization and degradation is highly
dependent on the chemical properties of the pesticide. Although some
selected pesticides are non-volatile and/or non-degradable, the
majority of available pesticides are vulnerable in varying degrees to
these mechanisms.
Volatilization is most significant during and immediately
following application. Loss during application is highly variable
depending on climatic conditions, pesticide formulation, and methods
of application 31 . Immediately following application, volatilization
losses remain at high levels for a number of days and then decrease
rapidly to low but continuous levels. During this time, variations in
volatilization rate are due to soil and pesticide characteristics, and
environmental conditions 34' 35 .
Degradation of pesticides is accomplished through chemical,
photochemical, and microbial mechanisms. The relative significance of
the various mechanisms is highly dependent on pesticide
characteristics and environmental conditions. Although laboratory
studies have been performed on the various degradation mechanisms, the
extrapolation of the results to field conditions is highly
questionable.
The volatilization and degradation models used in the PTR Model
are derived from theoretical considerations and laboratory results.
Consequently, the use of these models to predict volatilization and
degradation losses from natural watersheds has not been substantiated
or verified. Hopefully future work will provide data on
which calibration of the models can be performed. Pesticide
concentration on the land surface is partially a function of
accumulated losses from volatilization and degradation. Also
pesticide loss from surface runoff is dependent on surface pesticide
concentrations. The present volatilization models have been included
in order to be available if sufficient data for calibration is
developed. The degradation model, a simple first-order decay, was
included to reflect a decrease in surface concentration over the
growing season in order to more realistically simulate the pesticide
lost by surface runoff.
Volatilization of Soil-Incorporated Pesticides
The sub-model for determining the volatilization of
soil-incorporated pesticides is based on work performed by Mayer,
Letey, and Farmer36 In that investigation, an analogy is drawn
between heat flow and pesticide diffusion through the soil. Five
- 32 -
-------
submodels were presented for differing boundary conditions. All
submodels consider volatilization a diffusion-based phenomenon and
ignored transport via mass flow. Thus .diffusion is assumed to be the
rate limiting mechanism for pesticide flux occurring from the land
surface for soil-incorporated pesticides. The basis for our model is
the following equation:
F = DCo/ /irDtT (6)
where: F = pesticide flux
D = diffusion coefficient
Co = initial pesticide concentration
t = time since application
Modification of the value of D, the diffusion coefficient,
accounts for the effects of various environmental factors such as
temperature, soil moisture, and physical soil properties. Ehlers et
al have investigated the effects of these environmental factors
on lindane diffusion in soils. The results of that work have been used
in our model to correct the value of D for environmental conditions.
The actual equation used in the model is an approximation to equation
(6) so that the mechanism can be described in a time-independent
manner. This was necessary in order to avoid problems associated with
a continuous bookkeeping of time since application, and pesticide
concentrations due to reapplication.
Model Assumptions - The validity of equation (6) is dependent on two
basic assumptions:
(1) Pesticide concentration above the soil surface remains at
zero, and
(2) The initial pesticide concentration, Co, remains essentially
constant at the lowest depth of soil incorporation.
In the heat flow analog, the first assumption would correspond to the
case of determining the heat flux from an infinite (constant
temperature) heat source, where the surface temperature is maintained
at a constant lower temperature. Farmer et al 34 and Mayer, Letey,
and Farmer 36 have shown that extremely small air flow rates are
needed to maintain a negligible pesticide concentration above the
surface. Thus, assumption (1) above appears reasonable except in
those cases where vegetal cover and crop canopies stagnate the air and
allow a buildup of pesticide concentration to occur at the soil
surface. The importance of this situation will need to be considered
and investigated when sufficient data is available for model testing.
Assumption (2) is obviously an approximation since the pesticide
concentration at the lowest depth of incorporation will decrease from
- 33 -
-------
pesticide degradation and mobility. However, the approximation seems
reasonable for an initial model upon which improvements and
modifications can be made. Calculations for dieldrin by Mayer, Letey,
and Farmer 36 using equation (6), have shown that 2,000 days would
be required for Co to be reduced by 1% at the bottom of the soil
column 11 cm deep (assuming a constant D = 2.3 mm 2 /wk). Thus in the
initial weeks following pesticide application, the approximation would
be valid. However, the long-term volatilization rates may be too high
and thus require adjustment.
Environmental Factors - The major environmental conditions which will
have an effect on volatilization of soil incorporated pesticides
include surface temperature, soil moisture, and physical soil
properties. The work of Ehlers et al 37 on lindane diffusion is
used to adjust the diffusion coefficient for the existing
environmental conditions.
Working with organochlorine insecticides, Farmer et al 34
reported that vapor pressure was the most significant factor in
volatilization losses, and that pesticide concentration was the next
most important. Igue et al 35 and Spencer, Claith, and Farmer 38
have shown that the vapor density (calculated from the ideal gas law,
vapor density = P x M/RT) of dieldrin will increase with relative
humidity and soil water content until the equivalent of one molecular
layer of water molecules is held in the soil. With further increases
in soil water content, the vapor density remains constant. The
conclusion to be drawn is that vapor density, and therefore, pesticide
volatilization, will remain constant with respect to soil water
content once a minimum value of soil moisture is obtained. Ehlers et
al 37 has reported a similar relationship between soil water content
and the diffusion coefficient of lindane. For a soil water content
greater than 4-5%, the diffusion coefficient of lindane remains
essentially constant Consequently, the model assumes that the
diffusion coefficient in equation (6) will be independent of soil
moisture for our agricultural lands.
Farmer 39 has noted that this is a reasonable assumption except
for the variation in soil moisture with depth. The problem here is
that during dry periods, pesticide will diffuse to the dry soil
surface and accumulate there because volatilization will be inhibited
by the lack of soil moisture. Consequently, this accumulated
pesticide will volatilize rapidly immediately following a storm period
when moisture is available. The model neglects this mechanism since
it is possible that the total amount lost will not be affected
significantly.
Variation of the diffusion coefficient with temperature changes
has been investigated by Ehlers et al 37 for the case of lindane in
Gila salt loam. Ehlers divided the diffusion coefficient into
components representing diffusion in the vapor and non-vapor phases of
the pesticide. The relationship between the total diffusion
coefficient (sum of vapor and non-vapor phases) and temperature was
shown to be exponential, and of the following form:
- 34 -
-------
D = AeBT (7)
where: D = Total diffusion coefficient, in mm2 /wk
T = Temperature in degrees, C
A, B = Constants
The values of A and B for lindane in Gila silt loam are 0.42 and 0.11,
respectively. Equation (7) is employed in the soil-incorporated
pesticide volatilization model. The values of A and B need to be
determined for the specific pesticide and field conditions, or from
experimental values.
At present, the influence of physical soil properties on the
diffusion coefficient has not been quantified to a significant extent.
Ehlers et al 37 has reported an almost linear relationship between
bulk density and the diffusion coefficient for lindane in Gila silt
loam. However, additional evidence has not been uncovered at this
time. Consequently, the model requires a diffusion coefficient for
the specific soil conditions. Future improvements in the model
could be aimed at possible adjustments in the diffusion coefficient
for the physical soil properties.
Volatilization of Surface Applied Pesticides
Surface applied pesticides present a slightly different problem
than soil incorporated pesticides. The main differences relate to the
higher concentrations of the surface applied pesticide, and the
greater direct exposure to environmental conditions, such as
temperature and wind. The model is based on investigations by
Farmer Lf° and on conventional empirical relations for determining
water evaporation. The resulting equation which is used is:
F = C x U x (EVC/M1/2) (8)
where: F = pesticide flux
U = Wind velocity
EVC= Equilibrium vapor concentration
M = Molecular weight of the pesticide
C = Constant
The equilibrium vapor concentration, EVC , is a function of
pesticide concentration and surface soil temperatures. The value of C
is determined through calibration for the specific watershed.
Model Assumptions - Farmer investigated pesticide flux from treated
sand for four different pesticides at three different air flow rates
across the surface of the sand. He obtained reasonable agreement
between experimental values and those values calculated from:
1 /?
F = K x (SVC/M ' ) (9)
- 35 -
-------
where: F = Pesticide flux
SVC = Saturation vapor concentration (equivalent to EVC
in equation 8)
M = Molecular weight
k = Constant
Values of k were determined experimentally for each pesticide for each
flow rate. An average value of k for all four pesticides was used to
determine the calculated flux at each flow rate. The main conclusion
here is that pesticide flux can be adequately represented as the
product of the pesticide vapor concentration and a constant, k, which
is a function of the air flow rate. This relationship is similar to
the following equation often used to determine water evaporation:
E = C x U x (ew - ea) (10)
where: E = Evaporation
U = Wind velocity
ew = Vapor pressure of water
ea = Vapor pressure of water in air
C = Constant
Equation (8) which is used initially in the model was developed
conceptually from equations (9) and (10). The value of k was replaced
by wind velocity times a constant, C, which is evaluated through
calibration and testing.
An obvious problem with the model is that volatilization will not
occur if wind velocity is zero. This problem was also recognized in
the case of water evaporation. The original Meyer equation for
evaporation, from which equation (11) was derived, is as follows:
E = CT (1 + C2U) (ew - ea) (11)
C] and C2 are constants. Thus, if wind velocity is zero, some
evaporation will still occur through the mechanism of molecular
diffusion. It was argued that this component was insignificant in
water evaporation. The occurrence of zero wind movement in a 24 hour
day is quite rare, e.g. in Denver only 2 days of zero wind movement
occurred in 20 years of record. For pesticide volatilization, this
adjustment for zero wind movement may be quite significant since
volatilization will likely occur in the absence of wind. However,
this adjustment is presently neglected until calibration and testing
results necessitate its inclusion. The evaporation equation is based
on a vapor pressure (or vapor concentration) difference between the
water and air. Our model essentially assumes that wind velocity will
be non-zero and large enough to disperse pesticide vapor at the soil
surface. Thus, the vapor concentration of the air at the soil surface
is assumed to be zero because of the wind component.
A major assumption in the model relates to the dependence of the
equilibrium vapor concentration on pesticide soil concentration.
- 36 -
-------
Spencer, Claith and Farmer *tl have shown that the vapor
concentration (or density) of dieldrin increases with dieldrin soil
concentration until a value of 25 ppm is reached in the soil. For
pesticide soil concentrations greater than 25 ppm, the vapor
concentration is essentially constant and equal to the vapor
concentration of pure dieldrin. Thus, it appears that surface
applications of pesticides will initially volatilize from the soil as
quickly as from the pure pesticide. Farmer et al ^2 reached a
similar conclusion when comparing lindane and DDT to dieldrin, except
that the long-term rates of volatilization would be smaller because
the pesticide loss would reduce the pesticide soil concentration.
However, the assumptions in our model will be that the equilibrium
vapor concentration in equation (8) will be that of the pure
substance. Long-term volatilization rates will have to be adjusted
when data is available to prove that this assumption is no longer
valid.
Environmental Factors - Temperature, wind, soil moisture, and physical
soil properties all have an effect on volatilization of surface
applied pesticides. Wind velocity is included directly in the model
equation. Soil moisture and physical soil properties will affect
volatilization largely by their influence on the competing mechanism
of pesticide-soil adsorption. The greater the adsorption to soil
particles, the less opportunity for volatilization. The model assumes
that volatilization of surface applied pesticides will be
similar to volatilization from the pure pesticide. Consequently, soil
properties should not have a major effect. If calibration and testing
prove this assumption to be invalid, then further adjustments and
improvements will be required.
The major effect of surface temperature will be to change the
pesticide vapor pressure which in turn affects the equilibrium vapor
concentration. Spencer and Claith 43,44 nave investigated the
effects of temperature on the vapor pressures of dieldrin and lindane.
The general form of the relationship is:
Log 1Q P = A - B/T (12)
where: P = Vapor pressure, mm of Hg.
T = Temperature, in degrees, K
A, B = Constants
The results of Spencer and Claith are used to adjust the vapor
pressure of the pesticide for temperature changes. The resulting
equilibrium vapor concentration is calculated from the ideal gas
law as follows:
EVC = P x M/RT (13)
This value of EVC is then used in Equation (8) to determine the
pesticide flux for any time interval. The values of A and B in the
- 37 -
-------
temperature adjustment are determined either experimentally or from
the literature.
Pesticide Degradation
As mentioned previously, pesticide degradation occurs by a
variety of mechanisms involving chemical, photochemical, and microbial
agents. As with volatilization and adsorption, environmental factors
have significant impact on the various degradation processes. The
quantification of the various degradation mechanisms and the effect of
environmental conditions is a current research topic of substantial
importance. However, no definitive models of total pesticide
degradation are currently available which would be amenable to
inclusion in the PTR Model. The staff at SERL is presently studying
various degradation mechanisms with the goal of developing such
models.
A few studies have shown that total degradation for certain
pesticides can be approximated by a first-order decay function
Fractional order or Michaelis-Menton kinetics have shown to be more
accurate in other situations Considering such inconclusive
evidence, a first-order degradation function has been assumed for the
PTR Model. This assumption is necessary so that the amount of
pesticide available for transport by surface runoff and sediment will
be gradually diminished over time. Thus, more realistic estimates of
the concentration and volume of pesticide in surface runoff can be
obtained. As research progresses in this area, the first-order decay
can be replaced by a model which would be capable of adjusting
degradation rates for the specific soil, pesticide, and environmental
conditions.
In the PTR Model, the user supplies a daily degradation factor,
which reduces the amount of pesticide in storage at the end of each
day. The same factor is applied to each of the pesticide storage
zones within the soil profile-surface, upper zone, and lower zone.
Since the majority of pesticides never reach the groundwater storage
zone, no pesticide degradation is considered to occur from that zone.
- 38 -
-------
SECTION VI
PTR MODEL STRUCTURE AND OPERATION
MODEL STRUCTURE
The PTR Model is structured about a main, or executive program,
which controls the operation of six (6) accompanying subprograms. The
MAIN program also controls input and output operations, manages the
time-keeping operations, and performs the transfer of data between
subprograms. The MAIN program and subprograms are listed in Table 2,
along with the time intervals and functions which are performed. The
six subprograms simulate pesticide volatilization and degradation,
surface runoff, sediment loss, and pesticide interaction in the
various soil storages. A complete listing of the PTR Model is
included in Appendix C.
MODEL OPERATION
The Model operates continuously on a number of different time
intervals. The basic manner of operation is shown in Figure 9. The
VOLDEG subprogram adjusts the pesticide storages on a daily basis for
loss of active pesticide by volatilization and degradation. For days
in which rain occurs, Model operation follows the solid line in Figure
9, operating on a five or fifteen minute interval for the subprograms
LANDS, SEDT, ADSRB1, ADSRB2, and ADSRB3. The choice of a five or
fifteen minute time interval depends on the time intervals of the
input rainfall. For days in which rainfall does not occur, the model
operation follows the dashed line in Figure 9: SEDT is bypassed;
LANDS operates on five or fifteen minutes; and ADSRB1, ADSRB2, and
ADSRB3 operate on a daily basis. The MAIN program monitors the
passage of real time and keys the operation of the separate
subprograms at the proper time intervals.
The PTR Model is written in the IBM FORTRAN IV language and was
developed and run on the Stanford University IBM 360/67 computer. The
Model operates most efficiently in a two-step procedure. The first
step involves the compilation of the program and the storage of the
compiled version on disk or magnetic tape. In step two, the compiled
Model is provided the necessary input data and is executed. Thus, the
- 39 -
-------
Model can operate a number of types of different input data with a
single compilation. The format of the input and output is described
below.
Table 2. PTR MODEL SUBPROGRAMS
Name
Function
Operation Time
Intervals
MAIN
LANDS
SEDT
ADSRB1
ADSRB2
ADSRB3
VOLDEG
Executive program
Hydrologic model
Sediment loss model
Surface Pesticide
Interaction
Upper Zone Pesticide
Interaction
Lower Zone and Ground-
water Pesticide
Interaction
Pesticide Volatilization
and Degradation
Not Applicable
5-min., 15-min,
5-min., 15-min,
5-min., 15-min,
Daily
5-min., 15-min.
Daily
5-min., 15-min.
Daily
Daily
MODEL INPUT AND OUTPUT
Model Input
Input data is accepted by the Model on a sequential basis in
English or Metric units. Model parameters, daily potential
evapotranspiration, average daily soil temperature, daily wind
movement, and rainfall data are input in that order as shown in Table
3. To simplify input procedures and reduce computer storage
requirements, the hydrometeorologic data is input on a calendar year
cycle. Each block of data in table 3 represents all daily values for
the full year or for that portion of the calendar year which is to be
simulated. Thus, if simulation begins on July 1, 1972 and ends on
February 15, 1973, the hydrometeorologic data sequence
(evapotranspiration, soil temperature, wind, rainfall) for July 1
through December 31, 1972 is followed by the hydrometeorologic data
for>January 1 through February 15, 1973.
- 40 -
-------
Figure 9. PTR Model structure and operation
PESTICIDE
RUNOFF
Hyd
r o c o m p
-------
Table 3. SEQUENCE OF INPUT DATA
|_ PTR Model Input Parameters
Potential Evapotranspiration - 1st Year
Soil Temperature - 1st Year
Wind Movement - 1st Year
Rainfall - 1st Year
Potential Evapotranspiration - 2nd Year
Soil Temperature - 2nd Year
Wind Movement - 2nd Year
: Rainfall - 2nd Year
r
etc.
- 42 -
-------
Model parameters are input in FORTRAN 'namelist1 format as shown
in Table 4. (Parameter evaluation is discussed in Section 6.4).
Daily potential evapotranspiration, average daily soil temperature,
and daily wind movement are input under identical format conditions.
Daily values are required for each day of simulation. The input
format consists of a 31 X 12 matrix, i.e. 12 columns of 31 rows each,
representing the 12 months with a maximum of 31 days each. The input
format is shown in Table 5. Input daily evapotranspiration and wind
movement are in integer form while average daily soil temperature is
in real number form correct to the first decimal place. Rainfall data
is input in five or fifteen minute intervals according to the format
described in Table 6. A complete listing of the input data sequence
is included in Appendix B.
Model Output
A number of options are available for the type of output to be
obtained from the Model. The form and substance of the output is
controlled by the input parameters HYCAL, PRINT, and UNIT. HYCAL
specifies whether the simulation is a calibration run (with or without
pesticide simulation), or a production run providing concentrations
and amounts of pesticide remaining in the various soil storages.
PRINT specifies the interval for the printing of output; daily,
hourly, and five or fifteen minute intervals are allowed. In
addition, monthly and yearly summaries are provided with all output
options. UNIT specifies whether the units of the output will be
English, Metric, or both. These three parameters are integers and can
only have values of -1, 0, or +1. The values for each ootion are
specified in the portion entitled 'Parameter Evaluation and Model
Calibration1.
Since each of the parameters can take on three possible values,
the possible output options numbers 27. In practice, only a few
combinations are used regularly and will produce specific output forms
with any degree of reliability. These combinations and their
description and use are as follows:
HYCAL = +1 PRINT= -1 UNIT = -1, 0, +1
This combination is used primarily for calibration of the LANDS
and SEDT subprograms. The output, as shown in Table 7, provides
flow and sediment loss values for five or fifteen minute
intervals during storm events. Monthly and yearly summaries
provide totals so that recorded and simulated volumes can be
compared. The input parameter HYMIN specifies the minimum flow
for which output is to be printed.
HYCAL = -1 PRINT = -1 UNIT = -1, 0, +1
- 43 -
-------
Table 4. PTR MODEL INPUT PARAMETERS IN 'NAMELIST1 FORMAT
SHYCL
&PRNT
GSTRT
CENCD
6TRVL
GLN01
&LND2
SLND3
£LND4
&PEST
&NAWE
&CRGP
&SMOL
SAMOL
6VOL1
SVOL2
SQEG1
HYCAL^
PRINT=
BGNCAY=
ENDDAY=
INTRVL =
UZSN=0.
IRC=0. 0
LZS = 20.
ICS=0.0
SSTR=5*
PNAME=«
COVMAX=
JRER=3.
CMAX=0.
D1FC=30
MOLEWT =
DEGCON=
BGNYR=1972 &ENO
» ENDYR=1973 6 END
0* HYMIN=0.001« UNIT=-1, INPUT=-1
1, BGNMON=7,
15, ENDMCN=
5 SEND
05. LZSN=18.0, INFIL=0.5, INTER=0.7
NN=0.20, L=160.» SS=0.05, A=0.00,
0, SGW=0.0, GWS=0.0, KV=0.0, K24L= 1
» OFS=0.0, IFS=0.0, K24EL=0.0, K3=0
13.4, APMODE=0, DEPTH=6.125 SEND
SEND
UZS=0.05
6ENC
40, EPXM=0.12 SEND
0.60,TIMST=182.,TIMAP=182.,TIMAT=274.,TIMHAR^334.
0. KRER=0.09, JSER=1.0, KSER=1.5, SRERI=9.0 &END
00001,DD = 0.0003,8ULKD=103.0,K=120.,N=2.,AREA=6.7
.0, TD!FC=30.0, CBDIF=0.11 SEND
335., APFAC=0.0, BPFAC=0.0, WCFAC=1.0 (LEND
0.0001 £END
SEND
SEND
-------
Table 5. SAMPLE INPUT AND FORMAT FOR EVAPORATION,
TEMPERATURE, AND WIND DATA
tn
i
COLUMN
NUMBER
MONTH
EVAP72
EVAP72
EVAP72
f!VAP72
EVA P 72
E IMP 72
CVAP72
CVAP72
HVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
CVAP72
EVA P 72
EVAP72
EVAP72
EVAP72
EVAP72
EV/AP72
€VAP72
C VAP72
EVAP72
EVAP72
EVAP72
f VAP72
EVAP72
EVAP72
EVAP72
EVAP72
>,
rd
C
•"3
27
27
43
41
41
70
43
119
54
54
54
54
54
59
108
124
103
0
49
11
11
11
65
59
97
97
22
0
27
27
27
t
3
t.
ji
Ol
32
176
410
252
44
63
32
139
57
0
132
63
76
69
189
63
76
151
265
277
69
88
31
38
32
69
101
76
113
o
I-
2!
96
141
148
118
74
192
163
126
155
148
155
141
215
126
126
89
118
67
74
S9
141
178
200
74
111
96
96
81
89
141
74
^
CL
154
49
84
91
105
140
140
154
109
161
70
112
126
147
252
175
280
224
210
168
196
42
189
102
238
112
98
•252
63
140
1
167
175
190
190
198
251
198
91
122
228
220
175
84
243
205
236
152
144
137
84
219
129
106
167
205
289
243
106
68
53
122
-------
Table 6. PTR MODEL RAINFALL INPUT DATA FORMAT
Column No. Description and Format
1 - Blank
2-7 - Year, Month, Day (e.g., January 1, 1940 is 400101).
8 - Card Number - each card represents a 3-hr, period, e.g.
Card #1 - Midnight to 3:00 AM
#2 - 3:00 AM to 6:00 AM
#3 - 6:00 AM to 9:00 AM
#8 - 9:00 PM to Midnight
All eight cards are required if rain occurred anytime
during the day. A card number of 9 signifies that no
rain occurred during the entire day, and no other
rainfall cards are required for that day.
9-80 - Rainfall data (OOO's of millimeters (OO's of inches).
15-minute intervals:
6 column per each 15-minutes in the 3-hour period of
each card. Number must be right justified, i.e.
number must end in the 6th column for the 15-minute
period.
5-minute intervals:
2 columns per each 5-minute interval - i.e., the 15-
minute period still occupies 6 columns, but it is
broken down into 3 5-minute intervals.
NOTES - 1. Appendix B contains a sample of input data.
2. At least one rainfall card is required for each
day of simulation.
3. Blanks are interpreted as zeros by the Model.
Consequently, zeros do not need to be input.
- 46 -
-------
SAMPLE OUTPUT: LA.IDS AND SEDT CALIBRATION RUN
DATE
FLOW(CFS - CMS)
SEDIMENKLBS - KG - GM/L)
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
20: 5
20: 10
20:15
20:20
20:25
20: 30
20:35
20:40
20:45
20:50
20:55
21: 0
21: 5
0:20
0:25
0:30
0:35
0:40
0:45
0:50
0:55
l: 0
l: 5
1:10
1:15
1:20
1:25
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.o
0.5
0.7
0.5
0.3
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.000
0.003
O.OD2
0.002
0.002
0.002
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.013
0.020
0.013
0.010
0.006
0.004
0.003
0.002
O.OC1
0.001
0.001
0.001
0.000
4.45
14.58
5.99
0.92
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.07
159.26
201.62
88.51
46.74
19.03
5.42
0.53
0.0
0.0
0.0
0.0
0.0
0.0
2.02
6.62
2.72
0.42
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
7.30
72.30
91.54
40.19
21.22
8.64
2.46
0.24
0.0
0.0
0.0
0.0
0.0
0.0
24.62
6.82
3.80
0.74
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.24
18.81
15.37
10.49
7.42
4.74
2.02
0.27
0.0
0.0
0.0
0.0
0.0
0.0
-------
The output from this combination of values is identical to the
one above, except that pesticide volumes and concentrations in
water and on sediment are also provided. The format for this
combination is shown in Table 8.
HYCAL = 0 PRINT = +1 UNIT = -1 or +1
This output format is referred to as a production run. The
amount and concentration of pesticide remaining in the various
soil storages is provided along with the total volume of
pesticide lost by surface runoff, sediment loss, and
volatilization and degradation. The format is shown in Table
9. A PRINT value of -1 or 0 allows the investigation of
pesticide concentration and movement during short time
intervals. However, if PRINT is -1 or 0 for a long simulation
run, the volume of output would be enormous. Also if the UNIT
option is specified as 0, the volume of output would double
because the format in Table 9 would simply be reproduced in
metric units during each time interval. Consequently, caution
should be used when specifying values of PRINT and UNIT other
than shown above for a production run (HYCAL = 0),
The format of the monthly and yearly summaries for calibration
and production runs is shown in Tables 10 and 11. The use of a UNIT
value of 0 would simply reproduce these tables in metric units, and
thus, double the amount of output for the summaries.
PARAMETER EVALUATION AND CALIBRATION PROCEDURES
Parameter evaluation and calibration are closely related
processes. The parameters of the PTR Model are determined for the
most part by physical and/or chemical characteristics. Those
parameters which are not fixed by natural characteristics are
evaluated through calibration. Initial estimates of these parameters
are used in the first calibration run of the Model. The comparison of
simulated and recorded values provides the basis for re-evaluating the
parameter values for subsequent calibration trials. The process is
re-iterated until the desired accuracy is achieved.
The following sections discuss the evaluation of the input
parameters for each of the subprograms of the PTR Model. Calibration
procedures are also discussed as they pertain to the specific
subprograms. A complete list of the Model input parameters is
provided in Table 12, while Table 13 includes namelist names,
- 48 -
-------
Table 8. SAMPLE OUTPUT: PESTICIDE CALIBRATION RUN
DATE
TIME
FLOWICFS - CMS)
SEDIMEWTILBS - KG - GM/L)
PESTICIDE (GM - PPM)
WATER SEDIMENT
UD
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
20: 5
20: 10
20:15
20:20
20:25
20: 30
20:35
20:40
20:4-5
20:50
20:55
21: 0
21: 5
0: 20
0:25
0:30
0: 35
0:40
0:45
0:50
0:55
1: 0
l: 5
1:10
1:15
1:20
1:25
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.7
0.5
0.3
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.000
0.003
0.002
0.002
0.002
0.002
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.013
0.020
0.013
0.010
0.006
0.004
0.003
0.002
O.OC1
0.001
0.001
0.001
o.oco
4.45
14.58
5.99
0.92
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.07
159.26
201.62
88. 51
46.74
19.03
5.42
0.53
0.0
0.0
0. 0
0.0
0.0
0.0
2.02
6.62
2.72
0.42
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7.30
72.30
91.54
40.19
21.22
8.64
2.46
0.24
0.0
0.0
0.0
0.0
0.0
0.0
24.62
6.82
3.80
0.74
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.24
18.81
15.37
10.49
7.42
4.74
2.02
0.27
0.0
0.0
0.0
0.0
0.0
0.0
2.729
9.446
3.255
0.501
0.0
0.0
0.0
0.0
0.0
0,0
0.0
0.0
0.0
1.471
9.862
12. 505
5.750
2.979
1.230
0.352
0.035
0.0
0.0
0.0
0.0
0.0
0.0
33.230
9.732
4.553
0.883
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.876
2.564
2.098
1.500
1.040
0.675
0.288
0.038
0.0
0.0
0.0
0.0
0.0
0.0
0.032
0.106
0.040
0.006
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
O.P
0.036
0.278
0.343
0. 147
0.078
0.032
O.C09
0.001
0.0
0.0
0.0
0.0
0.0
0.0
16.043
16.036
14.852
14.849
0.0
0.0
0.0
n.o
0.0
0.0
0.0
0.0
0.0
4.902
3.842
3. 744
3.664
3.659
3.656
3.656
3.656
0.0
0.0
0.0
0.0
0.0
0.0
-------
Table 9. SAMPLE OUTPUT: PRODUCTION RUN-DAILY OUTPUT INTERVAL
24; 0 ON
_JiUJL_
JL222-
ZONE 1
ZONE 2
ZONE 3
ZONE
ZONE 5
TOTAL
fcATER, INCHES
PRECIPITATION
RUNOFF
OVERI_AND_FLOW
INTERFLOW
IMPERVIOUS
TOTAL
8ASE_FLOU
CRQ*ATER_RECHARGE
EVAPORATION
POTENTI AL
NET
STORAGES
UPPtR_ZONE
LOt,ER_ZONE
GROLNDWATER
INTERCEPTION
QVEBLAND_FLOW
IMTfRFLOW
WATF.R_BALANCE = 0.0
SEDIMENT, TONS
TCTAL SEDIMENT LOSS
FINES DEPOSIT
IMPERVIOUS EROSION
SURFACE LAYEP PESTICIDE
PESTICIDE , LBS
ACSOPBEO
CRYSTALLINE
DISSOLVED
PESTIC IDE , PPM
ACSORSED
CRYSTALLINE
D ISSOLVED
REMOVAL. LBS
SED IMENT
OVERLAND FLOW
PERCOLATION
UPPER ZCNF LAYER PESTICIDE
PESTICIDE, LBS
ACSORBED
CRYSTALLINE
DISSOLVED
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0=0
O.G
0.0
0.0
0.0
0.0
0.0
0.0
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
o.iei
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0. 161
0.0
0.0
2.568
2. 568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
l.BOO
0.161
C.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
9.000
0.0
0.804
0.804
C.O
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
19.296
19.288
0.0
0.008
50
-------
Table 9. (continued)
PESTICIDE. PPM 4.335 4.335 4.335 4.335 4.335 4.335
ADSORBED 2.566 2.566 2.566 2.566 2.566 2.566
CRYSTALLINE 0.0 0.0 0.0 0.0 0.0 0.0
DISSOLVED 1.768 1.768 1.768 1.768 1.768 1.768
REMOVAL, LBS 0.0 0.0 0.0 0.0 0.0 0.0
INTERFLOW 0.0 0.0 0.0 0.0 0.0 0.0
PERCOLATION 0.0 0.0 0.0 0.0 0.0 0.0
LOWER ZCNE LAYCR PESTICIDE
PESTICIDE, L8S 0.0
ADSORBED 0.0
CRYSTALLINE 0.0
DISSOLVED 0.0
PESTICIDE . PPM
ACSCRBEO C.O
CRYSTALLINE 0.0
DISSOLVED 0.0
REMOV4L. LBS 0.0
PERCCLATION 0.0
GRCLNC^ATER LAYER PESTICIDE
PESTICIDE, LBS 0.0
ADSORBED 0.0
CRYSTALLINE C.O
DISSOLVED 0.0
PESTICICE VOLATILIZATION LOSS. LBS.
TCTAL 0.540
F«CM SURFACE 0.0
FROM UPPEB /ONE 0.540
PESTICIDE OECRAOATION LOSS. LBS.
TCTAL 0.219
FRCP SURFACE 0.009
FRCM UPPEfi ZONE 0.210
FRCM LOWEF ZONE 0.0
- 51 -
-------
Table 10. SAMPLE OUTPUT: CALIBRATION RUN - MONTHLY SUMMARY
WATERf INCHES
PBECIPITATION
RUNOFF
OVERLAND_FLOW
INTERFLOW
IMPERVIOUS
TOTAL
BASE FLOW
GRDWATER_RECHARGE
EVAPORATION
POTENTIAL
NET
STORAGES
UPPER_ZONE
LOWEP_ZQNE
GROUNDWATER
INTERCEPTION
OVERLANO_FLOW
INTERFLOW
WAT£R_BALANCE= 0.0006
SEDIMENT, TONS
TOTAL SEDIMENT LOSS
FINES DEPOSIT
IMPERVIOUS EROSION
PESTICIDE REMOVAL, LBS.
OVERLAND FLOW REMOVAL
SEDIMENT REMOVAL
INTERFLOW REMOVAL
ZONE 1 ZONE 2
2.190
0.834
0.117
0.952
0.519
0.673
1972
ZONE 3
2.190
0.307
0.145
0.452
ZONE 4
2.190
0.177
0.124
0.301
ZONE 5
2.190
0.094
0. 146
0.240
5.259
3.574
0.096
17.432
0.0
0.039
.0.0
0.0
2.068
0.633
0.148
0.143
0.005
0.0
5.259
3.574
0.095
17.432
0.0
0.039
0.0
0.0
1.642
1.058
0.042
0.040
0.002
0.0
5.259
3. 574
0.095
17.432
0.0
0.039
0.0
0.0
1.161
1.539
0.016
0.015
0.001
0.0
5.259
3.574
0.095
17.432
0.0
0.039
0.0
0.0
0.731
1.970
0.008
0.008
0.301
0.0
5.259
3.574
0.094
17.432
0.0
0.039
0.0
0.0
0.416
2.284
0.005
0.004
0.000
0.0
TOTAL
2.190
0.386
0.137
0.0
0.523
0.0
0.573
5.259
3.574
0.096
17.432
0.0
0.039
0.0
0.0
6.019
7.483
0.0
0.220
0.210
0.009
0.0
PESTICIDE VOLATILIZATION LOSS, LBS.
TOTAL
FROM SURFACE
FROM UPPER ZONE
PESTICIDE DEGRADATION LOSS, LBS.
TOTAL
FROM SURFACE
FROM UPPER ZONE
FRUM LOWER ZONE
PESTICIDE_BALANCE- -0.0033
0.0
0.0
0.0
5.756
0.751
5.005
0.000
- 52 -
-------
Table 11. SAMPLE OUTPUT: PRODUCTION RUN - MONTHLY SUMMARY
.SiiM,MAB.X_F_CR._M.aNIli_CE
ZONE 1 ZONE 2
kATER, INCHES
PRECIPITATION
RUNOFF
OVEPL4KD_FLOW
INTERFLOW
IMPFRVIOUS
TOTAL
BASF_FLUH
GRCWAT FR._RFChARGE
2.190
0.834
0.117
0.9f>2
2.190
0.519
0.15*
0.673
L2J2
ZONE 3
2.190
0.307
0.1*5
0.*52
ZONE *
2.190
0.177
0.12*
0.301
ZONE 5
2. 190
O.C9*
0. 1*6
0.2*0
HATFR_tALANCE= 0.3006
SECIf-ENT, TONS
TOTAL «EOIMENT LOSS 2.066
FINES 1)1 POSIT 0.63*
I«P£R'J IPUS ER1SION
PESTICICE. PCUNOS
SURFACE LAYER
Arsnp BED
CRYSTALLINE
DISSOLVED
IPPER ZONE LAYER
ACSORBED
CRYSTALLINE
DISSOLVED
LOHER ZONE LAYER
ACSORBEO
CRYSTALLINE
DISSOLVED
GPCUNDWATER LAYER
ATSORPED
CRYSTALL INE
0 ISSCLVFO
PESTICICE REMOVAL. LBS.
OVERLAKO FLOW REMOVAL
SEDIMENT REMOVAL
INTERFLOW REMOVAL 0
PESTICIDE VOLATILIZATION LCSS, LBS.
TCTAL
FRTM SLRFACE
FROM UPPEK ZONE
PESTICIDE DEGRADATION LOSS, LBS.
TOTAL
FROM SURFACE
FROM UPPER ZOJF
FROV LCWER ZU'JE
PFSTICIDE_BALANCE= -0.0033
1.6*2
1.058
1. 161
1.539
0.731
1.970
0.416
2.28*
TCTAL
2.190
0.386
0.137
0.0
0.523
0.0
0.573
EVAPORATION
PCTFNT1 AL
NET
STORAGES
UPPFR_/fNE
LOW Fk_ZONE
GHOLNOWATER
INT FPCFPT ION
OVFPt.AND_FLOW
INT GRFLOW
5.259
3.57*
0.096
17. ',32
0.0
0. 039
0.0
0.0
5.259
3.57*
0.095
17. *32
0.0
0.039
0.0
0.0
5.259
3. 57*
0.095
17.*32
0.0
0.039
0.0
0.0
5.259
3.57*
0.095
17.*32
0.0
0.039
0.0
0.0
5.259
3.57*
0.09*
17.*32
0.0
0. 039
0.0
0.0
5.259
3.57*
0.096
17.*32
0.0
0.039
0.0
0.0
6.017
7.*85
0.0
0.021
0.021
0.0
0.0
2.699
2.719
0.0
0.010
.1*8
.1*3
.005
.0
0.020
0.020
0.0
0.0
2.807
2.826
0.0
0.011
0.042
0.0*0
0.002
0.0
0. 019
0.019
0.0
0.0
2.833
2.852
0.0
0.011
0.016
0.015
0.001
0.0
0.019
0.019
0.0
0.0
2.8*1
2.661
0.0
0.011
0.008
0.008
0.001
0.0
0.018
0.019
0.0
0.0
2. 8*5
2.865
0.0
0.011
0.005
0.00*
0.000
0.0
0.097
0.098
C.O
0.0
14.02*
1*.12*
0.0
0.055
0.000
0.0
0.0
C.O
0.0
0.0
0.0
0.0
0.220
0.210
0.009
0.0
0.0
0.0
0.0
5.756
0.751
5.005
0.000
53
-------
Table 12. PTR MODEL INPUT PARAMETER DESCRIPTION
HYCAL: Indicates mode of operation
-1 - Calibration run with pesticide simulation
0 - Production run
+1 - Calibration run without pesticide simulation
HYMIN: Minimum flow for printing output during a time interval.
INPUT: Input units; English (-1), Metric (1).
UNIT: Output units; English (-1), Metric (1), Both (0).
PRINT: Denotes frequency of printing output; Each interval (-1),
each hour (0), or each day (1).
BGNDAY, BGNMON, BGNYR: Date simulation begins; Day, month, year
ENDDAY, ENDMON, ENDYR: Date simulation ends; Day, month, year.
INTRVL: Time interval of operation (5 or 15 minutes).
UZSN: Nominal upper zone storage.
LZSN: Nominal lower zone storage.
INFIL: Index infiltration rate.
INTER: Interflow parameter, alters runoff timing.
IRC: Interflow recession rate.
NN: Manning's n for overland flow.
L: Length of overland flow to channel.
SS: Average overland flow slope.
A: Fraction of area that is impervious.
UZS: Initial upper zone storage.
LZS: Initial lower zone storage.
SGW: Initial groundwater storage.
GWS: Initial slope of the groundwater table.
- 54 -
-------
KV: Parameter to allow variable recession rate for groundwater
discharge.
K24L: Fraction of groundwater recharge percolating to deep
groundwater.
KK24: Groundwater recession rate.
ICS: Initial interception storage.
OFS: Initial overland flow storage.
IPS: Initial interflow storage.
K24EL: Fraction of watershed area where groundwater is within
reach of vegetation.
K3: Index to actual evaporation.
EPXM: Maximum interception storage.
PNAME: Pesticide name (8 characters, maximum).
WSNAME: Watershed name (8 characters, maximum).
SSTR: Total pesticide application (on zonal basis).
APMCDE: Mode of application; surface applied (0), soil incorporated
(1).
DEPTH: Depth of soil incorporation and/or upper zone depth.
COVMAX: Maximum surface area covered by fully matured vegetation.
TIMST: Time simulation starts (Julian day, i.e., day of the year
(e.g., January 1 is 1, December 31, is 365 or 366, etc.).
TIMAP: Time of pesticide application (Julian day).
TIMAT: Time of crop maturity (Julian day).
TIMHAR: Time of harvest (Julian day).
JRER: Exponent of rainfall intensity in soil splash equation.
KRER: Coefficient in soil splash equation.
JSER: Exponent of overland flow in surface scour equation.
KSER: Coefficient in surface scour equation.
- 55 -
-------
SRERI: Initial fines deposit.
CMAX: Maximum solubility of pesticide in water.
DD: Permanently fixed capacity (Ib pesticide/lb soil).
BULKD: Bulk density of soil.
K: Coefficient in Freundlich adsorption curve.
N: Exponent in Freundlich adsorption curve.
AREA: Watershed area.
DIFC: Pesticide diffusion coefficient.
TDIFC: Temperature at which DIFC was measured.
CBDIFC: Exponent of temperature correction for diffusion coefficient.
MOLEWT: Molecular weight of pesticide.
APFAC, BPFAC: Constants for temperature adjustment of pesticide vapor
pressure.
WCFAC: Wind calibration factor.
DEGCON: First order daily pesticide decay rate.
- 56 -
-------
Table 13. PTR MODEL INPUT PARAMETER ATTRIBUTES
Namelist
Name
HYCL
PRNT
STRT
ENDD
TRVL
LND1
LND2
LND4
LND4
* - dashes
Parameter
Name
HYCAL
HYMIN
UNIT
INPUT
PRINT
BGNDAY
BGNMON
BGNYR
ENDDAY
ENDMON
ENDYR
INTRVL
UZSN
LZSN
INFIL
INTER
IRC
NN
L
SS
A
UZS
LZS
SGW
GWS
KV
K24L
KK24
ICS
OFS
IFS
denote dimensionless
Type
Integer
Real
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
values.
English* Metric*
Units Units
.
Cubic feet per Cubic meters
second (CFS) per second
(CMS)
-
-
-
_ _
-
-
_ _
-
-
Minutes (min) Minutes (min)
Inches (in) Millimeters (mm)
Inches (in) Millimeters (mm)
Inches per Millimeters per
hour (in/hr) hour (mm/hr)
-
_ «
_ -
feet (ft) meters (m)
-
-
inches (in) millimeters (mm)
inches (in) millimeters (mm)
inches (in) millimeters (mm)
-
-
-
-
inches (in) millimeters (mm)
inches (in) millimeters (mm)
inches (in) millimeters (mm)
- 57 -
-------
PEST
NAME
CROP
SMDL
K24EL
K3
EPXM
SSTR
APMODE
DEPTH
PNAME
WSNAME
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
KSER
SRERI
Real
Real
Real
Real
Integer
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
AMDL
VOL1
VOL2
DEG1
CMAX
DD
DEGCON
Real
Real
Real
inches(in) millimeters (mm)
pounds (Ib) kilograms (kg)
inches(in) millimeters (mm)
tons
tonnes (t)
BULKD
K
N
AREA
DIFC
TDIFC
CBDIF
MOLEWT
APFAC
BPFAC
WCFAC
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
(lb/lb)
pounds per
cubic foot
(Ib/ft3)
-
_
acres (ac)
-
_
-
-
pounds per kilograms per
pound (lb/lb) kilogram (kg/kg)
pound pesticide kilogram pesticide
per pound soil per kilogram soil
(kg/kg)
grams per cubic
centimeter
(g/cc)
hectares (ha)
millimeters square
per week (mm2/wk) +
degrees Celcius
grams per mole
(g/mole)+
+only allowable input units
- 58 -
-------
Operational Parameters
The operational parameters are employed largely in the MAIN
subprogram, and are involved with real-time and input/output
operations of the Model. These parameters are included in the
namelists HYCL, PRNT, STRT, ENDD, and TRVL as shown in Table 13. They
are essentially self-explanatory, and are not involved in calibration.
LANDS Parameters
The LANDS subprogram parameters are included in namelists LND1,
LND2, LND3, and LND4. The majority of the LANDS parameters are
explained in detail in Appendix A. In fact, the only parameters not
described in Appendix A are the initial storage parameters UZS, LZS,
SGW, GWS, ICS, OFS, and IPS, which are defined in Table 12. Except
for UZS and LZS, the remaining storage parameters are generally set to
zero. Since these parameters refer to initial conditions, their
effect on long-term simulation is negligible. The initial storages
UZS and LZS are directly related to the surface response of the
watershed during the initial months of simulation. Consequently their
values have a direct impact on surface runoff for three to six months
after simulation begins. Their effect in the long term is also
negligible.
The values for UZS and LZS are generally close to their nominal
values, UZSN and LZSN. If simulation begins in a wet season their
values are either equal to or greater than the nominal values. On the
other hand, the initial storages will be less than nominal if
simulation begins in a dry season.
Calibration of the LANDS subprogram initially involves the
adjustment of the parameters so that recorded annual and monthly
runoff volumes are simulated as closely as possible. Once the volume
simulation is attained, simulated and recorded storm hydrographs are
compared. Peak flow rates, timing, and hydrograph shape are the major
characteristics investigated. Appendix A contains numerous
suggestions on the effects of varying the different parameters. The
interactions of the 15 major parameters (excluding moisture storages)
are quite complex. A detailed reading of Appendix A plus a number of
calibration trials should provide the user with a certain 'feel1 for
the parameters involved.
Watershed and Pesticide Application Parameters
This group of parameters is used throughout the PTR Model for the
special purposes of pesticide application nomenclature, and crop
cover. Calibration does not involve these parameters as they are
completely defined.
The namelist, PEST, includes parameters which apply to the amount
of pesticide applied (SSTR), the method of application (APMODE), and
- 59 -
-------
the depth of the upper zone and/or the soil-incorporated pesticide
(DEPTH). SSTR is programmed to allow an areal variation in
application across the five surface zones of the watershed. Thus, a
uniform application of 100 pounds can be specified either as 'SSTR =
20., 20., 20., 20., 20.' or 'SSTR = 5 * 20.' , within the namelist
format. The DEPTH parameter specifies both the bottom depth of the
upper zone and of the soil-incorporated pesticides. Thus, a uniform
pesticide concentration throughout the upper zone is assumed for
soil-incorporated pesticides, and total upper zone moisture (UZS) is
employed when determining pesticide loss by interflow and percolation.
The namelist CROP includes parameters which define the variable
interception of rainfall by the crop canopy over the growing season.
COVMAX defines the maximum percent of the watershed area covered by
the crop canopy when fully matured. The parameters TIMST, TIMAP,
TIMAT, and TIMHAR define the first day of simulation, and the days of
application, crop maturity, and harvesting, respectively. The MAIN
program, which monitors the passage of real time, provides a linear
increase in crop cover from TIMAP to TIMAT up to a maximum value
defined by COVMAX. Crop cover remains at COVMAX until harvesting time
when it is returned to a value of zero. The crop canopy affects both
the interception of rainfall in LANDS, and the soil splash production
of fines in SEDT.
SEPT Parameters
The SEDT parameters are included within the namelist SMDL. The
only experience with the SEDT parameters, other than the subject
research effort, has been presented by Negev 19 . His work was used as
a guide in calibrating the sediment loss model to the experimental
watersheds. Much additional research is needed into the sensitivity
of the SEDT parameters and their relationship to soil properties. The
values obtained from calibration on the experimental watersheds
provides some guidance for use of the model on watersheds with similar
characteristics.
Calibration procedures for SEDT are similar to those for LANDS.
Recorded annual and monthly volumes are compared to simulated values.
The soil splash parameters, JRER and KRER, appear to have the most
significant impact on the seasonal distribution of sediment loss.
Increasing JRER provides a greater relative impact to the
high-intensity summer storms than the low-intensity long duration
winter storms. KRER and SRERI, the initial surface storage of soil
fines, were adjusted so that the surface fines deposit did not get
progressively larger or smaller throughout the simulation period. The
surface scour and pick-up parameters, JSER and KSER, have a direct
effect on sediment loss concentrations during the storm events.
Adjustment of both JSER and KSER is required in order to reproduce the
rate of sediment loss recorded during storm events.
In summary, the above guidelines are the result of calibration
trials on the experimental watersheds in this research effort.
- 60 -
-------
Application on other watersheds will require separate calibration
trials, and caution should be used when transferring calibrated values
from one watershed to another.
Pesticide Adsorption-Desorption and VOLDEG Parameters
The parameters in this group need to be determined largely from
laboratory experiments. Consequently very little adjustment during
calibration is justifiable. Some variation in parameters for natural
environmental conditions different from laboratory conditions is
expected. However, this should be minimal for the parameters
involved.
The pesticide adsorption-desorption parameters used in
subprograms ADSRB1, ADSRB2, and ADSRB3 are contained in namelist AMDL.
These are described sufficiently in Tables 12 and 13. CMAX and BULKD
can be obtained from experimental results or published reports. DD,
K, and N must be evaluated by an experimental determination of the
adsorption isotherm for the specific pesticide and soil system. DD
would be the intercept at zero solution concentration, while K and N
describe the isotherm in terms of the Freundlich equation ( Section
V).
The namelists, VOL1 and VOL2, include the parameters for surface
and soil-incorporated pesticide volatilization. The majority of these
parameters must also be determined from laboratory experiments or
published reports. For soil-incorporated pesticides, the parameters
DIFC, TDIFC, and CBDIF define the temperature dependence of the total
diffusion coefficient as described in Section III. For surface
applied pesticides, APFAC and BPFAC define the temperature dependence
of the pesticide vapor pressure (Section III ), while WCFAC provides
a calibration tool for the effect of wind movement on volatilization.
Since no data was available for calibration or verification of these
models, they were not operated during calibration trials of the PTR
Model. To bypass the volatilization models, DIFC (soil-incorporated),
and both APFAC and BPFAC (surface-applied) are set equal to zero.
The namelist, DEG1 contains the soil degradation parameter,
DEGCON, which defines the daily first-order degradation rate for the
pesticide. DEGCON was calculated by applying a first-order
degradation to the initial application, and determining that rate
which would reproduce the amount of pesticide remaining on the
watershed as determined by the October 30 sampling analysis. Thus,
the value is purely empirical and is used to allow a realistic
reduction in pesticide concentration on the watershed as a result of
degradation.
Conclusion
Calibration initially involves only the LANDS and SEDT
subprograms (HYCAL= +1) until an adequate simulation of runoff and
- 61 -
-------
sediment loss is obtained. Then pesticide simulation is included
(HYCAL= -1) and a number of trials may be run for minute adjustments
in the pesticide parameters. Finally production runs (HYCAL = 0) are
performed to obtain pesticide concentrations and amounts remaining on
the land surface.
In summary, efficient calibration and parameter evaluation
requires a thorough knowledge of the operation of the PTR Model. This
is best acquired through repeated use of the Model and its components
under'varying climatic and edaphic conditions. Additional experience
on use of the model is necessary.
The parameter values for the final trials on the PI watershed are
shown in Table 14. Separate values for Paraquat and Enide are also
included. Section VIII presents the results obtained from application
of the PTR Model to the experimental watersheds.
Table 14. PARAMETER VALUES AND INITIAL CONDITIONS
FROM PI CALIBRATION
(English Units)
Parameter Value
UZSN
LSZN
INFIL
INTER
IRC
NN
L
SS
A
KV
K24L
KK24
K24EL
K3
EPXM
APMODE
DEPTH
DEGCON
0.05
18.0
0.5
0.7
0.0
0.20
160.0
0.05
0.0
0.0
1.0
0.6
0.0
0.40
0.12
0.0
6.125
(P) 0.0001
(D 0.0109
Parameter Value
Parameter
Value
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
KSER
CMAX (P)
(D)
DD (P)
(D)
BULKD
K (P)
(D)
N (P)
(D)
AREA
0.60
182.0
182.0
274.0
334.0
3.0
0.09
1.0
1.5
0.00001
0.00026
0.0003
0.0
103.0
120.0
1.8
2.0
1.6
6.7
DIFC
TDIFC
CBDIF
MOLEWT
APFAC
BPFAC
WCRAC
Initial Conditions
UZS
LZS
SGW
GWS
ICS
OFS
IFS
SRERI
0.0*
1.0*
1.0*
0.0*
0.0*
0.0*
1.0*
Value
0.05
20.0
0.0
0.0
0.0
0.0
0.0
9.0
SSTR (P) 5x13.4
(D)
5x4.02
(P)
CD)
Paraquat
Diphenamid
- These values are input to bypass the volatilization models
- 62 -
-------
SECTION VII
EXPERIMENTAL PROGRAM* AND MODELING METHODOLOGY
EXPERIMENTAL PROGRAM
The PTR Model development was conducted in support of an
experimental data gathering program at the U. S. Environmental
Protection Agency's Southeast Environmental Research Laboratory (SERL)
in Athens, Georgia. The program was a joint effort between the SERL
and the U. S. Agricultural Research Service's Southern Piedmont
Conservation Research Center (ARS-SPCRC) in Watkinsville, Georgia.
Experimental watersheds at the SPCRC in Watkinsville (Figure 10) were
instrumented for the continuous monitoring and sampling of runoff and
sediment during the 1972 growing season. Samples collected during
storm events were analyzed for pesticide concentrations at the SERL
laboratories. A gas-liquid chromatograph was used in the pesticide
analysis.
The watersheds are located on the Piedmont Plateau of the
Southeastern U. S. in Oconee County, Georgia. The soils
classification is a Cecil sandy loam with high acidity and clay
content and low organic matter content. The moderate slopes, two to
six percent, provide a slight to moderate erosion hazard, while
surface runoff, infiltration and water capacity are considered
moderate.
The 1972 program included two large watersheds (PI - 2.7 ha and
P3 - 1.2 ha), two small runoff plots (SP1 (9x22 meters) and SP3 (26x39
meters)), and 12 20x30 ft. (6x9 meters) attenuation plots (Figure
10). The experimental areas were planted with soybeans for the 1972
growing season. The herbicides, paraquat (1,1'-dimethyl-4,4'-bipyri-
dinium ion), diphenamid (N,N-dimethyl-2,2-diphenylacetamide), and
trifluralin (a,a,ortrifluoro-2, 6-dinitro-N, N-dipropyl-p-toluidine)
were applied at ten, three and one pound per acre (11.2, 3.4, and 1.1)
kg/ha, respectively. These herbicides were chosen to facilitate the
study of pesticide transport in the adsorbed phase (paraquat) in the
dissolved phase (diphenamid), and discrete particles (trifluralin).
Runoff and sediment samples were automatically collected during storm
events at short time intervals, and refrigerated on-site for later
analysis at the SERL. Core samples were taken and analyzed
Contact Dr. George W. Bailey at the SERL, Athens, Georgia for
further details on the continuing experimental program.
-63-
-------
I
cr>
Figure 10. Location of experimental watersheds
Experimental
Area
GEORGIA
SP1
PI (0.02 ha)
(2.71 ha)
(0.10 ha)1
SP3
2000
PESTICIDE
RUNOFF
Hyd
r o c o m p
-------
immediately after pesticide application and periodically during the
growing season to determine the extent of vertical movement and the
remaining volumes of the applied pesticides. Recording rain gages
were established at each major watershed, and a weather station was
set up at the attenuation plots to record air temperature, pan
evaporation, and wind data. Also, the attenuation plots were
instrumented to record soil moisture and temperature at various soil
depths, wind velocity and direction, solar intensity, net radiation,
air temperature, and relative humidity at different heights above the
soil surface. This data was automatically recorded on magnetic tape
by a PDP-8 computer. The extensive data gathered during the program
is to be included in the EPA STORET data system, available to
interested agencies.
MODELING METHODOLOGY
Hydrocomp's support of the experimental program involved review
of the data gathering procedures and development of the PTR Model to
simulate the recorded loss of pesticide from the watersheds. The
guiding philosophy in Hydrocomp's modeling effort was to develop a
model capable of:
(1) Simulating the pesticide lost from, and remaining on the land
surface.
(2) Reproducing the gross movement of pesticide within the soil
profile.
(3) Performing a mass-balance of the applied pesticide.
(4) General application in various regions of the country.
(5) Performing continuously for up to 1 or 2 years at reasonable
computer cost.
Although the existing Model is not capable of completely
satisfying all of these goals, the basic framework of the Model is
designed to perform these functions.
The modeling approach was on an area-wide watershed basis, with
an attempt to 'piggyback1 the pesticide onto the movement of water and
sediment from the land surface. Detailed consideration was not
directed to the microscale movement and minute interactions of the
pesticide within numerous layers of the soil profile. The reasons for
this approach are three fold: first, variations in soil
characteristics, sediment movement, and pesticide movement across a
watershed are too complex to simulate in detail; second, a detailed
model would be so watershed-dependent as to lose its general
applicability; third, a useful model must employ only parameters and
constants which are generally available with reasonable effort for
watersheds to which it is to be applied.
- 65 -
-------
With this approach, the modeling effort was concerned largely with
the data collected on the larger watersheds, PI and P3. Data from SP1
and SP3 was investigated for comparison purposes, and
hydrotneteorological data was provided by the instrumentation at the
attenuation plots. Also, historical rainfall and runoff data for the
period 1940-44 for the ARS Wl (7.77 ha ) watershed at Watkinsville was
provided by the SPCRC for calibration purposes. Calibration for
runoff and sediment was performed on the PI watersheds. Results from
simulation on the Wl watershed with the calibrated parameters were
used to check the validity of the PI calibration. Both the PI and P3
watersheds were run with the calibrated parameters to determine
pesticide loss. Since P3 is a terraced watershed, some deviation was
expected from recorded values of runoff, sediment and pesticide loss,
because the parameters were calibrated on the PI non-terraced
watershed. The results of the simulation runs and problems
encountered in calibration are discussed in Section VIII.
- 66
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SECTION VIII
PTR MODEL RESULTS AND DISCUSSIONS
GENERAL
As in most research endeavors, the results obtained from the PTR
Model development and testing were both good and bad. A number of
questions have been answered but many more have been raised. The
initial portion of this section discusses calibration trials on PI and
Wl, and presents results of pesticide trials on PI. Analytical
problems in obtaining an adsorption isotherm for trifluralin,
prevented simulation of that herbicide. The results of runoff,
sediment, and pesticide trials on the P3 watershed using the
parameters calibrated on PI are provided. Certain inconsistencies in
the recorded data are also discussed. Finally, a general discussion
of the simulation results is presented.
PI WATERSHED RESULTS
Calibration - Runoff and Sediment Loss
Seven and one-half months of continuous rainfall, runoff,
sediment, and pesticide residue data provided the basis for
calibration of the PI watershed. During this period, only four
significant runoff-producing events occurred, three of which were
within one and one-half months from the beginning of the simulation
period. Generally, calibration is performed continuously for a
minimum of about two years. The reasons for this are two-fold:
first, two years of data will likely include both dry and wet periods
so that the hydrologic response can be calibrated under a variety of
soil moisture conditions; secondly, a long period of record will
overcome the effects of initial soil moisture and surface sediment
conditions which can have a significant effect for three to six months
into the calibration period.
The available data for the PI calibration violated both of the
above conditions. The lack of a long period of data could not be
corrected. Initially, the starting soil moisture conditions were
obtained by simulating the three month period prior to the July 1
starting date. Rainfall data from the Athens Weather Service Office
- 67 -
-------
was used. Because of the variability of thunderstorms in Georgia and
resulting soil moisture conditions, the results of the three-month
simulation yielded starting soil moisture conditions much lower than
expected. Consequently, the values of initial soil moisture were
adjusted in the final calibration trials in order to reflect the
recorded runoff volumes.
Table 14 in Section VI presented the calibration parameters of
the PI watershed. The simulated and recorded monthly volumes of
runoff and sediment are shown in Figure 11 and Table 15, along with
recorded rainfall values. Simulated runoff volumes are generally
within 10 percent of recorded values. The error in the simulated
runoff for January is partially the result of the January 7-8 storm
during which snowmelt occurred. Also, rainfall data on the storm of
January 21-22 was received too late to be included in the simulation.
This would have an effect on antecedent soil moisture conditions.
Simulated sediment volumes closely follow the recorded values except
for the month of December.
Rainfall intensity, and recorded and simulated runoff and
sediment concentrations for the four major storm events during the
calibration period are shown in Figures 12, 13, 14, and 15. Simulated
peak runoff and ' sediment concentrations are reasonably close to
recorded values. The high intensity, short duration summer storms
(Figures 12, 13, 14) appear to be more accurately simulated than the
long low-intensity winter storm (Figure 15). Note the time variation
between the summer and winter storms. For the summer storms, peak
runoff occurs within five minutes of peak rainfall intensity. Also,
peak sediment concentration occurs immediately prior to peak runoff.
For the winter storm, the recorded hydrologic response is somewhat
sluggish with peak runoff occurring up to 15 minutes following peak
rainfall intensity. Also, detailed sediment concentrations are
lacking at critical moments during the winter storm. One would expect
the peak sediment concentration to occur between 22:30 and 22:10.
Unfortunately no sediment values were recorded during this time
period.
It should be noted that a weir pond formed in front of the PI
gage during runoff events. This pond likely had a significant effect
on recorded runoff and sediment loss, and the resulting pesticide
loss. The storage of water provided by the pond would tend to delay
the rising portion of storm hydrographs and extend the recession side
of the hydrograph. The recorded peak flow would be less than that
which actually occurred from the watershed. The estimated volume of
the PI weir pond appears to be sufficient to change the simulated
hydrograph of the July 28 storm (Figure 12) to closely approximate the
recorded hydrograph.
The effect of the pond on sediment loss would be to:
(1) more evenly distribute sediment concentrations recorded at
the gage, and
(2) allow larger sediment particles to settle out and not pass
through the gage.
- 68 -
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F 160 -
JULY AUG. SEPT. OCT. NOV. DEC. JAN.
FEB.
JULY
AUG.
SEPT. OCT. NOV. DEC. JAN. FEB.
10
d)
GO
o
o
LU
6.0
5.0
4.0
3.0
2.0
1.0
Ufti
JULY
AUG. SEPT. OCT. NOV. DEC.
JAN. FEB.
Figure 11. PI watershed: monthly summary of rainfall, runoff,
and sediment loss, (1972-73)
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 69 -
-------
Table 15. 1972 SUMMARY OF RAINFALL, RUNOFF, SEDIMENT
AND PESTICIDE LOSS FROM THE PI WATERSHED
(RECORDED AND SIMULATED)
July
August
September October November December Total
Rainfall (mm)
Runoff (rum)
Recorded
Simulated
Sediment loss
Recorded
S imu lated
Diphenamid
On- water
Recorded
Simulated
On sediment
Recorded
Simulated
Total
Recorded
Simulated
Paraquat loss
On water
Recorded
Simulated
On Sediment
Recorded
Simulated
Total
Recorded
Simulated
55.6
14.7
13.3
(tonnes)
6.00
5.47
0.057
0.095
0.004
0.004
0.061
0.099
(kg)
0.001
0.0
1.211
1.145
1.212
1.145
54.4
12.7
10.8
3.79
3.28
0.018
0.005
0.003
0.000
0.021
0.005
0.0
0.0
0.883
0.664
0.883
0.664
36.8
0.1
0.4
0.01
0.05
0.000
0.000
0.000
0.000
0.000
0.000
0.0
0.0
0.001
0.051
0.001
0.051
59.9
0.2
1.9
0.03
0.28
0.000
0.000
0.000
0.000
0.000
0.000
0.0
0.0
0.001
0.055
0.001
0.055
91.4
0.1
1.0
0.00
0.07
0.000
0.000
0.000
0.000
0.000
0.000
0.0
0.0
0.000
0.013
0.000
0.013
216.4
22.0
23.9
0.42
1.73
0.000
0.000
0.000
0.000
0.000
0.000
0.0
0.0
0.021
0.329
0.021
0.329
514.5
49.8
51.3
10.25
10.88
0.075
0.100
0.007
0.004
0.082
0.104
0.001
0.0
2.117
2.257
2.118
2.257
- 70 -
-------
60
. 50
>-
i—
i 40
LU
I—
~ 30
_l
£ 20
<
ct:
10
2000
2010
2020
2030
2040
2050
0.30
"0.20
o
z
ID
o:
0.10
2000
2010
2020
2030
2040
2050
25. -
20. ~
in -, r
o 15.
a
LU
10. -
5. -
2000
2010
2020
2030
2040
2050
Figure 12. PI watershed: storm of July 28, 1972
_ RECORDED
-- SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 71 -
-------
£ 100
E
E
^ 80
i—
g 60
z
~ 40
eC
^ 20
—
-
!
•"
— 1 — 1 J 1 1
1640
1650
1700
1710
1720
1730
o
z
=>
OL
1640
1650
1700
1710
1720
1730
IS)
o
Q
LU
OO
25
20
15
10
5
1640 1650 1700 1710 1720
Figure 13. PI watershed: storm of July 31, 1972
1730
RECORDED
SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 72 -
-------
"i- 100
E
£: so
i—i
co
I 60
rj 40
-------
<
ct:
80
60
40
20
Jl
2100
2120
2140
2200
2220
2240
2300
210-
2100
2140
2200
2220
2240
2300
OO
oo
o
Q
LjJ
oo
6.0
5.0
4.0
3.0-
2.0
1.0
0 2100 2120 2140 2200 2220 2240
Figure 15. PI watershed: storm of December 14, 1972 —
2300
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 74 -
-------
The existence of the pond biased the calibrated LANDS and SEDT
parameters, but the extent of the effect is impossible to determine
with the existing data.
In general, the calibration appears to adequately reproduce the
hydrologic response to the high-intensity summer thunderstorms, but
overestimates both runoff and sediment volumes for the one long
duration, low-intensity winter storm.
To test the calibration on PI, the nearby Watkinsville Wl
watershed (Figure 10) was run continuously for two years (1941-42)
with the PI parameters. The PI parameters will not apply to Wl
without modification, but conclusions can be drawn about regional
stability of parameters and the problem of extending parameters to
ungaged streams. Simulated and recorded monthly runoff volumes are
shown in Figure 16, while Figures 17 and 18 present the simulated and
recorded hydrographs for the four major storms during the two year
period. Although monthly volumes are underestimated during high
runoff periods, storm hydrograph shape and flow peaks are accurately
reproduced. Since Wl is approximately three times larger than PI,
subsurface flow and groundwater components are likely to contribute
more significantly to runoff on the Wl watershed. Parameter
adjustments to increase these subsurface flows on Wl would increase
runoff volumes (Figure 16) but would not significantly change peak
flows (Figure 17 and 18). The existence of subsurface components are
not reflected in the PI calibration parameters, as is typical of very
small watersheds. Also the PI infiltration characteristics are likely
to be somewhat inaccurate because calibration period was extremely dry
and too short to perform a precise water balance. In summary, the Wl
trial with the PI calibration parameters accurately simulates the
surface hydrologic response but is somewhat inaccurate on monthly
runoff volumes because of the data and conditions for calibration. It
shows encouraging evidence of the ability to transfer parameters that
control the surface hydrologic response which in turn controls the
transport of sediment and pesticide.
The lack of detailed sediment loss data prevented any comparison
of simulated and recorded values for the Wl watershed. Estimates by
the Soil Conservation Service of annual soil loss from a 135 square
meter (1450 square feet) plot near the watershed amounted to 21.89
tonnes/ha /yr (9.75 tons/ac/yr) k6 . Simulated annual loss from the
Wl watershed was 21.40 tonnes/ha /yr (9.53 tons/ac/yr). Consequently,
simulated volumes appear to be within range of expected values.
However, the validity of the simulated sediment concentrations and the
monthly distribution of sediment loss on Wl is unknown. Redeposition
and channel processes, which are neglected in the model, might become
a significant source of error on larger watersheds.
Pesticide Loss
Monthly vi ,
runoff (water and sediment) are presented in Figure 19. Paraquat,
75 -
Monthly values of paraquat and diphenamid collected in surface
ff (water and sediment) are presented ii
-------
•-J
CD
100
80
E
fe 60
^
=3
01
40
20
A S 0 N D
RECORDED
SIMULATED
Figure 16. Wl watershed: monthly runoff volumes, (1941-42)
PESTICIDE
RUNOFF
Hyd
r o c o m p
-------
1100 1120 1140 1200
JULY 11, 1941
1220
1240
1250
10
(J
o
:D
a;
0.8 -
0.6 _
0.4 -
0.2 -
240
300
320 340 400
MAY 15, 1942
440
RECORDED
SIMULATED
Figure 17. Wl watershed:
storms of July 11, 1941 and
May 15, 1942
PESTICIDE
RUNOFF
Hydrocor
n p
- 77 -
-------
1/1
o 0.4
u_
u_
o
z
;D
o;
0.2
2320 2340 2400 020 040 100
AUGUST 16-1/, 1942
O
o
13
1.2
0.8
0.4
2200 2220 2240 2300 2320 2340
AUGUST 17-18, 1942
2400
0200
Figure 18. Wl watershed:
storm of August 16 and 17, 1942
RECORDED
SIMULATED
PESTICIDE
RUNOFF
H y d r o c o
m p
- 78 -
-------
01
CO
-------
quite predictably, closely follows the monthly sediment loss values
presented previously in Figure 11. Again, the overestimate of
sediment loss in December is reflected in an overestimate of paraquat
loss. The variations in diphenamtd loss are more difficult to
explain. The first-order degradation function included in the Model
may possibly underestimate diphenamTd degradation during July and
overestimate the rate in later periods; hence, the deviation from
recorded values. ,
Paraquat and diphenamid concentrations on runoff and sediment
during the three summer storm events are shown in Figures 20 and 21.
These were the only major events for which extensive pesticide
sampling was performed. The fluctuations of pesticide concentrations
recorded on sediment during storm events are essentially unexplained.
The pesticide concentrations do not correlate with variations in flow
or sediment concentration. Also, analysis of sediment samples
collected during the storm produced no obvious relationship between
pesticide concentration and clay content or particle surface area.
The accuracy of laboratory analytical procedures corroborates the
existence of the concentration variations. Thus, the variations must
result from the complex interaction of the pesticide exposed to the
natural environment.
Although the concentration variations are unexplained their
significance may be negligible. The loss of pesticide from the
watershed is demonstrated more clearly by the mass movement of
pesticide past the gage during the storm event. Figures 22 and 23
show the rate of sediment and pesticide loss on sediment (paraquat and
diphenamid) for the July 28 and August 10 storms, respectively. The
area under each curve represents the total volume of sediment or
pesticide lost during the event. These figures demonstrate the
significance of sediment loss as a mechanism of pesticide transport.
This is especially true for paraquat, but is also important for
diphenamid. A closer simulation of sediment loss would have improved
the simulated pesticide loss. The error in simulated diphenamid loss
appears to be greater than the errors in sediment or paraquat. This
possibly indicates inaccuracies in the pesticide adsorption model and
may warrant further investigation.
The rate of diphenamid loss on water is presented in Figure 24
for the July 28 and August 10 storms. These figures should be
compared with the storm hydrographs of Figures 12 and 14. Although
the simulated runoff agrees reasonably well with recorded values,
substantial errors exist between recorded and simulated diphenamid
loss as shown in Figure 24. The amount of pesticide lost by surface
runoff is calculated in the pesticide adsorption-desorption model
which determines the division between the adsorbed and dissolved
phases of the pesticide. The discrepancies in Figure 24 demonstrate
that further investigation and modification of the
adsorption-desorption model is warranted.
- 80 -
-------
Q_
CL
O
O
-------
CL
Q.
o
o
1.80
1.60
1.20
0.80
0.04
„. WATER
2000 2010 2020 2030
JULY 28, 1972
2040
2050
2100
1640
Q.
Q_
O
^
O
2.0
D-
Q
1650
1700 1710 1720
JULY 31, 1972
1730
1740
2020 2030 2040 2050 2100
AUGUST 10, 1972
2110 2120
RECORDED
SIMULATED
Figure 21. PI watershed:
Diphenamid in water and
sediment during storm events
PESTICIDE
RUNOFF
Hydrocom
P
- 82 -
-------
E
en
00
oo
o
400
300
200
100
2000
O
LlJ
OO
OO en
00
O
<
Cr
D£
Du
80
60
40
20
0
2000
2010
2020
2030
2040
2050
00 C
oo T-
O E
Q. Q
I—I l_Lj
Q OO
0.250
0.200
0.150
0.100
0.050
_L
2000
2010
2020
2030
2040
Figure 22. PI watershed:
rate of sediment and pesticide
loss on sediment - July 28, 1972
2050
RECORDED
SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 83 -
-------
E
CD
c/i
oo
O
300
200
100
2050
2100
2110
2120
2130
oo •--.
oo E
O en
<=C -z.
H> LU
o-s:
o; Q
Ct LU
Q. 00
60
50
40
30
20
10
0
2050
2100
2110
2120
2130
o •
c
OO •!-
oo E
o •--.
-I E
01
Q- Q
i—i LU
o oo
0.30
0.20
0.10
2050
2100
2110
2120
2130
Figure 23. PI watershed:
rate of sediment and pesticide
loss on sediment - August 10,1972
RECORDED
SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 84 -
-------
3.0 -
Di
UJ
H-1 C
00 E
CO \
O E
2.0
D.
t—i
O
1.0
2000 2010 2020 2030 2040
JULY 28, 1972
2050
Di
LU
-------
P3 WATERSHED RESULTS
Simulation with PI Parameters
The parameters calibrated on the PI watershed and partially
verified on the Wl watershed were used in simulation runs on the P3
watershed. Calibration was not performed separately on the P3
watershed because of data inconsistencies (explained below). The P3
watershed is terraced, has a smaller slope and a grass-lined channel,
and is approximately one-half the size of the PI watershed. Simulated
and recorded monthly runoff and sediment loss is shown in Figure 25,
along with recorded rainfall. The simulation period extended over
five months from July to November. There is considerable discrepancy
between recorded and simulated volumes of runoff and sediment loss.
This discrepancy is also reflected in the monthly pesticide loss shown
in Figure 26.
Monthly runoff volumes are generally underestimated, while the
sediment loss values are reasonable, except for the month of
September. Paraquat loss consistently followed the volumes of
sediment lost, while diphenamid loss was overestimated in July as on
the PI watershed.
Sources of Error and Data Problems
A number of possible sources of error could explain the
discrepancies in the P3 simulation results. The PI and Wl watersheds
are of similar shape and both experience natural drainage. Their
average land slopes are similar (0.05 for PI, and 0.07 for Wl).
Although PI is one-third the area of Wl, the PI calibration parameters
behaved reasonably well on the Wl watershed. The P3 watershed, on the
other hand, is a terraced watershed; it has a grass-lined channel and
its average slope is 0.03. Consequently, one should expect some
variation in the hydro!ogic response of the two watersheds. Both the
Wl and P3 simulation runs underestimated volumes during high runoff
periods. For the Wl watershed, inaccuracies in infiltration
characteristics and the lack of a groundwater component were mentioned
as possible sources of error. Although groundwater is unlikely to
significantly affect the P3 watershed, an overestimate of infiltration
capacity could explain the discrepancies in runoff volumes. Also, the
grass-lined channel could have a significant effect on runoff timing
and sediment loss.
Although high runoff volumes were underestimated on both the Wl
and P3 watersheds, the Wl simulated hydrographs accurately reproduced
recorded values; whereas, the P3 simulated hydrographs varied
considerably from the observed. From the terracing and smaller slope,
one would expect less runoff to occur than simulated. However, the
exact opposite occurred. Thus, the hydrologic data was investigated
as another source of error. The P3 watershed is rectangular in shape
and is separated from the surrounding land by a soil berm. The
- 86 -
-------
*f
U-
z
t—1
<
150
125
100
75
50
25
0
_L
JULY AUG. SEPT. OCT.
NOV.
o
tx.
25
20
15
10
5
0
I
JULY AUG. SEPT.
OCT. NOV.
Ol
c
c
o
+J
2.5
2.0
1.5
1.0
0.5
0
JULY
AUG. SEPT. OCT.
NOV.
Figure 25. P3 watershed:
summaries of rainfall, runoff,
and sediment loss (1972)
RECORDED
SIMULATED
PESTICIDE
RUNOFF
Hydrocor
n p
- 87 -
-------
oo
O
•=c
o-
o:
Q.
0.5
0.4
0.3
0.2
0.1
JULY
AUG. SEPT. OCT.
NOV.
0.5
0.4
GO
O
0.2
0.1
JULY AUG.
SEPT.
OCT. NOV.
Figure 26. P3 watershed
monthly summaries of
pesticide loss (1972)
RECORDED
SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
- 88 -
-------
probability is small that subsurface drainage conforms to this
watershed boundary. Consequently, both surface and subsurface
drainage that crosses the boundary would introduce substantial error
in the recorded runoff data. A small 0.1 ha (0.25 ac) surface
drainage plot, SP3, is located adjacent to the P3 watershed. Table 16
presents the recorded rainfall, runoff, and sediment loss for the SP3
and P3 watersheds. It can be seen that recorded depth of runoff
exceeds rainfall on SP3 for three storms (7/28/72, 8/10/72, 9/4/72).
This can only occur if outside drainage is entering SP3. This
drainage likely originates in the P3 watershed which is located
upslope. The possibility of outside drainage crossing the P3
watershed boundary is another possible source of error which could
explain the discrepancies in simulated and recorded values.
In summary, the error in the simulation results on the P3
watershed is due to both surface characteristics not reflected in the
calibrated parameters and inconsistences in the recorded data.
Further calibration trials and additional inspections of the watershed
will help to reduce these errors and improve simulation results.
DISCUSSION OF SIMULATION RESULTS
In spite of inconclusive results from the P3 watershed
simulation, the Model results from the PI and Wl watersheds
simulations provide ample evidence of the capabilities and
inadequacies of the PTR Model. It is evident that a reliable
hydrologic and sediment transport simulation is paramount to
simulating pesticide loss from agricultural lands. The calibration of
the PI watershed and the verification of the parameters on the Wl
watershed demonstrates that the LANDS subprogram can reliably simulate
surface runoff from agricultural watersheds. Moreover, the calibrated
parameters are applicable to the geographic region rather than to
specific individual watersheds. Based on surface runoff and input
rainfall, the SEDT subprogram estimates of monthly sediment loss are
reasonably close to recorded values. However, simulated hydrographs
and sediment concentrations appear to recede more rapidly than
recorded during the storm events. The weir pond formed in front of
the PI gage may be partially responsible.
Also, lack of experience with sediment loss calibration and/or
inaccuracies in the basic algorithms could be partially at fault.
The pesticide functions of adsorption-desorption, volatilization,
and degradation performed reasonably well for Paraquat but were in
error when simulating diphenamid loss. The reasons for this are as
follows:
(1) Volatilization and degradation are major attenuation
mechanisms for diphenamid but are not significant for paraquat. The
volatilization functions were not operated during simulation runs
because of the lack of necessary parameters and verification data.
Degradation was assumed to be first order.
- 89 -
-------
Table 16. RAINFALL, RUNOFF, AND SEDIMENT LOSS FOR SP3 AND P3 WATERSHEDS
o
I
SP3 Watershed
(0.10 ha)
P3 Watershed
(1.25 ha)
Storm date
7/02/72
7/02/72
7/28/72
7/31/72
8/09/72
8/10/72
8/23/72
9/04/72
9/30/72
10/27/72
Rainfall
(mm)
19.05
6.10
21.84
11.43
8.13
10.92
17.27
49.28
12.70
35.56
Runoff
(mm)
6.43
0.51
38.35
7.87
2.08
13.56
11.30
75.18
0.64
16.69
Sediment loss
(kg/ha)
1121
113
5426
794
191
1157
327
1856
"
_
Runoff
(mm)
3.40
0.02
10.67
6.60
1.14
5.33
5.33
21.08
0.01
0.52
Sediment loss
(kg/ha)
454
2
611
395
25
140
134
350
-
_
-------
(2) For degradable pesticides, rates of degradation are high
during the first days and/or weeks following application. In later
periods, the rate usually decreases to essentially constant baseline
values. The first-order rate used in the Model was determined by
calculating the daily rate which would allow remaining pesticide
levels to be approximately those determined by the soil core samples
of October 30, 1972. This first-order rate likely underestimates
losses immediately after application, and overestimates losses in
later periods. This would introduce error into the monthly pesticide
losses.
(3) The attraction of paraquat to soil particles is such that the
entire paraquat application is adsorbed in the surface layer. In
soils with a high capacity for adsorption, the Model assumes that all
the paraquat is adsorbed and consequently bypasses the Freundlich
adsorption curve.
(4) Since diphenamid is not permanently fixed to soil particles,
the distribution between adsorbed and dissolved phases is determined
by the Freundlich adsorption curve. Errors in the
adsorption/desorption parameters and/or the basic
adsorption/desorption algorithm would thus have a greater effect on
diphenamid loss than on paraquat. The effect of the adsorption model
on diphenamid loss is shown most dramatically in Figures 22, 23, and
24. Although recorded and simulated flow and sediment loss are in
substantial agreement, simulated diphenamid loss deviates from the
observed.
The source-zone approach, described in Section V, produced an
areal variation in runoff, sediment and pesticide loss. Tables 17 and
18 summarize simulation results for 1972 for paraquat and diphenamid,
respectively. The Model predicts that 50% or more of total runoff,
sediment, and diphenamide loss is derived from 20% of the total
watershed area. Paraquat loss is more widespread; 50 percent
originating from approximately 30 percent of the watershed area. The
variable loss of pesticide from the source-zones also produced an
areal variation in pesticide remaining as shown in Tables 17 and 18.
The sampling on October 30 determined that the remaining amounts of
paraquat and diphenamid were 29.96 kg(65.91 Ib) and 2.33 kg (5.13 Ib);
corresponding simulated values were 28.23 kg (62.11 Ib) and 2.39 kg
(5.26 Ib). This close agreement is a result of the calculated
degradation constant. The main emphasis is that although total
volumes agree, the recorded and simulated concentrations across the
watershed deviate considerably. Since concentration is highly
dependent on the mass of soil available, the depths of the soil zones
in the Model would have a major effect on simulated concentrations.
Additional work with a variety of soil zone depths would help to
determine the importance of this factor.
- 91 -
-------
Table 17. 1972 SUMMARY:
PARAQUAT SIMULATION ON
PI WATERSHED
WATER, INCHES
PtECIPITATION
ZONE 1
20.2oO
/ONE 2
20.260
ZONE 3
20.260
ZONE 4
20.260
ZONE 5
20.260
TUTAL
20.260
RUNUFF
OVKRL ANO_FLOW
INTERFLOW
IMPERVIOUS
TOTAL
tiRiJrt'ATER_RECHARGE
EVAPORATION
POTENTIAL
NET
STORAGES
UPPER_ZONE
LUdtR_ZONE
GKOUNDWATER
INTERCEPTION
CWORLAND_FLOH
INTERFLOW
WATLR_BALANCE= 0.0011
SEDIMENT, TONS
TOTAL SEDIMENT LOSS
FINFS DEPOSIT
IMPERVIOUS EROSION
PESTICIDE, POUNDS
SURFACE LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
UPPER ZONE LAYER
ADSORBED
CRYSTALLINE
01 SSOLVtD
LOWER ZONE LAYER
ADSORBED
CRYSTALLINE
01 SSOLVED
GROUNDWATER LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
A. 275
1.2J2
5.478
1.417
0.804
2.221
0.683
0.576
1.259
0.360
0.372
0.732
0. lol
0.259
0.420
1.379
0.6
-------
Table 18. 1972 SUMMARY:
DIPHENAMID SIMULATION ON
PI WATERSHED
SUMMARY FOR 1977
VATER. INCHES
PRECIPITATION
RLNOFF
CVECLAND.FLOW
INTERFLOW
IMPERVIOUS
TOTAL
BASE_Finw
GRO*ATER_RECHARGE
ZONE 1
20.260
4.275
1.202
5.478
ZONE 2
20.260
1.417
0.804
2.221
ZONE 3
20.260
0.683
0.576
1.259
ZONE 4
20.260
0.360
0.372
0.732
ZCNE 5
20.260
C.161
0.259
0.420
HAT ER_ BALANCE* 0.0011
JEDIfENT. TONS
TOTAL SEDIMENT LOSS
FINES CEPOSIT
IMPERVIOUS EROSION
4.069
0.063
3.374
0.759
2.325
1.807
1.456
2.677
0.748
3. 385
TOTAL
2C.260
1.379
0.643
0.0
2.022
0.0
5.241
EVAPORATION
POTENTIAL
NET
STORAGFS
UPPEP_ZCNE
LO^ £R_ZGNE
GRCUNDWATER
INTERCEPTION
OVEPLANO_FLOW
INTERFLOW
19.717
14.588
0.085
18.417
0.0
0.0
0.0
0.0
19.717
14.588
0.017
18.417
0.0
0.0
0.0
0.0
19.717
14.588
0.0
18.417
0.0
0.0
0.0
0.0
19.717
14.588
0.0
18.417
0.0
0.0
0.0
0.0
19.717
14.588
O.C
18.417
O.C
O.C
O.C
0.0
19.717
14.588
0.022
16.417
0.0
0.0
0.0
0.0
11.971
8.691
0.0
PESTICIDE. PCLMDS
SURFACE L AYFR
CRYSTALL INE
DISSOLVED
LPPER ZONE LAYER
ADSORBED
CRYSTALLINE
DISSOL VEO
LChER ZONf LAYER
ADSOSHFO
CRYSTALLINE
DISSOLVED
CRCUNDWATFR LAYER
ATSORBED
CRYSTALLINE
DISSOLVED
FESTICIDF REMOVAL, LBS.
OVERLAP FLOW REMOVAL
SEDIMENT REMOVAL
INTERFLOW REMOVAL
PESTICIDE VOLATILIZATION LCSS, LBS.
TCTAL
FROM SURFACE
FROM UPPER ZONE
PESTICIOF DEGRADATION LOSS. LBS.
TCTAL
FRCM SURFACE
FPCH UPPER ZONE
FROM LCwFR ZONF
PESTIC IDE_8ALANCe» -0.0484
0.000
0.000
0.0
0.0
0.419
0.425
0.0
0.000
.153
.148
>.005
.0
0.000
0.000
0.0
0.0
0.528
0. 533
0.0
0. 000
0.045
0.042
0.003
0.0
0.000
0.000
0.0
0.0
0.555
0.560
0.0
0.0
o.oie
0.017
0.002
0.0
0.000
0.000
0.0
0.0
0.563
0.569
0.0
0.0
0.010
0.009
0.001
0.0
0.000
0.000
0.0
0.0
0.568
0.574
0.0
0.0
0.005
0.004
0.001
0.0
0.001
0.001
0.0
0.0
2.633
2.662
0.0
0.001
0.000
c.o
0.0
0.0
0.0
0.0
c.o
0.0
0.231
0.220
0.011
0.0
0.0
0.0
0.0
17.223
0.778
16.445
0.000
- 93
-------
Table 19 presents an approximate mass-balance of the pesticides
applied to the PI watershed. Less than 1 percent of the applied
diphenamid was lost by runoff and sediment loss; almost 7 percent of
the paraquat was lost by these mechanisms. Since paraquat is
essentially non-degradable, the discrepancy in the mass-balance is
likely due to errors in the application equipment and/or problems in
obtaining representative samples across the watershed. For
diphenamid the mass balance error in Table 19 is due to various
mechanisms of degradation. Table 19 demonstrates the relative
importance of the various loss mechanisms for each of the pesticides.
The simulated vertical movement of the pesticides can be
determined from Tables 17 and 18. Paraquat remained completely in the
surface zone, whereas diphenamid was moved to the upper zone by
percolating water. The observed vertical movement of the pesticides
was much greater than simulated. Paraquat was recorded at depths down
to 10 cm (4 inches) or more, while diphenamid was much more evenly
distributed and observed at even lower depths. This is further
evidence that the adsorption-desorption routine requires additional
investigation.
In summary, the basic conclusions that can be observed from the
PTR Model results are as follows:
1. The PTR Model was developed to simulate the transport of
pesticides in solution and on sediment. The Model uses physically -
based submodels to calculate runoff volumes and sediment
concentrations. Initial model tests show good results for transport
of pesticides on sediment and fair-to-good results for transport in
solution.
2. Surface runoff from agricultural lands in the Southern
Piedmont can be simulated with reasonable accuracy with the PTR Model.
The hydrologic submodel has been used extensively in other studies,
and past experience indicates that similar simulation accuracy for
runoff volumes can be expected in other geographical regions.
3. Simulation of monthly sediment loss agrees adequately with
recorded volumes; however, sediment concentrations during storm events
vary somewhat from the observed values. The general nature of the
sediment submodel shows promise of applicability to other regions,
although experience is limited at the present time.
4. The PTR Model has demonstrated the capability of providing
reasonable estimates of surface runoff and sediment loss from
agricultural watersheds in the Southern Piedmont. These routes are
the major modes of transport of pesticides and other non-point source
pollutants to waterbodies. Consequently, further refinement of the
pesticide functions (adsorption/desorption, volatilization, and
degradation) will upgrade the capability of the model to predict the
pesticide input to waterbodies from surface washoff. Moreover, the
- 94 -
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Table 19. PESTICIDE MASS-BALANCE ON PI WATERSHED
ON OCTOBER 30, 1972 (kg)
PARAQUAT
DIPHENAMID
Field Simulation Field Simulation
Measurement Measurement
Application Amount*
Pesticide Lost by
Runoff and Sediment
Degradation Loss**
Pesticide Remaining on
Watershed
Mass Balance Error
30.45
2.097
_
-
1.915
0.356
9.14
0.082
_
-
0.104
6.690
29.96*
+1.607
28.23
2.33*
-6.728
2.39
* These values are affected by errors in equipment operation
for application and problems of obtaining representative
samples over the watershed.
** These values assume first order degradation with an empirical
constant based on the pesticide remaining on the watershed on
October 30, 1972.
- 95 -
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PTR Model can provide the basis for the simulation of other non-point
source pollutants (nutrients, fertilizers, etc.), and thus estimate
the water quality of surface runoff from agricultural lands.
5. The loss of paraquat from the experimental watershed is
simulated reasonably well by assuming complete adsorption onto
sediment particles. Pesticides with a similar attraction to soil
particles would likely produce similar results.
6. The single-valued (reversible) Freundlich adsorption isotherm
appears to be inadequate in simulating the division between adsorbed
and dissolved phases of diphenamid in runoff from the watersheds.
This was also evidenced by the inability to simulate the observed
vertical movement of the pesticides.
7. The observed variations in pesticide concentrations during
runoff events appears to be of little consequence in predicting total
pesticide loss; total mass movement of pesticide (grams/minute) past
the gage during a storm event is a more valid comparison between
simulated and observed pesticide loss.
8. Although simulated and recorded pesticide amounts remaining
on the watersheds agreed reasonably well, concentrations within the
soil profile were in error. The assumed depths of the soil zones is
largely responsible for this discrepancy because the pesticide
concentration is dependent on the total mass of soil in the zone.
- 96 -
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SECTION IX
RECOMMENDATIONS FOR FUTURE RESEARCH
The subject research effort has produced a model which shows
considerable promise in simulating the pesticide contribution to
waterbodies by agricultural runoff. The PTR Model, developed on small
agricultural watersheds, requires additional development and further
refinement before it can be utilized in the regulation of pesticides
released to the environment. In addition to internal improvements,
suggested in the conclusions of Section I, complimentary research
needs to be conducted to support the Model, if it is to be used in
regulation.
One philosophy of regulation might involve the use of the PTR
Model to estimate the fraction of applied pesticide that would reach
the aquatic environment. Standards could be established denoting the
maximum amount (or concentration) of pesticide that could be accepted
by the aquatic environment without damage to man or the ecosystem.
Such standards in conjunction with the fractions obtained from the PTR
model could establish a maximum allowable pesticide application rate.
To develop a viable methodology, such rates would have to be
determined for groups of pesticides with similar toxicity and
transport characteristics. Also, the rates would need to be evaluated
under varying edaphic and climatic conditions across the country. In
water quality management, a 'control point1 or 'point of use1 is often
specified to establish standards of pollutant concentrations at a
critical point in the watershed. Similarly in pesticide regulation, a
control point would be necessary to specify at what point pesticides
leave the agricultural field environment and enter the aquatic
environment where substantial harm is possible. This would be
necessary for regulation purposes. Intermittent streams, topography,
and climatic conditions all contribute to the complexity of specifying
a control point and the corresponding drainage area. The PTR Model
would need to be run in various regions in order to determine the
pesticide fractions for these control size watersheds which would
likely vary across the country. The pesticide fractions, control-size
watersheds, and pesticide standards would provide the basis for
regulating pesticides releases to the environment.
Other philosophies of regulation might emphasize the need to
control the concentrations of pesticides entering the aquatic
- 97 -
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ecosystem. The PTR Model could be used to estimate pesticide
concentrations in agricultural runoff from certain storm events.
Extended simulation runs of 20 or 30 years could provide frequency
distributions of pesticide concentrations, which, in turn, would help
to evaluate the risk of contamination. Hopefully, methodologies such
as these would help to equitably compromise the concerns of the
agriculturist and the ecologist. The use of pesticides needs to be
regulated to provide efficient pest control without undue harm to the
environment.
From the results and conclusions of the present research effort,
and considering future uses of the PTR Model in pesticide regulation,
the following recommendations are presented:
1. Continuous simulation of pesticide transport, as opposed to
static steady-state investigations, has been shown to be a valid
methodology for performing a materials balance of pesticides applied
to agricultural lands. The dynamic nature of continuous simulation
allows the full accounting of: (a) pesticides remaining on the land
surface, (b) pesticide concentrations and volume lost during storm
events, and (c) accumulated amounts of pesticide lost to the aquatic
ecosystem during a growing season. Consequently this approach to the
investigation of pesticide transport warrants further refinement.
2. An understanding of the mechanisms of surface runoff and
sediment loss is paramount to the study of the importance of non-point
source pollutants on water quality. The PTR Model has demonstrated
the capability of representing these mechanisms. Consequently, the
coupling of the PTR Model with additional pollutant attenuation,
adsorption, and degradation functions could provide the structure for
modeling the transport of plant nutrients, fertilizers, animal wastes,
and other non-point source pollutants. The effects of silvicultural
and agricultural management techniques on water quality could be
evaluated through the PTR Model by their effect on the transport
mechanisms of the non-point source pollutants. Development of such
submodels needs to be undertaken in order to realize the full
potential of the Model as a management tool.
3. For further refinement of the existing PTR Model, future
research needs to be concerned with:
a. additional testing and calibration of the hydrologic model to
more accurately evaluate model algorithms and land surface
parameters.
b. calibration and possible refinement of the sediment loss
model to better reproduce recorded sediment concentrations and to
gain experience with the sensitivity of the sediment loss
parameters.
c. refinement and testing of the adsorption-desorption model to
better determine the division between the adsorbed and dissolved
- 98 -
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phases of pesticides which are transported on both sediment and
water. The inclusion of a nonsingle-valued adsorption-desorption
model warrants further investigation. These refinements are
critical to the reliable prediction of pesticide lost in water
and on sediment during storm events.
d. additional development and testing of the volatilization and
degradation models on actual field data, so that an accurate
pesticide materials balance can be performed. The effects of
environmental factors on these mechanisms needs to be
investigated.
4. To determine the general applicability of the PTR Model, the
following tasks are recommended:
a. Calibration and testing of the Model for runoff and sediment
loss on watersheds in various regions of the country. This would
allow investigation of changes in parameter values with varying
soil and climatic characteristics, and would demonstrate the
behavior of the Model under varying conditions.
b. Evaluation of model performance on watersheds ranging from 20
to 200 hectaries in order to determine required improvements in
the Model for larger watersheds. This would provide insight into
the effects of channel processes on runoff and sediment loss, and
demonstrate the efficacy of existing model algorithms to
simulate the hydrologic and erosion processes.
5. If the PTR Model is to be considered as a tool for regulating
the release of pesticides, the following areas need to be
investigated:
a. determination and definition of control-size watersheds in
various regions of the country which would be most amenable to
pesticide release regulations.
b. classification and grouping of pesticides according to
toxicity, transport, and persistence characteristics
c. establishment of maximum seasonal releases of pesticides
within each classification which would not inflict serious
consequences on man or the aquatic ecosyste. The fractions of
applied pesticides which reach waterbodies could then be
evaluated by the PTR Model and provide a basis for regulating
pesticide releases.
- 99 -
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SECTION X
REFERENCES
(1) The Pollution Potential in Pesticide Manufacturing. Pesticide
Study Series Volume #5. Environmental Protection Agency, Office of
Water Programs, Washington, D. C. June 1972. p. 5-2.
(2) Integrated Pest Management. Council on Environmental Quality,
Washington, D. C. November 1972. p. 3.
(3) Man's Impact on the Global Environment. Mass. Institute of
Technology. Cambridge, Mass. 1970. p. 127-128.
(4) Rudd, Robert L. Pesticides and the Living Landscape. University
of Wisconsin Press, 1964. p. 153.
(5) Carson, Rachel. Silent Spring. New York, N.Y. Fawcett World
Library Press, 1962. p. 304.
(6) Graham, Frank Jr. Since Silent Spring. New York, N.Y., Fawcett
World Library Press, 1970. p. 288.
(7) Langham, Max R., Joseph C. Headley, and W. Frank Edwards.
Agricultural Pesticides: Productivity and Externalities. In:
Environmental Quality Analysis, Edited by A.V. Kneese and B. T.
Bower. RFF, John Hopkins Press, 1972. p. 181-212.
(8) President's Council on Environmental Quality. Washington, D. C.
Annual Report, 1972. p. 27.
(9) Laws and Institutional Mechanisms Controlling the Release of
Pesticides into the Environment, Pesticide Study Series Volume #11.
Office of Water Programs, Environmental Protection Agency, Washington,
D. C. 1972. p. 140.
(10) The Effects of Agricultural Pesticides in the Aquatic
Environment, Irrigated Croplands, San Joaquin Valley. Pesticide Study
Series, Volume #6. Office of Water Programs, Environmental Protection
Agency. Washington, D. C. June 1972. p. 210-268.
- 100 -
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(11) Nicholson, H. P. The Pesticide Burden in Water and its
Significance, In: Agricultural Practices and Water Quality, Iowa
State University Press. Ames, Iowa, 1970. p. 183-193.
(12) Pesticide Use on Non-irrigated Croplands of the Midwest.
Pesticide Studies Series, Volume #4. Office of Water Programs,
Environmental Protection Agency. Washington, D. C. June 1972. p.
5-D.
(13) Glossary - Water and Wastewater Control Engineering. APHA, ASCE,
AWWA, WPCF. 1969. p. 168.
(14) Crawford, N. H. and R. K. Linsley. Digital Simulation in
Hydrology; Stanford Watershed Model IV. Department of Civil
Engineering, Stanford University. Technical Report No. 39. July
1966. p. 210.
(15) Hydrocomp Simulation Programming: Operations Manual. Hydrocomp
Incorporated. 2nd Edition. Palo Alto, California. 1969. p. 1-1 to
1-27, 3-5 to 3-16.
(16) Wischmeier, W. H., and D. D. Smith. Rainfall Energy and Its
Relationship to Soil Loss. Transactions AGU. April 1958. p.
285-291.
(17) Gottschalk, L.C. Reservoir Sedimentation. In: Handbook of
Applied Hydrology, V.T. Chow (ed.). New York, N.Y., McGraw-Hill,
1964. p. 17-27.
(18) Wischmeier, W. H., and D. D. Smith. Predicting
Rainfall-Erosion Losses from Cropland East of the Rocky Mountains.
Agr. Handbook No. 282. USDA. 1965. p. 47.
(19) Negev, M. A Sediment Model on a Digital Computer. Department of
Civil Engineering, Stanford University. Technical Report No. 76.
March 1967. p. 109.
(20) Bailey, G. W. and J. L. White. Factors Influencing the
Adsorption, Desorption, and Movement of Pesticides in Soil. Residue
Reviews. 32:29-92, 1970.
(21) King, D. H. and P. L. McCarty. The Movement of Pesticides in
soils. Presented at Purdue Industrial Wastes Conference, Purdue
University. May 3-5, 1966. p. 25.
(22) Edwards, C. A. Insecticide Residues in Soils. Residue Reviews.
13:83-132, 1966.
(23) Edwards, C. A. Persistent Pesticides in the Environment, CRC
Critical Reviews in Environmental Control, 1(1): 6-67, February 1970.
- 101 -
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(24) Weber, J. B., and S. B. Weed. Adsorption and Desorption of
Diquat, Paraquat, and Prometone by Montmorillonite and Kaolinite Clay
Minerals. Soil Sci. Soc. Amer. Proc. 32:485-487. 1968.
(25) Weed, S. B., and J. B. Weber. The Effect of Cation Exchange
Capacity on the Retention of Diquat (+2) and Paraquat (+2) by
Three-layer type Clay Minerals: I Adsorption and Release. Soil Sci.
Soc. Amer. Proc. 33:379-382. 1969.
(26) Faust, S. D., and A. Zarins. Interaction of Diquat and
Paraquat with Clay Minerals and Carbon in Aqueous Solution. Residue
Reviews. 29:151-170. 1969.
(27) Davidson, J. M., and J. R. McDougal. Experimental and
Predicted Movement of Three Herbicides in a Water-Saturated Soil.
Journal of Environmental Quality, p. 2:428-433. 1973.
(28) Van Genuchten, M. Th.f J. M. Davidson and P. J. Wierenga.
An Evaluation of Kinetic and Equilibrium Equations for the Prediction
of Pesticide Movement through Porous Media. Soil Sci. Soc. Amer. Proc.
Jan.-Feb., 1974. (Accepted for publication).
(29) Davidson, J. M., R. S. Mansell, and D. R. Baker. Herbicide
Distributions Within a Soil Profile and Their Dependence Upon
Adsorption-Desorption. Soil and Crop Science Society of Florida
Proceeding. 1973.
(30) Hornsby, A. G., and J. M. Davidson. Solution and Adsorbed
Fluometuron Concentration Distribution in a Water-Sautrated Soil:
Experimental and Predicted Evaluation. Soil Sci. Soc. Amer. Proc.
Nov.-Dec., 1973. (Accepted for publication).
(31) Pionke, H. B. and G. Chesters. Pesticide-Sediment-Water
Interactions. Journal of Environmental Quality. 2(l):29-45. 1973.
(32) Hall, J. K., M. Paulus, and E. R. Higgins. Losses of
Atrazine in Runoff Water and Soil Sediment. Journal of Environmental
Quality. 1(2):172-176, 1972.
(33) The Fate of Pesticides Applied to Irrigated Agricultural Land.
California Dept. of Water Resources, The Resources Agency. Bulletin
No. 174-1. May 1968. p. 30.
(34) Farmer, W. J., K. Igue, W. F. Spencer, J. P. Martin.
Volatility of Organochlorine Insecticides from Soil: I Effect of
Concentration, Temperature, Air Flow Rate, and Vapor Pressure. Soil
Sci. Soc. Amer. Proc. 36:443-447, 1972.
- 102 -
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(35) Igue, K. W. J. Farmer, W. F. Spencer, J. P. Martin.
Volatility of Organochlorine Insecticides from Soil: II Effect of
Relative Humidity and Soil Water Content on Dieldrin Volatility. Soil
Sci. Soc. Amer. Proc. 36:447-450,1972.
(36) Mayer, R., J. Letey, W. J. Farmer. Models for Predicting
Volatilization of Soil-Incorporated Pesticides. (Accepted for
publication in 1974 by Soil Sci. Soc. Amer. Proc.).
(37) Ehlers, W., J. Letey, W. F. Spencer, W. J. Farmer. Lindane
Diffusion in Soils: II Water Content, Bulk Density, and Temperature
Effects. Soil Sci. Soc. Amer. Proc. 33:505-508, 1969.
(38) Spencer, W. F., M. M. Claith, W. J. Farmer. Vapor Density
of Soil-Applied Dieldrin as Related to Soil-Water Content, Temperature
and Dieldrin Concentration. Soil Sci. Soc. Amer. Proc.
33:509-511, 1969.
(39) Farmer, W. J. (Personal Communication).
(40) Farmer, W. J. (Unpublished Material).
(41) Spencer, W. F., M. M. Claith, W. J. Farmer. Vapor Density
of Soil-Applied Dieldrin as Related to Soil-Water Content, Temperature
and Dieldrin Concentration. Soil Sci. Soc. Amer. Proc.
33:509-511, 1969.
(42) Farmer, W. J., K. Igue, W. F. Spencer, J. P. Martin.
Volatility of Organochlorine Insecticides from Soil: I Effect of
Concentration, Temperature, Air Flow Rate, and Vapor Pressure. Soil
Sci. Soc. Amer. Proc. 36:443-447, 1972.
(43) Spencer, W. F. and M. M. Claith. Vapor Density of Dieldrin.
Envir. Sci. and Tech. 3:670-674, 1969.
(44) Spencer, W. F. and M. M. Claith. Vapor Density and Apparent
Vapor Pressure of Lindane ( BHC). Journal of Agr. and Food Chem.
18:529-530, 1970.
(45) Zindahl, R. L., V. H. Freed, M. L. Montgomery, and W. R.
Furtick. The Degradation of Triazine and Uracil Herbicides in Soil.
Weed Research. 10(1);18-26, March 1970.
(46) Burschel, P. and V. H. Freed. The Decomposition of Herbicides
in Soils. Weeds. 7(2):157-161, April 1959.
(47) Hamaker, J. W., C. R. Youngson, and C. A. I. Goring. Rate
of Detoxification of 4-Amino-3, 5, 6-tricloropiralinic acid in Soil.
Weed Research. 8:46-57, 1968.
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(48) Carreker, J. R., and A. P. Barnett. Rainfall and Runoff
Characteristics on a Small Watershed in the Southern Piedmont. USDA
Soil Conservation Service. SCS-TP-114. Washington, D. C. August
1953. p. 16.
(49) Phillips, J.R. The Theory of Infiltration; 1. The Infiltration
Equation and its Solution. Soil Science 83: 345-375, 1957.
(50) Linsley, R.K. M.A. Kohler, and J.L.H. Paulus. Hydrology for
Engineers New York, N.Y., McGraw-Hill, 1958. p. 99 - 119.
(51) Barnes, B.S. Discussion of Analysis of Runoff Characteristics.
Trans. ASCE 105:106, 1940.
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SECTION XI
APPENDICES
Page
A. Description of HSP LANDS 106
B. PTR Model Sample Input Data 129
C. PTR Model Listing 146
- 105 -
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APPENDIX A
DESCRIPTION OF HSP LANDS*
Figure 27 presents the flowchart of the HSP LANDS program. The hydro-
logic processes and functions sketched are discussed in the following
sections.
INTERCEPTION
The first loss to which falling precipitation is subjected is inter-
ception, or retention on leaves, branches and stems of vegetation.
Interception in any single storm is small in amount and is not important
in flood producing storms. However, in the aggregate interception may
have a significant effect on annual runoff.
In nature, interception is a function of the type and extent of vegeta-
tion and, for deciduous vegetation, the season of the year. In HSP,
interception is modeled by defining an interception storage capacity
EPXM+ as an input parameter. All precipitation is assumed to enter
interception storage until it is filled to capacity. Water is removed
from interception storage by evapotranspiration at the potential rate.
Evapotranspiration may occur even during rain so that after the storage
is filled there is a continuing interception equal to the potential eva-
potranspiration.
IMPERVIOUS AREA
Precipitation on impervious areas that are adjacent to or connect with
stream channels will contribute directly to surface runoff. An input
parameter"1" A in HSP represents this "impervious" fraction of the total
watershed area. Precipitation minus interception is multiplied by the
impervious area fraction to determine the impervious area contribution
to streamflow.
*This appendix is abstracted directly from the Hydrocomp Operations
Manual'b
+Input parameters appear in uppercase roman letters.
- 106 -
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Figure 27 Lands flowchart
KEY
PRECIPITATION
POTENTIAL
EVAPOTRAN5PIRATION
PESTICIDE
RUNOFF
Hyd
ro c o m p
- 107
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The impervious area is usually a very small percentage of the total
watershed, except in urban areas. In rural watersheds impervious area
does not contribute large amounts of runoff. However, for the light
rains with relatively dry soil impervious area may be the sole contri-
bution of runoff to the stream. During the calibration phase the im-
pervious area term is usefull in reproducing these small runoff events.
It enhances the detailed understanding of the hydrologic process during
simulation. In urban areas the "impervious area" term becomes very im-
portant.
Lakes, swamps, and reservoirs create a special class of impervious area.
"Runoff" results from all of the precipitation that reaches these surfaces
and potential (lake) evaporation occurs continuously. The parameter A
does not include the surface area of major lakes and reservoirs, but does
include the surface area of small ponds and stream channels.
Calculations in the land surface phase of MSP are carried in terms of
water depth (inches or millimeters) over a unit area. Only at the
beginning of the channel phase are these depths multiplied by area to
derive actual volumes of runoff. This system is used to allow investi-
gation of various amounts of impervious area or reservoir surface area
without altering the response from pervious areas.
INFILTRATION
The process of infiltration is essential and basic to simulation of the
hydrologic cycle. Infiltration is the movement of water through the
soil surface into the soil profile. Infiltration rates are highly var-
iable and change with the moisture content of the soil profile, and in-
filtration is the largest single process diverting precipitation from
immediate streamflow. Usually more than half of the water which in-
filtrates is retained in the soil until it is returned to the atmosphere
by evapotranspiration. However, not all infiltrated water is permanently
diverted from streamflow. Some infiltrated water may move laterally
through the upper soil to the stream channels as interflow, and some may
enter temporary storages and later discharge into the stream channels as
base or groundwater flow.
Water which does not infiltrate directly into the soil moves over the
land surface and is subject to delayed infiltration and retention in
surface depressions. The delayed infiltration is introduced by the
upper zone function.
The infiltration capacity, the maximum rate at which a soil will accept
infiltration, is a function of fixed characteristics of the watershed-
soil type, permeability, land slopes and vegetal cover; and of variable
characteristics - primarily soil moisture content. Soils containing
clay colloids may expand as moisture content increases, thus reducing
pore space and infiltration capacity. The actual infiltration rate at
a point at any time is equal to the infiltration capacity or the supply
rate (precipitation minus interception plus surface detention), which-
- 108 -
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ever is least.
Traditionally infiltration has been represented by an infiltration
capacity curve in which the capacity is an exponential function of time.
This is in accord with experimental evidence provided the supply rate
always exceeds the capacity. Since supply rates are frequently less than
infiltration capacity, the variation of infiltration capacity is con-
trolled by accumulation of soil moisture and may not be described by any
smooth function of time.
Infiltration relationships used for continuous simulation must:
(1) Represent segment mean infiltration rates continuously.
Since variable moisture supply rates preclude continuous
functions of time, expressions for infiltration as a
function of soil moisture content are used. (Note: A seg-
ment is a portion of a watershed with uniform climatic and
hydrologic characteristics.)
(2) Represent the area! variation in infiltration - the
distribution of infiltration capacities that will exist
at any time about the segment average.
To meet requirement (1) HSP uses a method based on infiltration equations
developed by Phillips^.
F = st3* + at (14)
f - sf ^ + a (15)
Where F is cumulative infiltration, f is infiltration rate, and t is time
The letters a and s are constants that depend on soil properties.
If the constant a is small Eqns. (14) and (15) can be written:
fF=ii (16)
2
Since s2/2 is constant, Eqn. (16) continuously relates infiltration rate
to cumulative infiltration or infiltrated volume. This is the type of
relation needed in hydrologic simulation.
Equation (16) will apply only approximately to intermittent infiltration
when the moisture distribution in the soil profile adjusts between rains.
Homogeneous soil is also assumed but a decrease in permeability as depth
increases is more common. Therefore Eqn. (16) is modified to:
fBb = constant 0?)
109 -
-------
where b is a constant greater than one. Numerous trials have resulted in
adoption of b = 2 as a standard value in HSP.
The second requirement listed above, representation of areal variations
in infiltration capacity, has not normally been considered in applications
of the infiltration concept. Areal variation results from differences in
soil type and permeability and from differences in soil moisture which in
turn result from differing vegetal cover, precipitation, and exposure to
evaporation. It can be expected that the
exist from point to point in a watershed
distribution about a mean value (Fig. 28),
infiltration capacity curve (Fig. 29) is of interest as a basis for run-
off volume calculations. The solid line sketched in Fig. 29 is plotted
from the example of an actual frequency distribution sketched in Fig. 28.
infiltration capacities that
at a given time will have some
. The corresponding cumulative
o-
LlJ
RELATIVE INFILTRATION CAPACITY
Figure 28. Schematic frequency distribution of infiltration
capacity in a watershed
The shape of the cumulative frequency distribution that will apply in a
watershed at any time is impractical to determine, and for mathematical
simplification the dashed line in Fig. 29, corresponding to the dashed
frequency distribution in Fig. 28, is assumed in HSP. The assumption of
a linear variation is reasonably well verified by the limited experi-
mental data that is available, and experience indicates that the assump-
tion yields satisfactory results.
- 110 -
-------
o
D_
O
2:
O
I—I
h-
-------
t
>-
o
-------
in a given time interval. Infiltration occurs and the variable soil
moisture storage LZS increases. In the next time interval f will de-
crease since LZS/LZSN in Fig. 31 has increased. The combination of
functions represented by Figures 30 and 31 simulates the complex time
and area! variation of infiltration over a segment of a watershed.
Since different parameters may be used in each segment of a watershed
large variations in topography and soil properties can be represented.
Simulation algorithms make infiltration a function of the supply rate
and vary the area contributing to runoff continuously.
INTERFLOW
Infiltration may lead to interflow, runoff that moves laterally in the
soil for some part of its path toward a stream channel. Interflow is
encouraged by any relatively impermeable soil layers and has been ob-
served to follow roots and animal borrows in the soil. Interflow may
come to the surface to join overland flow if its flow path intersects the
surface. Fig. 30 is extended (Fig. 32) to represent interflow by adding
a line to represent transient infiltration for the interflow process.
The variable c is defined by:
c= INTERFLOW x 2(LZS/LZSN) (19)
an empirical equation that results in the variation with soil moisture
sketched in Fig. 33. INTERFLOW is an input parameter that governs the
volume assigned to interflow. INTERFLOW is shortened to INTER in the
PTR LANDS sub-program.
This simulation scheme makes interflow a function of the local infiltra-
tion rate and of soil moisture. That is, the higher the soil moisture
the greater the fraction of infiltration which becomes interflow. The
combination of interflow and infiltration functions yields a smooth
response to variations in moisture supply in any time interval. Fig. 34
illustrates this response.
UPPER ZONE
Moisture that is not infiltrated directly will increase surface detention
storage. The increment to surface detention calculated from Fig. 32
will either contribute to overland flow or enter upper zone storage.
Depression storage and storage in highly permeable surface soils are
modelled by the upper zone. The upper zone inflow percentage £ is inde-
pendent of rainfall intensity but upper zone storage capacity Ts low.
Moisture is lost from the upper zone by evaporation and percolation to
the lower zone and groundwater storages.
- 113 -
-------
MOISTURE
SUPPLY
(INCHES)
I
INCREMENT TO
SURFACE
DETENTION
INCREMENT TO
INTERFLOW
DFTFNTTnN
INFILTRATION
CAPACITY
(INCHES)
Figure 32. Cumulative frequency distribution of infiltration capacity
showing infiltration volumes, interflow and surface detention
4.0
3.0
2.0
1.0
0.0
c vs LZS/LZSN
for INTERFLOW =1.0
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
LOWER ZONE SOIL MOISTURE RATIO (LZS/LZSN)
Figure 33. Interflow c as a funtion of LZS/LZSN
- 114 -
-------
00
•z.
o
D_
OO
Q_
OO
O
oo
o
O-
o
C_J
INCREASE IN OVERLAND
FLOW SURFACE DETENTION
INCREASE IN INTERFLOW DETENTION
NET
INFILTRATION
I ' i . i . i .
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
MOISTURE SUPPLY
Figure 34. Components of HSP response vs. moisture supply
The following expressions are used to calculate the response of the
upper zone storage. The upper zone has a nominal capacity given by the
input parameter UZSN. The percentage? of a potential addition to over-
land flow surface detention that is held in the upper zone is a function
of the upper zone storage UZS and the nominal capacity UZSN (Fig. 35).
When the ratio UZS/UZSN is less than two,
£-=10° i1-0-
-------
The upper zone storage prevents overland flow from a portion of the
watershed depending on the value of the ratio UZS/UZSN, but since the
nominal capacity UZSN is small, the upper zone retention percentage de-
creases rapidly with early increments of accretion.
PERCENT OF THE INCREASE IN
SURFACE DETENTION
RETAINED BY THE UPPER ZONE
i— *
ro -p> cri co o
0 0 0 0 0 0
^
-\
\
\
\
\
\
\.
0.5 1.0 1.5 2.0 2,5 3.
UPPER ZONE SOIL MOISTURE RATIO (UZS/UZSN)
Figure 35. Surface detention retained in the upper zone
Percolation (PERC) occurs from the upper zone to the groundwater and
lower zone storages when the upper zone storage ratio UZS/UZSN exceeds
the lower zone storage ratio LZS/LZSN. This is calculated as
PERC = 0.1 x INFILTRATION xUZSN x [(UZS/UZSN) - (LZS/LZSN)] 3 (24)
where INFILTRATION is the infiltration level input parameter and PERC is
the percolation rate in inches/hour. Evapotranspiration occurs from the
upper zone storage at the potential rate of UZS/UZSN is greater than
2.0. If UZS/UZSN is less than 2.0 the portion of the potential evapo-
transpiration (PET) that is satisfied by upper zone is given by
ET (actual) = 0.5 (UZS/UZSN) xPET
(25)
Potential evapotranspiration that is not assigned to the upper zone is
passed to the lower zone. The assignment from Eqn. 25 models direct
evaporation from near-surface soil. Moisture loss from the lower zone
models transpiration by vegetation.
The use of a nominal rather than an absolute capacity for the upper zone
storage permits a smooth increase in overland flow rates as upper zone
storage increases. If an absolute capacity were used, there would be an
- 116 -
-------
abrupt increase in overland flow when the capacity was attained. Such
an abrupt change is not consistent with experience nor with the obser-
vation that a truly "saturated" state is rarely, if ever, observed.
Because of the use of a nominal capacity it is not possible to define
upper zone storage in any rigorous physical sense. It is best viewed as
an input parameter representing moisture retention at and near the soil
surface.
OVERLAND FLOW
The movement of water in surface or overland flow is an important land-
surface process. Interactions between overland flow and infiltration
need to be considered since both processes occur simultaneously. The
variations in rates of infiltration described above allow overland flow
in areas with low infiltration while preventing overland flow in other
areas. During overland flow, water held in detention storage remains
available for infiltration. Surface conditions such as heavy turf or
very mild slopes that restrict the velocity of overland flow tend to
reduce the total quantity of runoff by allowing more time for infiltra-
tion. Short, high intensity rainfall bursts are attenuated by surface
detention storage reducing the maximum outlow rate from overland flow.
A wide range of methods for the calculation of unsteady overland flow
were considered. The only rigorous general methods for simulating un-
steady overland flow are finite difference techniques for the numerical
solution of the partial differential equations of continuity and momentum.
These methods have a major disadvantage for continuous simulation since
substantial amounts of computer time are needed. In a natural watershed
there are area! variations in the amount of runoff moving in overland
flow because of areal variations in infiltration. Average values must be
used in the calculations for the length, slope, and roughness of over-
land flow. Hence the accuracy gained by using finite difference methods
for overland flow is subject to question because of the limitations on
the input data.
In HSP, overland flow is treated as a turbulent flow process. Since
continuous surface detention was chosen as the parameter to be related
to overland flow discharge. Using the Chezy-Manning equation, the
relation between surface detention storage at equilibrium cy, the supply
rate to overland flow i, Manning'sn and the length L and slope S of the
flow plane is
D = 0.000818i°-6n °'6 L 1'6 (26)
O
Using the ratio of detention depth at any instant to detention depth at
equilibrium D£ as an index of the distribution of flow over the overland
plane, an empirical expression relating outflow depth and detention
storage which fits experimental data quite well is
- 117 -
-------
= x
e ->
(27)
Susti tuting Eqn. (27) in the Chezy-Manning Equation the rate of discharge
from overland flow in ft^/sec/ft is
= 0.6 ( -
5/3
(28)
n e
where Deis a function of the current supply rate to overland flow and is
calculated from Eqn. (26). During recession flow when D is less than
D the ratio D/De is assumed to be one. HSP continuous!/ solves a
continuity equation
D=D]
A D - q~
(29)
where At is the time interval used, D2 is the surface detention at the
end of the current time interval, D, is the surface detention at the end
of the previous time interval, A D is the increment added to surface
detention in the time interval, and q is the overland flow into the
stream channel during the time interval. The discharge q~is a function
of the moisture supply rate and of (D^ + D2) /2, the average detention
storage during the time interval (D in Eq. (28).
The system of equations can be solved numerically with good accuracy if
the time interval of the calculation is sufficiently small so that the
value of discharge in any time interval remains a small fraction of the
volume of surface detention. Calculations of discharge from overland
flow are made on a 15-minute time interval, but shorter time intervals
can be used if required by the characteristics of the flow plane, or
if justified by the input data.
The overland flow calculations enter the delayed infiltration process
through the fact that any water remaining in detention at the end of an
interval is added to the rainfall minus interception of the next period
to give the supply rate for the infiltration calculations. Overland
flow detention is an important part of the total delay time in runoff on
small watersheds. Figures 36 and 37 illustrate the "fit" of the HSP
simulation of overland flow to experimental data. Fig. 38 shows that on
a watershed of 0.26 square miles, overland flow simulation closely
approximates the actual outflow hydrograph indicating that overland flow
delay is much more important than channel storage in controlling hydro-
graph shape. Fig. 39 shows a similar comparison for a watershed of 18.5
square miles which is partly urbanized. Here, the overland flow effects
on hydrograph shape are relatively small although the effect through
delayed infiltration is still present.
- 118 -
-------
C/5
UJ
4.0
3.0-
2.0.
1.0-
SIMULATED OVERLAND FLOW (WATERSHED MODEL)
SIMULATED OVERLAND FLOW (MORGALI)
- OBSERVED OVERLAND FLOW
SURFACE - TURF
RAINFALL - 3.60 in/hr
S = 0.04
L = 72 ft.
01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
TIME (MINUTES)
Figure 36. HSP Overland flow simulation
4.0-
3.0-
oo
UJ
2.0"
u_
o
i.o-
SIMULATED OVERLAND FLOW (WATERSHED MODEL)
SIMULATED OVERLAND FLOW (SCHAAKE)
OBSERVED OVERLAND FLOW
SURFACE - CRUSHED SLATE
RAINFALL- 3.68 in/hr
S - 0.04 ft./ft.
L - 72 ft.
Figure 37,
6 8 10 12 14 16 18 20 22
TIME (MINUTES)
HSP Overland flow simulation
24 26 28
- 119 -
-------
-------
INTERFLOW STORAGE
The calculation of an increment to interflow detention storage SRGX was
described in Sec. 1.5. Outflow from this storage to the stream is
calculated on a 15-minute time interval by the equation
INTF = LIRC4 xSRGX (30)
where
LIRC4 = 1.0 - (IRC)1/96 (31)
IRC, an input parameter, is the daily recession constant for the inter-
flow discharge at any instant to the interflow discharge twenty-four
hours earlier.
LOWER ZONE AND GROUNDWATER STORAGE FUNCTION
This function operates on the direct or immediate infiltration (Fig. 32)
and the percolation from upper zone storage (PERC in Eqn. 24). The
available water is divided between the lower zone or soil moisture
storage and the groundwater storage. The division is based on the lower
zone storage ratio LZS/LZSN where LZS is the quantity of moisture in the
lower zone storage and LZSN is the lower zone nominal capacity. The
percentage of the infiltration plus percolation that enters groundwater
storage (Fig. 40) is given by
p = inn .k^S- ( 1>Q }Z (3?)
Kg IUU LZSN ( 1.0 + z ' l^;
when LZS/LZSN is less than one and by
z
P = 100 (l.O- ( i o+ z ) } (33)
y v. * j
when LZS/LZSN is greater than one. z is defined by
z = 1.5
LZS - 1.0
+ 1.0 (34)
LZSN
These relationships are plotted in Fig. 40.
LOWER ZONE STORAGE
The lower zone storage is the main moisture storage for the land surface
in HSP. Like the upper zone storage it is defined in terms of a
nominal capacity LZSN, the storage level at which half of the incoming
infiltration enters the lower zone and half moves to groundwater. This
use of a nominal rather than an absolute capacity serves the same
- 121 -
-------
100
CD
-------
Figure 41. Groundwater flow
The outflow from active ground-
water storage at any time is
based on the simplified model in
Fig. 41. The discharge of an
aquifer is proportional to the
product of the cross-sectional
area and the energy gradient
of the flow. A representative
cross-sectional area of flow
is assumed proportional to the
groundwater storage level
computed by HSP.
The energy gradient is estim-
ated as a basic gradient plus
a variable gradient that depends
on groundwater accretion. The
groundwater outflow GWF at any
time is given by
GWF = LKK4 x (1.0 + KV GWS) x SGW
(35)
where GWS is
variable GWS
storage and
groundwater slope and SGW is
is an antecedent index based
is calculated daily as
groundwater storage. The
on inflow to groundwater
GWS = 0.97 (GWS + inflow to groundwater storage) (36)
Groundwater outflow is calculated on 15-minute intervals. The identifier
LKK4 is defined as
LKK4 = 1..0 - (KK24)1/%
(37)
where KK24 is the minimum observed daily recession constant of ground-
water flow, the ratio of current groundwater discharge to the ground-
water discharge twenty-four hours earlier. When the parameter KV is
zero and inflow to groundwater storage is zero, Eqn. (35) reproduces the
commonly used logarithmic depletion curve, i.e., the flow after a period
of n days decreases by (KK24) , and a semi-logarithmic plot of discharge
vs. time is a straight line.
KV is introduced to allow variable groundwater recession rates. When
KV is non-zero a semi-log plot of discharge vs. time is not linear. For
example, if the typical daily dry-season recession rate in a stream is
0.99 and a recession of 0.98 is more typical when groundwater storages
are being recharged, the value of KK24 can be set to 0.99 and the value
of the parameter KV can be adjusted so that 1.0 + KVxGWS will reduce the
effective recession rate to 0.98 during recharge periods. This added
flexibility in groundwater outflow simulation, introduced at the cost of
- 123 -
-------
an additional input parameter, is useful in many watersheds.
EVAPOTRANSPIRATION
The volume of water that leaves a watershed as evaporation and trans-
piration exceeds the total volume of stream flow in most hydrologic
regimes. Continuous estimates of actual evapotranspiration must there-
fore be made by HSP. There are two separable issues involved in esti-
mating actual evapotranspiration. Potential evapotranspiration must be
selected, and actual evapotranspiration must be calculated as a function
of moisture conditions and the potential evapotranspiration.
Potential evapotranspiration is assumed to be equal to lake evaporation
estimated from U.S. Weather Bureau Class A pan records50. This procedure
is more convenient than an approach based on meteorological data since
input requirements are less stringent. A single variable, adjusted pan
evaporation data, serves a purpose that would otherwise require input of
several variables. If pan evaporation data are not available, the input
data for potential evapotranspiration may be estimated by an appropriate
method. The relationship of actual evapotranspiration to potential evapo-
transpiration over large areas should logically be a function of moisture
conditions. Even if transpiration from vegetation is independent of soil
moisture until the wilting point is reached, variable soil moisture will
cause wilting in some parts of a watershed but not in others. Evaporation
from soil, a component of the total process, is dependent on moisture
conditions.
When near surface storage is depleted, the concept of evapotranspiration
opportunity is defined as the maximum quantity of water accessible for
evapotranspiration in a time interval at a point in the watershed. It is
analogous to infiltration capacity and would have a cumulative distri-
bution similar to that in Fig. 29. The cumulative evapotranspiration
opportunity curve will be a function of watershed soil moisture conditions,
and will give estimates of actual evapotra-nspi ration for any quantity of
potential evapotranspiration, just as the cumulative infiltration
capacity curve estimates net infiltration for any moisture supply.
Evapotranspiration occurs from interception storage at the potential
rate. Evapotranspiration opportunity controls evapotranspiration from
the lower zone storage. Evaporation from streams and reservoir surfaces,
and evapotranspiration from groundwater storages is also simulated.
Daily lake evaporation or potential evapotranspiration data, or average
daily rates for semi-monthly periods are used as input. HSP computes
hourly values from the daily totals using an empirical diurnal variation.
Potential evapotranspiration will result in a water loss or actual
evapotranspiration only if water is available. HSP first attempts to
satisfy the potential from interception storage and from the upper zone
in that order. The contribution to actual evapotranspiration of the
upper zone is limited if UZS/UZSN is less than 2.0 (Eq. 25). Any
remaining potential enters as E in Fig. 42. Since evapotranspiration
- 124 -
-------
opportunity in a watershed on a given day may be expected to vary
through a considerable range, a cumulative frequency distribution
similar to those found for infiltration capacity in Fig. 30 might be
reasonable. Following the assumption made for infiltration capacity
cumulative frequency distribution of evapotranspiration opportunity
assumed to be linear (Fig. 42)
the
is
Q_
oo
<=£ 00
O
D-
O
C_
EVAPOTRANSPIRATION
O
m
GO
O -a
-o o
-a —i
o 72
;o 3=-
—I ^.
d GO
z -a
25
50
75
100
PERCENT OF AREA WITH A DAILY EVAPOTRANSPIRATION
OPPORTUNITY EQUAL TO OR LESS THAN THE INDICATED VALUE
Figure 42. Potential and actual evapotranspiration
The quantity of water lost by evapotranspiration from the lower zone
when Ep Is less than ris given by the cross-hatched trapezoid of Fig. 42.
The variable r is an index given by
0.25
170 - K3
-) x (-
LZS
LZSN
(38)
Evapotranspiration is further limited when K3 is less than 0.5. A
fraction of the segment area given by 1.0-2xK3 is considered devoid of
vegetation that can draw from the lower zone storage. K3 is an input
parameter that is an index to vegetation density.
PARAMETER EVALUATION
The process of applying LANDS to a watershed requires a fitting or
calibration of parameters for the specific watershed. Some parameters
are measured directly from topographic maps, or are easily found by
conventional hydrologic procedures. Other parameters are established by
computer runs. Numerical values for the LANDS parameters are required
for each simulation trial. Methods for estimating these parameters
follow:
- 125 -
-------
A:
EPXM:
UZSN:
LZSN:
K3:
K24L,
K24EL:
A is the fraction representing the impervious area in a
segment. Usually A will be neglible for agricultural
watersheds, except in cases of extensive outcrops along
channel reaches.
This interception storage parameter is a function of cover
density.
Grassland
Forest cover (light)
Forest cover (heavy)
0.10 in.
0.15 in.
0.20 inc.
The nominal storage in the upper zone is related to LZSN and
watershed topography.
Low depression storage,
steep slopes, limited
vegetation O.OSxLZSN
Moderate depression storage
slopes and vegetation O.OSxLZSN
High depression storage,
soil fissures, flat
slopes, heavy vegetation 0.14xLZSN
The nominal lower zone soil moisture storage parameter is
related to the annual cycle of rainfall and evapotranspiration.
Approximate values range from 5.0 to 20.0 inches for most of
the continental U.S. depending on soil properties. The
proper value will need to be checked by computer trials.
Index to actual evaporation. Values range from 0.25 for open
land and grassland to 0.7-0.9 for heavy forest. The area
covered by forest or deep rooted vegetation as a fraction of
total watershed area is an estimate of K3.
These parameters control the loss of water from near surface
or active groundwater storage to deep percolation and trans-
piration respectively. K24L is the fraction of the ground-
water recharge that percolates to deep groundwater table.
Thus a value of 1.0 for K24L would preclude any groundwater
contribution to surface runoff. K24EL is the fraction of
watershed area where shallow water tables put groundwater
within reach of vegetation.
INFILTRATION: This parameter is also a function of soil characteristics.
As for LZSN, approximate or initial values will need to be
checked by computer trails. INFILATRATION can range from
0.01 to 1.0 in/hr depending on the cohesiveness and permeabi-
lity of the soil.
- 126 -
-------
INTERFLOW: This parameter alters runoff timing, and is closely related
to INFILTRATION and LZSN. Examples of its effect are dis-
cussed below.
L: Length of overland flow is obtained from topographic maps
and approximates the length of travel to a stream channel.
Its value can be approximated by dividing the watershed area
by twice the length of the stream channel.
SS: Average overland flow slope is also obtained from topographic
maps. The average slope can be estimated by superimposing a
grid pattern on the watershed, estimating the land slope at
each point of the grid, and obtaining the average of all
values measured.
NN: Manning's n for overland flow. Approximate values are:
Asphalt
Packed Clay
Turf
Heavy Turf and
Forest Litter
0.014
0.03
0.25
0.35
IRC; KK24: These parameters are the interflow and groundwater recession
rates. They can be estimated graphically^! or found by tria'
from simulation runs.
IRC =
KK24 =
Interflow discharge on any day
Interflow discharge 24 hours earlier
Groundwater discharge on any day
Groundwater discharge 24 hours earlier
(39)
(40)
KV:
The parameter KV (Eqn. 35) is used to allow a variable
recession rate for groundwater discharge. If KV = 1.0 the
effective recession rate for different levels of KK24 and
the variable groundwater slope parameter GWS is as follows:
GWS
KK24 0.0
0.5
1.0
2.0
0.99
0.98
0.97
0.96
0.99
0.98
0.97
0.96
0.985
0.97
0.955
0.94
0.98
0.96
0.94
0.92
0.97
0.94
0.91
0.88
CALIBRATION
Among all of the parameters used in HSP, LZSN, INFILTRATION and INTER-
FLOW are three that are not clearly defined from physical watershed
characteristics. LZSN and INFILTRATION affect both runoff volumes and
runoff timing. INTERFLOW effects only runoff timing.
- 127 -
-------
(a)
(b)
(c)
(d)
UJ
CJ3
a:
-------
LZSN and INFILTRATION: The nominal lower zone storage and the infiltr-
ation index control runoff volumes, if the parameter UZSN is based on
LZSN. Runoff volumes over extended periods are governed by the water
balance equation:
Precipitation - Actual Evapotranspiration - Deep percolation
= Streamflow (41)
Thus when deep percolation is small and precipitation is known, actual
evapotranspiration must be changed to cause a change in long-term run-
off volume Increasing LZSN will increase actual evapotranspiration
loss, and decreasing LZSN will reduce actual evapotranspiration loss.
The parameter INFILTRATION is also directly related to actual evapotrans-
piration. Lowering INFILTRATION will usually reduce actual evapotrans-
piration.
When correct annual runoff volumes are obtained, the seasonal distribu-
tion of runoff should be checked using the monthly summaries from the
LANDS load module. INFILTRATION parameter adjustments are effective in
altering groundwater recharge for improved seasonal distribution. When
detailed hydrographs from the CHANNEL load module are available the
INFILTRATION parameter can be closely checked. Fig. 43 shows the possible
results. Cases (a) and (b) indicate that an increase and decrease
respectively is in order for the INFILTRATION parameter. Cases (c) and
(d) give conflicting indications and unrepresentative input data is
likely. These cases should be ignored in the fitting process.
Trial runs are used to determine if cases (a) and (b) predominate during
the calibration period and the INFILTRATION parameter is adjusted
accordingly. Note that Fig. 43 (a) and (b) illustrate the effect of
INFILTRATION on groundwater flows. Groundwater flow is a very useful
indicator of infiltration levels.
INTERFLOW: The interflow parameter can be used effectively to alter
hydrograph shape after storm runoff volumes have been correctly adjusted.
INTERFLOW has no effect on runoff volumes. Its effect is illustrated
in Fig. 44 for which values of INTERFLOW were (a) 1.4, (b) 1.8, (c) 1.0.
TIME
Figure 44. Example of the response to the INTERFLOW parameter
- 129 -
-------
Appendix B
//PESTICID JOB (0510,510,15,30),'TONY.SP1.PARAQUAT'
/* SERVICE CLASS=B, BLOCK=W
//JOBLIB DD DSNAME=C510.TONY.PEST10,DISP=(OLD,KEEP),
// UNIT=2314,VOL=SER=SYS13
//STEP1 EXEC PGM=PEST
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT05F001 DD *
&HYCL
&PRNT
&STRT
&ENDD
&TRVL
HYCAL= 0, HYMIN=0.001, UNIT=-1, INPUT—I &END
PRINT= 1 &END
BGNDAY=1, BGNMON=75
ENDDAY=15.
INTRVL=5
BGNYR=1972 &END
ENDMON=2, ENDYR=1973 SEND
SEND
&LND1 UZSN=0.05, LZSN=18.0, INFIL=0.5, INTER=0.7 &END
&LND2 IRC=0.0, NN=0.20, L=160., SS=0.05, A=0.00, UZS=0.05 &END
&LND3 LZS=20.0, SGW=0.0, GWS=0.0, KV=0.0, K24L=1.0, KK24=0.6 &END
&LND4 ICS=0.0, OFS=0.0, IFS=0.0, K24EL=0.0, K3=0.40, EPXM=0.12 &END
&PEST SSTR=5*13.4, APMODE=0, DEPTH=6.125 SEND
&NAME PNAME= ' PARAQUAT ', WSNAME= ' PI ' &END
&CROP COVMAX=0.60,TIMST=182. ,TIMAP=182. ,TIMAT=274. ,TIMHAR=334. &END
&SMDL JRER=3.0, KRER-0.09, JSER=1.0, KSER=1.5, SRERI=9.0 SEND
&AMDL CMAX=0. 00001, DD=0.0003,BULKD= 103. 0,K=120. ,N=2. ,AREA=6.7 &END
&VOL1 DIFC=30.0, TDIFC=30.0, CBDIF=0.11 &END
&VOL2 MOLEWT=335., APFAC=0.0, BPFAC=0.0, WCFAC=1.0 &END
&DEG1 DEGCON=0.0001
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
27
27
43
41
41
70
43
119
54
54
54
54
54
59
108
124
103
0
49
11
11
11
65
59
97
32
176
410
252
44
63
32
139
57
0
132
63
76
69
189
63
76
151
265
277
69
88
31
38
32
&END
96
141
148
118
74
192
163
126
155
148
155
141
215
126
126
89
118
67
74
89
141
178
200
74
111
154
49
84
91
105
140
140
154
189
161
70
112
126
147
252
175
280
224
210
168
196
42
189
182
238
167
175
198
190
198
251
198
91
122
228
220
175
84
243
205
236
152
144
137
84
219
129
106
167
205
185
200
162
285
116
239
231
231
216
162
285
185
200
231
216
200
216
185
131
154
154
377
370
223
216
245
262
93
93
23,4
234
141
141
125
177
177
148
171
158
199
206
67
152
92
272
211
195
227
158
141
124
194
219
242
200
89
259
280
283
157
112
132
107
129
164
119
115
156
174
150
204
205
59
133
137
145
139
138
238
140
169
92
75
203
106
133
114
113
151
149
159
73
61
129
152
108
112
129
108
84
93
116
116
67
45
124
142
144
109
172
97
29
112
82
89
146
114
56
52
156
148
66
86
208
17
7
52
61
61
61
107
71
81
29
53
53
53
47
97
68
29
78
78
78
53
20
30
30
30
30
30
30
30
34
19
19
14
5
5
5
10
25
16
21
21
21
31
16
47
16
26
26
78
31
42
130 -
-------
Appendix B (continued)
EVAP72 97 69 96
EVAP72 22 101 96
EVAP72 0 76 81
EVAP72 27 113 89
EVAP72 27 141
EVAP72 27 74
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
112 289 277 141
98 243 123 111
252 106 39 322
63 68 246 99
140 53 285 158
122 109
24
25
24
25
23
21
20
22
22
22
23
24
27
25
27
26
25
26
26
25
26
28
28
28
26
26
25
25
25
23
21
.4
.6
.4
.6
.9
.4
.6
.2
.8
.2
.9
.7
.2
.9
.4
.1
.9
.0
.3
.3
.3
.9
.3
.9
.6
.3
.4
.4
.1
.2
.6
66
70
68
72
69
67
64
67
64
67
64
126
123
156
151
187
48
117
141
104
94
90
186
75
58
75
184
23
25
24
26
25
24
26
26
24
24
23
25
24
24
25
26
24
27
27
27
24
23
22
24
25
24
24
24
25
23
22
.0
.0
.3
.1
.9
.9
.7
.5
.7
.7
.4
.3
.4
.4
.8
.7
.9
.4
.3
.7
.9
.6
.7
.4
.2
.6
.8
.4
.4
.8
.7
28
79
46
58
59
31
75
71
71
40
35
22
22
24
25
20
20
19
20
24
24
19
21
21
23
25
24
24
23
25
26
22
23
24
23
23
23
25
24
26
20
.9
.5
.1
.0
.8
.0
.9
.9
.6
.4
.3
.1
.7
.4
.2
.7
.9
.3
.5
.1
.3
.9
.2
.3
.3
.3
.3
.6
.3
.8
25
24
21
40
47
32
9
26
42
48
24
14
14
16
18
19
17
18
16
17
19
16
17
21
20
21
19
22
18
16
11
6
13
17
20
13
14
14
13
16
15
16
58
.5
.2
.6
.4
.7
.7
.8
.9
.6
.2
.1
.5
.7
.6
.7
.4
.2
.8
.6
.3
.9
.8
.4
.9
.8
.8
.8
.6
.6
.4
.4
41
48
45
44
16
60
72
48
47
78
25
30
71
4
43
102
14
16
21
15
12
9
9
12
14
15
9
7
5
7
8
6
7
7
5
3
8
7
6
10
8
6
.9
.9
.4
.2
.8
.3
.2
.1
.0
.6
.9
.7
.9
.5
.6
.2
.8
.8
.3
.3
.3
.5
.1
.6
.8
.3
14
79
57
58
58
88
70
83
16
46
46
16
52
104
36
42
62
2.2
8.3
16.1
18.3
10.4
9.0
31
32
32
37
48
88
78
44
44
45
88
- 131
-------
Appendix B (continued)
WIND72 67 15 12 29 47 17
WIND72 70 26 13 28 83 73
WIND72 30 18 43 33 93 69
WIND72 0 20 17 25 63
WIND72 75 39 27 46 33
WIND72 33 49 22 48 57
WIND72 30 26 22 39 58
WIND72 35 21 19 99 58
WIND72 31 39 53 73 68
WIND72 36 34 20 39 17
WIND72 30 28 10 46 56
WIND72 50 27 37 40 56
WIND72 66 18 30 60 56
WIND72 66 15 35 27 56
WIND72 78 19 22 78 57
WIND72 110 20 18 73 90
WIND72 106 26 32 16 65
WIND72 106 24 50 103 80
WIND72 53 44 61 38 68
WIND72 39 52 31
7207019000000000000000000000000000000000000000000000000000000000000000000000000
7207029000000000000000000000000000000000000000000000000000000000000000000000000
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7207034000000000000000000000000000000000000000000000000000000000000000000000000
7207035000000000000000000000000000000000000000000000000000000000000000000000000
7207036000000000000000000000000000000000000000000000000000000000000000000000000
7207037000000000000000000000000000000000000000000000015040001000100000000000000
7207038000000000000000000000000000000000000000000000000000000000000000000000000
7207041000000000000000000000000000000000000000000000000000000000000000000000000
7207042000000000000000000000000000000000000000000000000000000000000000000000000
7207043000000000000000000000000000000000000000000000000000000000000000000000000
7207044000000000000000000000000000000000000000000000000000000000000000000000000
7207045000000000000000000000000000000000000000000000000000000000000000000000000
7207046000000000000000000000000000000000000000000000000000000000000000000000000
7207047000000000000000000000000000000000000000000000000000101000001000100010000
7207048000000000000000000000000000000000000000000000000000000000000000000000000
7207051000010050801010000000000010000000000000000000000000000000000000000000000
720705200000000000000000000000000000000000000000000000Q.OOOOOOOOOOOOOOOOOOOOOOOO
7207053000000000000000000000000000000000000000000000000000000000000000000000000
7207054000000000000000000000000000000000000000000000000000000000000000000000000
7207055000000000000000000000000000000000000000000000000000000000000000000000000
7207056000000000000000000000000000000000000000000000000000000000000000000000000
7207057000000000000000000000000000000000000000000000000000000000000000000000000
7207058000000000000000000000000000000000000000000000000000000000000000000000000
7207069000000000000000000000000000000000000000000000000000000000000000000000000
7207079000000000000000000000000000000000000000000000000000000000000000000000000
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7207099000000000000000000000000000000000000000000000000000000000000000000000000
720710900000000000000000000000000000000000000 000000000000000000000000000000000
7207119000000000000000000000000000000000000000000000000000000000000000000000000
- 132 -
-------
Appendix B (continued)
7207129000000000000000000000000000000000000000000000000000000000000000000000000
7207139000000000000000000000000000000000000000000000000000000000000000000000000
7207149000000000000000000000000000000000000000000000000000000000000000000000000
7207159000000000000000000000000000000000000000000000000000000000000000000000000
7207169000000000000000000000000000000000000000000000000000000000000000000000000
7207179000000000000000000000000000000000000000000000000000000000000000000000000
7207189000000000000000000000000000000000000000000000000000000000000000000000000
7207199000000000000000000000000000000000000000000000000000000000000000000000000
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7207243000000000000000000000000000000000000000000000000000000000000000000000000
7207244000000000000000000000000000000000000000000000000000000000000000000000000
7207245000000000000000000000000000000000000000000000000000000000000000000000000
7207246000000000000000000000000020300000000000000000000000000000000000000000000
7207247000000000000000000000000000000000000000000000000000000000000000000000000
7207248000000000000000000000000000000000000000000000000000000000000000000000000
7207259000000000000000000000000000000000000000000000000000000000000000000000000
7207269000000000000000000000000000000000000000000000000000000000000000000000000
7207279000000000000000000000000000000000000000000000000000000000000000000000000
7207281000000000000000000000000000000000000000000000000000000000000000000000000
7207282000000000000000000000000000000000000000000000000000000000000000000000000
72072830000000000000000000000000000000000000000000000000QOOOOOOOOOOOOOOOOOOOOOO
7207284000000000000000000000000000000000000000000000000000000000000000000000000
7207285000000000000000000000000000000000000000000000000000000000000000000000000
7207286000000000000000000000000020101010000000000000000000000000000000004110301
7207287010000000000000000000000000000000000000000000000082121200200020001000000
7207288000000000000000000000000000000000000000000000000000000000000000000000000
7207299000000000000000000000000000000000000000000000000000000000000000000000000
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7207311000000000000000000000000000000000000000000000000000000000000000000000000
7207312000000000000000000000000000000000000000000000000000000000000000000000000
7207313000000000000000000000000000000000000000000000000000000000000000000000000
7207314000000000000000000000000000000000000000000000000000000000000000000000000
7207315000000000000000000000000000000000000000000000000000000000000000000000000
7207316000000000000000000000000000000000000002524010301010101040100000000000000
7207317000000000000000000000000000000000000000000000000000000000000000000000000
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7208079000000000000000000000000000000000000000000000000000000000000000000000000
7208089000000000000000000000000000000000000000000000000000000000000000000000000
- 133 -
-------
Appendix B (continued)
7208091000000000000000000000000000000000000000000000000000000000000000000000000
7208092000000000000000000000000000000000000000000000000000000000000000000000000
7208093000000000000000000000000000000000000000000000000000000000000000000000000
7208094000000000000000000000000000000000000000000000000000000000000000000000000
7208095000000000000000000000000000000000000000000000000000000000000000000000000
7208096000000000000000000000000000000000000000000000000000000000000000000000000
720809700000000000000000000000000000000000000000000000000 000000003070302050400
7208098000000000000000000000000000000000000000000000000000000000000000000000000
7208101000000000000000000000000000000000000000000000000000000000000000000000000
7208102000000000000000000000000000000000000000000000000000000000000000000000000
7208103000000000000000000000000000000000000000000000000000000000000000000000000
7208104000000000000000000000000000000000000000000000000000000000000000000000000
7208105000000000000000000000000000000000000000000000000000000000000000000000000
7208106000000000000000000000000000000000000000000000000000000000000000000000000
7208107000000000000000000000000000000000000000000000000000503010000000012162417
7208108150900000000000000000000000000000000000000000000000000000000000000000000
7208111000000000000000000000000000000000000000000000000000000000000000000000000
7208112000000000000000000000000000000000000000000000000000000000000000000000000
7208113000000000000000000001103010100000000000000000000000000000000000000000000
7208114000000000000000000000000000000000000000000000000000000000000000000000000
7208115000000000000000000000000000000000000000000000000000000000000000000000000
7208116000000000000000000000000000000000000000000000000000000000000000000000000
7208117000000000000000000000000000000000000000000000000000000000000000000000000
7208118000000000000000000000000000000000000000000000000000000000000000000000000
7208129000000000000000000000000000000000000000000000000000000000000000000000000
7208139000000000000000000000000000000000000000000000000000000000000000000000000
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7208159000000000000000000000000000000000000000000000000000000000000000000000000
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7208189000000000000000000000000000000000000000000000000000000000000000000000000
7208199000000000000000000000000000000000000000000000000000000000000000000000000
7208209000000000000000000000000000000000000000000000000000000000000000000000000
7208219000000000000000000000000000000000000000000000000000000000000000000000000
7208229000000000000000000000000000000000000000000000000000000000000000000000000
7208231000000000000000000000000000000000000000000000000000000000000000000000000
7208232000000000000000000000000000000000000000000000000000000000000000000000000
7208233000000000000000000000000000000000000000000000000000000000000000000000000
7208234000000000000000000000000000000000000000000000000000000000000000000000000
7208235000000000000000000000000000000000000000000000000000000000000000000000000
7208236000000000000000000000000000000000000000000000033090402000000000000000000
7208237000000000000000000000000000000000000000000000000000000000000000000000000
7208238000000000000000000000000000000000000000000000000000000000000000000000000
7208249000000000000000000000000000000000000000000000000000000000000000000000000
7208259000000000000000000000000000000000000000000000000000000000000000000000000
7208269000000000000000000000000000000000000000000000000000000000000000000000000
7208271000000000000000000000000000000000000000000000000000000000000000000000000
7208272000000000000000000000000000000000000000000000000000000000000000000000000
- 134 -
-------
Appendix B (continued)
7208273000000000000000000000000000000000000000000000000000000000000000000000000
7208274000000000000000000000000000000000000000000000000000000000000000000000000
7208275000000000000000000000000000000000000000000000000000000000000000000000000
7208276000000000000000000000000000000000000000505070700000000000000000000000000
7208277000000000000000000000000000000000000000000000000000000000000000000000000
7208278000000000000000000000000000000000000000000000000000000000000000000000000
7208289000000000000000000000000000000000000000000000000000000000000000000000000
7208299000000000000000000000000000000000000000000000000000000000000000000000000
7208309000000000000000000000000000000000000000000000000000000000000000000000000
7208319000000000000000000000000000000000000000000000000000000000000000000000000
7209019000000000000000000000000000000000000000000000000000000000000000000000000
7209029000000000000000000000000000000000000000000000000000000000000000000000000
7209039000000000000000000000000000000000000000000000000000000000000000000000000
7209041000000000000000000000000000000000000000000000000000000000000000000000000
7209042000000000000000000000000000000000000000000000000000000000000000000000000
7209043000000000000000000000000000000000000000000000000000000000000000000000000
7209044000000000000000000000000000000000000000000000000000000000000000000000000
7209045000000000000000000000000000000000000000000000000000000000000000000000000
7209046000000000000000203020303050701010004050901000000010300000000000000010203
7209047000101030101010001030100000000000100000000000000000000000000000000000000
7209048000000000000000000000000000000000000000000000000000000000000000000000000
7209051000000000000000000000000000000000000000000000000000000000000000000000000
7209052000000000000000000000000000000000000000000000000000000000000000000000000
7209053000000000000000000000000000000000000000000000000000000000000000000000000
7209054000000000000000000000000000000000000000000000001010201010202010000000000
7209055000000000000000000000000000000000000000000000000000001010101000000000000
7209056000000000000000000000000000000000000000000000000000000000000000000000000
7209057000000000000000000000000000000000000000000000000000000000000000000000000
7209058000000000000000000000000000000000000000000000000000000000000000000000000
7209069000000000000000000000000000000000000000000000000000000000000000000000000
7209079000000000000000000000000000000000000000000000000000000000000000000000000
7209089000000000000000000000000000000000000000000000000000000000000000000000000
7209099000000000000000000000000000000000000000000000000000000000000000000000000
7209109000000000000000000000000000000000000000000000000000000000000000000000000
7209119000000000000000000000000000000000000000000000000000000000000000000000000
7209129000000000000000000000000000000000000000000000000000000000000000000000000
7209139000000000000000000000000000000000000000000000000000000000000000000000000
7209149000000000000000000000000000000000000000000000000000000000000000000000000
7209159000000000000000000000000000000000000000000000000000000000000000000000000
7209169000000000000000000000000000000000000000000000000000000000000000000000000
7209171000000000000000000000000000000000000000000000000000000000000000000000000
7209172000000000000000000000000000000000000000000000000000000000000000000000000
7209173000000000000000000000000000000000000000000000000000000000000000000000000
7209174000000000000000000000000000000000000000000000004010000000000000000000000
7209175000000000000000000000000000000000000000000000000000000000000000000000000
7209176000000000000000000000000000000000000000000000000000000000000000000000000
7209177000000000000000000000000000000000000000000000000000000000000000000000000
7209178000000000000000000000000000000000000000000000000000000000000000000000000
- 135 -
-------
Appendix B (continued)
7209181000000000000000000000000000000000000000000000000000000000000000000000000
7209182000000000000000000000000000000000000000000000000000000000000000000000000
7209183050302000000000000000000000000000000000000000000000000000000000000000000
7209184000000000000000000000000000000000000000000000000000000000000000000000000
7209185000000000000000000000000000000000000000000000000000000000000000000000000
7209186000000000000000000000000000000000000000000000000000000000000000000000000
7209187000000000000000000000000000000000000000000000000000000000000000000000000
7209188000000000000000000000000000000000000000000000000000000000000000000000000
7209199000000000000000000000000000000000000000000000000000000000000000000000000
7209209000000000000000000000000000000000000000000000000000000000000000000000000
7209219000000000000000000000000000000000000000000000000000000000000000000000000
7209229000000000000000000000000000000000000000000000000000000000000000000000000
7209239000000000000000000000000000000000000000000000000000000000000000000000000
7209249000000000000000000000000000000000000000000000000000000000000000000000000
7209259000000000000000000000000000000000000000000000000000000000000000000000000
7209269000000000000000000000000000000000000000000000000000000000000000000000000
7209279000000000000000000000000000000000000000000000000000000000000000000000000
7209289000000000000000000000000000000000000000000000000000000000000000000000000
7209299000000000000000000000000000000000000000000000000000000000000000000000000
7209301000000000000000000000000000000000000000000000000000000000000000000000000
7209302000000000000000500000212040200000000000000000101020000000101000000000000
7209303000100010204000000000000010103010000000000000000000000000000000000000000
7209304000000000000000000000000000000000000000000000000000000000000000000000000
7209305000000000000000000000000000000000000000000000000000000000000000000000000
7209306000000000000000000000000000000000000000000000000000000000000000000000000
7209307000000000000000000000000000000000000000000000000000000000000000000000000
7209308000000000000000000000000000000000000000000000000000000000000000000000000
7210019
7210029
7210039
7210049
7210051
7210052 2 1
7210053 11 121 114 1133221
7210054
7210055
7210056
7210057
7210058
7210069
7210079
7210089
7210099
7210109
7210119
7210129
7210131
7210132
- 136 -
-------
Appendix B (continued)
7210133 7
7210134
7210135
7210136
7210137
7210138
7210149
7210159
7210169
7210179
7210189
7210199
7210209
7210219
7210229
7210231
7210232
7210233
7210234
7210235
7210236
7210237 5 4 1111
7210238 12431 131
7210249
7210259
7210269
7210271
7210272
7210273
7210274 1123112232 116 1 3141
7210275 1 1 1 1112 1 7 814 4 3 4 2 141
7210276 11551 41 2 113544321 13221
7210277
7210278 2 2
7210281 2 2
7210282
7210283
7210284
7210285
7210286
7210287
7210288
7210299
7210309
7210319
7211019
7211029
7211031
- 137 -
-------
Appendix B (continued)
7211032 1 4
7211033 1 2 4
7211034
7211035
7211036
7211037
7211038
7211049
7211059
7211069
7211071
7211072
7211073 1 1 1
7211074 21131 236 12
7211075 2 121 111 2
7211076 2481 121134433111
7211077 224632
7211078
7211089
7211099
7211109
7211119
7211129
7211131
7211132
7211133
7211134
7211135
7211136
7211137
7211138000017051800000101020000000101040000000003010101010000000000000000000000
7211149
7211159
7211169
7211179
7211189
7211191 100000000
7211192 1
7211193 102 1 101020000
72111940001 1 201 101 101010001
7211195 10100020004040001010204020603
72111960300020404000100010201010402010101 403020301 101
72111970101
7211198
7211209
7211219
7211229
7211239
- 138 -
-------
Appendix B (continued)
7211249
7211251
7211252
7211253 101030101010201010101010101010202010101000000010101
72112540101 1010102 202010101010101010101010101 1 1
7211255020100050501000101010102020 204
7211256
7211257
7211258
7211269
7211279
7211289
7211299
7211301
7211302
7211303 50101 10101030302
7211304
7211305
7211306
7211307
7211308
7212019
7212029
7212039
7212049
7212051
7212052
7212053
7212054
7212055
7212056
7212057
7212058
7212061 11111311 2222
7212062 1
7212063
7212064 1 1
7212065
7212066
7212067
7212068
7212079
7212081
7212082
7212083
7212084 5 2 1
7212085
7212086
202010000030204010303030101 1010101
222221 1121111111
2 2
2111
1111
- 139 -
-------
Appendix B (continued)
7212087
7212088
7212099
7212109
7212119
7212129
7212139
7212141
7212142
7212143
7212144 232 1
7212145 1 1 1
7212146
7212147 722222222
7212148 122782792
7212151 111 11
7212152 11851 121
7212153
7212154 2 2 210 5 3 2 1 1
7212155
7212156
7212157
7212158
7212169
7212179
7212189
7212199
7212201
7212202
7212203
7212204
7212205
7212206
7212207
7212208
7212211 423511152
7212212 11111
7212213 1211 11 1
7212214 111 1123
7212215
7212216
7212217 11111
7212218 1111
7212221
7212222
7212223
7212224
7212225
1 111
1211
22222221
3 410 31212 899151 2 2 11 1
1101010 823232323237311121
1112 315 3114232411233411
1111 1 211
3
1112 11
7 3 1
811131 4432415181661, 1
11 11
1 1 1 1 1 1 1 111111
3133111211235213221111
333
11111 24
11111 1
1111111111
2247
111 4
1
11115
11111
334 2
4111
1
1
1 I 3
1111
- 140 -
-------
Appendix B (continued)
7212226
7212227
7212228
7212239
7212249
7212259
7212269
7212279
7212289
7212299
7212309
7212311
7212312
7212313 2
7212314
7212315
7212316
7212317
7212318
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
1 1
1 1
108
54
43
49
76
27
27
27
0
54
27
27
32
43
43
38
22
38
54
86
76
81
97
86
43
70
43
59
65
1 1 1
1
50
31
132
31
82
19
63
44
88
63
50
44
31
19
69
120
76
25
57
57
101
145
132
101
82
25
82
88
322323112121
21421 212111 111 1111
2
11111 11111 11111
- 141 -
-------
Appendix B (continued)
53
0
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
TEMP72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
24.4
25.6
24.4
25.6
23.9
21.4
20.6
22.2
22.8
22.2
23.9
24.7
27.2
25.9
27.4
26.1
25.9
26.0
26.3
25.3
26.3
28.9
28.3
28.9
26.6
26.3
25.4
25.4
25.1
23.2
21.6
66
70
68
72
69
67
64
67
64
67
64
67
70
30
0
23.0
25.0
24.3
26.1
25.9
24.9
26.7
26.5
24.7
24.7
23.4
25.3
24.4
24.4
25.8
26.7
24.9
27.4
27.3
27.7
24.9
23.6
22.7
24.4
25.2
24.6
24.8
24.4
25.4
23.8
22.7
28
79
46
58
59
31
75
71
71
40
35
15
26
18
20
22.9
22.5
24.1
25.0
20.8
20.0
19.9
20.9
24.6
24.4
19.3
21.1
21.7
23.4
25.2
24.7
24.9
23.3
25.5
26.1
22.3
23.9
24.2
23.3
23.3
23.3
25.3
24.6
26.3
20.8
25
24
21
40
47
32
9
26
42
48
24
12
13
43
17
14.5
14.2
16.6
18.4
19.7
17.7
18.8
16.9
17.6
19.2
16.1
17.5
21.7
20.6
21.7
19.4
22.2
18.8
16.6
11.3
6.9
13.8
17.4
20.9
13.8
14.8
14.8
13.6
16.6
15.4
16.4
41
48
45
44
16
60
72
48
47
78
25
29
28
33
25
14.9
16.9
21.4
15.2
12.8
9.3
9.2
12.1
14.0
15.6
9.9
7.7
5.9
7.5
8.6
6.2
7.8
7.8
5.3
3.3
8.3
7.5
6.1
10.6
8.8
6.3
14
79
57
58
58
88
70
83
16
46
46
47
83
93
63
2.2
8.3
16.1
18.3
10.4
9.0
31
32
32
37
48
88
78
44
44
45
88
17
73
69
- 142 -
-------
Appendix B (continued)
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
7301019
7301029
7301031
7301032
7301033
7301034 111112
7301035 1 1
7301036
7301037
7301038
7301041 1 1
7301042
7301043
7301044
7301045
7301046
7301047
7301048
7301051
7301052
7301053
7301054
7301055
7301056
7301057 7 3 1
7301058 11111
7301061 1111111111
7301062 11111
7301063
7301064
73UJ.C3I
7301066
75
33
30
35
31
36
30
50
66
66
78
110
106
106
53
39
1 1 1
1 2
1 1 1
2 2
11111
311111223
39
49
26
21
39
34
28
27
18
15
19
20
26
24
44
52
1 1
1
27
22
22
19
53
20
10
37
30
35
22
18
32
50
61
1
3
1 1
46
48
39
99
73
39
46
40
60
27
78
73
16
103
38
31
1 1
2 1
1
1
1 1 1
3 2
33
57
58
58
68
17
56
56
56
56
57
90
65
80
68
1
1 1
1
1 1 1
1 1
1 1
- 143 -
-------
Appendix B (continued)
7301067
7301068
7301071
7301072
7301073
7301074
7301075 11111 1 1111 1 1 1 1 1
7301076 1 111111 111 112112121111.
7301077 111111221222222222222222222222222222
7301078 222222222222221111111112111111111111
7301081 111212121212111111111111
7301082
7301083
7301084
7301085
7301086
7301087
7301088
7301099
7301109
7301119
7301129
7301139
7301149
7301159
7301169
7301179
7301189
7301191
7301192 53 11 11112222111311414121
7301193
7301194
7301195
7301196
7301197
7301198
7301209
7301219
7301229
7301239
7301249
7301251
7301252
7301253
7301254
7301255
7301256
7301257
- 144 -
-------
Appendix B (continued)
7301258 4 111111111
7301261 11334221111 1 11 11 11 11111232422
7301262 4221122222 4111111
7301263
7301264
7301265
7301266
7301267
7301268
7301279
7301281
7301282
7301283
7301284
7301285
7301286
7301287 111 11
7301288 1 1 1
7301299
7301309
7301319
7302019
7302029
7302039
7302049
7302059
7302061
7302062
7302063
7302064 3211
7302065
7302066
7302067
7302068
7302079
7302081
7302082
7302083 111 11
7302084 1 1
7302085 1111133211111
7302086
7302087
7302088
7302099
7302101
7302102
7302103
7302104
- 145 -
-------
Appendix B (continued)
7302105
7302106
7302107
7302108
7302119
7302129
7302139
7302149
7302151
7302152 232111111111
7302153 121121112111211111 1 1 1
7302154
7302155
7302156
7302157
7302158
/*
- 146 -
-------
Appendix C PTR MODEL LISTING
//PESTICID JOB (C510,510,3,8),'TONY.PEST10.COMPILED1
/* SERVICE CLASS=B, BLOCK=NIGHT
//STEP1 EXEC FORTHCL,PARM.FORT='OPT=2,MAP,XREF'
//FORT.SYSIN DD *
C
C
C
C PESTICIDE MODEL -- MAIN PROGRAM
C
C
IMPLICIT REAL(L)
C
DIMENSION RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), IEVAP(12,31)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION UZSBMT(5), RESBMT(5), SRGXMT(5), SRERMT(5)
DIMENSION STSMET(5), SASMET(5), SCSMET(5), SDSMET(5)
DIMENSION UTSMET(5), UASMET(5), UCSMET(5), UDSMET(5)
DIMENSION ERSNMT(5)
DIMENSION RAIN(288), IRAIN(288), MNAM(24), ROSB(5)
DIMENSION PRTTOT(5), PRTTOM(5), IWIND(12,31), ITEMP(12,31)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, IFLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCST, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
- 147 -
-------
Appendix c (continued)
INTEGER BGNDAY, BGNMON, BGNYR, ENDDAY, ENDMON, ENDYR
INTEGER DYSTRT, DYEND, YEAR, MONTH, DAY, H, HYCAL, TIME
INTEGER YR, MO, DY, CN, TF, PRNTKE, PRINT, DA, APMODE, UNIT
INTEGER INPUT
C
REAL IRC, NN, KV, K24L, KK24, INFIL, INTER
REAL IPS, ICS, K24EL, K3, NEPTOM, NEPTOT
REAL JRER, KRER, JSER, KSER
REAL M, MM, N, NI, K, MUZ, MU, ML, MLZ
REAL INFTOM, INFTOT, MOLEWT, ITEMP
REAL MMPIN, METOPT, KGPLB, QMETRC
REAL UZSMET, LZSMET, SGWMET, SCEPMT, RESSMT, TWBLMT
REAL SRGXTM, SRRTMT, SASTMT, SCSTMT, SDSTMT, STSTMT
REAL UTSTMT, UASTMT, UCSTMT, UDSTMT, LSTRMT, LASMET
REAL LCSMET, LDSMET, GSTRMT, GASMET, GCSMET, GDSMET
REAL VLTMMT, TPBALM, ERSNTT, EIMMET, ERSNMT
REAL*8 PNAME, WSNAME
C
DATA PRINT/I/
DATA. COUNT, TIMAT, TIMST, TIMAP/4*0.0/
DATA ICS, OFS, TPBAL, DEGT/4*0.0/
DATA PRTTOM, PRTTOT, PRT, VOLT, TOTPAP/13*0.0/
C
C
C DATA INPUT -- SINGLE-VALUED VARIABLES
C
NAMELIST /HYCL/ HYCAL, HYMIN, UNIT, INPUT
NAMELIST /PRNT/ PRINT
NAMELIST /STRT/ BGNDAY, BGNMON, BGNYR
NAMELIST /ENDD/ ENDDAY, ENDMON, ENDYR
NAMELIST /TRVL/ INTRVL
NAMELIST /LND1/ UZSN, LZSN, INFIL, INTER
NAMELIST /LND2/ IRC, NN, L, SS, A, UZS
NAMELIST /LND3/ LZS, SGW, GWS, KV, K24L, KK24
NAMELIST /LND4/ ICS, OFS, IFS, K24EL, K3, EPXM
NAMELIST /PEST/ SSTR, APMODE, DEPTH
NAMELIST /NAME/ PNAME, WSNAME
NAMELIST /CROP/ COVMAX, TIMST, TIMAP, TIMAT, TIMHAR
NAMELIST /SMDL/ JRER, KRER, JSER, KSER, SRERI
NAMELIST /AMDL/ CMAX, DD, BULKD, K, N, AREA
NAMELIST /VOL1/ DIFC, TDIFC, CBDIF
NAMELIST /VOL2/ MOLEWT, APFAC, BPFAC, WCFAC
NAMELIST /DEG1/ DEGCON
C
C INPUT PARAMETER DESCRIPTION
C HYCAL : INDICATES WHAT FACTORS ARE TO BE SIMULATED
C = -1 CALIBRATION RUN WITH PESTICIDE
- 148 -
-------
Appendix C (continued)
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
=
HYMIN
INPUT
UNIT
PRINT
BGNDAY
ENDDAY
INTRVL
UZSN
LZSN
INFIL
INTER
IRC
NN
L
SS
A
UZS
LZS
SGW
GWS
KV
K24L
KK24
ICS
OFS
IPS
K24EL
K3
EPXM
PNAME
WSNAME
SSTR
APMODE
DEPTH
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
BGNMON, BGNYR
ENDMON, ENDYR
TIME INTERVAL
0 PRODUCTION RUN
1 CALIBRATION RUN, NO PESTICIDES
MINIMUM FLOW FOR OUTPUT DURING A TIME INTERVAL (CFS, CMS)
INPUT UNITS; ENGLISH(-l), METRIC(l)
OUTPUT UNITS; ENGLISH(-l), METRIC(l), BOTH(O)
DENOTES FREQUENCY OF OUTPUT; EACH INTERVAL(-l),
EACH HOUR(O), OR EACH DAY(l)
: DATE SIMULATION BEGINS
: DATE SIMULATION ENDS
( 5 OR 15 MINUTES)
NOMIMAL UPPER ZONE STORAGE (IN, MM)
NOMINAL LOWER ZONE STORAGE (IN, MM)
INFILTRATION RATE (IN/HR, MM/HR)
INTERFLOW PARAMETER, ALTERS RUNOFF TIMING
INTERFLOW RECESSION RATE
MANNING'S N FOR OVERLAND FLOW
LENGTH OF OVERLAND FLOW TO CHANNEL (FT, M)
AVERAGE OVERLAND FLOW SLOPE
FRACTION OF AREA THAT IS IMPERVIOUS
INITIAL UPPER ZONE STORAGE (IN, MM)
INITIAL LOWER ZONE STORAGE (IN, MM)
INITIAL GROUNDWATER STORAGE (IN, MM)
GROUNDWATER SLOPE
PARAMETER TO ALLOW VARIABLE RECESSION RATE FOR GROUNDWATER
DISCHARGE
FRACTION OF GROUNDWATER RECHARGE PERCOLATING TO DEEP
GROUNDWATER
GROUNDWATER RECESSION RATE
INITIAL INTERCEPTION STORAGE (IN, MM)
INITIAL OVERLAND FLOW STORAGE (IN, MM)
INITIAL INTERFLOW STORAGE (IN, MM)
FRACTION OF WATERSHED AREA WHERE GROUNDWATER IS WITHIN
REACH OF VEGETATION
INDEX TO ACTUAL EVAPORATION
MAXIMUM INTERCEPTION STORAGE (IN, MM)
PESTICIDE NAME (8 CHARACTERS)
WATERSHED NAME (8 CHARACTERS)
PESTICIDE APPLICATION FOR EACH ZONE (LB, KG)
APPLICATION MODE; SURFACE APPLIED(O), SOIL INCORPORATED(l)
DEPTH OF SOIL INCORPORATION (IN, MM)
MAXIMUM SURFACE AREA COVERED BY VEGETATION
TIME SIMULATION STARTS (JULIAN DAY)
TIME OF PESTICIDE APPLICATION (JULIAN DAY)
TIME OF CROP MATURITY (JULIAN DAY)
TIME OF HARVEST (JULIAN DAY)
EXPONENT OF RAINFALL INTENSITY IN SOIL SPLASH EQUATION
COEFFICIENT IN SOIL SPLASH EQUATION
EXPONENT OF OVERLAND FLOW IN SURFACE SCOUR EQUATION
- 149 -
-------
Appendix C (continued)
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
KSER
SRERI
CMAX
DD
BULKD
K
N
AREA
DIFC
TDIFC
CBDIFC
MOLEWT
APFAC,
WCFAC
DEGCON
C
C
C
COEFFICIENT IN SURFACE SCOUR EQUATION
INITIAL FINES DEPOSIT (TONS, TONNES)
MAXIMUM SOLUIBILITY OF PESTICIDE IN WATER (LB/LB)
PERMANENTLY FIXED CAPACITY (LB PESTICIDE/LB SOIL)
BULK DENSITY OF PESTICIDE (6/CM(3))
COEFICIENT IN FREUNDLICH ADSORPTION CURVE
EXPONENT IN FREUNLICK ADSORPTION CURVE
WATERSHED AREA (AC, HA)
PESTICIDE DIFFUSION COEFFICIENT (MM(2)/WK)
TEMPERATURE AT WHICH DIFC WAS MEASURED (DEGREES CELCIUS)
COEFICIENT FOR DIFFUSION
MOLECULAR WEIGHT OF PESTICIDE (G/MOLE)
BPFAC : CONSTANTS FOR PRESSURE AND TEMPERATURE ADJUSTMENT
WIND CALIBRATION FACTOR
FIRST ORDER PESTICIDE DECAY RATE (PER DAY)
READ (5,HYCL)
READ (5,PRNT)
READ (5,STRT)
READ (5,ENDD)
READ (5JRVL)
READ .(5.LND1)
READ (5,LND2)
READ (5,LND3)
READ (5,LND4)
READ (5,PEST)
READ (5,NAME)
READ (5,CROP)
READ (5,SMDL)
READ (5.AMDL)
READ (5,VOL1)
READ (5,VOL2)
READ (5.DEG1)
PRINTING OF INPUT PARAMETERS
1001
1002
C
C
IF (HYCAL) 1002, 1001, 1002
WRITE (6,1091)
WRITE (6,1092)
GO TO 1003
WRITE (6,1093)
WRITE (6,1092)
1003 WRITE (6,1107) WSNAME
WRITE (6,1106) PNAME
- 150 -
-------
Appendix C (continued)
IF (INPUT .EQ. -1) WRITE (6,1108)
IF (INPUT .EQ. 1) WRITE (6,1109)
C
C
C
IF (APMODE .EQ. 1
IF (APMODE .EQ. 0
WRITE (6,1092)
WRITE (6,1105)
WRITE (6,1104)
WRITE (6,STRT)
WRITE (6,1092)
WRITE 6.ENDD
WRITE (6,1092)
WRITE (6.TRVL)
WRITE (6,1092)
WRITE (6.LND1)
WRITE (6,1092)
WRITE (6.LND2)
WRITE (6,1092)
WRITE (6,LND3)
WRITE (6,1092)
WRITE (6,LND4)
WRITE (6,1092)
WRITE (6,PEST)
WRITE (6,1092)
WRITE (6,CROP)
WRITE (6,1092)
WRITE (6.SMDL)
WRITE (6,1092)
WRITE (6,AMDL)
WRITE (6,1092)
WRITE (6.VOL1)
WRITE (6,1092)
WRITE (6.VOL2
WRITE (6,1092)
WRITE (6,DEG1)
WRITE (6,1092)
WRITE (6,HYCL)
WRITE (6,1092)
WRITE (6,PRNT)
WRITE (6,1092)
IF (INPUT .EQ. -1) GO TO 950
CONVERSION OF METRIC INPUT DATA TO ENGLISH UNITS
HYMIN= HYMIN*35.3
UZSN = UZSN/MMPIN
LZSN = LZSN/MMPIN
INFIL= INFIL/MMPIN
L = L*3.281
UZS = UZS/MMPIN
- 151 -
-------
Appendix C (continued)
LZS =
SGW =
ICS =
OFS =
IPS =
EPXM =
DEPTH=
SRERI=
AREA =
DO 501
LZS/MMPIN
SGW/MMPIN
ICS/MMPIN
OFS/MMPIN
IFS/MMPIN
EPXM/MMPIN
DEPTH/MMPIN
SRERI/METOPT
AREA*2.471
1 = 1,5
501 SSTR(I)=SSTR(I)/0.4536
C
C
C
ADJUSTMENT OF
950 H = 60/INTRVL
CONSTANTS
TIMFAC = INTRVL
INTRVL = 24*H
C
KRER = KRER*H**(JRER-1.)
KSER = KSER*H**(JSER-1.)
NI = l./N
C
MM = BULKD/96.
MU = BULKD*((DEPTH 0.125)/12.)
ML = BULKD*6.
M = MM*43560.*AREA*0.2
MUZ = MU*43560.*AREA*0.2
MLZ = ML*43560.*AREA
C
DO 1000 1= 1,5
1000 TOTPAP = TOTPAP + SSTR(I)
C
IFLAG=1
COUNT = 0.
IF (APMODE .EQ. 0) GO TO 1004
CONCIU = (TOTPAP*6./((MUZ + M)*5.))*1000000.*(BULKD/62.43)
C
DO 999 1= 1,5
USTR(I) = SSTR(I)
SSTR(I) = SSTR(I)*(0.125/DEPTH)
999 USTR(I) = USTR(I) - SSTR(I)
CADIF = DIFC/(EXP(CBDIF*TDIFC))
C
1004 FP = DD*M
FPUZ = DD*MUZ
FPLZ = DD*MLZ
- 152 -
-------
Appendix C (continued)
1005
DO 1005 1=1,5
SRER(I) = SRERI*0.2
UZSB(I) = UZS
RESB(I) = OFS
SRGX(I) = IPS
CONTINUE
RESS1 = OFS
RESS = OFS
SCEP = ICS
SCEP1 = ICS
SRGXT = IFS
SRGXT1 = IFS
SGW1 = SGW
C
C
C
PROGRAM EXECUTION
998
DO 1070 YEAR=BGNYR,ENDYR
IF (YEAR .EQ. BGNYR) GO TO 998
TIMST = 0.0
TIMAP = 0.0
TIMAT = TIMAT - 365.
IF (TIMAT .LE. 0.0) TIMAT = 0.0
TIMHAR = TIMHAR - 365.
IF (TIMHAR .LE. 0.0) TIMHAR = 0.0
COUNT = TIMST
COVER =0.0
MNSTRT = 1
MNEND = 12
IF (YEAR .EQ. BGNYR) MNSTRT = BGNMON
IF (YEAR .EQ. ENDYR) MNEND = ENDMON
C
C
C EVAPORATION, TEMP, AND WIND
C
DO 1007 NO = 1,31
DO 1006 MK = 1,12
IEVAP(MK,NO) = 0
ITEMP(MK,NO) = 0.0
1006 IWIND(MK,NO) = 0
1007 CONTINUE
DATA INPUT
C
C
1008
DO 1008 DA = 1,31
READ (5,1264) (IEVAP(MN,DA), MN =1,12)
- 153 -
-------
Appendix C (continued)
DO 1013 DA = 1,31
1013 READ(5,1265) (ITEMP(MN,DA), MN=1,12)
C
DO 1014 DA = 1,31
1014 READ(5,1264) (IWIND(MN.DA), MN=1,12)
IF (INPUT .EQ. -1) GO TO 625
DO 700 DA=1,31
DO 650 MN=1,12
IEVAP(MN,DA) = IEVAP(MN,DA)*3.937
IWIND(MN,DA) = IWIND(MN,DA)*0.6214
650 CONTINUE
700 CONTINUE
C
C
625 IF (INPUT .EQ. 1) GO TO 628
DO 627 DA=1,31
DO 626 MN=1,12
ITEMP(MN,DA) = (ITEMP(MN,DA) - 32.)*.5556
626 CONTINUE
627 CONTINUE '
628 DO 1060 MONTH=MNSTRT,MNEND
IF (HYCAL .EQ. 0) GO TO 1009
WRITE (6,1263)
WRITE (6,382)
WRITE (6,1092)
1009 DYSTRT = 1
IF (MOD(YEAR,4)) 1012, 1010, 1012
1010 GO TO (31,29,31,30,31,30,31,31,30,31,30,31), MONTH
1012 GO TO (31,28,31,30,31,30,31,31,30,31,30,31), MONTH
28 DYEND = 28
GO TO 1015
29 DYEND = 29
GO TO 1015
30 DYEND = 30
GO TO 1015
31 DYEND = 31
C
1015 IF (YEAR .NE. BGNYR) GO TO 1017
IF (MONTH .NE. BGNMON) GO TO 1017
DYSTRT = BGNDAY
C
1017 IF (YEAR .NE. ENDYR) GO TO 1018
IF (MONTH .NE. ENDMON) GO TO 1018
DYEND = ENDDAY
C
1018 DO 1050 DAY=DYSTRT,DYEND
IF ((MONTH .EQ. 1) .AND. (DAY .EQ. 1)) COUNT = 1.
- 154 -
-------
Appendix C (continued)
TIME = 0
RAINT = 0.0
EP = IEVAP(MONTH,DAY)/1000.
TEMP = ITEMP(MONTH,DAY)
WIND = IWIND(MONTH,DAY)
1016 DO 1019 I=1,INTRVL
IRAIN(I) = 0
RAIN(I) = 0.0
1019 CONTINUE
C
C CROP CANOPY EFFECTS - ASSUMES LINEAR GROWTH TO MAX. % COVER
C
COUNT = COUNT + 1.
IF (COUNT - TIMAP) 1047, 1047, 104'6
1046 IF (COUNT .LT. TIMAT) GO TO 1045
COVER = COVMAX
IF (COUNT .GE. TIMHAR) COVER=0.0
GO TO 1047
1045 COVER = COVMAX*((COUNT-TIMAP)/(TIMAT-TIMAP))
C
C
1047 IF (INTRVL .EQ. 288) GO TO 1021
DO 1020 J=l,8
JK = J*12
JJ - JK - 11
READ (5,1094) YR, MO, DY, CN, (IRAIN(I), I=JJ,JK)
IF (INPUT .EQ. -1) GO TO 704
DO 702 I=JJ,JK
IRAIN(I) = IRAIN(I)*3.937 + 0.5
702 CONTINUE
704 IF (CN .EQ. 9) GO TO 1025
YR = YR + 1900
IT = (YEAR-YR) + (MONTH-MO) + (DAY-DY) + (J-CN)
IF (IT .EQ. 0) GO TO 1020
WRITE (6,1090) J, MONTH, DAY, YEAR, CN, MO, DY, YR
GO TO 1080
1020 CONTINUE
GO TO 1023
C
1021 DO 1022 J=l,8
JK = J*36
JJ = JK - 35
READ (5,1095) YR, MO, DY, CN, (IRAIN(I), I=JJ,JK)
IF (INPUT .EQ. -1) GO TO 708
DO 706 I=JJ,JK
IRAIN(I) =IRAIN(I)*3.937 + 0.5
706 CONTINUE
708 IF (CN .EQ. 9) GO TO 1025
- 155 -
-------
Appendix C (continued)
YR = YR + 1900
IT = (YEAR-YR) + (MONTH-MO) + (DAY-DY) + (J-CN)
IF (IT .EQ. 0) GO TO 1022
WRITE (6,1090) J, MONTH, DAY, YEAR, CN, MO, DY, YR
GO TO 1080
1022 CONTINUE
C
1023 DO 1024 I=1,INTRVL
RAIN(I) = IRAIN(I)/100.
RAINT = RAINT + RAIN(I)
1024 CONTINUE
C
IF (RAINT) 1025, 1025, 1026
C
C
C USE RAIN LOOP IF MOISTURE STORAGES ARE NOT EMPTY
C
1025 IF ((RESS .GE. 0.001).OR.(SRGXT .GE. 0.001)) GO TO 1026
GO TO 1040
C
C
C RAIN LOOP
C
1026 DO 1036 I=1,INTRVL
TIME = TIME + 1
TF = 1
PR = RAIN(I)
C
IMIN = MOD(TIME,H)
IHR = (TIME - IMIN)/H
IMIN = TIMFAC*IMIN
PRNTKE = 0
IF (PRINT) 1027, 1028, 1029
1027 PRNTKE = 1
GO TO 1030
1028 IF (IMIN .LT. 1) PRNTKE = 1
GO TO 1030
1029 IF (IHR .EQ. 24) PRNTKE = 1
C
1030 IF (PRNTKE .EQ. 0) GO TO 1031
IX = 2*MONTH
IZ = IX - 1
C
IF (HYCAL .NE. 0) GO TO 1031
C
1037 WRITE (6,1101) IHR, IMIN, DAY,MNAM(IZ),MNAM(IX),YEAR
WRITE (6,1102)
WRITE (6,1103)
- 156 -
-------
Appendix C (continued)
C
1031 CALL LANDS
IF ((RESS .GE. 0.001).OR.(PR -ST. 0.001)) GO TO 1034
DO 1033 J=l,5
ERSN(J) = 0.0
1033 CONTINUE
IF (PRNTKE .EQ. 0) GO TO 1035
1034 CALL SEDT
1035 IF ((HYCAL .EQ. 1) .OR. (COUNT ,LT. TIMAP)) GO TO 1036
CALL ADSRB1
CALL ADSRB2
CALL ADSRB3
IF (IHR .EQ. 24) GO TO 1038
VOLU = 0.0
VOLS = 0.0
DEGS = 0.0
DEGU = 0.0
GO TO 1036
1038 CALL VOLDEG
1036 CONTINUE
C
GO TO 1050
C
C NO RAIN LOOP
C
C
1040 TF = INTRVL
PR = 0.0
P3 = 0.0
DO 1042 1=1,5
1042 RESBl(I) = 0.0
PRNTKE = 1
IMIN = 00
IHR = 24
IX = 2*MONTH
IZ = IX - 1
IF (HYCAL .NE. 0) GO TO 1043
WRITE (6,1101) IHR, IMIN, DAY, MNAM(IZ), MNAM(IX), YEAR
WRITE (6,1102)
WRITE (6,1103)
C
1043 CALL LANDS
SRERT =0.0
ERSNT =0.0
EIM = 0.0
DO 1041 J=l,5
SRERT = SRERT + SRER(J)
ERSN(J) = 0.0
- 157 -
-------
Appendix C (continued)
1041 CONTINUE
IF (HYCAL .NE. 0) GO TO 1044
IF (UNIT .EQ. 1) GO TO 1081
WRITE (6,1209)
WRITE (6,1210) ERSN, ERSNT
WRITE (6,1211) SRER, SRERT
WRITE (6,1212) EIM
1081 IF (UNIT .EQ. -1) GO TO 1044
C METRIC CONVERSIONS FOR OUTPUT
ERSNTT=ERSNT*METOPT
SRRTMT=SRERT*METOPT
EIMMET=EIM*METOPT
DO 1163 1=1,5
ERSNMT(I)=ERSN(I)*METOPT
SRERMT(I)=SRER(I)*METOPT
1163 CONTINUE
WRITE (6,1208)
WRITE (6,1210) ERSNMT, ERSNTT
WRITE (6,1211) SRERMT, SRRTMT
WRITE (6,1212) EIMMET
1044 IF ((HYCAL .EQ. 1) .OR. (COUNT .LT. TIMAP)) GO TO 1050
CALL ADSRB1
CALL ADSRB2
CALL ADSRB3
CALL VOLDEG
C
1050 CONTINUE
C
C MONTHLY SUMMARY
C
DO 1051 1= 1,5
1051 PRTTOM(I) = PRSTOM(I) + PROTOM(I) + UPITOM(I)
C
C
VOLTOM = VOLSOM + VOLUOM
VOLT = VOLT + VOLTOM
DEGTOM = DEGSOM + DEGUOM + DEGLOM
DEGT = DEGT + DEGTOM
C
PRTM = SPROTM + SPRSTM + UPRITM
PRT = PRT + PRTM
C
PBAL = STST + UTST + LSTR + GSTR + PRT
X + VOLT + DEGT - TOTPAP
IF ((PBAL .LE. 0.0).AND.(PBAL .GE. -0.0009)) PBAL = 0.0
TPBAL = TPBAL + PBAL
C
IX = 2*MONTH
- 158 -
-------
Appendix C (continued)
IZ = IX - 1
WRITE (6,1200) MNAM(IZ), MNAM(IX), YEAR
WRITE (6,1201)
WRITE (6,1103)
IF (UNIT .EQ. 1)
WRITE (6,360)
GO TO 1053
WRITE (6,361
WRITE (6,362
WRITE (6,363
PRTOM, PRTOM, PR'
ROBTOM, ROSTOM
WRITE (6,364) INFTOM, RINTOM
WRITE (6,365) RITOM
WRITE 6,366 ROITOM, RUTOM
WRITE (6,380) BASTOM
WRITE 6,381
WRITE (6,367
WRITE (6,368
WRITE (6,369
RCHTOM
EPTOM, EPTOM, EP
NEPTOM.NEPTOM,
WRITE (6,370)
WRITE (6,371) UZSB.UZS
WRITE (6,372) LZS,LZS,LZS,LZS,LZS,LZS
WRITE (6,373) SGW,SGW,SGW,SGW,SGW,SGW
WRITE (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
WRITE (6,375) RESB.RESS
WRITE (6,376) SRGX.SRGXT
WRITE (6,377) TWBAL
WRITE (6,1209)
WRITE (6,1210) ERSTOM,
WRITE (6,1211) SRER,
WRITE (6,1212)
IF (HYCAL .NE.
WRITE (6,1220)
STST
SAST
SCST
SDST
UTST
UAST
UCST
UDST
ERSNTM
SRERT
EIMTM
0) GO TO 1052
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
,1221)
,1222)
,1223)
,1227)
,1224)
,1222)
,1223
,1227)
,1228)
,1229)
,1230)
,1231)
,1232)
,1229)
,1230)
,1231)
STS,
SAS,
SCS,
SDS, :
UTS,
UAS,
UCS,
UDS,
LSTR
LAS
LCS
LDS
GSTR
GAS
GCS
GDS
- 159 -
-------
c
c
c
Appendix C (continued)
1052 WRITE (6,1240) PRTTOM, PRTM
WRITE (6,1241) PROTOM, SPROTM
WRITE (6,1242) PRSTOM, SPRSTM
WRITE (6,1243) UPITOM, UPRITM
IF (HYCAL .EQ. 1) GO TO 1053
WRITE (6,1244)
WRITE (6,1245) VOLTOM
WRITE (6,1246) VOLSOM
WRITE (6,1247) VOLUOM
WRITE (6,1248)
WRITE (6,1245) DEGTOM
WRITE (6,1246) DEGSOM
WRITE (6,1247) DEGUOM
WRITE (6,1252) DEGLOM
WRITE (6,1266) TPBAL
'l053 IF (UNIT .EQ. -1) GO TO 1055
CONVERSIONS TO METRIC
NEW PARAMETERS DEFINED FOR VARIABLES NOT RESET TO ZERO.
PRTOM =PRTOM*MMPIN
ROSTOM=ROSTOM*MMPIN
RINTOM=RINTOM*MMPIN
RITOM =RITOM*MMPIN
RUTOM =RUTOM*MMPIN
BASTOM =BASTOM*MMPIN
RCHTOM=RCHTOM*MMPIN
EPTOM =EPTOM*MMPIN
NEPTOM =NEPTOM*MMPIN
UZSMET=UZS*MMPIN
LZSMET=LZS*MMPIN
SGWMET=SGW*MMPIN
SCEPMT=SCEP*MMPIN
RESSMT=RESS*MMPIN
TWBLMT=TWBAL*MMPIN
SRGXTM=SRGXT*MMPIN
SEDIMENT
ERSNTM=ERSNTM*METOPT
SRRTMT=SRERT*METOPT
EIMTM=EIMTM*METOPT
PESTICIDE
STSTMT=STST*KGPLB
SASTMT=SAST*KGPLB
SCSTMT=SCST*KGPLB
SDSTMT=SDST*KGPLB
UTSTMT=UTST*KGPLB
UASTMT=UAST*KGPLB
UCSTMT=UCST*KGPLB
- 160 -
-------
Appendix C (continued)
UDSTMT=UDST*KGPLB
LSTRMT=LSTR*KGPLB
LASMET=LAS*KGPLB
LCSMET=LCS*KGPLB
LDSMET=LDS*KGPLB
GSTRMT=GSTR*KGPLB
GASMET=GAS*KGPLB
GCSMET=GDS*KGPLB
GDSMET=GDS*KGPLB
PRTM =PRTM*KGPLB
SPROTM=SPROTM*KGPLB
SPRSTM=SPRSTM*KGPLB
UPRITM=UPRITM*KGPLB
VLTMMT=VOLTOM*KGPLB
VOLSOM=VOLSOM*KGPLB
VOLUOM=VOLUOM*KGPLB
DEGTMT=DEGTOM*KGPLB
DEGSMT=DEGSOM*KGPLB
DEGUMT=DEGUOM*KGPLB
DEGLMT=DEGLOM*KGPLB
TPBALM=TPBAL*KGPLB
C
C ARRAY METRIC MODIFICATIONS
DO 1048 1=1,5
ROBTOM(I)=ROBTOM(I)*MMPIN
INFTOM(I)=INFTOM(I)*MMPIN
ROITOM(I)=ROITOM(I)*MMPIN
UZSBMT(I)=UZSB(I)*MMPIN
RESBMT(I)=RESB(I)*MMPIN
SRGXMT(I)=SRGX(I)*MMPIN
ERSTOM(I)=ERSTOM(I)*METOPT
SRERMT(I)=SRER(I)*METOPT
STSMET(I)=STS(I)*KGPLB
SASMET(I)=SAS(I)*KGPLB
SCSMET(I)=SCS(I)*KGPLB
SDSMET(I)=SDS(I)*KGPLB
UTSMET(I)=UTS(I)*KGPLB
UASMET(I)=UAS(I)*KGPLB
UCSMET(I)=UCS(I)*KGPLB
UDSMET(I)=UDS(I)*KGPLB
PRTTOM(I)=PRTTOM(I)*KGPLB
PROTOM(I)=PROTOM(I)*KGPLB
PRSTOM(I)=PRSTOM(I)*KGPLB
UPITOM(I)=UPITOM(I)*KGPLB
1048 CONTINUE
WRITE (6,460)
WRITE (6,461) PRTOM,PRTOM,PRTOM,PRTOM,PRTOM,PRTOM
WRITE (6,362)
- 161 -
-------
Appendix C (continued)
WRITE (6,463) ROBTOM,ROSTOM
WRITE (6,464) INFTOM,RINTOM
WRITE (6,465) RITOM
WRITE (6,466) ROITOM,RUTOM
WRITE (6,480) BASTOM
WRITE (6,481) RCHTOM
WRITE (6,367)
WRITE (6,468) EPTOM,EPTOM,EPTOM,EPTOM,EPTOM,EPTOM
WRITE (6,469) NEPTOM,NEPTOM,NEPTOM,NEPTOM,NEPTOM,NEPTOM
WRITE (6,370)
WRITE (6,471) UZSBMT,UZSMET
WRITE (6,472) LZSMET,LZSMET,LZSMET,LZSMET,LZSMET,LZSMET
WRITE (6,473) SGWMET,SGWMET,S6WMET,SGWMET,SGWMET,S6WMET
WRITE (6,474) SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT
WRITE (6,475) RESBMT,RESSMT
WRITE (6,476) SRGXMT,SRGXTM
WRITE (6,477) TWBLMT
WRITE (6,1208)
WRITE (6,1210) ERSTOM,ERSNTM
WRITE (6,1211) SRERMT,SRRTMT
WRITE (6,1212) EIMTM
IF (HYCAL .NE. 0) GO TO 1049
WRITE (6,1207)
WRITE (6,1221) STSMET,STSTMT
WRITE (6,1222) SASMET,SASTMT
WRITE (6,1223) SCSMET,SCSTMT
WRITE (6,1227) SDSMET,SDSTMT
WRITE (6,1224) UTSMET,UTSTMT
WRITE (6,1222) UASMET,UASTMT
WRITE (6,1223) UCSMET,UCSTMT
WRITE (6,1227) UDSMET,UDSTMT
WRITE (6,1228) LSTRMT
WRITE (6,1229) LASMET
WRITE (6,1230) LCSMET
WRITE (6,1231) LDSMET
WRITE (6,1231) GSTRMT
WRITE (6,1229) GASMET
WRITE (6,1230) GCSMET
WRITE (6,1231) GDSMET
1049 WRITE (6,1239) PRTTOM,PRTM
WRITE (6,1241) PROTOM,SPROTM
WRITE (6,1242) PRSTOM.SPRSTM
WRITE (6,1243) UPITOM,UPRITM
IF (HYCAL .EQ. 1) GO TO 1055
WRITE (6,1238)
WRITE (6,1245) VLTMMT
WRITE (6,1246) VOLSOM
WRITE (6,1247) VOLUOM
- 162 -
-------
Appendix C (continued)
WRITE (6,1249)
WRITE (6,1245) DEGTMT
WRITE (6,1246) DEGSMT
WRITE (6,1247) DEGUMT
WRITE (6,1252) DEGLMT
WRITE (6,1266) TPBALM
ZEROING OF VARIABLES
C
C
1055 PRTOM =0.0
RUTOM =0.0
NEPTOM =0.0
ROSTOM =0.0
RITOM =0.0
RINTOM = 0.0
BASTOM =0.0
RCHTOM =0.0
EPTOM =0.0
ERSNTM-= 0.0
EIMTM = 0.0
PRTM =0.0
SPROTM =0.0
SPRSTM =0.0
UPRITM = 0.0
VOLSOM = 0.0
VOLUOM =0.0
DEGSOM =0.0
DEGUOM =0.0
DEGLOM =0.0
C
DO 1058 1=1,5
ERSTOM(I) =0.0
ROBTOM(I) = 0.0
INFTOM(I) = 0.0
PRTTOM(I) =0.0
PROTOM(I) =0.0
PRSTOM(I) =0.0
UPITOM(I) = 0.0
1058 ROITOM(I)= 0.0
C
1060 CONTINUE
YEARLY SUMMARY
DO 1061 1= 1,5
1061 PRTTOT(I) = PRSTOT(I) + PROTOT(I) + UPITOT(I)
VOLTOT = VOLSOT + VOLUOT
DEGTOT = DEGSOT + DEGUOT + DEGLOT
C
C
- 163 -
-------
Appendix C (continued)
PRTT = SPROTT + SPRSTT + UPRITT
C
C
WRITE (6,1250)
WRITE (6,1251)
WRITE (6,1103)
YEAR
IF (UNIT .EQ. 1) 60 TO 1066
WRITE (6,360)
WRITE (6,361) PRTOT,PRTOT,PRTOT,PRTOT,PRTOT,PRTOT
WRITE (6,362)
WRITE (6,363) ROBTOT, ROSTOT
WRITE (6,364) INFTOT, RINTOT
WRITE (6,365) RITOT
WRITE (6,366) ROITOT, RUTOT
WRITE (6,380) BASTOT
WRITE (6,381) RCHTOT
WRITE (6,367)
WRITE (6,368) EPTOT,EPTOT,EPTOT,EPTOT,EPTOT,EPTOT
WRITE (6,369) NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT
WRITE (6,370)
WRITE (6,371) UZSB,UZS
WRITE (6,372) LZS,LZS,LZS,LZS,LZS,LZS
WRITE (6,373) SGW,SGW,SGW,SGW,SGW,SGW
WRITE (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
WRITE (6,375) RESB,RESS
WRITE (6,376) SRGX,SRGXT
WRITE (6,377) TWBAL
WRITE (6,1209)
WRITE (6,1210) ERSTOT.
WRITE (6,1211)
WRITE (6,1212)
IF (HYCAL .NE. 0)
WRITE (6,1220)
WRITE (6,1221) STS, STST
WRITE (6,1222) SAS, SAST
WRITE (6,1223) SCS, SCST
WRITE (6,1227) SDS, SDST
WRITE (6,1224) UTS, UTST
WRITE (6,1222) UAS, UAST
WRITE (6,1223)
WRITE (6,1227)
WRITE (6,1228)
ERSNTT
SRER, SRERT
EIMTT
GO TO 1063
WRITE (6,1229)
WRITE (6,1230)
WRITE (6,1231)
WRITE (6,1232)
UCS, UCST
UDS, UDST
LSTR
LAS
LCS
IDS
GSTR
- 164 -
-------
Appendix C (continued)
GAS
GCS
GDS
PRTTOT, PRTT
PROTOT, SPROTT
PRSTOT, SPRSTT
UPITOT, UPRITT
1) GO TO 1066
VOLTOT
VOLSOT
VOLUOT
WRITE (6,1229)
WRITE (6,1230)
WRITE (6,1231)
1063 WRITE (6,1240)
WRITE (6,1241)
WRITE (6,1242)
WRITE (6,1243)
IF (HYCAL .EQ
WRITE (6,1244)
WRITE (6,1245)
WRITE (6,1246)
WRITE (6,1247)
WRITE (6,1248)
WRITE (6,1245) DEGTOT
WRITE (6,1246) DEGSOT
WRITE (6,1247) DEGUOT
WRITE (6,1252) DEGLOT
WRITE (6,1266) TPBAL
1066 IF (UNIT .EQ. -1) GO TO 1065
: CONVERSIONS
PRTOT =PRTOT*MMPIN
ROSTOT=ROSTOT*MMPIN
RINTOT=RINTOT*MMPIN
RITOT =RITOT*MMPIN
RUTOT =RUTOT*MMPIN
BASTOT=BASTOT*MMPIN
RCHTOT=RCHTOT*MMPIN
EPTOT =EPTOT*MMPIN
NEPTOT=NEPTOT*MMPIN
UZSMET=UZS*MMPIN
LZSMET=LZS*MMPIN
SGWMET=SGW*MMPIN
SCEPMT=SCEP*MMPIN
RESSMT=RESS*MMPIN
TWBLMT=TWBAL*MMPIN
SRGXTM=SRGXT*MMPIN
ERSNTT=ERSNTT*METOPT
SRRTMT=SRERT*METOPT
EIMTT =EIMTT*METOPT
: PESTICIDE
STSTMT=STST*KGPLB
SASTMT=SAST*KGPLB
SCSTMT=SCST*KGPLB
SDSTMT=SDST*KGPLB
UTSTMT=UTST*KGPLB
UASTMT=UAST*KGPLB
UCSTMT=UCST*KGPLB
UDSTMT=UDST*KGPLB
- 165
-------
Appendix C (continued)
LSTRMT=LSTR*KGPLB
LASMET=LAS*KGPLB
LCSMET=LCS*KGPLB
LDSMET=LDS*KGPLB
GSTRMT=GSTR*KGPLB
GASMET=GAS*KGPLB
GCSMET=GDS*KGPLB
GDSMET=GDS*KGPLB
PRTT =PRTT*KGPLB
SPROTT=SPROTT*KGPLB
SPRSTT=SPRSTT*KGPLB
UPRITT=UPRITT*KGPLB
VLTMMT=VOLTOT*KGPLB
VOLSOT=VOLSOT*KGPLB
VOLUOT=VOLUOT*KGPLB
DEGTMT=DEGTOT*KGPLB
DEGSMT=DEGSOT*KGPLB
DEGUMT=DEGUOT*KGPLB
DEGLMT=DEGLOT*KGPLB
TPBALM=TPBAL*KGPLB
C METRIC MODIFICATION OF ARRAYS
DO 1062 1=1,5
ROBTOT(I)=ROBTOT(I)*MMPIN
INFTOT(I)=INFTOT(I)*MMPIN
ROITOT(I)=ROITOT(I)*MMPIN
UZSBMT(I)=UZSB(I)*MMPIN
RESBMT(I)=RESB(I)*MMPIN
SRGXMT(I)=SRGX(I)*MMPIN
ERSTOT(I) = ERSTOT(I)*METOPT
SRERMT(I)=SRER(I)*METOPT
STSMET(I)=STS(I)*KGPLB
SASMET(I)=SAS(I)*KGPLB
SCSMET(I)=SCS(I)*KGPLB
SDSMET(I)=SDS(I)*KGPLB
UTSMET(I)=UTS(I)*KGPLB
UASMET(I)=UAS(I)*KGPLB
UCSMET(I)=UCS(I)*KGPLB
UDSMET(I)=UDS(I)*KGPLB
PRTTOT(I)=PRTTOT(I)*KGPLB
PROTOT(I)=PROTOT(I)*KGPLB
PRSTOT(I)=PRSTOT(I)*KGPLB
UPITOT(I)=UPITOT(I)*KGPLB
1062 CONTINUE
C
WRITE (6,460)
WRITE (6,461) PRTOT,PRTOT,PRTOT,PRTOT,PRTOT,PRTOT
WRITE (6,362)
WRITE (6,463) ROBTOT,ROSTOT
- 166 -
-------
Appendix C (continued)
WRITE (6,464) INFTOT.RINTOT
WRITE (6,465) RITOT
WRITE (6,466) ROITOT.RUTOT
WRITE (6,480) BASTOT
WRITE (6,481) RCHTOT
WRITE (6,367)
WRITE (6,468) EPTOT,EPTOT,EPTOT,EPTOT,EPTOT,EPTOT
WRITE (6,469) NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT
WRITE (6,370)
WRITE (6,471) UZSBMT.UZSMET
WRITE (6,472) LZSMET,LZSMET,LZSMET,LZSMET,LZSMET,LZSMET
WRITE (6,473) SGWMET,SGWMET,SGWMET,SGWMET,SGWMET,SGWMET
WRITE (6,474) SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT
WRITE (6,475) RESBMT.RESSMT
WRITE (6,476) SRGXMT.SRGXTM
WRITE (6,477) TWBLMT
WRITE (6,1208)
WRITE (6,1210) ERSTOT.ERSNTT
WRITE (6,1211) SRERMT.SRRTMT
WRITE (6,1212) EIMTT
IF (HYCAL .NE. 0) GO TO 1064
WRITE (6,1207)
WRITE (6,1221) STSMET.STSTMT
WRITE (6,1222) SASMET.SASTMT
WRITE (6,1223) SCSMET.SCSTMT
WRITE (6,1227) SDSMET,SDSTMT
WRITE (6,1224 UTSMET.UTSTMT
WRITE (6,1222 UASMET.UASTMT
WRITE (6,1223 UCSMET.UCSTMT
WRITE (6,1227) UDSMET.UDSTMT
WRITE (6,1228) LSTRMT
WRITE (6,1229) LASMET
WRITE (6,1230) LCSMET
WRITE (6,1231) LDSMET
WRITE (6,1231) GSTRMT
WRITE (6,1229) GASMET
WRITE (6,1230) GCSMET
WRITE (6,1231) GDSMET
1064 WRITE (6,1239) PRTTOT.PRTT
WRITE (6,1241) PROTOT.SPROTT
WRITE (6,1242) PRSTOT.SPRSTT
WRITE (6,1243) UPITOT.UPRITT
IF (HYCAL .EQ. 1) GO TO 1065
WRITE (6,1238)
WRITE (6,1245) VLTMMT
WRITE (6,1246) VOLSOT
WRITE (6,1247) VOLUOT
WRITE (6,1249)
- 167 -
-------
Appendix C (continued)
WRITE (6,1245) DEGTMT
WRITE (6,1246) DEGSMT
WRITE (6,1247) DEGUMT
WRITE (6,1252) DEGLMT
WRITE (6,1266) TPBALM
C
C
C
ZEROING OF VARIABLES
1065 PRTOT =0.0
RUTOT =0.0
NEPTOT =0.0
ROSTOT =0.0
RITOT = 0.0
RINTOT = 0.0
BASTOT =0.0
RCHTOT =0.0
EPTOT =0.0
ERSNTT =0.0
EIMTT =0.0
PRTT = 0.0
SPROTT =0.0
SPRSTT =0.0
UPRITT = 0.0
VOLSOT =0.0
VOLUOT =0.0
DEGSOT =0.0
DEGUOT =0.0
DEGLOT =0.0
DO 1068 1=1,5
ERSTOT(I) = 0.0
ROBTOT(I) =0.0
INFTOT(I) = 0.0
PRTTOT(I) = 0.0
PRSTOT(I) = 0.0
PROTOT(I) =0.0
UPITOT(I) = 0.0
1068 ROITOT(I) = 0.0
1070 CONTINUE
C
C
C
1080 CONTINUE
WRITE (6,1260)
FORMAT STATEMENTS
1090 FORMAT ('I1,'*****ERROR***** INCORRECT INPUT DATAJg DESIRED '
* 'CARD ',11,' FOR ',I2,'/1,I2,1/M4,1; READ CARD ',11,' FOR
- 168 -
-------
Appendix C (continued)
1091
1092
1093
1094
1095
1101
1102
1103
* 12,'
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
Of2'7
('0'
('!'
(IX,
(IX,
( ' 1 '
( '+'
Co1
C 5X,' TOTAL
1104
1105
1106
1107
1108
1109
1200
1201
1208
1207
1209
1210
1211
1212
1220
1221
1222
1223
1224
1227
1228
1229
1230
1231
1232
1239
1238
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
('0'
('0'
('0'
('0'
('0'
Co1
C r
( ' + '
Co1
('0'
Co1
( ' •
( ' '
( ' '
Co-
Co1
/ 1 i
( ' '
Co1
! ' '
('0'
I ' '
( ' '
{ ' '
('0'
Co-
Co1
Co-
! ' '
( ' '
( ' '
Co1
( ' '
i * '
/ 1 i
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('!'
,25X
)
,25X
312,
312,
,25X
,25X
,35X
i \
,32X
,32X
,32X
,32X
,32X
,32X
,25X
,25X
,8X,
,5X,
, 8X
,11X
,11X
,11X
,5X,
, 8X
,11X
,11X
, 8X
,11X
, 8X
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',14)
,'THIS IS A PRODUCTION RUN1)
,'THIS IS A CALIBRATION RUN FOR LANDS')
11,1216)
11,3612)
,I2,':',I2,1 ON ',I2,1X,A4,A4,1X,I4)
' )
,'ZONE 1 ZONE 2 ZONE 3 ZONE 4 ZONE 5',
, 'APPLICATION: SURFACE-APPLIED')
, 'APPLICATION: SOIL-INCORPORATED1)
, 'PESTICIDE: ' ,A10)
, 'WATERSHED: ' ,A10)
,' INPUT UNITS: ENGLISH')
,' INPUT UNITS: METRIC')
,' SUMMARY FOR MONTH OF ' ,A4,A4,1X,I4)
i i \
'SEDIMENT, TONNES')
'PESTICIDE, KILOGRAMS')
,' SEDIMENT, TONS')
, 'TOTAL SEDIMENT LOSS ' ,5(3X,F7.3) ,4X,F7.3)
, 'FINES DEPOSIT' ,6X,5(3X,F7.3) ,4X,F7.3)
,' IMPERVIOUS EROSION', 55X,F7. 3)
"PESTICIDE, POUNDS')
, -SURFACE LAYER' ,10X,5(3X,F7.3),3X,F8.3)
,' ADSORBED ' ,12X,5(3X,F7. 3), 3X,F8. 3)
,' CRYSTALLINE ' ,9X,5(3X,F7. 3), 3X,F8. 3)
, 'UPPER ZONE LAYER',7X,5(3X,F7.3),3X,F8.3)
,' DISSOLVED ',11X,5(3X,F7. 3), 3X,F8. 3)
,' LOWER ZONE LAYER' ,60X,F8.3)
, "ADSORBED1 ,65X,F8.3)
, 'CRYSTALLINE', 62X,F8. 3)
, 'DISSOLVED', 64X,F8. 3)
,'GROUNDWATER LAYER' ,59X,F8.3)
'PESTICIDE REMOVAL, KGS. ' ,2X,5(F7.3,3X) ,F8.3)
'PESTICIDE VOLATILIZATION LOSS, KGS.')
, 'PESTICIDE REMOVAL, LBS. ' ,2X,5(F7.3,3X) ,F8.3)
, 'OVERLAND FLOW REMOVAL ' ,1X,5(F7.3,3X) ,F8.3)
, 'SEDIMENT REMOVAL' ,6X,5(F7.3,3X),F8.3)
,' INTERFLOW REMOVAL' ,5X,5(F7.3,3X) ,F8.3)
'PESTICIDE VOLATILIZATION LOSS, LBS.')
,'TOTAL',68X,F7.3)
,'FROM SURFACE', 61X,F7. 3)
,'FROM UPPER ZONE',58X,F7.3)
'PESTICIDE DEGRADATION LOSS, LBS.1)
'PESTICIDE DEGRADATION LOSS, KGS.1)
,' SUMMARY FOR ',14)
- 169 -
-------
Appendix
(continued)
1251
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1252 FORMAT
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FORMAT
C + ',25X
5 /
(' MIX, 'FROM LOWER ZONE ' ,58X,F7.3)
('!')
(2X,A4,A4,2X,I2,2X,I2,':',I2)
('l',7X, 'DATE', 4X, 'TIME', 6X,'FLOW(CFS - CMS)',6X,
'SEDIMENT(LBS - KG - GM/L) MIX, ' PESTICIDE (GM - PPM)')
(8X.12F6.1) *
(8X,12I6)
('0 ,11X, 'PESTICIDE BALANCED ',F8. 4)
CO
CO
CO
C
C
C
C
CO
C
C
Co
C
C
C
C
C
C
Co
( '+
Co
/ 1
C
Co
Co
/ 1
C
C
C
Co
C
C
C
/ 1
( '
C
( '
C
C
,8X,
,nx
,11X
,14X
,14X
,14X
,14X
,11X
,14X
,14X
,11X
,14X
,14X
,14X
,14X
,14X
,14X
,11X
,25X
,11X
,11X
,78X
,8X,
,11X
,14X
,14X
,14X
,14X
,11X
, 11X
,14X
,14X
,14X
,14X
,14X
,14X
,14X
,14X
Co ,iix
'WATER, INCHES')
, 'PRECIPITATION', 6X,5(3X
, 'RUNOFF')
, 'OVERLAND FLOW' ,3X,5(3X
,' INTERFLOW', 7X,5(3X,F7.
,' IMPERVIOUS', 55X,5X,F7.
,'TOTAL',11X,5(3X,F7.3),
,' EVAPORATION')
, 'POTENTIAL', 7X,5(3X,F7.
,'NET',13X,5(3X,F7.3),4X
, 'STORAGES')
, 'UPPER ZONE',6X,5(3X,F7
, 'LOWER ZONE',6X,5(3X,F7
,F7
,F7
3),
3)
4X,
3),
,F7
.3)
• 3)
.3)
• 3)
4X,
F7.
4X,
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,4X
,4X
,4X
,4X
F7.
3)
F7.
,F7
,F7
,F7.
,F7.
3)
3)
.3)
.3)
3)
3)
,'GROUNDWATER',5X,5(3X,F7.3),4X,F7.3)
,' INTERCEPTION', 4X,5(3X,
, 'OVERLAND FLOW' ,3X,5(3X
, 'INTERFLOW', 7X,5(3X,F7.
,'WATER_BALANCE=',F8.4)
,F5. 1)
,'BASE FLOW',64X,F7.3)
,'GRDWATER RECHARGE ' ,56X
, 'WATER ',13X, 'SEDIMENT')
'WATER, MILLIMETERS')
, 'PRECIPITATION', 6X,5(3X
, 'OVERLAND FLOW' ,3X,5(3X
,' INTERFLOW', 7X,5(3X,F7.
,' IMPERVIOUS', 60X,F7. 2)
,'TOTAL',11X,5(3X,F7.2),
,'BASEFLOW',65X,F7.2)
,'GRDWATER RECHARGE ' ,56X
, 'POTENTIAL', 7X,5(3X,F7.
,'NET',13X,5(3X,F7.2),4X
, 'UPPER ZONE',6X,5(3X,F7
, 'LOWER ZONE',6X,5(3X,F7
F7.
,F7
3),
,F7
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F7.
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F7.3)
,F7.
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,F7.
,F7.
2)
2)
.2)
.2)
3)
2)
2)
,'GROUNDWATER',5X,5(3X,F7.2),4X,F7.2)
,' INTERCEPTION' ,4X,5(3X,
, 'OVERLAND FLOW1 ,3X,5(3X
, 'INTERFLOW1, 7X,5(3X,F7.
, ' WATER BALANCE=',F8.3)
F7.
,F7
2),
2),
.2)
4X,
4X,
,4X
F7.
F7.2)
,F7.
2)
2)
- 170 -
-------
Appendix C (continued)
STOP
END
C
C
BLOCK DATA
C
C
C BLOCK DATA TO INITIALIZE VARIABLES
C
C
IMPLICIT REAL(L)
C
DIMENSION RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5), ROSB(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, I FLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCST, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, Ml, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
- 171 -
-------
Appendix C (continued)
C
INTEGER PRNTKE, UNIT
C
REAL M, K, NI, MUZ, MLZ
REAL LZSN, IRC, NN, L, LZS, KV, K24L, KK24, INFIL, INTER
REAL IPS, K24EL, K3, NEPTOM, NEPTOT
REAL INFTOM, INFTOT, INTF
REAL MMPIN, METOPT, KGPLB
C
DATA PRTOT, ERSNTT, EIMTT, PRTT/4*0.0/
DATA PRTOM, ERSNTM, EIMTM, PRTM/4*0.0/
DATA RUTOM, ROSTOM, RITOM, RINTOM, NEPTOM/5*0.0/
DATA RUTOT, ROSTOT, RITOT, RINTOT, NEPTOT/5*0.0/
DATA ROBTOM, ROBTOT, INFTOM, INFTOT, ROITQM, ROITOT/30*0.0/
DATA PROTOM, PROTOT, PRSTOM, PRSTOT, UPITOM, UPITOT/30*0.0/
DATA TWBAL, RESB, SRGX, INTF, ERSTOM, ERSTOT, SDST/27*0.0/
DATA RESB1, BASTOM, RCHTOM, BASTOT, RCHTOT/9*0.0/
DATA SPROTM, SPRSTM, EPTOM, EPTOT/4*0.0/, PRNTKE/0/
DATA STS, STST, SAST, SCST, UTS, UTST, UAST, UCST,UDST/17*0.0/
DATA PR, P3, RXB, RGX, RUZB, UZSB, PERCB, DPST/28*0.0/
DATA TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS/9*0.0/
DATA A, UZS,- LZS, SGW, GWS, KV, K24L, KK24/8*0.0/
DATA IFS, K24EL, K3, EPXM, COVER, COVMAX/6*0.0/
DATA ERSN/5*0.0/, SRER/5*0.0/, SRERT/0.0/
DATA SAS/5*0.0/, SCS/5*0.0/, SDS/5*0.0/, AREA, M, K/3*0.0/
DATA NI, FP, CMAX, SSTR/8*0.0/
DATA SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT/9*0.0/
DATA UAS/5*0.0/, UCS/5*0.0/, UDS/5*0.0/, USTR, MUZ, FPUZ/7*0.0/
DATA UPRP/5*0.0/, UPRITT, UPRITM/2*0.0/
DATA LSTR, LAS, LCS, LDS, MLZ, LPRP/6*0.0/
DATA GSTR, GAS, GCS, GDS, FPLZ/5*0.0/
DATA MNAM/' JAN','UARY','FEBR','UARY',' MA'.'RCH ',' AP1,
* 'RIL V MA'.'Y ',' JUVNE ',' JU'.'LY ',' AUG' ,
* 'UST ' ,'SEPT1,'MBERV OCT','OBER1,'NOVE','MBER','DECE',
* 'MBER1/
DATA VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS/6*0.0/
DATA MMPIN/25.4/, METOPT/0.9072/, KGPLB/0.4536/
DATA DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS/6*0.0/
DATA DEGLOM, DEGLOT/2*0.0/
C
END
C
C
SUBROUTINE LANDS
C
C
C HSP LANDS
C
- 172 -
-------
Appendix C (continued)
IMPLICIT REAL(L,K)
DIMENSION RXB(5), RGB(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5)5 PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION SHRD(5), RXX(5), ROSB(5), DEEPL(5)
DIMENSION UZRA(5), R6X(5), RESBMT(5), SRGXMT(5), UZSBMT(5)
DIMENSION PRE(5), INFL(5), UZI(5), DPERCB(5)
DIMENSION EVDIST(24), ROSINT(5), MNAM(24)
DIMENSION ARXB(5), ARGX(5), ADPRCB(5), ARIB(5)
DIMENSION AROSB(5), AINTF(5), AROSIT(5)
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSB, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, I FLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGB, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
INTEGER TF, PRNTKE, HYCAL, DAY, UNIT
REAL INFIL, INTER, NN, INFLT, IRC, INTF, INFL
REAL IRC4, ICS, IFS, NEPTOM, NEPTOT
- 173 -
-------
Appendix C (continued)
REAL INFTOM, INFTOT, QMETRC
REAL MMPIN, METOPT, K6PLB
REAL UZSMET, LZSMET, SGWMET, SCEPMT, RESSMT
REAL TWBLMT, SRGXTM, RESBMT, SRGXMT
C
DATA SIMP/0.0/, IHRR/0/
DATA PERC, INFLT/0.0,0.0/
DATA SBAS/0.0/
DATA RG, RIBT/2*0.0/, DPERCB, DPERC/6*0.0/
DATA SNET1, SNET, SRCH/3*0.0/, NUMI/0/
DATA ROSINT/5*0.0/, REPIN, EPIN1, AETR, KF/4*0.0/
DATA EVDIST/6*0.0,0.019,0.041,0.067,0.088,0.102,3*0.11,0.105,
C 0.095,0.081,0.055,0.017,5*0.O/
DATA ARXB, ARGX, ADPRCB, ARIB, ADPST, APR, AEPIN/23*0.0/
DATA AROSE, AINTF, AROSIT/15*0.0/
DATA ARU, ARUI, AROS, ARGXT, ASNET, ASBAS, ASRCH/7*0.0/
C
C ZEROING OF VARIABLES
C
LZS1 = LZS
UZS1 = UZS
IHRR = 1
NUMI = 0
DPST = 0.0
C
PA=1.0-A
IRC4=IRC**(1.0/96.0)
LIRC4=1.0-IRC4
KK4=KK24**(1.0/96.0)
LKK4= 1.0 - KK4
C
IF ((24.*60./TIMFAC) .61. 100.) GO TO 185
GO TO 187
185 LIRC4 = LIRC4/3.0
LKKR = LKK4/3.0
C
187 DEC= 0.00982*((NN*L/SQRT(SS))**0.6)
SRC= 1020.*SQRT(SS)/(NN*L)
C
RESS =0.0
LNRAT=LZS/LZSN
D3FV=(2.0*INFIL)/(LNRAT*LNRAT)
D4F= (TIMFAC/60.)*D3FV
RATIO= INTER*EXP(0.693147*LNRAT)
IF ((RATIO).LT.(1.0)) RATIO=1.0
D4RA= D4F*RATIO
DO 155 111=1,TF
- 174 -
-------
Appendix C (continued)
LNRAT = LZS/LZSN
IF (TF .LT. 2) GO TO 4
H = TF/24
NUMI =NUMI + 1
IF (NUMI .EQ. H) GO TO 2
GO TO 4
2 NUMI = 0
C
4 RX = 0.0
SBAS = 0.0
SRCH = 0.0
ROS = 0.0
RU = 0.0
GWF = 0.0
RGXT = 0.0
PERC = 0.0
INFLT = 0.0
C
C TIMFAC - TIME INTERVAL IN MINUTES
C L - LENGTH OF OVERLAND SLOPE
C NN - MANNING'S N FOR OVERLAND SLOPE
C A - IMPERVIOUS AREA
C PA - PERVIOUS AREA
C
C
C
C
C PR IS INCOMING RAINFALL
C P3 IS RAIN REACHING SURFACE(.OO'S INCHES)
C P4 IS TOTAL MOISTURE AVAILABLE( IN.
C RESS IS OVERLAND FLOW STORAGE( IN.
C D4F IS 'B' IN OP. MANUAL
C RATIO IS 'C1 IN OP. MANUAL
C EP - DAILY EVAP (IN.)
C EPHR - HOURLY EVAP
C EPIN - INTERVAL EVAP
C
C
IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0)) GO TO 202
GO TO 197
202 IF (TF .LT. 2) GO TO 200
IHRR = IHRR + 1
GO TO 201
200 IHRR = IHR + 1
201 IF (IHRR .61. 24) IHRR = 24
EPHR • EVDIS?(!H!lR)*EP
IF (EPHR.LE.(0.0001)) EpHR=0-0
- 175 -
-------
Appendix C (continued)
EPIN= EPHR
EPIN1=EPIN
C
C
C * * * INTERCEPTION FUNC. * * *
C
C
C EPXM - MAX. INTERCEPTION STORAGE
C SCEP - EXISTING INTER. STORAGE
C EPX - AVAILABLE INTER. STORAGE
C SIMP - SUM OF IMPERVIOUS RUNOFF
C RUI - IMPERVIOUS RUNOFF DURING INTERVAL
C
C
C
197 IF (COVER - 0.0001) 198,198,204
198 SNET = SNET + SCEP
SCEP = 0.0
EPX = 0.0
GO TO 203
C
C
204 EPX=EPXM*(COVER/COVMAX)-SCEP
IF(EPX.LT.(0.0001)) EPX=0.0
IF (PR-EPX) 205,203,203
203 P3= PR-EPX
RU= P3*A
RUI=RU
SIMP=SIMP+RU
SCEP = SCEP+EPX
GO TO 206
205 SCEP = SCEP+PR
P3=0.0
RU=0.0
RUI=0.0
C
C
C
C * * * INTERCEPTION EVAP * * *
C
C
206 IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0)) GO TO 207
GO TO 221
C
207 IF (SCEP) 221,221,208
208 IF (SCEP-EPIN) 209,210,210
209 EPIN = EPIN - SCEP
SNET = SNET + SCEP
- 176 -
-------
Appendix C (continued)
SCEP - 0.0
GO TO 221
210 SCEP=SCEP-EPIN
220 SNET=SNET+EPIN
EPIN - 0.0
C
221 REPIN=0.0
C
C *** INFILTRATION FUNC. ***
C P4 IS TOTAL MOISTURE IN STORAGE BLOCK
C SHRD(I) = SURFACE DETENTION AND INTERFLOW FROM BLOCK I
C RXX(I) = SURFACE DETENTION FROM BLOCK I
C RGXX(I) = INTERFLOW COMPONENT FROM BLOCK I
C RGX(I) = VOLUME TO INTER. DETEN STOR. FROM BLOCK I
C
C
C BEGINNING OF BLOCK LOOP
C
C
DO 100 1=1,5
P4 = P3 + RESB(I)
RESBI(I) = RESB(I)
IF ((10.*P4)-(((2.*I)-1)*D4F)) 10,10,15
10 SHRD(I)=0.0
GO TO 25
15 SHRD(I)= (P4-(((2.*!)-!.0)*D4F/10.))
16 IF ((10.*P4)-(((2.*!)-!.0)*D4RA)) 25,25,30
25 RXX(I)= 0.0
GO TO 31
30 RXX(I)= (P4-(((2.*!)-!.0)*D4RA/10.))
31 RGXX = SHRD(I)-RXX(I)
C
C
C *** UPPER ZONE FUNCTION ***
C
C PRE(I) - % SURFACE DETENTION TO OVERLAND FLOW
C UZSB(I) - UPPER ZONE STORAGE IN EACH BLOCK
C UZS - TOTAL UPPER ZONE STORAGE
C RUZB(I) - ADDITION TO U.Z. STORAGE DURING INTERVAL
C
UZRA(I)= UZSB(I)/UZSN
IF (UZRA(I)-2.0) 7,7,8
7 UZI(I)= 2.0*ABS((UZRA(I)/2.0)-1.0) +1.0
PRE(I)= (UZRA(I)/2.0)*((1.0/(1.0+UZI(I)))**UZI(I))
GO TO 9
8 UZI(I)= (2.0*ABS(UZRA(I)-2.0))+1.0
PRE(I)= 1.0-((1.0/(1
9 RXB(I)= RXX(I)* PRE(I)
- 177 -
-------
Appendix C (continued)
RGX(I)=RGXX*PRE(I)
RGXX=0.0
RUZB(I)=SHRD(I)-RGX(I)-RXB(I)
UZSB(I)=UZSB(I)+RUZB(I)
C
RIB(I) - P4 - RXB(I)
C
C
C
C * * * UPPER ZONE EVAP * * *
C
C
C REPIN - EVAP POT. FOR I.I. AND GRDWATER, I.E
C PORTION NOT SATISFIED FROM U.Z.
C
C
IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0)) GO TO 235
GO TO 290
C
235 IF (EPIN.LE.(O.O)) GO TO 290
EFFECT-1.0
IF(UZRA(I)-2.0) 230,230,240
240 IF (UZSB(I)-EPIN) 270,270,260
260 UZSB(I)=UZSB(I)-EPIN
RUZB(I)= RUZB(I)-EPIN
SNET=SNET+PA*EPIN*0.20
GO TO 290
230 EFFECT= 0.5*UZRA(I)
IF (EFFECT.LT.(0.02)) EFFECTED.02
IF (UZSB(I)-EPIN*EFFECT) 270,270,280
280 UZSB(I)=UZSB(I) - (EPIN*EFFECT)
RUZB(I)= RUZB(I)-(EPIN*EFFECT)
EDIFF= (1.0-EFFECT)*EPIN
REPIN=REPIN + EDIFF*0.20
EDIFF=0.0
SNET= SNET + (PA*EPIN*EFFECT)*0.20
GO TO 290
270 EDIFF= EPIN - UZSB(I)
REPIN= REPIN + EDIFF*0.20
EDIFF=0.0
SNET= SNET + PA*UZSB(I)*0.2rO
UZSB(I)=0.0
RUZB(I)=0.0
C
C
c * * * * INTERFLOW FUNCTION * * *
C
- 178 -
-------
Appendix C (continued)
C SRGX(I) - INTERFLOW DETENTION STORAGE FROM BLOCK I
C INTF(I) - INTERFLOW LEAVING STORAGE FROM BLOCK I
C SRGXT - TOTAL INTERFLOW STORAGE
C RGXT - TOTAL INTERFLOW LEAVING STORAGE DURING INTERVAL
C
290 INTF(I) = LIRC4*SRGX(I)
SRGX(I)=SRGX(I)+(RGX(I)*PA)-INTF(I)
RU-RU + INTF(I)*0.20
SRGXT= SRGXT + (RGX(I)*PA-INTF(I))*0.20
RGXT=RGXT + INTF(I)*0.20
C
C *** OVERLAND FLOW ROUTING ***
C
C
C RXB(I) = VOLUME TO OVERLAND SURFACE DETENTION FROM BLOCK I
C ROSB(I) = VOLUME OF OVERLAND FLOW TO STREAM FROM BLOCK I
C RESB(I) = VOLUME OF OVERLAND Q REMAINING ON SURFACE
C FROM BLOCK I
C
Fl= RXB(I)-(RESB(I))
F3= (RESB(I))+ RXB(I)
IF (RXB(I)-(RESB(I))) 34,34,32
32 DE= DEC*((F1)**0.6)
GO TO 35
34 DE= (F3)/2.0
35 IF (F3-(2.0*DE)) 38,38,36
36 DE=(F3)/2.0
38 IF ((F3)-(.005)) 40,40,42
40 ROSB(I)= 0.0
GO TO 43
42 DUMV=(1.0+0.6*(F3/(2.0*DE))**3.)**1.67
ROSB(I)=(TIMFAC/60.)*SRC*((F3/2.)**1.67)*DUMV
IF ((ROSB(I)).GT.(.95*RXB(I))) GO TO 122
GO TO 43
122 IF ((RXB(I)).GT.(0.0)) GO TO 121
GO TO 43
121 ROSB(I)=(.95)*RXB(I)
43 RESB(I)= RXB(I)-ROSB(I)
ROSB(I) = ROSB(I)*PA
ROSINT(I) = ROSB(I) + INTF(I)
C
C
C
C * * * UPPER ZONE DEPLETION * * *
C
C DEEPL(I) - DIFFERENCE IN UPPER AND LOWER ZONE RATIOS
C PERCB(I) - UPPER ZONE DEPLETION FROM EACH BLOCK
C PERC - TOTAL U.Z. DEPLETION
- 179 -
-------
Appendix C (continued)
C INFLT - TOTAL INFILTRATION
C ROS - TOTAL OVERLAND FLOW TO THE STREAM FROM ALL BLOCKS
C
IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0)) GO TO 44
PERCB(I) = 0.0
GO TO 47
C
44 DEEPL(I)= ((UZSB(I)/UZSN)-(LZS/LZSN))
IF (DEEPL(I)-.Ol) 47,47,45
45 PERCB(I)=0.1*INFIL*UZSN*(DEEPL(I)**3)
UZSB(I)=UZSB(I)-PERCB(I)
PERC=PERC+PERCB(I)*0.2
RUZB(I) = RUZB(I) - PERCB(I)
47 INFL(I)= P4-SHRD(I)
INFLT=INFLT + INFL(I)*0.20
RESS = RESS + RESB(I)*0.2
UZS= UZS + RUZB(I)*0.20
ROS = ROS + ROSB(I)*0.2
100 RX = RX + RXB(I)*0.2
IF (UZS .LE. 0.0001) UZS=0.0
C
C END OF BLOCK LOOP
C
RU=RU + ROS
IF ((RESS).LT.(0.0001)) GO TO 301
GO TO 302
301 LZS = LZS + RESS
RESS = 0.0
DO 306 IK= 1,5
306 RESB(IK)= 0.0
302 IF (SRGXT.LT.(0.0001)) GO TO 303
GO TO 305
303 LZS = LZS + SRGXT/PA
SRGXT = 0.0
DO 304 IK= 1,5
304 SRGX(IK)= 0.0
C
C
C * * * LOWER ZONE AND GROUNDWATER * * *
C
C SBAS - BASE STREAMFLOW
C SRCH - SUM OF GRDWATER RECHARGE
C PREL - % OF INFILTRATION AND U.Z. DEPLETION ENTERING L.Z
C F1A - GROUNDWATER RECHARGE - IE. PORTION OF INFIL.
C AND U.Z. DEPLETION ENTERING GRDWATER
C K24L - FRACTION OF F1A LOST TO DEEP GRDWATER
C
305 LZI=1.5*ABS((LZS/LZSN)-1.0)+1.0
- 180 -
-------
Appendix C (continued)
PREL=(1.0/(1.0+LZI))**LZI
IF (LZS.LT.LZSN) PREL=1.0-PREL*LNRAT
F3= PREL*(INFLT)
F1A = (1.0-PREL)*INFLT
IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0)) GO TO 308
GO TO 309
308 F3 = F3 + PREL*PERC
F1A = F1A + (1.0-PREL)*PERC
309 LZS= LZS+F3
Fl= F1A*(1.0 - K24L)*PA
6WF=SGW*LKK4*(1.0 + KV*GWS)
SBAS= GWF
SRCH= F1A*K24L*PA
SGW-SGW - GWF + Fl
GWS=GWS + Fl
C
C * * * GROUNDWATER EVAP * * *
C
C
C LOS - EVAP LOST FROM GROUNDWATER
C
C
IF (IHR .EQ. 24) GO TO 307
GO TO 101
307 IF (GWS .61. 0.0001) GWS = 0.97*GWS
LOS= SGW*K24EL*REPIN*PA
SGW=SGW - LOS
GWS=GWS - LOS
SNET= SNET + LOS
REPIN= REPIN - LOS
IF (GWS.LT.(0.0)) GWS=0.0
C
C * * * LOWER ZONE EVAP * * *
C
C AETR - EVAP LOST FROM L.Z.
C
C
LNRAT = LZS/LZSN
IF (REPIN.LT.(0.0001)) GO TO 101
IF (K3-1.0) 300,310,310
310 KF=50.0
GO TO 320
300 KF=0.25/(1.0-K3)
320 IF (REPIN - (KF*LNRAT)) 330,330,340
330 AETR= REPIN*(1.0-(REPIN/(2.0*KF*LNRAT)))
GO TO 350
340 AETR- 0.5*(KF*LNRAT)
- 181 -
-------
Appendix C (continued)
350 IF (K3.LT.(0.50)) AETR=AETR*(2.0*K3)
LZS=LZS - AETR
SNET= SNET + PA*AETR
101 SNETI = SNET - SNET1
C
C
C
C WBAL - WATER BALANCE IN THE INTERVAL
C TWBAL - ACCUMULATED WATER BALANCE
C
C
352 WBAL = (LZS-LZS1+UZS-UZS1+RESS-RESS1)*PA+(SNET-SNET1+SGW-SGW1+
X SCEP-SCEP1+SRCH+SRGXT-SRGXT1+GWF+RU-PR)
IF ((WBAL .LE. 0.0001).AND.(WBAL .GE. -0.0001)) WBAL = 0.0
TWBAL=TWBAL+WBAL
C
DPS = F1A*PA
DPST = DPST + DPS
C
C
C RESETTING VARIABLES
C
LZS1=LZS
UZS1=UZS
RESS1=RESS
SCEP1=SCEP
SRGXT1=SRGXT
S6W1=SGW
SNET1=SNET
C
C
C ZEROING OF VARIABLES
C
C
C PREPARATION OF OUTPUT
C
ADPST = ADPST + DPST
ASBAS = ASBAS + SBAS
ASRCH = ASRCH + SRCH
APR = APR + PR
ARU = ARU + RU
ARUI = ARUI + RUI
AROS = AROS + ROS
ARGXT = ARGXT + RGXT
IF ((NUMI.EQ.O).AND.(IMIN.EQ.O)) GO TO 146
GO TO 148
146 AEPIN = AEPIN + EPIN1
ASNET = ASNET + SNETI
- 182 -
-------
Appendix C (continued)
C
C
C
C
C
148 DO 150 1=1,5
DPERCB(I) = RIB(I) - RGX(I) - RUZB(I) + 2*PERCB(I)
ARXB(I) = ARXB(I) + RXB(I)
ARGX(I) = ARGX(I) + RGX(I)
ADPRCB(I) = ADPRCB(I) + DPERCB(I)
ARIB(I) = ARIB(I) + RIB(I)
AROSB(I) = AROSB(I) + ROSB(I)
AINTF(I) = AINTF(I) + INTF(I)
AROSIT(I) = AROSIT(I) + ROSINT(I)
150 CONTINUE
155 CONTINUE
IF (PRNTKE .EQ. 0) GO TO 180
RX = 0.0
RG = 0.0
RIBT = 0.0
DPERC = 0.0
DO 158 1=1,5
DPERC = DPERC + ADPRCB(I)*0.2
RG = RG + ARGX(I)*0.2
RX = RX + ARXB(I)*0.2
RIBT = RIBT + ARIB(I)*0.2
158 CONTINUE
CUMULATIVE RECORDS
PRTOM = PRTOM + APR
EPTOM = EPTOM + AEPIN
RUTOM = RUTOM + ARU
ROSTOM = ROSTOM + AROS
RITOM = RITOM + ARUI
RINTOM = RINTOM + ARGXT
NEPTOM = NEPTOM + ASNET
BASTOM = BASTOM + ASBAS
RCHTOM = RCHTOM + ASRCH
157
DO 157 1=1,5
ROBTOM(I) =
ROBTOT(I) =
INFTOM(I) =
INFTOT(I) =
ROITOM(I) =
ROITOT(I) =
ROBTOM(I)
ROBTOT(I)
INFTOM(I)
INFTOT(I)
ROITOM(I)
ROITOT(I)
AROSB(I)
AROSB(I)
AINTF(I)
AINTF(I)
AROSIT(I)
AROSIT(I)
- 183 -
-------
Appendix C (continued)
C
C
C
C
159
C
C
C
PRTOT = PRTOT + APR
EPTOT = EPTOT + AEPIN
RUTOT = RUTOT + ARU
ROSTOT = ROSTOT + AROS
RITOT = RITOT + ARUI
RINTOT =RINTOT + ARGXT
NEPTOT = NEPTOT + ASNET
BASTOT = BASTOT + ASBAS
RCHTOT = RCHTOT + ASRCH
IF (HYCAL) 159, 160, 159
OUTPUT FOR HSP LANDS CALIBRATION RUN
IF (TF .61. 2) GO TO 170
RU = (RU*AREA*43560.)/(TIMFAC*720.)
IF (RU .LT. HYMIN) GO TO 170
QMETRC=RU*.0283
WRITE (6,379) MNAM(IZ),MNAM(IX),DAY,IHR.IMIN
WRITE (6,378) RU,QMETRC
GO TO 170
OUTPUT FOR HSP LANDS PRODUCTION RUN AND SUMMARIES
160
IF (UNIT .EQ. 1) GO TO 161
WRITE (6,360)
WRITE (6,361) APR, APR, APR, APR, APR, APR
WRITE (6,362)
WRITE (6,363) AROSE, AROS
WRITE (6,364) AINTF, ARGXT
WRITE (6,365) ARUI
WRITE (6,366) AROSIT,ARU
WRITE (6,380) ASBAS
WRITE (6,381) ASRCH
WRITE (6,367)
WRITE (6,368) AEPIN, AEPIN5AEPIN, AEPIN, AEPIN
WRITE (6,369) ASNET, ASNET, ASNET, ASNET, ASNET
WRITE (6,370)
WRITE (6,371) UZSB,UZS
WRITE (6,372) LZS,LZS,LZS,LZS,LZS,LZS
WRITE (6,373) SGW,SGW,SGW,SGW,SGW,SGW
WRITE (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
WRITE (6,375) RESB,RESS
WRITE (6,376) SRGX,SRGXT
WRITE (6,377) TWBAL
, AEPIN
, ASNET
- 184 -
-------
Appendix C (continued)
161 IF (UNIT .EQ. -1) GO TO 170
METRIC CONVERSIONS FOR OUTPUT
APR =APR*MMPIN
AROS =AROS*MMPIN
ARGXT =ARGXT*MMPIN
ARUI =ARUI*MMPIN
ARU =ARU*MMPIN
ASBAS =ASBAS*MMPIN
ASRCH =ASRCH*MMPIN
AEPIN =AEPIN*MMPIN
ASNET =ASNET*MMPIN
UZSMET=UZS*MMPIN
LZSMET=LZS*MMPIN
SGWMET=SGW*MMPIN
SCEPMT=SCEP*MMPIN
RESSMT=RESS*MMPIN
TWBLMT=TWBAL*MMPIN
SRGXTM=SRGXT*MMPIN
DO 162 1=1,5
AROSB(I) =AROSB(I)*MMPIN
AINTF(I) =AINTF(I)*MMPIN
AROSIT(I)=AROSIT(I)*MMPIN
UZSBMT(I)=UZSB(I)*MMPIN
RESBMT(I)=RESB(1)*MMPIN
SRGXMT(I)=SRGX(I)*MMPIN
162 CONTINUE
WRITE (6,460)
WRITE (6,461) APR,APR,APR,APR,APR,APR
WRITE (6,362)
WRITE (6,463) AROSB,AROS
WRITE (6,464) AINTF,ARGXT
WRITE (6,465) ARUI
WRITE (6,466) AROSIT.ARU
WRITE (6,480) ASBAS
WRITE (6,481) ASRCH
WRITE (6,367)
WRITE (6,468) AEPIN,AEPIN,AEPIN,AEPIN,AEPIN,AEPIN
WRITE (6,469) ASNET,ASNET,ASNET,ASNET,ASNET,ASNET
WRITE (6,370)
WRITE (6,471) UZSBMT, UZSMET
WRITE (6,472) LZSMET, LZSMET, LZSMET, LZSMET,
WRITE (6,473) SGWMET, SGWMET, SGWMET, SGWMET,
WRITE (6,474) SCEPMT, SCEPMT, SCEPMT, SCEPHT!
WRITE (6,475) RESBMT, RESSMT
WRITE (6,476) SRGXMT, SRGXT
WRITE (6,477) TWBLMT
GO TO 170
LZSMET, LZSMET
SGWMET, SGWMET
SCEPMT, SCEPMT
- 185 -
-------
Appendix C (continued)
C
C
C
C
360 FORMAT ('0
361 FORMAT ('0
362 FORMAT ('0
363 FORMAT ('
364 FORMAT ('
365 FORMAT ('
366 FORMAT ('
367 FORMAT ('0
368 FORMAT ('
369 FORMAT ('
370 FORMAT ('0
371 FORMAT ('
372 FORMAT ('
373 FORMAT ('
374 FORMAT ('
375 FORMAT ('
376 FORMAT ('
377 FORMAT ('0
378 FORMAT ('+
379 FORMAT (2X
380 FORMAT ('0
381 FORMAT ('
460 FORMAT ('0
461 FORMAT ('0
463 FORMAT ('
464 FORMAT ('
465 FORMAT ('
466 FORMAT ('
480 FORMAT ('0
481 FORMAT ('
468 FORMAT ('
469 FORMAT ('
471 FORMAT ('
472 FORMAT ('
473 FORMAT ('
474 FORMAT ('
475 FORMAT ('
476 FORMAT ('
477 FORMAT ('0
C
170 APR = 0.0
ADPST = 0.(
AEPIN = 0.(
ARU = 0.0
F
,8X,
,11X
,11X
,14X
,14X
,14X
,14X
,11X
,14X
,14X
,11X
,14X
,14X
,14X
,14X
,14X
,14X
,11X
,25X
A4,A
,11X
,11X
,8X,
,11X
,14X
,14X
,14X
,14X
,11X
,11X
,14X
,14X
,14X
,14X
,14X
,14X
, 14
,14X
,11X
)
)
FORMAT STATEMENTS
,8X,'WATER, INCHES')
PRECIPITATION',6X,5(3X,F7.3),4X,F7.3)
RUNOFF1)
OVERLAND_FLOW',3X,5(3X,F7.3),4X,F7.3)
INTERFLOW',7X,5(3X,F7.3),4X,F7.3)
IMPERVIOUS1,55X,5X,F7.3)
TOTAL',11X,5(3X,F7.3),4X,F7.3)
EVAPORATION1)
POTENTIAL',7X,5(3X,F7.3),4X,F7.3)
NET',13X,5(3X,F7.3),4X,F7.3)
STORAGES')
UPPER_ZONE',6X,5(3X,F7.3),4X,F7.3)
LOWER_ZONE',6X,5(3X,F7.3),4X,F7.3)
GROUNDWATER',5X,5(3X,F7.3),4X,F7.3)
INTERCEPTION',4X,5(3X,F7.3),4X,F7.3)
OVERLAND_FLOW',3X,5(3X,F7.3),4X,F7.3)
INTERFLOW',7X,5(3X,F7.3),4X,F7.3)
WATER_BALANCE=',F8.4)
,25X,F5.1,3X,F5.3)
A4,A4,2X,I2,2X,I2,':',I2)
'BASE_FLOW',64X,F7.3)
'GRDWATER_RECHARGE',56X,F7.3)
,8X,'WATER, MILLIMETERS')
1 PRECIPITATION',6X,5(3X,F7.2),4X,F7.2)
'OVERLAND_FLOW',3X,5(3X,F7.2),4X,F7.2)
1 INTERFLOW',7X,5(3X,F7.2),4X,F7.2)
1 IMPERVIOUS',60X,F7.2)
'TOTAL',11X,5(3X,F7.2),4X,F7.2)
'BASEFLOW1,65X,F7.2)
1GRDWATER_RECHARGE',56X,F7.2)
1 POTENTIAL',7X,5(3X,F7.2),4X,F7.2)
'NET',13X,5(3X,F7.2),4X,F7.2)
'UPPER_ZONE',6X,5(3X,F7.2),4X,F7.2)
'LOWER_ZONE',6X,5(3X,F7.2),4X,F7.2)
'GROUNDWATER',5X,5(3X,F7.2),4X,F7.2)
1 INTERCEPTION',4X,5(3X,F7.2),4X,F7.2)
,'OVERLAND_FLOW',3X,5(3X,F7.2),4X,F7.2)
,14X,'INTERFLOW' ,7X,5(3X,F7.2),4X,F7.2)
WATER BALANCE=',F8.3)
- 186 -
-------
Appendix C (continued)
ARUI = 0.0
AROS =0.0
ARGXT =0.0
ASNET = 0.0
ASBAS =0.0
ASRCH = 0.0
DO 172 1=1,5
ARXB(I) = 0.0
ARGX(I) = 0.0
ADPRCB(I) = 0.0
ARIB(I) = 0.0
AROSB(I) = 0.0
AINTF(I) = 0.0
AROSIT(I) = 0.0
172 CONTINUE
C
180 CONTINUE
RETURN
END
C
C
SUBROUTINE SEDT
C
C
C SEDIMENT EROSION MODEL
C
C
DIMENSION RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION AERSN(5), ROSB(5), AERSNM(5), SRERMT(5)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, I FLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCST, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
- 187 -
-------
Appendix C (continued)
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24,'K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
COMMON GSTR, GAS, GCS, GDS !
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEG.U, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
C
INTEGER PRNTKE, HYCAL, UNIT
C
REAL JRER, KRER, JSER, KSER, A
REAL ERSNTT, SRRTMT, EIMMET
REAL MMPIN, METOPT, KGPLB
C
DATA ERSNT/0.0/, AERSN, AEIM/6*0.0/
C
C ZEROING OF VARIABLES
C
SRERT =0.0
ERSNT =0.0
AR = 0.2*AREA
C
C SOIL EROSION LOOP
C
DO 4452 1=1,5
RER = (1.0 - COVER)*KRER*PR**JRER
SRER(I) = SRER(I) + RER*AR
EIM = A*RER*AREA
IF (ROSB(I)+RESB(I)) 4442, 4442, 4444
C
4442 ERSN(I) = 0.0
SER = 0.
GO TO 4446
C
4444 SER = KSER*SRER(I)*(ROSB(I)+RESB(I))**JSER
IF ((SER*AR) .GT. SRER(I)) SER = SRER(I)/AR
ERSN(I) = (SER)*AR*(ROSB(I)/(ROSB(I)+RESB(I)))
SRER(I) = SRER(I) - ERSN(I)
IF (SRER(I) .LT. 0.) SRER(I) = 0.
- 188 -
-------
Appendix C (continued)
C
4446 AERSN(I) = AERSN(I) + ERSN(I) + EIM*0.2
4452 CONTINUE
C
AEIM = AEIM + EIM
C
IF (PRNTKE .EQ. 0) GO TO 4490
C
DO 4456 1=1,5
ERSNT = ERSNT + AERSN(I)
SRERT = SRERT + SRER(I)
ERSTOM(I) = ERSTOM(I) + AERSN(I)
ERSTOT(I) = ERSTOT(I) + AERSN(I)
4456 CONTINUE
C
C CUMULATIVE RECORDS
C
ERSNTM = ERSNTM + ERSNT
EIMTM = EIMTM + AEIM
C
ERSNTT = ERSNTT + ERSNT
EIMTT = EIMTT + AEIM
C
ERSNTP =0.0
ERSNTK = 0.0
ERSNCE = 0.0
ERSNCM = 0.0
C
IF (HYCAL .EQ. 0) GO TO 4460
IF (RU .LT. HYMIN) GO TO 4487
C
C CONVERSION OF SEDIMENT LOSS TO IBS., KGS., AND GM/L FOR OUTPUT
C
ERSNTP = ERSNT*2000.
ERSNTK = ERSNTP*.454
ERSNCM = ERSNTP*454./(RU*TIMFAC*60.*28.32)
WRITE (6,4484) ERSNTP, ERSNTK, ERSNCM
GO TO 4487
C
C
C PRINTING OF OUTPUT
C
4460 IF (UNIT .EQ. 1) GO TO 4462
WRITE (6,4480)
WRITE (6,4481) (AERSN(I), 1=1,5), ERSNT
WRITE (6,4482) (SRER(I), 1=1,5), SRERT
WRITE (6,4483) EIM
- 189 -
-------
Appendix C (continued)
4462 IF (UNIT .EQ. -1) GO TO 4487
ERSNTT=ERSNT*METOPT
SRRTMT=SRERT*METOPT
EIMMET=EIM*METOPT
DO 4461 1=1,5
AERSNM(I)=AERSN(I)*METOPT
SRERMT(I)=SRER(I)*METOPT
4461 CONTINUE
WRITE (6,4485)
WRITE (6,4481) AERSNM, ERSNTT
WRITE (6,4482) SRERMT, SRRTMT
WRITE (6,4483) EIMMET
FORMAT STATEMENTS
4480 FORMAT ('0',8X,'SEDIMENT, TONS')
4481 FORMAT (' ',11X,'ERODED SEDIMENT1,4X,5(3X,F7.3),4X,F7.3)
4482 FORMAT (' ',11X,'FINES DEPOSIT',6X,5(3X,F7.3),4X,F7.3)
4483 FORMAT (' ' ,11X,'IMPERVIOUS EROSION',55X,F7.3)
4484 FORMAT ('+',40X,3(3X,F7.2))
4485 FORMAT ('0',8X,'SEDIMENT, TONNES')
'4487 AEIM = 0.0
DO 4489 1=1,5
AERSN(I) = 0.0
4489 CONTINUE-
C
C
C
4490 CONTINUE
RETURN
END
C
C
C
C
C
C
C
SUBROUTINE ADSRB1
SURFACE SOLUTION ADSORPTION MODEL
DIMENSION RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SR6X(5), RESB(5), MNAM(24)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5),, STSJ5), ROSB(5
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION SAPS(5), SCPS(5), SPRS(5), SPRO(5)
- 190 -
-------
Appendix C (continued)
DIMENSION SPR(5), SPS(5), SCSC(5)S SASC(5), SDSC(5),SPOFS(5)
DIMENSION ASPR(5), ASPRS(5), ASPRO(5), ASPRP(5)
DIMENSION STSMET(5), SASMET(5), SCSMET(5), SDSMET(5)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, IFLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IKS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
C
INTEGER PRNTKE, HYCAL, UNIT
C
REAL M, NI, K, KK, INFW
REAL STSTMT, SASTMT, SCSTMT, SDSTMT
REAL STSMET, SASMET, SCSMET, SDSMET
REAL MMPIN, METOPT, KGPLB
C
DATA SPST, SASCT, SCSCT, SPRT, SPRST/5*0.0/
DATA SPROT, SPRPT/2*0.0/, INFW/0.0/
DATA ASPR, ASPRS, ASPRO, ASPRP/20*0.0/
DATA SDSC, SPOFS, SDSCT/11*0.0/
C
C ZEROING VARIABLES
C
STST = 0.0
SAST = 0.0
SCST = 0.0
- 191 -
-------
Appendix C (continued)
SDST = 0.0
ERSNT =0.0
C
C ADSORPTION-SOLUTION LOOP
C
Z = 1000000.**(NI-1)
KK = M*K*Z
DO 5320 1=1,5
INFW = 0.2*AREA*(P3+RESB1(I))*226512.
PTOT = SAS(I) + SCS(I) + SDS(I) + SSTR(I)-0.2*(VOLS+DEGS)
IF (PTOT .LE. 0.0) PTOT = 0.0
IF (PTOT-FP) 5310, 5310, 5315
5310 SAS(I) = PTOT
SCS(I) = 0.0
SDS(I) = 0.0
GO TO 5320
5315 X = KK*CMAX**NI + FP
PSLD = PTOT - X - INFW*CMAX
IF (PSLD .LT. 0.0) GO TO 5316
SAS(I) = X
SCS(I) = PSLD
SDS(I) = CMAX*INFW
GO TO 5320
C
5316 SCS(I) = 0.0
C
C = CMAX*PTOT/(X + INFW*CMAX)
5317 X = KK*C**NI + FP
Q = (PTOT/(X+INFW*C)) - 1.
IF (ABS(Q) - 0.01) 5319, 5319, 5318
5318 C = C*PTOT/(X + INFW*C)
GO TO 5317
C
5319 IF (INFW .LE. 0.001) X = PTOT
C
SDS(I) = (C*INFW)*(PTOT/(X+C*INFW))
SAS(I) = X*(PTOT/(X+C*INFW))
C
5320 CONTINUE
C
C PESTICIDE REMOVAL LOOP
C
5325 DO 5330 1=1,5
C
QS = 2000.*ERSN(I)/M
IF (QS .GT. 1.0) QS = 1.0
SAPS(I) = SAS(I)*QS
SCPS(I) = SCS(I)*QS
- 192 -
-------
Appendix C (continued)
SPRS(I) = SAPS(I) + SCPS(I)
SAS(I) = SAS(I) - SAPS(I)
SCS(I) = SCS(I) - SCPS(I)
C
SPRO(I) = 0.0
SPOFS(I) = 0.0
SPRP(I) = 0.0
SPR(I) = 0.0
IF (P3 +RESB1(I)) 5327, 5327, 5328
5327 GO TO 5329
C
5328 SPRO(I) = SDS(I)*(ROSB(I)/(RESB1(I)+P3))
C
SPOFS(I) = SDS(I)*(RESB(I)/(RESB1(I)+P3))
SPRP(I) = SDS(I) - SPRO(I) - SPOFS(I)
IF (SPRP(I) .IT. 0.0) SPRP(I) = 0.0
SPR(I) = SPRO(I) + SPRS(I) + SPRP(I)
5329 SDS(I) = SPOFS(I)
C
C
C
5330
C
IF
C
C
C
ASPR(I) =
ASPRS(I)
ASPRO(I)
ASPRP(I)
RESBl(I)
CONTINUE
(PRNTKE .
DO 5335
SPRT =
SPROT
SPRST
SPRPT
SAST =
SCST =
SDST =
ASPR(I) + SPR(I)
= ASPRS(I) + SPRS(I)
= ASPRO(I) + SPRO(I)
= ASPRP(I) + SPRP(I)
= 0.0
EQ. 0) GO TO 5390
PREPARATION OF OUTPUT
1=1,5
SPRT +ASPR(I)
= SPROT +ASPRO(I)
= SPRST +ASPRS(I)
= SPRPT +ASPRP(I)
SAST + SAS(I)
SCST + SCS(I)
SDST + SDS(I)
SASC(I) = (SAS(I)/M)*1000000.
SASCT = SASCT + SASC(I)*0.2
SCSC(I) = (SCS(I)/M)*1000000.
SCSCT = SCSCT + SCSC(I)*0.2
SDSC(I) = (SDS(I)/M)*1000000.
- 193 -
-------
Appendix C (continued)
C
C
c
C
C
5335
5337
C
C
C
C
SDSCT = SDSCT + SDSC(I)*0.2
STS(I) = SAS(I) + SCS(I) + SDS(I)
SPS(I) = SASC(I) + SCSC(I) + SDSC(I)
STST = STST + STS(I)
SPST = SPST + SPS(I)*0.2
ERSNT - ERSNT + ERSN(I)
CONTINUE
CUMULATIVE RESULTS
DO 5337 1= 1,5
PRSTOM(I) = PRSTOM(I) + ASPRS(I)
PROTOM(I) = PROTOM(I) + ASPRO(I)
PROTOT(I) = PROTOT(I) + ASPRO(I)
PRSTOT(I) = PRSTOT(I) + ASPRS(I)
SPROTM - SPROTM + SPROT
SPRSTM = SPRSTM + SPRST
SPRTT = SPRTT + SPRT
SPROTT = SPROTT + SPROT
SPRPTT = SPRPTT + SPRPT
SPRSTT = SPRSTT + SPRST
•\
IF (HYCAL .EQ. 0) GO TO 5340
IF (HYCAL .EQ. 1) GO TO 5370
IF (RU ,LT. HYMIN) GO TO 5370
SPRTGW = SPROT*454.
SPRTCW = (SPROT/(RU*TIMFAC*60.*62.43))*1000000
SPRTGS = SPRST*454.
IF (ERSNT .LE. 0.0) GO TO 5338
SPRTCS = (SPRST/(ERSNT*2000.))*1000000.
GO TO 5339
5338 SPRTCS - 0.0
5339 WRITE (6,5360) SPRTGW, SPRTCW, SPRTGS, SPRTCS
GO TO 5370
PRINTING OF OUTPUT
5340 IF (UNIT .EQ. 1) GO TO 5341
WRITE (6,5350)
WRITE (6,5351) STS, STST
WRITE (6,5352) SAS, SAST
WRITE (6,5353) SCS, SCST
WRITE (6,5361) SDS, SDST
WRITE (6,5354) SPS, SPST
WRITE (6,5352) SASC, SASCT
- 194 -
-------
Appendix C (continued)
WRITE (6,5353)
WRITE (6,5361)
WRITE (6,5355)
WRITE (6,5356)
WRITE (6,5357)
WRITE (6,5359)
SCSC, SCSCT
SDSC, SDSCT
ASPR, SPRT
ASPRS, SPRST
ASPRO, SPROT
ASPRP, SPRPT
5341 IF (UNIT .EQ. -1) GO TO 5370
METRIC CONVERSIONS FOR OUTPUT
STSTMT=STST*KGPLB
SASTMT=SAST*KGPLB
SCSTMT=SCST*KGPLB
SDSTMT=SDST*KGPLB
SPRT =SPRT*KGPLB
SPRST =SPRST*KGPLB
SPROT =SPROT*KGPLB
SPRPT =SPRPT*KGPLB
DO 5343 1=1,5
STSMET(I)=STS(I)*KGPLB
SASMET(I)=SAS(I)*KGPLB
SCSMET(I)=SCS(I)*KGPLB
SDSMET(I)=SDS(I)*KGPLB
ASPR(I) =ASPR(I)*KGPLB
5343
ASPRS(I)
ASPRO(I)
ASPRP(I)
CONTINUE
WRITE (6,5350)
(6
(6
(6
(6
=ASPRS(I)*KGPLB
=ASPRO(I)*KGPLB
=ASPRP(I)*KGPLB
5345
WRITE
WRITE
WRITE
WRITE
IF (UNIT
WRITE (6
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
5363) STSMET, STSTMT
5352) SASMET, SASTMT
5353) SCSMET, SCSTMT
5361) SDSMET, SDSTMT
.EQ. 0) GO TO 5345
C
C
C
5354) SPS, SPST
5352) SASC, SASCT
5353) SCSC, SCSCT
5361) SDSC, SDSCT
5374) ASPR,SPRT
5356) ASPRS, SPRST
(6,5357) ASPRO, SPROT
(6,5359) ASPRP, SPRPT
FORMAT STATEMENTS
(6
(6
(6
(6
(6
5350
5351
5352
5353
FORMAT ('0', 5X,1SURFACE LAYER PESTICIDE1)
FORMAT ('0',8X,'PESTICIDE, LBS',9X,5(3X,F7.3),3X,F8.3)
FORMAT (' ',11X,'ADSORBED1,12X,5(3X,F7.3),3X,F8.3)
FORMAT (' ',11X,'CRYSTALLINE1,9X,5(3X,F7.3),3X,F8.3)
- 195 -
-------
Appendix C (continued)
5354
5355
5356
5357
5359
5360
5361
5363
5374
C
C
C
5370
5380
s
'5390
5391
FORMAT ('0',8X,'PESTICIDE, PPM1,9X,5(3X,F7.3),3X,F8.3)
FORMAT ('0',8X,'REMOVAL, IBS',11X,5(3X,F7.3),3X,F8.3)
FORMAT (' ',11X,'SEDIMENT',12X,5(3X,F7.3),3X,F8.3)
FORMAT (' MIX,'OVERLAND FLOW ,7X,5(3X,F7.3),3X,F8.3)
FORMAT (' MIX, 'PERCOLATION1,9X,5(3X,F7.3),3X,F8.3)
FORMAT ('+',70X,2(2X,F8.3,3X,F7.3))
FORMAT (' ',11X,'DISSOLVED',11X,5(3X,F7.3),3X,F8.3)
FORMAT ('0',8X,'PESTICIDE, KGS',9X,5(3X,F7.3),3X,F8.3)
FORMAT ('0',8X,'REMOVAL, KGS M1X,5(3X,F7.3) ,3X,F8. 3)
ZEROING VARIABLES
DO 5380 1=1,5
ASPR(I) = 0.0
ASPRO(I) = 0.0
ASPRS(I) = 0.0
ASPRP(I) = 0.0
CONTINUE
SPST = 0.0
SASCT =0.0
SCSCT =0.0
SDSCT =0.0
SPRT = 0.0
SPRST =0.0
SPROT =0.0
SPRPT = 0.0
DO 5391 1= 1,5
SSTR(I) = 0.0
RETURN
END
C
C
C
C
C
C
C
SUBROUTINE ADSRB2
UPPER ZONE SOLUTION ADSORPTION MODEL
DIMENSION RXB(5), R6X(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5), ROSB(5)
- 196 -
-------
Appendix C (continued)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION JNFW(5), UDSCJ5), UPRIS(5)
DIMENSION UPR(5), UPS(5), UCSC(5), UASC(5)
DIMENSION AUPR(5), AUPRI(5), AUPRP(5)
DIMENSION UTSMET(5), UASMET(5), UCSMET(5), UDSMET(5)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSB, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, II, DAY, IFLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
C
INTEGER PRNTKE, HYCAL, UNIT
C
REAL UTSTMT, UASTMT, UCSTMT, UDSTMT
REAL UTSMET, UASMET, UCSMET, UDSMET
REAL MUZ, NI, K, KK, JNFW, INTF, JJ
REAL MMPIN, METOPT, KGPLB
C
DATA UPST, UASCT, UCSCT, UPRT, UPRIS/9*0.0/
DATA UDSCT, UPRPT/2*0.0/, JNFW/5*0.0/, UPRIT/0.0/
DATA AUPR, AUPRI, AUPRP/15*0.0/
C
C ZEROING VARIABLES
C
UTST = 0.0
UAST = 0.0
- 197 -
-------
Appendix C (continued)
UCST = 0.0
UDST = 0.0
C
C SOLUTION ADSORPTION LOOP
C
DO 6305 I = 1,5
JNFW(I) = AREA*(RIB(I)+UZSB(I)-RUZB(I)+(2*PERCB(I)))*45302.4
6305 CONTINUE
C
Z = 1000000.**(NI-1)
KK = MUZ*K*Z
C
DO 6320 1=1,5
JJ = JNFW(I)
PTOT =UAS(I)+UCS(I)+ UDS(I) + SPRP(I) + USTR(I)-0.2*(DEGU+VOLU)
IF (PTOT .LE. 0.0) PTOT = 0.0
C5 = 0.0
IF (JJ .61. 0.0) C5 = -UDS(I)/JJ
C
IF (PTOT-FPUZ) 6310, 6310, 6315
6310 UAS(I) = PTOT
UCS(I) = 0.0
UDS(I) = 0.0
GO TO 6320
C
6315 X = KK*CMAX**NI + FPUZ
PSLD = PTOT - X - JJ*CMAX
IF (PSLD .LT. 0.0) GO TO 6316
UAS(I) = X
UCS(I) = PSLD
UDS(I) = CMAX*JJ
GO TO 6320
C
6316 UCS(I) = 0.0
C
C = C5
IF (C .LE. 0.0) C = 0.001
6317 X = KK*C**NI + FPUZ
Q = (PTOT/(X+JJ*C)) - 1.
IF (ABS(Q)-O.Ol) 6319, 6319, 6318
6318 C = C*PTOT/(X+JJ*C)
GO TO 6317
C
6319 IF (JJ .LE. 0.001) X = PTOT
C
UDS(I) = (C*JJ)*(PTOT/(X+C*JJ))
UAS(I) = X*(PTOT/(X+C*JJ))
- 198 -
-------
Appendix C (continued)
C
6320 CONTINUE
C
c PESTICIDE REMOVAL LOOP
C
6325 DO 6330 1=1,5
C
IF (JNFW(I) .LE. 0.0001) GO TO 6327
QSP = (RIB(I)-RGX(I)-RUZB(I)+(2*PERCB(I)))/JNFW(I)
IF (QSP .61. 1.0) QSP = 1.0
UPRP(I) = UDS(I)*QSP
QSI = R6X(I)/JNFW(I)
IF (QSI .61. 1.0) QSI = 1.0
IF (SRGX(I) .LT. 0.0001) GO TO 6329
UPRII = UDS(I)*QSI
UPRIS(I) = UPRIS(I) + UPRII
UPRI(I) = (UPRIS(I))*(INTF(I)/SR6X(I))
UPRIS(I) = UPRIS(I) - UPRI(I)
GO TO 6328
6327 UPRP(I) = 0.0
6329 UPRI(I) = 0.0
6328 UDS(I) = UDS(I) - UPRP(I) - UPRI(I)
IF (UDS(I) .LT. 0.0) UDS(I) = 0.0
UPR(I) = UPRP(I) + UPRI(I)
C
AUPR(I) = AUPR(I) + UPR(I)
AUPRI(I) = AUPRI(I) + UPRI(I)
AUPRP(I) = AUPRP(I) + UPRP(I)
C
6330 CONTINUE
C
IF (PRNTKE .EQ. 0) GO TO 6380
C
C PREPARATION OF OUTPUT
C
DO 6335 1=1,5-
UPRT = UPRT +
UPRIT = UPRIT
UPRPT = UPRPT
UAST = UAST +
UCST = UCST +
UDST = UDST +
AUPR(I)
+ AUPRI(I)
+ AUPRP(I)
UAS(I
UCS(I
UDS(I
UASC(I) = (UAS(I)/MUZ)*1000000.
UCSC(I) = (UCS(I)/MUZ)*1000000.
IF (UZSB(I) .LE. 0.0001) GO TO 6333
UDSC(I) = (UDS(I)/(UZSB(I)*AREA*45302.4))*1000000.
- 199 -
-------
Appendix c (continued)
6333
6334
C
C
C
C
C
C
C
C
GO TO 6334
UDSC(I) = 0.0
UPS(I) = UASC(I)
UCSC(I) + UDSC(I)
UASCT = UASCT
UCSCT = UCSCT
UDSCT = UDSCT
UPST = UPST +
+ UASC(I)*0.
+ UCSC(I)*0,
+ UDSC(I)*0,
UPS(I)*0.2
UTS(I) = UAS(I) + UCS(I) + UDS(I)
UTST = UTST + UTS(I)
6335
CONTINUE
CUMULATIVE RESULTS
6340
DO 6340 1= 1,5
UPITOM(I) = UP-ITOM(I)
UPITOT(I) = UPITOT(I)
UPRITM = UPRITM + UPRIT
UPRITT = UPRITT + UPRIT
AUPRI(I)
AUPRI(I)
IF (HYCAL .NE. 0) 60 TO 6365
PRINTING OF OUTPUT
IF (UNIT .EQ.
WRITE (6,6350)
WRITE (6,6351)
WRITE (6,6352)
WRITE (6,6353)
WRITE (6,6354)
WRITE (6,6355)
WRITE (6,6352)
WRITE (6,6353)
WRITE (6,6354)
WRITE (6,6357)
WRITE (6,6358)
WRITE (6,6359)
1) GO TO 6342
UTS, UTST
UAS, UAST
UCS, UCST
UDS, UDST
UPS, UPST
UASC, UASCT
UCSC, UCSCT
UDSC, UDSCT
AUPR, UPRT
AUPRI, UPRIT
AUPRP, UPRPT
6342 IF (UNIT .EQ. -1) GO TO 6365
C
C METRIC CONVERSIONS FOR OUTPUT
UTSTMT=UTST*KGPLB
UASTMT=UAST*KGPLB
UCSTMT=UCST*KGPLB
UDSTMT=UDST*KGPLB
UPRT =UPRT*KGPLB
- 200 -
-------
Appendix C (continued)
UPRIT =UPRIT*KGPLB
UPRPT =UPRPT*KGPLB
DO 6344 1=1,5
UTSMET(I)=UTS(I)*KGPLB
UASMET(I)=UAS(I)*KGPLB
UCSMET(I)=UCS(I)*KGPLB
UDSMET(I)=UDS(I)*KGPLB
AUPR(I) =AUPR(I)*KGPLB
AUPRI(I) =AUPRI(I)*KGPLB
AUPRP(I) =AUPRP(I)*KGPLB
6344 CONTINUE
WRITE (6,6350)
WRITE (6,6360) UTSMET, UTSTMT
WRITE (6,6352) UASMET, UASTMT
WRITE (6,6353) UCSMET, UCSTMT
WRITE (6,6354) UDSMET, UDSTMT
IF (UNIT .EQ. 0) GO TO 6345
WRITE (6,6355) UPS, UPST
(6,6352) UASC, UASCT
(6,6353) UCSC, UCSCT
(6,6354) UDSC, UDSCT
(6,6361) AUPR, UPRT
(6,6358) AUPRI, UPRIT
6345
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
C
C
C
(6,6359) AUPRP, UPRPT
6350
6351
6352
6353
6354
6355
6357
6358
6359
6360
6361
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
'0'
'0'
'0'
i i
i i
'0'
'0'
C
C
C
FORMAT STATEMENTS
, 5X,'UPPER ZONE LAYER PESTICIDE')
,8X,'PESTICIDE, LBS1,9X,5(3X,F7.3),3X,F8.3)
1 ADSORBED',12X,5(3X,F7.3),3X,F8.3)
'CRYSTALLINE' ,9X,5(3X,F7.3),3X,F8.3)
1 DISSOLVED',11X,5(2X,F8.3),3X,F8.3)
PESTICIDE, PPM',9X,5(3X,F7.3),3X,F8.3)
REMOVAL, LBS',11X,5(3X,F7.3),3X,F8.3)
1 INTERFLOW',11X,5(3X,F7.3),3X,F8.3)
'PERCOLATION',9X,5(3X,F7.3),3X,F8.3)
,8X,'PESTICIDE, KGS',9X,5(3X,F7.3),3X,F8.3)
,8X,'REMOVAL, KGS1,11X,5(3X,F7.3),3X,F8.3)
ZEROING VARIABLES
,8X,
,8X,
6365 DO 6370 1=1,5
AUPR(I) = 0.0
AUPRI(I) = 0.0
AUPRP(I) = 0.0
6370 CONTINUE
- 201 -
-------
Appendix C (continued)
C
6380 UPST =0.0
UASCT =0.0
UCSCT = 0.0
UDSCT = 0.0
UPRT = 0.0
UPRPT = 0.0
UPRIT = 0.0
C
DO 6381 1= 1,5
6381 USTR(I) = 0.0
C
RETURN
END
C
C
SUBROUTINE ADSRB3
C
C
C LOWER ZONE AND GROUNDWATER
C SOLUTION ADSORPTION MODEL
C
C
IMPLICIT REAL(L)
C
DIMENSION RXB(5), R6X(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5), ROSB(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
C
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, I FLAG, COVER, COVMAX
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCST, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
- 202 -
-------
Appendix C (continued)
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
C
INTEGER PRNTKE, HYCAL, UNIT
C
REAL KNFW, MLZ, K, KK, NI
REAL LSTRMT, LASMET, LCSMET, LDSMET
REAL GSTRMT, GASMET, GCSMET, GDSMET
REAL MMPIN, METOPT, KGPLB
C
DATA ALPRP/0.0/
C
C SOLUTION ADSORPTION LOOP
C
LCS - 0.0
LAS = 0.0
LDS = 0.0
C
KNFW = AREA*(LZS+DPST)*226512.
Z = 1000000.**(NI-1)
KK = MLZ*K*Z
C
DO 7305 1=1,5
LSTR = LSTR + UPRP(l)
7305 CONTINUE
C
IF (LSTR .LE. 0.0001) GO TO 7330
C
PTOT = LSTR
C5 = 0.0
IF (KNFW .61. 0.0) C5 = LAS/KNFW
IF (PTOT-FPLZ) 7310, 7310, 7315
7310 LAS = PTOT
LCS = 0
LDS = 0
GO TO 7320
- 203 -
-------
Appendix C (continued)
C
7315 X = KK*CMAX**NI + FPLZ
PSLD = PTOT - X - KNFW*CMAX
IF (PSLD .LT. 0.0) GO TO 7316
LAS = X
LCS = PSLD
LDS = CMAX*KNFW
GO TO 7320
C
7316 LCS = 0.0
C
C = C5
IF (C .LE. 0.0) C - 0.001
7317 X = KK*C**NI + FPLZ
Q = (PTOT/(X+KNFW*C)) - 1.
IF (ABS(Q)-O.Ol) 7319, 7319, 7318
7318 C = C*PTOT/(X+KNFW*C)
GO TO 7317
C
7319 IF (KNFW .LE. 0.001) X = PTOT
C
LDS = (C*KNFW)*(PTOT/(X+C*KNFW))
LAS = X*(PTOT/(X+C*KNFW))
C
7320 CONTINUE
C
C PESTICIDE REMOVAL LOOP
C
LPRP = LDS*DPST/(DPST+LZS)
LDS = LDS - LPRP
C
LSTR = LAS + LCS + LDS
C
ALPRP = ALPRP + LPRP
C
7330 IF ((PRNTKE .EQ. 0).OR.(HYCAL .NE. 0)) GO TO 7380
C
C PREPARATION OF OUTPUT
C
C
LASC = (LAS/MLZ)*1000000.
LCSC = (LCS/MLZ)*1000000.
LDSC = (LDS/(LZS*AREA*226512.))*1000000.
C
C PRINTING OF OUTPUT
C
IF (UNIT .EQ. 1) GO TO 7340
WRITE (6,7350)
- 204 -
-------
Appendix C (continued)
WRITE (6,7351) LSTR
WRITE (6,7352) LAS
WRITE (6,7353) LCS
WRITE (6,7354) IDS
WRITE (6,7355)
WRITE (6,7352) LASC
WRITE (6,7353) LCSC
WRITE (6,7354) LDSC
WRITE (6,7357) ALPRP
WRITE (6,7359) ALPRP
^7340 IF (UNIT .EQ. -1) GO TO 7379
3 METRIC CONVERSIONS FOR OUTPUT
LSTRMT=LSTR*KGPLB
LASMET=LAS*KGPLB
LCSMET=LCS*KGPLB
LDSMET=LDS*KGPLB
ALPRP =ALPRP*KGPLB
WRITE (6,7350)
WRITE (6,7360) LSTRMT
WRITE (6,7352) LASMET
WRITE (6,7353) LCSMET
WRITE (6,7354) LDSMET
IF (UNIT .EQ. 0) GO TO 7345
WRITE (6,7355)
WRITE (6,7352) LASC
WRITE (6,7353) LCSC
WRITE (6,7354) LDSC
7345 WRITE (6,7361) ALPRP
WRITE (6,7359) ALPRP
C
C
C
7350 FORMAT
7351 FORMAT
7352 FORMAT
7353 FORMAT
7354 FORMAT
7355 FORMAT
7357 FORMAT
7359 FORMAT
7360 FORMAT
7361 FORMAT
C
C
C
FORMAT STATEMENTS
('0', 5X,'LOWER ZONE LAYER PESTICIDE1)
('0',8X,'PESTICIDE, LBS',62X,F8.3)
(' ',11X,'ADSORBED1,65X,F8.3)
(' ',11X,'CRYSTALLINE',62X,F8.3)
(' ',HX,'DISSOLVED1,64X.F8.3)
('O'.SX,1PESTICIDE, PPM',62X,F8.3)
('0',8X,'REMOVAL, LBS' ,64X,F8.3)
(' MIX,'PERCOLATION1,62X.F8.3)
('0',8X,'PESTICIDE, KGS',62X,F8.3)
('0',8X,'REMOVAL, KGS',64X,F8.3)
ZEROING OF VARIABLES
7379 ALPRP = 0.0
- 205 -
-------
Appendix C (continued)
C
7380 CONTINUE
C
C
C GROUNDWATER ADSORPTION MODEL
L
c
GSTR = GSTR + LPRP
IF (FPLZ .GT. 0.0) GO TO 7520
GAS = 0.0
GDS = GSTR
GCS = 0.0
C
7520 GAS = GSTR
GCS = 0.0
GDS = 0.0
C
IF ((PRNTKE .EQ. 0).OR.(HYCAL .NE. 0)) GO TO 7580
C
C PRINTING OF OUTPUT
C
IF (UNIT .EQ. 1) GO TO 7530
WRITE (6,7550)
WRITE (6,7551) GSTR
WRITE (6,7552) GAS
WRITE (6,7553) GCS
WRITE (6,7554) GDS
7530 IF (UNIT .EQ. -1) GO TO 7580
C
C METRIC CONVERSIONS FOR OUTPUT
GSTRMT=GSTR*KGPLB
GASMET=GAS*KGPLB
GCSMET=GCS*KGPLB
GDSMET=GDS*KGPLB
WRITE (6,7550)
WRITE (6,7555) GSTRMT
WRITE (6,7552) GASMET
WRITE (6,7553) GCSMET
WRITE (6,7554) GDSMET
C
C FORMAT STATEMENTS
C
7550 FORMAT ('0',5X,'GROUNDWATER LAYER PESTICIDE1)
7551 FORMAT ('0',8X,'PESTICIDE, LBS',62X,F8.3)
7552 FORMAT (' ',11X,'ADSORBED1,65X,F8.3)
7553 FORMAT (' ',11X,'CRYSTALLINE',62X,F8.3)
7554 FORMAT (' ',11X,'DISSOLVED1,64X,F8.3)
7555 FORMAT ('0',8X,'PESTICIDE, KGS1,62X,F8.3)
- 206 -
-------
Appendix C (continued)
7580 CONTINUE
RETURN
END
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
SUBROUTINE VOLDEG
VOLATILIZATION-DEGRADATION MODEL
PESTICIDE VOLATILIZATION
DIFC - DIFFUSION COEFFICIENT (MM2/WK) AT TEMPERATURE
TDIFC (C DEGREES)
CONCIU - INITIAL PEST. CONC. IN U.Z. IN G/CC
BULKD - BULK DENSITY OF SOIL IN G/CC
AREA - WATERSHED AREA IN ACRES
EVC - EQUIL. VAPOR PRES. IN MICROGRAMS/CC
MOLEWT - MOLECULAR WEIGHT
WIND - WIND IN MI/DAY
WCFAC - WIND CALIBRATION CONSTANT
APMODE - APPLICATION MODE - 0=SURFACE, 1=SOIL
PRES - PRESSURE IN MM OF HG
APFAC - CONSTANT FOR PRES-TEMP ADJUSTMENT
BPFAC - CONSTANT FOR PRES-TEMP ADJUSTMENT
DIMENSION RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
DIMENSION ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
DIMENSION ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
DIMENSION ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
DIMENSION PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
DIMENSION UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
DIMENSION SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
DIMENSION UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
DIMENSION AERSN(5), ROSB(5)
COMMON PRTOT, ERSNTT, EIMTT, PRTT, IHR
COMMON PRTOM, ERSNTM, EIMTM, PRTM, IMIN
COMMON RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
COMMON RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
COMMON ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
COMMON PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
COMMON RES5; B£SB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
COMMON MNAM, IX, IZ, DAY, IFLAG, COVER.
- 207 -
-------
Appendix C (continued)
COMMON SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
COMMON STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
COMMON PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
COMMON TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
COMMON A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
COMMON IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
COMMON SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
COMMON SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
COMMON SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
COMMON UAS, UCS, UDS, USTR, MUZ, FPUZ
COMMON UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
COMMON FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
COMMON GSTR, GAS, GCS, GDS
COMMON APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
COMMON MOLEWT, APFAC, BPFAC, WCFAC
COMMON VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
COMMON DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
COMMON DEGLOM, DEGLOT
C
C
REAL KFAC, MOLEWT, LSTR
REAL TVOLMT, VOLSMT, VOLUMT
REAL MMPIN, METOPT, KGPLB
INTEGER APMODE, PRNTKE, HYCAL , UNIT
C
C
DATA TVOL, TFLUX/2*0.0/, DEGL/0.0/
C
C
C
C SOIL INCORPORATED PESTICIDE VOLATILIZATION
C AND DEGRADATION
C
IF (UTST .LE. 0.0) GO TO 8040
DEGU = DEGCON*UTST
IF (APMODE .EQ. 0) GO TO 8022
IF (CADIF .EQ. 0.0) GO TO 8022
C
DIFCI = CADIF*(EXP(CBDIF*TEMP))
KFAC = (SQRT(DIFCI/2198.))*CONCIU
IF (IFLAG .EQ. 1) GO TO 8010
GO TO 8020
8010 FLUXI = KFAC*(2.0)
IFLAG = 0
GO TO 8025
8020 FLUXI = (2*((KFAC)**2))/TFLUX
8025 VOLU = FLUXI*AREA*(40.469)/1000.
TFLUX = TFLUX + FLUXI
VOLU = VOLU/.454
- 208 -
-------
appendix C (continued)
C
8022 UTST = UTST - VOLU - DEGU
IF (UTST) 8026,8027,8029
8026 VOLU =0.0
DEGU =0.0
8027 UTST =0.0
8029 DO 8030 I =1,5
UTS(I) = UTS(I) - 0.2*(VOLU+DEGU)
8030 IF (UTS(I) .LE. 0.0) UTS(I) = 0.0
C
C
C
C SURFACE PESTICIDE VOLATILIZATION AND DEGRADATION
C
8040 IF (STST .LE. 0.0) GO TO 8070
DEGS =DEGCON*STST
IF ((APFAC .EQ. 0.0).AND.(BPFAC .EQ. 0.0)) GO TO 8042
C
PRES = 10.**(APFAC-BPFAC/(TEMP+273.))
EVC = PRES*MOLEWT*10E6/((TEMP+273.)*62365.6)
FLUXI = WCFAC*WIND*EVC/SQRT(MOLEWT)
VOLS = FLUXI*AREA*(40.469/1000.)
VOLS = VOLS/.454
C
8042 STST = STST - VOLS - DEGS
IF (STST) 8050,8055,8057
8050 VOLS =0.0
DEGS = 0.0
8055 STST =0.0
8057 DO 8060 1= 1,5
STS(I) = STS(I) - 0.2*(VOLS+DEGS)
8060 IF (STS(I) .LE. 0.0) STS(I) = 0.0
C
C
C LOWER ZONE PESTICIDE DEGRADATION
C
8070 IF (LSTR .LE. 0.0) GO TO 8090
DEGL = DEGCON*LSTR
LSTR = LSTR - DEGL
IF (LSTR) 8076, 8077, 8090
8076 DEGL =0.0
8077 LSTR = 0.0
C
C
8090 CONTINUE
C
C
- 209 -
-------
Appendix C (continued)
C
C
C
CUMULATIVE RESULTS
VOLSOM
VOLSOT
VOLUOM
VOLUOT
DEGSOM
DEGSOT
DEGUOM
DEGUOT
DEGLOM
DEGLOT
VOLSOM
VOLSOT
VOLUOM
VOLUOT
DEGSOM
DEGSOT
DEGUOM
DEGUOT
DEGLOM
DEGLOT
VOLS
VOLS
VOLU
VOLU
DEGS
+ DEGS
+ DEGU
+ DEGU
+ DEGL
+ DEGL
TVOL = VOLU + VOLS
TDEG = DEGS + DEGU
+ DEGL
C
C
IF ((PRNTKE .EQ. 0).OR.(HYCAL .NE. 0)) GO TO 8600
IF (UNIT .EQ. 1) GO TO 8200
WRITE (6,8500)
WRITE (6,8501) TVOL
WRITE (6,8502) VOLS
WRITE (6,8503) VOLU
WRITE (6,8505)
WRITE (6,8501) TDEG
WRITE (6,8502) DEGS
WRITE (6,8503) DEGU
WRITE (6,8507) DEGL
8200 IF (UNIT .EQ. -1) GO TO 8600
METRIC CONVERSIONS FOR OUTPUT
TVOLMT=TVOL*KGPLB
VOLSMT=VOLS*KGPLB
VOLUMT=VOLU*KGPLB
TDEGMT=TDEG*KGPLB
DEGSMT=DEGS*KGPLB
DEGUMT=DEGU*KGPLB
DEGLMT=DEGL*KGPLB
WRITE (6,8504)
WRITE (6,8501) TVOLMT
WRITE (6,8502) VOLSMT
WRITE (6,8503) VOLUMT
WRITE (6,8506)
WRITE (6,8501) TDEGMT
WRITE (6,8502) DEGSMT
WRITE (6,8503) DEGUMT
WRITE (6,8507) DEGLMT
- 210 -
-------
Appendix C (continued)
C
C
8500 FORMAT ('0'
8501 FORMAT (' '
8502 FORMAT (' '
8503 FORMAT (' '
8504 FORMAT ('0'
8505 FORMAT ('0'
8506 FORMAT ('0'
8507 FORMAT (' '
,5X,'PESTICIDE VOLATILIZATION LOSS, LBS.
,8X,'TOTAL',72X,F7.3)
,8X,'FROM SURFACE',65X,F7.3)
,8X,'FROM UPPER ZONE1,62X,F7.3)
,5X,'PESTICIDE VOLATILIZATION LOSS, KGS.
,5X,'PESTICIDE DEGRADATION LOSS, LBS.1)
,5X,'PESTICIDE DEGRADATION LOSS, KGS.')
,8X,'FROM LOWER ZONE',62X,F7.3)
8600 RETURN
END
/*
//LKED.SYSLMOD DD DSNAME=C510.TONY.PEST10,DISP=(NEW,KEEP),
// SPACE=(TRK,(15,1,1),RLSE),UNIT=2314,
// VOL=SER=SYS13
//LKED.SYSIN DD *
NAME PEST
/*
- 211 -
MJ.S. GOVERNMENT PRINTING OFFICE: 1974 546-318/383 1-3
-------
SELECTED WATER
RESOURCES ABSTRACTS
INPUT TRANSACTION FORM
4. Title
PESTICIDE TRANSPORT AND RUNOFF MODEL FOR
AGRICULTURAL LANDS
7. Author(s)
N.H. Crawford and A.s. Doniaian. Jr
9. Organization
Hydrocomp, Inc.
1502 Page Mill Road
Palo Alto, California 94304
11. Contract/Grant No.
68-01-0887
75, Supplementary Notes
Environmental Protection Agency report number, EPA-660/2-74-013, December 1973.
16. Abstract
The development and testing of a mathematical model to simulate the loss of pesticides
from agricultural lands is presented. The Pesticide Transport and Runoff (PTR) Model
is composed of submodels concerned with hydrology, sediment loss, pesticide-soil
interaction, and pesticide attenuation functions. The Model 'piggybacks' the applied
pesticide onto the movement of water through the soil profile and the loss of water and
sediment from the land surface. The pesticide-soil interaction is based on the
Freundlich adsorption-desorption isotherm. Attenuation functions of volatilization and
degradation are provided but were not tested due to lack of data.
Comparison of simulated and recorded runoff and sediment loss showed considerable agree-
ment. Simulated pesticide loss agreed reasonably well with recorded values for those
pesticides completely adsorbed on sediment particles. The Freundlich adsorption model
did not accurately predict the division between the adsorbed and dissolved'states for
those pesticides which are transported by runoff and sediment loss. Recommendations
for future work include further calibration and testing of the PTR Model, and additional
development on the pesticide adsorption and attenuation functions. The regulation of
pesticide releases to the environment are explored as possible eventual uses of the
PTR Model.
17a. Descriptors
Pesticide pollution, Regulation, *Pesticide transport, Simulation, *Hydrologic
modeling, *Sediment transport, *Pesticide-soil adsorption, Volatilization.
17b. Identifiers
*Pesticide transport, Agricultural runoff, Sediment transport.
17c. CO WRR Field & Group
IS. Availability
Abstractor A.S. Donigian,Jr
WATER RESOURCES SCIENTIFIC INFORMATION CENTER
U.S. DEPARTMENTOFTHE INTERIOR
WASHINGTON, D. C. 2O24O
nstitution Hvdrocomp. Inc.
••--'C102IREV JUNE 1971)
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