xvEPA
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30613
EPA/600/3-89/048b
July 1989
Research and Development
Risk of
Unsaturated/Saturated
Transport and
Transformation of
Chemical
Concentrations (RUSTIC)
Volume II: User's Guide
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EPA/600/3-89/048b
RISK OF UNSATURATED/SATURATED TRANSPORT
AND TRANSFORMATION OF CHEMICAL
CONCENTRATIONS (RUSTIC)
Volume II: User's Guide
by
J.D. Dean, P,S. Huyakorn, A.S. Donigian, Jr.,3
K.A. Voos,1 R.W. Schanz,1 and R.F. Carsel4
Woodward-Clyde Consultants
Oakland, CA 94607
n
HydroGeologic^1
Herndon, VA 22070
AQUA TERRA Consultants3
Mountain View, CA 94043
Contract No. 68-03-6304
Project Officer
Robert F. Carsel
Assessment Branch
Environmental Research Laboratory
U.S. Environmental Protection Agency
Athens, GA 30613
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GA 30613
:'••'•-• -.nraental Protection
- \ :-''.veT-j L.-I,-16)
V:,.lcrn ijj.-ejt, Boom 1670
-...fe-o, IL 60604
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DISCLAIMER
The information in this document has been funded wholly or in part by the
United States Environmental Protection Agency under Contract No. 68-03-6304
to Woodward-Clyde Consultants. It has been subject to the Agency's peer and
administrative review, and it has been approved for publication as an EPA
document. Mention of trade names of commercial products does not constitute
endorsement or recommendation for use by the U.S. Environmental Protection
Agency.
ii
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FOREWORD
As environmental controls become more costly to implement and the penalties
of judgment errors become more severe, environmental quality management
requires more efficient analytical tools based on greater knowledge of the
environmental phenomena to be managed. As part of this Laboratory's research
on the occurrence, movement, transformation, impact, and control of
environmental contaminants, the Assessment Branch develops management or
engineering tools to help pollution control officials reach decisions on the
registration and restriction of pesticides used for agricultural purposes.
The pesticide regulatory process requires that the potential risk to human
health resulting from the introduction or continued use of these chemicals be
evaluated. Recently much of this attention has been focused on exposure
through leaching of pesticides to groundwater and subsequent ingestion of
contaminated water. To provide a tool for evaluating this exposure, the
RUSTIC model was developed. RUSTIC simulates the transport of field-applied
pesticides in the crop root zone, the unsaturated zone, and the saturated
zone to a drinking water well, taking into account the effects of
agricultural management practices. The model further provides estimates of
probable exposure concentrations by taking into account the variability in
the natural systems and the uncertainties in system properties and processes.
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, Georgia
iii
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ABSTRACT
This publication contains documentation for the RUSTIC model. RUSTIC links
three subordinate models in order to predict pesticide fate and transport
through the crop root zone, unsaturated zone, and saturated zone to drinking
water wells: PRZM, VADOFT, and SAFTMOD. PRZM is a one-dimensional finite-
difference model which accounts for pesticide fate and transport in the crop
root zone. This release of PRZM incorporates several features in addition to
those simulated in the original PRZM code: specifically, soil temperature
simulation, volatilization and vapor phase transport in soils, irrigation
simulation and a method of characteristics (MOC) algorithm to eliminate
numerical dispersion. PRZM is now capable of simulating fate and transport
of the parent compound and up to two daughter species. VADOFT is a one-
dimensional finite-element code which solves the Richard's equation for flows
in the unsaturated zone. The user may make use of constitutive relationships
between pressure, water content, and hydraulic conductivity to solve the flow
equations. VADOFT may also simulate the fate and transport of two parent and
two daughter products. SAFTMOD is a two-dimensional finite-element model
which simulates saturated solute flow and transport in either an X-Y or X-Z
configuration. The codes are linked together with the aid of a flexible
execution supervisor which allows the user to build models which are tailored
to site-specific situations. In order to perform exposure assessments, the
code is equipped with a Monte Carlo pre- and post-processor.
This report on the RUSTIC modeling system was submitted in fulfillment of two
separate work assignments under Contract No. 68-03-6304 by Woodward-Clyde
Consultants under the sponsorship of the U.S. Environmental Protection
Agency. RUSTIC was developed by a Project Team headed by Woodward-Clyde
Consultants as the prime contractor, with AQUA TERRA Consultants and
HydroGeologic, Inc. as subcontractors. This report describes the work
performed from January 1986 through September 1988. The final manuscript was
prepared for publication and printing by AQUA TERRA Consultants under EPA
Contract No. 68-03-3513.
iv
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TABLE OF CONTENTS
Page
Foreword [[[
Abstract [[[ iv
Figures [[[ ^viii
Tables [[[ x
Acknowledgments [[[ xiv
1 . 0 Introduction [[[ 1
1 . 1 Background and Obj ectives .................................. 1
1.2 Concept of Risk and Exposure Assessment .................... 2
1 . 3 Overview of RUSTIC ......................................... 7
1.3.1 Overview of PRZM .................................... 9
1.3.1.1 Features ................................... 9
1.3.1.2 Limitations ................................ 9
1.3.2 Overview of the Vadose Zone Flow and
Transport Model (VADOFT) ............................ 11
1.3.2.1 Features ................................... 11
1.3.2.2 Limitations ................................ 11
1.3.3 Overview of the Saturated Zone Flow and
Transport Model (SAFTMOD) ........................... 12
1.3.3.1 Features ................................... 12
1.3.3.2 Limitations ................................ 12
1.3.4 Model Linkage ....................................... 13
1.3.4.1 Temporal Model Linkage ..................... 13
1.3.4.2 Spatial Linkages ........................... 13
1.3.5 Monte Carlo Processor ............................... 14
1.3.6 Overview Summary .................................... 15
1.4 Overview of Volumes I and II ............................... 15
2 . 0 Model Installation and Execution ................................ 17
2 . 1 Hardware and Software Requirements ......................... 17
2.1.1 Software Requirements for IBM-PC/Definicon
Systems ............................................. 17
2 . 2 RUSTIC Installation Procedures ............................. 18
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TABLE OF CONTENTS (continued)
Page
3.0
4.0
Modules and Logistics
3.1 EXESUP - - The Execution Supervisor
3.2 PRZM - - The Root Zone Fate and Transport Module
3.2.1 Special Actions Option
3.3 VADOFT - - The Vadose Zone Fate and Transport Module
3.4 SAFTMOD -- The Saturated Zone Fate and Transport Module.
3.5 MCARLO -- The Monte Carlo Simulation Module
3.6 Model Structure
3.6.1 Subroutine Descriptions
3.6.2 Intra/Intermodule Communication
3.7 Limitations
3.8 Data Bases
Model Building
4.1 System Abstraction
4.1.1 Idealizing the System
4.1.2 Grid Specification
4.2 Description of Input Sequences
4.2.1 Execution Supervisor
4.2.1.1 Option Records
4.2.1.2 File Records
4.2.1.3 Global Parameters Records
4.2.1.4 Trace Level Record
4.2.1.5 Echo Level Record
4.2.1.6 Example Input File
4.2.2 PRZM Input
4.2.2.1 Meteorological File
4.2.2.2 PRZM Parameter File
4.2.3 VADOFT Input '
4.2.4 SAFTMOD Input
4.2.5 The Monte Carlo Input
4.2.5.1 Data Group 1: Simulation Control
Parameters
4.2.5.2 Data Group 2: Input Distribution
Parameters
4.2.5.3 Data Group 3: Empirical Distribution
Data
4.2.5.4 Data Group 4: Output Options
4.2.5.5 Data Group 5: Correlated Input
Variables
22
22
23
23
24
25
25
26
26
26
26
46
47
47
47
51
54
54
56
56
56
56
62
65
65
66
66
83
89
121
121
122
123
124
124
vi
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TABLE OF CONTENTS (concluded)
Page
5.0 Parameter Estimation 130
5 .1 Execution Supervisor 130
5 . 2 PRZM Parameters 130
5.2.1 Hydrology Parameters 135
5.2.2 Crop Parameters 153
5.2.3 Pesticide Parameters 154
5.2.4 Soil Temperature 174
5.2.5 Soils Parameters 179
5.2.6 Parameter Estimation for Irrigation 193
5 . 3 VADOFT Parameters 200
5 .4 SAFTMOD Parameters 209
6.0 Example Problems 217
6.1 The Physical Setting 217
6 .2 The Pesticide Aldicarb 219
6 . 3 Example Problems 219
6.3.1 Example Problem One -- PRZM to SAFTMOD
Linkage (P2S) 219
6.3.1.1 PRZM Input 222
6.3.2 SAFTMOD Input 222
6.3.2.1 Flow 222
6.3.2.2 Transport 224
6.3.3 Example Problem Two -- PRZM/VADOFT/
SAFTMOD Linkage (P2V) 224
6.3.3.1 VADOFT Flow 228
6.3.3.2 VADOFT Transport 230
6.3.4 Simulation Results 230
6.3.4.1 Vadose Zone Results 230
6.3.4.2 Saturated Zone Results 230
7.0 References 234
8.0 Appendices 240
8 .1 Error Messages and Warnings 240
8 . 2 Variable Glossary 240
vii
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FIGURES
Page
1.1 Decision path for risk assessment 3
1.2 Time series plot of toxicant concentrations 5
1.3 Frequency distribution of toxicant concentrations 5
1.4 Cumulative frequency distribution of toxicant concentrations.... 5
1.5 Time series of toxicant concentrations with moving average
window of duration TQ 6
1.6 Linked modeling system configuration 8
4.1 Decision tree for groundwater flow scenarios 49
4.2 Decision tree for solute transport scenarios 50
4.3 Discretization of aquifer regions: (a) regular region, and
(b) irregular region 53
4.4 Example execution supervisor input file (RUSTIC.RUN 55
4. 5 Example MONTE CARLO input file 122
5 .1 Pan evaporation correction factors 133
5.2 Diagram for estimating soil evaporation loss 134
5.3 Representative regional mean storm duration (hours) values
for the U.S 145
5.4 Diagram for estimating soil conservation service soil
hydrologic groups 151
5.5 Numerical dispersion associated with space step (Ax) 172
5.6 Physical dispersion (D) associated with advective transport 173
"^.7 Average temperature of shallow groundwater 177
1/3-bar soil moisture by volume 181
viii
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FIGURES (concluded)
Page
5.9 15-bar soil moisture by volumes 182
5 .10 Mineral bulk density 189
5.11 Estimation of drainage rate AD versus number of
compartments 194
5.12 Porosity components as a function of grain size 214
6 .1 Map of the Wickham Field Study Site 220
6.2 Longitudinal cross-section of Wickham Field Study Site 221
6 . 3 Schematic of aldicarb environmental chemical pathways 221
6.4 PRZM input data set - PRZM to SAFTMOD linkage 223
6 . 5 SAFTMOD flow input data set 225
6 . 6 SAFTMOD transport input data set 226
6.7 PRZM input data set - PRZM to VADOFT link 227
6 . 8 VADOFT flow input data set 229
6 . 9 VADOFT transport input data set 229
6.10 Observed and simulated aldicarb distribution in the
vadose zone , December 1979 231
6.11 Simulated aldicarb concentrations in the saturated zone,
December 1979 (P2S) 232
6.12 Simulated aldicarb concentrations in the saturated zone,
December 1979 (P2V) 232
6.13 Comparison of observed and simulated concentrations in the
saturated zone , December 1979 233
ix
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TABLES
Page
3-1 List of subroutines by module and a description of
their functions 27
3-2 Common block names and descriptions 39
3-3 Parameter statements utilized in the RUSTIC code 43
4-1 Input formats for the execution supervisor module (EXESUP) 57
4-2 The effect of the echo level on the output of EXESUP 63
4-3 The effect of the echo level on the output of EXESUP,
VADOFT, and SAFTMOD 64
4-4 Relative execution times as a function of echo level and
trace level 64
4-5 Relative output file sizes as a function of echo level 65
4-6 Variable designations for plotting files 80
4-7 MONTE CARLO labels for PRZM variables 125
4-8 MONTE CARLO labels for VADOFT variables 127
4-9 MONTE CARLO labels for SAFTMOD variables 128
5-1 Typical values of snowmelt factor as related to
forest cover 132
5-2 Mean duration of sunlight for latitutes 0° TO 50° in
the northern and southern hemispheres 135
5-3 Indications of the general magnitude of the
soil/erodibility factor, K 137
5-4 Values of the erosion equation's topographic factor, LS, for
specified combinations of slope length and steepness 142
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TABLES (continued)
Page
5-5 Values of support-practice factor, P 139
5-6 Generalized values of the cover and management factor, C,
in the 37 states east of the Rocky Mountains 140
5-7 Mean storm duration values for selected cities 144
5-8 Interception storage for major crops 146
5-9 Agronomic data for major agricultural crops in the
United States 147
5-10 Runoff curve numbers for hydrologic soil-
cover complexes 148
5-11 Method for converting crop yields to residues 149
5-12 Residue remaining from tillage operations 149
5-13 Reduction in runoff curve numbers caused by conservation
tillage and residue management 150
5-14 Values for estimating WFMAX in exponential FOLIAR Model 150
5-15 Pesticide soil application methods and distribution 159
5-16 Degradation rate constants of selected pesticides
on foliage 157
5-17 Estimated values of Henry's constant for selected pesticides.... 160
5-18 Physical characteristics of selected pesticides for use
in development of partition coefficients and reported
degradation rate constants in soil root zones 167
5-19 Octanol water distribution coefficients and soil degradation
rate constants for selected chemicals 167
5-20 Albedo factors of natural surfaces for solar radiation 175
5-21 Emissivity values for natural surfaces at normal
temperatures 176
5-22 Thermal properties of some soil and reference materials 178
5-23 Coefficients for linear regression equations for prediction
of soil water contents at specific matric potentials 180
xi
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TABLES (continued)
Page
5-24 Hydrologic properties by soil textures 184
5-25 Descriptive statistics and distribution model for
field capacity 185
5-26 Descriptive statistics and distribution model for
wilting point 186
5-27 Correlations between transformed variables of organic
matter, field capacity, and wilting point 187
5-28 Mean bulk density for five soil textural classifications 190
5-29 Descriptive statistics for bulk density 191
5-30 Descriptive statistics and distribution model for organic
matter 192
5-31 Adaptations and limitations of common irrigation methods 195
5-32 Water requirements for various irrigation and soil types 195
5-33 Representative furrow parameters described in the literature.... 197
5-34 Furrow irrigation relationships for various soils,
slopes , and depths of application 197
5-35 Suitable side slopes for channels built in various kinds
of materials 198
5-36 Value of "N" for drainage ditch design 198
5-37 Representative permeability ranges for sedimentary materials.... 199
5-38 Values of Green-Ampt parameters for SCS hydrologic
soil groups 199
5-39 Descriptive statistics for saturated hydraulic conductivity 201
5-40 Descriptive statistics for van Genuchten water retention
model parameters , a, ft, 7 203
5-41 Descriptive statistics for saturation water content (#s)
and residual water content (0r) 204
5-42 Statistical parameters used for distribution approximation 205
xii
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TABLES (continued)
Page
5-43 Correlations among transformed variables presented with
the factored covariance matrix 207
6-1 Aldicarb application rates - Wickham Farm 218
6-2 Key parameter values used in the simulation of groundwater
flow and pesticide transport in the saturated zone 224
6-3 Parameter values used in the simulation of infiltration
and pesticide transport in the VADOSE Zone 228
8-1 RUSTIC error messages, warnings, and troubleshooting
approaches 241
8-2 EXESUP program variables 253
8-3 PRZM program variables, units, location, and variable
designation 258
8-4 VADOFT program variables, units, location, and variable
designations 301
8-5 SAFTMOD program variables, units, location, and variable
designation 315
8-6 MONTE-CARLO program variables 339
xiii
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ACKNOWLEDGMENTS
A number of individuals contributed to this effort. Their roles are
acknowledged in the following paragraphs.
Mr. Voos of Woodward-Clyde Consultants (WCC) programmed the execution
supervisor and linked the models within the overall model architecture and
design of RUSTIC developed by Mr. Jack Kittle of Aqua Terra Consultants. The
model linkage was conceived by Mr. Dean and Dr. Atul Salhotra of WCC, Mr.
Kittle, and Dr. Huyakorn. Dr. Huyakorn and his staff wrote the time/space
bridging subroutines for the linkage.
Release II - PRZM was written by Dr. J. Lin and Mr. S. Raju of Aqua Terra
Consultants under the direction of Mr. Donigian. Mr. Schanz (WCC) and
Ms. Meeks (WCC) wrote the irrigation and MOC algorithms. Mr. Dean wrote the
daughter products algorithms which were implemented by Dr. Lin.
The VADOFT code was written and documented by Dr. Huyakorn, H. White, J.
Buckley, and T. Wadsworth of HydroGeologic. Mr. John Imhoff of Aqua Terra
performed many useful testing simulations with VADOFT. SAFTMOD was written
and documented by Dr. Huyakorn and J. Buckley.
The Monte Carlo pre- and post-processors were written by Dr. Salhotra, Mr.
Phil Mineart, and Mr. Schanz of Woodward-Clyde. Final model assembly of the
code and documentation and model testing was performed by Woodward-Clyde
Consultants. Drs. Salhotra and P. Mangarella (WCC) peer reviewed the final
documentation. The support of the editorial and graphics staff of WCC is
appreciated.
The final manuscript was prepared for publication, incorporating review
comments, by Ms. Susan Sharp-Hansen, Mr. S. Raju, and Ms. Dorothy Inahara of
Aqua Terra Consultants.
The authors would like to acknowledge the support of the U.S. Environmental
Protection Agency, and Mr. Lee Mulkey, Chief, Assessment Branch; Mr. Bob
Carsel, Project Officer; and Dr. Rudy Parrish for their suggestions, input,
and helpful comments.
xiv
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SECTION 1
INTRODUCTION
This publication contains documentation for a linked model, known as RUSTIC,
for contaminant transport in the root, vadose and saturated zones. A brief
section on background and objectives for the model development effort
follows in this introduction (Section 1.1). Section 1.2 gives a synopsis of
risk and exposure assessment concepts. The reader who has sufficient
background in these concepts may proceed to Section 1.3, which provides an
overview of the RUSTIC linked modeling system, including major features and
limitations. The documentation consists of two volumes, and Section 1.4
gives a synopsis of the contents of each. This introduction is common to
both volumes.
1.1 BACKGROUND AND OBJECTIVES
The U.S. Environmental Protection Agency is continually faced with issues
concerning the registration and restriction of pesticides used for
agricultural purposes. Each of these regulatory processes requires that the
potential risk to human health resulting from the introduction or continued
use of these chemicals be evaluated. Recently, much of this attention has
been focused on exposure through leaching of pesticides to groundwater and
subsequent ingestion of contaminated water.
The capability to simulate the potential exposure to pesticides via this
pathway has two maj or facets:
• Prediction of the fate of the chemical, after it is applied, as it
is transported by water through the crop root zone, the vadose
zone, and saturated zone to a drinking water well
• Evaluation of the probability of the occurrence of concentrations
of various magnitudes at the drinking water well
There are a number of models which are capable of simulating the fate and
transport of chemicals in the subsurface and in the root zone of
agricultural crops. However, none of these models have been linked together
in such a way that a complete simulation package, which takes into account
the effects of agricultural management practices on fate and transport, is
available for use either by the Agency or the agricultural chemical industry
to address potential groundwater contamination problems. Without such a
package, the decision maker must rely on modeling scenarios that are either
incomplete or potentially incorrect. Each time a new scenario arises,
recurring questions must be answered:
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• What models should be used?
• How should mass transfer between models be handled?
The resolution of these issues for each scenario is both expensive and time
consuming. Furthermore, it precludes consistency of approach to evaluation
of contamination potential for various scenarios.
The modeling package described in this report seeks to overcome these
problems by providing a consistent set of linked models which have the
flexibility to handle a wide variety of hydrogeological, soils, climate, and
pesticide scenarios. However, the formulation of the risk analysis problem
requires more than a simple, deterministic evaluation of potential exposure
concentrations. The inherent variability of force, capacitance and
resistance in natural systems, combined with the inability to exactly
describe these attributes of the system, suggests that exposure
concentrations cannot be predicted with certainty. Therefore, the
uncertainty associated with the predictions must be quantified.
Consequently, this simulation package also seeks to provide this capability
by utilizing Monte Carlo simulation techniques.
Stated more concisely, the objectives of this model development effort were
to provide a simulation package which can:
• Simulate the fate and transport of field-applied pesticides in the
crop root zone, the unsaturated zone, and the saturated zone to a
drinking water well, taking into account the effects of
agricultural management practices
• Provide probabilistic estimates of exposure concentrations by
taking into account the variability in the natural systems and
uncertainty in system properties and processes
Furthermore, it was desirable that the simulation package be easy to use and
parameterize, and execute on the Agency's DEC/VAX machines. As a result,
considerable effort has gone into providing parameter guidance for both
deterministic and probabilistic applications of the model and software
development for facile model implementation.
1.2 CONCEPT OF RISK AND EXPOSURE ASSESSMENT
Exposure assessment, as defined in the Federal Register (1984) for human
impacts, is the estimation of the magnitude, frequency, and duration at
which a quantity of a toxicant is available at certain exchange boundaries
(i.e., lungs, gut, or skin) of a subject population over a specified time
interval. Exposure assessment is an element of the larger problems of risk
assessment and risk management, as demonstrated in Figure 1.1. The
concentration estimates generated during an exposure assessment are combined
with demographic and toxicological information to evaluate risk to a
population--which can be used, in turn, to make policy decisions regarding
the use or disposal of the chemical.
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REGULATORY CONCERN
SCIENTIFIC DATA Population
Exposure
Product Lite Cycle
General Information Gathering
Prelimma'v Exposure
Assessment
In-Deptr- Exposure
Assessment
Most Probable Areas of Exposure
Preliminary Exposure Assessment
ntilication Toxicny
etc
J
Preliminary Rnk Analysis
Decision
6*9"i In-Depth
Exposure Assessment
No Need for Future
Exposure
Multi-Disciplinary
Peer Review
Design Atteumenl Study Plan
Comprehensive Data Gathering
Conduct Refined Exposure Modeling
IrvDepth Exposure Aueoment
Regulatory Response
Decition
nel Review
Hazard Input
1
Formal Risk Assessment
Decision
Regulatory Proposal
Examined Exposures
Present No Unreasonable Risk
Figure 1.1. Decision path for risk assessment
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Major components of risk assessment are indicated below. Of these, the
first three constitute the important steps for exposure assessment and are
discussed in detail here.
• Characterization and quantification of chemical sources
• Identification of exposure routes
• Quantification of contaminant movement through the exposure routes
to the receptor population/location
• Characterization of the exposed population
• Integration of quantified environmental concentrations with the
characteristics of the exposed populations to yield exposure
profiles
Characterization of sources(s) requires in a broad sense the estimation of
the loading of a chemical into various environmental media. For the
groundwater contamination problem, on a regional scale, this requires data
on chemical uses and distribution of those uses (spatially and temporally).
It also requires information on the crops being grown, registered or
proposed chemical uses of those crops, and regional management practices.
For a specific field-scale area, similar data would be needed to support an
assessment; however, greater detail may be necessary.
The identification of exposure pathways involves a qualitative (or
semiquantitative) assessment of how the chemical is thought to move from the
source to the exposed population. Important fate processes which may serve
to reduce the concentration of the chemical(s) along various pathways in
different environmental media are also identified. For the case of
groundwater exposure, important pathways and processes are predefined to a
large extent in the models to be used.
The quantification of concentrations in a medium, given the source strength,
pathways, and attenuation mechanisms along each pathway, is the next step,
and is the major benefit of using models such as RUSTIC. The guidelines are
very specific in the requirement that concentrations be characterized by
duration and frequency as well as magnitude. These characteristics can be
determined through the analysis of time series exposure data generated by
the model.
The model produces a time series of toxicant concentrations in groundwater
such as appears in Figure 1.2. The time series can be compared to a
critical value of the concentration y. (This might be, for instance, the
ADI [average daily intake level].) This type of analysis easily shows
whether the criterion is exceeded and gives a qualitative feel for the
severity of the exceedance state. A frequency distribution of the values of
y (Figure 1.3) can be created by determining how often y is at a particular
level or within a specific range. By choosing any value of y in Figure 1.2
and determining the area under the curve to the right of that value, Figure
'.4, which is a cumulative frequency distribution of the toxicant
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c
o
o
u
o
o
Time (t)
Figure 1.2. Time series plot of toxicant concentrations
«
O
CO
•o
o
9
O
X
o
i
Concentration (y)
Concentration (y)
Figure 1.3.
Frequency distribution of
toxicant concentrations.
Figure 1.4.
Cumulative frequency
distribution of toxicant
concentrations.
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concentration can be plotted. The cumulative frequency distribution shows
the chance that any given value y will be exceeded. If the example time
series is long enough, then the "chance" approaches the true "probability"
that y will be exceeded.
Thus far, only the concentration to which the organism will be exposed has
been discussed and nothing has been said concerning the duration of the
event. If a window of length "t" is imposed on the time series at level yc
(Figure 1.5), and moved incrementally forward in time, a statement can be
made concerning the toxicant concentration within the duration window.
Normally, the average concentration within the window is used. The
resulting cumulative frequency distribution shows the chance that the moving
average of duration tc will exceed the critical value of y, yc. The moving
average window should be the same length as that specified for yc. For
instance, in the case of cancer risk, a 70-year (lifetime) window is
normally used to average the data in the simulated time series. The use of
the moving window for averaging the time series allows us to compare both
the concentration and duration against the standard. The chance or
probability that the moving average concentration exceeds the standard is
the essence of the exposure assessment. This type of information provides a
precursor to the estimates of risk taken in using this chemical under the
conditions of the model simulation. The use of models like RUSTIC which
provide data of environmental concentrations and probability of occurrence
ends here.
The next step in exposure assessment involves the characterization of the
exposed population. Such factors as habits, age, sex, and location with
respect to the source are of importance. The integration of concentration
estimates and population characteristics makes possible the counting of the
conditional events of concentration in an environmental medium and the
e
o
**
(0
*rf
c
o
c
o
o
Figure 1.5
Time (t)
Time series of toxicant concentrations with
moving average window of duration tc .
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opportunity for the population to be exposed to these concentrations. The
exposure assessment ends at this point. The actual intake of chemicals,
their fate within the human body (e.g., pharmacokinetics, toxicology), and
their effects on the exposed population are not considered. These, however,
are elements of the risk assessment.
Although the concepts underlying an exposure assessment are relatively
simple, the actual application of these concepts is complicated because of
large variations in source-specific and environment-specific characteristics
and the necessity to integrate specialized knowledge from a number of
different fields. This variability underscores the need to use a model such
as RUSTIC in the evaluation of exposure concentrations.
1.3 OVERVIEW OF RUSTIC
This section gives an overview of the RUSTIC model highlighting the features
and limitations of the simulation package as a whole and the component
models PRZM, VADOFT, and SAFTMOD. The RUSTIC code was designed to provide
state-of-the-art deterministic simulation of the fate and transport of
pesticides, applied for agricultural purposes, in the crop root zone, the
vadose zone, and the saturated zone. The model is capable of simulating
multiple pesticides or parent/daughter relationships. The model is also
capable of estimating probabilities of concentrations or fluxes in or
fromthese various media for the purpose of performing exposure assessments.
To avoid writing an entirely new computer code, it was decided to make use
of existing codes and software to the extent possible. Thus, due to its
comprehensive treatment of important processes, its dynamic nature, and its
widespread use and acceptability to the Agency and the agricultural chemical
industry, the Pesticide Root Zone model (PRZM) (Carsel et al., 1984) was
selected to simulate the crop root zone.
Having selected PRZM, several options were evaluated for developing the
RUSTIC linked model to meet the objectives stated in Section 1.1. The first
involved use of PRZM and a saturated zone model only. In this
configuration, PRZM would be used to simulate both the root zone and the
vadose zone. This option was eliminated because the assumptions of the
elementary soil hydraulics in PRZM (i.e., drainage of the entire soil column
to field capacity in one day) were considered inadequate for simulating flow
in a thick vadose zone. The second involved the use of PRZM and a 2-D or 3-
D variably saturated flow model. In this configuration, the latter model
would represent both the vadose and saturated zones. Although the interface
between the unsaturated and saturated zones would be more rigorously
simulated, it was felt that the resulting code would be computationally too
intensive for the intended application scenarios, and especially for use in
a Monte Carlo (probabilistic) mode. Therefore, this option was also
dropped.
The option finally selected is depicted in Figure 1.6. In this
configuration, an enhanced version of PRZM is linked to a two-dimensional
(single or two-layer) saturated zone model either directly or through a one-
dimensional vadose zone flow and transport model. Both the vadose and
saturated zone models simulate water flow and solute transport. A new code
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(VADOFT) was written to perform the flow and transport simulation in
the vadose zone, and an existing code was modified to produce the two-
dimensional X-Y, X-Z or axisymmetric simulation model for the saturated zone
(SAFTMOD).
A significant problem associated with this type of linkage is the difference
in time scales of the individual models. While the vadose zone models are
required to operate on a daily or less-than-daily time step, the time step
of the saturated zone model could vary from days to months. A second
problem is the correct interfacing of the vadose and saturated zone models,
especially for the case of a fluctuating water table. This requires special
handling of the spatial discretizations in the two models. The solution to
these linkage problems is discussed in detail in Section 5.
PRZM
(1—D Flow and Transport)
VADOSE
ZONE
MODEL
m
VADOFT
(1—D Flow and Transport)
SATURATED ZONE MODEL
SAFTMOD
(2-D Flow and Transport)
Figure 1.6. Linked modeling system configuration
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1.3.1 Overview of PRZM
1.3.1.1 Features--
The Pesticide Root Zone Model (PRZM) is a one-dimensional, dynamic,
compartmental model that can be used to simulate chemical movement in
unsaturated soil systems within and immediately below the plant root zone.
It has two major components: hydrology (and hydraulics) and chemical
transport. The hydrologic component for calculating runoff and erosion is
based on the Soil Conservation Service curve number technique and the
Universal Soil Loss Equation. Evapotranspiration is estimated either
directly from pan evaporation data, or based on an empirical formula.
Evapotranspiration is divided among evaporation from crop interception,
evaporation from soil, and transpiration by the crop. Water movement is
simulated by the use of generalized soil parameters, including field
capacity, wilting point, and saturation water content. With a newly added
feature, irrigation may also be considered.
The chemical transport component can simulate pesticide application on the
soil or on the plant foliage. Dissolved, adsorbed, and vapor-phase
concentrations in the soil are estimated by simultaneously considering the
processes of pesticide uptake by plants, surface runoff, erosion, decay,
volatilization, foliar washoff, advection, dispersion, and retardation. Two
options are available to solve the transport equations: (1) the original
backwards-difference implicit scheme that may be affected by excessive
numerical dispersion at high Peclet numbers, or (2) the 'method of
characteristics' algorithm which eliminates numerical dispersion while
increasing model execution time.
Predictions are made on a daily basis. Output can be summarized for a
daily, monthly, or annual period. Daily time series values of various
fluxes or storages can be written to sequential files during program
execution for subsequent analysis. In addition, a 'Special Action' option
allows the user to output soil profile pesticide concentrations at user-
specified times. With the Special Action option, the user can also change
the values of certain parameters during the simulation period.
1.3.1.2 Limitations--
There were significant limitations in the original (Release I) version of
PRZM. A few were obvious to the developers, while others were pointed out
subsequently by model users. These are broken into four categories:
• Hydrology
• Soil hydraulics
• Method of solution of the transport equation
• Deterministic nature of the model
The Release II version of PRZM has been suitably modified to overcome many
of these limitations.
Hydrologic and hydraulic computations are still performed in PRZM on a daily
time step even though for some of the processes involved (evaporation,
runoff, erosion) finer time step might be used to ensure greater accuracy
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and realism. For instance, simulation of erosion by runoff depends upon the
peak runoff rate, which is in turn dependent upon the time base of the
runoff hydrograph. This depends to some extent upon the duration of the
precipitation event. PRZM retains its daily time step primarily due to the
relative availability of daily versus shorter time step meteorological data.
This limitation has in part been mitigated by enhanced parameter guidance.
In PRZM, Release I, the soil hydraulics were simple--all drainage to field
capacity water content was assumed to occur within 1 day. (An option to
make drainage time dependent was also included, but there is not much
evidence to suggest that it was utilized by model users to any great
extent.) This had the effect, especially in deeper soils, of inducing a
greater-than-anticipated movement of chemical through the profile. While
this representation of soil hydraulics has been retained in PRZM, the user
has the option of coupling PRZM to VADOFT. PRZM is then used to represent
the root zone, while VADOFT, with a more rigorous representation of
unsaturated flow, is used to simulate the thicker vadose zone. The code
VADOFT is discussed in more detail in a subsequent section. For short
distances from soil surface to the water table, PRZM can be used to
represent the entire vadose zone without invoking the use of VADOFT as long
as no layers which would restrict drainage are present.
The addition of algorithms to simulate volatilization has brought into focus
another limitation of the soil hydraulics representation. PRZM simulates
only advective, downward movement of water and does not account for
diffusive movement due to soil water gradients. This means that PRZM is
unable to simulate the upward movement of water in response to gradients
induced by evapotranspiration. This process has been identified by Jury et
al. (1984) as an important one for simulating the effects of volatilization.
However, the process would seem less likely to impact the movement of
chemicals with high vapor pressures. For these chemicals, vapor diffusion
would be a major process for renewing the chemical concentration in the
surface soil.
Another limitation of the Release I model was the apparent inadequacy of the
solution to the transport equation in advection-dominated systems. The
backward difference formulation of the advection term tends to produce a
high degree of numerical dispersion in such systems. This results in
overprediction of downward movement due to smearing of the peak and
subsequent overestimation of loadings to groundwater. In this new release,
a new formulation is available for advection-dominated systems. The
advective terms are decoupled from the rest of the transport equation and
solved separately using the Method of Characteristics (MOC). The remainder
of the transport equation is then solved as before, using the fully implicit
scheme. This approach effectively eliminates numerical dispersion with,
however, an increase in the computation time. In low-advection systems, the
MOC approach reduces to the original PRZM solution scheme, which becomes
exact as velocities approach zero.
The final limitation is the use of field-averaged water and chemical
transport parameters to represent spatially heterogeneous soils. Several
researchers have shown that this approach produces slower breakthrough times
than are observed using stochastic approaches. This concern has been
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addressed by adding the capability to run PRZM in a Monte Carlo framework.
Thus, distributional, rather than field-averaged, values can be utilized as
inputs which will produce distributional outputs of the relevant variables
(e.g., flux to the water table).
1.3.2 Overview of the Vadose Zone Flow and Transport Model (VADOFT)
VADOFT is a finite-element code for simulating moisture movement and solute
transport in the vadose zone. It is the second part of the three-component
RUSTIC model for predicting the movement of pesticides within and below the
plant root zone and assessing subsequent groundwater contamination. The
VADOFT code simulates one-dimensional, single-phase moisture and solute
transport in unconfined, variably saturated porous media. Transport
processes include hydrodynamic dispersion, advection, linear equilibrium
sorption, and first-order decay. The code predicts infiltration or recharge
rate and solute mass flux entering the saturated zone. Parent/daughter
chemical relationships may be simulated. The following description of
VADOFT is adapted from Huyakorn et al. (1988a).
1.3.2.1 Features--
The code employs the Galerkin finite-element technique to approximate the
governing equations for flow and transport and allows for a wide range of
nonlinear flow conditions. Boundary conditions of the variably saturated
flow problems may be specified in terms of prescribed pressure head or
prescribed volumetric water flux per unit area. Boundary conditions of the
solute transport problem may be specified in terms of prescribed
concentration or prescribed solute mass flux per unit area. All boundary
conditions may be time dependent. An important feature of the algorithm is
the use of constitutive relationships for soil water characteristic curves
based on soil texture.
1.3.2.2 Limitations--
Major assumptions of the flow model are that the flow of the fluid phase is
one-dimensional, isothermal and governed by Darcy's law and that the fluid
is slightly compressible and homogeneous. Hysteresis effects in the
constitutive relationships of relative permeability versus water saturation,
and water saturation versus capillary pressure head, are assumed to be
negligible.
Major assumptions of the solute transport model are that advection and
dispersion are one-dimensional, and fluid properties are independent of
contaminant concentrations. Diffusive/dispersive transport in the porous-
medium system is governed by Pick's law. The hydrodynamic dispersion
coefficient is defined as the sum of the coefficients of mechanical
dispersion and molecular diffusion. Adsorption and decay of the solute is
described by a linear equilibrium isotherm and a lumped first-order decay
constant. Steady-state transport can not be simulated when decay is
considered.
The code handles only single-phase flow (i.e., water) and ignores the
presence of a second phase--i.e., air. The code does not take into account
sorption nonlinearity or kinetic sorption effects which, in some instances,
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can be important. The code considers only single-porosity (granular) soil
media. It does not simulate flow or transport in fractured porous media or
structured soils.
1.3.3 Overview of the Saturated Zone Flow and Transport Model (SAFTMOD)
SAFTMOD is a finite element code for simulating groundwater flow and solute
transport in the saturated zone. It is the third part of the three-
component RUSTIC model for predicting the movement of pesticides within and
below the plant root zone and assessing subsequent groundwater
contamination. SAFTMOD performs two-dimensional simulations in an areal
plane or a vertical cross section. In addition, the code can also perform
axisymmetric simulations. Both single (unconfined or confined) and leaky
two-aquifer systems can be handled. Transport of dissolved contaminants may
also be simulated within the same domain. Transport processes accounted for
include hydrodynamic dispersion, advection, linear equilibrium sorption, and
first-order decay. Parent/daughter chemical relationships may be simulated.
The following description of SAFTMOD is adapted from Huyakorn et al.
(1988b).
1.3.3.1 Features--
The two dimensional analyses can be done in a transient or steady-state mode
using SAFTMOD. The code employs the Galerkin finite-element technique to
approximate the governing equations for flow and transport. For groundwater
flow simulations the code accommodates water table conditions, recharge by
infiltration or precipitation, and well pumping or injection. Boundary
conditions of the saturated flow problem are specified in terms of
prescribed hydraulic head (defined as the sum of pressure head and
elevation), or prescribed volumetric water flux. Boundary conditions of the
solute transport problem are specified in terms of prescribed concentration
or prescribed solute mass flux. All boundary conditions can be time
dependent.
1.3.3.2 Limitations- -
The SAFTMOD code contains both saturated flow and solute transport models.
Major assumptions of the flow model are that: Darcy's law is valid and
hydraulic head gradients are the only significant driving mechanism for
fluid flow, the porosity and hydraulic conductivity are constant with time,
and gradients of fluid density, viscosity, and temperature do not affect the
velocity distribution. For areal flow simulation in two-aquifer systems,
vertical leakage is assumed in aquitards.
Major assumptions of the transport model are that fluid properties are
independent of concentrations of contaminants. Contaminants are miscible
with in-place fluids. For areal transport simulations, advection in
aquitards is assumed to be negligible. Diffusive/dispersive transport in
the porous medium system is governed by Pick's law. The hydrodynamic
dispersion coefficient is defined as the sum of the coefficients of
mechanical dispersion and molecular diffusion. Adsorption and decay of the
solute may be described by a linear equilibrium isotherm and a first-order
decay constant. Steady-state transport can not be simulated when decay is
considered.
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Other limitations of the SAFTMOD code are that it simulates only single-
phase water flow and solute transport in saturated porous media. It does
not consider unsaturated or fractured media. Non-Darcy flow that may occur
near pumping wells is neglected. The code does not take into account
sorption nonlinearity or kinetic sorption effects which, in some instances,
can be important.
1.3.4 Model Linkage
One of the more challenging problems in this model development effort was
the temporal and spatial linkage of the component models. In the section
which follows, these linkages are discussed.
1.3.4.1 Temporal Model Linkage- -
The resolution of the temporal aspects of the three models was
straightforward. PRZM runs on a daily time step. The time step in VADOFT
is dependent upon the properties of soils and the magnitude of the water
flux introduced at the top of the column. In order for the nonlinear
Richards' equation to converge, VADOFT may sometimes require time steps on
the order of minutes. The SAFTMOD time step, on the other hand, would
normally be much longer than one day.
For simplicity, it was decided that the time step of SAFTMOD would always be
an integer multiple of PRZM's daily time step. This makes the direct
linkage of PRZM to SAFTMOD in a temporal sense very straightforward. PRZM
is simply run for the number of days of the SAFTMOD time step, SAFTMOD is
then run, and so forth. The water and pesticide fluxes from PRZM are summed
and averaged over the length of the SAFTMOD time step. SAFTMOD receives the
time-averaged fluxes as input.
For the linkage of PRZM, through VADOFT, to SAFTMOD, the resolution of time
scales is also straightforward. VADOFT is prescribed to simulate to a
"marker" time value, specifically to the end of a day. The last
computational time step taken by VADOFT is adjusted so that it coincides
with the end of the day. PRZM's daily water fluxes are used as input to
VADOFT. VADOFT utilizes this flux as a constant over the day and adjusts
its internal computational time step in order to converge. PRZM and VADOFT
execute for the number of days corresponding to the SAFTMOD time step. The
output from VADOFT is then time averaged and used as input to SAFTMOD.
1.3.4.2 Spatial Linkages--
The spatial linkages utilized for the models are more complex. The
principal problem is the presence of a fluctuating water table, which
complicates the interfacing of the vadose (or root zone) and saturated zone
codes. A second problem is that of the incompatibility between the
hydraulics in PRZM and VADOFT. Of course, any linking scheme utilized must
provide a realistic simulation of the flow of water and transport of solutes
at the interfaces and must ensure mass balance.
PRZM and VADOFT--The major problem with the interfacing of these two models
is that while VADOFT solves the Richards' equation for water flow in a
variably saturated medium, PRZM uses simple "drainage rules" to move water
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through the soil profile. Because of this incompatibility, there may be
times when PRZM produces too much water for VADOFT to accommodate within one
day. This is very likely to happen in agricultural soils, where subsoils
are typically of lower permeability than those of the root zone, which have
been tilled and perforated by plant roots and soil biota. The result of
this would be water ponded at the interface which would belong neither to
PRZM or VADOFT.
The solution was to prescribe the flux from PRZM into VADOFT so that VADOFT
accommodates all the water output by PRZM each day. This eliminates the
problem of ponding at the interface. However, it does force more water into
the vadose zone than might actually occur in a real system, given the same
set of soil properties and meteorological conditions. The consequence is
that water and solute are forced to move at higher velocities in the upper
portions of the vadose zone. If the vadose zone is deep, then this
condition probably has little impact on the solution. If it is shallow,
however, it could overestimate loadings to groundwater, especially if
chemical degradation rates are lower in the vadose zone than in the root
zone.
VADOFT and SAFTMOD--The principal problem here is that of the presence of a
dynamic boundary (rising and falling water table) between these models. Two
approaches were considered to handle the problem. The first was to expand
or contract the spatial domains of the models to accommodate the moving
boundary. This is not a particularly insurmountable problem for the vadose
zone model; however, for the saturated zone model it would require the
addition of nodes at the upper boundary. This would require a constant
evaluation and switching of the set of nodes receiving fluxes from the
vadose zone. This appeared undesirable.
The second option was to overlap the spatial domains of the models and
interpolate values for fluxes based upon the position of the water table.
This latter approach was ultimately utilized. It has the additional feature
of eliminating the effects of the bottom boundary conditions prescribed for
the vadose zone model on the simulation of solute transport just above the
water table. A detailed discussion of these spatial linkages is given in
Section 5.
1.3.5 Monte Carlo Processor
RUSTIC can be run in a Monte Carlo mode so that probabilistic estimates of
pesticide loadings to the saturated zone or concentrations in a well
downgradient from the source area can be made. The input preprocessor
allows the user to select distributions for key parameters from a variety of
distributions; the Johnson family (which includes the normal and lognormal),
uniform, exponential and empirical. If the user selects distributions from
the Johnson family, he may also specify correlations between the input
parameters. The Monte Carlo processor reads the standard deterministic
input data sets for each model, then reads a Monte Carlo input file which
specifies which parameters are to be allowed to vary, their distributions,
the distribution parameters, and correlation matrix. The model then
executes for a prespecified number of runs.
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The output processor is capable of preparing statistics of the specified
output variables including mean, maximum values and quantiles of the output
distribution. The output processor also can tabulate cumulative frequency
histograms of the output variables and send them to a line printer for
plotting.
1.3.6 Overview Summary
A modeling system (RUSTIC) has been developed for the U.S. Environmental
Protection Agency which is capable of simulating the fate and transport of
pesticides, following application, through the crop root zone, vadose zone
and saturated zone and into drinking water wells. The model is envisioned
for use in simulating farms or small communities, and was designed to handle
a variety of geometries likely to be encountered in performing evaluations
for pesticide registration or special reviews. A major objective was also
to keep the model simple and efficient, yet allow use in a Monte Carlo mode
to generate probabilistic estimates of pesticide loadings or well water
concentrations. The model consists of three major computational modules;
PRZM, which performs fate and transport calculations for the crop root zone
and is capable of incorporating the effects of management practices; VADOFT,
which simulates one-dimensional flow and transport within the vadose zone;
and SAFTMOD, which simulates two dimensional flow and transport in the
saturated zone. SAFTMOD is capable of simulating a variety of aquifer
geometries (water table, confined and leaky two-aquifer systems) and can
perform areal, cross-sectional and axisymmetric simulations. Linkage of
these models is accomplished through the use of simple bridging algorithms
which conserve water and solute mass.
1.4 OVERVIEW OF VOLUMES I AND II
Documentation for RUSTIC has been produced in two volumes. The subject of
Volume I is model theory and code verification or testing. It contains a
description of the theory underlying the PRZM, VADOFT, and SAFTMOD codes.
The description of each code includes a brief Overview highlighting the
features and limitations of each code. This is followed by detailed
descriptions of the algorithms involved in each code and how they are solved
numerically. The description of each of these models concludes with a
section on algorithm testing. The fifth section of Volume I contains a
description of the theory behind the linkage of the three codes to provide a
cohesive simulation of the movement of pesticides following application,
through the root zone, the vadose zone, and the saturated zone to a drinking
water well. Section 6 of Volume I covers the theory behind the uncertainty
preprocessor. Volume I concludes with model development references and
appendices.
Volume II is a model user's guide. It opens with an introduction and a
section on the installation of the code on the target computer systems.
Section 3 has a user-directed overview of the model software, simulation
modules, and a description of data bases for simulation support and
parameter estimation. The fourth section takes the user through problem
definition and model setup, and module input sequence building. Section 5
covers parameter estimation for the execution supervisor and each
computational module. Section 6 takes the user through an example problem.
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Following the references (Section 7), Section 8 contains appendices which
include a listing of error messages and warnings, and a variable glossary.
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SECTION 2
MODEL INSTALLATION AND EXECUTION
This section describes how to install the RUSTIC software on a computer,
(including compilation and linking), how to verify that the software has been
loaded correctly, and how to execute RUSTIC. The hardware and software
required for installing RUSTIC are also discussed. As the program evolves
over time, the installation and operational information described here is
likely to change. If problems are experienced with this software, users
should contact the EPA Center for Exposure Assessment Modeling (CEAM) at the
Athens Environmental Research Laboratory in Athens, Georgia.
2.1 HARDWARE AND SOFTWARE REQUIREMENTS
RUSTIC development, testing, and maintenance at the EPA Athens Environmental
Research Laboratory is performed on a Digital Equipment Corporation VAX
machine running the VMS operating system. RUSTIC has also been successfully
executed on a Prime 50 Series computer running PRIMOS, a Sun Microsystems
386i workstation (Unix/Sun OS), and an IBM-PC using a Definicon DSI-020
coprocessor board (with 2 MBytes of installed memory) and the MS-DOS
operating system. The coprocessor board is required because of the large
run-time memory requirements of the RUSTIC code. Although not yet tested,
it should be possible to run RUSTIC on an IBM-PC compatible computer with 2-3
MBytes of memory, running under the OS/2 operating system, and using a
FORTRAN compiler designed for OS/2 operation.
General hardware requirements for RUSTIC are 7 MBytes of available hard disk
storage and 2-3 MBytes of available memory, depending on the specific
computer and FORTRAN compiler being used. In addition, a 9-track tape drive
or IBM-PC compatible 5.25 inch floppy disk drive (360 KB or 1.2MB) is
required to download the software.
Software requirements are the operating system (listed above) and the
standard FORTRAN 77 compiler and code linker for the target computer.
Compilation of the code is necessary because the executable version (e.g EXE
file) of a program is incompatible with other computer systems.
2.1.1 Software Requirements for IBM-PC/Definicon Systems
If the Definicon version of the RUSTIC code is being used, the program will
be executed using the MS-DOS operating system. The RUSTIC software is
compatible with the MS-DOS 3.21 and other versions which are able to
recognize file directories. In addition, the Definicon Systems software
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loader must be available. The Deflnicon Systems documentation describes the
modifications to the PATH statement necessary to accomplish this.
The DOS supplied ANSI.SYS driver must also be available. This driver is
installed by modifying the DOS specific CONFIG.SYS file (located in the root
directory) to contain the following line:
DEVICE = ANSI.SYS
where is the location (directory) of the ANSI.SYS file. This file
will normally be located in a directory containing other DOS specific files.
The CONFIG.SYS file may have to be modified to specify how many files are
available to the RUSTIC software. It the statement 'FILES =' is not present
or it is present but with a value less than 20 (e.g., 'FILES = 10') then the
statement 'FILES - 20' (without the quote marks) should be added to the
CONFIG.SYS file.
If the installation disks are 360K Byte format, it will be necessary to have
the DOS RESTORE command available. The RESTORE command is necessary since
some files are larger than 360K and must be stored on more than one disk.
The DOS RESTORE command (and the corresponding write utility, BACKUP) provide
a convenient method to write and read files which are too large to fit on one
disk.
2.2 RUSTIC INSTALLATION PROCEDURES
The installation procedures described in this section are intentionally
general. Specific details will depend on the actual hardware, operating
system, and file/directory structure of the target computer.
2.2.1 Copying the software to the hard disk
Transfer the software from tape or floppy disk to a new directory or
subdirectory on the hard disk. The software consists of FORTRAN source code
files (.FOR), FORTRAN 'include' files (.CMM .CMN .PRM .INC), and test input
and output files. Depending on the computer and FORTRAN compiler, the
FORTRAN source files may need to be renamed. The source code is distributed
with .FOR suffixes, reflecting the VAX VMS FORTRAN compiler's standard name
convention. Other compilers use the suffixes .F77 and . f to represent
FORTRAN source code. Operating system commands using wild-card conventions
for globally renaming similarly-named files should be utilized whenever they
are available. For example, the DOS command for renaming all .FOR files to
.F77 files is:
REN *.FOR *.F77.
2.2.2 Code modifications
As much as possible, the RUSTIC code has been designed to be implementation
independent where an implementation would include computer make and type,
input/output devices used, and brand and version of FORTRAN 77 compiler used.
However, some incompatibilities may exist, particularly in the format of the
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INCLUDE statement and file OPEN statements. In these cases, code
modifications may be necessary, and should be made by someone having
knowledge of FORTRAN programming on the target computer. Any text processor
that operates on ASCII files without introducing control characters may be
used to edit the source and/or 'include' files.
2.2.3 Compilation and linking
The FORTRAN source code must be compiled and linked using the existing
FORTRAN compiler and code linker on the computer system. The 'include' files
must be present in the same directory as the source files, and each source
file must be compiled individually to produce a corresponding object file.
For example, the VAX VMS command to compile a FORTRAN source file is: FOR
, where is the name of the FORTRAN source code file, and
the resulting object file has the suffix .OBJ.
The 'link' step combines the object files (produced in the compilation step)
with standard library routines, and creates the executable program file.
This process may be facilitated by a link 'command' file that lists all
object files to be linked, specifies a name for the executable file, and
other information needed by the linker software (e.g. the location of
libraries to be searched for needed routines). The standard VAX VMS link
command appropriate for RUSTIC is: LINK RUSTIC.OBJ RUSTIC.OLE/LIB, where
RUSTIC.OBJ is the object file generated by compiling the 'main' program file
(RUSTIC.FOR), and RUSTIC.OLB is the name of a library file containing all
other RUSTIC object modules.
For VAX VMS systems, the command file RUSTBLD.COM is provided to perform both
the compilation and link steps automatically. This file compiles all of the
source files, stores the resulting object code in the library file
RUSTIC.OLB, and links the program to produce the executable file RUSTIC.EXE.
2.2.4 Executing the test runs
The input and output files corresponding to two standard test runs are
included in the RUSTIC software package to verify that the installation has
been performed correctly. These tests are described in detail in Section 6
of this document, and general information regarding RUSTIC execution is
included in Section 2.3 below. The RUSTIC run control input for these runs
is contained in the files RUSTIC.RN1 AND RUSTIC.RN2, and the corresponding
output files have the suffixes .VF1 and VF2. Since the program requires the
run control input file to be named RUSTIC.RUN, the test runs can be executed
individually by: (1) copying the appropriate test input file to a new file
with the name RUSTIC.RUN, (2) erasing any output files from a previous run,
if present, and (3) typing RUSTIC (followed by a return) to start execution.
During execution of the test runs, RUSTIC creates an output file named
KECHO.PRN, and several component model output files with the suffix or
extension .PRn, where n is 1 or 2. The KECHO.PRN file (for each run) and
each of the PRn output files should be compared to the corresponding VFn
output file, supplied for verification of the code, either by using an editor
to visually compare the results, or by using a compare utility such as
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DIFFERENCES on a VAX VMS computer or COMP or FC on DOS computers. If the PRn
files do not match the VFn files, some aspect of the installation may be
incorrect. Small differences in the values of floating point output may
occur normally between different computer hardware/software systems;
therefore, depending on the magnitude of the differences, a decision can be
made to consult with EPA CEAM personnel at the Athens ERL to determine the
source of the problem. The VFn files distributed with the software were
generated using a VAX VMS system, so very few or no differences should be
observed on VAX computers.
2.3 GENERAL PROCEDURES FOR RUSTIC EXECUTION
This section contains descriptions of techniques and suggestions for normal
(operational) execution of RUSTIC. It is generally convenient to locate all
of the input files pertaining to a specific problem within their own
subdirectory. The input files should be developed using the file
descriptions of Section 4.2. It will normally be useful to start with the
test input data files provided and edit them to meet the specifications of
the problem at hand.
The names chosen for the input files should be specific to the problem to
reduce the potential for confusing these files with those developed for
alternate problems -- RUSTIC enforces no restrictions on the names chosen,
either for the prefix or extension (the operating system used may, however,
enforce restrictions; e.g., MS-DOS limits the prefix to 8 characters and the
extension to 3 characters). The file name length, including the path, must
be less than 81 characters. It is good practice to choose an extension which
implies an input file (e.g., .IN or .DAT) or output file (e.g., .OUT or
.PRN). The names of the files used and the path describing where the files
are located (the subdirectory) are defined in the file RUSTIC.RUN, the run
control file (see Section 4.2.1). The file RUSTIC.RUN must be in the
subdirectory from which the execution is initiated. When all of the
necessary files are complete, RUSTIC can be executed by typing RUSTIC
followed by a return.
The PATH statement in the RUSTIC.RUN file can be used to specify the
directories or subdirectories where the model input and output files are
located. When the PATH statement is used in the RUSTIC.RUN file, the defined
path must include all necessary characters such that, when inserted in front
of the file name, a complete path + file name string is formed. For the MS-
DOS operating system, this implies that the path must end with the backslash
character ('\'). The defined path will pertain to all files which are
defined after that path statement until another path is defined.
The path record can also be used to define files which are located on
different drives. This is potentially useful to increase execution speed.
Scratch (unformatted) files are used by PRZM, VADOFT, and SAFTMOD for
temporary storage of data and these scratch files are opened with the last
defined path. If the last path indicates that the files are to be located on
a RAM disk (if a RAM disk is available with the operating system used),
execution speed will be measurably increased.
20
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Input files are opened with STATUS = 'OLD', which means that they must exist
(in the directory specified) for the software to continue the current
simulation. If the specified file does not exist, a fatal error is issued
and the simulation is terminated.
Output files are opened with STATUS = 'NEW. Different FORTRAN compiler/
operating systems have different responses to opening a file with STATUS =
'NEW when the file is present. Some will ignore the file's presence and
overwrite it. Others will issue a fatal error and the simulation will be
terminated. If this is the case, the user will have to ensure that the names
chosen for output files of the current simulation do not correspond to
existing file names.
In addition to file opening errors, the RUSTIC code checks for errors within
the input files and internal (calculation) errors. A complete list of the
error messages provided for error conditions currently recognized by the code
is provided in Appendix 8.1.
A screen management routine has been supplied to display the software in
operation. This routine lists RUSTIC execution information (line-by-line) to
the standard output device. Note that the echo level and trace level (see
Section 4.2.1) can be used to increase or decrease the amount of information
being sent to the screen.
21
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SECTION 3
MODULES AND LOGISTICS
The RUSTIC model consists of six major modules. These are:
• EXESUP, which controls the simulation
• IMPREA, which performs data input and initialization functions and
echoes input to an output file
• PRZM, which performs fate and transport computations for the crop
root zone
• VADOFT, which performs fate and transport computations for the
vadose zone
• SAFTMOD, which performs fate and transport calculations for the
saturated zone
• MCARLO, which performs functions related to Monte Carlo simulation
These components combine to allow the user to simulate the fate and transport
of agriculturally applied chemicals through the root zone, vadose zone, and
saturated zones, and to determine the magnitude of the resulting
concentrations in drinking water wells. Some special features that the model
possesses are the capability of PRZM to simulate volatilization and vapor
phase movement of chemical, irrigation effects on pesticide leaching and the
capability of each of the modules to simulate the formation, fate, and
transport of daughter products. SAFTMOD has the capacity to simulate a
variety of aquifer configurations. In the following sections, the functions
and capabilities of each of these modules are discussed more fully.
3.1 EXESUP - THE EXECUTION SUPERVISOR
The execution supervisor controls the simulation. It opens files, reads in
and initializes data (by calling INPREA), controls the "networking" of
computational modules, controls any Monte Carlo simulation, and eventually
closes files and terminates the simulation. The execution supervisor has its
own input file, the contents of which are detailed in Section 4.2.1. In
order to set up this input file, the user must have planned out the module
networking and system configuration, starting and ending simulation dates,
and other data to control the simulation and model output.
22
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3.2 PRZM - THE ROOT ZONE FATE AND TRANSPORT MODULE
PRZM performs computations of fate and transport in the crop root zone. To
do this, it requires soils, pesticides, crop, meteorology, and agronomic
practice input data. PRZM input data are discussed in Section 4.2.2.
In order to use PRZM, two files are required. The first is a meteorologic
file which contains precipitation, temperature, pan evaporation, wind speed,
and solar radiation data. Some meteorologic data files available to aid the
user in applying PRZM or RUSTIC are described in Section 3.8. The second
file required is the PRZM parameter file. Guidance for estimating PRZM
parameters is given in Section 5.2.
In the framework of the linked modeling system, the major outputs of PRZM are
the time series of water and chemical flux to the vadose zone, or saturated
zone, depending upon how the network is defined by the user. PRZM, however,
produces two individual output files:
1) A summary file containing:
• A hydrologic summary (daily, monthly, or annual)
• A pesticide mass summary (daily, monthly, or annual)
• A pesticide concentration summary (daily, monthly, or annual)
2) A time series file which writes out daily values of the requested
variables as either a time series or cumulative time series
3.2.1 Special Actions Option
The Special Actions option in PRZM allows the user to output soil profile
pesticide concentrations at user-specified times during the simulation
period, and to change selected model parameters to better represent chemical
behavior and the impacts of agricultural management practices. The required
input format and parameters are specified in Volume II, Section 4.
By using the 'SNAPSHOT' capability of Special Actions, the user can output
the pesticide concentration profile, i.e., the total concentration in each
soil compartment, for any user-specified day during the simulation period.
In this way, the user can run PRZM with only monthly or annual output
summaries and still obtain simulation results for selected days when field
data were collected. There is no inherent limit to the number of SNAPSHOTS
that can be requested in a single run. When more than one chemical is being
simulated, the concentration profiles are provided by the order of the
chemical number, i.e., NCHEM.
To better represent the expected behavior of the chemical being simulated, or
the impacts of tillage or other agricultural practices, the following
parameters can be reset to new values at any time during the simulation
period:
Solution Decay Rate (DWRATE)
Sorbed Decay Rate (DSRATE)
Partition Coefficient (KD)
23
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Bulk Density (BD)
Curve Number (CN)
USLE Cover Factor (USLEC)
Thus, for chemicals that demonstrate seasonal decay rates or partition
coefficients, or different values for the period following application
compared to later in the crop season, the appropriate parameters can be
changed at user-specified times to mimic the observed, or expected, behavior
of the compound.
Similarly, for agricultural practices or specific tillage operations that
impact the soil bulk density, curve number, or cover factor, these parameter
values can be altered during the simulation in an attempt to better represent
their impacts. The parameter guidance provided in Volume II, Section 5.2,
may help the user in determining adjustments for these parameters. Users
should note that adjustments to the bulk density, and possibly the partition
coefficient, may impact the pesticide balance calculation.
3.3 VADOFT - THE VADOSE ZONE FATE AND TRANSPORT MODULE
VADOFT performs computations of fate and transport in the vadose or
unsaturated zone. In order to do this, it requires soil, soil-water, and
chemical specific information. It also requires initial (water and chemical)
conditions and boundary conditions. Input data file building is discussed in
Section 4.2.2 and parameter estimation guidance is given in Section 5.3. In
the linked system, it obtains its upper boundary conditions from PRZM output
(i.e., root zone water and chemical fluxes). Note that if VADOFT is run in
tandem with PRZM, then VADOFT always uses a prescribed water flux at the
upper boundary and a prescribed head at the lower boundary node for flow
simulation and a prescribed mass flux at the upper boundary node and zero
concentration condition at the lower boundary node for transport simulation.
If SAFTMOD is being run, it obtains an updated lower boundary condition
(hydraulic head) based on SAFTMOD flow results after each executed SAFTMOD
timestep. Unlike PRZM, which performs flow and transport computations in a
single "pass", VADOFT may be used to solve either flow or transport problems
individually. Because of this, VADOFT must be called twice in the linked
model (once for flow, once for transport) for each day of transport
simulation. A scratch file is used to save flow information (velocities,
etc.) in order to perform a transport simulation. For guidance on the
required scratch file, please refer to Section 4.2.1.
Another consideration for the use of VADOFT is that it solves a set of non-
linear equations for flow, and hence an iterative solution is required. This
has the dubious distinction of making the flow portions of VADOFT one of the
slower portions of the code to execute and introduces the possibility of
model failure due to nonconvergence. With judicious parameter selection, the
latter can be avoided.
In the context of the linked modeling system, the most important VADOFT
outputs are the water and mass fluxes to user-designated SAFTMOD nodes. Of
course, the model produces various outputs. The primary line printer output
from the flow model of VADOFT includes nodal values (at various time steps)
24
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of pressure head and nodal values of vertical Darcy velocity and saturation
at various time steps. The frequency of printing of the flow and transport
results is controlled by the frequency of printing of PRZM hydrology and
pesticide summaries.
3.4 SAFTMOD - THE SATURATED ZONE FATE AND TRANSPORT MODULE
SAFTMOD performs chemical fate and transport computations in the saturated
zone. Like VADOFT, it requires data on aquifer materials, chemical
properties, and initial and boundary conditions. Some of the network
information for PRZM or VADOFT linkage to SAFTMOD is also input in its
parameter file. Boundary conditions (i.e., water and mass fluxes) are
provided by VADOFT or PRZM when the model is run as part of the linked
system.
Like VADOFT, SAFTMOD solves either a flow or transport problem and uses
scratch files to store parameters and initial conditions to -restart flow or
transport simulations. It uses a scratch file, like VADOFT, to store
information (velocities, etc.) for a transport simulation from a
corresponding flow simulation. Guidance for setting up the scratch file is
found in Section 4.2.1.
In a typical application of the linked modeling system, the SAFTMOD output of
greatest interest would be the time series of pesticide concentrations in the
drinking water well or at a specific location in the aquifer. Other outputs
(e.g., head or concentration maps) may be useful for calibration purposes.
The primary line printer output from the flow model of SAFTMOD includes model
values (at various time levels) of hydraulic head and nodal values of Darcy
velocity components at various time steps. The primary line printer output
of the transport model of SAFTMOD includes nodal values (at various time
levels) of solute concentration. When run in the linked nodes. SAFTMOD like
VADOFT, keys off the printing frequency of PRZM (daily, monthly, or annual).
3.5 MCARLO - THE MONTE CARLO SIMULATION MODULE
MCARLO performs all the functions necessary to execute a Monte Carlo
simulation. It reads special data for parameters to be varied (e.g.,
distribution types and moments) and output variables to be observed,
generates random numbers, correlates them and performs transformations,
exchanges these generated values for RUSTIC parameters, performs statistical
analysis on the output variables, and writes out statistical summaries for
the output variables.
The MCARLO module makes use of an input and output file. Inputs to the
MCARLO module are discussed in Section 4.2.5. The user should be aware that
any of the parameters entered in the Monte Carlo input file once designated
as constants will be used in lieu of that same parameter value entered in the
standard input file.
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3.6 MODEL STRUCTURE
This section discusses model structure. Specifically, it gives information
on model modules and subroutines and module communication.
3.6.1 Subroutine Descriptions
As discussed above, RUSTIC is organized into a number of modules, each having
subroutines which perform various specific functions. In this section, Table
3-1 gives a listing of all the major subroutines called by the program.
Subroutines are organized alphabetically by module or calling subroutine. A
brief description of the function of each subroutine is also given.
A glossary of the major internal variable names used within the RUSTIC code
is provided in Appendix 8.2.
3.6.2 Intra/Intermodule Communication
Communication between subroutines and modules utilizes common blocks and
argument lists. The primary modes of data transfer are via common blocks.
Common blocks utilized in the program are listed in Table 3-2 along with a
brief topical description of their contents.
Subroutine arguments are used to transfer some data within the program. This
method is primarily used where common blocks are infeasible and to isolate
variables in certain sections of the code which may have the same name. For
instance, the array DIS is used in both VADOFT and SAFTMOD as the storage
array for head and concentration values. Subroutine arguments are used to
pass values of DIS from VADOFT and SAFTMOD into the execution supervisor
(EXESUP) in which both of these arrays must be known.
Dimensions of most program arrays are specified in PARAMETER statements. The
variables used to size program arrays are listed in Table 3-3 along with a
description and default value. The user should select values for these
variables based upon the array sizes necessary to simulate the problem of
interest.
3.7 LIMITATIONS
The user should be aware of several limitations of the code as it is
currently implemented. Originally, the intent was to allow for multiple
applications of the PRZM and VADOFT codes to simulate large scale lateral
heterogeneities in a physical system. Although a perusal of the code will
show that there is a substantial amount of logic built in to accommodate this
feature, it has not yet been fully implemented.
The second limitation is that, in the current version, daughter products
cannot be simulated in either aquitards or a multiaquifer system. There, the
simulation of daughter products is limited to PRZM, VADOFT, and a single
water table aquifer system.
The final limitation is that only a small number of input variables may be
changed at random by invoking the Monte Carlo routines. It is not difficult
to add additional variables, however.
26
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS
Module/
Calling
Subroutine
Subroutine Function
RUSTIC
EXESUP
CLOSIT Closes all files prior to program termination.
DISPLY Displays a message on the screen and on the output
echo file.
DONBAR Tracks the "Percentage Completion" of the
simulation.
EXESUP Execution supervisor. Calls computational
modules and linkage routines.
INITEM Determines user defined options, opens files, and
reads global data.
INITMC Initializes Monte Carlo parameters.
INPREA Driver subroutines for reading and initializing
program input data.
OUTPUT Generates Monte Carlo output.
RANDOM Generates random numbers for Monte Carlo
simulations.
READM Reads user defined Monte Carlo variables.
SCREEN Screen manager routine.
STATIS Calculates statistical summaries of Monte Carlo
output variables.
DISPLY See "RUSTIC".
DONBAR See "RUSTIC".
ENDDAY Calculates the last day of the current simulation
period.
GLOMAS Performs global mass balance for the linked system.
SMIOIN Reads SAFTMOD input file.
SUBIN Tracks entries into subroutines.
27
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
SUBOUT Tracks exits from subroutines.
XFLOW Calls VADOFT and/or SAFTMOD for flow simulations.
XPRZM Calls PRZM module and stores intermediate results
for linkage with VADOFT and SAFTMOD.
XTRANS Calls VADOFT and/or SAFTMOD for transport
simulations.
ZONEAV Calculates zonal average values of water table
elevations/pressure heads.
INITEM ADDSTR Adds a character string to another character
string.
BMPCHR Capitalizes a character string.
CHKFIL Checks "Open" status of a file.
CLOZE1 Closes one file.
COMRD Allows user to insert comments in data files by
ignoring comments when reading.
COMRD2 Comment reading routine which handles end-of-file
read.
ECHOGD Echoes global data input.
ECHOF Echoes the names of files opened.
ELPSE Printing utility.
ERRCHK Writes error messages, closes files if fatal error.
LFTJUS Left justifies a character string.
NAMFIX Left justifies and capitalizes a character string.
OPECHO Printing utility.
OPENF Opens files.
28
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
INPREA
XPRZM
Subroutine
PRZDAY
SCREEN
TDCALC
VALDAT
CLOZE1
ECHO
ERRCHK
INIACC
INITL
MCPRZ
PRZMRD
RSTPT1
RSTPUT
SCREEN
SMIOIN
SUBIN
SUBOUT
VADINP
DISPLY
PRZM
SUBIN
Function
Transfers simulation start and end dates to PRZM
common.
See "RUSTIC".
Calculates total number of days in a simulation.
Checks user supplied date for validity.
See "INITEM"
Echoes PRZM input file read.
See "INITEM"
Initializes PRZM accumulators.
Initializes PRZM variables.
Transfers random values to PRZM variables for Monte
Carlo simulations.
Reads PRZM input file.
Stores PRZM initial conditions on unformatted file.
Stores PRZM initial conditions on unformatted file.
See "RUSTIC".
See "EXESUP".
See "EXESUP".
See "EXESUP".
Reads VADOFT input sequence.
See "RUSTIC".
Performs root zone fate and transport calculations.
See "EXESUP".
29
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine Subroutine Function
SUBOUT See "EXESUP".
XFLOW DISPLY See "RUSTIC".
LINTRP Interpolates daily values of the water table
elevation (Function 1) and interpolates daily
values of pesticides flux using PRZM/VADOFT normal
fluxes to estimate flux to the water table.
MCSFT Transfers Monte Carlo simulation variables to
SAFTMOD arrays.
MCVAD Transfers Monte Carlo simulation variables to
VADOFT commons.
SCREEN See "RUSTIC".
SFTMOD Performs saturated zone fate and transport
calculations.
SMIOIN See "EXESUP".
SUBIN See "EXESUP".
SUBOUT See "EXESUP".
SWFLX Loads water fluxes from PRZM/VADOFT into SAFTMOD
arrays.
VADINP See "INPREA".
VADOFT Performs vadose zone fate and transport
calculations.
WMFCAL Integrates daily PRZM/VADOFT fluxes to pass into
SAFTMOD.
ZONEAV See "EXESUP".
XTRANS DISPLY See "RUSTIC".
INITDK Initializes VADOFT chemical array.
30
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
LINTRP See "XFLOW".
MASCOR Corrects nodal concentrations of PRZM/VADOFT nodes
beneath the water table based on SAFTMOD simulation
results.
MCSFT Transfers SFTMOD output to Monte Carlo variables.
MCVAD Transfers VADOFT output to Monte Carlo variables.
SFTCHM Loads chemical specific data into SAFTMOD arrays.
SFTFLX Loads PRZM/VADOFT mass fluxes into SAFTMOD arrays.
SFTMOD See "XFLOW"
SMIOIN See "EXESUP".
SUBIN See "EXESUP".
SWFLX See "XFLOW".
VADINP See "INPREA".
VADCHM Loads chemical specific data into VADOFT arrays.
VADOFT See "XFLOW".
WMFCAL See "XFLOW".
PRZM ACTION Identifies and performs special action.
BLOCK DATA Initializes CNDMO, CMONTH and CDATE values for
common block MISC.
CANOPY Calculates the vertical transport resistances of
plant canopy.
EROSN Computes loss of pesticide due to erosion.
EVPOTR Computes daily potential evapotranspiration, canopy
evaporation and actual evapotranspiration from soil
layers.
31
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine Subroutine Function
FTIME Stores current time and date (for PDF 11/70 only).
FURROW Computes flow and infiltration down a furrow using
a kinematic wave with Green-Ampt infiltration.
HYDROL Performs hydrologic calculations for snowmelt, crop
interception, runoff and infiltration.
HYDRl Performs hydraulic calculations for soil under free
drainage conditions.
HYDR2 Performs hydraulic calculations for soil under
restricted drainage conditions.
INFIL Computes Green-Ampt infiltration assuming a
constant depth over time step DT.
IRRIG Determines soil moisture deficit, decides if
irrigation is needed, and calculates irrigation
depths.
KDCALC Calculates Kd values based on (1) Karickhoff (2)
Kenaga or (3) Chiou method.
KHCORR Corrects temperature effect on Henry's constant.
MASBAL Calculates mass balance error terms for both
hydrology and pesticide transport.
MOC Computes pesticide transport due to advective
influence.
OUTCNC Prints daily, monthly or annual pesticide
concentration profiles.
OUTHYD Accumulates and outputs daily, monthly or annual
summaries for water.
OUTIRR Outputs irrigation results.
OUTPST Accumulates and outputs daily, monthly or annual
summaries for pesticides.
32
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
OUTRPT Prints daily, monthly and annual pesticide
concentration profiles to MODOUT and SNAPSHOT
output files.
OUTTSR Outputs user specified time series to time series
plotting files.
PESTAP Computes amount and location of pesticide
application (foliage, soil surface, soil
incorporated).
PLGROW Determines plant growth parameters.
PLPEST Computes loss of pesticide in plant canopy
compartment.
PSTLNK Provides linkage for transformation and source
terms for parent/daughter relationships.
SLPSTO Sets up the coefficient matrix for the solution of
the soil pesticide transport equation considering
advection (implicit backwards difference
formation).
SLPST1 Sets up the coefficient matrix for the solution of
the soil pesticide transport equation without
considering advection (MOC formulation).
SLTEMP Calculates soil temperature profile.
THCALC Computes field capacity and wilting point for each
soil layer.
TRDIAG Solves a system of equations with a tridiagonal
coefficient matrix in double precision.
TRDIA1 Solves a system of equations with a tridiagonal
coefficient matrix in single precision.
VADOFT ASSEMF Computes and assembles element matrices for
variably saturated water flow simulation.
33
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
ASSEMT Computes and assembles element matrices for solute
transport simulation.
BALCHK Performs mass balance computation and computes
total solute mass flux at each node.
CONVER Determines limiting value of water saturation that
will be used in the subsequent computation.
DSWFUN Computes moisture capacity function.
HFINTP Determines the prescribed head or flux values by
interpolation.
INTERP Performs linear interpolation using tabulated data
of relative permeability versus water saturation,
and pressure head versus water saturation.
PKWFUN Computes relative permeability function.
SWFUN Computes water saturation function.
TRIDIV Performs tri-diagonal matrix solution.
VARCAL Computes current nodal values of head or
concentration.
VSWCOM Computes nodal values of water saturation and Darcy
velocity.
SFTMOD ANDCAL Computes nodal lengths and areas.
ASSEMV Assembles element matrices and right-hand-side
vector into global coefficient matrix and global
right-hand-side vector. Also incorporates nonzero
flux boundary conditions into the global right-
hand- side vector.
BALCHS Performs the mass balance computation.
BUPDAT Updates boundary conditions arrays.
34
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
CPCAL Computes the hydraulic head or concentration
distribution in aquitard soil columns.
OXYGEN Generates row and column coordinates of the
rectangular mesh.
EBFIND Computes matrix bandwidths and determines the
maximum bandwidth.
EBFOR1
Computes seepage element matrices for rectangular
elements.
EBFOR2
Computes transport element matrices for rectangular
elements.
FILHED Transfers head or concentration data for the final
simulation time step to and from file unit number
8.
FILVEL Transfers velocity and saturated thickness data at
each time step to and from file unit number 9.
FILPLT Transfers mesh data and simulation results to file
unit number 10 for use in subsequent plotting.
FILPRW Performs output windowing and printing option.
FIVEIO Performs input/output operation of an integer array
on a binary file with assigned unit number.
FRVEIO Performs input/output operation of a real array on
a binary file with assigned unit number.
HFINTP Determines prescribed function and flux values for
a particular time step using linear interpolation.
MATMOD Modifies the global matrix and right-hand-side
vector to account for leakage fluxes between
aquifers and confining aquitard.
MESHGN Generates nodal coordinates and element nodal
connections for the specified rectangular grid.
35
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
PBC Allocates the prescribed Dirichlet and flux
boundary conditions.
QCAL Computes nodal values of integrated fluid fluxes.
RUPDAT Updates values of recharge rate and integrated
nodal values of recharge flux.
SOLVEC Solves a linear set of algebraic equations using
Gauss elimination scheme for asymmetric banded
matrix.
SOLVEP Solves a set of algebraic equations using a Gauss
elimination scheme for symmetric banded matrix.
SUPDAT Updates values of solute (pesticide) application
rate and integrated nodal values of solute mass
flux.
THUPDT Updates saturated thickness of an unconfined
aquifer.
TRIMOD Performs a direct solution of a tri-diagonal matrix
equation using the Thomas algorithm.
VARCAL Performs a driving function by calling relevant
subroutines to compute nodal values of head or
concentration at the end of the current time step.
VELCOM Computes element centroidal values of Darcy
velocity components (and nodal values of saturated
thickness in the case of areal analysis).
MCARLO BMPCHR Capitalizes character variables.
COMRD3 Reads and checks if input lines contain comments,
data, or end of file.
DECOMP Decomposes correlation matrix into coefficient
matrix required to generate correlated random
numbers.
36
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (continued)
Module/
Calling
Subroutine
Subroutine Function
EMPCAL Generates empirically distributed random numbers by
interpolating linearly from an input empirical
distribution.
ERRCHK Writes error messages when unexpected end-of-file
is encountered in input data file.
EXPRN Generates exponentially-distributed random numbers.
FRQPLT Writes plots of cumulative frequency distributions.
FRQTAB Writes tabulated cumulative frequency
distributions.
INITMC Initializes statistical summation arrays,
reorganizes Monte-Carlo input arrays to account for
constant variables, and performs other
miscellaneous Monte-Carlo initializations.
LFTJUS Left-justifies character variables (i.e., removes
blanks from the left-side of character strings).
LNGSTR Finds the length of character variables (i.e., the
number of non-blank characters).
MCECHO Writes Monte-Carlo input data to the shell output
file.
MTPV Multiplies a vector of uncorrelated variables by a
coefficient matrix to reform a vector of correlated
variables.
NAMFIX Left-justifies and capitalizes character variables.
NMB Generates normally-distributed random numbers.
OUTFOR Writes frequency tables and plots.
OUTPUT Writes out statistical summaries of Monte-Carlo
runs to the Monte-Carlo output file.
RANDOM Generates a vector of random numbers from specified
distributions.
37
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Table 3-1. LIST OF SUBROUTINES BY MODULE AND A DESCRIPTION OF THEIR
FUNCTIONS (concluded)
Module/
Calling
Subroutine
Subroutine Function
READM Reads Monte-Carlo input data from a user-specified
input file unit number.
STATIS Performs summations required to compute statistical
moments for random model inputs and model outputs
over all Monte-Carlo runs.
STOUT
TRANSB
TRANSM
UNIF
Computes statistical moments (mean, standard
deviation, skewness, kurtosis, correlations,
minimum and maximum) from summations computed by
STATIS. Statistics are then written out to the
Monte-Carlo output file.
Transforms normally-distributed number to an SB
distributed number.
Transforms normally-distributed numbers to numbers
having the appropriate user-specified distributions
(i.e., log-normal SB, SU).
Generates uniform random numbers ranging
between 0 and 1.
38
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Table 3-2. COMMON BLOCK NAMES AND DESCRIPTIONS
Common Block
Description
ABOUN
ACCUM
ADISC
ASOLV
ATEMP
BALANC
BCDATA
BDATA
BNDOUT
BSOLV
CHAR
CHNEW
CNDF
CONTR
CONTR1
CONTR2
CONTR3
CONTR4
CONTR5
CROP
ECHOIT
ELSTOR
Boundary condition variables.
PRZM cumulative terms carried from one time step to the
next, primarily for outputting water and pesticide
summaries.
VADOFT coordinate and time-value arrays.
VADOFT solution arrays.
Temporary arrays for printing SAFTMOD boundary conditions,
RUSTIC global mass balance terms.
SAFTMOD boundary condition data.
SAFTMOD boundary condition data.
SAFTMOD boundary sink term data.
VADOFT solution arrays.
Character variables used in PRZM.
MONTE CARLO variable.
PRZM accumulated number of days in each month (with and
without leap year).
VADOFT timekeeping and simulation control variables.
SAFTMOD simulation control parameters.
SAFTMOD simulation control variables.
SAFTMOD timekeeping and simulation control variables.
SAFTMOD simulation and output control data.
SAFTMOD control variables for boundary conditions.
PRZM crop timing and growth related terms.
Information for RUSTIC output level.
SAFTMOD solution matrix arrays.
39
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Table 3-2. COMMON BLOCK NAMES AND DESCRIPTIONS (continued)
Common Block
Description
ESTORE
FDATA
GLOBRZ
GLOBSF
GLOBVD
HYDR
IN2SM
IN2VAD
INTDAT
IRGT
LNKSTO
MASCON
MCVAR
MCRVAR
MCSTR
MDATA
MDATAQ
MDATAT
MET
MISC
MSHDAT
MSHPAR
SAFTMOD aquifer geometry arrays.
SAFTMOD flux data.GLOBPZGlobal mass balance data from PRZM.
Global mass balance variables.
Global mass balance data from SAFTMOD.
Global mass balance data from VADOFT.
PRZM surface and soil hydrology related terms.
Transfer variables for input routines to computational
routines of SAFTMOD.
Decay rates, etc. of VADOFT chemicals.
SAFTMOD initial conditions arrays.
PRZM irrigation related terms.
Arrays used in linking VADOFT to SAFTMOD.
Array to convert VADOFT dissolved to total concentrations.
PRZM variables related to MONTE CARLO.
Parameters and random number arrays used in MONTE CARLO
routines.
Storage of VADOFT/SAFTMOD outputs for MONTE CARLO.
VADOFT material property data.
SAFTMOD material property data.
SAFTMOD aquitard property data.
PRZM meteorological related terms.
PRZM miscellaneous terms including output flags and time-
keeping variables.
SAFTMOD grid connectivity data.
SAFTMOD grid coordinate data.
40
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Table 3-2. COMMON BLOCK NAMES AND DESCRIPTIONS (continued)
Common Block
Description
NDSTOR
NDSTOS
PEST
PRCNTR
PRTCHK
PRZSTO
PTAP
RCHDAT
RDATA
RTDATA
SAZDAT
SCHMDK
SFTEXP
SFTSTO
SWHDA
TMDATA
TRACE
TRACHR
TRIDIA
VADSTO
VASOLV
SAFTMOD aquifer geometry data (also contains an array to
convert from dissolved to total concentration).
SAFTMOD aquitard leakage data arrays.
PRZM pesticide fate, transport, and application related
terms.
SAFTMOD output control variables.
Logical arrays to determine when VADOFT/SAFTMOD will write
output.
Storage of PRZM chemical fluxes and concentrations for
linkage with VADOFT/SAFTMOD.
PRZM/global mass balance solute input accumulator array.
SAFTMOD recharge data.
SAFTMOD chemical retardation and decay data.
SAFTMOD time varying recharge data.
SAFTMOD time varying solute flux data.
SAFTMOD chemical decay array.
Decay ratio, etc. of SAFTMOD chemicals.
SAFTMOD heads and concentrations.
VADOFT tabular data or permeability and pressure head
versus water saturation.
SAFTMOD timekeeping data.
RUSTIC program tracing variables.
Subroutine names used in RUSTIC trace.
SAFTMOD coefficients for tridiagonal matrix solution.
VADOFT heads, fluxes, etc. for linkage w/SAFTMOD.
VADOFT tridiagonal matrix coefficient arrays.
41
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Table 3-2. COMMON BLOCK NAMES AND DESCRIPTIONS (concluded)
Common Block
Description
VCHMDK
VDISC
VELEM
VONTR1
VONTR2
WELEM
VWORKM
WAVE
WAVEl
WAVE2
WELEM
WORKA
WORKM
WORKN
WORKP
WORKS
ZONWHT
VADOFT chemical decay.
SAFTMOD solution arrays for vertical aquitard columns.
SAFTMOD velocity field data.
VADOFT simulation control data.
VADOFT mass balance variables.
Variables related to VADOFT nodal velocities.
VADOFT daughter products simulation arrays.
SAFTMOD solution and matrix assembly arrays.
SAFTMOD matrix assembly arrays.
SAFTMOD matrix assembly arrays.
VADOFT intermediate arrays for developing solution arrays.
SAFTMOD matrix assembly variables.
SAFTMOD temporary arrays for reading/writing scratch files.
VADOFT residual saturation and capacity factor arrays.
SAFTMOD daughter product simulation arrays.
SAFTMOD temporary arrays for mass balance calculations.
Weighting factors for linking PRZM/VADOFT/SAFTMOD pesticide
zones.
42
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Table 3-3. PARAMETER STATEMENTS UTILIZED IN THE RUSTIC CODE
Parameter
Description
Default
Value
File Name
MCMAX Maximum number of MONTE CARLO random input
variables.
MXMAT Maximum number of VADOFT material types.
MXMBF Maximum band width of SAFTMOD coefficient matrix.
MXMBW Maximum band width of SAFTMOD coefficient matrix
(transport) or maximum semiband width (flow).
MXNAQF Maximum number of SAFTMOD aquifers.
MXNBFV Maximum number of SAFTMOD transient flux boundary
conditions.
MXNBHV Maximum number of SAFTMOD transient Dirichlet
boundary conditions.
MXNBOU Maximum number of SAFTMOD aquifer nodes where the
net integrated flux values are required.
MXNBTO Maximum number of SAFTMOD steady-state Dirichlet
boundary conditions.
MXNCAT Maximum number of SAFTMOD aquitard columns.
MXNCOL Maximum number of SAFTMOD grid lines parallel
to Y-axis.
MXNCOW Maximum number of SAFTMOD aquitard columns in
an output window section.
MXNDFL Maximum number of SAFTMOD steady-state flux
model boundary conditions.
MXNE Maximum number of SAFTMOD elements in the
aquifer mesh.
MXNLAY Maximum number of VADOFT layers. A VADOFT layer
is a collection of contiguous elements having a
uniform material type.
MXNMAD Maximum number of aquitard materials.
5 MCRVAR.CMN
20 MXMAT.PRM
62 MXMBF.PRM
62 MXMBW.PRM
2 MXNAQF.PRM
20 MXNBFV.PRM
20 MXNBHV.PRM
75 MXNBOU.PRM
100 MXNBTO.PRM
1250 MXNCAT.PRM
75 MXNCOL.PRM
30 MXNCOW.PRM
50 MXNDFL.PRM
400 MXNE.PRM
20 MXNLAY.PRM
10 MXNMAD.PRM
43
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Table 3-3. PARAMETER STATEMENTS UTILIZED IN THE RUSTIC CODE (continued)
Parameter
MXNMAT
MXNNDW
Maximum
Maximum
Description
number
number
of
of
SAFTMOD
SAFTMOD
aquifer materials .
nodes
in an output
Default
Value
20
30
File Name
MXNMAT
MXNNDW
.PRM
.PRM
window section.
MXNOBS Maximum number of SAFTMOD observation nodes
where monitoring of head or concentration values
is desired.
MXNOD Maximum number of VADOFT nodes.
MXNP Maximum number of SAFTMOD nodes.
MXNPAT Maximum total number of SAFTMOD nodes in an
aquitard.
MXNPCO Maximum number of SAFTMOD nodes per finite element
column in an aquitard.
MXNPSZ Maximum number of nodes per SAFTMOD solute flux
zone.
MXNRC The maximum of MXNCOL and MXNROW.
MXNROW Maximum number of SAFTMOD grid lines parallel
to X-axis.
MXNRZ Maximum number of nodes per recharge zone (used
in global mass balance).
MXNRZO Maximum number of SAFTMOD recharge zones.
MXNSZO Maximum number of SAFTMOD solute application zones.
MXNTCL Maximum number of SAFTMOD time values at which head
or concentration values at selected nodes are to be
printed.
MXNTNF Maximum number of SAFTMOD values on the graph of
time vs. flux at a transient flux node (not used
in linked simulation).
MXNTNH Maximum number of SAFTMOD values on the graph of
time vs. head or concentration at a transient
Dirichlet node (not used in linked simulation).
24 MXNOBS.PRM
120
470
4000
MXNOD.PRM
MXNP.PRM
MXNPAT.PRM
20 MXNPCO.PRM
150 MXNPSZ.PRM
75
75
MXNRC.PRM
MXNROW.PRM
450 MXNRZ.PRM
4
4
40
MXNRZO.PRM
MXNSZO.PRM
MXNCL.PRM
25 MXNTNF.PRM
25 MXNTNH.PRM
44
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Table 3-3. PARAMETER STATEMENTS UTILIZED IN THE RUSTIC CODE (continued)
Parameter
Description
Default
Value
File Name
MXNTRP Maximum number of SAFTMOD interpolation control
points on the graph of recharge rate (not used
in linked simulation).
MXNTS Maximum number of SAFTMOD time steps (not used
in linked simulation).
MXNTSZ Maximum number of SAFTMOD interpolation control
points on the graph of solute mass application
rate versus time (not used in linked simulation).
MXPRT
MXTIM
MXTMV
MXVDT
MXWNDO
NAPP
NC
NCMPTS
NCMAX
NEMP
NMAX
NMXFIL
NPMAX
Maximum number of VADOFT observation nodes.
Maximum number of VADOFT iterations necessary
for problem solutions.
Maximum number of VADOFT time interpolation values.
Maximum number of VADOFT timesteps (days) per
SAFTMOD simulation.
Maximum number of SAFTMOD output window sections.
Maximum number of PRZM pesticide applications.
Maximum number of PRZM cropping periods.
Maximum number of PRZM compartments in the soil
profile.
Maximum number of MONTE CARLO variables for which
cumulative distributions are plotted.
60 MXNTRP.PRM
200 MXNTS.PRM
100 MXNTSZ.PRM
1
30
30
31
10
10
5
118
MXPRT.PRM
MXTIM.PRM
MXTMV.PRM
MXVDT.PRM
MXWNDO.PRM
PARM.INC
PARM.INC
PARM.INC
3 MCRVAR.CMN
Maximum number of MONTE CARLO empirical distribution 20
points.
MCRVAR.CMN
Maximum number of MONTE CARLO summary output
variables.
8 MCRVAR.CMN
Maximum number of files which can be open at one 20 IOUNITS.PAR
time.
Maximum length of MONTE CARLO output averaging
periods.
5 MCRVAR.CMN
45
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Table 3-3. PARAMETER STATEMENTS UTILIZED IN THE RUSTIC CODE (concluded)
Parameter Description Default File Name
Value
NRMAX Maximum number of MONTE CARLO runs. 2000 MCRVAR.CMN
NPII Maximum number of PRZM particles used in MOC 600 FARM.INC
algorithm.
3.8 DATA BASES
There are currently under development, four data bases which will be
available for use with the RUSTIC code in the future: a meteorological data
base, a geographic soils data base, a soils properties and cropping practices
data base, and a descriptive statistics data base for Monte Carlo simulation
(Carsel 1988, personal communication). These data bases will be useful in
assisting the user to select appropriate model parameters and input time
series data. The data bases should be available in late 1989 from the
Environmental Protection Agency, Environmental Research Laboratory in Athens,
Georgia (Carsel 1989, personal communication).
46
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SECTION 4
MODEL BUILDING
In this section, the steps which are preliminary to actual model application
are discussed. It is not meant to be a general treatise on modeling, but
instead it will offer some specific guidance applicable to RUSTIC. The
experienced model user may want to skip the first part of this section which
deals with such concepts as problem definition, idealizing the physical
system, etc., and proceed directly to the description of the input data
sequence and format. However, for some, the review of these concepts may
prove valuable.
4.1 SYSTEM ABSTRACTION
In order to model a natural system, which is inherently complex, the model
user must idealize or abstract certain aspects of it. As a basic first step,
it is essential to find out as much as one can about the system and to
formulate a mental "model" of how it functions. Simultaneously, the user
must have in mind a set of goals for the modeling exercise and must keep in
mind the limitations of the model. The intersection of modeling goals, model
capabilities and the "reality" of the natural system (including the data
available to describe it) form the background for the model application.
Hopefully, Sections 1 and 3 have given the user an understanding of the
capabilities of the RUSTIC model. It remains to idealize the system,
translate that abstraction into a model configuration, formulate a simulation
strategy, estimate parameters, and execute the model.
4.1.1 Idealizing the System
The RUSTIC code is capable of representing very simple or fairly complex
subsurface systems. As previously discussed, it has three modules: PRZM,
which simulates pesticide fate and transport in the crop root zone; VADOFT,
which simulates pesticide fate and transport between the crop root zone and
the water table; and SAFTMOD, which simulates fate and transport in the
saturated zone. If the water table is close to the crop root zone and the
root zone soils are fairly permeable, then the user may choose to exclude
VADOFT from the simulation. If it can be done, this offers two advantages:
• it eliminates the possibility of nonconvergence of VADOFT flow
algorithms and
• it will speed up model execution time.
47
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If the system has a thick vadose zone with relatively slow draining soils,
then the use of VADOFT is essential to overcome the limitations of the
elementary soil hydraulics in PRZM.
PRZM and VADOFT are one-dimensional models and formulated so that vertical
material layering can be accounted for. In PRZM, these material zones are
referred to as horizons, while in VADOFT, they are referred to as material
layers. The maximum number of materials is controlled by a parameter
statement in each model.
The user may want to differentiate horizons or material layers for several
reasons. There may be differences, for instance, in hydraulic properties
which would affect water transmission rates, organic carbon content which
would affect solute adsorption, or soil pH which might affect chemical
degradation by hydrolysis. In PRZM, both water and chemical properties are
entered for each horizon. In VADOFT, however, the user has the option of
specifying a different material layering for flow simulations and for
transport simulations. For example, in VADOFT, the user may specify one
material layer for flow and three material layers for transport. In PRZM,
for the same case, the user would have to select three horizons to
differentiate the transport properties, and enter the same set of hydraulic
properties redundantly for these horizons.
Spatial heteorgeneities can be addressed by using Monte Carlo simulation.
Although the multiple application of PRZM and/or VADOFT modules is currently
not implemented, water and solute fluxes to SAFTMOD may be specified over
different areas or lengths.
With SAFTMOD, the user can allow for both lateral and vertical heterogeneity.
The user may simulate a one- or two-aquifer system with an intervening
aquitard. Laterally, the user can specify "material zones" which may have
porous media with contrasting properties. The user also has the capability
to specify recharge zones, or pesticide flux zones at SAFTMOD upper boundary
nodes. This capability allows the user to translate the nonuniformity in
water and solute flux to the saturated zone represented by SAFTMOD.
SAFTMOD is a two-dimensional model and is capable of being run in an X-Y, X-
Z or axisymmetric configuration. The user must decide which configuration
best meets the modeling goals and concurrently, best describes the real
system. Decision trees for aiding the user in selecting a configuration are
shown in Figures 4.1 and 4.2. Figure 4.1 shows options for simulating flow,
while 4.2 shows options for solute transport configurations. The terms
"radius of influence" (small, medium, or large) and "full or partial
penetration," of course, refer to the interaction of the well with the
aquifer system. In order for the flow and solute transport models to
interact properly, they must both be configured the same way (e.g., both 2-D,
X-Z). The decision trees were designed for use with rather simple systems
and may not be appropriate when idealizing complex groundwater systems.
As an example, in Figure 4.1, suppose that the system is an unconfined
surficial aquifer in which the well has a large radius of influence. This
suggests that the regional flow pattern is dominated by the gradients induced
48
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PHYSICAL SCENARIOS
SURFICIAL
UNCONFINED;
SYSTEM
,LROI
MROI
SPATIAL DIMENSION
2-D (r - z)
2-D (r - z) or 2-D (x - y)
"SROI
2-D (x - y) or 2-D (x - z)
LEAKY
TWO-AQUIFER
SYSTEM
LROI
MROI
2-D (r - z)
2-D (r - z) or 2-D (x - y) layered
2-D (x - y) or 2-D (x - z) layered
SROI - SMALL RADIUS OF INFLUENCE
KROI * MEDIUM RADIUS OF INFLUENCE
LRIO * LARGE RADIUS OF INFLUENCE
Figure 4.1. Decision tree for groundwater flow scenarios.
49
-------
PHYSICAL SCENARIOS
SPATIAL DIMENSION
UNCONFINED
AQUIFER
SYSTEM
2-D (x - y)
2-D (x - z) or 2-D (r - z)
LEAKY
TWO-AQUIFER
SYSTEM
2-D (x - y) layered
2-D (x - z) or 2-D (r - z)
FP = FULLY PENETRATING
PP = PARTIALLY PENETRATING
Figure 4.2. Decision tree for solute transport scenarios.
50
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by well pumping and, therefore, an axisymmetric analysis is appropriate. If,
on the other hand, the well has a small radius of influence, then the
regional gradient is dominant and an X-Y (plan view) simulation is suggested.
Similarly, if the well only partially penetrates the aquifer (inducing
selective withdrawal from the upper part of the aquifer and vertical
velocities) then an R-Z (axisymmetric) simulation could be used for transport
(see Figure 4.2). However, if the R-Z axisymmetric grid is used for
transport, it must also be used for flow.
In addition to decisions involving the configuration of the grid, the user
must decide whether to consider recharge of water to SAFTMOD in the analysis.
Obviously, there are situations in which this may be important to the
analysis and situations in which it will not be. For instance, in a thick
aquifer in which the recharge produces only minor fluctuations in the water
table elevations, the error caused by not considering recharge will be small.
This is especially true where the pumping will fully penetrate the aquifer or
where an X-Y simulation is used (concentrations will be averaged over the
entire aquifer thickness). On the other hand, for shallow aquifers in which
the well is screened in the top of the formation, water table fluctuations
could have a sizeable impact on the resulting concentrations in the pumping
well.
4.1.2 Grid Specification
Once the configuration of the model is decided upon, the next step is to set
up the grid systems for each of the models. In PRZM, the user may select
various sizes of soil layers (think of the midpoints of these layer
thicknesses as nodes) by horizon. If a volatile chemical is being simulated,
it is suggested that a fine nodal spacing be chosen near the soil surface as
the simulation of the pesticide concentration gradient here is very important
(see Volume I, Section 2). In this case, the top 20 cm or so should have
layers (i.e., nodal spacing) from 0.5 to 1 cm in thickness. For nonvolatile
chemicals, this near surface definition is less important. Nodal spacing in
PRZM or in VADOFT can be wider where solute velocities are higher. However,
nodal spacings which are too wide can lead to oscillations in the solution
surface.
In designing a finite element grid for variably saturated flow simulations,
one should select nodal spacings that will yield reasonable approximations to
the expected moisture profiles. Small nodal spacings should be used in the
zones where head gradients or moisture fronts are steep. The nodal spacings
may be gradually increased in the zone where no abrupt changes in hydraulic
conductivities occur and the head gradients are gradually sloping. The
variably saturated flow simulation can be performed using either the Picard
or the Newton-Raphson solution algorithm, or a Newton-Raphson algorithm
modified by Huyakorn (1987, personal commnication). For one-dimensional
cases where convergence difficulties are not expected, the efficiency of
these three algorithms has been found to be similar. For certain steady-
state cases involving higher nonlinear soil moisture characteristics, the use
of either Newton-Raphson algorithm is preferable, particularly when the
Picard algorithm fails to converge within a reasonable number of iterations
(say between 10 and 20). Experience with the VADOFT code indicates that when
51
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running in the linked mode, the modified Newton-Raphson method, (i.e., INEWT
=2) should be used for the iteration procedure.
In designing a finite element grid for transport simulations, one should
select nodal spacings that will yield reasonable approximations to the
expected concentration distributions. The selection of nodal spacing (Az)
and time step value (At) should follow the so-called Peclet number and
Courant number criteria where possible. These two criteria are given as
follows:
Az
— < 4 (Peclet number)
At/Az < 1 (Courant number) (4-2)
where
Vsol
where a^ is the longitudinal dispersivity , V i is the solute velocity, V is
the Darcy velocity, 6 is the water content, and R is the retardation
coefficient.
The VADOFT code also provides the user with the option of using upstream
weighting to curb numerical oscillations that may occur in solving the
advective-dispersive transport equation. The recommended value of w, the
weighting factor, is determined by using the following formulae:
w = 1 - 4aL/,e, 2 > 4aL (4-4)
w = 0 , £ < 4aL (4-5)
where a^ is the longitudinal dispersivity, and 2 is the length of the
element.
Spatial discretization of the solution region is performed automatically in
the SAFTMOD code given user instructions. A rectangular grid (Figure 4.3) is
used to represent the modeled two-dimensional region. In a cross-sectional
or an axisymmetric analysis, the modeled region corresponds to the entire
system, which is considered as one unit where different materials are
accommodated by assigning different sets of material properties for different
elements. In an areal analysis, a distinction is made between aquifer and
aquitard regions. Each aquifer region is discretized using a network of
rectangular elements. The same nodal arrangement and element configurations
are used for different aquifers . Nodes and elements are numbered
52
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sequentially as shown in Figure 4.3a. An irregular boundary is accommodated
by assigning zero material number to elements outside the region of interest
(Figure 4.3b). In the areal simulation of a system comprising two aquifers
separated by an aquitard, numbering of nodes and elements is performed,
aquifer by aquifer, starting from the bottom aquifer. The confining aquitard
region is treated as a series of vertical columns each of which is bounded at
both ends by two opposite aquifer nodes. Each aquitard column is represented
by a one-dimensional linear finite element grid. The one-dimensional grid is
generated automatically using a local dimensionless vertical coordinate,
£,defined as £ = z'/b', where z' is the height measured from the bottom of
the aquitard, and b' is the thickness of the aquitard column. Aquitard nodes
in the column are numbered sequentially from the bottom to top of the column.
One of the requirements of the linkage of either PRZM or VADOFT to SAFTMOD is
the overlap of model spatial domains to provide for continuity and realistic
simulation at model interfaces. This is discussed in some detail in Section
5 of Volume I. When SAFTMOD is used in a 2D X-Y configuration, the PRZM or
VADOFT grid should extend through the saturated zone to the base of the
uppermost aquifer. It is suggested that the nodal spacing be relatively
close in the zone in which the water table is fluctuating. This will allow
for more accurate interpolation of solute flux to the saturated zone. Below
this zone, the user can space the nodes more widely, as the results of the
vertical simulation below the water table in the X-Y SAFTMOD configuration
are meaningless and close spacing in this region will increase execution
time. The user should use nodal spacing, however, sufficiently close to curb
numerical oscillations. In the X-Z or axisymmetric simulation of the
saturated zone, the PRZM or VADOFT grid should also extend to the base of the
uppermost aquifer.
Once the model configuration and grid layout is decided upon, the user can
begin to build parameter files, which is the subject of the next section.
4.2 DESCRIPTION OF INPUT SEQUENCES
In the following sections, the input sequences for the Execution Supervisor,
PRZM, VADOFT, SAFTMOD and MCARLO are discussed. All of the input files to
these modules, except for the meteorological data file, may have embedded
comment lines. A comment line is any line beginning with three adjacent
asterisks ('***'). These comment lines are ignored by the code.
4.2.1 Execution Supervisor
This section describes the development of the input data file of the
execution supervisor. This file is used to define: 1) which options are to
be selected for a simulation, 2) the file names to be used for input and
output, and 3) certain globally-defined data (e.g., the start and end dates
of the simulation). This file, RUSTIC.RUN, is the only input data file which
may not have an optional (user-selected) name or path (i.e., the file must be
located in the default directory). An example execution supervisor input
file is shown in Figure 4.4. Its components are discussed below.
54
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PRZM
VADOFT
SAFTMOD
MONTE CARLO
TRANSPORT SIMULATION
ENDRUN
*** INPUT FILES
PATH
METEOROLOGY
PATH
SAFTMOD INPUT
PATH
VADOFT INPUT
PRZM INPUT
MCIN
*** output files
PATH
TIME SERIES
MCOUT
MCOUT2
PRZM OUTPUT
SAFTMOD OUTPUT
VADOFT OUTPUT
*** SCRATCH FILES
VADOFT TAPE10
PRZM RESTART
SAFTMOD TAPES
SAFTMOD TAPE9
SAFTMOD TAPE10
ENDFILES
START DATE
END DATE
TIME STEP
NUMBER OF CHEMICALS
PARENT OF 2
PARENT OF 3
ENDDATA
ECHO
TRACE
ON
OFF
ON
ON
ON
D:\SU\EPAB3\DATA\ONECHM\
MD1.DAT
C:\EPAB3\EXESUP\RUSTIC\DATA\CHKRATES\
SINXZWR.DAT
C:\EPAB3\EXESUP\RUSTIC\DATA\CHKELEV\
VIN.DAT
P2VLNK.DAT
eRANDU.IN
D:\RUSTIC\TEST\
KFLTS.PRN
MCOUT.PRN
MCOUT2.PRN
POUT.PRN
SOUT.PRN
VOUT.PRN
DUMT10
RESTART.DUM
TAPES
TAPE 9
TAPE10
020177
020178
15
1
1
2
ON
OFF
Figure 4.4. Example Execution Supervisor Input File (RUSTIC.RUN)
55
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4.2.1.1 Option Records--
All of the potential options have default values and, if an option is not
specifically set, the default value will be assumed. These defaults are as
follows: PRZM=ON, VADOFT=OFF, SAFTMOD=OFF, MONTE CARLO=OFF, and TRNSIM
(simulate solute transport)=OFF. The options section must end with
ANAME="ENDRUN" (see Table 4-1) even if all of the default values are to be
used.
4.2.1.2 File Records--
The file records (EXESUP2) of the execution supervisor input are used to
assign the user supplied names and locations of the files required for a
simulation to the appropriate RUSTIC file. If files are defined within these
records which are not necessary because of the options selected in the
options records (EXESUP1), the file name is ignored.
4.2.1.3 Global Parameters Records--
The global parameters records (EXESUP3) are used to define certain
environmental and simulation control parameters which need to be defined for
all phases of the simulation. These parameters include the start and end
dates, the number of days per SAFTMOD timestep (NLDLT), the number of
chemical species being simulated (NCHEM), and the index number of the parent
species for each chemical species being simulated (IPARNT(ICHEM)).
NLDLT is required only if SAFTMOD is on. If NLDLT is set larger than the
total number of days of the simulation, the code issues a warning and resets
NLDLT to the total number of days.
The number of chemicals being simulated (NCHEM) must be defined if TRNSIM is
ON. Values of 1, 2, or 3 are valid. If NCHEM is greater than 1, the parent
species index number must be provided. Chemical 1 can have no parent; the
code assumes this and the user does not have to specify it. Chemical 2 can
have no parent (IPARNT[2]=0) or can have chemical 1 as a parent
(IPARNT[2]=1). Chemical 3 can have no parent or can have chemical 1 or 2 as
the parent species.
4.2.1.4 Trace Level Record--
The trace level record (EXESUP4) is, in general, only necessary for debugging
problems with a simulation. If the trace level is set to a value greater
than one, the subroutine that RUSTIC is currently executing during a
simulation, as well as the path of subroutine calls to access that
subroutine, are displayed on the standard output device. A trace can be
useful for debugging a simulation but, when used, can increase execution time
considerably. If the trace level record is not included in the EXESUP data
set, a default value of 3 is assumed. A value of 3 indicates that subroutine
calls three levels deep will be displayed. For example, if RUSTIC calls
subroutine "A" which calls subroutine "B" which calls subroutine "C" which
calls subroutine "D," the string "RUSTIC>A>B>C" will be displayed while "C"
is being executed and while "D" is being executed since the call to "D" is
greater than 3 levels deep.
56
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Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
Variable Description Units
OPTION RECORDS EXESUP1 -- ANAME, STATS
FORMAT(A24.A56)
ANAME Name of simulation option which can
be one of the following list:
Value Module or variable
'PRZM' Pesticide/Root Zone
'VADOFT' Vadose Zone
'SAFTMOD' Saturated Zone
'TRNSIM' Solute Transport
'MONTE CARLO' Monte Carlo module
'ENDRUN' End of option's
selection
STATS Either "ON" or "OFF" for the
modules. The following are the
default values which are assumed
if the module status is not
explicitly defined.
Module Stats
PRZM ON
VADOFT OFF
SAFTMOD OFF
TRNSIM OFF
MONTE CARLO OFF
FILE RECORDS EXESUP2 -- ANAME, FNAME
FORMAT(A24.A56)
ANAME Label defining which input or
output file is being defined.
The following values of ANAME
can be used and must be present
if they are required.
57
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Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
(continued)
Variable
Description
Units
Value
'PATH'
'METEOROLOGY'
'PRZM INPUT'
'SAFTMOD INPUT'
'VADOFT INPUT'
'MCIN'
'PRZM OUTPUT'
'TIME SERIES'
'SAFTMOD OUTPUT'
'VADOFT OUTPUT'
'MCOUT'
'MCOUT2'
'SAFTMOD TAPES'
When Required
never required but
is available as a
convenience in
defining file
location
always
PRZM is ON
SAFTMOD is ON
VADOFT is ON
MONTE CARLO is ON
PRZM is ON and
MONTE CARLO is OFF
PRZM is ON and
MONTE CARLO is OFF
SAFTMOD is ON
VADOFT is ON
MONTE CARLO is ON
MONTE CARLO is ON
SAFTMOD is ON
File Represented
Path for following files.
Meteorological data (time
series) input file.
PRZM definition input file.
SAFTMOD definition input file.
VADOFT definition input file.
Monte Carlo simulation
definition (input) file.
PRZM hydrology, pesticides, and
concentrations output file.
PRZM time series output
file.
SAFTMOD output file.
VADOFT output file.
Monte Carlo output summary.
Monte Carlo output of
individual simulations.
Unformatted file with the
nodal values of concentra-
tions completed at final
time step.
58
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Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
(continued)
Variable
Description
Units
Value
'SAFTMOD TAPE9'
'SAFTMOD TAPE10'
'VADOFT TAPE10'
'PRZM RESTART'
'ENDFILES'
FNAME
When Required
SAFTMOD is ON
SAFTMOD is ON
VADOFT is ON and
TRNSIM is ON
PRZM is ON
File Represented
Unformatted file with
velocity and saturated
thickness data.
Unformatted file with Darcy
velocities and nodal
fluxes.
Unformatted file with nodal
values of element Darcy
velocity components and
water-phase saturation.
Unformatted file with
intermediate PRZM data
values. Used to
restart PRZM from
previous simulation.
Always - at end of
file definition - to
indicate that all
necessary files have
been defined.
The name of the file
defined by ANAME. If
the path of a previous
PATH record is to be ig-
nored, the first character
of FNAME must be '@'. If
FNAME is a path (i.e.,
ANAME was defined as 'PATH'),
it should end with the
character '\' (for MSDOS
microcomputers).
59
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Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
(continued)
Variable
Description
Units
GLOBAL PARAMETERS RECORDS EXESUP3--ANAME, FNAME
FORMAT (A24.A56)
ANAME
'START DATE'
'END DATE'
Label defining which
global parameter is
being defined. The
following values of
ANAME can be used
and must be present
if they are
required.
When Required
Always
Always
'TIME STEP' If SAFTMOD is on
(NLDLT) will be in the FNAME
'NUMBER OF
CHEMICALS'
'PARENT OF n'
'ENDDATA'
If TRNSIM is on
If TRNSIM is on and
NUMBER OF CHEMICALS
greater than 1
Always - at the end
of global parameters
definition
Comments
Indicates that the date of the
start of simulation will be
present in the FNAME field.
Indicates that the date of the
end of simulation will be
present in the FNAME field.
Indicates that the time step
field.
Indicates that the number of
chemicals will be in the FNAME
field.
Indicates that the index of
the parent species of this
chemical (n should be 1, 2, or
3) will be in the FNAME field.
60
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Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
(continued)
Variable
Description
Units
FNAME
ANAME
'START DATE'
'END DATE'
'TIME STEP'
'NUMBER OF
CHEMICALS'
'PARENT of n'
Integer values with
the following formats
dependent on the
value of ANAME
Variable
ISDAY, ISMON, ISTYR
IEDAY, IEMON, IEYR
NLDLT
NCHEM
IPARNT(n)
Description Format
Starting day, month, year (312)
Ending day, month, year (312)
Number of days per SAFTMOD (BN.I56)
simulation
Number of chemicals to be (BN,I56)
simulated in a transport
simulation
Index of the parent species(BN,156)
chemical n [n is read with
the format (BN.I25)]
ECHO LEVEL RECORD EXESUP4 (optional)--ANAME, STATS
FORMAT(A24.A56)
ANAME
STATS
The label 'ECHO'
Either 'ON' or 'OFF'
or an integer be-
tween 0 and 9. If
an integer, it is
read with FORMAT
(BN.I56)
61
-------
Table 4-1. INPUT FORMATS FOR THE EXECUTION SUPERVISOR MODULE (EXESUP)
(concluded)
Variable Description Units
TRACE LEVEL RECORD EXESUP5 (optional)--ANAME, STATS
FORMAT(A24,A56)
ANAME The label 'TRACE'
STATS Either 'ON' or 'OFF'
or an integer value.
If an integer, it is
read with FORMAT (BN.I56)
4.2.1.5 Echo Level Record--
The echo level record (EXESUP5) allows the user to determine how much data
will be written to the output echo file and to the VADOFT and SAFTMOD output
files. This record is available as a convenience to the user and allows the
user to obtain more information when debugging a simulation. It also allows
the user to speed up a simulation and reduce the size of output files during
a production simulation. If this record is not supplied in the EXESUP data
set, a default value of 5 will be assumed if MONTE CARLO is off (1 if MONTE
CARLO is on).
Table 4-2 demonstrates the effect of the echo level on the output of the
execution supervisor. An 'x' in the table signifies that the output feature
is enabled with the listed value of echo level. As an example in using this
table, a subroutine trace will be written to the standard output device if an
echo level of 4 or greater is selected (note that if the trace level is set
to 0, the subroutine trace will not appear even if the echo level is greater
than or equal to 4).
An echo level of 8 or 9 is useful during the early stages of defining a
simulation. An echo level of 8 (or 9) will result in a trace of which line
the code is attempting to read (for example, the line "Reading record [la]"
would appear in the output echo file as the code attempts to read SAFTMOD
group 7a). SAFTMOD group 7a is required as a function of options defined
earlier in the SAFTMOD input stream, and it might be possible that the user
thought that the options were set such that group 7a was not required. Thus,
an echo level of 8 or 9 is useful for defining what the code is actually
trying to read versus what the user expects the code to be reading.
An echo level of 9 will result in a trace of each line being read in by the
code to appear in the output echo file before that line is interpreted as
data. This is useful for displaying typographical errors which the user may
have made in developing an input file. For example, if the user had
62
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Table 4-2. THE EFFECT OF THE ECHO LEVEL ON THE OUTPUT OF EXESUP
Echo Level
Output Feature 0 1
Percent complete bar graph* XXXXXXXXX
Simulation status write to screen XXXXXXXX
Simulation status write to output X X X X X X X
echo file
Subroutine trace available X X X X X X
Warnings written to screen X X X X X
and output echo file
Results of linkage routines X X X X
written to output echo file
Additional data on simulated XXX
values of water/solute flux
written to output echo file
Additional data on simulated X X
values of head/concentrations
written to output echo file
Echo of which line is currently X X
being read
Echo of image of the line which X
is currently being read
* Applies to IBM PC compatible computers.
inadvertently inserted a character within a data field which was to be read
as a real number, the echo of the line displaying the faulty data field would
appear before the fatal error message which would result when that field was
being interpreted as a real number.
Lower values of the echo level are useful for speeding up a simulation and
for reducing the amount of output. The amount of output generated by EXESUP,
VADOFT, and SAFTMOD is controlled by the echo level. The amount of PRZM
output is controlled by defining the output frequency of hydrology,
concentration, and pesticide results (see Section 4.2.2).
63
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The effect of the echo level on the data output of EXESUP, VADOFT, and
SAFTMOD is defined in Table 4-3. At a level of 4 or greater, the results of
mass balance calculations become available (global mass balance as calculated
by EXESUP and the internal mass balance calculations of VADOFT and SAFTMOD).
At a level of 5 or greater, the results of the calculations at VADOFT and/or
SAFTMOD observation nodes are printed. Note that observation nodes must be
defined for VADOFT and/or SAFTMOD using the input file options described for
these modules (see Sections 4.2.3 and 4.2.4).
*
Table 4-3. THE EFFECT OF THE ECHO LEVEL ON THE OUTPUT OF EXESUP, VADOFT, AND
SAFTMOD
Echo Level
Data Group
8
Mass balance results
Results at observation nodes
Standard output
X X X X X X
X X X X X
X X X X
Echo levels less than 4 are generally only of use when using the Monte Carlo
simulation option since most of EXESUP, and all of PRZM, VADOFT, and SAFTMOD
output will be suppressed.
Table 4-4 presents the relative time of execution as a function of the echo
and trace levels. These times are relative to the execution time of a 2-
month deterministic run (MONTE CARLO - 'OFF') using the defaults ECHO = ON
and TRACE = OFF.
Table 4-4. RELATIVE EXECUTION TIMES AS A FUNCTION OF ECHO LEVEL AND TRACE
LEVEL (PRZM - ON, VADOFT - ON, SAFTMOD - ON, MONTE CARLO - OFF,
TRNSIM - ON)
Trace Level
0 (OFF)
Echo Level
5 (ON)
0 (OFF) 0.97
3 (ON)
5
1.
1
2
00
.64
.30
1.
1
2
00
.65
.35
1.
1
2
09
.72
.43
1.
1
2
21
.82
.50
Table 4-5 presents the relative file sizes which result from selected values
of the echo level. The file sizes are relative to the file sizes which
resulted from the simulation described above.
64
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Table 4-5. RELATIVE OUTPUT FILE SIZES AS A FUNCTION OF ECHO LEVEL
Echo Level
Output Echo
PRZM Output
VADOFT Output
SAFTMOD Output
0 (OFF)
0.83
0
0
0
4
0.95
1.00
1.00*
0.80
5 (ON)
1.00
1.00
1.00
1.00
6
1.29
1.00
2.25
7.00
9
1.80
1.00
2.25
7.00
* Since no observation nodes were selected for VADOFT output, the VADOFT
output file was the same size with echo levels 4 and 5.
Tables 4-4 and 4-5 are useful guides, but the relative execution times and
file sizes will change as a function of selected options (e.g., Monte Carlo)
and the time period being simulated. As an example, the size of the output
echo file is not much larger with an echo level of 5 than with an echo level
of 0 (see Table 4-5). This results from the input data echoes of the PRZM,
VADOFT, and SAFTMOD modules always appearing in the output echo file. These
input echoes are a relatively large portion of what would appear in the
output echo file from a relatively short simulation period (2 months).
4.2.1.6 Example Input File--
An example input file for the Execution Supervisor is presented in Figure 4-
4. Note that in this example, some of the ANAME fields are indented. It is
possible to put the label required for ANAME anywhere within the first 24
columns. If an MS-DOS-compatible computer is being used, lower-case
characters can be used in defining the label. Embedded blanks in the label
are significant. For example, if the label should be 'ENDRUN', a blank space
between 'END' and 'RUN' would result in the code not recognizing the label.
Note also that in this example VADOFT is OFF, but the input and output files
are still declared in the files section of the input file. These lines will
be ignored by the code. In general, the records within the options records
section, file records section, and global parameter records section can be in
any order. The one exception is that the number of chemicals must be defined
before the parent species of those chemicals.
4.2.2 PRZM Input
This section will describe the development of data input files for PRZM.
Brief descriptions of each parameter are included in the input format
65
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guidance in this section. A more detailed discussion that will aid the user
in assigning values to specific input parameters is found in Section 5.
4.2.2.1 Meteorological File--
Information for one day only is included in each record of the meteorological
file. An example of the read statement and record format appears below:
READ(FLMT,1000,END=999) MM, MD, MY, PRECIP, PEVP, TEMP, WIND, SOLRAD
1000 FORMAT (IX, 312, 5F10.0)
(1234567890123456789012345678901234567890123456789) [COLUMN NUMBER]
010179 1.50 0.340 17.2 fExample Record1
The format identifier, 312, indicates that there are six spaces (columns) for
designating the month (MM), day (MD), and year (MY) of the meteorological
data. The example 010179 indicates month 01, day 01, and year 1979. The
3F10.0 indicates that precipitation, (PRECIP), potentral evaporation, (PEVP),
and temperature (TEMP) are to be found in three separate blocks consisting of
10 columns each. PEVP and TEMP are not always required together; various
combinations are possible depending on the observed data or climate (i.e.,
geographical areas in which snow accumulation and melting should be simulated
will require temperature data). Wind speed and solar radiation data are
required when the options to simulate volatilization and soil temperature are
selected.
4.2.2.2 PRZM Parameter File--
Each line (representing a record) in the parameter file has a specified
number of parameters in it. Each line has a formatted designation and is
right justified. The user should make sure that the parameters for each line
(record) required for a specific run has a value specified so that the READ
statement will not go to the next line searching for a parameter file value
(this will probably initiate an error message). Each line and the input data
parameters for each line are discussed below (in the order required by the
model). Sample PRZM input files are shown in Figures 6.4 and 6.7.
RECORD 1. TITLE
FORMAT (20A4)
TITLE(IO): A specific title is developed for the simulation which appears
in output files, e.g., Calibration Run Albany, Georgia. A
total of 80 characters can be input to the title record.
RECORD 2. HTITLE
FORMAT (20A4)
HTITLE(10): This record provides a comment line of 80 characters for the
user to input information regarding hydrology parameters.
66
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RECORD 3. PFAC, SFAC, IPEIND, ANETD, INICRP, ISCOND
FORMAT (2F8.0, 18, F8.0, 218)
PFAC: Pan factor, dimensionless. This factor is multiplied by daily
pan evaporation to estimate daily evapotranspiration (ET). If
daily air temperatures are used for ET, any number can be input
for PFAC (e.g., 0.75).
SFAC: Snow factor, cm-snowmelt/°C above freezing. Values of snow
factor are in the order of 0.45. If snowmelt is not to be
calculated, enter 0.00 for SFAC.
IPEIND: Pan evaporation flag. If IPEIND = 0, pan evaporation data are
read. If IPEIND = 1, temperature data are read and used to
calculate potential ET. If IPEIND = 2, then pan evaporation, if
available, is used in the meteorologic file; if not, temperature
is used to compute potential ET.
ANETD: Minimum depth, cm, to which evaporation is extracted year round
(e.g., 20.0).
INICRP: User specified initial crop number if simulation starting date
is before first crop emergence date (see record 8).
ISCOND: User specified surface condition after harvest corresponding to
INICRP (either fallow, cropping, or residue, corresponding to
dimensionless integer of 1, 2 or 3).
RECORD 3A. DT (Only if IPEIND = 1 or 2; DO NOT include this record if
IPEIND = 0)
FORMAT (6F8.0)
DT(12): Average daily hours of daylight for each month. A total of 12
values (one for each month) are input using two lines in the
parameter file.
RECORD 4. ERFLAG
FORMAT (18)
ERFLAG: Erosion flag. If erosion losses are not to be calculated,
ERFLAG - 0, otherwise ERFLAG = 1.
RECORD 4A. USLEK. USLELS, USLEP, AFIELD, TR (Only if ERFLAG = 1; DO NOT
include this record if ERFLAG = 0).
FORMAT (5F8.0)
USLEK: Universal soil loss equation (K) soil erodibility parameter
(e.g., 0.15).
67
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USLELS: Universal soil loss equation (LS) topographic factor (e.g.,
0.14).
USLEP: Universal soil loss equation (P) supporting practice factor
(e.g., 1.0).
AFIELD: Area of field or plot (ha).
TR: Average duration of rainfall from runoff producing storms (hrs)
RECORD 5. NDC
FORMAT (18)
NDC: Number of different crops used in the simulation (minimum of 1)
RECORD 6. ICNCN, CINTCP, AMXDR, COVMAX, ICNAH, CN, USLEC, WFMAX, HTMAX
FORMAT (18, 3F8.0, 18, 3(1X, 13), 3(1X, F3.0), 2F8.0)
NOTE: This record must be repeated for each of the crops (NDC).
ICNCN: Crop number.
Maximum interception storage of the crop (cm).
Maximum active root depth of the crop (cm).
Maximum areal coverage of the crop at full canopy (percent).
CINTCP:
AMXDR:
COVMAX:
ICNAH:
Soil surface condition after crop harvest (1 = fallow, 2 =
cropping, 3 = residue).
CN: Runoff curve number for antecedent soil water condition II, for
fallow, crop, and residue fractions of the growing season (e.g.
86, 78, 82).
USLEC: Universal soil loss equation cover management factor. Three
values must be entered in the same order as (CN), fallow, crop,
and residue. Values only are required if ERFLAG = 1. Leaving
them in the input file will have no effect if ERFLAG = 0 (e.g.,
0.20).
o
WFMAX: Maximum dry foliage weight of the crop at full canopy kg m .
Only required if the exponential filtration model is used for
pesticide application (values of WFMAX will not affect the
simulation if FAM = 1 or 2, see record 12).
HTMAX: Maximum height of crop canopy at maturity (cm). Only used when
simulating volatilization. Otherwise, it should be set to zero.
68
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RECORD 7. NCPDS
FORMAT (18)
NCPDS: Number of cropping periods in the simulation (minimum of 1). If
three cropping years of continuous corn are simulated, NCPDS =
3. If two winter cover crops are in the middle of the three
years of corn, NCPDS = 5.
RECORD 8. EMD, EMM, IYREM, MAD, MAM, IYRMAT, HAD, HAM, IYRHAR, INCROP
FORMAT (2X, 312, 2X, 312, 2X, 312, 18)
NOTE: This record must be repeated for each cropping period
(NCPDS).
EMD: Day of month of crop emergence (e.g., 20).
EMM: Month of crop emergence (e.g., 4).
IYREM: Year of crop emergence (e.g., 82).
MAD: Day of month of crop maturation (e.g., 15).
MAM: Month of crop maturation (e.g., 10).
IYRMAT: Year of crop maturation (e.g., 82).
HAD: Day of month of crop harvest (e.g., 20).
HAM: Month of crop harvest (e.g., 10).
IYRHAR: Year of crop harvest (e.g., 82).
INCROP: Crop number of crop growing in current period (e.g., 1).
RECORD 9. PTITLE
FORMAT (20A4)
PTITLE(IO): This record provides a comment line of 80 characters for the
user to input information regarding pesticide parameters.
RECORD 10. NAPS, NCHEM
FORMAT (1018)
NAPS: Number of pesticide applications (minimum of 1).
NCHEM: Number of different chemicals being simulated (minimum of 1).
69
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RECORD 10A. PSTNAM
FORMAT (3A20)
PSTNAM: Name of pesticide. Up to three can be entered in the sequence
in which they are to be simulated.
RECORD 11. APD, APM, IAPYR, DEPI, TAPP
FORMAT (2X, 312, 2F8.0)
NOTE: This record should be repeated for each application up to the
total number of applications (NAPS).
APD: Day of the month of pesticide application (e.g., 10).
APM: Month of pesticide application (e.g., 5).
IAPYR: Year of pesticide application (e.g., 82).
DEPI: Depth of pesticide incorporation (cm).
TAPP: Total pesticide application (kg ha'^). An application rate is
required for each chemical in the sequence in which they are to
be simulated.
RECORD 12. FAM, IPSCND, FILTRA
FORMAT (218, F8.0)
FAM: Pesticide application model. There are three options: FAM = 1
indicates application to soil only, FAM = 2 indicates a foliar
application using a linear model (based on the percent of areal
coverage by crop) and FAM - 3 indicates a foliar application
using the exponential filtration model.
User-specified condition for disposition of pesticide remaining
on foliage after harvest (either surface-applied, complete
removal, or pesticide remains in plant compartment, conditions
corresponding to integers 1, 2, or 3).
Filtration parameter for exponential model (only required if FAM
=- 3).
PLVKRT, PLDKRT, FEXTRC (only if FAM - 2 or 3; DO NOT include
this record if FAM = 1). This record must be repeated NCHEM
times.
IPSCND:
FILTRA:
RECORD 12A.
FORMAT (3F8.0)
PLVKRT: Pesticide volatilization rate from plant foliage (day"1).
70
-------
PLDKRT: Pesticide decay rate on plant foliage (day ).
FEXTRC: Foliar extraction coefficient for pesticide washoff per
centimeter of precipitation (e.g., 0.10).
RECORD 13. STITLE
FORMAT (20A4)
STITLE(IO): This record provides a comment line of 80 characters for the
user to input information regarding soils properties.
RECORD 14. CORED, UPTKF, BDFLAG, THFIAG, KDFLAG, HSWZT, MCFIAG, IRFLAG,
ITFLAG, IDFLAG
FORMAT (2F8.0, 814)
CORED: Total depth of soil core (cm).
UPTKF: Plant uptake efficiency factor; UPTKF - 0 indicates no plant
uptake simulated, UPTKF - 1 indicates uptake is simulated and is
equal to the normalized crop transpiration rate times the
dissolved phase concentration, 0
-------
ITFLAG: Soil temperature simulation; ITFLAG = 0 for no temperature
simulation; ITFLAG = 1 if soil temperature is simulated. The
only reason to simulate soil temperature is to correct Henry's
constant for temperature effects. Therefore, unless chemical
volatilization is being simulated, soil temperature simulation
should be suppressed.
IDFLAG: Thermal conductivity and heat capacity flag; IDFLAG = 0 if
values are to be entered, IDGLAG = 1 if values are to be
calculated by the model. These calculations are only required
to support soil temperature calculations.
RECORD 15. DAIR, HENRYK, ENPY
FORMAT (F8.0, 3(2F8.0))
DAIR:
HENRYK:
ENPY:
o -I
Diffusion coefficient (cm day ) for the pesticide in air (one
value). Only required if HENRYK > 0.
Henry's Law constant for each of the NCHEM pesticides.
Enthalpy of vaporization (kcal mole ) for each of the NCHEM
pesticides. Only required if ITFLAG = 1 and HENRYK > 0. ENPY =
0 indicates no temperature correction for Henry's Constant.
RECORD 15.1 IRTYPE, FLEACH, PCDEPL, RATEAP (Only if IRFLAG = 1)
FORMAT (15, 3F8.0)
IRTYPE: Irrigation flag which determines the type of irrigation modeled
by PRZM.
IF IRTYPE - 0, no irrigation water is applied
= 1, flood irrigation
- 2, furrow irrigation
= 3, over-canopy sprinkler irrigation
- 4, under-canopy sprinkler irrigation.
FLEACH: Leaching factor, as fraction of irrigation water depth. FLEACH
allows extra water to be added for leaching of salts from saline
soils.
PCDEPL: Fraction of the available water capacity at which irrigation is
triggered. If the average root zone soil moisture level falls
below PCDEPL, irrigation water is applied.
RATEAP: Maximum rate of water sprinklers can deliver, cm hr. This
variable is required only if sprinkler irrigation is used
(IRTYPE = 3,4).
72
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RECORD 15.1A QO, BT, Z, SF, EN, XL, XFRAC (Included only for furrow
Irrigation, IRTYPE = 2 and IRFLAG = 1)
FORMAT (7F8.0)
QO: Flow rate of water entering the heads of individual furrow
(mV1).
BT: Bottom width of furrows (m).
Z: Slope of furrow channel side walls (horizontal/vertical).
SF: Slope of furrow channel bottom (vertical/horizontal).
EN: Manning's roughness coefficient for the furrow.
XL: Length of the furrow (m).
XFRAC: Location in furrow where PRZM infiltration calculations are
performed, as fraction of the furrow length XL. IF XFRAC = -1,
average furrow infiltration depths are used in PRZM.
RECORD 15.IB KS, HF (included only for furrow irrigation, IRTYPE - 2 and
IRFLAG = 1)
FORMAT (2F8.0)
KS: Saturated hydraulic conductivity of soil in which furrows are dug
(m/s).
HF: Green-Ampt infiltration suction parameter (m).
RECORD 16A. PCMC, SOL (Only if KDFLAG = 1, DO NOT include if KDFLAG - 0)
FORMAT (18, F8.0)
PCMC: Calculation flag for model to estimate pesticide soil partition
coefficients. There are three options: PCMC = 1, PCMC = 2, and
PCMC = 3.
SOL: Pesticide solubility. The units vary according to the model
(PCMC) selected; PCMC = 1, mole fraction; PCMC = 2, mg liter"1;
PCMC = 3, micromoles liter"1.
RECORD 16B. ALBEDO, EMISS, ZWIND (Only if ITFLAG = 1, do not include if
ITFLAG - 0)
FORMAT (14F5.0)
73
-------
ALBEDO(12): Monthly values of soil surface albedo (fraction).
EMISS: Reflectivity of the soil surface to longwave radiation
(fraction).
ZWIND: Height of wind speed measurement (m) above soil surface.
RECORD 16C. BBT (Only if ITFLAG = 1, do not include if ITFLAG - 0)
FORMAT (12F5.0)
BBT(12): Average monthly values of bottom boundary soil temperatures
(°C).
RECORD 17. NHORIZ
FORMAT (18)
NHORIZ: Total number of soil horizons (minimum of 1).
NOTE: Records ISA through 22 are read in (as required) for each
soil horizon up to number of horizons (NHORIZ) input. The
specific records read depend on the values of flags set on
record 14.
RECORD ISA. HORIZN, THKNS, BD, THETO, DISP, DWRATE, DSRATE, DGRATE (Include
only if HSWZT = 0)
FORMAT (IS, 3F8.0, 16X, 4F8.0)
HORIZN: Soil horizon number.
THKNS: Soil horizon thickness (cm).
BD:
THETO :
Soil bulk density (if BDFLAG - 0) and/or mineral bulk density
(if BDFLAG - 1) .
Initial soil water content in the horizon (cm cm ) .
f\ -I
DISP: Hydrodynamic solute dispersion coefficient (cm day ) (only
required here if NCHEM = 1).
DWRATE:
Dissolved phase pesticide decay rate in the horizon (day )
(only required here if NCHEM - 1) .
DSRATE: Adsorbed phase pesticide decay rate in the soil horizon (day )
(only required here if NCHEM - 1).
DGRATE: Vapor phase pesticide decay rate in the soil horizon (day )
(only required here if NCHEM - 1).
74
-------
RECORD 18B. HORIZN, THKNS, BD, THETO, AD, DISP, DWRATE, DSRATE, DGRATE
(Include only if HSWZT - 1)
FORMAT (18, 4F8.0, 8X, 4F8.0))
HORIZN:
THKNS:
BD:
THETO:
AD:
DISP:
DWRATE:
DSRATE:
DGRATE:
RECORD 19A.
FORMAT
DPN:
THEFC:
THEWP:
KD:
RECORD 19B.
See record ISA.
See record 18A.
See record ISA.
See record 18A.
Soil horizon drainage parameter (day ). Used only if HSWZT =
1, otherwise the value is ignored.
See record 18A.
See record 18A.
See record 18A.
See record 18A.
DPN, THEFC, THEWP, KD (Only if THFLAG - 0, BDFLAG - 0, and
KDFLAG - 0)
(8X, 3F8.0, 8X, F8.0)
Soil layer depth in soil horizon.
o o
Field capacity soil water content in horizon (cnr cm ) .
1 o
Wilting point soil water content in horizon (cm cm ).
Sorption partition coefficient for soil horizon/pesticide
combination (cnr* g" ) (only required here if NCHEM - 1).
DPN, THEFC, THEWP, OC, KD (Only if KDFLAG - 0, THFLAG - 0 and
BDFLAG - 1)
FORMAT (8X, 5F8.0)
DPN: See record 19A.
THEFC: See record 19A.
THEWP: See record 19A.
OC: Organic carbon content of the soil horizon (percent).
KD: See record 19A.
RECORD 19C. DPN, THEFC, THEWP, OC (Only if THFLAG = 0 and KDFLAG = 1)
FORMAT (8X, 4F8.0)
DPN: See record 19A.
THEFC: See record 19A.
THEWP: See record 19A.
OC: Organic carbon content of soil horizon (percent). This value is
also required if BDFLAG - 1.
75
-------
RECORD 19D. DPN, SAND, CLAY, OC, KD (Only if THFLAG = 1 and KDFLAG = 0)
FORMAT (8X, 5F8.0)
DPN: See record 19A.
SAND: Percent sand In soil horizon.
CLAY: Percent clay in soil horizon.
OC: Organic carbon content of soil horizon (percent). This value is
also required if BDFLAG =< 1.
KD: Sorption partition coefficient for soil horizon/pesticide
combination (cm3 g"1) (only required here if NCHEM = 1).
RECORD 19E. DPN, SAND, CLAY, OC (Only if THFLAG = 1 and KDFLAG = 1)
FORMAT (8X, 4F8.0)
DPN: See record 19A.
SAND: Percent sand in soil horizon.
CLAY: Percent clay in soil horizon.
OC: Organic carbon content of soil horizon (percent). This value is
also required if BDFLAG - 1.
RECORD 20A. SPT, SAND, CLAY, OC (Only if ITFLAG = 1, IDFLAG = 1, and
THFLAG - 0)
FORMAT (8X, 4F8.0)
SPT: Initial temperature of soil horizon (°C).
SAND: See record 19D.
CLAY: See record 19D.
OC: See record 19C.
RECORD 2OB. SPT (Only if ITFLAG = 1, IDFLAG = 1 and THFLAG = 1)
FORMAT (8x, F8.0)
SPT: See record 20A.
RECORD 20C. SPT, THCOND, VHTCAP (Only if ITFLAG = 1 and IDFLAG = 0)
FORMAT (8X, 3F8.0)
SPT: See record 20A.
THCOND: Thermal conductivity of soil horizon (cal cm"1 day"1 °C"1).
76
-------
o
VHTCAP: Heat capacity per unit volume of soil horizon (cal cm
°c-1).
RECORD 21A. DWRATE, DSRATE, DGRATE, KD, DISP (Record 21A is repeated for
each chemical for the horizon, only required if NCHEM > 1).
FORMAT (8X, 5F8.0)
DWRATE: See record ISA.
DSRATE: See record 18A.
DGRATE: See record ISA.
KD: See record ISA.
DISP: See record ISA.
RECORD 22. DKRT12. DKRT13, DKRT23 (Record 22 is only required if NCHEM > 1)
FORMAT (8X, 3F8.0)
DKRT12: Transformation rate from pesticide 1 to 2 (day"1).
DKRT13: Transformation rate from pesticide 1 to 3 (day"1).
DKRT23: Transformation rate from pesticide 2 to 3 (day"1).
RECORD 23. ILP, CFLAG
FORMAT (218)
ILP: Initial level of pesticide indicator. Signals user to input an
initial pesticide storage. ILP = 0, indicates no initial levels
input; ILP = 1, indicates initial levels are being input.
CFLAG: Conversion flag for initial pesticide level input. CFLAG=0,
indicates input in mg kg" ; CFLAG = 1, indicates input in kg
ha" . This flag need not be assigned if ILP = 0.
RECORD 23A. PESTR (Only if ILP = 1)
FORMAT (8F8.0)
PESTR: Initial pesticide level in each compartment (up to NCOM2) for
each chemical. Note that the user must calculate the total
number of compartments by using the thickness of each horizon
and its associated compartment thickness. Enter NCOM2/8 lines
of data for each chemical. Repeat for each chemical. Input
must be either in mg kg or kg ha" .
77
-------
RECORD 24. ITEM1, STEP1, LFREQ1, ITEM2, STEP2, LFREQ2, ITEM3, STEP3, LFREQ3
FORMAT (3(4X, A4, 4X, A4, 18))
NOTE: For hard copy output.
ITEM1: Hydrologic output summary indicator. WATR is inserted to call
hydrologic summaries. A blank is left for ITEM1 if hydrologic
summaries are not desired.
STEP1: Time step of output. Three options are available: DAY for
daily, MNTH for monthly, or YEAR for annual output.
LFREQl: Frequency of soil compartment reporting. Example: LFREQ1 — 1,
every compartment is output; LFREQ = 5, every fifth compartment
is output.
ITEM2: Pesticide output summary indicator. PEST is inserted to call
pesticide mass summaries. A blank is inserted for ITEM2 if
pesticide summaries are not desired.
STEP2: Same as STEPl.
LFREQ2: Same as LFREQl.
ITEM3: Pesticide concentration profile indicator. CONG is inserted to
call pesticide concentration profile summaries. A blank is
inserted if concentration profiles are not desired.
STEP3: Same as STEPl.
LFREQ3: Same as LFREQl.
RECORD 25. NPLOTS, STEP4
FORMAT (18, 4X, A4)
NPLOTS: Number of time series plots to be output (maximum of 7)
STEP4: Time step of output. This option outputs pesticide runoff flux,
pesticide erosion flux and pesticide leaching below core depth
to a single output data file for later use. Three options are
available: DAY for daily, MNTH for monthly, or YEAR for annual
output.
RECORD 25A. PLNAME, INDX, MODE, IARG, CONST, (Only if NPLOTS > 0) (Repeat
record for each time series output)
FORMAT (4X, A4, Al, 3X, A4, 18, F8.0, 7X, Al, 18)
PLNAME: Name of time series. Possible options are listed in Table 4-6.
78
-------
INDX:
MODE:
IARG:
CONST:
RECORD 26.
FORMAT
ATITLE:
RECORD 27.
FORMAT
SADAY:
SAMON:
SAYR:
SPACT:
NACTS:
SPACTS:
Index to identify pesticide; INDX values of 1, 2, 3 indicate
pesticide 1, 2, or 3. Leave blank if other timeseries are
plotted.
Plotting mode. Two options are available: TSER provides the
time series as output, TCUM provides the cumulative time series.
Argument of variable identified in PLNAME. Example: INFL is
specified which corresponds to AINF within the FORTRAN program.
AINF is dimensioned from 1 to NCOM2. IARG must be specified to
identify the soil compartment (1 to NCOM2) reporting for AINF
(IARG is left blank for sealers).
Specifies a constant with which the user can multiply the times
series for unit conversion, etc. If left blank a default of 1.0
is used.
ATITLE
(20A4)
Title for special action.
SADAY, SAMON, SAYR, SPACT, NACTS, SPACTS
(2X, 312, IX, 2A4, IX, 13, 3F8.0)
Day of month of special action.
Month of special action.
Year of special action.
Variable affected by special action.
Horizon or crop numbers affected by special action.
New value(s) for the special action. Possible values for NACTS
and SPACTS are listed below:
SPACT
(2A4)
BD
CN
DSRATE
DWRATE
KD
SNAPSHOT*
USLEC
NACTS
(13)
Horizon no.
Crop no.
Horizon no.
Horizon no.
Horizon no.
—
Crop no.
SPACTS
(3F8.0)
New value
New values
New values
New values
New values
New values
(3)
(3)
(3)
(3)
(3)
* Display pesticide concentration profile
79
-------
Table 4-6. VARIABLE DESIGNATIONS FOR PLOTTING FILES
Variable
Designation
(PLNAME)
Water Storage
INTS
SWTR
SNOP
THET
Water Fluxes
PRCP
SNOF
THRF
INFL
FORTRAN
Variable
CINT
SW
SNOW
THETN
PRECIP
SNOWFL
THRUFL
AINF
Description
Interception storage
on canopy
Soil water storage
Snow pack storage
Soil water content
Precipitation
Snowfall
Canopy throughfall
Percolation into each
Arguments
Required
Units (IARG)
cm None
cm 1-NCOM2
cm None
cm cm"1 1-NCOM2
cm day None
cm day None
cm day None
cm day"1 1-NCOM2
RUNF RUNOF
CEVP CEVAP
SLET ET
TETD TDET
Sediment Flux
ESLS SEDL
Pesticide Storages
FPST FOLPST
soil compartment
Runoff depth
Canopy evaporation
Actual evapotrans-
piration from each
compartment
Total daily actual
evapotranspiration
Event soil loss
Foliar pesticide
storage
cm day
-1
-1
cm day
cm day
-1
Tonnes-
day
g cm
-2
None
cm day None
-1
1-NCOM2
None
None
None
80
-------
Table 4-6. VARIABLE DESIGNATIONS FOR PLOTTING FILES (continued)
Variable
Designation
(PLNAME)
Pesticides Storages
TPST
SPST
Pesticide Fluxes
TPAP
FPDL
WFLX
DFLX
AFLX
DKFX
UFLX
RFLX
FORTRAN
Variable
PESTR
SPESTR
TAPP
FPDLOS
WOFLUX
DFFLUX
ADFLUX
DKFLUX
UPFLUX
ROFLUX
Description
Total soil pesticide
storage in each soil
compartment
Dissolved pesticide
storage in each soil
compartment
Total pesticide
application
Foliar pesticide
decay loss
Foliar pesticide
washoff flux
Individual soil
compartment pesticide
net diffusive flux
Pesticide advective
flux from each soil
compartment
Pesticide decay flux
in each soil compart-
ment
Pesticide uptake flux
from each soil compart-
ment
Pesticide runoff flux
Arguments
Required
Units (IARG)
-3
g cm
-3
g cm
-2
gem i
" \
day
-2
g cm^
day
-2
g cm^
day
-2
g cm ,
day
-2
g cm^
day
-2
Sent i
— I
day
-2
g cm i
day
-2
gcni-,
- 1
day
1-NCOM2
1-NCOM2
None
None
None
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
None
EFLX
ERFLUX
Pesticide erosion flux g
-2
None
day
81
-------
Table 4-6. VARIABLE DESIGNATIONS FOR PLOTTING FILES (concluded)
Variable
Designation
(PLNAME)
FORTRAN
Variable
Description
Arguments
Required
Units (IARG)
Pesticide Fluxes
RZFX RZFLUX
TUPX SUPFLX
TDKF SDKFLX
PCNC TCNC
VFLX PVFLUX
FPVL FPVLOS
Soil Temperature
STMP SPT
Canopy Height
CHGT
Net pesticide flux g
past the maximum root day
depth
Total pesticide uptake g
flux from entire soil day
profile
-2
-2
Total pesticide decay g
flux from entire profile day
Pesticide concentration g cm
in canopy
-2
Soil pesticide
volatilization flux
Foliar pesticide
volatilization flux
Soil temperature in
each soil compartment
-3
g en
day
g en
day
-2
-2
None
None
None
None
None
None
1-NCOM2
HEIGHT Canopy height
cm
None
82
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4.2.3 VADOFT Input
VADOFT is designed, to run one flow and/or a flow and associated transport
problem when run in the linked mode. When running both flow and transport,
the flow and transport input data are stacked in the same input file. An
example input file is shown in Section 6. Figure 6.8 shows the flow portion
of the file and Figure 6.9 shows the transport portion.
An option is provided in VADOFT for automatic generation of Default initial
values. This option is invoked by setting the input variables NONU and NPIN
equal to zero. When this is the case, all nodal values of the dependent
variable (head or concentration) will be set equal to the default initial
value.
Any set of consistent units for physical parameters may be used for VADOFT
input. However, when run in the linked mode, length units should be cm and
time units in days. To facilitate data entry, the input data is divided into
15 groups, arranged as follows:
1. Title record
2. Control records
3. Input/output control record
4. Time discretization record
5. Spatial discretization data
6. Default initial and boundary condition record
7. Boundary condition record
8. Saturated material property records
9. Soil moisture data records
10. Initial condition records
11. Solute transport parameter records
12. Velocity input file control record
13. Time-dependent boundary condition records for node 1
14. Time-dependent boundary condition records for node NP
15. Observation node records.
This sequence must be strictly followed when entering data into the program.
A description of the input variables and data formats follows.
Group 1. Title record (A80)
One record.
Col. 1-80 TITLE: Title of problem.
Group 2. Control records
(a) Simulation control record (1115)
One record.
Col, 1-5 NP: Total number of nodal points in the selected
grid.
83
-------
6-10
NTS:
11-15 NMAT:
16-20 NONU:
Total number of time steps. For a steady-
state simulation, the code automatically sets
NTS - 1. (Note that steady-state simulation
is not allowed when running in the linked
mode. See the ITRANS parameter, this group.)
When running in the linked mode, NTS is a
dummy input. The value input here is
overridden with a value (NLDLT) from the
execution supervisor.
Number of different porous materials.
Parameter indicating if the initial condition
is nonuniform;
- 0 if no,
» 1 if yes and data is read from the input
file.
21-25 ITRANS: Parameter indicating if the simulation is to
be performed in a transient mode. Currently,
the program will stop if PRZM is being run
with VADOFT and ITRANS is not set to 1 here.
= 1 if yes,
- 0 if no.
26-30 IMODL: Parameter indicating the type of simulation.
Note that when running a coupled flow and
transport simulation, the flow simulation
input must be first in the input file.
- 1 for water flow.
= 0 for solute transport.
31-35 IKALL: Time stepping index;
- 1 for backward difference scheme,
- 0 for central difference scheme.
For a steady-state simulation, the code
automatically uses the backward difference
scheme.
36-40 IMBAL: Parameter indicating if mass balance
computation is required. This must be set to
1 in order for mass balance information to be
output.
= 1 if yes,
- 0 if no.
*** if performing the transport simulation, leave columns 41-45 blank.
41-45 INTSPC: Parameter specifying if the initial condition
for the flow simulation is in terms of
pressure head or hydraulic head;
84
-------
= 1 for hydraulic head specification,
- 0 for pressure head specification.
46-50 IHORIZ: Parameter indicating if the flow direction is
horizontal; this should always be set to 0 for
linked model applications;
=1 if yes,
=0 if no.
51-55 ICHAIN: Parameter indicating if the simulation
involves simulation of daughter products;
=1 if yes,
=0 if no.
In the case of flow simulation, the code
automatically sets ICHAIN to zero.
(b) Iteration control record (315, E10.3)
*** Omit if performing transport simulation.
One record.
Col. 1-5 NITMAX: Maximum number of nonlinear iterations allowed
per time step.
6-10 INEWT: Parameter indicating the type of nonlinear
iterative procedure to be used in solving the
variably saturated flow equation;
= 0 for Picard procedure,
= 1 for standard Newton-Raphson procedure,
= 2 for modified Newton-Raphson procedure.
The use of modified Newton-Raphson procedure
is recommended for cases of highly nonlinear
soil moisture curves and when running VADOFT
in the linked mode with PRZM.
1-15 IRESOL: Maximum number of refinements of time steps
allowed if the solution of the variably
saturated flow equation does not converge.
Suggested value of IRESOL = 1.
16-25 HTOL: Head tolerance to be used in the nonlinear
solution. As a guide, the value of HTOL
should be set to (a/100)*(difference between
expected maximum and minimum head values) for
a% accuracy. The absolute limit of a is 10
for single precision and 10 for double
precision.
85
-------
Group 3. Input/output control record (815)
One record.
Col. 1-5 KPROP: Parameter indicating the type of relationships
of relative permeability versus saturation,
and pressure head versus saturation;
- 1 if functional parameters are to be
supplied,
- 0 if tabulated values are to be supplied.
Set KPROP - 0 for a fully saturated flow or a
transport analysis.
6-10 ITSGN: Parameter indicating if time values are to be
generated;
=1 if yes,
=0 if no.
For linked mode operation, it is most
convenient to set ITSGN to 1.
For a steady-state simulation, the code
automatically sets ITSGN - 1.
11-15 ITMARK: Parameter indicating if marker time values at
which output is to be written on backup files
differ from computational time values. This
parameter should be set to 1 when running in
the linked mode.
=1 if yes,
- 0 if no.
16-20 NSTEP: Parameter controlling printout of computed
nodal values. When NSTEP - n, results are
printed for each nth time step. Note that
when running in linked mode, this is a dummy
input. Printing frequency of flow and
transport output is controlled by the water
and pesticide reporting frequency specified in
PRZM.
21-25 NVPR: Parameter controlling printout of velocities.
Set NVPR = n if nodal velocities are to be
printed for each nth time step. When running
in linked mode, this is a dummy input. Nodal
velocities are written to the file at the end
of each marker time value (i.e., each day).
26-30 IOBSND: Parameter indicating if values at some
specified observation nodes are to be recorded
for all time steps;
=1 if yes,
=0 if no.
86
-------
31-35 NOBSND: Number of observation nodes. Leave blank or
set - 0, if IOBSND = 0.
36-40 IPRCHK: Print check parameter;
=1 if printcheck is required,
=0 if no printcheck is required.
If IPRCHK is set to 1, detailed information
utilized in the flow and transport matrix
assembly routines is printed.
Group 4. Temporal data
*** Omit if performing steady-state simulation (ITRANS-0).
(a) Time parameters (4E10.3)
If ITSGN - 0, leave Cols. 11-40 blank.
One record.
Col. 1-10 TIMA: Initial time value. For running in linked
mode, this should be set to 0.0
11-20 TIN: Initial time step value. For linked mode
operation, should be set to 1.0.
21-30 TFAC: Time-step multiplier. For linked mode
operation, should be set to 1.0.
31-40 TMAX: Maximum value of time step allowed. For
linked mode operation, should be set to 1.0.
(b) Computational time values (8E10.3)
*** Omit if ITSGN = 1.
Number of records - NTS/8 + 0 or 1.
Col. 1-10 TKVEC(I): Time values, where I = 1 NTS.
11-20
etc.
Note: TMVEC(l) must be greater than zero, and TMVEC(l) through
TMVEC(NTS) must be in sequential order of increasing value.
(c) Output marker time data
*** Omit if ITMARK = 0.
First record (2I5,2E10.3)
87
-------
Col. 1-5 NTOMT: Number of backup file output maker time
values. For linked mode operation, this
should be set to the number of days in a
SAFTMOD timestep (i.e., same value as NLDLT in
the Execution Supervisor input).
6-10 ITMGEN: Parameter indicating if marker time values are
to be generated by the code;
=1 if yes,
=0 if no.
For linked mode operation, set ITMGEN to 1.
11-20 STMARK: Starting marker time value. For linked mode
operation set STMARK to -0.01.
21-30 DTMARK: Marker time increment. If ITMGEN = 0, omit
Cols. 11-30. For linked mode operation, set
DTMARK equal to 1.0.
Remaining records (8E10.3)
*** Omit if ITMGEN - 1.
Number of records •= NTOMT/8 + 0 or 1.
Col. 1-10 TMFOMT(I): Output marker time values for backup
files, where I = 1 NTOMT.
11-20
etc.
Group 5. Spatial discretization data
(a) Control parameter (15)
Col. 1-5 NLAYRG: Number of layers (horizons) that need to be
discretized.
(b) Grid layer data (3I5.E10.3)
Number of records - NLAYRG.
Col. 1-5 ILAYR: Layer number.
6-10 NELM(ILAYR): Number of finite elements in layer ILAYR.
11-15 IMATL(ILAYR): Material number of layer ILAYR.
16-26 THL(ILAYR): Thickness of layer ILAYR.
88
-------
Group 6. Initial condition data (5(E10.3,15))
One record.
Col. 1-10 HINV: Default initial value of head (or concentration).
In the water flow simulation, the code allows
HINV to be either the initial pressure head or
the initial hydraulic head.
11-15 NPIN: Number of (nondefault) nodes where initial value
differs from the default value. For multiple
chemicals read in the values of HINV and NPIN
for each chemical sequentially on this line.
Group 7. Boundary condition record (215,2E10.3,215,2E10.3)
One record.
Col. 1-5
6-10
11-20
21-30
IBTND1: Boundary condition type specification for the
first node;
=1 if pressure head (or concentration) is
specified,
=0 if water (or solute) flux is specified.
When run in linked mode (i.e., PRZM is ON)
IBTND1 is automatically set to 0 in the code.
IBTNDN: Boundary condition type specification for the
last node;
=1 if pressure head (or concentration) is
specified,
=0 if water (or solute) flux is specified.
When run in linked mode (i.e., SAFTMOD is ON),
IBTNDN is automatically set to 1.
VALND1: For a flow simulation, VALNDl denotes pressure
head (if IBTND1 - 1) or water flux (if IBTND1
- 0) at node 1. For a transport simulation^
VALNDl denotes concentration at node 1 (if
IBTND1 - 1) or solute mass flux entering node
1 if (IBTNDl = 0). (The sign convention for
the flux is: positive for influx and negative
for efflux). This is a dummy input when
operating in linked mode.
VALNDN: For a flow simulation, VALNDN denotes pressure
head at node N (if IBTNDN - 1) or water flux
at node N (if IBTNDN = 0). For a transport
simulation, VALNDN denotes concentration at
node 1 (if IBTNDN =1). If the fluid is
discharging or exiting at node NP, set VALNDN
= 0 or leave blank. In linked mode, this is a
dummy input; VALNDN is specified by SAFTMOD
89
-------
for flow and set to zero for transport
automatically by the code.
31-35 ITCNDl: Parameter specifying if the boundary condition
at node 1 is transient;
- 1 if yes,
- 0 if no.
When running in linked mode, this is a dummy
input. The code automatically sets ITCNDl to
0.
36-40 ITCNDN: Parameter specifying if the boundary condition
at node NP is transient;
- 1 if yes,
- 0 if no.
When running in linked mode, this is a dummy
input. The code automatically sets ITCNON to
0.
41-50 FLX1: For a flow simulation, set FLX1 - 0. For a
transport simulation, FLX1 is the value of
fluid flux entering node 1. If the fluid is
discharging or exiting at node 1, set FLX1 -
0, or leave blank. Not required for linked
mode simulation (if PRZM is ON).
51-60 FLXN: For a flow simulation, set FLXN - 0. For a
transport simulation, FLXN is the value of
fluid flux entering node NP. If the fluid is
discharging or exiting at node NP, set FLXN -
0 or leave blank. Not required for linked
mode simulation (if SAFTMOD is ON).
Group 8. Saturated material property records (8E10.3)
Number of records — NMAT.
If performing a flow simulation, specify the following information:
Col. 1-10 PROP(I.l): Saturated hydraulic conductivity of
material I.
11-20 PROP(I,2): Effective porosity of material I.
21-30 PROP(I,3): Specific storage of material I.
31-40 PROP(I,4): Air entry pressure head value of material
I.
***
90
-------
*** If performing a transport simulation, specify the following
information. Note that, for multiple chemicals, properties 3 and 4
are repeated for each chemical sequentially on this line (i.e.,
(3,4)i=1, (3,4)i=2, (3,4)i=3):
Col. 1-10 PROP(I,1): Longitudinal dispersivity of material I.
11-20 PROP(I,2): Effective porosity of material I.
21-30 PROP(I,3):
31-40 PROP(I,4)
Retardation coefficient for chemical i in
material I under fully saturated conditions
(Rsat - 1 + Kd,/0).
Molecular diffusion coefficient for
chemical i in material I.
Group 9. Soil moisture data records
(a) Functional coefficient values (5E10.3)
*** Omit if KPROP - 0 or if performing transport simulation (IMODL - 0).
Number of records - NMAT.
Col. 1-10
11-20
FVAL(I,1)
FVAL(I,2)
Residual water phase saturation of (Swr) of
material number IMAT.
Parameter (n) of the relative permeability
versus saturation relationship. If n is
greater than zero , the following
relationship is used:
v = <;n
Krw *e
where Se is the effective saturation
defined as
se - (VS
If n is less than or equal to zero, the
following relationship is used:
21-30 FVAL(I,3):
where 7 corresponds to FVAL(I,5).
Leading coefficient (a) of the saturation
versus capillary head relationship,
Sw * Swr
91
-------
31-40 FVAL(I,4): Power index (ft) of the saturation versus
capillary head relationship.
41-50 FVAL(I,5): Power index (7) of the saturation versus
capillary head relationship.
(b)
Relative permeability versus saturation data
*** Omit if KPROP - 1 or if performing transport simulation (IMODL =0)
Number of record sets - NMAT. Each set contains the following:
Header record (15)
(i)
Col. 1-5
NUMK(I):
(ii) Data records (8E10.3)
Col. 1-10 SMV(I.l)
Number of entry pairs of relative
permeability and saturation values for
material I.
Value of water phase saturation for data
point 1 of material number I.
11-20 PKRW(I.l): Value of relative permeability at data
point 1.
21-30 SMV(I,2): etc.
31-40 PKRW(I,2): etc.
(c) Pressure head versus saturation data
*** Omit if KPROP - 1 or if performing transport simulation (IMODL = 0).
Number of record sets = NMAT. Each set contains the following:
(i) Header record (15)
Col. 1-5 NUMP(K): Number of entry pairs of pressure head versus
saturation values for material I.
(ii) Data records (8E10.3)
Col. 1-10 SSWV(I.l)
Value of water phase saturation for data
point 1 of material number I.
11-20
21-30
31-40
HCAP(I.l): Value of pressure head for data point 1 of
material number I.
SSWV(I,2)
etc.
HCAP(I,2): etc.
92
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Note: Values of water phase saturation must be arranged in increasing
order.
Group 10. Initial condition records (5(15,E10.3))
*** Omit if the initial condition is uniform (NONU - 0).
Number of records - NPIN/5 + 0 or 1. Nondefault initial conditions
for multiple chemicals should be entered sequentially for each
chemical (i.e., all nodes for chemical 1, all nodes for chemical 2,
etc.). Begin reading nodes for each new chemical with a new record.
For instance, if there are 3 nondefault nodes for chemical 1, and 3
for chemical 2, do not append the chemical 2 nondefault nodes to the
end of the chemical 1 record, begin chemical 2 with a new record.
Col. 1-5 N: Node number.
6-15 P1NT(I): Nondefault initial value of head or
etc. concentration, where I = 1 NPIN.
Group 11. Solute transport parameter records (I5.4E10.3)
*** Omit if performing flow simulation (IMODL - 1).
(a) General data
Number of records - NMAT.
Col. 1-5 I: Porous material number.
6-15 VDFI(I): Default value of Darcy velocity for material
I.
16-25 SWDFI(I): Default value of water saturation for
material I.
26-35 DLAMDI(I): Value of decay constant of the solute for
material I.
36-45 UWFI(I): Value of upstream weighting factor for
material I. Set UWF - 0, if no upstream
weighting is required.
(b) Transport parameter records (I5.6E10.3)
Number of records = 1 for each material.
Col. 1-5 I: Porous material number.
6-15 CLAMDI(I,1): Decay coefficient of chemical 1 in
material I.
93
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16-25 CRACMP(I.l):
26-36 CLAMDI(I,2):
37-47 CRACMP(I,2):
48-58 CLAKDI(I,3):
59-69 CRACMP(I,3):
Transformation mass fraction of chemical
1 in material I.
Decay coefficient of chemical 2 in
material I. Omit if NCHEM is less than
2.
Transformation mass fraction of chemical
2 in material I. Omit if NCHEM is less
than 2.
Decay coefficient of chemical 3 in
material I. Omit if NCHEM is less than
3.
Transformation mass fraction of chemical
3 in material I. Omit if NCHEM is less
than 3.
Group 12. Velocity input file control record (215)
*** Omit if performing flow simulation (IMODL - 1).
One record.
Col. 1-5 NVREAD: Parameter indicating if velocities will be
read from a scratch file. In linked mode,
this parameter is reset to 1;
=1 if yes,
- 0 if no.
6-10 IVSTED: Parameter indicating if the velocity field is
at a steady state. If PRZM is ON, this
parameter will be reset to 1; (note: this
implies that the velocity field is at steady-
state within each day, not over the course of
the simulation);
=1 if yes,
=0 if no.
Group 13. Time-dependent boundary condition records for node 1
*** Omit if ITCND1 = 0 or if PRZM is on.
If performing a water flow simulation, supply the following
information:
(a) Control record (15)
One record.
94
-------
Col. 1-5 NTSNDH(l): Number of selected time values on the time
graph of pressure head (or water flux if
IBTND1 - 0) at node 1.
(b) Time value records (8E10.3)
Number of records - NTSNDH(l)/8 + 0 or 1.
Col. 1-10 TMHV(1,J): Time values at the selected
11-20 interpolation points on the time graph
etc. of pressure head (or water flux if IBTND1 =
0) at node I, where J - 1 NTSNDH(l).
(c) Functional value record (8E10.3)
Number of records - NTSNDH(l)/8 + 0 or 1.
Col. 1-10 HVTM(l.J): Value of pressure head (or water flux
11-20 if IBTND1 = 0) corresponding to
etc. TMHV(1,J), where J - 1 NTSNDH(l).
If performing solute transport simulation, supply the following
information:
(a) Control record (15)
One record.
Col. 1-15 NTSNDH(l): Number of selected time values on the time
graph of concentration (or solute flux if
IBTND1 - 0).
(b) Time value records (8E10.3)
Number of records - NTSNDH(l)/8 +0 or 1.
Col. 1-10 TMHV(l.J): Time values at the selected
11-20 interpolation points on the time graph
etc. of concentration (or solute flux if IBTND1
- 0) at node 1, where J - 1, . . . , NTSNDH(l).
(c) Functional value record (8E10.3)
Number of records - NTSNDH(l)/8 = 0 or 1.
Col. 1-10 HVTM(l.J): Value of concentration (or solute flux
11-20 if IBTND1 - 0) corresponding to
etc. TMHV(l.J), where J = 1 NTSNDH(l).
(d) Fluid flux value records (8E10.3)
*** Omit if performing a water flow simulation (IMODL = 1).
95
-------
Number of records = NTSNDF(l)/8 + 0 or 1.
Col. 1-10 QVTM(l.J): Volumetric water flux values
11-20 corresponding to TMHV (1,J), where J -
1 NTSNDH(l).
etc.
Group 14. Time-dependent boundary condition records for node NP
*** Omit if ITCNDN =0, or if SAFTMOD is on.
If performing a water flow simulation, supply the following
information:
(a) Control record (15)
One record.
Col. 1-5 NTSHDH(2): Number of selected time values on the time
graph of pressure head (or water flux if
IBTNDN = 0) at node NP.
(b) Time value records (8E10.3)
Number of cards = NTSNDH(2)/8 -t- 0 or 1.
Col. 1-10 TMHV(2,J): Time values at the selected
11-20 interpolation points on the time graph
etc. of pressure head (or water flux if IBTNDN =
0) at node NP, where J - 1 NTSNDH(2).
(c) Functional value record (8E10.3)
Number of records - NTSNDH(2)/8 + 0 or 1.
Col. 1-10 HVTM(2,J): Value of pressure head (or water flux
11-20 if IBTNDN - 0) corresponding to
etc. TMHV(2,J), where J - 1 NTSNDH(2).
If performing solute transport simulation, supply the following
information:
(a) Control record (15)
One record.
Col. 1-5 NTSNDH(2): Number of selected time values on the time
graph of concentration (or solute flux if
IBTNDN = 0) .
96
-------
(b) Time value records (8E10.3)
Number of records = NTSNDH(2)/8 + 0 or 1.
Col. 1-10 TMVH(2,J): Time values at the selected
11-20 interpolation points on the time graph
etc. of concentration (or solute flux if IBTNDN
- 0) at node NP, where J - 1 NTSNDH(2) .
(c) Functional value record (8E10.3)
Number of records - NTSNDH(2)/8 + 0 or 1.
Col. 1-10 HVTM(2,J): Value of concentration (or water flux
11-20 if IBTNDN = 0) corresponding to
etc. TMHV(2,J), where J - 1 NTSNDH(2).
(d) Fluid flux value records (8E10.2)
*** Omit if performing a water flow simulation (IMODL - 1).
Number of records = NTSNDH(2)/8 + 0 or 1.
Col. 1-10 QVTM(2,J): Volumetric water flux values
11-20 corresponding to TMHV(2,J), where J -
etc. 1 NTSNDH(2).
Group 15. Observation node records (1615)
*** Omit if IOBSND or NOBSND = 0.
Number of records = NOBSND/16 + 0 or 1.
Col. 1-5 NDOBS(I): Increasing sequential numbers of
6-10 observation nodes 1 through NOBSND, where J
etc. - 1 , NOBSND.
4.2.4 SAFTMOD Input
The code is designed to perform the analysis of one flow and/or a flow and
associated transport problem when run in the linked mode. An option is
provided for automatic generation of default initial values. This option is
invoked by setting the input variables NONU and NPIN equal to zero. When
this is the case, all nodal values of the dependent variables will be set
equal to the default initial values. In addition, the code is designed to
allow any set of consistent units to be assigned to the input variables.
When run in linked mode, the unit of length is meters. Note that this is
different from the length unit used in PRZM and VADOFT, which is centimeters.
97
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The input data required for fluid flow and solute transport simulations are
classified into 24 groups given in their sequential order as follows:
1. Problem title
2. Problem control record
3. Simulation control parameter
4. Time stepping and iteration control record
5. Input/output control record
6. Temporal discretization data
7. Mesh generation control data
8. Aquifer identification data
9. Aquifer material property data
10. Aquifer thickness and base elevation data
11. Aquitard identification data
12. Aquitard material property and head data
13. Solute property data
14. Groundwater recharge and pesticide loading data
15. Boundary condition control
16. Steady-state Dirichlet boundary condition data
17. Steady-state flux boundary condition data
18. Transient Dirichlet boundary condition data
19. Transient flux boundary condition data
20. Initial condition data
21. Velocity and saturated thickness data
22. Sequential numbers of nonzero flux nodes
23. Output printout control data
24. Sequential numbers of observation nodes
This sequence must be strictly adhered to when entering the data into the
program. If a transport simulation is to follow a flow simulation, the data
set defining the transport simulation must immediately follow the data set
defining the flow simulation. A sample input file is shown in Figures 6.4
and 6.6. A description of input variables and data formats is presented on
the following pages.
Group 1. Title record (A80)
One record.
Col. 1-80 TITLE: Title of problem.
Group 2. Problem control record (815)
One record.
Col. 1-5 IMODL: Parameter indicating the type of modeling
problem;
= 0 for solute transport modeling,
= 1 for groundwater flow modeling.
98
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6-10 IAREAL: Parameter indicating orientation of the finite
element grid;
= 0 if cross-sectional,
= 1 if areal.
In an cross-sectional analysis, the code
assumes that the y-axis is oriented
vertically.
11-15 IAXSYM: Parameter indicating if the cross-sectional
analysis is to be performed using axisymmetric
coordinates;
- 0 if no,
= 1 if yes.
Note that IAXSYM is automatically set equal to
zero if IAREAL = 1.
16-20 NAQFR: For the case of areal analysis (IAREAL = 1),
NAQFR denotes the number of aquifer units, and
its value may be equal to 1 or 2. For the
case, of cross-sectional or axisymmetric
analysis (IAREAL = 0), set NAQFR = 1. In any
case, NAQFR must be greater than 0 and not
greater than 2.
21-25 NAQTRD: For the case of areal analysis, NAQTRD is a
parameter indicating if there is a confining
semipermeable layer (aquitard) separating two
aquifers;
= 0 if no,
= 1 if yes.
For the case of cross-sectional or
axisymmetric analysis, set NAQTRD = 0.
26-30 IWATP: Parameter indicating the hydraulic boundary
condition of the aquifer system;
= 0 for confined system,
= 1 for unconfined or partially unconfined
system.
31-35 IVRECH: Parameter indicating if precipitation recharge
(or infiltration) and water table conditions
are to be taken into account in solving the
flow or transport problem;
=0 if no,
= 1 if yes.
36-40 NZONRT: Number of zones with time-dependent recharge
rates. Set NZONRT = 0 if there are no such
zones.
99
-------
Group 3. Simulation control parameter (815)
One record.
Col. 1-5 IEXEC: Code execution control parameter; = 0 if
execution is to be stopped after generation
and output of mesh data, = 1 if execution is
to continue until simulation is completed.
6-10 ITRANS: Parameter indicating if the simulation is to
be performed in a transient or steady-state
mode;
= 0 for steady-state simulation,
= 1 for transient simulation.
11-15 NTS: Number of simulation time steps. For a
steady-state simulation, NTS is automatically
set - 1. In linked mode, this value will be
overridden by the value specified in the
execution supervisor file.
16-20 ITSGN: Parameter indicating if simulation time values
are to be generated by the code;
= 0 if no,
= 1 if yes.
For a steady-state simulation, ITSGN is
automatically set - 1.
21-25 NP: Total number of nodal points in the finite
element grid to be used in the analysis.
26-30 NE: Total number of elements in the grid.
31-35 IOUTLT: Parameter indicating if integrated values of
fluid or solute fluxes at certain specified
nodes are to be determined by the code;
=0 if no,
= 1 if yes.
36-40 IMBAL: Parameter indicating if mass balance
computation is required;
= 1 if yes,
=0 if no.
When IMBAL = 1, IOUTLT should also be equal to
1. Otherwise the code will set IMBAL to zero.
Group 4. Time stepping and iteration control record (215, 2E10.3)
One record.
Col. 1-5 IKALL: Parameter indicating the type of time-stepping
scheme required;
100
-------
=* 0 for central difference,
- 1 for backward difference.
For a steady-state analysis, IKALL is
automatically set = 1.
6-10 NITMAX: Maximum number of nonlinear iterative
solutions allowed per time step. In the case
of unconfined or partially unconfined flow,
NITMAX should be greater than 1 (recommended
value is 5 for transient analysis, and 10 for
steady-state analysis). Otherwise, NITMAX
should be set equal to 1.
11-15 HTOL: Iteration tolerance for hydraulic head (or
concentration). The selected value of HTOL
should be less than or equal to 0.01 of the
maximum expected value of head (or
concentration). In any case, HTOL must be
greater than zero.
Group 5. Input/output control record (1115)
One record.
Col. 1-5 IPRD: Parameter indicating printed output
requirements pertaining to the finite element
mesh and initial condition data;
=0 if mesh and initial condition data are
to be printed,
=1 if element identification data printout
is not required,
=2 if the entire mesh data printout is not
required,
- 3 if mesh and initial condition data
printouts and boundary condition print
checks are not required.
6-10 NSTEP: Parameters controlling the interval of
printing of computed values of head (or
concentration) at all nodal points. When
NSTEP - n, these nodal values are printed for
each nth time step. If the nodal printout is
to be suppressed for all time steps, set NSTEP
- 0.
11-15 NVPR: Parameter controlling printout of element
Darcy velocities. When NVPR = n, these
velocities are printed for each nth time step.
If the velocity printout is to be suppressed
for all time steps, set NVPR - 0.
101
-------
16-20 NOWRIT: Parameter indicating if computed values of
head (or concentration) at the final time
level are to be written to a scratch file;
- 0 if no,
- 1 if yes.
Note: The purpose of the NOWRIT - 1 option is
to create head or concentration data that can
be used as the initial condition for a restart
run.
21-25 NVWRIT: Parameter specifying if computed element
velocities and, if needed, nodal values of
saturated thickness are to be written to a
scratch file for use in the associated
transport run;
- 0 if no output is to be written,
- 1 if the output is to be written.
NVWRIT is irrelevant in the transport
simulation and is automatically set — 0.
26-30 NPLOT: Parameter specifying if time and computed
values of head or concentration are to be
written for use in subsequent plotting;
=0 if no output is to be written,
= n if the output is to be written for each
nth time step.
31-35 NVREAD: Parameter specifying if element Darcy velocity
and saturated thickness data are to be input
using a separate scratch data file;
=0 if no,
- 1 if yes.
In the flow simulation (IMODL-1), the code
automatically sets the value of NVREAD equal
to 0 to avoid reading a velocity file.
36-40 IVSTED: Parameter indicating if the velocity field to
be used in the transport simulation is in
steady-state;
=0 if no,
- 1 if yes.
41-45 IOBSND: Parameter indicating if values at some
specified nodes are to be recorded and printed
for all time steps;
- 0 if no,
- 1 if yes.
46-50 IPRCON: Parameter indicating if the output printout
windowing option is to be used;
=0 if no,
= 1 if yes.
102
-------
51-55 IPRCHK: Matrix computation print check parameter;
- 0 if print checks are not needed,
- 1 if print checks are needed.
(This parameter controls the printing of
intermediate matrix calculations. It should
normally be set to zero, but for it to be
effective the echo level defined in the
Execution Supervisor input should be set to 6
or greater.)
Group 6. Temporal discretization data
*** Omit if PRZM is on or if VADOFT is on.
(a) Time step control parameters (4E10.3)
*** Omit if ITRANS (group 3, parameter 2) - 0, or if 1TSGN (group
3, parameter 4) - 0.
One record.
Col. 1-10 TIMA: Starting time value of the simulation.
11-20 TIN: Value of the first time step.
21-30 TFAC: Multiplier used to compute values of
subsequent time steps.
31-40 TMAX: Maximum allowable value of time step generated
by the code.
(b) User-supplied time values (8E10.3)
*** Omit if ITRANS - 0, or if ITSGN - 1.
Number of records - NTS/8 + (0 or 1).
Col. 1-10 TMVEC(I): Time values at the end of time
11-20 steps 1 through NTS, where I = 1,...., NTS.
etc.
Group 7. Mesh generation control data
These data are required by the code to generate a rectangular finite
element mesh. Nodal coordinates are set up along the x and y axes.
In the case of areal simulation, these axes lie in an areal flow
plane. In the case of vertical cross-sectional simulation, x and y
axes are oriented in the horizontal and vertical directions,
respectively. In the case of axisymmetric simulation, the x and y
coordinates correspond to the radial and vertical cylindrical
coordinates, respectively.
103
-------
(a) Control parameter record (515)
One record.
Col. 1-5 NROWS: Number of nodal grid lines parallel to the
direction of the x-axis. NROWS must be
greater than or equal to 2.
6-10 NCOLS: Number of nodal grid lines parallel to the
direction of the y-axis. NCOLS must be
greater than or equal to 2.
11-15 IXYRED: Parameter indicating if coordinates of the
grid lines parallel to x and y axes are to be
input;
=0 if no,
= 1 if yes.
16-20 ISWAP: Parameter indicating the sequential numbering
of the nodes in the rectangular mesh to be
generated;
=0 if the nodes are sequentially numbered
in the y-direction,
=1 if the nodes are sequentially numbered
in the x-direction.
The purpose of ISWAP is to achieve a smaller
matrix bandwidth.
21-25 ICOVR: Parameter indicating if nodal coordinates
generated by the code are to be overridden by
user-supplied values;
= 0 if no,
= 1 if yes.
(b) Mesh spacing control parameters (8E10.3)
*** Omit if IXYRED - 1.
One record.
Col. 1-10 DX: Nodal spacing in the x-direction of the first
grid block.
11-20 DY: Nodal spacing in the y-direction of the first
grid block.
21-30 SCFX: Multiplier used to compute values of remaining
nodal spacings in the x-direction.
31-40 SCFY: Multiplier used to compute values of remaining
nodal spacings in the y-direction.
104
-------
41-50 DXMAX: Maximum allowable value of nodal spacing in
the x-direction.
51-60 DYMAX: Maximum allowable value of nodal spacing in
the y-direction.
61-70 XO: Length along the x-direction of the
discretized rectangular region.
71-80 YO: Length along the y-direction of the
discretized rectangular region.
(c) User-supplied x-coordinates of grid lines (8E10.3)
*** Omit if IXYRED - 0.
Number of records - NCOLS/8 + (0 or 1).
Col. 1-10 XW(J): x-coordinates of grid lines 1 through
11-20 NCOLS, WHERE J - 1, NCOLS.
etc.
(d) User-supplied y-coordinates of grid lines (8E10.3)
*** Omit if IXYRED = 0.
Number of records = NROWS/8 + (0 or 1).
Col. 1-10 YW(J): y-coordinates of grid lines 1 through
11-20 NROWS, where J = 1 NROWS.
etc.
(e) User-supplied nodal coordinates (15, 2E10.3)
*** Omit if ICOVR = 0.
Number of records = NP.
Col. 1-5 N: Node number.
6-15 CORD (N,l): x-coordinate.
16-20 CORD (N,2): y-coordinate.
Group 8. Aquifer identification data
(a) General identification records (315)
Number of records = NAQFR.
Each record contains the following information:
105
-------
Col. 1-5 I: Aquifer unit number.
6-10 NMATAQ(I): Number of different soil materials in
aquifer unit 1. In determining the value
of NMATAQ(I), disregard the zones where the
material number is assigned zero value.
11-15 IAQTYP(1): Type of aquifer unit I;
= 0 for confined aquifer,
= 1 for unconfined or partially unconfined
aquifer.
(b) Aquifer material identification records (615)
Number of records = as many as needed.
Each record contains the following information:
Col. 1-5 IAQFR: Aquifer unit number.
6-10 IZONO: Material number assigned to a particular zone
of the aquifer. Ignore zones that are
impermeable or outside the modeled region.
For those zones the code automatically sets
IZONO = 0.
11-15 IEST: Starting sequential element number in the
specified material zone.
16-20 IEFIN: Ending sequential element number in the
specified material zone.
21-25 IEINCR: Element number increment.
26-30 IPAUSE: Parameter indicating if this record is the
last record in this group;
=0 if no,
- 1 if yes.
Group 9. Aquifer material property data (5E10.3)
There is one set of records per aquifer unit. Record set L contains
properties of soil materials in aquifer unit L.
For IMODL - 1 (flow modeling), each record contains the following
information pertaining to porous material number I in aquifer L:
Number of records in the set = NMATAQ(L).
Col. 1-10 PROP(I,L,1): Hydraulic conductivity component K^ of
material number I in aquifer unit L.
11-20 PROP(I,L,2): Hydraulic conductivity component K^ of
material number I in aquifer unit L.
21-30 PROP(I,L,3): Specific storage of material I in
aquifer unit L. (Enter 0 if flow is
steady-state).
31-40 PROP(I,L,4): Specific yield of material I in aquifer
unit L. (Enter 0 if flow is steady-
state and/or the aquifer is confined).
106
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41-50 PROP(I,L,5): Hydraulic conductivity component KZZ of
material number I in aquifer unit L. In
the areal flow simulation, KZZ
corresponds to the vertical hydraulic
conductivity, and is not allowed to
exceed 0.1*minimum (K^, IC™) . In the
cross-sectional or axisymmetric flow
simulation, the code automatically sets
Kzz=Kyy
For IMODL — 0 (transport modeling), each record or group of records
contains the following information pertaining to porous material
number I in aquifer L:
Number of records - (1 + (NCHEM-1))NMATAQ(L)
(a) Col. 1-10 PROP(I,L,1): Longitudinal dispersivity of material I
in aquifer unit L.
11-20 PROP(I,L,2): Transverse dispersivity of material I in
aquifer unit L.
21-30 PROP(I,L,3): Molecular diffusion coefficient
component DQ of material I in aquifer
unit L for first chemical.
31-40 PROP(I,L,4): Effective porosity of material I in
aquifer unit L.
41-50 PROP(I,L,5): Vertical dispersivity of material I in
aquifer unit L. Note that in the cross-
sectional or axisymmetric simulation,
the code automatically sets PROP (I,L,5)
- PROP (I,L,2).
(b) Molecular diffusion coefficient component DQ of material I in
aquifer unit L for remaining chemicals (20X. F10.0)
*** Omit if NCHEM (defined in RUSTIC.RUN) is less than 2.
Number of records - NCHEM - 1.
Col. 21-30 CPROP Molecular diffusion coefficient
(I.L.ICHEM): component DQ of material I in aquifer unit L
of chemical ICHEM.
Group 10. Aquifer thickness and base elevation data (15, 2E10.3, 415)
*** Omit if performing transport analysis (IMODL=0) or performing
cross-sectional or axisymmetric flow analysis (IAREAL=0).
Number of records = as many as needed. Each record contains the
following information:
107
-------
Col. 1-5 IAQFR: Aquifer unit number.
6-15 ZBASE: Zonal value of elevation of the aquifer base
above the datum plane.
16-25 THCKMX: Zonal value of total thickness of the aquifer
unit. If THCKMX is not specified, the code
assumes an infinite thickness.
26-30 INDST: Starting sequential node number in this zone.
31-35 INDEND: Ending sequential node number in this zone.
36-40 NDINCR: Nodal increment.
41-45 IPAUSE: Parameter indicating whether this record is
the last record in this group;
- 0 if no,
— 1 if yes.
Group ll.Aquitard identification data
*** Omit if there is no aquitard (NAQTRD - 0).
(a) General identification data (315)
One record.
Col. 1-5
6-10
11-15
NMATRD: Number of different soil materials in the
aquitard unit.
NPATCO: Number of nodal points in each aquitard
column. For a steady-state simulation, NPATCO
is automatically set equal to 2. For a
transient simulation, suggested value of
NPATCO is between 5 and 10.
IZATRD: Parameter indicating if the dimensionless
vertical coordinate of the aquitard is to be
read;
- 0 if no,
- 1 if yes.
(b) Aquitard material identification records (515)
Number of records - as many as needed.
following information:
Each record contains the
Col. 1-5 IZONO: Material number assigned to a particular zone
of the aquitard.
6-10 ICOLST: Starting sequential number of the vertical
aquitard-soil column in the zone.
11-15 ICOLEN: Ending sequential number of the vertical
aquitard-soil column in the zone.
16-20 ICINCR: Increment of sequential column numbers.
21-25 IPAUSE: Parameter indicating whether this record is
the last record in this group;
=0 if no,
- 1 if yes.
108
-------
(c) Dimensionless vertical coordinates of aquitard
(8E10.3)
*** Omit if IZATRD = 0.
Number of records = NPATCO/8 + 0 or 1.
Col. 1-10 DIST(I): Nodal values of dimensionless
11-20 transverse coordinate in the aquitard layer
where I - etc.l NPATCO.
Group 12. Aquitard material property and head data (3E10.3)
*** Omit if there is no aquitard (NAQTRD=0).
Number of records = NMATRD.
For IMODL = 1 (flow modeling). each record contains the following
information pertaining to porous material number I of the aquitard:
Col. 1-10 ATPROP(I,1): Vertical component of saturated
hydraulic conductivity of material I in
the aquitard.
11-20 ATPROP(I,2): Specific storage of material I in the
aquitard.
21-30 ATPROP(I,3): Thickness of material I in the aquitard.
For IMODL = 0 (transport modeling') . each record contains the following
information pertaining to material number I of the aquitard:
Col. 1-10 ATPROP(I,1): Dispersion coefficient for material I in
the aquitard.
11-20 ATPROP(I,2): Porosity of material I in the aquitard.
21-30 ATPROP(I,3): Thickness of aquitard material I in the
aquitard.
Group 13. Solute property data and default overriding values
*** Omit if performing groundwater flow analysis (IMODL=1).
(a) Aquitard default solute property values (2E10.3)
*** Omit if NAQTRD (group 2, parameter 5) = 0.
One record.
Col. 1-10 RPCOEF: Default value of retardation coefficient
assigned to the aquitard.
11-20 DPLAM: Default value of decay coefficient assigned to
the aquitard.
109
-------
(b) Default overriding control parameters (215)
One record.
Col. 1-5 IATROR: Parameter indicating if assigned aquitard
default values are to be overridden;
=0 if no,
= 1 if yes.
(c) Aquitard overriding solute property values
(2E10.3,415)
*** Omit if IATROR - 0.
Number of records - as many as needed. Each record contains the
following information:
Col. 1-10 RPCOR: Overriding value of retardation coefficient in
the aquitard.
11-20 DPLOR: Overriding value of decay coefficient.
21-25 ICOLST: Starting sequential number of aquitard column
having the overriding solute property values.
26-30 ICINCR: Ending sequential number of aquitard column
having the overriding solute property values.
31-35 ICINCR: Increment of column numbering in the aquitard
zone having the overriding solute property
values.
36-40 IPAUSE: Parameter indicating if this record is the
last record in subgroup 13(c).
(d) Transport parameter records (3E10.3)
Number of records - NAQFR * NMATAQ (IAQ) * NCHEM. (Values should be
defined for all chemicals in material 1 of aquifer 1, then all
chemicals in material 2 of aquifer 1, etc.)
Col. 1-10 RCOFP(IMAT,IAQ,ICHEM): Aquifer retardation coefficient
in material IMAT of aquifer IAQ, for chemical
ICHEM
11-20 DLAMP(IMAT,IAQ,ICHEM): Decay coefficient of chemical
ICHEM in material IMAT of aquifer IAQ
21-30 FRACMP(IMAT,IAQ,ICHEM): Transformation of chemical
ICHEM in material IMAT of aquifer IAQ.
Group 14. Groundwater recharge and solute (pesticide) loading data
*** Omit groups 14a-14b if IWECH (record group 2, col, 31-35) is equal
to zero.
110
-------
(a) Groundwater recharge control records (15, E10.3, 415)
Number of records - as many as needed. Each record contains the
following information:
In preparing the data in this group, special attention should be
paid to the case of cross-sectional or axisymmetric analysis
Involving an unconfined aquifer system. For this case, all top
boundary nodes of the grid that need to receive specific yield
treatment must be identified and included in this data group.
Hence, for this case, the zone with zero recharge value must also be
included. For other cases, the zone(s) with zero recharge value may
be omitted.
Col. 1-5 IRZON: Recharge zone number.
6-15 RCHVAL: Steady-state default value of the recharge
rate for the zone. A dummy value for RCHVAL
may be set if NZONRT is greater than zero (see
next card group) or PRZM or VADOFT is on. In
either of these cases, RCHVAL will not be
used.
16-20 INDMIN: Lowest sequential nodal number in the recharge
zone.
21-25 INDMAX: Highest sequential nodal number in the
recharge zone.
26-30 NDINCR: Nodal increment.
31-35 IPAIJSE: Parameter indicating if this is the last
record in this group:
=0 if no,
= 1 if yes.
(b) Transient recharge records
*** Omit if NZONRT (record group 2, col. 36-40) is equal to zero.
Number of record sets = NZONRT.
These records are divided into three subgroups as follows:
(i) Control record (215)
One record.
Col. 1-5 IRZON: Recharge zone number.
6-10 NTSRCH: Number of control (interpolation) points on
the graph of recharge rate versus time. Set
to 2 when running with VADOFT or PRZM on.
(ii) Time value records (8E10.3)
Number of records - NTSRCH
111
-------
Col. 1-10 TMRCH(IRZON,J): Time values corresponding to
11-20 control points on the graph of
21-30 recharge rate versus time, where J -
etc. 1, NTSRCH. (When running with
PRZM or VADOFT ON, dummy values may be
entered as they will be overridden
during the simulation).
(iii) Recharge value records (8E10.3)
Number of records - NTSRCH.
Col. 1-10 RCHTM(IRZON,J): Recharge rate values
11-20 corresponding to control points
21-30 on the graph of recharge rate
etc. versus time. Dummy values may be
entered if either PRZM or VADOFT is ON
as they will be overridden during the
simulation by fluxes from either of
the two models, where J - 1,....,
NTSRCH.
(c) Solute (pesticide) loading zone identification records
These records are divided into two subgroups as follows:
(i) Control record (215)
One record.
Col. 1-5 IPSZP: Zone number (must be 1 in current version of
RUSTIC).
6-10 NDCOUN: Number of nodes in the zone.
(ii) Zone identification records (1615)
Col. 1-5 NDIDEN(I,IPSZO): Node numbers of the nodes
6-10 that lie in zone #IPSZO, where I =
etc. 1, NDCOUN.
(d) Solute (pesticide) mass loading records
These records are divided into three subgroups as follows:
(i) Control record (215)
Col. 1-5 IPSZO: Zone number (must be 1 in current version of
RUSTIC).
6-10 NTSPSZ: Number of control (interpolation) points on
the graph of solute mass application rate
versus time.
112
-------
(ii) Time value records (8E10.3)
Number of records = NTSPSZ.
Col. 1-10 TMPSZ(IPSZO.J): Time values corresponding to
11-20 control points on the graph of
21-30 solute mass application rate
etc. versus time, where J-=l NTSPSZ.
When running with PRZM or VADOFT ON,
dummy values may be entered as they
will be overridden during the
simulation.
(iii) Solute mass application rate records (8E10.3)
Number of records = NTSPSZ.
Col. 1-10 SFLXTM(IPSZO,J): Values of solute mass
11-20 application rate corresponding
21-30 to control points on the graph
etc. of solute mass application rate versus
time, where J-l, NTSPSZ. Dummy
values may be entered if either PRZM
or VADOFT is ON as they will be
overridden during the simulation by
fluxes from either of the two models.
Group 15. Boundary condition control record (415)
One record.
Col. 1-5 NBTO: Number of nodes where steady-state functional
values (hydraulic head or concentration) are
prescribed.
6-10 NDFLUX: Number of nodes where steady-state fluid (or
solute) flux values are prescribed.
11-15 NBHVAR: Number of boundary nodes for which functional
values (hydraulic head or concentration) are
time dependent.
16-20 NBFVAR: Number of boundary nodes for which fluid (or
solute) flux values are time dependent.
Note: In determining the values of NDFLUX and NBFVAR, do not count
those groundwater recharge or pesticide flux nodes if they have
already been accounted for in record group 14.
Group 16. Steady-state Dirichlet boundary condition data (I5,E10.3)
*** Omit if NBTO = 0.
Number of records - NBTO. Each record contains the following
information:
113
-------
Col. 1-5 NODV(I): Aquifer node number.
6-15 VAVO(I): Prescribed value of the unknown function (head
or concentration).
Group 17. Steady-state flux boundary condition data (I5.2E10.3)
*** Omit if NDFLUX = 0.
Number of records = NDFLUX. Each record contains the following
information:
Col. 1-5 NODF(I): Aquifer node number.
6-15 FLUXVO(I): For groundwater flow analysis (IMODL=1),
FLUXVO(I) denotes the net integrated fluid
flux (L1 T"1) at the aquifer node. For
solute transport analysis (IMODL-0),
FLUXVO(I) denotes the net solute flux (MT"1)
entering the aquifer node.
16-25 QVALV(I): For groundwater flow analysis (IMODL-1), set
QVALV(I) - 0 or leave blank. For solute
transport analysis, QVALV(I) denotes the net
volumetric fluid flux entering the aquifer
node.
Note: The sign convention for fluxes is positive for influxes and
negative for effluxes. In performing solute transport simulation,
the code automatically treats the nodes that correspond to pumping
wells and efflux boundary nodes. Hence, such nodes can be excluded
from data group 17.
Group 18. Transient Dirichlet boundary condition data
*** Omit if NBHVAR = 0.
Number of record sets - NBHVAR.
Each set contains the following records:
(a) Control record (315)
One record.
Col. 1-5 NDHVAR(I): Node number of a time-dependent Dirichlet
(prescribed head or concentration) node in
the aquifer region.
6-10 NTSNDH(I): Number of control (interpolation) points on
the graph of the function (head or
concentration) versus time.
114
-------
11-15 IREPB: Parameter indicating if transient boundary
condition data in record types 18b and 18c
of this record set are the same as those of
the preceding record set; — 1 if yes, — 0 if
no.
(b) Time value records (8E10.3)
*** Omit if IREPB = 1.
Number of records = NTSNDH(I)/8 + (0 or 1).
Col. 1-10 TMHV(I.J): Time values corresponding to control
11-20 points on the graph of function (head
etc. or concentration) versus time, where J -
1 NTSNDH(I).
(c) Functional value records (8E10.3)
*** Omit if IREPB - 1.
Number of records - NTSHDN(I)/8 + (0 or 1).
Col. 1-10 HVTM(K.J): Value of function (head or
11-20 concentration) corresponding to
etc. TMHV(I.J), where J - 1
NTSNDH(I).
Group 19. Transient flux boundary condition data
*** Omit if NBFVAR = 0.
Number of record sets = NBFVAR. Each set contains the following
information:
(a) Control record (315)
One record.
Col. 1-5 NDFVAR(I): Node number of a time-dependent flux
boundary node in the aquifer region.
6-10 NTSNDF(I): Number of control (interpolation) points on
the graph of fluid (or solute) flux versus
time.
11-15 IREPB: Parameter indicating if transient boundary
condition data in record types 19b-19d are
the same as those of the preceding record
set;
=0 if no,
= 1 if yes.
115
-------
(b) Time value records (8E10.3)
*** Omit if IREPB = 1.
Number of records - NTSNDF(I)/8 + (0 or 1).
Col. 1-10 TMHF(I.J): Time values corresponding to control
11-20 points on the graph of fluid (or
etc. solute flux versus time, where J = 1,
NTSNDF(I).
(c) Flux value records (8E10.3)
*** Omit if IREPB = 1.
Number of records = NTSNDF(I)/8 + (0 or 1).
Col. 1-10 FVTM(I.J): For groundwater flow analysis,
11-20 interpret these as volumetric fluid
etc. flux values corresponding to TMHF (I,J).
For solute transport analysis, interpret
these as injected mass flux values
corresponding to TMHF (I,J), where J - 1,
NTSNDF(I).
(d) Fluid flux value records (8E10.3)
*** Omit if performing groundwater flow analysis (IMODL-1) or if
IRPEB - 1.
Number of records - NTSNDF(I)/8 + (0 or 1).
Col. 1-10 QVTM(I.J): For solute transport analysis,
11-20 interpret these as injected fluid
etc. volumetric flux values corresponding to
TMHF(I,J), where J = 1, , NTSNDF(I).
Note: In performing solute transport simulation, the code
automatically treats the nodes that correspond to pumping wells and
efflux boundary nodes. Hence, such nodes can be excluded from data
group 19.
Group 20. Initial condition data
(a) Default initial values for aquifers (2E10.3)
One record.
Col. 1-10 HIAQFR(I): Default initial values of hydraulic
11-20 head (or concentration) in aquifer
unit 1 to NAQFR, where 1=1, 2.
116
-------
(b) Default initial value for aquitard (E10.3)
*** Omit If there is no aquitard (NAQTRD = 0).
One record.
Col. 1-10 HIATRD: Default initial value of hydraulic head (or
concentration) in the aquitard.
(c) Initial condition control parameters (315)
One record.
Col. 1-5 NONU:
Parameter indicating the uniformity of the
initial condition;
= 0 if the initial condition is uniform and
may be expressed using the default
values,
= 1 if the initial condition is nonuniform
and may be described using the default
initial values in conjunction with the
overriding initial value input option
(subgroup 20 (d)),
= 2 if the initial condition is nonuniform
and the initial values are to be
supplied from a separate data file, unit
number 8.
The value of NONU = 2 is normally used when
performing a restart simulation.
Number of nodes where overriding initial
values need to be input and included in this
data file. The value of NPIN must be set to
zero when NONU = 0 or NONU = 2.
Number of aquitard columns where overriding
initial values need to be input and included
in this data file. The value of NPINAT must
be set to zero when NONU = 0 or NONU = 2.
(d) Overriding initial values for aquifer nodes
(5(15,E10.3))
*** Omit if NPIN - 0.
Number of records = NPIN/5 + 0 or 1.
Col. 1-5 NDNO: Node number.
6-10 HINT(NDNO): Overriding initial value of hydraulic
etc. head (or concentration), where NDNO
= 1 NPIN.
6-10 NPIN:
11-15 NPINAT:
117
-------
(e) Overriding initial values for aquitard columns
(5(15,E10.3))
*** Omit if NPINAT = 0.
Number of records - NPINAT/5 + 0 or I.
Col. 1-5 ICOL: Column number.
6-10 HICOL(ICOL): Overriding initial value of hydraulic
etc. head (or concentration), where ICOL
- 1 NPINAT.
Group 21. Velocity and saturated thickness data (15,3E10.3,215)
*** Omit if performing flow simulation (IMODL-1).
(a) Default velocity and saturated thickness values
Number of records = NAQFR.
Col. 1-5 IAQFR: Aquifer unit number.
6-15 VXDF: Default value of Darcy velocity component in
the x-direction.
16-25 VYDF: Default value of Darcy velocity component in
the y-direction.
26-35 STHDF: Default value of aquifer saturated
thickness.
Note: For a cross-sectional or axisymmetric
simulation, the code automatically sets
STHDF = 1.
36-40 NEOVEL: Number of elements that need overriding
velocity values.
41-45 NEOTHK: Number of elements that need overriding
saturated thickness values.
(b) Overriding Darcy velocity values (2(15,2E10.3))
*** Omit if NEOVEL - 0.
Number of records = NEOVEL/2 + 0 or 1.
Col. 1-5 IEL: Element number.
6-15 VELX(IEL): Overriding value of x-velocity component.
16-30 VELY(IEL): Overriding value of y-velocity
etc. component IEL - 1 NOEVEL.
(c) Overriding saturated thickness values (5(15,ElO.3))
*** Omit if NEOTHK = 0.
Number of records = NEOTHK/5 + 0 or 1.
118
-------
Col. 1-5 IEL: Element number.
6-15 THEL(IEL): Overriding value of saturated
etc. thickness, where IEL - 1 , NEOTHK.
Group 22. Sequential numbers of nonzero flux nodes
*** Omit if IOUTLT (group 3, parameter 7) = 0.
(a) Control parameter (15)
One record.
Col. 1-5 NBOUT: Number of aquifer nodes where the net
integrated fluid or solute flux values are
nonzero.
(b) Sequential node numbers (1615)
Number of records - NBOUT/16 + (0 or 1).
Col. 1-5 NDOUT(I): Sequential numbers of the nodes where
6-10 the flux values are required, where I
etc. = 1, NBOUT.
(c) Computed values of fluid fluxes (8E10.3)
*** Omit if performing fluid flow simulation (IMODL=1) or if performing
transport simulation (IMODL=0) but using the option (NVREAD=1) by
which velocity and fluid flux data are supplied to the code from
file unit 9.
Number of records = NBOUT/8 + 0 or 1.
Col. 1-10 QNDOUT(I): Net values of volumetric fluid fluxes
11-20 at the nodes, where I = 1 , NBOUT.
etc.
Group 23.Output printout control data
*** Omit if IPRCON (group 5, parameter 10) =0.
(a) Control parameters (215)
One record.
Col. 1-5 NTCLIP: Number of time values at which head (or
concentration) values at selected nodes are
to be printed.
6-10 NWINDO: Number of output window sections in the
modeled region.
119
-------
(b) Selected time values (8E10.3)
Number of records = NTGLIP/8 + (0 or 1).
Col. 1-10 TMCLIP(I): Time values at which head (or
11-20 concentration) values in the window
etc. sections are to be printed. These time
values must be arranged in order of
increasing magnitude, where 1=1,
NTCLIP.
(c) Windowing section data (915)
Number of records - as many as needed.
Col. 1-5 IWNO: Window section number.
6-10 1NDST: Starting aquifer node number of output
traversing line (transect).
11-15 1NDEND: Ending aquifer node number of output
transect.
16-20 NDINCR: Nodal increment.
*** If printed output for aquitard columns are not required, leave
columns 21-35 blank.
21-25 ICOLST: Starting sequential number of aquitard
column passing through output transect.
26-30 ICOLEN: Ending sequential number of aquitard column
passing output transect.
31-35 ICINCR: Column number increment.
36-40 IRECW: Parameter indicating if this record is the
first record for window number IWNO;
=0 if no,
=1 if yes.
41-45 IPAUSE: Parameter indicating if this record is the
last record of subgroup 23(c);
=0 if no,
- 1 if yes.
Group 24. Sequential numbers of observation nodes
*** Omit if IOBSND (group 5, parameter 9) = 0.
(a) Control record (15)
One record.
Col. 1-5 NNOBS: Number of observation nodes for which
continuous monitoring of head (or
concentration) versus time is required.
120
-------
(b) Sequential node numbers (1615)
Number of records = NNOBS/16 + (0 or 1).
Col. 1-5 NDOBS(I): Sequential numbers of observation
6-10 nodes, where I - 1 , NNOBS.
etc.
4.2.5 The Monte Carlo Input
The Monte Carlo module uses one input file to specify the distributions of
variables and to select output options. This section describes the format of
this input file and the available user options. A sample input file is shown
in Figure 4.5.
The Monte Carlo input file consists of data lines and two types of general
utility lines. The first type of utility line is the comment line, indicated
by the presence of three asterisks ('***') as the first non-blank characters
in the line. These lines are ignored by the model and are provided to allow
the user to type in comments, table headings, and other information useful in
making the input file more understandable. Comment lines may be inserted
anywhere in the data set. The second type of utility line is the END line,
used by the code to mark the end of specific data groups. These lines are
indicated by the word 'END' in the first three columns of the input line, and
should be used only where specified in the following discussion.
Monte Carlo input data are comprised of five data groups: (1) simulation
control parameters, (2) input distribution parameters, (3) empirical
distribution data, (4) output options, and (5) correlated variable input.
Data are read sequentially starting with Data Group 1 and ending with Data
Group 5. Specific formats for each Data Group are shown below.
4.2.5.1 Data Group 1: Simulation Control Parameters--
This data group consists of two lines of data describing simulation options.
The first line contains the (alphanumeric) title for the run and is used to
label the output. The second line contains the number of Monte Carlo runs
and the percentile confidence level to be used in the simulation.
RECORD 1. TITLE
FORMAT(A80)
TITLE (80): Title for Monte Carlo simulation.
RECORD 2. NRUNS, PALPH
FORMAT(I5,F10.0)
NRUNS: Number of Monte Carlo runs.
PALPH: Confidence level for percentile confidence bounds, entered as a
percent (%).
121
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4.2.5.2 Data Group 2: Input Distribution Parameters--
This data group consists of one line of data for each model parameter to be
varied (i.e., random input variables). The first entry on each line is a
label, of length up to 20 characters, and 2 array indices used to identify
the parameter to be varied. The remaining data on these lines consist of
frequency distribution parameters for the selected variables. After a data
line is provided for each desired random variable, an END card must be
supplied to mark the end of this data group. Note that by setting the
distribution flag VAR(5) to 0, the user can specify a parameter as a
constant. In this case, the mean value of the parameter will be used in the
simulations. This option allows the user to vary the parameters to be Monte
Carloed without extensive modification of the input file. The user should be
aware that parameters which are designated as constants in the Monte Carlo
input file are used in lieu of the same parameter value in the standard input
file.
RECORD 3. PNAME(20), INDI, INDZ, VAR(l), VAR(2), VAR(3), VAR(4), VAR(5)
FORMAT(A20, 215, 5F10.0)
PNAME(20) A label of up to 20 characters identifying the parameter to be
varied. Labels used for various parameters are shown in
TEST OF MONTE CARLO SHELL
***
*** CONTROL PARAMETERS
***
2000 90.0
***
*** MONTE CARLO INPUTS
***PARAMETER NAME MEAN 3RD DEV.
DISPERSION 1 2 50.0 15.00
DISPERSION 1 4 50.0 15.00
DISPERSION 1 5 50.0 20.00
HOC 1 1 6.9 0.0997
HYDRAULIC CONDUC 1 432.0 50.0
END
*** EMPIRICAL DISTRIBUTION DATA
11
89.7 0.10
82.9 0.20
76.1 0.30
69.3 0.40
62.5 0.50
55.7 0.55
48.9 0.60
42.1 0.70
35.3 0.80
28.5 0.90
21.7 1.00
*** MONTE CARLO OUTPUT
DISPERSION 1 5
KXX 1 1 CDF
SAFT ADVECTION 1 1
***HYDRAULIC CONDUC 1 CDF
VAD ADVECTION 1
SAFT DISPERSION 1 1 CDF
VAD DISPERSION 1 CDF
END
*** CORRELATED VARIABLES
DISPERSION 1 4 DISPERSION 1
END
MIN MAX
10.0 90.0
10.0 90.0
0.0 120.0
0.1 7.5
200.0 600.0
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
5 .900
DIST
7
1
1
2
0
1
1
5
1
1
5
1
Figure 4.5. Example MONTE CARLO input file.
122
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Tables 4.7, 4.8 and 4.9.
of Section 4.)
(These tables are located at the end
INDI: An index for the soil horizon number, application number, or
material number corresponding to the parameter identified by
PNAME. Appropriate indexes are shown in Tables 4.7, 4.8, and
4.9.
INDZ: For SAFTMOD variables, INDZ refers to the aquifer number
corresponding to the variable PNAME. Leave blank for PRZM and
VADOFT variables.
VAR(l): The mean of the distribution
VAR(2): The standard deviation of the distribution
VAR(3): The minimum value for the random variable
VAR(4): The maximum value for the random variable
VAR(5): A flag specifying the distribution of the random variable:
0 = Constant
1 = Normal
2 - Log-Normal
3 - Exponential
4 — Uniform
5 = Johnson SU
6 = Johnson SB
7 - Empirical distribution, to be supplied in Data Group 3.
8 = Triangular
4.2.5.3 Data Group 3: Empirical Distribution Data--
This data group contains the piecewise linear descriptions of cumulative
frequency distributions for empirically distributed parameters specified in
Data Group 2 (by setting the value of VAR(5) to 7). Empirical distributions
are read in the order in which parameters were read in Data Group 2. The
first data line for each distribution contains the number of data pairs NDAT
used to describe the cumulative distribution. This is followed by NDAT data
lines, each containing (1) a value of the parameter and (2) the corresponding
cumulative probability, expressed as a decimal between 0.0 and 1.0, for the
specified value. These data pair lines should be supplied in order of
increasing cumulative probability. If no parameters are empirically
distributed, then this data group is not necessary.
RECORD 4. NDAT
FORMAT(I5)
NDAT: Number of data pairs used to describe the piecewise linear
cumulative distribution.
RECORD 5. DIST(I,1), DIST(I,2)
FORMAT(2F10.0)
DIST(I,1): The value (quantile) for data pair I.
DIST(I,2): The cumulative probability associated with the quantile
1).
123
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4.2.5.4 Data Group 4: Output Options--
This data group specifies the statistical output options for each parameter
to be written out. This data group consists of one line for each output
parameter containing: (1) a character label up to 20 characters long and two
array indexes identifying the output parameter, (2) a flag indicating if a
cumulative distribution should be plotted for this parameter (selected by
supplying the "CDF" here), (3) a flag indicating if values of the parameter
are to be written out for each Monte Carlo run (selected by supplying the
word "WRITE" here), and (4) the averaging period for output. Labels used to
identify input parameters or output variables for PRZM, VADOFT, and SAFTMOD
parameters are shown in Tables 4.7, 4.8 and 4.9. A statistical summary table
will be printed out for all parameters selected in this data group. An END
card is supplied to mark the end of this Data Group after a data line is
supplied for each output parameter.
RECORD 6. SNAME(l), 1NDI, INDZ, SNAME(2), SNAME(3), NAVG
FORMAT(A20, 215, 2A20, 15)
SNAME(l): A label used to identify which parameter is to be statistically
summarized.
INDI: The index for the output parameter (see Tables 4.7, 4.8 and
4.9).
INDZ: The aquifer number for SAFTMOD output parameters. Leave blank
for PRZM and VADOFT parameters.
SNAME(2): A flag which indicates if cumulative distributions should be
plotted for the parameter SNAME(l). This option is selected by
inputting "CDF" here.
SNAME(3): A flag which indicates if values of the parameter are to be
written out for each Monte Carlo run (selected by inputting
"WRITE" here).
NAVG: The length of the averaging period for the output parameter.
All time series model outputs are written out as maximum N-day
moving average values for each Monte Carlo run. Thus, if N=l,
statistics are calculated for the maximum daily value. If N=5,
statistics are calculated for the maximum 5-day average value
in each Monte Carlo run. For PRZM variables, NAVG is the
number of days in the average moving period (N). For VADOFT
and SAFTMOD variables, NAVG is the number of SAFTMOD time steps
in the averaging period.
4.2.5.5 Data Group 5: Correlated Input Variables--
This data group is used to indicate which of the input parameters specified
in Data Group 2 are correlated. Note that only parameters with normal, log-
normal, Johnson SU, and/or Johnson SB distributions can be correlated. One
line of data is provided for each pair of correlated parameters. The first
two entries on this line are labels identifying the two parameters that are
124
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TABLE 4-7. MONTE CARLO LABELS FOR PRZM VARIABLES
Parameter
Monte Carlo Label
Index
Random Model Inputs
Soil Bulk Density (g/cm3)
Wilting Point (cm3/cm3)
Field Capacity (cnr/cnr)
Organic Carbon Content (%)
Application Mass, Chemical 1 (kg/ha)
Application Mass, Chemical 2 (kg/ha)
Application Mass, Chemical 3 (kg/ha)
o
Dispersion Coeff., Chemical 1 (cm /day)
o
Dispersion Coeff., Chemical 2 (cm /day)
r\
Dispersion Coeff., Chemical 3 (cm /day)
Decay Rate in Water, Chemical 1 (days )
Decay Rate in Water, Chemical 2 (days )
Decay Rate in Water, Chemical 3 (days )
Decay Rate of Vapor, Chemical 1 (days )
Decay Rate of Vapor, Chemical 2 (days )
Decay Rate of Vapor, Chemical 3 (days )
Decay Rate of Sorbed Chemical 1 (days )
_ 1
Decay Rate of Sorbed Chemical 2 (days )
Decay Rate of Sorbed Chemical 3 (days )
Henry's Constant, Chemical 1
Henry's Constant, Chemical 2
Henry's Constant, Chemical 3
Irrigation Moisture Level (Fraction)
Application Year
Julian Application Day
BULK DENSITY
WILTING POINT
FIELD CAPACITY
ORGANIC CARBON
APPLICATION 1
APPLICATION 2
APPLICATION 3
DISPERSION 1
DISPERSION 2
DISPERSION 3
WATER DECAY 1
WATER DECAY 2
WATER DECAY 3
VAPOR DECAY 1
VAPOR DECAY 2
VAPOR DECAY 3
SORBED DECAY 1
SORBED DECAY 2
SORBED DECAY 3
HENRY'S CONSTANT 1
HENRY'S CONSTANT 2
HENRY'S CONSTANT 3
IRRIG LEVEL
APP YEAR
APP DAY
Horizon
Horizon
Horizon
Horizon
Application
Application
Application
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Application
Application
125
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TABLE 4-7. MONTE CARLO LABELS FOR PRZM VARIABLES (concluded)
Parameter
Monte Carlo Label
Index
Model Outputs
Soil Water Content (cm3/cm^)
Total Soil Pesticide, Chemical 1 (kg/ha)
Total Soil Pesticide, Chemical 2 (kg/ha)
Total Soil Pesticide, Chemical 3 (kg/ha)
Infiltration Depth (cm)
Runoff depth (cm)
Precipitation (cm)
Evapotranspiration (cm)
Flood or Furrow Irrigation Depth
Runoff Flux, Chemical 1 (kg/ha/day)
Runoff Flux, Chemical 2 (kg/ha/day)
Runoff Flux, Chemical 3 (kg/ha/day)
Erosion Flux, Chemical 1 (kg/ha/day)
Erosion Flux, Chemical 2 (kg/ha/day)
Erosion Flux, Chemical 3 (kg/ha/day)
Decay Flux, Chemical 1 (kg/ha/day)
Decay Flux, Chemical 2 (kg/ha/day)
Decay Flux, Chemical 3 (kg/ha/day)
Volat. Flux, Chemical 1 (kg/ha/day)
Volat. Flux, Chemical 2 (kg/ha/day)
Volat. Flux, Chemical 3 (kg/ha/day)
Plant Flux, Chemical 1 (kg/ha/day)
Plant Flux, Chemical 2 (kg/ha/day)
Plant Flux, Chemical 3 (kg/ha/day)
Root Zone Flux, Chemical 1 (kg/ha/day)
Root Zone Flux, Chemical 2 (kg/ha/day)
Root Zone Flux, Chemical 3 (kg/ha/day)
THETA
SOIL PESTICIDE 1
SOIL PESTICIDE 2
SOIL PESTICIDE 3
INFILTRATION
RUNOFF
PRECIPITATION-
EVAPOTRANSPIRATION
IRRIG DEPTH
RUNOFF FLUX 1
RUNOFF FLUX 2
RUNOFF FLUX 3
EROSION FLUX 1
EROSION FLUX 2
EROSION FLUX 3
DECAY FLUX 1
DECAY FLUX 2
DECAY FLUX 3
VOLAT. FLUX 1
VOLAT. FLUX 2
VOLAT. FLUX 3
PLANT FLUX 1
PLANT FLUX 2
PLANT FLUX 3
ROOT FLUX 1
ROOT FLUX 2
ROOT FLUX 3
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
126
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Table 4-8. MONTE CARLO LABELS FOR VADOFT VARIABLES
Parameter
Monte Carlo Label
Index
Random Model Inputs
Hydraulic Conductivity
Residual Saturation
Van-Genuchten Alpha
Van-Genuchten N
Decay Rate Chemical 1
Decay Rate Chemical 2
Decay Rate Chemical 3
Dispersion Coefficient, Chemical 1
Dispersion Coefficient, Chemical 2
Dispersion Coefficient, Chemical 3
Retardation, Chemical 1
Retardation, Chemical 2
Retardation, Chemical 3
HYDRAULIC CONDUC Material
RESIDUAL SATURATION Material
V-G ALPHA Material
V-G POWER N Material
VADOFT DECAY 1 Material
VADOFT DECAY 2 Material
VADOFT DECAY 3 Material
VAD DISPC 1 Material
VAD DISPC 2 Material
VAD DISPC 3 Material
VAD RETARD 1 Material
VAD RETARD 2 Material
VAD RETARD 3 Material
Model Outputs (Note: Fluxes are net fluxes for the entire soil column)
Total Water Flux
Advective Flux, Chemical 1
Advective Flux, Chemical 2
Advective Flux, Chemical 3
Dispersion Flux, Chemical 1
Dispersion Flux, Chemical 2
Dispersion Flux, Chemical 3
Decay Flux, Chemical 1
Decay Flux, Chemical 2
Decay Flux, Chemical 3
Concentration, Chemical 1
Concentration, Chemical 2
Concentration, Chemical 3
VAD WATER FLUX
VAD ADVECTION 1
VAD ADVECTION 2
VAD ADVECTION 3
VAD DISPERSION 1
VAD DISPERSION 2
VAD DISPERSION 3
VAD DECAY FLUX 1
VAD DECAY FLUX 2
VAD DECAY FLUX 3
VAD CONG 1
VAD CONG 2
VAD CONG 3
Node
Node
Node
127
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Table 4-9. MONTE CARLO LABELS FOR SAFTMOD VARIABLES
Parameter
Monte Carlo Label
Index
Random Model Inputs
Hydraulic Conductivity KXX
Hydraulic Conductivity KYY
Longitudinal Dispersivity
Transverse Dispersivity
Chemical Decay Rate, Chemical 1
Chemical Decay Rate, Chemical 2
Chemical Decay Rate, Chemical 3
Retardation Coeff., Chemical 1
Retardation Coeff., Chemical 2
Retardation Coeff., Chemical 3
KXX
KYY
LONG DISP
TRANS DISP
SAFT DECAY 1
SAFT DECAY 2
SAFT DECAY 3
SAFT RETARD 1
SAFT RETARD 2
SAFT RETARD 3
Material
Material
Mateiral
Material
Material
Material
Material
Material
Material
Material
Model Outputs (Note: Fluxes are net fluxes for the entire SAFTMOD grid)
Total Water Flux
Advective Flux, Chemical 1
Advective Flux, Chemical 2
Advective Flux, Chemical 3
Dispersion Flux, Chemical 1
Dispersion Flux, Chemical 2
Dispersion Flux, Chemical 3
Decay Flux, Chemical 1
Decay Flux, Chemical 2
Decay Flux, Chemical 3
Concentration, Chemical 1
Concentration, Chemical 2
Concentration, Chemical 3
SAFT WATER FLUX
SAFT ADVECTION 1
SAFT ADVECTION 2
SAFT ADVECTION 3
SAFT DISPERSION 1
SAFT DISPERSION 2
SAFT DISPERSION 3
SAFT DECAY FLUX 1
SAFT DECAY FLUX 2
SAFT DECAY FLUX 3
SAFT CONC 1
SAFT CONC 2
SAFT CONC 3
Node
Node
Node
128
-------
correlated. The third entry on these data lines is the value of the
correlation coefficient. After a data line is supplied for each correlated
pair of parameters, an END card must be provided to mark the end of the data
group.
RECORD 7. PNAME(l), INDIl, INDZ1, PNAME(2), 1NDI2, INDZ2, CORR(1,2)
FORMAT(2(A20, 215), FlO.O)
PNAME(l): A 1- to 20-character label identifying the first correlated
parameter. Distribution type must be normal, log-normal,
Johnson SU, or Johnson SB.
INDIl: The array index for the first parameter (see Tables 4.7, 4.8
and 4.9).
INDZ1: The aquifer number for the first parameter (SAFTMOD variables
only). Leave blank for PRZM and VADOFT variables.
PNAME(2): A 1- to 20-character label identifying the second correlated
parameter. Distribution type must be normal, log-normal,
Johnson SU, or Johnson SB.
INDI2: The array index for the second parameter (see Tables 4.7, 4.8
and 4.9).
INDZ2: The aquifer number for the second parameter (SAFTMOD variables
only). Leave blank for PRZM and VADOFT variables.
CORR(1,2): The value of the correlation coefficient for parameters 1
and 2.
129
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SECTION 5
PARAMETER ESTIMATION
This section describes procedures and provides information to aid the user of
RUSTIC in estimating model input parameters. Parameters in each of the
modules are covered beginning with the Executive Supervisor (5.1), PRZM
(5.2), VADOFT (5.3), and SAFTMOD (5.4).
5.1 EXECUTION SUPERVISOR
The execution supervisor does not have any parameters per se. The input data
stream to the execution supervisor is composed of what options are selected,
file names, and global data. The global data consists of the start and
ending dates of the current simulation, the number of days per SAFTMOD
simulation (required only if the SAFTMOD option is selected), the number of
chemicals which will be simulated (if solute transport is being simulated),
and the index of the parent species of each solute being simulated (required
only if solute transport is being simulated and the number of chemicals being
simulated is greater than one).
With the exception of the parameter defining the number of days in a SAFTMOD
simulation, the values selected for the execution supervisor input stream
result from the definition of the problem being simulated. A reasonable
value of the SAFTMOD time step can be determined from examining the grid
spacing and expected velocities in the saturated zone.
The number of days in a SAFTMOD simulation can be as small as one and as
large as the total number of days being simulated. Lower values of this
parameter will increase accuracy and also increase the execution time
(restart data have to be read from disk files each time a new SAFTMOD
simulation is started). The most accurate simulations will be obtained by
defining the time step such that water will move, on the average, across one
SAFTMOD element each time step. The water velocity can be estimated by
multiplying the head gradient times the hydraulic conductivity. Dividing the
average SAFTMOD element length by this velocity estimate will provide an
estimate of the time step.
5.2 PRZM PARAMETERS
With a few exceptions, PRZM Release II has the same parameter requirements as
PRZM Release I (Carsel et al. 1984). This section contains guidance for
original PRZM parameters, extracted from the original report, as well as
additional parameters required by the addition of several new capabilities.
New capabilities include:
130
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• Volatilization
• Daughter Products Simulation
• Soil Temperature Simulation
PRZM relates pesticide fate in the upper soil to temporal variations in
hydrologic, agronomic, and pesticide chemical factors. A minimum of
generally accessible input is required for successful use of PRZM.
The module does utilize some parameters, however, that users may find
difficult to obtain or calculate. The following sections describe these
parameters and provide detailed procedures for estimating or obtaining the
required values. In addition, where information was available, coefficients
of variation (CV) and distribution type are provided for use with Monte Carlo
simulation. Parameters appear in the same general order that they appear in
the input file. Options are available in the program to directly estimate
several parameters (e.g., soil water content at field capacity, wilting
point, bulk density, adsorption partition coefficients, soil thermal
conductivity, heat capacity), when related information is supplied by the
user.
5.2.1 Hydrology Parameters
SFAC and PFAC--Snow Factor and Pan Factor--
When the mean air temperature (T) falls below 0.0°C, any precipitation that
falls is considered to be in the form of snow. When the mean air temperature
is above 0.0°C, however, the snow accumulation is decreased by a snowmelt
factor, SFAC. The amount of snowmelt is calculated by the degree-day factor
and was described in Section 2 (Volume I). The mean air temperature is read
from the meteorological file.
The snowmelt factor, SFAC, usually increases as forest cover decreases;
typical minimum and maximum ranges for SFAC for different forest cover
conditions are provided in Table 5-1. Climatic and physiographic factors
will have an effect on values selected for site-specific conditions. Higher
values of SFAC are appropriate for sites with windy conditions and where
south-facing slopes are predominant (Anderson 1978). The snow factor would
be applicable only to those areas where the climatology produces temperatures
conducive to snow fall and snow melt.
The pan factor (PFAC) is a dimensionless number used to convert daily pan
evaporation to daily potential ET. The pan factor generally ranges between
0.60-0.80. Figure 5.1 illustrates typical pan factors in specific regions of
the United States.
ANETD--Soil Evaporation Moisture Loss During Fallow, Dormant Periods--
The soil water balance model considers both soil evaporation and plant
transpiration losses and updates the depth of water extraction by plant roots
during the growing season. The total evapotranspiration demand (ET) is
subtracted sequentially in a linearly weighted manner from each layer until a
minimum moisture level (wilting point) is reached within each layer.
Evaporation is initially assumed to occur in the top 10 cm of the soil
profile with the remaining demand, crop transpiration, occurring from
compartments below the 10-cm zone and down to the maximum depth of rooting.
131
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Table 5-1. TYPICAL VALUES OF SNOWMELT FACTOR (SFAC) AS RELATED TO FOREST
COVER
Forest Cover
Snowroelt Factor. SFAC (cm "C'1 day'1)
Minimum Maximum
Coniferous forest - quite dense
Mixed forest
coniferous plus open areas
and/or deciduous
Predominantly deciduous forest
Open areas
0.08 - 0.12
0.10 - 0.16
0.14 - 0.20
0.20 - 0.36
0.20 - 0.32
0.32 - 0.40
0.40 - 0.52
0.52 - 0.80
Source: Anderson, E.A., "Initial Parameter Values for the Snow Accumulation
and Ablation Model", Part IV.2.2.1, National Weather Service River
Forecast System - User's Manual, NWS/NOAA, U.S. Dept. of Commerce,
Silver Springs, MD, March 31, 1978.
These assumptions allow simulation of reduced levels of ET during fallow,
dormant periods and increased levels during active plant growth. Values for
(ANETD) used to estimate soil evaporation losses are provided in Figure 5.2.
The values for ANETD in Figure 5.2 are only applicable for soil hydraulics
option 1, the free drainage model, and would not be appropriate for use with
hydraulics option 2, the limited drainage model. The limited drainage model
allows more available soil water and, hence, more ET extraction. If drainage
option 2 is selected, it is recommended that ANETD be set to equal 10 cm.
Calibration may be required if results are not consistent with local water
balance data.
DT--Average Day Time Hours for a Day in Each Month--
The values of DT are used to calculate total potential ET using Hamon's
Formula if daily pan evaporation data do not exist. Values of DT for
latitudes 0 - 50° north or south of the equator are provided in Table 5-2.
Values for DT are determined by:
Step 1. Finding the approximate degree latitude north or south of
the equator for the location.
Step 2. Inputting the twelve monthly numbers under the degree
latitude column into the parameter file in calendar order,
i.e., January through December.
132
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Example: 40° north latitude
10.1, 10.0, 12.4, 13.3, 14.9, 15.0,
15.2, 14.2, 12.5, 11.5, 10.0, 9.7
USLEK, USLELS, USLEP, USLEC--Universal Soil Loss Equation Parameters--
The role of erosion on pesticide loss decreases with decreasing chemical
affinity for soil. The total mass of pesticide loss by this means for most
highly soluble pesticides will be quite small. If the apparent distribution
coefficient is less or equal to 5.0, erosion can usually be neglected (i.e.,
the erosion flag ERFLAG can be set to zero). For a compound having a
distribution coefficient greater than 5.0, erosion losses (and subsequent
pesticide loss) should be estimated and the erosion flag set (to one)
accordingly.
Soil characteristics, climatic conditions, agronomic practices, and
topography contribute to the potential erosion rate from a field. During an
erosion-producing runoff event, soil particles and aggregates are carried
across the field. These aggregates consist of coarse, medium, and fine
particles, with the finer particles carried the greatest distances across the
field. Sediment is the principal carrier of sorbed pesticides.
The Universal Soil Loss Equation (USLE) developed by USDA is a simple method
used to determine erosion losses. The USLE is most accurate for long-term
average erosion losses. The soil loss equation used in PRZM uses the
modification described by Williams (1975). The Williams modification
replaces the R (rainfall erosivity) term with an energy term. The energy
term enables the estimation of event totals for erosion from the field. The
modified universal soil loss equation (MUSLE) requires the remaining four
USLE factors with no modifications.
USLEK--Soil Erodibility Factor--
USLEK is a soil specific parameter. Specific values for various soils are
obtainable from local Soil Conservation Service (SCS) offices. Approximate
values (based on broad ranges of soil properties) can be estimated from
Table 5-3.
USLELS--Slope Length and Steepness Factor--
USLELS is a topographic parameter and is dimensionless. Values for LS can be
estimated from Table 5-4.
USLEP--Supporting Practice Factor--
USLEP is a conservation supporting practice parameter and is dimensionless.
Values range from 0.10 (extensive practices) to 1.0 (no supporting practice).
Specific values for P can be estimated from Table 5-5.
USLEC--Cover and Management Factor--
USLEC is a management parameter and is dimensionless. Values range from
0.001 (well managed) to 1.0 (fallow or tilled condition). One value for each
of the three growing periods (fallow, cropping, residue) is required.
Specific local values can be computed from Wischmeier and Smith (1978) or
obtained from the local SCS office. Generalized values are provided in Table
5-6.
136
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Table 5-3. INDICATIONS OF THE GENERAL MAGNITUDE OF THE SOIL/ERODIBILITY
FACTOR, Ka
Organic Matter Content
Texture Class
Sand
Fine Sand
Very fine sand
Loamy sand
Loamy fine sand
Loamy very fine sand
Sandy loam
Fine sandy loam
Very fine sandy loam
Loam
Silt loam
Silt
Sandy clay loam
Clay loam
Silty clay loam
Sandy clay
Silty clay
Clay
<0.5%
0.05
0.16
.42
.12
.24
.44
.27
.35
.47
.38
.48
.60
.27
.28
.37
.14
.25
2%
0.03
0.14
.36
.10
.20
.38
.24
.30
.41
.34
.42
.52
.25
.25
.32
.13
.23
0.13-0.29
4%
0.02
0.10
.28
.08
.16
.30
.19
.24
.33
.29
.33
.42
.21
.21
.26
.12
.19
The values shown are estimated averages of broad ranges of specific-soil
values. When a texture is near the borderline of two texture classes, use
the average of the two K values. For specific soils, Soil Conservation
Service K-value tables will provide much greater accuracy. (Control of
Water Pollution from Cropland, Vol. I, A Manual for Guideline Development.
U.S. Environmental Protection Agency, Athens, GA EPA-600/2-75-026a).
137
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Table 5-5. VALUES OF SUPPORT-PRACTICE FACTOR, Pc
Practice
1.1-2.0
Land Slope (percent)
2.1-7.0 7.1-12.0 12.1-18.0 18.1-24.0
(Factor P)
Contouring (Pc) 0.60 0.50 0.60 0.80 0.90
Contour Strip
cropping (Psc)b
R-R-M-M 0.30 0.25 0.30 0.40 0.45
R-W-M-M 0.30 0.25 0.30 0.40 0.45
R-R-W-M 0.45 0.38 0.45 0.60 0.68
R-W 0.52 0.44 0.52 0.70 0.90
R-0 0.60 0.50 0.60 0.80 0.90
Contour listing
or ridge
planting (Pcl)
Contour terracing
0.30
d0.6/7n"
No support practice 1.0
0.25
1.0
0.30
1.0
0.40
0.45
0.8/Vn"
1.0 1.0
Control of Water Pollution from Cropland, Vol. I, A Manual for Guideline
Development. U.S. Environmental Protection Agency, Athens, GA. EPA-
600/2-75-026a.
R — rowcrop, W = fall-seeded grain, 0 = spring-seeded grain, M = meadow.
The crops are grown in rotation and so arranged on the field that rowcrop
strips are always separated by a meadow or winter-grain strip.
These Pt values estimate the amount of soil eroded to the terrace channels
and are used for conservation planning. For prediction of off-field
sediment, the Pfc values are multiplied by 0.2.
n - number of approximately equal-length intervals into which the field
slope is divided by the terraces. Tillage operations must be parallel to
the terraces.
139
-------
Table 5-6. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FACTOR, C,
37 STATES EAST OF THE ROCKY MOUNTAINS3'b
IN THE
Line
No,
Base
Corn
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Crop, Rotation, and Management0
value: continuous fallow, tilled up and down slope
C, RdR, fall TP, conv (1)
C, RdR, spring TP, conv (1)
C, RdL, fall TP, conv (1)
C, RdR, we seeding, spring TP, conv (1)
C, RdL, standing, spring TP, conv (1)
C, fall shred stalks, spring TP, conv (1)
C(silage)-W(RdL, fall TP) (2)
C, RdL, fall chisel, spring disk, 40-30% re (1)
C(silage) , W we seeding, no-till pi in c-k (1)
C (RdL) -W) RdL, spring TP) (2)
C, fall shred stalks, chisel pi, 40-30% re (1)
C-C-C-W-M, RdL, TP for C, disk for W (5)
C, RdL, strip till row zones, 55-40% re (1)
C-C-C-W-M-M, RdL, TP for C, disk for W (6)
C-C-W-M, RdL, TP for C, disk for W (4)
C, fall shred, no- till pi, 70-50% re (1)
C-C-W-M-M, RdL, TP for C, disk for W (5)
C-C-C-W-M, RdL, no-till pi 2nd & 3rd C (5)
C-C-W-M, RdL, no-till pi 2d C (4)
C, no-till pi in c-k wheat, 90-70% re (1)
C-C-C-W-M-M, no-till pi 2d & 3rd C (6)
C-W-M, RdL, TP for C, disk for W (3)
C-C-W-M-M, RdL, no-till p!2d C (5)
C-W-M-M, RdL, TP for C, disk for W (4)
C-W-M-M-M, RdL, TP for C, disk for W (5)
C, no-till pi in c-k sod, 95-80% re (1)
Productivity
High
C Value
1.00
0.54
.50
.42
.40
.38
.35
.31
.24
.20
.20
.19
.17
.16
.14
.12
.11
.087
.076
.068
.062
.061
.055
.051
.039
.032
.017
Leveld
Mod.
1.00
0.62
.59
.52
.49
.48
.44
.35
.30
.24
.28
.26
.23
.24
.20
.17
.18
.14
.13
.11
.14
.11
.095
.094
.074
.061
.053
Gotten6
27 Cot, conv (Western Plains) (1)
28 Cot, conv (South) (1)
Meadow
29 Grass & Legume mix
30 Alfalfa, lespedeza or Sericia
31 Sweet clover
0.42
.34
0.004
.020
.025
0.49
.40
0.01
140
-------
Table 5-6. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FACTOR, C, IN THE
37 STATES EAST OF THE ROCKY MOUNTAINS3'b (continued)
Productivity Level
High Mod.
Line
No.
Crop, Rotation, and Management
C Value
Base value: continuous fallow, tilled up and down slope 1.00
1.00
Sorghum, grain (Western Plains)6
32 RdL, spring TP, conv (1)
33 No-till pi in shredded 70-50% re
Soybeans6
34 B, RdL, spring TP, conv (1)
35 C-B, TP annually, conv (2)
36 B, no-till pi
37 C-B, no-till pi, fall shred C stalks (2)
0.43
.11
0.48
.43
.22
.18
0.53
.18
0.54
.51
.28
.22
Wheat
38 W-F, fall TP after W (2)
39 W-F, stubble mulch, 500 Ibs re (2)
40 W-F, stubble mulch, 1000 Ibs re (2)
41 Spring W, RdL, Sept TP, conv (N&S Dak) (1)
42
43
44
45
46
47
48
49
Winter W, RdL, Aug TP, conv (Kansas) (1)
Spring W, stubble mulch,
Spring W, stubble mulch,
Winter W, stubble mulch,
Winter W, stubble mulch,
W-M, conv (2)
W-M-M, conv (3)
W-M-M-M, CONV (4)
750 Ibs re (1)
1250 Ibs re (1)
750 Ibs re (1)
1250 Ibs re (1)
0.38
.32
.21
.23
.19
.15
.12
.11
.10
.054
.026
.021
a This table is for illustrative purposes only and is not a complete list of
cropping systems or potential practices. Values of C differ with rainfall
pattern and planting dates. These generalized values show approximately
the relative erosion-reducing effectiveness of various crop systems, but
locationally derived C values should be used for conservation planning at
the field level. Tables of local values are available from the Soil
Conservation Service.
Control of Water Pollution from Cropland, Vol. I, A Manual for Guide-line
Development. U.S. Environmental Protection Agency, Athens, GA. EPA-600/3-
75-026a.
c Numbers in parentheses indicate number of years in the rotation cycle. No.
(1) designates a continous one-crop system.
High level is exemplified by long-term yield averages greater than 75 bu.
corn or 3 tons grass-and-legume hay; or cotton management that regularly
provides good stands and growth.
141
-------
Table 5-6. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FACTOR, C, IN THE
37 STATES EAST OF THE ROCKY MOUNTAINS3'b (concluded)
e Grain sorghum, soybeans, or cotton may be substituted for corn in lines 12,
14, 15, 17-19, 21-25 to estimate C values for sod-based rotations.
Abbreviations defined;
B - soybeans F - fallow
C - corn M - grass & legume hay
c-k - chemically killed pi - plant
conv - conventional W - wheat
cot - cotton we - cover
Ibs re - pounds of crop residue per acre remaining on surface after new
crop seeding
% re - percentage of soil surface covered by residue mulch after new
crop seeding
70-50% re - 70% cover for C values in first column; 50% for second column
RdR - residues (corn stover, straw, etc.) removed or burned
RdL - all residues left on field (on surface or incorporated)
TP - turn plowed (upper 5 or more inches of soil inverted, covering
residues)
142
-------
TR--Storm Duration--Peak Runoff Rate--
Total runoff is easily calculated with the curve number technique, but the
problem remains to estimate the peak runoff rate. Most runoff producing
storms occur over a short duration. The model assumes a trapezoidal
hydrograph (see Section 2, Vol. I) with storm duration (TR) specified as an
input. Unfortunately, data to estimate TR are not often readily available.
TR is entered as an average, although in reality this parameter changes
seasonally as well as with individual storm type. Because most erosion
losses occur shortly after plowing or other tillage prior to crop emergence,
the value of TR should be appropriate for this period. Several references
(Heimstra and Crease, 1970; Grace and Eagleson 1966; Varas and Linsley, 1977;
Eagleson, 1978; and Dean, 1979) give representative values of storm duration.
Table 5-7 provides estimates of TR for selected locations in the United
States for both mean annual and summer time periods, while Figure 5.3
provides "regionalized" values for different areas of the U.S. If more
detailed site-specific information is desired, representative storm durations
can be estimated from analysis of hourly rainfall records. Soil loss
estimates can be adjusted by calibrating this parameter to match annual soil
loss estimates. The soil loss estimates are proportional to 1/,/TR (a four-
fold decrease in TR will produce a two-fold increase in soil loss).
CINTCP--Maximum Crop Interception--
The crop interception parameter (CINTCP) estimates the amount of rainfall
that is intercepted by a fully developed plant canopy and retained on the
plant surface, cm. A range of 0.1 - 0.3 cm for a dense crop canopy is
reported USDA (1980). Values for several major crops are provided in Table
5-8.
AMXDR--Active Crop Rooting Depth--
PRZM requires input of the maximum active crop rooting depth (AMXDR), in
centimeters, for the simulated crop (or the deepest root zone of multiple
crop simulations) measured from the land surface. Generalized information
for corn, soybeans, wheat, tobacco, grain sorghum, potatoes, peanuts, and
cotton are provided in Table 5-9. If minor crops, such as mint, are
simulated, or site specific information alters the generalized information,
consulting with USDA Handbook No. 283 (Usual Planting and Harvesting Dates),
or the Cooperative Extension Service in the specific locale is advisable.
CN--Runoff Curve Number--
The interaction of hydrologic soil group (soil) and land use and treatment
(cover) is accounted for by assigning a runoff curve number (CN) for average
soil moisture condition (AMC II) to important soil cover complexes for the
fallow, cropping, and residue parts of a growing season. The average curve
numbers for each of the three soil cover complexes are estimated using Tables
5-10 through 5-14. The following steps provide a procedure for obtaining the
correct curve numbers. Corn planted in straight rows will be used as an
example.
143
-------
Table 5-7. MEAN STORM DURATION (TR) VALUES FOR SELECTED CITIES
Storm Duration (hrs) Storm Duration (hrs)
Summer Summer
Mean (June- Mean (June-
Location Annual Sept) Location Annual Sept)
Great Lakes
Champaign-Urbana, IL
Chicago , IL
Davenport , IA
Detroit, MI
Louisville, KY
Minneapolis , MN
Steubenville, OH
Toledo, OH
Zanesville, OH
Lansing, MI (30 Yr)
Lansing, MI (21 Yr)
6.1
5.7
6.6
4.4
6.7
6.0
7.0
5.0
6.1
5.6
6.2
4.6
4.5
5.3
3.1
4.5
4.5
5.9
3.7
4.3
4.2
5.1
Lower Mississippi Valley
Memphis, TN
New Orleans , LA
Shreveport, LA (17 Yr)
Lake Charles , LA
Texas and Southwest
Abilene, TX
Austin, TX
Brownsville , TX
Dallas, TX
El Paso, TX
Waco, TX
Phoenix , AZ
Northwest
Portland, OR (25 Yr)
Portland, OR (10 Yr)
Eugene, OR
Seattle, WA
6.9
6.9
7.8
7.7
4.2
4.0
3.5
4.2
3.3
4.2
3.2
5.4
15.5
29.2
21.5
4.7
5.0
5.3
5.9
3.3
3.3
2.8
3.2
2.6
3.3
2.4
4.5
9.4
15.0
12.7
Southeast
Greensboro , NC
Columbia, SC
Atlanta, GA
Birmingham, AL
Gainesville , FL
Tampa , FL
Rocky Mountains
Denver, CO (8 Yr)
Denver, CO (25 Yr)
Denver, CO (24 Yr)
Rapid City, SD
Salt Lake City, UT
Salt Lake City, UT
California
Oakland, CA
San Francisco, CA
Northeast
Caribou, ME
Boston, MA
Lake George , NY
Kingston, NY
Poughkeepsie, NY
New York City, NY
Mineola, LI, NY
Upton LI, NY
Wantagh, LI, NY (2 Yr)
Long Island, NY
Washington, DC
Baltimore, MD
5.0
4.5
8.0
7.2
7.6
3.6
4.3
4.8
9.1
8.0
4.5
7.8
4.3
5.9
5.8
6.1
5.4
7.0
6.9
6.7
5.8
6.3
5.6
4.2
5.9
6.0
3.6
3.4
6.2
5.0
6.6
3.1
3.2
3.2
4.4
6.1
2.8
6.8
2.9
11.2
4.4
4.2
4.5
5.0
4.9
4.8
4.5
4.6
4.0
3.4
4.1
4.2
Source:
* -
Woodward-Clyde Consultants, "Methodology for Analysis of Detention
Basins for Control of Urban Runoff Quality," prepared for U.S. EPA,
Office of Water, Nonpoint Source Division, September, 1986.
These values may be misleading in arid regions or regions with
pronounced seasonal rainfall patterns.
144
-------
PERIOD
MEAN STORM DURATION (hours)
ZONE
34567
ANNUAL
MEAN
C.V.
SUMMER
MEAN
C.V.
5.8
1.05
4.4
1.14
5.9
1.05
4.2
1.09
6.2
1.22
4.9
1.33
7.3
1.17
5.2
1.29
4.0
1.07
3.2
1.08
3.6
1.02
2.6
1.01
20.0
1.23
11.4
1.20
4.5
0.92
2.8
0.80
4.4
1.20
3.1
1.14
Mean - mean value
C.V. - Coefficient of variation
Source: Woodward-Clyde Consultants, "Methodology for Analysis of Detention
Basins for Control of Urban Runoff Quality", prepared for U.S. EPA,
Office of Water, Nonpoint Source Division, September, 1986.
Figure 5.3. Representative regional mean storm duration
(hours) values for the U.S.
145
-------
Table 5-8. INTERCEPTION STORAGE FOR MAJOR CROPS
Crop
Density
CINTCP (cm)
Corn
Soybeans
Wheat
Oats
Barley
Potatoes
Peanuts
Cotton
Tobacco
Heavy
Moderate
Light
Light
Light
Light
Light
Moderate
Moderate
0.25
0.20
0.0
0.0
0.0
0.0
0.0
0.20
0.20
-0.30
- 0.25
- 0.15
- 0.15
-0.15
-0.15
-0.15
- 0.25
- 0.25
Step 1. From Appendix B of the users manual for PRZM, Release I
(Carsel et al., 1984) or from Section 4 of the SCS National
Engineering Handbook (Soil Conservation Service, 1972) find
the hydrologic soil group for the particular soil that is in
the area under consideration. There are four different soil
classifications (A, B, C, and D), and they are in the order
of decreasing percolation potential and increasing slope and
runoff potential. Soil characteristics associated with each
hydrologic group are as follows:
Group A: Deep sand, deep loess, aggregated silts, minimum
infiltration of 0.76 - 1.14 (cm hr'1)
Group B: Shallow loess, sandy loam, minimum infiltration
0.38 - 0.76 (cm hr'1)
Group C: Clay loams, shallow sandy loam, soils low in
Group D:
organic content, and soils usually high in clay,
minimum infiltration 0.13 - 0.38 (cm hr"1)
Soils that swell significantly when wet, heavy
plastic clays, and certain saline soils, minimum
infiltration 0.03 - 0.13 (cm hr'1)
If the soil series or soil properties are not known, the
hydrologic soil group can be estimated from Figure 5.4.
Care must be exercised, however, in use of this figure.
Considerable spatial aggregation was made in order to
develop the generalized map over such a large area. Where
possible, development of more highly resolved data is
preferable.
146
-------
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147
-------
Table 5-10. RUNOFF CURVE NUMBERS FOR HYDROLOGIC SOIL-COVER COMPLEXES3
(ANTECEDENT MOISTURE CONDITION II, AND I. = 0.2 S)
Land Use
Fallow
Row crops
Small
grain
Close-
seeded
legumes
or rota-
tion
meadow
Pasture
or range
Meadow
Woods
Farmsteads
Roads (Dirt)
(Hard
Cover
Treatment
or Practice
Straight Row
Straight Row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Contoured
Contoured
Contoured
c
Surface)0
Hydrologic
Condition
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Fair
Good
Poor
Fair
Good
Good
Poor
Fair
Good
Hydrologic Soil Group
A B C D
77
72
67
70
65
66
62
65
63
63
61
61
59
66
58
64
55
63
51
68
49
39
47
25
6
30
45
36
25
59
72
74
86
78
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
58
66
60
55
74
82
84
91
85
85
84
82
80
78
84
83
83
81
79
78
85
81
83
78
80
76
86
79
74
81
75
70
71
77
73
70
82
87
90
94
91
89
88
86
82
81
88
87
87
84
82
81
89
85
85
83
83
80
89
84
80
88
83
79
78
83
79
77
86
89
92
a Mockus, 1972.
Close-drilled or broadcast.
c Including right-of-way.
148
-------
Table 5-11. METHOD FOR CONVERTING CROP YIELDS TO RESIDUE3
Cropb
Barley
Corn
Oats
Rice
Rye
Sorghum
Soybeans
Winter wheat
Spring Wheat
Straw/Grain
Ratio
1.5
1.0
2.0
1.5
1.5
1.0
1.5
1.7
1.3
Bushel
Weight
(Ibs)
48
56
32
45
56
56
60
60
60
a Crop residue = (straw/grain ratio) * (bushel weight in Ib/bu) * (crop
yield in bu/acre).
b Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff,
and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
Table 5-12. RESIDUE REMAINING FROM TILLAGE OPERATIONS3
Tillageb
Operation
Chisel Plow
Rod weeder
Light disk
Heavy disk
Moldboard plow
Till plant
Fluted coulter
V Sweep
Residue
Remaining
(%)
65
90
70
30
10
80
90
90
a Crop residue remaining = (crop residue from Table 10) * (tillage factor(s)
b Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff,
and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
149
-------
Table 5-13. REDUCTION IN RUNOFF CURVE NUMBERS CAUSED BY CONSERVATION TILLAGE
AND RESIDUE MANAGEMENT3
Large
Residue
Cropb
(Ib/acre)
0
400
700
1,100
1,500
2,000
2,500
6,200
Medium
Residue
Crop0
(Ib/acre)
0
150
300
450
700
950
1,200
3,500
Surface
Covered
by Residue
(%)
0
10
19
28
37
46
55
90
Reduction
in Curve
Number
(%)
0
0
2
4
6
8
10
10
3 Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff,
and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
Large-residue crop (corn).
c Medium residue crop (wheat, oats, barley, rye, sorghum, soybeans).
Percent reduction in curve numbers can be interpolated linearly. Only
apply 0 to 1/2 of these percent reductions to CNs for contouring and
terracing practices when they are used in conjunction with conservation
tillage.
Table 5-14. VALUES FOR ESTIMATING WFMAX IN EXPONENTIAL FOLIAR MODEL
Crop
Corn
Sorghum
Soybeans
Winter wheat
Yield3
(Bu/Ac)
110
62
35
40
Bushel3
dry wt.
(Ibs/Bu)
56
56
60
60
Straw/Grain
Ratio
1.0
1.0
1.5
1.7
Units
Conversion
Factor
1.1214 * 10'4
1.1214 * 10'4
1.1214 * 10'4
1.1214 * 10'4
WFMAX
1.38
0.78
0.59
0.72
10-year average
150
-------
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151
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Step 2. From Table 5-10 find the land use and treatment or practice
that is to be simulated (e.g., row crops, straight row).
Step 3. From Table 5-10 find the hydrologic condition of the soil
that is to be simulated (e.g., good).
Step 4. From Table 5-10 find the curve number for antecedent
moisture condition II for the site selected. Example:
Hydrologic group = A, treatment practice is straight row,
land use is row crops, hydrologic condition is good. The
curve number for the cropping season is 67.
Step 5. Follow the same procedure for the fallow portion of the
growing season using only the hydrologic soil group.
Example: Hydrologic soil group A, land use fallow, curve
number for condition II is 77.
Step 6. The post-harvest or residue portions of the year requires
numbers that reflect the extent of surface cover after
harvest. This can be quite variable and in many, cases may
require considerable judgement. Under "average" conditions
a value set to the mean of the fallow and growing period
numbers (from steps 4, 5) is appropriate. In the example
case, this number will be the mean of 77 and 67, or 72.
Step 7. The curve number input sequence is now written as
77 67 72
Additional guidance for management practices
Pesticides are being increasingly used in conjunction with conservation
practices to reduce erosion and runoff. Most notable among these practices
is the use of conservation tillage. The idea is to increase the soil surface
residue and hence reduce erosion and runoff by increasing infiltration. The
curve numbers developed in steps 1-7 assume conventional practices and must
be further modified to reflect the changes in management. Both the fall and
growing season numbers must be modified. For purposes of this example,
assume the corn is produced by using chisel plows rather than the
conventional tillage assumed above. The following steps now apply.
Step 8. From Table 5-11 find the straw/grain ratio for corn, which
is 1.0.
Step 9. From Table 5-11 find the bushel weight of corn, which is 56.
Step 10. From Table 5-9 find bushel/acre yield of corn, which is 110.
Step 11. Multiply straw/grain ratio * bushel weight * bushel
weight/acre = crop residue produced by the crop. For corn,
1.0 * 56 * 110 = 6160.
152
-------
Step 12. From Table 5-12 find the tillage practice desired for the
crop use site (e.g. chisel plow).
Step 13. Multiply the crop residue determined in step 11 by the
tillage factor from step 12 to determine residue remaining,
i.e. 6160 * 0.65 = 4004.
Step 14. From Table 5-13 find the reduction in curve number for
AMC II, crop curve number produced from residue remaining
after harvest determined in step 12. For corn at
4000 pounds per acre, a 10% reduction in curve number is
produced.
Step 15. Determine the curve number for antecedent moisture condition
(AMC) II. From Steps 1-5, AMC II was 67. 67 * 0.10 -
6.7, which is rounded to 7.0. The modified curve numbers
are 67 - 7 = 60 and 77 - 7 - 70.
Step 16. The post-harvest curve number must also now be reduced by
averaging the fallow and growing season numbers, that is, 70
and 60 to yield 65.
5.2.2 Crop Parameters
COVMAX--Maximum Areal Crop Coverage--
PRZM estimates the ground cover as the crop grows to some maximum value,
COVMAX, by linear interpolation between emergence and maturity dates. The
maximum areal coverage (COVMAX) determines the fraction of ground covered by
the crop and thus influences the mass of pesticide that reaches the ground
from an application event. For most crops, the maximum coverage will be on
the order of 80 to 100 percent.
WFMAX--Maximum Foliar Dry Weight--
If the user chooses to have the model estimate the distribution between
plants and the soil by an exponential function when a pesticide is applied,
then WFMAX must be specified. The maximum foliar dry weight, WFMAX, of the
plant above ground (kg m ) is the exponent used in the exponential foliar
pesticide application model. Estimates of WFMAX for several major crops were
given in Table 5-14. Estimates for other crops will require yield
information that is available from the USDA crop reporting service, WFMAX is
computed by finding the product of columns 2, 3, and 5, and by multiplying
this number by the straw/grain ratio (col. 4) plus 1.0. The straw/grain
ratio defines the amount of straw associated with the final grain product.
Both the straw and grain should be accounted for to determine the maximum
weight. Thus, the straw-to-grain ratio should have 1.0 added to it when used
to compute WFMAX. An example is provided for barley.
Step 1. Yield, bushel dry wt., and straw/grain ratio for barley are
42.0, 48.0, and 1.5, respectively.
Step 2. WFMAX = Bu/Ac * Lbs/Bu * (straw/grain ratio + 1.) *
conversion factor to yield (kg m" ) for PRZM input.
153
-------
Step 3. Conversion factor - 2.47 Ac * 1 ha * 0.454 kg - 1.1214*10"4
ha lOSn2 Lbs
Step 4. WFMAX = 42.0 * 48.0 * (1.5 + 1.0) * 1.1214 x 10'4, which
equals 0.56.
EMD, EMM, IYREM, MAD, MAM, IYRMAT, HAD, HAM, IYRHAR--Cropping Information for
Emergence, Maturity, and Harvest--
Generalized cropping information including date of emergence (EMD, EMM,
IYREM), maturity (MAD, MAM, IYRMAT), and harvest (HAD, HAM, IYRHAR) for eight
major crops including corn, soybeans, wheat, tobacco, grain sorghum,
potatoes, and peanuts were provided in Table 5-9. Simulations involving
minor crops such as mint, or where site specific information alters the
general practices provided, may require consultation with USDA Handbook No.
283 (Usual Planting and Harvesting Dates) or the local Cooperative Extension
Service.
HTMAX--Maximum Canopy Height--
HTMAX is the maximum height of the crop canopy at maturation, and is used in
the calculation of the pesticide flux through the plant compartment. Users
should have site-specific information for HTMAX since it varies with crop
type and species, climate, and environmental conditions. Specific ranges of
HTMAX for different crops are listed below:
CROP HEIGHT (cm) REF.
Barley 20 - 100 A
Grain Sorghum 90 - 110 B
Alfalfa 10-50 A
Corn 80 - 300 A
Potatoes 30-60 A
Soybeans 90 - 110 B
Sugarcane 100 - 400 A
References
A. Szeicy et al. (1969)
B. Smith et al. (1978)
5.2.3 Pesticide Parameters
Pesticides can be applied directly to the soil surface, the plant canopy, or
to both. Two modeling problems arise when one considers this. First, the
initial distribution of the applied pesticide between plant foliage and the
soil surface must be estimated. Second, the remaining foliar deposited
pesticides then become available for degradation (photolysis) or removal
(volatilization, washoff). Recall that two options are available for
distributing the applied pesticide (the FAM parameter).
TAPP--Total Pesticide Application--
The total pesticide application per event is entered in terms of kg-active
ingredient (a.i.) ha" . Typical application rates are included on the
product's registration label. According to Smith (personal communication,
1988) the coefficient of variation for granular application is 40 to 70% and
about 25% for spray application.
154
-------
DEPI--Depth of Incorporation--
This variable is only needed if soil application of chemical is specified
(i.e., FAM = 1). Typical incorporation depths are 5-10 cm. If soil
injection is being simulated, user should be aware that injection below 15 to
20 cm is difficult to achieve and represents an approximate upper limit of
incorporation depth (Matthews 1979). Representative values for several soil
application methods are given in Table 5-15.
Table 5-15. PESTICIDE SOIL APPLICATION METHODS AND DISTRIBUTION
Method of
Application
Common Procedure
Distribution
DEPI
Broadcast
Spread as dry granules
or spray over the whole
surface
Remains on the
soil surface
0.0
Disked-in
Chisel-plowed
Surface banded
Disking after broadcast
application
Chisel plowing after
broadcast
Spread as dry granules
or a spray over a fraction
of the row
Assume uniform 10.0
distribution to
tillage depth
(10 cm)
Assume linear 15.0
distribution to
tillage depth
(15 cm)
Remains on soil
surface
0.0
Banded incorporated
Spread as dry granules
or a spray over a fraction
of the row and incorporated
in planting operation
Assume uniform
distribution to
depth of incor-
poration (5 cm)
5.0
FILTRA--Initial Foliage to Soil Distribution--
The filtration parameter (FILTRA) relates to the equation for partitioning
the applied pesticide between the foliage and ground (this applies when FAM -
3). Lassey (1982) suggests values in the range of 2.3 -3.3 m^ kg"1. Miller
(1979) suggested a value of 2.8 m kg for pasture grasses. Most of the
variation appears to be due to the vegetation and not the aerosol.
155
-------
FEXTRC--Foliar Washoff Extraction Coefficient--
Washoff from plant surfaces is modeled using a relationship among rainfall,
foliar mass of pesticide, and an extraction coefficient. The parameter
(FEXTRC) is the required input parameter to estimate the flux of pesticide
washoff. Exact values are varied and depend upon the crop, pesticide
properties, and application method. Smith and Carsel (1984) suggest 0.10 is
suitable for most pesticides.
1PSCND--Foliage Pesticide Condition Factor--
IPSCND is required only when FAM = 2 or 3. It is a dimensionless number
which indicates the disposition of the pesticide remaining on the foliage at
and following harvest. IPSEND = 1 indicates that the remaining pesticide on
foliage is converted to surface application to the top soil layer; IPSCND = 2
indicates that the remaining foliage pesticide is completely removed with the
harvest; and IPSCND - 3 indicates that the remaining foliage pesticide is
retained on the surface residue and continues to undergo the same decay and
volatilization processes as before.
PLVKRT--Foliage Pesticide First-Order Volatilization Rate--
Pesticide volatilization from plant leaf surfaces is represented as a first-
order process controlled by the user-specific rate constant PLVKRT. For
organophosphate insecticides, Stamper et al. (1979) have shown that the
disappearance rate from leaf surfaces can be estimated by a first-order
kinetic approach. Similar observations for first-order kinetics were found
for volatilization of 2,4-D iso-octyl ester from leaf surfaces by Grover
et al. (1985). Volatilization losses of toxaphene and DDT from cotton plants
decreased exponentially with time and were linearly related to the pesticide
load on these plants (Willis et al. 1983).
Table 5-16 shows disappearance rates for selected pesticides on plant
foliage; these rates are also applicable to estimation of PLVKRT since the
overall disappearance rate (PLDKRT) includes loss associated with
volatilization. The user must be consistent in specifying these two rates:
if PLDKRT includes volatilization processes then PLVKRT should be zero, and
if PLVKRT is non-zero then PLDKRT should include all attenuation processes
except volatilization. Also, the pesticide volatilization flux controlled by
PLVKRT contributes to the calculated pesticide vapor concentration in the
atmosphere of the plant canopy, whereas the decay flux controlled by PLDKRT
does not.
Recent information (Willis and McDowell 1987) is available for estimating
degradation rates of pesticides on plant foliage. In the work cited above,
observed half-lives (days) were grouped by chemical family. These were:
• Organochlorine 5.0 ± 4.6
• Organophosphorus 3.0 ± 2.7
• Carbamate 2.4 ± 2.0
• Pyrethroid 5.3 ± 3.6
These mean half-lives correspond to degradation rates of 0.14, 0.23, 0.29 and
0.13 day"-*-, respectively. These are in reasonable agreement with the values
156
-------
Table 5-16. DEGRADATION RATE CONSTANTS OF SELECTED PESTICIDES ON FOLIAGE3
Class
Group
Decay Rate (days )
Organochlorine
Organophosphate
Fast
(aldrin, dieldrin, ethylan,
heptachlor, lindane,
methoxychlor).
Slow
(chlordane, DDT, endrin,
toxaphene).
Fast
(acepate, chlorphyrifos-methyl,
cyanophenphos, diazinon, depterex,
ethion, fenitrothion, leptophos,
malathion, methidathion, methyl
parathion, phorate, phosdrin,
phosphamidon, quinalphos, alithion,
tokuthion, triazophos, trithion).
Slow
(azinphosmethyl, demeton, dimethoate,
EPN, phosalone).
0.2310 - 0.1386
0.1195 - 0.0510
0.2772 - 0.3013
0.1925 - 0.0541
Carbamate
Pyrethroid
Pyridine
Benzole acid
Fast
(carbofuran)
Slow
(carbaryl)
(permethrin)
(pichloram)
(dicamba)
0.630
0.1260 - 0.0855
0.0196
0.0866
0.0745
a Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff,
and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
157
-------
in Table 5-16 with the exception of the pyrethroids, which appear to have a
faster breakdown than Table 5-16 suggests.
If the user has monitoring data which shows the degradation of plant foliar
concentrations with time, then the coefficient can be calibrated to cause the
simulated concentrations to mimic the observed data. The exponential decay
model is given by:
C - CQ e-(FKXIRC)t (5.1}
where
C — simulated concentration
CQ - initial concentration
FEXTRO first order decay rate (day"1)
t - time elapsed since application (days)
In linear form the equation is
In (C/C0) - -(FEXTRC)t (5-2)
Therefore, the coefficient FEXTRC is the slope of the plot of the natural log
of the normalized concentrations (C/CQ) vs. time in days. Obviously, loss
rates calculated in this way include volatilization losses and the above
caveats apply.
DAIR--Vapor Phase Diffusion Coefficient--
The diffusion coefficient is defined by Pick's first law, as the
proportionality between the chemical flux and the spatial gradient in its
concentration (Nye 1979) (see Volume I, Section 2.3.4). DAIR for a chemical
depends on both its own characteristics and those of the medium through which
the chemical is diffusing.
In soil, vapor phase diffusion occurs in the soil air space. Each chemical
will in general have its own characteristic diffusion coefficient depending
on its molecular weight, molecular volume, and shape (Streile 1984).
However, Jury et al. (1983) have concluded that the diffusion coefficient
will not show significant variations for different pesticides at a given
temperature; they recommend using a constant value of 0.43 m day" for all
pesticides.
Thibodeaux and Scott (1985) calculated values in the range of 0.39 to 0.78 m2
day" for 12 benchmark chemicals exhibiting a broad spectrum of
characteristics. Included in this list was chlorpyrifos and DDT, with
calculated vapor diffusion coefficients of 0.39 and 0.40 m2 day"1,
respectively. Consequently, we concur with Jury's recommendation to use 0.43
158
-------
m day" for all pesticides, unless other chemical-specific data are
available to justify a different value. Note that the DAIR parameter is
entered in cur day . The user should take care to convert the above
recommended values.
Although the magnitude of DAIR will likely increase with soil temperature,
very little information is available to quantify this dependence.
Accordingly, no temperature adjustment for DAIR is performed in the
volatilization model.
HENRYK--Henry's Constant--
Henry's constant is a ratio of a chemical's vapor pressure to its solubility.
It represents the equilibrium between the vapor and solution phases, and can
be calculated as follows:
H - P/S (5-3)
where
o -I
H - Henry's constant (atm-mj mole"-1-)
P - partial pressure of pesticide (atm)
o
S - aqueous solubility of pesticide (mole m" )
It is quite common in environmental modeling applications to express Henry's
constant on a dimensionless basis, which implies a variant form of
relationship, as follows:
CV
KH - — (5-4)
where
- dimensionless Henry's constant
saturated vapor density of pes
aqueous solubility of pesticide (mg 1 )
— saturated vapor density of pesticide (mg 1 )
o i
Henry's constant can be converted from atm-nr mole"-1- to a dimensionless
number by multiplying by 44.64 (Schnoor et al. 1987). Specific values of
HENRYK for selected pesticides can be found in Table 5-17. Many of these
values are calculated from Equation (2) by estimating the saturated vapor
density and aqueous solubility of the pesticide. Both Jury et al. (1984) and
Schnoor et al. (1987) have tabulated values of HENRYK for other chemicals.
159
-------
Table 5-17. ESTIMATED VALUES OF HENRY'S CONSTANT FOR SELECTED PESTICIDES
Compound
Alachlor
Aldrin
Anthracene
Atrazine
Bentazon
Bromacil
Butylate
Carbaryl
Carbofuran
Chlorpyrifos
Chrysene
Cyanazine
DDT
Diazinon
Dicamba
Dieldrin
Diuron
Endrin
EPTC
Ethoprophos
Fenitrothion
Fonofos
Heptachlor
Lindane
Linuron
Mai a th ion
Me thorny 1
Methyl Parathion
Metolachlor
Metribuzin
Monuron
Napropamide
Parathion
Permethrin
Picloram
Prometryne
Simazine
Terbufos
Toxaphene
Triallate
Trichlorfon
Trifluralin
2,4-D (acid)
2,4,5-T (acid)
Henry's Constant (dimensionless)
1.3E-06
6.3E-04
4.4E-05
2.5E-07
2.0E-10
3.7E-08
3.3E-03
1.1E-05
1.4E-07
1.2E-03
4.7E-05
1.2E-10
2.0E-03
5.0E-05
3.3E-08
6.7E-04
5.4E-08
1.8E-05
5.9E-04
6.0E-06
6.0E-06
2.1E-04
1.7E-02
1.3E-04
2.7E-06
2.4E-06
4.3E-08
4.4E-06
3.8E-07
9.8E-08
7.6E-09
7.9E-07
6.1E-06
6.2E-05
1.9E-08
5.6E-07
1.3E-08
1.1E-03
2 . 3E+00
7.9E-04
1.5E-09
6.7E-03
5.6E-09
7.2E-09
References
A
D
D
A
A
C
A
A
A
A
D
A
C
C
A
C
C
D
C
C
B
A
D
B
A
B
A
A
A
A
C
C
C
A
B
C
A
A
A
C
B
A
A
B
References: A - Donigian et al. (1986); B
C - Jury et al. (1984); D
Spencer et al. (1984)
Schnoor et al. (1987)
160
-------
ENPY--Enthalpy of Vaporization--
This parameter is used in the temperature correction equation for Henry's
constant (as described in Volume I, Section 2.3.4). In our limited
literature search, we could find only two pesticides with ENPY values
reported: 18.488 Kcal mole" for lindane and 20.640 Kcal mole" for
napropamide (Streile 1984). Chemical-specific values are needed for ENPY,
however, it appears that a value of 20 Kcal mole is a reasonable first
guess.
KD--Pesticide Soil-Water Distribution Coefficient--
The user can enter directly the distribution coefficient(s) or the model will
calculate a value given other pesticide properties. If the parameter KDFLAG
is set to a value of 0, then direct data input is made as the parameter KD.
If KDFLAG is set to 1, however, the following additional information is
required.
PCMC, SOL--Options for Use in Estimating Distribution Coefficients from
Related Input Data--
The fate of pesticides in soil and water is highly dependent on the sorptive
characteristics of the compound. Sorptive characteristics affect the
physical movement of pesticides significantly. The sorptive properties of
pesticides generally correlate well with the organic carbon content of soils.
The carbon content of most soils decreases with depth.
PRZM allows the user to estimate an organic carbon partition coefficient for
the pesticide from one of three models based on water solubility. The K is
subsequently multiplied by organic carbon to obtain the partition
coefficient. The three models are:
PCMC1 Log KQC - (-0.54 * Log SOL) + 0.44 (5-5)
KQC = organic carbon distribution coefficient
where SOL = water solubility, mole fraction
PCMC2 Log KQC = 3.64 - (0.55 * Log SOL) (5-6)
where SOL = water solubility, mg 1
PCMC3 Log KQC = 4.40 - (0.557 * Log SOL) (5-7)
where SOL = water solubility, micromoles 1
These models are selected by setting PCMC to values of 1, 2, or 3,
respectively. These methods were selected because of referenced
documentation and provisions for direct use with the most commonly reported
physical pesticide parameter, water solubility. The three models used in
PRZM for estimating partitioning between soil and water are limited to
specific types of pesticides. These equations are best used for pesticides
having melting points below 120 °C. Solubilities above these temperatures
are affected by crystalline energy and other such physical properties. The
three models are not appropriate for pesticides whose solubilities are
161
-------
affected by crystalline energy or other physical properties, and would have a
tendency to overestimate the partitioning between soil and water. Of the
three models, the first model is for true equilibrium of completely dispersed
particles of soil/water concentrations less than 10.0 g 1 . The second and
third models are for soil/water concentrations greater than 10.0 g 1 and
for short equilibrium periods of 48 hours or less. For most applications,
the first model would be the most appropriate.
Some pesticides having properties amenable for use with the water solubility
models are provided in Table 5-18. The pesticide solubility, SOL, must also
be input. Units must be consistent with the model chosen. Table 5-18 also
provides pertinent values for the selected pesticides.
Alternative methods are available to calculate KQC. A useful relationship
exists between the octanol-water distribution coefficient and the organic
carbon distribution coefficient. This relationship can be used when measured
soil distribution coefficients are not available, or the pesticides posses
crystalline energy properties that would preclude the use of any water
solubility models.
The octanol-water distribution coefficient can be used for calculating
distribution coefficients for pesticides that possess monomer-associated
properties for solubility in water. Karickhoff et al. (1979) proposed a
relationship between KQW and KQC given by
log Koc = 1.00 (log Kow) - 0.21 (5-8)
where
K = octanol-water distribution coefficient (cm g )
-1,
K = organic carbon distribution coefficient (cm g )
Carbofuran is a pesticide that exhibits crystalline energy relationships and
its apparent distribution coefficient should be estimated using its log KQW,
which is 2.44. Substituting into equation (5-8)
log KQC = 1.00 (2.44) - 0.21 - 2.23
KQC = 102-23 = 169.8
For a soil with 0.5% organic carbon the K^ of the pesticide is
(percent organic carbon) ,,.
Kd = Koc - 100 - (
169.8 (0.5)
Kd -- 100 -- °'85
162
-------
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165
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This compares to an estimated K^ of 2.68 using the PCMC1 water solubility
model and the same organic carbon content. Selected pesticides having
properties suitable use with the octanol water distribution model by
Karickhoff are provided in Table 5-19.
DWRATE, DSRATE--Degradation Rate Constants--
The processes that contribute to pesticide disappearance in soils are varied
and depend on environmental factors as well as chemical properties.
Unfortunately, only rarely are process-specific rate constants (e.g.,
hydrolysis) reported for the soil environment. In most cases, a lumped
first-order rate constant is assumed. This is the model used in PRZM, with
the additional enhancement of allowing the user to specify different decay
rates for the solution (DWRATE) and sorbed (DSRATE) phases. Although such an
approximation is imprecise, most modeling efforts follow a similar approach
and many pesticides appear to behave in this manner. For example, Nash
(1980) found that disappearance of many compounds was highly correlated to a
first order approximation with R^ > 0.80. More recently, Rao et al. (1984)
reported that pesticide disappearance rate constants in surface horizons of
soils (root zone) are reasonably constant across soils. This is encouraging
from a modeling standpoint because of the decrease in sensitivity testing
required for dissipation rates.
The dissipation rate of pesticides below the root zone, however, is virtually
unknown. Several studies have suggested the rate of dissipation decreases
with depth; however, no uniform correction factor was suggested between
surface/subsurface rates. First order dissipation rates for selected
pesticides in the root zone were tabulated in Tables 5-18 and 5-19.
For most cases, the same values should be used for both DWRATE and DSRATE,
and for all depths, unless specific investigations indicate otherwise. For
example, Macalady and Wolfe (1984) have concluded that the relative abiotic
hydrolysis rates for the solution and sorbed phases for several pesticides
depend on the specific hydrolytic pathway. For neutral hydrolysis, the rates
are similar in both phases; whereas for alkaline hydrolysis the sorbed phase
rate is retarded (i.e., less than), and for acid hydrolysis it is accelerated
(i.e., greater than) compared to the solution phase rate. Although it is not
currently possible to predict the magnitude of the change for all pesticides
(Wolfe 1988), specific laboratory studies for organophosphate insecticides
showed a decrease in alkaline hydrolysis rates by a factor of 10 for the
sorbed phase compared to the solution phase (Macalady and Wolfe 1985).
With regard to changes in the decay rate with depth, studies by Lavy et al.
(1973) and Wehtje et al. (1984) for atrazine showed a 0.5 and 0.3 reduction,
respectively, between surface and subsurface attenuation rates; work by Smelt
et al. (1978) on aldicarb showed similar reductions. Studies of this type,
and those noted above on hydrolysis rates, can be used to justify different
decay rates for DWRATE and DSRATE, and for different soil depths, if the
experimental conditions are similar or consistent with the site being
simulated.
166
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Table 5-19. OCTANOL WATER DISTRIBUTION COEFFICIENTS (log K ) AND SOIL
DEGRADATION RATE CONSTANTS FOR SELECTED CHEMICALS
Chemical Name
Alachlor
Aldicarb
Altosid
Atrazine
Benomyl
Bifenox
Bromacil
Cap tan
Carbaryl
Carbofuran
Chloramben
Chlordane
Chloroacetic Acid
Chloropropham
Chloropyrifos
Cyanazine
Dalapon
Dialifor
Diazinon
Dicamba
Dichlobenil
Dichlorofenthion
2,4, -Dichlorophenoxy-
acetic Acid
Dichloropropene
Dicofol
Dinoseb
Diuron
Endrin
Fenitrothion
Fluometuron
Linuron
Malathion
Me thorny 1
Methoxychlor
Methyl Parathion
Monolinuron
Monuron
MSMA
Nitrofen
Parathion
Permethrin
Phorate
Degradation Rate
Log KQW Constant (days' )
2.78
0.70
2.25
2.45
2.42
2.24
2.02
2.35
2.56
2.44
1.11
4.47
-0.39
3.06
4.97
2.24
0.76
4.69
3.02
0.48
2.90
5.14
2.81
1.73
3.54
2.30
2.81
3.21
3.36
1.34
2.19
2.89
0.69
5.08
3.32
1.60
2.12
-3.10
3.10
3.81
2.88
2.92
0.0384
0.0322
0.0149
0.1486
0.1420
0.1196
0.0768
0.0020
0.0058
0.0495
0.0462
0.0330
0 . 2140
0.0116
0.0693
0.0462
0.0035
0.1155
0.0231
0.0280
02.91 -
0.0046
0.2207
0.0046
0.2961
0.0396
0.0363
- 0.0116
- 0.0063
- 0.0023
- 0.0768
- 0.0079
- 0.0007
- 0.00267
- 0.0231
- 0.0067
- 0.0197
- 0.0039
- 0.0231
- 0.0231
- 0.0014
- 0.0578
- 0.0039
0.4152
- 0.0033
- 0.0020
- 0.0046
- 0.0040
Reference
A
A
A
A
A
A
A
D
C
D
A
A
D
D
D
A
C
A
A
A
A
D
A
E
A
167
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Table 5-19. OCTANOL WATER DISTRIBUTION COEFFICIENTS (log KQW) AND SOIL
DEGRADATION RATE CONSTANTS FOR SELECTED CHEMICALS (concluded)
Chemical Name
Log K
ow
Degradation Rate
Constant (days' ) Reference
Phosalone
Phosmet
Picloram
Propachlor
Propanil
Propazine
Propoxur
Ronnel
S imaz ine
Terbacil
Terbufos
Toxaphene
Trifluralin
Zineb
4.30
2.83
0.30
1.61
2.03
2.94
1.45
4.88
1.94
1.89
2.22
3.27
4.75
1.78
0.0354
0.0231
0.693
0.0035
0.0539
0.0046
0.0956
0.0512
- 0.0019
- 0.0139
- 0.231
- 0.0017
- 0074
- 0.0026
A
D
D
D
A
E
A
A
A Nash, R. G. 1980. Dissipation Rate of Pesticides from Soils. Chapter 17.
IN CREAMS: A Field Scale Model for Chemicals, Runoff, and Erosion
from Agricultural Management Systems. W. G. Knisel, ed. USDA
Conservation Research Report No. 26. 643pp.
B Smith, C. N.
Partition Coefficients (Log K )
Athens Environmental Research Laboratory, Athens, GA.
report, 1981.
for Selected Chemicals.
Unpublished
p
Herbicide Handbook of the Weed Science Society of America, 4th ed. 1979.
Control of Water Pollution from Cropland, Vol. I, a manual for guideline
development, EPA-600/2-75-026a.
E Smith, C. N. and R. F. Carsel. Foliar Washoff of Pesticides (FWOP) Model:
Development and Evaluation. Accepted for publishing in Journal of
Environmental Science and Health - Part B. Pesticides, Food
Contaminants, and Agricultural Wastes, B 19(3), 1984.
168
-------
DGRATE--First Order Decay Rate for Vapor-Phase Pesticide--
Pesticide degradation in soils is a kinetic phenomenon. Pesticides are
degraded by different mechanisms, and at different rates, depending upon
whether they are in vapor, liquid or adsorbed phase (Streile 1984). A lumped
first-order rate constant is assumed for DGRATE. In general, a zero value of
DGRATE is recommended, unless chemical-specific data is available to justify
a non-zero value. For example, if the user is calibrating for a highly
volatile and/or photo-sensitive chemical, vapor phase attenuation processes
in the upper 1-2 mm of the soil surface may be very important. Field studies
have shown that photo chemical loss of organic chemicals may be rapid and
substantial immediately following application to the land surface, especially
in the case of hydrophobic or cationic organics that sorb to soil particles
(Miller et al. 1987).
UPTKF--Plant Uptake of Pesticides--
The plant uptake efficiency factor, or root reflection coefficient (UPTKF)
provides for removal of pesticides by plants and is a function of the crop
root distribution and the interaction of soil, water, and the pesticide.
Several approaches to modeling the uptake of nutrients/ pesticides have been
proposed ranging from process models that treat the root system as a
distribution sink of known density or strength to empirical approaches that
assume a relationship to the transpiration rate. Dejonckheere et al. (1983)
reported the mass of uptake into sugar beets for the pesticides aldicarb and
thiofanox for three soils (sandy loam, silt loam, and sandy clay loam). Mass
removal expressed as a percentage of applied material for aldicarb on sandy
loam, silt loam, and clay loam ranged from 0.46-7.14%, 0.68-2.32%, and 0.15-
0.74%, respectively. For thiofanox, 2.78-20.22%, 0.81-8.70%, and 0.24-2.42%
removals were reported for the respective soils. The amount of uptake was
higher for sandy soils and increased with available water. Other reviews
have suggested ranges from 4-20% for removal by plants.
The procedure adopted for PRZM estimates the removal of pesticides by plant
uptake based on the assumption that uptake of the pesticide is directly
related to the transpiration rate. Sensitivity tests conducted with PRZM
indicate an increase in the uptake by plants as the root zone depth
increases, and as the partition coefficient decreases. For highly soluble
pesticides and for crop root zones less than 120 cm, the model simulates
total uptake within the range reported by Dejonckheere, et al. For highly
soluble pesticides and for crop root zones of greater than 120 cm, values of
greater than 20% were simulated. For initial estimates a value of 1.0 for
UPTKF is recommended. If more than 20-25% of the pesticide is simulated (to
be removed by plant uptake), UPTKF should be calibrated to a value less than
1.0.
CORED--Thickness of Soil Column--
The user will want to enter a typical value for the crop root zone unless
PRZM is not used in conjunction with VADOFT. For use with SAFTMOD, the PRZM
core depth would be extended to the bottom of the first aquifer.
DISP--Dispersion--
The dispersion or "smearing out" of the pesticide as it moves down in the
soil profile is attributed to a combination of molecular diffusion and
169
-------
hydrodynamic dispersion. The transport equations solved in PRZM also produce
truncation error leading to a purely mathematical or numerical dispersion.
The terms dropped from the Taylor's series expansion from which the finite
difference equations were formulated lead to errors that appear identical to
the intentional expressions for hydrodynamic dispersion. For these reasons
the DISP parameter must be evaluated in light of both "real" and "numerical"
components.
Molecular diffusion, Dm, in soils will be lower than free-water diffusion and
has been estimated by Bresler (1973)
Dm = Dw aeb(? (5-10)
where
? 1
DW= molecular diffusion in free water, cm day"
a= soil constants having a range of 0.001 to 0.005
b= soil constant having an approximate value of 10
o o
6= volumetric water content, cm cm
The free-water diffusion coefficient, DW, can be estimated from procedures
outlined by Lyman et al, (1982). In any case, values are quite low,
typically less than 10 cm day , and can usually be ignored.
Hydrodynamic dispersion is more difficult to estimate because of its site-
soil specificity and its apparent strong dependence upon water velocity.
Most investigators have established an effective diffusion or dispersion
coefficient that combines both molecular and hydrodynamic terms. This
combined expression can then be related to system variables by developing
expressions from field measurements. Most notable among these expressions is
D = 0.6 + 2.93 v1-11 (5-11)
where
f\ -i
D= effective dispersion coefficient, cm day"
v= pore water velocity, cm day"
by Biggar and Nielsen (1976). Note in Equation (5-11) D is a time-and depth-
varying function since v is both time and depth-varying. The problem remains
to estimate the assumed constant value for DISP, the effective dispersion
coefficient.
As previously noted, the backward difference numerical scheme in the PRZM
code for solution of the transport equation produces numerical dispersion.
This dispersion is also related to the magnitude of the velocity term. Other
170
-------
variables that influence the truncation error include the time and space
steps. Because this dispersion is a function of velocity, it is not possible
to illustrate the entire range for all anticipated modeling problems. A
sensitivity analysis was performed, however, to examine the influence of the
spatial step, Ax. Results are given in Figure 5.5. For these runs the DISP
parameter was set to 0.0.
The influence of the DISP parameter superimposed on the numerical dispersion
created by the model at a Ax value of 5.0 cm is shown in Figure 5.6.
Clearly, even when moderate values for DISP are used, substantial dispersion
is produced. If equation 5-11 is used along with typical simulated values
for velocity (0.1 - 22 cm day ), then calculated DISP values range from 0.83
- 91 cm day . It is clear that if this procedure is used, the desired
dispersion will be substantially higher than it should be because of the
model's numerical dispersion.
A number of modeling studies were performed to investigate the impact of
model parameters other than DISP on the apparent dispersion. From these
rather exhaustive studies, the following guidance is offered:
(1) A spatial step or compartment size of 5.0 cm will mimic observed
field effective dispersion quite well and should be used as an
initial value.
(2) No fewer than 30 compartments should be used in order to minimize
mass balance errors created by numerical dispersion.
(3) The DISP parameter should be set to 0.0 unless field data are
available for calibration.
(4) If DISP calibration is attempted, the compartment size should be
reduced to 1.0 cm to minimize the numerical dispersion.
(5) Equation 5-11 can be used to bound the values only should the
need arise to increase dispersion beyond that produced by the
numerical scheme.
If the user chooses the MOC algorithm to simulate advection transport, then
numerical dispersion will be eliminated and a typical value for field-
observed dispersion should be entered. Use of the MOC algorithm will result
in increased model execution time.
DPN--Layer Depth in Each Horizon--
The DPN parameter allows the user to specify a different layer depth (i.e.,
compartment thickness) for each soil horizon. In general, a smaller DPN will
generate more accurate results and provide greater spatial resolution, but
will also consume more CPU time. From the volatilization viewpoint, a finer
DPN in the top horizon is required for better estimation of the
volatilization flux from the soil surface. In addition, since pesticide
runoff is calculated from the surface layer, a smaller layer depth allows a
better representation of surface-applied chemicals. For the surface horizon,
171
-------
Pesticide concentration in total soil
(10~7xgcm~3)
Depth
(cm)
I I I I I I I I I
100-
AX= 1
D = 0.0
150
Figure 5.5.
Numerical dispersion associated with
space step (Ax) .
172
-------
0.0
50-
100-
Depth
(cm)
150H
200-
Pesticide concentration in total soil
(1CT7xg-cm-3)
1.0
D = 0.0
AX = 5
250 -I
Figure 5.6.
Physical dispersion (D) associated with advective
transport. (Note: Numerical dispersion included),
173
-------
DPN values in the range of 0.5 to 2.0 cm are recommended; a 1.0 cm value for
DPN is commonly used. Smaller values down to 0.1 cm can be used for highly
volatile compounds where volatilization is a major loss mechanism. For
subsurface soil horizons, DPN values in the range of 5.0 to 30.0 cm are
recommended depending on the spatial resolution needed at the lower depths.
APD, APM, IAPYR--Pesticide Application--
The use of PRZM requires the establishment of a pesticide application
procedure. The user should follow the two steps described below in
establishing representative application dates:
• establish an application period window covering the range of
possible application dates
• adjust the application dates within the window so that application
does not occur on a day immediately before, during, or immediately
after a rainfall event (pesticides are not normally applied to a
field with high moisture content or under conditions where the
efficacy would be diminished).
5.2.4 Soil Temperature
ALBEDO--Soil Surface Albedo --
To simulate soil temperatures, ALBEDO values at start of each month must be
specified. Soil surface albedo values depend on soil surface conditions
during the period of simulation. As the surface condition changes, the
albedo value changes accordingly. There is no simple procedure available
that would estimate the albedo values for different surface conditions. The
albedo values for some natural surface conditions are given in Table 5-20. A
detailed procedure to calculate the albedo of soil surface covered with mulch
and canopy is presented by Cruse et al. 1980.
ENMISS--Infrared Emissivity--
Most natural surfaces have an infrared emissivity lying between 0.9 and 0.99.
Values for all natural surfaces are not well known, but it is usually close
to unity. Specific values of emissivity for some natural surfaces are given
in Table 5-21.
ZWIND--Height of Wind Speed Measuring Instrument--
The wind speed measuring anemometer is usually fixed at 10 meters (30 feet)
above the ground surface. But this height may be different at some stations
such as at class A weather stations where the anemometer may be attached to
the evaporation pan. The correct value for ZWIND can be obtained from the
meteorological data reports for the station whose data is used in the
simulation.
BBT--Bottom Boundary Temperature--
In order to simulate soil temperature, BBT values at start of each month must
be specified. The BBT soil temperature for shallow core depths may vary
significantly with time throughout the year. For deep cores, the BBT will be
relatively constant. Average monthly soil temperature values at core depth
can be estimated from the climatological data reports published by NOAA,
174
-------
Table 5-20. ALBEDO FACTORS OF NATURAL SURFACES FOR SOLAR RADIATION
Surface
Fresh Dry Snow
Clean, Stable Snow Cover
Old and Dirty Snow Cover
Dry Salt Cover
Lime
White Sand, Lime
Quartz Sand
Granite
Dark Clay, Wet
Dark Clay, Dry
Sand, Wet
Sand, Dry
Sand, Yellow
Bare Fields
Wet Plowed Field
Newly Plowed Field
Grass, Green
Grass, Dried
Grass , High Dense
Prairie, Wet
Prairie, Dry
Stubble Fields
Grain Crops
Alfalfa, Lettuce, Beets, Potatoes
Coniferous Forest
Deciduous Forest
Forest with Melting Snow
Yellow Leaves (fall)
Desert, Dry Soils
Desert, Midday
Desert, Low Solar Altitude
Water (0 to 30° )a
Water (60°)a
Water (85° )a
Reflectivity
0.80-0.90
0.60-0.75
0.30-0.65
0.50
0.45
0.30-0.40
0.35
0.15
0.02-0.08
0.16
0.09
0.18
0.35
0.12-0.25
0.05-0.14
0.17
0.16-0.27
0.16-0.19
0.18-0.20
0.22
0.32
0.15-0.17
0.10-0.25
0.18-0.32
0.10-0.15
0.15-0.25
0.20-0.30
0.33-0.36
0.20-0.35
0.15
0.35
0.02
0.06
0.58
References
Van Wijk, W.R. 1963. Physics of Plant Environment, p. 87. North-Holland
Publishing Co., Amsterdam.
Brutsaert, W. 1982. Evaporation into the Atmopshere: Theory, History,
and Applications. D. Reidel Publishing Co., Dordrecht, Holland.
angle of solar incidence.
175
-------
Table 5-21. EMISSIVITY VALUES FOR NATURAL SURFACES AT NORMAL TEMPERATURES'
Surface
Emissivity
Sand (dry-wet)
Mineral Soil (dry-wet)
Peat (dry-wet)
Firs
Tree Vegetation
Grassy Vegetation
Leaves
Water
Snow (old)
Snow (fresh)
0.95-0.98
0.95-0.97
0.97-0.98
0.97
0.96-0.97
0.96-0.98
0.94-0.98
0.95
0.97
0.99
References
Van Wijk, W.R. 1963. Physics of Plant Environment, p. 87. North-Holland
Publishing Co., Amsterdam.
Brutsaert, W. 1982. Evaporation into the Atmosphere: Theory, History,
and Applications, D. Reidel Publishing Co., Dordrecht, Holland.
Department of Commerce. Depending on the core depth used in the simulation,
the average temperature of shallow groundwater, as shown in Figure 5.7, may
be used to estimate the BBT values.
THCOND and VHTCAP--Thermal Conductivity and Volumetric Heat Capacity of Soil
Horizon--
If the user chooses to have the model simulate the soil temperature profile
and sets the IDFLAG to zero, then the thermal conductivity and heat capacity
values for each soil horizon must be specified. Representative values for
some soil types are given in Table 5-22. Note that the value of THCOND is
entered in PRZM in units of cal cm"1 "C"1 day"1, therefore the values *"
Table 5-22 should be multiplied by 86,400.
in
If IDFLAG = 1, thermal conductivity and heat capacity are calculated from
soil composition (% sand, % clay, % OC), and the thermal properties of the
soil components and water, based on the method in de Vries (1963).
SPT--Initial Soil Temperature Profile--
To simulate the soil temperature profile, initial SPT values for each soil
horizon must be specified. Since PRZM is often used for long periods of
simulation, the initial temperature profile will not have any significant
effect on the predicted temperature profile after a few days or weeks of
simulation unless the core is deep. Lower horizons in the core should be
assigned values corresponding approximately to parameter BBT.
176
-------
S-l
0)
-p
oj
O
M
Cn
O
(0
^!
in
OJ
in
-P
03
0)
-t-l
0)
Dl
03
r^
LD
0)
3
•H
177
-------
5.2.5 Soils Parameters
The amount of available moisture in the soil is affected by such properties
as temperature and humidity, soil texture and structure, organic matter
content, and plant characteristics (rooting depth and stage of growth). The
moisture remaining in a soil after "gravity drainage" has ceased is known as
field capacity. The moisture content in a soil below which plants cannot
survive is called the wilting point. The wilting point, which varies among
specific soils is influenced by colloidal material and organic matter, but
most soils will have a similar wilting point for all common plants.
The PRZM model simulates soil water retention in the context of these bulk
soil properties. Drainage of "excess water" is simulated as a simple daily
value or as a daily rate. Most specific model parameters can be input
directly by the user and some can be internally estimated given certain
related soil properties as inputs.
THEFC, THEWP--Moisture Holding Capacity--
Field capacity (THEFC) and wilting point (THEWP) are required as user inputs.
Often these soil-water properties have been characterized and values can be
found from soils data bases. Where such data are not available, one of the
three estimation methods given below can be used. Method one requires the
textural properties (percent sand, silt, and clay), organic matter content
(%), and bulk density (g cm" ) of a specific soil. Method two utilizes a
soil texture matrix for estimating soil water content if only the sand (%)
and clay (%) contents are known. Method three provides mean field capacity
and wilting points if only the soil texture is known.
Method 1 (also done within the code if THFLAG = 1)
The regression equation from Rawls and Brakensiek (1982) is used to
estimate the matric water potential for various soils:
9X = a + [b * SAND(%)] + [c * CLAY(%)] + [d * ORGANIC MATTER(%)]
+ [e * BULK DENSITY (g cm'3)] (5-12)
where
3 3
9X = water retention cm cm for a given matric potential (field
capacity = -0.33 bar and wilting point - -15.0 bar)
a-e - regression coefficients
Step 1. From Table 5-23 find the matric potential for field capacity
and wilting point (-0.33 bar and -15.0 bar).
179
-------
Table 5-23.
COEFFICIENTS FOR LINEAR REGRESSION EQUATIONS FOR PREDICTION OF
SOIL WATER CONTENTS AT SPECIIC MATRIC POTENTIALS3
Matric
Coefficient
-0.20
-0.33
-0.60
-1.0
-2.0
-4.0
-7.0
-10.0
-15.0
Intercept
a
0.4180
0.3486
0.2819
0.2352
0.1837
0.1426
0.1155
0.1005
0.0854
Sand
b
-0.0021
-0.0018
-0.0014
-0.0012
-0.0009
-0.0007
-0.0005
-0.0004
-0.0004
Clay
c
0.0035
0.0039
0.0042
0.0043
0.0044
0.0045
0 . 0045
0 . 0044
0 . 0044
Organic
Matter
d
0.0232
0.0228
0.0216
0.0202
0.0181
0.0160
0.0143
0.0133
0.0122
Bulk
Density
(g cm'3)
e
-0.0859
-0.0738
-0.0612
-0.0517
-0.0407
-0.0315
-0.0253
-0.0218
-0.0182
R2
0.75
0.78
0.78
0.76
0.74
0.71
0.69
0.67
0.66
a Rawls, W. J., U.S. Department of Agriculture, Agricultural Research
Service, Beltsville, MD. Personal Communication.
Step 2. For each matric potential, find the regression coefficients (a-
e) that are required in the Rawls and Brakensiek equation
(e.g., for -0.33 potential, coefficients a-e are 0.3486, -
0.0018, 0.0039, 0.0228, and -0.0738).
Step 3,
For any given soil (example: Red Bay Sandy Loam where sand
(%), 72.90; clay (%), 13.1; organic matter (%), 0.824; and bulk
density (g cm~^), 1.70) solve the equation for the -0.33 and -
15.0 potential. For this example, THEFC = 0.170, THEWP -
0.090.
Method 2
Use Figure 5.8 for estimating the field capacity and Figure 5.9 for
estimating the wilting point of any soil, given the percent sand and clay.
*
Step 1. Example: Red Bay Sandy Loam (field capacity). Find the
percent sand across the bottom of Figure 5.8 (i.e., 73.0)
Step 2. Find the percent clay of the soil along the side of the
triangle (i.e., 13.0).
Step 3. Locate the point where the two values intersect on the triangle
and read the field capacity by interpolating between the
contour lines, THEFC - 0.17.
180
-------
100
0.55
0.50
0.45
0.5% Organic matter
0.0% Porosity change
0.40
0.35
0.30
0.25
0.20
0.15
0.10
100
Figure 5.8.
1/3-bar soil moisture by volume, (provided by
Dr. Walter J. Rawls, U.S. Department of
Agriculture,Agricultural Research Service,
Beltsville, Maryland) .
181
-------
100
0.40
0.35
0.5% Organic matter
0.0% Porosity change
0.30
25
10 20 30 40 50 60 70
0.20
0.15
0.10
80
0.05
90 100
Sand (%)
Figure 5.9. 15-bar soil moisture by volume, (provided by
Dr. Walter J. Rawls, U.S. Department of
Agriculture, Agricultural Research Service/
Beltsville, Maryland).
182
-------
Step 4. Follow Steps 2-4 for wilting point using Figure 5-9.
THEWP - 0.09.
Method 3
Step 1. Use Table 5-24 to locate the textural class of the soil of
choice.
Step 2. After locating the textural class, read the mean field capacity
and wilting point potentials (cm cm ), to the right of the
textural class. Example: Sandy loam. The mean field capacity
(THEFC) and wilting point (THEWP) potentials are 0.207 and
0.095, respectively.
Guidance for estimating distributional properties for THEFC and THEWP is
given in Tables 5-25 and 5-26. These tables show the arithmetic means and
coefficients of variation for Hydrologic Groups A, B, C and D soils with
depth. Also shown is the type of distribution which is most appropriate.
Jury (1985) indicates overall CV for wilting point water content (15 bar
tension) to be lower, at 24 percent. The values in the tables would tend to
be more appropriate for regional assessments, whereas a lower value, such as
that reported by Jury, would be more appropriate for single fields. He also
indicates that the most appropriate distribution for static soil properties
such as these is the normal.
Table 5-27 presents correlation coefficients between organic matter, field
capacity, and wilting point for different soil strata and hydrologic groups,
for use in Monte Carlo analyses.
Correlation coefficients between field capacity, and wilting point have
moderate to high values ranging from 0.64 to 0.85 (Carsel et al. 1988).
BD--Bulk Density and Field Saturation--
Soil bulk density (BD) is required in the basic chemical transport equations
of PRZM and is also used to estimate moisture saturation values. Values for
BD can be input directly. When such data are not available for the site of
interest, methods have been developed for their estimation. Two methods are
provided for estimating BD of various soils. Method one requires the
textural properties (percent sand, clay, and organic matter). Method two
uses mean bulk density values if only the soil texture is known. The
following steps provide procedures for estimating bulk density.
Method 1 (Also done within the code if BDFLAG - 1)
A procedure from Rawls (1983) is used to estimate bulk density for any
given soil, provided the percent sand, clay, and organic matter contents
are known. Example: Marlboro fine sandy loam--sand 80.0%, clay 5.0%, and
organic matter 0.871%. Using the following equation:
183
-------
Table 5-24. HYDROLOGIC PROPERTIES BY SOIL TEXTURE3
Texture
Class
Sand
Loamy Sand
Sandy Loam
Loam
Silt Loam
Sandy Clay
Loam
Clay Loam
Silty Clay
Loam
Sandy Clay
Silty Clay
Clay
Range of
Textural Properties
(Percent)
Sand Silt Clay
85-100 0-15 0-10
70-90 0-30 0-15
45-85 0-50 0-20
25-50 28-50 8-28
0-50 50-100 0-28
45-80 0-28 20-35
20-45 15-55 28-50
0-20 40-73 28-40
45-65 0-20 35-55
0-20 40-60 40-60
0-45 0-40 40-100
Water Retained at
-0.33 Bar Tension
3 -3
cnr cm
0.091b
(0.018 - 0.164)c
0.125
(0.060 - 0.190)
0.207
(0.126 - 0.288)
0.270
(0.195 - 0.345)
0.330
(0.258 - 0.402)
0.257
(0.186 - 0.324)
0.318
(0.250 - 0.386)
0.366
(0.304 - 0.428)
0.339
(0.245 - 0.433)
0.387
(0.332 - 0.442)
0.396
(0.326 - 0.466)
Water Retained at
-15.0 Bar Tension
3 -3
cm cm
0.033b
(0.007 - 0.059)c
0.055
(0.019 - 0.091)
0.095
(0.031 - 0.159)
0.117
(0.069 - 0.165)
0.133
(0.078 - 0.188)
0.148
(0.085 - 0.211)
0.197
(0.115 - 0.279)
0.208
(0.138 - 0.278)
0.239
(0.162 - 0.316)
0.250
(0.193 - 0.307)
0.272
(0.208 - 0.336)
a Rawls, W. J., D. L. Brakensiek, and K. E. Saxton. Estimation of Soil Water
Properties. Transactions ASAE Paper No. 81-2510, pp. 1316 - 1320.
1982.
Mean value.
c One standard deviation about the mean.
184
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Table 5-25. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR FIELD
CAPACITY (PERCENT BY VOLUME)
Oricinal
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size
52
50
42
39
456
454
435
373
371
362
336
290
230
208
178
146
Mean
11.8
9.6
7.3
7.1
19.5
18.8
18.7
17.5
22.4
22.8
22.7
22.2
24.1
26.1
25.0
24.1
Median
9.4
8.1
5.9
5.8
19.1
18.8
18.7
17.5
22.5
23.2
22.9
21.3
24.2
26.3
25.6
24.4
Data
s.d.
9.2
7.9
5.8
5.0
8.3
7.4
7.1
7.6
7.8
7.8
8.6
8.9
9.1
9.3
8.2
8.1
CV
Distribution Model
(%) Transform Mean
78
82
79
70
42
39
39
43
35
34
38
40
38
36
33
33
In
In
In
In
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
2.25
1.99
1.73
1.73
0.316
0.311
0.298
0.288
0.363
0.369
0.368
0.359
0.387
0.419
0.403
0.390
s.d.
0.65
0.73
0.73
0.71
0.13
0.12
0.11
0.12
0.12
0.12
0.13
0.13
0.14
0.14
0.13
0.12
CV = coefficient of variation
s.d. = standard deviation
Source: Carsel et al. (1988)
185
-------
Table 5-26. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR WILTING
POINT (PERCENT BY VOLUME)
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.6-1.2
Class C
0.3-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size
118
119
113
105
88
883
866
866
678
677
652
582
495
485
437
401
Mean
4.1
3.2
2.9
2.6
9.0
9.4
9.1
8.6
10.8
12.2
12.2
11.8
14.6
16.9
16.6
15.7
Original
Median
3.1
2.3
2.1
1.9
8.7
9.3
8.9
8.4
10.4
12.1
11.9
11.5
13.8
17.0
16.3
15.1
Data
s.d.
3.4
2.4
2.3
2.3
4.0
4.3
4.4
4.6
5.1
5.6
6.0
5.7
7.6
7.3
7.4
7.6
CV
Distribution Model
(%) Transform Mean
82
75
81
87
45
46
48
53
48
46
49
48
52
43
45
48
In
In
SB
SB
SU
SU
su
su
su
su
su
su
su
su
su
su
1.83
0.915
3.32
3.43
0.150
0.156
0.151
0.143
1.63
0.202
0.201
0.194
1.26
0.277
0.271
0.257
s.d.
0.64
0.71
0.88
0.92
0.066
0.071
0.072
0.076
0.62
0.091
0.096
0.092
0.76
0.12
0.12
0.12
CV = coefficient of variation
s.d. = standard deviation
Source: Carsel et al. (1988)
186
-------
Table 5-27. CORRELATIONS BETWEEN TRANSFORMED VARIABLES OF ORGANIC MATTER,
FIELD CAPACITY, AND WILTING POINT
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
N
118
119
111
98
877
870
844
780
673
664
627
543
488
472
420
384
OM + WP
Corr.
0.738
0.630
0.487
0.456
0.545
0.372
0.375
0.392
0.495
0.473
0.457
0.434
0.538
0.434
0.456
0.415
N
52
49
42
38
459
446
419
347
369
355
321
264
228
201
171
137
FC + OM
Corr.
0.624
0.404
0.427
0.170
0.609
0.384
0.336
0.412
0.577
0.409
0.434
0.456
0.496
0.454
0.369
0.106
N
51
49
42
39
455
450
429
370
370
361
334
289
226
204
174
145
FC -I- WP
Corr.
0.757
0.759
0.811
0.761
0.675
0.639
0.714
0.762
0.745
0.775
0.784
0.751
0.847
0.845
0.782
0.687
OM = organic matter; WP = wilting point; FC
size; Corr. = correlation.
Source: Carsel et al. 1988.
= field capacity; N = sample
187
-------
%OM 100.0 - %OM (5-13)
OMBD + MBD
where
BD = soil bulk density, g cm
OM = organic matter content of soil, %
OMBD = organic matter bulk density of soil, g cm"3 = 0.224
o
MBD = mineral bulk density, g cm
NOTE: MBD must be entered if BDFLAG - 1.
Step 1. Locate the percent sand (80.0) along the bottom of Figure 5.10.
Step 2. Locate the percent clay (5.0) along the side of Figure 5.10.
Step 3. Locate the intersection point of the two values and read the
mineral bulk density (1.55).
Step 4. Solve the Rawls equation for BD (e.g., 1.47).
Method 2
Step 1. Use Table 5-28 to locate the textural classification of the
soil.
Step 2. Read mean bulk density for the general soil texture. Example:
Sandy loam. The mean bulk density is 1.49 g cm" .
Table 5-29 shows distributional properties of bulk density information. The
information given is categorized by Hydrologic Soil Group (A, B, C, D). The
most appropriate distribution for this property is the normal (Jury 1985) .
Jury indicates slightly lower CVs, on the order of 9 percent. Values of CV
in the table are more appropriate for regional simulations, whereas a value
of 9-10% is more appropriate for a single field.
OC--Percent of Soil Organic Carbon--
Soil organic carbon (OC) is conventionally related to soil organic matter as
%OC — %OM/1.724. Guidance on estimating percent organic matter is found in
Table 5-30. Information is categorized by Hydrologic Soil Group and by
depth. Also shown are coefficients of variation for each soil group and
depth. Carsel et al. (1988) determined that the Johnson SB distribution
provides the best fit to this data.
Rao and Wagenet (1985) and Nielsen et al. (1983) have reported that these
values are often normally distributed. Carsel et al. (1988) have noted that
organic carbon is weakly correlated with field capacity and wilting point
188
-------
100
o
10 20 30
40 50
Sand (%)
90
I
100
Figure 5.10.
Mineral bulk density (gcm~3). (provided by
Dr. Walter J. Rawls, U.S. Department of
Agriculture, Agricultural Research Service,
Beltsville, Maryland).
189
-------
water content with correlation coefficients ranging from 0.1 to 0.74.
Strength of correlation decreases with depth, as shown previously in Table 5-
27.
Table 5-28. MEAN BULK DENSITY (g cm'3) FOR FIVE SOIL TEXTURAL
CLASSIFICATIONS3
Soil Texture Mean Value Range Reported
Silt Loams
Clay and Clay Loams
Sandy Loams
Gravelly Silt Loams
Loams
All Soils
1.32
1.30
1.49
1.22
1.42
1.35
0.86 -
0.94 -
1.25 -
1.02 -
1.16 -
0.86 -
1.67
1.54
1.76
1.58
1.58
1.76
a Baes, C.F., III and R.D. Sharp. 1983. A Proposal for Estimation of Soil
Leaching Constants for Use in Assessment Models. J. Environ. Qual.
12(1): 17-28.
AD--Soil Water Drainage Rate (for HSWZT - 1)--
The HSWZT flag indicates which drainage model is invoked for simulating the
movement of recharging water. Drainage model 1 (HSWZT - 0) is for freely
draining soils; drainage model 2 (HSWZT - 1) is for more poorly drained
soils. For soils with infiltration rates of more than 0.38 cm hr
(associated with SCS hydrologic soils groups A, B, and some C), setting HSWZT
= 0 is recommended. For soils with infiltration rates of less than 0.38 cm
hr (associated with groups D and some C) setting HSWZT - 1 is recommended.
The drainage rate parameter (AD), required when HSWZT - 1, is an empirical
constant and dependent on both soil type and the number of compartments to be
simulated. Although there is limited experience using this option, an
analysis was performed to determine the best value for AD over a range of
soil types on which agricultural crops are commonly grown. Each of three
soil types was tested with a constant soil profile depth (125 cm). The
profile was divided into a variable number of compartments and the optimum
value of AD for each soil/compartment combination was obtained.
The analysis was performed by comparing the storage of water in the soil
profile following the infiltration output from SUMATRA-1 (van Genuchten
1978). This model was used as "truth" because field data were lacking and
SUMATRA-1 is theoretically rigorous. The amount of water moving out of the
profile changed by only 1-2% over the range of compartments tested (15 -
40) for the three soils evaluated. Calibrating PRZM by comparison was
accomplished and estimates of AD calculated. Suggested values of AD for clay
190
-------
Table 5-29. DESCRIPTIVE STATISTICS FOR BULK DENSITY (g cm'3)
Str atxom
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size
40
44
38
34
459
457
438
384
398
395
371
326
259
244
214
180
Mean
1.45
1.50
1.57
1.58
1.44
1.51
1.56
1.60
1.46
1.58
1.64
1.67
1.52
1.63
1.67
1.65
Median
1.53
1.56
1.55
1.59
1.45
1.53
1.57
1.60
1.48
1.59
1.65
1.68
1.53
1.66
1.72
1.72
s.d.
0.24
0.23
0.16
0.13
0.19
0.19
0.19
0.21
0.22
0.23
0.23
0.23
0.24
0.26
0.27
0.28
cv
(%)
16.2
15.6
10.5
8.4
13.5
12.2
12.3
12.9
15.0
14.5
14.2
14.0
15.9
16.0
16.3
17.0
CV - coefficient of variation
s.d. - standard deviation
Source: Carsel et al. (1988)
191
-------
Table 5-30. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR ORGANIC
MATTER (PERCENT BY WEIGHT)
Original Data
Stratum
(a)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.3-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size
162
162
151
134
1135
1120
1090
1001
838
822
780
672
638
617
558
493
Mean
0.86
0.29
0.15
0.11
1.3
0.50
0.27
0.18
1.45
0.53
0.28
0.20
1.34
0.65
0.41
0.29
Median
0.62
0.19
0.10
0.07
1.1
0.40
0.22
0.14
1.15
0.39
0.22
0.15
1.15
0.53
0.32
0.22
s.d.
0.79
0.34
0.14
0.11
0.87
0.40
0.23
0.16
1.12
0.61
0.27
0.21
0.87
0.52
0.34
0.31
CV
(%)
92
114
94
104
68
83
84
87
77
114
96
104
66
80
84
105
Distribution Modela
Mean s.d.
-4.53
-5.72
-6.33
-6.72
-4.02
-5.04
-5.65
-6.10
-3.95
-5.08
-5.67
-6.03
-4.01
-4.79
-5.29
-5.65
0.96
0.91
0.83
0.87
0.76
0.77
0.75
0.78
0.79
0.84
0.83
0.88
0.73
0.78
0.82
0.86
CV - coefficient of variation
s.d. - standard deviation
Source: Carsel et al. (1988)
a
Johnson SD transformation is used for all cases in this table.
192
-------
loam, loamy sand, and sand as a function of the number of compartments are
given in Figure 5.11.
5.2.6 Parameter Estimation for Irrigation
Guidance is provided below for estimating parameters simulating irrigation
with PRZM.
IRTYPE--Selecting the Type of Irrigation--
The variable IRTYPE is used to indicate which type of irrigation is to be
simulated, and should reflect agricultural practices in the region of
interest. Table 5-31 lists conditions under which furrow, sprinkler, and
flood irrigation are usually used. In general, furrow (IRTYPE =1) and flood
(IRTYPE — 2) irrigation are more commonly used in the West and Midwest, where
topography is more level. Sprinklers (IRTYPE = 3,4) can be used in any type
of terrain and for any crop, and are increasingly popular due to concerns
about erosion from furrow- and flood-irrigated fields. Sprinklers are more
expensive to install, but generally apply water more efficiently than do
other irrigation methods.
PCDEPL--Fraction of Available Water Capacity Where Irrigation is Triggered--
The moisture level where irrigation is required is defined by the model user
as fraction of the available water capacity. This fraction will depend upon
the soil moisture-holding characteristics, the type of crop planted, and
regional agricultural practices. In general, PCDEPL should range between 0.0
and 0.6, where a value of 0.0 indicates that irrigation begins when soil
moisture drops to the wilting point, and 0.6 indicates the more conservative
practice of irrigating at 60 percent of the available water capacity. Schwab
et al. (1966) recommend values of PCDEPL ranging between 0.45 and 0.55.
FLEACH--Leaching Factor--
The leaching factor is used to specify the amount of water added by
irrigation to leach salts from saline soil, and is defined as a fraction of
the amount of water required to meet the soil water deficit. For instance, a
value of 0.0 indicates no water is added for leaching, while a value of 0.25
indicates that 25 percent extra water is added to the irrigation water volume
required to meet the soil water deficit.
RATEAP--Maximum Sprinkler Application Rate--
RATEAP is used to limit sprinkler applications to volumes that the sprinkler
system is capable of delivering in a PRZM time step, and is defined as a
maximum depth (cm) of water delivered per hour. Table 5-32 lists appropriate
sprinkler delivery rates for different slopes and soil types; sprinkler
systems designed for these conditions should be capable of supplying at least
the amounts of water listed in Table 5-32. Other guidance for estimating
maximum application rates can be found in industry specifications for
different sprinkler systems.
Furrow Irrigation Parameters--The first set of furrow irrigation parameters
define the flow characteristics and geometry of furrow channels. These
parameters will generally depend upon soil characteristics and topography,
with some variation occurring due to different local agricultural practices.
193
-------
20 25 30 35
Number of compartments
40
Figure 5.11. Estimation of drainage rate AD (day ) versus number
of compartments.
194
-------
Table 5-31. ADAPTATIONS AND LIMITATIONS OF COMMON IRRIGATION METHODS
Irrigation
Method
Adaptations
Limitations
Furrow Light, medium-and fine-
textured soils; row crops.
Sprinklers All slopes; soils; crops.
Flood Light, medium, and heavy
soils.
Slopes up to 3 percent in
direction of irrigation; row
crops; 10 percent cross slope.
High initial equipment cost;
lowered efficiency in wind and
hot climate.
Deep soils; high cost of land
preparation; slopes less than 2
percent.
Source: Adapted from Todd (1970).
Table 5-32. WATER REQUIREMENTS FOR VARIOUS IRRIGATION AND SOIL TYPES
Typical
Slope
(%)
Sprinkling 0-2
2-5
5-8
8-12
Application
Coarse
Sandy
Soils
2.0
2.0
1.5
1.0
Rate (Inches/Hour) bv Sprinklers
Light
Sandy
Loam
0.75
0.75
0.50
0.40
Medium
Silt
Loam
0.5
0.5
0.4
0.3
Clay
Loam
Soils
0.20
0.20
0.15
Source: Adapted from Todd (1970).
195
-------
Typical values of these parameters for furrows described in the literature
are listed in Table 5-33.
QO Flow Rate into a Single Furrow - QO is defined as the volume of
water entering the furrow per unit time. Flow rates are usually
set so that sufficient water reaches the end of the furrow without
causing excessive erosion. Table 5-34 lists maximum non-erosive
flow rates for various furrow channel slopes.
BT Bottom Width of Furrows - BT will depend mostly upon the type of
equipment used to dig the furrow channels and the spacing between
furrows.
Z Side Slope of Furrows - This parameter is defined as the slope
of the channel walls, horizontal distance/vertical distance. Z
will depend upon the cohesiveness of soils and the type of
equipment used to dig the furrows. Table 5-35 lists suitable side
slopes for different types of soils, with values ranging from 1.5
to 3.0 for unconsolidated materials.
SF Channel Slope - SF is determined by regional topography and the
design grades of the furrows, and is defined as vertical drop in
elevation per horizontal distance of the bed. Furrows are usually
used only in relatively level terrain, with slopes no greater than
.03 (Todd 1970). A few representative slopes are listed in Table
5-33.
EN Manning's Roughness Coefficient - Manning's roughness coefficient is
a well-known measure of the resistance of open channels to flow.
Chow (1959) suggests that values of EN range from .016 to .033 in
excavated or dredged earth channels. Values for furrows listed in
Table 5-33 range from .01 to .048. Table 5-36 lists values of EN
suggested by the USDA Soil Conservation Service for drainage
ditches with various values of hydraulic radius (defined as the
flow area divided by the wetted perimeter).
XL Length of Furrow - XL will depend upon the size of the field and the
local topography. Table 5-34 lists maximum furrow lengths for
various soil textures, irrigation application depths, and furrow
slopes.
The second set of furrow parameters are related to the infiltration
characteristics of the soil, and are used in the Green-Ampt infiltration
model:
KS Saturated Hydraulic Conductivity - This parameter represents the
limiting infiltration rate when the soil column is saturated and
suction pressure is no longer important. KS depends upon soil
mineralogy, texture, and degree of compaction. Ranges of values
for various unconsolidated materials are shown in Table 5-37. KS
has also been correlated with SCS Hydrologic Soil Groups
(Brakensiek and Rawls 1983); ranges of values for each soil group
are shown in Table 5-38.
196
-------
Table 5-35. SUITABLE SIDE SLOPES FOR CHANNELS BUILT IN VARIOUS KINDS OF
MATERIALS
Material Side slope
Rock Nearly vertical
Muck and peat soils k:1
Stiff clay or earth with concrete lining h:l - 1:1
Earth with stone lining, or earth for large channels 1:1
Firm clay or earth for small ditches 14:1
Loose sandy earth 2:1
Sandy loam or porous clay 3:1
Source: Adapted from Chow (1959).
Table 5-36. VALUE OF "N" FOR DRAINAGE DITCH DESIGN
Hydraulic radius (ft) EN
< 2.5 0.040 - 0.045
2.5-4.0 0.035-0.040
4.0 - 5.0 0.030 - 0.035
> 5.0 0.025 - 0.030
Source: Adapted from U.S. Dept. of Agric. Soil Conservation Service,
198
-------
Table 5-37. REPRESENTATIVE PERMEABILITY RANGES FOR SEDIMENTARY MATERIALS
Hydraulic
Conductivity
Material
Clay
Silty clay
Sandy clay
Silty clay loam
Sandy loam sand
Silt
Silt loam
Loam
Sandy loam
(m/s)
ID'12
io-12
lO'11
io-10
io-9
1C'9
io-9
io-9
10'8
- io-9
- io-9
- 10"8
- io-7
- 10"6
- 10'6
- 10"6
- 10'6
- lO-7
St
Excluding cavernous limestone;
Material
Very fine sand
Find sand
Medium sand
Coarse sand
Gravel and sand
Gravel
Sandstone
Limestone
Shale
Source : Adapted
Hydraulic
Conductivity
(m/s)
io-7 -
10"6 -
ID'5 -
IO-5 -
ID'5 -
io-5 -
10"6 -
io-7 -
1C'7 -
io-4
ID"3
ID'3
io-2
io-2
io-2
1C'3
io-4
10'4
from Todd (1970) .
Table 5-38. VALUES OF GREEN-AMPT PARAMETERS FOR SCS HYDROLOGIC SOIL GROUPS
SCS
Hydrologic
Soil Group
A
B
C
D
Saturated Hydraulic*
Conductivity Kg
(cm hr'1)
1.0 - 10.0
0.60 - 1.0
0.20 - 0.60
0.005 - 0.20
Suction
Parameter HF
(cm)
10
10 - 20
15 - 10
20 - 150
Source: Adapted from Brakensiek and Rawls (1983)
a Also see Table 5-30.
199
-------
HF Suction Parameter - HF represents water movement due to suction in
unsaturated soils, and has units of length (meters). As in the
case of KS, HF has been correlated with SCS Hydrologic Soil Groups
(Brakensiek and Rawls 1983); ranges of values for each soil group
are shown in Table 5-38.
5.3 VADOFT PARAMETERS
Input data for variably saturated flow simulations include the following:
(1) System Geometry
• Soil column dimensions (L)
(2) Porous Medium Properties
• Saturated hydraulic conductivity, Kg (LT~^)
• Specific storage, Ss (L~ )
• Effective porosity,
(3) Constitutive Relationships for Variably Saturated Flow
• Tabulated data of krw versus Sw, or values of parameters of
analytic expressions for k versus Sw
• Tabulated data of Sw versus ^>, or values of parameters of analytic
expressions for Sw versus ij>.
(4) Initial and Boundary Conditions
• Prescribed values of pressure head, \j> (L)
• Prescribed values of nodal fluid flux (infiltration rate), I
(LT-1)
Input data for the transport model include the following:
(1) System Geometry
• Soil column dimensions (L)
(2) Porous Medium Properties
• Longitudinal dispersivity a-^, (L)
* 91
• Molecular diffusion coefficients, D (L T )
• Effective porosity,
(3) Properties of Solute Species
200
-------
• Decay coefficient, A (T"1)
• Retardation coefficient, R
(4) Darcy Velocity, V (L T"1)
(5) Water Saturation, Sw
(6) Initial and Boundary Conditions
_ o
• Prescribed value of concentration, CQ (M L )
• Prescribed value of solute flux, VcQ (M L"2 T"1)
Guidance for certain of these parameters is given in the following
paragraphs.
Saturated Hydraulic Conductivity--
Saturated hydraulic conductivity represents the rate at which a porous medium
can transmit water under saturated conditions. Table 5-39 gives
representative values for various soil types. Also note the values of the
coefficient of variation in column three. These CVs are for many soils
nationwide which fall into this texture category. CVs for a single soil are
likely to be lower. Jury (1985) gives a CV of 120% for this parameter which
may be more representative. The most likely shape for the distribution is
lognormal.
Table 5-39. DESCRIPTIVE STATISTICS FOR SATURATED HYDRAULIC CONDUCTIVITY
(cm hr'1)
Soil Type
x
Hydraulic Conductivity (Ks)'
s CV
n
Clay**
Clay Loam
Loam
Loamy Sand
Silt
Silt Loam
Silty Clay
Silty Clay Loam
Sand
Sandy Clay
Sandy Clay Loam
Sandy Loam
0.20
0.26
1.04
14.59
0.25
0.45
0.02
0.07
29.70
0.12
1.31
4.42
0.42
0.70
1.82
11.36
0.33
1.23
0.11
0.19
15.60
0.28
2.74
5.63
210.3
267.2
174.6
77.9
129.9
275.1
453.3
288.7
52.4
234.1
208.6
127.0
114
345
735
315
88
1093
126
592
246
46
214
1183
n = Sample size, x = Mean, s = Standard deviation, CV — Coefficient of
variation (percent)
Agricultural soil, less than 60 percent clay
Source: Carsel and Parrish (1988).
201
-------
Soil-Water Characteristic Data--
The user is allowed two options: either to input these data as a set of
paired functions (water saturation [Sw] vs. relative conductivity [Krw] and
pressure head [ij>] vs. water saturation [Sw] or to input parameters of the
analytic expressions for these functions in the code. The parameterization
of the latter functions is discussed here.
In order to provide a linkage for these parameters to widely known or easily
obtained soils data (such as soil texture), Carsel and Parrish (1988) fit
these analytic functions to data from soils all over the U.S. and tabulated
corresponding parameter values by texture. These are shown in Table 5-40.
The required parameters are a, ft, and 7 of the van Genuchten model (see
Section 3, Volume I). Mean values of these parameters are shown along with
CVs for each by soil texture. Other parameters required to use these
relationships are the air entry pressure head (^a) and the residual water
content (0r). The air entry pressure head is normally taken to be zero.
Values of the residual water content are given in Table 5-41 along with their
respective CVs. Table 5-42 from Carsel and Parrish (1988) shows the types of
probability density functions used to fit the sample distributions of
saturated hydraulic conductivity, residual water content, and van Genuchten
parameters a and ft.
Note that 7 is related to ft by the relationship:
7 = 1 - I/ft (5-14)
In'addition, Table 5-43 gives the correlations between these parameters by
soil textural classification.
Specific Storage--
For unsaturated zone flow, the specific storage is set to 0.
Effective Porosity--
The mean values of saturation water content (00) and residual water content
o
(#r) shown in Table 5-41 can be used to estimate effective porosity. The
saturation water content (0S) is equal to the total porosity of the soil.
The effective porosity can be roughly approximated as the difference of &s
and 9 in Table 5-41. CVs for soil texture categories are also shown in
Table 5-41. According to Jury (1985) the normal distribution is an
appropriate probability density function for this parameter.
Longitudinal Dispersivity--
The user should refer to the discussion of the dispersion coefficient (cm2
day ) in Section 5.2.3. Dispersion coefficients are calculated by the model
as the product of the seepage velocity and the dispersivity input by the
user. In the absence of site-specific values it is recommended that the
dispersivity be chosen as one-tenth of the distance of the flow path or:
202
-------
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203
-------
Table 5-41. DESCRIPTIVE STATISTICS FOR SATURATION WATER CONTENT (0g) AND
RESIDUAL WATER CONTENT (0r)
Saturation
Soil Type
Clay**
Clay Loam
Loam
Loamy Sand
Silt
Silt Loam
Silty Clay
Silty Clay Loam
Sand
Sandy Clay
Sandy Clay Loam
Sandy Loam
X
0
0
0
0
0
0
0
0
0
0
0
0
.38
.41
.43
.41
.46
.45
.36
.43
.43
.38
.39
.41
Water
s
0.09
0.09
0.10
0.09
0.11
0.08
0.07
0.07
0.06
0.05
0.07
0.09
n = Sample size, x = Mean, s
variation (percent)
Agricultural soil, less than
Content (6 s) Residual Water
Statistic*
CV
24.1
22.4
22.1
21.6
17.4
18.7
19.6
17.2
15.1
13.7
17.5
21.0
n
400
364
735
315
82
1093
374
641
246
46
214
1183
- standard
60 percent
0
0
0
0
0
0
0
0
0
0
0
0
X
.068
.095
.078
.057
.034
.067
.070
.089
.045
.100
.100
.065
deviation,
clay.
0
0
0
0
0
0
0
0
0
0
0
0
CV
s
.034
.010
.013
.015
.010
.015
.023
.009
.010
.013
.006
.017
Content (9^.)
L
CV
49.9
10.1
16.5
25.7
29.8
21.6
33.5
10.6
22.3
12.9
6.0
26.6
n
353
363
735
315
82
1093
371
641
246
46
214
1183
= coefficient of
Source: Carsel and Parrish, 1988
(5-15)
where
- the thickness of the vadose zone.
Molecular Diffusion--
See the discussion in Section 5.2.3.
Pesticide Decay Coefficients--
See the discussion in Section 5.2.3.
Retardation Factors - -
In VADOFT, in contrast to PRZM, the user inputs the retardation factor R
instead of the distribution coefficient, KD(cm^ g ). The retardation factor
is defined for saturated conditions in the input:
204
-------
Table 5-42. STATISTICAL PARAMETERS USED FOR DISTRIBUTION APPROXIMATION
Soil
Tex-
**
ture
S
S
S
S
SL
SL
SL
SL
LS
LS
LS
LS
SIL
SIL
SIL
SIL
SI
SI
SI
SI
C
C
c
c
SIC
SIC
SIC
SIC
sc
sc
sc
sc
SICL
SICL
SICL
SICL
Hydrau-
lic
Variable
Ks
a
P
Ks
a
ft
Ks
Q
P
KS
8
a
P
Ks
a
P
Ks
d
a
P
Ks
a
P
Ks
a
^
Ks
8
a.
P
Limits of
Transfer- Variation
mation A B
SB
LN
SB
LN
SB
SB
SB
LN
SB
SB
NO
SB
LN
SB
LN
SB
LN***
ND***
NO
NO
SB
su**
SB**
LN**
LN
NO
LN
SB
LN
SB
LN
LN
SB
NO
SB
NO
0.0
0.0
0.0
1.5
0.0
0.00
0.00
1.35
0.0
0.0
0.0
1.35
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.2
0.0
0.0
0.0
0.9
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
70.0
0.1
0.25
4.0
30.0
0.11
0.25
3.00
51.0
0.11
0.25
5.00
15.0
0.11
0.15
2.0
2.0
0.09
0.1
1.6
5.0
0.15
0.15
1.4
1.0
0.14
0.15
1.4
1.5
0.12
0.15
1.5
3.5
0.115
0.15
1.5
-0
-3
0
0
-2
0
-0
0
-1
0
0
-1
-2
0
-4
-0
0
1
-5
0
-4
0
-5
0
-5
-1
-4
1
-3
0
-5
0
-2
1
Mean
.39387
.11765
.37768
.97813
.49047
.38411
.93655
.63390
.26908
.07473
.12354
.11095
.18691
.47752
.09937
.37036
-2.20
0.042
.01688
.37815
.75949
.44537
.14805
.00021
.68562
.06971
.65849
.28378
.04036
.72496
.76810
.20209
.31256
.08871
.75043
.23640
Estimated
Standard
Deviation
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
.15472
.22369
.43895
.10046
.52854
.70011
.76383
.08162
.40000
.56677
.04345
.30718
.49414
.58156
.55542
.52557
0.7000
0.0145
0
0
2
0
1
0
1
0
0
0
2
0
0
0
1
0
0
0,
.00611
.03729
.32884
.28178
.29310
.11800
.31421
.02337
.58445
.82074
.01721
.70000
.56322
.07788
.61775
.00937
.60529
.06130
*
D
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
Truncation Limits
on Transformed
Variable
.045
.053
.050
.063
.029
.034
.044
.039
.036
.043
.027
.070
.046
.073
.083
.104
.168 -2.564
.089 0.013
.252
.184
.122
.058 0.0065
.189 -5.01
.131 0.00
.205
.058
.164
.069
.130
.078
.127
.100
.049
.056
.082
.082
-0.337
0.049
0.834
0.912
0.315
205
-------
Table 5-42. STATISTICAL PARAMETERS USED FOR DISTRIBUTION APPROXIMATION
(concluded)
Soil Hydrau-
Tex- lie
ture Variable
CL Ks
CL er
CL a
CL ft
SCL Ks
SCL er
SCL a
SCL ft
L Ks
J-J V «
L a
L ft
Transfor-
mation
SB***
SU
LN
SB
SB
SB***
SB
LN
SB
SB
SB
SU
Limits of
Variation
A B
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
7.5
0.13
0.15
1.6
20.0
0.12
0.25
2.0
15.0
0.12
0.15
2.0
Mean
-5.87171
0.67937
-4.21897
0.13248
-4.03718
1.65387
-1.37920
0.38772
-3.71390
0.63872
-1.27456
0.53169
Estimated
Standard
Deviation
2.92220
0.06005
0.71389
0.72498
1.84976
0.43934
0.82327
0.08645
1.77920
0.48709
0.78608
0.09948
^
Truncation Limits
on Transformed
D**** Variable
0.058 -8.92
0.061
0.052
0.035
0.047
0.077 0.928
0.048
0.043
0.019
0.064
0.039
0.036
2.98
2.94
**
***
For distribution of transformed variables.
S = sand, SL = sandy loam, LS — loamy sand, SIL - silty loam, SI - silt,
C = clay, SIC = silty clay, SC - sandy clay, SICL = silty clay loam,
CL = clay loam, SCL - sandy clay loam, L — loam.
Truncated form of the distribution.
****
Kolmogorov-Smirnov test statistic.
Source: Carsel and Parrish, 1988.
206
-------
Table 5-43. CORRELATIONS AMONG TRANSFORMED VARIABLES PRESENTED WITH THE
FACTORED COVARIANCE MATRIX*
Silt **(n
Ks
a
ft
Clay (n -
Ks
'r
a
ft
Silty Clay
Ks
a
ft
Sandy Clay
Ks
'r
a
ft
Sand (n —
Ks
S*
a
ft
Sandy Loam
Ks
a
ft
Loamy Sand
Ks
*r
a
ft
Ks
- 61)
0.5349258
-0.204
0.984
0.466
95)
1.9614077
0.972
0.948
0.908
(n - 123)
1.2512845
0.949
0.974
0.908
(n - 46)
2.0172105
0.939
0.957
0.972
237)
1.0370702
-0.515
0.743
0.843
(n - 1145)
1.6026856
-0.273
0.856
0.686
(n - 313)
1.4754063
-0.359
0.986
0.730
'r
-0.0015813
0.0075771
-0.200
-0.610
0.0701669
0.0170159
0.890
0.819
0.0082067
0.0027392
0.964
0.794
0.8827527
0.3241979
0.937
0.928
-0.1092256
0.1816914
0.119
-0.858
-0.1529235
0.5378436
0.151
-0.796
-0.2005639
0.5215473
-0.301
-0.590
a
0.0030541
0.0000021
0.0005522
0.551
0.5645309
-0.0798488
0.1716520
0.910
0.3143268
0.0404171
0.0608834
0.889
0.5391195
0.0634106
0.1501651
0.932
0.3276629
0.2583835
0.1429585
0.298
0.0372713
0.0174500
0.0142626
0.354
0.0372713
0.0174500
0.0142626
0.354
ft
0.0128700
-0.0145118
0.0144376
0.0133233
0.0475514
-0.0142394
0.0021973
0.0164640
0.3674505
-0.0858769
0.0660396
0.1305065
0.0756103
0.0035688
-0.0010668
0.0178225
0.0805436
-0.0471785
-0.0013674
0.0167064
0.2108253
-0.1943369
0.0193794
0.1084945
0.2108253
-0.1943369
0.0193794
0.1084945
207
-------
Table 5-43. CORRELATIONS AMONG TRANSFORMED VARIABLES PRESENTED WITH THE
FACTORED COVARIANCE MATRIX* (concluded)
Silt Loam (n - 1072)
K
0
a
ft
1.4754063
-0.359
0.986
0.730
-0.02005639
0.5215473
-0.301
-0.590
0.5245489
0.0300399
0.0820163
0.775
0.3525548
-0.1696100
0.2341768
0.1583593
Silty Clay Loam (n - 591)
K
a
ft
1.6177521
0.724
0.986
0.918
s
'r
Clay Loam (n = 328)
1.9200165
0.790
a 0.979
ft 0.936
Sandy Clay Loam (n = 212)
K
Loam (n
Ks
a
/9
1.8497610
0.261
0.952
0.909
664)
1.4083953
0.204
0.982
0.632
0.0056509
0.0053780
0.777
0.549
0.0395603
0.0307122
0.836
0.577
0.1020156
0.3775754
0.392
-0.113
-0.0995016
0.4775039
-0.086
-0.748
0.5116521
0.0475299
0.0731704
0.911
0.5886263
-0.0619715
0.1060875
0.909
0.7838769
0.1223451
0.2198684
0.787
0.6110671
0.0727710
0.0926351
0.591
0.0486478
-0.0089569
0.0080399
0.0171716
0.5417671
-0.1536351
0.0653030
0.1159401
0.0766289
-0.0305588
•0.0078559
0.0155766
0.0545016
-.0545793
0.0256843
0.0288861
* Entries in the lower triangular portion of the matrix are sample Pearson
product-moment correlations given to three decimal places. The diagonal
and upper triangular entries form the triangular Cholesky decomposition of
the sample covariance matrix.
** N - Sample size.
Source: Carsel and Parrish, 1988.
208
-------
R = 1 + -s— (5-16)
and is adjusted internally for values of 6 < 0g. In the above equation, a is
the soil bulk density (g cm" ) and 9& is the saturation water content (cm
cm"^). In making this calculation, the user should directly use the value
for p, if known. If necessary, p can be approximated according to:
P = 2.65 (1 - 0g) (5-17)
The CV of the retardation factor, R, can be computed knowing the
uncertainties in K^, p and 8S (Taylor 1982). The fractional uncertainties
add to give an upper bound error on R (Cvmax) or are combined as a root mean
square for independent random errors. Thus,
CVmaY = (CV, + CV,, + CVJ
max
+ CVKd + C
or
CV = 100 [(CV0 /100)2 + (CVR /100)2 + (CV^/lOO)2]1* (5-19)
The uncertainty in the value of K^ will depend upon whether it is measured,
calculated as the product of KQC and % organic carbon, and whether the KQC is
calculated from a surrogate parameter such as octanol water partition
coefficient (K w) or solubility (s). Directly measured values would
obviously have lower CVs. Assuming that K^ is calculated from a measured
soluble concentration, then it is possible that the CV would be on the order
of 60 - 130% (Jury 1985). For Kd derived from KQW or solubility, the CV
could be on the order of 1000%.
5.4 SAFTMOD PARAMETERS
Data required for the groundwater flow simulation include values of the
saturated hydraulic conductivity and specific storage of each aquifer and
aquitard material, the geometry and configuration of the flow region, as well
as initial and boundary conditions associated with the flow equation. Data
required for the simulation of solute transport include values of
longitudinal and transverse dispersivities and porosity, retardation and
decay constants, and values of Darcy velocity components, as well as initial
and boundary conditions associated with the transport equation.
Input data of the flow model include the following:
(1) System Geometry
• Horizontal and vertical dimensions, including layering and other
heterogeneities (L)
209
-------
(2) Porous-Medium Properties
For each aquifer,
• Hydraulic conductivity component, K-^ (L T"^)
• Hydraulic conductivity component, V^22 ^ ^" '
• Hydraulic conductivity component, K^ (L T )
• Specific storage, Ss (L )
• Specific yield, S
For each aquitard,
• Hydraulic conductivity, K' (L T"1)
• Specific storage, Sg (L"1)
(3) Initial Boundary Conditions
• Prescribed values of pressure head, h (L)
• Prescribed values of nodal fluid flux, Qn (L3 T"1)
• Recharge rate, 1 (L T"1)
Input data of the transport model include the following:
(1) System Geometry
• Horizontal and vertical dimensions, including
layering and other heterogeneities (L)
(2) Porous-Medium Properties
For each aquifer,
• Longitudinal dispersivity, a^ (L)
• Transverse dispersivity, a^ (L)
• Molecular diffusion coefficient, DQ (L2 T"1)
• Effective porosity, ^
For each aquitard,
• Diffusion coefficient, D' (L2 T'1)
• Effective porosity, 4>'
210
-------
(3) Solute Properties
• Decay constant, A (T )
• Retardation in aquifer, R
• Retardation in aquitard, R'
(4) Darcy velocity components of groundwater, V-^ and \^ (L T )
(5) Initial and Boundary Conditions
o
• Initial values of concentration, CQ (M L )
• Prescribed values of concentration, c (M L )
• Prescribed values of solute flux, ft (M T )
Hydraulic Conductivity--
In the absence of site-specific measurements, the hydaulic conductivity can
be estimated using Tables 5-37 and 5-39. An alternative, though often a poor
one, is to calculate the hydraulic conductivity by means of an approximate
functional relationship. One such relationship, the Karman-Cozney equation
(Bear 1979), can be expressed as:
" - ^ ^ d2 (5-20)
1.8
where
K = the hydraulic conductivity (cm sec )
•5
p = the density of water (Kg m )
o
g = acceleration due to gravity (m sec )
f\
H = the dynamic viscosity of water (N sec m )
d = mean particle diameter (cm) and
= the effective porosity (dimensionless)
In Eq. 5-20 the constant 1.8 includes a unit conversion factor. Both the
density of water (p) and the dynamic viscosity of water (yu) are functions of
temperature. Values can be computed using regression equations presented in
CRC (1981). Note that at 15°C, the value of [pg/1.8/*] is about 478.
Ranges of values for various aquifer materials can be found in Freeze and
Cherry (1979). Kg is probably log-normally distributed, having a range of
values from 0.0001 to 0.48 cm sec"1 (Federal Register 1986).
211
-------
Specific Storage--
Specific storage (L ) can be calculated from the expression:
Ss - pg (a + 6sft) (5-21)
where
Sg = the specific storage (I/-*-)
p - fluid density (M L"3)
r\
g - the acceleration of gravity (L T )
a - compressibility of the aquifer (L T2 M"^)
O o
9S — saturated water content or porosity (L L" )
ft - compressibility of water (L T2 M"1)
The value of ft can be taken as 4.4*10~10 m2 N"1 (Freeze and Cherry 1979).
The value of a for various aquifer materials may be taken from the following
data:
Aquifer
Compressibility, a
Material (m2 N'1)
Clay W~6 - la'9
Sand 10'' - 10~*
Gravel 10"J - 10" "
Jointed Rock 10~I - 10 "I"
Sound Rock 10 - 10
o o
The term pg is equal to 9.8*10" N m and can be considered constant.
Therefore, Ss is a function of the aquifer type and porosity:
S. - 9.8*103 (a + 4.4*10"10 BJ (5-22)
o S
Q
A typical value for a predominantly sandy aquifer (a = 10"°, 0S - 0.30) would
be 9.9*10'5 m'1.
Dispersion Coefficients--
The model computes the longitudinal and either lateral or vertical dispersion
coefficients as the product of the seepage velocity and longitudinal (CUT ) ,
transverse (a^) or vertical (a^) dispersivities. A literature review
indicated generalized theory to describe dispersivities, although a strong
dependence on scale has been noted (EPRI 1985). In the absence of user
specified values, the following guidance is given.
212
-------
Based on the values presented in the Federal Register (1986) , the
longitudinal and transverse dispersivities are:
c«T = 0.1 x_ (5-23)
J-» L.
(5-24)
where x_ is the distance from the source to the receptor well. The vertical
dispersivity, a™, can be assumed to be uniformly distributed in the range of
,025 to . 1 of the longitudinal dispersivity. Distributional properties of
the longitudinal and transverse dispersivities are unknown.
Saturated Water Content (Porosity)--
In the absence of a user- specified distribution for the saturated water
content (porosity), it can be calculated from the particle diameter using the
following empirical relationship (Federal Register 1986) :
0- - 0.261 - 0.0385 ln(d) (5-25)
where d — the mean particle diameter [cm] . Given a typical range of d
(Federal Register 1986) of 0.0004 to 0.10 cm for aquifer materials, 6S would
be 0.30 to 0.56 if Equation 5-25 is used. Its distribution is probably
normal or uniform.
Effective Porosity/Specific Yield- -
In the absence of site specific information, the effective porosity or
specific yield can be approximated using Figure 5.12.
Solute Decay- -
Degradation of solute may be the result of many factors . General guidance
for chemical degradation is given in Section 5.2.3. If hydrolysis is the
principal degradation pathway, then the following procedures may be used to
establish hydrolysis rates.
The acid-catalyzed, neutral, and base-catalyzed hydrolysis rates are all
influenced by groundwater temperature. This effect is often quantified using
the Arrhenius equation, that yields:
KI,n,b " #nfb exp [ Ea/Rg
213
-------
s>|oog
|9ABJ6 9SJBOQ
J9ABJ6
PUBS 9SJBOQ
pUBS 9U
H'.S
AB|O 9UIJ
0)
N
•H
C
•H
Cn
C
o
•H
-P
U
C
W 00
(0 en
H
U
•P ^
8s
0)
>,^
•H M
W 0)
O -P
M 4-1
m
-------
where
T - temperature of the groundwater [°C]
Tr - reference temperature [°C]
T T
Ka u and K_rtj- the second-order acid- and base-catalysis hydrolysis
rates at temperature T and T respectively [Pinole yr]
T T
ICrand K^ - the neutral hydrolysis rate at temperatures Tr and T
respectively [yr~ ]
universal gas const;
Ea - Arrhenius activation energy [Kcal mole ]
R - universal gas constant [1.987E-3 Kcal "C"1 mole"1]
O
Note that using the generic activation energy of 20 Kcal/mole recommended by
Wolfe (1985), the factor Efl/R has a value of about 10,000.
The acid-catalyzed, base-catalyzed, and neutral hydrolysis rate constants can
be combined (Mill et al., 1983) to yield the composite, first-order,
dissolved phase hydrolysis rate:
Xl - K£ [H+] + Kj + Kj [OH'] (5-27)
where
[H+] - the hydrogen ion concentration [mole i" ]
[OH"] - the hydroxyl ion concentration [mole £' ]
Note that [H+] and [OH"] can both be computed from the pH of the aquifer,
i.e.,
[H+] - 10'PH (5-28)
[OH"] - l(T<14"PH> (5-29)
For the case of sorbed phase hydrolysis, evidence suggests that base
neutralized hydrolysis can be neglected and that the acid neutralized
hydrolysis rate is enhanced by a factor of a. Thus, the effective sorbed
phase decay rate can be expressed as:
A2 = aK£[H+] + Kj (5-30)
where a — acid-catalysis hydrolysis rate enhancement factor for sorbed phase
with a typical value of 10.0.
215
-------
Retardation Factor--
As discussed in Section 5.3, the retardation factor is a function of the
distribution coefficient (K^), bulk density (p), and saturation water content
Kd P
R - 1 + -2— (5-32)
If values of bulk density p are not known, they can be approximated using
equation 5-17.
The relationship most suited for relating the chemical distribution
coefficient, K^, to soil or porous medium properties is discussed in detail
by Karichoff (1984). In the absence of user-specified values, hydrophobic
binding is assumed to dominate the sorption process. For this case, the
distribution coefficient can be related directly to soil organic carbon
using:
Kd - Koc foc <5
where
KQC = normalized distribution coefficient for organic carbon
fQC = fractional organic carbon in the saturated zone
The discussion of the magnitude of the coefficient of variation for the
distribution of K^ also applies here. The distribution of fQC used for waste
sites from all over the U.S. has a range of 0.001 to 0.01, a mean of 0.00315,
and a standard deviation of 0.0003 (Woodward-Clyde Consultants, 1988). It is
lognormally distributed with a transformed mean of -5.76 and a transformed
standard deviation of 3.17 (Federal Register 1986). Section 6.3.3 of Volume
I discusses the lognormal distribution and provides equations for calculating
the transformed mean and standard deviation parameters.
216
-------
SECTION 6
EXAMPLE PROBLEMS
The example problem presented in this section is for the simulation of fate
and transport of the pesticide aldicarb on Long Island. The section opens
with a brief description of the site, and proceeds to discuss the values of
various model parameters utilized in the simulation. Results are then
presented, which are compared to field-measured data.
6.1 THE PHYSICAL SETTING
This problem has previously been simulated by Lafleur et al. (1981). The
following description of the site hydrogeology and local setting is taken
from that report.
Long Island extends from New York City eastward about 120 miles. It is
underlain by consolidated bedrock, which in turn is overlain by a wedge-
shaped mass of unconsolidated sedimentary materials. The top of the
bedrock, which is at or near the land surface in the northern part of the
island, slopes to the southeast to depths of about 2000 feet below sea
level at the south shore. The general hydrologic situation has been
reviewed by Cohen, Franke and Foxworthy (1968) and will not be repeated in
detail.
The materials that overlie the bedrock and constitute the groundwater
reservoir consist of Pleistocene deposits and Cretaceous unconsolidated
fluvial and deltaic deposits of gravel, sand, silt, and clay. Groundwater
in the uppermost part of the saturated zone, mainly the upper glacial
aquifer, but locally also in the Magothy aquifer, is generally under
water-table (unconfined) conditions. Artesian (confined) conditions
predominate in most other parts of groundwater reservoirs of Long Island.
There are several distinctive features about Long Island which affect its
hydrogeological behavior and hence influence how solutes such as aldicarb
may move. First, both the Pleistocene upper glacial material, in which
the water-table aquifer is developed and on which are the island soils,
and the deeper, dominantly artesian, Cretaceous Magothy aquifer are mainly
unreactive quartz (Si02> grai-ns with little clay or organic material and
an almost total absence of carbonate minerals. Because of an absence of
reactive minerals, the chemistry of groundwater tends to be like that of
recharging rainfall--slightly acid, pH 5-6, with low salt content.
Further, the scarcity of reactive clay minerals and organic material tends
to limit the sorbtive capacity of the aquifers.
217
-------
The glacial and Magothy materials, in addition to their lack of chemical
reactivity, have an above-average ability to transmit water. Thus,
rainfall on the island tends to percolate readily through the soil to the
zone of saturation where it moves laterally as groundwater to discharge to
the ocean or to streams and inlets along the shore. Because of this
ability of Long Island soils to accept rainfall and transmit it directly
downward, little rainfall runs off the surface of the soil, and thus there
is little development of streams on the island.
The site selected for the modeling of pesticide transport on a local scale
was the Wickham farm. The study field is located on the south shore of
the North Fork of Long Island near the town of Cutchogue. According to
the data supplied INTERA by the EPA, the field comprises approximately 6.5
acres, and was subjected to three biannual aldicarb applications, as
indicated in Table 6-1. This site was chosen from a number of possible
alternatives for the following reasons:
1. No source of aldicarb exists up the hydraulic gradient from this
field. Thus, all aldicarb found in groundwater under and
downgradient from the field must be from aldicarb applied to the
field.
2. The depth of the water table under the field is shallow enough that
wells for sample collection could be installed and samples of the
unsaturated soil down to the water table taken with minimal
difficulty.
3. The amounts and dates of aldicarb application to the field were
known.
4. The hydrogeological conditions and aldicarb application rates
are representative of those on the Island.
Table 6-1. ALDICARB APPLICATION RATES - WICKHAM FARM
Nominal Aldicarb Mass Applied
Date (Ibs active ingredient/acre)
April 15, 1977
June 10, 1977
April 15, 1978
June 20, 1978
April 15, 1979
June 10, 1979
2.0
1.2
2.25
1.8
2.85
2.25
Actual application dates may have varied ± one week, depending on the stage
of growth of the crop.
218
-------
A map of the Wickham field site is shown in Figure 6.1 and an idealized
longitudinal cross-section is shown in Figure 6.2.
6.2 THE PESTICIDE ALDICARB
Aldicarb is a nematicide, acaricide, and a systemic insecticide. Its
environmental fate is dominated by two factors: the fact that it forms two
toxic daughter products, and its high mobility in soils. Degradation of the
toxic residues of the compound is of intermediate duration compared to other
pesticides.
Aldicarb is a white crystalline solid which is incorporated into soil as a
granule containing either 10% or 15% active ingredient. In order to be
effective, it must dissolve in water. Once this happens in soils, the
compound begins to transform and degrade.
The current theory is that aldicarb, 2-methyl-2-(methylthio)propionaldehyde
0-(methylcarbamoyl)oxime, is fairly rapidly oxidized to aldicarb sulfoxide,
2-methyl-2-(methylsulfinyl)propionaldehyde 0-(methylcarbamoyl) oxime, which
in turn is more slowly oxidized to aldicarb sulfone, 2-methyl-2-
(methylsulfonyl)propionaldehyde 0-(methylcarbamoyl)oxime. Concurrently,
these three carbamates are transformed by hydrolysis to corresponding oximes.
Hydrolysis is a chemical reaction in which water breaks up an organic
molecule (RX), such as aldicarb, by breaking a carbon-X bond and replacing it
with OH from the water molecule:
R-X + H2 -» R-OH + X '+ H+ (6-1)
These products of hydrolysis are far less toxic than aldicarb, its sulfoxide
or its sulfone, and are of little environmental concern (Smelt et al. 1978).
A schematic of these processes is shown in Figure 6.3.
Recently, Dean et al. (1988) performed a detailed review of degradation and
transformation rates and adsorption coefficients for aldicarb and its two
daughter products.
6.3 EXAMPLE PROBLEMS
Two example model applications are discussed in this section. The first,
P2S, is a PRZM to SAFTMOD linkage. The second, P2V, is a PRZM/
VADOFT/SAFTMOD linkage.
6.3.1 Example Problem One - PRZM To SAFTMOD Linkage (P2S")
In the first example problem, PRZM and SAFTMOD are linked directly, without
using VADOFT. SAFTMOD is used in the X-Z mode. It was assumed that aldicarb
flux, but no recharge water, enters the aquifer. A problem of this type is
probably the simplest which can be addressed with the linked model.
219
-------
Figure 6.1. Map of the Wickham Field study site.
220
-------
RECHARGE
u m n n »ii n H mi in
0.30 m^ ALDICARB APPLICATION
ZUINt
^ \ : — •••'•• • — • • • •••••'••• '• -r~
* VADOSE ZONE
* yn m *i* *fi>o m *i* i/o m ^
SATURATED ZONE
kY
^ ^^ — c mrr — mm'v'viim'v CVTTX ^
x
U- - . e: o 7 i-n ».
-1
2.2J
t
7.2
> m
m
Figure 6.2. Longitudinal cross-section of Wickham Field
study site.
Aldicarb
Aldicarb Sulfoxide
Aldicarb Sulfone
CH, O *J. O CH, 0 *2 O CH,
I J II II I J II II t J
0
n
CH
,S - C - CH = NOCNHCH,—CH,S - C - CH =» NOCNHCH, — CH,S - C - CH - NOCNHCH,
3 | J J I J J|| | J
CH,
CH,
O CH,
(Hydrolysis)
(Hydrolysis)
Nontoxic Oximes and Nitriles
Figure 6.3. Schematic of aldicarb environmental chemical pathways.
221
-------
Specifics of the model input sequences are described below, followed by
simulation results and comparison to observed data.
6.3.1.1 PRZM Input--
PRZM was used to simulate the crop root zone, the upper 60 cm of the profile.
The PRZM input data set is shown in Figure 6.4. Pertinent features of the
input are discussed below.
Uptake of the pesticide was simulated (UPTKF=1.0). Soil temperature was
simulated (ITFLAG=1) with a bottom boundary temperature of 26°C. Values of
thermal conductivity and heat capacity were estimated by the model
(IDFLAG=1). For the purpose of calculating soil thermal properties, the
sand, clay, and organic carbon percentage of each horizon was assumed to be
60, 30, and 2, respectively. Volatilization was also simulated. The -
diffusion coefficient for the_chemical in air was set to 4300 cm day . A
Henry's Law Constant (cm cm ) of 1.7 x 10 was used. No temperature
correction was made for the Henry's Law Constant (ENPY = 0.0). Monthly
values of albedo (ALBEDO) for the temperature simulation were set to 0.15,
the emissivity of the soil set to 0.97, and the measurement height of the
wind speed in the meteorological file was assumed to be 2 meters.
Three soil horizons were utilized. The first was 30 cm in thickness, with-5-
cm layer thicknesses (DPN). Bulk density (BD) in the layer was 1.38 g cm ,
and the field capacity and wilting point water contents (THEFC and THEWP)
were set to 0.24 and 0.10 cm cm , respectively.
o -1
The chemical partition., coefficient was set at 0.20 cm g and the decay rate
was set to 0.0035 day" .
In the second horizon (total thickness of 30 cm),-layer thickness was greater
(10 cm), bulk density slightly higher (1.59 g cm ), and field capacity and
wilting point water contents lower (0.042 and 0.02, respectively). The
partition coefficient was set to 0.20 cm g and the decay rate was 0.0035
day
The third horizon (885 cm) was similar to the second, with layer thicknesses
of 8.85 cm, slightly lower THEFC (0.035), THEWP (0.015), and partition
coefficient (0.16). The decay rate was set to 0.00076 day" . The user
should note that a total of 115 soil layers were used to simulate the PRZM
domain from the soil surface to the base of the aquifer.
6.3.2 SAFTMOD Input
6.3.2.1 Flow--
For the SAFTMOD flow simulation, 451 nodes (400 elements) were used in an X-Z
configuration with no recharge. Nodal spacing was 13.125 meters in the 'x'
direction and 0.72 m in the 'z' direction. The aquifer was assumed, to be
approximately 7.2m thick, with a constant gradient of 0.00074 m m . This
results from the use of constant head conditions at the upstream and
downstream boundaries of 7.6 m and 7.2m, respectively. The..hydraulic
conductivity in the 'x' direction was taken to be 100 m day and in the 'z'
direction, 10 m day . The specific storage of the aquifer material was
222
-------
taken to be 1.0 x 10"5 m"1; the specific yield, 0.24. The SAFTMOD flow input
data set is shown in Figure 6.5. Specific values of key parameters for flow
and transport are summarized in Table 6-2.
Table 6-2. KEY PARAMETER VALUES USED IN THE SIMULATION OF GROUNDWATER FLOW
AND PESTICIDE TRANSPORT IN THE SATURATED ZONE (EXAMPLE NUMBER 1)
Parameter
Saturated hydraulic conductivity, K^
Saturated hydraulic conductivity, KZ
Porosity, (f>
Specific yield, S
Specific storage, Sg
Longitudinal dispersivity, a-^
Vertical dispersivity, a^
Retardation factor, R
Decay coefficient, 7
Coarse
Sand
100
10
0.3
0.24
io-5
10
0.2
1.86
7.6xlO"4
Units
m d
m d
-1
m
m
m
d-1
6.3.2.2 Transport- -
For transport, the same grid spacing was used as for flow.
An effective
porosity of 0.3 m m was used along with a retardation factor of 1.86 and a
degradation constant of 0.00076 day . A dispersivity of 10 m in the 'x'
direction and 0.2 m in the ' z' direction was used. The SAFTMOD transport
input data set is shown in Figure 6.6.
6.3.3 Example Problem Two - PRZM/VADOFT/SAFTMQD Linkage (P2V)
The second example problem involves the use of all three models. The
simulation conditions were essentially the same except that VADOFT was used
to simulate the lower portion of the vadose zone. The PRZM input file
(Figure 6.7) was modified so that it would simulate only the top of the
vadose zone.
The VADOFT flow and transport input sequences are discussed below.
224
-------
6.3.3.1 VADOFT Flow--
The VADOFT flow input sequence is shown in Figure 6.8. For the
PRZM/VADOFT/SAFTMOD linkage, the vadose zone was divided into three layers.
Layer 1 is 90 cm thick and discretized into 12 linear elements, layer 2 is 75
cm thick with 8 elements while layer 3 is 720 cm thick with 72 elements.
Although Figure 6.8 shows that two soil materials were assigned to the vadose
zone, the material properties were assumed homogeneous. The saturated
hydraulic conductivity was taken to be 432 cm day . The effective porosity
was estimated to be 0.3 cm cm , the specific storage of the material 0.0,
and the air entry pressure head 0.0 cm. The closed-form solution embedded in
the codes for generating soil water characteristic curves was used. The
coefficient values used were n = -0.1, a = 0.124, /3 = 2.28, and 7 = 0.56.
Residual water-phase saturation was taken to be 0.14 cm cm . Table 6-3 is
a summary of the key parameters for vadoze zone flow and transport.
Table 6-3. PARAMETER VALUES USED IN THE SIMULATION OF INFILTRATION AND
PESTICIDE TRANSPORT IN THE VADOSE ZONE (VADOFT SIMULATIONS)
Parameter
Saturated hydraulic conductivity, K
S3.C
Porosity, (j>
Residual water saturation. S
' wr
Air entry value , Va
Moisture retention parameter, a
Moisture retention parameter, /3
Moisture retention parameter, -y
Dispersivity , a^
Retardation factor, R
Decay coefficient, A
Loamy
Sand Units
432cm d"1
0.4
0.14
0.0
0.124 cm"1
2.28
0.56
4 . 0 cm
1.86
0.00076 d"1
Flow boundary conditions are prescribed for VADOFT at the upper boundary and
head boundary conditions are prescribed at the lower end. The head boundary
value is based on SAFTMOD-predicted water level elevation.
228
-------
6.3.3.2 VADOFT Transport--
For transport, the same grid spacing was used. The VADOFT transport input
sequence is shown in Figure 6.9. Three soil materials were assigned to the
vadose zone. Effective porosity, retardation and decay rate were assumed
uniform for each material with values of 0.3, 1.86 and 7.6x10 day ,
respectively. Dispersivity was assumed to be the same for materials 1 and 2
with a value of 4 cm while a dispersivity of 10 cm was assumed for material
3.
VADOFT transport boundary conditions are prescribed when run in the linked
mode. At the upper boundary, a prescribed flux is utilized, the value of
which is determined by PRZM solute flux output. At the lower boundary, a
zero-concentration condition is utilized.
6.3.4 Simulation Results
The simulation was run for three years using meteorological data provided by
EPA (Bob Carsel, personal communication). For the most part, initial
parameter values were taken from Lafleur et al. (1981) and were adjusted
through the calibration process to achieve results which correlated as well
as possible with the observed data. Data taken during December 1979 for the
vadose zone and the saturated zone were utilized for comparison to simulation
results.
6.3.4.1 Vadose Zone Results--
Observed and simulated concentration profiles using PRZM (P2S simulation) and
VADOFT (P2V simulation) are shown in Figure 6.10. Note that the observed
data are shown for three of the five cores having concentrations reported by
Lafleur et al. (1981). The two remaining cores contained almost no residues
and were not plotted. Simulated peak concentrations were on the order of 175
mg kg and occurred at a depth of approximately 100 cm below land surface,
whereas the observed concentrations were much lower, with the peak occurring
at approximately 75 cm below land surface (the peak concentration observed
was 118 mg kg ). With the parameters selected for these simulations,
groundwater concentrations of sufficient magnitude to match the observed
values could not be simulated without this magnitude of concentrations in the
vadose zone. With the selected parameters, both PRZM and VADOFT simulate
movement of the peak further into the profile than the observed data.
However, it should be noted that earlier calibration work by Carsel et al.
(1985), with PRZM I, by Donigian (personal communication, 1989) with PRZM II,
and by Huyakorn et al. (1988) with VADOFT, demonstrated much better agreement
between observed and simulated vadose zone concentrations using a decay rate
of 0.002 day" (instead of 0.00076 day ) for the vadose region between the
root zone and the water table. The results presented here are only for
demonstration of the linked operation of RUSTIC.
6.3.4.2 Saturated Zone Results--
Figure 6.11 shows simulated aldicarb concentrations in an X-Z cross section
of the modeled area in December 1979 for P2S linkage and Figure 6.12 show the
same for the P2V linkage. Note that the concentrations are skewed to the
right, indicating the influence of the regional gradient. Figure 6.13 shows
230
-------
the simulated versus observed concentrations with depth for Wells W-2, -4, -
5 and -7. Note the position of these wells from Figure 6.1. The
simulationof concentrations in Wells W-2 and W-4, which are essentially in
the field, are excellent with the exception of the shallowest depth (0.8 m
below the water table) in Well W-2. Observed concentrations at Wells W-5 and
W-7, which are further downgradient, are less well matched by the simulation
results. In these wells, the shallowest observations are better simulated
than the deeper observations.
ui
o
LL
DC
V)
O
tr,
(3
1
Ui
CO
UJ
o
225
50
100
200
250
ALDICARB CONCENTRATION (ppb)
Figure 6.10.
Observed and simulated aldicarb distribution
in the vadose zone, December 1979.
231
-------
-65.6m-
Recharge Zone
525m
Pesticide Application Zone „
265.6m
•194.8m
Figure 6.11. Simulated aldicarb concentrations in the
saturated zone, December 1979 (P2S).
Recharge Zone
525m
* Pesticide Application Zone
265.6m
194.8m
7.2m
J L
Figure 6.12.
Simulated aldicarb concentrations in the
saturated zone, December 1979 (P2V).
232
-------
UI
o
5
z
g
t-
cc
UI
o
UJ
5
O
cc
UJ
(qdd) NOUVH1N30NOO SdVOKnV
(qdd) NOUVH1N3ONOO aHVOKHV
TJ QJ
O) C
4-> O
I/I (O
C +->
ro n3
CD O)
> ^
S- 4->
O)
01 C
4- C f->
O O CTi
CM
O
Z
ui
5
z
cc
ui
in
O
Z
ui
5
cc
LU
C +->
O n3 •>
in S- 1-
•r- -(-> O)
S- C -Q
ro O) E
Q. O O>
ECU
O O OJ
O O Q
O)
s_
3
cn
(Odd) NOU.VHIN30NOO aUVOIQlV
(qdd) NOLIVUJ>BONOO SHVOiaiV
233
-------
SECTION 7
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Smith, C.N., R.A. Leonard, G.W. Langdale, and G.W. Baily. 1978. Transport of
Agricultural Chemicals from Small Upland Peidmont Watersheds. EPA-600/3-
78-056. U.S. EPA, Environmental Research Laboratory, Athens, Georgia.
Soil Conservation Service. 1972. Hydrology. Section 4, SCS National
Engineering Handbook. U.S. Department of Agriculture, Washington, DC.
NEH-Notice 4-102.
Spencer, W.F., W.A. Jury, and W.J. Farmer. 1984. Importance of
Volatilization as a Pathway for Pesticide Loss from Forest Soils. In:
Chemical and Biological Controls In Forestry. American Chemistry
Society, pp. 193-209.
Stamper, J.H., H.N. Nigg, and J.C. Allen. 1979. Organophosphate Insecticide
Disappearance from Leaf Surfaces: An Alternative to First-Order
Kinetics. Environ. Sci. and Tech., 13:1402-1405.
Streile, G.P. 1984. The Effect of Temperature on Pesticide Phase
Partitioning, Transport, and Volatilization from Soil. Ph.D.
dissertation, University of California, Riverside.
Szeicy, G.G. Endrodi, and S. Tajchman. 1969. Aerodynamic and Surface Factors
in Evaporation. Water Resour. Res. 5(2):380-394.
238
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Taylor, J.R. 1982. An Introduction to Error Analysis. University Science
Books, Mill Valley, California.
Thibodeaux, L.J., and H.D. Scott. 1985. Air/Soil Exchange Coefficients. In
Environmental Exposure from Chemicals. Vol. I, eds. W.B. Neely and G.E.
Blau, pp. 65-90, CRC Press, Inc.
Todd, O.K. 1970. The Water Encyclopedia. Water Information Center, Port
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U.S. Department of Agriculture. 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff and Erosion from Agricultural Management Systems. W.G.
Knisel, ed. Conservation Res. Rep. No. 26, Washington, B.C.
Varas, E.A., and R.K. Linsley. 1977. Rainfall Synthesis with Scanty Data.
J. Hydrol., 34:235-249.
van Genuchten, M.Th. 1978. Mass Transport in Saturated-Unsaturated Media:
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Wehtje, G., L.N. Mielke, JH.R.C. Leavitt, and J.S. Schepers. 1984. Leaching
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Willis, G.H., and L.L. McDowell. 1987. Pesticide Persistance of Foliage.
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Willis, G.W., L.L. McDowell, L.A. Harper, L.M. Southwick and S. Smith. 1983.
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239
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SECTION 8
APPENDICES
8.1 ERROR MESSAGES AND WARNINGS
The RUSTIC Code contains a number of error and warning messages which
indicate either fatal or non-fatal routine conditions. A list of the current
error (fatal) and warning (non-fatal) conditions which are recognized by the
code is given in Table 8-1. Along with each message, troubleshooting
approaches are described. Error or warning messages originating in RUSTIC
(the main code) are numbered beginning with 1000; PRZM, 2000; VADOFT, 3000;
SAFTMOD, 4000; and the Monte Carlo module, 5000. Note that error numbers
less than 1000 may appear. These numbers are being supplied by the FORTRAN
compiler which was used to compile RUSTIC and its associated modules. These
errors will probably be associated with reading input data; e.g., problems
such as inappropriate characters in an input field which the code is
attempting to interpret as an integer or a disk drive being unavailable for
reading data. Consult the compiler errors list for the exact cause.
Note also that if the compiler used uses numbers in the range of 1000 to 5000
for these file access errors an error number may appear which seems to be an
EXESUP/PRZM/VADOFT/SAFTMOD error. The error message will not, however,
correspond to the messages in Table 8-1. The message will be something such
as: "Error in attempting to open file []" or "Error in input...."
Again, check the compiler's run time error list for the exact cause.
When errors and warnings are reported in the output echo file, three lines of
information are provided. The first line reports the number and whether the
condition was an error (fatal) or warning (non-fatal). The second line
supplies the associated message. The third line supplies the subroutine
trace of where the error occurred. For example, the third line might be:
'RUSTIOINPREA>VADINP' . This implies that the error occurred in the
subroutine VADINP (the VADOFT input routine), which was called from
subroutine INPREA, which was called from the RUSTIC main program. This third
line will not appear if an error occurs in the routine INITEM, which is the
routine to read the RUSTIC.RUN file and initialize the simulation.
8.2 VARIABLE GLOSSARY
This section presents the major variables used in the RUSTIC code. Table 8-2
presents variables used in the EXESUP module, Table 8-3 presents PRZM
variables, Table 8-4 presents VADOFT variables, Table 8-5 presents SAFTMOD
variables and Table 8-6 presents variables used in the Monte Carlo module.
240
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
Error or
Warning
Troubleshooting Approach/Explanation
1000 nnn negative water fluxes
into SAFTMOD zone nn
1010 Water table is above
vadose zone
1020 Water table is above
root zone
1040 Zero aquifer thickness in
MASCOR
1050 Zero or negative mass in
VADOFT/PRZM nodes below
the water table
1060 VADOFT and SAFTMOD were
selected w/o PRZM
1070 Error in the file name
input, line with...
1080 Value of NLDLT [nnnn]
exceeds MXVDT [nnnn]
1082 Value of NLDLT [nnnn] is
less than 1
Negative values of water flux have been set
to zero in WMFCAL. This is normal. No
action necessary.
The water table has accumulated to above the
top of the vadose zone. Use higher
conductivities in SAFTMOD or increase the
thickness of the vadose zone.
The water table is above the top of the root
zone. Use higher conductivities in SAFTMOD
or increase the thickness of the root zone.
Check the values used to define the SAFTMOD
grid in the SAFTMOD input data.
This is a warning only, the concentration
values in the VADOFT or PRZM nodes below the
water table will not be adjusted for the
current timestep by what mass was simulated
within the SAFTMOD common volume. If this
warning appears repeatedly, the VADOFT or
PRZM and SAFTMOD geometry might have to be
adjusted.
If VADOFT and SAFTMOD are run in linked
mode, PRZM must also be utilized. Turn PRZM
"ON" in global parameter file and provide a
PRZM input parameter file.
An incorrect (or misspelled) identifier was
supplied for a file (ANAME of group EXESUP2,
see Section 4.2.1.2).
The number of PRZM/VADOFT timesteps
specified exceeds the maximum number in the
PARAMETER statement. Reduce the number of
timesteps or increase the maximum number in
the PARAMETER statement. If the latter,
recompile the code.
NLDLT must be greater than 0 in the global
data group of RUSTIC input.
241
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
1084 NLDLT [nnnn] has been The value of NLDLT which was input was
reset to the calculated greater than the total number of days
total number of days [nnn] calculated from the starting and ending
dates. This is a warning only, but to avoid
it, set NLDLT to be less than or equal to
the total number of days between the start
and end of simulation.
1090 Bad value [nnnn] for
number of chemicals
1092 Bad index [nnnn] of
chemical
1100 Bad value [nnnn] for
chemical parent species
1190 Bad identifier reading
global data []
The number of chemicals must be between 1
and 3, inclusive. Change the number in the
global data group of RUSTIC input file.
An invalid index was provided for input
record EXESUP3 with ANAME = 'PARENT OF'.
Values less than 1 or greater than NCHEM are
not valid.
Check input values. Chemical
a parent of 0 only. Chemical
parent of 0 or 1. Chemical 3
parent of 0, 1, or 2.
1 can have a
2 can have a
can have a
An invalid label appears in the global data
section (EXESUP) of the RUSTIC.RUN input
file.
1200 End date is before start
date
1202 End date and start date
are the same
1210 Unrecognized label
[
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
1240 End of file on RUSTIC
run file
1250 Error reading RUSTIC
run file data. . .
1270 Too many files requested
to be open at once
1280 ENDFILE statement present
before file [nn] was
opened
1290 Request to close file
[nn] which was not open
1300 Unknown unit number to
open file
1310 Too many lines required
for Trace Option
1320 Argument [] too
large for EXP
1330 Negative or zero argument
[] to LOG
Recheck the global data group of the RUSTIC
input file. There is an error in the input
sequence; an option was set which required
more lines of data than supplied.
Error in reading RUSTIC input data, most
likely there are inappropriate characters in
a data field which is attempting to be
interpreted as integer data.
The maximum number of files allowed (defined
in the include file IOUNITS.PAR) is too
small a number for the (recently modified)
version of RUSTIC. This error should not
appear in the current version of RUSTIC.
An input file, which is required for the
current RUSTIC simulation configuration, has
not been identified in the file group of the
RUSTIC input file.
Should never occur in current version of
RUSTIC. Implies that recent code
modifications have been made which did not
properly account for which files were open.
Should never occur in current version of
RUSTIC. Implies that recent code
modifications have been made which did not
properly account for which files were open.
Should never occur in current version of
RUSTIC. Implies that recent code
modifications have been made resulting in a
very high level of subroutine nesting.
Attempt to take the exponential of too large
a. real, number.
Attempt to take the log of a zero or
negative argument.
243
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
1350 Single precision overflow
1360 Negative argument
[] to SQRT
1390 Invalid index [nnnn]
in reading record
[]
1400 Error reading PRZM data
1500 ENDDATA before starting
day was provided
1510 ENDDATA before end day
was provided
1520 ENDDATA before time step
was provided
1530
1540
ENDDATA before number of
chemicals was provided
ENDDATA before the parent
of chemical n was
provided
A mathematical operation resulted in a
number too large for the real value being
calculated.
Attempt to take the square root of a
negative number. Subroutine trace
accompanying error message will show in
which routine the error occurred.
A bad index value in a VADOFT or SAFTMOD
read, probably initial condition data.
Probable causes are inappropriate characters
in an input field for integer or real reads.
The label 'ENDDATA' appears in the global
parameters section of RUSTIC.RUN file before
the 'START DATE' record was provided.
The label 'ENDDATA' appears in the global
parameters section of RUSTIC.RUN file before
the 'END DATE' record was provided.
The label 'ENDDATA' appears in the global
parameters section of RUSTIC.RUN file before
the 'TIME STEP' record was provided. The
'TIME STEP' record is required if SAFTMOD is
on.
The label 'ENDDATA' appears in the global
parameters section of RUSTIC.RUN file (with
TRNSIM = 'ON') before the 'NUMBER OF
CHEMICALS' record was provided. The 'NUMBER
OF CHEMICALS' record is required for a
transport simulation.
The label 'ENDDATA' appears in the global
parameters section of RUSTIC.RUN file (with
TRNSIM = 'ON' and NUMBER OF CHEMICALS
greater than 1) before the 'PARENT OF n'
record was provided.
244
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
1550 dd/mm/yy - Invalid START
(or END) DATE
1560 End of file [] encountered
1570 Monte Carlo simulation -
Echo level reset to 1
2000 Simulation date
(dd/nun/yy) ,
meteorological date
(dd/mm/yy) do not match
2005 WDMS file not supported
in RUSTIC version of PRZM
2010 Number of chemicals in
PRZM [nn] O number of
chemicals in EXESUP [nn]
2040 NPI [nnnn] + NEW [nnnn]
is greater than NPII
[nnnn] in subroutine MOC
2050 Solution for tridiagonal
matrix not found,
previous day's values
used
An invalid date has been entered in the
global parameters section of the RUSTIC.RUN
input file. Check to see if the month being
specified had the number of days which is
being implied (e.g., 31/02/88 is not valid).
The end of the file specified was reached
while still attempting to read data.
If an echo level greater than 3 is requested
with Monte Carlo on, the echo level will be
reset to 1. No action required.
The meteorological data file is not aligned
with the simulation data. There is
probably a missing record in the data file
or the simulation start and end dates
specified in RUSTIC.RUN do not correspond to
the dates in the meteorological data file.
This error should not occur in current
version of RUSTIC. If it does appear,
recent and probably inappropriate changes
have been made to the code.
The value supplied to the PRZM input file
for the number of chemicals being simulated
does not agree with the number supplied to
the RUSTIC input file.
Decrease the number of PRZM compartments or
increase the parameter NPII. If the latter,
recompile the code. This error only occurs
if the MOC rather than backward difference
transport solution technique is used.
If this message appears repeatedly, the PRZM
problem definition geometry should be
reevaluated.
245
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
2060 NDC [nnnn] is greater
than NC fnnnnl
2070 NCPDS [nnnn] is greater
than NC [nnnn]
2080 NAPS [nnnn] is greater
than NAPP [nnnn]
2090 NHORIZ [nnnn] is greater
than NCMPTS [nnnn]
2100
2110
2120
2130
NCOM2+1 [nnnn] is greater
than NCMPTS [nnnn]
NPLOTS [nnnn] is greater
than 7
Change PRZM problem definition geometry so
that the input value of NDC is less than or
equal to the parameter NC or change the
value of NC and recompile.
Change PRZM problem definition geometry so
that the input value of NCPDS is less than
or equal to the parameter NC or change the
value of NC and recompile.
Change PRZM problem definition geometry so
that the input value of NAPS is less than or
equal to the parameter NAPP or change the
value of NAPP and recompile.
Change PRZM problem definition geometry so
that the input value of NHORIZ is less than
or equal to the parameter NCMPTS or change
the value of NCMPTS and recompile.
Change PRZM problem definition geometry so
that the input value of NCOM2 is less than
the parameter NCMPTS or change the value of
NCMPTS and recompile.
Reduce the number of requested plots.
Sum of horizon thicknesses Change PRZM problem definition geometry so
exceeds depth that the sum of horizon thickness is equal
to the user supplied total depth.
Soil profile description
is incomplete, data
available for xx.xx of
xx.xx cm
2140 Calculated field capacity
water content exceeds the
saturation value
Change PRZM problem definition file so that
soil profile data are supplied for the
entire defined depth.
Either decrease the soil bulk density or
adjust the parameters for calculating field
capacity water content (if THFLAG=1) or
lower the supplied value of field capacity
water content (if THFLAG=0).
246
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
3000 Fatal error in HFINTP, The current time in VADOFT exceeds the
interpolation failed supplied values of the interpolation time
vector in attempting to interpolate head or
flux values. This error should not occur
when running VADOFT in linked mode. If
running VADOFT alone, increase the number of
time periods of the interpolation time and
head/flux vectors.
3010 VARCAL - timestep nnn The maximum number of time refinements was
solution fails to converge exceeded due to non-convergence. Relax the
converge criterion, change the iterative
scheme or revise VADOFT parameters.
after nnn reductions
3020 Attempt to run VADOFT
w/PRZM on and ITRANS
.ne. 1
3030 Incorrect value for IMODL
3040 in VADOFT input
3050 Requested value of NOBSND
[nnnn] greater than
MXPRT [nnnn]
3060 Transport simulation,
NVREAD reset to 1
3070 PRZM is on; IVSTED reset
to 1
The user has attempted to run VADOFT with
PRZM on and ITRANS not equal to one. Set
ITRANS to 1 and make the appropriate changes
to the VADOFT parameter file.
An incorrect value has been entered for
IMODL in the VADOFT input file. Check the
values entered; IMODL = 0 for transport,
IMODL = 1 for flow.
The value entered for the number of
observation nodes in VADOFT (NOBSND) exceeds
the maximum (MXPRT). Reduce the number of
observation modes or increase MXPRT in the
PARAMETER statement. If the latter,
recompile the model.
The value of NVREAD supplied by the user was
reset to 1 since a transport simulation was
requested; no action required.
The value of IVSTED supplied by the user was
reset to 1; no action required.
247
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
3080 PRZM is on; flow boundary
conditions will be over-
written
3090 PRZM is on; transient
data at top node ignored
If PRZM is on and linked to VADOFT, a
prescribed flux b.c. will be used at the
VADOFT top node. Daily values of water and
solute flux are generated by PRZM. Related
boundary conditions in the VADOFT impact
file are overwritten. IBTND1 is set to 0;
no action required.
If PRZM is on, any transient flow data
relevant to VADOFT's upper boundary is
overwritten. ITCND1 is set to 0; no action
required.
3100 SAFTMOD is on; flow b.c. If SAFTMOD is on, a prescribed head boundary
at bottom node overwritten condition is used for VADOFT's bottom
boundary. The head is determined by SAFTMOD
input. IBTNDN is set to 1; no action
required.
3110 SAFTMOD is on; transient
data at bottom ignored
3120 PRZM is on; transport
boundary conditions will
be overwritten
3130 PRZM is on; transient
data at top node ignored
3140 SAFTMOD is on; transport
3150 b.c. at bottom node
overwritten
If SAFTMOD is ON, any transient flow data
input by the user relevant to VADOFT's lower
boundary is overwritten. ITCNDN is set to
0. No action required.
PRZM .output will overwrite VADOFT upper
boundary condition for solute transport.
PRZM generates daily volume of solute flux.
IBTNDI is set to 0. No action required.
If PRZM is on, any transient solute flux
data the user has input for the upper
boundary in VADOFT is ignored. ITCNDN is
set to 0. No action required.
If SAFTMOD is on, then the user defined b.c.
for the lower VADOFT boundary is
overwritten. A zero concentration boundary
is used. IBTNDN is set to 1. No action
required.
248
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
3160 SAFTMOD is on, transient
data at bottom ignored
3170 Invalid index [nnn] in
reading PINT
If SAFTMOD is on, any transient data
relevant to VADOFT's lower boundary is
ignored. ITCNDN is set to 0. No action
required.
An invalid index (less than 1 or greater
than the parameter NP) was supplied for an
initial condition value. Supply proper
value.
3180
3190
3200
3210
NTOMT
ITMGENol in linked mode,
results may be unpredict-
able
NTS reset to EXESUP
supplied value (NLDLT)
[]
End of file reading
VADOFT Darcy velocities
4000 Daughter products not
currently implemented
for aquitards
4010 Invalid IRZON value
[] reading group
The supplied value of NTOMT in the VADOFT
input file was less than the value of NLDLT
supplied for the execution supervisor.
NTOMT has been reset to NLDLT. No action
necessary but check the VADOFT echo of 'LIST
OF BACKUP FILE OUTPUT MARKER TIME VALUES' to
ensure that the integer day numbers from 1
to NLDLT are greater than or equal to the
values in this list. If not, a read error
of Darcy velocities may occur.
The user is supplying output marker time
values which, potentially, could result in a
read error of Darcy velocities during the
VADOFT transport simulation.
No action necessary. The value of NTS in
the VADOFT input file will be overwritten by
the value of NLDLT supplied to the execution
supervisor input.
Check to see if warning 3180 or 3190
occurred prior to this fatal error. Make
necessary changes to VADOFT input file.
The model is currently only configured to
run daughter products simulation in a single
aquifer system. They cannot be simulated in
aquitards or the lower of a two aquifer
system.
A value for IRZON has been entered which is
less than or equal to zero or which is
greater than the maximum number of nodes.
Check input sequence.
249
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
4020 Flow simulation with
IMODL.NE.l
4030 Transport simulation
with IMODL.NE.l
4040 Value specified for NP
[nnn] greater than max
allowed [nnn]
4050 No. of aquifer elements
[nnn] not as specified
[nnn]
4060 IEXEC is zero in SAFTMOD
4070 Daughter products not
implemented for multiple
aquifers
4080 Default values of Darcy
velocities will be
overwritten
4090 SAFTMOD input error
reading group
User is attempting to run a flow simulation
(SAFTMOD is on) with IMODL not equal to one.
Reset IMODL to 1 in the SAFTMOD input file.
The value of IMODL is not set to zero in the
SAFTMOD transport input file (SAFTMOD is
on). Reset IMODL to 0 in the SAFTMOD input
file.
The number of nodes in SAFTMOD exceeds the
maximum value (MXNP). Decrease the number
of nodes (and elements) in the SAFTMOD input
file or increase MXNP in the PARAMETER
statement. If the latter, recompile the
code.
The number of elements calculated by the
mesh generator is not equal to the number in
the input file. Recheck NE in the input
file.
The program has terminated at the user's
request because IEXEC = 0. Set IEXEC = 1
for the program to proceed after mesh
generation is done.
Daughter products (ICHAIN = 1) can only be
simulated in a single aquifer system. Set
ICHAIN to zero in the SAFTMOD parameter
file.
For a transport simulation in the linked
system, default Darcy velocities will be
overwritten. NUREAD is reset to 1. No
action required.
The format in the input file does not match
the format for the indicated group. Most
likely, the preceeding group has additional
or missing records. Check the SAFTMOD input
file.
250
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(continued)
Error or
Warning
Troubleshooting Approach/Explanation
4100 Invalid IPSZO value
[] reading group
5000 Format error in reading
Monte Carlo input file
5010 Premature end of
Monte Carlo input file
A value for IPSZO has been entered which is
less than or equal to zero or which is
greater than the maximum number of zones.
Check in input sequence.
Check Monte Carlo input file. Illegal
characters are in inappropriate data file
columns.
Check Monte Carlo input file. Insufficient
data lines have been provided given the
users problem definition.
5020 Uniform random number Random exponential distribution variates
could not be generated for could not be generated. Probable cause is
exponential distribution inappropriate distribution parameters being
supplied in the Monte Carlo input file.
5030 Cannot have a negative
mean for a log normal
distribution. Mean
equals
5040 Subroutine DECOMP
terminated, matrix BBT
is not positive definite
5050 The number of [MONTE
CARLO RUNS] is greater
than maximum of
5060 The number of [MONTE
CARLO VARIABLES] is
greater than maximum of
5070 The number of [EMPIRICAL
DIST. DATA POINTS] is
greater than maximum of
A negative mean was calculated for a log
normal distribution. Check distribution
parameters supplied in the Monte Carlo input
file.
Monte Carlo solution matrix could not be
decomposed. Check distribution parameters
supplied in Monte Carlo input file.
Too large a value was chosen for the number
of Monte Carlo runs. Reduce number in input
file or change NRMAX in parameter file and
recompile.
Reduce number in input file or change MCMAX
and recompile.
Reduce number in input file or change NEMP
and recompile.
251
-------
Table 8-1. RUSTIC ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING APPROACHES
(concluded)
Error or
Warning
Troubleshooting Approach/Explanation
5080 The number of [MONTE
CARLO OUTPUT VARIABLES]
is greater than maximum
of
5090 The number of [DAYS IN
OUTPUT AVG. PERIOD] is
greater than maximum of
5100 The number of [REQUESTED
OUTPUT CDFS] is greater
than maximum of
5110 First element for horizon
[] not found
Reduce number in input file or change NMAX
and recompile.
Reduce number in input file or change NPMAX
and recompile.
Reduce number in input file or change NCMAX
and recompile.
The PRZM horizon value provided for a
variable defined in the Monte Carlo input
file is probably invalid (does not match the
PRZM horizon/element number description
provided in the PRZM input file).
252
-------
Table 8-2. EXESUP PROGRAM VARIABLES
Variable Units Type Description
Sub- Common
routine Block I,M,0
AVHEAD m Array Average head in a recharge
zone of the top SAFTMOD
aquifer.
AVSFLX ppb m day"1 Array
AVWFLX cm day" Array
Average solute flux to the
saturated zone over the
SAFTMOD timestep.
Average water flux to the
saturated zone over the
SAFTMOD timestep.
AVZWT m Array Average water table
elevation in a recharge
zone of the top SAFTMOD
aquifer.
BASEND -- Scalar Number of bottom PRZM node
within a given PRZM zone.
BOTFLX cm day" Array
-2
DAFLUX q cm Array
day
DAVFLX ppm cm day Array
DISUNS ppm „ Array
(q cm )
Water flux from VADOFT base
node for each timestep.
Dispersive-advective flux
at each PRZM node in each
zone for each chemical
(positive).
Nodal values of dispersive
advective flux from VADOFT.
Temporary storage of
VADOFT (or PRZM) nodal
concentrations for mass
correction computations.
EDAT
FLOSIM
ICHEM
Array Ending day, month, year of
PRZM simulation.
Logical Flow simulation indicator.
Scalar Counter for number of
chemicals.
EXESUP LNKSTO M
EXESUP LNKSTO M
EXESUP LNKSTO M
EXESUP LNKSTO M
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
M
EXESUP VADSTO M
EXESUP PRZSTO M
EXESUP VADSTO M
M
M
M
M
IDAYe
Scalar Starting day of PRZM
simulation.
EXESUP
M
253
-------
Table 8-2. EXESUP PROGRAM VARIABLES (continued)
Variable Units
ILDLT
IMON0
IPRZM
IPZONE
ISZONE
ITSAFT
IYRe
MFLX cm day
LISTS days
NCHEM
NDAYS days
NLDLT
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Description
Counter for PRZM or VADOFT
time steps within a SAFTMOD
timestep.
Starting month of PRZM
simulation.
Counter for number of PRZM
zones .
Counter for VADOFT zones.
Counter for SAFTMOD
recharge zones .
Counter for SAFTMOD
timesteps .
Starting year of PRZM
simulation.
Chemical efflux from
bottom (equivalenced
with WFLX) .
Number of days in final
SAFTMOD timestep.
Number of chemicals .
Number of days in a SAFTMOD
timestep minus one.
Number of PRZM or VADOFT
timesteps within a SAFTMOD
timestep.
Sub-
routine
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
INITEM
EXESUP
INPREA
INITEM
EXESUP
EXESUP
INPREA
INITEM
Common
Block I.M.O
M
M
M
M
M
M
M
LNKSTO M
I
0
I
I
0
M
I
I
0
NP
NPNARY
Scalar Total number of SAFTMOD
nodes.
Array Number of VADOFT nodes
in all VADOFT zones.
EXESUP CONTR2
EXESUP
M
254
-------
Table 8-2. EXESUP PROGRAM VARIABLES (continued)
Variable Units
NPRZM
NPV
NPZONE
NPZ
NSZONE
NTSAFT
PINT L
M/L**3
PRZMON
PRZMPF q cm"2
day
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Logical
Array
Description
Number of PRZM zones.
Number of VADOFT nodes
in a given zone .
Number of VADOFT zones.
Temporary storage for the
amount number of PRZM or
VADOFT nodes.
Number of SAFTMOD recharge
or pesticide flux zones.
Number of SAFTMOD
timesteps .
VADOFT corrected values
of head or concentration.
PRZM on indicator.
Daily chemical flux from
the base of PRZM.
Sub-_
routine
EXESUP
INPREA
INITEM
EXESUP
EXESUP
INPREA
INITEM
EXESUP
EXESUP
INPREA
INITEM
EXESUP
INITEM
EXESUP
EXESUP
INPREA
INITEM
EXESUP
Common
Block I,M,0
I
I
0
I
I
I
0
M
I
I
0
I
0
VADSTO M
I
I
0
PRZSTO M
PRZMWF cm day
P2SWHT
P2VWHT
REDAT
-1
Array Daily water flux from the
base of PRZM.
Array Weight for transfer of
water or chemical flux from
PRZM to SAFTMOD.
Array Weighting factors for trans-
fer of water or chemical
flux from PRZM to VADOFT.
Array Ending day, month, year of
PRZM simulation within a
given SAFTMOD timestep.
EXESUP PRZSTO M
EXESUP ZONWHT M
EXESUP ZONWHT M
EXESUP - - M
255
-------
Table 8-2. EXESUP PROGRAM VARIABLES (continued)
Variable Units
Type Description
Sub- Common
routine Block I,M,0
RSDAT
RSTFG
Array Starting day, month, year EXESUP
of PRZM simulation within
a given SAFTMOD timestep.
Scalar PRZM restart flag, I if EXESUP
first time through, 2
thereafter.
M
M
SAFTON
Logical SAFTMOD on indicator.
EXESUP
INPREA
INITEM
I
I
0
SAVCNC ppm
SAVHED
SDAT
cm
SFLDT ppm cm day,
(gem day )
SVSCNC ppb
SVSHED
m
Array Concentrations at each
VADOFT node from previous
timestep.
Array Previous timestep VADOFT
heads by node.
Array Starting day, month, year
of PRZM simulation.
Array Interpolated values of
solute flux at the water
table by zone and timestep
for VADOFT (PRZM).
Array Nodal concentrations from
previous SAFTMOD timestep.
Array Initial head values for
SAFTMOD.
EXESUP VADSTO
M
EXESUP VADSTO M
EXESUP
M
EXESUP LNKSTO M
EXESUP SFTSTO M
EXESUP SFTSTO M
TIME1
TIME2
days
days
TOPFLX cm day
(jug cm
day )
-1
-2
TOWFLX cm day
-1
Scalar Beginning time of current
SAFTMOD timestep.
Scalar Ending time of current
SAFTMOD timestep.
Array Weighted water (or pesti-
cide) flux leaving the
base of PRZM.
Array Water flux from PRZM to top
of VADOFT for each timestep.
EXESUP
EXESUP
EXESUP VADSTO M
EXESUP VADSTO M
256
-------
Table 8-2. EXESUP PROGRAM VARIABLES (concluded)
Variable Units
Type Description
Sub- Common
routine Block I,M,0
TRNSIM
TRTERM
Logical Indicator for flow and
transport simulation.
cm
day
".2
TRTERM ppb m day
VADFON
-1
Array Chemical decay within
VADOFT nodes
Array Chemical decay within
SAFTMOD nodes
Logical VADOFT on indicator.
EXESUP - - I
INPREA I
INITEM 0
EXESUP VCHMDK M
EXESUP SCHMDK M
EXESUP
INPREA
INITEM
I
I
0
VD2TC
V2SWHT
WFLX
WHGT
WTZLT
ZPESTR
cm day
-1
m
g cm
-2
day
-1
Array VADOFT correction factors
for converting from
dissolved to total solute
concentration.
Array Weight for transfer of
water or chemical flux from
VADOFT to SAFTMOD.
Array Water efflux from bottom
(equivalenced with MFLX).
Scalar Temporary variable for
storing flux weighting
factors.
Array Daily interpolated value of
water table elevation within
the SAFTMOD timestep.
Array PRZM chemical flux by zone,
compartment, time period,
and chemical.
EXESUP VADSTO
M
EXESUP - - M
EXESUP LNKSTO M
EXESUP - - M
EXESUP LNKSTO M
EXESUP PRZSTO M
257
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
Variable Units
A day
AAA cm
ABSOIL fraction
AD day"1
ADFLUX g cm"2
day
ADS mg kg"
AFIELD ha
AINF cm
Type
Array
Scalar
Scalar
Array
Array
Array
Scalar
Array
Description
Lower Diagonal Element
of Solution Matrix (1-1)
A Variable Used to
Calculate the Average
Temperature Gradient
in the Top Compartment
Daily Value of Soil
Surface Albedo
Soil Horizon Drainage
Parameter
Advective Flux of
Pesticide
Adsorbed Portion of
Pesticide in Each
Compartment
Area of Field
Percolation Into Each
Soil Compartment
Sub-
routine
SLPSTO
SLPST1
TRDIAG
SLTEMP
SLTEMP
PRZMRD
ECHO
INITL
HYDR2
SLPSTO
SLPST1
OUPPST
OUTTSR
MASBAL
OUTCNC
PRZMRD
EROSN
HYDROL
HYDR1
HYDR2
Common
Block I,M,0
PEST 0
0
I
M
M
HYDR 0
I
I
I
PEST 0
0
I
I
I
HYDR 0
HYDR 0
I
I
AIRDEN gm cm
-3
Scalar Density of Air at
Ambient Temperature
-1
AIRLMD cal cm - Scalar Thermal Conductivity
day" °C of Air
AKAY
Array K-Factor in the Soil
Thermal Conductivity
Equation
SLTEMP
SLTEMP
SLTEMP
M
M
258
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
ALAMDA cal cm"
day °C~
ALBEDO fraction
AMXDR cm
ANETD cm
ANUM cm
APD
APDEP cm
APM
ATEMP °C
Type
1 Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
Thermal Conductivity
of Soil Constituent
Soil Surface Albedo at
Start of Each Month
Maximum Rooting Depth
of Each Crop
Minimum Depth from Which
ET is Extracted Year
Around
Total Available Water
in Profile
Day of Month of Pesticide
Application
Depth of irrigation water
applied to soil
Month of Pesticide
Application
Air Temperature
Sub- Common
routine Block I,M,0
SLTEMP
PRZMRD MET 0
SLTEMP I
PRZMRD CROP 0
INITL I
PLGROW I
PRZMRD CROP 0
INITL I
EVPOTR
PRZMRD
IRRIG - - 0
PRZMRD
Main 0
o .3
AVSTOR cm0 cm Scalar
AW
B
BBB
day
-1
Scalar
Array
K cm Scalar
BBT
Array
Available Water Storage
Fraction of Soil Voids
Occupied by Water
Diagonal Element of
Solution Matrix (I)
A Variable Used to
Calculate the Average
Temperature Gradient
in the Top Compartment
Bottom Boundary Temperature
at Start of Each Month
HYDR2
EVPOTR
SLPSTO PEST
SLPST1
TRDIAG
SLTEMP
PRZMRD
SLTEMP
MET
0
M
0
I
259
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
-3
BD g cm
BDFLAG - -
BT m
C day"1
CB kg ha"1
CC g
CELLBG - -
CEVAP cm
CFLAG
CHANGE g
CINT cm
CINTB cm
Type
Array
Scalar
Scalar
Array
Scalar
Array
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Mineral Soil Bulk Density
Bulk Density Flag (0 =
Whole Soil BD Entered,
1 = Mineral BD and OC
Entered)
Bottom width of furrows
Upper Diagonal Element of
Solution Matrix (1+1)
Cumulative Pesticide
Balance Error
Total mass associated
with a moving point
First location in a
compartment
Current Daily Canopy
Evaporation Depth
Conversion Flag for
Initial Pesticide Input
Change in total pesticide
mass per compartment
Current Crop Interception
Storage
Crop Interception From
Previous Time Step
Sub - Common
routine Block
SLTEMP HYDR
PRZMRD
ECHO
INITL
FURROW IRGT
SLPSTO PEST
SLPST1
TRDIAG
OUTPST
MOC PEST
INITL
INITL
EVPOTR HYDR
MASBAL
OUTHYD
OUTTSR
PRZMRD MISC
INITL
MOC
INITL HYDR
HYDROL
EVPOTR
MASBAL
OUTHYD
OUTTSR
PRZM HYDR
MASBAL
OUTHYD
I.M.O
I
0
I
I
I
0
0
I
M
M
0
I
I
I
0
I
M
0
I
I
I
I
I
0
I
I
260
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
CINTCP cm
CLAY percent
CONC
CONDUC cm day"1
CONST - -
CORED cm
COVER fraction
COUNT - -
COVMAX fraction
CN
CNCPND g cm"3
Type
Array
Array
Alpha-
numeric
Scalar
Scalar
Scalar
Scalar
Array
Array
Array
Scalar
Description
Maximum Interception
Storage of Each Crop
Percent Clay in Each Soil
Horizon
Flag for Output of Soil
Pesticide Concentration
Profile
Canopy Conductance
Including Boundary
Layer ' s Conductance
Constant Values Used to
Multiply Each Time Series
Output
Total Depth of Soil
Profile
Current Areal Cover of
Crop Canopy
Number of moving points
in a compartment
Maximum Areal Coverage
of Each Crop at Full
Canopy Development
Runoff Curve Numbers for
Antecedent Soil Moisture
Condition II
Concentration of
pesticide in inflowing
Sub - Common
routine Block
PRZMRD CROP
ECHO
PLGROW
SLTEMP HYDR
PRZM
PRZM PEST
SLPSTO
SLPST1
PRZMRD
ECHO
OUTTSR
PRZMRD HYDR
ECHO
INITL
SLTEMP CROP
MOC
PRZMRD CROP
ECHO
INITL
PLGROW
PRZMRD HYDR
ECHO
HYDROL
MOC PEST
INITL
I.M.O
0
I
I
I
0
I
I
0
I
I
0
I
I
I
M
0
I
I
I
0
I
I
I
CNDBDY cm day
-1
water
Scalar Boundary Layer's
Conductance
PRZM
261
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
CNDM
CNDMO
CPBAL g cm
-2
Array Accumulated Number of
Days in Each Month (With
and w/o Leap Year)
Array Accumulated Number of
Days in Each Month
Scalar Cumulative Pesticide
Balance Error
CRC
day m Array Canopy Resistance
CRCNC day m Array Canopy Resistance
CTOT g
CURVN
CWBAL cm
m
Scalar Concentration of
consolidated points
Scalar Current Value of Runoff
Curve Number
Scalar Cumulative Water Balance
Error
Scalar Zero Displacement Height
9 -1
DAIR cur day Scalar Molecular Diffusivity
in the Air
DAY
DELT
day
Alpha- Flag for Daily Output of
numeric Water or Pesticide
Summary
Scalar Time Step
PRZM
SLTEMP MISC
MASBAL PEST
OUTPST
CANOPY
PRZM PEST
OUTPST
MOC
HYDROL
M
I
0
I
0
M
MASBAL HYDR
OUTHYD
CANOPY
SLTEMP
ECHO PEST
PRZM
PRZMRD
SLPSTO
SLPST1
PRZM
INITL MISC
HYDR2
PLPEST
SLPSTO
SLPST1
MASBAL
M
I
0
M
I
I
0
I
I
0
I
I
I
I
I
262
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
DELTA °K
DELX cm
DELXSQ cm"2
DEN
DENOM cm
DENOM cm hr"1
DEPI cm
DFFLUX g cm^2
day
DGAIR cm2 day"1
DGRATE day"1
Type
Scalar
Array
Scalar
Array
Scalar
Scalar
Array
Array
Array
Array
Description
Convergence Criteria
in the Newton -Raphs on
Solution Technique
Compartment Thickness
Compartment Thickness
Squared
Point density. The
number of points in the
horizon divided by the
depth of the horizon.
Total Voids in the Soil
Profile
Available Water for Runoff
During a Storm
Depth of Pesticide
Incorporation
Diffusive/Dispersive
Flux of Pesticide Leaving
Each Soil Compartment
Molecular Diffusivity
in the Soil Air Pore
First Order Decay Rate
for Vapor- Phase Pesticide
Sub - Common
routine Block
SLTEMP
SLTEMP HYDR
INITL HKYDR
SLPSTO
SLPST1
INITL HYDR
EVPOTR
EROSN
PRZMRD PEST
ECHO
PESTAP
SLPSTO PEST
SLPST1
OUTPST
OUTTSR
SLPSTO
SLPST1
ECHO PEST
INITL
PRZMRD
SLPSTO
SLPST1
I.M.O
M
I
0
M
0
I
0
0
I
I
I
I
I
I
0
I
I
9 -1
DIFFCH nr day Scalar Eddy Diffusivity at
o -1
DIFFCO cm^ day Array Diffusivity of Soil
Canopy Height
Diffusivity
Compartment
CANOPY
SLTEMP
M
263
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
DIFK m2 day"1
DIN cm
DISP cm2
day
DISS mg I"1
DKFLUX g cm
DKRATE day"1
DKRT12 day"1
DKRT13 day"1
DKRT23 day"1
Type
Scalar
Scalar
Array
Array
Array
Array
Array
Array
Array
Description
Eddy Diffusivity
Current Plant Canopy
Interception Potential
Dispersion/Diffusion
Coefficient
Dissolved Portion of
Pesticide in Each
Compartment
Decay Flux of Pesticide
From Each Compartment
Pesticide Decay Rate in
Each Soil Horizon
Transformation Rate from
Parent Pesticide to First
Daughter Product
Transformation Rate from
Parent Pesticide to Second
Daughter Product
Transformation Rate from
First Daughter Product to
Second Daughter Product
Sub - Common
routine Block
CANOPY
PLGROW HYDR
HYDROL
OUTHYD
PRZMRD PEST
ECHO
INITL
SLPSTO
SLPST1
OUTCNC
SLPSTO PEST
SLPST1
MASBAL
OUTPST
OUTTSR
PRZMRD PEST
ECHO
INITL
SLPSTO
SLPST1
ECHO PEST
PRZMRD
INITL
PSTLNK
ECHO PEST
PRZMRD
INITL
PSTLNK
ECHO PEST
PRZMRD
INITL
PSTLNK
I.M.O
0
0
I
I
0
I
I
I
I
0
0
I
I
I
0
I
I
I
I
I
0
0
I
I
0
0
I
I
0
0
I
264
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
DOM
DPN cm
DT hr
DVF kg ha"1
day
DW Fraction
DX m
EF kg ha"1
ELTERM day "1
EMD
EMM
EMMISS fraction
EN
ENP Kcal
mole
ENPY Kcal
mole
Type
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
Number of Current Day of
Month of Simulation
Layer Depth in Each
Horizon
Average Hours of Daylight
for a Day Falling in Each
Month
Daily Foliage Pesticide
Volatilization Flux
Available porosity in soil
column
Spatial stop used in furrow
finite difference model
Daily Erosion Flux
Erosion Loss Term for
Pesticide Balance
Day of Month of Crop
Emergence
Month of Crop Emergence
Infrared Emissivity of
Soil Surface
Manning ' s roughne s s
coefficient for furrows
Enthalpy of Vaporization
Enthalpy of Vaporization
Sub- Common
routine Block I,M,0
SLTEMP MISC
ECHO HYDR
PRZMRD
PRZMRD MET
ECHO
EVPOTR
OUTPST
IRRIG IRGT
FURROW
FURROW IRGT
IRRIG
OUTPST
EROSN PEST
SLPSTO
SLPST1
PRZMRD
ECHO
PRZMRD
ECHO
PRZMRD MET
SLTEMP
FURROW IRGT
KHCORR
ECHO PEST
PRZM
PRZMRD
I
I
0
0
I
I
0
0
M
I
0
I
I
0
I
I
I
I
I
0
265
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub-
routine
Common
Block I,M,0
ENRICH - -
ERFLAG
ERFLUX g cm
-2
Scalar Enrichment Ratio for
Organic Matter
Scalar Erosion Flag (0= Not
Calculated, 1= Calculated)
Scalar Erosion Flux of Pesticide
From Soil Surface
EVAP cm day
-1
Scalar Daily Evaporation from the
Top 5 cm of Soil After
Adjusting for Crop
evapotranspiration
EXTRA cm cm Scalar Extra Water Occurring in
a Compartment Over the
Allowed Saturation Amount
EROSN
PRZMRD HYDR
PRZM
SLPSTO PEST
SLPST1
MASBAL
OUTPST
SLTEMP
FO/
FAIH
g cn
day
-2
kg ha
-1
Array Vector of Source Terms
for Each Compartment
(Tri-diagonal Matrix)
Scalar Current Foliar Pesticide
Storage
Scalar Stability Function for
Sensible Heat
OUTTSR
HYDR2
SLPSTO PEST
SLPST1
TRDIAG
OUTPST
CANOPY
0
I
0
0
I
I
M
0
0
I
FAIM
Scalar Stability Function for
Momentum
CANOPY
0
FAM
FC
cm
Scalar Pesticide Application
Flag (1= Soil, 2= Linear
Foliar, 3= Exponential
Foliar)
Array Field Capacity Water
Depth in Soil Compartment
PRZMRD PEST
ECHO
PESTAP
INITL HYDR
EVPOTR
0
I
I
0
266
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
FCV
FDAY
FEXTRC cm
-1
FIRST
Array Regression Coefficients
for Prediction of Field
Capacity Soil Water
Content
Scalar Loop Limit, First Day
Scalar Foliar Extraction Coef-
ficient for Foliar Wash-
off Model
THCALLC
FILTRA m2 kg"1 Scalar
Filtration Parameter
for Exponential Foliar
Application Model
Scalar Index of first point
under interface with
Ratio greater than 2
FL kg ha Scalar
FLEACH Fraction Scalar
Foliar Pesticide Decay
Loss
Leaching factor, as
fraction of soil moisture
deficit
FOLPO/ g cm
-2
FP kg ha
FPDLOS g cm"2
-1
FPVLOS g cm
day
-2
Scalar Foliar Pesticide Storage
From Previous Time Step
Scalar Current Daily Foliar
Pesticide Storage
Scalar Current Daily Foliar
Pesticide Decay Loss
Array Daily Foliage Pesticide
Volatilization Flux
PRZM
PRZMRD PEST
ECHO
PLPEST
PRZMRD PEST
ECHO
PESTAP
MOC HYDR
OUTPST
IRRIG IRGT
PLPEST PEST
MASBAL
OUTPST
PRZM
OUTPST
PLPEST PEST
MASBAL
OUTPST
OUTTSR
MASBAL PEST
OUTPST
PLPEST
0
I
I
0
I
I
M
I
0
I
I
I
0
I
I
I
I
I
0
267
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
FPWLOS g cm
-2
Scalar Current Daily Pesticide
Washoff Loss
PLPEST
FRAG
Scalar Fraction of the Distance
a Curve Number is Between
Increments of Ten
PRZMRD
FRAG
FRAG
Scalar Fraction of the Current PLGROW
Crop Growing Season
Completed
Array Number of Compartments EVPOTR
Available to Extraction
of ET
FRACOM --
Scalar Fraction of Layer Attri-
buted to the Current
Horizon
INITL
FS m
Array Infiltration depth at each
station in furrow
FURROW IRGT
IRRIG
0
I
FX1 "IT
FX2 °K3
GAMMA
Scalar Fourth Order Energy
Balance Equation in
Terms of Soil Surface
Temperature
Scalar Derivative of Energy
Balance Equation in
Terms of Soil Surface
Temperature
Array Pesticide Uptake Effi-
ciency by Plant
SLTEMP
M
SLTEMP
PLGROW PEST
SLPSTO
SLPST1
M
0
I
I
GEE Fraction Array
GFLD Fraction Scalar
Depolarization Factors of SLTEMP
Soil Constituent in Three
Dimensions
Depolarization Factor of SLTEMP
Entrapped Air at Field
Capacity Water Content
M
M
268
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
GRADT "Cm
GRADW day1
HAD
HAM
HEIGHT cm
HENRY cm3 cm"3
HENRYK cm3 cm"3
HF m
HGT m
HORIZN --
HSWZT - -
HTEMP °C
HTITLE - -
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Alpha-
numeric
Description
Temperature Gradient
Wind Speed Gradient
Day of Month of Crop
Harvest
Month of Crop Harvest
Canopy Height
Henry ' s C ons tant
Henry 's Constant
Green-Ampt Suction head
parameter
Thickness of Each Layer
in the Canopy
Soil Horizon Number
Hydraulics Flag (0= Free
Draining Soils, 1= Res-
tricted Drainage)
Average Air Temperature
Comment Line to Enter
Information about Hydro -
Sub- Common
routine Block
CANOPY
CANOPY
PRZMRD
ECHO
PRZMRD
ECHO
PRZM CROP
OUTPST
PLGROW
SLTEMP
KHCORR
ECHO PEST
PRZM
PRZMRD
FURROW IRGT
INFIL
CANOPY
PRZMRD MISC
ECHO
INITL
OUTHYD
OUTPST
OUTCNC
PRZMRD
ECHO
INITL
PRZM
CANOPY
PRZMRD
ECHO
I.M.O
0
0
I
I
0
I
I
I
I
0
I
I
0
0
I
I
I
I
I
0
I
I
I
0
logy Parameters
269
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
HTMAX cm
I
IAPDY
IAPYR
IARG
IARG1
IB
IBM1
ICNAH --
ICNCN
ICROSS - -
Type
Array
Scalar
Array
Array
Array
Scalar
Scalar
Scalar
Array
Array
Scalar
Description
Maximum Canopy Height
Loop Counter
Julian Day of Pesticide
Application
Year of Pesticide
Application
Argument of Variable
Identified by 'PLNAME'
Argument of Variable
Identified by 'PLNAME'
Backward Loop Index
Counter
Soil Surface Condition
After Harvest
Crop Number
Number of horizon inter-
faces where points need
Sub - Common
routine Block
ECHO CROP
PLGROW
PRZMRD
SLTEMP
KHCORR
CANOPY
PRZMRD MISC
ECHO
PRZM
PRZMRD MISC
ECHO
PRZM
PRZMRD MISC
ECHO
OUTTSR
OUTTSR
INITL
HYDR2
INITL
PRZMRD HYDR
ECHO
PLGROW
PRZMRD CROP
ECHO
INITL
PLGROW
INITL HYDR
MOC
I.M.O
I
M
0
0
I
I
0
I
I
0
I
I
0
I
I
0
I
I
I
M
IDEL
to be consolidated, i.e.,
Ratio greater than 2.
Scalar Number of points which
are consolidated
MOC
M
270
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
IDFLAG - -
IEDAY - -
IEDY
IEMER - -
IEMON - -
IEND
I ERROR - -
IEYR
IFIRST --
IHAR
II
IJ
Type
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Flag to Identify if Soil
Thermal Conductivity and
Heat Capacity are Input
or Simulated in the Model
Ending Day of Simulation
Counter
Julian Day of Crop
Emergence
Ending Month of Simula-
tion
Index of point at which
consolidation ends
Error Flag if Tri-
Diagonal Matrix Cannot
be Saved
Ending Year of Simulation
Flag to Print Output
Heading and Initialize
Output Array
Julian Day of Crop Harvest
Loop Counter
Loop Counter
Sub- Common
routine Block
ECHO MET
PRZMRD
SLTEMP
OUTCNC
PRZMRD MISC
PRZM
ECHO
INITL
PRZMRD CROP
ECHO
INITL
PLGROW
PRZMRD MISC
ECHO
PRZM
MOC
SLPSTO
SLPST1
TRDIAG
PRZMRD MISC
ECHO
PRZM
OUTTSR
PRZMRD CROP
ECHO
INITL
PLGROW
OUTPST
PRZM
I.M.O
I
0
I
I
0
I
I
0
I
I
I
0
I
I
M
0
I
I
0
I
I
I
271
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
ILP
INABS cm
INCROP - -
INICRP - -
Type Description
Scalar Initial Level of Pesti-
cide Flag (0= No Pesti-
cide, 1= Initial Pesticide)
Scalar Initial Abstraction of
Water from Potential
Surface Runoff
Array Crop Growing in Current
Cropping Period
Scalar Initial Crop Number if
Simulation Starting Date
is Before First Crop
Emergence Date
Sub- Common
routine Block
PRZMRD MISC
ECHO
HYDROL HYDR
EROSN
PRZMRD CROP
ECHO
INITL
PLGROW
OUTHYD
OUTPST
PRZMRD CROP
ECHO
INITL
I.M.O
0
I
0
I
0
I
I
I
I
0
I
I
INTFC
I OUT
IPEIND - -
IPSCND
IRTYPE
Scalar Whole Layer (s) Attributed INITL
to the Current Horizon
Scalar Index of first point MOC
outside flow domain
Scalar Pan Evaporation Indica- PRZMRD
tor Flag (0= Data Read ECHO
In, 1= Calculated)
Scalar Foliage Pesticide ECHO
Condition after Harvest: PLGROW
1. Surface Applied PRZMRD
2 . Removed
3. Surface Residue
Scalar Irrigation type flag: IRRIG
0=No irrigation
l=Flood irrigation
2=Furrow irrigation
3=0ver - canopy sprinklers
4=Unde r - c anopy sp r inkl ers
MET
CROP
IRGT
M
0
I
I
M
0
272
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
ISCOND --
ISDAY - -
ISDY
ISMON --
I START --
ISTYR --
ITEM1 - -
ITEM2
ITEM3 - -
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
Description
Surface Condition After
Harvest Corresponding to
'INICRP'
Starting Day of Simula-
tion
Counter
Starting Month of Simu-
lation
Index of point at which
consolidation starts
Starting Year of Simula-
tion
Hydrology Output Summary
Indicator
Pesticide Output Summary
Indicator
Soil Pesticide Concentra-
tion Profile Output
Indicator
Sub - Common
routine Block
PRZMRD HYDR
ECHO
PLGROW
HYDROL
EROSN
PRZMRD MISC
ECHO
INITL
PRZM
INITL
PRZMRD MISC
ECHO
INITL
PRZM
MOC
PRZMRD MISC
ECHO
INITL
PRZM
PRZMRD MISC
ECHO
OUTHYD
PRZMRD MISC
ECHO
OUTPST
PRZMRD MISC
ECHO
PRZM
I.M.O
0
I
I
I
I
0
I
I
I
0
I
I
I
M
0
I
I
I
0
I
I
0
I
I
0
I
I
ITEMP °C
Scalar Mean Daily Temperature
Rounded to Next Lowest
Whole Number
EVPOTR MISC
273
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
ITFLAG -- Scalar Soil Temperature Flag
ITMP - - Scalar Number of Compartments
Pesticide is Applied to
When Incorporated
IY - - Annual Loop Counter
IYREM - - Array Year of Crop Emergence
IYRHAR -- Array Year of Crop Harvest
IYRMAT - - Array Year of Crop Maturation
J - - Scalar Loop Counter
Sub- Common
routine Block
ECHO MET
PRZM
OUTCNC
PRZMRD
PESTAP
PRZM
PLGROW
OUTHYD
OUTPST
OUTTSR
OUTCNC
PRZMRD CROP
ECHO
INITL
PLGROW
PRZMRD CROP
ECHO
INITL
PLGROW
PRZMRD CROP
ECHO
INITL
PLGROW
PRZM
PRZMRD
ECHO
INITL
PLGROW
OUTHYD
OUTPST
I.M.O
I
I
I
0
I
I
I
I
I
I
0
I
I
I
0
I
I
I
0
I
I
I
JJ
JP1
JP1T10
Scalar Loop Counter
Scalar Counter (J+l)
Scalar Counter (JP1*10)
PRZMRD
PRZMRD
PRZMRD
274
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
JT10
JULDAY --
K
KD cm3 g"1
KDFLAG - -
KH cm cm
KK
KOC cm3 g"1
-oc
KS m/s
L
LAYERS - -
LBTEMP ° C
Type
Scalar
Scalar
Scalar
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Counter (J*10)
Julian Day
Loop Counter
Adsorption/partition
Coefficient for Soil
Compartment
Partition Coefficient
Flag (0= Kd Read In,
1= Kd Calculated)
Henry's Constant at
Current Time
Loop Counter
Organic Carbon Partition
Coefficient
Saturated hydraulic
conductivity of soil
Loop Counter
Number of Layers in Canopy
Daily Value of Bottom
Sub- Common
routine Block
PRZMRD
PRZM MISC
PLGROW
OUTHYD
OUTPST
SLTEMP
PRZMRD PEST
ECHO
INITL
KDCALC
PESTAP
SLPSTO
SLPST1
MASBAL
OUTPST
OUTTSR
OUTCNC
PRZMRD
ECHO
PRZM
PRZM PEST
SLPSTO
SLPST1
PRZMRD
KDCALC
FURROW IRGT
INFIL
SLTEMP
CANOPY
SLTEMP
I.M.O
0
I
I
I
0
I
I
0
I
I
I
I
I
I
I
0
I
I
0
I
I
I
I
I
0
M
Boundary Temperature
275
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
LDAY
LEAP
LFREQ1 --
LFREQ2 - -
LFREQ3 - -
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Loop Limit (Last Day)
Additional Day Flag for
Leap Year
Frequency of Soil Com-
partment Reporting in
Water Output Summary
Frequency of Soil Com-
partment Reporting in
Pesticide Output Summary
Frequency of Soil Com-
partment Reporting in
Sub- Common
routine Block
PRZM
SLTEMP
PRZMRD MISC
OUTHYD
PRZMRD MISC
OUTPST
PRZMRD MISC
OUTCNC
I.M.O
I
0
I
0
I
0
I
LL
LOGO
LOGKOC - -
LOGZO - -
M
MAD
MAM
MASS g
MASSO g
Concentration Profile
Output Summary
Scalar Loop counter MOC
Scalar Logarithm of Zero CANOPY
Displacement Height
Scalar Natural Log of Koc KDCALC
Scalar Logarithm of Roughness CANOPY
Length
Scalar Loop counter MOC
Scalar Day of Month of Crop PRZMRD
Maturation ECHO
Scalar Month of Crop Maturation PRZMRD
ECHO
Array Current pesticide mass MOC
in compartment
Array Total pesticide mass in MOC
each compartment at INITL
previous time step
0
PEST
M
M
276
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
MAT
MCFLAG - -
MD
MDOUT kg ha"1
MEOUTW cm
MINPP kg ha"1
MINPP1 kg ha"1
MINPP2 kg ha"1
MINPW cm
MINPW1 cm
MINPW2 cm
MINTH
Type
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Alpha-
numeric
Description
Julian Day of Crop
Maturation
Transport solution
technique flag (0 -
PRZM, 1= MOCPRZM)
Number of Day Read from
Meteorologic File
Monthly Pesticide Decay
from Each Compartment
Monthly ET from Each Soil
Compartment
Monthly Advection/Disper-
sion Flux from Each
Compartment
Monthly Foliar Applied
Pesticide
Monthly Soil Applied
Pesticide
Monthly Infiltration into
Each Soil Compartment
Monthly Precipitation
Monthly Snowfall
Flag for Monthly Output
Summary (for Either Water
Sub-
routine
PRZMRD
ECHO
INITL
PLGROW
ECHO
PRZMRD
PRZM
PRZM
OUTPST
OUTHYD
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
PRZM
Common
Block
MISC
PEST
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I.M.O
0
I
I
I
I
M
M
M
M
M
M
M
M
MM
MNTHP1 - -
or Pesticide)
Scalar Number of Month Read from PRZM
Meteorologic File
Scalar Current Month Plus 1 OUTHYD
(Month + 1)
277
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
MODFC
MONTH
MOUTP kg ha
MOUTP1 kg ha
MOUTP2 kg ha
MOUTP 3 kg ha
MOUTP4 kg ha
MOUTP5 kg ha
MOUTP6 kg ha
MOUTW cm
MOUTW1 cm
MOUTW2 cm
MOUTW3 cm
MOUTW4 cm
MOUTW5 cm
MOUTW6 MTonne
-1
-1
-1
-1
-1
-1
-1
Scalar Fraction Multiplier
Scalar Number of Current Month
of Simulation
Array Monthly Pesticide Uptake
from Each Compartment
Scalar Monthly Pesticide Washoff
Flux
Scalar Monthly Pesticide Runoff
Flux
Scalar Monthly Pesticide Erosion
Flux
Scalar Monthly Foliar Pesticide
Decay Loss
Scalar Monthly Pesticide Uptake
Flux from Profile
Scalar Monthly Pesticide Decay
Flux from Profile
Array Monthly Exfiltration from
Each Compartment
Scalar Monthly Canopy Evapo-
ration
Scalar Monthly Thrufall
Scalar Monthly Runoff
Scalar Monthly Snowmelt
Scalar Monthly Evapotrans-
piration
Scalar Total Monthly Sediment
Loss
INITL
SLTEMP MISC I
OUTPST ACCUM
OUTPST ACCUM
OUTPST ACCUM
OUTPST ACCUM
OUTPST ACCUM
OUTPST ACCUM
OUTPST ACCUM
OUTHYD ACCUM
OUTHYD ACCUM
M
M
M
M
M
M
M
M
M
OUTHYD ACCUM M
OUTHYD ACCUM M
OUTHYD ACCUM M
OUTHYD ACCUM M
OUTHYD ACCUM
278
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub - Common
routine Block I,M,0
MSTART - -
MSTR cm
MSTR1 cm
MSTR2 cm
-1
Scalar
Array
Scalar
Scalar
MSTRP kg ha Array
MSTRP1 kg ha"1 Scalar
Flag for Positioning
Meteorologic File
Previous Month Storage
of Water in Each Soil
Compartment
Monthly Canopy Inter-
ception
Monthly Accumulation of
Snow
Storage of Pesticide from
Previous Month in Each
Soil Compartment
Storage of Foliar Pesti-
cide from Previous Month
PRZM
OUTHYD ACCUM
OUTHYD ACCUM
OUTHYD ACCUM
OUTPST ACCUM
OUTPST ACCUM
M
M
M
M
M
MY
N
Scalar Number of Year Read from
Meteorologic File
Scalar Loop Counter
PRZM
CANOPY
SLTEMP
NAPPC
NAPS
NBYR
NCELL
NCOMO/ - -
Scalar Pesticide Application
Counter
Scalar Number of Pesticide
Applications in the
Simulation
Scalar Beginning Year of Crop
Growth for Current Crop
(Loop Limit)
Scalar Compartment number in
which a point is located
Scalar Number of Compartments
from Which ET is Extracted
Year Round
PRZM PEST
PESTAP
PRZMRD PEST
ECHO
INITL
PRZM
INITL
PLGROW
MOC
INITL
INITL HYDR
PLGROW
0
I
0
I
I
I
M
0
I
279
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
NCOM1 - -
NCOM2 - -
NCOM2M --
NCOMRZ - -
NCP
NCPDS - -
NCROP
NDC
NDCNT - -
NDYRS
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Current Number of Com-
partments , that ET is
Extracted From
Number of Compartments
in Soil Profile
Number of Compartments
in Soil Profile Minus 1
(NCOM2 - 1)
Number of Compartments
in the Root Zone
Number of Current Crop-
ping Period
Number of Cropping
Periods in the Simulation
Number of Current Crop
Number of Different Crops
in Simulation
Number of Days Since Crop
Emergence for Current
Crop
Number of Years Between
Emergence and Maturation
Sub-
routine
PLGROW
EVPOTR
OUTHYD
SLTEMP
INITL
SLPSTO
SLPST1
INITL
SLPSTO
SLPST1
OUTHYD
OUTPST
INITL
PLGROW
PRZMRD
ECHO
INITL
PLGROW
INITL
PLGROW
HYDROL
EROSN
PRZMRD
ECHO
INITL
PLGROW
INITL
PLGROW
INITL
PLGROW
Common
Block
HYDR
HYDR
HYDR
CROP
CROP
CROP
CROP
CROP
MISC
I,M,0
0
I
I
I
0
I
I
0
I
I
I
I
0
I
0
I
I
I
0
I
I
I
0
I
I
I
0
I
of a Crop
280
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub-
routine
Common
Block I,M,0
NET g
Array Net change in mass due
to advection
HOC
M
NEW
Scalar Number of new points
entering the flow domain
NEWK cm cm Array Henry's Constant
NEXDAY --
Scalar Extra Day Added for Leap
Year
HOC
KHCORR
PLGROW
M
NEYR
NHORIZ - -
Scalar Ending Year of Crop
Growth for Current Crop
Scalar Total Number of Soil
Horizons
INITL
PLGROW
PRZMRD MISC
ECHO
INITL
KDCALC
0
I
I
I
NLINES - -
NM1
NOPRT
Scalar Numbers of Lines for
Listing Initial Pesticides
in Profile (Loop Limit)
Scalar Number of Compartments
in Profile Minus 1
(NCOM2 - 1)
Scalar Print Flag
ECHO
TRDIAG
OUTHYD
OUTPST
NPI
NPLOTS
Scalar Current number of
moving points in soil
profile
Scalar Number of Time Series to
be Output (Maximum of 7)
MOC
INITL
HYDR
PRZMRD MISC
ECHO
PRZM
OUTTSR
M
0
I
I
I
NRZCOM - -
Scalar Current Number of Layers
in Root Zone
PLGROW
NSPACE
Scalar Number of furrow stations
for finite difference
FURROW IRGT
IRRIG
M
I
281
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
NSUM
NSUMM - -
NUM
NUM
Type
Scalar
Scalar
Scalar
Scalar
Description
Cumulative Sum of Com-
partment Numbers
Termination Loop Index
for Summary Output
Number of Soil Compartment
Intial number of moving
points per compartment
Sub- Common
routine Block I,M,0
EVPOTR
OUTHYD
OUTPST
KHCORR I
HOC HYDR I
INITL
NUMDYS - - Scalar
OC percent Array
o -3
OKH cnr cm Array
ORGM percent Scalar
OSNOW cm
OUTPUT
Scalar
Array
-1
PA kg ha Scalar
PB kg ha" Scalar
-2
PBAL g cm
Scalar
Number of Days in a Month
Organic Carbon in Each
Soil Horizon
Henry's Constant at
Previous Time
Organic Matter Content
of a Soil Horizon
Snow Accumulated at the
End of the Previous Time
Step
Output Array for Time
Series
Daily Foliar Pesticide
Application
Pesticide Balance
Current Pesticide Balance
Error
SLTEMP
SLTEMP PEST
INITL
PRZM HYDR
HYDROL
MASBAL
OUTTSR
OUTPST
OUTPST
MASBAL PEST
OUTPST
PCDEPL Fraction Scalar
Fraction of available water IRRIG
capacity where irrigation
is triggered
IRGT
M
INITL PEST
PRZM
SLPSTO
SLPST1
0
I
I
I
0
I
I
282
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
PCMC
PCOUNT - -
PESTR g cm"3
PET cm
PETP cm
PEVP cm
PFAC
PI
PLDKRT day"1
PLNAME - -
PLNTAP g cm"2
Type
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Alpha-
numeric
Scalar
Description
Partition Coefficient
Model Flag (1= Karick-
hoff , 2= Kenega,
3= Chiou)
Number of points crossing
an interface with Ratio
greater than 2 .
Total Pesticide in Each
Soil Compartment
Total Daily Potential
Evapotranspiration
Running Total of Avail-
able Evapotranspiration
Pan Evaporation
Pan Factor for ET
3.1415926
Foliar Pesticide Decay
Rate
Time Series Output Iden-
tifier (Options Listed
in User' s Guide)
Pesticide Applied to Crop
Canopy
Sub- Common
routine Block
PRZMRD MISC
KDCALC
INITL HYDR
MOC
PRZMRD PEST
ECHO
INITL
PRZM
PESTAP
MASBAL
OUTPST
EVPOTR
EVPOTR
PRZM MET
EVPOTR
PRZMRD MET
ECHO
EVPOTR
CANOPY
PRZMRD PEST
ECHO
PLPEST
PRZMRD MISC
OUTTSR
PESTAP PEST
OUTPST
OUTTSR
I.M.O
0
I
M
0
I
I
I
I
I
I
0
I
0
I
I
0
I
I
0
I
0
I
I
283
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
PLVKRT day'1
PNBRN - -
PRECIP cm
.3
PTEMP g cm
PTITLE - -
PVFLUX g cm"2
day
Type Description
Array Foliage Pesticide
Volatilization Rate
Array Output Array for Time
Series
Scalar Precipitation
Array Temporary storage of
total pesticide mass
per cc water after
advection step
Alpha- Comment Line to Input
numeric Information About Pesti-
cide Parameters
Array Daily Soil Pesticide
Volatilization Flux
Sub- Common
routine Block
ECHO PEST
PLPEST
PRZMRD
OUTTSR
PRZM MET
HYDROL
EROSN
MASBAL
OUTHYD
OUTTSR
MOC
PRZMRD MISC
ECHO
MASBAL PEST
OUTPST
OUTRPT
OUTTSR
SLPSTO
SLPST1
I.M.O
I
I
0
0
I
I
I
I
I
M
0
I
I
I
I
I
0
0
PWIND m day
_3
-1
Q
QC1
QEVF
QGHF
m"
Array Wind Velocity
Scalar Runoff Volume
-2
cal cm " Scalar Sensible Heat Flux Term
day °K
_2
cal cm Scalar Evaporation Heat Flux
day
cal cm" Scalar Soil Heat Flux Term
day °K
PRZM
EROSN
SLTEMP
SLTEMP
SLTEMP
M
M
M
284
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
_2
QLW1 cal cm .Scalar
day °K
_2
QLW2 cal cm 1 Scalar
day °K
QO nr/s Scalar
QQP m" sec" Scalar
QS
Array
-2
QSWR cal cm Scalar
day
RATIO
Array
RETEAP cm/hr Scalar
RF kg ha" Scalar
RINUM - - Scalar
RMULT - - Scalar
RMULT1 - -
RMULT3
RNSUM
Scalar
Scalar
Scalar
-2
RNUM ha cm Scalar
Atmospheric Longwave
Radiation Component Term
Longwave Radiation Flux
Term Emitted by Soil
Surface
Flow rate entering head of
furrow
Runoff Energy Factor
Flow rate in furrow at each
downstream station
Net Shortwave Radiation
Flux Term
The ratio of point
densities between
adjacent horizons.
Maximum rate of water that
sprinklers can deliver
Pesticide Runoff Flux
Richardson Number
Multiplication Factor for
Time Series Output
Multiplication Factor for
Curve Number AMC I
Multiplication Factor for
Curve Number AMC III
Converts NSUM to a Real
Number
Numerator of Peak Runoff
Rate
SLTEMP
SLTEMP
FURROW IRGT
EROSN
FURROW IRGT
SLTEMP
INITL HYDR
MOC
IRRIG IRGT
OUTPST
CANOPY
OUTTSR
PRZMRD
PRZMRD
EVPOTR
EROSN
M
M
M
M
M
285
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
RODPTH - -
ROFLUX g cm'2
day
RTR day"1
RUNOF cm
RVEL
RZD cm
RZFLUX g cm"2
RZI
SA kg ha
SAIM
SAND percent
Type
Scalar
Scalar
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
Number of Soil Compart-
ments that Affect Runoff
Runoff Flux of Pesticide
From Land Surface
Transformation Term
from Daughter Product
Consideration
Current Runoff Depth
Retarded solute velocity
Maximum Root Zone Depth
for All Crops
Dispersive/Advective Flux
of Pesticide Past the
Bottom Root Zone Com-
partment
Active Root Zone Flag
Application of Pesticide
to the Soil
Integrated Momentum
Stability Parameter
Percent Sand in Each Soil
Sub- Common
routine Block
HYDROL
SLPSTO PEST
SLPST1
MASBAL
OUTHYD
OUTTSR
PSTLNK PEST
SLPSTO
SLPST1
HYDROL HYDR
PRZM
EROSN
SLPSTO
SLPST1
MASBAL
OUTHYD
OUTTSR
MOC
INITL
OUTHYD
SLPSTO PEST
SLPST1
OUTTSR
INITL MISC
PLGROW
OUTPST
CANOPY
SLTEMP HYDR
I.M.O
0
0
I
I
I
0
I
I
0
I
I
I
I
I
I
I
M
0
0
I
0
I
0
I
Horizon
286
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
SD
SDKFLX
SEDL
SF
SFAC
kg ha
-2
g cm^
day
MTonne
day
Fraction
cm °C
Type Description
Scalar Sum of the Decay Fluxes
From All Compartments
in Soil Profile
Scalar Sum of the Decay fluxes
From All Compartments in
Soil Profile
Scalar Erosion Sediment Loss
Scalar Slope of furrow channel
(vertical/horizontal)
Scalar Snowmelt Factor
Sub- Common
routine Block I,M,0
OUTPST
SLPSTO PEST
SLPST1
OUTPST
PRZM HYDR
EROSN
OUTHYD
FURROW IRGT
PRZMRD MET
ECHO
HYDROL
0
0
I
0
M
0
I
0
I
I
SIGMAO
SIGMA1 cal cm
°C day
SIGMA2 - -
-1
-1
SJDAY - -
SLKGHA kg hj
day
SMDEF cm
SMELT cm
-1
Scalar Summation Variable Used to
Calculate K Factor in the
Soil Thermal Conductivity
Equation
Scalar Total Numerator Value in
the Soil Thermal Conduc-
tivity Equation
Scalar Total Denominator Value
in the Soil Thermal
Conductivity Equation
Scalar Starting Day of Simulation
Scalar Erosion Sediment Loss
Scalar Soil moisture deficit
requiring irrigation
Scalar Current Daily Snowmelt
Depth
SLTEMP
SLTEMP
SLTEMP
INITL
EROSN
IRRIG IRGT
HYDROL HYDR
EROSN
OUTHYD
M
M
M
0
287
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type
SNOW
SNOWFL
SOILAP
SOL
SOLRAD
SPESTR
SPT
SPTEMP
SRC
SRCFLX
cm Scalar
cm Scalar
-2
g cm Array
mole Scalar
fraction
mg 1" ^
umoles 1
-2
cal cm Scalar
day
-3
g cm Array
°C Array
-3
g cm Array
-3
g cm.. Array
day
-2
g cm.. Array
day
Description
Snowpack Accumulation
Depth
Current Snowfall Depth
Pesticide Applied to the
Soil
Pesticide Solubility -
Karickhoff Model
Kenaga Model
Chiou Model
Shortwave Solar Radiation
Dissolved Pesticide in
Each Soil Compartment
Temperature of Soil in
Each Compartment
Temporary storage of
dissolved pesticide
mass per cc water after
advection step
Source Term from Daughter
Product Consideration
Source Flux of Pesticide
from Each Soil Compartment
Sub - Common
routine Block I,M,0
SLTEMP HYDR
HYDROL MET
MASBAL
OUTHYD
OUTTSR
PESTAP PEST
PRZM
OUTPST
OUTTSR
PRZMRD
KDCALC
PRZMRD MET
SLTEMP
INITL PEST
PRZM
PESTAP
SLPSTO
SLPST1
SLTEMP MET
PRZM
MOC PEST
SLPST1
INITL
PSTLNK PEST
SLPSTO
SLPST1
SLPSTO PEST
SLPST1
OUTPST
I
0
I
I
I
0
I
I
I
0
I
0
I
0
I
I
I
I
0
I
M
0
I
I
0
0
I
188
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
STEMP °C
STEP1 - -
STEP2 - -
STEPS --
STITLE - -
STK °K
STTDET cm day'1
SU kg ha"1
SUMC g
SUMXP kg ha"1
SUPFLX g cm"2
day
Type Description
Array Soil Compartment
Temperature
Alpha- Time Step of Water Output
numeric Summary
Alpha- Time Step of Pesticide
numeric Output Summary
Alpha- Time Step of Concentration
numeric Profile Output
Summary
Alpha- Comment Line to Input
numeric Information About Soil
Parameters
Scalar Soil Surface Temperature
in Kelvin Scale
Scalar Daily Evaporation from the
Top 5cm of Soil
Scalar Sum of the Uptake Fluxes
From All Soil Compart-
ments
Array Sum of mass in a
compartment
Scalar Sum of Soluble Pesticide
in Profile
Scalar Sum of the Uptake Fluxes
From All Soil Compart-
ments
Sub - Common
routine Block
KHCORR
PRZMRD MISC
ECHO
OUTHYD
PRZMRD MISC
ECHO
OUTPST
PRZMRD MISC
ECHO
OUTCNC
PRZMRD MISC
ECHO
SLTEMP
SLTEMP MET
EVPOTR
OUTPST
MOC
OUTPST
SLPSTO PEST
SLPST1
OUTPST
OUTTSR
I.M.O
I
0
I
I
0
I
I
0
I
I
0
I
M
I
0
M
0
0
I
I
SV kg ha
day
-1
Scalar Daily Soil Pesticide
Volatilization Flux
OUTPST
0
289
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
SW cm
T
TA day'1
TAPP g cm"2
TB day"1
TC day"1
TCNC g cm"3
TCORR mole.
cal
TEMP °C
TEMPK °K
Type
Array
Scalar
Array
Array
Array
Array
Array
Scalar
Scalar
Scalar
Description
Current Water Depth in
Each Soil Compartment
Fraction Compartment
Check
Lower Diagonal Element of
Tridiagonal Matrix
Total Pesticide Applied
Per Application
Diagonal Element of
Tridiagonal Matrix
Upper Diagonal Element of
Tridiagonal Matrix
Average Pesticide
Concentration in Canopy
Temperature Correction
Factor
Ambient Air Temperature
Air Temperature in
Sub- Common
routine Block
INITL HYDR
HYDROL
EVPOTR
HTDR1
HYDR2
SLPSTO
SLPST1
OUTTSR
INITL
SLTEMP
PRZMRD PEST
ECHO
INITL
PESTAP
SLTEMP
SLTEMP
OUTPST
KHCORR
SLTEMP MET
SLTEMP
I.M.O
0
I
I
I
I
I
I
I
M
0
I
I
I
M
M
0
M
I
M
TEND day
TERM
Kelvin Scale
Scalar Time required for point MOC
to move to compartment
boundary
Scalar Exponential Pesticide PLPEST
Washoff Term
M
290
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub- Common
routine Block I,M,0
TERM1
TERM2
TF
TFRAC
Scalar
Scalar
Array
Scalar
o .3
THAIR cnr cm Array
THCOND cal cm" 1 Array
day °C
i -3
THEFC cm-3 cm Array
THETAS cm3 cm" Array
THETH cm3 cm Scalar
o .3
THETN cnr cm Array
Exponential Pesticide
Decay Term
Product of Washoff and
Decay Terms
Vector of Previous Time
Step Soil Compartment
Temperature
Total Fraction of Com-
partments Available for
Evapotranspiration
Extraction
Volumetric Air Content
Thermal Conductivity of
Soil Compartment
Field Capacity Water
Content for Each Soil
Horizon
Soil Compartment Water
Content at Saturation
Soil Moisture Content Half
Way Between Wilting Point
and Field Capacity in the
Top Soil Compartments
Soil Water Content at the
End of the Current Day
for Each Soil Compartment
PLPEST
PLPEST
SLTEMP
EVPOTR
SLPSTO
SLPST1
SLTEMP
SLTEMP HYDR
SLTEMP HYDR
INITL
HYDROL
HYDR1
HYDR2
PRZM
SLPSTO
SLPST1
MASBAL
OUTHYD
OUTPST
OUTTSR
OUTCNC
HYDR
HYDR
M
0
0
M
0
I
0
0
I
I
I
I
I
I
I
I
291
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
THETO cm3 cm"3
THEWP cm3 cm"3
THFLAG - -
THKLY1 cm
THKNS cm
THRUFL cm
THZERO cal cm"
TITLE
TLEFT day
TMPK °K
TNDGS day
Type
Array
Array
Scalar
Scalar
Array
Scalar
Array
Alpha-
numeric
Scalar
Scalar
Array
Description
Soil Water Content at the
End of the Previous Day
for Each Soil Compartment
Wilting Point Water Content
for Each Soil Horizon
Soil Water Content Flag
(0= Field Capacity and
Wilting Point are Input,
1= Field Capacity and
Wilting Point are
Calculated)
Thickness of Top
Compartment
Soil Horizon Thickness
Precipitation that Falls
Past the Crop Canopy to
the Soil Surface
Thermal Conductivity of
Soil at Water Content
and Wilting Point
Title of the Simulation
(User Supplied)
Travel time left in
current time step
Soil Temperature
Total Number of Days in
Each Growing Season
Sub - Common
routine Block
SLTEMP HYDR
SLTEMP HYDR
PRZMRD MISC
ECHO
PRZM
SLTEMP
PRZMRD MISC
ECHO
INITL
HYDROL
HYDROL MET
OUTHYD
OUTTSR
SLTEMP
PRZMRD MISC
ECHO
MOC
KHCORR
INITL CROP
PLGROW
I.M.O
I
I
0
I
I
0
I
I
I
0
I
I
M
0
I
M
M
0
I
TOL
Scalar Fraction Compartment
Check
INITL
292
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
TOP
TOT day m"
TOTAL rag kg'1
TOTR day m"
TR hr
TRFLUX g cm"2
day
T .3
TS cnr cm
TSRCFX g cm"2
day
TSW cm
TTHKNS cm
TTRFLX g cm"2
day
Type
Array
Scalar
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Array
Description
Location of top compart-
ment in horizon where
points are consolidated
Canopy Resistance
Total Pesticide in Each
Compartment
Total Canopy Resistance
Duration of Average
Erosive Storm Event
Transformation Flux of
Pesticide from Each Soil
Compartment
Previous Soil Compartment
Water Content Minus
Evapo transpiration
Sum of the Source Flux
from All Compartments in
Soil Profile
Total Soil Water in
Compartments Available
for Evapotranspiration
Extraction
Total Thickness of Soil
Profile (For Computa-
tional Check)
Sum of the Transformation
Flux from All Compartments
in Soil Profile
Sub- Common
routine Block I,M,0
INITL, HYDR
HOC
CANOPY
OUTCNC
CANOPY
PRZMRD MET
ECHO
EROSN
SLPSTO PEST
SLPST1
OUTPST
HYDR2
SLPSTO PEST
SLPST1
OUTPST
EVPOTR
INITL
SLPSTO PEST
SLPST1
OUTPST
M
0
0
0
I
I
0
0
I
0
0
I
0
0
I
TWLVL cm cm
-1
Scalar Fraction of Water to Soil
Depth for Runoff
Calculation
HYDROL
293
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
TWP cm
U
UBT °C
UPF kg ha"1
UPFLUX g cm
UPTKF
URH m day"1
USLEC
USLEK
USLELS - -
USLEP - -
Type
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Description
Total Wilting Point Depth
in Compartments Available
for Evapotranspiration
Extraction
Upper Decomposed Matrix
Upper Boundary or Soil
Surface Temperature
Daily Pesticide Uptake
Flux in Profile
Uptake Flux of Pesticide
From Each Soil Compartment
Plant Pesticide Uptake
Efficiency Factor
Wind Velocity at Reference
Height
Universal Soil Loss
Equation 'C' Factor
Universal Soil Loss
Equation 'K' Factor
Universal Soil Loss
Equation 'Ls' Factor
Universal Soil Loss
Equation 'P' Factor
Sub- Common
routine Block
EVPOTR
TRDIAG
SLTEMP
OUTPST
SLPSTO PEST
SLPST1
OUTPST
PRZMRD PEST
ECHO
PLGROW
CANOPY
PRZM
PRZMRD HYDR
ECHO
EROSN
PRZMRD HYDR
ECHO
EROSN
PRZMRD HYDR
ECHO
EROSN
PRZMRD HYDR
ECHO
EROSN
I.M.O
M
0
0
I
0
I
I
I
0
0
I
I
0
I
I
0
I
I
0
I
I
USTAR m day
UTEMP °C
-1
Scalar Friction Velocity
Array Air Temperature
CANOPY
CANOPY
0
I
294
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units Type Description
Sub-
routine
Common
Block I,M,0
UWIND
VAPLMD
VAR1
m day
-1
Array Wind Velocity
-1
cal cm Scalar Thermal Conductivity of
day"1°C"-1- Vapor in the Soil Pores
kg ha
-1
VAR2 kg ha
VAR2D cm
VAR2M cm
-1
VAR2RZ kg ha
VAR2Y cm
-1
VAR3
VEL
kg ha
-1
-1
VHTCAP cal ..cm
°c~L
-3
Scalar Daily Advection/Disper-
sion Flux of Pesticide
Into a Compartment
Scalar Daily Advection/Disper-
sion Flux of Pesticide
Out of a Compartment
Scalar Water Storage in a Single
Compartment for the
Previous Day
Scalar Water Storage in a Single
Compartment for the
Previous Month
Scalar Daily Advection/Disper-
sion Flux of Pesticide
Out of the Root Zone
Scalar Water Storage in a Single
Compartment for the
Previous Year
Scalar Pesticide Storage in a
Single Compartment for
the Previous Day
cm day Array
Water Velocity in Each
Soil Compartment
Array Heat Capacity Per Unit
Volume of Soil
CANOPY
SLTEMP
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTPST
OUTHYD
OUTPST
HYDR1 HYDR
HYDR2
SLPSTO
SLPST1
SLTEMP
0
0
I
I
M
295
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
VLFLAG - -
VOLCOR --
WBAL cm
WEIGHT kg m"2
WF kg ha"1
WFMAX kg m"
WIND cm sec
WLVL cm
WOFLUX g cm"
day
WP cm
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Array
Description
Advection flux flag
(0 = all soil water
velocities are zero,
1 = soil water velocity
is nonzero)
A Variable Used to Convert
Weight Percents of Soil
Constituents to Volume
Fractions of Bulk Volume
Current Water Balance
Error
Current Plant Dry Foliage
Weight
Daily Pesticide Washoff
Flux
Maximum Plant Dry Foliage
Weight at Full Canopy
Wind Speed
Total Soil Water in the
Compartments that Affect
Runoff
Washoff Flux of Pesticide
From Plant Foliage
Wilting Point Water Depth
in a Soil Compartment
Sub- Common
routine Block
HYDR1 HYDR
PRZM
HYDR2
SLTEMP
MASBAL HYDR
OUTHYD
PLGROW CROP
PESTAP
OUTPST
PRZMRD CROP
ECHO
INITL
PRZMRD MET
SLTEMP
PRZM
HYDROL
SLPSTO PEST
SLPST1
OUPPST
HYDR 0
EVPOTR
I.M.O
I
0
I
0
I
0
I
I
0
I
I
0
0
I
WPV
Array Regression Coefficients
for Prediction of Wilting
Point Soil Water Content
THCALC
296
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
WTERM g cm
X g cm
XFRAC Fraction
XL m
XP g cm"3
XVOL fraction
Y
YDOUT kg ha
YEAR
YEOUTW cm
Type
Scalar
Array
Scalar
Scalar
Array
Array
Array
Array
Alpha-
numeric
Array
Description
Current Daily Pesticide
Washoff Loss
Dissolved Pesticide in
Each Soil Compartment
Location in furrow where
infiltration is to be used
in PRZM transport
calculations (as fraction
of total furrow length)
Length of furrows
Total Pesticide in
Each Soil Compartment
Volume Fraction of Soil
Constituent
Intermediate Matrix Solu-
tion Array
Annual Pesticide Decay
From Each Soil Compartment
Flag for Annual Water and
Pesticide Summary Output
Annual Evapotranspiration
Sub - Common
routine Block
PLPEST PEST
SLPSTO
SLPST1
TRDIAG PEST
SLPSTO PEST
SLPST1
MASBAL
OUTPST
OUTTSR
OUTCNC
PRZM
IRRIG IRGT
IRRIG IRGT
FURROW
MASBAL
SLTEMP
TRDIAG
OUTPST ACCUM
PRZM
OUTHYD ACCUM
I.M.O
0
I
I
0
0
I
I
I
I
I
I
I
I
0
M
M
YINPP kg ha
-1
From Each Soil Compartment
Array Annual Advective/Disper-
sive Flux Into Each Soil
Compartment
OUTPST ACCUM
M
297
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
YINPP1
YINPP2
YINPW
YINPW1
YINPW2
YOUTP
YOUTP1
YOUTP2
YOUTP 3
YOUTP4
YOUTP5
YOUTP 6
YOUTW
YOUTW1
YOUTW2
YOUTW3
YOUTW4
kg ha
kg ha"
cm
cm
cm
kg ha
kg ha
kg ha
kg ha
kg ha
kg ha"
kg ha"
cm
cm
cm
cm
cm
Type
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Description
Annual Pesticide Applied
to Foliage
Annual Pesticide Applied
to Soil
Annual Infiltration Into
Each Soil Compartment
Annual Precipitation
Annual Snowfall
Annual Pesticide Uptake
From Each Soil Compartment
Annual Pesticide Washoff
Flux
Annual Pesticide Runoff
Flux
Annual Pesticide Erosion
Flux
Annual Foliar Pesticide
Decay Flux
Total Annual Pesticide
Uptake Flux
Total Annual Pesticide
Soil Decay Flux
Annual Exfiltration From
Compartment
Annual Canopy Evaporation
Annual Trufall
Annual Runoff
Annual Snowmelt
Sub - Common
routine Block
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I.M.O
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
298
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(continued)
Variable Units
YOUTW5 cm
YOUTW6 MTonne
YSTR cm
YSTR1 cm
YSTR2 cm
YSTRP kg ha"1
YSTRP1 kg ha"1
Z Fraction
Z
ZC
ZCH m
PRZM
ZCTOT
ZIN
ZO m
Type
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Scalar
0
Scalar
Array
Scalar
Description
Total Annual Evapotrans-
piration
Total Annual Sediment
Loss
Previous Year Storage of
Water in Each Soil Com-
partment
Annual Canopy Interception
Annual Snow Accumulation
Storage of Pesticide From
Previous Year in Each
Soil Compartment
Storage of Foliar Pesticide
Side slope of furrow
channel walls
(horizontal/vertical)
Location of moving
points
Location of fixed
compartment center
Canopy Height
Concentration weighted
locations of consolidated
points
Temporary storage of
new point locations
Roughness Height
Sub-
routine
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTPST
OUTPST
FURROW
MOC
INITL
MOC
INITL
CANOPY
SLTEMP
MOC
MOC
CANOPY
SLTEMP
Common
Block
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
IRGT
HYDR
HYDR
-
I.M.O
M
M
M
M
M
M
M
M
I
M
M
I
M
M
M
0
M
299
-------
Table 8-3. PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATION
(concluded)
Variable Units Type Description
Sub- Common
routine Block I,M,0
ZRH m
ZTOT
ZWIND m
Scalar Reference Height
CANOPY
PRZM
Scalar Location of consolidated MOC
Points
Scalar Distance Above the Ground PRZMRD
Where Wind Speed was PRZM
Measured SLTEMP
I
0
M
0
0
I
300
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS
Variable Units
A
ASTORN
B
BALSTO
BSTOR1
BSTORN
C
CORD L
CSTOR1
CTRFAC
D
Type
ARRAY
SCALAR
ARRAY
ARRAY
SCALAR
SCALAR
ARRAY
ARRAY
SCALAR
ARRAY
ARRAY
Description
Left Diagonal of a
Tridiagonal Matrix
Value of A(NP) Where
NP=Number of Nodes
Main Diagonal of a
Tridiagonal Matrix
Array Containing Mass
Balance Information
Value of B(l)
Value of B(NP) Where
NP=Number of Nodes
Right Diagonal of a
Tridiagonal Matrix
Nodal Coordinates
Value of C(l)
Coordinate Transform-
ation Factors for
Different Soil Materials
Right-Hand-Side Vector
of a Tridiagonal Matrix
Sub- Common
routine Block I,M,0
ASSEMF ASOLV M
ASSEMT
ASSEMF WORKA M
ASSEMT
BALCHK
ASSEMF ASOLV M
ASSEMT
VADINP M
BALCHK 0
ASSEMF WORKA M
ASSEMT
BALCHK
ASSEMF WORKA M
ASSEMT
BALCHK
ASSEMF ASOLV M
ASSEMT
VADINP ADISC I
VSWCOM
ASSEMF WORKA M
ASSEMT
BALCHK
CONVER WORKN M
DSWFUN
VADINP
ASSEMF ASOLV M
ASSEMT
DETAND
ARRAY Nodal Storage Factor
ASSEMF WELEM
M
301
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
DIS L
M/L**3
DLAMDA 1/t
DLAMND 1/t
DPKND L/t
DPKRAV L**2
DSTOR1
DSTORN
DTEPS
DTMARK
DX
EL L
Type
ARRAY
SCALAR
SCALAR
ARRAY
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
Description
Current Nodal Value of
Head of Concentration
Value of Decay Constant
for the Node Currently
Being Evaluated
Nodal Value of Decay
Constant
Nodal Values of Hyd.
Conductivity Increment
Value of Rel. Perm, for
Node Currently Being
Solved
The Value of D(l)
The Value of D(NP)
Where NP = Number of
Nodes
Time Step Tolerance
Parameter
Marker Time Increment
DX = THL(I) NEL
Elemental Values for
Finite -Element Element
Length Formulation
Sub-
routine
VADINP
ASSEMF
BALCHK
VARCAL
VSWCOM
VADINP
ASSEMT
VARCAL
VADINP
ASSEMT
BALCHK
VARCAL
ASSEMF
ASSEMF
PKWFUN
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
BALCHK
VADINP
VADINP
VADINP
VADINP
ASSEMF
ASSEMT
BALCHK
VARCAL
VSWCOM
Common
Block I,M,0
BSOLV M
0
CONTR M
WELEM I
WELEM M
M
WORKA M
WORKA M
M
M
M
WELEM M
302
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units Type
ETAND - - ARRAY
FLX1 L**3/t SCALAR
FLXN L**3/t SCALAR
FVAL - ARRAY
HAVE L SCALAR
HCAP L ARRAY
HCRIT L SCALAR
HDOBS L ARRAY
M/L**3
HINV L SCALAR
M/L**3
HTOL L SCALAR
Description
Nodal Values of Fluid
Storage Factor
Value of Fluid Flux
Entering Node 1
(for Flow FLX1 =0.0)
Value of Fluid Flux
Entering the Last Node
(for Flow FLX1 =0.0)
Functional Coefficient
Values for the Soil
Moisture Relationship
Average Head Value
Value of Pressure
Head on Press. Head
vs. Sat. Curve
Critical Head Value
Head or Concentration
of Observation Node
for Current Time
Default Value of Initial
Head or Concentration
Head Tolerance Allowed
for Nonlinear Solution
Sub-
routine
ASSEMF
ASSEMT
BALCHK
VADINP
ASSEMT
HFINTP
VARCAL
VADINP
ASSEMT
HFINTP
VARCAL
VADINP
ASSEMT
HFINTP
SWFUN
CONVER
DSWFUN
ASSEMF
SWFUN
DSWFUN
VADINP
ASSEMF
INTERP
ASSEMF
SWFUN
DSWFUN
VADINP
VADINP
VADINP
ASSEMF
VARCAL
DSWFUN
Common
Block I,M,0
WELEM M
CONTR M
CONTR M
MDATA M
M
SWHDA M
I
DAOBS M
0
I
CONTR I
303
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
HVTM L
IBTND1
IBTNDN
ICONVG
Type Description
ARRAY Value of function
corresponding to
Time Value s(TMHV)
SCALAR Last Node Boundary
Condition Code (l=lst
type, 0=3rd type)
SCALAR Last Node Boundary
Condition Code (l=lst
Type, 0=3 rd type)
SCALAR Convergence Flag
(l=Convereed, 0=Not
Sub- Common
routine Block I,M,0
VADINP M
HFINTP
VADINP I
ASSEMF
ASSEMT
VARCAL
ASSEMF I
VADINP
ASSEMT
VARCAL
VADINP I
VARCAL
IHORIZ
IKALL
ILAYR
IMAT
IMATL
IMBAL
Converged)
SCALAR Simulation Orientation
Indicator (0=Vertical,
l=Horizontal)
SCALAR Time Stepping Scheme
Indicator (l=Backward,
0=Central)
SCALAR Current Layer Number
SCALAR Counter Used in Looping
with Respect to Materials
ARRAY
SCALAR
VADINP
VADINP
VADINP
VADINP
ASSEMF
ASSEMT
INTERP
PKWFUN
SWFUN
CONVER
DSWFUN
Material Identifing VADINP
Number for Current Layer
Mass Balance Computation
Indicating Parameter
VADINP
CONTR
304
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
IMOD
IMODL
INEWT
INOCTS
INPFL
INTSPC
IOBSND
IPRCHK
I PROP
IREP
Type
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
ARRAY
SCALAR
Description
For Modified Newton
Raphson Solution
Procedure
Simulation Identifier
(Flow or Transport)
Nonlinear Iterative
Procedure Flag
(l=Newton, 0=Picard,
2=Modified N-R)
Number of Computation
Time Steps Required to
Simulate This Target
Time Step
Unit Number for Input
File
Initial Condition
Specifier for Head
Conversion Convert
Initial Head Values
(l=Yes, 0-No)
Observation Node Index
Print Check Flag
(Triggers Additional
Diagnostic Output)
Generated Material
Property Identifiers
Time Step Refinement
Counter
Sub-
routine
VADINP
DSWFUN
VADINP
BALCHK
VARCAL
VADINP
ASSEMF
VARCAL
VADINP
VARCAL
VADINP
VADINP
WORKA
VADINP
ASSEMF
ASSEMT
BALCHK
VARCAL
CONVER
VADINP
ASSEMF
ASSEMT
VADINP
VARCAL
Common
Block I,M,0
CONTR1 I
CONTR I
CONTR I
I
I
I
I
I
MDATA I
M
305
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
IREPMX
IRESOL
IRLTYP
ITCND1
ITCNDN
ITER
ITMARK
ITMFC
ITMGEN
ITRANS
Type
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
Description
Maximum Number of
Nonlinear Solution
Cycles
Maximum Number of
Time Step Refinements
Flag for the Type of
Relative Function Being
Evaluated
Node 1 Boundary
Condition Flag
(1 = Transient,
0 = Steady State)
Node 1 Boundary
Condition Flag
(1 = Transient,
0 = Steady State)
Iterative Counter
(Current Iteration
Number)
Backup File Output
Indicator
Marker Time Increasing
Parameter
Marker Time Value
Generation Indicator
Transient Steady- State
Flag (1=TR, 0=SS)
Sub-
routine
VADINP
VARCAL
VADINP
VARCAL
ASSEMF
INTERP
VADINP
HFINTP
VADINP
HFINTP
VADINP
ASSEMF
ASSEMT
BALCHK
VARCAL
VSWCOM
VADINP
VSWCOM
VADINP
VSWCOM
VADINP
VADINP
ASSEMF
VARCAL
Common
Block I,M,0
I
I
I
WELEM I
WELEM I
M
M
M
I
CONTR I
ITSGN
SCALAR Time Step Generation
Indicator
VADINP
306
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
Type
Description
Sub - Common
routine Block I,M,0
ITSTH
IVSTED
ARRAY Identifies Location of VADINP
Previous Time Value of HFINTP
Time Graph
SCALAR Steady-State Velocity VADINP
Field Indicator
KPROP
MARK
MM
SCALAR Flag for Perm-Saturation
and Pressure Head-
Saturation Curves
(1-Functional,
0=Tabulated)
SCALAR Flow Direction Flag
(l=Vertical,
0=Horizontal)
SCALAR Place Holder for Loop
Incrementer
VADINP CONTR
ASSEMF
VARCAL
VADINP
ASSEMF
VARCAL
VSWCOM
CONTR
M
MXMAT
SCALAR Maximum Number of
Materials Allowed
(Due to the Size of
Arrays)
VADINP
ASSEMF
ASSEMT
INTERP
PKWFUN
SWFUN
DSWFUN
MXNODE
SCALAR Maximum Number of Nodes
Allowed (Due to the Size
of Some Arrays)
VADINP
ASSEMF
ASSEMT
BALCHK
TRIDIV
VARCAL
VSWCOM
MXTMV t
NDCOUN
SCALAR Maximum Time Value to VADINP
be Interpolated HFINTP
SCALAR Material Number VADINP
Temporary Counter
M
307
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
NDM1
NDOBS
NE
NEL
Type
SCALAR
ARRAY
SCALAR
SCALAR
Description
Counter Minus One
NDM1 = NDCOUN
Nodal Values of
Observation Nodes
Number of Elements in
the Linear Representation
Storage Location for the
Sub-
routine
VADINP
VADINP
VADINP
VSWCOM
VADINP
Common
Block I,M,0
M
DAOBS I
CONTR I
M
NELM
NITMAX
NLAYRG
ARRAY
SCALAR
SCALAR
Number of Finite Elements
in the Current Layer
NELM(I)
Number of Finite Elements
in the Current Layer
Maximum Number of
Nonlinear Iterations
Allowed per Time Step
VADINP
VADINP
VARCAL
CONTR
Number of Layers That VADINP
Need to be Descritized
NMAT
SCALAR Number of Soil Materials
VADINP
CONVER
NOBSND
NONU
SCALAR Number of Observation VADINP
Nodes in the Simulation
SCALAR Nonuniform Initial VADINP
Condition Indicator
NOWRIT
SCALAR Restart Data Writing
Indicator
VADINP
NP
SCALAR Total Number of Nodal
Points
VADINP
ASSEMF
ASSEMT
BALCHK
TRIDIV
VARCAL
VSWCOM
CONTR
308
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
NPIN
NPROB
NSTEP
NTN1
NTNP
NTOMT
NTS
NTSNDH
NUMK
Type
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
ARRAY
ARRAY
Description
Number of Nondefault
Initial Values
Number of Simulations
to be Made
Nodal Value Printout
Control Parameter
Storage Location for
NTSNDH (1)
Storage Location for
NTSNDH (NP)
Number of Backup File
Output Marker Time
Values
Number of Time Steps
in This Simulation
Number of Time Values
on the Time Graph
([1]-CONC, [2]=HEAD)
Values of Permeability
from the Permeability
vs Saturation Table
Sub - Common
routine Block I,M,0
VADINP I
VADINP I
VADINP CONTR I
BALCHK
VADINP M
VADINP M
VADINP I
VSWCOM
VADINP M
VADINP I
HFINTP
VADINP SWHDA I
ASSEMF
INTERP
NUMP
NUMT
NVPR
NVREAD
ARRAY
SCALAR
SCALAR
SCALAR
for Each Material
Number of Pressure VADINP
Head vs. Saturation ASSEMF
Values for Each Material INTERP
Time Step Incrementor
VADINP
Velocity Printout Control VADINP
Parameter VSWCOM
Velocity Reading
Indicator
VADINP
SWHDA
CONTR
I
I
309
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units Type
OUTFL - - SCALAR
PCUR L ARRAY
M/L**3
PINT L ARRAY
M/L**3
PKND L/t ARRAY
PKRW L**2 ARRAY
PKWOUT L**2 SCALAR
PROP ARRAY
Description
Output File Unit Number
Current Value of
Pressure Head or
Concentration for the
Current Time Step
Initial Value of
Pressure Head or
Concentration
Nodal Values of
Hydraulic Conductivity
Value of Relative
Permeability (on
Perm. vs. Sat. Curve)
Relative Permeability
Computed Using Function
Then Passed Back
Saturated Material
Properties (Flow or
Transport) Flow-
Sub - Common
routine Block I,M,0
VADINP I
ASSEMF
ASSEMT
BALCHK
INTER?
VARCAL
VSWCOM
ASSEMF BSOLV M
VARCAL
VADINP BSOLV I
ASSEMF
ASSEMT
BALCHK
VARCAL
VADINP WELEM M
ASSEMF
VSWCOM
VADINP SWHDA M
ASSEMF
INTERP
PKWFUN M
VADINP MDATA I
ASSEMF
ASSEMT
QVTM
L**3/t ARRAY
Hydraulic Conductivity
Porosity, Specific
Storage Air Entry
Pressure Transport-
Dispersivity, Porosity,
Retardation Diffusion
Volumetric Water Flux
Values Corresponding
to Time Values
VADINP
HFINTP
M
310
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
SLOPE
SSWV
STMARK t
SWAVE
SWDFI
SWND
SWNDPT
SWRKP
SWV
TAPS
TAP10
Type
SCALAR
ARRAY
SCALAR
SCALAR
ARRAY
ARRAY
ARRAY
ARRAY
ARRAY
SCALAR
SCALAR
Description
Slope of the Line
Between the Points
Being Interpolated
Value of Water Phase
Saturation (on Press,
Head vs Sat. Curve)
Starting Marker Time
Value
Average Water Saturation
Default Value of Water
Saturation for the
Current Material
Current Water Saturation
at the Node Being
Evaluated
Water Saturation for the
Node at Previous Time
Step
Temporary Working Array
Value of Water Phase
Saturation (on Perm.
vs. Sat. Curve)
Unit Number for Restart
File
Unit Number of Flow- to -
Transport File (Darcy
Sub- Common
routine Block
HFINTP
INTERP
ASSEMF SWHDA
INTERP
VADINP
ASSEMF
PKWFUN
VADINP
VADINP WELEM
ASSEMF
ASSEMT
VARCAL
VSWCOM
VADINP WELEM
VSWCOM
CONVER WORKN
VADINP SWHDA
ASSEMF
INTERP
VADINP
VADINP MDATA
VSWCOM
I.M.O
M
M
M
M
I
M
M
M
M
I
I
TDIFF
Vel. & Water Sat.)
TDIFF-TMCUR-TMVECX
VADINP
M
311
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units Type
Description
Sub-
routine
Common
Block I,M,0
TERIFL
TEROFL
TFAC
THETA
THETM1
THL
TIN
TIMA t
TIMAKP t
TITLE
SCALAR Unit Number for Input
File
SCALAR Unit Number for Output
File
SCALAR Time Step Multiplier
SCALAR Value Used in the Time
Stepping Scheme
(Theta=0.5 for Central
Difference Scheme,
Theta=1.0 for Backward
Difference Scheme)
SCALAR Theta Minus One
ARRAY Thickness of Current
Layer
SCALAR Value of Initial Time
Step
SCALAR Initial Time Value of
the Simulation
SCALAR Storage Location for
the Value of Time Where
Iteration Computation
is Taking Place
ALPHA- Title of Simulation
NUMERIC
ARRAY
VADINP
VADINP
VADINP
VADINP
ASSEMT
BALCHK
VARCAL
VADINP
ASSEMT
BALCHK
VARCAL
VADINP
VADINP
ASSEMF
ASSEMT
BALCHK
VARCAL
VADINP
VSWCOM
VADINP
VADINP
CONTR
CONTR
I
M
M
M
M
I
TMACCU L**3 SCALAR Quantitative Storage VADINP
M Water Volume or Solute BALCHK
Mass
CONTR
M
312
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (continued)
Variable Units
TMAX t
TMCUR t
TMDCAY M
TMFOMT t
TMHV t
TMVEC t
TMVECX t
UWF
UWFI
VALND1
VALNDN
Type
SCALAR
SCALAR
SCALAR
ARRAY
ARRAY
ARRAY
SCALAR
SCALAR
ARRAY
SCALAR
SCALAR
Description
Maximum Time Step Size
Current Time Value
Cumulative Solute Mass
Decay
Time Values for Output
to the Backup File
Time Values at the
Interpolation Points
([Ij-CONC, [2]=HEAD)
Values of Time Generated
by the Code, to be Used
in the Simulation
Extra Time Value Due
to the Reduction of a
Time Step When Solution
is not Converging
Value of Upstream
Weighting Factor for
the Node Currently
Being Evaluated
Value of Upstream-
Weighting Factor for
the Current Material
Value of First Node
(Depending on: Type of
Run & Type of Boundary
Value of Last Node
(Depending on: Type of
Run & Type of Boundary
Sub-
routine
VADINP
VADINP
VSWCOM
VADINP
BALCHK
VADINP
VSWCOM
VADINP
HFINTP
VADINP
BALCHK
VADINP
BALCHK
HFINTP
VARCAL
VADINP
ASSEMT
VARCAL
VADINP
VADINP
ASSEMF
ASSEMT
HFINTP
VARCAL
VADINP
ASSEMF
ASSEMT
HFINTP
VARCAL
Common
Block I,M,0
I
M
CONTR M
ADISC I
M
ADISC I
M
M
CONTR M
M
M
M
313
-------
Table 8-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATIONS (concluded)
Variable Units Type
VDAR L/t ARRAY
VDARPT L/t ARRAY
Description
Darcy Velocity for Each
Node
Nodal Darcy Velocities
at Previous Time
Sub-
routine
VADINP
ASSEMF
BALCHK
VARCAL
VSWCOM
VADINP
VSWCOM
Common
Block
WELEM
WELEM
I.M.O
M
0
M
VDFI L/t ARRAY Default Value of Darcy
Velocity for Current
Material
VADINP
XX
YY
SCALAR The X value Passed in INTERP
INTERP (to be Used in
the Interpolation)
SCALAR The Y Value Passed in INTERP
INTERP (to be Used in
the Interpolation
M
M
314
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION
Variable
A
AA
All
AK
ALPHAL
ANODE
AR
ATPROP
B
BR
C
CC
Units Type
Array
Array
Array
Array
Scalar
L**2 Array
Array
Report Array
Array
Array
Array
M Array
Description
Left Diagonal of
Tridiagonal Matrix
Element Coefficient Matrix
Element Right Hand Side
Vector
Global Coefficient Matrix
Longitudinal Dispersivity
Nodal Areas
Right -Hand Side Vector
of Tridiagonal System
Aquitard Properties
Central Diagonal of
Tridiagonal Matrix
Working Array for
Formulation of Tridiagonal
System
Right Diagonal of
Tridiagonal Matrix
Element Mass Storage
Matrix
Sub - Common
routine Block
TRIMOD TRIDIA
ASSEMV ELSTOR
EBFOR1
EBFOR2
ASSEMV ELSTOR
BALCHS
EBFOR1
EBFOR2
ASSEMV WAVE1
MATMOD
SOLVEC
SOLVEP
ASSEMV
SMIOIN NDSTOR
ANDCAL
BALCHS
MATMOD
CPCAL TRIDIA
SMIOIN MDATAT
CPCAL
TRIMOD
SMIOIN TRIDIA
CPCAL
TRIMOD
BALCHS ELSTOR
EBFOR1
EBFOR2
I,M,0
M
M,0
M,0
M
M,0
M.O
M
M
M
M
M
M
M,0
I
M
M,0
M
M
M,0
M,0
315
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable
CD
CON
CONVND
CORD
CR
CTEMP
D
DAKP
DEMND
DIS
Units Type
M Array
M/L**3 Array
L/t ARRAY
Array
Array
Array
Array
L**2 Array
L**3/t Array
L Array
M/L**3
Description
Element Mass Decay Matrix
Nodal Values of Concentra-
tion In Aquitard Column
Nodal Value of Vertical
Conductivity
Nodal Coordinates
Working Array for the
Formulation of the
Tridiagonal System
Temporary Working Array
Right-Hand Side Vector of
Tridiagonal System
Element Nodal Area
Nodal Values of Vertical
Leakage Coefficient
Current Nodal Values of
the Dependent Variable
(Head/Cone . )
Sub - Common
routine Block
BALCHS ELSTOR
EBFOR2
CPCAL TRIDIA
SMIOIN NDSTOS
MATMOD
SMIOIN MSHDAT
FILPLT
FILPRW
MESHGN
CPCAL TRIDIA
BALCHS WORKS
TRIMOD
SMIOIN ESTORE
EBFOR1
EBFOR2
SMIOIN NDSTOS
MATMOD
SMIOIN WAVE
BALCHS
CPCAL
EBFOR1
EBFOR2
FILHED
FILPLT
FILPRW
MATMOD
PBC
QCAL
THUPDT
VARCAL
VELCOM
I.M.O
M
M,0
0
M
M,0
0
M
M,0
M
M
0
M
M
0
0
M
M
M
316
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
DIST
DLAM 1/t
DLEL 1/t
DPCOL 1/t
DX L
DYMAX L
DXV L
DY L
Ell
EL L
ELKP L
Type
Array
Array
Array
Array
Scalar
Scalar
Array
Array
Scalar
Array
Sub - Common
Description routine Block
Dimensionless Nodal SMIOIN VDISC
Coordinate for an
Aquitard Column CPCAL
Aquifer Decay Coefficient SMIOIN RDATA
Elemental Value of Aquifer SMIOIN RDATA
Decay Coefficient EBFOR2
Default Value of the Decay SMIOIN RDATA
Coefficient of an Aquitard CPCA
Column
Nodal Spacing of First SMIOIN
Grid Block Nodal Spacing CPCAL
in the X-Direction OXYGEN
MESHGEN
Maximum Allowable Value SMIOIN
of Nodal Spacing OXYGEN
MESHGEN
Vertical Spacing of the CPCAL VDISC
Nodes in the Aquitard
Column
Nodal Spacing in the SMIOIN
Y-Direction MESHGN
Element Right Hand Side BALCHS ELSTOR
Vector
Length of a Particular EBFOR1 ESTORE
Rectangular Element EBFOR2
VELCOM
Lengths of All Rectangular SMIOIN ESTORE
Elements in the Grid ANDCAL
EBFOR1
EBFOR2
VELCOM
I.M.O
I.M.O
M
1,0
M
M
1,0
M
M
1,0
M
1,0
M
M
M
M
M
M
317
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable
EM
EMKP
ETA
ETAKP
FIZ1
FLUXV
FLUXVO
FNDSTO
FVAL
FVTM
GAMKP
Units Type
L Array
L Array
Scalar
Array
Array
L**3/t Array
M/t
L**3/t Array
M/t
Array
Array
L**3/t Array
L**3/t Array
Description
Width of a Particular
Rectangular Element
Widths of All Rectangular
Elements in the Grid
Back Substitution
Parameter
Back Substitution
Parameter of All Aquitard
Columns
Working Element Right Hand
Side Vector
Current Values of
Prescribed Nodal
(Fluid/Solute) Fluxes
Default Steady- State
values of Prescribed Nodal
(Fluid/Solute) Fluxes
Array Containing Mass
Balance Information
Working Arrays of Nodal
Values
Prescribed Nodal Fluxes at
Various Times
Values of Vertical Leakage
Sub- Common
routine Block
EBFOR1
EBFOR2
VELCOM
SMIOIN ESTORE
ANDCAL
EBFOR1
EBFOR2
VELCOM
CPCAL
TRIMOD
SMIOIN VDISC
BALCHS
CPCAL
MATMOD
ASSEMV ELSTOR
EBFOR1
BUPDAT FDATA
HFINTP
PBC
SMIOIN FDATA
BUPDAT
BALCHS WORKS
ASSEMV ABOUN
PBC
SMIOIN BCDATA
HFINTP
CPCAL VDISC
I.M.O
M
M
M
M
M
M
M,0
M,0
M
M
M
M
I
M,0
M
1,0
M
Fluxes for Various Aquitard
Columns
318
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units Type
Description
Sub- Common
routine Block I,M,0
GAMMA
L**3 Scalar
GDECAY 1/t Array
GMZ - - Array
HDOBS
HOTEL
HIAQFR
HICOL
HINT
HPATD
HSTORE
L Array
M/L**3
Array
L Array
M/L**3
L Array
M/L**3
L Array
M/L**3
L Array
M/L**3
L Array
M/L**3
Value of Vertical Leakage CPCAL
Flux for Particular TRIMOD
Aquitard Column
TRIDIA M,0
M
Nodal Values of Solute
Mass Decay Rates
Nodal Storage Array
Values (Head/Cone.) at
Specified Observation
Nodes
BALCHS WORKS M
ASSEMV
BALCHS
FILVEL
SMIOIN
VARCAL
Element Bookkeeping Array EBFOR1
Initial Values of the SMIOIN
Dependent Variable
(Head/Cone.) in Aquifers
Overriding Initial Values SMIOIN
(Head/Cone.) in Aquitard CPCAL
Columns
Nodal Values (Head/Cone.)
in Aquifers At Previous
Time Levels
Current Values (Head/
Cone.) in the Aquitard
SMIOIN
BALCHS
EBFOR1
EBFOR2
FILMED
FILPRW
MATMOD
SMIOIN
CPCAL
FILHED
FILPRW
Array Containing Temporary VARCAL
Values (Head/Cone.) at the
Aquifer Nodes
ESTORE M
M
BNDOUT 0
M
ELSTOR M
INTDAT
INTDAT I,M,0
I,M,0
M
WORKM M
M,0
WAVE M
319
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units Type
HTOL L Scalar
HVAR L Array
M/L**3
HVTM L Array
M/L**3
IAQ - - Scalar
IAQNO - - Array
IAQTYP - - Array
IAREAL - - Scalar
IATP - - Array
Description
Head Tolerance for Matrix
Solution of the Unconfined
Areal Flow Problem
Intermediate Values
(Head/Cone.) at Aquifer
Nodes
Current Values of
Prescribed (Head/Cone.)
Aquifer Numbering Index
Aquifer No. Identification
of Various Rectangular
Elements
Aquifer Type Specification
Areal Modeling Indicator
Material Identification
for Aquitard Columnb
Sub- Common
routine Block
SMIOIN CONTR3
VARCAL
VARCAL WAVE
SMIOIN BCDATA
HFINTP
SMIOIN
ASSEMV
BALCHS
CPCAL
EBFOR1
EBFPR2
MATMOD
VARCAL
VELCOM
SMIOIN MDATAQ
ASSEMV
EBFOR1
EBFOR2
MATMOD
VELCOM
SMIOIN MDATAQ
EBFOR1
VARCAL
SMIOIN CONTR1
ASSEMV
BALCHS
EBFOR1
FILVEL
VARCAL
VELCOM
SMIOIN MDATAT
CPCAL
FILPRW
MATMOD
I.M.O
1,0
M
1,0
M
0
0
M
M
M,0
M
M
1,0
1,0
M
320
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
IAXSYM
IBSUB
I CALL
ICEND
I COORD
I COUNT
IDPCOL
Type Description
Scalar Parameter Indicating if
AXSYM Used (1-YES, 0=NO)
Scalar Matrix Solution Index
Scalar Option Specification
Parameter for Matrix
Modifications
Scalar Ending Node Number
Scalar Coordinate Generation
Index (1 for X- COORD,
2 for Y-COORD)
Scalar Dummy Integer Variable
Scalar Aquitard Column Numbers
Where Nodal (Head/Cone . )
Sub - Common
routine Block
SMIOIN CONTR1
ANDCAL
ASSEMV
BALCHS
EBFOR1
EBFOR2
QCAL
CPCAL
ASSEMV
MATMOD
CPCAL
TRIMOD
FIVEIO
FRVEIO
OXYGEN
SMIOIN
ASSEMV
BALCHS
EBFIND
FILMED
FILPLT
FILPRW
FILVEL
FIVEIO
FRVEIO
HFINTP
MATMOD
PBC
QCAL
SMIOIN PRCNTR
FILPRW
I.M.O
1,0
0
I.M.O
I.M.O
M
M,0
M
0
0
0
M
M
M
M
M
M
IDPND
are to be Printed
Array Aquifer Node Numbers Where SMIOIN
Nodal (Head/Cone.) are to FILPRW
be Printed
PRCNTR
M
321
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
IDTEMP
IHALFB
III
IKALL
IMBAL
IMODL
Type Description
Array Temporary Integer Flag
Array
Scalar Half Bandwith of Global
Matrix
Scalar Unconfined Flow Nonlinear
Iteration Index
Scalar Time Stepping Scheme
(0=Central Diff,
l=Backward Diff)
Scalar Mass Balance Computation
Requirement Index
Scalar Modeling Type Index
(l=Flow, 0=Transport)
Sub - Common
routine Block I,M,0
ASSEMV ATEMP M,0
PBC M
SMIOIN
ASSEMV
EBFIND M
MATMOD
QCAL
SOLVEC
SOLVEP
VARCAL
ASSEMV
VARCAL 0
SMIOIN CONTR3 1 , 0
VARCAL
SMIOIN CONTR1
BALCHS
FILVEL
SMIOIN CONTR1 1 , 0
ASSEMV
BALCHS
FILPRW
MATMOD
QCAL
VARCAL
INDKP
INDSTO
10
Scalar
Array
Scalar
Node Number of Flux Nodes SMIOIN WAVE2
QCAL
Nodal Indices of Flux
Nodes
BALCHS WORKS M,0
Parameter Controlling FIVEIO
Functions of Subroutine FRVEIO
(1 for Read, 2 for Write)
322
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
IOBSND
IOUTLT
IPRCHK
IPRCON
IPRD
Requirements
1PROP REPORT
IRCOUN
IREPB
IRND
Type
Scalar
Scalar
Scalar
Scalar
Scalar
ASSEMV
Array
Array
Scalar
Array
Description
Observation Node Parameter
Specification
(1=YES, 0=NO)
Fluxe Computation Index
(1=YES, 2=NO)
Matrix Computation Print -
Check Index (1-YES, 2=NO)
Printout Deletion Index
(1=YES, 0=NO)
Printed Output
Material Property Number
of the Element
Recharge Nodal Index Array
Parameter Indicating
if Transient Boundary
Condition Date is the
same as Preceding Record
(1=YES, 0=NO)
Nodal Recharge Index
Sub - Common
routine Block I,M,0
SMIOIN CONTR4 1 , 0
SMIOIN CONTR2 1,0
SMIOIN CONTR4 1 , 0
CPCAL
EBFOR1
EBFOR2
MATMOD
SMIOIN CONTR4 1,0
SMIOIN CONTR5 1,0
SMIOIN MDATAQ 0
ASSEMV
EBFOR1
EBFOR2
MATMOD
VARCOM
SMIOIN RCHDAT M,0
BALCHS
MATMOD
SMIOIN I
RUPDAT
SMIOIN RCHDAT M
MATMOD M
IRZKP
IRZON
Array Zone Numbers of the
Recharge Nodes
Scalar Recharge Zone Number
SMIOIN RTDATA
SMIOIN
323
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units Type
Description
Sub - Common
routine Block I,M,0
ISLNO
I START
Scalar Aquifer Numbering Index
Array Starting Node Number of
Related Aquifer
SOLVEC
SOLVEP
ASSEMV ASOLV
BALCHS
EBFIND
QCAL
M
I SUB
I SWAP
ITAP10
ITAP6
Scalar Matrix Integration Index ASSEMV
for Multilayer Aquifer VARCAL
System
Scalar Parameter Indicating SMIOIN
Direction of Sequential ANDCAL
Node Numbering MESHGN
Scalar Parameter Specifying Which SMIOIN
Data is Written to Tape 10 FILPLT
(1 for MESH DATA, 2 for
HEAD or CONCENTRATION)
Scalar Output Control Parameter
SMIOIN
FILPRW
1,0
ITAP8
ITAP9
ITCLIP
ITDP
Scalar
Scalar
Scalar
Array
Parameter-Controlling SMIOIN
Functions of Subroutine FILMED
(1 for READ, 2 for WRITE)
Input/Output Control
Parameter (1=READ,
2=WRITE)
SMIOIN
FILVEL
Windowing Node Numbers for SMIOIN PRCNTR
Specific Section
Parameters Indicating if SMIOIN RTDATA
the Recharge Rate at the RUPDAT
Nodes are Time Dependent
M
324
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable
ITER
ITRANS
ITSTF
ITSTH
IVECT
IVRECH
IVSTED
Field in
IWATP
IXYRED
Units Type
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Transport
Scalar
Scalar
Description
Current Iteration
Simulation Mode
(1=TRANSIENT,
0=STEADY- STATE)
Time Level Counter
Time Level Counter
Temporary Vector for
Storing Integer Variable
Recharge of Infiltration
Included (1-YES, 0=NO)
Steady State Velocity
MATMOD
(1=YES, 0=NO)
Aquifer System Unconfined
(1=YES, 0=NO)
Parameter Indicating if
Grid Lines are to be Input
Sub- Common
routine Block I,M,0
SMIOIN CONTR2 0
BALCHS
CPCAL
EBFOR1
EBFOR2
HFINTP
MATMOD
VARCAL
VELCOM
SMIOIN CONTR2 1,0
SMIOIN BCDATA M
HFINTP M
SMIOIN BCDATA M
HFINTP M
FILPLT M
FIVEIO 1,0
SMIOIN CONTR1 1 , 0
ASSEMV
BALCHS
SMIOIN CONTR4 1,0
SMIOIN CONTR1 1 , 0
ASSEMV
BALCHS
CPCAL
VELCOM
SMIOIN 1,0
MESHGN 0
IZONO
Scalar Material Number Assigned
to a Particular Zone of
the Aquifer
RUPDAT
325
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
JJJ
JTSRKP
KKK
LENREC
LENV
LV
MM
MXNAQF
Type Description
Scalar Solving Option (1=MATRIX
SOLUTION, 2=BACK
SUBSTITUTION)
Array Dummy Bookkeeping Array
Scalar Solving Option (1=COMPLETE
MATRIX SOLUTION, 2=BACK
SUBSTITUTION)
Scalar Binary Record Length
Scalar Length of Vector
Array Used In Matrix Bandwidth
Computation
Scalar Element Number
Scalar Maximum Number of Aquifers
Allowed (Code Limit
Parameter)
Sub - Common
routine Block I,M,0
SOLVEC
VARCAL
SMIOIN RTDATA M
RUPDAT
SOLVEP
VARCAL
FILMED
FIVEIO M
FRVEIO M
FILHED M
FILPLT M
FILVEL M
FIVEIO
FRVEIO
EBFIND ASOLV M
EBFOR1
EBFOR2
SMIOIN MDATAQ
ASSEMV
BALCHS
CPCAL
EBFOR1
EBFOR2
FILHED
FILPLT
FILPRW
MATMOD
VARCAL
VELCOM
N
Scalar Number of Rows in
Tridiagonal Matrix
TRIMOD
326
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
NAQFR
NAQTRD
NB
NBAND
NBFVAR
NBHVAR
Type Description
Scalar Number of Aquifers
Scalar Aquitard Separating 2
Aquifers (1=YES, 0=NO)
Scalar Number of Nodes with
Prescribed Concentration
Array Storage Array Used in
Computation of Matrix
Bandwidth
Scalar Number of Transient Flux
Boundary Conditions
Scalar Number of Transient
DIRICHLET Boundary
Conditions
Sub - Common
routine Block I,M,0
SMIOIN CONTR1 1 , 0
ASSEMV
BALCHS
CPCAL
EBFIND
VARCAL
SMIOIN CONTR1 1,0
ASSEMV
BALCHS
FILHED
FILPRW
VARCAL
ASSEMV CONTR5
BUPDAT
PBC M
SOLVEC
SOLVEP
ASSEMV ASOLV
BALCHS
EBFIND M
QCAL
SMIOIN CONTR5 1 , 0
HFINTP
PBC
SMIOIN CONTR5 1 , 0
HFINTP
PBC
NBOUT
NBTO
Scalar Number of Nodes Where SMIOIN
NONZERO Nodal Flux Values ASSEMV
are to be Computed BALCHS
FILVEL
QCAL
Scalar Number of Steady DIRICHLET SMIOIN
Boundary Conditions BUPDAT
HFINTP
PBC
CONTR5 1,0
CONTR5 1,0
327
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
NCATRD
NCOL
NCOLS
NCOLWN
NDFIX
NDFLUX
NDFLX
NDFVAR
Type Description
Scalar Maximum Number of Aquitard
Columns
Scalar Number of Grid Lines
Parallel to Y-Axis
Scalar Number of Columns in
Rectangular Mesh
Array Number of Nodal Columns
in the Window
Array Nodal Value Fixing Flag
Scalar Number of Steady Flux
Boundary Conditions
Array Local Node Number of a
Flux Boundary Condition
Node
Array Node Numbers of Time
Dependent Flux Boundary
Sub - Common
routine Block I,M,0
SMIOIN CONTR2 0
CPCAL
MESHGN M
SMIOIN 1,0
ANDCAL
MESHGN M
SMIOIN PRCNTR M
FILPRW
ASSEMV ABOUN
PBC M
SOLVEC
SOLVEP
SMIOIN CONTR5 1,0
BUPDAT
HFINTP
PBC
ASSEMV ABOUN
PBC M
SMIOIN BCDATA 1 , 0
HFINTP
NDHVAR
NDOBS
NDOUT
Array
Array
Array
Nodes
Node Numbers of Time
Dependent DIRICHLET
(Prescribed Head or Cone.)
Global Sequential Numbers
of Observation Nodes
SMIOIN BCDATA
SMIOIN
VARCAL
Global Sequential Node SMIOIN
Numbers of the Nodes for ASSEMV
Which Nodal Fluid or BALCHS
Solute Flux Values are to QCAL
be Computed
BNDOUT
BNDOUT
1,0
1,0
0
328
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
NDREV
NE
NEQ
NESL
NITMAX
NMATAQ
NN
NNDFLX
NNDSL
Type Description
Array Reverse Node Numbering
Index
Scalar Total Number of Elements
in the Grid
Scalar Number of Algebraic
Equations in the Matrix
System
Scalar Number of Elements Per
Aquifer
Scalar Maximum Number of Time
Steps
Array Number of Materials
Associated With the
Receptive Aquifer
Scalar Number of Nodes on the
Grid Line
Scalar Number of Nodal Flux
Values in the Current
Time Step
Scalar Number of Nodes Per
Aquifer
Sub- Common
routine Block I,M,0
SMIOIN VDISC M
CPCAL
SMIOIN CONTR2 1,0
ANDCAL
FILPLT
FILVEL
MATMOD
THUPDT
VELCOM
SOLVEC M
VARCAL M
SMIOIN CONTR5 M
ASSEMV
BALCHS
EBFIND
SMIOIN CONTR3 1 , 0
VARCAL
SMIOIN MDATAQ 1,0
OXYGEN M
SOLVEC M
ASSEMV CONTR5
PBC M
SMIOIN CONTR5 M
ASSEMV
BALCHS
CPCAL
EBFIND
MATMOD
SOLVEC
SOLVEP
VARCAL
329
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units Type
Description
Sub- Common
routine Block I,M,0
NNDWN
NNOBS
NNP
NODF
NODV
NOP
Array
Scalar
Scalar
Array
Array
Array
NOWRIT
NP
Scalar
Scalar
Number of Nodes in the SMIOIN PRCNTR
Current Window Section FILPRW
Number of Observation SMIOIN CONTR5
Nodes VARCAL
Number of Algebraic SOLVEP
Equations in the Matrix
System
Aquifer Node Numbers With SMIOIN FDATA
Steady-State Flux HFINTP
Boundary Condition PBC
Aquifer Node Numbers With SMIOIN BDATA
Steady-State DIRICHLET HFINTP
Boundary Condition PBC
Element Connection Data SMIOIN MSHDAT
for Finite Element Network ANDCA
ASSEMV
BALCHS
EBFIND
EBFOR1
EBFOR2
FILPLT
MATMOD
MESHGN
THUPDT
VELCOM
Head Values Written to
Tape 8 (l=Yes, 0=No)
Total Number of Nodal
Points in the Grid
SMIOIN CONTR4
SMIOIN
ANDCAL
BALCHS
FILHED
FILPLT
FILVEL
MATMOD
PBC
THUPDT
VARCAL
VELCOM
CONTR2
M
1,0
1,0
M
1,0
M
M
1,0
1,0
330
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
NPATCO
NPATD
NPLOT
NRCOUN
NREC
NROW
NROWS
NSTEP
NTCLIP
NTS
NTSNDF
Type
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Number of Nodal Points in
Each Aquitard Column
Total Number of Nodes
in All Aquitards
Time and Head Value
Written to TAPE 10
(0-No, N=N th)
Recharge Node Counter
Number of Records
Number of Grid Lines
Parallel to X-Axis
Number of Rows in
Rectangular Mesh
Controls Printout of
Computed Values for
Nodal points
Number of Time Steps
Where Printed
Output is Required
Number of Time Steps
Number of Control Points
Sub - Common
routine Block I,M,0
SMIOIN CONTR2 1 , 0
CPCAL
FILPRW
SMIOIN CONTR2 M
FILMED
SMIOIN CONTR4 1,0
SMIOIN CONTR1 M,0
BLCHK
MATMOD
FIVEIO M
FRVEIO M
MESHGN
SMIOIN 1,0
ANDCAL
BALCHS M
MESHGN
SMIOIN CONTR4 1 , 0
BALCHS
SMIOIN CONTR4 1,0
SMIOIN CONTR2 1,0
SMIOIN BCDATA I
on the Flux-vs-Time Graph HFINTP
for Transient Flux
Boundary Condition
331
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
NTSNDH
NTSRCH
NTSRKP
NUMT
NVPR
NVREAD
NVWRIT
NWINDO
NZONRT
OUTFL
Type
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Number of Control Points
on the Graph of Head
(or Cone.) vs. Time Graph
for Transient Dirichlet
Boundary Condition
Number of Control (Inter-
polation) Points on the
Graph of Recharge Rate vs .
Time
Number of Control Inter-
polation Points for
Individual Recharge Zones
Time Step Number
Controls Printout of
Computed Element Darcy
Velocities
Velocity Input From Unit
Number 9 (1-Yes, 0=No)
Element Velocities Written
to Tape 9 (l=Yes, 0=No)
Number of Output Window
Sections in Aquifer Region
Number of Zones with Time-
Dependent Recharge Rates
Unit Number of Printer
Output File
Sub - Common
routine Block
SMIOIN BCDATA
HFINTP
SMIOIN
RUPDAT
SMIOIN RTDATA
RUPDAT
SMIOIN
FILPRW
SMIOIN CONTR4
VELCOM
SMIOIN CONTR4
SMIOIN CONTR4
SMIOIN CONTR4
FILPRW
Main CONTR1
SMIOIN
AS'SEMV
BALCHS
CPCAL
FILHED
FILPLT
FILPRW
FILVEL
QCAL
VARCAL
VELCOM
I.M.O
I
I
I
M
1,0
1,0
1,0
1,0
I
0
332
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES„ UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable
PROP
PVAL
QNDOUT
QVAL
QVALV
QVTEMP
QVTM
Rl
Units
REPORT
L
M/L**3
L**3/t
L**3/t
L**3/t
L**3/t
L**3/t
L**3/t
Type
Array
Array
Array
Array
Array
Array
Array
Array
Description
Material Property of the
Porous Matrix
Temporary Value Storage
Array for Head/
Concentration
Net Values of Prescribed
Volumetric Fluid Fluxes
at the Nodes
Storage Array for Flux
from Prescribed Boundary
Condition During Matrix
Assembly and Solution
Nodal Volumetric Fluid
Flux for Steady- State Flux
Boundary Condition
Temporary Storage Array
for Flux Values During
Assembly and Solution of
Element Matrix
Injected Fluid Volumetric
Flux that are Time
Dependent (Transpor
Simulation Only)
Integrated Values of Nodal
Recharge Fluxes
Sub-
routine
SMIOIN
ASSEMV
EBFOR1
EBFOR2
VELCOM
ASSEMV
PBC
SOLVEC
SOLVEP
SMIOIN
BALCHS
FILVEL
QCAL
ASSEMV
PBC
SMIOIN
HFINTP
PBC
ASSEMV
SMIOIN
HFINTP
SMIOIN
ASSEMV
BALCHS
FILPRW
MATMOD
SOLVEP
VARCAL
VELCOM
Common
Block
MDATAQ
ABOUN
BNDOUT
ABOUN
FDATA
ATEMP
BCDATA
WAVE
I,M,0
0
M
1,0
M
M
M
I
M
0
1,0
M,0
M
M
M
M
M
M
M
333
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
RADS L
RCEL
RCHND
RCHTM L/t
Type
Array
Array
Array
Array
Description
Aquitard Vertical
Coordinator Aquifer
Retardation for each
Element in F.E. Network
Nodal Recharge Value Array
Recharge Rate Values
Sub-
routine
CPCAL
SMIOIN
EBFOR2
SMIOIN
SMIOIN
Common
Block
TRIDIA
RDATA
RCHDAT
RTDATA
I.M.O
M
M
M
I
RCHVAL
ROWA
ROWR
RPCOL
RTEMP
S
SCFX
L/t Scalar
RCOEF - - Array
RFLUXV L**3/t Array
Array
Array
Array
L Array
M/L**3
Scalar
Corresponding to Control
Points on the Graph of
Recharge Rate vs. Time
Steady-State Default Value RUPDAT
of the Recharge Rate for
the Zone
Aquitard Retardation
Factor
SMIOIN
Nodal Recharge Flux Values SMIOIN
ASSEMV
BALCHS
Row Value Storage Array
of the Solution Matrix
Right-Hand Side Row of
the Right Solution Matrix
Retardation Parameter
for Specific Columns of
Aquitard
Array Temporary Storage Array
Storage of Head or Cone.
During Back Substitution
Multiplier to Compute
Remaining Node Spacings
in the X-Direction
ASSEMV
BALCHS
QCAL
ASSEMV
BALCHS
QCAL
SMIOIN
CPCAL
BALCHS
CPCAL
TRIMOD
SMIOIN
MESHGN
RDATA
RCHDAT
WAVE2
WAVE2
RDATA
WORKS
TRIDIA
1,0
M
M
M
M
M
1,0
334
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
SCFY
SS1KP
SS2KP
TAP10
TAPS
TAP9
THEL L
THETA
THETM1
THMXND L
Type
Scalar
Array
Array
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Description
Multiplier to Compute
Remaining Node Spacings
in the Y-Direction
Element Storage Array
Element Storage Array
File Unit 10
File Unit 8
File Unit 9
Saturated Thickness of
Element
Time Weighting Factor
THETA -1
Nodal Value of Aquifer
Thickness
*
Sub- Common
routine Block I,M,0
SMIOIN 1,0
MESHGN
BALCHS ESTORE
EBFOR1 M
EBFOR2 M
BALCHS ESTORE
EBFOR2 M
SMIOIN
FILPLT
SMIOIN
FILMED
SMIOIN
FILVEL
SMIOIN ESTORE I,M
EBFOR1
EBFOR2
FILVEL M
THUPDT M
VELCOM M , 0
SMIOIN CONTR3 M
BALCHS
CPCAL
EBFOR1 0
EBFOR2 0
VARCAL M
BALCHS CONTR3
CPCAL
EBFOR1
EBFOR2
VARCAL M
SMIOIN NDSTOR M
MATMOD
VELCOM
335
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable Units
TIMA t
TIME t
TIN t
TMACCU L
TMCLIP
TMDCAY M
TMHF t
TMHV t
TMK t
TMK1 t
Type
Scalar
Scalar
Scalar
Scalar
Array
Array
Array
Array
Scalar
Scalar
Description
Starting Time of the
Simulation
Time Value
Value of the First Time
Step
Cumulative Fluid Storage
Printout Control for
Head/Cone .
Total Accumulated Mass
Decay
Time Values to Control
Points on Fluid Flux vs .
Time Plot
Time Values to Control
Points on the Graph of
Head (or Cone.) vs. Time
Current Time Value
Preceeding Time Value at
Sub - Common
routine Block
SMIOIN CONTR3
SMIOIN
FILPRW
SMIOIN CONTR3
BALCHS
CAPCAL
EBFOR1
EBFOR2
FILPRW
SMIOIN CONTR1
BALCHS
SMIOIN PRCNTR
SMIOIN CONTR1
BALCHS
SMIOIN TMDATA
HFINTP
SMIOIN TMDATA
HFINTP
RUPDAT
RUPDAT
I.M.O
1,0
M,0
1,0
M
M
M,0
1,0
M
M
I
1,0
M
M
TMK2
TMRCH
Scalar
Array
the Interpolation Points
Subsequent Time Value at
the Interpolation Points
RUPDAT
Time Values Corresponding SMIOIN
to Control Points on the RUPDAT
Graph of Recharge Rate vs.
Time
RTDATA
M
336
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (continued)
Variable
TMVEC
UNIT
VABSND
VALV
VALVO
VELX
VELY
VTEMP
XCTR
Units Type
t Array
Scalar
L/t Array
L Array
M/L**3
L Array
M/L**3
L/t Array
L/t Array
L**3/t Array
L Array
Description
Time Values of Time Steps
One Through NTS
Parameter- Specifying File
Unit for Output
Absolute Darcy Velocity
for Each Node
Storage of VAL During
Iterative Solution Process
Prescribed Value of Head
or Cone, for Steady- State
Dirichlet Boundary
Condition
Element Value of Darcy
Velocity in the
X-Direction
Element Value of Darcy
Velocity in the
Y-Direction
Temporary Storage of Nodal
Fluid Flux Values
Element Centroidal Value
in the X-Direction
Sub- Common
routine Block I,M,0
SMIOIN TMDATA I.M.O
FILPRW
HFINTP
FRVEIO
FIVEIO
MATMOD NDSTOS M
SMIOIN BDATA 1,0
BUPDAT M
HFINTP M
PEG
SMIOIN BDATA 1,0
BUPDAT
SMIOIN VELEM 1,0
EBFOR2
FILVEL M
MATMOD
VELCOM M,0
SMIOIN VELEM 1,0
EBFOR2
FILVEL M
MATMOD
VELCOM M,0
ASSEMV ATEMP M,0
PBC M
SMIOIN VELEM M,0
ANDCAL
EBFOR1
EBFOR2
FILPLT
XFAC
Scalar Multiplier Used to Compute SMIOIN
Remaining Nodal Spacing OXYGEN
337
-------
Table 8-5. SAFTMOD PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (concluded)
Variable
XFST
XLIM
XLNODE
XO
XS
xw
YCTR
YO
YW
ZBND
ZI
ZIKP
Units
--
L
L
L
L
L
L
L
L
L
--
Type
Scalar
Scalar
Array
Scalar
Array
Array
Array
Scalar
Array
Array
Scalar
Array
Description
Starting Coordinate Value
Length of Domain
Nodal Length
Length Along X- Direction
Temporary Storage of X & Y
Coordinates of Grid Lines
While Being Computed by
the Code
X- Coordinates of Grid
Lines One Through NCOLS
Read from User File
Element Centre idal Value
in the Y-Direction
Length Along Y-Direction
Y- Coordinate of Grid Lines
One Through NROWS Read
from User File
Zonal Value of Elevation
of the Aquifer Base Above
the Datum Plane
Back Substitution
Parameter
Storage of ZI at Aquifer
Aquitard Interface
Sub- Common
routine Block
OXYGEN
OXYGEN
SMIOIN NDSTOR
ANDCAL
MATMOD
SMIOIN
CPCAL
MESHGN
ASSEMV MSHPAR
ANDCAL
OXYGEN
SMIOIN MSHPAR
ANDCAL
OXYGEN
MESHGN
SMIOIN VELEM
FILPLT
SMIOIN
MESHGN
SMIOIN MSHPAR
OXYGEN
MESHGN
SMIOIN NDSTOR
THUPDT
VELCOM
TRIMOD
CPCAL
SMIOIN VDISC
BALCHS
CPCAL
MATMOD
I.M.O
M
1,0
M
M
M
1,0
M
M,0
1,0
1,0
M
M
M
M
M,0
M
338
-------
Table 8-6. MONTE-CARLO PROGRAM VARIABLES
Variable
BBT
CORR
DECOM
DIST
IN2
IOUT
IOUT2
IRUN
IVAR
LARR
MCMAX
MCVAR
NCMAX
NDAT
Type
Double
Precision
Double
Precision
Array
Integer
Real
Array
Integer
Integer
Integer
Integer
Integer
Integer
Array
Integer
Integer
Integer
Integer
Array
Description
Correlation matrix for Monte -Carlo
inputs .
Array of correlation terms for
summary output variables .
Decomposed correlation matrix for
Monte -Carlo inputs.
Array storing empirical
distributions .
Monte -Carlo input file number.
Monte -Carlo summary output file
unit number.
Output file unit number for results
of each Monte -Carlo run.
Do loop counter for Monte -Carlo
runs.
Do loop counter for variable number.
Array storing array addresses for
random input variables .
Maximum possible number of random
input variables .
Number of random input variables.
Maximum possible number of variables
for which cumulative distributions
can be plotted.
Number of values in empirical
distributions.
Subroutine
Main program,
READM, INITMC
Main Program,
STSTIS, OUTPUT
Main Program,
INITMC, RANDOM
Main Program,
READM, Random
Main Program,
READM
Main Program,
READM, OUTPUT
Main Program,
STATIS
Main Program,
STATIS
Main Program
Main Program,
READM, INITMC
Main Program
Main Program,
READM, INITMC,
RANDOM
Main Program
Main Program,
READM, RANDOM
NEMP Integer Maximum number of empirical
distribution value-probability
pairs.
Main Program,
READM, RANDOM
339
-------
Table 8-6. MONTE-CARLO PROGRAM VARIABLES (concluded)
Variable Type
Description
Subroutine
NMAX Integer Maximum possible number of variables
for which summary statistics can be
printed.
NRMAX Integer Maximum number of Monte-Carlo runs
allowed.
Main Program
Main Program
NRUNS
NVAR
Integer
Number of Monte-Carlo Runs.
Main Program
READM, OUTPUT
Number of summary output variables. Main Program
PNAME
RMC
SNAME
STAT
VAR
XCDF
XMC
Character
Array
Real
Array
Character
Array
Double
Precision
Array
Real
Array
Real
Array
Real
Array
Input labels used to flag random
input variables.
Array of randomly- generated numbers.
Input labels used to flag summary
output variables
Array of summary statistics for
output variables .
Array storing distribution
parameters for random input
variables .
Array storing values of selected
variables for plotting cumulative
distributions .
Array storing values of summary
output variables.
Main Program,
READM, INITMC
Main Program,
RANDOM
Main Program,
READM, OUTPUT
Main Program,
STSTIS, OUTPUT
Main Program,
READM, INITMC,
RANDOM
Main Program,
STATIS, OUTPUT
Main Program,
STATIS
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