EPA/600/R-05/111
September 2005
PRZM-3, A Model for Predicting Pesticide and Nitrogen
Fate in the Crop Root and Unsaturated Soil Zones:
Users Manual for Release 3.12.2
by
L.A. Suarez
Ecosystems Research Division
National Exposure Research Laboratory
Athens, GA 30605-2700
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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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-C6-0020 to HydroGeoLogic, Inc. It has been subjected to the Agency's peer and
administrative review, and has been approved for publication as an EPA document. Additional peer and administra-
tion review and testing is ongoing, but not yet completed. Mention of trade names of commercial products does not
constitute endorsement or recommendation for use by the U.S. Environmental Protection Agency.
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FOREWORD
As environmental protection measure become more costly to implement, and the penalties of judgment errors
become more severe, environmental quality management requires more efficient assessment tools based on greater
knowledge of the environmental phenomena to be managed. As part of this Division's research on the occurrence,
movement, transformation, impact, and control of environmental contaminants, this Division develops management
and engineering tools to help pollution control officials reach decisions on the registration and restriction of
pesticides used for agricultural purposes.
The pesticide and nutrient 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
human and ecosystem exposure through the leaching of pesticides and nitrogen to groundwater and the subsequent
ingestion of the contaminated ground water. To provide a tool for evaluating pesticide exposure, the PRZM-2 model
was developed; subsequent enhancements: expanded capabilities to include nitrogen simulation. PRZM-3 simulates
the fate and transport of field-applied pesticides in the crop root zone down throughout the vadose zone, taking into
account the effects of agricultural management practices. The model provides estimates of probable exposure
concentrations by taking into account the variability in the natural system and the uncertainties in system properties
and processes. To enable evaluation of nitrogen (particularly nitrate) exposure via groundwater, PRZM-3 includes a
septic system module and capabilities for modeling soil nitrogen fate and transport.
Eric J. Weber, Ph.D.
Acting Director
Ecosystems Research Division
National Exposure Research Laboratory
Athens, Georgia
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ABSTRACT
This publication contains documentation for the PRZM-3 model. PRZM-3 is the most recent version of a modeling
system that links two subordinate models - PRZM and VADOFT - in order to predict pesticide transport and
transformation down through the crop root and unsaturated soil zones. Enhancements to Release 3.0 reported herein
include algorithms that also enable modeling of the nitrogen cycle soil kinetic processes, with the ability to track
nitrogen discharges from a septic tank into the soil environment and its subsequent movement to groundwater.
Additional included enhancements enable better simulation of physicochemical processes, increased flexibility in
representing agronomic practices, and improved post-processing and data interpretation aids.
PRZM is a one-dimensional, finite-difference model that accounts for pesticide and nitrogen fate in the crop root
zone. PRZM-3 includes modeling capabilities for such phenomena as soil temperature simulation, volatilization and
vapor phase transport in soils, irrigation simulation, microbial transformation, and a method of characteristics
(MOC) algorithm to eliminate numerical dispersion. PRZM is capable of simulating the transport and the transfor-
mation of a given parent compound, and at most as two daughter species. VADOFT is a one-dimensional, finite-
element code that solves the Richard's equation for flow in the unsaturated zone. The user can use constitutive
relationships between pressure, water content, and hydraulic conductivity to solve the flow equations. VADOFT can
simulate the fate of two parent compounds, each with two daughter products. The PRZM and VADOFT codes are
linked together with the aid of a flexible execution supervisor that allows the user to build loading models tailored to
the user's site-specific situations. In order to perform probability-based exposure assessments, the code is also
equipped with a Monte Carlo pre- and post-processor.
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ACKNOWLEDGMENTS
The manual forPRZM-3 p was written by R.F. Carousel (USEPA), J.C. Imhoff, P.R. Hummel (AQUA TERRA
Consultants), and J.M. Cheplick (Waterborne Environmental), and A.S. Donigian, Jr.(AQUA TERRA Consultants).
This edition represents a major rewrite.
PRZM-3 is the result of fifteen years of focused model development effort. The original PRZM model was released
in 1984, with the accompanying user's manual written by R.F. Carousel (EPA), C.N. Smith (EPA), L.A. Mulkey
(EPA), J.D. Dean (Anderson Nichols) and P. Jowise (Anderson Nichols).
Release 2.0 (PRZM-2) became the official version of PRZM in the early 1990's. Several components of PRZM-2
were excerpted from the RUSTIC model. The following contributors to the RUSTIC model are acknowledged: Mr.
K. A. Voos of Woodward-Clyde Consultants (WCC) programmed the execution supervisor and linked the models.
The linkage was conceived by Mr. J.D. Dean and Dr. Atul Salhotra of WCC and Dr. P. S. Huyakorn of
HydroGeoLogic. Dr. Huyakorn and his staff wrote the time/space bridging subroutines for the linkage. Mr. R.W.
Schanz (WCC) and Y.J. Meeks (WCC) wrote the irrigation and MOC algorithms. The volatilization routines were
written by Dr. J. Lin and Mr. S. Raju of AQUA TERRA Consultants. Mr. Dean wrote the daughter products
algorithms that were implemented by Dr. Lin. Mr. JL. Kittle implemented modifications to allow multiple segment
(zone) simulation capability.
The original VADOFT code was written and documented by Dr. Huyakorn, Mr. H. White, Mr. J. Buckley, and Mr.
T. Wadsworth of HydroGeoLogic. The Monte Carlo pre- and post-processors were written by Dr. Salhotra, Mr. P.
Mineart, and Mr. Schanz of WCC.
Final assembly of the PRZM-2 model code, documentation and model testing were performed by AScI Corporation.
The authors of the PRZM-2 user's manual (1993) were J.A. Mullins (AScI), R.F. Carousel (EPA), J.E. Scarbrough
(AScI) and A.M. Ivery (AScI).
Prior to the release of PRZM-3, the model underwent a series of enhancements that resulted in intermediate releases.
Changes were related to linking PRZM-2 with the HSPF, WASP and PATRIOT modeling systems, and the linking
of PRZM-2 with the WDM database structure. Modifications to the soil moisture depth for runoff and surface
pesticide ' mixing zone' calculations were documented in an addendum to the PRZM-2 user's manual written by A. S.
Donigian, Jr. (AQUA TERRA), R.F. Carousel (EPA), J.C. Imhoff (AQUA TERRA) and P.R. Hummel (AQUA
TERRA). Changes related to a non-uniform extraction algorithm for estimating pesticide runoff, the bi-phase
transformation of a parent compound and its metabolites, the ability to transform parent compounds ins an adsorbed
phase to metabolites, the metabolite loading transfer into EXAMS-2.98, the enhanced flexibility in chemical
applications, and the improved output functions were implemented by Waterborne Environmental, Inc. and
documented by R.F. Carousel (EPA), J.M. Cheplick (Waterborne) and W.M. Williams (Waterborne).
PRZM-2 evolved into PRZM-3 in 1995 when a septic system loading module On-site Wastewater Disposal System
(OSWDS) and algorithms for modeling soil nitrogen fate and transport were added to the PRZM modeling system to
provide a tool for defining wellhead protection strategies relative to nitrate contamination. This latter work was
performed and documented by J.C. Imhoff, P.R. Hummel, A.S. Donigian and B.R. Bicknell, all of AQUA TERRA
Consultants.
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TABLE OF CONTENTS
DISCLAIMER ii
FOREWORD iii
ABSTRACT iv
ACKNOWLEDGMENTS v
SECTION 1
Introduction 1-1
1.1 Background and Objectives 1-1
1.2 Concept of Risk and Exposure Assessment 1-2
1.3 Overview of PRZM-3 K7
1.3.1 Overview of PRZM 1-7
1.3.1.1 Features 1-7
1.3.1.2 Limitations 1-8
-10
1.3.2 Overview of the Vadose Zone Flow and Transport Model (VADOFT)
1.3.2.1 Features
1.3.2.2 Limitations .
1.3.3 Overview of the Monte Carlo Simulation Module .
1.3.4 Model Linkage
1.3.4.1 Temporal Model Linkage
1.3.4.2 Spatial Linkages
1.3.5 Monte Carlo Processor.
1.3.6 Overview Summary . . .
-10
-10
-11
-11
-11
-11
-12
-12
SECTION 2
Model Development, Distribution, and Support 2-1
2.1 Development and Testing 2-1
2.2 Distribution 2-1
2.3 Obtaining a Copy of the PRZM-3 Model 24
2.3.1 Internet 2-1
2.4 General/minimum Hardware and Software Installation and Run Time Requirements 2-2
2.4.1 Installation Requirements 2-2
2.4.2 Run Time Requirements 2-2
2.5 Installation 2-2
2.6 Installation Verification and Routine Execution 2-2
2.7 Code Modification 2^2
2.8 Technical Help 2-1
via
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2.9 Disclaimer 2-3
2.10 Trademarks 2-3
SECTION 3
Modules and Logistics 3-1
SECTION 4
Input Parameters for PRZM-3.12 4-1
4.1 Input File Summary 4-1
4.2 Time-series Files 4-1
4.2.1 Meteorological Data File 4-2
4.2.2 Atmospheric Deposition File 4-2
4.2.3 Septic Effluent File 4-3
4.2.4 WDM Time-series File 4-4
4.3 Execution Supervisor File (PRZM3.RUN) 4^4
4.3.1 Execution Supervisor Input Examples 4-4
4.3.1.1 Example Execution Supervisor (PRZM3.RUN) Input File: One Zone 4-5
4.3.1.2 Example Execution Supervisor (PRZM3.RUN) Input File: Two Zones with
Monte Carlo Option 4-5
4.3.1.3 Example Execution Supervisor (PRZM3.RUN) Input File: One PRZM Zone with
Nitrogen and WDM in Use 4-6
4.3.2 Execution Supervisor (PRZM3.RUN) Input Guide 4-8
4.4 PRZM INPUT FILE 4-10
4.4.1 Example PRZM Input Files 4-11
4.4.1.1 Example PRZM Input File for PRZM-3: Pesticide Simulation-No erosion
4-11
4.4.1.2 Example PRZM Input File for PRZM-3: Pesticide Simulation-Erosion 4-12
4.4.1.3 Example PRZM Input File for PRZM-3: Nitrogen Simulation 4-13
4.4.2 PRZM Input Guide 4-15
4.4.2.1 PRZM Input Guide for All PRZM-3 Runs 4-16
4.5 VADOFTInputFile 4-45
4.5.1 Example VADOFT Input File 4-46
4.5.2 VADOFT Input Guide for Flow 4-46
4.6 MONTE CARLO INPUT FILE 4-58
4.6.1 Example MONTE CARLO Input File 4-59
4.6.2 MONTE CARLO Input Guide 4-59
SECTION 5
Parameter Estimation 5-1
5.1 EXESUP (Execution Supervisor) 54
5.2 PRZM (Pesticide Root Zone Model) 54
5.2.1 Nitrogen Calibration Procedures and Parameter Estimation 5-24
5.3 VADOFT Input Parameters 5-25
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SECTION 6
Pesticide Root Zone Model (PRZM) Code and Theory 6-1
6.1 Introduction and Background (PRZM) 6-1
6.1.1 Introduction 6-1
6.1.2 Background 6-2
6.2 Features and Limitations 6-2
6.2.1 Features 6-2
6.2.2 Limitations 6-4
6.3 Description of the Algorithms 6-5
6.3.1 Chemical Transport in Soil 6-6
6.3.2 Water Movement 6-10
6.3.3 Chemical Application and Foliar Washoff 6-15
6.3.4 Chemical Dissolved in Runoff 6-16
6.3.5 Soil Erosion 6-19
6.3.6 Volatilization 6-21
6.3.7 Irrigation Equations 6-36
6.3.8 Nitrogen Species Algorithms 6-39
6.4 Numerical Solution Techniques 6-45
6.4.1 Chemical Transport Equations 6-45
6.4.2 Volatilization 6-47
6.4.3 Soil Temperature 6-49
6.4.4 Furrow Irrigation 6-50
6.5 Results of PRZM Testing Simulations 6-52
6.5.1 Transport Equation Solution Options 6-52
6.5.2 Testing Results of Volatilization Subroutines 6-54
6.5.3 Testing Results of Soil Temperature Simulation Subroutine 6-66
6.5.4 Testing of Daughter Products Simulation 6-70
6.5.5 Testing of Nonuniform Extraction Model for Runoff and Revisions in the Distribution of
Residues 6-75
6.6 Biodegradation Theory and Assumptions 6-79
SECTION 7
Vadose Zone Flow and Transport Model (VADOFT) Code and Theory 7-1
7.1 Introduction 7-1
7.2 Overview of VADOFT 74
7.2.1 Features 7-1
7.3 Description of Flow Module 7-2
7.3.1 Flow Equation 7-2
7.3.2 Numerical Solution 7-4
7.4 Description of the Transport Module 7-6
7.4.1 Transport Equation 7-6
7.4.2 Numerical Solution of the Transport Equation 7-14
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7.5 Results of VADOFT Testing Simulations 7-15
7.5.1 Flow Module (Variably Saturated Flow Problems) 7-15
7.5.2 Transport Module 7-16
7.5.3 Combined Nonlinear Flow and Transport Modules 7-29
SECTION 8
Uncertainty Preprocessor 8-1
8.1 Introduction 8-1
8.2 Overview of the Preprocessor 8-1
8.2.1 Description of the Method 8-1
8.2.2 Uncertainty in the Input Variables 8-2
8.3 Description of Available Parameter Distributions 8-3
8.3.1 Uniform Distribution 8-3
8.3.2 Normal Distribution 8-4
8.3.3 Log-Normal Distribution 8-4
8.3.4 Exponential Distribution 8-4
8.3.5 The Johnson System of Distributions 8-5
8.3.6 Triangular Distribution 8-5
8.3.7 Empirical Distribution 8-6
8.3.8 Uncertainty in Correlated Variables 8-6
8.3.9 Generation of Random Numbers 8-9
8.4 Analysis of Output and Estimation of Distribution Quantiles 8-9
8.4.1 Estimating Distribution Quantiles 8-9
SECTION 9
Linking PRZM-3 with Other Environmental Models 9-1
9.1 HSPF 94
9.1.1 PZ2HSPF Bridge Program 9-1
9.1.2 Application Procedure 9-2
9.1.3 Example Input and Test Run 9-3
9.1.4 Lateral Drainage Modifications to PRZM-3 9-6
9.2 WASP M
9.2.1 PRZWASP Bridge Program M
9.2.2 Application Procedure 9-11
9.2.3 Example Input and Test Run 9-11
9.3 On-site WastewaterDisposal System(OSWDS 9-15
SECTION 10
REFERENCES 10-1
SECTION 11
Appendices 11-1
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11.1 Error Messages and Warnings 11-1
11.2 Variable Glossary 11-1
11.3 PRZM and VADOFT Example Input Files 11-72
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List of Figures
Figure 1.1 Decision path for risk assessment 1-3
Figure 1.2 Time series plot of toxicant concentration 1-5
Figure 1.3 Frequency distribution of toxicant concentration 1-5
Figure 1.4 Cumulative frequency distribution of toxicant concentration 1-5
Figure 1.5 Time series of toxicant concentration with moving average concentration window of duration tc.
1-6
Figure 1.6 Linked modeling system configuration 1-6
Figure 5.1 Estimation of drainage rate AD (day"1) versus number of compartments 5-3
Figure 5.2 Diagram for estimating soil evaporation loss 5-4
Figure 5.3 Mineral bulk density (g cm"3) 5-5
Figure 5.4 Average temperature of shallow groundwater 5-6
Figure 5.5 Diagram for estimating Soil Conservation Service soil hydrologic groups 5-7
Figure 5.6 Physical dispersion (D) associated with advective transport. (Includes: Numerical dispersion).
5-10
Figure 5.7 Numerical dispersion associated with space step (Ax) 5-11
Figure 5.8 Approximate geographic boundaries for SCS rainfall distribution 5-14
Figure 5.9 Pan evaporation correction factors 5-17
Figure 5.10 1/3-bar soil moisture by volume 5-20
Figure 5.11 15-bar soil moisture by volume 5-21
Figure 5.12 Representative regional mean storm duration (hours) values for the U.S 5-22
Figure 6.1 Pesticide Root Zone Model 6-3
Figure 6.2 Schematic representation of a single chemical in a soil layer 6-7
Figure 6.3 Extraction model for pesticide runoff. 6-17
Figure 6.4 Illustration of chemical application methods 6-18
Figure 6.5 Schematic of pesticide vapor and volatilization processes 6-21
Figure 6.6 Variability of infiltration depths within an irrigation furrow 6-38
Figure 6.7 PRZM-3 soil/plant nitrogen transformations 6-40
Figure 6.8 Schematic of the top two soil compartments and the overlaying surface compartment (a) without
plant canopy, (b) with plant canopy 6-48
Figure 6.9 Comparison of simulation results at high Peclet number 6-53
Figure 6.10 Comparison of simulation results at low Peclet number 6-54
Figure 6.11 Comparison of volatilization flux predicted by PRZM and Jury's analytical solution: Test cases #1
and #2 6-56
Figure 6.12 Comparison of volatilization flux predicted by PRZM and Jury's Analytical solution. Test cases #3
and #4 6-57
Figure 6.13 Sensitivity of cumulative volatilization flux to Kd and decay rate 6-60
Figure 6.14 Effects of DELX on volatilization flux and pesticide decay 6-63
Figure 6.15 Comparison of constant and two-step decay rates 6-64
Figure 6.16 Effects of two-step decay rates on volatilization flux and pesticide decay 6-65
Figure 6.17 Comparison of soil temperature profiles predicted by analytical and finite difference solutions
(Time Step=l HR) 6-68
Figure 6.18 Comparison of soil temperature profiles predicted by analytical and finite difference solutions
(Time Step=l day) 6-69
Figure 6.19 Schematic of a system of parent and daughter pesticide relationships 6-71
Figure 6.20 Conversion of C1 to C2 to C3 with no adsorption without decay 6-73
Figure 6.21 Conversion of C1 to C2 to C3 with no adsorption without decay 6-74
Figure 6.22 Conversion of aldicarb to aldicarb sulfoxide to aldicarb sulfone 6-75
Figure 6.23 Comparison of PRZM-2.2 and PRZM-3 at Georgia study site. (PRZM-3 results are the same as
those generated by the experimental version 2.3) 6-77
Figure 6.24 Comparison of PRZM-2.2 and PRZM-3 at Tennessee study site. (PRZM-3 results are the same as
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those generated by the experimental version 2.3) 6-78
Figure 7.1 Logarithmic plot of constitutive relations for clay, clay loam, and loam sandy soils: (a) saturation
vs. capillary head and (b) relative permeability vs. saturation.
7-7
Figure 7.2 Logarithmic plot of constitutive relations for silt, silty clay loam, silty clay, and silty loam soils.
7-8
Figure 7.3 Logarithmic plot of constitutive relations for sandy clay, sandy clay loam, sandy loam, and sandy
soils 7-9
Figure 7.4 Standard plot of relative permeability vs. saturation for clay, clay loam, loam and loam sandy soils.
7-10
Figure 7.5 Standard plot of relative permeability vs. saturation for silt, silt clay loam, silty clay and silty loam
soils 7-11
Figure 7.6 Standard plot of relative permeability vs. saturation for sandy clay, sandy clay loam, sandy loam
and sandy soils 7-12
Figure 7.7 Finite element discretization of soil column showing node and element numbers 7-13
Figure 7.8 Simulated pressure head profiles for the problem of transient upward flow in a soil column.
(Adapted from Battelle and GeoTrans, 1988) 7-18
Figure 7.9 Simulated profile of water saturation for the problem of transient upward flow in a soil column.
7-19
Figure 7.10 Simulated pressure head profiles for five cases of the problem of steady infiltration in a soil
column. (Adapted from Springer and Fuentes, 1987) 7-21
Figure 7.11 Simulated profiles of water saturation for five cases of the problem of steady infiltration in a soil
column. (Adapted from Springer and Fuentes, 1987) 7-22
Figure 7.12 Simulated concentration profiles for the problem of solute transport in a semi-infinite soil column.
7-23
Figure 7.13 Simulated concentration profiles for two cases of the problem of solute transport in a soil column
of finite length, (a) A = 0 d'1, and (b) A = 0.25 d'1 7-27
Figure 7.14 Simulated outflow breakthrough curve for case 1 of the problem of solute transport in a layered
soil column 7-30
Figure 7.15 Simulated outflow breakthrough curve for case 2 of the problem of solute transport in a layered
soil column 7-31
Figure 7.16 One-dimensional solute transport during absorption of water in a soil tube. (Adapted from
Huyakorn et al., 1985) 7-36
Figure 7.17 Simulated profiles of water saturation during absorption of water in a soil tube. (Adapted from
Huyakorn et al., 1984a) 7-37
Figure 7.18 Simulated concentration profiles for the problem of one-dimensional solute transport during
adsorption of water in a soil tube. (Adapted from Huyakorn, etal., 1985) 7-38
Figure 7.19 Problem description for transient water infiltration and contaminant transport in the vadose zone.
7-39
Figure 7.20 Water Infiltration rate vs. time relationship used in numerical simulation 7-40
Figure 7.21 Simulated water saturation profiles 7-41
Figure 7.22 Simulated pressure head profiles 7-42
Figure 7.23 Simulated vertical Darcy velocity profiles 7-43
Figure 7.24 Simulated solute concentration profiles 7-44
Figure 8.1 Triangular probability distribution 8-6
Figure 9.1 Schematic of an example PRZWASP test run 9-14
Figure 9.2 Schematic Representation of the On-site Wastewater Disposal System (OSWDS) Nitrogen
Module 9-17
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List of Tables
Table 3.1 List of Subroutines and Functions and a Brief Description of Their Purpose 3-1
Table 3.2 List of All Parameters Files, Parameter Dimensions, and a Brief Description 3-9
Table 4.1 Variable Designations for Plotting Files 4-41
Table 4.2 Monte Carlo Input and Output Labels 4-61
Table 5.1 Typical Values of Snowmelt (SFAC) as Related to Forest Cover 5-27
Table 5.2 Mean Duration (Hours) of Sunlight for Latitudes in the Northern and Southern Hemispheres3
5-28
Table 5.3 Indications of the General Magnitude of the Soil credibility Factor, Ka 5-28
Table 5.4 Interception Storage for Major Crops 5-29
Table 5.5 Values of the Erosion Equation's Topographic Factor, LS, for Specified Combinations of Slope
Length and Steepness* 5-30
Table 5.6 Values of Support-practice Factor, P3 5-30
Table 5.7 Generalized Values of the Cover and Management Factor, C, in the 37 States East of the Rocky
Mountains3* 5-31
Table 5.8 Mean Storm Duration* (TR) Values for Selected Cities 5-34
Table 5.9 Agronomic Data for Major Agricultural Crops in the United States 5-37
Table 5.10 Runoff Curve Numbers for Hydrologic Soil-cover Complexes3 (Antecedent Moisture
Condition II, and Ia = 0.2 S) 5-39
Table 5.11 Method for Converting Crop Yields to Residue" 5-40
Table 5.12 Residue Remaining from Tillage Operations3 5-41
Table 5.13 Reduction in Runoff Curve Numbers Caused by Conservation Tillage and Residue Management3
5-41
Table 5.14 Values for Estimating Wfmax in Exponential Foliar Model 5-42
Table 5.15 Pesticide Soil Application Methods and Distribution 5-42
Table 5.16 Maximum Canopy Height at Crop Maturation 5-43
Table 5.17 Degradation Rate Constants of Selected Pesticides on FOLIAGE3 5-43
Table 5.18 Estimated Values of Henry's Constant for Selected Pesticides 5-44
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients
(Using Water Solubility) and Reported Degradation Rate Constants in Soil Root Zone 5-46
Table 5.20 Octanol Water Distribution Coefficients (Log K^) and Soil Degradation Rate Constants for
Selected Chemicals 5-54
Table 5.21 Albedo Factors of Natural Surfaces for Solar Radiation* 5-56
Table 5.22 Emissivity Values for Natural Surfaces at Normal Temperatures* 5-57
Table 5.23 Coefficients for Linear Regression Equations for Prediction of Soil Water Contents at Specific
Matric Potentials3 5-58
Table 5.24 Thermal Properties of Some Soil and Reference Materials* 5-58
Table 5.25 Hydrologic Properties by Soil Texture3 5-60
Table 5.26 Descriptive Statistics and Distribution Model for Field Capacity (Percent by Volume) 5-61
Table 5.27 Descriptive Statistics and Distribution Model for Wilting Point (Percent by Volume) 5-62
Table 5.28 Correlations among Transformed Variables of Organic Matter, Field Capacity, and Wilting Point
5-63
Table 5.29 Mean Bulk Density (g cm'3) for Five Soil Textural Classifications 3 5-64
Table 5.30 Descriptive Statistics for Bulk Density (g cm'3) 5-64
Table 5.31 Descriptive Statistics and Distribution Model for Organic Matter (Percent by Volume) 5-65
Table 5.32 Adaptations and Limitations of Common Irrigation Methods 5-66
Table 5.33 Water Requirements for Various Irrigation and Soil Types 5-66
Table 5.34 Representative Furrow Parameters Described in the Literature 5-67
Table 5.35 Furrow Irrigation Relationships for Various Soils, Slopes, and Depths of Application 5-68
Table 5.36 Suitable Side Slopes for Channels Built in Various Kinds of Materials 5-69
Table 5.37 Value of "N" for Drainage Ditch Design 5-69
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Table 5.38 Representative Permeability Ranges for Sedimentary Materials 5-69
Table 5.39 Values of Green-ampt Parameters for SCS Hydrologic Soil Groups 5-70
Table 5.40 Descriptive Statistics for Saturated Hydraulic Conductivity (cm hr1) 5-70
Table 5.41 Descriptive Statistics for Van Genuchten Water Retention Model Parameters, a, p, y 5-71
Table 5.42 Descriptive Statistics for Saturation Water Content (0S) and Residual Water Content (0r) . . . 5-71
Table 5.43 Statistical Parameters Used for Distribution Approximation 5-72
Table 5.44 Correlations among Transformed Variables Presented with the Factored Covariance Matrix*
5-74
Table 5.45 Examples of Nitrogen Gains, Losses, and Transformations (In Kg/ha/yr) for Eight Different
Cropping Systems" 5-78
Table 5.46 Recommended Manning's Roughness Coefficients for Overland Flow 5-79
Table 6.1 Coefficients for Calculation of Unit Peak Discharge 6-20
Table 6.2 Aerodynamic parameters for wind speed computations 6-24
Table 6.3 Summary of Soil Temperature Model Characteristics 6-30
Table 6.4 Input Parameters for the Test Cases - Analytical Solution 6-59
Table 6.5 Trifluralin Volatilization Losses, Amounts Remaining in Soil, and Estimated Losses via Other
Pathways for the 120-day Field Test 6-61
Table 6.6 Input Parameters for the Test Cases - Watkinsville Site 6-61
Table 6.7 Simulation Results Using Different Compartment Depth (DELX) 6-62
Table 6.8 Simulated Soil Temperature Profile after One Day for Different Compartment Thicknesses (Time
Step = 1 Day) 6-70
Table 7.1 Soil Properties and Discretization Data Used in Simulating Transient Flow in a Soil Column
7-17
Table 7.2 Soil Properties Used in Simulating Steady-state Infiltration 7-19
Table 7.3 Iterative Procedure Performance Comparison 7-20
Table 7.4 Values of Physical Parameters and Discretization Data Used in Simulating One-dimensional
Transport in a Semi-infinite Soil Column 7-20
Table 7.5 Concentration Profile Curves at / = 25 hr and t = 50 hr Showing Comparison of the Analytical
Solution and Results from VADOFT 7-24
Table 7.6 Values of Physical Parameters and Discretization Data Used in Simulating One-dimensional
Transport in a Finite Soil Column 7-25
Table 7.7 Concentration Profile Curves Showing Comparison of the Analytical Solution and VADOFT
7-28
Table 7.8 Values of Physical Parameters Used in the Simulation of Transport in a Layered Soil Column
7-32
Table 7.9 Breakthrough Curves (at z = 86.1 Cm) Computed Using the Analytical Solution and VADOFT
(Case 1) 7-32
Table 7.10 Breakthrough Curves (at z = 86.1 cm) Computed Using the Analytical Solution and VADOFT
(Case 2) 7-33
Table 7.11 Values of Physical Parameters and Discretization Data Used in Simulating Transport in a Variably
Saturated Soil Tube 7-35
Table 7.12 Values of Physical Parameters and Discretization Data Used in Simulating Transient Infiltration
and Contaminant Transport in the Vadose Zone 7-35
Table 9.1 Input Guide forthe PZ2HSPF Bridge Program 9-3
Table9.2 Example Input File for PZ2HSPF 9^6
Table 9.3 Input Guide forthe PRZWASP Bridge Program 9-9
Table 9.4 Example InputFile for PRZWASP 9-13
Table 9.5 Output Nonpoint Source File for PRZWASP Test Run1 9-15
Table 9.6 Output Runoff Information File for PRZWASP Test Run1 9-15
Table 9.7 Typical Mean Concentration Values (mg/1) for Nitrogen Species in Septic Tank Effluent .... 9-17
Table 9.8 Input Guide for On-site Wastewater Disposal System (OSWDS) Module 9-19
Table 9.9 Example Input File for On-site Wastewater Disposal System (OSWDS) Module 9-20
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Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches 11-2
Table 11.2 EXESUP Program Variables 11-10
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation 11-13
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations 11-47
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations 11-53
Table 11.6 Monte Carlo Program Variables 11-65
Table 11.7 PZ2HSPF Bridge Program Variables 11-67
Table 11.8 PRZWASP Bridge Program Variables 11-69
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SECTION 1
Introduction
This publication contains documentation for a soil column to groundwater loading model, PRZM-3, for the
simulation of chemical contaminant transport down through the crop root and vadose zones. PRZM-3 enables
modeling of organic chemicals, such as pesticides, as well as organic and inorganic nitrogen species. This release of
PRZM-3 incorporates several new features in addition to those presented in the previous release of the model
(PRZM-2.2): a nonuniform extraction algorithm for estimating pesticide runoff; bi-phase transformation of parent
compound and metabolites; the ability to transform a parent compound in a sorbed phase to metabolites; metabolite
loading transfer into EXAMS v. 2.98; enhanced flexibility in chemical applications; improved output features; and
inclusion of nitrogen routines for assessing septic tank waste effluent.
A brief section on the background and objectives for the PRZM-3 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 prefer to proceed to Section 1.3, that provides an overview of the
PRZM-3 modeling system, including its major features and limitations.
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 such chemicals be evaluated. Recently,
much of this attention has been focused on exposure through leaching of pesticides and nitrates to groundwater and
subsequent ingestion of contaminated water.
The capability to simulate the potential exposure to pesticides or nitrates via this pathway has two major facets:
• D Prediction of the fate of the chemical, after it is applied, as it is transported by water down through
the crop root and soil vadose zones.
• D Evaluation of the probability of the occurrence of contaminant concentrations of various
magnitudes at various depths.
Several public domain models are capable of simulating the transport and transformation of chemicals in the
subsurface and in the root zone of agricultural crops. However, none of these models had been linked together prior
to PRZM-3, in such a way that a complete simulation package, that takes into account the effects of agricultural
management practices on contaminant fate was available for use, either by the Agency or the agricultural chemical
industry, to address groundwater contamination problems. Without such a scientifically credible modeling package,
the decision maker must rely on modeling scenarios that are either incomplete or potentially incorrect. Each time a
new scenario arose, recurring questions had to be answered:
• D What models should be used?
• D How should mass transfer between models be handled?
The resolution of these issues on a per-scenario basis is both expensive and time consuming. Furthermore, it
precludes consistency of approach for the evaluation of contamination potential for across scenarios.
The modeling package described in this report seeks to overcome these problems by providing a consistent set of
linked unsaturated zone models that have the flexibility to handle a wide variety of hydrogeological, soils, climate,
and chemical scenarios. However, the formulation of the risk analysis problem requires more than a simple,
deterministic evaluation of potential exposure concentrations. The inherent variability offeree, 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
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predictions must also 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 that
can:
• Simulate the transport and transformation of field-applied pesticides in the crop root zone and the
underneath unsaturated zone, taking into account the effects of agricultural management practices
• D Simulate the transport and transformation of nitrogen, introduced by atmospheric deposition and/or
septic systems in the crop root zone and the underneath unsaturated zone
• D Provide probabilistic estimates of potential exposure concentrations by taking into account the
variability in natural system, population and processes, and the uncertainty in out ability to
quantify these properties and processes.
Furthermore, it was desirable that the simulation package be easy to use and parameterize, and execute on IBM or
IBM-compatible PCs and 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 on software development
for facile model implementation.
1.2 Concept of Risk and Exposure Assessment
Exposure assessment, as defined for human impacts (U.S. Environmental Protection Agency 1984, 1992), is the
estimation of the magnitude, frequency, duration, and route by which a quantity of a toxicant becomes available at
certain exchange boundaries (i.e., lungs, gut, or skin) of a subject population over a specified time interval. Exposure
assessment is a constitutive element of the larger problems of risk assessment and risk management, as illustrated in
Figure 1.1. The concentration estimates generated during an exposure assessment must be combined with
demographic and lexicological information to evaluate risk to a population - that can be used, in turn, to make
policy decisions regarding the use or disposal of the chemical.
Major components of risk assessment are indicated in the following text. Of these, the first three constitute the
important steps for exposure assessment and are discussed in detail here.
Characterization and quantification of chemical sources
1. Identification of exposure routes
2. Quantification of contaminant movement through the exposure routes to the receptor population/location
3. Characterization of the exposed population
4. 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 sources/uses and distribution of those sources/uses (spatially and temporally). For pesticides, it also
requires information on the crops being grown, registered or proposed chemical uses on 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.
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REGULATORY CONCERN
SCIENTIFIC DATA
Population
Exposure
Product Life Cycle
General Information Gathering
Preliminary Exposure
Assessement
Hazard Identification Toxicity
env. cone., etc.
Most Probable Areas of Exposure
Preliminary Exposure Assessment
Preliminary Risk Analysis
Decision
Begin In-Depth
Exposure Assessment
1
Exposure Assessment
Multi-Disciplinary
Peer Review
In-Depth Exposure
Assessment
Design Assessment Study Plan
Comprehensive Data Gathering
T
Conduct Refined Exposure Modeling
Regulatory Response
Scienc
In-Depth Exposure Assessment
I
e Panel Review
Decision
Hazard Input
Formal Risk Assessment
Decision
T
Regulatory Proposal
T
Examined Exposures
Present No Unresonable Risk
Figure 1.1
Decision path for risk assessment.
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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 that may serve to reduce the
concentration of the chemical(s) along various pathways in different environmental media are also identified. For the
case of ingested groundwater exposure, important contaminant loading pathways and fate processes are predefined
to a large extent in the models available for use. The quantification of contaminant concentrations in a medium,
given the source strength, transport pathways, and attenuation mechanisms along each pathway, is the next step, and
is the major benefit of using models such as PRZM-3. 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.
PRZM-3 produces time series of estimated toxicant concentrations, such as those in Figure 1.2. Each time series can
be compared to a critical value of the concentration y. This type of analysis easily shows whether the criterion is
exceeded and gives a qualitative feel for the severity of the exceedance state. If we determine how often a
contaminant is at a particular level or within a specified range, a frequency distribution of the values of y (Figure
1.3) can be created. If, in addition, we choose any value of y in Figure 1.2 and determine the area under the curve to
the right of that value, we can plot Figure 1.4, the cumulative frequency distribution of the toxicant concentration.
The cumulative frequency distribution indicated the chance that any given value y that we select will be exceeded. If
the example time series is long enough, then the "chance" approaches the true "probability" thaty 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 we take the same concentration time series and impose a window of
length "V" on it at level yc (Figure 1.5) and move that incrementally forward in time, we can make a statement
concerning the toxicant concentration within the duration window. Normally, the average concentration within the
window is used. The resulting cumulative frequency distribution indicates the chance that the moving average
concentration duration tc will exceed the critical value ofy, yc.
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Time (t)
Figure 1.2 Time series plot of toxicant concentration.
100
1
I
01
ro
i-
•5
Concentration (y)
£ 0
Concentration (y)
Figure 1.3 Frequency distribution of toxicant
concentration.
Figure 1.4 Cumulative frequency distribution of
toxicant concentration.
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Time (t)
Figure 1.5 Time series of toxicant concentration with moving average concentration window of duration tc.
PRZM-3
(1-D Flow and Transport)
VADOSE
ZONE
MODEL
VADOFT
(1-D Flow and Transport)
Figure 1.6 Linked modeling system configuration.
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The moving average time window should be the same length as that specified foryc. 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 involved using a
given chemical under the conditions of the model simulation. The use of models like PRZM-3 that provide data the
necessary data for environmental concentrations, duration 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 the concentration estimates
with population characteristics makes possible the counting of the conditional events of concentration in an
environmental medium and the opportunity for the population to be exposed to these concentrations. The exposure
assessment ends at this point. The actual intake of the chemicals, their fate within the human body (i.e., their
pharmacokinetics), and their effects (i.e., toxicology) on the exposed population are not considered during exposure
assessment.. These later issues, however, are also essential elements of 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 PRZM-3 in the evaluation of exposure concentrations.
1.3 Overview of PRZM-3
This section gives an overview of the PRZM-3 model, highlighting the features and limitations of the simulation
package as a whole as well as those of the component models PRZM and VADOFT. The PRZM-3 code was
designed to provide state-of-the-art deterministic simulation of the fate of pesticides, applied for agricultural
purposes, both in the crop root zone and the underlying vadose zone. The model is capable of simulating multiple
pesticides and/or parent/daughter relationships. The model is also capable of estimating the probabilities of
concentrations or fluxes in or from the various media components 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. 1985) was selected to simulate the crop root zone.
Having selected PRZM, two options were evaluated for developing a model to meet the objectives stated in Section
1.1. The first involved use of PRZM only. In this configuration, PRZM would be used to simulate both the root zone
and the vadose zone. This option was rejected because the assumptions of the elementary soil hydraulics in PRZM
(i.e., drainage of the entire soil column to field capacity in 1 day) were considered inadequate for simulating flow in
a thick vadose zone. The second option involved PRZM linked to a to be determined unsaturated zone model. The
option finally selected has been previously depicted in Figure 1.6. In this configuration, an enhanced version of
PRZM was to be linked to a one-dimensional vadose zone flow and contaminant transport model. Both the vadose
and PRZM models would simulate water flow and solute transport. Subsequently, a new code (VADOFT) was
written to perform the necessary flow and chemical transport simulation in the vadose zone for this option.
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
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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.
The chemical transport component can simulate pesticides and organic and inorganic nitrogen species. For
pesticides, the transport component can simulate pesticide application on the soil or on the plant foliage.
Biodegradation can be modeled in the root zone. Dissolved, adsorbed, and vapor-phase concentrations in the soil are
estimated by simultaneously considering the processes of pesticide uptake by plants, surface runoff and erosion,
decay/transformation, volatilization, foliar washoff, advection, dispersion, and retardation/sorption. For nitrogen,
simulation of surface applications, atmospheric deposition, and septic effluent discharge can all be simulated. The
nitrogen species of nitrate, ammonia, and four forms of organic nitrogen (i.e. paniculate organic nitrogen (labile and
refractory) and dissolved organic nitrogen (labile and refractory)) are represented. The soil nitrogen processes
considered include plant uptake of nitrate and ammonium, return of plant nitrogen as organic nitrogen, denitrification
or reduction of nitrate-nitrite, immobilization of nitrate-nitrite and ammonium, mineralization of organic nitrogen,
fixation of atmospheric nitrogen, volatilization of ammonium, and the adsorption/desorption of ammonium and the
organic forms.
Two options are available to solve the transport equations: (1) the original backwards-difference implicit scheme that
can produce excessive numerical dispersion at high Peclet numbers; or (2) the method of characteristics algorithm
that eliminates numerical dispersion, but slightly increases model execution time.
PRZM has the capability to simulate multiple zones. This allows PRZM and VADOFT to combine different root
zone and vadose zone characteristics into a single simulation. Zones can be visualized as multiple vertical land
segments joined together in a horizontal manner. There are three reasons a user may choose for implementing
multiple zones:
(1) to simulate heterogenous PRZM root zones linked to a homogeneous vadose zone
(2) to simulate a homogeneous root zone linked to heterogenous vadose zones
(3) to simulate multiple homogeneous root zones linked to multiple homogeneous vadose zones
Weighing multiple zones together and their use are discussed in detail in Section 5.
Another feature for pesticide simulation is the ability to simulate as many as three chemicals simultaneously as either
separate compounds or as a parent-daughter relationship. This gives the user the option to observe the behavior of
multiple chemicals without making additional runs, or the ability to enter a mass transformation factor from a parent
chemical to one or two daughter products and follow the behavior of all three.
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.
1.3.1.2 Limitations
There were significant limitations in the original (Release I) version of PRZM. A few were obvious to the
developers; others were pointed-out subsequently by model users. These limitations are broken out into four
categories:
• D Hydrology
• D Soil hydraulics
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• D Method of solution of the transport equation
• D Deterministic nature of the model
Modifications made for PRZM-2 and PRZM-3 have 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 steps might be used to ensure greater accuracy 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 been mitigated, in part, 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 also was included, but there is little evidence to
suggest that it was utilized by model users to any great extent.) This 1-day drainage assumption 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 now has the alternative 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 VADOFT code is discussed in more detail in a
subsequent section. For short distances from the soil surface to the water table, PRZM can be used to represent the
entire vadose zone without invoking the use of VADOFT so long as no layers that 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 PRZM-2 and PRZM-3, an
alternative 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 only a small 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 addressed by adding the capability to run PRZM-2 and
PRZM-3 in a Monte Carlo framework. Thus, distributional, rather than field-averaged, values can be utilized as
inputs that will produce distributional outputs of the relevant variables (e.g., flux to the water table).
The Special Actions option in PRZM-3 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 Section 4.
By using the 'SNAPSHOT' capability of Special Actions, the user can output the pesticide concentration profile, i.e.,
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the total concentration in each soil compartment, for any user-specified day during the simulation period. In this
way, the user can run PRZM-3 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 (BCD)
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 affect 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 in Section 5 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 affect the pesticide
balance calculation.
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 two-component PRZM-3 model for predicting the movement of pesticides or nitrogen species
within and below the plant root zone and assessing subsequent groundwater contamination. The VADOFT code uses
Richards' equation to simulate one-dimensional, single-phase moisture and solute transport in unconfmed, 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. The following description of VADOFT is adapted from Huyakorn et al.(1988).
1.3.2.1 Features
The code, which employs the Galerkin finite-element technique to approximate the governing equations for flow and
transport, 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 that fluid
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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. Parent/daughter chemical relationships may be
simulated.
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 that, in some instances, 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 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 PRZM-3 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. The user should be aware that many 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.
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.
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 two 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.
For the linkage of PRZM-3, through VADOFT 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.
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. 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.
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 through the soil
profile. Because of this incompatibility, there may be times when PRZM produces too much water for VADOFT to
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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.
1.3.5 Monte Carlo Processor
PRZM-3 can be run in a Monte Carlo mode so that probabilistic estimates of pesticide loadings to the saturated zone
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 or she 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 that specifies which parameters are to be allowed to vary, their
distributions, the distribution parameters, and correlation matrix. The model then executes a prespecified number of
runs.
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 (PRZM-3) has been developed for the U.S. Environmental Protection Agency that is capable of
simulating the transport and transformation of pesticides, following application, down through the crop root zone
and underlying vadose zone. The modeling system was designed to handle a variety of geometries likely to be
encountered in performing evaluations for pesticide registration or special reviews. Recent enhancements have
expanded modeling capabilities to include simulation of nitrogen species as well, enabling the model to be used for
evaluation of subsurface nitrate contamination. A major objective was to keep the model simple and efficient enough
so that it could be operated on an IBM-PC or IBM-compatible PC and used in a Monte Carlo mode to generate
probabilistic estimates of pesticide loadings or water concentrations. The model consists of two major computational
modules - PRZM, which performs pollutant fate calculations for the crop root zone and is capable of incorporating
the effects of management practices, and VADOFT, which simulates one-dimensional transport and transformation
within the vadose zone.
Linkage of these models is accomplished through the use of simple bridging algorithms that conserve water and
solute mass.
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SECTION 2
Model Development, Distribution, and Support
Refer to the README.TXT file for the most recent and detailed PRZM-3 model development, distribution, and
support information. A copy of the README.TXT file is included in the distribution package or it can be viewed or
downloaded from the Center for Exposure Assessment Modeling (CEAM) Internet site (Refer to Section 2.3,
Obtaining a Copy of the PRZM-3 Model System).
2.1 Development and Testing
The distribution version of the PRZM-3 model system is built with the Lahey/Fujitsu Fortran 95 Pro compiler,
version 7.1. Refer to Section 2.4.2 for specific hardware and software run time requirements for the host system for
the PRZM-3 model system.
2.2 Distribution
The PRZM-3 model system and all support files and programs are available through the Internet from CEAM at no
charge (Refer to Section 2.3, Obtaining a Copy of the PRZM-3 Model System).
Included in the distribution set are:
• D an interactive installation program
• D test input and output files for installation verification
• D an executable task image file for the PRZM-3 model system
• D Fortran source code files
• D command and "make" files to compile, link, and run the task image file
• D a PRZM-3 general execution and user support guide (README.TXT) file.
The README.TXT file contains a section entitled File Name and Content that provides a brief functional
description of each PRZM-3 file by name or file name extension type. Other sections in this document contain
further information about:
• D system documentation
• D installation procedure
• D verifying installation
• D development system
• D code modification
• D technical help contacts.
2.3 Obtaining a Copy of the PRZM-3 Model
2.3.1 Internet
PRZM-3 and other software, data, and documents can be downloaded from the Internet via the EPA Exposure
Assessment Models Web site maintained by CEAM. The Exposure Assessment Models home page is located at the
Uniform Resource Locator (URL):
http ://www. epa. gov/ceampubl/
A complete list of software models, data, and documents distributed by CEAM is available at the URL:
http://www.epa.gov/ceampubl/products.htm
2-1
-------
If you do not have access to the Internet, contact CEAM to request a copy of the model distribution package on disk.
Refer to Section 2.8, Technical Help, for CEAM contact information.
2.4 General/minimum Hardware and Software Installation and Run Time Requirements
Refer to the README.TXT file for the most recent and complete information concerning hardware and software
installation and run time requirements.
2.4.1 Installation Requirements
• D CD-ROM drive (if installed from CD-ROM)
• D approximately 6 MB available hard disk storage
• D Windows 9x/NT/2K/XP operating system
2.4.2 Run Time Requirements
• D DOS or operating system capable of emulating a DOS console (e.g., Win9x/NT/2K/XP)
• D approximately 6 MB available hard disk storage
A Fortran compiler is not required to execute any portion of the model.
2.5 Installation
The PRZM-3 model system and related support files are distributed within an automated installation program which
may be acquired either from the Internet or CD-ROM. In either case, the model and related support files are
contained in the file Install_PRZM312.EXE. Save the installation program to a local disk before running the
installation program.
To install PRZM-3:
• D Close all applications
• D Click on the installation program "Install_PRZM312.EXE"
• D Follow the instructions presented by the installation program.
2.6 Installation Verification and Routine Execution
Refer to the following sections in the README.TXT file for complete instructions concerning installation
verification and routine execution of the PRZM-3 model:
• D File Name and Content
• D Installation Verification
2.7 Code Modification
Included in the distribution file are:
• D an executable task image file for the PRZM-3 model system
• D Fortran source code files
• D command and "make" files to compile, link, and run the task image file (PRZM3.EXE).
If the user wishes to modify the model or any other program, it will be up to him or her to supply or obtain:
• D an appropriate text editor that saves files in ASCII (non-binary) text format
2-2
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• Fortran development tools to recompile and link edit any portion of the model.
CEAM cannot support, maintain, or be responsible for modifications that change the function of any executable task
image (*.EXE), DOS batch command (*.BAT), or "make" utility file(s) supplied with this model package.
2.8 Technical Help
For further information on installation and execution, refer to the Installation and Installation Verification sections of
the README.TXT file. For questions or information concerning the distribution or installation of PRZM-3
software, documentation, or data please contact CEAM at:
• Phone: 706-355-8400
• Fax: 706-355-8104
• E-mail: ceam@epamail.epa.gov
• Mail:
Center for Exposure Assessment Modeling (CEAM)
National Exposure Research Laboratory - Ecosystems Research Division
U.S. Environmental Protection Agency (U.S. EPA)
960 College Station Road
Athens, Georgia 30605-2700
CEAM operates and maintains a listserver system named CEAM-USERS. The CEAM-USERS listserver is an
automated mailing list system which broadcasts up-to-date information concerning CEAM software product updates
and releases as well as hints on software installation and use. Subscribers may broadcast messages to other list
subscribers to ask and answer questions about exposure assessment modeling topics. Instructions for subscribing,
posting messages, and managing membership setting are available on the CEAM Web site at the URL:
http://www.epa.gov/ceampubl/listserv.htm
2.9 Disclaimer
Mention of trade names or use of commercial products does not constitute endorsement or recommendation for use
by the United States Environmental Protection Agency.
Execution of the PRZM-3 model system, and modifications to the DOS system configuration files (i.e.,
\CONFIG.SYS and \AUTOEXEC.BAT) must be used and/or made at the user's own risk. Neither the U.S. EPA nor
the program authors can assume responsibility for model and/or program modification, content, output,
interpretation, or usage.
The PRZM-3 program and files have been extensively tested and verified. However, as for all complex software
products, the programs herein may not be completely free of errors and may not be applicable for all cases. In no
event will the U.S. EPA be liable for direct, indirect, special, incidental, or consequential damages arising out of the
use of the programs and/or associated documentation.
2.10 Trademarks
LF95 is a registered trademark of Lahey Computer Systems, Inc. All other Lahey products are
trademarks of Lahey Computer Systems, Inc.
2-3
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SECTION 3
Modules and Logistics
The PRZM-3 model consists of four major modules. These are:
• D EXESUP, which controls the simulation
• D PRZM, which performs transport and transformation simulations for the root zone
• D VADOFT, which performs transport and transformation simulations for the vadose zone
• D MONTE CARLO, which performs sensitivity analysis by generating random inputs
In this section, Table 3.1 gives a listing of all subroutines and functions organized by module calling routines. Table
3.2 gives a listing of all parameter files and their dimensions. A brief description for each listing is also given.
Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
EXESUP
INIT initializes common block CONST.INC
ECHOF echo names of files opened.
ENDDAY used to determine Julian day and simulation progress.
FILOPN opens and assigns file unit numbers.
ECHOGD echoes global data input.
DONBAR calculates percent complete bar.
ADDSTR add string to end of existing string.
INPREA reads and initializes program input.
BMPCHR converts character to uppercase.
CENTER centers string message on screen.
COMRD checks input for end of file.
COMRD2 checks input for comment lines.
COMRD3 checks input for END statement.
DISPLAY display data to echo file and screen.
ECHORD echoes line numbers read from input.
ELPSE add trailing string and fill middle.
ERRCHK write error messages.
EXPCHK check argument for exponential limits.
FILCLO closes open files.
3-1
-------
Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
OPECHO flags the printing utility.
RELTST checks argument as a real number.
SQRCHK gives square root with error checking.
SUBIN tracks entry into a subroutine.
SUBOUT tracks exit from a subroutine.
TRCLIN writes subroutine tracking to screen.
SCREEN controls display to screen.
LFTJUS left justifies a character string.
LNCHK takes natural log of a number.
LNGSTR returns length of a character string.
LOGCHK takes base 10 logarithm of a number with error checking
provided.
NAMFIX left justifies and capitalizes a string.
CLEAR clears the display screen.
FILCHK checks that necessary files are open.
EXESUP controls calls to PRZM, VADOFT, and MONTE CARLO
INITEM determines global data.
FILINI initializes file unit numbers.
PRZM3 controls model calling routines.
LSUFIX performs internal reads.
PRZM
BIODEG perform time dependant solution for microbiodegradation.
SLPST1 set up coefficient matrix for the solution of pesticide
transport.
PRZMRD reads PRZM input file.
HYDR2 perform soil hydraulic calculations.
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIBIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIBIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
PLGROW determines plant growth parameters for use in other
subroutines.
3-2
-------
Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
FARM insures pesticide application is applied during adequate
moisture conditions
INIDAT provides common block CMISC.INC values.
TRDIA1 solves tridiagonal matrix.
HYDROL calculates snowmelt, crop interception, runoff, and
infiltration.
HFINTP determines boundary for head, concentration or flux.
PESTAP computes amount of pesticide application.
PLPEST determines amount of pesticide which disappears by first
order decay and pesticide washoff.
SLPSTO sets up the matrix for transport of pesticide.
CANOPY calculates the overall vertical transport resistance.
MOC solves the advection component of the pesticide transport
process.
MASB AL calculates mass balance error terms for both flow and
transport.
PSTLNK provides linkage for transformation and source terms of
parent/daughter.
OUTCNC prints daily, monthly, and annual pesticide concentration
profiles.
TRDIAG solves tridiagonal matrix.
OUTRPT prints daily, monthly, and annual concentration profiles
plus snapshots.
VALDAT checks simulation dates against calendar dates.
XPRZM performs PRZM execution calls.
INITDK initializes amount of pesticide decay each chemical which
could have daughter products.
OUTPST prints daily, monthly, and annual pesticide flux profiles.
INITL initializes PRZM arrays.
OUTTSR prints daily, monthly, and annual time series data.
OUTHYD accumulates summaries for water flow.
3-3
-------
Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
HYDR1 performs hydraulic calculations assuming a uniform soil
profile.
PRZECH echoes PRZM input to files.
RSTPUT writes PRZM input to a restart file.
RSTGET reads PRZM input from a restart file.
RSTPT1 writes PRZM input to a restart file.
RCALC function to compute biodegradation.
RSTGT1 reads PRZM input from a restart file.
PRZEXM creates input file for EXAMS model.
PRZDAY transfers start and end dates to common block.
THCALC computes moisture for PRZM.
INIACC initializes PRZM storage arrays.
KDCALC computes KD.
MCPRZ computes MONTE CARLO inputs for PRZM.
FNDCHM function to find a chemical number.
FNDHOR function to find a horizon number.
PZCHK checks horizontal values for consistency.
KHCORR corrects Henry's law constant.
ACTION performs special actions.
GETMET reads in meteorological data.
IRRIG performs irrigation algorithm.
FURROW computes furrow irrigation.
INFIL computes Green-Ampt infiltration.
EVPOTR computes evapotranspiration.
EROSN computes erosion losses.
SLTEMP calculates soil temperatures.
PRZM performs calls to PRZM routines.
TDCALC calculates total days in a simulation.
3-4
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Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
NITR simulate nitrogen behavior in detail.
NITRXN perform reactions on all nitrogen forms.
S V calculate adsorption/desorption of nitrogen constituents
using the single value freundlich method
ITER iterate until a sufficiently close approximation for the
adsorbed and solution values on the
freundlich isotherm is reached.
FIRORD calculate adsorption/desorption fluxes using
temperature dependent first order kinetics.
CRDYFR determine number of days in month each crop is
growing and fraction of monthly target plant uptake for
each crop.
YUPINI calculate initial values of the daily plant uptake target on
last day of previous month.
CRPSEL determine which, if any, of the current crop seasons
includes the current day and month.
YUPTGT calculate daily yield-based plant uptake targets for each soil
layer based on user-specified monthly fractions of the
annual target and a trapezoidal function to
interpolate between months.
YUPLAY calculate daily yield-based plant uptake targets for a soil
layer based on user-specified monthly fractions of the
annual target and a trapezoidal function to interpolate
between months.
LPYEAR returns a leap year flag which is set to on if the year is a
leap year
PRZNRD read nitrogen input parameters for PRZM nitrogen
simulation
OMSG output an error or warning message from nitrogen
simulation code.
OMSINI reset output message parameters for nitrogen simulation
code.
OMSTI save an integer value to output with nitrogen simulation
message.
OMSTR save a real value to output with nitrogen simulation
message.
3-5
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Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
OMSTC save a character value to output with nitrogen simulation
message.
OMSTC save a date to output with nitrogen simulation message.
DAYVAL linearly interpolate a value for this day, given values for the
start of this month and next month.
NITMOV set up the coefficient matrix for the solution of the soil
transport equation for nitrogen species.
NITMOV set up the coefficient matrix for the solution of the soil
transport equation for nitrogen species, then call equation
solver for the tridiagonal matrix.
NITECH echo user input nitrogen simulation parameters.
OUTNIT accumulate and output daily, monthly, and annual
summaries for nitrogen species.
WDTGET retrieve buffer of time-series data from specified data set on
WDM file.
WDBSGR retrieve real type attribute from specified data set on WDM
file.
VADOFT
VADCAL calls relevant subroutines to compute nodal head and
concentration.
BALCHK mass balance calculation.
READTM reads in HVTM, TMHV, QVTM from input.
VADINP reads in flow and transport input.
TRIDIV performs tridiagonal matrix solution.
VADOFT saves information between flow and transport.
IRDVC reads in integer vectors.
VSWCOM computes nodal values of water saturation and Darcy
velocities.
VADCHM transfers chemical specific data to VADOFT variables.
INTERP performs linear interpolation using tabulated data of relative
permeability versus water saturation.
SWFUN computes water saturation values for grid element.
3-6
-------
Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
PKWFUN computes relative permeability.
DSWFUN computes moisture capacity.
XTRANS controls transport calling routines.
RDPINT reads non-default nodes data.
VARCAL computes nodal head and concentration values.
ASSEMF assembly routine for flow.
VADPUT writes VADOFT input to restart file.
VADGET reads VADOFT input from a restart file.
ASSEMT assembly routine for transport.
XFLOW controls flow calling routines.
MCVAD determines MONTE CARLO variables for VADOFT.
READVC reads in vectors.
CONVER computes the limiting values of water saturation for each
material.
MTPV calculates vectors.
OUTPUT write summary statistics.
INITMC initializes statistical summation arrays.
DECOMP decomposes the matrix BBT (N by N) into a lower
triangular form.
RANDOM controls random numbers generation.
NMB generates normal (0-1) random numbers.
UNIF generates uniform random numbers.
EXPRN generates exponentially distributed random numbers.
EMPCAL generates values from empirical distributions.
TRANSM converts normally distributed correlated vectors to the
parameter set returned to the model.
TRANSB transforms variables from normal space to SB space or
vice-versa.
OUTFOR writes tables and plots of cumulative distribution.
3-7
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Table 3.1
List of Subroutines and Functions and a Brief Description of Their Purpose
MODULE CALLING
ROUTINE
SUBROUTINE or
FUNCTION
PURPOSE
STOUT initializes the amount of pesticide decay.
FRQTAB prints tabular frequency output.
FRQPLT plots cumulative distributions.
MONTE CARLO
MCECHO echoes MONTE CARLO input.
READM reads in MONTE CARLO input.
MAXAVG computes maximum daily average output.
STATIS performs summations for MONTE CARLO.
-------
Table 3.2 List of All Parameters Files, Parameter Dimensions, and a Brief Description
FILE PARAMETER
CTRACE.INC MAXSUB=50
MAXLIN=10
PMXMAT.INC MXMAT=5
PMXNLY.INC MXNLAY=20
PMXPRT.INC MXPRT=100
PMXTIM.INC MXTIM=31
PMXTMV.INC MXTMV=31
PMXVDT.INC MXVDT=31
PCMPLR.INC REALMX=1.0D+30
REALMN=1.0D-30
MAXINT=2 147483647
MAXREC=512
EXNMX=-53.0
EXPMN=REALMN
EXPMX=53.0
WINDOW=.TRUE.
PCASCI=.TRUE.
NONPC=.FALSE.
PMXNOD.INC MXNOD=100
PMXZON.INC MXZONE=10
PP ARMING NCMPTS=100
NAPP=50
NC=5
NPII=800
NCMPP2=NCMPTS+2
MXCPD=150
PENANCE KNOUT=6
DESCRIPTION
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
number of subroutines.
number of lines for trace option.
number of VADOFT materials.
number of layers in VADOFT.
number of VADOFT observation nodes.
number of VADOFT iterations allowed.
number of VADOFT time interpolation values.
number of VADOFT time steps.
real number.
minimum real number.
maximum
maximum
maximum
integer value.
record length.
negative exponential number.
minimum exponential real number.
maximum positive exponential number.
allows screen window on or off.
allows attributes for PC's for displays.
allows attributes for non-PC's for displays.
maximum
maximum
maximum
maximum
maximum
maximum
number of VADOFT nodes allowed.
number of PRZM zones.
number of compartments in PRZM.
number of applications in PRZM.
number of crops allowed in PRZM.
number of PRZM particles in MOC.
maximum number of compartments plus 2 for top and
bottom ends.
maximum
number of cropping periods in PRZM.
screen unit number.
3-9
-------
Table 3.2
List of All Parameters Files, Parameter Dimensions, and a Brief Description
FILE
PARAMETER
DESCRIPTION
NMXFIL=99 maximum number of file units open.
FILBAS=30 base file unit number.
PMXNSZ.INC
CMCRVR.INC
MXNSZO= 10 maximum number of VADOFT zones allowed.
MCMAX=50 maximum number of random input variables.
NMAX=10 maximum number of summary output variables.
NCMAX=10 maximum number of CDF's.
NRMAX=1000
NEMP=20
maximum number of MONTE CARLO runs.
maximum number of empirical distributions.
maximum number of random input and output variables.
MCSUM =
MCMAX+NMAX
NPMAX=5
maximum length of MONTE CARLO averaging periods.
3-10
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SECTION 4
Input Parameters for PRZM-3.12
4.1 Input File Summary
PRZM-3 utilizes up to five input files, depending on the features and modules to be simulated:
• Execution Supervisor file (PRZM3.RUN). The Execution Supervisor file determines which
modules are chosen for simulation; the number of zones used in a simulation; input, output, and
scratch file names with optional path statements; the starting and ending date of a simulation; the
number of chemicals (either separate or daughter); weighting parameters between PRZM and
VADOFT zones; and global echo and trace levels during execution.
• PRZM parameter input file. The PRZM parameter input file specifies regional climatological
information, hydrology and erosion parameters, crop characteristics including emergence and
harvest dates, pesticide properties and application rates, and soil characteristics.
• Time-series files. Various time-series data are input via files specified in the execution supervisor.
These include meteorological, nitrogen atmospheric deposition, and septic effluent data. Only the
file containing meteorologic data is required for all PRZM-3 runs.
• VADOFT parameter input file. The VADOFT input file, containing soil horizon and chemical
properties, is required if VADOFT or TRANSPORT SIMULATION are specified as "ON" in the
execution supervisor (PRZM3.RUN) file.
• MONTE CARLO input file. This file is required when MONTE CARLO is specified as "ON" in
the execution supervisor file. The file indicates parameter input values, distributions, and
correlations.
All of these files, except for the time-series files, may have embedded comment lines. A comment line is any line
beginning with three asterisks (***). These lines are ignored by the code during execution. To better understand
record formats used in model input, an example record format statement appears below:
FORMAT 3I2,2X,F8.0,E10.3,1X,2(I5,1X,F8.0)
where input would look like:
010181 0.340 2.40EOO 1 0.340 1 0.340
The format identifier, 312, specifies there are three integers with two columns each. The format identifier, 2X,
specifies there are two blank spaces. The format identifier, F8.0, specifies there is one floating point field with eight
columns and also a decimal point with no precision (although up to seven of these columns may be points of
precision with the eighth column being the decimal point since this is a FORTRAN read statement). The format
identifier, E10.3, specifies there is one field often columns that may include an exponential suffix. The format
identifier, 2(15,1X,F8.0), specifies that there are two sequential sets of 15,1X,F8.0 entered. All format specifiers
should be right justified so that unused columns in a field are assumed to be zeros by the code.
Each of these module files along with their examples are discussed in the following pages. For further descriptions,
see Section 4 on parameter estimation.
4.2 Time-series Files
The PRZM-3 model requires the input of various time-series data. These are input via files specified in the execution
supervisor. The meteorological file is the only time-series file which is required for all PRZM-3 runs. The nitrogen
4-1
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atmospheric deposition and septic effluent files are only required when nitrogen species are being simulated and
atmospheric deposition and/or septic effluent is being considered.
4.2.1 Meteorological Data File
PRZM-3 requires the use of a meteorological file that is specified in the execution supervisor. Information on daily
precipitation, pan evaporation, temperature, wind speed, and solar radiation is included in each record of the
meteorological file. Data format requirements and an example input file are shown below:
Meteorological File Input Guide
RECORD FORMAT: 1X,3I2,5F10.0
READ STATEMENT: MM, MD, MY, PRECIP, PEVP, TEMP, WIND, SOLRAD
where
MM
MD
MY
PRECIP
PEVP
TEMP
WIND
SOLRAD
meteorological month
meteorological day
meteorological year
precipitation (cm day"1)
pan evaporation data (cm day"1)
temperature (Celsius)
wind speed (cm sec"1)
solar radiation (Langley)
Example Meteorological File
1
1
1
1
1
1
1
1
1
1
164
264
364
464
564
664
764
864
964
1064
0
0
0
1
0
1
0
3
0
0
.000
.000
.000
.041
.203
.143
.000
.048
.000
.000
0
0
0
0
0
0
0
0
0
0
.149
.242
.227
.164
.211
.186
.181
.216
.229
.172
-0.278
8.611
13.611
9.444
9.722
10.278
6.389
12.222
7.778
2.500
388.925
388.925
388.925
388.925
388.925
388.925
388.925
388.925
388.925
388.925
225.
226.
227.
228.
229.
230.
231.
232.
233.
235.
597
408
280
211
200
248
353
515
733
006
4.2.2 Atmospheric Deposition File
When nitrogen species are being simulated in PRZM-3, daily inputs of atmospheric deposition of nitrogen may be
input using a file that is specified in the execution supervisor. Daily values for both dry and wet deposition of
ammonia, nitrate, and organic N are included on each record of the file. Which dry and wet constituents being
simulated are specified on record N4 of the PRZM input file (see Section 4.4.2.2) via a set of six flags (3 dry, 3 wet).
Only the constituents with a flag value of -1 will be read from the atmospheric deposition file. Data format
requirements and an example input file are shown below:
Atmospheric Deposition File Input Guide
RECORD FORMAT: 1X,3I2,6F10.0
READ STATEMENT: MM, MD, MY, AMMD, NITRD, ORGND, AMMW, NITRW, ORGNW
where
4-2
-------
MM = calendar month
MD = calendar day
MY = calendar year
AMMD = ammonia concentration (g cm"1)
NITRD = nitrate concentration (g cm"1)
ORGND = organic N concentration (g cm"1)
AMMW = ammonia concentration (g cm"1)
NITRW = nitrate concentration (g cm"1)
ORGNW = organic N concentration (g cm"1)
Example Atmospheric Deposition File
182
282
382
482
582
682
782
882
982
1 1082
01
01
01
01
01
01
01
01
01
01
,005
,005
,005
,005
,005
,005
,005
,005
,005
,005
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
If daily time series of atmospheric deposition are not available, monthly values may be input on record N5 in the
PRZM input file (see Section 4.4.2.2). This is indicated by entering flag values of -2 on record N4. The monthly
values will be divided equally among the days in the respective months. Additionally, nitrogen applications with
fertilizers or manure may be accomplished in a manner analogous to pesticide applications (see records N6 - N7 in
Section 4.4.2.2).
4.2.3 Septic Effluent File
When nitrogen species from septic tank effluent are being simulated, PRZM-3 requires the use of a septic effluent
file that is specified in the execution supervisor. Daily values for water, ammonia, nitrate, and organic nitrogen are
included on each record of the file. These files are generated as output from the On-site Wastewater Disposal System
(OSWDS) model (see Section 9.3). Data format requirements and an example input file are shown below:
RECORD FORMAT:
READ STATEMENT:
Septic Effluent File Input Guide
1X,3I2,4F10.0
MM, MD, MY, INFLOW, AMMON, NITR, ORGN
where
MM
MD
MY
INFLOW
AMMON
NITR
ORGN
effluent month
effluent day
effluent year
amount of water (cm)
ammonia concentration (g cm"1)
nitrate concentration (g cm"1)
organic N concentration (g cm"1)
Example Septic Effluent File
4-3
-------
1
1
1
1
1
1
1
1
1
1
157
257
357
457
557
657
757
857
957
1057
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
133
133
133
133
133
133
133
133
133
133
0
0
0
0
0
0
0
0
0
0
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
.5101E-040.
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
1697E-04
4.2.4 WDM Time-series File
Any of the time-series data described above may be accessed using the Watershed Data Management (WDM) utility
(Lumb et al. 1990) instead of flat files. WDM is a robust data management tool which can maintain and compress
large amounts of time-series data. It also allows faster input and output of time-series data than a flat file. WDM also
comes with an interactive interface (ANNIE) which allows the user to perform detailed management and display of
the time-series data on WDM files. Additional information about WDM and its acquisition may be found on the
internet at "http://h2o.usgs.gov/software/lib.html".
If any time-series data is to be input using a WDM file, the appropriate record is inserted in the execution supervisor
file (see Section 4.3 for details) to specify the WDM file name. The actual time-series data to be read from the WDM
file are specified on the appropriate records of the PRZM input file (see Section 4.4 for details). The location of the
data on the WDM file is specified by the data-set number(s) provided on the PRZM input file record(s). If a WDM
file is specified, but no data-set number is given for a specific time-series data, it is assumed that the data will come
from a flat file (also specified in the execution supervisor file) or not be input at all. A brief summary of where
various WDM time-series data sets are specified follows:
Time-series Data
Meteorological
Atmospheric Deposition
Septic Effluent
PRZM Input File Location
Record 3, columns 49-68
Record N4, columns 1-30
Record N2, columns 11-30
An additional method for retrieving pan evaporation data as monthly average values exists using WDM. Instead of
placing the data-set number for pan evaporation in columns 54-58, a value of -1 may be used. This indicates that the
monthly values for pan evaporation are found as attribute values on the data set for precipitation data (specified in
columns 49-53). Thus, this method requires that precipitation time-series values are coming from WDM as well. The
monthly values are divided equally among each day of corresponding month.
Output of time-series data may also be sent to a WDM file instead of a flat file in the same manner as input time
series. On record 43 of the PRZM input file, a " W" is placed in column 40 and a data-set number is placed in
columns 41-48. The results for the variable specified in columns 5-8 on that record will be output to this data set.
4.3 Execution Supervisor File (PRZM3.RUN)
The PRZM-3 model requires existence of a control file (PRZM3 .RUN) also known as the execution supervisor file.
This file specifies options by the user to control the overall (global) parameters during model execution. The file
must always be resident in the current directory where the execution is performed.
4.3.1 Execution Supervisor Input Examples
The following pages contain examples of the execution supervisor input file. The first example demonstrates a run
4-4
-------
with only one PRZM zone and one VADOFT zone. The second example demonstrates a ran with two PRZM and
two VADOFT zones with Monte Carlo capability in use. The third example demonstrates a ran with only one PRZM
zone with nitrogen simulation being performed and WDM capabilities in use.
4.3.1.1 Example Execution Supervisor (PRZM3 .RUN) Input File: One Zone
*** option records
PRZM ON
EXAMS ON
VADOFT ON
MONTE CARLO OFF
TRANSPORT SIMULATION ON
*** zone records
PRZM ZONES 1
EXAMS ENV. 1
VADOFT ZONES 1
ENDRUN
*** input file records
PATH C:\PRZM3\INPUT\
MCIN MC.INP
METEOROLOGY 1 MET.INP
PRZM INPUT 1 PRZM3.INP
EXAMS INPUT 1 EXAMS.EXA
VADOFT INPUT 1 VADF3.INP
*** output file records
PATH C:\PRZM3\OUTPUT\
TIME SERIES 1 PRZM.ZTS
PRZM OUTPUT 1 PRZM.OUT
EXAMS REPORT 1 EXAMS3.XMS
EXAMS PLOT 1 EXAMS3.PEX
VADOFT OUTPUT 1 VADF.OUT
MCOUT MC.OUT
MCOUT2 MC2.OUT
*** scratch file records
PATH C:\PRZM3\OUTPUT
PRZM RESTART 1 RESTART.PRZ
VADOFT FLOW RS 1 VFLOW.RST
VADOFT TRANS RST 1 VTRANS.RST
VADOFT TAPE10 1 VADF.TAP
ENDFILES
*** global records
START DATE 010181
END DATE 311283
NUMBER OF CHEMICALS 3
PARENT OF 2 1
PARENT OF 3 2
ENDDATA
*** display records
ECHO 4
TRACE OFF
NOTE: Three asterisks (***) denote a comment line and are ignored by the program.
4.3.1.2 Example Execution Supervisor (PRZM3 .RUN) Input File: Two Zones with Monte Carlo Option
***0ptions
PRZM ON
EXAMS OFF
4-5
-------
VADOFT ON
MONTE CARLO ON
TRANSPORT SIMULATION ON
PRZM ZONES 2
VADOFT ZONES 2
ENDRUN
***Input files
MCIN MC.INP
METEOROLOGY 1
METEOROLOGY 2
PRZM INPUT 1
PRZM INPUT 2
VADOFT INPUT
VADOFT INPUT
''Output files
TIME SERIES 1
TIME SERIES 2
1
MET.INP
METx.INP
PRZM.INP
PRZMx.INP
1 VADF.INP
2 VADFx.INP
PRZM OUTPUT
PRZM OUTPUT 2
VADOFT OUTPUT
VADOFT OUTPUT
MCOUT MC.OUT
MCOUT2 MC2.OUT
***Scratch files
PRZM RESTART 1
PRZM RESTART 2
VADOFT FLOW RST
VADOFT FLOW RST
VADOFT TRANS RST
VADOFT TRANS RST
VADOFT TAPE10 1
TIMES.OUT
TIMESx.OUT
PRZM.OUT
PRZMx.OUT
1 VADF.OUT
2 VADFx.OUT
VADOFT TAPE10
ENDFILES
START DATE 010181
END DATE 311281
NUMBER OF CHEMICALS
PARENT OF 2 1
PARENT OF 3 2
WEIGHTS
1.0 0.0
0.0 1.0
ENDDATA
ECHO ON
TRACE OFF
RESTART.PRZ
RESTARTx.PRZ
1 VFLOW.RST
2 VFLOWx.RST
1 VTRANS.RST
2 VTRANSx.RST
VADF10.TAP
VADFlOx.TAP
NOTE: Three asterisks (***) denote a comment line and are ignored by the program.
4.3.1.3 Example Execution Supervisor (PRZM3 .RUN) Input File: One PRZM Zone with Nitrogen and WDM in
Use
***0ptions
PRZM ON
EXAMS OFF
VADOFT OFF
MONTE CARLO ON
TRANSPORT SIMULATION OFF
NITROGEN SIMULATION ON
***Zone records
4-6
-------
PRZM ZONES
ENDRUN
***Input files
PATH
MCIN
***Met stations - all
WDM FILE 1
SEPTIC EFFLUENT 1
PRZM INPUT 1
***0utput files
PATH
TIME SERIES 1
PRZM OUTPUT 1
MCOUT
MCOUT2
***Scratch files
PRZM RESTART 1
ENDFILES
***Global records
START DATE
END DATE
NUMBER OF CHEMICALS
ENDDATA
***Display records
ECHO
TRACE
C:\PRZM3.0\INPUT\
MCNIT.INP
on main wdm file
PRECIP.WDM
SEPTIC.INP
TESTNIT.INP
C:\PRZM3.0\OUTPUT\
TIMES.OUT
TESTNIT.OUT
MC.OUT
MC2.OUT
RESTART.PRZ
010157
311257
3
4
OFF
NOTE: Three asterisks (***) denote a comment line and are ignored by the program.
4-7
-------
4.3.2 Execution Supervisor (PRZM3.RUN) Input Guide
RECORD 1 - OPTIONS
LABEL (Col. 1-18)
PRZM
EXAMS
VADOFT
MONTE CARLO
TRANSPORT
NITROGEN
FORMAT A18,6X,A56
EXECUTION STATUS (Col. 25-78)
ON or OFF (the root zone model execution)
ON or OFF (the aquatic exposure assessment model)
ON or OFF (the vadose zone model execution)
ON or OFF (Monte Carlo execution)
ON or OFF (vadose zone transport execution)
ON or OFF (nitrogen model execution)
RECORD 2 - ZONES
LABEL (Col. 1-18)
PRZM ZONES
EXAMS AQEs
VADOFT ZONES
ENDRUN
FORMAT A18,6X,I2
ZONE NUMBER (Col. 25-78)
Ito 10
ItolO
Ito 10
(total number of PRZM land zones)
(total number of EXAMS aquatic
environments / PRZM run)
(total number of VADOFT land zones)
(specifies end of OPTIONS and ZONE
records)
RECORD 3 - INPUT FILES
LABEL (Col. 1-18)
PATH
METEOROLOGY
PRZM INPUT
EXAMS INPUT
VADOFT INPUT
MCIN
SEPTIC EFFLUENT
NITROGEN DEPOSIT
WDM FILE
FORMAT A18,1X,I2,3X,A56
ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)
directory (optional)
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
filename
1 to 10 filename
1 to 10 filename
filename
4-8
-------
RECORD 4 - OUTPUT FILES
LABEL (Col. 1-18)
PATH
TIME SERIES
PRZM OUTPUT
EXAMS REPORT
EXAMS PLOT
VADOFT OUTPUT
MCOUT
MCOUT2
FORMAT A18,1X,I2,3X,A56
ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)
directory (optional)
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
RECORD 5 - SCRATCH FILES
LABEL (Col. 1-18)
PATH
PRZM RESTART
VADOFT FLOW RESTART
VADOFT TRANS RESTART
VADOFT TAPE
ENDFILES
FORMAT A18,1X,I2,3X,A56
ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)
directory (optional)
1 to 10 filename
1 to 10 filename
1 to 10 filename
1 to 10 filename
(specifies end of file name records)
RECORD 6 - GLOBAL RECORDS
FORMAT
A18,1X,3I2
LABEL (Col. 1-18)
START DATE
END DATE
NUMBER OF CHEMICALS
PARENT OF 2
PARENT OF 3
WEIGHTS
VALUE (Col. 20-25)
ddmmyy
ddmmyy
Ito3
1
Ior2
(starting day, month, year)
(ending day, month, year)
(number of chemicals)
(parent of the second chemical if
TRANSPORT=ON and if more than one
chemical)
(parent of third chemical if
TRANSPORT=ON and if more than one
chemical)
(indicates next values are weights)
4-9
-------
NOTE: enter next lines only if PRZM or VADOFT have multiple zones. Enter a line for every increasing PRZM
zone containing a fractional weight to each VADOFT zone. FORMAT: 10(F8.2)
1.0
0.0
ENDDATA
0.0
1.0
(PRZM zone 1 weight to VADOFT zone
land 2)
(PRZM zone 2 weight to VADOFT zone
land 2)
(specifies end of GLOBAL data)
RECORD 7 - DISPLAY RECORDS FORMAT A18,6X,A56
LABEL (Col. 1-18) VALUE (Col. 25-78)
ECHO
TRACE
Ito9
ON or OFF
(amount output increasingly displayed to
the screen and to files)
(tracking of subroutines for debugging)
EFFECT OF THE ECHO LEVEL ON MODEL OUTPUT
ECHO LEVEL
Percent bar graph
Simulation status to screen
Simulation status to files
Subroutine trace available
Warnings displayed
Results of linkage routines
Detailed water/solute data
Detailed head/concentration data
Echo of line being read from input
Echo of image being read from input
1
/
2
/
/
3
/
/
/
4
/
/
/
/
5
/
/
/
/
/
6
/
/
/
/
/
/
7
/
/
/
/
/
/
/
/
/
8
/
/
/
/
/
/
/
/
/
/
9
/
/
/
/
/
/
/
4.4 PRZM INPUT FILE
The PRZM-3 model requires a PRZM input file if the PRZM option is specified "ON" in the execution supervisor
file.
4.4.1 Example PRZM Input Files
4-10
-------
The following pages show three examples of PRZM input files. The first example shows an input sequence for
pesticide simulation without erosion. The second example shows an input sequence for pesticide simulation with
erosion. The third example shows an input sequence for nitrogen simulation.
4.4.1.1 Example PRZM Input File for PRZM-3: Pesticide Simulation-No erosion
PRZM3 Input File
3 chemicals, foliar application for chemical 1
0.74 0.52 0 0.25 1 1
0
2
1 0.25 60.00 80.00 3 86 80 86 0.00 100.00
2 0.25 60.00 80.00 3 86 80 86 0.00 100.00
2
22656 251056 261156 1
22657 251057 261157 2
Chemical Input Data:
2300
cheml-aerial chem2-granular chem3-injected
11 756 02 0.00 1.00 0.95 0.01 4 2.00 0.50 1.00 0.00 8 4.00 0.75 1.00 0.00
11757 020.00 1.000.950.0100.00 0.000.000.0000.00 0.000.000.00
0. 1 1.0 1 0.0 1 0.0
0.0 .005 0.1
0.0 .000 0.0
0.0 .000 0.0
0.00 0.00 0.00
Soil Series: LEESBURG OK185-3
165.00 000000000
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2
1 10.000 1.400 0.462 0.000 0.000 0.000
0.022 0.011 0.033 0.022 0.011 0.033 0.000 0.000 0.000
0.100 0.462 0.100 0.725 5.000 81.000 0.600
0.000 0.000 0.000 0.000 0.000 0.000
2 155.000 1.400 0.462 0.000 0.000 0.000
0.022 0.011 0.033 0.022 0.011 0.033 0.000 0.000 0.000
1.000 0.462 0.100 0.725 5.000 81.000 0.600
0.000 0.000 0.000 0.000 0.000 0.000
0
WATR YEAR 10 PEST YEAR 10 CONC YEAR 10 1
6
11 -—
12 -—
13 -—
4 DAY
PRCP TSER 0 0
RUNF TSER 0 0
RFLX TSER 0 0 1.E5
RZFX TSER 0 0 1.E5
4.4.1.2 Example PRZM Input File for PRZM-3: Pesticide Simulation-Erosion
4-11
-------
190.0 0.0 760.0
34.0 0.0 137.0
650.0 0.0 2280.0
***AG-N Litter N
5.0 3.0
WATR YEAR 1 NITR MNTH 1 CONC YEAR 1
6 YEAR
RUNF TSER 0 0
STMP TSER 5 5
STMP TSER 14 14
STMP TSER 19 19
STMP TSER 25 25
STMP TSER 33 33
4.4.2 PRZM Input Guide
The following pages describe the input records used by the PRZM input file. The input record descriptions are
divided into two sections. The first section describes the records which are used for all PRZM simulations. The
second section describes the additional records which are needed for nitrogen simulation in PRZM.
When performing nitrogen species simulation in PRZM, the nitrogen input records are to be inserted between
records 40 and 41 of the PRZM input sequence described in the previous section. This keeps a logical order of
specifying desired outputs after defining all other simulations. Record 41 contains the only modification to an
existing PRZM input record. Output element ITEM2 is used to specify pesticide output summaries for a desired time
interval and compartment frequency by entering PEST in its position. This element may now be set to NITR to
achieve the same type of summary for nitrogen species output. As with pesticides, setting the element ITEMS to
CONC generates nitrogen storage output.
All required records in the PRZM-2 input sequence must be preserved in PRZM-3 input sequences. This means
including record numbers 12 -17 (or 12 -16 if CAM on record 16 is set to 1) and elements of records 25, 33, 35, and
36 for pesticide parameters even though pesticides are not simulated when running nitrogen simulation. PRZM-2
requires at least one pesticide application (0 is not valid). Thus, to make an existing PRZM-2 input sequence
upwardly compatible with PRZM-3, at least one "dummy" pesticide must be defined.
To minimize new input records, the existing cropping dates entered in the PRZM input sequence are used by the
nitrogen module to define the crop growing periods. However, the nitrogen module requires a planting date and no
planting date is entered in the PRZM input. It is thus assumed that the emergence date entered in the PRZM input
will be used as the planting date in the nitrogen module. The cropping period for the crops being simulated must not
vary from year to year as the nitrogen algorithm expects the same cropping periods each year. If the cropping period
for a crop does vary in the input sequence, the first year's cropping period for that crop will be used throughout the
simulation. Cropping periods may still wrap around the end of a year. However, the maximum number of cropping
periods that the nitrogen module can simulate is three (versus the existing PRZM-2 limit of five).
Soil temperature must be simulated in PRZM-3 when running nitrogen simulation, as soil temperature values are
used significantly in the nitrogen reaction algorithms. This requires ITFLAG to be set to 1 on record 19 and records
30, 31, and 39 to be defined.
4.4.2.1 PRZM Input Guide for All PRZM-3 Runs
RECORD 1 FORMAT A78
4-15
-------
col: 1-78
TITLE:
label for simulation title.
RECORD 2
col: 1-78
FORMAT A78
HTITLE:
label for hydrology information title.
RECORDS FORMAT 2F8.0,I8,F8.0,2I8,5I4
col: 1-8 PFAC: pan factor used to estimate daily evapotranspiration.
col: 9-16 SFAC: snowmelt factor in cm/degrees Celsius above freezing.
col: 17-24 IPEIND: pan factor flag.
0 = pan data read,
1 = temperature data read,
2 = either available used.
col: 25-32 ANETD: minimum depth of which evaporation is extracted (cm).
col: 33-40 INICRP: (INICRP is only used when ERFLAG=0.)
Indicates the initial crop if the simulation date occurs before the
emergence date of all cropping periods (see record 10). Value:
= 0 : no;
> 0 : its value designates the number of the crop whose data
is to be used in the initialization, i.e., the conditions present
before the first emergence date in the first simulation period.
INICRP must be equal to one of the values of ICNCN
(Record 9) and INCROP (Record 11).
col: 41-48 ISCOND: surface condition of initial crop if INICRP>0.
1 = fallow, 2 = cropping, 3 = residue. ISCOND is ignored when
ERFLAG > 0; In this case PRZM will determine the current crop
conditions.
col: 49-52 DSN: WDM data set number for precipitation data
col: 53-56 DSN: WDM data set number for potential evaporation data
col: 57-60 DSN: WDM data set number for temperature data
col: 61-64 DSN: WDM data set number for wind speed data
col: 65-68 DSN: WDM data set number for solar radiation data
RECORD 4 FORMAT 6F8.0
Only if IPEIND = 1 or 2 (see record 3).
col: 1-48
DT:
monthly daylight hours for January - June.
4-16
-------
RECORDS FORMAT 6F8.0
Only if IPEIND = 1 or 2 (see record 3).
col: 1-48
DT:
monthly daylight hours for July - December.
RECORD 6 FORMAT 18
col: 1-8 ERFLAG:
flag to calculate erosion.
ERFLAG=0, no erosion
ERFLAG=1, not used (raises error condition)
ERFLAG=2, MUSLE
ERFLAG=3, MUST
ERFLAG=4, MUSS
RECORD? FORMAT 4F8.0, 8X, 18, 2F8.0
Only if ERFLAG = 2, 3, 4 (see record 6).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 40-48
col: 49-56
col: 57-64
USLEK:
USLELS:
USLEP:
AFIELD:
IREG:
SLP:
HL:
universal soil loss equation (K) of soil credibility.
universal soil loss equation (LS) topographic factor.
universal soil loss equation (P) practice factor.
area of field or plot in hectares.
location of NRCS 24-hour hyetograph.
land slope (%)
hydraulic length (m)
RECORDS
col: 1-8
FORMAT 18
NDC:
number of different crops in the simulation (1 < NDC < NC).
RECORD 9 FORMAT I8,3F8.0,I8,3(1X,I3),2F8.0
Repeat this record up to NDC (see record 8).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
ICNCN:
CINTCP:
AMXDR:
COVMAX:
ICNAH:
crop number of the different crop.
maximum interception storage of the crop (cm).
maximum rooting depth of the crop (cm).
maximum area! coverage of the canopy (percent).
surface condition of the crop after harvest date (see record 11).
1 = fallow, 2 = cropping, 3 = residue. ICNAH is ignored when
ERFLAG > 0; In this case PRZM will determine the current crop
conditions.
4-17
-------
RECORD 10 FORMAT 18
col: 1-8 NCPDS:
number of cropping periods (sum of NDC for all cropping dates in
record 11). (1 < NCPDS < MXCPD)
RECORD 11 FORMAT 2X,3I2,2X,3I2,2X,3I2,I8
Repeat this record up to NCPDS (see record 10).
col: 3-4
col: 5-6
col: 7-8
col: 11-12
col: 13-14
col: 15-16
col: 19-20
col: 21-22
col: 23-24
col: 25-32
EMD:
EMM:
IYREM:
MAD:
MAM:
IYRMAT:
HAD:
HAM:
IYRHAR:
INCROP:
integer day of crop emergence.
integer month of crop emergence.
integer year of crop emergence.
integer day of crop maturation.
integer month of crop maturation.
integer year of crop maturation.
integer day of crop harvest.
integer month of crop harvest.
integer year of crop harvest.
crop number associated with NDC (see record 8).
RECORD 12 FORMAT A78
col: 1-80 PTITLE:
label for pesticide title.
RECORD 13 FORMAT 418
col: 1-8 NAPS:
col: 9-16
col: 17-24
NCHEM:
FRMFLG:
total number of pesticide applications occurring at different dates (1 to
800). Note: if two or more pesticides are applied on the same date then
NAPS= 1 for that day.
number of pesticide(s) in the simulation. This value should equal the
number in the execution supervisor file (1 to 3).
flag for testing of ideal soil moisture conditions for the application of
pesticide(s) relative to the target date (see record 15 for target date
information). 1, 2, and 3 = yes, 0 = no.
1 = check preceding days (WINDAY, record 16) after the target
application date(APD, record 16) for ideal moisture conditions;
2 = check moisture conditions only on the target application date;
3 = check preceding days (WINDAY, record 16) after the target
application date(APD, record 16) for ideal moisture conditions. Also,
check soil moisture conditions on the target application date.
4-19
-------
col: 25-32
DK2FLG
flag to allow input of bi-phasic half-life l=yes, 0=no
RECORD 14 FORMAT 3(4X,2I2,I8))
Only if DKFLG2=1, Repeat for each chemical
col: variable
col: variable
col: variable
DKDAY: day when first half-life begins.
DKMNTH: month when first half-life begins.
DKNUM: number of days after first half-life begins that half-life is changed to
second half-life.
RECORD 15 FORMAT 3A20
col: 1-60 PSTNAM:
names of pesticide(s) for output titles.
RECORD 16 FORMAT 2X,3I2,I3,3(I2,F5.0,F6.0,F5.0,F5.0)
Repeat this record up to NAPS (see record 13).
col: 3-4
col: 5-6
col: 7-8
col: 9-11
APD: integer target application day.
APM: integer target application month.
IAPYR: integer target application year.
WIND AY: number of days in which to check soil moisture values following the
target date for ideal pesticide(s) applications. Required if FRMFLG =
1, 2, or 3 else set to 0 (see record 13).
4-20
-------
col: variable
CAM:
col: variable
DEPI:
col: variable
col: variable
col: variable
TAPP:
APPEFF:
DRFT:
Chemical Application Method.
1 = soil applied, soil incorporation depth of 4 cm, linearly decreasing
with depth;
2 = interception based on crop canopy, as a straight-line function of
crop development; chemical reaching the soil surface is incorporated to
4 cm;
3 = interception based on crop canopy, the fraction captured increases
exponentially as the crop develops; chemical reaching the soil surface
is incorporated to 4 cm;
4 = soil applied, user-defined incorporation depth (DEPI), uniform
with depth;
5 = soil applied, user-defined incorporation depth (DEPI), linearly
increasing with depth;
6 = soil applied, user-defined incorporation depth (DEPI), linearly
decreasing with depth;
7 = soil applied, T-Band granular application, user-defined
incorporation depth (DEPI), use DRFT input variable to define fraction
of chemical to be applied in top 2 cm, remainder of chemical will be
uniformly incorporated between 2 cm and the user-defined depth;
8 = soil applied, chemical incorporated entirely into depth specified by
user (DEPI) (modified CAM 1)
9 = linear foliar based on crop canopy, chemical reaching the soil
surface incorporated to the depth given by DEPI (modified CAM 2);
10 = nonlinear foliar using exponential filtration, chemical reaching
the soil surface incorporated to the depth given by DEPI (modified
CAMS);
NOTE: DEPI must be set greater than 0.0 for CAM=4-10. If DEPI =
0, or DEPI < the depth of the first (top) surface soil layer, chemical
reaching the soil surface is distributed into the first surface soil layer.
depth of the pesticide(s) application (cm). Use with CAM=4-10. For
CAM=2 or 3, chemical not intercepted by the crop foliage is
incorporated to 4 cm. The default incorporation depth for CAM=2 or 3
can only be over-ridden by selecting CAM = 9 or 10 and entering a
value >0.0 for DEPI. Should DEPI be zero, or a value less than the
depth of the top soil compartment, chemical is distributed uniformly
throughout the depth of the top soil compartment.
target application rate of the pesticide(s) (kg ha"1).
application efficiency (fraction), target application rate may be reduced
to account for application inefficiencies
spray drift (fraction), used for spray drift loading to EXAMS pond.
However, (1-DRFT) should be >= to application efficiency. DRFT is
also used when CAM=7 to represent fraction of chemical which is
incorporated into top 2 cm (drift will be set to 0 for EXAMS pond
loadings)
RECORD 17 FORMAT F8.0,3(I8,F8.0)
col: 1-8 FILTRA: filtration parameter. Required if CAM = 3 else set to 0.0.
4-21
-------
col: variable
IPSCND: condition for disposition of foliar pesticide after harvest. 1 = surface
applied, 2 = complete removal, 3 = left alone. Required if CAM=2 or
col: variable
UPTKF: plant uptake factor. 0 = no uptake is simulated. 1 = uptake is equal to
transpiration * diss. phase concentration, 0.001 to 0.99 = uptake is a
fraction of transpiration* dissolved phase concentration.
NOTE: Repeat IPSCND & UPTKF for each chemical (see example
input file.
RECORD 18 FORMAT 3F8.0
Only if CAM=2 or 3, repeat this record up to NCHEM.
col: 1-8
col: 9-16
col: 17-24
PLVKRT:
PLDKRT:
FEXTRC:
pesticide volatilization decay rate on plant foliage (days'1).
pesticide decay rate on plant foliage (days'1).
foliar extraction coefficient for pesticide washoff per centimeter of
rainfall.
RECORD ISA FORMAT 3F8.0
Only if CAM=2 or 3, and NCHEM >1.
col: 1-8
col: 9-16
col: 17-24
PTRAN12:
PTRAN13:
PTRAN23:
foliar transformation rate for chemical 1-2
foliar transformation rate for chemical 1-3
foliar transformation rate for chemical 2-3
RECORD 19 FORMAT A78
col: 1-78 STITLE:
label for soil properties title.
RECORD 20 FORMAT F8.0,8X,9I4
col: 1-8
col: 17-20
col: 21-24
CORED: total depth of soil core in cm. (must be sum of all horizons thicknesses
(THKNS) in record 33 and at least as deep as the root depth in record
9).
BDFLAG: bulk density flag. 0 = apparent bulk density known and entered in
record 33,1 = mineral value entered
THFLAG: field capacity and wilting point flag. 0 = water contents are entered, 1
= calculated by the model
4-22
-------
col: 25-28
col: 29-32
col: 33-36
col: 37-40
col: 41-44
col: 45-48
col: 49-52
KDFLAG: soil/pesticide adsorption coefficient.
0 = KD value entered in record 37;
1 = KD value calculated by the model (see record 30);
2 = normalized Freundlich KD value entered in record 37 and the
Freundlich exponent 1/n entered in record 30A;
3 = aged sorption is implemented with the Freundlich KD value
entered in record 37. Compound specific aging factors are entered in
records SOB and 30C.
HSWZT: drainage flag, 0 = free draining, 1 = restricted
MOC: method of characteristics flag. l=yes, 0=no.
IRFLAG: irrigation flag.
0 = no irrigation simulated
1 = year round,
2 = during cropping period only.
ITFLAG: soil temperature simulation flag. 1 or 2 =yes, 0=no.
Flag value must = 1 if nitrogen is being simulated.
Flag value must = 2 if soil temperature is simulated with the use of
temperature and moisture corrected degradation (record 32A).
IDFLAG: thermal conductivity and heat capacity flag. l=yes, 0=no
BIOFLG: biodegradation flag. l=yes, 0=no.
RECORD 21 FORMAT 5F8.0
Only if BIOFLG = 1 (see record 20)
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
AM:
AC:
AS:
AR:
KE:
maintenance coef. of metabolizing Xm population (day"1)
maintenance coef. of co-metabolizing Xc population (day-1).
maintenance coef. of sensitive Xs population (day"1).
maintenance coef. of non-sensitive X, population (day"1).
average enzyme content of the Xc population (dimensionless).
RECORD 22 FORMAT 7F8.0
Only if BIOFLG = 1 (see record 20).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
KSM:
KCM:
KC:
MKS:
saturation constant of the metabolizing X,,, population with respect to
pesticide concentration.
saturation constant of the metabolizing X,,, population with respect to
carbon concentration.
saturation constant of the co-metabolizing Xc population.
saturation constant of the sensitive Xs population.
4-23
-------
col: 33-40
col: 41-48
col: 49-56
KR:
KIN:
KSK:
saturation constant of the non-sensitive X, population.
inhibition constant (mg g"1 dry soil).
carbon solubilization constant (day"1).
RECORD 23 FORMAT 6F8.0
Only if BIOFLG =1 (see record 20).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
KLDM:
KLDC:
KLDS:
KLDR:
KL1:
KL2:
death rate of metabolizing X,,, population (day"1).
death rate of co-metabolizing Xc populatio^day"1).
death rate of sensitive Xs population (day"1).
death rate of non-sensitive X, population (day"1).
second order death rate of Xs population (mg g"1 day"1).
dissociation constant of enzyme substrate complex (day"1).
RECORD 24 FORMAT 5F8.0
Only if BIOFLG = 1 (see record 20).
col: 1-8
col: 9-16
USM:
UCM:
col: 17-24
col: 25-32
col: 33-40
MUC:
US:
UR:
growth rate of metabolizing X,,, population with respect to pesticide
concentration (day"1).
specific growth rate of metabolizing X,,, population with respect to
carbon concentration (day"1).
specific growth rate of co-metabolizing Xc population (day"1).
specific growth rate of sensitive Xs population (day"1).
specific growth rate of non-sensitive X, population (day"1).
RECORD 25 FORMAT 5F8.0
Only if BIOFLG = 1 (see record 20).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
YSM:
YCM:
YC:
YS:
YR:
true growth yield of the metabolizing X,,, population with respect to
pesticide concentration (mg(dry wt.)/mg).
true growth yield of the metabolizing X,,, population with respect to
carbon concentration (mg(dry wt.)/mg).
true growth yield of the co-metabolizing Xc population (mg(dry
wt.)/mg).
true growth yield of the sensitive X,. population (mg(dry wt.)/mg).
true growth yield of the non-sensitive X, population (mg(dry wt.)/mg).
4-24
-------
RECORD 26 FORMAT 9F8.0
col: variable DAIR: diffusion coefficient for the pesticide(s) in the air (cm2 day"1). Only
required if HENRYK is greater than 0 else set to 0.0 for each NCHEM
col: variable HENRYK: Henry'slaw constant of the pesticide(s) for each NCHEM
(dimensionless).
col: variable ENPY: enthalpy of vaporization of the pesticide(s) for each NCHEM (kcal
mole"1).
RECORD 27 FORMAT I8,3F8.0
Only if IRFLAG = 1 or 2 (see record 20).
col: 1-8 IRTYP: type of irrigation:
1 = flood irrigation,
2 = furrow,
3 = over canopy (may generate runoff),
4 = under canopy sprinkler (may generate runoff),
5 = over canopy without runoff generation,
6 = over canopy, user-defined rates, with runoff generation,
7 = over canopy, user-defined rates, without runoff generation.
col: 9-16 PLEACH: leaching factor as a fraction of irrigation water depth.
col: 17-24 PCDEPL: fraction of available water capacity at which irrigation is applied.
Usually -0.45 - 0.55; PRZM accepts values between 0.0 and 0.9
col: 25-32 RATEAP: maximum rate at which irrigation is applied (cm hr"1).
RECORD 28 FORMAT 7F8.0
Only if IRFLAG = 1 or 2 and IRTYP = 2 (see record 20).
col: 1-8 QO: flow rate of water entering heads of individual furrows (m3 s"1).
col: 9-16 BT: bottom width of the furrows (m).
col: 17-24 ZRS: slope of the furrow channel walls (horizontal/vertical).
col: 25-32 SF: slope of the furrow channel bottom (vertical/horizontal).
col: 33-40 EN: Manning's roughness coefficient for the furrow.
col: 41-48 X2: length of the furrow (m).
col: 49-56 XFRAC: location in furrow where PRZM infiltration calculations are performed,
as a fraction of the furrow length (X2). If XFRAC = -1, average depths
are used in PRZM.
4-25
-------
RECORD 29 FORMAT 2F8.0
Only if IRFLAG = 1 or 2 and IRTYP = 2 (see record 20).
col: 1-8
col: 9-16
KS:
HF:
saturated hydraulic conductivity of the soil in which furrows are dug
(ms-1).
green-amp infiltration suction parameter (m).
RECORD 30 FORMAT I8,3F8.0
Only if KDFLAG = 1 (see record 20).
col: 1-8
col: variable
PCMC:
SOL:
flag to select which model is used to estimate KD (see record 36). 1 =
mole fraction, 2 = mg liter"1, 3 = micromoles liter"1, 4 = KOC entered
(dimensionless).
pesticide(s) solubility entered according to PCMC flag above for each
NCHEM.
RECORD 30A FORMAT 3F8.0
Only if KDFLAG = 2 or 3 (see record 20).
col: variable
FKNDCF:
Freundlich exponent 1/n (dimensionless) for each NCHEM.
RECORD 30B FORMAT 1515
Only if KDFLAG = 3 (see record 20).
col: variable
BAKD:
Days for the definition of the aging factors VAKD (record 30C) for
each NCHEM. Expressed as off-sets from the application date.
Although most sensible for single applications, PRZM restarts the
sequence at the date of additional pesticide applications. Up to 5
values. The first day must be 0 (zero), so that the sequence will start at
the application date.
RECORD 30C FORMAT 15F5.0
Only if KDFLAG = 3 (see record 20).
col: variable
VAKD:
time dependent factor changing on days BAKD (record SOB) to
calculate an aged sorption (dimensionless) for each NCHEM Up to 5
values.
RECORD 31 FORMAT 14F5.0
Only if ITFLAG = 1 or 2 (see record 20).
col: 1-60
col: 61-65
ALBEDO: monthly values of soil surface albedo (12 values).
EMMISS: reflectivity of soil surface to longwave radiation (fraction).
4-26
-------
col: 66-70 ZWIND: height of wind speed measurement above the soil surface (m)
RECORD 32 FORMAT 12F5.0
Only if ITFLAG = 1 or 2 (see record 20).
col: 1-60 BBT: average monthly values of bottom boundary soil temperatures in °C
(12 values).
RECORD 32A FORMAT 6F8.0 (I.e., QFAC(l).. QFAC(Nchem) TBASE(l).. TBASE(Nchem))
Only if ITFLAG = 1 or 2 (see record 20).
col: variable QFAC: factor for rate increase when temperature increases by 10°C. (If QFAC
is set equal to zero, PRZM will not simulate degradation change with
temperature.)
col: variable TBASE: temperature during the test of biodegradation.
RECORD 32B FORMAT 3(I8,2F8.0)
Only if ITFLAG = 2 (see record 20). One set (MSFLG, MSEFF, MSLAB) for each NCHEM.
col: variable MSFLG: flag to select moisture corrected degradation:
= 1 : reference soil moisture is absolute to field capacity (FC),
= 2 : reference soil moisture is relative to FC.
col: variable MSEFF: exponent of moisture corrected degradation (moisture relationship
according to WALKER).
col: variable MSLAB: reference soil moisture.
RECORD 33 FORMAT 18
col: 1-8 NHORIZ: total number of horizons (minimum of 1).
RECORD 34 FORMAT I8,7F8.0
Repeat records 34-38 in data sets up to NHORIZ.
col: 1-8 HORIZN: horizon number in relation to NHORIZ.
col: 9-16 THKNS: thickness of the horizon.
col: 17-24 BD: bulk density if BDFLAG=0 or mineral density if BDFLAG=1.
col: 25-32 THETO: initial soil water content in the horizon (cm3 cm"3).
col: 33-40 AD: soil drainage parameter if HSWZT = 1, else set to 0.0 (day1).
col: 41-variable DISP: pesticide(s) hydrodynamic solute dispersion coefficient for each
NCHEM.
4-27
-------
col: variaable ADL: lateral soil drainage parameter if HSWZT= 1
RECORD 35 FORMAT 8X,5F8.0
Only if BIOFLG = 1 (see record 20).
col: 9-16 Q: average carbon content of the population, (dimensionless).
col: 17-24 CM1: mineralizable carbon (mg g"1).
col: 25-32 Yl: cone, of metabolizing microbial population (mg g"1).
col: 33-40 Y2: cone, of co-metabolizing microbial population (mgg"1).
col: 41-48 Y3: cone, of sensitive microbial population (mg g"1).
col: 49-56 Y4: cone, of non-sensitive microbial population (mg g"1).
RECORD 36 FORMAT 8X,9F8.0
(i.e., DWRATE(l).. DWRATE(Nchem) DSRATE(l).. DSRATE(Nchem) DGRATE(l).. DGRATE(Nchem))
Only if DKFLG2=0 (see record 13).
Note: set DWRATE and DSRATE equal to simulate lumped first-order degradation.
col: variable DWRATE: dissolved phase pesticide(s) decay rate for each NCHEM (day"1).
col: variable DSRATE: adsorbed phase pesticide(s) decay rate for each NCHEM (day"1).
col: variable DGRATE: vapor phase pesticide(s)decay rate for each NCHEM (day"1).
RECORD 36 FORMAT 8X,9F8.0
Only if DKFLG2=1 (see record 13).
col: variable DWRAT1: dissolved phase pesticide(s) decay rate for first phase of bi-phase
reaction for each NCHEM (day"1).
col: variable DSRAT1: adsorbed phase pesticide(s) decay rate for first phase of bi-phase
reaction for each NCHEM (day"1).
col: variable DGRAT1: vapor phase pesticide(s)decay rate for first phase of bi-phase reaction
for each NCHEM (day"1).
RECORD 36A FORMAT 8X,9F8.0
Only if DKFLG2=1 (see record 13).
col: variable DWRAT2: dissolved phase pesticide(s) decay rate for second phase of bi-phase
reaction for each NCHEM (day"1).
col: variable DSRAT2: adsorbed phase pesticide(s) decay rate for second phase of bi-phase
reaction for each NCHEM (day"1).
4-28
-------
col: variable DGRAT2: vapor phase pesticide(s)decay rate for second phase of bi-phase
reaction for each NCHEM (day"1).
RECORD 37 FORMAT 8X,7F8.0
col: 9-16 DPN: thickness of compartments in the horizon (cm).
col: 17-24 THEFC: field capacity in the horizon (cm3 cm"3).
col: 25-32 THEWP: wilting point in the horizon (cm3 cm"3).
col: 33-40 OC: organic carbon in the horizon (percent).
col: variable KD: pesticide(s) partition coefficient for each NCHEM. Required if
KDFLAG = 0, 2, or 3 (see record 20), else set to 0.0 (cm"3 g"1).
RECORD 38 FORMAT 8X,5F8.0
Only if ITFLAG = lor 2 (see record 20).
col: 9-16 SPT: initial temp, of the horizon (Celsius).
col: 17-24 SAND: sand content in the horizon. Required if THFLAG = 1, else set to 0.0
(percent).
col: 25-32 CLAY: clay content in the horizon. Required if THFLAG = 1, else set to 0.0
(percent).
col: 33-40 THCOND: thermal conductivity of the horizon (cm"1 day"1). Required if IDFLAG
= 0, else set to 0.0.
col: 41-48 VHTCAP: heat capacity per unit volume of the soil horizon (cm"3 Celsius"1).
Required if IDFLAG = 0, else set to 0.0.
RECORD 39 FORMAT 8X,6F8.0
Only if DKFLG2=0 and NCHEM>1 (see record 13). Note: this record is used for parent/daughter relationship.
Set to zero for simulating independent parent chemicals.
col: 9-16 DKRW12: dissolved transformation fraction for chemical 1 to 2.
col: 17-24 DKRW13: dissolved transformation fraction for chemical 1 to 3. If NCHEM = 2,
set to 0.0 .
col: 25-32 DKRW23: dissolved phase transformation fraction for chemical 2 to 3. If
NCHEM = 2, set to 0.0 .
col: 3 3 -40 DKRS12: sorbed phase transformation fraction for chemical 1 to 2.
col: 41-48 DKRS13: sorbed phase transformation fraction for chemical 1 to 3. If NCHEM =
2, set to 0.0 .
col: 49-56 DKRS23: sorbed phase transformation fraction for chemical 2 to 3. If NCHEM =
2, set to 0.0.
4-29
-------
RECORD 39 FORMAT 8X,3F8.0
Only if DKFLG2=1 and NCHEM >1 (see record 13).
Note: this record is used for parent/daughter relationship.
Set to zero for simulating independent parent chemicals.
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
col: 49-56
DKW112: dissolved phase transformation fraction for first phase of bi-phase
transformation for chemical 1 to 2.
DKW 113: dissolved phase transformation fraction for first phase of bi-phase
transformation for chemical 1 to 3. If NCHEM = 2, set to 0.0.
DKW123: dissolved phase transformation fraction for first phase of bi-phase
transformation for chemical 2 to 3. If NCHEM = 2, set to 0.0 .
DKS112: sorbed phase transformation fraction for first phase of bi-phase
transformation for chemical 1 to 2.
DKS 113: sorbed phase transformation fraction for first phase of bi-phase
transformation for chemical 1 to 3. If NCHEM = 2, set to 0.0
DKS 123: sorbed phase transformation fraction for first phase of bi-phase
transformation for chemical 2 to 3. If NCHEM = 2. set to 0.0.
RECORD 39A FORMAT 8X,3F8.0
Only if DKFLG2=1 and NCHEM >1 (see record 13).
Note: this record is used for parent/daughter relationship.
Set to zero for simulating independent parent chemicals.
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
col: 49-56
DKW212: dissolved phase transformation fraction for second phase of bi-phase
transformation for chemical 1 to 2.
DKW213: dissolved transformation fraction for second phase of bi-phase
transformation for chemical 1 to 3. If NCHEM = 2, set to 0.0.
DKW223: dissolved phase transformation fraction for second phase of bi-phase
transformation for chemical 2 to 3. If NCHEM = 2, set to 0.0.
DKS212: sorbed phase transformation fraction for second phase of bi-phase
transformation for chemical 1 to 2.
DKS213: sorbed phase transformation fraction for second phase of bi-phase
transformation for chemical 1 to 3. If NCHEM = 2, set to 0.0.
DKS223: sorbed phase transformation fraction for second phase of bi-phase
transformation for chemical 2 to 3. If NCHEM = 2, set to 0.0.
RECORD 40
col: 1-8
FORMAT 218
ILP:
flag for initial pesticide(s) levels before simulation start date. 1 = yes, 0
= no.
4-30
-------
col: 9-16
CFLAG:
conversion flag for initial pesticide(s) levels. 0 = mg/kg"1, 1 = kg/ha"1.
Leave blank if ILP = 0.
RECORD 41 FORMAT 8F8.0
Only if ILP = 1 (see record 40).
NOTE: number of lines = THKNS(I) divided by DPN(I) where I = HORIZN.
Maximum of 8 values per line. Enter this record in data sets for each NCHEM.
col: 1-80
PESTR:
initial pesticide(s) levels.
RECORD 42
col: 5-8
col: 13-16
col: 17-24
FORMAT 3(4X,A4,4X,A4,I8),I4
ITEM1:
STEP1:
LFREQ1:
col: 29-32
col: 37-40
col: 41-48
col: 53-56
col: 61-64
col: 65-72
col: 73-76
ITEM2:
STEP2:
LFREQ2:
ITEM3:
STEP3:
LFREQ3:
EXMFLG:
hydrologic hardcopy output flag. WATR is inserted or leave blank.
time step of hydrologic output. DAY = daily, MNTH = monthly,
YEAR = yearly.
frequency of hydrologic output given by a specific compartment
number.
pesticide flux output flag. PEST is inserted or leave blank.
same as STEP1.
same asLFREQl.
pesticide concentration output flag. CONC is inserted or leave blank.
same as STEP 1.
same asLFREQl.
flag for reporting output to file for EXAMS model. 1 = yes, 0 = no. If
ERFLAG=0, EXMFLG is automatically set to 0
RECORD 43 FORMAT 18
Only if EXMFLG = 1 (see record 42)
col: 1-8
EXMENV:
EXAMS environment catalog number
RECORD 44 FORMAT I8,A16,2I8,F8.0
Only if EXMFLG = 1 (see record 42), repeat RECORD 44 for each chemical
col: 1-8
col: 9-24
col: 24-32
EXMCHM:
CASSNO:
NPROC:
EXAMS chemical catalog number
chemical CASS Number (optional)
signals the type of process transforming parent to metabolite in
EXAMS, (see Burns 2000, Section 6)
4-31
-------
col: 33-40
col: 41-48
RFORM gives the reactive molecular form from the transformation of parent to
metabolite in EXAMS, (see Burns 2000, Section 6)
YIELD product yield from the transformation pathway dimensions of mole of
transformation product produced per mole of parent compound reacted
RECORD 45 FORMAT I8,4X,A4
col: 1-8 NPLOTS: number of times series plots (max. of 12).
col: 13-16 STEP4: output time step. This option outputs pesticide runoff and erosion flux
and pesticide leaching below core depth. Three options are available:
DAY for daily, MNTH for monthly, YEAR for yearly.
RECORD 46 FORMAT 4X,A4,A1,3X,A4,1X,I3,1X,I3,F8.0,7X,A1,I8
Only if NPLOTS > 0 (see record 45) and ECHO > 2. (Echo level is set in PRZM3.RUN file).
NOTE: repeat this record up to NPLOTS.
col: 5-8
col: 9-9
col: 13-16
col: 18-20
col: 22-24
col: 25-32
col: 40-40
col: 41-48
PLNAME: name of plotting variable (see Table 4.1).
INDX: index to identify which pesticide if applicable. 1 = first chemical, 2 =
second chemical, 3 = third chemical.
MODE: plotting mode. TSER (daily), TCUM (cumulative), TAVE (daily
average over multiple compartments), TSUM (daily sum over multiple
compartments)
IARG: argument value for PLNAME (see Table 4.1).
IARG2: argument value for PLNAME (see Table 4.1). (If TSER or TCUM
enter same value as IARG
CONST: constant with which to multiply for unit conversion. Leave blank for
default to 1.0.
PLTYP: input W for WDM file, P for printer (not required unless running
PATRIOT)
PLTDSN: WDM data set number for the output time series (Not required unless
running PATRIOT)
RECORD 47 FORMAT A78
Only if special actions are desired (see record 48).
col: 1-78
ATITLE:
label for special actions title.
RECORD 48 FORMAT 2X,3I2,1X,A8,1X,I3,3F8.0
Only if special actions are desired.
Repeat this record for each special action required (up to 12).
4-32
-------
col: 3-4
col: 5-6
col: 7-8
col: 10-17
col: 19-21
col: variable
SAD AY: day of special action.
SAMON: month of special action.
SAYR: year of special action.
SPACT: special action variable (see below).
NACTS: horizon or crop number affected by special actions (see below).
SPACTS: new value(s) for the special action
SPACT
BD
CN
DSRATE
DWRATE
KD
SNAPSHOT*
USLEC
* Used to displa
NACTS
HORIZON NO.
CROP NO.
HORIZON NO.
HORIZON NO.
HORIZON NO.
CROP NO.
y pesticide concentration profile.
SPACTS
NEW VALUE(S)
NEW VALUES
NEW VALUE(S)
NEW VALUE(S)
NEW VALUE(S)
NEW VALUE(S)
Format
(F8.0)
(318)
(3F8.0)
(3F8.0)
(3F8.0)
(3F8.0)
4.4.2.2 PRZM Input Guide for PRZM-3 Nitrogen Simulation Records
RECORD Nl FORMAT A78
col: 1-78 NTITLE: title for nitrogen simulation.
RECORD N2 FORMAT I5,F5.0,4I5
col: 1-5 SEPHZN: horizon number into which septic effluent is introduced
col: 6-10
col: 11-30
ORGFRC: fraction of organic nitrogen in septic effluent which is refractory (the rest
becomes labile).
SEPDSN: data-set numbers from WDM file (if in use) for septic effluent values in the
following order: water, ammonia, nitrate, organic N.
RECORD N3 FORMAT 815
col: 1-5
VNUTFG:
flag to allow plant uptake parameters to vary throughout the year. 1 = yes,
0 = no.
4-3
-------
col: 6-10
col: 11-15
col: 16-20
col: 21-25
col: 26-30
col: 3 1-35
FORAFG:
ITMAXA:
NUPTFG:
FIXNFG:
AMVOFG:
ALPNFG:
col: 36-40
VNPRFG:
method for simulating adsorption and desorption of ammonium. 0 = first-
order kinetics, 1 = single value Freundlich.
maximum number of iterations to be attempted in solving Freundlich
equation (only needed if FORAFG =1).
method for simulating plant uptake of nitrogen. 0 = first-order kinetics, 1 =
yield-based algorithm.
flag to simulate nitrogen fixation. 1 = yes, 0 = no. (If FIXNFG = 1,
NUPTFG must be 1 also).
flag to simulate ammonia volatilization. 1 = yes, 0 = no.
flag to simulate above-ground and litter compartments for plant nitrogen. 1
= yes, 0 = no.
flag to allow plant return parameters to vary throughout the year. 1 = yes, 0
= no.
RECORD N4 FORMAT 615
col: 1-30
NIADFG:
array of flags indicating the source of atmospheric deposition data for
nitrogen species (ammonia, nitrate, organic N). Three flags for dry
deposition are followed by three flags for wet deposition.
0 = no deposition for this species,
-2 = monthly values entered on ensuing record (N5),
-1 = deposition values come from file specified in execution supervisor,
> 0 = values come from this data-set number on WDM file.
RECORD N5 FORMAT 12F5.0
Repeat this record for each occurrence of NIADFG=-2 in record N4.
col: 1-60
NIAFXM/NIA monthly values for nitrogen
CNM:
atmospheric deposition (NIAFXM = dry deposition, NIACNM = wet deposition).
RECORD N6 FORMAT 215
col: 1-5 NNAPS:
col: 6-10
NFRMFG:
total number of agricultural nitrogen applications occurring at different
dates (0 to 50).
flag for testing of ideal soil moisture conditions for the agricultural nitrogen
application relative to target dates (see record N6 for target dates
information).
1 = yes, 0 = no.
4-34
-------
RECORD N7 FORMAT 2X,3I2,I8,5F8.0
Repeat this record up to NNAPS (see record N5).
Not required if NAPS=0.
col: 3-4 NAPD: integer target application day.
col: 5-6 NAPM: integer target application month.
col: 7-8 NAPYR: integer target application year.
col: 9-16 NWNDAY: number of days in which to check soil moisture values following the target
dates for ideal nitrogen applications. Required if NFRMFG=1, else set to 0.
col: 17-24 NDEPI: depth of the nitrogen application (cm).
col: 25-48 NTAPP: total application of the nitrogen species (kg ha"1) in the following order:
ammonia, nitrate, organic N.
col: 49-56 NAPFRC: fraction of organic N applied which becomes refractory (the rest becomes
labile).
RECORD N8 FORMAT 8F8.0
Only if NUPTFG = 0 and VNUTFG = 0 (see record N5).
NOTE: number of lines = (NHORIZ divided by 8) plus 1
Maximum of 8 values per line.
col: 1-64 KPLN: plant nitrogen uptake reaction rate parameters for each soil horizon (/day).
RECORD N9 FORMAT 12F5.0
Only if NUPTFG = 0 and VNUTFG = 1 (see record N5).
Repeat this record up to NHORIZ.
col: 1-60 KPLNM: monthly plant nitrogen uptake reaction rate parameters for each soil
horizon (/day).
RECORD N10 FORMAT 2F8.0
Only if NUPTFG = 1 (see record N5).
col: 1-8 NUPTGT: total annual target for plant uptake of nitrogen for all soil layers and all
crops during the calendar year (kg/ha/yr).
col: 9-16 NMXRAT: ratio of the maximum uptake rate to the optimum (target) rate when the
crop is making up a deficit in nitrogen uptake.
RECORD Nil FORMAT 12F5.0
Only if NUPTFG = 1 (see record N5).
col: 1-60 NUPTFM: monthly fractions of the total annual nitrogen plant uptake target (see
record 52) applied to each month (total of values must sum to 1.0).
4-35
-------
RECORD N12 FORMAT 12F5.0
Only if NUPTFG = 1 (see record N5).
Repeat this record up to NHORIZ.
col: 1-60
NUPTM: fractions of the monthly nitrogen plant uptake target applied to each soil
horizon (values across soil horizons must sum to 1.0 for each month).
RECORD N13 FORMAT 8F8.0
Only if ALPNFG = 1 and VNUTFG = 0 (see record N5).
NOTE: number of lines = (NHORIZ divided by 8) plus 1
Maximum of 8 values per line.
col: 1-64
ANUTF:
above-ground plant uptake fractions for each soil horizon.
RECORD N14 FORMAT 12F5.0
Only if ALPNFG = 1 and VNUTFG = 1 (see record N5).
Repeat this record up to NHORIZ.
col: 1-60
ANUFM: monthly fractions of plant uptake which go to above-ground plant N
storage.
RECORD N15 FORMAT 10F8.0
col: 1-8 GNPM(l): fraction of nitrogen uptake which comes from nitrate (GNPM(l) and
GNPM(2) must sum to 1.0).
col: 9-16 GNPM(2): fraction of nitrogen uptake which comes from ammonium (GNPM(l) and
GNPM(2) must sum to 1.0).
col: 17-24 GNPM(3): temperature coefficient for plant uptake (only needed if NUPTFG = 0).
col: 25-32 GNPM(4): temperature coefficient for ammonium desorption (only needed if
FORAFG = 0).
col: 33-40 GNPM(5): temperature coefficient for ammonium adsorption (only needed if
FORAFG = 0).
col: 41-48 GNPM(6): temperature coefficient for nitrate immobilization.
col: 49-56 GNPM(7): temperature coefficient for organic N ammonification.
col: 57-64 GNPM(8): temperature coefficient for NO3 denitrification.
col: 65-72 GNPM(9): temperature coefficient for nitrification.
col: 73-80 GNPM(IO): temperature coefficient for ammonium immobilization.
4-36
-------
RECORD N16 FORMAT 8F8.0
Repeat this record up to NHORIZ.
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
col: 49-56
col: 57-64
NPM(l):
NPM(2):
NPM(3):
NPM(4):
NPM(5):
DNTHRS:
NPM(6):
NPM(7):
first-order reaction rate for ammonium desorption for each soil horizon
(only needed if FORAFG = 0) (/day).
first-order reaction rate for ammonium adsorption for each soil horizon
(only needed if FORAFG = 0) (/day).
first-order reaction rate for nitrate immobilization for each soil horizon
(/day).
first-order reaction rate for organic N ammonification for each soil horizon
(/day).
first-order reaction rate for denitrification for each soil horizon (/day).
fraction of saturated water content at which denitrification begins to occur.
first-order reaction rate for nitrification for each soil horizon (/day).
first-order reaction rate for ammonium immobilization (/day).
RECORD N17 FORMAT F8.0
Only if FORAFG = 1 (see record N5).
col: 1-8
GNPM( 11): maximum solubility of ammonium in water (ppm).
RECORD N18 FORMAT 3F8.0
Only if FORAFG = 1 (see record N5).
Repeat this record up to NHORIZ.
col: 1-8 NPM(8): maximum concentration (on the soil) of ammonium which is permanently
fixed to the soil for each soil horizon (ppm).
col: 9-16 NPM(IO): coefficient parameter for the Freundlich adsorption/desorption equation for
each soil horizon (-).
col: 17-24 NPM(ll): exponent parameter for the Freundlich adsorption/desorption equation for
each soil horizon.
RECORD N19 FORMAT 8F8.0
Only if AMVOFG = 1 (see record N5)
NOTE: number of lines = (NHORIZ+2 divided by 8) plus 1
Maximum of 8 values per line.
col: 1-8
col: 9-16
THVOL: temperature correction coefficient for ammonia volatilization (needed on
first record only).
TRFVOL: reference temperature for the correction (needed on first record only) (deg
C).
4-37
-------
col: 17-64 KVOL: ammonia volatilization rates for each soil horizon (/day).
Note: ammonia volatilization is performed in the nitrogen simulation code (i.e., not in the volatilization portion
of the PRZM pesticide code) using these parameters.
RECORD N20 FORMAT 4F8.0
Repeat this record up to NHORIZ.
col: 1-8 ORNPM(l): paniculate/soluble partitioning coefficient for labile organic N.
col: 9-16 ORNPM(2): paniculate/soluble partitioning coefficient for refractory organic N.
col: 17-24 ORNPM(3): first-order conversion rate of labile to refractory paniculate organic N
(/day).
col: 25-32 ORNPM(4): associated temperature correction coefficient.
RECORD N21 FORMAT 8F8.0
Only if VNPRFG = 0 (see record N5)
NOTE: number of lines = (NHORIZ+1 divided by 8) plus 1
Maximum of 8 values per line.
col: var* KRETBN: first-order return rates of below-ground plant N to organic N storage for
each soil horizon.
* column locations depend on # of horizons
col: var** BGNPRF: fraction of plant N return that becomes paniculate refractory organic N (the
rest becomes paniculate labile).
** column location depends on # of fields filled by values for KRETBN; BGNPRF value location follows last
KRETB value
RECORD N22 FORMAT 3F8.0
Only if ALPNFG = 1 and VNPRFG = 0 (see record N5).
col: 1-8 AGKPRN: first-order fall rate of above-ground plant N to litter N (/day).
col: var* KRETAN: first-order return rates of litter N to organic N storage in the top soil
horizon (/day).
* column locations depend on # of horizons
col: var** LINPRF: fraction of litter N return that becomes paniculate refractory organic N (the
rest becomes paniculate labile).
** column location depends on # of fields filled by values for KRETBN; BGNPRF value location follows last
KRETB value
4-38
-------
RECORD N23 FORMAT 12F5.0
Only if VNPRFG = 1 (see record N5).
Repeat this record up to NHORIZ.
col: 1-60 KRBNM: monthly first-order return rates of below-ground plant N to organic N for
each soil horizon (/day).
RECORD N24 FORMAT 12F5.0
Only if VNPRFG = 1 (see record N5).
col: 1-60 BNPRFM: monthly fractions of below-ground plant N return which becomes
paniculate refractory organic N (the rest becomes paniculate labile).
RECORD N25 FORMAT 12F5.0
Only if ALPNFG = 1 and VNPRFG = 1 (see record N5).
col: 1-60 KRANM: monthly first-order return rate of above-ground plant N to litter N (/day).
RECORD N26 FORMAT 12F5.0
Only if ALPNFG = 1 and VNPRFG = 1 (see record N5).
col: 1-60 KRLNM: monthly return rates of litter plant N to paniculate labile organic N for the
top soil horizon (/day).
RECORD N27 FORMAT 12F5.0
Only if ALPNFG = 1 and VNPRFG = 1 (see record N5).
col: 1-60 LNPRFM: monthly fractions of litter N return which becomes paniculate refractory
organic N (the rest becomes paniculate labile).
RECORD N28 FORMAT 8F8.0
Repeat this record up to NHORIZ.
col: 1-8 NIT(l): initial storage of paniculate labile organic N in each soil horizon (in kg/ha).
col: 9-16 NIT(2): initial storage of adsorbed ammonium in each soil horizon (kg/ha).
col: 17-24 NIT(3): initial storage of solution ammonium in each soil horizon (kg/ha).
col: 25-32 NIT(4): initial storage of nitrate in each soil horizon (kg/ha).
col: 33-40 NIT(5): initial storage of plant N in each soil horizon (kg/ha).
col: 41-48 NIT(6): initial storage of paniculate refractory organic N in each soil horizon
(kg/ha).
col: 49-56 NIT(7): initial storage of solution labile organic N in each soil horizon (kg/ha).
4-39
-------
col: 57-64 NIT(8): initial storage of solution refractory organic N in each soil horizon (kg/ha).
RECORD N29 FORMAT 2F8.0
Only if ALPNFG = 1 (see record N5).
col: 1-8 AGPLTN: initial storage of above-ground plant N (kg/ha).
col: 9-16 LITTRN: initial storage of litter N (kg/ha).
4-40
-------
Table 4.1 Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
Water Storage
INTS
SWTR
SNOP
THET
Water Fluxes
PRCP
SNOF
THRF
INFL
RUNF
CEVP
SLET
TETD
OUTF
IRRG
Sediment Flux
ESLS
Pesticide Storages
FPST
TPST
SPST
Pesticide Fluxes
TPAP
Fortran
Variable
CINT
SW
SNOW
THETN
PRECIP
SNOWFL
THRUFL
AINF
RUNOF
CEVAP
ET
TDET
OUTFL
IRRR
SEDL
FOLPST
PESTR
SPESTR
TAPP
Description
Interception storage on canopy
Soil water storage
Snow pack storage
Soil water content
Precipitation
Snowfall
Canopy throughfall
Percolation into each
compartment
Runoff depth
Canopy evaporation
Actual evapotranspiration from
each compartment
Total daily actual
evapotranspiration
Lateral water outflow
Applied irrigation
Event soil loss
Foliar pesticide storage
Total soil pesticide storage in
each soil compartment
Dissolved pesticide storage in
each soil compartment
Total pesticide application
Units
cm
cm
cm
cm cm"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
cm day"1
Tonnes day"1
gem"2
gem"2
gem"2
g cm"2 day"1
Arguments
Required
(IARG)
None
1-NCOM2
None
1-NCOM2
None
None
None
1-NCOM2
None
None
1-NCOM2
None
None
None
None
None
1-NCOM2
1-NCOM2
None
4-41
-------
Table 4.1 Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
FPDL
WFLX
DFLX
AFLX
DKFX
DWRT
DSRT
UFLX
RFLX
EFLX
RZFX
LTFX
COFX
TUPX
TDKF
PCNC
VFLX
FPVL
STMP
KDFR
Canopy Height
Fortran
Variable
FPDLOS
WOFLUX
DFFLUX
ADFLUX
DKFLUX
DWRATE
DSRATE
UPFLUX
ROFLUX
ERFLUX
RZFLUX
LATFLX
DCOFLX
SUPFLX
SDKFLX
TCNC
PVFLUX
FPVLOS
SPT
KD
Description
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
compartment
Dissolved decay rate from each
soil compartment
Sorbed decay rate from each soil
compartment
Pesticide uptake flux from each
soil compartment
Pesticide runoff flux
Pesticide erosion flux
Net pesticide flux past the
maximum root depth
Lateral pesticide outflow
Pesticide outflow below soil core
Total pesticide uptake flux from
entire soil profile
Total pesticide decay flux from
entire profile
Pesticide concentration in canopy
Soil pesticide volatilization flux
Foliar pesticide volatilization flux
Soil Temperature
Soil temperature in each soil
compartment
KD for each soil compartment
Units
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
day"1
day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
g cm"2 day"1
gem"3
g cm"2 day"1
g cm"2 day"1
°C
cm3 g"1
Arguments
Required
(IARG)
None
None
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
None
None
None
None
None
None
None
None
None
None
1-NCOM2
1-NCOM2
4-42
-------
Table 4.1 Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
CHGT
Curve Number
CURV
Soil
Concentration*
TCON
ACON
GCON
DLYS
DCON
'Default concentratior
Nitrogen Storages
PLON
AMAD
AMSU
NO3
PLTN
SLON
PRON
SRON
AGPN
LITN
Nitrogen Fluxes
ELON
EAMA
ERON
RAMA
Fortran
Variable
HEIGHT
CVNUM
TCON
ACON
GCON
DLYS
DCON
units may be co
NIT(I,1)
NIT(I,2)
NIT(I,3)
NIT(I,4)
NIT(I,5)
NIT(I,6)
NIT(I,7)
NIT(I,8)
AGPLTN
LITTRN
SEDN(l)
SEDN(2)
SEDN(3)
RON(l)
Description
Canopy height
Curve number
Total soil concentration
Adsorbed soil concentration
Gas soil concentration
Dissolved soil concentration
weighted for sphere of influence
Dissolved soil concentration
averted using multiplication factor
Paniculate labile organic N
Adsorbed ammonium
Solution ammonium
Nitrate
Plant nitrogen
Solution labileorganic N
Paniculate refractory organic N
Solution refractory organic N
Above ground plant nitrogen
Litter nitrogen
Labile organic N erosion loss
Adsorbed ammonium erosion loss
Refractory organic N erosion loss
Solution ammonium runoff loss
Units
cm
none
mg/kg
mg/kg
mg/1
mg/1
mg/1
kg ha'1
kg ha1
kg ha1
kg ha'1
kg ha'1
kg ha'1
kg ha1
kg ha'1
kg ha'1
kg ha1
kg ha1 day'1
kg ha"1 day'1
kg ha"1 day"1
kg ha1 day'1
Arguments
Required
(IARG)
None
None
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
None
None
None
None
None
None
4-43
-------
Table 4.1 Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
RNO3
RLON
RRON
PSAM
OSAM
PSNI
OSNI
DENI
AMNI
AMIM
ONMNZ
DDAM
DDNI
DDON
WDAM
WDNI
WDON
NFIX
PSLN
OSLN
Fortran
Variable
RON(2)
RON(3)
RON(4)
PSAMS
OSAMS
PSNO3
OSNO3
DENIF
AMNIT
AMIMB
ORNMN
NIADDR(l)
NIADDR(2)
NIADDR(3)
NIADWT(l)
NIADWT(2)
NIADWT(3)
NFIXFX
PSSLN
OSSLN
Description
Nitrate runoff loss
Labile organic N runoff loss
Refractory organic N runoff loss
Solution ammonium flux from
each compartment
Solution ammonium lateral
outflow from each compartment
Nitrate flux from each
compartment
Nitrate lateral outflow from each
compartment
Denitrification
Ammonia nitrification
Ammonia immobilization
Organic nitrogen mineralization
Dry atmospheric deposition of
ammonia
Dry atmospheric deposition of
nitrate
Dry atmospheric deposition of
organic N
Wet atmospheric deposition of
ammonia
Wet atmospheric deposition of
nitrate
Wet atmospheric deposition of
organic N
Nitrogen fixation
Labile organic N flux from each
compartment
Labile organic N lateral outflow
from each compartment
Units
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
Arguments
Required
(IARG)
None
None
None
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
4-44
-------
Table 4.1 Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
PSRN
OSRN
NIIM
AMVO
LARF
ANIU
AAMU
BNIU
BAMU
REAG
ARLN
ARRN
BRLN
BRRN
Fortran
Variable
PSSRN
OSSRN
NUMB
AMVOL
REFRON
NIUPA
AMUPA
NIUPB
AMUPB
RETAGN
RTLLN
RTRLN
RTLBN
RTRBN
Description
Refractory organic N flux from
each compartment
Refractory organic N lateral
outflow from each compartment
Nitrate immobilization
Ammonia volatilization
Labile to refractory conversion
Above-ground nitrate plant
uptake
Above-ground ammonia plant
uptake
Below-ground nitrate plant
uptake
Below-ground ammonia plant
uptake
Plant return to litter
Litter return to labile organic N
Litter return to refractory organic
N
Below-ground return to labile
organic N
Below-ground return to refractory
organic N
Units
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
kg ha"1 day"1
Arguments
Required
(IARG)
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
1-NCOM2
4.5 VADOFT Input File
PRZM-3 requires a VADOFT flow input file if VADOFT is specified "ON" in the execution supervisor
(PRZM3.RUN) file. Also if TRANSPORT SIMULATION is specified "ON", VADOFT transport input must follow.
When nitrogen simulation is being performed, VADOFT simulates the three nitrogen constituents as if they were
three chemicals. Thus, a VADOFT input sequence for modeling nitrogen must contain parameters for all three
species. This effects records 11, 14, 20, and 22 of VADOFT input for transport (see Section 4.5.3). Output from
VADOFT is still reported by chemical number. Thus, chemical number one is ammonia, chemical number two is
nitrate, and chemical number 3 is total organics.
4.5.1 Example VADOFT Input File
4-45
-------
**********************************pLQ^y*************************************
3 CHEMICAL, 2 HORIZON, 1 MATERIAL, VADOSE ZONE FLOW SIMULATION FOR ZONE 1
61 11111 1
20 2 1 .01
1 11101 2
0.0 1.0 1.0
1 0.0 1.0
2
1 20 1 50.0
2 40 1 80.0
O.OEOO 0
0 1 0.0 O.OEOO 0
7.12E02 .43EOO O.OEOO 0
0.045EOO -l.OEOO 0.145EOO
YEAR
1 0 0
1 0
1.0
000
.OEOO
2.68EOO 0.626EOO 5 10
*********************************'pj^J^]\f§pQ]^rp* ************ ************ ********
3 CHEMICAL, 2 HORIZON, 1 MATERIAL,
61 11101
0 11001 2
0.0 1.0 1.0
1 0.0 1.0
2
1 20 1 50.0
2 40 1 80.0
VADOSE TRANSPORT SIMULATION FOR ZONE 1
1
1.0
5
YEAR
O.OEOO 0 O.OEOO 0 O.OEOO 0
0 0 0.0 0.0 0 0 0 0
30E01 .43EOO
OOEOO 1.01EOO l.OOEOO O.OEOO O.OEOO O.OEOO
0.0 1.0 O.OEOO
2.000E-2 O.OOEOO 7.00E-3 O.OOEOO 2.30E-2 O.OEOO
1
10
4.5.2 VADOFT Input Guide for Flow
RECORD 1 FORMAT A80
col: 1 -80 TITLE: label for flow simulation title.
RECORD 2 FORMAT 1015
col: 1-5 NP: total number of VADOFT nodal points (max of 100).
col: 6-10 NMAT: total number of different porous materials (maximum of 5).
col: 11-15 NONU: flag to indicate if initial condition is non-uniform. 1 = yes, 0 = no.
4-46
-------
col: 16-20 ITRANS: flag to indicate if running in transient or steady-state. Must be set to 1 if
PRZM is ON. 1 = transient, 0 = steady-state.
col: 21-25 IMODL: flag to indicate if running flow or transport model. 1 = flow, 0 = transport.
Set to 1 here.
col: 26-30 IKALL: time stepping index. 1 = backward difference, 0 = central difference. This
flag is automatically set to 1 in FLOW.
col: 31-35 IMBAL: flag to indicate if mass balance computation is required. 1 = yes, 0 = no.
col: 36-40 INTSPC: flag to indicate initial conditions for head values. 1 = hydraulic head, 0 =
pressure head.
col: 41-45 IHORIZ: flag to indicate if flow direction is horizontal. 1 = yes, 0 = no. Set to 0 if
PRZM is ON.
col: 46-50 ICHAIN: flag to indicate if daughter products are used. 1 = yes, 0 = no.
Automatically set to 0 for flow.
RECORD 3 FORMAT 3I5,E10.3
col: 1-5 NITMAX: maximum number of iterations per time step. Suggested value of 20.
col: 6-10 INEWT: flag to indicate nonlinear iterative procedure for solving saturated flow
equation. 0 = Picard, 1 = standard Newton-Raphson, 2 = modified Newton-
Raphson. Suggested value of 2 if PRZM is ON.
col: 11-15 IRESOL: maximum number of refinements each time step if solution does not
converge. Suggested value of 1.
col: 16-25 HTOL: head tolerance for the nonlinear solution (length). Suggested value of 0.01.
RECORD 4
col: 1-5
col: 6-10
col: 11-15
col: 16-20
col: 21-25
FORMAT 815
KPROP:
ITSGN:
ITMARK:
NSTEP:
NVPR:
flag to indicate relationship between relative permeability versus saturation
and pressure head versus saturation. 1 = functional parameters supplied in
record 15, 0 = model calculated.
flag to indicate if output time values are to be model calculated. 1 = yes, 0
= no.
flag to indicate if output time values differ from computational time values
(see records 6 and 7). 1 = yes, 0 = no.
value of which time step to output nodal values from. When NSTEP = n,
then output is printed. Must be from 1 up to 31 (days).
value of which time step to output nodal velocities. When NVPR = n, then
output is printed. Must be from 1 up to 31 (days).
4-47
-------
col: 26-30 IOBSND: flag to indicate if values are printed at certain observation nodes. 1 = yes, 0
= no. NOTE: Echo level must be greater than or equal to 6 in
PRZM3.RUN file.
col: 31-35 NOBSND: number of observation node(s) to be printed. NOBSND must not be greater
than NP (see record 2). If IOBSND = 0 then set NOBSND = 0.
col: 36-40 IPRCHK: flag to indicate if detailed information is generated in the flow matrix. 1 =
yes, 0 = no.
RECORDS FORMAT 4E10.3
Only if ITRANS = 1 (see record 2).
col: 1-10 TIMA: initial time value (t). Suggested value if PRZM is ON: 0.0
col: 11-20 TIN: initial time step value(t). Suggested value if PRZM is ON: 1.0. Omit if
ITSGN = 0.
col: 21-30 TFAC: time step multiplier. Suggested value if PRZM is ON: 1.0. Omit if ITSGN
= 0.
col: 31-40 TMAX: maximum time step value allowed (t). Suggested value if PRZM is ON: 1.0
Omit if ITSGN = 0.
RECORD 6 FORMAT 8E10.3
Only if ITGSN = 0 (see record 4) and ITRANS = 1.
col: 1-80 TMVEC(I): time values corresponding to the number of time steps where I = 1...31 (t).
Input up to 8 values per line.
RECORD? FORMAT I5,2E10.3
Only if ITMARK = 1 and ITRANS = 1.
col: 1-5 ITMGEN: flag to indicate if backup file marker time values are used. 1 = yes, 0 = no.
col: 6-15 STMARK: starting marker time value (t). If PRZM and TRANSPORT are ON, set to
0.0.
col: 16-25 DTMARK: marker time value increment (t). If PRZM and TRANSPORT are ON, set
to 1.0.
RECORD 8 FORMAT 8E10.3
Only if ITRANS = 1, ITMARK = 1 and ITMGEN = 0.
col: 1-80 TMFOMT: output marker file time values (t) corresponding to TMVEC(I) (see record
6). Input up to 8 values per line.
RECORD 9 FORMAT 15
4-48
-------
col: 1-5
NLAYRG:
number of soil horizons to be discretized.
RECORD 10 FORMAT 3I5,E10.3
Repeat this record up to NLAYRG (see record 9).
col: 1-5 ILAYR: horizon number in relation to NLAYRG.
col: 6-10 NELM: number of finite elements in ILAYR.
col: 11-15 IMATL: porous material number related to NMAT (see record 2) in ILAYR.
col: 16-25 THL: thickness of the horizon (ILAYR).
RECORD 11 FORMAT E10.3,I5
col: 1-10 CHINV:
col: 11-15
CNPIN:
default initial values of pressure (1) or hydraulic head (m P) for nodes in the
matrix.
number of non-default nodes in the matrix related to the default initial
values (CHINV) if NONU = 1 (see record 2), else set to 0.
RECORD 12
col: 1-5
col: 6-10
col: 11-20
col: 21-30
col: 3 1-35
col: 36-40
col: 41-50
col: 51-60
FORMAT
IBTND1:
IBTNDN:
VALND1:
VALNDN:
ITCND1:
ITCNDN:
FLX1:
FLXN:
2I5,2E10.3,2I5,2E10.3
type of boundary condition for the first node. 1 = pressure head, 0 = water
flux.
type of boundary condition for the last node. 1 = pressure head, 0 = water
flux.
value of the pressure head or water flux at the first node. The value should
be positive for influx and negative for efflux. Set to 0.0 if PRZM is ON.
value of the pressure head or water flux at the last node. The value should
be positive for influx and negative for efflux. Set to 0.0 if fluid is exiting
the last node.
flag to indicate if the boundary condition at the first node is transient. 1 =
yes, 0 = no. Automatically set to 0 if PRZM is ON.
flag to indicate if the boundary condition at the last node is transient. 1 =
yes, 0 = no. Automatically set to 0 if PRZM is ON.
fluid flux injected into the first node (I31). Automatically set to 0.0 for
FLOW if PRZM is ON.
fluid flux injected into the last node (I31). Automatically set to 0.0 for
FLOW if PRZM is ON.
RECORD 13 FORMAT 4E10.3
Repeat this record up to NMAT (see record 2).
4-49
-------
col: 1-10 PROP1: saturated hydraulic conductivity of the material (use cm day"1 if PRZM is
ON).
col: 11-20 PROP2: effective porosity of the material.
col: 21-30 PROPS: specific storage of the material. For unsaturated flow, set to 0.0.
col: 31-40 PROP4: air entry pressure head of the material.
RECORD 14 Omit for FLOW simulation.
RECORD 15 FORMAT 5E10.3
Repeat this record up to NMAT if KPROP = 1.
col: 1-10 F VAL1: residual water phase saturation of the material (residual water content /
saturated water content).
col: 11-20 FVAL2: parameter n of the relative permeability versus saturation relationship.
Suggested value of 0.0 or negative value.
col: 21-30 FVAL3: leading coefficient of the saturation versus capillary head relationship
(alpha).
col: 31 -40 F VAL4: power index of the saturation versus capillary head relationship (beta).
col: 41-50 FVAL5: power index of the saturation versus capillary head relationship (gamma).
Suggested value of 1.0 - (1.0/FVAL4).
RECORD 16 FORMAT 15
Repeat records 16-19 in data sets up to NMAT if KPROP = 0.
col: 1-5 NUMK: number of entry pairs of relative permeability and saturation of the
material.
RECORD 17 FORMAT 8E10.3
Only if KPROP = 0.
col: 1-10 SMV1: value of water phase saturation for point 1 of the entry pairs related to
NUMK.
col: 11-20 PKRW1: value of relative permeability (I2) for point 1 of the entry pairs related to
NUMK.
col: 21-30 SMV2: etc.
col: 31-40 PKRW2: etc.
4-50
-------
RECORD 18 FORMAT 15
OnlyifKPROP = 0.
col: 1-5 NUMP: number of entry pairs of pressure head versus saturation values for the
material.
RECORD 19 FORMAT 8E10.3
OnlyifKPROP = 0.
col: 1-10 SSWV1: value of water phase saturation for point 1 of the entry pairs related to
NUMP.
col: 11-20 HCAP1: value of the pressure head (1) for point 1 of the entry pairs related to
NUMP.
col: 21-30 SSWV2: etc.
col: 31-40 HCAP2: etc.
RECORD 20 FORMAT 5(I5,E10.3)
OnlyifNONU= 1.
NOTE: enter next two variables sequentially for every non-default node (CNPIN).
col: 1-5 N: non-default node number relative to CNPIN (see record 11).
col: 6-15 PINT: non-default initial value of pressure head (1) or hydraulic head (m I3) of the
node number (n).
RECORD 21 Omit for FLOW simulation.
RECORD 22 Omit for FLOW simulation.
RECORD 23 Omit for FLOW simulation.
RECORD 24 FORMAT 15
Only if ITCND1 = 1 and PRZM is OFF.
col: 1-5 NTSNDH1: number of selected time values of pressure head or water flux for transient
simulation at first node.
RECORD 25 FORMAT 8E10.3
Only if ITCND1 = 1 and PRZM is OFF.
4-51
-------
col: 1-80 TMHV1: time values in relation to NTSNDH1 at the first node for pressure head or
water flux (t). Enter up to 8 values per line up to NTSNDH1 lines.
RECORD 26 FORMAT 8E10.3
Only if ITCND1 = 1 and PRZM is OFF.
col: 1-80 HVTM1: values of pressure head or water flux corresponding to TMHV1 at the first
node (length). Enter up to 8 values per line up to NTSNDH1 lines.
RECORD 27 Omit for FLOW simulation.
RECORD 28 FORMAT 15
Only if ITCNDN =1 and PRZM is OFF.
col: 1-5 NTSNDH2: number of selected time values of pressure head or water flux for transient
simulation at the last node.
RECORD 29 FORMAT 8E10.3
Only if ITCNDN = 1 and PRZM is OFF.
col: 1-80 TMHV2: time values in relation to NTSNDH2 at the last node for pressure head or
water flux (t). Enter up to 8 values per line up to NTSNDH2 lines.
RECORD 30 FORMAT 8E10.3
Only if ITCNDN = 1 and PRZM is OFF.
col: 1-80 HVTM2: values of pressure head or water flux corresponding to TMHV2 at the last
node (length). Enter up to 8 values per line up to NTSNDH2 lines.
RECORD 31 Omit for FLOW simulation.
RECORD 32 FORMAT 1615
OnlyifIOBSND = l.
col: 1-80 NDOBS: increasing sequential numbers of observation nodes. Enter up to 16 per line
up to NOBSND (see record 4).
RECORD 33 FORMAT A4
col: 1-4 OUTF: output time step for printing. Enter DAY for daily, MNTH for monthly,
YEAR for yearly.
4-52
-------
4.5.3 VADOFT Input Guide for Transport
RECORD 1 FORMAT A80
col: 1-80 TITLE: label for transport simulation title.
RECORD 2
col: 1-5
col: 6-10
col: 11-15
col: 16-20
col: 21-25
col: 26-30
col: 3 1-35
col: 36-40
col: 41-45
col: 46-50
FORMAT
NP:
NMAT:
NONU:
ITRANS:
IMODL:
KALL:
IMBAL:
INTSPC:
fflORIZ:
ICHAIN:
1015
total number of VADOFT nodal points.
total number of different porous materials (maximum of 5).
flag to indicate if initial condition is non-uniform. 1 = yes, 0 = no.
flag to indicate if running in transient or steady-state. Must be set to 1 if
PRZM is ON. 1 = transient, 0 = steady-state.
flag to indicate if running flow or transport model. 1 = flow, 0 = transport.
Set to 0 here.
time stepping index. 1 = backward difference, 0 = central difference. This
flag is automatically set to 1 for steady-state simulation.
flag to indicate if mass balance computation is required. 1 = yes, 0 = no.
flag to indicate initial conditions for head values. 1 = hydraulic head, 0 =
pressure head. Automatically set to 0 for transport.
flag to indicate if flow direction is horizontal. 1 = yes, 0 = no. Set to 0 if
PRZM is ON.
flag to indicate if daughter products are used. 1 = yes, 0 = no.
RECORD 3 Omit for transport simulation.
RECORD 4
col: 1-5
col: 6-10
col: 11-15
col: 16-20
col: 21-25
FORMAT 815
KPROP:
ITSGN:
ITMARK:
NSTEP:
NVPR:
flag to indicate relationship between relative permeability versus saturation
and pressure head versus saturation. Set to 0 for Transport simulation.
flag to indicate if output time values are to be model calculated. 1 = yes, 0
= no.
flag to indicate if output time values differ from computational time values
(see records 6 and 7). 1 = yes, 0 = no.
value of which time step to output nodal values from. When NSTEP = n,
then output is printed. Must be from 1 up to 31 (days).
value of which time step to output nodal velocities. When NVPR = n, then
output is printed. Must be from 1 up to 31 (days).
4-53
-------
col: 26-30 IOBSND: flag to indicate if values are printed at certain observation nodes. 1 = yes, 0
= no. NOTE: Echo level must be greater than or equal to 6 in
PRZM3.RUN file.
col: 31-35 NOBSND: number of observation node(s) to be printed. NOBSND must not be greater
than NP (see record 2). If IOBSND = 0 then set NOBSND = 0.
col: 36-40 IPRCHK: flag to indicate if detailed information is generated in the flow matrix. 1 =
yes, 0 = no.
RECORDS FORMAT 4E10.3
Only if ITRANS = 1 (see record 2).
col: 1-10 TIMA: initial time value (t). Suggested value if PRZM is ON: 0.0
col: 11-20 TIN: initial time step value(t). Suggested value if PRZM is ON: 1.0. Omit if
ITSGN = 0.
col: 21-30 TFAC: time step multiplier. Suggested value if PRZM is ON: 1.0. Omit if ITSGN
= 0.
col: 31-40 TMAX: maximum time step value allowed (t). Suggested value if PRZM is ON: 1.0
Omit if ITSGN = 0.
RECORD 6 FORMAT 8E10.3
Only if ITGSN = 0 (see record 4) and ITRANS = 1.
col: 1-80 TMVEC(I): time values corresponding to the number of time steps where I = 1...31 (t).
Input up to 8 values per line.
RECORD? FORMAT I5,2E10.3
Only if ITMARK = 1 and ITRANS = 1.
col: 1-5 ITMGEN: flag to indicate if backup file marker time values are used. 1 = yes, 0 = no.
col: 6-15 STMARK: starting marker time value (t). If PRZM and TRANSPORT are ON, set to
0.0.
col: 16-25 DTMARK: marker time value increment (t). If PRZM and TRANSPORT are ON, set
to 1.0.
RECORD 8 FORMAT 8E10.3
Only if ITRANS = 1, ITMARK = 1 and ITMGEN = 0.
col: 1-80 TMFOMT: output marker file time values (t) corresponding to TMVEC(I) (see record
6). Input up to 8 values per line.
RECORD 9 FORMAT 15
4-54
-------
col: 1-5
NLAYRG:
number of soil horizons to be discretized.
RECORD 10 FORMAT 3I5,E10.3
Repeat this record up to NLAYRG (see record 9).
col: 1-5
col: 6-10
col: 11-15
col: 16-25
ILAYR:
NELM:
IMATL:
THL:
horizon number in relation to NLAYRG.
number of finite elements in ILAYR.
porous material number related to NMAT (see record 2) in ILAYR.
thickness of the horizon (ILAYR).
RECORD 11 FORMAT E10.3,I5
Repeat for each NCHEM.
col: 1-10 CHINV: default initial values of concentration (m P) for nodes in the matrix.
col: 11-15 CNPIN: number of non-default nodes in the matrix related to the default initial
values (CHINV) if NONU = 1 (see record 2), else set to 0.
RECORD
col: 1-5
col: 6-10
col: 11-20
col: 21-30
col: 31-35
col: 36-40
col: 41-50
col: 51-60
12 FORMAT 2I5,2E10.3,2I5,2E10.3
IBTND1:
IBTNDN:
VALND1:
VALNDN:
ITCND1:
ITCNDN:
FLX1:
FLXN:
type of boundary condition for the first node. 1 = concentration, 0 = solute
flux.
type of boundary condition for the last node. 1 = concentration, 0 = solute
flux.
value of the concentration or solute flux at the first node. The value should
be positive for influx and negative for efflux. Set to 0.0 if PRZM is ON.
value of the concentration or solute flux at the last node. The value should
be positive for influx and negative for efflux. Set to 0.0 if fluid is exiting
the last node.
flag to indicate if the boundary condition at the first node is transient. 1 =
yes, 0 = no. Automatically set to 0 if PRZM is ON.
flag to indicate if the boundary condition at the last node is transient. 1 =
yes, 0 = no. Automatically set to 0 if PRZM is ON.
fluid flux injected into the first node (I31). Automatically set to 0.0 if
PRZM is ON.
fluid flux injected into the last node (I31). Automatically set to 0.0 if PRZM
is ON.
RECORD 13 FORMAT 2E10.3
Repeat records 13-14 in data sets up to NMAT.
4-55
-------
col: 1-10 CPROP1: longitudinal dispersivity of the material.
col: 11-20 CPROP2: effective porosity of the material.
RECORD 14 FORMAT 3(2E10.3)
col: variable CPROP3: retardation coefficient for the material. Enter this value up to NCHEM.
col: variable CPROP4: molecular diffusion for the material. Enter this value up to NCHEM.
RECORD 15 Omit for TRANSPORT
RECORD 16 Omit for TRANSPORT
RECORD 17 Omit for TRANSPORT
RECORD 18 Omit for TRANSPORT
RECORD 19 Omit for TRANSPORT
RECORD 20 FORMAT 5(I5,E10.3)
Only if NONU = 1. Repeat this record up to NCHEM.
NOTE: enter next two variables sequentially for every non-default node (CNPIN).
col: 1-5
col: 6-15
N:
PINT:
non-default node number relative to CNPIN (see record 11).
non-default initial value of concentration (m I3) of the node number (n).
RECORD 21 FORMAT I5,3E10.3
Repeat records 21-22 in data sets up to NMAT.
col: 1-5
col: 6-15
col: 16-25
col: 26-35
I:
VDFI:
SWDFI:
UWFI:
porous material number in relation to NMAT.
default value of darcy velocity.
default value of water saturation.
value of upstream weighting factor. Set to 0.0 if no upstream weighting is
desired.
RECORD 22 FORMAT I5,6E10.3
4-56
-------
col: 1-5 I: porous material number in relation to NMAT.
col: variable CLAMDI: decay coefficient of the material. Enter this value up to NCHEM.
col: variable CRACMP: transformation mass fraction of the material. Enter this value up to
NCHEM.
RECORD 23 FORMAT 215
col: 1-5
col: 6-10
NVREAD:
IVSTED:
flag to indicate if darcy velocities will be read from internal scratch files. If
PRZM and TRANSPORT are ON, but not FLOW, then NVREAD is set to
1. 1 =yes, 0 = no.
flag to indicate if the velocities are at steady-state. This implies steady-state
within each day, not the entire simulation. 1 = yes , 0 = no. If PRZM is ON
then IVSTED is set to 1.
RECORD 24 FORMAT 15
Only if ITCND1 = 1 and PRZM is OFF.
col: 1-5
NTSNDH1: number of selected time values of concentration or solute flux for transient
simulation at first node.
RECORD 25 FORMAT 8E10.3
Only if ITCND1 = 1 and PRZM is OFF.
col: 1-80
TMHV1: time values in relation to NTSNDH 1 at the first node for pressure head or
water flux (t). Enter up to 8 values per line up to NTSNDH1 lines.
RECORD 26 FORMAT 8E10.3
Only if ITCND1 = 1 and PRZM is OFF.
col: 1-80
HVTM1: values of concentration or solute flux corresponding to TMHV1 at the first
node (length). Enter up to 8 values per line up to NTSNDH1 lines.
RECORD 27 FORMAT 8E10.3
Only if IBTND1 = 0 and PRZM is OFF.
col: 1-80
QVTM1: volumetric fluxes corresponding to TMHV1 at the first node. Enter 8
values per line up to NTSNDH 1.
RECORD 28 FORMAT 15
Only if ITCNDN =1 and PRZM is OFF.
4-57
-------
col: 1-5 NTSNDH2: number of selected time values of concentration or solute flux for transient
simulation at the last node.
RECORD 29 FORMAT 8E10.3
Only if ITCNDN = 1 and PRZM is OFF.
col: 1-80 TMHV2: time values in relation to NTSNDH2 at the last node for concentration or
solute flux (t). Enter up to 8 values per line up to NTSNDH2 lines.
RECORD 30 FORMAT 8E10.3
Only if ITCNDN = 1 and PRZM is OFF.
col: 1-80 HVTM2: values of pressure head or water flux corresponding to TMHV2 at the last
node (length). Enter up to 8 values per line up to NTSNDH2 lines.
RECORD 31 FORMAT 8E10.3
Only if ITCNDN = 1 and PRZM is OFF.
col: 1-80 QVTM2: volumetric fluxes corresponding to TMHV2 at the last node. Enter 8 values
per line up to NTSNDH2.
RECORD 32 FORMAT 1615
OnlyifIOBSND = l.
col: 1-80 NDOBS: increasing sequential numbers of observation nodes. Enter up to 16 per line
up to NOBSND (see record 4).
RECORD 33 FORMAT A4
col: 1-4 OUTT: output time step for printing. Enter DAY for daily, MNTH for monthly,
YEAR for yearly.
4.6 MONTE CARLO INPUT FILE
PRZM-3 requires a Monte Carlo input file when MONTE CARLO is specified "ON" in the execution supervisor
file. The following is an example Monte Carlo input file.
4.6.1 Example MONTE CARLO Input File
MONTE CARLO TEST INPUT
***Number of runs and confidence level
100 90.0
***Monte Carlo inputs
KOC 1 1 800. 1400. 10.10000.
4-58
-------
FIELD CAPACITY
WILTING POINT
ORGANIC CARBON
FIELD CAPACITY
WILTING POINT
ORGANIC CARBON
DISPERSION 1 1
***Empirical Distribution Data
4
89.7 0.10
82.9 0.20
76.1 0.30
69.3 0.40
***Monte Carlo outputs
INFILTRATION 1 1
DISPERSION 1 1 1
END
* "Correlations
FIELD CAPACITY 1 1
FIELD CAPACITY 1 1
FIELD CAPACITY 2 1
FIELD CAPACITY 2 1
END
1
1
1
2
2
2
1
1 .316
1 .150
1 1.30
1 .288
1 .143
1 .110
50.0
.130
.066
.870
.110
.076
.070
15.0
CDF
CDF
WILTING POINT
ORGANIC CARBON
WILTING POINT
ORGANIC CARBON
0.05
0.03
0.01
0.04
0.03
0.01
10.0
0.60
0.30
5.00
.540
.030
1.00
90.0
WRITE
WRITE
0.757
1 0.609
0.757
1 0.170
NOTE: The above Monte Carlo input file contains lines beginning with three asterisks (***). These are considered
comment lines and will be ignored by the program.
4.6.2 MONTE CARLO Input Guide
RECORD 1 FORMAT A80
col: 1-80 TITLE: label for Monte Carlo simulation title.
RECORD 2 FORMAT I5,F10.0
col: 1-5 NRUN: number of Monte Carlo runs (1 to 1000).
col: 6-15 PALPH:
confidence level for percentile confidence bounds. Entered as a percent(%).
Default of 90.
RECORD 3 FORMAT A20,2I5,5F10.0
Repeat this record for number of inputs desired up to 50 records.
col: 1-20 PNAME: Monte Carlo input variable name (up to 20 characters). See Table 4.2.
col: 21-25 IND1: integer index for horizon, application, or material. See Table 4.2.
col: 26-30 INDZ: zone number (1 to 10).
4-59
-------
col: 31-40
col: 41-50
col: 51-60
col: 61-70
col: 71-80
VAR1:
VAR2:
VAR3:
VAR4:
VAR5:
the mean value of the distribution variable.
the standard deviation of the distribution variable.
the minimum value for the variable.
the maximum value for the variable.
flag to indicate the type of the variable distribution.
0 = constant,
1 = normal,
2 = log-normal,
3 = exponential,
4 = uniform,
5 = Johnson SU,
6 = Johnson SB,
7 = empirical, entered in record 4,
8 = triangular
RECORD 4 FORMAT A3
col: 1-3 ENDIT: enter "END" to indicate end of record 3
RECORDS FORMAT 15
only if VAR5 = 7 (see record 3).
col: 1-5
NDAT:
number of data pairs in empirical cumulative distribution (1 to 20).
RECORD 6 FORMAT 2F10.0
only if VAR5 = 7 (see record 3).
Note: repeat record 5 for every time VAR5 =7.
col: 1-10 DIST1: value of quantile for data pair I where I = 1....NDAT.
col: 11-20 DIST2: cumulative probability for data pair I where I = 1....NDAT.
RECORD 7 FORMAT A20,2I5,2(A20),I5
repeat this record for number of outputs desired up to 10 records.
col: 1-20
col: 21-25
col: 26-30
col: 3 1-50
col: 51-70
SNAME:
IND1:
INDZ:
SNAME2:
SNAME3:
Monte Carlo output variable name. See Table 4.2.
integer index for horizon, application, or material number. See Table 4.2.
zone number (1 to 10).
enter "CDF" to indicate if cumulative distributions are plotted.
enter "WRITE" to indicate if values are written as output for each Monte
Carlo run (NRUN).
4-60
-------
col: 71-75
NAVG:
length of the averaging period (in days) for output variables (1 to 5).
RECORDS FORMAT A3
col: 1-3 ENDIT: enter "END" to indicate end of output variables.
RECORD 9 FORMAT A20,2I5,A20,2I5,F10.0
onlyifVAR5=l,2, 5, or 6
Note: this record may be repeated up to half of the number of inputs in record 3 if correlation is desired.
variable (PNAME) in record 3 to be correlated.
integer index for horizon, application, or material number (1 to 10).
zone number (1 to 10).
variable (PNAME) in record 3 to be correlated with NAME1.
same as IND1 above.
same as INDZ above.
the value of the correlation coefficient for NAME1 and NAME2.
col: 1-20
col: 21-25
col: 26-30
col: 3 1-50
col: 51-55
col: 56-60
col: 61-70
NAME1:
IND1:
INDZ:
NAME2:
IND1:
INDZ:
CORR:
RECORD 10 FORMAT A3
col: 1-3 ENDIT:
enter "END" to indicate end of correlation inputs.
Table 4.2 Monte Carlo Input and Output Labels
Parameter
Monte Carlo Label
Index
Random PRZM Model Inputs
Soil Bulk Density (g/cm3)
Wilting Point (cnrVcm3)
Field Capacity (cnrVcm3)
Organic Carbon Content (%)
Application Mass, Chem 1 (kg/ha)
Application Mass, Chem 2(kg/ha)
Application Mass, Chem 3 (kg/ha)
Dispersion Coeff, Chem I(cm2/day)
Dispersion Coeff, Chem 2(cm2/day)
BULK DENSITY
WILTING POINT
FIELD CAPACITY
ORGANIC CARBON
APPLICATION 1
APPLICATION 2
APPLICATION 3
DISPERSION 1
DISPERSION 2
Horizon
Horizon
Horizon
Horizon
App.
App.
App.
Horizon
Horizon
4-61
-------
Table 4.2 Monte Carlo Input and Output Labels
Parameter Monte Carlo Label Index
Dispersion Coeff., Chem 3(cm2/day)
Decay Rate in Water, Chem l(days"')
Decay Rate in Water, Chem 2(days"')
Decay Rate in Water, Chem 3(days"')
Decay Rate in Vapor, Chem l(days"')
Decay Rate in Vapor, Chem 2(days"')
Decay Rate in Vapor, Chem 3(days"')
Decay Rate of Sorbed, Chem l(days"')
Decay Rate of Sorbed, Chem 2(days"')
Decay Rate of Sorbed, Chem 3(days"')
Henry's Constant, Chem 1
Henry's Constant, Chem 2
Henry's Constant, Chem 3
Irrigation Moisture Level (Fraction)
Application Year
Julian Application Year
Soil Water Content (cnrVcm3)
Total Soil Pesticide, Chem 1 (kg/ha)
Total Soil Pesticide, Chem 2(kg/ha)
Total Soil Pesticide, Chem 3 (kg/ha)
Infiltration Depth (cm)
Runoff Depth (cm)
Precipitation (cm)
Evapotranspiration
Flood or Furrow Irrigation Depth
Nitrate Application (kg/ha)
Ammonia Application (kg/ha)
Organic N Application (kg/ha)
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
APPYEAR
APPDAY
THETA
SOIL PESTICIDE 1
SOIL PESTICIDE 2
SOIL PESTICIDE 3
INFILTRATION
RUNOFF
PRECIPITATION
EVAPOTRANSPIRATION
IRREG DEPTH
NO3 APPLICATION
NH3 APPLICATION
ORGN APPLICATION
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
Horizon
App.
App.
Comp.
Comp.
Comp.
Comp.
Comp.
App.
App.
App.
4-62
-------
Table 4.2 Monte Carlo Input and Output Labels
Parameter
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
Random VADOFT Model Outputs
Total Water Flux
Advection Flux, Chemical 1
Advection Flux, Chemical 2
Advection 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
Monte Carlo Label Index
V-G POWER N
VADOFT DECAY 1
VADOFT DECAY 2
VADOFT DECAY 3
VAD DISPC 1
VAD DISPC 2
VAD DISPC 3
VAD RETARD 1
VAD RETARD 2
VAD RETARD 3
Material
Material
Material
Material
Material
Material
Material
Material
Material
Material
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 CONC 1
VAD CONC 2
Node
Node
! VAD CONC 3 ! Node
NOTE: Monte Carlo output of nitrogen constituents is achieved by using the existing VADOFT Model
Outputs, with Chemical 1 being equivalent to Ammonia, Chemical 2 being equivalent to Nitrate, and Chemical 3
being equivalent to Total Organics (see Section 4.5).
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SECTION 5
Parameter Estimation
This section describes estimation the values of those parameters identified in Section 2 to order to aid the user with
an aid in constructing the required records for EXESUP, PRZM, and VADOFT modules. For convenience to the
user, all variables (or parameters) from Section 4 are categorized by module name and alphabetized to ensure quick
reference.
5.1 EXESUP (Execution Supervisor)
The Execution Supervisor generally consists of labels and options; therefore, only parameters with obscure
definitions are defined.
ECHO - This value can be entered as an integer value (1-9) to control the amount of display sent to the screen and
output files. Also entering "ON" or "OFF", rather than an integer value, defaults the echo level to 5 (ON) or a
minimal display of 1 (OFF). For MONTE CARLO simulations, the echo level defaults to 1 automatically to prevent
excessive output.
ENDDATE - A valid calendar date that specifies the day at which all of the simulation processes stop. The user
must choose this date with respect to meteorological file dates to ensure adequate weather data exist for the total
elapsed time (STARTDATE to ENDDATE) of the simulation.
NUMBER OF CHEMICALS - This value (1-3) controls the number of pesticides being simulated. As many as
three separate chemicals are allowed per simulation. Whether these multiple chemicals have a parent-daughter
relationship depends upon transformation mass fractions entered in the PRZM and VADOFT input files.
PARENT OF 2 - This value implies the NUMBER OF CHEMICALS is greater than 1 and that a possible parent-
daughter relationship exists.
PARENT OF 3 - This value implies the NUMBER OF CHEMICALS is greater than 2 and that a possible parent-
daughter relationship exists.
PATH - A computer-specific drive and directory statement allowing any proceeding file names to be read or written
in this area.
STARTDATE - A valid calendar date that specifies the day at which all simulation processes begin. The user must
choose this date with respect to meteorological file dates to ensure adequate weather data exists from this date
forward to the ENDDATE.
TRACE - Primarily a tool for code debugging. By entering "ON" or "OFF", the user has the option to track
subroutine calling processes during a simulation.
WEIGHTS - Values entered that specify a fractional percent effluxes between PRZM and VADOFT zones. These
values are ordered into a matrix with a sum of 1.0 for each PRZM zone.
5.2 PRZM (Pesticide Root Zone Model)
AC - Maintenance coefficient of the co-metabolizing Xc population. This value specifies the amount of energy
required to maintain co-metabolizing (inhibited growth) microorganisms.
AD - Soil water drainage rate. This value is required if HSWZT = 1. It is an empirical constant, dependent on both
soil type and the number of compartments (DPN(I)/THKNS(I), where I = number of horizons) to be simulated.
Although there is limited experience using this option, three soils were evaluated for testing AD. The analysis was
5-1
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performed by comparing the storage of water in the soil profile following the infiltration output from SUMATRA-1
(van Genuchten 1978). Each soil had a profile depth of 125 cm. The amount of water moving out of the profile
changed by only 1 to 2% over the range of compartments (15-40) used in the simulation. Calibrating PRZM by
comparison was accomplished and estimates of AD calculated. Suggested values of AD for clay loam, loamy sand,
and sand as a function of the number of compartments are given in Figure 5.1.
AFIELD - This is the erosion area or plot size in hectares.
ALBEDO - Soil surface albedo. To simulate soil temperatures, ALBEDO values for each must be specified for each
month. As the surface condition changes, the ALBEDO values change accordingly. Values for some natural surface
conditions are provided in Table 5.21.
AM - Maintenance coefficient of the metabolizing Xm population. This parameter is used in biodegradation processes
to express the amount of energy required to maintain metabolizing (enhanced) microorganism growth rates.
AMXDR - The maximum active rooting depth of crops. PRZM requires this parameter in centimeters to estimate the
actual root depth from the land surface. For ranges of specific root depths, consult the USDA Handbook No. 283
(Usual Planting and Harvesting Dates), or the local Cooperative Extension Service. For general information, Table
5.9 shows the ranges for major crops.
5-2
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Number of compartments
Figure 5.1
Estimation of drainage rate AD (day"1) versus number of compartments.
ANETD - This value represents soil evaporation moisture loss during a fallow, dormant period. Evaporation is
initially assumed to occur in the top 10 cm of soil with remaining moisture losses occurring below 10 cm up to the
maximum rooting depth. Values for ANETD apply when there is no growing season, allowing a reduced level of
moisture loss through evaporation. For soils with limited drainage, set ANETD to 10 cm. Values for free drainage
soils are shown in Figure 5.2.
5-3
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II 10-15 cm
15-20 cm
20-30 cm
30-35 cm
Figure 5.2
Diagram for estimating soil evaporation loss.
APPEFF - Application efficiency of pesticide application (TAPP). TAPPis multiplied by APPEFF to calculate
effective rate of application.
AR - Maintenance coefficient of the non-sensitive Xr microbial population. This parameter specifies the energy
required to sustain non-sensitive (indifferent) microorganisms.
AS - Maintenance coefficient of the sensitive^ population. This parameter specifies the energy required to sustain
sensitive (lethally affected) microorganisms.
BD - Soil bulk density. This value is required in the basic chemical transport equations of PRZM, and is also used to
estimate moisture saturation values. Two methods are provided for estimating BD if site data are not available.
Method one requires percent sand, clay and organic matter. The procedure of Rawls (1983) is used to estimate BD
via Equation 5.1:
Method 1
BD =
100
%OM
OMBD
100 - %OM
MBD
(5.1)
where
BD = soil bulk density, g cm"3
5-4
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OM = organic matter content of the soil, (percent)
OMBD = organic matter bulk density of the soil, gem"3
MBD = mineral bulk density, g cm"3
Step 1. Locate the percent sand along bottom of Figure 5.3.
Step 2. Locate the percent clay along side of Figure 5.3.
Step 3. Locate the intersection point of the two values and read the mineral bulk density.
Step 4. Solve the Rawls equation for BD.
Method 2
Step 1. Use Table 5.29 to locate the textural class.
Step 2. Read mean BD for the general soil texture.
Table 5.30 shows distributional properties of BD information.
100-
O
70-
60-
50-
40-
30-
20-
10-
0 10 20 30 40 50 60 70 80 90 100
Sand (%)
Figure 5.3 Mineral bulk density (g cm"3).
Figure 5.3 provided by Dr. Walter J. Rawls, U.S. Department of Agriculture, Agricultural Research Service,
Beltsville Maryland.
BBT - Bottom boundary soil temperatures. BBT values for each month must be specified. The BBT soil temperature
for shallow core depths will vary significantly with time throughout the year. For deep cores, BBT will be relatively
constant. BBT can be estimated from NOAA data reports, Department of Commerce. Depending on core depth used
in the simulation, the average temperature of shallow groundwater, as shown in Figure 5.4. can be used to estimate
BBT.
5-5
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Average Temperature
of Shallow
Ground Water
~v n^ivKS/"' \*"
•^—A—•—•— *
•N. • A_V >•
TEMPERATURE
IN DEGREES F.
Figure 5.4 Average temperature of shallow groundwater.
BDFLAG - Flag to indicate bulk density calculation.
BIOFLG - Biodegradation flag. This flag allows the user to simulate the degradation of pesticides by
microorganisms in the root zone. Parameters associated with biodegradation are very specific and can be difficult to
obtain for specific soil conditions. As an alternative, estimates of biological parameters can be found in literature on
the kinetics of microbial growth in liquid culture.
BT - Bottom width of the furrows. BT will depend mostly upon the type of equipment used to dig the furrow
channels and the spacing between the furrows.
CAM - Chemical application model flag. This flag specifies how the pesticide is applied to soil or foliage. CAM = 1
should be used for surface-applied chemicals and results in a linearly decreasing concentration distribution in the soil
to a depth of 4 cm. CAM = 2 results in linear interception by the crop foliage based on the degree of crop canopy
development. CAM = 3 results in nonlinear interception by the crop foliage, i.e., the fraction of pesticide captured by
the foliage increases exponentially as the crop canopy matures. CAM = 4 is used for uniform incorporation into the
soil to a depth specified by the user. CAM = 5 results in linearly increasing incorporation to a user-defined depth.
CAM = 6 results in linearly decreasing incorporation to a depth specified by the user. CAM = 7 approximates T-
Band application to a user-defined incorporation depth. The variable DRFT is used to define the fraction of chemical
to be applied in the top 2 cm. The remainder of the chemical is uniformly incorporated between 2 cm and the user-
defined depth. CAM = 8 incorporates chemical directly to the depth specified by the user (modification of CAM 1).
CAM = 9 is a modification of CAM 2 allowing a user-specified depth (DEPI) of incorporation of chemical not
intercepted by the foliage. CAM = 10 is a modification of CAM 3 allowing a user-specified depth (DEPI) of
incorporation of chemical not intercepted by the foliage.
CFLAG - Conversion flag for initial pesticide levels. This flag is valid when ILP = 1. If CFLAG = 0, then initial
5-6
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pesticide levels (PESTR) are in units of mg kg"1. If CFL AG = 1, then initial pesticide levels (PESTR) are in units of
kg ha1. Leave CFLAG blank if ILP = 0.
CINTCP - The maximum rainfall interception storage of the crop (cm). This parameter estimates the amount of
rainfall that is intercepted by a fully developed plant canopy and retained on the plant surface. A range of 0.1 to 0.3
for a dense crop canopy is reported by USDA (Knisel 1980). Values for several major crops are provided in Table
5A_.
CM - Mineralizable carbon (mg g"1). This value represents the carbon substrate in the soil solution originating from a
fraction of the carbon compounds of the solid phase.
CN - Runoff curve numbers at antecedent moisture condition II. The interaction of soil hydrologic groups (Figure
5.5) and land use treatment (cover) is accounted-for by assigning a runoff curve number (CN) for the average soil
moisture condition (AMCII) to important soil-cover complexes for fallow, cropping, and residue parts of a growing
season. Tables 5.10 through 5.14 can be used to help estimate the correct curve numbers.
Figure 5.5
Diagram for estimating Soil Conservation Service soil hydrologic groups.
Figure 5.5 from (from EPA Field Guide for Scientific Support Activities Associated with Superfund Emergency
Response. U.S. EPA, Corvallis, OR)
Legend:
A: Well drained soils
B: Moderately well drained soils
C: Poorly drained soils
D: Very poorly drained soils
5-7
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CORED - The total depth of the soil core in centimeters. This value specifies the maximum depth in which PRZM
simulates vertical movement. CORED must be greater or equal to the active crop root depth (AMXDR). For
simulation using PRZM and VADOFT, the core depth (CORED) is usually equal to the root zone (AMXDR).
COVMAX - This is the maximum area! crop coverage(%). PRZM simulates crop ground cover up to the maximum
value, COVMAX, by linear interpolation between the emergence and maturity dates. As a crop grows, its ground
cover increases, thereby influencing the mass of pesticide that reaches the ground from an above-surface application
event. For most crops, the maximum coverage will be on the order of 80% to 100%.
DAIR - Vapor phase diffusion coefficient. When Henry's law constant (HENRYK) is greater than zero, vapor phase
diffusion is used to calculate the equilibrium between vapor and solution phases. Pick's first law defines the diffusion
coefficient as the proportionality between the chemical flux and its concentration spacial gradient (Nye 1979). In
soil, vapor phase diffusion occurs in the soil air space. Each chemical will have its own characteristic diffusion
coefficient depending on its molecular weight, molecular volume, and shape (Streile 1984). Jury et al. (1983b)
concluded that the diffusion coefficient will not show significant variations for different pesticides at a given
temperature; they recommended using a constant value of 0.43 m2 day"1 for all pesticides. This value is
recommended herein unless other chemical-specific data are available. Note that DAIR is entered in PRZM in cm2
day"1. The user should be sure to convert the above recommended value to the correct units.
DEPI - The depth(s) of pesticide incorporation. This variable is only required when CAM = 4, 5, 6, 7, 8, 9, or 10.
Typical depths are 5 to 10 centimeters. Representative values for several soil application methods are given in Table
5.15. If DEPI is set to zero, or to a depth less than that of the surface soil compartment, pesticide is incorporated to
the depth of that first compartment.
DGRAT1, DGRAT2 - Vapor phase degradation rate constant(s). DGRAT1 is used for single phase vapor
degradation or as the first phase of a bi-phase reaction. DGRAT2 is only used if simulating a second phase of a bi-
phase reaction. Pesticides are degraded by different mechanisms, and at different rates, depending upon whether they
are in the vapor, liquid or absorbed phase (Streile 1984). A lumped, first-order rate is assumed for DGRATx. In
general, a zero value of DGRATx is recommended, unless chemical-specific data are 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 to 2 mm of the soil surface may be very important. Field studies have shown that
photo chemical loss of organic chemicals can 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).
DISP - Dispersion of pesticide(s). 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 hydrodynamic dispersion. Molecular diffusion, Dm,
in soils will be lower than free-water diffusion, and has been estimated by Bresler (1973):
where
Z)w = molecular diffusion in free water, cm2 day"1
a = soil constants having a range of 0.00 1 to 0.005
b = soil constants (approximately 10)
6 = volumetric water content, cm3 cm"3
Hydrodynamic dispersion is more difficult to estimate because of its site-soil specificity and its apparent strong
dependence on water velocity. Most investigators have established an "effective" diffusion or dispersion coefficient
that combines molecular and hydrodynamic terms. Most notable among these is
D = 0.6 + 2.93vL11 (5.2)
where
D = "effective" dispersion coefficient, cm2 day"1
v = pore water velocity, cm day"1
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by Biggar and Nielsen (1976). Note in equation 5.2 that D is a time and depth varying function since v is both time
and depth-varying. The problem remains to estimate the assumed constant for DISP, the effective dispersion
coefficient. As noted earlier, the backward difference numerical scheme in PRZM produces numerical dispersion.
This dispersion is also related to the magnitude of the velocity term. Other variables that influence the truncation
error include the time and space steps. A sensitivity test was performed to examine the influence of the spatial step,
AX. Results are given in Figure 5.7. For these runs, the DISP parameter was set to 0.0. The influence of DISP
superimposed on the numerical dispersion created by the model at a AX value of 5.0 cm is shown in Figure 5.7. A
number of studies were performed to investigate the impact of model parameters other than DISP on the apparent
dispersion. From these, the following guidance is offered:
1) A spatial step or compartment size of 5.0 cm will mimic the 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
numerical dispersion.
5) The Biggar and Nielsen (1976) equation previously noted can be used to bound the values 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 data dispersion should be entered. Use of the MOC algorithm will result in
increased model execution time.
DKFLG2, DKDAY, DKMNTH, DKNUM - Flag to allow input of bi-phase degradation of chemicals and/or bi-
phase transformation of chemicals to daughter products. First-phase rates are initiated by a user-specified month and
day (DKMNTH, DKDAY). Second-phase rates are enacted after a set number of days as specified by the user
(DKNUM). See also chemical decay parameters DWRAT1, DSRAT1, DGRAT1, DWRAT2, DSRAT2, DGRAT2
and transformation parameters DKW112, DKW113, DKW123, DKS112, DKS113, DKS123, DKW212, DKW213,
DKW223, DKS212, DKS213, DKS223.
DKW112, DKW113, DKW123, DKS112, DKS113, DKS123 - Transformation rate from a parent chemical (1 or 2)
to a daughter chemical (2 and/or 3) for dissolved (DKW) and sorbed (DKS) phase residues. When multiple
chemicals are specified in PRZM3.RUN, either a parent/daughter relationship exists or the chemicals are
independent (chosen by the user). For a parent/daughter relationship, DKWxx or DKSxx is the mass fraction
degrading from parent x to daughter x. By setting DKWxx or DKSxx to 0.0, the user is specifying that the multiple
chemicals (xx) are independent parents.
DKW212, DKW213, DKW223, DKS212, DKS213, DKS223 - Same as above except that transformation rates
reflect the second phase of the bi-phase reaction (see DKFLG2).
DPN - Thickness of the compartments in the horizon. The DPN parameter allows the user to specify a different layer
depth for each soil horizon. The value of each DPN can be divided by each horizon thickness (THKNS) to obtain the
total number of compartments in PRZM. In general, a smaller DPN will generate more accurate results and provide
greater spatial resolution, but will also consume more CPU time. From a volatilization viewpoint, a smaller 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 and
simulation of surface-applied chemicals. Values of 0.1 cm are recommended for the initial 10 cm of the soil profile
and where volatilization is a major loss mechanism. DPN can be gradually increased with depth (i.e., 1.0 cm to 2.5
cm to 5.0 cm in the deeper horizons). For the deepest subsurface soil horizons, DPN values in the range of 5.0 to
30.0 cm can be used depending on the spatial resolution needed at lower depths.
5-9
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Pesticide concentration in total soil
(1CT7xgcm-3)
Depth
(cm)
AX= 1
D = 0.0
100-
150
Figure 5.7
Numerical dispersion associated with space step (Ax).
DRFT - Spray drift fraction used to calculate drift loading in the EXAMS transfer file. DRFT is also used for a T-
Band application (CAM = 7) to represent the fraction of the chemical application which will be incorporated into the
top 2 cm in which drift will be set to 0.0 for the EXAMS transfer file.
DSRAT1, DSRAT2 - Absorbed phase degradation rate constant(s). DSRAT2 is only used with bi-phase reactions
(see DKFLG2). See DWRAT1, DWRAT2 for guidance.
DT - Daylight hours for each month in relation to latitude. These values are used to calculate total potential ET if
daily pan evaporation data do not exist. Table 5.2 lists monthly daylight hours for the northern hemisphere.
DWRAT1, DWRAT2 - Solution phase degradation rate constant(s). DWRAT1 is used for single phase degradation
or as the first phase of a bi-phase reaction. This rate constant contributes to the disappearance of pesticide(s) through
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decay. DWRAT2 is only used if simulating a second phase in a bi-phase reaction. For most cases, the same values
should be used for solution (DWRATx) and adsorbed (DSRATx) phases for a specific depth. This will allow a
lumped first-order degradation rate constant. 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 5.19 and 5.20.
EMMISS - Infrared Emissivity. Most natural surfaces have an infrared emissivities 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 EMMISS for
some natural surfaces are given in Table 5.22.
EN - Manning's roughness coefficient. The well-known measure of the resistance of open channels to flow. Chow
(1959) suggests the values of EN range from 0.016 to 0.033 in excavated or dredged earth channels. EN values for
the furrows listed in Table 5.34 range from 0.01 to 0.048. Table 5.37 lists the values of EN suggested by the USDA
Soil Conservation Service for drainage ditches with various hydraulic radii (defined as the flow area divided by the
wetted perimeter).
ENPY - Enthalpy of vaporization. This parameter is used in the temperature correction equation for Henry's Law
constant. In a limited literature search, we could find only two pesticides for which ENPY values reported: 18.488
kcal mole"1 for lindane and 20.640 kcal mole"1 for napropamide (Streile 1984). Chemical-specific values are needed
for ENPY, but a value of 20 kcal mole"1 is a reasonable approximation.
ERFLAG - Erosion flag used to determine whether erosion losses are to be calculated during a simulation. The total
mass of pesticide loss by erosion is determined using the chemicals affinity for soil. The amount of pesticide loss by
these means is quite small for highly soluble pesticides. If the apparent distribution coefficient is less than or equal to
5.0, erosion can usually be neglected. For a compound having a greater distribution coefficient, erosion losses should
be estimated. To not simulate erosion set ERFLAG =0.
EXMFLG - Flag for reporting output into the EXAMS model file format. This flag allows a user to create an input
file for the EXAMS model through PRZM output if so desired. The EXAMS input file created has the name
PRZMSEXA.Dxx where xx is the year of PRZM simulation. ERFLAG must be set to 1.
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 variable and depend upon the crop, pesticide
properties, and application method. Smith and Carsel (1984) suggest that a value of 0.10 is suitable for most
pesticides.
FILTRA - The filtration parameter of initial foliage to soil distribution. This parameter relates to the equation for
partitioning the applied pesticide between foliage and the ground. Lassey (1982) suggests values in the range of 2.3
to 3.3 m2 kg"1. Miller (1979) suggested a value of 2.8 m2 kg"1 forpasture grasses. Most of the variation appears to be
due to the vegetation and not the aerosol. FILTRA only applies if CAM=3.
FLEACH - The leaching factor as a fraction of irrigation water depth. This 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 0.25 indicates that 25% extra water is added to meet the soil
water deficit.
FRMFLG - Flag for testing of ideal soil moisture conditions. This flag specifies whether to check preceding days
(WINDAY) after the target application date (APD) for moisture levels being ideal for pesticide application. If a
preceding date has adequate moisture levels and the target date does not, then the application date is changed
automatically. If the soil moisture after a specified number of days (WINDAY) fails to meet ideal conditions,
execution is halted.
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HENRYK - 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 (see Equation 6.17). It is quite common to express HENRYK as a
dimensionless number. Specific values for HENRYK for selected pesticides can be found in Table 5.18.
HF - Suction parameter. HF represents water movement due to suction in unsaturated soils, and has units of length
(meters). As with KS, HF has been correlated with SCS hydrologic soil groups (Brakensiek and Rawls 1983) and are
shown in Table 5.39.
HORIZN - Horizon number. The horizon number in relation to the total number of horizons (NHORIZ) must be
specified when inputing parameters for each of the PRZM horizons.
HSWZT - Flag to indicate soil water drainage calculation. The HSWZT flag indicates which drainage model is
invoked for simulating the movement of recharging water. Drainage model 1 (HSZWT = 0) is for freely draining
soils; drainage model 2 (HSZWT = 1) is for more poorly drained soils and requires the user to enter a soil water
drainage rate (AD).
HTMAX - Maximum canopy height of the crop at maturation in centimeters. Canopy height increases during crop
growth resulting in pesticide flux changes in the plant compartment. Users should have site-specific information on
HTMAX since it varies with climate, crop species, and environmental conditions. General ranges for different crops
are listed in Table 5.16.
ICNAH - This is the surface condition after crop harvest. Three values are allowed— fallow, cropping, and residue
(foliage remains on ground).
ICNCN - The crop number of the different crop. This value is in relation to NDC (number of different crops). This
allows separate crop parameters to be specified for each different crop in a simulation.
IDFLAG - Thermal conductivity and volumetric heat capacity flag. This flag allows a user to simulate soil
temperature profiles. If IDFLAG = 0, the user must enter thermal conductivity (THCOND) and volumetric heat
capacity (VHTCAP). If IDFLAG = 1, the model automatically simulates soil temperature profiles.
ILP - Initial pesticide levels flag. ILP should be set to 1 when evidence of pesticide is present before the simulation
start date (STARTDATE). See also CFLAG and PESTR.
INCROP - The crop number associated with the number of different crops (NDC). INCROP should be an increasing
integer from the first different crop to the last different crop grown.
INICRP - Initial crop flag. This flag indicates that before the simulation date occurs, a previous crop existed. The
IREG - SCS rainfall distribution region. For time period May 1 to September 15, IREG will be used in time of
concentration calculation of peak flow. For rest of year IREG=2. See ? for appropriate region.
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Rainfall
Distribution
Type I, IREG=1 Type IA, IREG=2 Type II, IREG=3 Type III, IREG=4
Figure 5.8
Approximate geographic boundaries for SCS rainfall distribution
Figure 5.8 from Soil Conservation Service {, 1986 #220}
IRFLAG - Flag to simulate irrigation. If irrigation is desired, the user has a choice of applying water for the whole
year or during a cropping period whenever a specified deficit exists.
IRTYP - Specifies the type of irrigation used. See Table 5.32.
IPEIND - Pan Factor flag. When this flag is set to 0, daily pan evaporation is read from the meteorological file.
When this flag is set to 1, pan data are calculated from daylight hours according to latitude. When this flag is set to
2, pan data are calculated through either the met file or daylight hours according to availability.
IPSCND - Flag indicating the disposition of pesticide remaining on foliage after harvest. This flag only applies if
CAM = 2 or 3. If IPSCND = 1, pesticide remaining on foliage is converted to surface application to the top soil
layer. If IPSCND = 2, remaining pesticide on foliage is completely removed after harvest. If IPSCND = 3, remaining
pesticide on foliage is retained as surface residue and continues to undergo decay.
ISCOND - The surface condition for the initial crop if applicable.
ITFLAG - Flag for soil temperature simulation. This flag allows a user to specify soil temperatures (BBT) for
shallow core depths. For deep cores (CORED), temperatures will remain relatively constant.
KC - Saturation constant of the co-metabolizing Xc population. See KSM and KCM for further explanation.
KCM - Saturation constant of the metabolizing Xm population with respect to carbon concentration. This value
represents an inhibition of growth rate in relation to soil carbon. Lower saturation constants result in decreased
carbon content consequently resulting in a lower growth rate.
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KE - Average enzyme content of the Xc population. This parameter specifies the amount of the enzyme necessary to
allow the population to break a pesticide down.
KD - Pesticide soil-water distribution coefficient. The user can enter KD directly if KDFLAG = 0 (see PCMC and
SOL) or allow the model to calculate KD automatically (KDFLAG =1).
KDFLAG - Flag to indicate soil/pesticide adsorption coefficient. A user may choose to enter KD by setting this flag
to 0 else the model automatically calculates the adsorption coefficient.
KIN - Inhibition constant of the Xi population. Evolution of the population requires a finite value controlling growth.
KIN accounts for natural variations found in metabolic activities affecting growth rates.
KL1 - Second-order death rate of the Xt population.
KL2 - Dissociation constant of the enzyme substrate complex.
KLDC - Death rate of the co-metabolizing Xc population.
KLDM - Death rate of the metabolizing Xm population.
KLDR - Death rate of the non-sensitive Xr population.
KLDS - Death rate of the sensitive^ population.
KR - Saturation constant of the non-sensitive Xr population. See KSM and KCM for further explanation.
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 for various unconsolidated materials are given in Table 5.38. KS has also been correlated with
SCS hydrologic soil groups (Brakensiek and Rawls 1983) shown in Table 5.39.
KSK - Carbon solubilization constant.
KSM - Saturation constant of the metabolizing Xm population with respect to pesticide concentration. This value
represents an inhibition of growth rate. Lower saturation constants result in lower bacteria rates, consequently
resulting in lower growth rates. Higher saturation constants increase bacteria growth, resulting in higher growth
rates.
MKS - Saturation constant of the sensitive Xs population. See KSM and KCM for further explanation.
MNGS - Mannings roughness coefficient for field. Up to 32 values may be entered per year. Value of 0.17
recommended as default value for typical row crop tillage. See Table 5.45 for values.
MOC - Flag to indicate method of characteristics calculation. The MOC algorithm is a two-pass solution technique
used to simulate advection and dispersion. The solution technique reduces truncation error. Because of the 24 hour
time step in PRZM, this method can lead to significant losses of mass under high velocity (greater than 120 cm per
day) conditions.
MUC - Specific growth rate of the co-metabolizingXc population.
NAPS - Number of pesticide applications. This is the total number of application dates specified during the
simulation. It is possible to apply up to three chemicals on the same application date, but for PRZM this still
constitutes one application.
5-15
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NCHEM - Number of chemicals in the simulation. PRZM and VADOFT allow up to three chemicals to be
specified. Using more than one chemical (i.e., NCHEM=3) indicates either a parent-daughter relationship or multiple
separate chemicals (determined by transformation mass fractions). NCHEM should be consistent with the number of
chemicals specified in the Execution Supervisor file.
NCPDS - Number of cropping periods. This is entered as a sum of all cropping dates from the beginning simulation
date to the ending simulation date.
NDC - The number of different crops in the simulation. This value determines how many separate crops will be
grown during a simulation. If only one type of crop is grown (ex: corn), then NDC = 1. This includes the crop type
of the initial crop also (INICRP).
NHORIZ - Total number of horizons. PRZM allows the user to specify how many horizons are to be simulated
within the core depth (CORED). The horizon should serve as a distinct morphologic zone generally described by
layers (i.e., surface, subsurface, substratum) according to soil pedon descriptions or soil interpretation records, if
available.
NPLOTS - Number of time series plots. PRZM can report several output variables (PLNAME) to a time series file.
NPLOTS specifies how many are written in a single simulation.
OC - Percent of soil organic carbon. OC is conventionally related to soil organic matter as %OC = %OM/1.724.
Guidance on estimating OM is found in Table 5.31. 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 (Rao and Wagenet 1985) and Nielsen et
al. (1973) have reported that these values are often normally distributed. Carsel et al. (1988) noted that organic
carbon is weakly correlated with field capacity and wilting point water content with the correlation coefficients
ranging from 0. 1 to 0.74. Strength of correlation decreases with depth, as shown previously in Table 5.28.
PCDEPL - Fraction of available water capacity where irrigation is triggered. The moisture level where irrigation is
required is defined by the user as a 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
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 between 0.45 and 0.55. PRZM will accept values of PCDEPL
between 0.0 and 0.90; if the input value is outside this range, PRZM sets PCDEPL to 0.5 and issues a warning
message.
PCMC - Flag for estimating distribution coefficients (KD). PRZM allows the user to estimate the KD by
multiplying the organic carbon partition coefficient (Koc) derived from the solubility (SOL). PCMC is the flag for
using one of four different models for estimating Koc. The four models are:
PCMC1 Log^oc = (-0.54 x Log SOL) + 0.44
Koc = organic carbon distribution coefficient
where SOL = water solubility, mole fraction
PCMC2 LogA:oc = 3.64 - (0.55 x Log SOL)
where SOL = water solubility, mg L"1
PCMC3 LogA:oc = 4.40 - (0.557 x Log SOL)
where SOL = water solubility, micromoles L"1
PCMC4 Koc = SOL
where SOL = K, dimensionless
5-16
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PESTR - Initial pesticide(s) levels. PESTR levels are required if evidence of pesticide(s) is present before the
simulation start date (ILP =1). PESTR is entered in units specified by CFLAG for each compartment in each
horizon and for all chemicals (NCHEM).
PFAC - The pan factor is a dimensionless number used to convert daily pan evaporation to daily potential
evapotranspiration (ET). Pan factor general ranges are between 0.60 to 0.80. See Figure 5.9 for specific regions of
the United States.
Figure 5.9 Pan evaporation correction factors
Figure 5.9 (from U.S. Weather Bureau)
PLDKRT - Foliage pesticide first-order decay rate. Pesticide degradation rates on plant leaf surfaces is represented
as a first-order process controlled by PLDKRT. The user must be consistent in specifying PLDKRT and PLVKRT
rates. If PLDKRT includes volatilization processes, then PLVKRT should be zero. If PLVKRT is non-zero then
PLDKRT should include all attenuation processes except volatilization. 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:
• D Organochlorine 5.0 ±4.6
• D Organophosphorus 3.0 ±2.7
• D Carbamate 2.4 ±2.0
• D Pyrethroid 5.3 ±3.6
These mean half-lives correspond to degradation rates of 0.14, 0.23, 0.29, and 0.13 day"1, respectively. These are in
reasonable agreement with values in Table 5.17.
5-17
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PLNAME - Name of plotting variable. When creating a time series plot, PLNAME specifies the variable in Table
4.1 for which that output data are written.
PLVKRT - Foliage pesticide first-order volatilization rate. Pesticide volatilization from plant leaf surfaces is
represented as a first-order process controlled by PLVKRT. For organophosphate insecticides, Stamper et al. (1979)
has shown that the disappearance rate from leaf surfaces can be estimated by a first-order kinetic approach. Similar
observations of 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 5.17 shows disappearance rates
for selected pesticides on plant foliage. These rates are applicable to estimation of PLVKRT since the overall decay
rate (PLDKRT) includes loss associated with volatilization.
PSTNAM - Pesticide(s) name. This is a label used to identify pesticide output. Pesticide names should be placed in
order of chemical 1, chemical 2, and chemical 3 if applicable (NCHEM=3)
PTRN12, PTRN13, PTRN23 - lumped foliar transformation rate (days"1)
Q - Average carbon content of the Xt population.
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
5.35 lists the maximum non-erosion flow rates for various furrow channel slopes.
QFAC - Factor for rate increase when temperature increases by 10°C. Set to 2 for doubling of microbial degradation
rate
RATEAP - Maximum sprinkler application rate. RATEAP is used to limit sprinkler applications to volumes that the
sprinkler system is capable of delivering per time step. This value is defined as a maximum depth (cm) of water
delivered per hour. Table 5.33 lists sprinkler rates.
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 0.03 (Todd 1970). A few representative slopes are listed in Table 5.34.
SFAC - The snowmelt factor is a used to calculate snowmelt rates in relation to temperature. Snow is considered any
precipitation that falls when the air temperature is below 0°C. In areas where climatology prevents snow fall, SFAC
should be set to 0.0. Typical ranges for SFAC are provided in Table 5.1.
SLP - Slope of hydraulic flow path.
SOL - Pesticide water solubility. By specifying a water solubility (SOL) for pesticides, the model can calculate the
Koc and KD by using one of the models specified for PCMC. SOL must be entered according to the PCMC model
selected. Table 5.19 provides pertinent values for selected pesticides for obtaining SOL. Methods are also available
to calculate Koc (SOL if PCMC=4). The octanol-water distribution coefficient can be used for calculating Koc with a
relationship to organic carbon (OC). Karickhoff et al. (1979) proposed a relationship between KOVI and Koc given by
= 1.0log(KoJ - 0.21
where
Km = octanol-water distribution coefficient (cm3 g"1)
Koc = organic carbon distribution coefficient (cm3 g"1)
Selected pesticides having properties suitable for use with the octanol-water distribution model by Karickhoff are
provided in Table 5.20.
5-18
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SPACT - Special action variable. During the course of a PRZM simulation, there may be a change in chemical
behavior or agricultural management practices. SPACT allows the user to specify a special action variable from
Section 4 and change its value at a user-specified time (SADAY,SAMON,SAYR). Also the SPACT variable
'SNAPSHOT' can allow a user to output soil profile pesticide concentrations at a user-specified time during the
simulation.
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 depth (CORED) is deep. Lower horizons in the core should be assigned values corresponding
approximately to the bottom boundary temperature (BBT).
TAPP - Target application rate for pesticide(s). For each pesticide and each application date, the amount of pesticide
is entered in kg-active ingredient ha"1. Typical rates are included on the product's registration label. Actual rates used
in the model are reduced by an application efficiency (APPEFF).
TBASE - temperature at which microbial degradation was determined
THCOND,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 flag to zero, then the thermal conductivity
(THCOND) and heat capacity (VHTCAP) must be specified. Representative values for some soil types are given in
Table 5.24. Note that the value of THCOND is entered in PRZM in units of cal cm"1 "C"1 day"1; therefore, the values
in Table 5.24 should be multiplied by 86,400. If IDFLAG = 1, then THCOND and VHTCAP are calculated by the
model from %Sand, %Clay, and %OC, based on the method described in de Vries (1963).
THEFC,THEWP - Field capacity and wilting point. Often these soil-water properties have been characterized and
can be found from soil data bases. Where such data are not available, one of three following estimation methods can
be used. Method one requires the textural properties (percent sand, silt, and clay), organic matter content (%), and
bulk density (g cm"3) 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 soil texture is known.
Method 1 - (Rawls 1983)
Qx = a + bx%Sand + c*%Clay + d*%Organic_Matter + e*Bulk_Density
where
6X = water retention cm3 cm"3 for a given matric potential (field capacity = -0.33 bar and wilting point =
-15.0 bar)
a-e = regression coefficients
Bulk_Density measured in g cm"3
Step 1. From Table 5.23 find the matric potential for field capacity and wilting point.
Step 2. For each matric potential, find the regression coefficient (a-e) that are in the Rawls and Brakensiek
equation.
Step 3. For any given soil solve the equation for the -0.33 and -15.0 potential.
Method 2
Use Figure 5.10 for estimating the field capacity and Figure 5.11 for estimating the wilting point, given the
percent sand and clay.
Method:
Use Table 5.25 to locate the textural class of the soil of choice. After locating the textural class, read the
5-19
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mean field capacity and wilting point potentials (cm cm ), to the right of the textural class.
Guidance for estimating distributional properties for THEFC and THEWP is given in Tables 5.26 and 5.27.
0.5% Organic matter
0.0% Porosity change
0 10 20 30 40 50 60 70 80 90 100
0.10
Sand <0/-
Figure 5.10 1/3-bar soil moisture by volume.
Figure 5.10 (provided by Dr. Walter J. Rawls, U.S. Department of Agriculture, Agricultural Research Service,
Beltsville, Maryland).
5-20
-------
0.40
0.35
0.5% Organic matter
0.0% Porosity change
30 40 50 60
Sand (%)
Figure 5.11 15-bar soil moisture by volume.
Figure 5.11 provided by Dr. Walter J. Rawls, U.S. Department of Agriculture, Agricultural Research Service,
Beltsville, Maryland.
THETO - Initial water content of the soil. This value provides the model with a starting calculation for moisture. If
site-specific data are not available, field capacity value is recommended for THETO.
THEWP - See THEFC for guidance.
THFLAG - Flag to indicate field capacity and wilting point calculation.
THKNS - Thickness of the horizon. This value is the depth (cm) of the horizon specified (HORIZN) in relation to
core depth (CORED).
TR - Storm duration peak runoff rate. TR is entered as an average, although in reality this parameter changes
seasonally as well as with each storm type. This value represents the time period when storms occur producing peak
runoff over a short duration. Table 5.8 provides estimates for TR for selected locations in the U.S. for both mean
summer and annual time periods while Figure 5.12 provides regionalized values for different areas in the United
States.
5-21
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Figure 5.12 Representative regional mean storm duration (hours) values for the U.S.
Mean Storm duration (hours)
Zone
Period
Mean (Annual)
C.V. (Annual)
Mean (Summer)
C.V. (Summer)
1
5.8
1.05
4.4
1.14
2
5.9
1.05
4.2
1.09
o
J
6.2
1.22
4.9
1.33
4
7.3
1.17
5.2
1.29
5
4.0
1.07
3.2
1.08
6
3.6
1.02
2.6
1.01
7
20.0
1.23
11.4
1.20
8
4.5
0.92
2.8
0.80
9
4.4
1.20
3.1
1.14
Mean - mean storm duration (hours)
C.V. - Coefficient of variation (hours)
Source: (Woodward-Clyde Consultants 1988a, b)
5-22
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checks for ideal moisture conditions. For this value to be valid, FRMFLG must equal 1. WIND AY should be less
than the difference of the target date (APD) to the next chronological target date.
WFMAX - The maximum dry foliar weight. This value is used only if a user desires to have the model estimate the
distribution between plants and the soil by an exponential function when a pesticide is applied. WFMAX of the plant
above ground (kg m"2) is the exponent used in the exponential foliar pesticide application model. Estimates of
WFMAX for several crops are given in Table 5.14.
X2 - Length of the furrow. X2 will depend upon the size of the field and the local topography. Table 5.35 lists
maximum furrow lengths for various slope textures, irrigation application depths, and furrow slopes.
XFRAC - Location of the furrow. XFRAC is a fraction of furrow length (X2) that specifies where PRZM infiltration
calculations are performed. To use the average depth of furrow infiltration depths, set XFRAC to -1.
Yl - Metabolizing (Xm) microbial population.
Y2 - Co-metabolizing (JQ microbial population.
Y3 - Sensitive (JQ microbial population.
Y4 - Non-sensitive (JQ microbial population.
YC - True growth yield of the co-metabolizing Xc population.
YCM - True growth yield of the metabolizingXm population with respect to carbon concentration.
YR - True growth yield of the non-sensitive Xr population.
YS - True growth yield of the sensitive^ population.
YSM - True growth yield of the metabolizingXm population with respect to pesticide concentration.
ZRS - Side slope of the furrows. This parameter is defined as the slope of the channel walls, horizontal
distance/vertical distance. ZRS will depend upon the cohesiveness of soils and the type of equipment used to dig the
furrows. Table 5.36 lists the suitable side slopes for different types of soils, with values ranging from 1.5 to 3.0 for
unconsolidated materials.
ZWIND - Height of wind speed measuring instrument. The wind speed anemometer is usually fixed at 10 meters (30
feet) above the ground surface. This height may differ at some weather stations such as at a class A station where the
anemometer may be attached to the evaporation pan. The correct value can be obtained from the meteorological data
reports for the station whose data are in the simulation.
5.2.1 Nitrogen Calibration Procedures and Parameter Estimation
Application of the nitrogen simulation capabilities in the PRZM-3 code focuses on the model's ability to reproduce
target levels of nitrogen storages and fluxes, along with available site-specific data; this approach necessitates model
calibration. Calibration of soil nitrogen models involves defining model inputs, estimating the nitrogen balance
expected for the soil/plant system being modeled, and adjusting model parameters to mimic the expected or observed
nitrogen balance, including both soil and plant storages and fluxes. Most of the soil nitrogen modeling work to date,
and the majority of the currently available literature on nitrogen balances, is based on studies of agricultural systems,
with a significantly smaller portion directed to forested systems. Table 5.45. from Frissel (1978, pp. 203-243) shows
examples of nitrogen balances developed from selected field studies for cultivated crops, grasslands, and a few
5-24
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forested ecosystems. This presentation of a nitrogen balance shows the N inputs or additions, such as
fertilizer/manure applications, N fixation, irrigation, and atmospheric deposition (described as 'sediments added' in
Table 5.45): the N removals, including crop harvest, denitrification, volatilization, and leaching and erosion/runoff;
and recycling process within the soil, such as mineralization, plant uptake, and residue return. It is important to note
that the largest components of most agricultural systems are the N additions (e.g. N applications and fixation) and
resulting plant uptake and removal. Thus, accurately defining these two components is key to modeling soil nitrogen
processes for these systems.
As noted previously, the soil N process algorithms that were integrated into PRZM-3 are the same as those included
in the most recent version of the HSPF model. Consequently, the best current source of relevant nitrogen parameter
information are prior and recent HSPF applications. Donigian (Donigian 1996) has compiled an (unpublished)
bibliography of HSPF-related documents that identifies nitrogen modeling applications in Iowa, Nebraska,
Tennessee, Georgia, Pennsylvania, Maryland, Virginia, and the general Chesapeake Bay Region. Bicknell et al.
(1996) describe the most recent modifications to the HSPF nitrogen modeling algorithms, which are included in
PRZM-3, and their application to forested watersheds in Maryland and Virginia. Donigian et al. (1995) describe the
nitrogen plant uptake formulations in HSPF, included in PRZM-3, along with parameter estimation and calibration
guidance for agricultural systems. Expected nitrogen balances for a variety of land uses, including cropland, hay,
pasture, forest, and urban, are presented by Donigian and Chinnaswamy (Donigian and Chinnaswamy 1996),along
with a discussion of their use in watershed modeling. The original report on the PRZM-3 nitrogen algorithms
(Imhoff et al. 1995) includes its application for nitrogen leaching from septic systems, along with an expected
nitrogen balance and initial parameters for an application site in Colorado; the example PRZM nitrogen input in
Section 4.5.1 includes the parameters used for the Colorado septic system application.
Users of the nitrogen capabilities in PRZM-3 should consult the above sources of parameter information, along with
the parameter definitions (Section 4.5.2) and the example input (Section 4.5.1), as part of the nitrogen parameter
estimation and calibration process, especially when site-specific data is not available for the application site.
5.3 VADOFT Input Parameters
Input data for variably saturated flow simulations include the following:
(l)System Geometry
• D Soil column dimensions (L)
(2)Porous Medium Properties
• D Saturated hydraulic conductivity, Ks (LT1)
• D Specific storage, Ss (I/1)
• D Effective porosity, cj>
(3 Constitutive Relationships for Variably Saturated Flow
• D Tabulated data of Km versus Sw, or values of parameters of analytic expressions for Km versus Sw
• D Tabulated data of Sw versus i|;, or values of parameters of analytic expressions for Sw versus i|;.
(4)Initial and Boundary Conditions
• D Prescribed values of pressure head, fy (L)
• D Prescribed values of nodal fluid flux (infiltration rate), / (LT"1)
Input data for the transport model include the following:
(l)System Geometry
• D Soil column dimensions (L)
(2)Porous Medium Properties
5-25
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• D Longitudinal dispersivity «L, (L)
• D Molecular diffusion coefficients, D* (L2T"')
• D Effective porosity, cj>
(3)Properties of Solute Species
• D Decay coefficient, A (T1)
• D Retardation coefficient, R
(4)Darcy Velocity, V (LT1)
(5)Water Saturation, Sw
(6)Initial and Boundary Conditions
• D Prescribed value of concentration, c0 (ML"3)
• D Prescribed value of solute flux, Vc0 (ML"2 T"1)
Guidance for certain of these parameters is given in the following paragraphs.
Saturated Hydraulic Conductivity - represents the rate at which a porous medium can transmit water under
saturated conditions. Table 5.40 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 that 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.
Soil-Water Characteristic Data - The user is allowed two options: either to input these data as a set of paired
functions (water saturation [Sw] versus relative conductivity [K^] and pressure head [i|;] versus 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.
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 United States and tabulated
corresponding parameter values by texture. These are shown in Table 5.41. The required parameters are a, p, and y
of the van Genuchten model. 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 (i|;a) and the residual water phase
saturation (•S1""). The air entry pressure head is normally taken to be zero. Values of the residual water phase
saturation are given in Table 5.42 along with their respective CVs. Table 5.43 from Carsel and Parrish (1988) shows
the types of probability density functions used to fit the sample distributions of saturated hydraulic conductivity,
residual water phase saturation, and van Genuchten parameters a and p. Note that y is related to p by the relationship
Y = 1 - 1/P.
In addition, Table 5.44 gives the correlations between these parameters by soil textural classification.
Specific Storage - For unsaturated zone flow, set the specific storage to 0.
Effective Porosity - Mean values of saturated water content (6S) and residual water content (6r) shown in Table 5.42
can be used to estimate effective porosity. The saturation water content (6S) is equal to the total porosity of the soil.
The effective porosity can be roughly approximated as the difference of 6S and 6r in Table 5.43. CVs for soil texture
categories are also shown in Table 5.43. 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 having units of
cm2 day"1.) 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
5-26
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as one-tenth of the distance of the flow path or:
a = 0.1 Xy
where
xv = the thickness of the vadose zone.
Molecular Diffusion - See the discussion in Section 5.2 for the variable DISP.
Pesticide Decay Coefficients - See the discussion in Section 5.2 for pesticide decay in PRZM.
Retardation Factors - In VADOFT, in contrast to PRZM, the user inputs the retardation factor R instead of the
distribution coefficient, Kd (cm3 g"1). The retardation factor is defined for saturated conditions in the input:
R = 1
9,
and is adjusted internally for values of 6 < 6S. In the above equation, p is the soil bulk density (g cm"3) and 6^ is the
saturation water content (cm3 cm"3). 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 - 6s)
The coefficient of variation (CV) of the retardation factor, R, can be computed knowing the uncertainties in Kd, p and
6^ (Taylor 1982). The fractional uncertainties may be added to determine an upper bound error on R (CVmiu),
- CV(Kd) + CV(p)
or are combined as a root mean square for independent random errors,
cvrms = 100 • .
100
+ ( CV(Kd)
100
100
The uncertainty in the value of Kd will depend upon whether it is measured, calculated as the product of Koc and the
percent organic carbon, and whether the Koc is calculated from a surrogate parameter such as octanol water partition
coefficient (K^) or solubility (s). Directly measured values would obviously have lower CVs. Assuming that Kd is
calculated from a measured soluble concentration, then it is possible that the CV would be on the order of 60 to
130% (Jury 1985). For Kd derived from KM or solubility, the CV could be on the order of 1000%.
Table 5.1 Typical Values of Snowmelt (SFAC) as Related to Forest Cover
Snowmelt Factor, (cm °C1 day ')
FOREST COVER MINI
Coniferous - quite dense 0.08 -
Mixed forest - coniferous,deciduous, open 0. 10 -
Predominantly deciduous forest 0.14 -
Open areas 0.20 -
MUM MAXIMUM
0.12 0.20-0.32
0.16 0.32-0.40
0.20 0.40 - 0.52
0.36 0.52-0.80
Source: (Anderson 1978)
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Table 5.2 Mean Duration (Hours) of Sunlight for Latitudes in the Northern and Southern Hemispheres8
Latitude North*
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Days In
Month
31
28
31
30
31
30
31
31
30
31
30
31
00
12.1
12.1
12.1
12.1
12.1
12.1
12.1
12.1
12.1
12.1
12.1
12.1
10
11.6
11.7
12.0
12.4
12.5
12.7
12.5
12.4
12.2
11.8
11.8
11.5
20
11.0
11.6
12.0
12.6
13.1
13.3
13.2
12.9
12.2
11.6
11.2
10.9
30
10.5
11.2
12.0
13.0
13.7
14.0
13.9
13.2
12.4
11.4
10.7
10.2
35
10.1
10.9
12.0
13.1
14.1
14.5
14.3
13.5
12.4
11.3
10.3
9.9
40
9.8
10.7
12.0
13.3
14.4
15.0
14.7
13.7
12.5
11.1
10.0
9.4
45
9.3
10.4
11.8
13.6
14.9
15.5
15.2
14.1
12.5
10.9
9.5
8.7
50
8.6
10.0
11.8
13.8
15.4
16.3
15.9
14.5
12.7
10.7
9.1
8.1
a -(Criddle 1958)
* - Values for the southern hemisphere were assumed equal to the northern hemisphere lagged by six months,
e.g., the duration for January in the northern hemisphere is the same as July in the southern hemisphere.
Table 5.3 Indications of the General Magnitude of the Soil credibility 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
< 0.5%
0.05
0.16
0.42
0.12
0.24
0.44
0.27
0.35
2%
0.03
0.14
0.36
0.10
0.20
0.38
0.24
0.30
4%
0.02
0.10
0.28
0.08
0.16
0.30
0.19
0.24
5-28
-------
Table 5.3 Indications of the General Magnitude of the Soil credibility Factor, Ka
Organic Matter Content
Texture Class
Very Fine Sandy Loam
Loam
Silt Loam
Silt
Sandy Clay Loam
Clay Loam
Silty Clay Loam
Sandy Clay
Silty Clay
Clay
< 0.5%
0.47
0.38
0.48
0.60
0.27
0.28
0.37
0.14
0.25
2%
0.41
0.34
0.42
0.52
0.25
0.25
0.32
0.13
0.23
0.13- 0.29
4%
0.33
0.29
0.33
0.42
0.21
0.21
0.26
0.12
0.19
a 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. (Stewart et al. 1975).
Table 5.4 Interception Storage for Major Crops
Crop
Corn
Soybeans
Wheat
Oats
Barley
Potatoes
Peanuts
Cotton
Tobacco
Density
Heavy
Moderate
Light
Light
Light
Light
Light
Moderate
Moderate
CINTCP (cm)
0.25-0.30
0.20-0.25
0.0 -0.15
0.0 -0.15
0.0 -0.15
0.0 -0.15
0.0 -0.15
0.20-0.25
0.20-0.25
5-29
-------
Table 5.6 Values of Support-practice Factor, Pa
Land Slope (percent)
Practice
1.1-2.0
2.1-7.0
7.1-12.0
12.1-18.0
18.1-24.0
(Factor P)
Contour Strip cropping (Psc)b
R-R-M-M
R-W-M-M
R-R-W-M
R-W
R-O
Contour listing or ridge
planting (Pcl)
Contour terracing
(Pt)c
No support practice
0.30
0.30
0.45
0.52
0.60
0.30
d 0.6 /n
1.0
0.25
0.25
0.38
0.44
0.50
0.25
0.5 /n
1.0
0.30
0.30
0.45
0.52
0.60
0.30
0.6 /n
1.0
0.40
0.40
0.60
0.70
0.80
0.40
0.8 /n
1.0
0.45
0.45
0.68
0.90
0.90
0.45
0.9 /n
1.0
a (Stewart et al. 1975)
bR = rowcrop, W = fall-seeded grain, O = 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 Pt values are multiplied by 0.2.
dn = number of approximately equal-length intervals into which the field slope is divided by the terraces. Tillage
operations must be parallel to the terraces.
Table 5.7 Generalized Values of the Cover and Management Factor, C, in the 37 States East of the Rocky
Mountainsa'b
Line Crop, Rotation, and Management c No.
Base value: continuous fallow, tilled up and down
Productivity Level d
High Mod.
C Value
1.00
Corn
1.00
1 C, RdR, fall TP, conv (1) 0.54 0.62
2 C, RdR, spring TP, conv (1) .50 .59
5-31
-------
Table 5.7 Generalized Values of the Cover and Management Factor, C, in the 37 States East of the Rocky
Mountains a>b
Line Crop, Rotation, and Management c No.
Base value: continuous fallow, tilled up and down
3
4
£
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Cottoi
27
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% rc(l)
C(silage),W we seeding, no-till pi in c-k(l)
C(RdL)-W(RdL, spring TP) (2)
C, fall shred stalks, chisel pi, 40-30% rc(l)
C-C-C-W-M, RdL, TP for C, disk for W (6)
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 2nd C (4)
C, no-till pi in c-k wheat, 90-70% re (1)
C-C-C-W-M-M, no-till pi 2nd & 3rd C (6)
C-W-M, RdL, TP for C, disk for W (3)
C-C-W-M-M, RdL, no-till pi 2nd 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)
ie
Cot, conv (Western Plains) (1)
Productivity Level d
High Mod.
C Value
1.00
.42
.40
.38
.35
.31
.24
.20
.20
.19
.17
.16
.14
.12
.11
.087
.076
.068
.062
.061
.055
.051
.039
.032
.017
0.42
1.00
.52
.49
.48
.44
.35
.30
.24
.28
.26
.23
.24
.20
.17
.18
.14
.13
.11
.14
.11
.095
.094
.074
.061
.053
0.49
5-32
-------
Table 5.7 Generalized Values of the Cover and Management Factor, C, in the 37 States East of the Rocky
Mountains a>b
Line Crop, Rotation, and Management c No.
Base value: continuous fallow, tilled up and down
28
Mead
29
30
31
Sorgh
32
33
Soybe
34
35
36
37
Whea
38
39
40
41
42
43
44
45
46
47
48
Cot, conv (South) (1)
DW
Grass & Legume mix
Alfalfa, lespedeza or Sericia
Sweet clover
um, grain (Western Plains)6
RdL, spring TP, conv (1)
No-till pi in shredded 70-50% re
anse
B, RdL, spring TP, conv (1)
C-B, TP annually, conv (2)
B, no-till pi
C-B, no-till pi, fall shred C stalks (2)
t
W-F, fall TP after W (2)
W-F, stubble mulch, 500 Ibs re (2)
W-F, stubble mulch, 1000 Ibs re (2)
Spring W, RdL, Sept TP, conv (N&S Dak) (1)
Winter W, RdL, Aug TP, conv (Kansas) (1)
Spring W, stubble mulch, 750 Ibs re (1)
Spring W, stubble mulch, 1250 Ibs re (1)
Winter W, stubble mulch, 750 Ibs re (1)
Winter W, stubble mulch, 1250 Ibs re (1)
W-M, conv (2)
W-M-M, conv (3)
Productivity Level d
High Mod.
C Value
1.00 1.00
.34 .40
.004 0.01
.020
.025
0.43 0.53
.11 .18
0.48 0.54
.43 .51
.22 .28
.18 .22
0.38
.32
.21
.23
.19
.15
.12
.11
.10
.054
.026
5-33
-------
Table 5.7 Generalized Values of the Cover and Management Factor, C, in the 37 States East of the Rocky
Mountainsa>b
Line Crop, Rotation, and Managementc No.
Productivity Leveld
High Mod.
C Value
Base value: continuous fallow, tilled up and down
1.00
1.00
49
W-M-M-M, conv (4)
! .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.
b (Stewart et al. 1975)
°Numbers in parentheses indicate number of years in the rotation cycle. No. (1) designates a continuous one-crop
system.
dHigh 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.
e Grain sorghum, soybeans, or cotton may be substituted for corn in lines 12, 14, 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 - plantconv - conventional
W - wheat
cot - cotton
we - cover
Ibs re - pounds of crop residue per acre remaining on surface after new crop seeding
% re - percentage
7-50% 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
Table 5.8 Mean Storm Duration* (TR) Values for Selected Cities
Location
Great Lakes
Champaign-UrbanaJL
Chicago, IL
Storm Duration (hrs)
Mean
Annual
6.1
5.7
Summer
(June-Sept)
4.6
4.5
Location
Southeast
Greensboro, NC
Columbia, SC
Storm Duration (hrs)
Mean
Annual
5.0
4.5
Summer
(June-Sept)
3.6
3.5
5-34
-------
Table 5.8 Mean Storm Duration* (TR) Values for Selected Cities
Location
Davenport, IA
Detroit, MI
Louisville, KY
Minneapolis, MN
Stubenville, OH
Toledo, OH
Zanesville, OH
Lansing, MI (30 Yr)
Lansing, MI (21 Yr)
Lower Mississippi Valley
Memphis, TN
New Orleans, LA
Shreveport, LA (17)
Lake Charles, LA
Texas and Southwest
Abilene, TX
Austin, TX
Brownsville, TX
Dallas, TX
El Paso, TX
Waco, TX
Phoenix, AZ
Northwest
Storm Duration (hrs)
Mean
Annual
6.6
4.4
6.7
6.0
7.0
5.0
6.1
5.6
6.2
6.9
6.9
7.8
7.7
4.2
4.0
3.5
4.2
3.3
4.2
3.2
Summer
(June-Sept)
5.3
3.1
4.5
4.5
5.9
3.7
4.3
4.2
5.1
4.7
5.0
5.3
5.9
3.3
3.3
1 Q
Z.O
3.2
2.6
3.3
2.4
Location
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 (2)
Upton LI, NY
Storm Duration (hrs)
Mean
Annual
8.0
7.2
7.6
3.6
4.3
40
.O
9.1
8.0
4.5
7.8
4.3
5.9
5.8
6.1
5.4
7.0
6.9
6.7
5.6
6.3
Summer
(June-Sept)
6.2
5.0
6.6
3.1
3.2
o *•>
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
4.0
4.6
5-35
-------
Table 5.8 Mean Storm Duration* (TR) Values for Selected Cities
Location
Portland, OR (25yr)
Portland, OR (lOyr)
Eugene, OR
Seattle, WA
Storm Duration (hrs)
Mean
Annual
5.4
15.5
29.2
21.5
Summer
(June-Sept)
4.5
9.4
15.0
12.7
Location
Wantagh, LI, NY
(2)
Long Island, NY
Washington, DC
Baltimore, MD
Storm Duration (hrs)
Mean
Annual
5.6
4.2
5.9
6.0
Summer
(June-Sept)
4.0
3.4
4.1
4.2
Source: (U.S. Environmental Protection Agency 1986)
* These values may be misleading in arid regions or regions with pronounced seasonal rainfall patterns.
5-36
-------
Table 5.9 Agronomic Data for Major Agricultural Crops in the United States
Crop
Corn
Soybeans
Cotton
Wheat
Potatoes
Peanuts
Tobacco
Representative
States of Major
States
Production3
IA, IL, IN, ME,
OH
IA, IL,
IN,MS,OH
TX, MS, CA,
AZ, AR
KS, OK, CA,
ND, MT, WA,
MN, ID
Long Island
NY, ME, ID,
WA, CA, OR
GA, TX, AL,
NC, VA
NC, SC, TN, KY,
VA
Planting Window,
Month, Day (Days
from (Julian Day)b
Planting)
April 25 (115)
to June 15 (166)
May 1(121) to June
25 (176)
March 1 (60) to
May25(145)[TXto
June 20 (171)]
Aug. 15 (227) to
Oct. 25 (298)
[WA to Nov. 20
(324),CAtoFeb. 15
(046)]
April 1 (091) to May
1 (121)
April 5 (095) to June
5 (156)
[TX Mar. 3 1(090) to
July 20 (201)]
April 5 (095) to June
20(171)
Crop Emergency
(Days from
Planting)
5-15
5-15
5-15
5-15
5-15
5-15
5-15
Planted in Field
as Seedling
Crop Maturity
Month, Day
(Julian Day)"
110-130
110-130
110-130
110-130
200-225
150-170
150-175
120-150
Harvest
Window,Yield/
Acre 1977-1979°
Sept. 25 (268
to Dec. 10 (344)
Sept. 15 (258)
to Dec. 10 (344)
Sept. 1 (244) to
Jan. 15 (015)
[TX Aug. 1(213)
to Dec. 20 (354)]
June 15 (166) to
Sept. 20 (263)
Sept. 1 (244) to
Oct. 1 (274)
Aug. 10 (222) to
Dec. 15 (349)
July 1 (182) to
Oct. 1 (274)
Average
Rooting
Depth (cm)
HObu
35 bu
670 Ibs
40 bu
335 cwt
2550 Ibs
2000 Ibs
Range of
Active Plant
60-120
30-60
30-90
15-30
15-45
30-60
30-60
5-37
-------
Table 5.9
Crop
Grain
Sorghum
Agronomic Data for Major Agricultural
Representative
States of Major
States
Production3
TX, KS, NE
Planting Window,
Month, Day (Days
from (Julian Day)b
Planting)
TX Mar. 1 (060) to
July 1 (182)
KS,NEMay5(125)
to July 1 (182)
Crops in the United States
Crop Emergency Crop Maturity Harvest
(Days from Month, Day Windov
Planting) (Julian Day)" Acre 19
Average
r,Yield/ Rooting Range of
77-1979c Depth (cm) Active Plant
5-15 120-150 TX July 1(182) 62 bu 15-30
to Nov. 20 (324) KS, NE Sept. 20
(263) to Dec. 1
(335)
a(Bay and Bellinghausen 1979)
b(Burkhead
(Kirkbride
etal. 1972)
1980)
5-38
-------
Table 5.10 Runoff Curve Numbers for Hydrologic Soil-cover Complexes8 (Antecedent
Moisture Condition II, and Ia = 0.2 S)
Land Use
Fallow
Row crops
Small grain
Close-seeded
legumesb or
rotation meadow
Pasture or range
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
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
Hydraulic Soil Group
A
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
B
86
81
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
C
91
88
85
84
82
80
78
84
83
83
81
79
78
85
81
83
78
80
76
86
79
74
81
75
70
D
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
5-39
-------
Table 5.10 Runoff Curve Numbers for Hydrologic Soil-cover Complexes8 (Antecedent
Moisture Condition II, and Ia = 0.2 S)
Land Use
Cover
Treatment or Practice Hydrologic Condition
Hydraulic Soil Group
A
B C
D
Meadow
Woods
Farmsteads
Roads
(dirt)c
(hard surface)0
Good
Poor
Fair
Good
—
30
45
36
25
59
72
74
58
66
60
55
74
82
84
71
77
73
70
82
87
90
78
83
79
77
86
89
92
a (Mockus 1972)
b Close-drilled or broadcast.
0 Including right-of-way.
Table 5.11 Method for Converting Crop Yields to Residue3
Crop"
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
5-40
-------
Table 5.11
Method for Converting Crop Yields to Residue3
Crop"
Straw/Grain Ratio
Bushel Weight (Ibs)
a Crop residue = (straw/grain ratio) x (bushel weight in Ib/bu) x (crop yield in bu/acre).
b (Knisel 1980)
Table 5.12
Residue Remaining from Tillage Operations"
Tillageb Operation
Residue Remaining(%)
Chisel Plow
65
Rod weeder
90
Light disk
70
Heavy disk
30
Moldboard plow
10
Till plant
80
Fluted coulter
90
V Sweep
90
a Crop residue remaining = (crop residue from Table 5.11) x (tillage factor(s).
b (Knisel 1980)
Table 5.13 Reduction in Runoff Curve Numbers Caused by Conservation Tillage and Residue
Management"
Large Residue Cropb
(Ib/acre)
0
400
700
1,100
1,500
2,000
2,500
6,200
Medium Residue Cropb
(Ib/acre)
0
150
300
450
700
950
1,200
3,500
Surface Covered by
Residue (%)
0
10
19
28
37
46
55
90
Reductive in Curve
Number" (%)
0
0
2
4
6
8
10
10
5-41
-------
a(Knisel 1980)
b Large-residue crop (corn).
0 Medium residue crop (wheat, oats, barley, rye, sorghum, soybeans).
d Percent reduction in curve numbers can be interpolated linearly. Only apply 0 to l/i 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.(lbs/Bu)
56
56
60
60
Straw/Grain
Ratio
1.0
1.0
1.5
1.7
Units
Conversion
Factor
1.1214 x 10'4
1.1214 x IQ-4
1.1214 x 10'4
1.1214 x IQ-4
WFMAX
1.38
0.78
0.59
0.72
a 10-year average
Table 5.15 Pesticide Soil Application Methods and Distribution
Method of Application
Broadcast
Disked-in
Chisel-plowed
Surface banded
Banded -
incorporated
Common Procedure
Spread as dry granules or spray over
the whole surface
Disking after broadcast application
Chisel plowing after broadcast
Spread as dry granules or a spray over
a fraction of the row
Spread as dry granules or a spray over
a fraction of the row and incorporated
in planting operation
Distribution
Remains on the soil
surface
Assume uniform
distribution to tillage
depth
Assume linear distribution
to tillage depth
Remains on soil surface
Assume uniform
distribution to depth of
incorporation
CAM
4
lor 6
4
4
7
Table 5.16
Maximum Canopy Height at Crop Maturation
Crop
Barley
Height (cm)
I 20 - 50
Reference
5-42
-------
Grain Sorghum
Alfalfa
Corn
Potatoes
Soybeans
Sugarcane
References:
A. (Szeicz et al. 1969)
B. (Smith etal. 1978)
90-110
10-50
80 - 300
30-60
90-110
100 - 400
B
A
A
A
B
A
Table 5.17 Degradation Rate Constants of Selected Pesticides on FOLIAGE"
Class
Organochlorine
Organophosphate
Carbamate
Pyrethroid
Pyridine
Benzoic acid
Group
Fast
(aldrin, dieldrin, ethylan, heptachlor,
lindane, methoxychlor).
Slow
(chlordane, DDT, endrin, toxaphene).
Fast
(acephate, 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).
Fast
(carbofuran)
Slow
(carbaryl)
(permethrin)
(pichloram)
(dicamba)
Decay Rate (days *)
0.231-0.1386
0.1195-0.0510
0.2772-0.3013
0.1925-0.0541
0.630
0.1260-0.0855
0.0196
0.0866
0.0745
a(Knisel 1980)
5-43
-------
Table 5.18 Estimated Values of Henry's Constant for Selected Pesticides
Compound
Alachlor
Aldrin
Anthracene
Atrazine
Bentazon
Bromacil
Bury late
Carbaryl
Carbofuran
Chlorpyrifos
Chrysene
Cyanazine
DDT
Diazinon
Dicamba
Dieldrin
Diuron
Endrin
EPTC
Ethoprophos
Fenitrothion
Fonofos
Heptachlor
Lindane
Linuron
Malathion
Methomyl
Methyl Parathion
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
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
5-44
-------
Table 5.18 Estimated Values of Henry's Constant for Selected Pesticides
Compound Henry's Constant (dimensionless) References
Metolachlor
Metribuzin
Monuron
Napropamide
Parathion
Permethrin
Picloram
Prometryne
Simazine
Terbufos
Toxaphene
Triallate
Trichlorfon
Trifluralin
2,4-D (acid)
2,4,5-T (acid)
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
A
A
C
C
C
A
B
C
A
A
A
C
B
A
A
B
References:
A. (Donigian et al. 1986)
B. (Spencer etal. 1984)
C. (Juryetal. 1984)
D. (Schnoor et al. 1987)
5-45
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Actellic
Alachlor
Antor
Aresin
Balan
Basalin
Baygon
Baygon Meb
Bayleton
Baythion
Baythion C
Betasan
Bromophos
Common Name
pirimiphosmethyl
alachlor
diethatyl ethyl
monol inuron
benefin
fluchloralin
propoxur
plifenate
triadimefon
phoxim
chlorphoxim
bensulide
bromophos
Solubility
in water
(20 - 25°C)
(mg/1)
5
220
105
735
70
0.7
2000
50
70
7
1.7
25
40
Reference
a
b
a
a
b
b
a
a
a
b
a
c
a
Mode of
Action
Insecticide
X
X
X
X
X
X
X
Herbicide
X
X
X
X
X
Fungicide
X
Nematocide
Acaricide
Molecular weight
6?
274
269.9
311.5
214.6
335.3
355.7
209
336.2
267.45
298
301.45
397.5
366
Reference
b
b
c
b
b
b
b
d
d
b
d
b
b
Partitioning
Model
PCMCl
(mole fraction)
3.28xlO'7
1.47xlO'5
6.07X10'6
6.17X10'6
3.76X10'6
3.55X10'8
1.72X10'4
2.68X10'6
4.72 xlO'6
4.23 xlO'7
1.02X10'7
1.13X10'6
1.97X10'6
11
^S
K»
5
220
105
735
70
0.7
2000
50
70
7
1.7
25
40
^ '•a
P
3 o
L*J
18
815
337
3430
209
2
9600
149
262
24
5.6
63
109
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0384
.0099-.0173
0.3349
0.0169
.0198
Reference
f
g
f
f
f
5-46
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Butachlor
Bux
Carbamult
Carbyne
Chlordimeform
Chlorfenvinphos
Chloro IPC
Chlorpyrifos
Co-Ral
Counter
DNOC
Dichlorprop
Dimetan
Common Name
butachlor
bufencarb
promecarb
barban
chlordimeform
Chlorfenvinphos
chlorpropham
chlorpyrifos
coumaphos
terbufos
DNOC
dichlorprop
dimetan
Solubility
in water
(20 - 25°C)
(mg/1)
23
1
92
11
250
110
108
2
1.5
15
130
350
30000
Reference
a
b
a
c
a
a
b
b
b
a
a
a
b
Insecticide
Mode of
Action
Herbicide
X
X!
X;
X
X
X!
1 x
xi
X!
X!
X
X
X
X
Fungicide
X
Nematocide
X
Acaricide
X
Molecular weight (g)
312
221.3
207
258.1
196.7
359.5
213.7
350.5
362.8
288
198.1
235
197.3
Reference
e
b
d
b
b
b
b
b
b
d
b
b
b
Partitioning
Model
PCMCl
(mole fraction)
1.33X10'6
8.14X10'8
S.OlxlO'6
7.70X10'7
2.30X10'5
5.51 xlO'6
9.11X1Q-6
1.03 xlO'7
7.45 xlO'8
9.38X10'7
LlSxlO'5
2.68X10'5
2.74X10'3
11
^S
K»
23
1.0
92
11
250
110
108
2.0
1.5
15
130
350
30000
^ '•a
P
3 o
L*J
74
5
444
43
1270
306
505
6
4
52
656
1490
152000
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0347
.0055
.0058-.00267
.0578-.0866
Reference
g
f
g
f
5-47
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Dimethoate
Dinitramine
Dinoseb
Dazomet
Devrinol
Elocron
Evik
Far-Go
Fongarid
Fornothion
Fuji-one
Gardona
Gesaran
Common Name
dimethoate
dinitroamine
dinoseb
dazomet
napropamide
dioxacarb
ametryn
triallate
furalaxyl
fornothion
isoprothiolane
tetrachlorvinphos
methoprotryne
Solubility
in water
(20 - 25°C)
(mg/1)
X=25000
1
52
1200
73
6000
185
4
230
2600
48
11
320
Reference
a
a
c
b
a
a
a
b
a
a
a
b
a
Mode of
Action
Insecticide
X
X
X
X
X
Herbicide
X
X
X
X
X
X
Fungicide
X
X
X
X
Nematocide
X
Acaricide
X
Molecular weight
6?
229.1
322.2
240.2
162.3
271.36
223
227
304.6
301
257
290
366
271
Reference
b
c
b
b
b
b
b
d
b
d
b
b
Partitioning
Model
PCMCl
(mole fraction)
1.97xlQ-3
5.60 xlO'8
3.90X10'6
1.33X10'4
4.85 xlO'6
4.85 xlO'4
1.47 xlO'5
2.37X10'7
1.38xlQ-5
1.82 xlO'4
2.98X10'6
5.42X10'7
O 1 "J v 1 n-5
z.ljxiu
II
25000
1
52
1200
73
6000
185
4
230
2600
48
11
320
^ '•a
P
3 o
109000
3
217
7390
269
26900
815
13
764
10100
166
30
1180
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0057
.0193-.0856
.0462-.0231
.3465-.0248
.0231-.0077
.0231-.0713
.1732-1386
Reference
f
g
f
g
g
5-48
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Goal
Guthion
Hoelon
Imidan
IPC
Linuron
Malathion
Mecoprop
MEMC
Merpelan AZ
Mesoranil
Mesurol
Methomyl
Common Name
oxyfluorfen
azinphos-methyl
diclofop methyl
phosmet
propham
linuron
malathion
mecoprop
MEMC
isocarbamid
aziprotryn
mercaptodimethur
methomyl
Solubility
in water
(20 - 25°C)
(mg/1)
0.1
29
30
25
250
75
145
620
50000
13000
75
2.7xl07
58000
Reference
c
a
a
b
b
a
a
a
a
a
b
a
a
Mode of
Action
Insecticide
X
X
X
X
X
Herbicide
X
X
X
X
X
X
X
Fungicide
X
Nematocide
Acaricide
Molecular weight
6?
361.7
317.3
340.9
317.3
179.2
249.1
330.4
214.6
295
185
225
225.3
162.2
Reference
c
b
d
b
b
b
b
b
d
d
b
b
b
Partitioning
Model
PCMCl
(mole fraction)
4.98xlQ-9
1.65X10'5
1.59X10'6
1.42 xlO'6
2.51X10'5
5.42X10'6
7.91 xlO'6
5.21X10'5
3.05xlQ-3
1.27X1Q-3
6.01 xlO'6
2.16
6.44 xlO'3
I|
0.1
29
30
25
250
75
145
620
50000
13000
75
2.7xl07
58000
^ '•a
P
3 o
0.3
91
88
79
1400
300
439
2890
169000
70300
333
1.2xl08
358000
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0231-.0173
.0533-.0014
.0347-.0116
.0280-.0039
2.91-.4152
Reference
c
f
g
f
f
5-49
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Methoxychlor
Meth-Parathion
Nemacur
Norton
Orthene
Oxamyl
Parathion
Patoran
Phorate
Propachlor
Propanil
Prowl
Common Name
methoxychlor
methyl Parathion
fenamiphos
ethofumesate
acephate
oxamyl
parathion
metabromuron
phorate
propachlor
propanil
pendimethalin
Solubility
in water
(20 - 25°C)
(mg/1)
0.1
X = 57.5
400
110
6.5xl05
2.8xl05
24
330
50
580
500
0.5
Reference
b
a
a
a
b
a
b
a
b
c
c
c
Mode of
Action
Insecticide
X
X
X
X
X
X
Herbicide
X
X
X
X
X
Fungicide
X
Nematocide
X
Acaricide
X
Molecular weight (g)
345.7
263.2
300
286
183.2
219
291.3
258.9
260.4
211.7
218
281.3
Reference
b
b
b
d
b
b
b
d
b
b
b
c
Partitioning
Model
PCMCl
(mole fraction)
5.21x10
3.94X10'6
2.38xlO'5
6.93 xlO'6
0.06
6.5xl05
0.023
2.8xl05
1.48X10'6
2.30X10'5
3.46X10'6
4.94X10'5
4.13X10'5
3.20X10'8
11
^s
K»
0.1
57.5
400
110
650000
280000
24
330
50
580
500
0.5
^ '•a
P
3 o
L*J
0.3
219
1320
385
355000
0
128000
0
82
1280
192
2740
2290
1 Q
1.0
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0046-.0033
.2207
.0354-.0646
.2962-.0046
.0234
.0363-.0040
.0231-.0139
.693-.231
Reference
f
f
f
f
f
f
g
g
5-50
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Chemical
Prynachlor
Quinalphos
Ronstar
Sancap
Semeron
Supracide
Tachigareu
Temik
Tolban
Trifluralin
Tsumacide
Tordon
Toxaphene
Common Name
prynachlor
quinalphos
oxadiazon
dipropetryn
desmetryn
methidathion
hymexazol
aldicarb
profluralin
trifluralin
MTMC
picloram
toxaphene
Solubility
in water
(20 - 25°C)
(mg/1)
500
22
0.7
16
580
240
85000
6000
0.1
24
2600
430
3
Reference
a
a
b
a
a
a
a
a
a
b
a
c
b
Mode of
Action
Insecticide
X
X
X
X
X
Herbicide
X
X
X
X
X
X
X
Fungicide
X
Nematocide
X
Acaricide
X
X
X
Molecular weight (g)
221.7
298
345.23
255.4
213
302
99.05
190.3
347.3
335.3
165
241.5
413
Reference
b
d
b
b
b
b
b
b
c
b
d
b
b
Partitioning
Model
PCMCl
(mole fraction)
4.06 xlO'5
1.33xlO-6
3.65xlO'8
1.13X10'6
4.91 xlO'5
1.43X10'5
0.02
5.68xlO'4
5.19xlO'9
1.29X10'6
2.84X10'4
3.21 xlO'5
1.31X10'7
11
^S
K»
500
22
0.7
16
580
240
85000
6000
0.1
24
2600
430
3
^ '•a
P
3 o
L*J
2260
74
2.0
63
2720
795
858000
31500
0.3
71
15800
1780
7
Degradation Rate
Constant in Soil
Root Zone
(days'1)
.0495-.0108
.0322-.0116
.0049
.0956-.0026
.0354-.0019
.0046
Reference
f
f
f
f
f
f
5-51
-------
Table 5.19 Physical Characteristics of Selected Pesticides for Use in Development of Partition Coefficients (Using Water Solubility) and Reported
Degradation Rate Constants in Soil Root Zone
Mode of
Action
Partitioning
Model
Chemical
Common Name
Solubility
in water
(20 - 25°C)
(mg/1)
ft.
Oq"
O h^
>—' ^
^o
o
Degradation Rate
Constant in Soil
Root Zone
(days'1)
Trichlorfon
! trichlorfon
120000
jajxj
257.35 id ! 8.40xlO'3 | 120000 j 466000 j
Calculations for the Karickhoff and Chiou partitioning equations are:
PCMC1:
millimole solubility (MMS) = (ppm solubility) / [molecular weight (g)]
molar solubility (MS) = MMS /103
mole fraction = MS / [55.5 (molar cone, water)]
Chiou:
millimole solubility (MMS) = (ppm solubility) / [molecular weight (g)]
urn/1 = MMS x 106 /103
References:
a Farm Chemicals Handbook (Meister Publishing Company 1981)
b Pesticide Manual (Martin 1968)
c Herbicide Handbook (Mullison 1979)
d Calculations based on information from Farm Chemicals Handbook (Meister Publishing Company 1981).
e (Beroza et al. 1981)
f (Nash 1980)
g (Stewart et al. 1975)
5-52
-------
Table 5.20 Octanol Water Distribution Coefficients (Log K^) and Soil Degradation Rate Constants for
Selected Chemicals
Chemical Name
Alachlor
Aldicarb
Altosid
Atrazine
Benomyl
Bifenox
Bromacil
Captan
Carbaryl
Carbofuran
Chloramben
Chlordane
Chloroacetic Acid
Chloropropham
Chloropyrifos
Cyanazine
Dalapon
Dialifor
Diazinon
Dicamba
Dichlobenil
Dichlorofenthion
2,4,-Dichlorophenoxy-acetic Acid
Dichloropropene
Dicofol
Dinoseb
Diuron
Log#J-
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
Degradation Rate
Constant (days *)
0.0384
0.0322-0.0116
0.0149 - 0.0063
0.1486-0.0023
0.1420
0.1196-0.0768
0.0768 - 0.0079
0.0020 - 0.0007
0.0058 - 0.00267
0.0495
0.0462-0.0231
0.0330-0.0067
0.2140-0.0197
0.0116-0.0039
0.0693-0.0231
0.0462-0.0231
0.0035-0.0014
Reference
A
A
A
A
A
A
A
D
C
D
A
A
D
D
D
5-53
-------
Table 5.20 Octanol Water Distribution Coefficients (Log K^) and Soil Degradation Rate Constants for
Selected Chemicals
Chemical Name
Endrin
Fenitrothion
Fluometuron
Linuron
Malathion
Methomyl
Methoxychlor
Methyl Parathion
Monolinuron
Monuron
MSMA
Nitrofen
Parathion
Permethrin
Phorate
Phosalone
Phosmet
Picloram
Propachlor
Propanil
Propazine
Propoxur
Ronnel
Simazine
Terbacil
Terbufos
Toxaphene
Log#J-
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
0 ££
Z.oo
2.92
4.30
2.83
0.30
1.61
2.03
2.94
1.45
4.88
1.94
1.89
2.22
3.27
Degradation Rate
Constant (days *)
0.1155-0.0578
0.0231
0.0280 - 0.0039
02.91-0.4152
0.0046 - 0.0033
0.2207
0.0046 - 0.0020
0.2961-0.0046
0.0396
0.0363 - 0.0040
0.0354-0.0019
0.0231-0.0139
0.693 -0.231
0.0035-0.0017
0.0539 - 0074
0.0046
Reference
A
C
A
A
A
A
D
A
E
A
A
D
D
D
A
E
5-54
-------
Table
5.20 Octanol Water Distribution Coefficients (Log K^)
Selected Chemicals
Chemical Name Log Kj>
Trifluralin 4.75
Zineb
A
B
C
D
E
1.78
(Nash 1980)
(Smith 1981)
(Mullison 1979)
(Stewart et al. 1975)
(Smith and Carsel 1984)
and Soil Degradation Rate Constants for
Degradation Rate Reference
Constant (days *)
0.0956 - 0.0026 A
0.0512 A
Table 5.21 Albedo Factors of Natural Surfaces for
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
Solar Radiation*
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
5-55
-------
Table 5.21 Albedo Factors of Natural Surfaces for
Surface
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°C to 30°C)a
Water (60°C)a
Water (85°C)a
References:
(Brutsaert 1982)
(van Wijk 1963)
a angle of solar incidence.
Solar Radiation*
Reflectivity
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
Table 5.22 Emissivity Values for Natural Surfaces at I
Surface
Sand (dry-wet)
Mineral Soil (dry-wet)
Peat (dry -wet)
Firs
Tree Vegetation
Grassy Vegetation
Leaves
formal Temperatures*
Emissivity
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
5-56
-------
Table 5.22
Surface
Water
Snow (old)
Snow (fresh)
References:
(van Wijk 1963)
(Brutsaert 1982)
Emissivity Values for Natural Surfaces at Normal Temperatures*
IEmissivity
0.95
0.97
0.99
Table 5.23 Coefficients for Linear Regression Equations for Prediction of Soil Water Contents at Specific
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 (%)
(1
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.
Table 5.24 Thermal Properties of Some Soil and Reference Materials*
Material
Clay
Light Soil w/Roots
Wet Sandy Soil
Dead Air
Water Content (%)
Heat Capacity
(cal cm 3 °C -1)
1.44
0.09
0.64
0.000312
Thermal Cond.
(cal cm-1 °C1 sec'1)
0.00288
0.00027
0.0064
0.00005
5-57
-------
Table 5.24 Thermal Properties of Some Soil and Reference Materials*
1 Heat Capacity Thermal Cond.
Water Content (%) J (cal cm 3 °C1) J (cal cm ' °C1 sec'1)
Dakota Sandy Loam
Black Cotton Soil
1.9
4.9
0.269
0.483
0.336
0.00059
0.0054
0.00037
References:
(Rosenberg 1974)
(Kilmer 1982)
Table 5.25 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
85-100
70-90
45-85
25-50
0-50
45-80
20-45
0-20
45-65
0-20
0-45
Silt
0-15
0-30
0-50
28-50
50-100
0-28
15-55
40-73
0-20
40-60
0-40
Clay
0-10
0-15
0-20
8-28
8-28
20-35
28-50
28-40
35-55
40-60
40-100
Water Retained at -0.33
Bar Tension cm3 cm"3
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 cm3 cm"3
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(Rawlsetal. 1982)
b Mean value.
0 One standard deviation about the mean.
Table 5.26
Descriptive Statistics and Distribution Model for Field Capacity (Percent by Volume)
Original Data
5-59
-------
Stratum(m)
Sample
Size
Mean
Median
s.d.
CV(%)
Distribution Model
Transform
Mean
s.d.
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
52
50
42
39
456
454
435
373
371
362
336
290
230
208
178
146
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
9.4
81
.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
9.2
7.9
5.8
5.0
8.3
7.4
7.1
7.6
1 £
/ .0
1 Q
/.O
8.6
8.9
9.1
9.3
8^>
.z
81
.1
78
82
79
70
42
39
39
43
35
34
o o
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
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)
5-60
-------
Table 5.27 Descriptive Statistics and Distribution Model for Wilting Point (Percent by Volume)
Original Data
Stratum(m)
Sample
Size
Mean
Median
s.d.
CV(%)
Distribution Model
Transform
Mean
s.d.
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
118
119
113
105
880
883
866
866
678
677
652
582
495
485
437
401
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
3.1
2.3
2.1
1.9
8.7
9.3
8.9
81
.4
10.4
12.1
11.9
11.5
13.8
17.0
16.3
15.1
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
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
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)
5-61
-------
Table 5.28 Correlations among Transformed Variables of Organic Matter, Field Capacity, and Wilting
Point
Stratum (m)
OM + WP
N
Corr.
FC + OM
N
Corr.
FC+WP
N
Corr.
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
118
119
111
98
877
870
844
780
673
664
627
543
488
472
420
384
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
52
49
42
38
459
446
419
347
369
355
321
264
228
201
171
137
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
51
49
42
39
455
450
429
370
370
361
334
289
226
204
174
145
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 = field capacity; N = sample size; Corr. = correlation.
Source: (Carsel et al. 1988)
5-62
-------
Table 5.29 Mean Bulk Density (g cm"3) for Five Soil Textural Classifications a
Soil Texture
Silt Loams
Clay and Clay Loams
Sandy Loams
Gravelly Silt Loams
Loams
All Soils
Mean Value
1.32
1.30
1.49
1.22
1.42
1.35
Range Reported
0.86 - 1.67
0.94- 1.54
1.25 - 1.76
1.02-1.58
1.16- 1.58
0.86 - 1.76
a (Baes and Sharp 1983)
Table 5.30 Descriptive Statistics for Bulk Density (g cm'3)
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
Sample Size
40
44
38
34
459
457
438
384
398
395
371
326
259
244
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
Medium
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
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
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
5-63
-------
Table 5.30 Descriptive Statistics for Bulk Density (g cm"3)
Stratum(m)
0.6-0.9
0.9-1.2
Sample Size
214
180
Mean
1.67
1.65
Medium
1.72
1.72
s.d.
0.27
0.28
CV(%)
16.3
17.0
CV = coefficient of variation
s.d. = standard deviation
Source: (Carsel et al. 1988)
Table 5.31 Descriptive Statistics and Distribution Model for Organic Matter (Percent by Volume)
Original Data
Stratum(m)
Sample
Size
Mean
Median
s.d.
CV(%)
Distribution Model
Mean
s.d.
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
162
162
151
134
1135
1120
1090
1001
838
822
780
672
638
617
558
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
0.62
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
0.79
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
92
114
94
104
68
83
84
87
77
114
96
104
66
80
84
-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
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
5-64
-------
Table 5.31
Descriptive Statistics and Distribution Model for Organic Matter (Percent by Volume)
Original Data
Stratum(m)
0.9-1.2
Sample
Size Mean Median
493 I 0.29 I 0.22
Distribution Model
s.d. CV(%) Mean 1 s.d.
0.31 I 105 I -5.65 I 0.86
CV = coefficient of variation
s.d. = standard deviation
Source: (Carsel et al. 1988)
a Johnson SB transformation is used for all cases in this table.
Table 5.32 Adaptations and Limitations of Common Irrigation Methods
Irrigation Method
Furrow
Sprinklers
Flood
Adaptations
Light, medium and fine.
All slopes; soils; crops.
Light, medium, and heavy soils.
Limitations
Slopes up to 3 percent in textured soils;
row crops.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: (Todd 1970)
Table 5.33 Water Requirements for Various Irrigation and Soil Types
Typical Application Rate (Inches/Hour) by Sprinklers
Sprinkling
Slope (%)
0-2
2-5
5-8
8-12
Coarse
Sandy Loam
2.0
2.0
1.5
1.0
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: (Todd 1970)
5-65
-------
Table 5.36 Suitable Side Slopes for Channels Buil
Material
Rock
Muck and peat soils
Stiff clay or earth with concrete lining
Earth with stone lining, or earth for large channels
Firm clay or earth for small ditches
Loose sandy earth
Sandy loam or porous clay
Source: Adapted from (Chow 1959).
t in Various Kinds of Materials
Side slope
Nearly vertical
!/4:l
'72:1 to 1:1
1:1
172:1
2:1
3:1
Table 5.37
Value of "N" for Drainage Ditch Design
Hydraulic radius (ft)
EN
less than 2.5
0.040-0.045
2.5 to 4.0
0.035-0.040
4.0 to 5.0
0.030-0.035
more than 5.0
0.025-0.030
Source: Adapted from U.S. Dept. of Agric. Soil Conservation Service.
Table 5.38 Representative Permeability Ranges for Sedimentary Materials
Material
Clay
Silty clay
Sandy clay
Silty clay loam
Sandy loam sand
Silt
Silt loam
Loam
Hydraulic Conductivity (m/s)
io-12 - io-9
io-12 - io-9
io-11 - io-8
io-10 - io-7
io-9 - io-6
io-9 - io-6
io-9 - io-6
io-9 - io-6
Material
Very fine sand
Find sand
Medium sand
Coarse sand
Gravel and sand
Gravel
Sandstone
Limestone*
Hydraulic Conductivity
(m/s)
io-7 - io-4
lO'6 - lO'3
io-5 - io-3
io-5 - io-2
io-5 - io-2
io-5 - io-2
lO'6 - lO'3
io-7 - io-4
5-68
-------
Table 5.38 Representative Permeability Ranges for Sedimentary
Material
Hydraulic Conductivity (m/s) Material
Sandy loam 10'8 - 10'7 Shale
Materials
Hydraulic Conductivity
(m/s)
10'7 - 10'4
* Excluding cavernous limestone.
Source: Adapted from (Todd 1970).
a See also Table 5.40.
Table 5.39
Values of Green-ampt Parameters for SCS Hydrologic Soil Groups
SCS Hydrologic
Soil Group
Saturated Hydraulic3
Conductivity KS (cm hr1)
Suction Parameter
HF(cm)
1.0 - 10.0
10
B
0.60 - 1.0
10-20
c
0.20 -0.60
15-10
D
0.005 - 0.20
20 - 150
Source: Adapted from (Brakensiek and Rawls 1983)
a Also see Table 5.30.
Table 5.40 Descriptive Statistics for Saturated Hydraulic Conductivity (cm hf ')
Hydraulic Conductivity (K)*
Soil Type
Clay**
Clay Loam
Loam
Loamy Sand
Silt
Silt Loam
Silty Clay
Silty Clay Loam
Sand
Sandy Clay
Sandy Clay Loam
X
0.20
0.26
1.04
14.59
0.25
0.45
0.02
0.07
29.70
0.12
1.31
s
0.42
0.70
1.82
11.36
0.33
1.23
0.11
0.19
15.60
0.28
2.74
CV
210.3
267.2
174.6
77.9
129.9
275.1
453.3
288.7
52.4
234.1
208.6
n
114
345
735
315
88
1093
126
592
246
46
214
5-69
-------
Table 5.40
Descriptive Statistics for Saturated Hydraulic Conductivity (cm hf ')
Hydraulic Conductivity (K)*
Soil Type
Sandy Loam
* n = Sample
** Agricultural
Source: (Carsel
size,
soil,
and
1 * i ;
j 4.42 j 5.63
CV 1 n
127.0 j 1183
x = Mean, s = Standard deviation, CV = Coefficient of variation (percent)
less than 60 percent clay
Parrish 1988)
Table 5.41 Descriptive Statistics for Van Genuchten Water Retention Model Parameters, a, p, y ((Carsel
and Parrish 1988))
Soil Type
Claya
Clay Loam
Loam
Loamy Sand
Silt
Silt Loam
Silty Clay
Silty Clay
Loam
Sand
Sandy Clay
Sandy Clay
Loam
Sandy Loam
Parameter a, cm *
X
0.008
0.019
0.036
0.124
0.016
0.020
0.005
0.010
0.145
0.027
0.059
0.075
SD
0.012
0.015
0.021
0.043
0.007
0.012
0.005
0.006
0.029
0.017
0.038
0.037
CV
160.3
77.9
57.1
35.2
45.0
64.7
113.6
61.5
20.3
61.7
64.6
49.4
N
400
363
735
315
88
1093
126
641
246
46
214
1183
Parameter p
X
1.09
1.31
1.56
2.28
1.37
1.41
1.09
1.23
2.68
1.23
1.48
1.89
SD
0.09
0.09
0.11
0.27
0.05
0.12
0.06
0.06
0.29
0.10
0.13
0.17
CV
7.9
7.2
7.3
12.0
3.3
8.5
5.0
5.0
20.3
7.9
8.7
9.2
N
400
364
735
315
88
1093
374
641
246
46
214
1183
Parameter y
X
0.08
0.24
0.36
0.56
0.27
0.29
0.09
0.19
0.62
0.18
0.32
0.47
SD
0.07
0.06
0.05
0.04
0.02
0.06
0.05
0.04
0.04
0.06
0.06
0.05
CV
82.7
23.5
13.5
7.7
8.6
19.9
51.7
21.5
6.3
34.7
53.0
10.1
N
400
364
735
315
88
1093
374
641
246
46
214
1183
x = Mean, SD = Standard Deviation, CV = Coefficient of Variation, N = Sample size
"Agricultural Soil, Clay 60%
Table 5.42 Descriptive Statistics for Saturation Water Content (0S) and Residual Water Content (0r)
Saturation Water Content (6S) Residual Water Content (6r) Statistic*
Soil Type
Clay**
x
0.38
s
0.09
CV
24.1
n
400
x
0.068
s
0.034
CV
49.9
n
353
5-70
-------
Table 5.42 Descriptive Statistics for Saturation Water Content (0S) and Residual Water Content (0r)
Saturation Water Content (6S) Residual Water Content (6r) Statistic*
Soil Type ] x | s | CV | n | x | s | CV | n
Clay Loam
Loam
Loamy Sand
Silt
Silt Loam
Silty Clay
Silty Clay Loam
Sand
Sandy Clay
Sandy Clay
Loam
Sandy Loam
0.41
0.43
0.41
0.46
0.45
0.36
0.43
0.43
0.38
0.39
0.41
0.09
0.10
0.09
0.11
0.08
0.07
0.07
0.06
0.05
0.07
0.09
22.4
22.1
21.6
17.4
18.7
19.6
17.2
15.1
13.7
17.5
21.0
364
735
315
82
1093
374
641
246
46
214
1183
0.095
0.078
0.057
0.034
0.067
0.070
0.089
0.045
0.100
0.100
0.065
0.010
0.013
0.015
0.010
0.015
0.023
0.009
0.010
0.013
0.006
0.017
10.1
16.5
25.7
29.8
21.6
33.5
10.6
22.3
12.9
6.0
26.6
363
735
315
82
1093
371
641
246
46
21
1183
* n = Sample size, x = Mean, s = standard deviation, CV = coefficient of variation (percent)
** Agricultural soil, less than 60 percent clay.
Table 5.43 Statistical Parameters Used for Distribution Approximation
Soil
Texture**
S
S
S
s
SL
SL
SL
SL
Hydraulic
Variable
Ks
er
a.
P
Ks
er
a
P
Transformation
SB
LN
SB
LN
SB
SB
SB
LN
Limits of
Variation
A
0.0
0.0
0.0
1.5
0.0
0.00
0.00
1.35
B
70.0
0.1
0.25
4.0
30.0
0.11
0.25
3.00
Mean
-0.39387
-3.11765
0.37768
0.97813
-2.49047
0.38411
-0.93655
0.63390
Estimated*
Standard
Deviation
1.15472
0.22369
0.43895
0.10046
1.52854
0.70011
0.76383
0.08162
Truncation Limits on
Transformed
D*** Variable
0.045
0.053
0.050
0.063
0.029
0.034
0.044
0.039
5-71
-------
Table 5.43 Statistical Parameters Used for Distribution Approximation
Soil
Texture**
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
Hydraulic
Variable
Ks
er
a
P
Ks
er
a
P
Ks
er
a
P
Ks
er
a
P
Ks
er
a
P
Ks
er
a
P
Ks
er
Transformation
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
Limits of
Variation
A
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
B
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
Mean
-1.26908
0.07473
0.12354
-1.11095
-2.18691
0.47752
-4.09937
-0.37036
-2.20
0.042
0.01688
1.37815
-5.75949
0.44537
-4.14805
0.00021
-5.68562
0.06971
-5.65849
-1.28378
-4.04036
1.72496
-3.76810
0.20209
-5.31256
0.08871
Estimated*
Standard
Deviation
1.40000
0.56677
0.04345
0.30718
1.49414
0.58156
0.55542
0.52557
0.7000
0.0145
0.00611
0.03729
2.32884
0.28178
1.29310
0.11800
1.31421
0.02337
0.58445
0.82074
2.01721
0.70000
0.56322
0.07788
1.61775
0.00937
Truncation Limits on
Transformed
D*** Variable
0.036
0.043
0.027
0.070
0.046
0.073
0.083
0.104
0.168 -2.564 -0.337
0.089 0.013 0.049
0.252
0.184
0.122
0.058 0.0065 0.834
0.189 -5.01 0.912
0.131 0.00 0.315
0.205
0.058
0.164
0.069
0.130
0.078
0.127
0.100
0.049
0.056
5-72
-------
Table 5.43 Statistical Parameters Used for Distribution Approximation
Soil
Texture**
SICL
SICL
CL
CL
CL
CL
SCL
SCL
SCL
SCL
L
L
L
L
Hydraulic
Variable
a.
P
Ks
er
a
P
Ks
er
a
P
Ks
er
a
P
Transformation
SB
NO
gg###
su
LN
SB
SB
gg###
SB
LN
SB
SB
SB
SU
Limits of
Variation
A
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
B
0.15
1.5
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
-2.75043
1.23640
-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
0.60529
0.06130
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
0.082
0.082
0.058 -8.92 2.98
0.061
0.052
0.035
0.047
0.077 0.928 2.94
0.048
0.043
0.019
0.064
0.039
0.036
* 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)
Table 5.44 Correlations among Transformed Variables Presented with the Factored Covariance Matrix*
K,
er
a
P
Silt **(n = 61)
Ks
er
a
P
0.5349258
-0.204
0.984
0.466
-0.0015813
0.0075771
-0.200
-0.610
0.0030541
0.0000021
0.0005522
0.551
0.0128700
-0.0145118
0.0144376
0.0133233
5-73
-------
Table 5.44 Correlations among Transformed Variables Presented with the Factored Covariance Matrix*
1* K 1« IP
Clay (n = 95)
Ks
er
a.
P
Silty Clay (n = 123)
Ks
er
a
P
Sandy Clay (n = 46)
Ks
er
a.
P
Sand (n = 237)
Ks
er
a.
P
Sandy Loam (n = 11
Ks
er
a
P
Loamy Sand (n = 31.
Ks
er
1.9614077
0.972
0.948
0.908
1.2512845
0.949
0.974
0.908
2.0172105
0.939
0.957
0.972
1.0370702
-0.515
0.743
0.843
15)
1.6026856
-0.273
0.856
0.686
3)
1.4754063
-0.359
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
0.5645309
-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
0.0475514
-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
5-74
-------
Table 5.44 Correlations among Transformed Variables Presented with the Factored Covariance Matrix*
IK. jer [a |p
a.
P
Silt Loam (n = 1072)
Ks
er
a
P
Silty Clay Loam (n =
Ks
er
a
P
Clay Loam (n = 328)
Ks
er
a
P
Sandy Clay Loam (n
Ks
er
a
P
Loam (n = 664)
Ks
er
a.
P
0.986
0.730
1.4754063
-0.359
0.986
0.730
591)
1.6177521
0.724
0.986
0.918
1.9200165
0.790
0.979
0.936
= 212)
1.8497610
0.261
0.952
0.909
1.4083953
0.204
0.982
0.632
-0.301
-0.590
-0.02005639
0.5215473
-0.301
-0.590
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
0.0142626
0.354
0.5245489
0.0300399
0.0820163
0.775
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
0.0193794
0.1084945
0.3525548
-0.1696100
0.2341768
0.1583593
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
5-75
-------
K,
er
a
P
Table 5.44
Correlations among Transformed Variables Presented with the Factored Covariance Matrix*
* 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)
5-76
-------
Table 5.45 Examples of Nitrogen Gains, Losses, and Transformations (In Kg/ha/yr) for Eight Different Cropping Systems*
Description of
Change
Additions
Added Fertilizer
Irrigation,floodwater
Sediments added
Nj-fixation
Removals
Harvested product
Denitrification
Volatilization of
ammonia
Leaching loss
Erosion and runoff
Recycling process
Uptake from soil
Manure from
grazing
Plant residues left
Mineralization from
humus
Grazed
Bluegrass
(No. Car.)
168
—
10
—
38
5
98b
—
14
151
60
113
48
Corn
Grains
and.)
112
10
10
—
85
15
—
15
16
126
—
41
50
Soybean
Seeds
(Ark)
0
—
10
123
90
15
—
10
16
120
—
30
15
Wheat
(Kansas)
34
—
6
—
36
5
—
4
5
56
—
20
28
Irish
Potatoes
(Maine)
168
—
6
—
80
15
—
64
15
145
—
65
65
Cotton
(Calif.)
179
50
3
—
79
20
—
83
50
127
—
48
48
Loblolly
Pine
(Miss.)
—
—
11
8
12
1
—
1
o
6
20
—
9
6
Douglas
Fir
(Wash.)
—
—
10
—
10
1
—
1
2
35
—
25
—
a A dash means that no measurement was made or the item does not apply to the system.
b Losses from voided animal urine and feces as ammonia gas.
Source: Data from (Frissel 1978, pp. 203-243)
5-77
-------
Table 5.46 Recommended Manning's Roughness Coefficients for Overland Flow
Cover of Treatment
Concrete or asphalt
Bare sand
Graveled surface
Bare clay -loam (eroded)
Fallow — no residue
Chisel plow
Disk/harrow
No-till
Moldboard Plow (Fall)
Coulter
Range (natural)
Range (clipped)
Grass (bluegrass sod)
Short grass prairie
Dense grass
Bermuda grass
Woods-Light underbrush
Woods-Dense underbrush
Residue Rate
(ton/acre)
1/4
1/4-1
1-
o
3
1/4
1/4-1
1-3
o
5
1/4
1/4-1
1-3
Value recommended
0.011
0.01
0.02
0.02
0.05
0.07
0.18
0.30
0.40
0.08
0.16
0.25
0.30
0.04
0.07
0.30
0.06
0.10
0.13
0.10
0.45
0.15
0.24
0.41
0.40
0.80
Range
0.01-0.013
0.010-0.016
0.012-0.03
0.012-0.033
0.006-0.16
0.006-0.17
0.07-0.34
0.19-0.47
0.34-0.46
0.008-0.41
0.10-0.41
0.14-0.53
0.03 - 0.07
0.01-0.13
0.16-0.47
0.02-0.10
0.05-0.13
0.01-0.32
0.02 - 0.24
0.39-0.63
0.10-0.20
0.17-0.30
0.30-0.48
5-78
-------
SECTION 6
Pesticide Root Zone Model (PRZM) Code and Theory
6.1 Introduction and Background (PRZM)
This section describes the theoretical background for the mathematical simulation model (PRZM) that has been
developed and tested to evaluate pesticide leaching from the crop root zone under field crop conditions. While the
model's focus has traditionally been on pesticides, it has been used over the years to simulate the behavior of other
organic chemicals; the most recent version of the model has been expanded to include capabilities for modeling
nitrogen species as well. The majority of this section is devoted to the discussion of the model's code and theory
related to pesticide simulation. A summary of the features of the new nitrogen code is included in Section 6.2.1; a
new section (6.3.8) describes the nitrogen code and theoretical considerations in greater detail.
Following an introduction, Section 6.2 describes the features and limitations of the model. A description of the
theory, including a detailed description of the equations solved, is provided in Section 6.3. An outline of the
numerical implementation techniques used by the model to apply the theory to the simulation of physical problems
follows. Section 6 concludes with a discussion of testing results for new algorithms that have been added in this
release.
6.1.1 Introduction
Pesticide leaching from agricultural fields as nonpoint source loads can lead to groundwater contamination.
Nonpoint source contamination is characterized by highly variable loadings, with rainfall and irrigation events
dominating the timing and magnitude of the loading of pesticides leaching below the root zone. The potentially
widespread, area! nature of resulting contamination makes remedial actions difficult because there is no single plume
emanating from a "point source" (the more common groundwater problem) that can be isolated and controlled. In
any case, a more prudent approach to prevention or reduction of groundwater contamination by pesticides must be
based on understanding the relationships among chemical properties, soil system properties, and the climatic and
agronomic variables that combine to induce leaching. Knowledge of these relationships can allow a priori
investigation of conditions that lead to problems, and appropriate actions can be taken to prevent widespread
contamination.
Many investigators have studied the factors contributing to pesticide leaching. These investigations have shown that
chemical solubility in water, sorptive properties, volatility, formulation, and soil persistence determine the tendency
of pesticides to leach through soil. Similarly, the important environmental and agronomic factors include soil proper-
ties, climatic conditions, crop type, and cropping practices. In short, the hydrologic cycle interacts with the chemical
characteristics to transform and transport pesticides within and out of the root zone. Vertical movement out of the
root zone can result in groundwater contamination and is the problem that the model is designed to investigate.
Numerical models to simulate the movement of solutes in porous media under steady-state, transient, homogenous,
and/or multi-layered conditions have been previously developed. Included in such models have been descriptions of
linear and nonlinear sorption, ion exchange, and chemical-specific reactions. These prior models and related
investigations have proven valuable in interpreting laboratory data, investigating basic transport processes, and
identifying the controlling factors in solute transport and transformation. As noted in a recent review of models for
simulating the movement of contaminants through groundwater flow systems, the successful use of such models
requires a great deal of detailed field data. This unfortunate conclusion arises from the scaling problems associated
with using laboratory experiments results for field-scale assessments, and the traditional solution of the appropriate
partial differential equations at points or nodes in a finite-difference or finite-element grid network. Each spatial
segment modeled must be properly characterized - a most expensive, if not impossible, task for many modeling
problems.
Such difficulties in modeling pesticide leaching with existing procedures are even more daunting when one
considers the need to evaluate the potential for future problems arising from pesticides not yet widely distributed or
6-1
-------
used. Models used to perform such prognostic evaluations should conform to the maximum possible extent to known
theory, but must be structured to enable efficient analysis of field situations with minimal requirements for
specialized field data. In short, the goal is to integrate the essential chemical-specific processes occurring during
leaching with reasonable estimates of water movement through soil systems. Data input must be reasonable for both
spatial and temporal requirements, and generally available from existing data bases. PRZM attempts to meet these
objectives.
In addition to pesticide simulation, the need has arisen to simulate nitrogen species (in particular, nitrate) in order to
assist in delineating rural wellhead protection areas. A model to perform these simulations would need to be able to
represent (1) nitrogen introduced as a result of on-site wastewater treatment systems, (2) soil nitrogen transport and
transformation processes within the unsaturated zone, and (3) certain potential influxes of nitrogen due to land
surface activities related to agriculture and atmospheric deposition. In 1995 the existing PRZM-2 modeling
framework was enhanced to develop a tool capable of simulating nitrogen soil fate processes, thereby providing a
means to project those loadings to groundwater. PRZM-3 is capable of modeling soil nitrogen transformation and
transport processes, thereby providing a valuable tool for defining wellhead protection strategies relative to nitrate
contamination.
6.1.2 Background
The Pesticide Root Zone Model (PRZM) (Carsel et al. 1984, Carsel et al. 1985) was designed and developed as a
code for Agency in simulating the transport and transformation of agriculturally applied pesticides in the crop root
zone. As such, PRZM attained a degree of acceptability in both the regulatory community and in the agricultural
chemical industry. Therefore, its utility in accomplishing the objective of this model development effort is obvious.
6.2 Features and Limitations
6.2.1 Features
PRZM Release III is a one-dimensional, dynamic, compartmental model for use in simulating chemical movement in
unsaturated soil systems within and immediately below the plant root zone (see Figure 6.1). PRZM allows the user
to perform simulations of potentially toxic chemicals, particularly pesticides, that are applied to the soil or to plant
foliage. Dynamic simulation allows the consideration of pulse loads, the prediction of peak events, and the
estimation of time-varying mass emission or concentration profiles, thus overcoming limitations of the more
commonly used steady-state models. Time-varying transport by both advection and dispersion in the dissolved phase
or diffusion in the gas phase are represented in the program.
PRZM has two major components - hydrology and chemical transformation and 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 from pan evaporation data, or by an empirical
formula if input pan data are unavailable. 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. Irrigation inputs can also be
modeled.
Dissolved, adsorbed, and vapor-phase contaminant concentrations in the soil are estimated by simultaneously
considering the processes of chemical uptake by plants, surface runoff, erosion, decay, volatilization, foliar washoff,
advection, dispersion, and retardation. The user can elect to solve the transport equations using one of two finite-
difference numerical techniques, the original backwards-difference implicit scheme featured in the first release, or a
Method of Characteristics algorithm that greatly reduces numerical dispersion, but increases model execution time.
The hydrologic components of the chemical transport equations (i.e., soil moisture content and soil-water velocities)
are decoupled, solved separately, and used to numerically integrate the equation in succeeding time steps.
Predictions are made on a daily basis. Output can be summarized on a daily, monthly, or annual frequency. Daily
time series of values for various fluxes or storages can be written to sequential files during program execution.
6-2
-------
Figure 6.1
Pesticide Root Zone Model.
Enhancements to PRZM in the most recent version of PRZM-3 have added the ability to simulate nitrogen
constituents in a manner similar to pesticides. The soil nitrogen storages and transformations included in PRZM-3
are based on the soil nitrogen modeling procedures included in HSPF AGCHEM Version No. 11 (Bicknell et al.
1995), with a few modifications to accommodate the PRZM soil profile representation, include a threshold for
denitrification based on soil moisture, and mesh with the daily time step in PRZM. The nitrogen species of nitrate,
ammonia, and four forms of organic nitrogen (i.e. paniculate organic nitrogen (labile and refractory) and dissolved
organic nitrogen (labile and refractory)) are represented. Allowable inputs of nitrogen include atmospheric
deposition, septic system effluent, and surface applications. The soil nitrogen fate processes include plant uptake of
nitrate and ammonium, return of plant nitrogen to organic nitrogen, denitrification or reduction of nitrate-nitrite,
immobilization of nitrate-nitrite and ammonium, mineralization of organic nitrogen, fixation of atmospheric
nitrogen, volatilization of ammonium, and the adsorption/desorption of ammonium and the organic forms. All
reactions and fluxes are computed on a daily basis and the storages then updated.
All water related transport processes are performed by existing PRZM routines. Water from septic system effluent is
introduced into the soil hydraulics routines in the same equations used to calculate lateral outflow. Since the nitrogen
reactions are performed in the newly incorporated soil nitrogen module, the water movement generated within
6-3
-------
PRZM can used to transport the nitrogen species. This was done by creating a modified version of the existing
pesticide transport/reaction routine (SLPSTO), that calls the same tri-diagonal matrix solver (TRDIAG) to calculate
only transport values. This new routine (NITMOV) uses the water movement calculations within PRZM to account
for all water-related nitrogen movement fluxes, including runoff, and erosion, leaching, and lateral outflow.
Agricultural nitrogen applications are modeled using the same rules for incorporation depth (soil application only) as
for a pesticide in PRZM-2. The PRZM-3 mass balance model houses the same code for the water balance as is found
in PRZM-2's mass balance.
Some processes are simulated in both the PRZM code and the nitrogen module, and it should be clarified which
modules are used for which simulation. Ammonia volatilization is performed using the new nitrogen simulation
code, not the pesticide volatilization routines in PRZM. Plant growth is simulated in both PRZM and the nitrogen
module. The plant growth algorithm used in the nitrogen module is only used in that module. All other plant growth
simulation is performed in existing PRZM modules.
Some assumptions from the HSPF nitrogen simulation had to be transferred to the nitrogen simulation in PRZM-3.
For example, atmospheric deposition and litter return are only incorporated into the surface and upper zones of
HSPF. In PRZM-3, it is assumed that atmospheric deposition and litter return are incorporated into the first horizon
only and are divided equally among the compartments in the first horizon.
6.2.2 Limitations
There were some severe limitations of the PRZM Release I Code, that were obvious to the developers, and some that
were pointed out subsequently by model users. These limitations can be broken into four categories:
• Hydrology
• Soil hydraulics
• Method of solution of the transport equation
• Deterministic nature of the model
In Release II, many of these limitations to an extent, were overcome, to an extent.
Hydraulic computations are performed in PRZM on a daily time step; however, some of the processes involved
(evaporation, runoff, erosion) are clearly among those that might be simulated on a finer time step to ensure greater
accuracy and realism. For instance, simulation of erosion by runoff depends on the peak water runoff rate, that is, in
turn, dependent on the time base of the water runoff hydrograph. All of this depends, to some extent, on the duration
of the precipitation event. PRZM retains its daily time step in this release primarily due to the relative availability of
daily versus shorter time step meteorological data. A portion of this limitation has been mitigated, we hope, by
enhanced parameter guidance.
The method of computing potential evapotranspiration using Hamon's formula, in the absence of actual evaporation
data, has also been retained. However, we noted that evapotranspiration from irrigated citrus in Florida was found to
be substantially under-predicted when using this method to estimate potential evapotranspiration (Dean and Atwood
1985a). Users should check the model's hydrologic simulation carefully when using this option.
The capability to simulate soil temperature was added to PRZM-2 and carried-over to PRZM-3 in order to correct
Henry's constant for the temperature occurring in various depths in the soil when performing vapor-phase
calculations. Removal of water by evaporation versus transpiration from the profile may have a pronounced effect on
soil temperature. This is due to the fact that more heat is removed during the process of evaporation because the
energy necessary to vaporize water leaves the system, producing a cooling effect. No differentiation is made
between evaporation and transpiration in PRZM at this time.
In PRZM Release I, the soil hydraulics were simple - all drainage to the field capacity water content was assumed to
occur within 1 day. (An option to make drainage time dependent value was also included, but there is not much
6-4
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evidence to suggest that this option was utilized by model users to any great extent). This assumption had the effect,
especially in deeper soil columns , of inducing a greater-than-anticipated and unrealistic movement of chemical
through the soil profile. While this representation of soil hydraulics has been retained in PRZM-3, the user has the
option, with the linked modeling system, of coupling PRZM to VADOFT. PRZM-3 is then used to model just the
root zone, while VADOFT, with a more rigorous representation of unsaturated flow, is used to simulate the rest of
the vadose zone. The difficulties in parameterizing the Richards equation for unsaturated flow in VADOFT is
overcome by using the technique of van Genuchten to generate soil water characteristic curves using soil textural
information. For thin soil columns, PRZM can be used to represent the entire vadose zone.
The addition of algorithms to simulate volatilization has brought into focus another limitation of the soil hydraulics
representation. PRZM-3 simulates only advective, downward movement of water and does not account for diffusive
movement due to soil water gradients.. This means that PRZM-3 is unable to simulate the upward movement of
water in response to when for simulating the effects of volatilization. This latter process has bee identified by jury at
al. (1984) to be important when simulating the effects of volatilization However, this process would seem less likely
to affect the movement of chemicals with high vapor pressures. For these later chemicals, vapor diffusion would
more likely be the major process for renewing the chemical concentration in the surface soil horizon(s).
Another limitation of the Release I model was the inadequacy of the solution to the chemical 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 over-prediction of downward chemical movement
due to smearing of the dissolved concentration peak and subsequent overestimation of chemical loadings to
groundwater. In PRZM-3, 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 a Method of Characteristics (MOC)
formulation. The remainder of the transport equation is then solved as before, using the fully implicit scheme. This
approach effectively eliminates numerical dispersion, but with some additional overhead expense in computation
time. In low-advection systems, the MOC approach reduces to the original PRZM solution scheme, which is exact
for water velocities approaching 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 addressed by adding the capability in PRZM-3 to run
PRZM in a Monte Carlo framework. Thus, distributional, rather than field-averaged, values can be utilized as inputs
thereby producing distributional outputs of the relevant variables (e.g., flux to the water table).
6.3 Description of the Algorithms
The description of the processes simulated by PRZM is broken-down in the following discussion into eight
categories:
• Chemical Transport in Soil
• Water Movement
• Chemical Application and Foliar Washoff
• Chemical dissolved in Runoff
• Soil Erosion
• Volatilization
• Irrigation
• Nitrogen Processes
The first two categories plus soil erosion were simulation options originally available in PRZM Release I. Since the
capability to simulate ponding is new, the mathematical basis of the ponding algorithms is described in detail.
Volatilization and irrigation simulation were added in the PRZM-2 release, and the sections on chemical application,
dissolved chemical runoff and nitrogen processes describe enhancements developed for the PRZM-3 release.
6.3.1 Chemical Transport in Soil
6-5
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The PRZM-3 model was derived from the conceptual, compartmentalized representation of the soil profile as shown
in Figure 6.2. From consideration of Figure 6.2. it is possible to writechemical mass balance equations for both the
surface and subsurface zones. Addition of the vapor phase and ponded water compartments in PRZM-3 require the
consideration of additional terms compared to previous PRZM releases. The surface zone mass balance expressions
for each of the dissolved, adsorbed, and vapor phases are:
Aba—^— = JD - JY - JDW - Jv- JQR + J^p + JFOF ± JTRN (6.1)
at
- ~JDS~ JER (6-2)
= JGD ~ JDG (6-3)
dt
where
A = cross-sectional area of soil column (cm2)
Az = depth dimension of compartment (cm)
Cw = dissolved concentration of pesticide (g cm"3)
Cs = sorbed concentration of pesticide (g g"1)
Cg = gaseous concentration of pesticide (g cm"3)
6 = volumetric water content of soil (cm3 cm"3)
a = volumetric air content of the soil (cm3 cm"3)
ps = soil bulk density (g cm"3)
/ = time (days)
JD = represents the effect of dispersion and diffusion of dissolved phase (g day"1)
Jv = represents the effect of advection of dissolved phase (g day"1)
JGD = represents the effect of dispersion and diffusion in vapor phase (g day"1)
JDW = mass loss due to degradation in the dissolved phase (g day"1)
JDG = mass loss due to degradation in the vapor phase (g day"1)
Jv = mass loss by plant uptake of dissolved phase (g day"1)
JQR = mass loss by removal in runoff (g day"1)
JJPP = mass gain due to pesticide deposition on the soil surface (g day"1)
Jpop = mass gain due to washoff from plants to soil (g day"1)
JDS = mass loss due to degradation of sorbed phase chemical (g day"1)
Jm = mass loss by dissolved removal on eroded sediments (g day"1)
JmN = mass gain or loss due to parent/daughter transformations (g day"1)
Equations for the subsurface zones are identical to Equations 6.1. 6.2. and 6.3 except that JQR, Jpop, and Jm are not
included. J^,P applies to subsurface zones only when the pesticides are incorporated into the soil. For subsurface
layers below the root zone, the term Jv is also not utilized.
Note that terms representing phase transfers (e.g., volatilization) are neglected in Equations 6.1 through 6.3 because
they cancel when the equations are added (see Equation 6.19).
6-6
-------
(Surface La
Runoff)
(Surface La
Erosion)
JER
^
J (J J ^
'-'QR WAPPI '-'FDF^
Diffusion
tJ°
SOLIDS
Cs
Ps
Adsorption/
Desorption
^
(Jos)
T Diffusion
Leaching A (Surface Layer:
1 Jv T GD Volatilization)
WATER
cw
e
(JTRN)
> —
^^1
~ (Jow)
i
Diffusion
Jo
GAS
Cg
a
Gas/Liquid
— ^. Equilibria
(Jos)
^ ^
Leaching Diffusion
Jy JGD
> Plant Uptake
Ju
Figure 6.2
Schematic representation of a single chemical in a soil layer.
Each term in Equations 6.1 through 6.3 is now further defined. Dispersion and diffusion in the dissolved phase are
combined and are described using Pick's law:
JD= -
(6.4)
where
Dw = diffusion-dispersion coefficient for the dissolved phase, assumed constant (cm2 day"1)
Cw= dissolved concentration of pesticide (g cm"3)
6 = volumetric soil water content (cm3 cm"3)
z = soil depth dimension (cm)
In a similar manner, dispersion and diffusion in the vapor phase are described by Pick's law:
JaD= - A Az Dg
(6.5)
where
Dg = molecular diffusivity of the pesticide in the air-filled pore space (cm2 day"1)
C = vapor-phase concentration of pesticide (g cm"3)
a = volumetric soil air content (cm3 cm"3)
The dependence of the molecular diffusivity of the pesticide in air-filled pore space of the volumetric soil air content
6-7
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is described by the Millington-Quirk expression (Jury et al. 1983a)
aio/3
where
a = the air-filled porosity (cm3 cm"3)
4> = total porosity (cm3 cm"3)
Da = molecular diffusivity of the chemical in air, assumed constant (cm2 day"1)
The mathematical theory underlying the diffusive and dispersive flux of pesticide in the vapor phase within the soil
and into the overlying air can be found in the section describing volatilization.
The advective term for the dissolved phase, Jr, describes the movement of pesticide in the bulk flow field:
d(C* fl^
Jv = A Az V — (6.7)
oz
where
V= velocity of water movement (cm day"1)
Vapor-phase advection has not been included as a flux in the transport equation. A number of researchers have
indicated a consensus that vapor-phase advection is not likely to be significant for agricultural situations (Jury et al.
1987). Early studies of water vapor movement suggested that the fluctuation of barometric pressure at the soil
surface could act as a pumping mechanism for vapor-phase advective transport (Fukuda 1955, Farrell et al. 1966,
Scotter and Raats 1970). However, using models for vapor emissions from landfills, Thibodeaux et al. (1982)found
that atmospheric pressure fluctuations increased the total emission rate for benzene by only 15%, compared to
constant pressure conditions. Therefore, it appears to be a reasonable assumption at this time to neglect vapor-phase
advection in modeling chemical migration for agricultural situations.
Degradation of a pesticide in or on soil can be due to such processes as hydrolysis, photolysis, and microbial decay.
If these processes follow pseudo first-order kinetics, the rate coefficients can be combined into a single, overall or
lumped decay coefficient. Assuming the same rate constants for the solid and dissolved phases, the rate of change of
chemical out of each phase due to decomposition is given by:
JDW = Ks Cw 9 A bz (6.8)
JDS = K.c,P.Ate (6.9)
JDO = KgCgaA ^ (6.10)
where
Ks = lumped, first-order decay constant for solid and dissolved phases (day"1)
Kg = lumped, first-order decay constant for vapor phase (day"1)
Cs = solid-phase concentration of pesticide (g g"1)
Plant uptake of pesticides is modeled by assuming that uptake is directly related to transpiration rate. The uptake is
given by:
Ju = / Cw 9 e A b* (6.11)
where
Ju = uptake of pesticide (g day"1)
/ = the fraction of total water in the zone used for transpiration (day"1)
e = an uptake efficiency factor or reflectance coefficient (dimensionless)
-------
Erosion and runoff losses as well as inputs to the surface zone from foliar washoff are considered in the surface
layer. The loss of pesticide due to runoff is
JQR = -7- c* A (6.12)
Aw
where
JQR = pesticide loss due to runoff (g day"1)
Q = the daily runoff volume (cm3 day"1)
Aw = watershed area (cm2)
and the loss of sorbed pesticide due to erosion is
P X r C A
(6.13)
w
where
Jm = the pesticide loss due to erosion (g day"1)
Xe = the erosion sediment loss (metric tons day"1)
rom = the enrichment ratio for organic matter (g g"1)
p = a units conversion factor (g tons"1)
Soil erosion is discussed in more detail in Section 6.3.5.
Pesticides can be applied to either bare soil if pre-plant conditions prevail or to a full or developing crop canopy if
post-plant treatments are desired. The pesticide application is an input mass rate that is calculated by one of the
application/deposition models discussed in Section 6.3.3. It is partitioned between the plant canopy and the soil
surface, and the rate at which it reaches the soil surface is designated J^,P.
Pesticides applied to the plant canopy can be transported to the soil surface as a result of rainfall washoff. This term,
JPOP, is defined as:
JFOF = E Pr M A (6.14)
where
E = foliar extraction coefficient (cm"1)
Pr = daily rainfall amount (cm day"1)
M = mass of the pesticide on the plant surface projected area basis (g cm"2)
The foliar pesticide mass, M, is subject to degradation, transformation to metabolites and losses through
volatilization. Its rate of change is given by
- KfMA - KtMA - JFOF + AFbA (6.15)
where
Kf = lumped first-order foliar degradation constant (day"1)
K, = lumped first-order foliar transformation constant (day"1)
Ap = application rate to the plant (g ha"1 day"1)
b = a units conversion factor (ha)
Adsorption and desorption in Equations 6.1 through 6.3 are treated as instantaneous, linear, and reversible processes.
Using this assumption, we can relate the sorbed phase concentration to the dissolved-phase concentration by:
C. - Kd Cw (6.16)
where
6-9
-------
Kd = partition coefficient between the dissolved and solid phases (cm3 g"1)
A similar expression can be developed to express the vapor phase concentration in terms of the dissolved-phase
concentration:
Cg = KHCW (6.17)
where
KH = dimensionless Henry's constant, i.e., distribution-coefficient between the vapor phase and the
liquid phase. KH = (Henry's constant [atm/mol]) / ® 7), where T is the temperature [kelvin],
and^ is the gas constant, 8.20574xlO'2 [liter atm mol'1 K'1].
The transformation of parent to daughter is assumed to be first order and is described by
JTRN = ~ KTRN Cw A Az Q (6.18)
where
KTRN = the transformation rate constant (day"1)
When simulating an end-of-chain daughter, JTRN can also be a source term equal to the sum of the first-order transfers
from any and all parents.
JTBN = E KTRN Cw A &Z Q (6A9)
in which the superscript k denotes a parent compound. For intermediate products, the solute transport equation can
also contain terms such as those shown in both Equations 6.18 and 6.19. The transformation of parent to daughter
compounds is discussed in detail in Section 6.5.4. That section includes a description of the equations used to
simulate this scenario.
Summing Equations 6.1. 6.2. and 6.3 and utilizing equations 6.16 and 6.17. produces the following expressions for
the mass balance of pesticide in the uppermost soil layer:
dz2 dz2
- C
KdPs) H- KgaKH
J EPM
(6.20)
4 A A 1KN W lKl\i. W
A Az Az £ *
Equation 6.20 is solved in PRZM-3 for the surface layer with/6 = 0, and an upper boundary condition that allows
vapor phase flux upward from the soil surface to the overlying air. This upper boundary condition is described more
fully in the section on volatilization. The lower boundary condition is one that allows advection, but no diffusion, out
of the bottom of the soil profile.
6.3.2 Water Movement
Because Fand 6 are not generally known and not generally measured as part of routine monitoring programs, it is
necessary to develop additional equations for these variables. In the general case, Darcy's law can be combined with
the continuity equation to yield the Richards equation (Richards 1931):
— = — K (6) — (6.21)
dt dz [ dt ^ '
where
6-10
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Infiltration is calculated as
INF =P+SM-Q-E (6.30)
where, assuming a unit area of 1 cm2,
P = precipitation as rainfall, minus crop interception (cm day"1)
SM = snowmelt (cm day"1)
Q = runoff (cm day"1)
E = evaporation (cm day"1)
The calculations of precipitation, snowmelt, and runoff on a daily time step are described in the following. The
disaggregation of these values and the calculation of the change in the depth of ponding on a finer time step is
included in Sections 6.3.7.4 and 6.4.4 that describe the simulation of furrow irrigation and ponded surface water.
Input precipitation is read in and pan evaporation and/or air temperature are additional inputs from which potential
evapotranspiration (PET) is estimated. Incoming precipitation is first partitioned between snow or rain, depending on
temperature. Air temperatures below 0°C produce snow that can result in the accumulation of a snowpack.
Precipitation first encounters the plant canopy, and once the interception storage capacity is depleted, the remaining
depth is available for runoff or infiltration.
The runoff calculation partitions the precipitation between infiltrating water and surface runoff. Infiltrating water can
pond on the soil surface for a period of time before it infiltrates, but this ephemeral process is described in a
following section. Runoff is calculated by a modification of the USD A Soil Conservation Service curve number
approach (Haith and Loehr 1979). Snowmelt is estimated on days in which a snowpack exists and above freezing
temperatures occur as
SM = CMT (6.31)
where
CM = degree-day snowmelt factor (cm "C"1 day"1)
T = average daily temperature (°C)
The precipitation and/or snowmelt are inputs to the SCS runoff equation written as
~ (P - . , N
Q - (6'32)
where S, the watershed retention parameter, is estimated by
<- w - '•
where
CN = SCS runoff curve number
Curve numbers (CN) are a function of soil type, soil drainage properties, crop type, and management practice.
Typically, specific curve numbers for a given rainfall event are determined by using the sum of the rainfall totals for
the previous 5 days, known as the 5-day antecedent moisture condition. In this release of PRZM, as in the original
version, the curve numbers are continuously adjusted each day as a function of the soil water status in the upper soil
layers. These algorithms were developed and reported by Haith and Loehr (1979 pp. 325 - 327). The approach used
in PRZM does not incorporate all of their modifications, however. The algorithm used by PRZM-3 considers the
contribution of snowmelt as a component in the runoff curve equation (via the snowmelt (SM) addition to
precipitation), but does not adjust the watershed retention parameter, S, to account for the effects of frozen ground.
In addition, field experience suggested an improvement to the mapping of antecedent soil moisture conditions to
daily curve numbers (R.F. Carousel 2004, Personal Communication). The initial approach stepped from AMC
(Antecedent Moisture Condition) I (driest, CN1) to II (average, CN2) to AMC III (wettest, CN3) based on absolute
(1 cm) moisture departures from field capacity. In PRZM-3. 12.2, field capacity is mapped to the midpoint between
6-12
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CN2 and CN3, and the wilting point is mapped to the mid-point between CN1 and CN2. The curve number for each
day is a linear interpolation between these set points. Because PRZM-3. 12.2 restricts apparent soil moisture to this
computational range, the effective CN is similarly restricted to the range from (CN1 + CN2)/2 to (CN2+CN3)/2.
CN1 and CN3 are calculated as in Chow et al. (1988):
™T1 4.2 UN 2
CN1 = - (6 34)
10 - 0.05RCN2 *• }
CN3 = (635)
10 + 0.13CN2 ( • '
The daily evapotranspiration demand is divided among evaporation from canopy, ponded surface water, soil
evaporation, and crop transpiration. Total demand is first estimated and then extracted sequentially from crop canopy
storage, ponded surface water, and then from each layer until wilting point is reached in each layer or until total
demand is met. Evaporation occurs down to a user-specified depth. The remaining demand, crop transpiration, is met
from the active root zone. The root zone growth function is activated at crop emergence and increases stepwise until
maximum rooting depth is achieved at crop maturity.
Actual evapotranspiration from a soil layer is estimated as:
ET, = Minimum [(SW, - WP} fdi, ET-\ ET.] (6.36)
y=i
where
ETt = the actual evapotranspiration from layer "7" (cm)
fdi = depth factor for layer "/'
WPt = wilting point water content in layer "7" (cm)
ETp = potential evapotranspiration (cm)
This equation states that the transpiration from any layer "/" is the minimum of the available water in layer "7" or the
demand remaining after extracting available water from layers above "/" in the profile.
The depth factor,^,, is internally set in the code. It linearly weights the extraction of ET from the root zone with
depth. A triangular root distribution is assumed from the surface zone to the maximum depth of rooting, with the
maximum root density assumed to be near the surface. This algorithm essentially views the plant as a pump and
assumes that it will expend the minimum energy possible in pumping. As long as the soil water is equally available,
water closest to the surface meets this criterion.
Evapotranspiration can also be limited by soil moisture availability. The potential rate cannot be met if sufficient soil
water is not available to meet the demand. In that case, PRZM-3 modifies the potential rate by
ETp =
ETp SW- WP z 0.6(FC- WP)
SMFAC*ETD WP < SW- WP < 0.6(FC- WP) (6.37)
p
SW < WP
where
FC = soil moisture content at field capacity (cm)
WP = soil moisture content at wilting point (cm)
SMFAC = soil moisture factor (dimensionless)
FC-WP = maximum soil moisture available to plants (cm)
SW-WP = plant-extractable soil moisture (cm)
The SMFAC concept has been used in other similar water balance models (Stewart et al. 1976, Haith and Loehr
1979) and is internally set in the code to linearly reduce ETp when soil water becomes limited. Finally, if pan
evaporation input data are available, ETp is related to this later input value:
6-13
-------
ETp = Cp PE (6.38)
where
PE = measured pan evaporation (cm day"1)
Cp = pan factor (dimensionless)
The pan factor is constant for a given location and is a function of the average daily relative humidity, average daily
wind speed, and location of the pan with respect to an actively transpiring crop.
In the absence of pan evaporation data, ETp can also be estimated by
ETp = 14000 L2d SVD (6.39)
where
Ld = possible hours of sunshine per day, in 12-hour units
SVD = saturated vapor density at the mean air temperature (g cm"1)
SVD = 0.622 SVPI (Rg T^)
SVP = saturated vapor pressure at the mean absolute air temperature (mb)
Rg = dry-air gas constant
Tabs = absolute mean air temperature (K)
The final term in the various soil profile layer water balance equations that must be defined is the percolation value,
/. Because the Richards equation is not solved in PRZM-3 utilizing soil water characteristic curves to predict water
movement, PRZM-3 resorts to "drainage rules" keyed to soil moisture storages and the time available for drainage.
Two options are included. Although bothe options are admittedly simplistic representations of soil moisture
redistribution, they are consistent with the objectives of PRZM-3 and its intended uses.
6.3.2.1 Option 1
Percolation, /, in this option is defined in the context of two bulk soil moisture holding characteristics commonly
reported for agricultural soils: field capacity and wilting point. Field capacity is a somewhat imprecise measure of
soil water holding properties and is usually reported as the moisture content that field soils attain after all excess
water is drained from the system under influence of gravity, usually at tensions of about 0.3 bar. The difficulty with
this concept is the fact that some soils will continue to drain for long periods of time, and thus field capacity is not a
constant. Admitting the lack of theoretical and physical rigor, we believe that the concept remains a useful measure
of soil moisture capacity that has been successfully used in a number of water balance models (Stewart et al. 1976,
Haith and Loehr 1979). Wilting point is a function of both the soil and the plants growing in the soil. It is defined as
the soil moisture content below which plants are unable to extract water, usually at tensions of about 15 bar.
Field capacity and wilting point are used operationally to define two reference states in each soil layer for predicting
percolation. If the soil water, SW, in any layer is calculated to be in excess of field capacity, then percolation is
allowed to remove the excess water to a lower zone/layer. The entire soil profile excess is assumed to drain within 1
day. The lower limit of soil water permitted is the wilting point. One outcome of these assumed "drainage rules" is
that the soil layers below the root zone tend to quickly reach field capacity and remain at that value. When this
condition is reached, all water percolated below the root zone is assumed to displace the water in the next lower soil
layer simulated, and so on to ground water. There is no allowance for lateral water movement. Water balance
accounting in this manner should be most accurate for sandy soils in which water movement is relatively unimpeded,
and least accurate for clay soils (Stewart et al. 1976).
6.3.2.2 Option 2
The second option is provided to accommodate soils having low permeability layers that restrict the "free drainage"
assumed in Option 1. In the context of the field capacity reference condition, two things can occur. First, conditions
may prevail that raise the soil moisture levels above field capacity for periods of time because the water is "backed
6-14
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up" above a relatively impermeable layer. Second, the excess water may not drain during the 1-day period assumed
in Option 1. To accommodate these two conditions, two additional parameters are needed. Maximum soil moisture
storage, 6S, is added to represent moisture contents under saturated conditions. The drainage rate also must be
modified to allow drainage to field capacity over periods in excess of 1 day (one time step). The drainage rate is
assumed to be a first-order function of the water content above field capacity and is modeled by
^(0-9/c)= -a(9-9/c) (6.40)
which has the solution
0[" = (6f - 0/c) exp(- aA/) + 0/c (6.41)
where
6 = soil layer water content (cm3 cm"3)
6/c = water content at field capacity (cm3 cm"3)
a = drainage rate parameter (day"1)
In this equation, t and t+l denote beginning and end of time step values, respectively, and "/' is the soil layer index.
The value /* denotes a value of time between the beginning and the end of the time step. The variable 6f denotes
current storage plus any percolation from the next layer above, before the occurrence of any drainage from the
current layer. Because Equation 6.41 is solved independently for each layer in the profile, there is a possibility of
exceeding the storage capability (saturation water content, 6S) of a low-permeability layer in the profile if a more
permeable layer overlies it. At each time step, once redistribution is complete, the model searches the profile for any
0(. > Qs. If this condition is found, the model redistributes water back into overlying layers, as if the percolation of
additional water beyond that necessary to saturate the low-permeability layer had not occurred. This adjustment is
necessary due to the nature of Equation 6.41 and the fact that these equations for each layer are not easily coupled.
The difficulty in coupling the equations for the entire profile arises from the dichotomy that only one of two factors
can limit percolation from any given stratum in the profile: either the rate at which that stratum can transmit water, or
the ability of the stratum below it to store or transmit water. This dichotomy leads to an iterative (or at least
corrective) approach to the explicit solution of a system of equations for 6, represented by Equation 6.41. It should
be noted, however, that the value of a selected by this approach is only relevant if the permeability of the soil
materials, and not storage considerations in the profile (i.e., the presence of a water table), is the limiting factor for
percolation of water.
6.3.3 Chemical Application and Foliar Washoff
The predecessor release of PRZM (PRZM-2.2) allowed for four different modes of pesticide application (input
parameter FAM): (1) direct application to soil; (2) foliar application based on a linear crop growth model; (3) foliar
application based on an exponential filtration model, and (4) chemical incorporation based on a uniform distribution
of chemical residues to a user-defined depth. With the first three methods, chemical residues directly applied to soil
(i.e., not intercepted by foliage) were uniformly distributed to a depth of 1 cm. Additionally, chemical residues from
foliar washoff were placed into the first soil compartment.
PRZM-3 contains 8 application options. CAMs 1 through 3 are equivalent to the previous FAMs 1 through 3 in
PRZM-2.2 except for the allocation of distributing residues in the soil profile from direct soil application and
chemical washoff. CAM 4 is equivalent to the previous FAM 4. CAMs 5 through 8 are new options.
CAM=1 Recommended for direct surface applications. Residues are distributed to 4 cm, linearly
decreasing with depth.
CAM=2 Application to foliage based on a crop canopy that varies linearly during the growing season.
This is the same as the previous FAM=2 in PRZM-2.
CAM=3 Pesticide foliar application using nonlinear exponential filtration. This is the same as the
6-15
-------
previous FAM=3 inPRZM-2.
CAM=4 Recommended for rototil incorporation. Uniform incorporation into the soil to a depth
specified by the user. This is the same as the previous FAM=4 in PRZM-2. Specifying a depth
of 1.0 cm will result in the same distribution used in the PRZM-2 for FAM=1.
CAM=5 Pesticide incorporation into an opened furrow that is then covered. Residues are distributed
through the soil linearly, increasing to a user-defined depth.
CAM=6 Similar to CAM=1 except that residues are linearly decreasing to a user-defined depth.
CAM=7 Recommended for T-Band granular application. User defines the fraction of chemical to be
applied in the top 2 cm, the remainder of the chemical is applied uniformly between 2 cm and
a user-defined incorporation depth.
CAM=8 Recommended for shank injection. Residues are incorporated into a single compartment at a
depth specified by the user.
CAM=9 Recommended for application to a linearly growing crop canopy. Chemical reaching the soil
surface is incorporated to the depth given by DEPI (modified CAM 2).
CAM=10 Recommended for nonlinearly growing canopy using exponential filtration. Chemical
reaching the soil surface is incorporated to the depth given by DEPI (modified CAM 3).
NOTE: DEPI must be set greater than 0.0 for CAM=4-10. If DEPI = 0, or DEPI < the depth of the first (top) surface
soil layer, the chemical reaching the soil surface is distributed into the first (top) surface soil layer.
Residue distribution in the soil for each of these application methods is presented in Figure 6.4.
Pesticide washoff from foliage is calculated in the same manner as PRZM-2.2 except that the disposition of washed-
off residues are processed differently. In PRZM-3, residues from washoff are distributed in the soil in the same
manner as CAM =1, that is linearly decreasing with depth to a depth of 4 cm (Figure 6.4). This differs from PRZM-
2.2 in which residues from washoff were distributed into the first soil compartment. In addition, IRTYPE 4 (under-
canopy sprinklers/drip irrigation) no longer removes pesticide from the crop canopy.
6.3.4 Chemical Dissolved in Runoff
In the previous release of PRZM (PRZM-2.2), chemical residues in the dissolved phase were uniformly and
completely available for runoff to a depth of 1 cm. Residues below 1 cm were unavailable for runoff. With the
nonuniform extraction model in PRZM-3, residues have decreasing availability with depth (non-linear model) under
the rationale that interaction between soil-pore water and excess precipitation (runoff) is diminished as a result of
obstructions in the soil structure. This phenomenon has been discussed by numerous researchers (Bailey et al. 1974,
Romkens and Nelson 1974, Bruce et al. 1975, Leonard et al. 1979, Ahuja et al. 1981, Sharpley et al. 1981, Ahuja
and Lehman 1983, Heathman et al. 1986, Emmerich et al. 1989)
The nonuniform extraction model employs an exponential curve (Figure 6.3) to restrict the amount of dissolved
phase chemical that is allowed to mix with runoff water as a function of soil depth according to:
DRI: = 0.7
1
0.9
(6.42)
Midtot.
\ '
in which DRIi is the fraction of dissolved-phase chemical present in compartment /' available for runoff, Midtot,. is the
6-16
-------
depth to the midpoint of compartment / (cm), 0.7 is an efficiency factor, and 0.9 = depth-reduction coefficient.
Calculations are performed for all compartments (/') from the surface to a depth of 2 cm.
Extraction Model for Pesticide Runoff
Q.
-------
Chemical Washoff
Fraction of Washed off Mass
DEPN2.0--This line will
correspond with any value
entered into DEPI other
than 0 (the default value)
Surface Application (CAM=1)
Mass (kg)
0.02 0.03
Application Mass = 1 kg
Foliar Application (CAM=2, CAM=3)
Mass (kg)
Application with Rototil (CAM=4)
Incorporation depths of 2.0 and 1.0 cm shown
Mass (kg)
0 0.05 0.1
0.35: :
0.75::
1.15::
1.55:;
1.95::
2.35::
2.75::
3.15;:
3.55: :
3.95
DEPI=2.0- This line will
correspond with any value
entered into DEPI other
than 0 (the default value)
DEPI=0 (default)
Mass will vary based on plant
coverage at time of application
For this example there was 50%
plant coverage (i.e. 50% of
applied mass onto soil)
0.15
0.35^
0.55
0.75-
0.95 -
1.15
1.35 •
1.55
1.75
1.95
-*-
Application Mass = 1kg
Application Mass = 1 kg
Furrow Incorporation (CAM=5)
Incorporation depths of 4.0 and 2.0 cm shown
Mass (kg)
CAM=6
Incorporation depths of 5.0, 2.0 and 1.0 cm shown
Mass (kg)
Application Mass = 1 kg
Application Mass = 1 kg
T-Band Application (CAM=7)
with DRFT=60% and DEPI=5.0
Shank Injection (CAM=8)
with DEPI=3.0
Mass (kg)
1.25::
1.65;:
2.05::
2.45;
2.85::
3.25:
3.65: :
4.05:
4.45::
Application Mass = 1 kg
Application Mass = 1 kg
Figure 6.4
Illustration of chemical application methods.
6-18
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6.3.5 Soil Erosion
Removal of sorbed pesticides on eroded sediments requires estimates for soil erosion. PRZM release 3 provides
three methods to estimate soil erosion: the Modified Universal Soil Loss Equation (MUSLE) as developed by
Williams (1975), contained in earlier versions of PRZM, plus two recent modifications, MUST and MUSS (Singh
1995):
MUSLE, Xe = 1.586 (Vr q^-56A°'nK LS CP (6.43)
MUST, Xe = 2.50 (Vr q/'5K LS C P (6.44)
MUSS, Xe = 0.79 (Vr q^0-65A°-omK LS CP (6.45)
in which
Xe = the event soil loss (metric tonnes day"1),
Vr = volume of daily runoff event (mm),
qp = peak storm runoff rate (mm/h),
A = field size (ha),
K = soil erodability factor (dimensionless),
LS = length-slope factor (dimensionless),
C = soil cover factor (dimensionless),
P = conservation practice factor (dimensionless).
MUST is a theoretical calculation and MUSS was specifically designed for small watersheds. The majority of
parameter values for Equations 6.43 through 6.45 are determined from other calculations within PRZM (e.g., Fr) or
are familiar terms readily available from handbooks.
Peak storm runoff rate, qp, is calculated using the Graphical Peak Discharge Method (Soil Conservation Service,
(Soil Conservation Service 1986):
9P - aquAVr Fp (6.46)
in which
qu = unit peak discharge rate, and
Fp = pond and swamp adjustment factor.
The parameter a is a units conversion factor. Fp has been preprogrammed to have a value of 1.0 in PRZM release 3.
The unit peak discharge rate, qu, is calculated by:
log(<7a) = C0 + qiog^) H- C2[log(7;)]2 (6.47)
in which Tc is the time of concentration (hour) and C0, C,, and C2 are regional coefficients that are related storm
intensity and precipitation volume (See Soil Conservation Service 1986). The meteorological files that drive PRZM
contain daily values of precipitation with no record of rainfall intensity over time. Therefore, rainfall intensity is
assumed to occur according to design storm distributions (Type I, IA, II, and III) developed by the Soil Conservation
Service from available National Weather Service duration-frequency data (Soil Conservation Service 1986).
Distributions and associated regions are provided in Figure 6.3 and Figure 5.8. respectively.
The SCS rainfall distributions were originally developed for flood control design and are biased to reflect intense,
brief rainfalls. As a result, seasonal modifications to the SCS design storms were introduced to better represent
periods that are characterized by longer duration precipitation events (e.g., frontal systems as opposed to
thunderstorms). Regional peak discharge coefficients derived from the rainfall distributions are contained in Table
6.1.
6-19
-------
The time of concentration, Tc, is defined as the time it takes water to flow from the furthest point in the watershed to
a point of interest within the watershed, and is a function of basin shape, topography, and surface cover. Tc is
calculated by summing the travel times for various designated flow segments within the watershed (Soil
Conservation Service 1986). PRZM release 3 is configured with two flow segmentsto be summed : sheet flow for the
first 100 meters and shallow, concentrated flow (unpaved) for the remaining portion of the hydraulic length. Tc is
given by:
T = a 0.007 (n L)(
(sheet flow)
,0.8
bL
3600(16.1345s°'5)
(shallow cone, flow)
in which
n
L
P
s
Manning's roughness coefficient for the watershed,
hydraulic flow length (m),
daily precipitation (cm), and
slope of the hydraulic grade line(land slope, m/m).
(6.48)
Table 6.1 Coefficients for Calculation of Unit Peak Discharge
Rainfall
Type
I
IA
IaP
0.10
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.10
0.20
0.25
0.30
0.50
C0
2.3055
2.2353
2.1821
2.1062
2.0030
1.8773
1.7631
1.6788
2.0325
1.9197
1.8384
1.7265
1.6341
c,
C2
0.01983
0.05754
0.00453
0.0
0.02633
0.0
Rainfall
Type
II
III
IaP
0.10
0.30
0.35
0.40
0.45
0.50
0.10
0.30
0.35
0.40
0.45
0.50
C0
2.55323
2.46532
2.41896
2.36409
2.29238
2.20282
2.47317
2.39628
2.35477
2.30726
2.24876
2.17772
c,
C2
-0.16403
-0.11657
-0.08820
-0.05621
-0.02281
-0.01259
-0.17083
-0.13245
-0.11985
-0.11094
-0.11508
-0.09525
Coefficients a and b are unit conversion factors. The term for shallow, concentrated flow is derived from Manning's
equation assuming a roughness coefficient, n, of 0.05 and a hydraulic radius of 0.2 (Soil Conservation Service 1986).
The enrichment ratio, rom, is the remaining term that needs to be defined to estimate the removal of sorbed pesticides
by erosion from the upper (top) soil layer. Because erosion is a selective process during runoff events, eroded
sediments become "enriched" in smaller particles. The sediment transport theory available to describe this process
requires substantially more hydraulic spatial and temporal resolution than is available in PRZM-3, leading to the
adoption of an empirical approach (Menzel 1972). The enrichment ratio for organic matter is calculated from
6-20
-------
ln(r ) = 2 - Q.2]n(X/AJ
(6.49)
6.3.6 Volatilization
Since volatilization was not included in the original release of PRZM, its theoretical basis is discussed in detail here.
The following key processes have been identified as being important in deriving the volatilization algorithms to
simulate vapor-phase pesticide transport within the soil/plant compartments:
• Vapor-phase movement of the pesticide in the soil profile
• Boundary layer transfer at the soil-air interface
• Vertical diffusion of pesticide vapor within the plant canopy
• Pesticide mass transfer between the plant (leaves) and the surrounding atmosphere
• Soil temperature effects on pesticide volatilization
The discussion of the volatilization algorithms is presented in four parts: influence of vapor phase pesticide in soil
and volatilization flux, volatilization flux through the plant canopy, volatilization flux from plant surfaces, and soil
temperature modeling and effects. Figure 6.5 is a schematic of the pesticide vapor and volatilization processes
considered in the PRZM-3 soil and plant compartments.
Plant Compartment
Volatilization Flux
»....
HORIZON
HORIZON 2
HORIZON 3
'{!
{;
{•
Volatilization
From Soil
Reference
Height
Canopy Height
Plant
Compartment
Air/Soil
Boundary Layer
Vapor Diffusion
Soil
Layer
Figure 6.5 Schematic of pesticide vapor and volatilization processes.
6.3.6.1 Soil Vapor Phase and Volatilization Flux
6-21
-------
The basic governing equations for chemical transport in the vapor phase were introduced previously in the
description of transport in the soil. Fluxes from the soil column in the vapor phase are summarized in that discussion
by Equations 6.3. 6.5. and 6.10. The terms in these equations are summed with the other flux terms to produce the
overall pesticide transport Equation 6.20. In addition to these new terms, the upper boundary of PRZM-3 was
changed from a zero-concentration boundary to a stagnant-layer boundary to allow diffusive transport upward from
the soil to the overlying atmosphere. This enhancement is discussed in detail in the following.
Surface boundary condition - When a pesticide is incorporated into the soil, the initial volatilization rate is a
function of the vapor pressure of the chemical at the surface as modified by adsorptive interactions with the soil. As
the concentration at the surface of the soil changes, the volatilization rate can become more dependent on the rate of
movement of the pesticide to the soil surface (Jury et al. 1983b).
The soil surface layer can be visualized as a membrane that allows water to pass through but keeps the solute behind.
Experimental results show that, within the top centimeter of the soil surface, pesticide concentrations can increase as
much as 10-fold due to the accumulation of chemical at the surface layer, resulting in higher vapor density. In order
to describe these phenomena, Jury et al. (1983a, 1983b) proposed a boundary layer model that states that the
controlling mechanism for pesticide volatilization is molecular diffusion through a stagnant surface boundary layer.
The layer of stagnant air may or may not form a significant barrier to volatilization loss for a given pesticide,
depending on a variety of factors. In general, if the diffusion rate through the air layer is able to match the upward
flux to the soil surface without having the surface concentration build up, then the stagnant layer is not acting as a
barrier to loss and the volatilization flux will not depend strongly on the thickness of the boundary layer. Conversely,
if the diffusion rate through the air is less than the flow to the surface by diffusion or mass flow, then the
concentration at the soil surface will not be close to zero, and the thickness of the air layer will regulate the loss by
volatilization. In other words, the significance of the boundary layer model depends on the ratio of the magnitudes
between the upward soil pesticide flux and the boundary layer diffusion flux. Only downward, advective movement
of water is treated in PRZM Release I. In this case, the sources that contribute to the upward soil pesticide flux are
only the diffusion processes in the vapor and dissolved phases, but not upward water advection.
The zero chemical concentration upper boundary condition in the first release was modified in accordance with
Jury's boundary layer model. The pesticide volatilization flux from the soil profile can be estimated as follows:
•A = ^J- (Cfti - C,) (6.50)
where
Jj = volatilization flux from soil (g day"1)
Da = molecular diffusivity of the chemical in air (cm2 day"1)
A = cross-sectional area of soil column (cm2)
d = thickness of stagnant air boundary layer (cm)
C , = vapor-phase concentration in the surface soil layer (g cm"3)
Cgd = vapor-phase concentration above the stagnant air boundary layer (g cm"3)
The thickness of the stagnant boundary layer can be estimated using a water vapor transport approach (Jury et al.
1983a). However, Wagenet and Biggar (1987) assumed a constant value of 5 mm for this thickness, which is
consistent with the values estimated by Jury. Consequently, the same assumption of a 5 mm thickness for the
stagnant layer has been used here pending the results of further sensitivity analyses. The value of C'gd can take on a
value of zero if the soil surface is bare or can be positive if a plant canopy exists.
6.3.6.2 Volatilization Flux Through the Plant Canopy
In pioneering work on this topic, Parmele et al. (1972) discuss a number of micrometeorological techniques for
calculating pesticide volatilization flux from observed aerial pesticide concentrations. Their procedures are based on
6-22
-------
the assumption that the vertical diffusivity coefficient (Kz) for pesticide vapor is analogous to the vertical diffusivity
for water vapor, energy, or momentum. The pesticide volatilization flux can be computed by Pick's first law of
diffusion, as follows
JZ(Z) - -Kz(Z) ^ (6.51)
where
JZ(Z) = pesticide gaseous phase flux at height Z (g m"2 s"1)
dP/dZ = pesticide gaseous phase concentration gradient (g m"2)
KZ(Z) = the vertical gaseous phase diffusivity at the height Z(m2 s"1)
The value of Kz depends on the turbulence of the atmosphere into which the pesticide vapor is dissipated. Therefore,
Kz is primarily a function of the prevailing meteorological conditions and not of any physical or chemical property of
the pesticide.
In order to apply these concepts, vapor phase pesticide concentrations at two or more heights are required to estimate
the pesticide gradient and the subsequent flux. For the estimation of vertical diffusivity, more extensive
meteorological information is also required. These data requirements pose significant limitations for a predictive
modeling approach.
In developing this PRZM-3 module, the following approaches circumvent the intensive data requirements. First, a
relationship for Kz is derived as a function of height within the canopy. Then only the pesticide concentration
gradient (or a suitable surrogate) is needed to compute the pesticide volatilization flux.
Estimation o£Kj7) - Mehlenbacher and Whitfield (1977) present the following formula to compute Kz at various
heights within a plant canopy.
K(Z) - K(ZCH) exp
4.0
ZCH
- 1.0
(6.52)
U* k(ZCH - D)
T- (6-53)
U* = " CH
*£/„„
(6.54)
ln[(ZCff-£)/ZJ H- 1rm$m
where
KZ(Z) = thermal eddy diffusivity at height Z (m2 s"1)
K*(ZCH) = thermal eddy diffusivity at canopy height (m2 s"1)
ZCH = canopy height (m)
Z0 = roughness length (m)
D = zero plane displacement height (m)
k = von Karman's constant, 0.4
if = friction velocity (ms"1)
4>A = stability function for sensible heat
tym($m) = integrated momentum stability parameter
4>m = stability function for momentum
UCH = wmd speed at the canopy height (m s"1)
For agricultural applications, the canopy height is used as a reference height for calculating if. The user must supply
the wind speed and the height at which the measurement was made. The wind speed at the canopy height (UCH) is
6-23
-------
u,
In
CH
U
In
(6.55)
where
Ur
Dr
DCH
Z0il.
Z
wind speed (m s"1) measured at the reference height Zr (m)
zero plane displacement height (m) associated with the measurement
zero plane displacement height (m) associated with the canopy
roughness length (m) associated with the measurement
roughness length (m) associated with the canopy
stability function for momentum associated with the canopy
stability function for momentum associated with the measurement
PRZM-3 assumes observations are made under neutrally stable atmospheric conditions. Under these conditions,
\|;m((j>m) is equal to zero and Equation 6.55 reduces to the standard logarithmic wind velocity profile:
U,
In
CH
U
In
(6.56)
Aerodynamic parameters for several conditions are given in Table 6.2. PRZM assumes Open Flat Terrain parameters
for wind speed computations. The user caan specify a reference height in the PRZM-3 input file (record 31,
ZWIND).
Table 6.2 Aerodynamic parameters for wind speed
Surface
Open Flat Terrain (Used
for Meteorological
Stations)
Class A Pan
Anemometer
FAO Reference
Short-Grass Crop
Open sea, fetch > 5km
Large Water Surfaces
Reference
height (m)
10
0.6
2
-
-
Roughness
Length Z0 (m)
0.03
0.01476
0.01476
0.0002
0.0001-0.0006
0.000228
computations. (Burns et
Zero Plane
Displacement D (m)
None (few isolated
obstacles)
0.08
0.08
Depends on sea state
Depends on sea state
al. 2005)
Reference
(U.S. Environmental
Protection Agency 2000)
Assumed approx. same as
FAO Short Grass
(Allen etal. 1998)
(U.S. Environmental
Protection Agency 2000)
The friction velocity if can be visualized as a characteristic of the flow regime within the uppermost part of the
plant canopy in which the logarithmic velocity distribution law holds. In Equation 6.54. if is calculated as a
6-24
-------
function of UCH, ZCH, Zm D, and i|;m. Rosenberg (1974) describes Z0 + D as the total height at which the velocity
profile above the canopy extrapolates to zero wind velocity. For very short crops (lawns, for example), Z0 adequately
describes the total roughness length, and little adjustment of the zero plane is necessary (i.e., D = 0).
For tall crops, Z0 is related to canopy height (ZCH) by
log Zo = 0.997 log(ZCT) - 0.883 (6.57)
In tall crops, Z0 is not an adequate description of the total roughness length; a value of D, the zero plane
displacement, is needed. For a wide range of crops and heights (0.02 m < ZCH < 25 m), the following equation for D
has been developed (Stanhill 1969).
logfi = 0.9793 log(ZCff) - 0.1536 (6.58)
This equation results from a linear regression analysis based on published data for 19 different crops with limited
data measured for the same crop at different growth stages. In the calculation of Kz, the PRZM-3 module uses these
latter two equations for estimation of Z0 and D, since there is no method available to justify any variations for crop
type, row spacing, or canopy density.
In PRZM, when the canopy height is less than or equal to 5 cm, D is assumed to be zero and Z0 is set to the value
given by Equation 6.57. evaluated at ZCH = 0.05 m.
With estimates of Z0 and D in hand, if (friction velocity) can be estimated if the values of the stability parameters
(i|;m and fyh) are known. These two variables are closely related to the Richardson number, Ri, which is the measure
of the rate of conversion of convective turbulence to mechanical turbulence. It is defined as follows (Wark et al.
1998):
R. = (gIT) (dT/dZ)
(dUldZy
2 (6.59)
where
g = acceleration of gravity (9.8m sec"2)
T = potential temperature (kelvin)
Z = height above ground surface (m)
U= wind velocity (m s"1)
Potential temperature is defined as the temperature that a parcel of dry air would acquire if brought adiabatically
from its initial pressure to a saturated pressure of 1000 millibars (Perkins 1974). In application of this model, the
measured temperature is used in the Richardson number calculation, as suggested by Rosenberg (1974).
The sign of Ri indicates the atmospheric condition, and its magnitude reflects the degree of the influence. There are
several different formulas for relating^/' to the atmospheric stability parameters; for present purposes, the sign of Ri
is of greater concern than its magnitude. WhenRi is larger than 0.003, the atmosphere exhibits little vertical mixing,
reflecting stable conditions; when the absolute value of Ri, \Ri, is less than 0.003, neutral stability conditions exist
(Oliver 1971); and whenRi is less than -0.003, convective mixing becomes dominant and atmospheric conditions are
unstable.
The nominal range of the Richardson number is -2.0
-------
c =
Ri
Ri
Ri>0
1 - 5Ri
(6.60)
The definition of C preserves the sign of the Richardson number (Ri), i.e., both quantities are positive, or both are
negative, or both are equal to zero. Therefore atmospheric stability can also be deduced from the sign of (. When the
estimated Ri> 0.2, PRZM-3 sets Ri to 0.19 and continues execution. For neutral conditions (Ri = 0 or ( = 0), cj>m = 4>h=
l,andi|fm = 0.
The stability functions for momentum (cj>m) and sensible heat (cj>h) are given by:
/4 C<0
C^o
(6.61)
c
1 V™
(6.62)
C
-------
Cgl = pesticide vapor concentration in top soil layer (g cm"3)
For those conditions in which plants can act as significant pesticide sources or sinks, another approach must be
taken. The influences of plant canopy require the formulation for the surface boundary condition as described in the
following two sections.
6.3.6.3 Volatilization Flux from Plant Surfaces
A detailed description of the controlling factors for volatilization from plant surfaces has been presented by Taylor
(1978). He indicated that the distribution of the pesticide residues over the plant surface appeared to be the dominant
factor. This, together with the influence of the microscale climate at the plant surface, makes accurate simulation of
plant volatilization processes very difficult.
For organophosphate insecticides, Stamper et al. (1979) have shown that the disappearance rates from leaf surfaces
can be estimated by a logarithmic or a first-order kinetics approach. Similar observations of first-order kinetics were
found for volatilization of 2,4-D iso-octyl ester from leaf surfaces by Grover et al. (1985). Thus, a simple rate
constant approach is possible that requires the user to input the first-order loss rate constant for volatilization. The
plant leaf volatilization flux can be estimated as follows.
JPi-
dz (6.68)
i
0.5 ZCH
!
where
Jpi = pesticide volatilization flux from the leaf (g cm"2 day"1)
M = foliar pesticide mass (g cm"2)
Kf = first-order volatilization loss rate constant (day"1)
A resistance type approach is also applicable for volatilization flux estimation from plant leaves. The current code
employs the first-order kinetics approach to calculate pesticide volatilization flux from plant leaf surfaces described
above. This approach, that requires the user to specify the first-order rate constants for plant leaf volatilization, was
selected because it is consistent with the foliar fate model in PRZM Release I.
Average pesticide concentration in plant canopy - Volatilization flux from plant leaves (Jpl) will be calculated only if
pesticide application to the plant foliage has been specified in the model input. Whenever a plant canopy exists, the
averagepesricide concentration in the air within the plant canopy can be estimated as follows.
^ - (^ + Jpl) £ *0.5 (6-69)
where
C* = average concentration in the air between the ground surface and the plant canopy height (g cm"3)
SR05 = canopy resistance from half canopy height to the top of the canopy
.3.6.4 Soil Temperature Simulation
Soil temperature is modeled in PRZM release 3 to correct the Henry's law constant, KH, for temperature effects, to
simulate temperature dependent degradation, and to limit infiltration during snowmelt and precipitation when the soil
is frozen. The interaction of its microclimate with the soil surface that results in a given soil temperature regime is
complex and dynamic. Soil surface configuration and plant residue cover, both affected by tillage, have significant
impacts on soil heat flux and, therefore, soil temperature. Studies of tillage and residue effects on soil temperature
have been dominated by qualitative observations and site-specific measurements. The lack of mathematical
evaluation and supporting field data has limited the ability of researchers to predict, beyond qualitative terms, the
6-27
-------
tillage and residue effect on soil temperature for soil and climatic conditions other than those under which data have
been collected.
The objective of the soil temperature model is to provide a scientifically sound and usable approach: (i) to predict
with reasonable accuracy the daily average soil temperatures at the soil surface and in and below the root zone,
utilizing basic soil physical and thermal properties, and daily climatic measurements taken at weather stations; and
(ii) to allow consideration of the residue, canopy, and tillage effects on soil temperature.
Several models are available to predict soil temperature under various soil surface conditions, but there are
restrictions to the general use of these models because either they need large data bases that are not available at
many places, or they are site-specific. Existing soil temperature models form two general groups: (1) process-
oriented models, which require detailed information on soil and surface characteristics, initial and boundary
conditions, and inputs, and (2) semi- or non-process-oriented models, which often utilize weather station information
and soil temperature information at one depth to develop empirical relationships for extrapolation to other locations.
Table 6.3 summarizes the key characteristics of the soil temperature models reviewed in this work. For both the
process and semi-process oriented models, the two primary components are estimation of soil surface (or upper
boundary) temperatures and soil profile temperature utilizing the calculated or estimated surface temperature as the
upper boundary condition. A number of the models utilize the same procedure for calculating temperature in the soil
profile (Gupta et al. 1981, Wagenet and Hutson 1987) and differ only in the procedures for specifying the surface
boundary condition.
Van Bavel and Hillel (1976b, 1976a) developed a dynamic numerical procedure to link the process-oriented
simulations of heat movement in the soil and the partition of heat and energy at the soil surface. Soil surface
temperature, Tm is calculated as a factor in predicting evaporation from a bare soil. Their technique utilized
simultaneous solutions of seven equations with seven unknowns: net radiative flux, evaporation rate, air sensible
heat flux, soil sensible heat flux, surface soil temperature, Richardson's number, and the saturation humidity at the
surface soil temperature. Heat and water (liquid) flows are each coupled at the soil surface. An iterative procedure
was used at each update to find the proper soil surface temperature. Soil profile temperatures were then estimated
(Wierenga and de Wit 1970) by using these estimates of T0 as the surface boundary condition. Inputs required for
this model include solar radiation, air and dewpoint temperature, wind speed, initial soil temperature profile, and the
surface roughness evaluated by its effect on the aerodynamic roughness parameter. No comparisons were made
between predicted and measured soil temperatures. Thibodeaux (1979) describes a similar energy-balance procedure
for calculating soil surface temperatures.
For modeling soil profile temperatures, Hanks et al. (1971) used a numerical approximation for the one-dimensional
soil-heat flow equation. This method requires the input of initial and boundary conditions, as well as the soil thermal
conductivity and heat capacity as a function of depth and time. Predicted root zone soil temperature profiles were
within 1°C of observed values for a 3-day period, but this model needs estimated or measured soil surface
temperatures as its upper boundary condition.
Using the Hanks et al. (1971) procedure for the root zone, Gupta et al. (1981, 1982, 1983, 1984) developed a model
for estimating hourly soil temperature by depth from meteorologic data. Inputs needed for this model include hourly
air temperature at the 2-m height; daily maximum and minimum soil temperatures; initial soil temperature with
depth; and soil thermal diffusivity, that can be estimated from soil mineral composition, organic matter percentage,
bulk density, and soil water content. The upper boundary temperature is estimated by a sine function. The amplitude
of the function is equal to the difference between daily maximum temperatures of air and soil surface or daily
minimum temperatures of air and soil surface. Empirical curves, relating daily maximum air temperature to daily
maximum soil surface temperature and daily minimum air temperature to daily minimum soil surface temperature,
were developed for different residue and tillage conditions for the specific application site. These relationships
provided a means of accounting for residue and tillage effects on soil temperature, but require site-specific data.
The correction for temperature dependent degradation is based on the Q10 equation (similar to an Arrhenius
equation).
6-28
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Q10FAC = QFAC(T~TBASE)'W (6.70)
where
Ql OF AC = correction factor for biodegradation based on the actual temperature
QFAC = factor for rate increase when temperature increases by 10°C
T = actual soil temperature
TBASE = temperature during the test of biodegradation
The soil temperature model in PRZM-3 is derived from a combination of the work by van Bavel and Hillel (1976b)
and Thibodeaux (1979) for estimating the soil surface/upper boundary temperature. The soil profile temperature
procedures are those developed by Hanks et al. (1971) and applied by Gupta et al. (1981, 1982, 1983)and Wagenet
and Hutson (1987).
6-29
-------
Table 6.3 Summary of Soil Temperature Model Characteristics
Gupta
etal.
vanBavel (1981,
andHillel Thibodeaux 1982, Parton
Model/Author(s) (1976a) (1979) 1983) (1984)
1) Type of Model:
a)Process-Oriented X X
b)Semi-Process-Oriented X X
c)Non-Process-Oriented
2)Heat Flow Process
a)Conduction X XX
b)Convection
c)Radiation X X
3)Upper Boundary Temperature
a)Est. by Energy Partitioning X X
b)Est. by Empirical Relationship X X
4)Soil Temperature Profile: (Solving 1-D Heat Flow Eqn. Using the Procedure of:)
a) Hanks etal. (1971) X EX
b)Wierenga and de Wit (1970) X*
c)Curve Fitting
5)Input Data Required
a)Daily Max and Min Air Temp. X X
Hasfurther
and Wagenet
Cruse et al. Burman Williams et and Hutson Chen et al.
(1980) (1974) al. (1984) (1987) (1983)
XXX
X X
XXX
X
X X AT
X ME AVE
X
X" X DD
XXX
6-30
-------
Table 6.3 Summary of Soil Temperature Model Characteristics
Model/Author(s)
b)Daily Max and Min Soil
Surface Temperature
c)Hourly Air Temperature
d)Hourly Solar Radiation
e) Surface Albedo
fjWind Velocity
g)Humidity/Dewpoint Temp.
h)Canopy Shadow/Ht. of Veg.
i)Soil Water Content
j)Soil Bulk Density
k)Soil Mineral Composition
l)Percentage Organic Matter
van Bavel
and Hillel
(1976a)
X
X
X
X
X
X
X
X
X
X
Thibodeaux
(1979)
X
X
X
X
X
X
Gupta
etal.
(1981,
1982,
1983)
Parton
(1984)
X I
X
X
X
X
X
XX
Cruse et al.
(1980)
XX
X
X
at 5 cm
X
X
X
Hasfurther
and
Burman
(1974)
Williams et
al. (1984)
XX
X
X
X
Wagenet
and Hutson
(1987)
X
X
X
X
Chen et al.
(1983)
X
X
X
X
X
6)Soil Surface Condition
a)Residue Cover XXX X 100%
b)Tillage Condition XXX
c) Crop Canopy X X X X X
7)Time Step
6-31
-------
Table 6.3 Summary of Soil Temperature Model Characteristics
Model/Author(s)
a)Hourly
b)Daily
van Bavel
and Hillel
(1976a)
X
Thibodeaux
(1979)
Gupta
et al. Hasfl
(1981, an
1982, Parton Cruse etal. Bun
1983) (1984) (1980) (19
XXX
irther
d Wagenet
nan Williams et and Hutson Chen et al.
74) al. (1984) (1987) (1983)
X X
X X X X X
* - Horton et al. (1984) used a 2-D heat flow equation.
** - Regression equation is fitted for soil temp at 5 -cm depth.
AVE - "Average" measured soil surface temperatures are used.
AT - Ambient air temperature is used as upper boundary temperature.
DD - Damping depth parameter is used to predict soil temperature at different depths.
XX - Total daily solar radiation.
EX - Explicit Finite Difference Scheme.
ME - Simplified mathematical relationship involving solar radiation, surface albedo, and daily min and max air temperatures.
6-32
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Estimating upper boundary temperature - An energy balance procedure is used in PRZM-3 to estimate soil surface
temperature (van Bavel and Hillel 1976b, Thibodeaux 1979). The same procedure is used in the POSSM model
(Brown and Boutwell 1986), that employed PRZM-2 as a framework for PCB fate simulation.
The basic energy-balance equation with terms having units of cal cm"2 day"1 at the air/soil interface is described as:
Rn - Hs - LEs - Gs = Am (6.71)
where
Rn = net radiation (positive downward)
Hs = sensible air heat flux (positive upward)
LES = latent heat flux (positive upward)
G^ = soil heat flux (positive downward)
ATH = change in thermal energy storage in the thin surface soil layer (cal cm"2 day"1)
The term ATH is also given by:
ATH = (pbd)s(Ti+l - T,.) (6.72)
where
pb = bulk density of soil (g cm"3)
d = thickness of a thin, surface soil layer (cm)
s = the specific heat capacity of soil (cal g"1 "C"1)
Tf Ti+l = the representative temperature for the surface layer at two consecutive time steps and can be
represented as the average of the temperatures at the top and bottom of thethin, surface
soil layer (°C"Vday).
For evaluating the heat exchange across the air/soil interface, the top soil layer thickness, d, can be set to a small
value so that ATH may be neglected. As a result, the right side of Equation 6.71 can be set equal to zero.
Net radiation flux at any surface can be represented as:
Rn = (*, - *.) + (*fa - *to) - *fc (6.73)
where
Rn = the net radiation flux (cal cm"2 day"1)
Rs = incident short-wave solar radiation (cal cm"2 day"1)
R^ = reflected short-wave solar radiation (cal cm"2 day"1)
Rla = incident long-wave atmospheric radiation (cal cm"2 day"1)
Rlar = reflected long-wave atmospheric radiation (cal cm"2 day"1)
R,s = long-wave radiation emitted by the soil (cal cm"2 day"1)
The terms Rs and R^. include both the direct and diffuse short-wave radiation, and are related as follows.
R,= aR* (6.74)
where
a = the albedo of the surface (dimensionless)
Therefore, the short-wave radiation component of the energy balance is
R. ~ Rv = */! - «) (6.75)
The incident short-wave radiation can either be measured directly using pyranometers or calculated using a variety
of available empirical relationships or nomographs. PRZM-3requires input of radiation daily time series, whether
measured or calculated, in order to simulate soil temperature.
6-33
-------
The albedo of a canopy-covered land surface can be estimated as:
o(l) - acC(i) + as[l - C(t)} (6.76)
where
a(t) = albedo on day t
ac = albedo of canopy cover (0.23 for vegetation)
C(t) = canopy cover on day t (fraction)
as = albedo of soil surface (dimensionless)
Since the albedo of a soil surface changes with the soil surface condition, its value must be defined by the user as 12
monthly values corresponding to the first day of each month; the albedo value for each day is interpolated between
the neighboring monthly values. For snow cover less than 0.5 cm, the surface albedo is estimated using Equation
6.76. and for snow cover above 0.5 cm, the surface albedo is set equal to the snow albedo value (0.80).
The incident long-wave atmospheric radiation, Rla, is represented as
Rla = ea ° T*a (6-77)
where
ea = emissivity of the atmosphere (dimensionless)
a = the Stefan-Boltzmann constant (11.7 x 10'8 cal cm2 K'4 day'1)
Ta = the air temperature (K)
Wunderlich (1972) proposed a correction to Equation 6.77 for the effects of cloud cover, that can increase Rla by up
to 25 percent under overcast conditions. However, this correction is not included in the model because it would
require input of a cloud cover time series, and the effect on the calculated soil surface temperature would be small.
The emissivity of the atmosphere varies from a low of 0.7 to almost unity. Numerous empirical relationships for
estimating ea have been proposed (Salhotra 1986). A simple, reliable method is the use of Swinbank's formula:
ea = 0.936xl(T5 T2a (6.78)
The reflected long-wave radiation, Rlar, can be expressed as:
Riar = Ria C1 - Y) (6.79)
where
Y = the reflectivity of the surface for long-wave radiation (dimensionless)
The resulting net atmospheric long-wave radiation component becomes:
R,a ~ Riar =" **,(! - Y) = 0.936X10-5 T6a o (1 - Y) (6.80)
The long-wave radiation component emitted by the soil surface is represented in an analogous equation to the
atmospheric component, as follows
Rls - e* ° TS (6-81)
where
es = infrared emissivity of soil (dimensionless)
o = the Stefan-Boltzmann constant (11.7 x 10'8 cal cm2 K'4 day"1)
Ts = soil surface temperature (K)
Since the soil emissivity and reflectivity are related as es = 1-y, we can replace (1 - y) in Equation 6.80 with ef
Combining the radiation components from Equations 6.75. 6.80. and 6.81. the net radiation flux is calculated as
6-34
-------
follows.
Rn = (1 - a)Rs + 0.936xlO-5orfl6es - esoT* (6.82)
The evaporative heat flux, LES, is estimated by:
LES = \iE pw (6.83)
where
p, = latent heat of vaporization/unit quantity of water (580.0 cal g"1)
E = evaporation rate (cm day"1)
pw = density of water (1.0 g cm"3)
The evaporation rate , E, is obtained from the evapotranspiration (EVPOTR) subroutine of PRZM. It is assumed that
the calculated evapotranspiration from the top 5 cm of soil represents the potential evaporation energy loss at the
air/soil interface. However, only a fraction of the evapotranspiration loss calculated by PRZM contributes to this
heat flux. This fraction is estimated as the portion of the land surface not covered by vegetation, (i.e., 1.0 - canopy
cover).
The sensible air heat flux, Hs, is given by:
H. = Pa Cpa * (TS ~ TJ (6.84)
where
pa = air density (g cm"3), estimated by pa = (-0.0042 Ta + 1.292) x 10"3 (Thibodeaux (1979))
Cpa = specific heat of air at constant pressure (0.2402 cal g"1 K"1)
h = heat transfer coefficient at air-soil interface (cm day"1)
Ta = the air temperature (°C)
The heat transfer coefficient is given by:
h-Klv^{^L-^\ (6.85)
V z° I
where
K] = Von Karman's number (0.4)
Fz = wind velocity (cm day"1)
Zm = reference height at which Vz is measured (m)
D = zero plane displacement (m)
Z0 = roughness height (m)
Equation 6.85 is valid only when the air temperature does not vary greatly with height, as is often the case near
sunrise or sunset or under cloudy skies or when canopy heights are relatively small. It appears to be a reasonable
approximation for most agricultural crops. Correlations have been developed relating Z0 and D to the canopy height
as described previously in this section by Equations 6.57 and 6.58. respectively.
From the fundamental equation of heat conduction, the soil heat flux, Gs, is given by:
G. - (Ts ~ 7",) VD, (6.86)
where
T\ = temperature of the soil at bottom of layer 1 (K)
Ts = soil surface temperature (K)
A! = thermal conductivity of layer 1 (cal cm"1 day"1 K"1)
Dl = thickness of layer 1 (cm)
6-35
-------
Substituting Equations 6.82. 6.83. 6.84. and 6.86 into Equation 6.71 produces a polynomial in Ts:
esaT* + [PaC h H- VD,] T, - [(l-o)*, + Q.936*W-5aT6aes +
(6.87)
The value of Ts at each time step is estimated by solving the above equation using an iterative solution based on the
Newton-Raphson method. The initial estimate of soil surface temperature is taken to equal to the measured air
temperature. The value for T, is obtained from the previous time step. These calculations are repeated until the
difference between two consecutive estimates for soil surface temperature is less than the preset convergence criteria
(setto0.1°C).
Simulation of heat flow through the soil profile - The soil profile temperature model is based on the one-dimensional
partial differential equation describing heat flow in soils:
where
d = the soil thermal diffusivity.
The thermal diffusivity is equal to the ratio of thermal conductivity and heat capacity of the soil. The procedures
used to estimate soil thermal conductivity and heat capacity are taken from de Vries (1963). They are calculated
from basic soil properties - soil water content, mineral composition, texture, and thermal conductivity of the
individual soil particles. These parameters are either input or supplied by the model in the simulation. The thermal
diffusivity is given by:
d = A/C (6.89)
where
d = thermal diffusivity of the soil layer (cm2 day"1)
A = thermal conductivity of the soil layer (cal cm"1 day"1 0C"')
C = heat capacity per unit volume of the soil layer (cal cm"3 "C"1)
Temperature effect - A detailed discussion of the temperature effect on the volatilization behavior of pesticides is
presented by Streile (1984). Two parameters that influence the vapor-phase transport in the soil profile are Henry's
constant and the vapor diffusion coefficient.
The equation used to correct Henry's constant for temperature effects is (Streile 1984):
KH(T) = KHl exp
Aff a
vap
R
(6.90)
where
KHtl = Henry's constant at the reference temperature Tl
klfvap = partial molar enthalpy of vaporization from solution (J mole"1)
The temperature effect on the vapor phase diffusion coefficient can be estimated from the Fuller correlation as
presented in Liley and Gambill (1973). However, it is not implemented in the PRZM-3 code due to the general lack
of information required to use it.
6.3.7 Irrigation Equations
PRZM-3 irrigation algorithms determine depths of irrigation water to be applied at the soil surface. These depths are
computed based on the available soil water deficit, and are added as infiltration to the first (uppermost) PRZM soil
compartment. Above- and below-canopy sprinklers, flooding, and furrow irrigation can be simulated. Methods for
6-36
-------
computing water application depths/amounts for each type of irrigation are described in the following paragraphs.
6.3.7.1 Soil Moisture Deficit
Irrigation is triggered in PRZM-3 when the soil moisture volume in the active root zone (whose depth increases
during crop development) falls to a user-defined fraction (with permissible range of 0.0 to 0.9) of the available water
capacity (Qfc-Qwp). The soil moisture deficit, D, is then given by:
D = (9/c - Bz) Zr (6.91)
where
D = soil moisture deficit (cm)
6Z = active root-zone soil moisture content (cm3cm"3) on the current day
Qfc = active root-zone soil moisture content at field capacity (cm3cm"3)
Zr = active root zone depth (cm) (varies with crop stage)
D is the depth of water over the unit area that must be added to the soil by irrigation to bring the soil water content
up to field capacity.
Rainfall can also occur on the same day as irrigation water has been applied: PRZM assumes that the decision to
irrigate has been made and implemented prior to the beginning of the rain event. This behavior probably imitates
most agricultural practice, i.e., apparent crop needs are likely in most instances to weigh more heavily than do
uncertain weather forecasts in a decision to irrigate the crop. This rather conservative, although not unreasonable,
assumption can lead to significant runoff and erosion producing events, for example, from a field that has been
irrigated in the morning and then receives an additional soaking from an afternoon convective storm.
6.3.7.2 Sprinkler Irrigation
Irrigation water from sprinklers can be applied either above or below the crop canopy. When applied above the crop
canopy, irrigation water is intercepted by the canopy and may or may not run off when it reaches the soil surface. (At
the user's option, however, runoff can be prevented, thereby invoking an assumption that irrigation rates are
generally controlled intentionally to avoid exceeding the infiltration rate.) The depth of water applied during a daily
PRZM-3 time step by overcanopy sprinklers is estimated from the soil moisture deficit as:
Da = D (1 + LF) + If (6.92)
where
Da = depth of irrigation water applied to the field (cm)
LF = a factor specified by the user to allow for the practice in saline soils of adding
water to leach salts out of the root zone (fraction of Da)
If = crop canopy interception capacity (cm)
The water depth Da is applied as "precipitation" above the crop canopy, and canopy interception is computed for the
current crop situation in the PRZM-3 crop growth subroutines. Unless the user specifies that irrigation is controlled
to prevent runoff, sprinkler runoff from the soil surface is estimated using the SCS curve number approach, assuming
that runoff characteristics of sprinkler water are similar to those of precipitation. Water that does not run off
infiltrates into the first (uppermost) PRZM-3 soil compartment.
Irrigation water applied below the crop canopy is not subject to canopy interception losses. The depth of water
applied by undercanopy sprinklers is therefore, is given by:
Da = D (1 + LF) (6.93)
The irrigation water depth APDEP is applied as throughfall to the soil surface; potential sprinkler runoff is also
estimated using the SCS curve number approach.
6-37
-------
In some instances, the sprinkler system may be unable, due to hydraulic limitations, to deliver water at the rate
needed to meet the required daily application depth. In these cases, the sprinkler application depth Da is set equal to
the maximum depth that the system can deliver. The user, therefore, is required to input the maximum water
application rate Rmm (cm hr"1) for the particular sprinkler system to be used.
6.3.7.3 Flood Irrigation
Flood irrigation, in this case, refers to the practice of flooding entire fields with irrigation water. Flood-irrigated
fields are diked around the edges to allow water to pond and infiltrate into the soil. In the PRZM irrigation
algorithm, it is assumed that this water ponds uniformly over the entire field. The amount of water applied to the soil
surface is then:
Da = D (1 + LF) (6.94)
Since the field is assumed to be diked around the edges, no water is allowed to run off from the field.
6.3.7.4 Furrow Irrigation
Furrow irrigation involves the release of water into numerous small channels that cut across the planted field.
Infiltration depths within furrows vary due to differences in times at which water reaches various locations down the
furrow, with less water infiltrating at the downstream end (Figure 6.6). Hydraulic characteristics of the furrow
determine how quickly water moves down the channel, while soil characteristics determine the rate of infiltration
once water reaches a location in the furrow.
a.
CD
Q
c
o
"on
Distance Along the Furrow
Inflow
End
Outflow
End
Figure 6.6
Variability of infiltration depths within an irrigation furrow.
The PRZM-3 furrow irrigation model computes daily infiltration depths at various locations down the length of the
furrow. This requires solution of the open channel flow equations of motion coupled with a soil infiltration model.
Model developers have made numerous attempts to solve the furrow-irrigation advance problem, ranging in
complexity from empirical volume-balance solutions (Fok and Bishop 1965, Wilke and Smerdon 1965) to numerical
solutions of the full open channel flow equations of motion (Bassett and Fitzsimmons 1974). In general, solutions of
the full equations of motion are too computationally intensive for this application, while simpler empirical models
involve infiltration parameters that are not easily related to physical soil characteristics.
The PRZM-3 furrow advance model uses the kinematic wave simplification of the equations of motion coupled with
the Green-Ampt infiltration model to determine furrow infiltration depths. Kinematic-wave theory neglects inertia!
6-38
-------
accelerations and assumes that the water surface slope is equal to the ground slope. The equations of motion then
reduce to:
dQ dA dq
—^ + = - —i- ((. 951
dz dt Bt l ' >
where
Q = flow rate in the channel (m3 s"1)
A = cross-sectional area of flow (m2)
z = distance down the furrow (m)
q = lateral flow infiltrated per unit length of channel (m3 /m s))
The flow area A is related to the flow rate Q by Manning's equation:
Q = 1 ARM S™ (6>%)
where
n = Manning's roughness coefficient
R = the hydraulic radius of flow (m)
S = the channel slope (vertical/horizontal)
Section 6.4.4 explains how the solution of the horizontal furrow irrigation equation is applied in PRZM-3.
6.3.8 Nitrogen Species Algorithms
Nitrogen species reactions can be divided between those that are chemical in nature and those that are a combination
of chemical and biological reactions. The adsorption and desorption of ammonium is a chemical process. The user
has the option of simulating ammonium adsorption and desorption by first order kinetics with subroutine FIRORD or
by the Freundlich isotherm method with subroutine SV (discussed in the following).
The other nitrogen species reactions are a combination of biological and chemical transformations. They all can be
simulated with first order kinetics, but plant uptake can optionally use another algorithm (described later). The
optimum first order kinetic rate constant is corrected for soil temperatures below 35°C by the generalized equation:
KK = KxTHT~35 (6.97)
where:
KK = temperature corrected first order transformation rate constant (per day)
K = optimum first order reaction rate constant (per day)
TH = temperature coefficient for reaction rate correction (dimensionless) (typically about 1.06)
T = soil layer temperature (°C)
Soil temperature must also be simulated when nitrogen species transformation processes are being simulated with
PRZM-3. When temperatures are greater than 35°C, the rate is considered optimum, that is, KK is set equal to K.
When the temperature of the soil layer is below 4°C or the layer is dry, no biochemical transformations occur.
Identifiers with a leading "K" (e.g., KDNI) are the optimum rates; those for corrected rates have both a leading and
trailing "K" (e.g., KDNIK). The corrected reaction rate constants are determined every day and multiplied by the
respective storages as shown in Figure 6.7 to obtain the reaction fluxes.
Denitrification is also modeled as a first-order rate, but it is dependent on soil moisture levels following procedures
used in GLEAMS (Knisel et al. 1994). The user controls the starting point of denitrification by specifying the initial
"% saturation" soil moisture level, the denitrification rate then increases linearly to a maximum at saturation (at the
current soil temperature value).
6-39
-------
The biochemical reaction rate fluxes that are shown in Figure 6.7 are coupled, that is, added to and subtracted from
the storages simultaneously. The coupling of the fluxes is efficient in use of computer time but has a tendency to
produce unrealistic negative storages when large reaction intervals and large reaction rates are used jointly. A
method has been introduced into PRZM-3 that modies the reaction fluxes so that they do not produce negative
storages. A warning message is issued when this modification occurs.
Den itrifi cation
Atmospheric
Deposition
Volatilization
Atmospheric
Deposition
•Return of above ground plant and litter N occurs to
surface solid horizon only
Figure 6.7
PRZM-3 soil/plant nitrogen transformations.
6.3.8.1 Ammonia Volatilization
Ammonia volatilization is included to allow large concentrations of ammonia in the soil resulting from OSWDS
(On-site Wastewater Disposal System, i.e., septic systems) inputs, animal waste, and fertilizer applications to be
attenuated by losses to the atmosphere. A simple, first-order rate expression is used in PRZM-3 with an adjustment
for air temperature. The original formulation (Reddy et al. 1979) was adjusted for the soil cation exchange capacity
(CEC) and wind speed, and automatically turned off after seven days. In the PRZM-3 implementation, we assume
that the constant CEC factor is incorporated into the first-order rate constant, and the wind speed (air flow) is always
large enough to result in maximum loss (Reddy's equation reduced volatilization only when wind was less than 1.4
km/day). Also, we calculate the volatilization rates for each soil horizon such that the rates decrease as the ammonia
moves down through the soil column. The volatilization flux in each layer is computed as:
where:
AMVOL =
AMSU =
AMVOL = AMSU-KVOL-TCVOLT~20
loss of ammonia (mg I"1 day"1)
dissolved ammonia concentration (mg/1)
(6.98)
6-40
-------
KVOL = rate constant at 20°C (day"1)
TCVOL = temperature correction coefficient (dimensionless)
T = air temperature (°C)
The temperature correction for volatilization of ammonia is slightly different than that described for the other first-
order rate processes. The reference temperature can be user-specified; since rates in the literature are often given at a
temperature of 20°C, we use this value as the default. Also, the rate will be adjusted upwards when the soil
temperature exceeds the reference temperature.
6.3.8.2 Sorption/Desorption of Ammonium
When FORAFG = 0, the adsorption and desorption reaction fluxes of ammonium chemicals are simulated with the
FIRORD subroutine using temperature-dependent first-order kinetics. The calculation of these fluxes by first-order
kinetics for soil temperatures less than 35°C takes the form:
DES = CMAD-KDS-THKDSTMp-35 (6.99)
ADS = CMSU-KAD-THKAD™p-35 (6.100)
where:
DES = current desorption flux of chemical (mass/area per time interval)
CMAD = storage of adsorbed chemical (mass/area)
KDS = first-order desorption rate constant (per time interval)
THKDS = temperature correction coefficient for desorption
TMP = soil layer temperature (°C)
ADS = current adsorption flux of chemical (mass/area per time interval)
CMSU = storage of chemical in solution (mass/area)
KAD = first-order adsorption rate constant (per time interval)
THKAD = temperature correction coefficient for adsorption; THKDS and THKAD are typically about 1.06.
All of the variables except the temperature coefficients can vary with the layer of the soil being simulated. As noted
previously, soil temperature must be simulated when nitrogen is being simulated. The temperature correction of the
reaction rate constant is based on the Arrhenius equation. At temperatures of 35°C or above, no correction is made.
When the temperature is at 0°C or below or the soil layer is dry, no adsorption and desorption occurs.
When FORAFG = 1, subroutine SV simulates sorption/desorption based on the Freundlich isotherm that unlike first-
order kinetics, assumes instantaneous equilibrium. That is, no matter how much chemical is added to a particular
phase, equilibrium is assumed to be established between the solution and adsorbed phase of the chemical. This
method also assumes that for any given amount of chemical in the soil, the equilibrium distribution of the chemical
between the soil solution and on the soil particle can be found from an isotherm.
The adsorbed and solution phases of ammonium in this subroutine are related by a modification of the standard
Freundlich equation. When the amount of chemical is less than the capacity of the soil particle lattice to permanently
bind the chemical (XFIX), then all the material is consider fixed. All the fixed chemical is contained in the adsorbed
phase of the soil layer storage. Otherwise, the Freundlich equation is used to determine ammonium/chemical
partitioning between into the adsorbed and solution phases is:
X= KFlxC1/N1 + XFIX (6.101)
where:
X = chemical adsorbed on soil (ppm of soil)
KF1 = single value Freundlich K coefficient
C = equilibrium chemical concentration in solution (ppm of solution)
Nl = single value Freundlich exponent
XFIX = chemical that is permanently fixed (ppm of soil)
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This equation is solved in subroutine ITER by an iteration technique. The parameters used in the computation can
differ for each soil horizon.
6.3.8.3 Nitrogen Inputs
Inputs of nitrogen to the surface and subsurface soil horizons can include OSWDS (On-site Wastewater Disposal
System, i.e., septic system effluent) loadings, atmospheric deposition, and nitrogen additions through fertilizer
and/or manure applications. All nitrogen inputs are defined in their elemental forms as NO3-N, NH4-N, and organic
N; for each of the three input forms further restrictions apply on the form and species of the applied amounts
(discussed in the following).
OSWDS loadings can be input as user-defined WDM files, or as output files generated by either or both of the
treatment options included in the OSWDS module; they are then input to a specific PRZM soil horizon defined by
the user (see Section 4.2.3 for a complete discussion).
Two basic types of atmospheric deposition are simulated. Dry deposition is considered to be a flux input over the
land surface independent of rainfall. Wet deposition is considered to be a concentration of a nitrogen species
dissolved in the input precipitation. If data is available as a total flux only, it should be input as dry deposition. All
deposition inputs are added to the surface soil horizon, and are assumed to be input as NO3-N, adsorbed NH4-N, and
paniculate labile organic N. See Section 4.2.2 for a discussion of input methods.
When atmospheric deposition is being simulated, the soil storage in the surface horizon is updated for each of these
three species of nitrogen using the formula:
N(I+ 1) = N(I) + ADFX + PRECxADCN (6.102)
where:
N(I) = storage of nitrogen species in the soil surface layer on day /, in mass/area
ADFX = dry or total atmospheric deposition flux in mass/area per time interval
PREC = precipitation depth
ADCN = concentration of nitrogen species in wet atmospheric deposition in mass/volume
Nitrogen applications with fertilizers or manure is accomplished in a manner analogous to pesticide applications.
Application dates are specified for the entire simulation period, along with the specific amounts of each N form,
NO3-N, NH4-N, and organic N, the depth of incorporation for each application, and the labile fraction of the applied
organic N. See Section 4 for a discussion of input methods.
6.3.8.4 Plant Uptake
Plant uptake of soil nutrients in PRZM-3 can be modeled by two alternative methods using the NITR module. When
NUPTFG = 0, plant uptake is represented as a first-order rate process with an Arrhenius temperature correction
adjustment based on simulated soil temperatures. The first-order plant uptake rates are defined by the user, can be
specified separately for each soil horizon within PRZM, and can vary for each month to approximate the monthly
pattern of crop growth and nutrient uptake. The rates are adjusted during calibration to mimic the expected annual
nutrient uptake and the seasonal pattern for the specific crop and practice. Plant uptake can be distributed between
nitrate and ammonium by input parameters intended to designate the fraction of plant uptake from each species.
Because this option uses first-order monthly uptake rates to represent time-varying plant nutrient uptake, the
calculated uptake amounts are highly sensitive to, and a direct function of, the available nutrients in the soil profile
and the specific nutrient input/application rates. This causes a problem when application rates are changed, such as
under nutrient reduction alternatives, because the uptake amounts are not a function of expected crop yields and
associated nutrient uptake; thus, even though sufficient nutrients may be available to satisfy crop needs under the
reduced application rates, the calculated uptake may be less than the crop needs because of the first-order
formulation.
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The situation just described and other issues related to the plant uptake algorithms in the AGCHEM modules of
HSPF have been reviewed, along with the primary alternative algorithms used in a number of other current
agricultural nutrient models (Donigian et al. 1995). Based on that review and the compatibility of alternative
functions with the AGCHEM and HSPF soil profile representation, the conceptual approach of the plant uptake
formulation in the NLEAP model (Shaffer et al. 1991) was selected for incorporation into AGCHEM/HSPF Version
No. 11 (Bicknell et al. 1995). This selection was based on the following characteristics of the NLEAP plant nutrient
uptake function:
• Calculates crop nutrient needs as a function of expected crop yield
• Allows seasonal uptake variation based on expected crop growth patterns
• Accommodates (or can be modified to accommodate) time steps less than one day
• Considers both NO3-N and NH4-N as available for N uptake
• Considers N fixation, double cropping, and uptake from different soil layers
• Except for N fixation, the N uptake functions can be adapted for P uptake in AGCHEM
• Overall level of detail is consistent and compatible with AGCHEM
Due to differing hydrology, soil moisture, and soil profile simulation procedures among NLEAP, AGCHEM, and
PRZM, the NLEAP plant N uptake functions required adaptation. The changes made primarily provided greater user
flexibility in defining the timing and distribution of plant uptake from the individual soil layers, whose depths are
also user-specified in both AGCHEM and PRZM, and to represent a wider potential range of land surface
conditions. The details of the changes are discussed by Donigian et al. (1995).
The yield-based plant nitrogen uptake formulation is selected when NUPTFG = 1, and is essentially the same in
PRZM-3 and AGCHEM/HSPF Version No. 11. A total annual nitogen uptake target, NUPTGT, is specified by the
user, and is then divided into monthly targets during the crop growing season for each soil horizon. The monthly
target for each horizon is calculated as:
MONTGT = NUPTGT x NUPTFM(MON) x NUPTM(MON) x CRPFRC(MONJCROP) (6.103)
where:
MONTGT = monthly plant uptake target for current crop, mass N/area
NUPTGT = total annual uptake target, mass N/area
NUPTFM = monthly fraction of total annual uptake target (dimensionless)
NUPTM = soil horizon fraction of monthly uptake target (dimensionless)
CRPFRC = fraction of monthly uptake target for current crop (dimensionless). Default value is 1.0,
unless the month contains parts of two or more crop seasons, in which case the monthly
uptake target is divided among the crops according to the number of days of the month
belonging to each crop season.
MON = current month
ICROP = index for current crop
Planting and harvesting dates can be specified for up to three separate crops during the year. Plant uptake is assumed
to occur only during its growing season, defined as the time period between planting and harvest. As stated
previously, When portions of two growing seasons are contained within one month, the total monthly target is
divided between the two crops in proportion to the number of days in each season in that month. The daily target is
calculated by starting at zero at the beginning of a crop season and using a trapezoidal rule to solve for monthly
boundaries; linear interpolation is used to solve for daily values between the monthly boundaries, and between a
monthly boundary and a planting or harvest date.
Yield-based plant uptake values only occur when the soil moisture is above the wilting point, specified by the user
for each soil horizon, and sufficient nutrients are available. No temperature rate adjustment is performed, but all
uptake is stopped when soil temperature is below 4°C. If the uptake target is not met during a given time interval,
whether due to nutrient, temperature, or moisture stress, then a deficit is accumulated and applied to the next time
interval's target. If uptake later becomes possible, the program will attempt to make-up the deficit by taking-up
nitrogen at a rate higher than the normal daily target, up to a user-specified maximum defined as a multiple of the
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target rate. The deficit is tracked for each soil layer, and is reset to zero at harvest, i.e. it does not carryover from one
crop season to the next.
When using the yield-based plant uptake option, it is also possible to represent leguminous plants (e.g. soybeans)
that will fix nitrogen from the atmosphere. The algorithm is designed to allow N fixation only to make up any
shortfall in soil nitrogen, i.e. fixation is only allowed if the available soil nitrogen (i.e. nitrate and solution
ammonium) is insufficient to satisfy the target uptake. The maximum daily nitrogen fixation rate is subject to the
same limits as the uptake under deficit conditions noted above.
6.3.8.5 Soil and Plant Nitrogen, and Litter Compartments
In the previous version of the NITR module in HSPF AGCHEM, plant N was a single "state variable" that
represented the cumulative amount of N taken up by plants from each soil layer. This material continues to "build
up" during the simulation, i.e., it is not converted to any other species in the soil. In AGCHEM Version 11 and
PRZM-3 a pathway has been added in each layer so that plant N can be converted (by first-order rate) to organic N
(labile paniculate) to represent the return of plant N to the soil through leaf fall or crop residues, and root decay. This
rate can be either constant or monthly variable.
Nitrogen that is taken up by the plants can be divided between above-ground and below-ground fractions (using a
simple fraction of the total uptake). The above-ground plant N return would first fall into a litter compartment before
returning to the soil organic N. Both of these rates - from above-ground N to litter and from litter to organic N - can
be either constant or monthly variable. The above-ground plant N and litter N are single compartments, while the
below-ground plant N storage will be maintained for each of the soil compartments. Note that under this option, the
old definition of plant N as the nitrogen that has been derived from a particular layer will not be correct since some
of the plant N derived from a layer will be allocated to the above-ground storage.
When ALPNFG = 1, plant nitrogen is divided into above-ground, litter, and below-ground compartments.
Above-ground plant N returns to the litter compartment, and litter N returns to paniculate organic N (with labile and
refractory fractions) in the surface soil horizon. Both of these reactions are simulated using first-order kinetics. No
other reactions affect these nitrogen storages except for plant uptake to the above-ground compartment, as calculated
in subroutine NITRXN.
Return of litter and below-ground plant N to paniculate organic N is divided into labile and refractory fractions,
which can be constant or monthly variable. Regardless of the option used to simulate plant uptake, if the
above-ground and litter compartments are being simulated, then the user can specify the fraction of uptake from each
layer that goes to the above-ground storage. The rest is assumed to remain within the below-ground plant N
compartment for that soil layer.
6.3.8.6 Organic Nitrogen Compartments and Reactions
The previous NITR module of AGCHEM contained a single organic N state variable in each soil layer. This material
was assumed to be a paniculate species that is increased from immobilization of nitrate and ammonia, and is
converted back to ammonia by mineralization in the soil. It also is transported on the surface by association with
sediment. In PRZM-3, this species is described as a "paniculate labile" fraction of organic N; it will undergo
conversion by first order rate to a "paniculate refractory" fraction, and it will partition to a "soluble labile" fraction.
The "paniculate refractory" species will also partition to a "soluble refractory" fraction. The two soluble species will
therefore be available for transport as runoff and leaching within the soil profile, and likewise, the new paniculate
fraction will be transported on the surface with sediment. The partitioning reactions are described by a simple ratio
of paniculate concentration to solution concentration, i.e. a standard linear partition coefficient. The four fractions
and their assorted reactions are illustrated in Figure 6.7. Note that the storages and transformations in this figure are
repeated in each soil horizon except for the aboveground plant N and the litter compartments.
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6.4 Numerical Solution Techniques
This section describes the numerical techniques that are used to solve the differential equations introduced in the
preceding section. Section 6.4.1 discusses the two numerical techniques available to solve the chemical transport
equations - a backwards-difference implicit scheme and a method of characteristics algorithm. The additional terms
and the adjustment in the upper soil boundary that are added into these transport equations to simulate volatilization
are described in Section 6.4.2. The numerical approximations used to calculate soil temperature are presented in
Section 6.4.3 and the numerical solution for furrow infiltration depths are presented in Section 6.4.4.
6.4.1 Chemical Transport Equations
The second-order partial differential equation outlined in Section 6.3 must be solved with appropriate boundary
conditions. The calculations for moisture contents, air contents, pore velocities, erosion, and runoff are decoupled
from, and solved in advance of, the transport equation. The resulting values, treated as constant for each specific
time step, are then used as coefficients in a discretized numerical approximation of the chemical transport equation.
Two techniques are currently available to solve the discretized chemical transport equation for the new dissolved
pesticide concentration at the end of the time step. The available techniques are:
• A backward-difference, implicit scheme to simulate all chemical transport processes
• A method of characteristics (MOC) algorithm that simulates diffusion, decay, erosion, runoff, and
uptake by the backward-difference technique, but uses the method of characteristics to simulate
advective transport
The user is allowed to select the desired solution technique in the input sequence. Details of these techniques are
provided below. Results from test simulations are provided in Section 6.5.1.
Identical discretizations and initial and boundary conditions are used with both numerical simulation techniques. A
spatial and temporal discretization step is used equal to those applied in the water balance equations. For boundary
conditions at the base of the soil column, the numerical technique uses
< - = 0 (6.104)
Az
in which the subscripts "/'" refer to soil layer numbers.
This condition corresponds to a zero concentration gradient at the bottom of the soil profile. The upper boundary
condition is discussed in more detail in Section 6.4.2.
A backwards-difference solution algorithm was the only solution option available in the original PRZM model. In
this method, the first derivative in space, the advection term, is written as a backward difference (i.e., involves the
difference CfiJJ-Cfi-lJJ). The second spatial derivative, the diffusion term, is centered in space (i.e., based on the
terms C[i-l,j]+C[i+l,j]-2C[i,j]). The time derivative is also calculated as a backward difference in the original
code, (Cfi,j]-Cfi,j-lJ). The equations are then made implicit by writing each concentration for the (/+l)th time step.
The advantage of this numerical scheme is that it is unconditionally stable and convergent. However, the terms
truncated in the Taylor's series expansion from which the finite difference expression are formulated lead to errors
that, in the advection terms, appear identical to the expressions for hydrodynamic dispersion. In the simulation
results, these terms manifest themselves as "numerical dispersion," which is difficult to separate from the physical
dispersion that is intentionally simulated. In systems exhibiting significant advection (i.e., high Peclet number), the
artificial numerical diffusion may dominate the physical dispersion. It can be larger by orders of magnitude, leading
to difficulty in the interpretation of simulation results.
To minimize the effects of numerical dispersion in systems having high Peclet numbers, a method of characteristics
6-45
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solution was added as an option to PRZM-3. This solution method avoids the backwards-difference approximation
for the advection term and the associated numerical dispersion by decomposing the governing transport equation. In
advection-dominated systems, as the dispersion term becomes small with respect to the advection term, the
advection-dispersion equation approaches a hyperbolic equation. According to the MOC theory, advection of the
solute can be simulated separately from the other processes governing the fate of that advected solute. Baptista et al.
(1984) state that no error is introduced by this decomposition provided that the advection equation is solved first by
an explicit procedure, and the diffusion equation is solved next by an implicit technique. This order was preserved in
the PRZM-3 model by utilizing a new explicit algorithm for advection that is always called first, and is immediately
followed by execution of a modified version of the existing implicit algorithm for simulation of other processes. The
advection algorithm employed was adapted from those described by Khalell and Reddell (1986) and Konikow and
Bredehoeft (1978). These techniques were modified to allow simulation of changes in saturation and adsorption of
the pesticide and variable compartment size.
In the new explicit advection algorithm, in addition to the fixed grid system, a set of moving points is introduced.
These points can be visualized as carrying the chemical mass contained within a small region in space surrounding
the point. Initially, these points are uniformly distributed throughout the flow domain. At each time interval, these
moving points are redistributed according to the local solute velocity in each compartment. New points may enter the
top of the flow domain, while old points may move out the bottom. When the moving points are transported in
horizons where the compartment size is larger and numerical resolution is less, the points may be consolidated to
conserve computational effort. After the new locations have been assigned to each point, the average concentration
in each compartment is computed based on the number and mass carried by the points contained within the
compartment at that time. This temporary average concentration is returned to the main program, and a subroutine
that assembles the terms in the transport equation (without advection) is called. Changes in concentration due to all
other transport and transformation processes (diffusion, decay, sources, etc.) are calculated for each compartment
exactly as in the original version of PRZM. These values are then returned to the main program, and one transport
step is complete.
When the MOC algorithm is called during the next time step, the exact location of each moving point has been
saved. The first task is to update the masses carried by each moving point using the changes calculated during the
last time step. Increases in mass are simply added equally to each point in the compartment, while decreases are
weighted by the actual value at each point before subtraction to avoid simulating negative masses. The updated
moving points are then relocated and the two-step process is repeated again until the end of the simulation.
Regardless of which method (backwards-difference or MOC) is selected to approximate the governing equation(s)
for transport, a tri-diagonal matrix solution (Thomas algorithm) is utilized by the model code. The key elements of
the tri-diagonal matrix are the lower diagonal element (A), the diagonal element (B), the upper diagonal element (C),
and the vector of source terms (F). The elements of the solution matrix for the transport equation are determined
based on the values supplied for numerous input parameters as identified below.
'A' Term
• DISP - dispersion/diffusion coefficient (cm2 day"1)
HENRYK - Henry's constant (cm3 cm3)
• DAIR - molecular diffusivity in the air (cm2 day"1)
'B' Term
• DISP - dispersion/diffusion coefficient (cm2 day"1)
HENRYK - Henry's constant (cm3 cm"3)
• DAIR - molecular diffusivity in the air (cm2 day"1)
• DWRATE - solution phase degradation rate constant (day"1)
• DSRATE - adsorbed phase degradation rate constant (day"1)
• DGRATE - vapor phase degradation rate constant (day"1)
• KD - adsorption/partition coefficient for soil (cm3 g"1)
• BD - mineral soil bulk density (g cm"3)
• UPTKF - plant pesticide uptake efficiency factor
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'C' Term
T' Term
PEVP
DKRT12
DKRT13
DKRT23
ELTERM
DKBIO
DISP
HENRYK
DAIR
KD
BD
HENRYK
DKRT12
DKRT13
DKRT23
FEXTRC
- pan evaporation data (cm day"1)
- transformation rate from parent pesticide to first daughter product (day"1)
- transformation rate from parent pesticide to second daughter product (day"1)
- transformation rate from first daughter product to second daughter product (day"1)
- erosion loss term, calculated from erosion input parameters
- biodegradation term, calculated from biodegradation input parameters
• dispersion/diffusion coefficient (cm2 day"1)
• Henry's constant (cm3 cm"3)
• molecular diffusivity in the air (cm2 day"1)
• adsorption/partition coefficient for soil (cm3 g"1)
• mineral soil bulk density (g cm"3)
• Henry's constant (cm3 cm"3)
• transformation rate from parent pesticide to first daughter product (day"1)
• transformation rate from parent pesticide to second daughter product (day"1)
• transformation rate from first daughter product to second daughter product (day"1)
• foliar extraction coefficient for foliar washoff model (cm"1)
6.4.2 Volatilization
The numerical techniques discussed in Section 6.4.1 are the basis of the simulation of chemical transport in all
phases. However, some modifications have been made to the upper boundary condition in order to model
volatilization of chemical from the soil surface.
In order to simulate vapor-phase pesticide movement past the soil surface, the zero concentration upper boundary
conditions used in the original PRZM code has to be modified. Jury's boundary layer model (Jury et al. 1983a, Jury
et al. 1983b) has been incorporated into the PRZM-3 code. The model states that the controlling mechanism for
pesticide volatilization is molecular diffusion through the stagnant surface boundary layer. The volatilization flux
from soil profile can be estimated by:
J - D°A
Jl ~T~
(6.105)
where
1
d
volatilization flux from soil (g day"1)
molecular diffusivity of the chemical in air (cm2day"1)
vapor-phase concentration in the surface soil layer (g cm"3)
vapor-phase concentration above the stagnant air boundary layer (= 0, for the no-canopy field
condition) (g cm"3)
thickness of stagnant air boundary layer (cm)
This equation defines the new flux-type boundary condition for the volatilization simulation. In order to incorporate
the new flux-type boundary condition into the PRZM-3 code, new mass balance equations were derived for the
surface soil and stagnant air layers. Figure 6.8(a) is a schematic of the top two soil layers and the stagnant surface
boundary layer when no plant canopy exists. Zero concentration is assumed for C"g4 under the no-canopy field
condition.
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d
Ax
*-o
cg,1 ~ KHC1
Ax
cg,2 ~ KHC2
(a) without plant canopy
c *• o
Ax
:g,1 ~ KHC1
Ax
cg,2 - KHC2
(b) with plant canopy
Figure 6.8 Schematic of the top two soil compartments and the
overlaying surface compartment (a) without plant
canopy, (b) with plant canopy.
A mass balance equation for the uppermost soil compartment is
= AD.
ACg
^Az~
D
- A—-C , - VK aC ,
d g' g &
(6.106)
where
V
A
a
molecular diffusivity of pesticide in air filled pore space (cm2day"')
volume of the compartment (cm3)
area of the compartment (cm2)
volumetric air content (cm3 cm"3)
first-order reaction rate constant (day"1)
By substituting Equation 6.108 into the overall (i.e., all phases) mass balance equation for the uppermost soil layer, a
flux-type upper boundary condition is obtained. Figure 6.8(b) reflects the field situation when a plant canopy exists.
Zero concentration is now assumed to exist above the top of the canopy compartment. The volatilization flux from
the plant canopy is defined as follows.
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± + zR (6<107)
. D" i
where
Jpc = volatilization flux through the plant canopy (g cm"2 day"1)
SR = vertical transfer resistance (day cm"1), described in Section 6.3.6.3)
C" = concentration above the plant canopy (assumed to be zero)
The first term of the right side of Equation 6.106 represents the gas diffusive flux into the surface soil layer, and the
second term denotes the gas diffusive output as governed by the stagnant boundary layer above the soil surface. By
using backward implicit finite differencing, the following is derived.
ri \~\ v /"^ ri w ii_ ^^ T~\ n «n v f~* n «n _L
a[l,n- i\ K.JJ Cw[l,n- 1J = L) \2,n\ KH CW[2,«J +
Az2
(6.108)
-*LDg[l,n]KH + a[\,n\KH(\ + Kg} + ^j^J CJl.n]
\ /
where
n = time index
By carrying out a similar mass balance using finite differences, the boundary condition that describes the field with
canopy existing is obtained.
6.4.3 Soil Temperature
Soil temperature is solved for numerically. Section 6.3.6.4 describes the theoretical basis for the simulation of soil
temperature. The distribution of temperature within the soil profile is summarized by Equation 6.88. This equation is
solved numerically for soil temperature, T, as a function of depth, Z, and time, /, based on the input thermal
diffusivity, d, for each soil compartment, and the following initial and boundary conditions.
Initial Condition:
rz,0 = r(z) (6.109)
Boundary Conditions:
To,t - W (6.110)
TV = TiV> (6.111)
where
T(z) = initial soil temperature in each soil compartment (°C)
Ts(t) = calculated soil surface temperature for each time step (°C)
TL(t) = lower boundary temperature condition at the bottom of the soil core (°C)
The lower boundary temperature is defined by the user as 12 monthly values corresponding to the first day of each
month; the value for each day is interpolated between the neighboring monthly values.
The following numerical approximation used in the model is taken from Hanks et al. (1971)
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A? Az2
'
Equation 6.107 is solved using a modified numerical solution procedure of Hanks etal. (1971), the same finite
difference technique and tridiagonal matrix solver (Thomas algorithm) used in PRZM (Carsel et al. 1984).
6.4.4 Furrow Irrigation
To simplify the algebra required to calculate the furrow infiltration volume as Manning's equation is substituted into
the kinematic wave model (Equation 6.95). Manning's equation is approximated as follows:
A = a Qm (6.113)
a and m are constants that are estimated by the model from the parameters of Manning's equation as follows:
\n(A2/Al)
A,
"• m (6.115)
where
Ai,A2 = cross-sectional areas (m2) at depthsyl andy2
Qi, Q2 = flow rates (m3 s"1) computed from Manning's equation (Equation 6.96) at depths^ and^2
yl 1 cm
y2 = 10 cm
The depths yl and^2 were chosen to represent the range of depths likely to occur in furrows.
Substituting Equation 6.113 into Equation 6.95 produces:
1 ~- ~ -j- (6.116)
No closed-form solution to the above equation is known when infiltration is time-variable. Equation 6.116 therefore,
is, solved for Q using the backwards-space, backwards-time finite-difference solution described by Li et al. (Li et al.
1975). Writing Equation 6.116 in finite-difference form produces:
Az A? A?
where
g* = flow rate at time k, station /'
Az = spatial step
A/ = time step
Infiltration volumes are computed using the Green-Ampt model:
IT-*-
(H + Hfl (6.118)
where
/* = infiltration depth (m) at time k, station i
Ks = saturated hydraulic conductivity of the soil (ms"1)
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H = ponded water depth (m)
Hs = suction parameter (m)
6 = available porosity (fraction)
/ = total volume of infiltrated water (m)
The solution of Equation 6.118. subject to the initial condition I(t0) = 0 , is
where
h = (H + H.) 6 (6.120)
This solution assumes / is a function of time only. Equation 6.119 has an explicit solution in terms of the Lambert W
function (Barry et al. 1995a, Barry et al. 1995b, Corless et al. 1996),
7(0 = -A [1 + W_,(z)] (6.121)
where
K (/-/„)
z = - Exp[- 1 - -* - °-] (6.122)
«
and W_ j (z) represents the branch of the Lambert W function with domain - Exp(- 1) < z < 0 and range
The Green- Ampt model has long been accepted as a model of the advance of the wetting front through the soil
column, and involves parameters that can be related to well-known soil properties. The volume of infiltration is
computed assuming /* is an average infiltration depth for the channel at location /':
qf = W* if (6.123)
where
cft = volume infiltrated at location /' (m3 m"1)
W'l = current flow width at location /' (m)
Furrow channels are assumed to be trapezoidal in shape. Equation 6.96 is solved at each station at the end of each
time step for the new flow rate (2/+Y • Because the equation is non-linear with respect to Q, the new value of flow is
found using second-order Taylor series iteration. Given the flow rate in the furrow, infiltration depths at each
location are then computed using the Green- Ampt model (Equation 6.106).
The PRZM-3 furrow irrigation model determines infiltration depths at various locations in the furrow. Irrigation
continues until the depth of water infiltrated at the downstream end of the furrow is sufficient to meet the soil
moisture deficit SMDEF. The depth of water applied as irrigation to the first PRZM-3 soil compartment is then set
equal to either the average furrow infiltration depth or the infiltration depth at a specific location in the furrow,
depending on options selected by the user. This depth of water then infiltrates through the root zone as determined
by the PRZM-3 soil hydraulic algorithms.
6.5 Results of PRZM Testing Simulations
This section includes the results of testing the two solute transport solution techniques and the volatilization
algorithm. Simulated results are compared with those from analytic solutions. Sensitivity analyses also were
performed to evaluate the effects of key model parameters on the prediction of volatilization rates. A test comparison
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of the model with field data from Georgia (soybeans) concludes the section.
The PRZM model has undergone additional performance testing with field data in New York and Wisconsin
(potatoes), Florida (citrus), and Georgia (corn) (Jones 1983, Jones et al. 1983, Carsel et al. 1985). The results of
these tests demonstrate that PRZM is a useful tool for evaluating groundwater threats from pesticide use. Please refer
to these references for information regarding the further testing of PRZM under field conditions.
6.5.1 Transport Equation Solution Options
Currently, two numerical solution options are available to the PRZM-3 user for the chemical transport equation. As
discussed in Section 6.4.1, the finite difference option (utilizing subroutine SLPSTO) is unconditionally stable and
convergent, but may result in excessive numerical dispersion in high Peclet number systems. The method of
characteristics algorithm (utilizing subroutines MOC and SLPST1) eliminates or reduces that numerical dispersion.
Two examples are provided that compare the alternate solutions methods at high Peclet number (greater than 5.0)
and at low Peclet number (less than 0.5).
6.5.1.1 High Peclet Number
Figure 6.9 presents the analytical solution (Hunt 1978) together with the SLPSTO and MOC/SLPST1 solutions at 6
days for the transport of a 69 mg cm"3 pesticide application in the uppermost compartment. The physical parameters
are as presented in the figure - notably the Peclet number is 5.1. The following table details pertinent features of the
simulation:
Method
Analytical
SLPSTO
MOC/SLPST1
Location of Peak
5.8
4.5
5.5
Value of Peak
(mg/cm3)
11.2
5.07
12.09
% Error at Peak
_
-54
+7
Runtime (sec)
_
88.5
112.4
At this relatively high Peclet number, the SLPSTO algorithm shows excessive numerical dispersion, capturing only
about half the amplitude of the peak concentration, while showing excessive mass in both tails. In addition, the
SLPSTO algorithm does not predict the location of the peak precisely. (It is lagged behind the location of the peak
given by the analytical solution and the MOC/SLPST1 solution.) The MOC/SLPST1 algorithm requires 27% more
runtime, but errs by only 7% in the peak and shows good agreement in the tails.
6-52
-------
1
5 7 9 11 13 15
COMPARTMENT NUMBER
17
19
Figure 6.9
Comparison of simulation results at high Peclet number.
6.5.1.2 Low Peclet Number
Figure 6.10 illustrates the results of a SLPSTO and MOC/SLPST1 simulation 8 days after an incorporation of 69
mg/cm3 in the sixth compartment using the parameters listed. The predicted concentrations at this lower Peclet
number, 0.46, are very similar in the peaks and the tails, and apparently little additional resolution is gained from
utilizing the MOC algorithm. However, the additional computational burden associated with the MOC algorithm is
only 7%.
6-53
-------
19
COMPARTMENT NUMBER
Figure 6.10 Comparison of simulation results at low Peclet number.
6.5.2 Testing Results of Volatilization Subroutines
To test and validate the operation of the volatilization algorithms, model results were compared with Jury's analytical
solution (Jury et al. 1983a), and against field data for trifluralin from Watkinsville, GA. Sensitivity analyses were
also performed to evaluate effects of key parameters on model predictions. The intent of this preliminary model
testing was to evaluate model operation by comparing the results for the volatilization flux from a soil surface
application.
6.5.2.1 Comparison with Analytical Solution
Jury et al. (1983a) presented a mathematical model for describing volatile loss and movement of soil-applied organic
chemicals. By making the following assumptions, they derived an analytical solution for evaluating the chemical
concentration profile within the soil and the volatilization flux at the soil surface:
• Uniform soil properties consisting of a constant water content, bulk density, liquid water flux
(either upward, downward, or zero), and a constant organic carbon fraction
• Linear equilibrium adsorption isotherm
• Linear equilibrium liquid-vapor partitioning (Henry's law)
• Uniform incorporation of a quantity of chemical to a specified depth below the surface
• Pesticide loss by volatilization through a stagnant air boundary layer at the soil surface
• Infinite depth of uniform soil below the depth of incorporation
The second through fifth assumptions are satisfied by the current PRZM-3 code. The sixth assumption defines zero
concentration for the bottom layer, which is somewhat different from PRZM's zero gradient bottom boundary
condition. However, as long as no chemical reaches the bottom layer, these two types of boundary conditions
produce identical results. Our test runs for volatilization were designed to satisfy this requirement. In order to
comply with the first assumption, the hydrological computation subroutines in PRZM were bypassed and replaced
6-54
-------
with a constant value for water flux. A positive flux value indicates a leaching condition, whereas a negative flux
value indicates an evaporating condition. The hydrological subroutines in PRZM-3 are based on a moisture-routing
method in which daily accounting of water inflow and outflow is recorded. One limitation of the moisture-routing
method is that it is unable to properly describe the upward movement of evaporating water. Evaporation loss is
removed from specific surface soil layers without accounting for movement between layers.
The pesticide 2,4-D was chosen as the test compound for our simulation; the input parameters are listed in Table 6.4
and were obtained from Jury et al. (1983a). The test run results for daily volatilization flux are presented in Figure
6.11(a). Figure 6.11(bX Figure 6.12(a). and Figure 6.12(¥). corresponding to the four test cases listed at the bottom
of Table 6.4. Two different soil compartment depths (DELX) of 1.0 and 0.1 cm were used to investigate the
sensitivity of the volatilization algorithms to the spatial discretization in the surface soil horizon.
Figure 6.11(a) shows the steady state situation (i.e., no evaporation and no leaching) without any advective
movement. The daily volatilization flux values predicted by the two different DELXs are almost identical. In this
case, the magnitude of DELX is relatively unimportant. The simulation results with a leaching rate of 0.01 cm day"1
are shown in Figure 6.11(¥). Because of the leaching influence, the predicted daily flux is smaller than the
corresponding daily value shown in Figure 6.11 (a). The differences between the analytical solution and the PRZM-3
predictions are due to the finite difference solution technique and the occurrence of advective movement by
leaching. The simulation results using the smaller DELX (0.1 cm) more closely match the analytical solution results,
and an even smaller DELX would have improved the agreement further. The slope of both DELX curves is the same
as the analytical solution, and the maximum differences (for the 1.0 cm DELX) from the analytical solution are 10%
or less.
Figure 6.12 shows the simulation results under evaporating conditions with the upward advective velocity at 0.01
(Figure 6.12(a)) and 0.25 (Figure 6.12(b)) cm day"1. The "wick effect" phenomenon (described in Section 6.3.6)
leading to enhanced upward movement of the pesticide can be observed in these two figures. The maximum daily
flux occurs on the first day for the leaching conditions. Depending on the magnitude of the evaporating water
velocity, the maximum daily flux no longer occurs on the first day of the pesticide application. Also the magnitude
of the maximum daily flux is enhanced by the magnitude of the evaporating water velocity. The effect of DELX
becomes more critical as the influence of advective movement increases. For simulations using a 1.0-cm DELX,
Figure 6.12(a) shows stable numerical behavior with a small discrepancy when compared to the analytical solution
result. As the advective movement becomes larger, the numerical behavior becomes more unstable, as shown in
Figure 6.12(¥). The smaller 0.1-cm DELX showed good agreement with the analytical solution for both test cases
shown in Figure 6.12.
Based on these test cases, it appears that a finer DELX, in the range of 0.1 to 0.5 cm, is needed for top soil layers
when volatilization processes are simulated with PRZM-3. However, this finer DELX requirements poses an
additional computational burden for PRZM-3 applications due to the increase in the number of soil compartments.
To circumvent this burden, the PRZM-3 code was modified to allow a variable compartment depth, which allows the
user to select a smaller DELX for the top horizon (or any other horizon) and a bigger DELX for the rest of the soil
profile. By selecting this variable compartment depth capability, a significant saving in CPU time may be achieved
while a better representation is provided for calculation of the surface volatilization flux. In conjunction with field
data comparisons (presented below), the results of model runs and CPU time are presented for simulation runs both
uniform and variable compartment depth.
6-55
-------
2X3
g 15
I
( E - 6 Kg/ ha-day )
00
"B
10
o
Analytic Sci'n
FftZM Results
DELX - 0.1 cm
DELX - 1 cm
24 6 8 10 12 14 16 18 20 22 24 26 28 30
(days)
No Evaporation & No Leaching
E
( E - 6 Kg/ ha-day )
20
15
1O
— Analytic Sol'n
PRZM Results
» DELX-0.1 cm
^ DELX - 1 cm
46 8 10 12 14 16 18 20 22 24 26 28 30
(days)
Leaching Rate - 0.01 cm/ day
Figure 6.11 Comparison of volatilization flux predicted by PRZM and Jury's analytical solution: Test cases #1
and #2
6-56
-------
( E - 6 Kg/ ha-day )
— Analytic Sol'n
PRZM Results
» DELX-0.1 cm
DELX - 1 cm
2 4 6 8 10 12 14 16 18 2O 22 24 26 28
Evaporation. Rate ™ 0.01 cm/ day
( E - 6 Kgf h«-day )
40
20
O
*
— Analytic Sol'n
PRZM Results
* DELX - O.i cm
DELX - 1 cm
i i i
2 46 8 10 12 14 16 18 2O 22 24 26 28 30
(day)
Evaporation Rate - 0.25 cm/ day
Figure 6.12 Comparison of volatilization flux predicted by PRZM and Jury's Analytical solution. Test cases #3
and #4
6-57
-------
6.5.2.2 Comparison with Field Data
Preliminary model testing with field observations also was performed to assess the ability to predict the general
magnitude of volatilization losses and daily fluxes under field conditions. Based on a review of available
volatilization field data sets, a USDA experimental watershed site in north-central Georgia was selected because of
its use of a volatile pesticide (trifluralin), surface-applied to a major crop (soybeans), with a comprehensive
micrometeorological and soil sampling plan.
The study site was located at Watkinsville, GA, on a 1.26-ha watershed comprised of Cecil soil (63.9% sand, 23.6%
silt, and 12.5% clay) with 0.55% organic carbon, a pH of 6.5, and a slope of 3.0%. Harper et al. (1976) present a
detailed description of the site, the equipment, and the installation procedures required for collecting microclimate
data. They also summarize the method, assumptions, and calculations used for determining pesticide volatilization
flux rates. Trifluralin was surface-applied as a spray to a bare soil surface, using a ground sprayer equipped with flat-
fan nozzles, at a rate of 1.12 kg/ha between 1220 and 1247 eastern daylight time (EDT) on 15 June 1973.
The field results shown in Table 6.5 were obtained from White et al. (1977). The values in columns 2, 4 and 5 of
Table 6.5 provide the cumulative volatilization flux, remaining pesticide in soil, and total cumulative decay losses,
respectively. A discrepancy is noted for the data in column 4 of Table 6.5: the pesticide remaining in soil at the 35th
day is smaller than that at the 49th day.
This discrepancy is most likely due to sampling variations, although data were not available to establish accuracy
limits on the data points. Meteorological data required for applying PRZM to the site, which include daily
precipitation and pan evaporation, were obtained from Smith et al. (1978).
The PRZM-3 input parameters for trifluralin and the Watkinsville site are listed in Table 6.6. Two additional key
parameters which influence the volatilization results are the decay rate and the adsorption partition coefficient. The
magnitude of the decay rate can be estimated from the data in column 5 of Table 6.5. assuming that decay accounts
for all losses from the soil other than volatilization. A value of 0.0206 per day for the first-order decay rate constant
obtained from these data points is consistent with the value of 0.0198 per day used by Donigian et al. (Donigianet
al. 1986) after reviewing the literature. An initial value for Kd was obtained from the organic carbon content of
0.55% and an organic-carbon partition coefficient (Koc) value of 13,700, resulting in a Kd of 75 ml/g. Figure 6.13
shows the results of sensitivity analyses runs for Kd and the decay rate; the observed data for trifluralin from Table
6.5 are also included for comparison. Figure 6.13(a) shows a good representation of the observed cumulative
volatilization curve. Figure 6.13(b) shows that a value of 40 for Kd, and a decay rate of 0.02 per day provides the
best representation of the decay rate values analyzed.
The simulation results for cumulative volatilization flux and cumulative pesticide decay are shown in Figure 6.14
for four different DELX combinations. For these simulations, DELX values of 1.0, 0.5, 0.25, and 0.1 cm were
chosen for the first horizon and 5-cm DELX for the rest of the profile. The field data are also included in the figures
for comparison. Table 6.7 shows the total volatilization flux for each of the four combinations using variable DELX,
as well as for a simulation using simulations, a constant 1.0-cm DELX throughout the whole soil profile. The CPU
requirements for each run are also included in Table 6.7. The predicted total volatilization flux using the smallest
DELX of 0.1 cm is closest to the field-measured value; the values for DELX of 0.25 cm and 0.50 cm are also quite
close to the field value. The saving of CPU time can be observed from Table 6/7.The simulation requires 129
seconds using 1.0 cm DELX for the whole soil profile, compared with only 39 seconds for the simulation using 1.0
cm for the top horizon and 5.0 cm for the rest of the profile. The results in Table 6.7 indicate that a DELX of 0.25 to
0.50 cm for the top horizon may be a reasonable compromise between simulation accuracy and CPU costs.
Velocity =1.82 cm/day Delta x = 1 cm
Diff coef = 4.0 cnf/day Delta / = 1 day
Retardation Coef = 11.74 Core Length = 20 cm
Decay = 0. I/day Peclet = 0.46
6-58
-------
Table 6.4
DG
DL
P
T
foe
e
a
M
L
KH
Koc
u
1
t
Jw
E
Test case #1:
Test case #2:
Test case #3:
Test case #4:
Input Parameters for the Test Cases - Analytical Solution
Air diffusion coefficient
Water diffusion coefficient
Porosity
Bulk density
Temperature
Organic carbon fraction
Water content
Air content
Pesticide applied
Depth of incorporation
Henry's constant for 2,4-D
Organic carbon partition coefficient for 2,4-D
Decay coefficient for 2,4-D
Total depth of soil column
Simulation period
Water flux
Evaporation flux
0.43 (m2 day"1)
4.3xlO"5(m2day"1)
0.5
1.35(kgm3)
25°C
0.0125
0.3
0.2
ICkgha"1)
O.lm
5.5 xlO"9
0.02 (m3 kg"1)
4.62 xlO"2 (day"1)
0.3m
30 days
no evaporation and no leaching (Jw = E = 0)
with leaching (Jw = 0.01 cm day"1)
with evaporation (E = 0.01 cm day"1)
with evaporation (E = 0.25 cm day"1)
6-59
-------
40
(% of applied)
§
"cc
N
30
CC20
CD
§10
3
E
O
/*'
Field Data
PRZM Results
KD = 30
KD = 40
KD = 50
KD = 75
12 24
36 48 60 72 84
Sensitivity of KD
96 108 120
(day)
40
(% of applied)
x
CO
N
CD
"•^
_CD
3
E
^
O
30
20
10
,x-
Field Data
PRZM Results
K = 0.01
K = 0.02
K = 0.03
12 24
36
48 60
72
84
Sensitivity of Decay Rate
96 108 120
(day)
Figure 6.13 Sensitivity of cumulative volatilization flux to Kd and decay rate.
6-60
-------
Table 6.5 Trifluralin Volatilization Losses, Amounts Remaining in Soil, and Estimated Losses via Other
Pathways for the 120-day Field Test
Time, (day)
Application
1
2
6
18
35
49
63
76
120
Cumulative Volatilized
% of Total
Applied
3.5
3.8
5.3
10.9
20.5
23.4
24.4
25.1
25.4
25.9
% of Total
Applied
13.3
14.8
20.3
42.2
79.1
90.2
94.1
96.9
98.2
100.0
Remaining* in Soil,
% Applied
-
89
72
64
51
33
35
23
20
11
Estimated Other
Losses,
% of Applied
-
7.2
22.7
25.1
28.5
43.6
40.6
48.9
54.6
63.1
Source: White et al. (1977).
* Based on amount remaining in soil at a 0 cm to 7.5 cm depth as compared with an initial 1.0 ng/g level at
application (rate was 1.12 kg/ha).
Table 6.6 Input Parameters for the Test Cases - Watkinsville Site
Simulation start date
Simulation end date
Trifluralin:
Henry's constant
Diffusion coefficient in air
Application date
Amount applied
Incornoration denth
1 Thickness DELX
(cm) (cm)
Field Capacity
14 June 1973
31 December 1973
6.7X10'3
0.43 m2 day1
15 June 1973
1.12 kg ha1
5 cm
Wilting Initial Water
Point 1 Content
1 5 0.1 0.207 0.095 0.166
2 10 5.0 0.207 0.095 0.217
3 15 5.0 0.339 0.239 0.318
6-61
-------
Horizon
4
Thickness
(cm)
60
DELX
(cm)
5.0
Field Capacity
0.320
Wilting
Point
0.239
Initial Water
Content
0.394
Table 6.7 Simulation Results Using Different Compartment Depth (DELX)
Horizon
\
r\
3
4
Total
Volatilization
Flux (kg/ha)
CPU (Sec)
Constant DELX
Depth
(cm)
5
10
15
60
DELX
(cm)
1.0
1.0
1.0
1.0
0.393
129
Variable DELX
DELX
(cm)
1.0
5.0
5.0
5.0
0.398
39
DELX
(cm)
0.5
5.0
5.0
5.0
0.338
46
DELX
(cm)
0.25
5.0
5.0
5.0
0.317
67
DELX
(cm)
0.1
5.0
5.0
5.0
0.316
106
Field
Value
0.290
Figure 6.15(a) reveals significant differences between the observed pesticide decay and the simulated values during
the first few weeks following application. In fact, the observed data appear to indicate a much higher attenuation rate
during the first few days following application, with a lower rate for the remaining period. To better match the decay
characteristics, and evaluate the potential impact on the volatilization simulation, a two-step decay procedure was
used with a rate of 0.1 per day for 5 days following application and a rate of 0.01 per day for the remaining period.
The results of these simulations in terms of pesticide remaining in the soil, shown in Figure 6.15. indicate a much
better agreement with the observed field values in Figure 6.15(b). The impact of the two-step decay on both
cumulative decay and volatilization flux is shown in Figure 6.16. The cumulative pesticide decay shown in Figure
6.16(a) improves considerably (compared to Figure 6.14(b)). while the results for cumulative volatilization flux
(Figure 6.16(b)) are slightly better than those in Figure 6.14(a).
6-62
-------
40
>,30
03
O
CD
Q
CD
T3
120
CD
Q_
CD
(% of applied)
10
O
0
Field Data
PRZM Results
•—DELX = 0.1 &5cm
DELX = 0.25 & 5 cm
••••DELX = 0.5&5cm
DELX= 1.0 & 5 cm
80
70
8 60
CD
Q
CD 50
T3
I 40
Q.
> 30
12 24 36 48 60 72 84
Effect of DELX on Volatilization Flux
(% of applied)
96 108 120
(day)
E
D
O
20
10
0
Field Data
PRZM Results
DELX = 0.1 &5cm
DELX = 0.25 & 5 cm
DELX = 0.5 & 5 cm
DELX= 1.0 & 5 cm
12 24 36 48 60 72 84 96 108 120
(day)
Effect of DELX on Pesticide Decay
Figure 6.14 Effects of DELX on volatilization flux and pesticide decay.
6-63
-------
(% of applied)
o
co
0)
w
0
0.
ra
CO
o>
Di
o
CO
0)
;g
'o
'•55
0
Q.
DJ
CO
0)
90
80
70
60
50
40
30
20
10
Field Data
PRZM Results
DELX = 0.1 & 5 cm
DELX = 0.25 & 5 cm
DELX = 0.5 & 5 cm
DELX = 1.0 & 5 cm
90
80
70
60
50
40
30
20
10
0
12 24 36 48 60 72 84 96 108
Simulations with Constant Decay Rate
(% of applied)
\'
Field Data
PRZM-2 Results
DELX = 0.1 &5cm
DELX = 0.25 & 5 cm
DELX = 0.5 & 5 cm
DELX = 1.0 & 5 cm
lay)
(b)
12 24 36 48 60 72 84 96 108
Simulations with Two-Step Decay Rates
Iffly)
Figure 6.15 Comparison of constant and two-step decay rates.
6-64
-------
X
80
70
I 60
-t->
05
N
=5 50
40
3 30
E
5 20
10
0
Field Data
PRZM Results
DELX=0.1 &5cm
DELX = 0.25 & 5 cm
DELX = 0.5 & 5 cm
DELX 1.0 & 5 cm
12 24 36 48 60 72 84 96
Simulation with Two-Step Decay Rate
(% of applied)
108 120
(day)
Field Data
PRZM Results
DELX = 0.1 & 5 cm
_._. DELX = 0.25 & 5 cm
DELX = 0.5 & 5 cm
DELX 1.0 & 5 cm
24 36 48 60 72 84 96 108 120
(day)
Simulations with Two-Step Decay Rates
Figure 6.16 Effects of two-step decay rates on volatilization flux and pesticide decay.
6.5.2.3 Conclusions from Volatilization Model Testing
6-65
-------
The primary conclusions derived from this preliminary model testing are as follows.
1) Comparisons with Jury's analytical solution indicate that the volatilization algorithms are operating
correctly, and that, with a very small DELX (0.1 cm or less), the results are in excellent agreement.
2) The preliminary field testing results with trifluralin in Watkinsville, GA, indicate good agreement
between measured and predicted volatilization flux when measured decay rates and adjusted KD
values are used.
3) Small soil layer depths (in the range of 0.25 and 0.50 cm) are needed to provide the best
presentation of volatilization flux at reasonable CPU times, based on the Watkinsville testing.
4) A two-step decay rate best represents the attenuation behavior of trifluralin using a higher rate for
the period immediately following application and a lower rate for the remaining period.
Further testing of the volatilization model should be performed to evaluate its capabilities for different compounds,
different regions, and other crops. In addition, the vapor transport and concentration calculations for the plant
compartment should be tested with the additional data available from the Watkinsville site and from other field data
sets (e.g., Grover et al. (1985) and Willis et al. (1983)).
6.5.3 Testing Results of Soil Temperature Simulation Subroutine
Preliminary testing of the simulation subroutine for the soil profile temperature was performed by comparing
predicted values with values obtained by an analytical solution to the governing heat flow equation. These testing
results are discussed in this section. Testing of the soil surface/upper boundary temperature simulation, estimated by
the energy balance procedure in the model, was not performed due to problems in obtaining observed meteorological
and soil temperature data for the Watkinsville, GA, test site.
An analytical solution presented in Kreysig (1972) for the classical one-dimensional heat flow partial differential
equation (described in Section 6.3.6.4) was used to calculate changes in the soil temperature profile with time, due to
a change in the upper boundary temperature. In order to develop a valid comparison between the analytical and finite
difference methods, three assumptions were made:
a) Uniform properties throughout the soil profile
b) Constant lower-boundary temperature
c) Uniform initial temperatures throughout the profile
To compare the results of the analytical solution with the finite difference solution from the soil temperature model,
the following parameters were used.
Depth of the soil profile =100 cm
Compartment thickness (DELX) =1.0 cm
Diffusivity of the soil profile = 864 cm2 day "'
Upper-boundary temperature, TM = 30°C
Lower-boundary temperature, TM = 20°C
Initial temperature, TM = 20°C
Figure 6.17 and Figure 6.18 show the comparison of soil temperature profiles predicted by both the analytical
solution and the finite difference soil temperature model after 1 day and 5 days of simulation. In Figure 6.17 the
finite difference solution is obtained by using a 1-hour time step, while in Figure 6.18 a 1-dav time step is used. The
following observations are evident from these testing results.
1) Comparison of the soil temperature profiles predicted by both methods indicate excellent
agreement when the smaller, 1-hour time step is used in the finite difference procedure, as shown
6-66
-------
in Figure 6.17.
2) The finite difference solution obtained by using the daily time steps deviates from the analytical
solution by about 1 °C, in the upper and middle portions of the soil profile (Figure 6.18). This
deviation is due to the assumption of a constant initial temperature profile and the abrupt change in
the upper-boundary temperature from 20°C to 30°C for the first daily time step.
3) As the steady-state condition is approached, irrespective of the time step used in the finite
difference solution, the soil temperature profiles predicted by both methods are in good agreement
(Figure 6.17(¥) and Figure 6.180))).
Table 6.8 shows that reducing the depth of the compartment from 1 cm to 0.1 cm does not produce any significant
change in the finite difference solution. These depths bracket the range of values for DELX (i.e., compartment
thickness) likely to be used for the surface soil horizon.
These test results show that, for smaller time steps, the finite difference solution will be in complete agreement with
the analytical solution. For a daily time step as used in PRZM-3, under expected environmental conditions, with a
non-uniform initial temperature profile, non-uniform soil characteristics, and smaller daily changes in the upper-
boundary temperature, the soil temperature profile estimated by the finite difference method used in the model is
expected to be capable of providing close agreement with observed temperature profile data. In addition to further
testing of the soil profile temperature model with field data, the procedure to estimate the upper-boundary
temperature should be tested to evaluate and demonstrate the validity of the entire soil temperature simulation model.
6-67
-------
30T
perature in degree C
N> N> N>
-»• •&>. ^j
Te
-»•
oo
-»•
cn
O
30r^
27--
O)
(U
•- 24f
§
0> 21+.
Q.
E
0)
"~ 18.
15
•.
,
Profile Initial Temp = 20° C
Upper Boundary Temp = 30° C
Lower Boundary Temp = 20° C
Time Step = 1 hr
Analytical Soln.
Finite Diff. Soln.
10 20 30 40 50 60 70 80 90 100
Depth of Soil Profile in cm
Profile Initial Temp = 20° C
Upper Boundary Temp = 30° C
Lower Boundary Temp = 20° C
Time Step = 1 hr
lv*,,
,
Analytical Soln.
Finite Diff. Soln.
0 10 20 30 40 50 60 70 80 90 100
Depth of Soil Profile in cm
Figure 6.17 Comparison of soil temperature profiles predicted by analytical and finite difference solutions
(Time Step=l HR).
6-68
-------
°IV
27
o
0)
T3
C
fc 21
a.
18-
15
\\
30
27"
O
24.-
D)
0)
T3
321
ro
18"
\ *,
\ '
's
Profile Initial Temp. = 20 C
Upper Boundary Temp= 30 C
Lower Boundary Temp= 20 C
Time Step = 1 day
Analytical Soln. - -
Finite Diff. Soln.
10
20
30 40 50 60
Depth of Soil Profile in cm
70
80
90
100
Profile Initial Temp = 20 C
Upper Boundary Temp = 30 C
Lower Boundary Temp = 20 C
Time Step = 1 day
Analytic! Soln. -
Finite Diff. Soln-
^-^ Finite Diff. Soln.
10 20 30 40 50 60
Depth of Soil Profile in cm
70
80
90 100
Figure 6.18 Comparison of soil temperature profiles predicted by analytical and finite difference
solutions (Time Step=l day).
6-69
-------
Table 6.8 Simulated Soil Temperature Profile after One Day for Different Compartment Thicknesses
(Time Step = 1 Day)
Depth (cm)
0.0
1.0
2.0
3.0
4.0
5.0
10.0
20.0
30.0
40.0
50.0
60.0
75.0
99.0
100.0
DELX = 1 cm
30.000
29.665
29.341
29.028
28.725
28.432
27.109
25.048
23.577
22.524
21.766
21.215
20.638
20.023
20.000
DELX = 0.1 cm
30.000
29.664
29.340
29.026
28.723
28.431
27.106
25.045
23.574
22.520
21.760
21.206
20.627
20.020
20.000
6.5.4 Testing of Daughter Products Simulation
The fate of pesticides in soils is a complex issue. Many processes (i.e., volatilization, degradation, etc.) must be
considered in order to adequately address this issue. One of these processes, which has been largely neglected in
pesticide leaching models, is that of the transformation of the parent compound to various toxic daughter products.
The tendency has been to lump all the toxic family into a "total toxic residue" and to model the fate of this composite
as a single chemical. This assumption may not be acceptable, especially if the daughters have very different decay
rates or adsorption partition coefficients from the parent or from each other.
Algorithms have been included in PRZM-3 to simulate parent/daughter relationships. An analytical solution to the
decay and transformation model was derived to check the numerical model.
6-70
-------
*
c
1
*
c
2
*
c
3
ADSORBED PHASE
Ci -^
C2
^ c3
DISSOLVED PHASE
Figure 6.19 Schematic of a system of parent and daughter pesticide relationships.
The system that was modeled is shown in Figure 6.19. The Ci are dissolved concentrations and the C" are adsorbed
concentrations. The Kt are adsorption partition coefficients, the kj are decay and transformation rates in the dissolved
species, the k" are adsorbed phase decay coefficients and 6 and p are the water content and soil bulk densities,
respectively. Notice that only the dissolved forms may be transformed from one toxic form to another. A system of
first order differential equations describing this system can be written as:
dt
(6.124)
c2e
dt
c2e
(6.125)
c3e
dt
= - ks C3Q + k4 C2Q
(6.126)
dt
(6.127)
dt
S~l*
C2 p
(6.128)
dt
c;
(6.129)
Making use of C, Kt = C* we can reduce the six equations above to three equations in three unknowns, namely:
6-71
-------
at
d C
d C,
- a, C, (6.130)
C,
dt
in which
= a2 Cj + a3 C2 (6.131)
= a4 C2 + a5 C3 (6.132)
(6.133)
0 + JST, p
a- = -— (6.134)
9(^3 + *4) - ^* ^2P
-^ ^ — (6.135)
0 + ,s:2 p
(6.136)
^3p
Q + K3p
(6.137)
These ordinary differential equations with constant coefficients can be solved analytically for Ch C2 and C3 using the
initial conditions C1 = C\ when / = 0 and C2 = C3 = 0 at / = 0. The solution is:
q(0 = C/ e"1' (6.138)
Cl r a*t a~t-i
\[e ~ e ]
CM) = -^-±± - - (6.139)
o1-a3
{ [(a3- a5)ea>' + (a5-ai)ga3'+ (a1-a3)ea5f] ^
(a,-a3) (aj-aj) («3-a5)
In PRZM-3, the equations are solved numerically as part of the general advection-dispersion equation for a solute in
a porous medium by using an implicit scheme. A new subroutine was added to set up the transformation (source and
sink) terms for the system. The relationship C1 -> C2 -* C3 may be modeled or the system can be configured for C1 ->
C2 and Cj -> C3 or for independent Ch C2 and C3 simply by selecting zero or positive values for the appropriate
transformation rate constants.
Figure 6.20 and Figure 6.21 show the results of a series of tests performed on the numerical model and checked by
the analytical model. In these figures, the solid line represents the "true" or analytical solution, and the dashed line
6-72
-------
represents the approximate numerical solution. In Figure 6.20. there was no decay of the dissolved phase chemicals
and no adsorption of any species. The rate of transformation from C1 to C2 was 0.2 day"1 and that from C2 to C3 was
0.5 day"1. After 20 days nearly all the chemical is in form C? The numerical model traces the decay and formation of
each constituent closely, being poorer in those regions where the rate of change of the concentrations are more rapid.
Figure 6.21 shows the same system with a decay rate of 0.01 day"1 in the dissolved phase.
LU
100
80
60
LU
O
o
O
40
20
8 10
TIME, IN DAYS
12
14 16
18 20
Figure 6.20 Conversion of C, to C2 to C3 with no adsorption without decay.
6-73
-------
100.
HI
8 80
LU
Q.
60
LU
o
O
O
40
20
NUMERICAL
ANALYTICAL
8 10 12
TIME, IN DAYS
14 16
18 20
Figure 6.21
Conversion of C, to C2 to C3 with no adsorption without decay.
Using the analytical model, the assumption of modeling the "total toxic residue" decay as a first-order process was
tested. Adsorption coefficients for aldicarb, aldicarb sulfoxide and aldicarb sulfone in a Woburn sandy loam (K1 =
0.55, K2 = 0.16 andK3 = 0.185) and decay and transformation rate constants (k1 = 0.07, k2 = 0.55, k3 = 0.01, k4 =
0.031 and ks = 0.0152) were taken from Bromilow etal. (1980). A soil bulk density of 1.45, a water content of 0.27
cm3 cm"3 and an initial aldicarb parent mass of 100 mg were also used. The model was run for 90 days and the results
are shown in Figure 6.22.
6-74
-------
* PARENT ALDICARB
• SULFOXIDE
O SULFONE
TOTAL
Figure 6.22 Conversion of aldicarb to aldicarb sulfoxide to aldicarb sulfone.
The results show that the decay of the sum of the dissolved aldicarb concentrations does not follow first-order
kinetics. The reason for this is the conversion of aldicarb parent to aldicarb sulfoxide. Because the sulfoxide has a
lower partition coefficient, the dissolved concentration increases until most of this conversion is complete. Once this
happens, however, the sum of the sulfoxide and the sulfone concentrations does follow a first-order decay curve.
6.5.5 Testing of Nonuniform Extraction Model for Runoff and Revisions in the Distribution of Residues
The nonuniform extraction model for runoff and revisions in the distribution of residues following washoff and
application (CAM=1) first appeared in an unofficial release of PRZM-2, developed by Waterborne Environmental,
Inc., referred to as PRZM-2.3 (Waterborne Environmental 1995). PRZM-2.3 was developed in response to data
indicating that PRZM-2.2 was over-predicting pesticide runoff for the herbicide atrazine by about an order of
magnitude at the Georgia, Tennessee, and Iowa study sites (Solomon et al. 1996). PRZM-2.3 provided significantly
better estimates of atrazine runoff compared at all three field sites; results comparisons for the Georgia and
Tennessee sites are shown in Figure 6.23 and Figure 6.24. respectively. Sites consisted of different geographical
areas, soil times, and climatological conditions.
• Shelby County. Tennessee. This study was conducted by Memphis State University and consisted
of 18 hectares. The upper 8 hectares were in pasture and the lower 10 hectares were planted in
corn (Klaine et al. 1988). Soils consisted of the Falaya silt loam, having a 1 to 2 percent slope and
classified as Hydrologic Soil Group D. Atrazine was applied at 0.92 kg a.i./ha
• Watkinsville. Georgia. Site monitored by the U. S. Environmental Protection Agency and U. S.
Department of Agriculture in 1972 through 1973 (Smith et al. 1978). The study was conducted on
6-75
-------
a 1.3-ha drainage area planted in corn. Soils consisted of the Cecil sandy loam (Hydrologic Soil
Group B) having a slope of 1 to 3 percent. Atrazine application at 3.36 kg a.i./ha
Monona County. Iowa. This study was conducted by Iowa State University as part of an
evaluation of the effect of tillage practices on the movement of pesticides and nutrients with water
and sediment (Baker and Johnson 1978). The study site consisted of a 0.78-ha drainage area
planted in corn. The predominant soil was the Ida silt loam having a slope of 12 to 18 percent and
classified as Hydrologic Soil Group C. Atrazine application at 2.24 kg a.i./ha
6-76
-------
160
140
160 •
140 '
120 •
100 '
40-
20-
PRZM-2.2
Atrazine Mass in Runoff
Observed
Simulated
Days after Application
PRZM-2.3
Atrazine Mass in Runoff
Observed
Simulated
8 12 17 25 26 28 29 30 33 40 41 45 48 58
12 17 25 26 28 29 30 33 40 41 45 48 58
Days after Application
Figure 6.23 Comparison of PRZM-2.2 and PRZM-3 at Georgia study site. (PRZM-3 results are the same as
those generated by the experimental version 2.3)
6-77
-------
PRZM-2.2
Atrazine Mass in Runoff
1200
1000
If 800
J5) -H-
ro 600
CD
400
200
Observed
Simulated
1200
1000
800
•S
t/J
re eoo
CD
I I ' • I I ""• I I I I I I I I I I
-29 -25 0 7 20 27 32 66 75 81 95 110 127 132 146
Days after Application
PRZM-2.3
Atrazine Mass in Runoff
400
200
Observed
Simulated
I
o-f 1 H-"H 1 ^" I 1 1 1 1 1 1 1 1 1
-29 -25 0 7 20 27 32 66 75 81 95 110 127 132 146
Days after Application
Figure 6.24 Comparison of PRZM-2.2 and PRZM-3 at Tennessee study site. (PRZM-3 results are the same as
those generated by the experimental version 2.3)
6-78
-------
6.6 Biodegradation Theory and Assumptions
The biodegradation model is based on Soulas (1982). The soil is divided into two phases: the solid phase, consisting
of the dry soil including the organic matter, and the aqueous phase dispersed within it, consisting of the soil
moisture, various organic substrates, and all the biomass. Some of the organic and inorganic components constituting
the solid phase can adsorb the pesticide. This adsorption is represented as a linear isotherm, instantaneous and
without hysteresis.
The microbial population is divided into four groups. The first two are responsible for the degradation of the
pesticide. These are the metabolizing and co-metabolizing populations. The former corresponds to normal metabolic
utilization, whereas the latter represents that fraction of the microflora which degrades without energy recovery.
The non-degrading population was divided into microorganisms that are sensitive to the lethal action of the chemical
and those that are indifferent.
In the original development of the equations, all concentrations were expressed with respect to the soil solution.
Soulas (Soulas 1982) reports that these concentrations are somewhat theoretical when considering the different
biomasses and are not easy to evaluate by experiment. Thus, all concentrations were expressed with respect to the
weight of the moist soil. For these biomasses, the simple proportionality
X{ = WSX°, i=m,c,s,r (6.141)
was chosen where
Xi = concentration of the Xj population in the moist soil
X° = concentration of the Xj population in the soil solution, and
HIP
where
H = weight of the aqueous phase (soil solution)
P = weight of the solid phase (dry soil)
For the metabolizing population, growth is described by:
dX X X
~f = ^W"S'K + wdst + ^C"]T " kdmXm (6'143)
This represents growth at the expense of both the pesticide (S) and the carbon (Q in the soil solution. The population
decreases as a result of a first-order death process with a death rate constant kdm.
For the co-metabolizing population,
dX X
_ c - ,, fi _ « _ _ L. V
"
This reflects growth only at the expense of soil carbon. Allowance was also made for possible antagonistic effects by
the non-degrading portion of the soil microflora. These antagonisms were assumed to result only in a reduction of
the growth rate of the co-metabolizing population. Michaelis-Menten kinetics with non-competitive inhibition were
used to simulate these conflicts.
For the sensitive population,
6-79
-------
dX
— - k, W, S. X - k, X
ir 1 a t s as s
(6.145)
This equation assumes a death process that follows second-order kinetics. For the non-sensitive, non-degrading
population Jf,, the population is given by:
dX
X
(6.146)
This is the basic relation of first-order growth and death terms.
The equation describing the pesticide concentration, S,,
X
dS. i
— = —W\i St
dt y_ s sm '
WdS,
- k,E.
(6.147)
has two parts. The first term describes the degradation due to the metabolizing population, while the second
describes the action of the co-metabolizing population. The concentration of carbon in the moist soil, Cw is given by:
dt
W,
w.
- Cw - -1-W, (6.148)
is derived on the basis that the concentration is determined by the difference between two reaction rates - the
solubilization rate of carbon compounds from solid soil organic matter and the rate of microbial consumption. It is
assumed that soluble carbon in the soil solution is, in first approximation, sufficiently low to be neglected when
compared to the saturation constant.
Definitions:
X,
s
7,
k,
Concentration of the Xi population in the moist soil (i = m, c, s, r)*
Pesticide concentration in the moist soil
Carbon concentration in the moist soil
Maximum specific growth rate of the Xi population (/' = sm, cm, c, s, r)*
Saturation constant of the Xt population (/' = sm, cm, c, s, r)*
Death rate of the Xi population (/' = m, c, s, r)*
True growth yield of the Xt population (/' = sm, cm, c, s, r)*
Second-order death rate of the Xs population
Dissociation constant of the enzyme-substrate complex
Inhibition constant
In addition,
where
KJ = distribution coefficient
1 + W
Kd+ W
W= -
(6.149)
(6.150)
and
H = weight of soil solution (aqueous phase)
P = weight of dry soil (solid phase)
These equations are to be solved simultaneously, and the results used to determine the amount of pesticide in the soil
6-80
-------
that is degraded biologically over the timestep interval.
These equations are solved in PRZM-3 using a fourth-order Runge-Kutta method. This subprogram uses the carbon
concentration and the pesticide concentration in the moist soil of each compartment as input. Using the populations
of organisms in each compartment, which is saved between calls, the subprogram solves the degradation algorithm
to determine the new pesticide amount, and thus the amount degraded, over the PRZM-3 time step. Also, the
changes to the organism populations are calculated and saved for use in the subsequent timestep.
6-81
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SECTION 7
Vadose Zone Flow and Transport Model (VADOFT) Code and Theory
7.1 Introduction
VADOFT is a finite-element code for simulating moisture movement and solute transport in the vadose zone. It is
the second part of the two-component PRZM-3 model for predicting the movement of pesticides within and below
the plant root zone and assessing consequent groundwater contamination. The VADOFT code simulates
one-dimensional, single-phase moisture movement in unconfined, variably saturated porous media. The code
considers only single-porosity media and also ignores the effects of hysteresis. 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. VADOFT also simulates solute transformations in order
to account for parent/daughter relationships.
7.2 Overview of VADOFT
7.2.1 Features
7.2.1.1 General Description
The VADOFT code can be used to perform one-dimensional modeling of water flow and transport of dissolved
contaminants in variably or fully saturated soil/aquifer systems. VADOFT can be operated as a stand-alone code or
operated in conjunction with the root zone model, PRZM. In the latter case, boundary conditions at the interfaces of
the modeled domains are established via model linkage procedures.
7.2.1.2 Process and Geometry
VADOFT performs one-dimensional transient or steady-state simulations of water flow and solute transport in
variably saturated porous media. The code employs the Galerkin finite-element technique to approximate the
governing equations for flow and transport. It allows for a wide range of nonlinear flow conditions, and handles
various transport processes, including hydrodynamic dispersion, advection, linear equilibrium sorption, and first-
order decay. Steady-state transport can not be simulated when decay is considered. Boundary conditions of the
variably saturated flow problems are specified in terms of prescribed pressure head or prescribed volumetric water
flux per unit area. Boundary conditions of the solute transport problem are specified in terms of prescribed
concentration or prescribed solute mass flux per unit area. All boundary conditions may be time dependent.
7.2.1.3 Assumptions
The VADOFT code contains both flow and solute transport models. Major assumptions of the flow model are:
• Flow of the fluid phase is one-dimensional and considered isothermal and governed by Darcy's
law.
• The fluid considered 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:
• Advection and dispersion are one-dimensional.
• Fluid properties are independent of concentrations of contaminants.
• 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.
7-1
-------
• Adsorption and decay of the solute may be described by a linear equilibrium isotherm and a first-
order decay constant.
• Vapor transport can be neglected.
7.2.1.4 Data Requirements
Data required for the simulation of variably saturated flow include values of the saturated hydraulic conductivity and
specific storage of the porous media, the geometry and configuration of the flow region, as well as initial and
boundary conditions associated with the flow equation. Soil moisture relationships are also required. These include
relative permeability versus water phase saturation and capillary head versus water phase saturation. These
relationships may be supplied to the code using tabulated data or functional parameters.
Data required for the simulation of solute transport in variably saturated soil include dispersivity and porosity values,
retardation and decay constants, Darcy velocity and water saturation values, as well as initial and boundary
conditions associated with the transport equation.
7.2.2 Limitations
Major limitations of the VADOFT code are:
• In performing a variably saturated flow analysis, the code handles only single-phase flow (i.e.,
water) and ignores the flow of a second phase (i.e., air) which, in some instances, can be
significant.
• The code ignores the effects of hysteresis on the soil moisture constitutive relations.
• The code does not take into account sorption nonlinearity or kinetic sorption effects which, in
some instances, can be important.
• The code considers only single-porosity (granular) soil media. It cannot handle fractured porous
media or structured soils.
• The code does not take into account transverse dispersion, which can be important for layered
media.
7.3 Description of Flow Module
7.3.1 Flow Equation
VADOFT considers the problem of variably saturated flow in a soil column in the vadose zone of an unconfined
aquifer. The code solves the Richards' equation, the governing equation for infiltration of water in the vadose zone:
where
i|; = the pressure head (L)
K = the saturated hydraulic conductivity (LT"1)
km = the relative permeability
z = the vertical coordinate pointing in the downward direction (L)
/ = time (T)
T| an effective water storage capacity (L"1) defined as:
dS...
where
Ss = specific storage (L"1),
7-2
-------
= water saturation
= the effective porosity.
Specific storage is defined by
where
cf = the fluid compressibility (LT2M4)
cs = the solid skeleton compressibility (LT2M-1)
p = the fluid density (ML"3), and
g = the gravitational acceleration (LT"2)
The initial and boundary conditions of the one-dimensional infiltration problem may be expressed as:
i|r(z,0) = i|r,
either
or
either
or
where
I
L
F
(7.3)
V(L,t) - 0
the initial pressure head value (L)
the pressure head at the upper boundary (L)
the pressure head at the lower boundary (L)
the rate of infiltration at the soil surface (LT"1)
the thickness of the vadose zone (L)
the vertical Darcy velocity (LT"1) (defined by Equation 7.12).
(7.4)
(7.5)
(7.6)
(7.7)
(7.8)
The boundary condition in Equation 7.8 is valid because the bottom boundary of VADOFT allows fluid to exit.
To solve the variably saturated infiltration problem, it is also necessary to specify the relationships of relative
permeability versus water saturation and pressure head versus water saturation. Two alternative function expressions
are used to describe the relationship of relative permeability versus water saturation. These functions are given by
Brooks and Corey (1966) and van Genuchten (1980):
k_.. = S"
and
where
,1/2 ,
(7.9)
(7.10)
n and y are empirical parameters
Se = the effective water saturation defined as Se = (Sw - Swr)/(l - Swr); Swr denotes the residual water
saturation.
7-3
-------
The relationship of pressure head versus water saturation is described by the function (Mualem 1976, van Genuchten
1980):
1
' "V " (7.11)
i - s.
wr
where
a, p, and y = empirical parameters; y = 1 - 1 / p,
i|;a = the air entry pressure head value (L)
Swr = the residual water phase saturation.
Descriptive statistical values for a, p, and y have been determined by Carsel and Parrish (Carsel and Parrish 1988)
for 12 soil classifications (see Section 5). Using the mean parameter values, the relationships of effective saturation
versus capillary head and relative permeability versus effective saturation are plotted. Logarithmic plots are shown in
Figure 7.1 through Figure 7.3. To show more vividly the high degree of nonlinearities, the relationships of relative
permeability versus effective saturation are also plotted on arithmetic scales and presented in Figure 7.4 through
Figure 7.6. It is important that the finite element flow module be capable of handling such high nonlinearities to be
successful in performing a Monte Carlo study of infiltration in the unsaturated zone.
Equation 7.1 is solved using the Galerkin finite element subject to the initial and boundary conditions given in
Equations 7.4 through 7.7. After the distributions of i|; and Sw have been determined, the Darcy velocity is computed
from:
/ Pilr ^
(7.12)
7.3.2 Numerical Solution
7.3.2.1 Numerical Approximation of the Flow Equation
A numerical approximation of the one-dimensional flow equation in the vadose zone is obtained using a Galerkin
finite-element formulation with spatial discretization performed using linear elements. Time integration is performed
using a backward finite difference approximation. This leads to a system of nonlinear algebraic equations. For a
typical node "7" in the finite-element grid (see Figure 7.7).
, = dt (7.13)
where k+l is the current time level, and cq, p;, y15 and dt are given by
(7.14)
(7.15)
(7.16)
7-4
-------
and Az, and A/t are the spatial and time increments, respectively. Note that braces ({ }) are used in the equationsabove
(and below) to denote the value of the enclosed quantity at the element centroid. The nonlinear system of equations
is solved for each time step. Three nonlinear schemes are provided in the VADOFT code. The first scheme is a
Picard-type iteration scheme, the second scheme is a Newton-Raphson, and the third is a Newton-Raphson scheme
modified by Huyakorn (1988, Personal Communication).
In the Picard scheme, the matrix coefficients, a;, ft, Yi, and dt, are first evaluated using an initial estimate of
pressure head values, -ty\. The resulting system of linearized equations is then solved for i|; f"1 using the Thomas
algorithm. Updating of the matrix coefficient is performed by recomputing values of nonlinear soil parameters.
Iterations are performed until the successive change in pressure head values is within a prescribed tolerance.
In the Newton-Raphson scheme, the nonlinear system of equations is treated by applying the Newton-Raphson
technique (see Huyakorn and Finder 1983) to Equation 7. 13. This leads to the following system of linearized
algebraic equations:
where superscript r is used to denote the r-th iterate; a;, ft, YI, and dt were defined previously; a*, p*, and y*, are
given by
P* = + + f ({e*>,A-, + WA.) * ^{K},, - ^{V}, (7.20)
/T/x
Y, = - -£-+ —{V}t+ - g— (7.21)
The initial solution and subsequent iterations of the Newton-Raphson scheme are performed in the same manner as
that described for the Picard scheme.
7.3.2.2 General Guidance on Selection of Grid Spacings and Time Steps, and the Use of Solution Algorithms
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.
In the analysis of the given variably saturated flow problem, 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 algorithm or one of the Newton-Raphson solution
algorithms. For one-dimensional cases where convergence difficulties are not expected, the efficiencies of these
algorithms have been found to be similar. For certain steady -state cases involving highly nonlinear soil moisture
characteristics, the use of either of the Newton-Raphson algorithms is preferable, particularly when the Picard
algorithm fails to converge within a reasonable number of iterations (say between 10 and 20).
7.4 Description of the Transport Module
7.4.1 Transport Equation
7-5
-------
The governing equation for one-dimensional transport of a nonconservative solute species in a variably saturated soil
takes the form
9 , T*. 9C x vrdC an s 9C i v
— (D—) - V— = QR(— + Ac) (722)
3z dz dz Bt ( '
where D is the apparent dispersion coefficient (L2T"'), c is the solute concentration (ML"3), 6 is the volumetric water
content (6 = 4>SW), R is the retardation coefficient, and A is the first-order decay constant (T1). Note that the apparent
dispersion coefficient is defined as D = «LV + cj>D*, where aL is the longitudinal dispersivity, and D* is the effective
molecular diffusion coefficient.
The initial and boundary conditions of the one-dimensional transport problem may be expressed as:
c(z,0) = ci (7.23)
either
-D |^(0,0 = V(c0-c) (7.24)
dz
or
c(0,/) = c0 (7.25)
^(£,0 - 0 (7.26)
dz
where ci is the initial concentration (ML"3), and c0 is the leachate concentration at the source (ML"3).
7-6
-------
10 10 10'
CAPILLARY HEAD, cm
(a)
10
tn
<
ui
QL
LU
D.
LU
LJJ
ce
10
10
10 10
SATURATION
(b)
10
Figure 7.1 Logarithmic plot of constitutive relations for clay, clay loam, and loam sandy soils: (a) saturation
vs. capillary head and (b) relative permeability vs. saturation.
7-7
-------
LLJ
o;
10'
10'
SUTY CLAY
SILTY CLAY LOAM
SILT
SILT LOAM
(a)
I I I I Mill I I I I Mill I I I I Hill I I I I
10 10 10
CAPILLARY HEAD, cm
(a)
1.0
10
10
10-
10
10"'
10"
10
t~-i-
(b) f/l/gM.|
i i 11 inn i i 11 inn i i 1111 in i' i i Mill
-in -in in 1 n
10'
10
SATURATION
(b)
10"
\"f
Logarithmic plot of constitutive relations for silt, silty clay loam, silty clay, and silty
Figure 7.2
loam soils.
7-8
-------
o:
w
10 10 10
CAPILLARY HEAD, cm
1.0
10
10
f. 10
_J
CD
£ io-4
s
cc
LU -5
D- 10
LU
LU
cc
-6
10"
10
SATURATION
Figure 7.3 Logarithmic plot of constitutive relations for sandy clay, sandy clay loam, sandy loam, and sandy
soils.
7-9
-------
ca
<
LJJ
LJJ
Q_
LJJ
3
LJJ
Of
0.0
0.2
0.4 0.6
SATURATION
Figure 7.4 Standard plot of relative permeability vs. saturation for clay, clay loam, loam and loam sandy soils.
7-10
-------
1.0
0.
5 0-6
I
Qf
LU
Q_
LU
>
LU
Of
0.2
0.4
0.0
SILT
SILTY CLAY
SILTY
'H™! i* B * * " j , aM
o.o
0.2
0.4 0.6
SATURATION
0.8
1.0
Figure 1.5 Standard plot of relative permeability vs. saturation for silt, silt clay loam, silty clay and silty loam
soils.
7-11
-------
1.0
03
LU
5
or
LU
Q_
LU
I
LU
0.6
0.4
0.2
0.0
0.0
Sand
Sandy Loam
Sandy Clay Loam
Sandy
r
0.2
0.4 0.6
SATURATION
Figure 7.6 Standard plot of relative permeability vs. saturation for sandy clay, sandy clay loam, sandy loam
and sandy soils.
7-12
-------
7.4.2 Numerical Solution of the Transport Equation
7.4.2.1 Numerical Approximation of the Transport Equation
A numerical approximation of the one-dimensional transport equation is obtained using an upstream-weighted finite-
element formulation with spatial discretization performed using linear elements. Time integration is performed using
a central finite-difference approximation. This leads to a system of linear algebraic equations. The equation
corresponding to node "/'" takes the form:
a,',*' + P,.c*+1 + Y,.^,' = dt (7.27)
where
,
a. = TO, +
, {e*}Az
. = TV- +
' Y'
i v / \ i i-1 ri i ii i+ Is ^ ^ \ i-1 / / ^ ^^ V j+1 / / ^/.ZoJ
a-r = "-r^1 ~ ^^n,.!
Az,._, 2
P-r = ^T^11 + ^T^ + T
Az,._, Az; 3
Y,r = -
6
with T and co denoting the time weighting factor and the upstream weighting factor, respectively.
To obtain a second-order temporal approximation, the value of T is set equal to l/i. This corresponds to using the
Crank-Nicholson central difference time stepping scheme. The upstream weighting factor co is introduced in the
above numerical approximation to curb numerical oscillations that may occur when the selected finite-element grid
is not sufficiently refined for a given value of longitudinal dispersivity. For each time step, the linear system of
algebraic equations is solved using the Thomas algorithm.
Transport of a daughter species in a decay chain can also be handled by the VADOFT code. In this case, the right
side of the governing equation for single species transport (Equation 7.22) is modified by adding a source term
accounting for transformation of parent components. This source term is given by
"P
m = - E QS^jlyRjCj (7-29)
y=i
where
subscripty' = the parent species
np = the number of parent species
EJ = the mass fraction of parent component that is transformed into the daughter species under
consideration
The numerical solution of the modified transport equation can be performed in the same manner as that described
7-14
-------
previously for a single species. The source term from Equation 7.29 is incorporated into the finite element matrix
equation by adding d] to the right side,
( "P \
(7.30)
In performing the solute transport analysis, the selection of nodal spacing (Az) and time step value (A/) should follow
the so-called Peclet number and Courant number criteria where possible. These two criteria are
Az .
- * 4 (7.31)
V ,Ar
(7.32)
Az
F«rf = ^ (7.33)
where
«L = the longitudinal dispersivity
Vsd = the solute velocity
V = Darcy velocity
6 = water content
R = 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 co, the
weighing factor, is given by
0) =
4CLL
-T *>4aL
0 0 < 4uL
(7.34)
where
aL = the longitudinal dispersivity
/ = the length of the element.
7.5 Results of VADOFT Testing Simulations
Three sets of benchmark problems were used to test the VADOFT code. The first set consists of two steady and
transient problems designed to test the variably saturated flow component of the code. The second set consists of
four transient one-dimensional transport problems. The third set consists of two coupled flow-transport problems.
Numerical results obtained from VADOFT are compared with analytical solutions and results obtained using two
other finite-element codes, UNSAT2 and SATURN. These test problems were simulated using VADOFT before it
was linked in PRZM-3.
7.5.1 Flow Module (Variably Saturated Flow Problems)
7.5.1.1 Transient Upward Flow in a Soil Column
This problem concerns transient, vertically upward moisture movement in a 20 cm long soil column. The soil
column is subject to zero pressure head at the base and zero flux at the top. The initial distribution of pressure head
7-15
-------
is hydrostatic: (/ = 0) = -90 + z cm, where z is the depth below the top of the soil column. Soil properties and
discretization data used in the simulation are presented in Table 7.1. The simulation was performed for 15 time steps
with constant time step value of / = 0.01 d. Numerical results given by the Picard and the Newton-Raphson schemes
are virtually identical. Both schemes require between 2 and 3 iterations per time step to converge to a head tolerance
of 0.01 cm. The simulation results obtained from VADOFT are compared with those obtained from UNSAT2 and
SATURN (the two-dimensional finite-element codes described by Davis and Neuman (1983), and Huyakorn et al.
(1984)) respectively. Shown in Figure 7.8 and Figure 7.9 are plots of distributions of pressure head and water
saturation, respectively. As can be seen, the results of VADOFT are in good agreement with the results of the other
two codes.
7.5.1.2 Steady Infiltration in a Soil Column
This problem concerns steady-state infiltration in a soil column. The column is 550 cm in length and is subject to an
infiltration rate of 4.07 cm day"1 at the top and zero pressure head at the bottom. Soil properties used in the
simulation are presented in Table 7.2. Five cases of varying degree of nonlinearity of relative permeability function
(k^ = S") were simulated. Both the Picard and the Newton-Raphson schemes were used in conjunction with a finite-
element grid having constant nodal spacing, z = 10 cm. The performance of the two iterative schemes are illustrated
in Table 7.3. Note that the Newton-Raphson scheme converges for all cases, whereas the Picard scheme fails to
converge when the nonlinear exponent n exceeds 4. Simulated distributions of pressure head and water saturation are
shown in Figure 7.10 and Figure 7.11. respectively. These results of the VADOFT code are virtually identical to
corresponding results obtained using the SATURN code.
7.5.2 Transport Module
7.5.2.1 Transport in a Semi-Infinite Soil Column
This problem concerns one-dimensional transport of a conservative solute species in a saturated soil column of
infinite length. The solute is introduced into the column at the inlet section where z = 0. The initial concentration is
assumed to be zero, and the dimensionless constant inlet concentration is prescribed as 1. Values of physical
parameters and discretization data used in the numerical simulation are given in Table 7.4. The finite-element grid
representing the soil column was 400 cm in length. The simulation was performed for 20 time steps. Thus the
duration of the simulation time of transport in the soil column was 50 hours. For this duration, the selected grid
length is sufficient to avoid the end boundary effect. The numerical solution obtained from the VADOFT code was
checked against the analytical solution of Ogata and Banks (1961). Shown in Figure 7.12 and Table 7.5 are
concentration values at / = 25 hours and / = 50 hours. As can be seen, the numerical and analytical solutions are in
excellent agreement.
7.5.2.2 Transport in a Finite Soil Column
In this problem, downward vertical transport of dissolved contaminants in a soil column above the water table of an
unconfmed aquifer is considered. The length of the soil column is 20 m and the Darcy velocity and water content are
assumed to be constant and equal to 0.25 m day"1 and 0.25, respectively. The initial concentration is zero, and water
with dimensionless solute concentration of 1 enters the soil surface at a rate of 0.25 m day"1. At the water table, a
zero dispersive-flux boundary condition is assumed. A list of physical parameter values and discretization data used
in the simulation is provided in Table 7.6. Two cases involving conservative and nonconservative species were
simulated. Results obtained from the VADOFT code are compared in Figure 7.13 and Table 7.7 with the analytical
solution given by van Genuchten and Alves (1982). There is excellent agreement between the numerical and
analytical solutions for both cases.
7.5.2.3 Transport in a Layered Soil Column
This problem concerns one-dimensional transport of a conservative solute species in a soil column consisting of
three layers. The initial concentration in the soil column is assumed to be zero, and the two boundary conditions
prescribed are a unit concentration at the top and a zero dispersive flux boundary condition at the bottom. A list of
7-16
-------
physical parameter values and discretization data used in the simulation is provided in Table 7.8. Two cases
corresponding to those considered by Shamir and Harleman (1967) were simulated. Both cases have contrasting
longitudinal dispersivity values among the three layers. The dispersivity values of the second case are ten times
those of the first case for the same layers. The intention here is to test the numerical scheme used in the VADOFT
code, as well as to check the validity of an approximate analytical solution presented by Shamir and Harleman
(1967) and Hadermann (1980). It should be noted here that the approximate solutions are valid only for relatively
small values of dispersivity. Therefore, for a small dispersivity value, the solutions can be employed to verify the
VADOFT code. Then with appropriate discretization, the VADOFT code could be used to determine the validity of
the analytical solutions at large dispersivity values.
Table 7.1 Soil Properties
Parameter
Length of soil column, L
Saturated hydraulic conductivity
Porosity, cj>
Residual water phase saturation,
Air entry value, i|;a
Constitutive relations:
krw = (Sw-SJ/(l-SJ
where i|;r = -100 cm.
Az = 0.5 cm
At = 0.01 d
and Discretization Data Used in Simulating Transient Flow in a Soil Column
Value
20cm
K lOcmd'1
0.45
Sm 0.333
0.0cm
,,-s.
7-17
-------
20
16
12
CD
LJJ
0
UNSAT2
O VADOFT
x SATURN
-20
-40 -60
PRESSURE HEAD, cm
-80 -100
Figure 7.8 Simulated pressure head profiles for the problem of transient upward flow in a soil column.
(Adapted from Battelle and GeoTrans, 1988).
7-18
-------
Constitutive relations:
*». = $,",«= 3,4,6,8,10
1
rw
s. =
where Se = (Sw - 8^)7(1 - SJ, a = 0.014 cm1, i|;a = 0 cm, p = 1.51, y = 0.338
Table 7.3
Iterative Procedure Performance Comparison
Case
Number of Nonlinear Iterations
Newton-Raphson
Picard
n= 3
12
33
56
n= 4
13
n= 6
19
n.c.*
n.c.
n.c.
n =
27
n=10
31
* No convergence. Head tolerance = 0.0001 cm. Grid spacing z = 10 cm.
Table 7.4 Values of Physical Parameters and Discretization Data Used in Simulating One-dimensional
Transport in a Semi-infinite Soil Column
Parameter
Value
Darcy velocity, V
1 cm hr"1
Porosity, cj>
Longitudinal dispersivity, aL
0.25
5 cm
Concentration at the source, cn
Az = 10 cm
At = 2.5hr
7-20
-------
100
n =
10
200
Q_
UJ
Q
300
400
500
600
-10
I I I
-20 -30 -40
PRESSURE HEAD, (cm)
-50
-60
Figure 7.10 Simulated pressure head profiles for five cases of the problem of steady infiltration in a soil
column. (Adapted from Springer and Fuentes, 1987).
7-21
-------
Sc
LJJ
O
Z
O
O
LJJ
LU
Of
— Analytic Soln
O VADOFT
0.4
0.2
0.0
50. 100.
150. 200. 250.
DISTANCE, cm
300.
350. 400.
Figure 7.12 Simulated concentration profiles for the problem of solute transport in a semi-infinite soil column.
7-23
-------
Table 7.5 Concentration Profile Curves at / = 25 hr and / = 50 hr Showing Comparison of the Analytical
Solution and Results from VADOFT
z Distance (cm)
260.0
270.0
280.0
290.0
300.0
310.0
320.0
330.0
Concentration Values
t = 25 hr
Analytical
VADOFT
t = 50 hr
Analytical
0.1058
0.0701
0.0444
0.0270
0.0157
0.0087
0.0046
0.0000
VADOFT
0.1193
0.0831
0.0563
0.0371
0.0239
0.0150
0.0092
0.0055
Table 7.6 Values of Physical Parameters and Discretization Data Used in Simulating One-dimensional
Transport in a Finite Soil Column
Parameter
Thickness of soil column, L
Darcy velocity, V
Water content, 6
Retardation coefficient, R
Longitudinal dispersivity, «L
Source leachate concentration, c0
Case 1:
Decay constant, A
Value
20m
0.25 md'1
0.25
1
4m
1
Od'1
Case 2:
Decay constant, A
0.25 d'1
Az= 1.0m
At = 0.5d
7-25
-------
0.0.
4.
8. 12.
DISTANCE, m
16. 20.
Analytic Soln.
VADOFT
8. 12.
DISTANCE, m
(b)X=0.25d1
Figure 7.13 Simulated concentration profiles for two cases of the problem of solute transport in a soil column
of finite length, (a) A = 0 d1, and (b) A = 0.25 d'1.
7-26
-------
Using the discretization data given in Table 7.8. the VADOFT code was run for 180 time steps. Simulated
breakthrough curves at the bottom end of the column (z = 86.1 cm) are presented in Figure 7.14 and Figure 7.15
and in Tables 7.9 and 7.10. As can be seen, the numerical solution of the VADOFT code compares very well with
the analytical solution for case 1: The small dispersivity case, where the analytical assumption of infinite ratio of
layer thickness to layer dispersivity-i.e., each layer extends to infinity-is fairly accurate. There is a slight
discrepancy of the analytical solution from the numerical solution for case 2, where the analytical assumption is less
accurate.
7.5.3 Combined Nonlinear Flow and Transport Modules
7.5.3.1 Transport During Absorption of Water in a Soil Tube
This problem is selected to provide simultaneous testing of the flow and the transport modules of VADOFT. The
problem is depicted schematically in Figure 7.16. A conservative solute species has a uniform initial concentration
and moisture content. The initial concentration is assumed to be zero, and the inlet concentration c0 is assumed to be
1 p.m. The solute is transported by dispersion and advection. Note that the solute front and the wetting front advance
at different rates. The solute velocity, Vsoh was previously defined as Equation 7.3 3. The velocity of the wetting front
is dependent upon the rate of water sorption into the soil, which is dependent on moisture diffusivity; thus,
calculation of the wetting front velocity requires integration of the mass balance equation. For the sake of
convenience, all physical data pertaining to the geometry of the soil tube and the physical parameter values are kept
the same as those used in the paper by Huyakorn et al. (1985). The complete set of data is listed in Table 7.11. The
simulation was performed in two stages. In the first stage, the transient water flow problem was analyzed to
determine the distributions of Darcy velocity and water saturation for each time level. These results are written on an
output file. In the second stage, the transient solute transport problem was analyzed to determine concentration
distributions using the velocity and water saturation data file obtained from the flow simulation.
The spatial and temporal discretization data used in running the VADOFT code are also given in Table 7.11. Both
the flow and the transport analyses were performed for 50 time steps. Results of the flow analysis are plotted in
Figure 7.17. The water saturation profiles given by VADOFT compare well with those obtained using the semi-
analytical solution of Phillip (1955) and the UNSAT2 finite-element flow code. Results of the transport analysis are
plotted in Figure 7.18. The concentration distributions given by VADOFT also compare well with those obtained
using the semi-analytical solution of Smiles et al. (1978) and the FEMWASTE finite-element transport code
documented by Yeh and Ward (1981).
7.5.3.2 Transient Infiltration and Contaminant Transport in the Vadose Zone
This problem, schematically depicted in Figure 7.19. involves variable infiltration and contaminant transport in a
layered system in which layer permeabilities differ by more than two orders of magnitude. The problem was chosen
to demonstrate the capability of VADOFT to handle a higher nonlinear situation involving soil materials with sharp
contrast in drainage properties. Shown in Table 7.12 are values of physical parameters and discretization data used in
the flow and transport simulations. For the unsaturated flow simulation, the transient infiltration rates illustrated in
Figure 7.20 were used. It was assumed that the initial condition corresponded to a hydrostatic pressure head
distribution in the soil with pressure head values at the water table and the top of the soil equal to 0 and -420 cm,
respectively. The simulation was performed for 20 time steps using A/ = 1 d. Shown in Figure 7.21 through Figure
7.23 are simulated profiles of water saturation, pressure head, and vertical Darcy velocity, respectively. As expected,
the two sand layers exhibit fast drainage response, whereas the intervening clay-loam layer exhibits slow drainage
response. This behavior is seen in Figure 7.21. The pressure head and velocity profiles depicted in Figure 7.22 and
Figure 7.23 directly reflect the effect of temporal change in the infiltration rate. Note that the values of Darcy
velocity at the soil surface (Figure 7.23) are equal to the values of infiltration rate for the same time values.
Following the unsaturated flow simulation, the transport simulation was performed using the Darcy velocity file
from the flow computation as an input file for the transport computation. Concentration profiles determined by the
code are plotted in Figure 7.24. As illustrated, the contaminant front exhibits slow movement through the clay loam
layer.
7-28
-------
O
fe
LJJ
O
Z
O
O
LJJ
LJJ
Of
0.0
600
Analytic Soln.
VADOFT
650
700
750
850
TIME, s
Figure 7.14 Simulated outflow breakthrough curve for case 1 of the problem of solute transport in a layered
soil column.
7-29
-------
O
*
LU
O
O
O
<
LU
CASE 2
•—A Analytic Soln.
0 VADOFT
400. 500. 600. 700. 800.
TIME,s
900. 1000.
Figure 7.15 Simulated outflow breakthrough curve for case 2 of the problem of solute transport in a layered
soil column.
7-30
-------
Table 7.8 Values of Physical Parameters Used in the Simulation of Transport in a Layered Soil Column
Parameter
Layer thickness, ^
Seepage velocity, Uj
Retardation coeff., P^
Decay constant, A,;
Source concentration, c0
Case 1:
Dispersivity, au
Case 2:
Dispersivity, au
Value for Layer I
Layer 1
25.48
0.127
1.0
0
1.0
0.076
0.76
Layer 2
30.31
0.123
1.0
0
Layer 3
30.31cm
0.121 cms'1
1.0
OS'1
0.174 0.436cm
1.74 4.36 cm
Az = 0.6888 cm
At=5s
Table 7.9 Breakthrough Curves (at z = 86. 1 Cm) Computed Using the Analytical Solution and VADOFT
(Case 1)
Time, t (s)
600
610
620
630
640
650
660
670
680
690
700
710
Concentration Values for Case 1
Analytical Solution
0.0204
0.0361
0.0596
0.0923
0.1354
0.1887
0.2514
0.3217
0.3971
0.4748
0.5518
0.6255
Numerical VADOFT
0.0262
0.0427
0.0665
0.0989
0.1410
0.1930
0.2543
0.3234
0.3981
0.4755
0.5526
0.6266
7-31
-------
Table 7.9 Breakthrough Curves (at z = 86. 1 Cm) Computed Using the Analytical Solution and VADOFT
(Case 1)
Time, t (s)
720
730
740
750
760
770
780
790
800
810
820
830
840
850
Concentration Values for Case 1
Analytical Solution
0.6935
0.7544
0.8072
0.8517
0.8881
0.9172
0.9400
0.9573
0.9704
0.9800
0.9870
0.9919
0.9950
0.9970
Numerical VADOFT
0.6951
0.7564
0.8096
0.8542
0.8907
0.9197
0.9421
0.9590
0.9715
0.9805
0.9869
0.9913
0.9943
0.9964
Table 7.10 Breakthrough Curves (at z = 86. 1 cm) Computed Using the Analytical Solution and VADOFT
(Case 2)
Time, t (s)
600
610
620
630
640
650
660
670
Concentration Values for Case 2
Analytical Solution
0.303
0.330
0.357
0.384
0.412
0.439
0.466
0.493
Numerical VADOFT
0.310
0.337
0.365
0.394
0.422
0.450
0.478
0.505
7-32
-------
Table 7.10 Breakthrough Curves (at z = 86. 1 cm) Computed Using the Analytical Solution and VADOFT
(Case 2)
Time, t (s)
680
690
700
710
720
730
740
750
760
770
780
790
800
810
820
830
840
850
900
Concentration Values for Case 2
Analytical Solution
0.519
0.544
0.569
0.593
0.617
0.639
0.661
0.681
0.701
0.720
0.738
0.755
0.771
0.787
0.801
0.815
0.828
0.840
0.889
Numerical VADOFT
0.532
0.558
0.584
0.608
0.632
0.655
0.677
0.698
0.718
0.737
0.755
0.772
0.788
0.804
0.818
0.831
0.844
0.856
0.904
7-33
-------
Table 7.11 Values of Physical Parameters
Variably Saturated Soil Tube
Parameter
Length of soil column, L
Saturated hydraulic conductivity, K
Initial pressure head, ^1
Remaining flow parameters
Initial concentration, q
Longitudinal dispersivity, aL
Molecular diffusion, D*
Decay constant, A
Retardation coefficient, R
Az = 0.25 cm
At = 0.0025 d
and Discretization Data Used in Simulating Transport in a
Value
20cm
1 cm d'1
-83.33 cm
See Table 12
Oppm
0 cm
1 cm2 d'1
Od'1
1
Table 7.12 Values of Physical Parameters and Discretization Data Used in Simulating Transient Infiltration
and Contaminant Transport in the Vadose Zone
Property
Saturated conductivity, K
Porosity, $
Residual Water Saturation, Sm
Air entry value, i|;a
Soil moisture parameter, a
Soil moisture parameter, p
Soil moisture parameter, y
Longitudinal dispersivity, aL
Retardation coefficient, R
Decay coefficient, A
Material l(Sand)
713
0.43
0.105
0.0
0.145
2.68
0.63
1.0
1.1
0.00274
Material 2 (clay loam)
6.24 cm d'1
0.41
0.232
0.0cm
0.019cm-1
1.31
0.24
1.0cm
1.5
0.00274 d'1
Az = 4 cm
At=ld
7-34
-------
Solute
Front
Wetting
Front
X|/=0
c= 1 ppm
I I
SfSSSSSSSSS
N
c or 0
FLOW
20 cm
xj; = -83.33 cm
^ n
-=°
X
Figure 7.16 One-dimensional solute transport during absorption of water in a soil tube. (Adapted from
Huyakornetal., 1985).
7-35
-------
1.0
0.8
-------
o
o
O 1.00
z"
o
!<
LU
O
z
o
o
LU
LU
o:
0.75
0.50
0.25
0
0 VADOFT
-*•- FEMWASTE
SEMI-ANALYTIC
SOLUTION
234
DISTANCE X, CM
Figure 7.18 Simulated concentration profiles for the problem of one-dimensional solute transport during
adsorption of water in a soil tube. (Adapted from Huyakorn, et al., 1985).
7-37
-------
20cm
120cm
280cm
l,cm/d
I I I I I I I
SANDKsat = 713cm/d
CLAY LOAM
Ksat = 6.24 cm/d
SAND
Ksat=713cm/d
WATER TABLE
Figure 7.19 Problem description for transient water infiltration and contaminant transport in the vadose zone.
7-38
-------
Q
O
z
O
£
I
u_
z 2
0
Ul
8 12
TIME, DAYS
16
20
Figure 7.20 Water Infiltration rate vs. time relationship used in numerical simulation.
7-39
-------
60 ••
120 ••
180 ••
I 240 f
300 ••
360 ••
420
* t = 3d
o t = 4d
x t = 5d
+ t = 8d
0.4 0.6
SATURATION
0.8
1.0
Figure 7.21 Simulated water saturation profiles.
7-40
-------
420
-100 -200 -300
PRESSURE HEAD, cm
-400
Figure 7.22 Simulated pressure head profiles.
7-41
-------
E
o
60
120
180
CL
LU
Q 240
300
360
420
2 3
VELOCITY, cm/d
Figure 7.23 Simulated vertical Darcy velocity profiles.
7-42
-------
SECTION 8
Uncertainty Preprocessor
8.1 Introduction
In recent years, the use of quantitative models to assess the transport and transformation of contaminants in the
environment has increased significantly. Typically these models include a set of algorithms that simulate the fate of a
contaminant within a medium (e.g., unsaturated zone, saturated porous media, air or a surface water body) based on
a number of user-specified parameters. These parameters describe the properties of the chemical, the transport
medium, and the effects that man has on the system.
Unfortunately, the values of these parameters are not known exactly due to measurement errors and/or inherent
spatial and temporal variability. Therefore, it is often more appropriate to express their value in terms of a
probability distribution rather than a single deterministic value and to use an uncertainty propagation model to assess
the effect of this variability on the transport and transformation of the contaminant.
This section describes the Monte Carlo method of uncertainty propagation and a Monte Carlo shell that is coupled
with the PRZM-3 model (subsequently referred to as the deterministic code in this report). The composite code (i.e.,
the uncertainty shell coupled with the deterministic code) can be used for the quantitative estimate of the uncertainty
in the concentrations at the monitoring point due to uncertainty in the (fate) model input parameters.
8.2 Overview of the Preprocessor
The objective of the uncertainty analysis/propagation method is to estimate the uncertainty in model output (e.g., the
concentration at a monitoring point) given the uncertainty in the input parameters and the transport and
transformation model. Alternatively stated, the objective is to estimate the cumulative probability distribution of the
concentration at a receptor location given the probability distribution of the input parameters. If Cw represents the
concentration at the receptor, then
Cw = g(X) (8.1)
where the function g represents the fate model and X represents the vector of all model inputs. Note that some or all
of the components of X may vary in an uncertain way, i.e. they are random variables defined by cumulative
probability distribution functions. Thus the goal of an uncertainty propagation method is to calculate the cumulative
distribution function Fc (Cj ) given a probabilistic characterization of X. Note that Fc (Cj ) is defined as:
FC(C*) = Probability(Cw *Cw*) (8.2)
where C^ is a given output concentration.
8.2.1 Description of the Method
Given a set of deterministic values for each of the input parameters, Xp X2, . . . Xn, the composite model computes
the output variable (e.g., a downgradient receptor well concentration CJ as:
Cw = g(Xl,X2,-,Xa) (8.3)
Application of the Monte Carlo simulation procedure requires that at least one of the input variables, X1. . . Xn, be
uncertain and the uncertainty represented by a cumulative probability distribution. The method involves the repeated
generation of pseudo-random number values of the uncertain input variable(s) (drawn from the known distribution
and within the range of any imposed bounds) and the application of the model using these values to generate a series
of model responses i.e. values of Cw. These responses are then analyzed statistically to yield the cumulative
probability distribution of the model response. Thus, the various steps involved in the application of the Monte Carlo
8-1
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simulation technique involve:
1. Selection of representative cumulative probability distribution functions for describing uncertainty
in the relevant input variables.
2. Generation of pseudo-random numbers from the distributions selected in (1). These values repre-
sent a possible set of values for the input variables.
3. Application of the model to compute the derived inputs and output(s).
4. Repeated application of steps (2) and (3).
5. Presentation of the series of output (random) values generated in step (3) as a cumulative
probability distribution function (CDF).
6. Analysis and application of the cumulative probability distribution of the output as a tool for deci-
sion making.
8.2.2 Uncertainty in the Input Variables
The parameters required by a transport and transformation model can be broadly classified into four different sets
that exhibit different uncertainty characteristics. These are:
• Chemical parameters. Examples of pesticide parameters include the octanol-water partition coeffi-
cient, acid, neutral, and base catalyzed hydrolysis rate, soil-adsorption coefficient, Henry's Law
Constant, etc. Examples of parameters for nitrogen species include rates for plant uptake and
return, ammonia adsorption/desorption, nitrate immobilization, organic N ammonification,
denitrification, nitrification, ammonia immobilization, and ammonia volatilization.
• Media parameters. Examples of these variables include the groundwater velocity, soil porosity,
organic carbon content, dispersivity values, etc
• Meteorological parameters. Examples include precipitation, evaporation, solar radiation.
• Management parameters. Examples include irrigation timing, pesticide application timing, well
pumping rates, etc.
Uncertainty in chemical parameters primarily arises due to laboratory measurement errors or theoretical methods
used to estimate the numerical values. In addition to experimental precision and accuracy, errors may arise due to
extrapolations from controlled (laboratory) measurement conditions to uncontrolled environmental (field) conditions.
Further, for some variables, semi-empirical methods are used to estimate the values. In this case, errors in using the
empirical relationships also contribute to errors/uncertainty in the model outputs.
Uncertainty in the second and third sets of parameters, identified above, may include both measurement and
extrapolation errors. However, the dominant source of uncertainty in these is the inherent natural (spatial and
temporal) variability. This variability can be interpreted as site-specific or within-site variation in the event that the
fate model is used to analyze exposure due to the use and/or the disposal of a contaminant at a particular site.
Alternatively it can represent a larger scale (regional/national) uncertainty if the model is used to conduct exposure
analysis for a specific chemical or specific disposal technology on a generic, nation-wide or regional basis. Note that
the distributional properties of the variables may change significantly depending upon the nature of the application.
Uncertainty in the fourth set of parameters may arise from a complex variety of factors including climate, sociology,
economics, and human error.
Whatever the source of uncertainty, the uncertainty preprocessor developed here requires that the uncertainty be
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quantified by the user. This implies that for each input parameter deemed to be uncertain, the user select a
distribution and specifies the parameters that describe the distribution.
The current version of the preprocessor allows the user to select one of the following distributions.
• Uniform
• Normal
• Log-normal
• Exponential
• Johnson SB distribution
• Johnson SU distribution
• Empirical
• Triangular
Depending on the distribution selected, the user is required to input relevant parameters of the distribution. The first
requires minimum and maximum values. The second and third distributions require the user to specify the mean and
the variance. The fourth distribution requires only one parameter - the mean of the distribution. For the empirical
distribution, the user is required to input the coordinates of the cumulative probability distribution function
(minimum 2 pairs, maximum 20 pairs) which is subsequently treated as a piecewise linear curve. For the triangular
distribution the user is required to input the minimum, maximum and the most likely value. Finally, the Johnson SB
and SU distribution requires four parameters - mean, variance, and the lower and upper bounds.
In addition to the parameters of the distribution, the user is required to input the bounds of each model parameter.
These bounds may be based on available data or simply physical considerations, e.g., to avoid the generation of
negative values. Values generated outside these bounds are rejected.
Of the above eight distributions, the characteristics of the majority are easily available in the literature (Benjamin
and Cornell 1970). The triangular distribution has been discussed in Megill (1977). Details of the Johnson system of
distributions are presented in McGrath and Irving (1973) and Johnson and Kotz (1970). Additional details for each
of these distributions are presented in the following discussion.
In some cases, it may be desirable to include correlations among the variables. For example, there may be a
correlation between hydraulic conductivity and particle size or between adsorption and degradation coefficients. The
uncertainty processor allows the generation of (linearly) correlated variables for cases where the underlying
distribution of the variables is either normal and/or lognormal.
8.3 Description of Available Parameter Distributions
The Monte Carlo shell has the ability to generate data from a number of probability distributions listed above. A
description of each of these distributions is provided in the following paragraphs, including parameters of the
distributions, equations for the probability and cumulative density functions, and a brief discussion of the properties
of each distribution.
8.3.1 Uniform Distribution
A uniform distribution is a symmetrical probability distribution in which all values within a given range have an
equal chance of occurrence. A uniform distribution is completely described by two parameters: 1) the minimum
value (lower bound) A, and 2) the maximum value (upper bound) B. The equation for the uniform probability density
distribution of the variable x is given by:
/.W = ^ (8-4)
where
fu(x) = the value of the probability density function at x
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The cumulative distribution F(x) is obtained by integrating Equation 8.4. This yields the probability distribution:
r, , N X- A
" = -J—7 (8.5)
where
Fu(x) = the probability that a value less than or equal to x will occur
8.3.2 Normal Distribution
The term "normal distribution" refers to the well known bell-shaped probability distribution. Normal distributions
are symmetrical about the mean value and are unbounded, although values further from the mean occur less
frequently. The spread of the distribution is generally described by the standard deviation. The normal distribution
has only two parameters) - the mean and the standard deviation. The probability density function ofx is given by:
( x- m \2
/„(*) = -j= exP ~ j ~~^ (8-6)
where
Sx = the standard deviation of x
m^ = the mean ofx
The cumulative distribution is the integral of the probability density function:
X
f (Y\ = F f ( Y\/!Y (Q *T\
Tables of values ofFn(x) are widely available in the statistical literature.
8.3.3 Log-Normal Distribution
The log-normal distribution is a skewed distribution in which the natural log of variable x is normally distributed.
Thus, ify is the natural log ofx, then the probability distribution of y is normal with mean my, and standard deviation
Sy and a probability density function similar to Equation 8.9. The mean and standard deviation ofx (mx and Sx) are
related to the log-normal parameters my and Sy as follows:
S2
m = expFw + —1
FL y 2 (8.8)
S2 = ^2[exp(S,2)- 1]
To preserve the observed mean and standard deviation ofx, the parameters of the log-normal distribution (my and Sy)
are selected such that the above relationships are satisfied. Note that my and Sy do not equal the natural log of mx and
Sx, respectively. Log-normal distributions have a lower bound of 0.0 and no upper bound, and are often used to
describe positive data with skewed observed probability distributions.
8.3.4 Exponential Distribution
The probability density function for an exponential distribution is given by:
= ^-6XP["^] (8-9)
8-4
-------
where mx is the mean of x. The cumulative distribution is given by:
Fe(x) = 1 - exp [--?-]
(8.10)
The probability density function has its maximum at x = 0 and decreases exponentially as x increases in magnitude.
8.3.5 The Johnson System of Distributions
The Johnson system involves two main distribution types - SB (log-ratio or bounded) and S0 (unbounded or
hyperbolic arcsine). These two distribution types represent two different transformations applied to a random
variable such that the transformed variable is normally distributed. The specific transformations are:
SD: Y = In
x-A
B-x
(8.11)
S: Y = sinh"
v
x-A
B-x
= In
x-A
B-x \
x- i
5^
(8.12)
where
x = untransformed variable, A < x < B
Y = the transformed variable with a normal distribution.
Selection of a particular Johnson distribution for sample data set is accomplished by plotting the skewness and
kurtosis of the sample data. The location of the sample point indicates the distribution for the sample data.
For additional details of the Johnson system of distributions, the reader is referred to McGrath and Irving (1973) and
Johnson and Kotz (1970).
8.3.6 Triangular Distribution
A triangular distribution is a relatively simple probability distribution defined by the minimum value, the maximum
value, and the mode (the most frequent value). Figure 8.1 shows an example triangular probability density function.
The cumulative distribution is given by:
F(x) - •
x1
-------
Figure 8.1
Triangular probability distribution.
8.3.7 Empirical Distribution
At times it may be difficult to fit a standard statistical distribution to observed data. In these cases, it is more
appropriate to use an empirical piecewise-linear description of the observed cumulative distribution for the variable
of interest.
Cumulative probabilities can be estimated from observed data by ranking the data from lowest (rank = 1) to highest
(rank = number of samples) value. The cumulative probability associated with a value of x is then calculated as a
function of the rank of x and the total number of samples. The cumulative probabilities of values between observed
data can be estimated by linear interpolation.
8.3.8 Uncertainty in Correlated Variables
In many cases model input variables are correlated due to various physical mechanisms. Monte Carlo simulation of
such variables requires not only that parameters be generated from the appropriate univariate distributions, but also
that the appropriate correlations be preserved in the generated input sequences. The Monte Carlo module currently
has the ability to generate correlated normal, log-normal, Johnson SB, and Johnson SU numbers; the procedures
used are described in the following paragraphs.
The correlation coefficient is a measure of the linear dependence between two random variables and is defined as:
8-6
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where
pxy = the correlation coefficient between the random variables x and y
cov(x,y) = the covariance of x and y as defined below
Pj, PJ, = the standard deviation of x andy.
The covariance of x andy is defined as:
co\(x,y) = EKx-mJCv-m)]
(8.15)
(x- mx)(y- mv)fx(x,y)dxdy
where
E = the expected value
/»„ OTJ, = the mean of the random variables x and y
fX:/x,y) = the j oint probability distribution of x and y.
Note that the linear correlation coefficient between x andy can be computed using
\
(E>W) -
'"' (8.16)
To generate correlated random variables, three steps are required. First uncorrelated, normally distributed random
numbers are generated. This vector is then transformed to a vector of normally distributed numbers with the desired
correlation. Finally, the normally distributed numbers are transformed to numbers with the desired distribution.
The transformation of uncorrelated to correlated normal numbers consists of multiplying the uncorrelated vector of
numbers with a matrix B:
Y' = Be (8.17)
where
e = the vector of uncorrelated, normally distributed random numbers.
B = an N by N matrix
Y' = a vector of standard normal deviates of mean zero and standard deviation of unity.
The matrix B is related to the variance-covariance matrix S as follows:
S = BBT (8.18)
where BT is the transpose of the B matrix. Since the normal variables Y' have means of zero and unit variances, the
variance-covariance matrix is equivalent to the correlation matrix.
Thus, if the correlation matrix S is known, B can be found from Equation 8.18 by using a Choleski decomposition
algorithm. This algorithm will decompose a symmetric positive definite matrix, such as S, into a triangular matrix
such as B (de Marsily 1986).
Having generated a vector of correlated normally distributed random numbers, the user can convert vector Y',
through appropriate transformations, to the distribution of choice. Thus for parameters Xi that have a normal
8-7
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distribution, the Y' numbers are transformed as follows.
Xt = mx + oj't (8.19)
For parameters that follow the lognormal distribution, the following transformation applies.
Xt = exP[7/0ln,. H- ^,.] (8.20)
where
Hln_t = the log mean of the /'* parameter
oln,. = the log standard deviation of the /'* parameter
For parameters with Johnson SB and SU distributions, the Y' are first transformed to normally distributed variables
Y with meanMj, and standard deviation ay:
Yt = My H- o,7/ (8.21)
Johnson SB numbers are then computed from Yt as follows.
Johnson SU numbers are computed by:
x, = A + (B
exp(7.)-exp(-7.) (8-23)
- A + eg- A)
2
Other distributions can be easily incorporated into the analyses at a later time when suitable transformations from the
normal distribution can be found. It is important to note that, in using this technique, the correlations are maintained
in normal space, so if these correlations are estimated using actual data, the data should be transformed to a normal
distribution before correlation coefficients are estimated.
For two correlated variables, one with a normal distribution (x2) and the other with a log normal distribution (x,), the
following equation is used to transform correlations to normal space (Meija and Rodriguez-Iturbe 1974).
-------
Thus, for log-normal variables, the user enters the values of the correlation coefficients in log-normal space;
Equations 8.24 and 8.25 are then used to transform the correlation coefficients into normal space.
No direct transformation of Johnson SB or SU correlations to normal correlations is currently known. For these
distributions, the user must supply the correlation coefficients between normal-transformed numbers. This may be
accomplished by first transforming Johnson SB and SU data to normal data using Equations 8.11 and 8.12. The
covariance matrix S is then derived using only normal, log-normal, and normal-transformed SB and SU data.
8.3.9 Generation of Random Numbers
Having selected the distribution for the various input parameters, the next step is the generation of random values of
these parameters. This requires the use of pseudo-random-number generating algorithms for Normal and Uniform
numbers. Numerous proprietary as well as non-proprietary subroutines can be used to generate random numbers.
Many of these are comparable in terms of their computational efficiency, accuracy, and precision. The performance
of the algorithms included in this preprocessor has been checked to ensure that they accurately reproduce the
parameters of the distributions that are being sampled (Woodward-Clyde Consultants 1988a, b).
8.4 Analysis of Output and Estimation of Distribution Quantiles
Model output generally will consist of a volume of data that represents a sample of outcomes. Given the natural
variability and the uncertainty of various model components, there will be variability in the output. All of the factors
that were allowed to vary within the model contribute to variability in model predictions. Taken as a whole, the
model output depicts possible events in terms of their relative frequency of occurrence. Values produced by the
model generally are treated as if they were observations of real field events. In interpreting these values, it is
important to maintain the perspective dictated by the design and scope of the study.
Model output can be analyzed in various ways depending upon current objectives. Many features of the distribution
may be characterized. Quite often, for example, it is of interest to estimate certain quantiles or percentiles of the
distribution. Since the model output is treated as a sample from an unknown parent population, the methods of
statistical inference normally are used to estimate distribution parameters and to associate measures of uncertainty
with these parameters.
One of the most frequently asked questions concerns the number of samples required for some given purpose. In
modeling, this translates into the number of model runs needed. For the most part, since methods of basic inference
are being applied in a Monte Carlo framework, resulting model output values are treated as observations forming a
random sample. The sample size required to estimate a given parameter depends on a number of factors. These
include the nature of the parameter that is being estimated, the form of the underlying distribution, the variability in
the observations, the degree of precision and/or accuracy desired, the level of confidence to be associated with the
estimate, and the actual statistical estimator used to provide the estimate.
Generally, if the output distribution is to be accurately characterized with respect to its many features, the number of
model runs needed will be higher than if only a few parameters are to be estimated. The simulation strategy should
be determined by the issues addressed by the modeling effort. It may be important, for example, to estimate the
extreme upper percentiles of the output distribution. In this case, the choice of simulation design should account for
the relative difficulty of obtaining such estimates. If it is not known exactly how the data will be utilized, then the
problem becomes one of establishing a distributional representation that is as good as possible under the most
extreme usage or estimation scenario. For example, if only a distribution mean were to be estimated, the sample size
required could be determined without concern for estimating, say, the 99th percentile.
8.4.1 Estimating Distribution Quantiles
In the following section, a summary is given for statistical techniques used to estimate distribution quantiles. Many
such methods are available to estimate a given percentile of an unknown distribution on the basis of sample data. In
the PRZM-3 code, four such methods can be used. Among these are distribution-free or nonparametric techniques as
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described below. Others include methods specific to certain distributions that assume a knowledge of the
distributional form. First, the point estimators are given, then the method for constructing a confidence interval is
briefly described.
The order statistics of a sample are merely the ordered values denoted by x(1), x(2), ..., x(n), where n represents the
sample size. The empirical cdf can be defined simply as
0
x< x,
•(i)
/ \ J -v ^f -v ^ t»
g(*)=\- *,)**<*(,,
1 TC > Tf
1 Jd £. -A-/.-\
Mathematically, g(x) is a step function discontinuous at each value x(I).
By definition, the lOOp-th percentile (i.e., the^-level quantile) is given by up where
p = Pr{X
-------
F(x) = - (8.31)
in which / is the rank of the outcome in the sample. The specific quantile of interest is then determined by
interpolation.
8.4.2 Confidence of up
Approximate confidence statements can be placed on up by selecting appropriate order statistics to serve as the upper
and lower confidence bounds. For a given distribution, the value up is such that exactly 100/>% of all values of this
distribution are less than up, and 100(1 -p)% exceed this value. An individual value selected randomly from the
distribution has probability p of being less than up.Ina random sample of size n from this distribution, the
probability of not exceeding up remains constant for each individual element of the sample. Thus, the number of
values in the sample that are less than or equal to up is distributed binomially. The probability that the random
interval (XQ), X0+1)) will contain up is equivalent to the probability that exactly / of the n elements of the sample will
be less than up. Hence, this probability is
i'(l-p)"-' (8.32)
v *y
which is a simple binomial probability.
This expression can be calculated for each pair of consecutive order statistics JQ,, X(i+1), for/' =1,..., n-l. However, it
is more convenient to deal with these several intervals by calculating cumulative probabilities of the form
Pr{Up]
2
The solution is
i = (n-p-0.5) + ^n-p(l-p) F~\a/2)
(8.37)
j = (n-p-0.5) + Jn-p(\-p) F~\l- a/2)
where F~l denotes the inverse cdf of the standard normal distribution (e.g., for 1-a = 0.90, F\l-a/2) = 1.645). For
example, with w=100, p=0.95, and l-a=0.90, /'=90 and7'=98, so that (X(90), Jf(98)) forms the approximate 90%
8-11
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confidence interval for up.
Although the expressions for the confidence interval do not depend in any way on the underlying distribution, the
expected width of the interval does. In particular, it depends on the expected values of the order statistics involved.
In the example above, if the sample is from a standard normal distribution, up = 1.645 and the expected half-width of
the interval is 0.349. If the sample is from a lognormal distribution based on a standard normal, up = 5.180 and the
expected half-width is 1.858. Also, note that, in normal sampling, the expected confidence interval half-width for
w=500 is 0.192 for the same estimate.
8-12
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SECTION 9
Linking PRZM-3 with Other Environmental Models
The popularity of the PRZM-3 model has led to a number of applications that were not originally envisioned.
Among these are the model's use (1) as a means of estimating chemical and sediment loadings to watershed-scale
modeling systems and (2) for evaluating wellhead protection strategies for nitrates. In order to support these
applications, supplemental software has been developed that offers expanded modeling opportunities for PRZM-3
model users. Three linkage opportunities are described below. In accordance with the format used for presenting
pertinent equations in the source documents for this section, the equations in Section 9 are not numbered.
9.1 HSPF
9.1.1 PZ2HSPF Bridge Program
The PZ2HSPF bridge program was developed to provide a linkage between the PRZM-2 (now PRZM-3) and HSPF
models; the bridge program represents pesticide flow and transport processes between the field and stream. The
program provides a simple means for the user to adjust field-generated pesticide fluxes for calibration of in-stream
pesticide concentrations, but does not simulate these processes from the first principles represented in governing
differential equations. In a sense, the bridge program is a lumped parameter model which simulates the total effects
of travel time and losses due to processes such as volatilization, decay and adsorption, as well as resettlement of
eroded sediment and water losses to deep groundwater, on the final pesticide load entering the stream. The bridge
program accepts four pesticide concentration fluxes generated by the PRZM-3 model and outputs a total surface and
subsurface runoff pesticide loading and a sediment associated pesticide loading for introduction into the RCHRES
portion of the HSPF model (Bicknell et al. 1993) which simulates in-stream processes. The degree of sophistication
of transport processes represented in the bridge program is dependent on the choice of the user and the amount and
quality of field data which may be used for calibration.
To generate the four pesticide fluxes which are lagged and attenuated in the bridge program, PRZM-3 was modified
to account for lateral drainage and associated lateral flux of pesticide (see Section 9.1.4). The other three pesticide
fluxes generated by PRZM-3 are erosion flux, surface runoff flux and groundwater flux, which is generated from the
bottom of the PRZM-3 soil column. The fluxes entered into the bridge program from PRZM-3 remain within their
"compartment". For example, infiltration of surface runoff along the flow path from field to stream is not represented
in the bridge program. As discussed above, the lack of representation of field-to-stream infiltration of the surface
runoff and interflow fluxes will result in conservative estimates of these edge-of-stream loadings, since infiltration of
these fluxes would increase lag in arrival times, thus also increasing magnitudes of mass losses due to decay and
sorption processes. In the bridge program, sorption processes are considered only as part of the permanent loss
processes and can be included in the fractional loss parameters for interflow and groundwater. Therefore, it is
important to keep in mind that this means of representing "inter-scale" transport may not be appropriate for
simulation of a highly sorbing chemical over long periods of time, where sorption/desorption processes play a large
role in determining edge-of-stream pesticide loads.
The bridge program reads in the four pesticide fluxes generated by PRZM-3 and then calculates the following
modifications of each flux:
Erosion flux - This flux may be lagged by an amount of time representative of the travel time between the field and
stream. The loadings may decay during this lag time, with a first-order rate similar to the sorbed decay rate in
PRZM-3. The flux may also be multiplied by a sediment delivery ratio (SEDRAT), such that the flux reaching the
stream would be SEDRAT*flux.
Surface runoff flux - This flux may be lagged to represent travel time to the stream. It may also decay during this lag,
with a first-order decay rate, similar to the PRZM-3 DWRATE for pesticide in water. This rate may differ from the
PRZM-3 water decay rate if the user wants to consider additional processes, such as volatilization along the flow
path from field to stream.
9-1
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Lateral flux - This flux may be lagged and may decay with a first-order decay rate for pesticide in water. Other loss
processes which may be better represented by a fractional loss equation (for example sorption processes) may be
taken into account by using a fractional loss term (LATRAT) which is implemented in the same manner as the
sediment delivery ratio, such that LATRAT*lateral flux = lateral flux delivered to the reach.
Groundwater flux - This flux can lag and decay, again with a first-order decay rate. In addition, there is a
multiplicative factor similar to the sediment delivery ratio for the "groundwater delivery" (GWRAT), such that
GWRAT*groundwater flux = groundwater flux delivered to the reach. The delivery ratio term may also be
considered to represent losses due to adsorption. The remaining groundwater flux enters the deep groundwater sink.
The three pesticide fluxes associated with water runoff (surface, lateral and groundwater runoff) are summed within
the bridge program to create a total daily edge-of-stream pesticide runoff mass flux. The transformed erosion
pesticide flux is accounted for separately, and is input to HSPF as a daily edge-of-stream sorbed pesticide flux.
These two daily flux time series generated by the bridge program are divided into 24 components (if the HSPF time
step is hourly) to produce the total hourly flux load to be input to HSPF RCHRES.
The four pesticide fluxes which are generated by PRZM-3 are in the form of WDM data sets (Lumb et al. 1990). The
bridge program then reads these input data sets and outputs the four transformed fluxes and the total runoff flux as
WDM data sets. The total pesticide runoff flux and pesticide erosion flux are used as input into HSPF, where the
daily fluxes are uniformly divided into the time step being used by the HSPF simulation. The bridge program also
creates an ASCII output file that echoes input data and summarizes total pesticide mass in each storage flux,
pesticide mass losses for each flux, and a pesticide mass balance. Note that the mass balance computations are
restricted to considering the processes (lag and loss) and configuration (connectivity and areal extent) specified by
the user as taking place in transition between the edge-of-field fluxes PRZM-3 computes and the edge-of-stream
watershed-scale receiving water inputs HSPF RCHRES requires.
Users of the bridge code should be mindful that the nature of a 'bridge' from a model that computes unit area
fluxes to a second model that requires information on both connectivity and areal extent of these fluxes does
not allow a true mass balance computation for the entire system that is evaluated by the combined models.
The bridge program consists of a single main program, PZ2HSPF, and two parameter files, PZ2HSPF.INC and
MASSBAL.INC. To run the bridge program requires an input parameter file and a WDM file containing the input
pesticide flux data sets and output data sets. To invoke the program, type PZ2HSPF . A detailed
description of the input variables is provided in Table 9.1. A full list of definitions for all variables used in PZ2HSPF
is included in the Appendices contained in Section 11.
9.1.2 Application Procedure
PZ2HSPF is a small, stand-alone program, which can be copied (using the DOS copy command) from the program
disk to the directory where it will be used. The only requirements are a 386/486 computer and extended memory.
The program has been structured to consider most scenarios that can take place in terms of connections between
PRZM-3 and HSPF models. Before the user can run the bridge program, a careful layout of the HSPF simulation
scenario is necessary to prepare the input parameter file. Most of the information for this file is extracted from the
PRZM-3 simulation runs; hence it is imperative that the PRZM-3 runs be entirely consistent with the PRZM-3/HSPF
scenario.
To execute the bridge program, an input parameter file is required. The input file contains information on the
modeling start and end dates, number of pesticides, pesticide names, crop area treated with each pesticide, WDM
dataset identification, and decay rates, 'delivery ratios', and lag times associated with the alternate flow paths of
pesticides (i.e., erosion, surface runoff, interflow, groundwater). An example of a bridge program input file is
provided in Table 9.2. A full list of definitions for all variables used in PZ2HSPF is included in the Appendices
contained in Section 11.
The PRZM-3 program disk contains the executable code for PZ2HSPF, an example test run input parameter file and
9-2
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the resulting output. The program disk also contains the PRZM-3 output files used in the example input file. To
invoke the program, type PZ2HSPF at the command prompt. The user will be prompted to enter the input parameter
filename and the names of the output nonpoint source and runoff files. All of the PRZM-3 generated files should be
present in the same directory, or else the path must be specified. A detailed description of the example input
parameter file and test run is given in the following section.
9.1.3 Example Input and Test Run
The ultimate function of the PZ2HSPF bridge program is to transform PRZM-3 generated, field-scale pesticide flux
timeseries contained in WDM files into watershed-scale pesticide flux timeseries, again contained in WDM files,
that are suitable for use as input to the receiving water module (RCHRES) of HSPF. The transformation includes
consideration of areal and temporal issues, as well as potential losses prior to arriving at the edge of the stream.
The linked models have been used to simulate pesticide transport in the Potomac River Basin for the years 1984
through 1987 (Christian et al. 1993). PRZM-3 simulations were completed for three pesticides (atrazine, metolachlor
and alachlor) to generate field-scale loadings, and the HSPF Chesapeake Bay Watershed Model (Donigian et al.
1991) was used to simulate in-stream transport. Three variations of the model applications were presented to
demonstrate model sensitivity to single and multiple PRZM-3 pesticide application scenarios, and the effects of
changing lag and loss parameters in the bridge program.
Table 9.1
Input Guide for the PZ2HSPF Bridge Program
RECORD 1 FORMAT A8
col: 1-80 DESCRP: description of run
RECORD 2 FORMAT I5,5X,A15,5X,6I5
col: 1-5 SEGNUM: model segment id number
col: 11-25 CRPNAM: crop name
col: 31-45 STDATE: simulation starting date: year, month, day
col: 46-60 END ATE: simulation ending date: year, month, day
RECORDS FORMAT 15
col: 1-5 NUMPST number of pesticides
RECORD 4 FORMAT A20,5X,F10.2
Repeat this record up to NUMPST
col: 1-20 PSTNAM: pesticide name
col: 26-35 CRPAREA: crop area treated with pesticide (ha)
RECORDS FORMAT 15
9-3
-------
col: 1-5 OPTFLG: option flag for writing to WDM file; write if > 1
RECORD 6 FORMAT 915
Repeat this record up to NUMPST
col: 1-20 INPDSN: input data set numbers for erosion, surface runoff, interflow and groundwater
pesticide fluxes
col: 21-45 OUTDSN: output data set numbers for transformed erosion, surface runoff, interflow,
groundwater and total aqueous runoff pesticide fluxes
RECORD? FORMAT 2A20
col: 1-20 WDFLNM: WDM file name
col: 21-40 OUFLNM: output file name
RECORD 8 FORMAT 4F10.4
Repeat this record up to NUMPST
col: 1-10 DSRATE: sediment associated pesticide decay rate (I/day)
col: 11-20 DRRATE: surface runoff associated pesticide decay rate (I/day)
col: 21-30 DLRATE: interflow associated pesticide decay rate (I/day)
col: 31-40 DGRATE: groundwater associated pesticide decay rate (I/day)
RECORD 9 FORMAT 3F10.4
Repeat this record up to NUMPST
col: 1-10 SEDRAT: sediment delivery ratio
col: 11-20 LATRAT: interflow "delivery ratio" (allows for loss of interflow component)
col: 21-30 GWRAT: groundwater "delivery ratio" (allows for fractional loss to deep groundwater)
RECORD 10 FORMAT 4F10.4
Repeat this record up to NUMPST
col: 1-10 TERO: sediment associated pesticide time lag from field to stream (days or fraction of a
day)
col: 11-20 TSUR: surface runoff associated pesticide time lag from field to stream (days or fraction of
a day)
col: 21-30 TLAT: interflow associated pesticide time lag from field to stream (days or fraction of a
day)
9-4
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col: 31-40 TGW: groundwater associated pesticide time lag from field to stream (days or fraction of
a day)
9-5
-------
Table 9.2 Example Input File
BRIDGE PROGRAM
160 CORN
3
ATRAZINE
METOLACHLOR
ALACHLOR
2
11 14 17
12 15 18
13 16 19
. . \POT.WDM
. 0231
. 0231
.0462
.15
.15
.15
0. 0
INPUT FOR
GRAIN CNT
20 1011
21 1012
22 1013
forPZ2HSPF
160CG.CNT
1984
1461. 38
673.22
443.34
1014 1017
1015 1018
1016 1019
01 01 1987 12 31
1020 2001
1021 2002
1022 2003
CGCNT . OUT
. 0231
. 0231
.0462
1.00
1.00
1.00
0. 0
.0231
.0231
.0462
1.00
1.00
1.00
0. 0
0116
0116
0231
5. 0
The example input sequence provided in Table 9.2 is one of the many developed for the study cited above. Given the
nature of the bridge program functions (i.e., accessing timeseries files, manipulating the data, writing the
transformed data to new files), output results for the example are not provided.
9.1.4 Lateral Drainage Modifications to PRZM-3
PRZM-3 has been modified to account for lateral outflow of pesticide from the soil column. A lateral water drainage
option was previously implemented in the PRZM model in a study of the fate and transport of aldicarb in Florida
(Dean and Atwood 1985b). The lateral drainage option is a part of the restricted vertical drainage option, which is
presently included and documented in PRZM-3. The lateral drainage portion is not documented in the users manual,
so it is briefly described here.
PRZM-3 simulates water and pesticide movement through a one-dimensional soil column, which is divided into a
number of soil compartments for numerical calculation. Drainage within the soil column is calculated for each soil
compartment, sequentially calculating water movement, starting with the top soil compartment and moving
downward through the soil column. If there is surface infiltration on the current simulation day, water is moved into
the soil column unrestrictedly, sequentially filling each soil compartment to saturation water content until the total
volume of surface infiltration is accounted for. If there is no surface infiltration event on the current simulation day,
the restricted drainage model is invoked. According to the restricted drainage rules, if the water content of a soil
compartment is initially below saturation, water drains vertically only, into the compartment below, the flow rate
controlled by the vertical drainage parameter of the exponential drainage model. Thus, during the drainage
calculations for the current time step, compartments receiving water infiltrating from above may become
oversaturated. If the compartment currently being considered has a water content above saturation content, then the
exponential drainage model computes vertical and lateral drainage until the compartment water content reaches
saturation. Once water content is below saturation, water continues to drain only vertically for the rest of the
simulation day.
To understand the restricted lateral and vertical drainage model, it is revealing to consider a conceptual model of
each soil compartment as a bucket with two holes of different sizes. One hole is near the bottom of the bucket, and
another is at some height along the wall of the bucket. The volume of the bucket below the elevation of the second
hole represents the quantity of water above field capacity required to fill the soil compartment to saturation. The
volume of water below the lower hole represents the quantity of water stored at field capacity. If the water level rises
above saturation due to infiltration from above, water will drain from both the side and the bottom hole at rates
9-6
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above saturation, to account for continued drainage during the same daily time step after water in excess of
saturation has already been drained. Thus, lateral flow only occurs when the soil compartment has a water content
above saturation.
The Dean and Atwood implementation of lateral drainage did not allow for associated lateral mass removal of
pesticide. We have modified PRZM-3 to account for lateral pesticide movement based on the pesticide concentration
within the soil compartment from which the lateral flow originates. Thus, the mass of pesticide which is removed
laterally from each compartment is simply the product of the pesticide concentration of the water in the soil
compartment and the volume of water which moves laterally from that soil compartment.
9.2 WASP
9.2.1 PRZWASP Bridge Program
The PRZWASP bridge program (Varshney et al. 1993) was developed to facilitate the use of PRZM-2 to generate
nonpoint loads for direct input to the WASP model (Ambrose et al. 1993). The bridge code is now operational with
PRZM-3, and it creates input nonpoint source and runoff files for the WASP model from the PRZM-3 generated
output file for EXAMS (Burns 2000). The program enables the user to read in multiple PRZM-3 output files for
several years of simulation runs and generates a single file with daily pesticide loads entering each WASP segment.
The program reads an input parameter file which contains information on the WASP segments, systems, and PRZM-
3 generated EXAMS input files. The PRZM-3 generated files contain information on the chemical application rate,
the time of application, number of applications, surface runoff depth, and runoff fluxes for each chemical. If erosion
is being simulated, then PRZM-3 output also contains the pesticide erosion fluxes as well as the soil loss in tonnes
per ha. The array size of some of the parameters in the bridge program is governed by the PRZM-3 and WASP
model dimensions, i.e., the maximum number of chemicals and applications that can be simulated during one
simulation run, and the total number of systems that can be considered. The bridge program is structured to consider
all scenarios and sequences that can possibly take place.
The input parameter file contains information on the starting date of the WASP simulation, the number of WASP
segments, and systems. The surface area of each WASP segment, as well as the tributary area associated with a
corresponding PRZM-3 segment, is required. Several flags, to check whether or not sediment is simulated, or how
many and which chemicals are being considered, are included. If sediment is simulated in PRZM-3 and WASP, an
option is available to distribute the total erosion load into three fractions, sand, silt, and clay for input to WASP.
Also included in the program is the capability to accommodate spray drift deposition on the surface area of the
WASP segments. If the flag is on, the user provides information on the mass loading rate of the chemical to be
accounted for in spray drift. After reading the input, the program checks whether chemicals and/or sediment are
simulated, proceeds with the calculation and generates the nonpoint source file. Next, the program writes the surface
runoff and precipitation in volume of water per day reaching each WASP segment to a separate runoff file.
The bridge program is written in FORTRAN 77 and compiled using the LAHEY 32-bit compiler. It consists of a
single main program, PRZWASP, which calls two functions, JULIAN and LENSTR, and a subroutine LPYEAR.
Function JULIAN converts calendar date to Julian date for any given year, and LENSTR gives the actual length of
the character array. Subroutine LPYEAR checks whether the year in simulation is a leap year or a calendar year and
accordingly sets up a flag which is then read in by JULIAN to calculate the day in consideration. The program can
detect and report a number of errors in the input files, and contains more than twenty error messages, to help the user
execute the model successfully.
The bridge program reads in the pesticide surface runoff and erosion fluxes and sediment loss generated by PRZM-3
and then calculates the following modifications of each flux:
Surface runoff and erosion flux - The surface runoff and erosion flux for each chemical are output by
PRZM-3 in kg/cnf/day. In the bridge program, they are multiplied by the tributary areas associated with
each PRZM-3 run, one for all PRZM-3 segments tributary to each WASP segment and converted into kg to
get the total nonpoint source load for each chemical on a daily basis (i.e. kg/day).
-------
Soil loss - The soil loss on a daily basis is multiplied by the tributary area associated with each PRZM-3
run for all corresponding WASP segments, and converted to kg/day.
Spray drift - Mass loading rate (kg/ha) of the chemical assumed to be deposited by spray drift is multiplied
by the surface area of the WASP segment on the day of application, for each chemical simulated.
The bridge program creates two ASCII output files: a nonpoint source file and a runoff file. The nonpoint source file
contains information directly echoed from the input parameter file as well as the chemical loads as a function of
system (chemical and/or sediment), segment, and day. The program sums the chemical loads from surface runoff,
erosion, and spray drift, and outputs the loads to the nonpoint source file in kg/day. The runoff file contains daily
totals of surface runoff volume and precipitation volume, both expressed in mVday. If multiple PRZM-3 segments
contribute to a WASP segment, the depth of precipitation falling on the WASP segment is assumed to be the area-
weighted average of the precipitation falling on the tributary PRZM-3 areas.
Since PRZM-3 generates separate EXAMS files for each year, all the yearly output files have to be specified in the
PRZWASP input parameter file; these files are processed one year at a time by the bridge program.
A detailed description of the input variables is provided in Table 9.3. A full list of definitions for all variables used in
PRZWASP is included in the Appendices contained in Section 11.
Table 9.3 Input Guide for the PRZWASP Bridge Program
RECORD 1 FORMAT 14,213
col: 1-10 WSDATE start date of WASP simulation - year, month, date
RECORD 2 FORMAT 315
col: 1-5 NUMSEG number of segments receiving nonpoint source loads
col: 6-10 INTOPT interpolation option; l=step function (only one in code now)
col: 11-15 NUMSYS number of WASP systems (chemicals, sediments) receiving nonpoint source loads
RECORD 3 FORMAT I5,F10.0
Repeat this record NUMSEG times
col: 1-5 NPSSEG segment number receiving loads
col: 6-15 SEGAREA area of the WASP segment receiving loads (ha)
RECORD 4 FORMAT 615
col: 1-30 NPSSYS WASP system number receiving loads
RECORD 5 FORMAT A15
Repeat this record NUMSYS times
9-9
-------
Table 9.3
Input Guide for the PRZWASP Bridge Program
col: 1-15 NPSNAME name or description of the WASP system receiving loads
RECORD 6 FORMAT 615
col: 1-5 NUMPRZ number of PRZM-3 segments
col: 6-10 NUMPYR number of calendar years for which PRZM-3 has been simulated; PRZM-3
generated EXAMS output files must be present for each year
RECORD 7 FORMAT
col: 1-75 HEADER this record is not read, the program skips this line
RECORDS FORMAT A8,2X,I2,6(5X,I1),3(4X,F4.0)
Repeat this record NUMPRZ times
col: 1-8 PRZMFILE name of the PRZM-3 file
col: 11-12 NTRIB number of tributary areas associated with this PRZM-3 file
col: 18-18 TNAPP total number of chemical applications
col: 24-24 ISED flag to ensure if erosion has been simulated (0=no, l=yes)
col: 30-42 ICHEM three flags indicating which chemicals are simulated in this PRZM-3 file (0=not
simulated, l=simulated)
col: 48-48 ISPRAY flag indicating whether spray drift occurred (0=no,l=yes)
col: 53-72 SOLFRC fractions of three sediment sizes
RECORD 8a FORMAT I5,F10.0
Repeat this record NTRIB times
col: 1-5 WASPID identification of the WASP segment
col: 6-15 TEMP tributary area associated with this PRZM-3 file corresponding to WASP segment
number (ha)
RECORD 8b FORMAT 3F10.0
Repeat this record TNAPP times
col: 1-30 MSPRAY
total mass of the chemical in spray drift that falls on the WASP segment for each
chemical in each application associated with this PRZM-3 file (kg/ha)
9.2.2 Application Procedure
9-10
-------
PRZWASP is a small, stand-alone program, which can be copied (using the DOS copy command) from the program
disk to the directory where it will be used. The only requirements are a 386/486 computer and extended memory.
The program has been structured to consider most scenarios that can take place in terms of connections between
PRZM-3 and WASP models. Before the user can run the bridge program, a careful layout of the WASP simulation
scenario is necessary to prepare the input parameter file. Most of the information for this file is extracted from the
PRZM-3 simulation runs; hence it is imperative that the PRZM-3 runs be entirely consistent with the PRZM-
3/WASP scenario.
To execute the bridge program, an input parameter file is required. The input file contains information on the WASP
start date, number of segments and systems to be considered in the WASP model, description of the systems, and
PRZM-3 information. PRZM-3 information consists of all the PRZM-3 generated files for EXAMS, the total number
of chemical applications in each, erosion information if simulated, and details on the amount of chemical deposited
by spray drift for each application. If erosion is being simulated, data for up to three solid fractions namely, sand,
silt, and clay can be input.
The PRZM-3 program disk contains the executable code for PRZWASP, an example test run input parameter file
and the resulting output. The program disk also contains the PRZM-3 output files used in the example input file. To
invoke the program, type PRZWASP at the command prompt. The user will be prompted to enter the input
parameter filename and the names of the output nonpoint source and runoff files. All of the PRZM-3 generated files
should be present in the same directory, or else the path must be specified. A detailed description of the example
input parameter file and test run is given in the following section.
Users of the bridge code should be mindful that the nature of a 'bridge' from a model that computes unit area
fluxes to a second model that requires information on both connectivity and areal extent of these fluxes does
not allow a mass balance computation for the entire system that is evaluated by the combined models. In the
absence of a mass balance computation, the person defining the bridge code parameters has a heightened
responsibility to assure an appropriate linkage between the two models.
9.2.3 Example Input and Test Run
Figure 9.1 shows a schematic of an example test run. In the stream section AA', the area receiving nonpoint source
load from PRZM-3 segments to be simulated by the WASP model is subdivided equally into six WASP surface
water segments. The WASP surface water segments identified from 1 to 6, receive loads from three PRZM-3 runs,
namely PRZM1, PRZM2, and PRZM3. The PRZM1 output file provides unit area daily loads (i.e. kg/cnf/day) for
the areas shown as Ab and A\; PRZM2 file provides the unit loads for A2, and A2; and PRZM3 file is for A3, A3,
and A"3. The area A'2 of segment PRZM2 also contributes to WASP surface water segment 2. Thus, a single PRZM
output file can provide unit loads to multiple WASP segments.
(Note: There are always four water column segments considered in the WASP model. The surface water (segment
1), subsurface water (segment 2), upper benthic (segment 3), and lower benthic (segment 4) segment. Here we are
dealing with only the surface water segments.)
The surface area of each WASP surface water segment must be known to calculate the mass of the pesticide
deposited from spray drift. Al through A3, and A\ through A"3 are the segment areas associated with each PRZM-3
output file, providing loads corresponding to respective WASP segments, as illustrated in Figure 9.1 by arrows.
Three pesticides atrazine, metolachlor, and alachlor were simulated in this example. Three PRZM-3 simulations
running consecutively for three years from 1978, were considered. Two sediment fractions, sand and silt, were
simulated along with the chemicals. The areas of PRZM-3 and WASP segments, the flags to indicate spray drift, the
solid fractions, and the mass of chemical deposited from spray drift are entered in the input file as shown in Table
9A
Tables 9.5 and 9.6 contain a representative portion of the output nonpoint source file and the runoff information file
for the test run shown in Table 9.4. The nonpoint source file is in a format which can be directly read by the WASP
model, whereas the runoff file contains volumetric water in cubic meters due to runoff and precipitation entering
9-11
-------
each WASP segment on a daily basis; this latter file can be utilized as an external flow file in a hydrodynamic
model, e.g. RIVMOD.
9-12
-------
Figure 9.1 Schematic of an example PRZWASP test ran.
PRZM1 through PRZM3 - PRZM-3 segments (i.e. separate output files)
WASP[ through WASP6 - Identification of WASP surface water segments
A! and A! - Tributary areas associated with PRZM1, ha
A2 and A'2 - Tributary areas associated with PRZM2, ha
A3, A3, A 3 - Tributary areas associated with PRZM3, ha
a{ through a6 - Surface area of each WASP segment, ha
9-14
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In order to enable PRZM-3 to be used as a tool for evaluating rural wellhead protection strategies for nitrates, a
stand-alone program that allows simplistic modeling of inputs of septic effluent nitrogen species to the PRZM soil
column has been developed (Imhoff et al. 1995). The On-site Wastewater Disposal System (OSWDS) module offers
an appropriate level of detail for representing inflows/processes/outflows by implementing a generalized module
(i.e., one not dependent on specific reaction kinetics) comprised of two treatment units (Figure 9.2). The first
treatment unit always represents the septic tank; the second treatment unit represents all transformations/ losses that
occur between the outlet of the septic tank and the inflow into the unaltered subsurface soil. (For our purposes
"unaltered" means below or beyond the area that has been modified for purposes of wastewater distribution and/or
treatment.)
The user has the option of whether to consider only the first treatment unit, or both treatment units. If only one
treatment unit is modeled, the output flow and N concentrations from the unit are directly input into the appropriate
PRZM-3 soil compartment. The PRZM-3 soil horizon into which OSWDS outflow is introduced is specified by the
user based on knowledge of the effluent depth compared to the soil horizon depths. If only one treatment unit is
modeled, this depth corresponds to the depth below the ground surface of the tank outlet; if two treatment units are
modeled, the effluent depth typically corresponds to the bottom depth of the area modified for wastewater
distribution/treatment.
Effluent is assumed to be homogeneously distributed throughout the horizon into which it is introduced.
Wastewater influent to the first treatment unit is characterized according to the following scheme:
(1) The user defines a "base" timeseries of wastewater flow (gal/day) and concentrations of associated
nitrogen species (mg/1). The base wastewater flow is defined by assigning a value for per person
wastewater generation (gal/capita), and one or more seasonal occupancy rates (# of persons
serviced by the On-site Wastewater Disposal System OSWDS).
(2) The ability to define seasonal occupancy is enabled. The user specifies the number of "occupancy
seasons" during the calendar year, the starting date of each season, and the number of occupants
serviced by the OSWDS.
(3) The modeling scheme assumes that the nitrogen species concentrations associated with a particular
OSWDS remain constant over time (i.e., the flows can vary seasonally, but the concentrations do
not vary with time). The user defines the concentrations of N species in the influent; reasonable
default values gleaned from the literature for residential systems are provided in Table 9.7.
(4) Nitrogen species that are modeled in the influent wastewater are total organic N, ammonium, and
nitrate-nitrite. While the literature consistently reports negligible amounts of nitrate-nitrite in
typical wastewater influent, for the sake of flexibility combined nitrate-nitrite is included as a
possible wastewater constituent for atypical situations. Given that the modeling of N
transformations within the OSWDS module is not mechanistic, there is no benefit to differentiating
between paniculate and dissolved organic N, or between labile and refractory organic N. However,
this distinction is needed prior to input to the soil region represented by PRZM-3 (see discussion
below).
9-16
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Q
/T7WO3-WO2
Q
1st Treatment Unit m°RGN
(TINH4
mNO3-NO2
Transformations/Losses
ORGN
«.
NO3-NO2
ORGN
0.25
0.00
0.00
NH4
0.70
0.99
0.00
NO3-NO2
0
0
1
LOSS1
0.05
0.01
0.00
LOSS1 = Settling, Physical Removal
LOSS2 = Denitrification
LOSS3 = Volatilization
2nd Treatment Unit
Transformations/Losses
ORGN
NH4
NO3-NO2
ORGN
0.7
0.0
0.0
NH4
0.25
0.50
0.00
NO3-NO2
0.0
0.3
0.7
LOSS1
0.05
0.00
0.00
LOSS2
0.0
0.1
0.3
LOSS3
0.0
0.1
0.0
OSWDS Nitrogen Module for PRZM-2
ITINH4
mNO3-NO2
Figure 9.2 Schematic Representation of the On-site Wastewater Disposal System (OSWDS) Nitrogen
Module.
Table 9.7 Typical Mean Concentration Values (mg/1) for Nitrogen Species in Septic Tank Effluent
Total N
NH4+-N
NO3'-N
Dissolved Org. N
Paniculate Org. N
EPA (1980)
46
Reneau (1989)
40-80
30-60
<1
10-20
<1
NVPDC (1990)
72
60
<1
12
<1
9-17
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The treatment effects of the septic tank are modeled as follows:
(5) The efficiency of the septic tank is defined by the user by (7) assigning values for a series of
transformation factors between N species and (/'/') defining a physical loss term for organic N due
to settling/storage within the tank and tank maintenance activities (i.e., pumping).
(6) N species that are modeled within the septic tank are total organic-N, ammonium, and nitrate-
nitrite. (Nitrate-nitrite concentrations are consistently reported at insignificant levels within septic
tanks, but nonetheless, for the sake of generality, we will include transformation factors that allow
user-controlled specification of this constituent.
The treatment effects of the second treatment unit are modeled as follows:
(7) As in the first treatment unit, the efficiency of the second unit is defined by the user by (/)
assigning values for a series of transformation factors between N species and (/'/') defining a
physical loss term for organic N. The transformation factors are expanded to allow representation
of the production, and loss of, elemental nitrogen via denitrification, and ammonia via
volatilization. (Literature suggests that transformation of ammonium to nitrate-nitrite can be
significant within the distribution/treatment area outside the septic tank, particularly in systems
engineered to facilitate nitrification/denitrification.) The physical loss term represents the sum of
loss due to settling, clogging, complexation or any other process that results in permanent physical
arrest of nitrogen species within the confines of the distribution/treatment area.
(8) N species that are modeled as state variables within the second treatment unit are total organic-N,
ammonium, and nitrate-nitrite.
Regarding the N constituent linkage between the OSWDS module and the PRZM-3 soil compartment, the modeling
strategy is borrowed from that used for modeling sediment in the HSPF model (Bicknell et al. 1993). In the same
manner that sediment is modeled as a single constituent in the HSPF land surface module (PERLND) and then
divided into sand, silt and clay fractions (via user input) prior to its input in the HSPF instream module (RCHRES),
total organic N is modeled as a single constituent throughout the OSWDS module, and capabilities are implemented
for user-defined allocation of total N into paniculate labile and paniculate refractory, (we have assumed that all
organic N effluent is paniculate) to parallel the N species scheme that are used in the PRZM-3 soil compartments.
The modeling approach assumes that all OSWDSs are located in the subsurface area that is represented by the
PRZM component of PRZM-3 (i.e., septic tanks do not generate direct fluxes to VADOFT). The linkage has
required the development of capabilities for representing lateral influxes of both water and chemical constituents into
specific PRZM-3 compartments. The soil horizon into which the lateral flows occur is user-defined. Specification of
effluent flow into a soil layer that is below the area modeled using the PRZM component (i.e., in the area modeled
using VADOFT) is not allowed, and results in an error message and termination of the run.
The OSWDS module has been implemented with the dual capability to (1) write to user-defined files, or (2) to
interact with the ANNIE/WDM capabilities for timeseries management and display of relevant flow and nitrogen
species. Users are able to provide the timeseries influent to the first treatment unit of the OSWDS module by
defining flows and concentrations in the module input sequence. Users are able to provide the timeseries influent to
the second treatment unit of the OSWDS module by direct use of values generated by the simulation of the first
treatment unit. The design assumes that all interactions between the OSWDS module and the PRZM-3 model occur
via PRZM-3 reading OSWDS module output files to obtain input to the soil compartment(s). These files contain
flow/chemical mass flux data derived from one of two different run options: (1) results generated by a simulation
that only considered the first treatment unit, (2) results generated by simulating both treatment units. Running the
OSWDS module "stand-alone" allows the user to develop scenarios related to different septic tank influent
conditions (e.g., occupancy rates, seasonal occupancy) and/or treatment options (e.g., nitnfication/denitrification
schemes); store the results of the scenarios; and use the results at a later date as input to various PRZM-3 soil
honzon conditions.
9-18
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A detailed description of the OSWDS input variables and an example of an input file are provided in Tables 9.8 and
9.9.
Table 9.8 Input Guide for On-site Wastewater Disposal System (OSWDS) Module
RECORD 1 - Control Parameters
Format (I5,F8.0,2I5,2(2X,2I2,I4),F8.0)
NSEA number of seasonal occupancies
UVOL unit volume (I/person/day)
NTR number of treatments (1 <= NTR <=2)
OFLG output flag (1 - WDM, 2 - Flat file)
SEDAT start/end date of simulation (ddmmyyyy)
LCHSIZ leach field size (m2)
RECORD 2 - Influent Volume Input
Format (2I2,I4,F8.1)
start date, number of occupants, (212,14,F8.1)
RECORD 3 - Influent Concentrations
Format (3F8.2)
Organic N, Ammonia, Nitrate/Nitrite (kg/1)
RECORD 4 - Primary Treatment Transformations/Losses
Format (4F8.2)
For each constituent, specify fraction resulting in:
OrgN, Ammonia, Nitrate/Nitrite, Settling/Removal
RECORD 5 - Secondary Treatment Transformations/Losses
Format (6F8.2)
For each constituent, specify fraction resulting in:
Org N, Ammonia, Nitrate/Nitrite, Settling/Removal, Denitrification, Volatilization
9-19
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SECTION 10
REFERENCES
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from forest soils. Pages 193-210 in Chemical and Biological Controls in Forestry. American Chemical
Society, Washington, DC, USA.
Stamper, J. H., H. N. Nigg, and J. C. Allen. 1979. Organophosphate insecticide disappearance from leaf surfaces: an
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Stanhill, G. 1969. A simple instrument for the field measurement of turbulent diffusion flux. Journal of Applied
Meteorology 8:509-513.
Stewart, B. A., D. A. Woolhiser, W. H. Wischmeier, J. H. Caro, and M. H. Frere. 1975. Control of Water Pollution
from Cropland: Volume I - A Manual for Guideline Development. EPA-600/2-75-026a, U.S.
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Stewart, B. A., D. A. Woolhiser, W. H. Wischmeier, J. H. Caro, and M. H. Frere. 1976. Control of Water Pollution
from Cropland: Volume II: - An Overview. EPA-600/2-75-026b, U.S. Environmental Protection Agency,
10-6
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Streile, G. P. 1984. The Effect of Temperature on Pesticide Phase Partitioning, Transport, and Volatilization from
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Szeicz, G., G. Endrodi, and S. Tajchman. 1969. Aerodynamic and surface factors in evaporation. Water Resources
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Taylor, A. W. 1978. Post-application volatilization of pesticides under field conditions. Journal of the Air Pollution
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Taylor, J. R. 1982. An Introduction to Error Analysis. University Science Books, Mill Valley, California, USA.
Thibodeaux, L. J. 1979. Chemodynamics: Environmental Movement of Chemicals in Air, Water, and Soil. John
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Thibodeaux, L. J. 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, Second
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Thibodeaux, L. J., C. Springer, and L. M. Riley. 1982. Models of mechanisms for the vapor phase emission of
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Todd, D. K., editor. 1970. The Water Encyclopedia: A Compendium of Useful Information on Water Resources.
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Willis, G. H., and L. L. McDowell. 1987. Pesticide persistence on foliage. Reviews of Environmental Contamination
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Willis, G. H., L. L. McDowell, L. A. Harper, L. M. Southwick, and S. Smith. 1983. Seasonal disappearance and
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Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall erosion losses - a guide to conservation planning.
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Unsaturated Porous Media. ORNL-5601, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
10-8
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SECTION 11
Appendices
11.1 Error Messages and Warnings
The PRZM-3 code contains a number of error and warning messages that indicate either fatal or non-fatal routine
conditions. A list of the current error (fatal) and warning (non-fatal) conditions that are recognized by the code is
given in Table 11.1. Along with each message, troubleshooting approaches are described. Error or warning messages
originating in PRZM-3 (the main code) are numbered beginning with 1000; PRZM pesticide routines, 2000;
VADOFT, 3000; PRZM nitrogen routines, 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 that was used to compile PRZM-3
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 that 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 uses numbers in the range of 1000 to 5000 for these file access errors, an error number
may appear that seems to be an EXESUP/PRZM/VADOFT error. The error message however, will not, correspond
to the messages in Table 11.1. 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 a subroutine trace to indicate where the error occurred. For example,
the third line might be: 'PRZM3>INPREA>VADINP'. This indicates that the error occurred in the subroutine
VADINP (the VADOFT input routine), which was called from subroutine INPREA, which was called from the
PRZM-3 main program. This third line will not appear if an error occurs in the routine INITEM, which is the routine
to read the PRZM3.RUN file and initialize the simulation.
11.2 Variable Glossary
This section presents the major variables used in the PRZM-3 code, as well as variables for the bridge codes that link
PRZM-3 to the HSPF and WASP models (see Sections 9.1 and 9.2). Table 11.2 presents variables used in the
EXESUP module, Table 11.3 presents all PRZM variables other than those specific to nitrogen simulation, Table
11.4 presents PRZM nitrogen simulation variables, Table 11.5 presents VADOFT variables, Table 11.6 presents
variables used in the Monte Carlo module, Table 11.7 presents PZ2HSPF bridge code variables, and Table 11.8
presents variables used in the PRZWASP bridge code.
11-1
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
1010
1020
1050
1070
1090
1092
1100
1190
1200
1202
1210
1220
1230
1240
Error or Warning
Water table is above vadose zone
Water table is above root zone
Zero or negative mass in VADOFT/PRZM
nodes below the water table
Error in the file name input, line with...
Bad value [nnnn] for number of chemicals
Bad index [nnnn] of chemical
Bad value [nnnn] for chemical parent
species
Bad identifier reading global data []
End date is before start date
End date and start date are the same
Unrecognized label [
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
1250
1260
1270
1280
1290
1300
1310
1320
1330
1350
1360
1390
1400
1500
1510
Error or Warning
Error reading PRZM-3 run file...
File type ['nn'] has already been specified
Too many files requested to be open at once
ENDFILE statement present before file [nn]
was opened
Request to close file [nn] which was not
open
Unknown unit number to open file
Too many lines required for Trace option
Argument [] too large for EXP
Negative or zero argument []
Single precision overflow
Negative argument [] to SQRT
Invalid index [nnnn]in reading record
[]
Error reading PRZM data
ENDDATA before starting end day was
provided
ENDDATA before end day was provided
Troubleshooting Approach/Explanation
Error in reading PRZM-3 input data, most likely there
are inappropriate characters in a data field that is
attempting to be interpreted as integer data.
A file with the same unit number has been open while
PRZM-3 is running. Should never occur in current
version of PRAM-3 .
The maximum number of files allowed (defined in the
include file IOUNITS.PAR) is too small a number for
the (recently modified) version of PRAM-3. This error
should not appear in the current version of PRAM-3 .
An input file, which is required for the current PRZM-
3 simulation configuration, has not been identified in
the file group of the PRAM-3 input file.
Should never occur in current version of PRAM-3 .
Implies that recent code modifications have been made
which did not properly account for which files were
open.
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 PRAM-3 .
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.
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 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 day was
provided parameters section of PRZM3.RUN file
before the record was provided.
The label 'ENDDATA' appears in the global
parameters section of PRZM3 .RUN file before the
'END DATE' record was provided.
11-3
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
1530
1540
1550
1560
1570
2000
2010
2020
2040
2050
2060
Error or Warning
ENDD ATA before number of chemicals
was provided
ENDD ATA before the parent of chemical n
was provided
dd/mm/yy - Invalid START (or END)
DATE
End of file [
number of chemicals in EXESUP [nn]
ERFLAG has invalid value
NPI [nnnn] + NEW [nnnn] is greater than
NPII [nnnn]
Solution for tridiagonal matrix not found,
previous day's values used
NDC [nnnn] is greater than NC [nnnn]
Troubleshooting Approach/Explanation
The label 'ENDD AT A' appears in the global
parameters section of PRZM3.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 'ENDD AT A' appears in the global
parameters section of PRZM3 .RUN file (with
TRNSIM = 'ON' and NUMBER OF CHEMICALS
greater than 1) before the 'PARENT OF n' record was
provided.
An invalid date has been entered in the global
parameters section of the PRZM3.RUN input file.
Check to see whether the month being specified had
the number of days which is being implied (e.g.,
3 1/02/88 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 Echo 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 match
record in the data file or the simulation start and end
dates specified in PRZM3.RUN do not correspond to
the dates in the meteorological data file.
The value supplied to the PRZM input file for the
number of chemicals being simulated does not agree
with the number supplied to the PRZM3.RUN input
file.
ERFLAG (Erosion method) may only take on values
of 0, 2, 3, or 4 (see ERFLAG in Chapter 1 1)
Decrease the number of PRZM compartments or
increase the parameter NPII. If the latter, in subroutine
MOC recompile the code. This error only occurs if the
MOC rather than backward difference
transportsolution technique is used.
If this message appears repeatedly, the PRZM problem
definition geometry should be reevaluated.
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.
11-4
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
2065
2070
2080
2090
2100
2110
2120
2130
2140
2150
2160
2170
2180
Error or Warning
CROPNO [n] not found in ICNCN(1:NDC):
Hi n2 ... nNDC
NCPDS [nnnn] is greater than NC [nnnn]
NAPS [nnnn] is greater than NAPP [nnnn]
NHORIZ [nnnn] is greater than NCMPTS
[nnnn]
NCOM2+1 [n] is greater than NCMPTS [n]
NPLOTS [nnnn] is greater than 7
Sum of horizon thicknesses exceeds depth
Soil profile description is incomplete, data
available for xx.xx of xx.xx cm
Calculated field capacity water content
exceeds the saturation value
Application [nn] failed to meet ideal soil
conditions
WIND AY [nn] for application [nn] is too
large
Horizon into which septic effluent is to be
introduced > number of horizons.
DEPI(,) changed from to
because CAM is equal to .
Troubleshooting Approach/Explanation
The crop number (CROPNO, record 9A) does not
match any of the crop numbers in ICNCN (record 9).
CROPNO should be present in one of the ICNCN of
the (multiple) records 9.
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.
The total number of compartments NCOM2 (roughly,
Sum(Ceiling(THKNS/DPN))) is greater than
NCMPTS, the dimension of the array DelX. Change
PRZM problem definition geometry so that the value
of NCOM2 is less than the parameter NCMPTS or
change the value of NCMPTS and recompile.
Reduce the number of requested plots.
Change PRZM problem definition geometry so that the
sum of horizon thickness is equal to the user supplied
total depth.
Change PRZM problem definition file so that profile
data are supplied for the entire 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).
The specified pesticide application did not meet soil
moisture criteria before the WIND AY value expired.
Currently this error will halt execution.
The value for WIND AY, specified in the PRZM input
sequence, causes overlap on a proceeding application
date. Reduce the value for WIND AY to a value lesser
than the difference of application dates.
Execution is halted.
If CAM is equal to 1, 2, or 3, and DEPI is not equal to
the default value (4 cm), then DEPI is set to the default
value. Execution continues.
11-5
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
2190
2200
2210
2220
2230
3000
3010
3020
3030
3040
3050
3060
3070
Error or Warning
DEPI(,) changed from to
because CAM is equal to .
CAM(,) == is out of the valid
range 1-10
PCDEPL changed from to 0.5
Sum(THKNS(l:NHORIZ)) [ n ] is less than
Max(AMXDR(l:NDC)) [ n ]
ffiGN [n] is greater than NCMPTS [n]
Fatal error in HFINTP, interpolation failed
VARCAL - timestep nnn solution fails to
converge after nnn reductions
Attempt to run VADOFT w/PRZM on and
ITRANS.ne.l
Incorrect value for IMODL in VADOFT
input
Requested value of NOBSND [nnnn]
greater than MXPRT [nnnn]
Transport simulation, NVREAD reset to 1
PRZM is on; IVSTED reset to 1
Troubleshooting Approach/Explanation
See section 4, Record 16: if CAM == 4-10, then DEPI
>= DPN(l). Execution continues.
See section 4, Record 16 for the valid range.
Execution is halted.
See section 4, Record 27 for the valid range.
Execution continues.
The maximum root zone depth is greater than the soil's
total thickness. The difference was added to the last
compartment and the user data adjusted. Execution
continues.
The total number of compartments IBGN (roughly,
Sum(Ceiling(THKNS/DPN))) is greater than
NCMPTS, the dimension of the array DelX. Execution
is halted.
The current time in VADOFT exceeds the 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.
The maximum number of time refinements was
exceeded due to non-convergence. Relax the converge
criterion, change the iterative scheme or revise
VADOFT parameters.
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;
DVIODL = 0 for transport, DVIODL = 1 for flow.
The value entered for the number of observation nodes
in VADOFT (NOBSND) exceeds the maximum
(MXPRT). Reduce the number of observation nodes 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.
11-6
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
3080
3090
3120
3130
3170
3190
3210
4000
4010
4020
4030
4040
Error or Warning
PRZM is on; flow boundary conditions will
be over-written
PRZM is on; transient data at top node
ignored
PRZM is on; transport boundary conditions
will
PRZM is on; transient data at top node
ignored
Invalid index [nnn] in reading PINT
ITMGENol in linked mode, results may
be unpredictable
End of file reading VADOFT Darcy
velocities
The horizon number specified to receive
septic influent [n] does not exist
If FIXNFG is 1, NUPTFG must be 1. As
NUPTFG is 0, FIXNFG will be set to 0
Sum of monthly plant uptake fractions over
the year [n] do not sum to 1
Sum of layered plant uptake fractions [n] do
not sum to 1 in month [n]
Sum of fraction of nitrogen uptake from
nitrate & ammonium [n] is not 1
Troubleshooting Approach/Explanation
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.
PRZM output will overwrite VADOFT upper
boundary condition for solute transport. PRZM
generates be overwritten 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.
An invalid index (less than 1 or greater than the
parameter NP) was supplied for an initial condition
value. Supply proper value.
The user is supplying output marker time values that,
potentially, could result in a read error of Darcy
velocities during the VADOFT transport simulation.
Check to see whether warning 3 190 occurred prior to
this fatal error. Make necessary changes to VADOFT
input file.
The horizon number into which the septic effluent is to
be introduced does not exist. This number must be
between 1 and NHORIZ (See Record 32 of the PRZM
input defns.)
The flag FIXNFG is set to 1, indicating nitrogen
fixation is to be simulated, but the flag NUPTFG is set
to zero. In order for nitrogen fixation to be simulated,
the yield-based algorithm for nitrogen uptake
(NUPTFG =1) must be used.
The monthly fractions for yield-based plant uptake of
nitrogen must sum to unity across the calendar year.
The monthly fractions for yield-based plant uptake of
nitrogen from soil layers must sum to one across the
number of horizons being simulated for each month.
The input parameters which designate the fraction of
nitrogen uptake that comes from nitrate and
ammonium must sum to unity.
11-7
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
5000
5010
5020
5030
5040
5050
5060
5070
5080
5090
5100
5110
6010
6020
Error or Warning
Format error in reading Monte Carlo input
file
Premature end of Monte Carlo input file
Uniform random number could not be
generated for exponential distribution
Cannot have a negative mean for a log
normal distribution. Mean equals
Subroutine DECOMP terminated, matrix
BBT is not positive definite
The number of [MONTE CARLO RUNS] is
greater than maximum of
The number of [MONTE CARLO
VARIABLES] is greater than maximum of
The number of [EMPIRICAL DIST. DATA
POINTS] is greater than maximum of
The number of [MONTE CARLO OUTPUT
VARIABLES] is greater than maximum of
The number of [DAYS IN OUTPUT AVG.
PERIOD] is greater than maximum of
The number of [REQUESTED OUTPUT
CDFS] is greater than maximum of
First element for horizon [] not
found
INFIL subroutine. (d+hf)*dw < 0
Subroutine INFIL. Value out of range.
Troubleshooting Approach/Explanation
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.
Random exponential distribution variates could not be
generated. Probable cause is inappropriate distribution
parameters being supplied in the Monte Carlo input
file.
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.
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 file).
The solution of the integrated Green- Ampt equation
requires (d+hf)*dw > 0 (see equations 6.121 and 6.19).
Execution is halted.
See discussion in the PRZM manual regarding the
Green- Ampt equation. The variable z is outside the
range of - e~
-------
Table 11.1 PRZM-3 Error Messages, Warnings, and Troubleshooting Approaches
6120
6130
6140
6150
Error or Warning
Subroutine Get_Crop_Params: Crop Height
is outside the nominal range 0.0 m < ZCH <
25m.
ITFLAG [ # ] was not 0, 1 or 2.
MSFLGCnO [ n2 ] was not 1 or 2.
Errors detected. PRZM stopped.
Troubleshooting Approach/Explanation
See discussion in the PRZM manual regarding the
Volatilization Flux. Execution is halted.
See PRZM input file record 20. Execution is halted.
See PRZM input file record 32B. Execution is halted.
Several fatal errors were detected. Execution is halted.
11-9
-------
Table 1 1.2 EXESUP Program Variables
Variable
BASEND
BOTFLX
DAFLUX
DAVFLX
DISUNS
EDAT
FLOSIM
ICHEM
IDAY0
ILDLT
IMON0
IPRZM
IPZONE
IYR0
LLSTS
NCHEM
Units
~
cm day"1
qcm2
ppm cm day"1
ppm (q cm"3)
-
-
-
-
-
-
-
-
-
days
Type
Scalar
Array
Array
day"1
Array
Array
Array
Logical
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Number of bottom
PRAM node within a
given PRZM zone.
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.
Ending day, month, year
of PRZM simulation.
Flow simulation
indicator.
Counter for number of
chemicals.
Starting day of PRZM
simulation.
Counter for PRZM or
VADOFT timesteps.
Starting month of PRZM
simulation.
Counter for number of
PRZM zones.
Counter for VADOFT
zones.
Starting year of PRZM
simulation.
Number of days in final
timestep.
Number of chemicals.
Subroutine
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
INITEM
EXESUP
INPREA
INITEM
Common
Block
~
VADSTO
PRZSTO
VADSTO
-
-
-
-
-
-
-
-
-
-
I,M,O
M
M
M
M
M
M
M
M
M
M
M
M
M
M
I
0
I
I
o
11-10
-------
Table 1 1.2 EXESUP Program Variables
Variable
NDAYS
NLDLT
NP
NPNARY
NPRZM
NPV
NPZONE
NPZ
PINT
PRZMON
PRZMPF
PRZMWF
P2VWHT
REDAT
RSDAT
Units
days
~
~
—
~
-
~
L
M/L3
~
qcm"2
day"1
cm day"1
~
Type
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Array
Logical
Array
Array
Array
Array
Array
Description
Number of days in a
timestep minus one.
Number of PRZM or
VADOFT timesteps.
Total number of nodes.
Number of VADOFT
nodes in all VADOFT
zones.
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.
VADOFT corrected
values of head or
concentration.
PRZM on indicator.
Daily chemical flux
from the base of PRZM.
Daily water flux from
the base of PRZM.
Weighting factors for
transfer of water or
chemical flux from
PRZM to VADOFT.
Ending day, month, year
of PRZM simulation
within a timestep.
Starting day, month,
year of PRZM
simulation within
timestep.
Subroutine
EXESUP
EXESUP
INPREA
INITEM
EXESUP
EXESUP
EXESUP
INPREA
INITEM
EXESUP
EXESUP
INPREA
INITEM
EXESUP
EXESUP
EXESUP
INPREA
INITEM
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
Common
Block
-
~
CONTR2
~
~
-
~
VADSTO
~
PRZSTO
PRZSTO
ZONWHT
~
I,M,O
M
I
I
O
I
M
I
I
O
I
I
I
O
M
M
I
I
0
M
M
M
M
M
11-11
-------
Table 1 1.2 EXESUP Program Variables
Variable
RSTFG
SAVCNC
SAVHED
SDAT
TOPFLX
TOWFLX
TRNSIM
VADFON
VD2TC
WHGT
ZPESTR
Units
~
ppm
cm
~
cm day"1
(g cm"2 day"1)
cm day"1
~
—
~
gem"2
day"1
Type
Scalar
Array
Array
Array
Array
Array
Logical
Logical
Array
Scalar
Array
Description
PRZM restart flag, 1 if
first time through, 2
thereafter.
Concentrations at each
VADOFT node from
previous timestep.
Previous timestep
VADOFT heads by node
Starting day, month,
year of PRZM
simulation
Weighted water (or
pesticide) flux leaving
the base of PRZM.
Water flux from PRZM
to top of VADOFT for
each timestep.
Indicator for flow and
transport simulation.
VADOFT on indicator.
VADOFT correction
factors for converting
from dissolved to total
solute concentration
Temporary variable for
storing flux weighting
factors.
PRZM chemical flux by
zone, compartment, time
period, and chemical.
Subroutine
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
INPREA
INITEM
EXESUP
INPREA
INITEM
EXESUP
EXESUP
EXESUP
Common
Block
~
VADSTO
VADSTO
~
VADSTO
VADSTO
~
~
VADSTO
~
PRZSTO
I,M,O
M
M
M
M
M
M
I
I
O
I
I
O
M
M
M
11-12
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
A
AAA
ABSOIL
AD
ADFLUZ
ADL
ADS
AFIELD
AINF
AIRDEN
AIRLMD
AKAY
ALAMDA
ALBEDO
AMXDR
Units
day"1
cm"1
fraction
day"1
gem"2
day"1
day"2
mg kg"1
ha
cm
gm cm"3
cal cm"1
day"1 °Cl
-
cal cm"1
day"1 "C"1
fraction
cm
Type
Array
Scalar
Scalar
Array
Array
Scalar
Array
Scalar
Array
Scalar
Scalar
Array
Array
Array
Scalar
Description
Lower Diagonal Element of
Solution Matrix (I- 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
Lateral Drainage
Time Constant
Adsorbed Portion of Pesticide
in Each Compartment
Area of Field
Percolation Into Each Soil
Compartment
Density of Air at Ambient
Temperature
Thermal Conductivity of Air
K-Factor in the Soil Thermal
Conductivity Equation
Thermal Conductivity of Soil
Constituent
Soil Surface Albedo at Start of
Each Month
Maximum Rooting Depth of
Each Crop
Subroutine
SLPEST
TRDIAG
SLTEMP
SLTEMP
READ
ECHO
INITL
HYDR2
SLPEST
MASBAL
OUTPST
OUTTSR
READ
ECHO
INITL
HYDR2
OUTCNC
READ
EROSN
HYDROL
HYDR1
HYDR2
SLTEMP
SLTEMP
SLTEMP
SLTEMP
READ
SLTEMP
READ
INITL
PLGROW
Common
Block
PEST
HYDR
PEST
HYDR
HYDR
HYDR
MET
CROP
I,M,
0
0
I
M
M
0
I
I
I
0
I
I
I
0
I
I
I
o
0
I
I
M
M
0
I
o
I
I
11-13
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
ANETD
ANUM
APD
APDEP
APM
ATEMP
AVSTOR
AW
B
BBB
BBT
BD
BDFLAG
BFLO
BT
C
CB
CC
CELLBG
Units
cm
cm
-
cm
-
°C
cm3 cm"3
-
day"1
°K cm"1
°C
gem"3
~
cm
m
day"1
kg ha"1
g
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Array
Array
Scalar
Array
Scalar
Array
Scalar
Array
Scalar
Description
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
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
Mineral Soil Bulk Density
Bulk Density Flag (0 = Whole
Soil BD Entered, 1 = Mineral
BD and OC Entered)
Monthly Baseflow Runoff
Accumulated for Output Table
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
Subroutine
READ
INITL
EVPOTR
READ
IRRIG
READ
Main
HYDR2
EVPOTR
SLPEST
SLTEMP
READ
SLTEMP
SLTEMP
READ
ECHO
INITL
OUTHYD
FURROW
IRREAD
SLPEST
TRDIAG
OUTPST
MOC1
INITL
INITL
Common
Block
CROP
-
PEST
MET
HYDR
TABLE
IRGT
PEST
PEST
I,M,
0
0
I
o
0
0
M
O
I
I
0
I
I
0
I
o
I
M
M
11-14
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
CEVAP
CFLAG
CHANGE
CINT
CINTB
CINTCP
CLAY
CONC
CONDUC
CONST
CORED
COVER
COUNT
COVMAX
Units
cm
-
g
cm
cm
cm
percent
—
cm day"1
~
cm
fraction
-
fraction
Type
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Alpha -
numeric
Scalar
Scalar
Scalar
Scalar
Array
Array
Description
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
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 Area! Cover of Crop
Canopy
Number of moving points in a
compartment
Maximum Areal Coverage of
Each Crop at Full Canopy
Development
Subroutine
EVPOTR
MASBAL
OUTHYD
OUTTSR
READ
INITL
MOC1
INITL
HYDROL
EVPOTR
MASBAL
OUTHYD
OUTTSR
PMAIN
MASBAL
OUTHYD
READ
ECHO
PLGROW
SLTEMP
PMAIN
MAIN
SLPSTO
SLPST1
READ
ECHO
OUTTSR
READ
ECHO
INITL
SLTEMP
MOC1
READ
ECHO
INITL
PLGROW
Common
Block
HYDR
MISC
HYDR
HYDR
CROP
HYDR
PEST
HYDR
CROP
CROP
I,M,
0
0
I
I
I
0
I
M
O
I
I
I
I
I
0
I
I
0
I
I
I
O
I
I
O
I
I
0
I
I
I
M
O
I
I
I
11-15
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
CN
CNCPON
D
CNDBDY
CNDM
CNDMO
CPBAL
CRC
CRCNC
CTOT
CURVN
CWBAL
D
DAIR
DAY
DELT
DELTA
DELX
Units
~
gem'3
cm day"1
—
-
gem2
day m"1
day m"1
g
-
cm
m
cm2 day"1
-
day
°K
cm
Type
Array
Scalar
Scalar
Array
Array
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Alpha-
numeric
Scalar
Scalar
Array
Description
Runoff Curve Numbers for
Antecedent Soil Moisture
Condition II
Concentration of pesticide in
inflowing water
Boundary Layer's
Conductance
Accumulated Number of Days
in Each Month (With and w/o
Leap Year)
Accumulated Number of Days
in Each Month
Cumulative Pesticide Balance
Error
Canopy Resistance
Canopy Resistance
Concentration of consolidated
points
Current Value of Runoff
Curve Number
Cumulative Water Balance
Error
Zero Displacement Height
Molecular Diffusivity in the
Air
Flag for Daily Output of
Water or Pesticide Summary
Time Step
Convergence Criteria in the
Newton-Raphson Solution
Technique
Compartment Thickness
Subroutine
READ
ECHO
HYDROL
MOC1
INITL
MAIN
PMAIN
SLTEMP
MASBAL
OUTPST
CANOPY
MAIN
OUTPST
MOC
HYDROL
MASBAL
OUTHYD
CANOPY
SLTEMP
ECHO
MAIN
READ
SLPSTO
SLPST1
PMAIN
INITL
HYDR2
PLPEST
SLPEST
MASBAL
SLTEMP
SLTEMP
Common
Block
HYDR
PEST
MISC
PEST
PEST
-
HYDR
PEST
MISC
HYDR
I,M,
0
0
I
I
I
0
I
M
I
0
o
I
M
0
I
o
M
I
I
O
I
I
O
I
I
I
I
M
I
11-16
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
DELXSQ
DEN
DENOM
DENOM
DEPI
DFFLUX
DGAIR
DGRATE
DIFFCH
DIFFCO
DIFK
DIN
DISP
DISS
DKFLUX
Units
cm"2
~
cm
cmhr"1
cm
gem2
day"1
cm2 day"1
day"1
m2 day"1
cm2 day"1
m2 day"1
cm
cm2
day"1
mgl"1
gem2
Type
Scalar
Array
Scalar
Scalar
Array
Array
Array
Array
Scalar
Array
Scalar
Scalar
Array
Array
Array
Description
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
Eddy Diffusivity at Canopy
Height
Diffusivity of Soil
Compartment
Eddy Diffusivity
Current Plant Canopy
Interception Potential
Dispersion/Diffusion
Coefficient
Dissolved Portion of Pesticide
in Each Compartment
Decay Flux of Pesticide From
Each Compartment
Subroutine
INITL
SLPEST
INITL
EVPOTR
EROSN
READ
ECHO
PESTAP
SLPEST
OUTPST
OUTTSR
SLPSTO
SLPST1
ECHO
INITL
READ
SLPSTO
SLPST1
CANOPY
SLTEMP
CANOPY
PLGROW
HYDROL
OUTHYD
READ
ECHO
INITL
SLPEST
OUTCNC
SLPEST
MASBAL
OUTPST
OUTTSR
Common
Block
HKYDR
HYDR
PEST
PEST
PEST
HYDR
PEST
PEST
I,M,
0
0
M
O
I
0
I
I
I
I
I
I
0
I
I
0
M
O
0
I
I
0
I
I
I
O
I
I
I
11-17
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
DKRATE
DKRT12
DKRT13
DKRT23
DOM
DPN
DT
DVF
DW
DX
EF
ELTERM
EMD
EMM
EMMISS
EN
Units
day1
day1
day1
day1
-
cm
hr
kg ha"1
day"1
Fraction
m
kg ha'1
day"1
-
-
fraction
Type
Array
Array
Array
Array
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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
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 roughness
coefficient for furrows
Subroutine
READ
ECHO
INITL
SLPEST
ECHO
READ
INITL
PSTLNK
ECHO
READ
INITL
PSTLNK
ECHO
READ
INITL
PSTLNK
SLTEMP
ECHO
READ
READ
ECHO
EVPOTR
OUTPST
IRRIG
FURROW
FURROW
IRRIG
OUTPST
EROSN
SLPEST
READ
ECHO
READ
ECHO
READ
SLTEMP
FURROW
IRREAD
Common
Block
PEST
PEST
PEST
PEST
MISC
HYDR
MET
IRGT
IRGT
PEST
MET
IRGT
I,M,
0
0
I
I
I
I
o
o
I
I
o
o
I
I
o
o
I
I
I
0
0
I
I
o
o
M
I
o
I
0
I
I
0
11-18
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
END YEA
R
ENP
ENPY
ENRICH
ERFLAG
ERFLUX
ERPST
EVAP
EVPO
EXTRA
F
FO/
FAIH
FAIM
FAM
Units
~
Kcal
mole"1
Kcal
mole"1
-
gem2
gem"2
cm day"1
cm
cm3 cm"3
gem"2
day"1
kg ha"1
-
-
~
Type
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Array
Scalar
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Description
Ending Year of Simulation
Used to Loop for Output
Table
Enthalpy of Vaporization
Enthalpy of Vaporization
Enrichment Ratio for Organic
Matter
Erosion Flag (0 = Not
Calculated, 1 is Not Used,
2 = Calculated by MUSLE,
3= Calculated by MUST,
4 = Calculated by MUSS)
Erosion Flux of Pesticide
From Soil Surface
Total Erosion Pesticide Load
Used for Output Table
Daily Evaporation from the
Top 5 cm of Soil After
Adjusting for Crop
evapotranspiration
Monthly Evapotranspiration
Accumulated for Output Table
Extra Water Occurring in a
Compartment Over the
Allowed Saturation Amount
Vector of Source Terms for
Each Compartment (Tri-
diagonal Matrix)
Current Foliar Pesticide
Storage
Stability Function for Sensible
Heat
Stability Function for
Momentum
Pesticide Application Flag (1=
Soil, 2= Linear Foliar, 3=
Exponential Foliar)
Subroutine
INITEM
KHCORR
ECHO
MAIN
READ
EROSN
READ
PMAIN
SLPEST
MASBAL
OUTPST
OUTPST
OUTNIT
SLTEMP
OUTHYD
OUTTSR
HYDR2
SLPEST
TRDIAG
OUTPST
CANOPY
CANOPY
READ
ECHO
PESTAP
Common
Block
TABLE
PEST
HYDR
PEST
TABLE
TABLE
PEST
PEST
I,M,
0
0
I
I
0
o
I
o
I
I
o
o
M
0
I
0
I
o
o
0
I
I
11-19
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
FC
FCV
FDAY
FEXTRC
FILTRA
FIRST
FL
PLEACH
FOLPO/
FP
FPDLOS
FPVLOS
FPWLOS
FRAC
FRAC
FRAC
Units
cm
~
~
cm'1
m2 kg1
~
kg ha'1
Fraction
gem'2
kg ha'1
gem2
gem2
day"1
gem2
~
-
Type
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Array
Description
Field Capacity Water Depth in
Soil Compartment
Regression Coefficients for
Prediction of Field Capacity
Soil Water Content
Loop Limit, First Day
Foliar Extraction Coefficient
for Foliar Wash off Model
Filtration Parameter for
Exponential Foliar
Application Model
Index of first point under
interface with Ratio greater
than 2
Foliar Pesticide Decay Loss
Leaching factor, as fraction of
soil moisture deficit
Foliar Pesticide Storage From
Previous Time Step
Current Daily Foliar Pesticide
Storage
Current Daily Foliar Pesticide
Decay Loss
Daily Foliage Pesticide
Volatilization Flux
Current Daily Pesticide
Washoff Loss
Fraction of the Distance a
Curve Number is Between
Increments of Ten
Fraction of the Current Crop
Growing Season Completed
Number of Compartments
Available to Extraction of ET
Subroutine
INITL
EVPOTR
THCALLC
PMAIN
READ
ECHO
PLPEST
READ
ECHO
PESTAP
MOC
OUTPST
IRRIG
IRREAD
PLPEST
MASBAL
OUTPST
PMAIN
OUTPST
PLPEST
MASBAL
OUTPST
OUTTSR
MASBAL
OUTPST
PLPEST
PLPEST
READ
PLGROW
EVPOTR
Common
Block
HYDR
PEST
PEST
HYDR
IRGT
PEST
PEST
PEST
I,M,
0
0
o
I
I
o
I
I
M
I
0
o
I
I
I
0
I
I
I
I
I
o
11-20
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
FRACOM
FS
FX1
FX2
GAMMA
GEE
GFLD
GRADT
GRADW
HAD
HAM
HEIGHT
HENRY
HENRYK
HF
HOT
Units
-
m
0K4
0K3
-
Fraction
Fraction
"Cm1
day1
-
-
cm
cm3 cm"3
cm3 cm"3
m
m
Type
Scalar
Array
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Fraction of Layer Attributed
to the Current Horizon
Infiltration depth at each
station in furrow
Fourth Order Energy Balance
Equation in Terms of Soil
Surface Temperature
Derivative of Energy Balance
Equation in Terms of Soil
Surface Temperature
Pesticide Uptake Efficiency
by Plant
Depolarization Factors of Soil
Constituent in Three
Dimensions
Depolarization Factor of
Entrapped Air at Field
Capacity Water Content
Temperature Gradient
Wind Speed Gradient
Day of Month of Crop
Harvest
Month of Crop Harvest
Canopy Height
Henry's Constant
Henry's Constant
Green-Ampt Suction head
parameter
Thickness of Each Layer in
the Canopy
Subroutine
INITL
FURROW
IRRIG
SLTEMP
SLTEMP
PLGROW
SLPEST
SLTEMP
SLTEMP
CANOPY
CANOPY
READ
ECHO
READ
ECHO
MAIN
OUTPST
PLGROW
SLTEMP
KHCORR
ECHO
MAIN
READ
FURROW
INFIL
IRREAD
CANOPY
Common
Block
IRGT
PEST
CROP
PEST
IRGT
I,M,
0
0
I
M
M
O
I
M
M
0
O
I
I
0
I
I
I
I
O
I
I
O
0
11-21
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
HORIZN
HSWZT
HTEMP
HTITLE
HTMAX
1
IAPDY
IAPYR
IARG
IARG1
IB
IBM1
ICNAH
ICNCN
Units
°C
~
cm
-
~
~
~
-
-
~
—
Type
Array
Scalar
Scalar
Alpha-
numeric
Array
Scalar
Array
Array
Array
Scalar
Scalar
Scalar
Array
Array
Description
Soil Horizon Number
Hydraulics Flag (O= Free
Draining Soils, 1= Restricted
Drainage)
Average Air Temperature
Comment Line to Enter
Information about Hydrology
Parameters
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
Subroutine
READ
ECHO
INITL
OUTHYD
OUTPST
OUTCNC
READ
ECHO
INITL
PMAIN
CANOPY
READ
ECHO
ECHO
PLGROW
READ
SLTEMP
KHCORR
CANOPY
READ
ECHO
PMAIN
READ
ECHO
PMAIN
READ
ECHO
OUTTSR
OUTTSR
INITL
HYDR2
INITL
READ
ECHO
PLGROW
READ
ECHO
INITL
Common
Block
MISC
CROP
MISC
MISC
MISC
HYDR
CROP
I,M,
0
0
I
I
I
I
I
o
I
I
I
o
I
M
O
0
I
I
o
I
I
o
I
I
o
I
I
o
I
I
11-22
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
ICROSS
IDEL
IDFLAG
IEDAY
IEDY
IEMER
IEMON
IEND
IEPJIOR
IEYR
IFIRST
IFLO
IHAR
II
IJ
ILP
Units
-
~
~
~
-
-
~
-
cm
~
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Description
Number of horizon interfaces
where points need to be
consolidated, i.e. Ratio greater
than 2.
Number of points which are
consolidated
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 Simulation
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
Monthly Interflow Runoff
Accumulated for Output Table
Julian Day of Crop Harvest
Loop Counter
Loop Counter
Initial Level of Pesticide Flag
(O= No Pesticide, 1= Initial
Pesticide)
Subroutine
INITL
MOC
MOC
ECHO
READ
SLTEMP
OUTCNC
READ
PMAIN
ECHO
INITL
READ
ECHO
INITL
PLGROW
READ
ECHO
PMAIN
MOC
SLPEST
TRDIAG
READ
ECHO
PMAIN
OUTTSR
OUTHYD
READ
ECHO
INITL
PLGROW
OUTPST
PMAIN
READ
ECHO
Common
Block
HYDR
-
MET
MISC
CROP
MISC
-
MISC
TABLE
CROP
MISC
I,M,
0
M
M
I
0
I
I
0
I
I
o
I
I
I
o
I
I
M
0
I
I
o
o
I
I
I
o
I
11-23
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
INABS
INCROP
INICRP
INTFC
IOUT
IPEIND
IPSCND
IRTYPE
ISCOND
ISDAY
ISDY
Units
cm
-
-
~
Type
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Initial Abstraction of Water
from Potential Surface Runoff
Crop Growing in Current
Cropping Period
Initial Crop Number if
Simulation Starting Date is
Before First Crop Emergence
Date
Whole Layer(s) Attributed to
the Current Horizon
Index of first point outside
flow domain
Pan Evaporation Indicator
Flag (O= Data Read In, 1=
Calculated)
Foliage Pesticide Condition
after Harvest:
1. Surface Applied
2. Removed
3. Surface Residue
Irrigation type flag:
0=No irrigation
l=Flood irrigation
2=Furrow irrigation
3=Over-canopy sprinklers
4=Under-canopy sprinklers
5=Over-canopy without runoff
6=Over-canopy, user-defined
rates, with runoff
7=Over-canopy, user-defined
rates, without runoff
Surface Condition After
Harvest Corresponding to
'INICRP'
Starting Day of Simulation
Counter
Subroutine
HYDROL
EROSN
READ
ECHO
INITL
PLGROW
OUTHYD
OUTPST
READ
ECHO
INITL
INITL
MOC1
READ
ECHO
ECHO
PLGROW
READ
IRRIG
IRREAD
READ
ECHO
PLGROW
HYDROL
EROSN
READ
ECHO
INITL
PMAIN
INITL
Common
Block
HYDR
CROP
CROP
MET
CROP
IRGT
HYDR
MISC
I,M,
0
0
I
0
I
I
I
I
o
I
I
M
0
I
o
M
I
I
0
o
I
I
I
I
0
I
I
I
11-24
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
ISMON
ISTART
ISTYR
ITEM1
ITEM2
ITEMS
ITEMP
ITFLAG
ITMP
IY
IYEAR
IYREM
Units
-
~
~
~
°C
~
~
Type
Scalar
Scalar
Scalar
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
Scalar
Scalar
Scalar
Scalar
Array
Description
Starting Month of Simulation
Index of point at which
consolidation starts
Starting Year of Simulation
Hydrology Output Summary
Indicator
Pesticide Output Summary
Indicator
Soil Pesticide Concentration
Profile Output Indicator
Mean Daily Temperature
Rounded to Next Lowest
Whole Number
Soil Temperature Flag
Number of Compartments
Pesticide is Applied to When
Incorporated
Annual Loop Counter
Number of Simulation Years
Used to Make Output Table
Year of Crop Emergence
Subroutine
READ
ECHO
INITL
PMAIN
MOC
READ
ECHO
INITL
PMAIN
READ
ECHO
OUTHYD
READ
ECHO
OUTPST
READ
ECHO
PMAIN
EVPOTR
ECHO
MAIN
OUTCNC
READ
PESTAP
PMAIN
PLGROW
OUTHYD
OUTPST
OUTTSR
OUTCNC
OUTPST
OUTHYD
OUTNIT
READ
ECHO
INITL
PLGROW
Common
Block
MISC
-
MISC
MISC
MISC
MISC
MISC
MET
CROP
I,M,
0
0
I
I
I
M
0
I
I
I
0
I
I
0
I
I
o
I
I
o
I
I
I
0
I
I
I
I
I
I
o
0
0
o
I
I
I
11-25
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
IYRHAR
IYRMAT
j
JJ
JP1
JP1T10
JT10
JULDAY
TV"
KD
KDFLAG
KH
KK
KOC
Units
-
-
-
~
-
cm3 g1
-
cm3 cm"3
cm3 g1
-oc
Type
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Array
Scalar
Scalar
Description
Year of Crop Harvest
Year of Crop Maturation
Loop Counter
Loop Counter
Counter (J+l)
Counter (JP1* 10)
Counter (J* 10)
Julian Day
Loop Counter
Adsorption/partition
Coefficient for Soil
Compartment
Partition Coefficient Flag (O=
Kd Read In, 1= Kd
Calculated)
Henry's Constant at Current
Time
Loop Counter
Organic Carbon Partition
Coefficient
Subroutine
READ
ECHO
INITL
PLGROW
READ
ECHO
INITL
PLGROW
PMAIN
READ
ECHO
INITL
PLGROW
OUTHYD
OUTPST
READ
READ
READ
READ
PMAIN
PLGROW
OUTHYD
OUTPST
SLTEMP
READ
ECHO
INITL
KDCALC
PESTAP
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC
READ
ECHO
PMAIN
MAIN
SLPSTO
SLPST1
READ
KDCALC
Common
Block
CROP
CROP
MISC
PEST
PEST
I,M,
0
0
I
I
I
0
I
I
I
0
I
I
I
0
I
I
0
I
I
I
o
I
I
o
I
I
o
I
I
I
11-26
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
KS
L
LA
LATFLX
LAYERS
LBTEMP
LDAY
LEAP
LFREQ1
LFREQ2
LFREQ3
LL
LOGO
LOGKOC
LOGZO
LTFLUX
M
MAD
Units
m/s
kg ha'1
gem'2
day1
°C
-
~
~
-
-
gem"2
day"1
-
Type
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Saturated hydraulic
conductivity of soil
Loop Counter
Daily Lateral Pesticide Flux
For Each Pesticide Used for
Output Table
Daily Lateral Pesticide Flux
For Each Pesticide from the
Entire Soil Column
Number of Layers in Canopy
Daily Value of Bottom
Boundary Temperature
Loop Limit (Last Day)
Additional Day Flag for Leap
Year
Frequency of Soil
Compartment Reporting in
Water Output Summary
Frequency of Soil
Compartment Reporting in
Pesticide Output Summary
Frequency of Soil
Compartment Reporting in
Concentration Profile Output
Summary
Loop counter
Logarithm of Zero
Displacement Height
Natural Log of Koc
Logarithm of Roughness
Length
Daily Lateral Pesticide Flux
For Each Pesticide from Each
Soil Compartment
Loop counter
Day of Month of Crop
Maturation
Subroutine
FURROW
INFIL
IRREAD
SLTEMP
OUTPST
INIACC
SLPSTO
MASBAL
OUTPST
OUTTSR
CANOPY
SLTEMP
PMAIN
SLTEMP
READ
OUTHYD
READ
OUTPST
READ
OUTCNC
MOC1
CANOPY
KDCALC
CANOPY
SLPSTO
OUTPST
MOC1
READ
ECHO
Common
Block
IRGT
PEST
MISC
MISC
MISC
PEST
I,M,
0
I
I
O
o
O
o
I
I
I
o
M
I
O
I
o
I
0
I
0
0
0
I
11-27
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
MAM
MASS
MASSO
MAT
MCFLAG
MD
MDOUT
MEOUTW
MINPP
MINPP1
MINPP2
MINPW
MINPW1
MINPW2
MINTH
MLOUT
MM
Units
-
g
g
~
-
kg ha'1
cm
kg ha'1
kg ha'1
kg ha'1
cm
cm
cm
~
gem'2
day1
-
Type
Scalar
Array
Array
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Alpha-
numeric
Array
Scalar
Description
Month of Crop Maturation
Current pesticide mass in
compartment
Total pesticide mass in each
compartment at previous time
step
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/Dispersion 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 or
Pesticide)
Monthly Lateral Pesticide
Outflow From Each Soil
Compartment For Each
Pesticide
Number of Month Read from
Meteorologic File
Subroutine
READ
ECHO
MOC1
MOC1
INITL
READ
ECHO
INITL
PLGROW
ECHO
READ
PMAIN
PMAIN
OUTPST
OUTHYD
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
PMAIN
INIACC
OUTPST
PMAIN
Common
Block
PEST
MISC
PEST
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I,M,
0
M
M
O
I
I
I
I
M
M
M
M
M
M
M
M
0
11-28
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
MNTHP1
MODFC
MONTH
MOUTP
MOUTP1
MOUTP2
MOUTP3
MOUTP4
MOUTP5
MOUTP6
MOUTP9
MOUTW
MOUTW1
MOUTW2
MOUTW3
MOUTW4
MOUTW5
MOUTW6
MSTART
MSTR
MSTR1
MSTR2
Units
-
—
-
kg ha"1
kg ha'1
kg ha"1
kg ha'1
kg ha"1
kg ha"1
kg ha"1
gem"2
day"1
cm
cm
cm
cm
cm
cm
MTonne
-
cm
cm
cm
Type
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Current Month Plus 1 (Month
+ 1)
Fraction Multiplier
Number of Current Month of
Simulation
Monthly Pesticide Uptake
from Each Compartment
Monthly Pesticide Washoff
Flux
Monthly Pesticide Runoff
Flux
Monthly Pesticide Erosion
Flux
Monthly Foliar Pesticide
Decay Loss
Monthly Pesticide Uptake
Flux from Profile
Monthly Pesticide Decay
Monthly Lateral Pesticide
Outflow From the Entire Soil
Column For Each Pesticide
Monthly Exfiltration from
Each Compartment
Monthly Canopy Evaporation
Monthly Thrufall
Monthly Runoff
Monthly Snowmelt
Monthly Evapotranspiration
Total Monthly Sediment Loss
Flag for Positioning
Meteorologic File
Previous Month Storage of
Water in Each Soil
Compartment
Monthly Canopy Interception
Monthly Accumulation of
Snow
Subroutine
OUTHYD
INITL
SLTEMP
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
INIACC
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
PMAIN
OUTHYD
OUTHYD
OUTHYD
Common
Block
MISC
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I,M,
0
I
M
M
M
M
M
M
M
O
M
M
M
M
M
M
M
M
M
11-29
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
MSTRP
MSTRP1
MY
N
NAPPC
NAPS
NBYR
NCELL
NCOMO/
NCOM1
NCOM2
NCOM2M
NCOMRZ
NCP
NCPDS
Units
kg ha'1
kg ha'1
-
-
-
—
-
-
—
-
—
-
Type
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Storage of Pesticide from
Previous Month in Each Soil
Compartment
Storage of Foliar Pesticide
from Previous Month
Number of Year Read from
Meteorologic File
Loop Counter
Pesticide Application Counter
Number of Pesticide
Applications in the Simulation
Beginning Year of Crop
Growth for Current Crop
(Loop Limit)
Compartment number in
which a point is located
Number of Compartments
from Which ET is Extracted
Year Round
Current Number of
Compartments, 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 Cropping
Period
Number of Cropping Periods
in the Simulation
Subroutine
OUTPST
OUTPST
PMAIN
CANOPY
SLTEMP
PMAINPES
TAP
READ
ECHO
INITL
PMAIN
INITL
PLGROW
MOC1
INITL
INITL
PLGROW
PLGROW
EVPOTR
OUTHYD
SLTEMP
INITL
SLPEST
INITL
SLPEST
OUTHYD
OUTPST
INITL
PLGROW
READ
ECHO
INITL
PLGROW
Common
Block
ACCUM
ACCUM
PEST
PEST
HYDR
HYDR
HYDR
HYDR
CROP
CROP
CROP
I,M,
0
M
M
O
I
0
I
I
I
M
0
I
O
I
I
I
0
I
0
I
I
I
0
I
0
I
I
I
11-30
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
NCROP
NDC
NDCNT
NDYRS
NET
NEW
NEWK
NEXDAY
NEYR
NHORIZ
NLINES
NM1
NOPRT
NPI
NPLOTS
NRZCOM
Units
-
—
g
-
cm3 cm"3
-
-
~
-
-
-
Type
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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 of
a Crop
Net change in mass due to
advection
Number of new points
entering the flow domain
Henry's Constant
Extra Day Added for Leap
Year
Ending Year of Crop Growth
for Current Crop
Total Number of Soil
Horizons
Numbers of Lines for Listing
Initial Pesticides in Profile
(Loop Limit)
Number of Compartments in
Profile Minus 1 (NCOM2 - 1)
Print Flag
Current Number of Moving
Points in Soil Profile
Number of Time Series to be
Output (Maximum of 7)
Current Number of Layers in
Root Zone
Subroutine
INITL
PLGROW
HYDROL
EROSN
READ
ECHO
INITL
PLGROW
INITL
PLGROW
INITL
PLGROW
MOC1
MOC1
KHCORR
PLGROW
INITL
PLGROW
READ
ECHO
INITL
KDCALC
ECHO
TRDIAG
OUTHYD
OUTPST
MOC1
INITL
READ
ECHO
PMAIN
OUTTSR
PLGROW
Common
Block
CROP
CROP
MISC
MISC
HYDR
MISC
I,M,
0
0
I
I
I
0
I
I
I
0
I
M
M
O
o
I
I
I
M
O
I
I
I
11-31
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
NSPACE
NSUM
NSUMM
NUM
NUM
NUMDYS
oc
OKH
ORGM
OSNOW
OUTFLO
OUTPUT
PA
PB
PBAL
PCDEPL
PCMC
PCOUNT
Units
-
-
-
-
—
percent
cm3 cm"3
percent
cm
cm day"1
kg ha"1
kg ha"1
gem2
Fraction
~
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
Number of furrow stations for
finite difference
Cumulative Sum of
Compartment Numbers
Termination Loop Index for
Summary Output
Number of Soil Compartment
Initial number of moving
points per compartment
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
Lateral Outflow of Water
from Each Soil Compartment
Output Array for Time Series
Daily Foliar Pesticide
Application
Pesticide Balance
Current Pesticide Balance
Error
Fraction of available water
capacity where irrigation is
triggered (range 0.0 - 0.9)
Partition Coefficient Model
Flag (1= Karick hoff, 2=
Kenega, 3= Chiou)
Number of points crossing an
interface with Ratio greater
than 2.
Subroutine
FURROW
IRRIG
EVPOTR
OUTHYD
OUTPST
KHCORR
MOC1
INITL
SLTEMP
SLTEMP
INITL
MAIN
SLPSTO
SLPST1
INITL
PMAIN
HYDROL
MASBAL
OUTSTR
OUTTSR
OUTPST
OUTPST
MASBAL
OUTPST
IRRIG
IRREAD
READ
KDCALC
INITL
MOC
Common
Block
IRGT
HYDR
PEST
PEST
HYDR
PEST
IRGT
MISC
HYDR
I,M,
0
M
I
I
I
M
I
O
I
I
I
0
I
I
0
O
I
O
0
I
M
11-32
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
PESTR
PET
PETP
PEVP
PFAC
PI
PLDKRT
PLNAME
PLNTAP
PLVKRT
PNBRN
PRDPTH
PRECIP
PTEMP
Units
gem'2
cm
cm
cm
~
day1
~
gem2
day1
—
cm
cm
gem'3
Type
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Alpha-
numeric
Scalar
Array
Array
Scalar
Scalar
Array
Description
Total Pesticide in Each Soil
Compartment
Total Daily Potential
Evapotranspiration
Running Total of Available
Evapotranspiration
Pan Evaporation
Pan Factor for ET
3.1415926
Foliar Pesticide Decay Rate
Time Series Output Identifier
(Options Listed in User's
Guide)
Pesticide Applied to Crop
Canopy
Foliage Pesticide
Volatilization Rate
Output Array for Time Series
Depth Used in the Extraction
of Pesticide Flux in Runoff
Precipitation
Temporary storage of total
pesticide mass per cc water
after advection step
Subroutine
READ
ECHO
INITL
PMAIN
PESTAP
MASBAL
OUTPST
EVPOTR
EVPOTR
PMAIN
EVPOTR
READ
ECHO
EVPOTR
CANOPY
READ
ECHO
PLPEST
READ
OUTTSR
PESTAP
OUTPST
OUTTSR
ECHO
PLPEST
READ
OUTTSR
SLPSTO
SLPST1
PMAIN
HYDROL
EROSN
MASBAL
OUTHYD
OUTTSR
MOC1
Common
Block
PEST
MET
MET
PEST
MISC
PEST
PEST
MET
I,M,
0
0
I
I
I
I
I
I
0
I
0
I
I
o
I
I
0
I
0
I
I
I
I
o
M
M
O
I
I
I
I
I
M
11-33
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
PTITLE
PVFLUX
PWIND
Q
QCl
QEVF
QGHF
QLW1
QLW2
QO
QQP
QS
QSWR
RAIN
RATIO
RETEAP
RF
RINUM
RMULT
Units
~
gem'2
day"1
m day"1
m3
cal cm"2
day"1 "K"1
cal cm"2
day"1
cal cm"1
day"1 "K"1
cal cm"2
day"1 °K"4
cal cm"2
day"1 "K"1
mVs
m6 sec"1
mVs
cal cm"2
day"1
cm
-
cm/hr
kg ha"1
Type
Alpha-
numeric
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Description
Comment Line to Input
Information About Pesticide
Parameters
Daily Soil Pesticide
Volatilization Flux
Wind Velocity
Runoff Volume
Sensible Heat Flux Term
Evaporation Heat Flux
Soil Heat Flux Term
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
Monthly Precipitation
Accumulated for Output Table
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
Subroutine
READ
ECHO
MASBAL
OUTPST
OUTRPT
OUTTSR
SLPSTO
SLPST1
MAIN
EROSN
SLTEMP
SLTEMP
SLTEMP
SLTEMP
SLTEMP
FURROW
IRREAD
EROSN
FURROW
SLTEMP
OUTHYD
INITL
MOC
IRRIG
IRREAD
OUTPST
CANOPY
OUTTSR
Common
Block
MISC
PEST
IRGT
IRGT
TABLE
HYDR
IRGT
I,M,
0
0
I
I
I
I
I
o
0
o
M
M
M
M
M
I
O
M
M
0
M
I
0
0
11-34
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
RMULT1
RMULT3
RNSUM
RNUM
RODPTH
ROFLUX
ROPST
RTR
RUNOF
RVEL
RZD
RZFLUX
RZI
SA
SAIM
SAND
Units
-
-
-
ha cm"2
-
gem'2
day1
gem"2
day1
cm
~
cm
gem'2
-
kg ha"1
-
percent
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
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
Number of Soil Compartments
that Affect Runoff
Runoff Flux of Pesticide From
Land Surface
Total Runoff Pesticide Load
Used for Output Table
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 Compartment
Active Root Zone Flag
Application of Pesticide to the
Soil
Integrated Momentum
Stability Parameter
Percent Sand in Each Soil
Horizon
Subroutine
READ
READ
EVPOTR
EROSN
HYDROL
SLPEST
MASBAL
OUTHYD
OUTTSR
OUTPST
PSTLNK
SLPSTO
SLPST1
HYDROL
PMAIN
EROSN
SLPEST
MASBAL
OUTHYD
OUTTSR
MOC1
INITL
OUTHYD
SLPEST
OUTTSR
INITL
PLGROW
OUTPST
CANOPY
SLTEMP
Common
Block
PEST
TABLE
PEST
HYDR
PEST
MISC
HYDR
I,M,
0
0
I
I
I
0
0
I
I
o
I
I
I
I
I
I
M
O
I
o
I
0
I
11-35
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
SD
SDKFLX
SEDI
SEDL
SF
SFAC
SIGMAO
SIGMA1
SIGMA2
SJDAY
SLKGHA
SMDEF
SMELT
SNOW
SNOWFL
Units
kg ha'1
gem'2
day'1
kg ha1
MTonne
day'1
Fraction
cm "C1
cal cm"1
°C day1
~
kg ha1
day"1
cm
cm
cm
cm
Type
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Sum of the Decay Fluxes
From All Compartments in
Soil Profile
Sum of the Decay fluxes From
All Compartments in Soil
Profile
Monthly Erosion
Accumulated For Output
Table
Erosion Sediment Loss
Slope of furrow channel
(vertical/horizontal)
Snowmelt Factor
Summation Variable Used to
Calculate K Factor in the Soil
Thermal Conductivity
Equation
Total Numerator Value in the
Soil Thermal Conductivity
Equation
Total Denominator Value in
the Soil Thermal Conductivity
Equation
Starting Day of Simulation
Erosion Sediment Loss
Soil moisture deficit requiring
irrigation
Current Daily Snowmelt
Depth
Snowpack Accumulation
Depth
Current Snowfall Depth
Subroutine
OUTPST
SLPEST
OUTPST
OUTHYD
PMAIN
EROSN
OUTHYD
FURROW
IRREAD
READ
ECHO
HYDROL
SLTEMP
SLTEMP
SLTEMP
INITL
EROSN
IRRIG
HYDROL
EROSN
OUTHYD
SLTEMP
HYDROL
MASBAL
OUTHYD
OUTTSR
Common
Block
PEST
TABLE
HYDR
IRGT
MET
IRGT
HYDR
HYDR
MET
I,M,
0
0
I
0
0
M
0
I
0
o
I
I
M
M
M
O
O
I
0
I
I
I
11-36
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
SOILAP
SOL
SOLRAD
SPESTR
SPT
SPTEMP
SRC
SRCFLX
STARTYR
STEMP
STEP1
STEP2
STEPS
STITLE
Units
gem'2
mole
fraction
mgl'1
umoles T1
cal cm'2
day1
gem'3
°C
gem3
gem3
day1
gem'2
day"1
~
°C
~
~
~
Type
Array
Scalar
Scalar
Array
Array
Array
Array
Array
Scalar
Array
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
Description
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
Starting Year of Simulation
Used to Loop for Output
Table
Soil Compartment
Temperature
Time Step of Water Output
Summary
Time Step of Pesticide Output
Summary
Time Step of Concentration
Profile Output Summary
Comment Line to Input
Information About Soil
Parameters
Subroutine
PESTAP
PMAIN
OUTPST
OUTTSR
READ
KDCALC
READ
SLTEMP
INITL
PMAIN
PESTAP
SLPEST
SLTEMP
MAIN
MOC1
SLPST1
INITL
PSTLNK
SLPSTO
SLPST1
SLPSTO
SLPST1
OUTPST
INITEM
OUTPST
OUTHYD
KHCORR
READ
ECHO
OUTHYD
READ
ECHO
OUTPST
READ
ECHO
OUTCNC
READ
ECHO
Common
Block
PEST
MET
PEST
MET
PEST
PEST
PEST
TABLE
MISC
MISC
MISC
MISC
I,M,
0
0
I
I
I
0
I
0
I
0
I
I
I
0
I
M
o
I
I
o
o
I
o
I
I
I
0
I
I
0
I
I
0
I
I
o
I
11-37
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
STK
STTDET
SU
SUMC
SUMXP
SUPFLX
SURF
sv
sw
T
TA
TAPP
TB
TC
TCNC
TCORR
TEMP
Units
°K
cm day"1
kg ha'1
g
kg ha1
gem'2
day'1
cm
kg ha'1
day'1
cm
day"1
gem2
day'1
day"1
gem3
mole
cal'1
°C
Type
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Array
Scalar
Array
Array
Array
Array
Array
Scalar
Scalar
Description
Soil Surface Temperature in
Kelvin Scale
Daily Evaporation from the
Top 5cm of Soil
Sum of the Uptake Fluxes
From All Soil Compartments
Sum of mass in a
compartment
Sum of Soluble Pesticide in
Profile
Sum of the Uptake Fluxes
From All Soil Compartments
Monthly Surface Runoff
Accumulated for Output Table
Daily Soil Pesticide
Volatilization Flux
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
Subroutine
SLTEMP
SLTEMP
EVPOTR
OUTPST
MOC1
OUTPST
SLPEST
OUTPST
OUTTSR
OUTHYD
OUTPST
INITL
HYDROL
EVPOTR
HYDR1
HYDR2
SLPEST
OUTTSR
INITL
SLTEMP
READ
ECHO
INITL
PESTAP
SLTEMP
SLTEMP
OUTPST
KHCORR
SLTEMP
Common
Block
MET
PEST
TABLE
HYDR
PEST
MET
I,M,
0
M
I
0
M
0
I
I
0
o
o
I
I
I
I
I
I
M
0
I
I
I
M
M
O
M
I
11-38
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
TEMPK
TEND
TERM
TERM1
TERM2
EF
IFLO
FRAC
THEIR
THCOND
THEFC
THETAS
THETH
THETN
Units
K
day
-
-
-
°C
cm
~
cm3 cm"3
cal cm"1
day"1 °C-'
cm3 cm"3
cm3 cm"3
cm3 cm"3
cm3 cm"3
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Array
Array
Array
Array
Scalar
Array
Description
Air Temperature in Kelvin
Scale
Time required for point to
move to compartment
boundary
Exponential Pesticide
Washoff Term
Exponential Pesticide Decay
Term
Product of Washoff and
Decay Terms
Vector of Previous Time Step
Soil Compartment
Temperature
Monthly Total Runoff
Accumulated for Output Table
Total Fraction of
Compartments 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
Subroutine
SLTEMP
MOC1
PLPEST
PLPEST
PLPEST
SLTEMP
OUTHYD
EVPOTR
SLPSTO
SLPST1
SLTEMP
SLTEMP
SLTEMP
INITL
HYDROL
HYDR1
HYDR2
PMAIN
SLPEST
MASBAL
OUTHYD
OUTPST
OUTTSR
OUTCNC
Common
Block
TABLE
HYDR
HYDR
HYDR
HYDR
I,M,
0
M
M
M
0
O
M
I
I
O
I
O
0
I
I
I
I
I
I
I
11-39
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
THETO
THEWP
THFLAG
THKLY1
THKNS
THRUFL
THZERO
TITLE
TLEFT
TMPK
TNDGS
TOL
TOP
TOT
TOTAL
TOTR
TR
Units
cm3 cm"3
cm3 cm"3
cm
cm
cm
cal cm"1
day"1 "C"1
-
day
K
day
~
day m"1
mg kg"1
day m"1
hr
Type
Array
Array
Scalar
Scalar
Array
Scalar
Array
Alpha-
numeric
Scalar
Scalar
Array
Scalar
Array
Scalar
Array
Scalar
Scalar
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 (O=
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
Fraction Compartment Check
Location of top compartment
in horizon where points are
consolidated
Canopy Resistance
Total Pesticide in Each
Compartment
Total Canopy Resistance
Duration of Average Erosive
Storm Event
Subroutine
SLTEMP
SLTEMP
READ
ECHO
PMAIN
SLTEMP
READ
ECHO
INITL
HYDROL
HYDROL
OUTHYD
OUTTSR
SLTEMP
READ
ECHO
MOC1
KHCORR
INITL
PLGROW
INITL
INITL
MOC
CANOPY
OUTCNC
CANOPY
READ
ECHO
EROSN
Common
Block
HYDR
HYDR
MISC
MISC
MET
MISC
CROP
HYDR
MET
I,M,
0
I
I
0
I
I
0
I
I
I
0
I
I
M
o
I
M
M
O
I
M
0
0
0
I
I
11-40
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
TRFLUX
TS
TSRCFX
TSW
TTHKNS
TTRFLX
TWLVL
TWP
U
UBT
UPF
UPFLUX
UPTKF
URH
USLEC
USLEK
Units
gem'2
day"1
cm3 cm"3
gem"2
day"1
cm
cm
gem"2
day"1
cm cm"1
cm
—
°C
kg ha"1
gem2
~
m day"1
~
Type
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Description
Transformation Flux of
Pesticide from Each Soil
Compartment
Previous Soil Compartment
Water Content Minus
Evapotranspiration
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 Computational Check)
Sum of the Transformation
Flux from All Compartments
in Soil Profile
Fraction of Water to Soil
Depth for Runoff Calculation
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
Subroutine
SLPSTO
SLPST1
OUTPST
HYDR2
SLPSTO
SLPST1
OUTPST
EVPOTR
INITL
SLPSTO
SLPST1
OUTPST
HYDROL
EVPOTR
TRDIAG
SLTEMP
OUTPST
SLPEST
OUTPST
READ
ECHO
PLGROW
CANOPY
MAIN
READ
ECHO
EROSN
READ
ECHO
EROSN
Common
Block
PEST
PEST
PEST
PEST
PEST
HYDR
HYDR
I,M,
0
0
I
I
0
0
I
o
o
I
M
0
I
0
I
I
I
0
o
I
I
o
I
I
11-41
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
USLELS
USLEP
USTAR
UTEMP
UWIND
VAPLMD
VAR1
VAR2
VAR2D
VAR2M
VAR2RZ
VAR2Y
VAR3
VEL
VHTCAP
VLFLAG
Units
~
~
m day"1
°C
m day"1
cal cm"1
day"1 °Cl
kg ha"1
kg ha"1
cm
cm
kg ha"1
cm
kg ha"1
cm day"1
cal cm"3
oC-l
Type
Scalar
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Description
Universal Soil Loss Equation
'Ls' Factor
Universal Soil Loss Equation
'P' Factor
Friction Velocity
Air Temperature
Wind Velocity
Thermal Conductivity of
Vapor in the Soil Pores
Daily Advection/Dispersion
Flux of Pesticide Into a
Compartment
Daily Advection/Dispersion
Flux of Pesticide Out of a
Compartment
Water Storage in a Single
Compartment for thePrevious
Day
Water Storage in a Single
Compartment for the Previous
Month
Daily Advection/Dispersion
Flux of Pesticide Out of the
Root Zone
Water Storage in a Single
Compartment for the Previous
Year
Pesticide Storage in a Single
Compartment for the Previous
Day
Water Velocity in Each Soil
Compartment
Heat Capacity Per Unit
Volume of Soil
Advection flux flag (0 = all
soil water velocities are zero,
1 = soil water velocity is
nonzero)
Subroutine
READ
ECHO
EROSN
READ
ECHO
EROSN
CANOPY
CANOPY
CANOPY
SLTEMP
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTPST
OUTHYD
OUTPST
HYDR1
HYDR2
SLPEST
SLTEMP
HYDR1
PMAIN
HYDR2
Common
Block
HYDR
HYDR
HYDR
HYDR
I,M,
0
0
I
I
0
I
I
0
I
I
0
I
I
M
I
11-42
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
VOLCOR
WBAL
WEIGHT
WF
WFMAX
WIND
WLVL
WOFLUX
WP
WPV
WTERM
X
XFRAC
XL
Units
cm
k_?
gm"
kg ha"1
kgm"2
cm sec"1
cm
gem"2
day"1
cm
—
gem"2
gem"3
Fraction
m
Type
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Array
Array
Scalar
Array
Scalar
Scalar
Description
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
Regression Coefficients for
Prediction of Wilting Point
Soil Water Content
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
Subroutine
SLTEMP
MASBAL
OUTHYD
PLGROW
PESTAP
OUTPST
READ
ECHO
INITL
READ
SLTEMP
MAIN
HYDROL
SLPEST
OUTPST
EVPOTR
THCALC
PLPEST
SLPEST
TRDIAG
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC
PMAIN
IRRIG
IRREAD
IRRIG
FURROW
IRREAD
Common
Block
HYDR
CROP
CROP
MET
PEST
HYDR
PEST
PEST
PEST
IRGT
IRGT
I,M,
0
0
I
0
I
o
I
I
o
I
I
0
I
o
o
I
0
I
I
I
I
I
I
I
o
I
I
0
11-43
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
XP
XVOL
Y
YDOUT
YEAR
YEOUTW
YINPP
YINPP1
YINPP2
YINPW
YINPW1
YINPW2
YLOUT
YOUTP
YOUTP1
YOUTP2
YOUTP3
YOUTP4
YOUTP5
Units
gem'3
fraction
-
kg ha"1
-
cm
kg ha'1
kg ha'1
kg ha'1
cm
cm
cm
gem2
kg ha'1
kg ha"1
kg ha"1
kg ha"1
kg ha"1
kg ha"1
Type
Array
Array
Array
Array
Alpha-
numeric
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Total Pesticide in Each Soil
Compartment
Volume Fraction of Soil
Constituent
Intermediate Matrix Solution
Array
Annual Pesticide Decay From
Each Soil Compartment
Flag for Annual Water and
Pesticide Summary Output
Annual Evapotranspiration
From Each Soil Compartment
Annual Advective/Dispersive
Flux Into Each Soil
Compartment
Annual Pesticide Applied to
Foliage
Annual Pesticide Applied to
Soil
Annual Infiltration Into Each
Soil Compartment
Annual Precipitation
Annual Snowfall
Annual Lateral Pesticide
Outflow From Each Soil
Compartment For Each
Pesticide
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
Subroutine
MASBAL
SLTEMP
TRDIAG
OUTPST
PMAIN
OUTHYD
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
INIACC
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
Common
Block
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I,M,
0
M
M
M
M
M
M
M
M
0
0
M
M
M
M
M
M
11-44
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
YOUTP6
YOUTP9
YOUTW
YOUTW1
YOUTW2
YOUTW3
YOUTW4
YOUTW5
YOUTW6
YSTR
YSTR1
YSTR2
YSTRP
YSTRP1
Z
Z
ZC
ZCH
ZCTOT
ZIN
zo
Units
kg ha'1
gem'2
cm
cm
cm
cm
cm
cm
MTonne
cm
cm
cm
kg ha'1
kg ha'1
Fraction
-
-
m
~
-
m
Type
Scalar
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Scalar
Scalar
Array
Scalar
Description
Total Annual Pesticide Soil
Decay Flux
Annual Lateral Pesticide
Outflow From the Entire Soil
Column for Each Pesticide
Annual Exfiltration From
Compartment
Annual Canopy Evaporation
Annual Thrufall
Annual Runoff
Annual Snowmelt
Total Annual Evapotrans piration
Total Annual Sediment Loss
Previous Year Storage of
Water in Each Soil
Compartment
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
Subroutine
OUTPST
INIACC
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
FURROW
IRREAD
MOC1
INITL
MOC1
INITL
CANOPY
MAIN
SLTEMP
MOC
MOC1
CANOPY
SLTEMP
Common
Block
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
IRGT
HYDR
HYDR
I,M,
0
M
0
0
M
M
M
M
M
M
M
M
M
M
M
M
I
0
M
M
I
O
M
M
M
0
M
11-45
-------
Table 11.3 PRZM Program Variables, Units, Location, and Variable Designation
Variable
ZRH
ZTOT
ZWIND
Units
m
-
m
Type
Scalar
Scalar
Scalar
Description
Reference Height
Location of consolidated
Points
Distance Above the Ground
Where Wind Speed was
Measured
Subroutine
CANOPY
MAIN
MOC
READ
MAIN
SLTEMP
Common
Block
I,M,
0
I
O
M
O
0
I
11-46
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
AGKPRN
AGPLTN
ALPNFG
AMIMB/
NCFX8
AMMINF
AMMON
AMNIT/
NCFX7
AMUPA/
NCFX21
AMUPB/
NCFX23
AMVOFG
AMVOL/
NCFX18
ANUFM
ANUTF
BGNPRF
BNPRFM
Units
day"1
kg/ha
-
kg/ha
kg/ha
g/cm2
kg/ha
kg/ha
kg/ha
—
kg/ha
—
—
—
Type
Scalar
Scalar
Scalar
Array
Array
Scalar
Array
Array
Array
Scalar
Array
Array
Array
Scalar
Array
Description
Above-ground plant
return rate
Above-ground plant N
storage
Flag for above-ground
and litter simulation
Ammonia
immobilization flux
from each compartment
Inflow of septic
ammonia in each
compartment
Daily inflow of septic
ammonia
Ammonia nitrification
flux from each
compartment
Above-ground ammonia
uptake flux from each
compartment
Below-ground ammonia
uptake flux from each
compartment
Flag for ammonia
volatilization simulation
Ammonia volatilization
flux from each
compartment
Monthly above-ground
uptake fraction for each
compartment
Above-ground plant
uptake fraction for each
compartment
Plant return refractory
fraction
Monthly below-ground
plant return refractory
fraction
Subroutine
PRZNRD
NITR
PRZNRD
NITR
PRZNRD
NITR
NITR
SEPTIN
NITBAL
GETMET
SEPTIN
NITR
NITR
NITR
PRZNRD
NITR
NITRXN
NITRXN
NITBAL
PRZNRD
NITR
PRZNRD
NITR
NITRXN
PRZNRD
NITR
NITRXN
PRZNRD
NITR
Common
Block
CNITR
CNITR
CNITR
CNITR
CSPTIC
CSPTIC
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
I,M,O
O
M
0
M
0
I
M
O
I
O
I
M
M
M
0
I
M
I
O
I
O
M
I
O
M
I
0
I
11-47
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
CNIT
CRPDAT
CRPDAY
CRPFRC
DENIF/
NCFX6
DNTHRS
FIXNFG
FORAFG
GNPM
INFLOW
ITMAXA
KPLN
KPLNM
Units
~
~
—
kg/ha
~
~
cm
~
day1
day'1
Type
Array
Array
Array
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Description
Concentration of
nitrogen constituents for
each compartment
Plant and harvest dates
for each crop
Number of days each
month for each crop
Fraction of monthly
plant uptake per crop
Denitrification from
each compartment
Fraction of water
saturation when
denitrification begins for
each compartment
Flag for nitrogen
fixation simulation
Ammonia
adsorption/desorption
calculation method
General nitrogen
parameters
(nitrate/ammonium
uptake fractions,temp
coeffs., max solubility of
ammonium)
Daily inflow of septic
water
Max iterations for
Freundlich solution
Plant uptake rate per
compartment
Monthly plant uptake
rate per compartment
Subroutine
NITMOV
NITR
CRDYFR
YUPINI
YUPTGT
CRDYFR
YUPINI
YUPTGT
CRDYFR
YUPINI
YUPTGT
NITR
NITBAL
PRZNRD
NITR
PRZNRD
NITR
NITRXN
PRZNRD
NITRXN
PRZNRD
NITRXN
GETMET
SEPTIN
PRZNRD
NITRXN
SV
PRZNRD
NITR
NITRXN
PRZNRD
NITR
NITRXN
Common
Block
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CSPTIC
CNITR
CNITR
CNITR
I,M,O
M
0
I
I
I
0
I
I
0
I
I
M
I
o
I
o
I
o
I
o
I
o
I
o
I
I
o
M
I
0
M
I
11-48
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
KRBNM
KRANM
KRETAN
KRETBN
KRLNM
KVOL
LINF
LINPRF
LITTRN
LNPRFM
NAPFRC
NBUFF
NCI
NCRP
NDFC
NECNT
Units
day1
day'1
day1
day1
day"1
day1
cm
-
kg/ha
-
~
—
—
-
kg/ha
Type
Array
Array
Array
Array
Array
Array
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Array
Array
Description
Monthly below-ground
plant return rate per
compartment
Monthly above-ground
plant return rate
Litter return rate for
compartments in first
horizon
Below-ground plant
return rate for each
compartment
Monthly litter return rate
Ammonia volatilization
rates for each
compartment
Inflow of septic water in
each compartment
Litter return refractory
fraction
Litter N storage
Monthly litter return
refractory fraction
Fraction of organic N
application that becomes
refractory
Data buffer for
atmospheric deposition
time-series values
Number of
compartments in first
horizon
Number of crop periods
each year
Yield-based plant uptake
deficit for each
compartment
Counter for error
messages
Subroutine
PRZNRD
NITR
PRZNRD
NITR
PRZNRD
NITR
PRZNRD
NITR
NITRXN
PRZNRD
NITR
PRZNRD
NITRXN
SEPTIN
HYDR1
HYDR2
PRZNRD
NITR
PRZNRD
NITR
PRZNRD
NITR
PRZNRD
NITRAP
GETMET
PRZNRD
NITR
NITR
NITRXN
YUPTGT
NITRXN
SV
Common
Block
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CSPTIC
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
I,M,O
0
I
0
I
0
M
0
M
I
O
I
O
I
0
I
I
0
M
0
M
O
I
O
I
O
0
M
0
M
M
M
11-49
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
NFIXFX/
NCFX12
NIACNM
NIADDR/
NCFX10
NIADWT/
NCFX11
NIADFG
NIAFXM
NUMB/
NCFX17
NIT
NITINF
NITR
NIUPA/
NCFX20
NIUPB/
NCFX22
NMXRAT
NPM
NRXF
Units
kg/ha
kg/ha
kg/ha
kg/ha
-
kg/ha
kg/ha
kg/ha
kg/ha
/ 2
g/cnr
kg/ha
kg/ha
-
day1
kg/ha
Type
Array
Array
Array
Array
Array
Array
Array
Array
Array
Scalar
Array
Array
Scalar
Array
Array
Description
Nitrogen fixation flux
for each compartment
Monthly dry
atmospheric deposition
flux values
Dry atmospheric
deposition fluxes
Wet atmospheric
deposition fluxes
Atmospheric deposition
flags
Monthly wet
atmospheric deposition
flux values
Nitrate immobilization
flux from each
compartment
Storage of nitrogen
constituents for each
compartment
Inflow of septic nitrate
in each compartment
Daily inflow of septic
nitrate
Above-ground nitrate
uptake flux from each
compartment
Below-ground nitrate
uptake flux from each
compartment
Ratio of max uptake to
target uptake
First order rates for each
compartment,
ammonium absorption
parameters
Daily reaction fluxes for
each compartment
Subroutine
NITRXN
NITBAL
PRZNRD
GETMET
GETMET
NITR
NITBAL
GETMET
NITR
NITBAL
PRZNRD
GETMET
PRZNRD
GETMET
NITR
PRZNRD
NITR
NITRXN
SEPTIN
NITRAP
SEPTIN
NITBAL
GETMET
SEPTIN
NITR
NITR
PRZNRD
NITRXN
PRZNRD
NITRXN
NITRXN
Common
Block
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CSPTIC
CSPTIC
CNITR
CNITR
CNITR
CNITR
CNITR
I,M,O
M
I
0
I
0
I
I
0
I
I
o
I
o
I
M
0
M
M
M
M
O
I
0
I
M
M
O
I
O
I
M
11-50
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
NUPTFG
NUPTFM
NUPTG
NUPTGT
NUPTM
NWCNT
ORGINF
ORGN
ORGRFC
ORNMN/
NCFX9
ORNPM
OSAMS/
NCFX3
OSNO3/
NCFX5
OSSLN/
NCFX14
OSSRN/
NCFX16
Units
~
~
day1
~
~
-
kg/ha
g/cnf
-
kg/ha
-
kg/ha
kg/ha
kg/ha
kg/ha
Type
Scalar
Array
Array
Scalar
Array
Array
Array
Scalar
Scalar
Array
Array
Array
Array
Array
Array
Description
Flag for plant uptake
method
Monthly fraction of
annual yield-based
uptake target
Yield-based plant uptake
target for each
compartment
Annual yield-based plant
uptake target
Fraction of monthly
yield-based uptake target
from each compartment
Counter for warning
messages
Inflow of septic organic
N in each compartment
Daily inflow of septic
organic N
Fraction of septic
organic N that becomes
refractory
Mineralization flux from
each compartment
Organic N parameters
for each compartment
Solution ammonia lateral
outflow from each
compartment
Nitrate lateral outflow
from each compartment
Labile organic N lateral
outflow from each
compartment
Refractory organic N
lateral outflow from
each compartment
Subroutine
PRZNRD
NITR
NITRXN
PRZNRD
YUPINI
YUPTGT
YUPTGT
NITRXN
PRZNRD
YUPINI
YUPTGT
PRZNRD
YUPINI
YUPTGT
NITRXN
OMSG
SEPTIN
NITBAL
GETMET
SEPTIN
PRZNRDSE
PTIN
NITR
PRZNRD
NITRXN
NITMOV
NITBAL
NITMOV
NITBAL
NITMOV
NITR
NITBAL
NITMOV
NITR
NITBAL
Common
Block
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CSPTIC
CSPTIC
CSPTIC
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
I,M,O
0
I
I
0
I
I
0
I
0
I
I
0
I
I
M
o
I
0
I
0
I
M
O
I
M
I
M
I
M
M
I
M
M
I
11-51
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
PNUTG
PSAMS/
NCFX2
PSNO3/
NCFX4
PSSLN/
NCFX13
PSSRN/
NCFX15
REFRON/
NCFX19
RETAGN/
NCFX24
RTLLN/
NCFX25
RTRLN/
NCFX26
RTLBN/
NCFX27
RTRBN/
NCFX28
SBUFF
SEDN/
NCFX1
SEPDSN
Units
day1
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
-
kg/ha
Type
Array
Array
Array
Array
Array
Array
Array
Array
Array
Array
Array
Array
Array
Array
Description
Yield-based plant uptake
target from each
compartment for end of
previous month
Solution ammonia
leaching output from
each compartment
Nitrate leaching output
from each compartment
Labile organic N
leaching output from
each compartment
Refractory organic N
leaching output from
each compartment
Labile to refractory
conversion flux for each
compartment
Above-ground plant
return to litter flux
Litter return to labile
organic N in first
horizon's compartments
Litter return to refractory
organic N in first
horizon's compartments
Below-ground plant
return to labile organic
N for each compartment
Below-ground plant
return to refractory
organic N for each
compartment
Data buffer for septic
effluent time-series
values
Sediment and runoff loss
fluxes
Data-set numbers for
septic effluent time-
series values
Subroutine
YUPINI
YUPTGT
NITMOV
NITBAL
NITMOV
NITBAL
NITMOV
NITBAL
NITMOV
NITBAL
NITR
NITR
NITR
NITR
NITR
NITR
GETMET
NITMOV
PRZNRD
GETMET
Common
Block
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CSPTIC
CNITR
CSPTIC
I,M,O
0
M
M
I
M
I
M
I
M
I
M
M
M
M
M
M
0
M
O
I
11-52
-------
Table 11.4 PRZM Nitrogen Simulation Variables, Units, Location, and Variable Designations
Variable
SEPHZN
THVOL
TNIT
TOTNIT
TONITO
TRFVOL
VNPRFG
VNUTFG
Units
-
~
kg/ha
kg/ha
kg/ha
°C
-
-
Type
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Horizon number for
septic effluent
Temperature correction
coeff for ammonia
volatilization
Total storage of nitrogen
constituents in soil
profile
Total nitrogen storage in
soil profile
Total nitrogen storage in
soil profile for previous
day
Reference temperature
for ammonia
volatilization
Flag for time-varying
plant return
Flag for time-varying
plant uptake
Subroutine
PRZNRD
SEPTIN
PRZNRD
NITRXN
NITR
PRZNRD
NITR
NITBAL
PRZNRD
NITBAL
PRZNRD
NITRXN
PRZNRD
NITR
PRZNRD
NITR
Common
Block
CSPTIC
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
CNITR
I,M,O
0
I
0
I
M
0
M
I
O
M
O
I
O
I
0
I
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
^
ASTORN
B
BALSTO
BSTOR1
BSTORN
Units
-
~
-
~
~
Type
Array
Scalar
Array
Array
Scalar
Scalar
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
Subroutine
ASSEMF
ASSEMT
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
MAIN
BALCHK
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
BALCHK
Common
Block
ASOLV
WORKA
ASOLV
WORKA
WORKA
I,M,O
M
M
M
M
0
M
M
11-53
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
C
CORD
CSTOR1
CTRFAC
D
DETAND
DIS
DLAMDA
DLAMND
DPKND
DPKRAV
DSTOR1
DSTORN
DTEPS
DTMARK
DX
Units
-
L
~
~
-
~
L
M/L3
1/t
1/t
L/t
L2
~
~
-
~
Type
Array
Array
Scalar
Array
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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
Nodal Storage Factor
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
Subroutine
ASSEMF
ASSEMT
MAIN
VSWCOM
ASSEMF
ASSEMT
BALCHK
CONVER
DSWFUN
MAIN
ASSEMF
ASSEMT
ASSEMF
MAIN
ASSEMF
BALCHK
VARCAL
VSWCOM
MAIN
ASSEMT
VARCAL
MAIN
ASSEMT
BALCHK
VARCAL
ASSEMF
ASSEMF
PKWFUN
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
BALCHK
MAIN
MAIN
MAIN
Common
Block
ASOLV
CORD
WORKA
WORKN
ASOLV
WELEM
BSOLV
CONTR
WELEM
WORKA
WORKA
I,M,O
M
I
M
M
M
M
M
0
M
I
M
M
M
M
M
M
M
11-54
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
EL
ETAND
FLX1
FLXN
FVAL
HAVE
HCAP
HCRIT
HDOBS
HINV
HTOL
HVTM
Units
L
~
L3/t
L3/t
L
L
L
LML'3
LML'3
L
L
Type
Scalar
Array
Scalar
Scalar
Array
Scalar
Array
Scalar
Array
Scalar
Scalar
Array
Description
Elemental Values for
Finite-Element Element
Length Formulation
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
Value of function
corresponding to Time
Values (TMHV)
Subroutine
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
ASSEMF
ASSEMT
BALCHK
MAIN
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMT
HFINTP
SWFUN
CONVER
DSWFUN
ASSEMF
SWFUN
DSWFUN
MAIN
ASSEMF
INTERP
ASSEMF
SWFUN
DSWFUN
MAIN
MAIN
MAIN
ASSEMF
VARCAL
DSWFUN
MAIN
HFINTP
Common
Block
WELEM
CONTR
CONTR
MDATA
SWHDA
DAOBS
CONTR
I,M,O
M
M
M
M
M
M
M
I
M
O
I
I
M
11-55
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
IBTND1
IBTNDN
ICONVG
IHORIZ
IKALL
ILAYR
IMAT
IMATL
IMBAL
IMOD
IMODL
INEWT
Units
~
~
~
~
~
~
~
—
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Last Node Boundary
Condition Code (1= 1st
type, 0=3 rd type)
Last Node Boundary
Condition Code (1= 1st
Type, 0=3rd type)
Convergence Flag
(l=Converged, 0=Not
Converged)
Simulation Orientation
Indicator (0= Vertical,
l=Horizontal)
Time Stepping Scheme
Indicator (l=Backward,
0=Central)
Current Layer Number
Counter Used in
Looping with Respect to
Materials
Material Identifying
Number for Current
Layer
Mass Balance
Computation Indicating
Parameter
For Modified Newton
Raphson Solution
Procedure
Simulation Identifier
(Flow or Transport)
Nonlinear Iterative
Procedure Flag
(l=Newton, 0=Picard)
Subroutine
MAIN
ASSEMF
ASSEMT
VARCAL
ASSEMF
MAIN
ASSEMT
VARCAL
MAIN
VARCAL
MAIN
MAIN
MAIN
MAIN
ASSEMF
ASSEMT
INTERP
PKWFUN
SWFUN
CONVER
DSWFUN
MAIN
MAIN
MAIN
DSWFUN
MAIN
BALCHK
VARCAL
MAIN
ASSEMF
VARCAL
Common
Block
CONTR
CONTR1
CONTR
CONTR
I,M,O
I
I
I
I
I
I
I
I
I
I
I
I
11-56
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
INOCTS
INPFL
INTSPC
IOBSND
IPRCHK
IPROP
IREP
IREPMX
IRESOL
IRLTYP
ITCND1
ITCNDN
Units
-
~
-
~
-
~
Type
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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
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)
Subroutine
MAIN
VARCAL
MAIN
MAIN
WORKA
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
CONVER
MAIN
ASSEMF
ASSEMT
MAIN
VARCAL
MAIN
VARCAL
MAIN
VARCAL
ASSEMF
MAIN
HFINTP
MAIN
HFINTP
Common
Block
MDATA
INTERP
I,M,O
I
I
I
I
I
I
M
I
I
I
I
I
11-57
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
ITER
ITMARK
ITMFC
ITMGEN
ITRANS
ITSGN
ITSTH
IVSTED
KPROP
MARK
MM
MXMAT
Units
-
-
-
~
-
~
-
-
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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)
Time Step Generation
Indicator
Identifies Location of
Previous Time Value of
Time Graph
Steady-State Velocity
Field Indicator
Flag for Perm-Saturation
and Pressure Head-
Saturation Curves
(l=Functional,
0=Tabulated)
Flow Direction Flag
(l=Vertical,
0=Horizontal)
Place Holder for Loop
Incrementer
Maximum Number of
Materials Allowed (Due
to the Size of Arrays)
Subroutine
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
VSWCOM
MAIN
VSWCOM
MAIN
VSWCOM
MAIN
MAIN
ASSEMF
VARCAL
MAIN
MAIN
HFINTP
MAIN
MAIN
ASSEMF
VARCAL
MAIN
ASSEMF
VARCAL
VSHCOM
MAIN
ASSEMF
ASSEMT
INTERP
SWFUN
DSWFUN
Common
Block
CONTR
CONTR
I,M,O
M
M
M
I
I
I
I
I
I
I
M
I
11-58
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
MXNODE
MXTMV
NDCOUN
NDM1
NDOBS
NE
NEL
NELM
NITMAX
NLAYRG
NMAT
NOBSND
NONU
NOWRIT
Units
t
-
-
-
~
~
~
-
-
-
-
-
Type
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Maximum Number of
Nodes Allowed (Due to
the Size of Some
Arrays)
Maximum Time Value
to be Interpolated
Material Number
Temporary Counter
Counter Minus One
NDM1 = NDCOUN
Nodal Values of
Observation Nodes
Number of Elements in
the Linear
Representation
Storage Location for the
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
Number of Layers That
Need to be Discritized
Number of Soil
Materials
Number of Observation
Nodes in the Simulation
Nonuniform Initial
Condition Indicator
Restart Data Writing
Indicator
Subroutine
MAIN
ASSEMF
ASSEMT
BALCHK
TRIDIA
VARCAL
VSWCOM
MAIN
HFINTP
MAIN
MAIN
MAIN
MAIN
VSWCOM
MAIN
MAIN
MAIN
VARCAL
MAIN
MAIN
CONVER
MAIN
MAIN
MAIN
Common
Block
DAOBS
CONTR
CONTR
I,M,O
I
I
M
M
I
I
M
I
I
I
I
I
I
I
11-59
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
NP
NPIN
NPROB
NSTEP
NTN1
NTNP
NTOMT
NTS
NTSNDH
NUMK
NUMP
NUMT
NVPR
NVREAD
Units
-
-
-
-
-
~
-
—
-
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Scalar
Description
Total Number of Nodal
Points
Number of Non-default
Initial Values
Number of Simulations
to be Made
Nodal Value Printout
Control Parameter
Storage Location for
NTSNDH(l)
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 for
Each Material
Number of Pressure
Head vs. Saturation
Values for Each
Material
Time Step incrementer
Velocity Printout
Control Parameter
Velocity Reading
Indicator
Subroutine
MAIN
ASSEMF
ASSEMT
BALCHK
TRIDIA
VARCAL
VSWCOM
MAIN
MAIN
MAIN
BALCHK
MAIN
MAIN
MAIN
VSWCOM
MAIN
MAIN
HFINTP
MAIN
ASSEMF
INTERP
MAIN
ASSEMF
INTERP
MAIN
MAIN
VSWCOM
MAIN
Common
Block
CONTR
CONTR
SWHDA
SWHDA
CONTR
I,M,O
I
I
I
I
M
M
I
M
I
I
I
I
I
I
11-60
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
OUTFL
PCUR
PINT
PKND
PKRW
PKWOUT
PROP
QVTM
SLOPE
sswv
Units
LML'3
LML'3
L/t
L2
L2
L3/t
~
Type
Scalar
Array
Array
Array
Array
Scalar
Array
Array
Scalar
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-
Hydraulic Conductivity
Porosity, Specific
Storage Air Entry
Pressure Transport-
Dispersivity, Porosity,
Retardation Diffusion
Volumetric Water Flux
Values Corresponding to
Time Values
Slope of the Line
Between the Points
Being Interpolated
Value of Water Phase
Saturation (on Press.
Head vs Sat. Curve)
Subroutine
MAIN
ASSEMF
ASSEMT
BALCHK
INTERP
VARCAL
VSWCOM
ASSEMF
VARCAL
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
MAIN
ASSEMF
VSWCOM
MAIN
ASSEMF
INTERP
PKWFUN
MAIN
ASSEMF
ASSEMT
MAIN
HFINTP
HFINTP
INTERP
ASSEMF
INTERP
Common
Block
BSOLV
BSOLV
WELEM
SWHDA
MDATA
SWHDA
I,M,O
I
M
I
M
M
M
I
M
M
M
11-61
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
STMARK
SWAVE
SWDFI
SWND
SWNDPT
SWRKP
SWV
TAPS
TAP10
TDIFF
TERIFL
TEROFL
TFAC
THETA
THETM1
Units
t
-
~
~
-
~
-
~
t
-
-
~
Type
Scalar
Scalar
Array
Array
Array
Array
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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
Vel. & Water Sat.)
TDIFF=TMCUR-
TMVECX
Unit Number for Input
File
Unit Number for Output
File
Time Step Multiplier
Value Used in the Time
Stepping Scheme
(Theta=0.5 for Central
Difference Scheme,
Theta=1.0 for Backward
Difference Scheme)
Theta Minus One
Subroutine
MAIN
ASSEMF
PKWFUN
MAIN
MAIN
ASSEMF
ASSEMT
VARCAL
VSWCOM
MAIN
VSWCOM
CONVER
MAIN
ASSEMF
INTERP
MAIN
MAIN
VSWCOM
MAIN
MAIN
MAIN
MAIN
MAIN
ASSEMT
BALCHK
VARCAL
MAIN
ASSEMT
BALCHK
VARCAL
Common
Block
WORKN
SWHDA
MDATA
I,M,O
M
M
I
M
M
M
M
I
I
M
I
I
I
M
M
11-62
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
THL
TIN
TIMA
TIMAKP
TITLE
TMACCU
TMAX
TMCUR
TMDCAY
TMFOMT
TMHV
TMVEC
TMVECX
UWF
Units
L
t
t
t
L3M
t
t
M
t
t
t
t
Type
Array
Scalar
Scalar
Scalar
Alpha-
Numeric
Scalar
Scalar
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Description
Thickness of Current
Layer
Value of Initial Time
Step
Initial Time Value of the
Simulation
Storage Location for the
Value of Time Where
Iteration Computation is
Taking Place
Title of Simulation
Quantitative Storage
Water Volume or Solute
Mass
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
([1]=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
Subroutine
MAIN
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
MAIN
VSWCOM
MAIN
MAIN
MAIN
BALCHK
MAIN
MAIN
VSWCOM
MAIN
BALCHK
MAIN
VSWCOM
MAIN
HFINTP
MAIN
BALCHK
MAIN
BALCHK
HFINTP
VARCAL
MAIN
ASSEMT
VARCAL
Common
Block
CONTR
CONTR
CONTR
CONTR
CONTR
I,M,O
M
I
I
M
I
I
M
I
M
M
I
M
I
M
M
M
11-63
-------
Table 11.5 VADOFT Program Variables, Units, Location, and Variable Designations
Variable
UWFI
VALND1
VALNDN
VDAR
VDARPT
VDFI
XX
YY
Units
~
L/t
L/t
L/t
~
~
Type
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Description
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)
Darcy Velocity for Each
Node
Nodal Darcy Velocities
at Previous Time
Default Value of Darcy
Velocity for Current
Material
The X value Passed in
INTERP (to be Used in
the Interpolation)
The Y Value Passed in
INTERP (to be Used in
the Interpolation)
Subroutine
MAIN
MAIN
ASSEMF
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMF
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMF
BALCHK
VARCAL
VSWCOM
MAIN
VSWCOM
MAIN
INTERP
INTERP
Common
Block
TPDEF
I,M,O
M
M
M
M
O
M
I
M
M
11-64
-------
Table 11.6 Monte Carlo Program Variables
Variable
BBT
CORR
DECOM
DIST
IN2
IOUT
IOUT2
IRUN
IVAR
LARR
MCMAX
MCVAR
NCMAX
NDAT
NEMP
NMAX
Units
Double Precision
Double Precision
Array
Integer
Real Array
Integer
Integer
Integer
Integer
Integer
Integer Array
Integer
Integer
Integer
Integer Array
Integer
Integer
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.
Maximum number of empirical
distribution value-probability pairs.
Maximum possible number of variables
for which summary statistics can be
printed.
Subroutine
Main program
RE ADM
INITMC
Main Program
STATIS
OUTPUT
Main Program
INITMC
RANDOM
Main Program
RE ADM
Random
Main Program
RE ADM
Main Program
RE ADM
OUTPUT
Main Program
STATIS
Main Program
STATIS
Main Program
Main Program
RE ADM
INITMC
Main Program
Main Program
RE ADM
INITMC
RANDOM
Main Program
Main Program
RE ADM
RANDOM
Main Program
RE ADM
RANDOM
Main Program
11-65
-------
Table 11.6 Monte Carlo Program Variables
Variable
NRMAX
NRUNS
NVAR
PNAME
RMC
SNAME
STAT
VAR
XCDF
XMC
Units
Integer
Integer
Character Array
Real Array
Character Array
Double Precision
Array
Real Array
Real Array
Real Array
Description
Maximum number of Monte-Carlo runs
allowed.
Number of Monte-Carlo Runs.
Number of summary output variables.
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.
Subroutine
Main Program
Main Program
RE ADM
OUTPUT
Main Program
Main Program
RE ADM
INITMC
Main Program
RANDOM
Main Program
RE ADM
OUTPUT
Main Program
STATIS
OUTPUT
Main Program
RE ADM
INITMC
RANDOM
Main Program
STATIS
OUTPUT
Main Program
STATIS
11-66
-------
Table 11.7
PZ2HSPF Bridge Program Variables
Variable
Description
CRPAREA Area of crop treated with pesticide (ha)
*
CRPNAM Name of crop (20 characters)
*
DECERO Total mass of pesticide lost due to decay in erosion (mass units)
*
DECGW Total mass of pesticide lost due to decay in groundwater (mass units)
*
DECLAT Total mass of pesticide lost due to decay in lateral flow (mass units)
...................................JL[[[
DECSUR Total mass of pesticide lost due to decay in surface flow (mass units)
...................................JL[[[
DESCRP Description of run (80 characters)
*
DGRATE Groundwater-associated pesticide decay rate (/day) (array of values for each pesticide)
*
DGW Total mass of pesticide in groundwater flow lost due to delivery ratio (mass units)
...................................JL[[[
DLRATE Interflow-associated pesticide decay rate (/day) (array of values for each pesticide)
*
DRERO Total mass of pesticide in erosion lost due to delivery ratio (mass units)
*
DRLAT Total mass of pesticide in lateral flow lost due to delivery ratio (mass units)
...................................JL[[[
DRRATE Surface runoff-associated pesticide decay rates (/day) (array of values for each pesticide)
*
DSRATE Sediment-associated pesticide decay rates (/day) (array of values for each pesticide)
JL
END ATE Simulation ending date (array containing year, month, day, hour, minute, and second; user
enters year, month, day)
...................................JL[[[
ERFLUX Total mass of pesticide in erosion after losses (mass units)
EROFLX Mass of chemical associated with erosion; array of values for each chemical and each day;
units are mass units/ha/day for input and mass units/day for output
JL
EROMB Mass balance on total pesticide in erosion (mass before losses - losses - mass after losses)
(mass units)
GROFLX Mass of chemical associated with groundwater runoff; array of values for each chemical and
each day; units are mass units/ha/day for input and mass units/day for output
*
-------
Table 11.7
PZ2HSPF Bridge Program Variables
Variable
Description
LAGL AT Total mass of pesticide lost due to lag of lateral flow (mass units)
*
LAGSUR Total mass of pesticide lost due to lag of surface flow (mass units)
JL
LATFLX Mass of chemical associated with lateral runoff; array of values for each chemical and each
day; units are mass units/ha/day for input and mass units/day for output
LATMB Mass balance on total pesticide in lateral flow (mass before losses - losses - mass after losses)
(mass units)
*
LATRAT Interflow "delivery ratio" (array of values for each pesticide)
*
LTFLUX Total mass of pesticide in lateral flow after losses (mass units)
...................................jh[[[
MXDAYS Maximum number of days that program can process in a run (current value = 1470)
MXPEST Maximum number of pesticides or chemicals that program can process in a run (current
value = 3)
*
NUMDAY Number of days in simulation run span
...................................jh[[[
NUMPST Number of pesticides or chemicals to be processed by the program
*
OPTFLG Option flag for writing to WDM file; program writes to WDM if > 1
*
OUFLNM Output file name (20 characters)
JL
OUTDSN Array of dataset numbers containing the output data, i.e. the data transformed by PZ2HSPF
bridge program and used as input to HSPF (erosion, surface runoff, interflow, groundwater
and total aqueous runoff pesticide fluxes)
*
OUTFL Unit number of output file for PZ2HSPF bridge program
JL
PBAL Mass balance on total pesticide (sum of EROMB, SURMB, LATMB, and GWMB) (mass
units)
...................................JL[[[
PSTNAM Names of pesticides (array of 20-character names)
*
SEDRAT Sediment delivery ratio (array of values for each pesticide)
*
SEGNUM Model segment ID number
STDATE Simulation starting date (array containing year, month, day, hour, minute, and second; user
enters year, month, day)
*
SUFLUX Total mass of pesticide in surface flow after losses (mass units)
JL
SURFLX Mass of chemical associated with surface runoff; array of values for each chemical and each
day; units are mass units/ha/day for input and mass units/day for output
SURMB Mass balance on total pesticide in surface flow (mass before losses - losses - mass after
losses) (mass units)
*
TERO Sediment-associated pesticide time lag from field to stream (days or fraction of a day)
*
-------
Table 1 1.7 PZ2HSPF Bridge Program Variables
Variable
Description
TOTFLX Total mass of chemical input to stream; array of values for each chemical and each day (mass
units/day)
TOTGW Total mass of pesticide in
TOTLAT Total mass of pesticide in
TOTSUR Total mass of pesticide in
TSUR Surface runoff-associated
groundwater flow before losses (mass units)
lateral flow before losses (mass units)
surface flow before losses (mass units)
pesticide time lag from field to stream (days or fraction of a day)
WDFLNM WDM file name (20 characters)
WDMSFL
Unit number of WDM file
Table 11.8 I
Variable
APPCTR
APDAY
CALYR
CHMNUM
CLINE
DOM
ERFLUX
EDAY
FDAY
HISEG
IAPM
IAPD
ICHEM
INP
INPFNM
INPS
INTOPT
ISED
ISDFRC
ISPRAY
>RZWASP Bridge Program Variables
Description
Application counter for the chemical
Date of pesticide application as JULIAN day
Calendar year determined from the WASP start date
Number of chemicals simulated in a PRAM-3 run
Character line specified to skip header of PRZM-3 file
Day of the month as output by PRZM-3
Erosion flux of pesticide, g/cm2/day
Ending day of WASP simulation for output
Beginning day of WASP simulation for output
Last segment number for output on current line
Month of pesticide application
Date of pesticide application
Flag to specify which chemical is being simulated in each PRAM-3 run
Unit number for the input file name to be read in by the main program PRZWASP
Name of the input parameter file
Flag to check if chemicals or sediment are simulated
Interpolation option; 1 = step function (only one in code now)
Flag to specify if erosion is being simulated (0=No, l=Yes)
Flag to specify which solid fractions are considered
Flag to specify if spray drift is simulated (0=No, l=Yes)
11-69
-------
Table 11.8 I
Variable
JULIAN
LEN
LDAY
LPYEAR
LOSEG
MSPRAY
MXSYST
MXAPPS
MXCHEM
MXSYST
MXWSEG
MXPRZM
MXSOLD
NLINES
NUMSYS
NPSFNM
NPSSEG
NPSSYS
NPSNAME
NUMPRZ
NUMPYR
NUMSEG
NPSTYP
NFS
NPSLOAD
NTRIB
OUTFLG
PRX
PRZMFILE
'RZWASP Bridge Program Variables
Description
Function to calculate Julian day from a given calendar date
Length of a PRZM-3 file name
Loading day counter
Subroutine to determine if the simulation year is a leap year
Beginning segment number for output on current line
Mass of pesticide considered as spray drift, kg/ha
Maximum number of systems possible
Maximum number of pesticide applications
Maximum number of chemicals that can be simulated
Maximum number of systems that can be simulated
Maximum number of WASP segments that can be simulated
Maximum number of PRZM-3 segments that can be simulated
Maximum number of solid fractions that can be simulated
Number of lines per loading day required for WASP output
Number of WASP systems receiving nonpoint source loads (see WASPS. 0 documentation for
detail)
Unit number of NFS file for WASP
WASP segment number receiving load
WASP system numbers receiving loads
Name or description of WASP systems receiving loads
Number of PRZM-3 segments considered
Number of years PRZM-3 runs have been made
Number of segments receiving nonpoint source loads
Name or description of the nonpoint source model or method of generation; this is echoed to
the output file
Unit number for the nonpoint source file
Nonpoint source load which each WASP segment receives on each day of a calendar year,
kg/day
Number of tributary areas of PRZM-3 contributing loads to a WASP segment
Flag to prompt generation of output on a nonzero loading day, (0=No, l=Yes)
Unit number for the PRZM-3 output file for EXAMS
Name of the PRZM-3 output file for EXAMS
11-70
-------
Table 11.8 I
Variable
PRECIP
RDAY
RNF
RNFFNM
RUNOF
ROFLUX
SEGAREA
SOLFRC
SLTNHA
TOTTRB
TNAPP
TRIBA
WSDATE
WASPID
WATNAM
YREXT
'RZWASP Bridge Program Variables
Description
Precipitation, cm/day
Day as real
Unit number for the runoff output file
Name of the bridge program output runoff file
Surface runoff depth generated by PRZM-3, cm/day
Surface runoff flux, g/cnf/day
Surface area of the WASP segment, ha
Solids fraction in the sediment
Soil loss, tonnes/ha
Total tributary area contributing to each WASP segment from all PRAM-3 segments, ha
Total number of pesticide applications in a PRZM-3 run
Tributary area of each PRZM-3 segment contributing to each WASP segment, ha
WASP simulation start date (year, month, day)
WASP segment ID number
Names of the two water systems namely, runoff and precipitation
Year extension at the end of a PRZM-3 file
11-71
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11.3 PRZM and VADOFT Example Input Files
The following pages contain examples using different options in PRZM and VADOFT. Below each example file is a
brief summary of the scenario illustrated.
1 CHEMICAL, 1 HORIZON, TEMP CORRECTION, BACKGROUND LEVELS HYDROLOGY PARAMETERS
(CROP DATA FROM USDA NO.283 HANDBOOK)
60.0
0.72
0
1
1
1
0.03
0
20.0
15.000 1
80.000 1
1
86
78
82
110582 300982 151082 1
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATION PARAMETERS
1 1
ALDICARB
120582
1 1
SOILS PARAMETERS
20.0
4.3E03
0
0 1.0 1.00
0
0
0
0.3 0 0
l.OE-7 5.5E-3
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.97 10.0
8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3
1
1 20.0 1.32
0.012 0.011
1.0 .330 .133
8.3 10.0 60.0
1 1
0.000 0.000 0.000
0.000 0.000 0.010
0.050 0.040 0.030
WATR YEAR 1
5 YEAR
TUPX1 TSER 1.0E05
RZFX1 TSER 1.0E05
CHGT TSER
PRCP TSER
VFLX1 TCUM 1.0E05
SPECIAL ACTIONS
120682 KD 1 0.5
170682 SNAPSHOT
0.330
0.000
1.0
0.0
0.000
0.020
0.020
PEST
0.0
0.3
0.0
0.000
0.030
YEAR
0.0
0.000
0.040
1
0.000
0.050
0.000
1.000
CONC
YEAR 1
This PRZM input file represents a scenario where one chemical is applied and background levels are present at the
bottom compartments of the root zone. Volatilization is simulated through the entire root zone. Plant uptake is
simulated until crop harvest. One soil horizon is specified of 20 cm with a compartment thickness of 1 cm. Output is
reported on a yearly basis for hydrology, flux, and concentration. Special actions are implemented following
chemical application.
11-72
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1 CHEMICAL, NO TEMPERATURE CORRECTION, PRZM INPUT FOR ZONE 1
HYDROLOGY PARAMETERS (CROP DATA FROM USDA NO.283 HANDBOOK)
0.00 0.00 0 15.000 1 1
0
1
1 0.15 20.0 80.000 1 86 78 82 0.0 0.0 0.0
0.0
1
110582 300982 151082 1
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATION PARAMETERS
1 1 0
ALDICARB
120582 0 2.5 1.00
1 1
SOILS PARAMETERS
20.0 0.0 00 0 0 0 0 0 0 0
O.OEO O.OEOO O.OEOO
1
1 20.0 1.45 0.233 0.0 0.0
0.012 0.012 0.000
2.5 .233 .050 1.0 1
0 0
WATR YEAR 1 PEST YEAR 1 CONC YEAR 1
3 YEAR
RFLX1 TSER 1.0E05
RUNF TSER
INFL TSER 12
This PRZM input file represents one chemical being applied 2.5 cm deep at a rate of 1.0 kg/ha. The soil horizon is
20 cm deep with a compartment thickness of 2.5 cm. This is an example of a basic sequence without any options.
3 CHEMICALS, 2 HORIZONS, EROSION, IRRIGATION, PRZM INPUT FOR ZONE 1
HYDROLOGY PARAMETERS (CROP DATA FROM USDA NO.283 HANDBOOK)
9.0
86 78 82 0.1 0.1 0.1
60.0
1
110582 300982 151082 1
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATION PARAMETERS
230
0.72
9.6 9
15.7
1
0.15
1
1 0
0.00
.7 12.2
14.5
0.14
.15 30.
2
13.6
12.5
1.0
.0
0.000
15.4
11.3
2.0
80.000
1
15.5
9.5
5.8
3
ALDICARB
SOILS
120582
120682
1 1
PARAMETERS
45.0
4.3E3
0
0
0.3
2.5
2.5
0
l.OE-7
ATRAZINE
1.0
1.0
0
2.5E-7
2.
2.
0
1.
.5
.5
.4E-7
2.00
1.00
0
CARBOFURAN
1.00
0.00
0
2
1
1
.00
.00
5.5E-5
5.5E-5
3 0.25
0.55
.78
1
5.5E-3
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.97 10.0
8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3
11-73
-------
2
1
0.015 0.000
15.0 1.45 0.233
0.012 0.000
0.5 .233 .050
8.3 10.0 60.0
0.000 0.000
2 30.0 1.45
0.012 0.000
2.5 .233 .050
8.3 10.0 60.0
0.000 0.000
0 0
WATR YEAR 1
2 YEAR
TSER 1.0E05
RUNF TSER
0.0
0.000
1.0
0.0
0.000
0.233
0.000
0.5
0.0
0.000
PEST
0.0
0.010
.1
0.0
0.0
0.005
.1
0.0
YEAR
0.0
0.010
1.
0.0
0.005
.5
1
0.0
0.000
.3
0.0
0.000
.1
0.015
0.0
0.015
COM
0.015 0.000
YEAR 1
2 YEAR
RFLX1
This PRZM input file represents 3 chemicals being applied at various incorporation depths and various applications
simultaneously. Erosion losses are calculated. Irrigation is triggered when water capacity falls below 55 percent
during the cropping period. Two soil horizons represent the 45 cm root zone with the first horizon occupying the first
15 cm and the second horizon the lower 30 cm. Pesticide runoff flux and runoff depth are plotted to a time series file.
11-74
-------
1 CHEMICAL, 2 HORIZONS, NO VOLATILIZATION, BIODEGRADATION, BACKGROUND LEVELS
HYDROLOGY PARAMETERS (CROP DATA FROM USDA NO.283 HANDBOOK)
0.00 0.00 2 0.000 1 3
9.6 9.7 12.2 13.6 15.4 15.5
15.7 14.5 12.5 11.3 9.5 9.0
0
1
1 0.00 45.0 80.000 3 50 50 500.00.00.0 60.0
1
110581 300981 151081 1
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATION PARAMETERS
2 1 0
ALDICARB
120281 0 0.5 0.00
120581 0 0.5 0.00
1 1
SOILS PARAMETERS
45.0 0.0 0 0 0 0 0 0 1 1 1
.005 .005 .005 .005 .001
0.2 0.4 0.35 0.4 0.3 0.1 .0025
0.01 0.02 0.01 0.01 10.0 1000.0
2.0 1.0 6.0 2.0 2.0
0.1 0.4 0.4 0.4 0.4
4.3E3 O.OEOO O.OEOO
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.97 10.0
8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3
2
1 15.0 1.50 0.350 0.0 0.0 0.0 0.0
0.5 0.5 .000001 .00001 0.05 0.05
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2.5 .350 .150 0.06 1.
8.3 10.0 60.0 0.0 0.0
2 30.0 1.50 0.350 0.0 0.0 0.0 0.0
0.5 0.5 .000001 .00001 0.05 0.05
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2.5 .350 .150 0.06 1.
8.3 10.0 60.0 0.0 0.0
1 0
8.8000 8.8000 8.8000 8.8000 8.8000 8.8000 8.8000 8.8000
8.8000 8.8000 8.8000 8.8000 8.8000 8.8000 8.8000 8.8000
8.8000 8.8000
WATR MNTH 1 PEST MNTH 1 CONC DAY 1
3 YEAR
RFLX1 TSER 1.0E05
THET TSER 2
INFL TSER 2
This PRZM input file represents a scenario where biodegradation is used. Aldicarb is applied with application
targeted for May 12, 1982. With the FRMFLG option set, a window application date of 10 days has been specified
to check for the ideal soil-moisture conditions around the target application date. Solid, liquid, and gas phase
degradation rates have been set to zero to observe only the decay resulting from biodegradation.
11-75
-------
***********************************pLQ^y**************************************
1 CHEMICAL, 3 MATERIAL, VADOSE ZONE FLOW SIMULATION FOR ZONE 1
61 3
20 2
1 1
1
3
1 20
2 20
3 20
O.OOEOO 0
0 1
7.12E02.43EOO
24.96EOO
1.06E02
0.045EOO
0.078EOO
0.065EOO
5 10
YEAR
01111
1 .01
11012
0.0 1.0 1.0
0.0 1.0
1 40.0
2 40.0
3 40.0
0.0 O.OEOO 0 0
O.OEOO O.OEOO
.43EOO O.OEOO O.OEOO
.41EOO O.OEOO O.OEOO
-l.OEOO 0.145EOO
-l.OEOO 0.036EOO
-l.OEOO 0.075EOO
1
0
0
1.0
0
2.68EOO 0.626EOO
1.56E000.358EOO
1.89E000.470EOO
1 CHEMICAL, 3 MATERIAL, VADOSE TRANSPORT SIMULATION FOR ZONE 1
1.0
61 3 1
0 1 1
0.
1 0.
3
1 20 1
2 20 2
3 20 3
O.OEOO 1
000.
0.12E02.43EOO
1.480EOO 0.
0.12E02.43EOO
1.480EOO 0.
0.12E02.41EOO
1.480EOO 0.
1 1.0
1 0.0
1 0.00 1EOO
2 0.0
2 0.005EOO
3 0.0
3 0.004EOO
1 1
5 10
YEAR
1 0 1
0012
.0 1.0 1.0
.0 1.0
40.0
40.0
40.0
.0 0.0 0 0
.OEOO
.OEOO
.OEOO
1.0 O.OEOO
O.OEOO
1.0 O.OEOO
O.OEOO
1.0 O.OEOO
O.OEOO
This VADOFT file represents a 1 chemical simulation with 61 nodes and 60 elements at a depth of 120 cm.
Retardation and degradation are simulated.
11-76